commit dde3e12476f98743f4a6b5ba91ec0ece46dff5ef Author: Bifang <915779419@qq.com> Date: Mon Sep 28 17:00:53 2026 +0800 初始化 Qwen-Asr 本地仓库 diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..45e18e6 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,70 @@ +# Git文件 +.git +.gitignore +.gitmodules + +# Python缓存 +__pycache__ +*.pyc +*.pyo +*.pyd +.Python +*.so + +# 虚拟环境 +venv +env +ENV +.venv + +# IDE文件 +.vscode +.idea +*.swp +*.swo + +# 系统文件 +.DS_Store +Thumbs.db + +# 日志文件 +*.log +logs/ + +# 临时文件 +temp/ +tmp/ +*.tmp +build-file/ + +# 模型文件（这些将通过volume挂载） +tts/third_party/CosyVoice/pretrained_models/ +models/ +*.pt +*.pth +*.bin +*.safetensors +*.tar.gz + +# 测试文件 +tests/ +*.test +coverage.* + +# 文档 +README.md +*.md +docs/ + +# 配置文件（可能包含敏感信息） +.env +.env.* + +# 本地音视频样本 +*.m4a +*.mp4 + +# 其他 +.pytest_cache +.coverage +node_modules diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..a83b43f --- /dev/null +++ b/.env.example @@ -0,0 +1,150 @@ +# Qwen3-ASR 环境变量覆盖示例 +# 复制为 .env 后，只取消你需要修改的项的注释即可。 + +# ----------------------------------------------------------------------------- +# 仅在需要 API Key 鉴权时开启。 +# ----------------------------------------------------------------------------- +# API_KEY=your_api_key_here + +# ----------------------------------------------------------------------------- +# Docker / 部署入口配置。 +# ----------------------------------------------------------------------------- +# NGINX_PORT：宿主机暴露的 Nginx 端口。 +# NGINX_PORT=17003 +# ASR_IMAGE：离线包或自定义部署时使用的镜像标签。 +# ASR_IMAGE=unis/qwen3-asr:gpu-latest +# ASR_VISIBLE_DEVICES：统一可见卡编号配置，多个用逗号分隔；程序会按当前 accelerator 自动映射到底层变量。 +# ASR_VISIBLE_DEVICES=0 +# METAX_DRI_DEVICE：沐曦 / MuXi 需要映射的 DRI 设备路径。 +# METAX_DRI_DEVICE=/dev/dri +# METAX_MXSMI_PATH：沐曦 / MuXi 的 mx-smi 工具路径。 +# METAX_MXSMI_PATH=/opt/mxdriver/bin/mx-smi +# METAX_PRIVILEGED：沐曦 / MuXi 容器是否以 privileged 方式运行。 +# METAX_PRIVILEGED=false +# ILUVATAR_USR_SRC：天数 / Iluvatar 容器内 usr/src 挂载路径。 +# ILUVATAR_USR_SRC=/usr/src +# ILUVATAR_LIB_MODULES：天数 / Iluvatar 容器内模块目录挂载路径。 +# ILUVATAR_LIB_MODULES=/lib/modules +# ILUVATAR_DEV：天数 / Iluvatar 设备目录挂载路径。 +# ILUVATAR_DEV=/dev +# ILUVATAR_HOME：天数 / Iluvatar 主目录挂载路径。 +# ILUVATAR_HOME=/home +# ILUVATAR_DATA：天数 / Iluvatar 数据目录挂载路径。 +# ILUVATAR_DATA=/data +# MTHREADS_DEV：摩尔线程 / Moore Threads 设备目录挂载路径。 +# MTHREADS_DEV=/dev +# MTHREADS_USR_SRC：摩尔线程 / Moore Threads 容器内 usr/src 挂载路径。 +# MTHREADS_USR_SRC=/usr/src +# MTHREADS_LIB_MODULES：摩尔线程 / Moore Threads 容器内模块目录挂载路径。 +# MTHREADS_LIB_MODULES=/lib/modules +# MTHREADS_HOME：摩尔线程 / Moore Threads 主目录挂载路径。 +# MTHREADS_HOME=/home +# MTHREADS_DATA：摩尔线程 / Moore Threads 数据目录挂载路径。 +# MTHREADS_DATA=/data +# MODEL_STORAGE_DIR：模型宿主机挂载目录。 +# MODEL_STORAGE_DIR=/opt/dep/asr/models +# DATA_STORAGE_DIR：业务数据宿主机挂载目录。 +# DATA_STORAGE_DIR=/opt/dep/asr/data +# NGINX_RATE_LIMIT_RPS：Nginx 全局限流，单位为每秒请求数。 +# NGINX_RATE_LIMIT_RPS=0 +# NGINX_RATE_LIMIT_BURST：Nginx 全局突发限流额度，0 表示自动按 RPS 处理。 +# NGINX_RATE_LIMIT_BURST=0 +# ASR_DEPLOY_TOPOLOGY：部署拓扑，isolated=每卡一个实例，sharded=单实例多卡分片，auto=优先 sharded 失败回退 isolated。 +# ASR_DEPLOY_TOPOLOGY=isolated + +# ----------------------------------------------------------------------------- +# 运行时模型选择。 +# 留空 QWEN3_ASR_MODEL 时会自动选择。 +# 支持值：qwen3-asr-0.6b、qwen3-asr-1.7b +# ----------------------------------------------------------------------------- +# ACCELERATOR：加速后端类型，常见值有 auto、cpu、nvidia、metax、iluvatar、mthreads。 +# ACCELERATOR=auto +# DEVICE：具体运行设备，常见值有 auto、cpu、cuda:0。 +# DEVICE=auto +# QWEN3_ASR_MODEL：手动指定离线识别模型，留空则自动挑选。 +# QWEN3_ASR_MODEL= + +# ----------------------------------------------------------------------------- +# 模型下载 / 缓存行为。 +# 在 Docker Compose 中，默认宿主机挂载目录是 /opt/dep/asr/models。 +# 实际模型目录通常位于： +# /opt/dep/asr/models/Qwen +# /opt/dep/asr/models/iic +# /opt/dep/asr/models/damo +# ----------------------------------------------------------------------------- +# MODELS_DIR：项目主模型目录。 +# MODELS_DIR=/opt/dep/asr/models +# MODELSCOPE_CACHE：ModelScope 缓存根目录，下面会生成 models/{publisher}/{model}。 +# MODELSCOPE_CACHE=/opt/dep/asr +# MODELSCOPE_PATH：ModelScope 实际模型目录，建议保持在 models 目录下。 +# MODELSCOPE_PATH=/opt/dep/asr/models + +# ----------------------------------------------------------------------------- +# 说话人注册 / pgvector 数据库配置。 +# 表结构与 Model-Test-New 保持一致：speakers(id, name, user_id, embedding)。 +# ----------------------------------------------------------------------------- +# SPEAKER_DB_ENABLED：是否启用说话人库与 pgvector 检索。 +# SPEAKER_DB_ENABLED=true +# DB_HOST：PostgreSQL 主机地址。 +# DB_HOST=127.0.0.1 +# DB_PORT：PostgreSQL 端口。 +# DB_PORT=5432 +# DB_USER：PostgreSQL 用户名。 +# DB_USER=postgres +# DB_PASSWORD：PostgreSQL 密码。 +# DB_PASSWORD=postgres +# DB_NAME：PostgreSQL 数据库名。 +# DB_NAME=asr_db +# SV_MODEL：说话人识别模型。 +# SV_MODEL=iic/speech_campplus_sv_zh-cn_16k-common +# SV_THRESHOLD：说话人相似度阈值，越大越严格。 +# SV_THRESHOLD=0.6 +# TEMP_DIR：临时文件目录。 +# TEMP_DIR=/opt/dep/asr/data/temp +# LOG_FILE：日志文件路径。 +# LOG_FILE=/opt/dep/asr/data/logs/qwen3-asr.log +# TASK_STATE_DIR：任务状态持久化目录。 +# TASK_STATE_DIR=/opt/dep/asr/data/tasks +# TASK_RETENTION_HOURS：任务结果保留时间，单位小时。 +# TASK_RETENTION_HOURS=24 + +# ----------------------------------------------------------------------------- +# CPU Rust 后端覆盖配置。 +# 一般不需要改；自动检测会检查 vendor/qwenasr/target/{release,debug}。 +# ----------------------------------------------------------------------------- +# QWENASR_LIBRARY_PATH：手动指定 libqwen_asr.so 的绝对路径。 +# QWENASR_LIBRARY_PATH=/absolute/path/to/libqwen_asr.so + +# ----------------------------------------------------------------------------- +# 调优参数。除非你在做特定瓶颈测试，否则建议保持默认。 +# ----------------------------------------------------------------------------- +# ASR_BATCH_SIZE：批处理大小，表示一次并行推理的片段数。 +# ASR_BATCH_SIZE=4 +# ASR_ENABLE_WORD_TIMESTAMPS：是否启用字词级时间戳。 +# ASR_ENABLE_WORD_TIMESTAMPS=false +# MAX_SEGMENT_SEC：离线 ASR 单段最大时长，单位秒。 +# MAX_SEGMENT_SEC=60 +# QWEN_RUST_CPU_WORKERS：Qwen Rust CPU 后端 worker 数。 +# QWEN_RUST_CPU_WORKERS=4 +# QWEN_RUST_ASR_CONCURRENCY：Qwen Rust ASR 并发数，0 通常表示自动。 +# QWEN_RUST_ASR_CONCURRENCY=0 +# QWEN_RUST_ALIGN_CONCURRENCY：Qwen Rust 对齐并发数，0 通常表示自动。 +# QWEN_RUST_ALIGN_CONCURRENCY=0 +# QWEN_GPU_MEMORY_UTILIZATION：GPU 显存使用比例。 +# QWEN_GPU_MEMORY_UTILIZATION=0.9 +# QWEN_VLLM_ENFORCE_EAGER：是否强制 vLLM eager 执行；国产卡/稳定优先建议 true，NVIDIA 性能测试可设 false。 +# QWEN_VLLM_ENFORCE_EAGER=true +# QWEN_FORCE_ALIGNER_GPU_MEMORY_UTILIZATION：forced aligner 预留的显存比例。 +# QWEN_FORCE_ALIGNER_GPU_MEMORY_UTILIZATION=0.15 +# ASR_ENABLE_NEARFIELD_FILTER：是否启用近场/远场过滤。 +# ASR_ENABLE_NEARFIELD_FILTER=true +# ASR_NEARFIELD_RMS_THRESHOLD：近场判断的 RMS 能量阈值。 +# ASR_NEARFIELD_RMS_THRESHOLD=0.01 + +# ----------------------------------------------------------------------------- +# 仅用于本地开发调试。 +# ----------------------------------------------------------------------------- +# LOG_LEVEL：日志级别。 +# LOG_LEVEL=INFO +# FUNASR_STARTUP_UI：FunASR 启动界面模式。 +# FUNASR_STARTUP_UI=auto diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..992df58 --- /dev/null +++ b/.gitignore @@ -0,0 +1,48 @@ +# Environment files (contain sensitive information) +.env +*.env.local +*.env.*.local + +# Build and deployment files +build-file/ + +# Downloaded models directory +/models/ + +# Large binary packages +*.tar.gz +*.tar +*.zip + +# Python cache +__pycache__/ +*.py[cod] +*$py.class +*.so + +# Model files (downloaded) +*.pt +*.pth +*.onnx +*.safetensors +*.bin + +# Log files +*.log +logs/ + +# OS files +.DS_Store +Thumbs.db + +# IDE +.idea/ +.vscode/ +*.swp +*.swo + +# Temporary files +*.tmp +*.temp +.cache/ +.code-review-graph/ \ No newline at end of file diff --git a/.pi-crg-mcp/README.md b/.pi-crg-mcp/README.md new file mode 100644 index 0000000..b29041b --- /dev/null +++ b/.pi-crg-mcp/README.md @@ -0,0 +1,36 @@ +# Code Review Graph MCP + +Generated from the `agent-tool-basic` template. + +## Contributions + +- Agent tool `echo_text` + +## Develop + +1. Open the Plugins page and use **Load development plugin**, pointing at this + directory. PI-Desktop reloads the plugin whenever you save a file here. +2. Verify the contributions from the command palette. +3. Validate and package: + +```bash +pnpm pi-plugin check . +pnpm pi-plugin pack . +# writes dist/crg-mcp-0.1.0.piplug +``` + +Install the resulting `.piplug` from the Plugins page to test it the way a +user would. + +### Panel top drag band + +PI-Desktop reserves exactly a transparent 46px frameless drag band above panel +content and renders a minimal fixed three-button window-control capsule in its +top-right corner. Normal-flow content is offset automatically. The panel title, +toolbar, and every other visible surface belong to the plugin. Development +panels show a reminder that the top 46px is not clickable outside the capsule. +For `position: fixed` or `position: sticky` content, use +`top: var(--pi-plugin-titlebar-height, 46px)` and account for the same value +in viewport-height calculations. Add `-webkit-app-region: drag` to a +plugin-owned toolbar when it should move the window, and +`-webkit-app-region: no-drag` to controls inside it. diff --git a/.pi-crg-mcp/crg.cmd b/.pi-crg-mcp/crg.cmd new file mode 100644 index 0000000..4b695f5 --- /dev/null +++ b/.pi-crg-mcp/crg.cmd @@ -0,0 +1,57 @@ +@echo off +chcp 65001 >nul +setlocal + +rem --------------------------------------------------------------------------- +rem code-review-graph MCP launcher for PI-Desktop. +rem +rem The MCP host spawns this with a minimal environment (PATH, SystemRoot, +rem windir, TEMP, TMP, LANG plus the manifest's env block) and cwd = plugin +rem directory, with stdin/stdout used for JSON-RPC. Never write to stdout. +rem --------------------------------------------------------------------------- + +if defined CRG_HOME goto have_home +set "CRG_HOME=D:\github-project\code-review-graph" +:have_home + +rem code_review_graph/constants.py calls Path.home() at import time; without a +rem user profile the server dies with "Could not determine home directory.". +if defined USERPROFILE goto have_profile +set "USERPROFILE=C:\Users\%USERNAME%" +:have_profile +if defined HOMEDRIVE goto have_hd +set "HOMEDRIVE=C:" +:have_hd +if defined HOMEPATH goto have_hp +set "HOMEPATH=\Users\%USERNAME%" +:have_hp +if defined APPDATA set "APPDATA=%USERPROFILE%\AppData\Roaming" +if defined LOCALAPPDATA set "LOCALAPPDATA=%USERPROFILE%\AppData\Local" + +set "PYTHONUTF8=1" +set "PYTHONIOENCODING=utf-8" + +rem The editable install's .pth points at D:\github_project\... (underscore) +rem while the checkout lives at D:\github-project\... (hyphen), so the package +rem is only importable with the checkout explicitly on sys.path. +set "PYTHONPATH=%CRG_HOME%" + +rem The venv's code-review-graph.exe is a broken uv trampoline +rem ("failed to canonicalize script path"), so prefer a working interpreter. +set "CRG_PY=%CRG_HOME%\.venv\Scripts\python.exe" +if exist "%CRG_PY%" goto py_ready +set "CRG_PY=python" +:py_ready + +rem Upstream ships 30 tools; keep the surface small unless overridden. +if defined CRG_TOOLS goto tools_ready +set "CRG_TOOLS=build_or_update_graph_tool,run_postprocess_tool,get_minimal_context_tool,get_review_context_tool,get_impact_radius_tool,query_graph_tool,semantic_search_nodes_tool,detect_changes_tool,list_graph_stats_tool,get_affected_flows_tool" +:tools_ready + +if defined CRG_REPO goto repo_ready +set "CRG_REPO=%CRG_HOME%" +:repo_ready + +"%CRG_PY%" -m code_review_graph serve --repo "%CRG_REPO%" + +endlocal diff --git a/.pi-crg-mcp/main.js b/.pi-crg-mcp/main.js new file mode 100644 index 0000000..3930880 --- /dev/null +++ b/.pi-crg-mcp/main.js @@ -0,0 +1,16 @@ +/** + * crg-mcp — thin loader for the code-review-graph MCP bridge. + * + * All wiring lives in manifest.json under `contributes.mcpServers`: the host + * spawns `crg.cmd`, speaks MCP over its stdio, and publishes each upstream tool + * as `plugin_crg_mcp_crg_`. The host owns the client, the retry on the + * next call, and the tool lifecycle, so there is nothing to register here. + */ + +async function onLoad() { + // Intentionally empty: the MCP client is owned by the host. +} + +async function onUnload() {} + +module.exports = { onLoad, onUnload }; diff --git a/.pi-crg-mcp/manifest.json b/.pi-crg-mcp/manifest.json new file mode 100644 index 0000000..44bfe1f --- /dev/null +++ b/.pi-crg-mcp/manifest.json @@ -0,0 +1,33 @@ +{ + "schemaVersion": 1, + "id": "crg-mcp", + "name": "Code Review Graph MCP", + "version": "1.0.0", + "description": "Bridges the locally deployed code-review-graph MCP server into the agent as native tools.", + "main": "main.js", + "contributes": { + "mcpServers": [ + { + "id": "crg", + "label": "Code Review Graph", + "transport": "stdio", + "command": "crg.cmd", + "env": { + "PYTHONPATH": "D:\\github-project\\code-review-graph", + "USERPROFILE": "C:\\Users\\admin", + "HOMEDRIVE": "C:", + "HOMEPATH": "\\Users\\admin" + } + } + ] + }, + "permissions": [ + "mcp.server.local" + ], + "engines": { + "piDesktop": ">=0.1.0" + }, + "activationEvents": [ + "onStartup" + ] +} diff --git a/.pi-crg-mcp/selfcheck.ps1 b/.pi-crg-mcp/selfcheck.ps1 new file mode 100644 index 0000000..5270319 --- /dev/null +++ b/.pi-crg-mcp/selfcheck.ps1 @@ -0,0 +1,96 @@ +<# + Self-check for the crg-mcp plugin. + + Drives crg.cmd exactly the way the PI-Desktop MCP host does — `cmd /c crg.cmd` + with piped stdio and cwd = this folder — then reports the MCP handshake, the + tool list, and one real tool call. Run it from PowerShell: + + powershell -NoProfile -ExecutionPolicy Bypass -File .\selfcheck.ps1 +#> +$ErrorActionPreference = 'Stop' + +$dir = Split-Path -Parent $MyInvocation.MyCommand.Path + +# Mirror the host's minimal environment plus the manifest's env block. +foreach ($k in 'PATH', 'SystemRoot', 'TEMP', 'TMP') { + if (-not (Test-Path "Env:$k")) { Write-Warning "missing $k in ambient env" } +} + +$psi = New-Object System.Diagnostics.ProcessStartInfo +$psi.FileName = 'cmd.exe' +$psi.Arguments = '/c crg.cmd' +$psi.WorkingDirectory = $dir +$psi.RedirectStandardInput = $true +$psi.RedirectStandardOutput = $true +$psi.RedirectStandardError = $true +$psi.UseShellExecute = $false +$psi.StandardOutputEncoding = [System.Text.Encoding]::UTF8 + +$proc = [System.Diagnostics.Process]::Start($psi) + +function Send($obj) { + $proc.StandardInput.WriteLine(($obj | ConvertTo-Json -Compress -Depth 8)) + $proc.StandardInput.Flush() +} + +function ReadLine([int]$waitSeconds = 25) { + $task = $proc.StandardOutput.ReadLineAsync() + if ($task.Wait([TimeSpan]::FromSeconds($waitSeconds))) { return $task.Result } + return $null +} + +Send @{ + jsonrpc = '2.0'; id = 1; method = 'initialize' + params = @{ + protocolVersion = '2025-06-18' + capabilities = @{} + clientInfo = @{ name = 'crg-selfcheck'; version = '1' } + } +} + +$initLine = ReadLine 40 +if (-not $initLine) { + Write-Host 'HANDSHAKE FAILED: no stdout from crg.cmd' -ForegroundColor Red + Write-Host '--- stderr ---' + Write-Host $proc.StandardError.ReadToEnd() + try { $proc.Kill() } catch { } + exit 1 +} + +$init = $initLine | ConvertFrom-Json +Write-Host ("HANDSHAKE OK server={0} {1}" -f $init.result.serverInfo.name, $init.result.serverInfo.version) -ForegroundColor Green + +Send @{ jsonrpc = '2.0'; method = 'notifications/initialized'; params = @{} } +Send @{ jsonrpc = '2.0'; id = 2; method = 'tools/list'; params = @{} } + +$toolsLine = ReadLine +if (-not $toolsLine) { + Write-Host 'tools/list returned nothing' -ForegroundColor Red + try { $proc.Kill() } catch { } + exit 1 +} +$tools = ($toolsLine | ConvertFrom-Json).result.tools +Write-Host ("TOOLS: {0}" -f $tools.Count) -ForegroundColor Green +foreach ($t in $tools) { Write-Host (" plugin_crg_mcp_crg_{0}" -f $t.name) } + +Send @{ + jsonrpc = '2.0'; id = 3; method = 'tools/call' + params = @{ name = 'list_graph_stats_tool'; arguments = @{} } +} +$callLine = ReadLine 40 +if ($callLine) { + $call = $callLine | ConvertFrom-Json + if ($call.result) { + $text = $call.result.content[0].text + Write-Host 'TOOL CALL OK' -ForegroundColor Green + Write-Host (' ' + ($text -split "`n")[0..3] -join ' | ') + } + else { + Write-Host ("TOOL CALL ERROR: {0}" -f ($call | ConvertTo-Json -Compress -Depth 6)) -ForegroundColor Red + } +} +else { + Write-Host 'TOOL CALL: no response' -ForegroundColor Red +} + +try { $proc.Kill() } catch { } diff --git a/Dockerfile.cpu b/Dockerfile.cpu new file mode 100644 index 0000000..ceda127 --- /dev/null +++ b/Dockerfile.cpu @@ -0,0 +1,73 @@ +FROM python:3.10-slim AS runtime + +ENV DEBIAN_FRONTEND=noninteractive \ + PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 \ + HF_HUB_DISABLE_SYMLINKS_WARNING=1 \ + HF_HUB_DISABLE_PROGRESS_BARS=1 \ + OPENBLAS_NUM_THREADS=1 \ + OMP_NUM_THREADS=1 \ + GOTO_NUM_THREADS=1 \ + QWENASR_LIBRARY_PATH=/opt/qwenasr/lib/libqwen_asr.so + +# Install system packages required for audio processing +RUN apt-get update && apt-get install -y --no-install-recommends \ + ffmpeg \ + sox \ + libsox-dev \ + libsndfile1 \ + libopenblas-dev \ + nginx \ + build-essential \ + curl + +WORKDIR /app + +ARG TARGETARCH +ARG QWENASR_RUST_TARGET_CPU=x86-64-v2 + +COPY vendor/qwenasr /tmp/qwenasr + +# Install Rust compiler (required for sudachipy on ARM64) +RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y +ENV PATH="/root/.cargo/bin:${PATH}" + +# Build QwenASR Rust shared library for CPU Qwen3-ASR inference. The default +# amd64 target stays portable across common AVX2-era servers; set +# QWENASR_RUST_TARGET_CPU=native only for self-built, host-specific images. +RUN if [ "$TARGETARCH" = "amd64" ]; then export RUSTFLAGS="-C target-cpu=${QWENASR_RUST_TARGET_CPU}"; fi \ + && cargo build --release -p qwen-asr --features ffi --manifest-path /tmp/qwenasr/Cargo.toml \ + && mkdir -p /opt/qwenasr/lib \ + && cp /tmp/qwenasr/target/release/libqwen_asr.so /opt/qwenasr/lib/libqwen_asr.so + +# Install Python dependencies (CPU mode) +COPY environments/cpu/pyproject.toml /app/environments/cpu/pyproject.toml +RUN python - <<'PY' > /tmp/qwen3-asr-cpu-reqs.txt +import tomllib +from pathlib import Path + +data = tomllib.loads(Path("/app/environments/cpu/pyproject.toml").read_text()) +for dep in data["project"]["dependencies"]: + print(dep) +print("asyncpg==0.31.0") +PY +RUN pip install --no-cache-dir -r /tmp/qwen3-asr-cpu-reqs.txt && \ + rm -f /tmp/qwen3-asr-cpu-reqs.txt + +# Clean build tools and cache to reduce image size +RUN apt remove -y build-essential curl && apt autoremove -y \ + && rm -rf /tmp/qwenasr \ + && rm -rf /root/.cargo /root/.rustup \ + && apt-get clean && rm -rf /var/lib/apt/lists/* + +# Copy application code +COPY . . + +# Create runtime directories +RUN mkdir -p /app/data/temp /app/data/logs /app/data/tasks \ + && chmod +x start.py /app/scripts/docker/entrypoint.sh + +EXPOSE 8000 + +ENTRYPOINT ["/app/scripts/docker/entrypoint.sh"] +CMD ["python", "start.py"] diff --git a/Dockerfile.gpu b/Dockerfile.gpu new file mode 100644 index 0000000..da75b2f --- /dev/null +++ b/Dockerfile.gpu @@ -0,0 +1,101 @@ +ARG PYTORCH_BASE_IMAGE=pytorch/pytorch:2.11.0-cuda13.0-cudnn9-runtime +FROM ${PYTORCH_BASE_IMAGE} + +ARG CUDA_NVCC_PACKAGE=cuda-nvcc-13-0 +ARG PYTORCH_CUDA_INDEX=https://download.pytorch.org/whl/cu130 +ARG TORCH_VERSION=2.11.0 +ARG TORCHAUDIO_VERSION=2.11.0 +ARG TORCHVISION_VERSION=0.26.0 +ARG VLLM_VERSION=0.20.0 +ARG TORCH_CUDA_ARCH_LIST=12.0+PTX + +ENV DEBIAN_FRONTEND=noninteractive \ + PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 \ + HF_HUB_DISABLE_SYMLINKS_WARNING=1 \ + HF_HUB_DISABLE_PROGRESS_BARS=1 \ + PIP_BREAK_SYSTEM_PACKAGES=1 \ + TORCH_CUDA_ARCH_LIST=${TORCH_CUDA_ARCH_LIST} + +# Add NVIDIA apt repository for CUDA packages +RUN apt-get update && apt-get install -y --no-install-recommends \ + ca-certificates \ + gnupg \ + wget \ + && wget -qO - https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/3bf863cc.pub | apt-key add - \ + && echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64 /" > /etc/apt/sources.list.d/cuda.list \ + && apt-get update + +# Install system packages required for audio processing +# Note: nvcc and build-essential are required for FlashInfer JIT compilation. +RUN apt-get install -y --no-install-recommends \ + ffmpeg \ + sox \ + libsox-dev \ + libsndfile1 \ + nginx \ + build-essential \ + ${CUDA_NVCC_PACKAGE} + +WORKDIR /app + +# Install Python dependencies directly into the image runtime Python. +# The image defaults to CUDA 13.0/cu130 for Blackwell-capable GPUs. Developers +# can rebuild with a different PyTorch CUDA backend by overriding: +# PYTORCH_BASE_IMAGE, PYTORCH_CUDA_INDEX, CUDA_NVCC_PACKAGE, TORCH_CUDA_ARCH_LIST. +COPY pyproject.toml /app/ +RUN python - <<'PY' > /tmp/qwen3-asr-gpu-reqs.txt +import tomllib +from pathlib import Path + +data = tomllib.loads(Path("/app/pyproject.toml").read_text()) +for dep in data["project"]["dependencies"]: + if dep.startswith("torch=="): + continue + if dep.startswith("torchaudio=="): + continue + if dep.startswith("torchvision=="): + continue + if dep.startswith("vllm=="): + continue + print(dep) +PY +RUN pip install --no-cache-dir -r /tmp/qwen3-asr-gpu-reqs.txt && \ + pip install --no-cache-dir \ + --index-url "${PYTORCH_CUDA_INDEX}" \ + --extra-index-url https://pypi.org/simple \ + "torch==${TORCH_VERSION}" \ + "torchaudio==${TORCHAUDIO_VERSION}" \ + "torchvision==${TORCHVISION_VERSION}" && \ + pip install --no-cache-dir "vllm==${VLLM_VERSION}" && \ + rm -f /tmp/qwen3-asr-gpu-reqs.txt + +# Fail the image build early if the runtime dependency chain is inconsistent. +RUN python - <<'PY' +import torch +import torchaudio +import transformers +import vllm +from transformers import PreTrainedModel + +print("torch", torch.__version__) +print("torchaudio", torchaudio.__version__) +print("transformers", transformers.__version__) +print("vllm", vllm.__version__) +print("PreTrainedModel", PreTrainedModel) +PY + +# Clean apt cache but keep build-essential and nvcc for FlashInfer JIT compilation. +RUN apt-get clean && rm -rf /var/lib/apt/lists/* + +# Copy application code +COPY . . + +# Create runtime directories +RUN mkdir -p /app/data/temp /app/data/logs /app/data/tasks \ + && chmod +x start.py /app/scripts/docker/entrypoint.sh + +EXPOSE 8000 + +ENTRYPOINT ["/app/scripts/docker/entrypoint.sh"] +CMD ["python", "start.py"] diff --git a/Dockerfile.iluvatar b/Dockerfile.iluvatar new file mode 100644 index 0000000..7bf1713 --- /dev/null +++ b/Dockerfile.iluvatar @@ -0,0 +1,51 @@ +ARG ILUVATAR_BASE_IMAGE=registry.iluvatar.com.cn:10443/customer/sz/vllm0.17.0-4.4.0-x86:v5 +FROM ${ILUVATAR_BASE_IMAGE} + +ARG PYTHON_BIN=python3 +ARG INSTALL_SYSTEM_PACKAGES=false + +ENV DEBIAN_FRONTEND=noninteractive \ + PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 \ + HF_HUB_DISABLE_SYMLINKS_WARNING=1 \ + HF_HUB_DISABLE_PROGRESS_BARS=1 \ + PIP_BREAK_SYSTEM_PACKAGES=1 \ + ACCELERATOR=iluvatar \ + DEVICE=auto \ + APP_PYTHON_BIN=${PYTHON_BIN} + +# Iluvatar production images should be built on top of the official Iluvatar +# vLLM image. The base image owns the IX runtime, PyTorch, vLLM, and kernels; +# this layer only adds generic project/runtime dependencies and app code. +RUN if [ "${INSTALL_SYSTEM_PACKAGES}" = "true" ]; then \ + apt-get update && apt-get install -y --no-install-recommends \ + python3 python3-pip python3-venv ffmpeg sox libsox-dev libsndfile1 nginx \ + && apt-get clean && rm -rf /var/lib/apt/lists/*; \ + fi + +WORKDIR /app + +COPY environments/iluvatar/requirements.txt /tmp/qwen3-asr-iluvatar-requirements.txt +RUN ${APP_PYTHON_BIN} -m pip install --no-cache-dir -r /tmp/qwen3-asr-iluvatar-requirements.txt && \ + rm -f /tmp/qwen3-asr-iluvatar-requirements.txt + +RUN ${APP_PYTHON_BIN} - <<'PY' +import importlib +import torch + +print("torch", torch.__version__) +print("torch.cuda.is_available", torch.cuda.is_available()) +for name in ("vllm",): + module = importlib.import_module(name) + print(name, getattr(module, "__version__", "unknown")) +PY + +COPY . . + +RUN mkdir -p /app/data/temp /app/data/logs /app/data/tasks \ + && chmod +x start.py /app/scripts/docker/entrypoint.sh + +EXPOSE 8000 + +ENTRYPOINT ["/app/scripts/docker/entrypoint.sh"] +CMD ["python3", "start.py"] diff --git a/Dockerfile.metax b/Dockerfile.metax new file mode 100644 index 0000000..81f304f --- /dev/null +++ b/Dockerfile.metax @@ -0,0 +1,55 @@ +ARG METAX_BASE_IMAGE +FROM ${METAX_BASE_IMAGE} + +ARG PYTHON_BIN=/opt/conda/bin/python +ARG INSTALL_SYSTEM_PACKAGES=true + +ENV DEBIAN_FRONTEND=noninteractive \ + PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 \ + HF_HUB_DISABLE_SYMLINKS_WARNING=1 \ + HF_HUB_DISABLE_PROGRESS_BARS=1 \ + PIP_BREAK_SYSTEM_PACKAGES=1 \ + ACCELERATOR=metax \ + DEVICE=auto \ + APP_PYTHON_BIN=${PYTHON_BIN} + +# MetaX production images should be built on top of an official MetaX vLLM +# image that already contains MACA, PyTorch, vLLM, and their compiled kernels. +# We only add generic project/runtime dependencies and the application code. +RUN if [ "${INSTALL_SYSTEM_PACKAGES}" = "true" ]; then \ + apt-get update && apt-get install -y --no-install-recommends \ + python3 python3-pip python3-venv ffmpeg sox libsox-dev libsndfile1 nginx \ + && apt-get clean && rm -rf /var/lib/apt/lists/*; \ + fi + +WORKDIR /app + +COPY environments/metax/requirements.txt /tmp/qwen3-asr-metax-requirements.txt +RUN ${APP_PYTHON_BIN} -m pip install \ + --no-cache-dir \ + --no-warn-conflicts \ + --root-user-action=ignore \ + -r /tmp/qwen3-asr-metax-requirements.txt && \ + rm -f /tmp/qwen3-asr-metax-requirements.txt + +RUN ${APP_PYTHON_BIN} - <<'PY' +import importlib +import torch + +print("torch", torch.__version__) +print("torch.cuda.is_available", torch.cuda.is_available()) +for name in ("vllm",): + module = importlib.import_module(name) + print(name, getattr(module, "__version__", "unknown")) +PY + +COPY . . + +RUN mkdir -p /app/data/temp /app/data/logs /app/data/tasks \ + && chmod +x start.py /app/scripts/docker/entrypoint.sh + +EXPOSE 8000 + +ENTRYPOINT ["/app/scripts/docker/entrypoint.sh"] +CMD ["/opt/conda/bin/python", "start.py"] diff --git a/Dockerfile.mthreads b/Dockerfile.mthreads new file mode 100644 index 0000000..112195b --- /dev/null +++ b/Dockerfile.mthreads @@ -0,0 +1,40 @@ +ARG MTHREADS_BASE_IMAGE=registry.mthreads.com/presale/devtech/vllm_musa:s4000_4.3.5_d0519 +FROM ${MTHREADS_BASE_IMAGE} + +ARG PYTHON_BIN=python3 +ARG INSTALL_SYSTEM_PACKAGES=true + +ENV DEBIAN_FRONTEND=noninteractive \ + PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 \ + HF_HUB_DISABLE_SYMLINKS_WARNING=1 \ + HF_HUB_DISABLE_PROGRESS_BARS=1 \ + PIP_BREAK_SYSTEM_PACKAGES=1 \ + ACCELERATOR=mthreads \ + DEVICE=auto \ + APP_PYTHON_BIN=${PYTHON_BIN} + +# Moore Threads production images should be built on top of the official MUSA +# vLLM image. Keep the vendor Python/runtime stack intact and only add the +# extra system tools needed by the application. +RUN if [ "${INSTALL_SYSTEM_PACKAGES}" = "true" ]; then \ + apt-get update && apt-get install -y --no-install-recommends \ + ffmpeg sox libsox-dev libsndfile1 nginx \ + && apt-get clean && rm -rf /var/lib/apt/lists/*; \ + fi + +WORKDIR /app + +COPY environments/mthreads/requirements.txt /tmp/qwen3-asr-mthreads-requirements.txt +RUN ${APP_PYTHON_BIN} -m pip install --no-cache-dir -r /tmp/qwen3-asr-mthreads-requirements.txt && \ + rm -f /tmp/qwen3-asr-mthreads-requirements.txt + +COPY . . + +RUN mkdir -p /app/data/temp /app/data/logs /app/data/tasks \ + && chmod +x start.py /app/scripts/docker/entrypoint.sh + +EXPOSE 8000 + +ENTRYPOINT ["/app/scripts/docker/entrypoint.sh"] +CMD ["python3", "start.py"] diff --git a/README.md b/README.md new file mode 100644 index 0000000..20dbc01 --- /dev/null +++ b/README.md @@ -0,0 +1,610 @@ +
+ +

Qwen3-ASR

+

Ready-to-use Local Speech Recognition API Service

+ +Speech recognition API service centered on [Qwen3-ASR](https://github.com/QwenLM/Qwen3-ASR), with NVIDIA CUDA vLLM, MetaX/MuXi MACA vLLM, and CPU Rust backends, OpenAI API compatibility, Alibaba Cloud Speech API compatibility, and a Paraformer realtime websocket capability. + +[简体中文](./docs/README_zh.md) + +--- + +![Static Badge](https://img.shields.io/badge/Python-3.10+-blue?logo=python) +![Static Badge](https://img.shields.io/badge/Torch-2.11.0-%23EE4C2C?logo=pytorch&logoColor=white) +![Static Badge](https://img.shields.io/badge/CUDA-13.0_default-%2376B900?logo=nvidia&logoColor=white) + +
+ +## Live Demo Site + +- **Web Demo**: https://asr.vect.one + +## Demo + +[![Demo](./demo/demo.png)](https://media.cdn.vect.one/qwenasr_client_demo.mp4) + +## Release 1.0.1 + +> `v1.0.1` is the current patch release. `v1.0.0` introduced a large breaking refactor relative to the earlier `main` branch. +> If you are upgrading from `main`, read the release notes before reusing old deployment assumptions. +> +> Key breaking changes: +> - Python dependency management is now `uv`-based (`pyproject.toml` + `uv.lock`); `requirements*.txt` are gone +> - Runtime stack changed to `NVIDIA/MetaX GPU -> vLLM`, `CPU/macOS -> vendored QwenASR Rust` +> - `MLX` / Apple Silicon GPU path has been removed; `mps` is normalized to `cpu` +> - macOS / Apple Silicon now defaults to `qwen3-asr-0.6b`; set `QWEN3_ASR_MODEL` to override it +> - `ENABLED_MODELS` has been removed + +## Features + +- **Hybrid Runtime Stack** - Uses auto-selected Qwen3-ASR for offline inference and Paraformer realtime for websocket streaming +- **Speaker Diarization** - Automatic multi-speaker identification using CAM++ model +- **OpenAI API Compatible** - Supports `/v1/audio/transcriptions` endpoint, works with OpenAI SDK +- **Alibaba Cloud API Compatible** - Supports Alibaba Cloud Speech RESTful API and WebSocket streaming protocol +- **WebSocket Streaming** - Real-time streaming speech recognition with low latency +- **Smart Far-Field Filtering** - Automatically filters far-field sounds and ambient noise in streaming ASR +- **Intelligent Audio Segmentation** - VAD-based greedy merge algorithm for automatic long audio splitting +- **GPU Batch Processing** - Batch inference support, 2-3x faster than sequential processing +- **Resource-Aware Runtime** - Auto-selects the appropriate Qwen3-ASR model for the current machine + +## Acknowledgements + +- [Qwen3-ASR](https://github.com/QwenLM/Qwen3-ASR) provides the official model family and multimodal/vLLM usage guidance +- [QwenASR](https://github.com/huanglizhuo/QwenASR) provides the CPU Rust backend vendored by this project + +## Quick Deployment + +### 1. Docker Deployment (Recommended) + +```bash +# Copy and edit configuration +cp .env.example .env +# Edit .env to set API_KEY (optional) + +# Compose defaults: +# /opt/dep/asr/models -> /app/models +# /opt/dep/asr/data -> /app/data +# /opt/dep/asr/data/logs, temp, tasks live under this data mount +# Optional: override any host mount root in .env +# export MODEL_STORAGE_DIR=/data/qwen3-asr-models +# export DATA_STORAGE_DIR=/data/qwen3-asr-data + +# Start service (NVIDIA GPU version) +docker-compose up -d + +# Or MetaX/MuXi GPU version +docker-compose -f docker-compose-metax.yml up -d + +# Or Iluvatar/Tianshu GPU version +docker-compose -f docker-compose-iluvatar.yml up -d + +# Or Moore Threads / MUSA GPU version +docker-compose -f docker-compose-mthreads.yml up -d + +# Or CPU version +docker-compose -f docker-compose-cpu.yml up -d + +# NVIDIA multi-GPU auto mode (one instance per visible GPU) +CUDA_VISIBLE_DEVICES=0,1,2,3 docker-compose up -d + +# MetaX/MuXi multi-GPU auto mode +METAX_VISIBLE_DEVICES=0,1 docker-compose -f docker-compose-metax.yml up -d + +# Iluvatar/Tianshu multi-GPU auto mode +ILUVATAR_VISIBLE_DEVICES=0,1 docker-compose -f docker-compose-iluvatar.yml up -d + +# Moore Threads / MUSA multi-GPU auto mode +MTHREADS_VISIBLE_DEVICES=0,1 docker-compose -f docker-compose-mthreads.yml up -d +``` + +Service URLs: +- **API Endpoint**: `http://localhost:17003` +- **API Docs**: `http://localhost:17003/docs` + +Optional built-in rate limit settings: +- `NGINX_RATE_LIMIT_RPS` (global requests/sec, `0` = disabled) +- `NGINX_RATE_LIMIT_BURST` (global burst, `0` = auto use RPS) + +**docker run (alternative):** + +```bash +# NVIDIA GPU version +docker run -d --name qwen3-asr \ + --gpus all \ + -p 17003:8000 \ + -e ACCELERATOR=nvidia \ + -e CUDA_VISIBLE_DEVICES=0,1,2,3 \ + -e API_KEY=your_api_key \ + -v /opt/dep/asr/models:/app/models \ + -v /opt/dep/asr/data:/app/data \ + unis/qwen3-asr:gpu-latest + +# MetaX/MuXi GPU version +docker run -d --name qwen3-asr-metax \ + --privileged \ + --network=host \ + --pid=host \ + --ipc=host \ + -v /dev:/dev \ + -v /opt/mxdriver:/opt/mxdriver:ro \ + -e ACCELERATOR=metax \ + -e PORT=17003 \ + -e METAX_VISIBLE_DEVICES=0 \ + -v /opt/dep/asr/models:/app/models \ + -v /opt/dep/asr/data:/app/data \ + unis/qwen3-asr:metax-latest + +# CPU version +docker run -d --name qwen3-asr \ + -p 17003:8000 \ + -v /opt/dep/asr/models:/app/models \ + -v /opt/dep/asr/data:/app/data \ + unis/qwen3-asr:cpu-latest +``` + +> **Note**: NVIDIA GPU images default to CUDA 13.0/cu130 with `torch 2.11.0` + `vllm 0.20.0`. +> Developers can rebuild `Dockerfile.gpu` for CUDA 12.6, CUDA 13.0, or another backend by overriding Docker build args. +> MetaX/MuXi images use `Dockerfile.metax` on top of an official MetaX vLLM image. In field deployments, use host networking plus privileged `/dev` and `/opt/mxdriver` mounts so both `mx-smi` and the MetaX PyTorch runtime can initialize devices. +> CPU images now support `qwen3-asr-0.6b` via the bundled QwenASR Rust backend. The default CPU image uses a portable Rust target; set `QWENASR_RUST_TARGET_CPU=native` only for self-built, host-specific images. +> On CUDA vLLM and CPU Rust, `word_timestamps=true` now triggers the forced aligner automatically. +> On macOS / Apple Silicon, Qwen3-ASR now runs through the Rust CPU backend. +> `start.py` now forces the vLLM multiprocessing method to `spawn` so startup does not hit CUDA re-initialization failures in forked subprocesses. + +**Custom GPU backend builds:** + +```bash +# Default GPU build: CUDA 13.0 / PyTorch cu130 +docker build -t qwen3-asr:gpu-cu130 -f Dockerfile.gpu . + +# CUDA 12.6 build for older deployments +docker build -t qwen3-asr:gpu-cu126 -f Dockerfile.gpu \ + --build-arg PYTORCH_BASE_IMAGE=pytorch/pytorch:2.11.0-cuda12.6-cudnn9-runtime \ + --build-arg PYTORCH_CUDA_INDEX=https://download.pytorch.org/whl/cu126 \ + --build-arg CUDA_NVCC_PACKAGE=cuda-nvcc-12-6 \ + --build-arg TORCH_CUDA_ARCH_LIST="8.0;8.6;8.9" \ + . + +# CUDA 13.0 build when your driver/toolchain requires it +docker build -t qwen3-asr:gpu-cu130 -f Dockerfile.gpu \ + --build-arg PYTORCH_BASE_IMAGE=pytorch/pytorch:2.11.0-cuda13.0-cudnn9-runtime \ + --build-arg PYTORCH_CUDA_INDEX=https://download.pytorch.org/whl/cu130 \ + --build-arg CUDA_NVCC_PACKAGE=cuda-nvcc-13-0 \ + --build-arg TORCH_CUDA_ARCH_LIST="12.0+PTX" \ + . + +# MetaX/MuXi build: fuse this project into an official MetaX vLLM image +./scripts/package_vendor_gpu_image.sh \ + --vendor metax \ + --base-image \ + -v n260-3.7.0.38 + +# Iluvatar/Tianshu build: fuse this project into the official Iluvatar vLLM image +docker pull registry.iluvatar.com.cn:10443/customer/sz/vllm0.17.0-4.4.0-x86:v5 +./scripts/package_vendor_gpu_image.sh \ + --vendor iluvatar \ + --base-image registry.iluvatar.com.cn:10443/customer/sz/vllm0.17.0-4.4.0-x86:v5 \ + -v vllm0.17.0-4.4.0-v5 + +# Moore Threads / MUSA build: fuse this project into the official MUSA vLLM image +docker pull registry.mthreads.com/presale/devtech/vllm_musa:s4000_4.3.5_d0519 +./scripts/package_vendor_gpu_image.sh \ + --vendor mthreads \ + --base-image registry.mthreads.com/presale/devtech/vllm_musa:s4000_4.3.5_d0519 \ + -v s4000_4.3.5_d0519 +``` + +For MetaX/MuXi offline delivery, see [docs/metax_offline_deployment.md](docs/metax_offline_deployment.md). +For Iluvatar/Tianshu offline delivery, see [docs/iluvatar_offline_deployment.md](docs/iluvatar_offline_deployment.md). +For Moore Threads / MUSA offline delivery, see [docs/mthreads_offline_deployment.md](docs/mthreads_offline_deployment.md). + +**Offline Deployment**: You can now build a timestamped offline delivery folder that includes the image archive, compose file, env template, host-dir init script, and usage docs. The export script uses plain `docker build` + `docker save`, so it does not depend on `buildx`: + +```bash +# 1. Build an offline delivery folder +./export_offline_bundle.sh --type gpu +# or +./export_offline_bundle.sh --type cpu +# or MetaX/MuXi GPU +./export_offline_bundle.sh \ + --type metax \ + --metax-base cr.metax-tech.com/public-ai-release/maca/vllm-metax:0.17.0-maca.ai3.5.3.307-torch2.8-py312-ubuntu22.04-amd64 \ + --skip-models +# or Iluvatar/Tianshu GPU +./export_offline_bundle.sh --type iluvatar --iluvatar-base registry.iluvatar.com.cn:10443/customer/sz/vllm0.17.0-4.4.0-x86:v5 +# or Moore Threads / MUSA GPU +./export_offline_bundle.sh --type mthreads --mthreads-base registry.mthreads.com/presale/devtech/vllm_musa:s4000_4.3.5_d0519 +# or build both in one bundle +./export_offline_bundle.sh --type all + +# 2. Prepare models separately, without deleting existing model files +./scripts/download-models.sh --models-dir /opt/dep/asr/models + +# 3. Copy the generated folder to the offline server +scp -r build-file/-all user@server:/opt/dep/asr/ + +# 4. On the offline server +cd /opt/dep/asr/-all +./init_host_dirs.sh +gunzip -c qwen3-asr-gpu--amd64.tar.gz | docker load +gunzip -c qwen3-asr-cpu--amd64.tar.gz | docker load +# NVIDIA GPU +docker compose up -d +# or MetaX/MuXi GPU +# docker compose -f docker-compose-metax.yml up -d +# or Iluvatar/Tianshu GPU +# docker compose -f docker-compose-iluvatar.yml up -d +# or Moore Threads / MUSA GPU +# docker compose -f docker-compose-mthreads.yml up -d +# or CPU +# docker compose -f docker-compose-cpu.yml up -d +``` + +> Detailed deployment instructions: [Deployment Guide](./docs/deployment.md) + +### Local Development + +**System Requirements:** + +- Python 3.10+ +- CUDA 13.0+ for the default GPU image; CUDA 12.6 / 13.0 can be built with Docker args +- FFmpeg (audio format conversion) + +**Installation:** + +Runtime dependency locks now default to the GPU stack at the repo root, with CPU kept as a specialized environment: + +| Mode | Command | Notes | +|------|---------|-------| +| NVIDIA GPU (default) | `uv sync` or `./scripts/sync_gpu_env.sh` | Syncs the root [pyproject.toml](/opt/qwen3-asr/pyproject.toml) and [uv.lock](/opt/qwen3-asr/uv.lock) into `.venv`, including CUDA 13.0/cu130 `torch 2.11.0` / `torchaudio 2.11.0` / `torchvision 0.26.0` / `vllm 0.20.0` | +| MetaX/MuXi GPU | `./scripts/sync_metax_env.sh` | Syncs common dependencies from [environments/metax/pyproject.toml](/opt/qwen3-asr/environments/metax/pyproject.toml); optional GPU-stack install uses the MetaX MACA PyPI index with `--no-deps` by default | +| Iluvatar/Tianshu GPU | `./scripts/sync_iluvatar_env.sh` | Syncs common dependencies from [environments/iluvatar/pyproject.toml](/opt/qwen3-asr/environments/iluvatar/pyproject.toml); GPU stack should come from the official Iluvatar vLLM image | +| Moore Threads / MUSA GPU | `./scripts/sync_mthreads_env.sh` | Syncs common dependencies from [environments/mthreads/pyproject.toml](/opt/qwen3-asr/environments/mthreads/pyproject.toml); GPU stack should come from the official Moore Threads MUSA vLLM image | +| CPU (specialized) | `./scripts/sync_cpu_env.sh` | Syncs the dedicated CPU lock in [environments/cpu/pyproject.toml](/opt/qwen3-asr/environments/cpu/pyproject.toml) into `.venv` | +| Auto | `./scripts/sync_accel_env.sh` | Chooses MetaX when `mx-smi` is present, Iluvatar when `ixsmi` is present, Moore Threads when `mthreads-gmi` is present, otherwise NVIDIA when `nvidia-smi` is present, otherwise CPU | + +```bash +# Clone project +cd qwen3-asr + +# Install dependencies (Linux/NVIDIA CUDA) +uv sync + +# Start service +source .venv/bin/activate +python start.py +``` + +MetaX/MuXi local development: + +```bash +./scripts/sync_metax_env.sh +source .venv/bin/activate +ACCELERATOR=metax python start.py +``` + +Local model storage defaults to `./models` under the project root: + +```text +./models/ + Qwen/ + iic/ + damo/ +``` + +Override it when needed: + +```bash +export MODELS_DIR=/data/qwen3-asr-models +export MODELSCOPE_CACHE=/data +export MODELSCOPE_PATH=$MODELS_DIR +``` + +macOS / Apple Silicon local development: + +```bash +./scripts/sync_cpu_env.sh +source .venv/bin/activate +python start.py +``` + +## Runtime Defaults + +Current runtime behavior on the mainline codebase: + +- `ACCELERATOR=auto` resolves to `metax` when `mx-smi` reports devices, then `iluvatar` when `ixsmi` reports devices, then `mthreads` when `mthreads-gmi` reports devices, otherwise `nvidia` when NVIDIA CUDA is available, otherwise `cpu` +- `DEVICE=auto` resolves to the active accelerator device (`cuda:0` for NVIDIA/MetaX/Iluvatar GPU, otherwise `cpu`) +- `DEVICE=mps` is normalized to `cpu` +- `Linux + NVIDIA CUDA` uses official `vLLM` +- `Linux + MetaX/MuXi MACA` uses the MetaX-compatible PyTorch/vLLM stack +- `Linux + Iluvatar/Tianshu` uses the Iluvatar official vLLM image stack +- `Linux + CPU` uses vendored `QwenASR` Rust +- `macOS / Apple Silicon` also uses vendored `QwenASR` Rust +- macOS / Apple Silicon defaults to `qwen3-asr-0.6b` +- `qwen3-asr-1.7b` on macOS is only used when `QWEN3_ASR_MODEL=qwen3-asr-1.7b` +- `word_timestamps=true` works on the current offline CUDA and CPU Rust paths +- WebSocket streaming does not currently return word-level timestamps +- CAM++ speaker diarization remains required and still follows `DEVICE`; on CPU its main hotspot is speaker verification embedding + +## API Endpoints + +### OpenAI Compatible API + +| Endpoint | Method | Function | +|----------|--------|----------| +| `/v1/audio/transcriptions` | POST | Audio transcription (OpenAI compatible) | +| `/v1/models` | GET | Offline model list | + +**Request Parameters:** + +| Parameter | Type | Default | Description | +|-----------|------|---------|-------------| +| `file` | file | Preferred when provided | Audio/video file | +| `audio_address` | string | Optional | Audio/video URL (HTTP/HTTPS), `file://`, or server-local path. Ignored when `file` is also provided | +| `language` | string | Auto-detect | Language code (zh/en/ja) | +| `enable_speaker_diarization` | bool | `true` | Enable speaker diarization | +| `enable_speaker_identification` | bool | `true` | Match registered speaker database when diarization is enabled | +| `enable_text_cleanup` | bool | `true` | Enable text deduplication, boundary-overlap trimming, and filler cleanup | +| `word_timestamps` | bool | `false` | Return word-level timestamps when the backend supports them. Qwen CUDA vLLM and CPU Rust automatically use the forced aligner when enabled. | +| `hotwords` | string | - | Hotwords, format: `word1 weight1 word2 weight2` | +| `response_format` | string | `verbose_json` | Output format | +| `prompt` | string | - | Prompt text (reserved) | +| `temperature` | float | `0` | Sampling temperature (reserved) | + +**Audio / Video Input Methods:** +- **File Upload**: Use `file` parameter to upload an audio file or a video container with an audio track +- **URL / Local Path**: Use `audio_address` parameter to provide an audio/video URL or server-local path, service will read it automatically +- **Precedence**: If both `file` and `audio_address` are provided, the service uses `file` and ignores `audio_address` + +**Usage Examples:** + +```python +# Using OpenAI SDK +from openai import OpenAI + +client = OpenAI(base_url="http://localhost:8000/v1", api_key="your_api_key") + +with open("audio.wav", "rb") as f: + transcript = client.audio.transcriptions.create( + file=f, + response_format="verbose_json" # Get segments and speaker info + ) +print(transcript.text) +``` + +```bash +# Using curl +curl -X POST "http://localhost:8000/v1/audio/transcriptions" \ + -H "Authorization: Bearer your_api_key" \ + -F "file=@audio.wav" \ + -F "model=qwen3-asr-0.6b" \ + -F "response_format=verbose_json" \ + -F "enable_speaker_diarization=true" \ + -F "enable_speaker_identification=true" \ + -F "enable_text_cleanup=true" \ + -F "hotwords=Qwen 2.0 ModelScope 1.5" +``` + +**Supported Response Formats:** `json`, `text`, `srt`, `vtt`, `verbose_json` + +### Alibaba Cloud Compatible API + +| Endpoint | Method | Function | +|----------|--------|----------| +| `/stream/v1/asr` | POST | Speech recognition (long audio support) | +| `/stream/v1/asr/models` | GET | Declared model/capability entries | +| `/stream/v1/asr/health` | GET | Health check | +| `/ws/v1/asr` | WebSocket | Qwen3-ASR streaming | +| `/ws/v1/asr/qwen` | WebSocket | Qwen3-ASR streaming (explicit path) | +| `/ws/v1/asr/funasr` | WebSocket | Removed; returns a deprecation error and asks clients to switch to `/ws/v1/asr/qwen` | + +**Request Parameters:** + +| Parameter | Type | Default | Description | +|-----------|------|---------|-------------| +| `audio_address` | string | `https://media.cdn.vect.one/podcast_demo.mp4` (docs example) | Audio/video URL, `file://`, or server-local path (optional; ignored when body content is uploaded) | +| `sample_rate` | int | `16000` | Sample rate | +| `enable_speaker_diarization` | bool | `true` | Enable speaker diarization | +| `enable_speaker_identification` | bool | `true` | Match registered speaker database when diarization is enabled | +| `enable_text_cleanup` | bool | `true` | Enable text deduplication, boundary-overlap trimming, and filler cleanup | +| `word_timestamps` | bool | `false` | Return word-level timestamps when the backend supports them. Qwen CUDA vLLM and CPU Rust automatically use the forced aligner when enabled. | +| `vocabulary_id` | string | - | Hotwords (format: `word1 weight1 word2 weight2`) | + +**Usage Examples:** + +```bash +# Basic usage +curl -X POST "http://localhost:8000/stream/v1/asr" \ + -H "Content-Type: application/octet-stream" \ + --data-binary @audio.wav + +# With parameters +curl -X POST "http://localhost:8000/stream/v1/asr?enable_speaker_diarization=true&enable_speaker_identification=true&enable_text_cleanup=true&vocabulary_id=Qwen%202.0%20ModelScope%201.5" \ + -H "Content-Type: application/octet-stream" \ + --data-binary @audio.wav +``` + +### Meeting Offline API + +| Endpoint | Method | Function | +|----------|--------|----------| +| `/api/v1/asr/transcriptions` | POST | Create an offline meeting transcription task | +| `/api/v1/asr/transcriptions/{task_id}` | GET | Query task status and result | + +`audio_address` is the required production input field for this endpoint. + +```json +{ + "audio_address": "https://example.com/media/meeting.mp4", + "config": { + "enable_speaker": true, + "match_speaker_registry": true, + "enable_text_cleanup": true, + "speaker_threshold": 0.6, + "word_timestamps": false, + "hotwords": [ + { "hotword": "Qwen", "weight": 2.0 }, + { "hotword": "ModelScope", "weight": 1.5 } + ] + } +} +``` + +**Response Example:** + +```json +{ + "task_id": "xxx", + "status": 200, + "message": "SUCCESS", + "result": "Speaker1 content...\nSpeaker2 content...", + "duration": 60.5, + "processing_time": 1.234, + "segments": [ + { + "text": "Today is a nice day.", + "start_time": 0.0, + "end_time": 2.5, + "speaker_id": "Speaker1", + "word_tokens": [ + {"text": "Today", "start_time": 0.0, "end_time": 0.5}, + {"text": "is", "start_time": 0.5, "end_time": 0.7}, + {"text": "a nice day", "start_time": 0.7, "end_time": 1.5} + ] + } + ] +} +``` + +## Speaker Diarization + +Multi-speaker automatic identification based on CAM++ model: + +- **Enabled by Default** - `enable_speaker_diarization=true` +- **Automatic Detection** - No preset speaker count needed, model auto-detects +- **Speaker Labels** - Response includes `speaker_id` field (e.g., "Speaker1", "Speaker2") +- **Smart Merging** - Two-layer merge strategy to avoid isolated short segments: + - Layer 1: Accumulate merge same-speaker segments < 10 seconds + - Layer 2: Accumulate merge continuous segments up to 60 seconds +- **Subtitle Support** - SRT/VTT output includes speaker labels `[Speaker1] text content` + +Disable speaker diarization: + +```bash +# OpenAI API +-F "enable_speaker_diarization=false" + +# Alibaba Cloud API +?enable_speaker_diarization=false +``` + +## Audio Processing + +### Intelligent Segmentation Strategy + +Automatic long audio segmentation: + +1. **VAD Voice Detection** - Detect voice boundaries, filter silence +2. **Greedy Merge** - Accumulate voice segments, ensure each segment does not exceed `MAX_SEGMENT_SEC` (default 60s) +3. **Silence Split** - Force split when silence between voice segments exceeds 3 seconds +4. **Batch Inference** - Multi-segment parallel processing, 2-3x performance improvement in GPU mode + +### WebSocket Streaming Limitations + +**Qwen3-ASR Streaming** (using `/ws/v1/asr` or `/ws/v1/asr/qwen`): +- ✅ Multi-language real-time recognition +- ✅ CUDA vLLM and CPU Rust both support the current streaming path +- ❌ Word-level timestamps are not available in the current streaming path + +### Qwen3 Runtime Matrix + +| Runtime | Backend | Offline | WebSocket Streaming | Word Timestamps Offline | Word Timestamps Streaming | Maturity | +|---------|---------|---------|---------------------|-------------------------|---------------------------|----------| +| Linux + NVIDIA GPU | Official vLLM 0.20.0 | ✅ | ✅ | ✅ | ❌ | Production-oriented | +| CPU / macOS | QwenASR Rust | ✅ | ✅ | ✅ (forced aligner) | ❌ | Recommended local fallback | + +## Offline-Capable Models + +| Model ID | Name | Description | Features | +|----------|------|-------------|----------| +| `qwen3-asr-1.7b` | Qwen3-ASR 1.7B | High-performance multilingual ASR, 52 languages + dialects; CUDA uses vLLM | Offline/Realtime | +| `qwen3-asr-0.6b` | Qwen3-ASR 0.6B | Lightweight multilingual ASR; CUDA uses vLLM, CPU/macOS uses Rust backend | Offline/Realtime | + +**Runtime selection:** +- **VRAM >= 32GB**: Select `qwen3-asr-1.7b` +- **VRAM < 32GB**: Select `qwen3-asr-0.6b` +- **No CUDA**: Select the vendored Rust-backed `qwen3-asr-0.6b` +- **macOS / Apple Silicon**: Always default to `qwen3-asr-0.6b`, regardless of memory size +- **Environment override**: Set `QWEN3_ASR_MODEL=qwen3-asr-1.7b` or `QWEN3_ASR_MODEL=qwen3-asr-0.6b` to bypass automatic selection + +At startup the service checks the current runtime model plan and downloads missing models from ModelScope by default. + +## Environment Variables + +Recommended public settings: + +| Variable | Default | Description | +|----------|---------|-------------| +| `API_KEY` | - | API authentication key (optional, unauthenticated if not set) | +| `LOG_LEVEL` | `INFO` | Log level (DEBUG/INFO/WARNING/ERROR) | +| `MAX_AUDIO_SIZE` | `2048` | Max audio file size (MB, supports units like 2GB) | +| `ASR_BATCH_SIZE` | `4` | ASR batch size for long-audio segment processing | +| `MAX_SEGMENT_SEC` | `60` | Max audio segment duration (seconds) | +| `ASR_ENABLE_NEARFIELD_FILTER` | `true` | Enable far-field sound filtering | +| `QWEN3_ASR_MODEL` | auto | Force `qwen3-asr-1.7b` or `qwen3-asr-0.6b` instead of VRAM-based selection | +| `QWEN_GPU_MEMORY_UTILIZATION` | `0.9` | Upper bound for vLLM GPU memory reservation; lower it on shared GPUs, raise it when KV cache is too small | +| `QWEN_VLLM_ENFORCE_EAGER` | `true` | Force vLLM eager execution for compatibility; set `false` to allow CUDA Graph optimization on supported NVIDIA deployments | + +Far-field filter notes: + +- `ASR_NEARFIELD_RMS_THRESHOLD=0.01` is the current default and recommended starting point +- raise it in noisy rooms to filter more background speech +- lower it in quiet rooms if soft speech is being dropped +- use `LOG_LEVEL=DEBUG` temporarily when you need to inspect filter behavior + +Advanced backend-specific settings: + +| Variable | Default | Description | +|----------|---------|-------------| +| `QWEN_RUST_CPU_WORKERS` | `4` | CPU Rust backend worker count (Rust ASR / forced align default to 4 runtimes) | +| `QWENASR_LIBRARY_PATH` | auto-detect | Override vendored Rust dylib/so path | + +## Resource Requirements + +**Minimum (CPU):** + +- CPU: 4 cores +- Memory: 16GB +- Disk: 20GB + +**Recommended (GPU):** + +- CPU: 4 cores +- Memory: 16GB +- GPU: NVIDIA GPU (16GB+ VRAM) +- Disk: 20GB + +## API Documentation + +After starting the service: + +- Swagger UI: `http://localhost:8000/docs` +- ReDoc: `http://localhost:8000/redoc` + +## Links + +- **Deployment Guide**: [Detailed Docs](./docs/deployment.md) +- **Qwen3-ASR**: [Qwen3-ASR GitHub](https://github.com/QwenLM/Qwen3-ASR) +- **FunASR**: [FunASR GitHub](https://github.com/alibaba-damo-academy/FunASR) +- **Chinese README**: [中文文档](./docs/README_zh.md) + +## License + +This project uses the MIT License - see [LICENSE](LICENSE) file for details. + +## Star History + +[![Star History Chart](https://api.star-history.com/svg?repos=Quantatirsk/qwen3-asr&type=Date)](https://star-history.com/#Quantatirsk/qwen3-asr&Date) + +## Contributing + +Issues and Pull Requests are welcome to improve the project! diff --git a/REALTIME_ASR_TROUBLESHOOTING.md b/REALTIME_ASR_TROUBLESHOOTING.md new file mode 100644 index 0000000..7bb3391 --- /dev/null +++ b/REALTIME_ASR_TROUBLESHOOTING.md @@ -0,0 +1,310 @@ +# 实时 ASR 当前问题排查记录 + +更新时间：2026-09-07 +排查范围：实时 ASR、原生流式 partial、说话人识别、声纹姓名匹配、WebSocket 输出和前端展示。 + +## 1. 当前结论 + +目前发现的问题分布在三个层次： + +```text +识别层 原生 partial 默认关闭，非原生路径会反复重识别窗口 +说话人层 短片段无条件继承上一位实名，并可能把复制的 embedding 写回记录池 +输出层 内部物理切段直接作为前端展示单元，导致同一说话人被拆成多行 +``` + +其中，“不同的人进入已经确定的说话人气泡”最明确的根因是说话人层的短段快路径：小于 1.6 秒的片段在提取新特征之前直接继承上一位已命名说话人。WebSocket 本身负责传递这些结果，但错误身份是在上游状态机中产生的。 + +“同一说话人被切成几行”主要是输出层问题。silence 和 max duration 可以结束一次内部识别片段，但不应直接决定前端展示换行。 + +## 2. 当前实时 WebSocket 链路 + +入口位于 [app/api/v1/websocket_asr.py](app/api/v1/websocket_asr.py)。`/ws/v1/asr` 和 `/ws/v1/asr/qwen` 最终都进入 [Qwen3ASRService.handle_connection](app/services/qwen3_websocket_asr.py#L2538)。 + +主流程如下： + +```text +客户端 start + ↓ +初始化 ConnectionContext 和 streaming state + ↓ +接收二进制音频 + ↓ +RMS/peak 判断是否有声音，维护 pre-roll 和当前 turn + ↓ +partial 解码并向前端发送 sentence_type=0 + ↓ +silence / max_duration / sentence_limit / stop 触发内部提交 + ↓ +最终重识别，创建 confirmed segment + ↓ +异步 speaker worker 处理说话人 + ↓ +通过相同 sentence_id 发送 speaker update + ↓ +stop 时发送最终 sentences 和 end +``` + +图谱分析显示 `handle_connection` 是当前实时链路的关键汇聚点；说话人解析又连接到 `RealtimeSpeakerClusterer.resolve_segment_speaker`、声纹注册服务和历史记录重聚类。因此直接修改主服务的影响范围较大，本次验证优先放在独立 demo 中。 + +## 3. 问题一：内部切段直接变成前端展示行 + +### 3.1 当前切段触发器 + +| 触发器 | 当前条件 | 当前作用 | 对展示的实际影响 | +|---|---|---|---| +| `silence` | 连续静音默认 `800ms` | 主力结束当前 turn | 直接产生一个 confirmed segment | +| `max_duration` | 缓冲默认最多 `12s` | 防止单段无限增长 | 长讲话被硬切成多个 segment | +| `sentence_limit` | 段内达到默认 `8` 句 | 兜底限制 | 可能提前提交 | +| `complete_sentence` | 标点结尾、时长达到阈值且字数足够 | 长独讲兜底 | 正常短句通常到不了该条件 | +| `final` | 客户端发送 `stop` | 提交最后一段 | 输出最终结果 | + +相关逻辑位于 [qwen3_websocket_asr.py](app/services/qwen3_websocket_asr.py#L2538)、[_should_commit_complete_sentence](app/services/qwen3_websocket_asr.py#L1100) 和 [_commit_retranscribe_turn](app/services/qwen3_websocket_asr.py#L2333)。 + +### 3.2 当前输出方式 + +每次内部提交都会： + +1. 创建一条 `confirmed_segments` 记录； +2. 使用该记录的 `index` 作为 `sentence_id`； +3. 立即发送 `sentence_type=1` 的 `sentences` 事件； +4. 把所有记录用换行连接成 `full_text`。 + +代码中 `full_text` 使用 `"\\n".join(...)`，位置在 [_commit_retranscribe_turn](app/services/qwen3_websocket_asr.py#L2464) 和 [_stop](app/services/qwen3_websocket_asr.py#L2916)。因此这里的 `sentence_id` 实际上是“物理切段编号”，不一定是语义完整句子编号。 + +项目协议文档明确区分了 `sentence_type=0` 的 partial 和 `sentence_type=1` 的 final，但没有把“内部 segment”和“前端 display block”分开，见 [realtime_meeting_websocket.md](docs/realtime_meeting_websocket.md#L162)。 + +前端可以根据相同 `sentence_id` 更新原记录，但不会把不同 `sentence_id` 且说话人相同的记录合并。因此停顿、12 秒硬切和前端换行目前形成了直接关系。 + +另外，图谱显示 [_should_force_stable_segment](app/services/qwen3_websocket_asr.py#L2051) 当前没有调用方，它不能实际改变实时切段；`complete_sentence` 主要是长段兜底。 + +## 4. 问题二：原生流式 partial 与输出合并不是同一层 + +当前配置中 `REALTIME_STREAM_CHUNK_SEC` 默认约为 `1.2s`，`REALTIME_PARTIAL_WINDOW_SEC` 默认约为 `8s`，原生 partial 开关默认关闭，相关默认值在 [app/core/config.py](app/core/config.py#L103)。 + +非原生路径会维护窗口并调用 `_transcribe_audio_text` 重识别；原生路径则通过： + +```text +create_stream → push_stream/feed_stream → finish_stream +``` + +对应 [qwen3_engine.py](app/services/asr/qwen3_engine.py#L570) 和 [qwen3_websocket_asr.py](app/services/qwen3_websocket_asr.py#L1162)。 + +原生 partial 影响的是： + +- partial 首次出现的延迟； +- 每次增量识别的计算量； +- 流式状态维护方式； +- 文本回滚和未固定 token 的处理。 + +它不应决定： + +- 是否因为停顿产生前端新行； +- 哪些物理 segment 合并成一个说话人气泡； +- 是否把某个 speaker 姓名写入展示结果。 + +所以两个优化需要同时做，但必须保持两个独立状态：ASR streaming state 和 display aggregation state。 + +## 5. 问题三：短音频无条件继承实名 + +### 5.1 已确认的代码路径 + +在 [realtime_speaker_clusterer.py](app/services/realtime_speaker_clusterer.py#L27) 中，`_FAST_ATTACH_MAX_SEC = 1.6`。 + +`resolve_segment_speaker` 的入口逻辑是： + +```text +duration_sec < 1.6s +且 speaker_records 非空 +且上一条记录存在实名身份 + ↓ +直接复制上一条记录的 speaker_id / speaker_name / user_id / registry_speaker_id +speaker_confidence = 0.0 +speaker_strategy = "short_attach" +``` + +该分支在 [realtime_speaker_clusterer.py](app/services/realtime_speaker_clusterer.py#L505)，早于 `extract_chunk_embeddings`。因此短段没有经过新的声纹特征验证。 + +### 5.2 为什么实名会直接通过稳定性判断 + +[_is_stable_speaker_info](app/services/qwen3_websocket_asr.py#L613) 首先检查 `registry_speaker_id` 或 `user_id`。短段继承时这两个字段被完整复制，所以即使 `speaker_confidence=0.0`，仍会被判断为 stable。 + +随后 [_resolve_segment_speaker](app/services/qwen3_websocket_asr.py#L2156) 会把该身份写入最终 segment，并由 [_emit_speaker_update](app/services/qwen3_websocket_asr.py#L498) 通过相同 `sentence_id` 推送给客户端。 + +这解释了为什么匿名 `SpeakerNN` 的继承可能被拦截，而带 `user_id` 或注册声纹 ID 的实名继承可以直接进入前端。 + +### 5.3 为什么短段是高风险场景 + +“嗯”“对”“好的”“可以”等短插话通常不到 1.6 秒，恰好是多人会议中最容易发生换人的场景。当前快路径把上一位实名当作新片段身份，产生的结果就是：新说话人的短句进入上一位实名气泡。 + +## 6. 问题四：特征提取失败分支与实际路径 + +代码中确实存在 `embedding_attach` 分支：特征提取后如果 `current_chunks` 为空，则尝试继承上一位实名，位置在 [realtime_speaker_clusterer.py](app/services/realtime_speaker_clusterer.py#L521)。 + +但当前 `extract_chunk_embeddings` 在没有 chunk 时会回退为整段单 chunk，因此正常情况下很难返回空列表；如果模型真正抛异常，异常会向上传递，最终由 [_resolve_and_emit_segment_speaker](app/services/qwen3_websocket_asr.py#L2220) 捕获，当前片段保持 pending。 + +准确结论是： + +- 模型抛异常：当前片段通常保持 `speaker_id=-1`，不会走继承； +- chunk 为空：代码意图是继承实名，但该分支近乎不可达； +- 模型不抛异常但产生垃圾 embedding：仍需单独检查零向量、NaN 和相似度边界； +- 当前最确定、最直接的错误来源是 `<1.6s` 的 `short_attach`。 + +## 7. 问题五：继承 embedding 造成污染链 + +短段继承返回时还会复制上一条记录的 `_embedding`、`_chunk_embeddings` 和 `_chunks`。下游 [_record_segment_speaker](app/services/qwen3_websocket_asr.py#L644) 只要发现 `_embedding` 非空，就会把它作为正常 speaker record 保存。 + +污染链如下： + +```text +上一位实名离场 + ↓ +新人的短段直接复制实名和 embedding + ↓ +复制结果成为新的 last_record + ↓ +后续短段继续继承这条记录 + ↓ +历史匹配池重复出现同一个 embedding + ↓ +相似度、聚类中心和重聚类结果被污染 +``` + +影响包括： + +1. 继承链持续延长，错误实名被不断续写； +2. 历史 embedding 被重复计数，匹配置信度可能虚高； +3. `cluster_records_with_ranges` 会把复制的 chunk 作为真实样本参与聚类； +4. 后续重聚类可能把错误身份回写到更多 segment。 + +## 8. 问题六：speaker update 是异步的 + +实时最终段先写入 `confirmed_segments`，speaker worker 再异步处理，处理完成后通过相同 `sentence_id` 发送更新，相关位置是 [_speaker_worker_loop](app/services/qwen3_websocket_asr.py#L442) 和 [_emit_speaker_update](app/services/qwen3_websocket_asr.py#L498)。 + +当前协议允许首次 final 没有姓名，后续再补姓名，这一点在 [realtime_meeting_websocket.md](docs/realtime_meeting_websocket.md#L240) 有说明。 + +这里有两个风险： + +- 前端如果把每次事件当成追加消息，会出现重复行； +- 上游异步继承结果如果带着错误实名，前端的幂等更新会把错误身份稳定显示出来。 + +因此上层 WebSocket 需要维护 segment 状态表，按 `sentence_id` 覆盖更新，然后根据完整状态重新生成 display block 快照。 + +## 9. 需要同时实施的两层优化 + +### 9.1 识别层：原生流式 partial + +目标是让同一轮语音持续使用一个 streaming state，通过增量音频推进识别，减少窗口重复重识别。 + +demo 应透传并记录： + +- `enable_native_partial_stream`； +- partial 产生时间； +- 每次 partial 的文本长度和修订次数； +- 首次 partial 延迟； +- final 延迟； +- 原服务返回的 chunk 或 segment 时间范围。 + +### 9.2 说话人与输出层 + +目标是把“身份确认”和“展示合并”分开： + +1. `<1.6s` 片段不继承上一位实名； +2. 没有新鲜声纹证据时保持 pending； +3. 特征提取异常时保持 pending； +4. 不复制上一条记录的 embedding； +5. 只有达到确认阈值的独立特征才能更新身份缓存； +6. 同一 `sentence_id` 的更新覆盖原片段； +7. 相邻且身份可信度一致的物理 segment 才合并为 display block； +8. A→B→A 保留时间顺序，不把非相邻发言重新拼接到一起； +9. 前端展示使用 display block，入库和诊断仍保留 raw segment。 + +推荐的状态关系是： + +```text +raw segment + ├─ ASR text state：partial / final + ├─ speaker evidence：pending / fresh / confirmed + ├─ speaker identity：cluster / registry / user + └─ display block：按时间和可信身份重新生成 +``` + +## 10. 服务器接口边界 + +已验证服务器 `10.100.53.199:8000` 可访问，原实时 WebSocket 地址为： + +```text +ws://10.100.53.199:8000/ws/v1/asr/qwen +``` + +当前公开接口包括： + +- `/ws/v1/asr/qwen`：原实时 WebSocket，已经包含主服务内部的 ASR、切段和 speaker 逻辑； +- `/v1/audio/transcriptions`：整段音频转写； +- `/api/v1/speakers/identify`：通过文件来源识别注册说话人； +- `/api/v1/speakers`：声纹注册和人员管理。 + +当前公开接口没有返回实时聚类所需的原始 embedding，也没有把 [Qwen3ASREngine](app/services/asr/qwen3_engine.py#L570) 的 `create_stream`、`feed_stream`、`finish_stream` 暴露为独立远程模型 RPC。 + +所以存在一个边界： + +- 上层 demo WebSocket 可以独立重写事件顺序、切段提交策略、pending 保护和展示合并； +- 如果要在 demo 中完整重建原项目的声纹聚类，服务器还需要提供 embedding 接口或模型 RPC； +- 只依赖当前 `/ws/v1/asr/qwen` 返回字段，无法重新计算已被原服务错误归类的长段身份。 + +## 11. 当前独立 demo 状态 + +独立验证项目位于 [realtime_asr_optimization_demo](realtime_asr_optimization_demo)。 + +当前结构： + +```text +浏览器 + ↓ ws://127.0.0.1:8082/ws +demo 上层 WebSocket + ↓ model_service.py 远程模型服务适配层 + ↓ ws://10.100.53.199:8000/ws/v1/asr/qwen +已部署模型服务 +``` + +当前 demo 已具备： + +- 原生 partial 参数透传； +- 同一 `sentence_id` 覆盖更新； +- raw event 与 display block 分离； +- 同一说话人的相邻片段合并； +- `<1.6s` 实名结果降级为 pending； +- 已命名身份不接受只有弱 cluster id 的片段加入； +- embedding 字段不进入 demo 状态池。 +- 模型服务与 WebSocket 解耦；demo 不启动或加载模型； +- 模型服务地址可通过页面或 `--model-service-url` 配置。 + +对应实现见 [server.py](realtime_asr_optimization_demo/server.py)、[model_service.py](realtime_asr_optimization_demo/model_service.py)、[speaker_assembler.py](realtime_asr_optimization_demo/speaker_assembler.py) 和 [static/app.js](realtime_asr_optimization_demo/static/app.js)。 + +这个 demo 当前主要验证上层协议、状态覆盖和展示保护。后续把模型服务部署到新服务器时，只需替换模型服务地址；若新模型服务协议不同，则替换 `model_service.py`，不改变 WebSocket 编排层。若要验证“demo 自己完成声纹特征提取、聚类和按需姓名匹配”，仍需服务器提供远程 embedding/model RPC。 + +## 12. 建议验证用例 + +| 用例 | 关注结果 | +|---|---| +| 单人连续讲话，中间停顿 800ms 以上 | 前端仍属于同一个 display block | +| A 讲话后，B 说“嗯/好的” | B 的短段保持 pending，不进入 A 的实名块 | +| A→B→A | 保持三个时间顺序块 | +| 同一 `sentence_id` 先 partial 后 final | 文本覆盖，不重复追加 | +| final 先返回，speaker update 后返回 | 原 block 更新并重新归并 | +| 声纹模型异常 | 片段保持 pending，不继承上一位实名 | +| 连续多个短插话 | 不复制上一条 embedding，不形成继承链 | +| 关闭 native partial | 只影响识别延迟和资源，不改变展示合并规则 | +| 关闭 display merge | 可以看到原始物理 segment，用于对照 | + +## 13. 当前未确认项 + +以下问题不能仅靠现有公开 WebSocket 字段确认： + +- 原服务是否存在未写入 OpenAPI 的内部 embedding/model RPC； +- 实际使用的 realtime speaker 模型是否会输出零向量或 NaN； +- 不同真实说话人被分配相同稳定 cluster id 的比例； +- speaker worker 完成时间与 `end` 事件之间是否存在竞态； +- 远程 Docker 是否还映射了独立的模型后端端口。 + +这些项目需要通过服务器日志、embedding 接口或带原始音频的端到端录音继续验证。 diff --git a/app/__init__.py b/app/__init__.py new file mode 100644 index 0000000..07b4988 --- /dev/null +++ b/app/__init__.py @@ -0,0 +1,6 @@ +# -*- coding: utf-8 -*- +"""Qwen3-ASR application package.""" + +__version__ = "1.0.1" +__author__ = "Nexa Team" +__description__ = "Qwen3-ASR speech recognition API service" diff --git a/app/api/__init__.py b/app/api/__init__.py new file mode 100644 index 0000000..64bce62 --- /dev/null +++ b/app/api/__init__.py @@ -0,0 +1,5 @@ +# -*- coding: utf-8 -*- +""" +API路由模块 +包含所有API端点的路由定义 +""" diff --git a/app/api/v1/__init__.py b/app/api/v1/__init__.py new file mode 100644 index 0000000..6adc06e --- /dev/null +++ b/app/api/v1/__init__.py @@ -0,0 +1,22 @@ +# -*- coding: utf-8 -*- +"""API v1版本路由""" + +from fastapi import APIRouter +from .asr import router as asr_router +from .websocket_asr import router as websocket_asr_router +from .openai_compatible import router as openai_router +from .meeting import router as meeting_router + +api_router = APIRouter() + +# 原有 API (阿里云兼容) +api_router.include_router(asr_router) + +# WebSocket ASR 端点（包含阿里云协议和 Qwen3 流式协议） +api_router.include_router(websocket_asr_router) + +# OpenAI 兼容 API +api_router.include_router(openai_router) + +# 独立会议离线/声纹管理 API（Model-Test-New 兼容形状） +api_router.include_router(meeting_router) diff --git a/app/api/v1/asr.py b/app/api/v1/asr.py new file mode 100644 index 0000000..ee67a69 --- /dev/null +++ b/app/api/v1/asr.py @@ -0,0 +1,481 @@ +# -*- coding: utf-8 -*- +""" +ASR API路由 +""" + +from fastapi import ( + APIRouter, + Request, + HTTPException, + Depends +) +from fastapi.responses import JSONResponse +from typing import Annotated, Optional +import time +import logging + +from ...core.config import settings +from ...core.exceptions import ( + AuthenticationException, + InvalidParameterException, + InvalidMessageException, + UnsupportedSampleRateException, + DefaultServerErrorException, + get_http_status_code, +) +from ...core.security import validate_token +from ...models.common import SampleRate +from ...models.asr import ( + ASRResponse, + ASRHealthCheckResponse, + ASRModelsResponse, + ASRSuccessResponse, + ASRErrorResponse, + ASRQueryParams, +) +from ...utils.common import generate_task_id +from ...services.asr.manager import get_model_manager +from ...services.asr.model_selection import validate_offline_model_id +from ...services.asr.runtime import get_runtime_router +from ...services.asr.audio_validation import validate_sample_rate +from ...services.asr.offline_transcription_service import ( + OfflineTranscriptionOptions, + PreparedAudio, + get_offline_transcription_service, +) + +# 配置日志 +logger = logging.getLogger(__name__) + +# 创建路由器 +router = APIRouter(prefix="/stream/v1", tags=["ASR"]) + + +def _build_asr_openapi_parameters() -> list[dict]: + parameters: list[dict] = [ + { + "name": "model", + "in": "query", + "required": False, + "schema": { + "type": "string", + "maxLength": 128, + "example": "qwen3-asr-0.6b", + }, + "description": "可选。离线 ASR 模型 ID；不传则使用服务当前默认模型", + }, + { + "name": "audio_address", + "in": "query", + "required": False, + "schema": { + "type": "string", + "maxLength": 512, + "example": "https://media.cdn.vect.one/podcast_demo.mp4", + }, + "description": "音频/视频文件地址，支持 HTTP/HTTPS URL、file:// 或服务端本地路径。仅当请求体为空时使用；若同时上传请求体，服务会忽略此参数", + }, + { + "name": "sample_rate", + "in": "query", + "required": False, + "schema": { + "type": "integer", + "enum": [8000, 16000, 22050, 24000, 32000, 44100, 48000], + "default": 16000, + "example": 16000, + }, + "description": "音频采样率（Hz）。音频会在服务端自动转换，通常保持默认值即可", + }, + { + "name": "enable_speaker_diarization", + "in": "query", + "required": False, + "schema": { + "type": "boolean", + "default": True, + "example": True, + }, + "description": "是否启用说话人分离。启用后响应会包含 speaker_id 字段", + }, + { + "name": "enable_speaker_identification", + "in": "query", + "required": False, + "schema": { + "type": "boolean", + "default": True, + "example": True, + }, + "description": "是否匹配已注册声纹库。仅在 enable_speaker_diarization=true 时生效，命中后响应会包含 speaker_name/user_id", + }, + { + "name": "enable_text_cleanup", + "in": "query", + "required": False, + "schema": { + "type": "boolean", + "default": True, + "example": True, + }, + "description": "是否启用文本去重、跨段重叠裁剪和口头语清理", + }, + { + "name": "vocabulary_id", + "in": "query", + "required": False, + "schema": { + "type": "string", + "maxLength": 512, + "example": "阿里巴巴 20 腾讯 15", + }, + "description": "热词字符串，格式：`热词1 权重1 热词2 权重2`。权重范围 1-100，建议 10-30。可提升特定词汇的识别准确率", + }, + { + "name": "X-NLS-Token", + "in": "header", + "required": False, + "schema": { + "type": "string", + "minLength": 1, + "maxLength": 256, + "example": "", + }, + "description": "访问令牌，用于身份认证。未配置 API_KEY 环境变量时可忽略", + }, + ] + if settings.ASR_ENABLE_WORD_TIMESTAMPS: + parameters.insert( + 5, + { + "name": "word_timestamps", + "in": "query", + "required": False, + "schema": { + "type": "boolean", + "default": False, + "example": False, + }, + "description": "是否返回字词级时间戳（默认关闭；启用时会自动调用 forced aligner）", + }, + ) + return parameters + + +async def get_asr_params(request: Request) -> ASRQueryParams: + """从请求中提取并验证ASR参数""" + # 从URL查询参数中获取 + query_params = dict(request.query_params) + + # 使用统一的验证器验证参数 + try: + # 验证采样率（转换为整数） + if "sample_rate" in query_params and query_params["sample_rate"]: + try: + sample_rate = int(query_params["sample_rate"]) # type: ignore + validated_rate = validate_sample_rate(sample_rate) + query_params["sample_rate"] = str(validated_rate) # type: ignore + except ValueError: + raise InvalidParameterException( + f"采样率必须是整数，收到: {query_params['sample_rate']}" + ) + + # 创建ASRQueryParams实例，Pydantic会自动验证和设置默认值 + return ASRQueryParams.model_validate(query_params) + except InvalidParameterException: + raise + except Exception as e: + raise InvalidParameterException(f"请求参数错误: {str(e)}") + + +@router.post( + "/asr", + response_model=ASRResponse, + responses={ + 200: { + "description": "识别成功", + "model": ASRSuccessResponse, + }, + 400: { + "description": "请求参数错误", + "model": ASRErrorResponse, + }, + 401: {"description": "认证失败", "model": ASRErrorResponse}, + 500: {"description": "服务器内部错误", "model": ASRErrorResponse}, + }, + summary="语音识别（支持长音频）", + description=""" +将音频文件转写为文本，兼容阿里云语音识别 RESTful API。 + +## 功能特性 +- 支持多种音频格式与常见含音轨视频容器：WAV, MP3, M4A, FLAC, OGG, AAC, AMR, PCM, WEBM, MP4, MOV, MKV, AVI 等 +- 自动音频格式检测和转换 +- 支持长音频自动分段识别（返回带时间戳的分段结果） +- 最大文件大小：{settings.MAX_AUDIO_SIZE // (1024 * 1024)}MB（可通过环境变量 MAX_AUDIO_SIZE 配置） + +## 音频输入方式 +1. **请求体上传**：将音频/视频二进制数据作为请求体发送 +2. **URL/本地路径读取**：通过 `audio_address` 参数指定音频/视频文件 URL（HTTP/HTTPS）或服务端本地路径 + +如果请求体和 `audio_address` 同时存在，服务会优先使用请求体，并忽略 `audio_address`。 + +## 注意事项 +- 默认使用服务当前启用的 Qwen3-ASR 模型；也可通过可选 `model` 参数指定当前可用离线模型 +- `vocabulary_id` 参数用于传递热词，格式：`热词1 权重1 热词2 权重2`（如：`阿里巴巴 20 腾讯 15`） +- `enable_speaker_identification` 仅在 `enable_speaker_diarization=true` 时生效，用于匹配已注册声纹库 +- `enable_text_cleanup` 控制识别后的文本去重、跨段重叠裁剪和口头语清理 +- 音频会自动转换为 16kHz 采样率进行识别 +""", + openapi_extra={ + "parameters": _build_asr_openapi_parameters(), + "requestBody": { + "description": "音频/视频文件二进制数据。支持格式：WAV, MP3, M4A, FLAC, OGG, AAC, AMR, PCM, WEBM, MP4, MOV, MKV, AVI 等。若同时提供 audio_address，服务会优先使用这里上传的内容", + "content": { + "application/octet-stream": { + "schema": {"type": "string", "format": "binary"} + } + }, + "required": False, + }, + }, +) +async def asr_transcribe( + request: Request, params: Annotated[ASRQueryParams, Depends(get_asr_params)] +) -> JSONResponse: + """语音识别API端点""" + task_id = generate_task_id() + prepared_audio: Optional[PreparedAudio] = None + + # 性能计时 + request_start_time = time.time() + + # 记录请求开始（此时文件已上传完成） + content_length = request.headers.get("content-length", "unknown") + logger.info(f"[{task_id}] 收到ASR请求, content_length={content_length}") + + transcription_service = get_offline_transcription_service() + + try: + # 验证请求头部（鉴权） + result, content = validate_token(request, task_id) + if not result: + raise AuthenticationException(content, task_id) + + model_id = validate_offline_model_id(params.model) + + # 使用音频服务处理音频 + target_sample_rate = int(params.sample_rate) if params.sample_rate else 16000 + prepared_audio = await transcription_service.prepare_from_request( + request=request, + audio_address=params.audio_address, + task_id=task_id, + sample_rate=target_sample_rate, + ) + + logger.info(f"[{task_id}] 开始调用 transcribe_long_audio (enable_speaker_diarization={params.enable_speaker_diarization})...") + asr_result = await transcription_service.transcribe( + prepared_audio, + OfflineTranscriptionOptions( + model_id=model_id, + sample_rate=int(params.sample_rate or SampleRate.RATE_16000), + hotwords=params.vocabulary_id or "", + enable_speaker_diarization=params.enable_speaker_diarization is not False, + enable_speaker_identification=( + params.enable_speaker_diarization is not False + and params.enable_speaker_identification is not False + ), + enable_text_cleanup=params.enable_text_cleanup is not False, + word_timestamps=( + settings.ASR_ENABLE_WORD_TIMESTAMPS + and params.word_timestamps is True + ), + task_id=task_id, + ), + ) + + logger.info(f"[{task_id}] 识别完成，共 {len(asr_result.segments)} 个分段，总字符: {len(asr_result.text)}") + + # 构建分段结果（始终返回 segments，短音频也是 1 个 segment） + segments_data = [] + for seg in asr_result.segments: + seg_dict = { + "text": seg.text, + "start_time": round(seg.start_time, 2), + "end_time": round(seg.end_time, 2), + } + if seg.speaker_id: + seg_dict["speaker_id"] = seg.speaker_id + if seg.speaker_name: + seg_dict["speaker_name"] = seg.speaker_name + if seg.user_id: + seg_dict["user_id"] = seg.user_id + # 添加字词级时间戳（如果存在） + if seg.word_tokens: + seg_dict["word_tokens"] = [ + { + "text": wt.text, + "start_time": round(wt.start_time, 3), + "end_time": round(wt.end_time, 3), + } + for wt in seg.word_tokens + ] + segments_data.append(seg_dict) + + # 计算请求处理时间 + request_duration = time.time() - request_start_time + + # 返回成功响应（统一数据结构） + response_data = { + "task_id": task_id, + "result": asr_result.text, + "status": 200, + "message": "SUCCESS", + "segments": segments_data, + "duration": round(asr_result.duration, 2), + "processing_time": round(request_duration, 3), + } + + return JSONResponse(content=response_data, headers={"task_id": task_id}) + + except ( + AuthenticationException, + InvalidParameterException, + InvalidMessageException, + UnsupportedSampleRateException, + DefaultServerErrorException, + ) as e: + e.task_id = task_id + logger.error(f"[{task_id}] ASR异常: {e.message}") + + # 使用标准错误格式 + response_data = e.to_dict() + return JSONResponse( + content=response_data, + headers={"task_id": task_id}, + status_code=get_http_status_code(e.status_code), + ) + + except Exception as e: + logger.error(f"[{task_id}] 未知异常: {str(e)}") + + # 使用标准错误格式 + from ...core.exceptions import create_error_response + response_data = create_error_response( + error_code="DEFAULT_SERVER_ERROR", + message=f"内部服务错误: {str(e)}", + task_id=task_id, + ) + return JSONResponse(content=response_data, headers={"task_id": task_id}) + + finally: + transcription_service.cleanup(prepared_audio) + + +@router.get( + "/asr/health", + response_model=ASRHealthCheckResponse, + summary="ASR 服务健康检查", + description=""" +检查语音识别服务的运行状态和资源使用情况。 + +## 返回信息 +- **status**: 服务状态（healthy/unhealthy/error） +- **model_loaded**: 默认模型是否已加载 +- **device**: 当前推理设备（cuda:0/cpu） +- **loaded_models**: 已加载的模型列表 +- **memory_usage**: GPU 显存使用情况（仅 GPU 模式） +""", +) +async def health_check(request: Request): + """ASR服务健康检查端点""" + # 鉴权 + result, content = validate_token(request) + if not result: + raise AuthenticationException(content, "health_check") + + try: + # 尝试获取默认模型的引擎 + try: + runtime_router = get_runtime_router() + default_model = runtime_router.resolve_model_id(None) + async with await runtime_router.acquire_engine(default_model) as engine: + model_loaded = True + device = engine.device + except Exception: + model_loaded = False + device = "unknown" + + runtime_router = get_runtime_router() + memory_info = runtime_router.get_memory_usage() + loaded_models = runtime_router.get_loaded_model_ids() + accelerator_info = memory_info.get("accelerator") + + return { + "status": "healthy" if model_loaded else "unhealthy", + "model_loaded": model_loaded, + "device": device, + "version": settings.APP_VERSION, + "message": ( + "ASR service is running normally" + if model_loaded + else "ASR model not loaded" + ), + "loaded_models": loaded_models, + "memory_usage": memory_info.get("gpu_memory"), + "accelerator": accelerator_info, + } + except Exception as e: + return { + "status": "error", + "model_loaded": False, + "device": "unknown", + "version": settings.APP_VERSION, + "message": str(e), + "accelerator": None, + } + +@router.get( + "/asr/models", + response_model=ASRModelsResponse, + summary="获取声明条目列表", + description=""" +返回系统声明的离线模型与 realtime capability 信息。 + +## 条目说明 + +| ID | 类型 | 说明 | +|----|------|------| +| qwen3-asr-1.7b | model | 离线/实时共用的 Qwen3-ASR 模型条目 | +| qwen3-asr-0.6b | model | 轻量版 Qwen3-ASR 模型条目 | +## 返回信息 +- **declared_entries**: 声明的模型与 capability 列表 +- **declared_count**: 声明项总数 +- **runtime**: 运行时加载状态 +""", +) +async def list_models(request: Request): + """获取声明条目列表端点""" + # 鉴权 + result, content = validate_token(request) + if not result: + raise AuthenticationException(content, "list_models") + + try: + + model_manager = get_model_manager() + runtime_router = get_runtime_router() + loaded_model_ids = runtime_router.get_loaded_model_ids() + entries = model_manager.list_declared_entries() + + return { + "declared_entries": entries, + "declared_count": len(entries), + "runtime": { + "loaded_model_ids": loaded_model_ids, + "loaded_count": len(loaded_model_ids), + "default_offline_model_id": runtime_router.resolve_model_id(None), + }, + } + except Exception as e: + logger.error(f"获取模型列表时发生错误: {str(e)}") + raise HTTPException(status_code=500, detail=f"获取模型列表失败: {str(e)}") diff --git a/app/api/v1/meeting.py b/app/api/v1/meeting.py new file mode 100644 index 0000000..1ec1326 --- /dev/null +++ b/app/api/v1/meeting.py @@ -0,0 +1,855 @@ +# -*- coding: utf-8 -*- +"""Independent meeting-style offline API compatible with Model-Test-New shapes.""" + +from __future__ import annotations + +import asyncio +import math +import time +import uuid +from pathlib import Path +from typing import Any, Optional +from urllib.parse import unquote, urlparse + +import requests +from fastapi import APIRouter, BackgroundTasks, Body, File, HTTPException, UploadFile +from pydantic import BaseModel, ConfigDict, Field + +from app.core.config import settings +from app.core.database import pg_speaker_db +from app.core.exceptions import InvalidParameterException +from app.core.task_store import create_task_record, get_task_record, update_task_record +from app.models.common import SampleRate +from app.services.asr.model_selection import validate_offline_model_id +from app.services.asr.offline_transcription_service import ( + OfflineTranscriptionOptions, + PreparedAudio, + get_offline_transcription_service, +) +from app.services.speaker_registry import get_speaker_registry_service + +router = APIRouter(prefix=settings.API_PREFIX) + +_UPLOAD_DIR = Path(settings.TEMP_DIR) / "api_uploads" +_UPLOAD_DIR.mkdir(parents=True, exist_ok=True) +_uploaded_files: dict[str, dict[str, str]] = {} + + +def _recognition_request_config_example() -> dict[str, Any]: + example: dict[str, Any] = { + "enable_speaker": True, + "match_speaker_registry": True, + "enable_text_cleanup": True, + "speaker_threshold": 0.6, + "hotwords": [ + {"hotword": "阿里巴巴", "weight": 2.0}, + {"hotword": "腾讯", "weight": 1.5}, + ], + } + if settings.ASR_ENABLE_WORD_TIMESTAMPS: + example["word_timestamps"] = False + return example + + +def _meeting_request_examples() -> dict[str, Any]: + return { + "url_with_hotwords": { + "summary": "URL + 结构化热词", + "description": "推荐的生产调用格式。", + "value": { + "model": "qwen3-asr-0.6b", + "audio_address": "https://example.com/media/meeting.mp4", + "config": _recognition_request_config_example(), + }, + }, + "local_path": { + "summary": "服务端本地路径", + "value": { + "audio_address": "/data/audio/meeting.m4a", + "config": { + "enable_speaker": True, + "match_speaker_registry": False, + "enable_text_cleanup": True, + }, + }, + }, + } + + +def _recognition_request_config_schema_extra(schema: dict[str, Any], _model: Any) -> None: + schema["example"] = _recognition_request_config_example() + if settings.ASR_ENABLE_WORD_TIMESTAMPS: + return + properties = schema.get("properties") + if isinstance(properties, dict): + properties.pop("word_timestamps", None) + required = schema.get("required") + if isinstance(required, list): + schema["required"] = [item for item in required if item != "word_timestamps"] + + +class HotwordItem(BaseModel): + model_config = ConfigDict(json_schema_extra={"example": {"hotword": "汇智", "weight": 2.0}}) + + hotword: str = Field( + ..., + title="热词内容", + description="需要优先匹配或纠正的业务词、专有名词、人名、产品名等。Qwen3-ASR 无原生热词能力，服务会在识别后做规则化热词纠正。", + examples=["通义千问"], + ) + weight: float = Field( + 1.0, + title="热词权重", + description="热词权重，兼容老项目 Model-Test-New 的传参格式；当前规则纠正主要使用热词文本，权重保留用于接口兼容和后续扩展。", + examples=[2.0], + ) + + +class RecognitionRequestConfig(BaseModel): + model_config = ConfigDict( + json_schema_extra=_recognition_request_config_schema_extra + ) + enable_speaker: bool = Field( + True, + title="是否启用说话人分离", + description="是否执行说话人分离。开启后返回的分段会带 speaker 字段；关闭后不做说话人分离和声纹库匹配。", + examples=[True], + ) + match_speaker_registry: bool = Field( + True, + title="是否匹配已注册声纹库", + description="是否在说话人分离后匹配已注册声纹库。只有 enable_speaker=true 时生效。", + examples=[True], + ) + enable_text_cleanup: bool = Field( + True, + title="是否启用文字去重", + description="是否启用识别结果去重/边界重叠裁剪，适合长音频分段识别后的重复文本清理。", + examples=[True], + ) + speaker_threshold: Optional[float] = Field( + None, + title="声纹匹配阈值", + description="本次声纹库匹配阈值，范围通常为 0~1；不传则使用服务默认配置。", + examples=[0.6], + ) + word_timestamps: bool = Field( + False, + title="是否返回字词级时间戳", + description="是否返回字词级时间戳。长会议场景建议默认关闭以降低耗时和结果体积。", + examples=[False], + ) + hotwords: str | list[HotwordItem] = Field( + default_factory=list, + title="热词", + description=( + "热词参数。推荐传老项目兼容的结构化数组：" + "[{\"hotword\":\"通义千问\",\"weight\":2.0}]；也兼容字符串：\"通义千问 2.0 ModelScope 1.5\"。" + "Qwen3-ASR 本身没有原生热词参数，服务会在识别后使用热词规则做文本纠正。" + ), + examples=[[{"hotword": "通义千问", "weight": 2.0}, {"hotword": "ModelScope", "weight": 1.5}]], + ) + sample_rate: int = Field( + 16000, + title="采样率", + description="音频处理采样率，默认 16000。", + examples=[16000], + ) + + +class TranscriptionCreateRequest(BaseModel): + model_config = ConfigDict( + json_schema_extra={ + "example": { + "model": "qwen3-asr-0.6b", + "audio_address": "https://example.com/meeting.m4a", + "config": { + "enable_speaker": True, + "match_speaker_registry": True, + "enable_text_cleanup": True, + "speaker_threshold": 0.6, + "hotwords": [ + {"hotword": "汇智", "weight": 2.0}, + {"hotword": "通义千问", "weight": 2.5}, + ], + }, + } + } + ) + model: Optional[str] = Field( + None, + title="离线 ASR 模型 ID", + description="可选。不传则使用服务当前默认模型；可传当前可用的本地模型 ID，如 qwen3-asr-0.6b 或 qwen3-asr-1.7b。", + examples=["qwen3-asr-0.6b"], + max_length=128, + ) + audio_address: str = Field( + ..., + title="音视频地址", + description="必填。音频/视频文件地址，支持 HTTP/HTTPS URL、file:// 地址或服务端本地音视频路径。该接口生产调用不使用 file_id/file_url。", + examples=["https://example.com/meeting.m4a"], + ) + config: RecognitionRequestConfig = Field( + default_factory=RecognitionRequestConfig, + title="识别配置", + description=( + "离线会议识别配置，包括说话人分离、声纹库匹配、文字去重和热词规则。" + if not settings.ASR_ENABLE_WORD_TIMESTAMPS + else "离线会议识别配置，包括说话人分离、声纹库匹配、文字去重、字词时间戳和热词规则。" + ) + ) + + +class SpeakerRegisterRequest(BaseModel): + model_config = ConfigDict( + json_schema_extra={"example": {"name": "张三", "user_id": "u001", "audio_address": "/data/speakers/zhangsan.wav"}} + ) + name: str + audio_address: Optional[str] = None + file_url: Optional[str] = None + file_id: Optional[str] = None + user_id: Optional[str] = None + + +class SpeakerIdentifyRequest(BaseModel): + model_config = ConfigDict(json_schema_extra={"example": {"audio_address": "/data/audio/sample.wav", "threshold": 0.6}}) + audio_address: Optional[str] = None + file_url: Optional[str] = None + file_id: Optional[str] = None + threshold: Optional[float] = None + + +def _get_meeting_transcription_description() -> str: + return """创建离线会议转写任务。该接口采用 **JSON 请求体**，适合业务系统直接提交会议音频/视频地址并异步轮询结果。 + +**音频输入方式：** +1. **HTTP/HTTPS URL**：`audio_address="https://example.com/meeting.mp4"` +2. **file:// 地址**：`audio_address="file:///data/audio/meeting.m4a"` +3. **服务端本地路径**：`audio_address="/data/audio/meeting.m4a"` + +> 生产调用只使用 `audio_address`。`file_id` / `file_url` 不属于该接口参数，上传接口仅用于测试页面调试。 + +**请求参数：** +| 字段 | 类型 | 必填 | 默认值 | 说明 | +|------|------|------|--------|------| +| `model` | string/null | 否 | 服务默认模型 | 离线 ASR 模型 ID；不传则使用 `QWEN3_ASR_MODEL` 或自动选择结果 | +| `audio_address` | string | 是 | - | 音频/视频文件地址，支持 HTTP/HTTPS、`file://`、服务端本地路径 | +| `config` | object | 否 | 默认配置 | 识别配置对象 | +| `config.enable_speaker` | boolean | 否 | `true` | 是否启用说话人分离 | +| `config.match_speaker_registry` | boolean | 否 | `true` | 是否匹配已注册声纹库，仅在 `enable_speaker=true` 时生效 | +| `config.enable_text_cleanup` | boolean | 否 | `true` | 是否启用文字去重、边界重叠裁剪 | +| `config.speaker_threshold` | number/null | 否 | 服务默认值 | 本次声纹库匹配阈值，通常为 `0~1` | +""" + ( + "| `config.word_timestamps` | boolean | 否 | `false` | 是否返回字词级时间戳；开启后会调用 Qwen3 ForcedAligner |\n" + if settings.ASR_ENABLE_WORD_TIMESTAMPS + else "" + ) + """ +| `config.hotwords` | array/string | 否 | `[]` | 热词。推荐老项目兼容数组格式：`[{\"hotword\":\"通义千问\",\"weight\":2.0}]` | +| `config.sample_rate` | integer | 否 | `16000` | 音频处理采样率 | + +**热词说明：** +- Qwen3-ASR 本身没有原生热词参数。 +- 本服务兼容 Model-Test-New 的热词格式，识别完成后通过规则做热词纠正。 +- 推荐格式：`[{\"hotword\":\"通义千问\",\"weight\":2.0},{\"hotword\":\"ModelScope\",\"weight\":1.5}]` +- 兼容字符串格式：`\"通义千问 2.0 ModelScope 1.5\"` + +**创建任务返回：** +| 字段 | 类型 | 说明 | +|------|------|------| +| `code` | integer | 业务状态码，`0` 表示成功 | +| `message` | string | 业务消息 | +| `data.task_id` | string | 任务 ID，用于查询识别进度与结果 | +| `data.status` | string | 初始状态，通常为 `queued` | +| `data.stage` | string | 当前阶段 | +| `data.percentage` | number | 当前进度百分比 | + +**查询任务返回：** +调用 `GET /api/v1/asr/transcriptions/{task_id}` 查询任务。完成后 `data.result` 包含完整结果。 + +| 字段 | 类型 | 说明 | +|------|------|------| +| `data.status` | string | `queued` / `processing` / `completed` / `failed` | +| `data.result.text` | string | 完整转写文本 | +| `data.result.segments` | array | 分段结果 | +| `data.result.segments[].start` | number | 分段开始时间，秒 | +| `data.result.segments[].end` | number | 分段结束时间，秒 | +| `data.result.segments[].text` | string | 分段文本 | +| `data.result.segments[].speaker_id` | string | 说话人 ID，启用说话人分离时返回 | +| `data.result.segments[].speaker_name` | string | 匹配到声纹库时返回姓名，否则通常等于 speaker_id | +| `data.result.segments[].user_id` | string | 匹配到声纹库用户时返回 | +| `data.result.segments[].word_tokens` | array | 字词级时间戳，仅 `word_timestamps=true` 且模型支持时返回 | +| `data.result.audio_duration_seconds` | number | 音频总时长，秒 | +| `data.result.speaker_count` | integer | 说话人数量 | +| `data.result.speakers` | array | 说话人列表 | +""" + + +def _ok(data: Any) -> dict[str, Any]: + return {"code": 0, "message": "ok", "data": data or {}} + + +def _public_task_payload(task_id: str, task: dict[str, Any]) -> dict[str, Any]: + payload: dict[str, Any] = {"task_id": task_id, "status": task["status"]} + for key in ( + "stage", + "message", + "percentage", + "detail", + "created_at", + "updated_at", + ): + if task.get(key) is not None: + payload[key] = task[key] + if ( + task["status"] == "processing" + and task.get("created_at") is not None + and task.get("percentage") not in (None, 0) + ): + elapsed_seconds = max(1.0, time.time() - float(task["created_at"])) + percentage = max(1.0, float(task.get("percentage") or 0)) + payload["eta_seconds"] = max( + 0, + int(math.ceil(elapsed_seconds * (100.0 - percentage) / percentage)), + ) + elif task["status"] == "completed": + payload["eta_seconds"] = 0 + if task.get("result") is not None: + payload["result"] = task["result"] + if task.get("error"): + payload["error"] = task["error"] + return payload + + +def _require_db() -> None: + if not pg_speaker_db.is_connected: + raise HTTPException( + status_code=503, + detail={"code": 1001, "message": "speaker database is not connected"}, + ) + + +def _normalize_hotwords_for_asr(hotwords: str | list[HotwordItem] | None) -> str: + if isinstance(hotwords, str): + return hotwords.strip() + parts: list[str] = [] + for item in hotwords or []: + text = item.hotword.strip() + if not text: + continue + parts.extend([text, f"{float(item.weight):g}"]) + return " ".join(parts) + + +async def _download_url(url: str) -> bytes: + loop = asyncio.get_running_loop() + + def _download() -> bytes: + response = requests.get(url, timeout=60) + response.raise_for_status() + return response.content + + return await loop.run_in_executor(None, _download) + + +def _is_http_url(value: str) -> bool: + return value.lower().startswith(("http://", "https://")) + + +def _source_filename(source: str) -> str: + parsed = urlparse(source) + path = unquote(parsed.path or source) + return Path(path).name or "audio" + + +async def _read_local_file(path_value: str) -> bytes: + parsed = urlparse(path_value) + local_path = Path(unquote(parsed.path)) if parsed.scheme == "file" else Path(path_value).expanduser() + if not local_path.is_file(): + raise HTTPException(status_code=404, detail={"code": 1001, "message": "local media file not found"}) + loop = asyncio.get_running_loop() + return await loop.run_in_executor(None, local_path.read_bytes) + + +async def _resolve_source_bytes(audio_address: Optional[str], file_id: Optional[str]) -> tuple[bytes, Optional[str]]: + if audio_address: + source = audio_address.strip() + if _is_http_url(source): + return await _download_url(source), _source_filename(source) + return await _read_local_file(source), _source_filename(source) + if file_id: + item = _uploaded_files.get(file_id) + if not item: + matched_files = sorted(_UPLOAD_DIR.glob(f"{file_id}.*")) + if not matched_files: + raise HTTPException(status_code=404, detail={"code": 1001, "message": "file_id not found"}) + matched_path = matched_files[0] + item = {"path": str(matched_path), "filename": matched_path.name} + _uploaded_files[file_id] = item + path = item["path"] + with open(path, "rb") as file_obj: + return file_obj.read(), item.get("filename") + raise HTTPException(status_code=400, detail={"code": 1001, "message": "audio_address or file_id is required"}) + + +def _cleanup_uploaded_file(file_id: Optional[str]) -> None: + if not file_id: + return + item = _uploaded_files.pop(file_id, None) + candidate_paths: list[Path] = [] + if item and item.get("path"): + candidate_paths.append(Path(item["path"])) + candidate_paths.extend(sorted(_UPLOAD_DIR.glob(f"{file_id}.*"))) + for path in candidate_paths: + try: + path.unlink(missing_ok=True) + except Exception: + pass + + +def _format_segments(asr_result: Any) -> list[dict[str, Any]]: + segments: list[dict[str, Any]] = [] + for segment in asr_result.segments: + item: dict[str, Any] = { + "start": round(float(segment.start_time), 2), + "end": round(float(segment.end_time), 2), + "duration": round(float(segment.end_time - segment.start_time), 2), + "text": segment.text, + } + if segment.speaker_id: + item["speaker_id"] = segment.speaker_id + if segment.speaker_name: + item["speaker_name"] = segment.speaker_name + elif segment.speaker_id: + item["speaker_name"] = segment.speaker_id + if segment.user_id: + item["user_id"] = segment.user_id + if segment.word_tokens: + item["word_tokens"] = [ + { + "text": token.text, + "start": round(float(token.start_time), 3), + "end": round(float(token.end_time), 3), + } + for token in segment.word_tokens + ] + segments.append(item) + return segments + + +def _build_transcription_result(asr_result: Any) -> dict[str, Any]: + segments = _format_segments(asr_result) + speakers: list[str] = [] + for segment in segments: + name = str(segment.get("speaker_name") or segment.get("speaker_id") or "").strip() + if name and name not in speakers: + speakers.append(name) + return { + "text": asr_result.text, + "segments": segments, + "audio_duration_seconds": round(float(asr_result.duration), 2), + "speaker_count": len(speakers), + "speakers": speakers, + } + + +def _update_task_progress( + task_id: str, + *, + status: str, + stage: str, + message: str, + percentage: int, + detail: Optional[dict[str, Any]] = None, +) -> None: + current_task = get_task_record(task_id) or {} + current_status = str(current_task.get("status") or "") + current_percentage = int(current_task.get("percentage") or 0) + incoming_percentage = max(0, min(100, int(percentage))) + if ( + current_status in {"queued", "processing"} + and status in {"queued", "processing"} + and incoming_percentage < current_percentage + ): + return + payload: dict[str, Any] = { + "status": status, + "stage": stage, + "message": message, + "percentage": incoming_percentage, + } + if detail is not None: + payload["detail"] = detail + update_task_record(task_id, payload) + + +async def _run_transcription_task( + *, + task_id: str, + model: Optional[str], + audio_address: Optional[str], + config: RecognitionRequestConfig, +) -> None: + service = get_offline_transcription_service() + prepared_audio: Optional[PreparedAudio] = None + _update_task_progress( + task_id, + status="processing", + stage="preparing", + message="任务已开始，正在准备音频文件。", + percentage=5, + detail={"segment_total": 0, "segment_completed": 0}, + ) + try: + audio_data, filename = await _resolve_source_bytes(audio_address, None) + _update_task_progress( + task_id, + status="processing", + stage="normalizing", + message="音频来源已读取，正在转换为识别格式。", + percentage=8, + detail={"segment_total": 0, "segment_completed": 0}, + ) + prepared_audio = await service.prepare_upload( + audio_data=audio_data, + filename=filename, + task_id=task_id, + sample_rate=config.sample_rate or int(SampleRate.RATE_16000), + ) + _update_task_progress( + task_id, + status="processing", + stage="transcribing", + message="音频已准备完成，正在执行离线识别。", + percentage=12, + detail={ + "segment_total": 0, + "segment_completed": 0, + "audio_duration_seconds": round(float(prepared_audio.duration), 2), + }, + ) + + def progress_callback( + stage: str, + message: str, + percentage: int, + detail: Optional[dict[str, Any]] = None, + ) -> None: + _update_task_progress( + task_id, + status="processing", + stage=stage, + message=message, + percentage=percentage, + detail=detail, + ) + + asr_result = await service.transcribe( + prepared_audio, + OfflineTranscriptionOptions( + model_id=model, + sample_rate=config.sample_rate or int(SampleRate.RATE_16000), + hotwords=_normalize_hotwords_for_asr(config.hotwords), + enable_speaker_diarization=config.enable_speaker, + enable_speaker_identification=( + config.enable_speaker and config.match_speaker_registry + ), + enable_text_cleanup=config.enable_text_cleanup, + word_timestamps=( + settings.ASR_ENABLE_WORD_TIMESTAMPS and config.word_timestamps + ), + task_id=task_id, + progress_callback=progress_callback, + ), + ) + if ( + config.enable_speaker + and config.match_speaker_registry + and config.speaker_threshold is not None + ): + _update_task_progress( + task_id, + status="processing", + stage="speaker_matching", + message="正在使用本次阈值匹配已注册声纹库。", + percentage=95, + detail={ + "segment_total": len(asr_result.segments), + "segment_completed": len(asr_result.segments), + "audio_duration_seconds": round(float(asr_result.duration), 2), + }, + ) + await get_speaker_registry_service().apply_registered_speakers( + asr_result, + threshold=config.speaker_threshold, + ) + result = _build_transcription_result(asr_result) + _update_task_progress( + task_id, + status="completed", + stage="completed", + message="离线识别已经完成。", + percentage=100, + detail={ + "segment_total": len(result.get("segments") or []), + "segment_completed": len(result.get("segments") or []), + "audio_duration_seconds": result.get("audio_duration_seconds"), + "speaker_count": result.get("speaker_count"), + "speakers": result.get("speakers"), + }, + ) + update_task_record(task_id, {"result": result}) + except Exception as exc: + _update_task_progress( + task_id, + status="failed", + stage="failed", + message="离线识别执行失败。", + percentage=100, + ) + update_task_record(task_id, {"error": str(exc)}) + finally: + service.cleanup(prepared_audio) + + +@router.post("/files", tags=["会议离线接口"], summary="上传音频文件") +async def upload_file(file: UploadFile = File(...)) -> dict[str, Any]: + file_id = uuid.uuid4().hex + suffix = Path(file.filename or "").suffix or ".audio" + path = _UPLOAD_DIR / f"{file_id}{suffix}" + content = await file.read() + path.write_bytes(content) + _uploaded_files[file_id] = {"path": str(path), "filename": file.filename or path.name} + return _ok({"file_id": file_id, "file_path": str(path)}) + + +@router.post( + "/asr/transcriptions", + tags=["会议离线接口"], + summary="创建离线会议识别任务", + description=_get_meeting_transcription_description(), + response_description="任务创建成功，返回 task_id；后续调用 GET /api/v1/asr/transcriptions/{task_id} 查询进度和结果。", + responses={ + 200: { + "description": "任务创建成功", + "content": { + "application/json": { + "example": { + "code": 0, + "message": "ok", + "data": { + "task_id": "task_6fd90daffd2f446f944b64fdf6c17572", + "status": "queued", + "stage": "queued", + "message": "任务已创建，等待进入处理流程。", + "percentage": 0, + }, + } + } + }, + }, + 400: { + "description": "请求参数错误", + "content": { + "application/json": { + "example": { + "detail": { + "code": 1001, + "message": "audio_address is required", + } + } + } + }, + }, + }, +) +async def create_transcription_task( + background_tasks: BackgroundTasks, + item: TranscriptionCreateRequest = Body( + ..., + description="离线会议识别任务 JSON 请求体。必须提供 audio_address，可在 config 中配置说话人分离、声纹库匹配、文字去重、热词等。", + examples=_meeting_request_examples(), + ), +) -> dict[str, Any]: + if not item.audio_address: + raise HTTPException(status_code=400, detail={"code": 1001, "message": "audio_address is required"}) + try: + model_id = validate_offline_model_id(item.model) + except InvalidParameterException as exc: + raise HTTPException(status_code=400, detail={"code": 1001, "message": exc.message}) from exc + task_id = f"task_{uuid.uuid4().hex}" + create_task_record( + task_id, + { + "status": "queued", + "stage": "queued", + "message": "任务已创建，等待进入处理流程。", + "percentage": 0, + "result": None, + "detail": {"segment_total": 0, "segment_completed": 0}, + }, + ) + background_tasks.add_task( + _run_transcription_task, + task_id=task_id, + model=model_id, + audio_address=item.audio_address, + config=item.config, + ) + return _ok( + { + "task_id": task_id, + "status": "queued", + "stage": "queued", + "message": "任务已创建，等待进入处理流程。", + "percentage": 0, + } + ) + + +@router.get( + "/asr/transcriptions/{task_id}", + tags=["会议离线接口"], + summary="查询离线会议识别任务", + description=( + "根据创建任务时返回的 `task_id` 查询离线会议识别进度和结果。" + "`status=completed` 时，`data.result` 会包含完整文本、分段、说话人和可选字词级时间戳。" + ), + responses={ + 200: { + "description": "查询成功", + "content": { + "application/json": { + "examples": { + "processing": { + "summary": "处理中", + "value": { + "code": 0, + "message": "ok", + "data": { + "task_id": "task_6fd90daffd2f446f944b64fdf6c17572", + "status": "processing", + "stage": "asr", + "message": "正在识别分段 80/127", + "percentage": 62, + "detail": {"segment_total": 127, "segment_completed": 80}, + "eta_seconds": 38, + }, + }, + }, + "completed": { + "summary": "已完成", + "value": { + "code": 0, + "message": "ok", + "data": { + "task_id": "task_6fd90daffd2f446f944b64fdf6c17572", + "status": "completed", + "stage": "completed", + "message": "识别完成。", + "percentage": 100, + "eta_seconds": 0, + "result": { + "text": "大家好，今天我们讨论通义千问和 ModelScope 的接入方案。", + "segments": [ + { + "start": 0.0, + "end": 4.28, + "duration": 4.28, + "text": "大家好，今天我们讨论通义千问和 ModelScope 的接入方案。", + "speaker_id": "speaker_1", + "speaker_name": "张三", + "user_id": "u001", + "word_tokens": [ + {"text": "大家好", "start": 0.12, "end": 0.68} + ], + } + ], + "audio_duration_seconds": 4669.05, + "speaker_count": 1, + "speakers": ["张三"], + }, + }, + }, + }, + } + } + }, + }, + 404: { + "description": "任务不存在", + "content": { + "application/json": { + "example": { + "detail": { + "code": 1004, + "message": "task not found", + } + } + } + }, + }, + }, +) +async def get_transcription_task(task_id: str) -> dict[str, Any]: + task = get_task_record(task_id, include_result=True) + if not task: + raise HTTPException(status_code=404, detail={"code": 1001, "message": "Task not found"}) + return _ok(_public_task_payload(task_id, task)) + + +@router.post("/speakers", tags=["声纹管理"], summary="注册声纹") +async def register_speaker(item: SpeakerRegisterRequest) -> dict[str, Any]: + _require_db() + temp_path: Optional[str] = None + try: + audio_data, filename = await _resolve_source_bytes(item.audio_address or item.file_url, item.file_id) + suffix = Path(filename or "").suffix or ".wav" + temp_path = await get_speaker_registry_service().save_upload_to_temp(audio_data, suffix=suffix) + result = await get_speaker_registry_service().register_file( + name=item.name, + file_path=temp_path, + user_id=item.user_id, + ) + return _ok(result) + finally: + get_speaker_registry_service().cleanup_file(temp_path) + _cleanup_uploaded_file(item.file_id) + + +@router.get("/speakers", tags=["声纹管理"], summary="获取声纹人员列表") +async def list_speakers() -> dict[str, Any]: + _require_db() + return _ok( + { + "items": await get_speaker_registry_service().list_speakers(), + "speaker_model": "CampPlus", + } + ) + + +@router.delete("/speakers/{speaker_id}", tags=["声纹管理"], summary="删除声纹人员") +async def delete_speaker(speaker_id: str) -> dict[str, Any]: + _require_db() + deleted = await get_speaker_registry_service().delete_speaker(speaker_id) + if not deleted: + raise HTTPException(status_code=404, detail={"code": 1001, "message": "Speaker not found"}) + return _ok({"id": speaker_id}) + + +@router.post("/speakers/identify", tags=["声纹管理"], summary="识别音频中的说话人") +async def identify_speaker(item: SpeakerIdentifyRequest) -> dict[str, Any]: + _require_db() + temp_path: Optional[str] = None + try: + audio_data, filename = await _resolve_source_bytes(item.audio_address or item.file_url, item.file_id) + suffix = Path(filename or "").suffix or ".wav" + temp_path = await get_speaker_registry_service().save_upload_to_temp(audio_data, suffix=suffix) + return _ok( + await get_speaker_registry_service().identify_file( + file_path=temp_path, + threshold=item.threshold, + ) + ) + finally: + get_speaker_registry_service().cleanup_file(temp_path) + _cleanup_uploaded_file(item.file_id) diff --git a/app/api/v1/openai_compatible.py b/app/api/v1/openai_compatible.py new file mode 100644 index 0000000..6071b9c --- /dev/null +++ b/app/api/v1/openai_compatible.py @@ -0,0 +1,692 @@ +# -*- coding: utf-8 -*- +""" +OpenAI 兼容 API +实现 OpenAI Audio API 规范，兼容 OpenAI SDK 和第三方客户端 +""" + +import asyncio +import json +import time +import logging +from typing import Any, Optional, List +from enum import Enum + +from fastapi import APIRouter, File, Form, UploadFile, Request, HTTPException +from fastapi.responses import JSONResponse, PlainTextResponse, StreamingResponse +from pydantic import BaseModel, Field + +from ...core.config import settings +from ...core.security import validate_openai_token +from ...core.exceptions import ( + create_error_response, + InvalidParameterException, +) +from ...services.asr.model_selection import ( + get_default_offline_model_id, + get_offline_model_ids, + validate_offline_model_id, +) +from ...services.asr.offline_transcription_service import ( + OfflineTranscriptionOptions, + PreparedAudio, + get_offline_transcription_service, +) + +logger = logging.getLogger(__name__) + +def _parse_hidden_bool(raw_value: Any) -> bool: + if isinstance(raw_value, bool): + return raw_value + if raw_value is None: + return False + return str(raw_value).strip().lower() in {"1", "true", "yes", "on"} + + +router = APIRouter(prefix="/v1", tags=["OpenAI Compatible"]) +HEARTBEAT_INTERVAL_SECONDS = 15.0 + + +# ============= 枚举类型 ============= + +class ResponseFormat(str, Enum): + JSON = "json" + TEXT = "text" + SRT = "srt" + VERBOSE_JSON = "verbose_json" + VTT = "vtt" + + +# ============= 响应模型 ============= + +class TranscriptionSegment(BaseModel): + """转写分段""" + id: int + seek: int = 0 + start: float + end: float + text: str + tokens: List[int] = Field(default_factory=list) + temperature: float = 0.0 + avg_logprob: float = 0.0 + compression_ratio: float = 0.0 + no_speech_prob: float = 0.0 + speaker: Optional[str] = Field(default=None, description="说话人ID") + + +class TranscriptionWord(BaseModel): + """转写词级别信息""" + word: str + start: float + end: float + + +class TranscriptionResponse(BaseModel): + """简单转写响应 (json 格式)""" + text: str + + +class VerboseTranscriptionResponse(BaseModel): + """详细转写响应 (verbose_json 格式)""" + task: str = "transcribe" + language: str + duration: float + text: str + segments: List[TranscriptionSegment] = Field(default_factory=list) + words: Optional[List[TranscriptionWord]] = None + + +class ModelObject(BaseModel): + """模型对象""" + id: str + object: str = "model" + created: int = Field(default_factory=lambda: int(time.time())) + owned_by: str = "qwen3-asr" + + +class ModelsResponse(BaseModel): + """模型列表响应""" + object: str = "list" + data: List[ModelObject] + + +# ============= 辅助函数 ============= + +def format_timestamp_srt(seconds: float) -> str: + """格式化时间戳为 SRT 格式 (HH:MM:SS,mmm)""" + hours = int(seconds // 3600) + minutes = int((seconds % 3600) // 60) + secs = int(seconds % 60) + millis = int((seconds % 1) * 1000) + return f"{hours:02d}:{minutes:02d}:{secs:02d},{millis:03d}" + + +def format_timestamp_vtt(seconds: float) -> str: + """格式化时间戳为 VTT 格式 (HH:MM:SS.mmm)""" + hours = int(seconds // 3600) + minutes = int((seconds % 3600) // 60) + secs = int(seconds % 60) + millis = int((seconds % 1) * 1000) + return f"{hours:02d}:{minutes:02d}:{secs:02d}.{millis:03d}" + + +def generate_srt(segments: List[TranscriptionSegment]) -> str: + """生成 SRT 字幕格式""" + lines = [] + for i, seg in enumerate(segments, 1): + start = format_timestamp_srt(seg.start) + end = format_timestamp_srt(seg.end) + lines.append(f"{i}") + lines.append(f"{start} --> {end}") + text = seg.text.strip() + if seg.speaker: + text = f"[{seg.speaker}] {text}" + lines.append(text) + lines.append("") + return "\n".join(lines) + + +def generate_vtt(segments: List[TranscriptionSegment]) -> str: + """生成 WebVTT 字幕格式""" + lines = ["WEBVTT", ""] + for seg in segments: + start = format_timestamp_vtt(seg.start) + end = format_timestamp_vtt(seg.end) + lines.append(f"{start} --> {end}") + text = seg.text.strip() + if seg.speaker: + text = f"[{seg.speaker}] {text}" + lines.append(text) + lines.append("") + return "\n".join(lines) + + +def detect_language(text: str, language: Optional[str]) -> str: + """检测识别语言。""" + if language: + return language + + import re + + if re.search(r"[\u4e00-\u9fff]", text): + return "zh" + return "en" + + +def build_transcription_payload( + *, + response_format: ResponseFormat, + asr_result, + audio_duration: float, + language: Optional[str], +) -> tuple[object, int, int]: + """构建 OpenAI 转写响应载荷，并返回 segments / words 计数。""" + segments: List[TranscriptionSegment] = [] + words: List[TranscriptionWord] = [] + + for i, seg in enumerate(asr_result.segments): + segments.append( + TranscriptionSegment( + id=i, + seek=int(seg.start_time * 100), + start=seg.start_time, + end=seg.end_time, + text=seg.text, + speaker=seg.speaker_id, + ) + ) + if seg.word_tokens: + for wt in seg.word_tokens: + words.append( + TranscriptionWord( + word=wt.text, + start=round(seg.start_time + wt.start_time, 3), + end=round(seg.start_time + wt.end_time, 3), + ) + ) + + detected_language = detect_language(asr_result.text, language) + + if response_format == ResponseFormat.VERBOSE_JSON: + payload = VerboseTranscriptionResponse( + task="transcribe", + language=detected_language, + duration=audio_duration, + text=asr_result.text, + segments=segments, + words=words if words else None, + ).model_dump() + elif response_format == ResponseFormat.JSON: + payload = {"text": asr_result.text} + elif response_format == ResponseFormat.TEXT: + payload = asr_result.text + elif response_format == ResponseFormat.SRT: + if not segments: + segments = [ + TranscriptionSegment( + id=0, + start=0, + end=audio_duration, + text=asr_result.text, + ) + ] + payload = generate_srt(segments) + elif response_format == ResponseFormat.VTT: + if not segments: + segments = [ + TranscriptionSegment( + id=0, + start=0, + end=audio_duration, + text=asr_result.text, + ) + ] + payload = generate_vtt(segments) + else: + payload = {"text": asr_result.text} + + return payload, len(segments), len(words) + + +def create_heartbeat_streaming_response( + *, + response_format: ResponseFormat, + inference_coro, + audio_duration: float, + language: Optional[str], + cleanup_callback, +) -> StreamingResponse: + """为长耗时 JSON 响应生成带心跳的流式输出。""" + + async def response_stream(): + inference_task = asyncio.create_task(inference_coro) + heartbeat_count = 0 + + try: + while True: + done, _pending = await asyncio.wait( + {inference_task}, + timeout=HEARTBEAT_INTERVAL_SECONDS, + return_when=asyncio.FIRST_COMPLETED, + ) + if inference_task in done: + break + + heartbeat_count += 1 + logger.info( + "[OpenAI API] 发送响应心跳: " + f"format={response_format}, heartbeat_count={heartbeat_count}" + ) + yield b" \n" + + asr_result = await inference_task + logger.info(f"[OpenAI API] 识别完成: {len(asr_result.text)} 字符") + + payload, segments_count, words_count = build_transcription_payload( + response_format=response_format, + asr_result=asr_result, + audio_duration=audio_duration, + language=language, + ) + response_bytes = json.dumps( + payload, + ensure_ascii=False, + separators=(",", ":"), + ).encode("utf-8") + + logger.info( + "[OpenAI API] 准备发送 JSON 响应: " + f"format={response_format}, " + f"segments={segments_count}, " + f"words={words_count}, " + f"payload_bytes={len(response_bytes)}, " + f"heartbeat_count={heartbeat_count}" + ) + yield response_bytes + finally: + cleanup_callback() + + return StreamingResponse( + response_stream(), + media_type="application/json", + headers={ + "Cache-Control": "no-cache", + "X-Accel-Buffering": "no", + }, + ) + + +# ============= API 端点 ============= + +def _get_openai_model_description() -> str: + """获取动态的模型描述""" + available_models = get_offline_model_ids() + default_model = get_default_offline_model_id() + + model_descriptions = { + "qwen3-asr-1.7b": "Qwen3-ASR 1.7B，52 种语言，vLLM 高性能", + "qwen3-asr-0.6b": "Qwen3-ASR 0.6B，轻量版，适合小显存环境", + "qwen3-asr": "自动路由到当前已启动的 Qwen3-ASR 版本", + } + + # 构建表格行 + table_rows = [] + for m in available_models: + desc = model_descriptions.get(m, "") + if m == default_model: + desc += "（默认）" + table_rows.append(f"| `{m}` | {desc} |") + + return f"""返回当前可用的离线 Qwen3-ASR 模型列表（OpenAI `/v1/models` 兼容）。 + +**可用离线模型：** + +| 模型 ID | 说明 | +|---------|------| +{chr(10).join(table_rows)} + +**兼容性说明：** +- 支持 OpenAI SDK 和第三方客户端调用 +- 当前默认模型根据显存自动选择；也可通过 `QWEN3_ASR_MODEL` 覆盖 +""" + + +@router.get( + "/models", + response_model=ModelsResponse, + summary="列出可用离线模型", + description=_get_openai_model_description(), +) +async def list_models(request: Request): + """列出可用离线模型 (OpenAI 兼容)""" + result, _ = validate_openai_token(request) + if not result: + response_data = create_error_response( + error_code="AUTHENTICATION_FAILED", + message="Invalid authentication", + ) + return JSONResponse(content=response_data, status_code=401) + + try: + # 使用动态模型列表 + model_ids = get_offline_model_ids() + + model_objects = [] + for model_id in model_ids: + model_objects.append(ModelObject( + id=model_id, + owned_by="qwen3-asr", + )) + + return ModelsResponse(data=model_objects) + except Exception as e: + logger.error(f"获取模型列表失败: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +def _get_transcription_description() -> str: + """获取动态的转写端点描述""" + return f"""将音频文件转写为文本（完全兼容 OpenAI Audio API）。 + +**支持的音频格式与常见含音轨视频容器：** +`mp3`, `mp4`, `mpeg`, `mpga`, `m4a`, `wav`, `webm`, `flac`, `ogg`, `amr`, `pcm`, `mov`, `mkv`, `avi` + +**音频输入方式：** +1. **文件上传**：通过 `file` 参数上传音频/视频文件（标准 OpenAI 方式） +2. **URL/本地路径读取**：通过 `audio_address` 参数提供音频/视频文件 URL（HTTP/HTTPS）或服务端本地路径 + +如果同时提供 `file` 和 `audio_address`，服务会优先使用 `file`，并忽略 `audio_address`。 + +**文件大小限制：** +- 最大支持 {settings.MAX_AUDIO_SIZE // (1024 * 1024)}MB（可通过 `MAX_AUDIO_SIZE` 环境变量配置） +- OpenAI 原生限制为 25MB + +**说话人分离：** +- 默认开启 (`enable_speaker_diarization=true`) +- 启用后 `verbose_json` 格式的 segments 会包含 `speaker` 字段（如 "说话人1"） +- 可设置 `enable_speaker_diarization=false` 关闭 + +**增强选项：** +- `enable_speaker_identification=true` 时会尝试匹配已注册声纹库，仅在说话人分离开启时生效 +- `enable_text_cleanup=true` 时会执行文本去重、跨段重叠裁剪和口头语清理 +- `hotwords` 可传热词字符串，格式：`热词1 权重1 热词2 权重2` + +**输出格式：** +| 格式 | Content-Type | 说明 | +|------|-------------|------| +| `json` | application/json | 简单 JSON，仅含 text 字段（默认） | +| `text` | text/plain | 纯文本 | +| `verbose_json` | application/json | 详细 JSON，含时间戳、分段和说话人 | +| `srt` | text/plain | SRT 字幕格式 | +| `vtt` | text/vtt | WebVTT 字幕格式 | + +**模型选择：** +- 默认使用当前服务启用的 Qwen3-ASR 模型 +- 可通过可选 `model` 表单字段指定当前可用离线模型 +- 通过 `QWEN3_ASR_MODEL` 控制服务端默认模型型号 +- `/v1/models` 仍可用于查看当前服务端实际在线模型 + +**兼容参数：** +`prompt`、`temperature`、`timestamp_granularities` 参数已保留；其中 `prompt` 可作为热词提示的兼容入口 +""" + + +@router.post( + "/audio/transcriptions", + summary="音频转写", + description=_get_transcription_description(), + responses={ + 200: { + "description": "转写成功", + "content": { + "application/json": { + "example": {"text": "今天天气不错，明天可能会下雨。"} + }, + "text/plain": { + "example": "今天天气不错，明天可能会下雨。" + }, + }, + }, + 400: { + "description": "请求错误", + "content": { + "application/json": { + "example": { + "error_code": "INVALID_PARAMETER", + "message": f"File too large. Maximum size is {settings.MAX_AUDIO_SIZE // (1024 * 1024)}MB", + "task_id": "", + "timestamp": "2025-01-31T12:00:00Z", + "details": {} + } + } + }, + }, + 401: { + "description": "认证失败", + "content": { + "application/json": { + "example": { + "error_code": "AUTHENTICATION_FAILED", + "message": "Invalid API key", + "task_id": "", + "timestamp": "2025-01-31T12:00:00Z", + "details": {} + } + } + }, + }, + }, +) +async def create_transcription( + request: Request, + # 1. 音频输入（二选一） + file: Optional[UploadFile] = File( + default=None, + description="要转写的音频/视频文件。若同时提供 audio_address，服务会优先使用这里上传的文件" + ), + audio_address: Optional[str] = Form( + default=None, + description="音频/视频文件地址，支持 HTTP/HTTPS URL、file:// 或服务端本地路径。仅当 file 为空时使用；若同时上传 file，服务会忽略此参数", + json_schema_extra={"example": "https://media.cdn.vect.one/podcast_demo.mp4"}, + ), + model: Optional[str] = Form( + default=None, + description="可选。离线 ASR 模型 ID；不传则使用服务当前默认模型", + examples=["qwen3-asr-0.6b"], + ), + language: Optional[str] = Form( + None, + description="音频语言代码（ISO-639-1），如 zh/en/ja，不填则自动检测", + examples=["zh", "en", "ja"], + ), + # 4. 功能开关 + enable_speaker_diarization: bool = Form( + True, + description="是否启用说话人分离（默认开启）。启用后响应 segments 会包含 speaker 字段" + ), + enable_speaker_identification: bool = Form( + True, + description="是否匹配已注册声纹库。仅在 enable_speaker_diarization=true 时生效" + ), + enable_text_cleanup: bool = Form( + True, + description="是否启用文本去重、跨段重叠裁剪和口头语清理" + ), + hotwords: Optional[str] = Form( + None, + description="热词字符串，格式：热词1 权重1 热词2 权重2" + ), + # 5. 输出选项 + response_format: ResponseFormat = Form( + ResponseFormat.VERBOSE_JSON, + description="输出格式", + examples=["verbose_json", "json", "text", "srt", "vtt"], + ), + # 6. 兼容性参数（暂不支持） + prompt: Optional[str] = Form(None, description="提示文本（暂不支持，保留兼容）"), # noqa: ARG001 + temperature: Optional[float] = Form(0, description="采样温度（暂不支持，保留兼容）"), # noqa: ARG001 + timestamp_granularities: Optional[List[str]] = Form( # noqa: ARG001 + None, + alias="timestamp_granularities[]", + description="时间戳粒度（暂不支持，保留兼容）" + ), +): + """音频转写 API (OpenAI Audio API 兼容)""" + # 标记暂不支持的参数（保留以兼容 OpenAI API） + _ = (temperature, timestamp_granularities) + hotword_text = (hotwords or prompt or "").strip() + form_data = await request.form() + requested_word_timestamps = _parse_hidden_bool(form_data.get("word_timestamps")) + word_timestamps = settings.ASR_ENABLE_WORD_TIMESTAMPS and requested_word_timestamps + + prepared_audio: Optional[PreparedAudio] = None + response_cleanup_managed = False + + logger.info(f"[OpenAI API] 收到转写请求: model={model or 'default'}, format={response_format}, " + f"speaker_diarization={enable_speaker_diarization}, word_level={word_timestamps}, " + f"audio_address={'有' if audio_address else '无'}") + + # 验证输入：至少提供一种输入源；若二者同时存在，优先 file + if not file and not audio_address: + response_data = create_error_response( + error_code="INVALID_PARAMETER", + message="必须提供 file（上传文件）或 audio_address（音频 URL）其中之一", + ) + return JSONResponse(content=response_data, status_code=400) + + transcription_service = get_offline_transcription_service() + + try: + result, _ = validate_openai_token(request) + if not result: + response_data = create_error_response( + error_code="AUTHENTICATION_FAILED", + message="Invalid authentication", + ) + return JSONResponse(content=response_data, status_code=401) + + model_id = validate_offline_model_id(model) + + # 处理音频输入：优先 file，其次 audio_address + if file is not None: + if audio_address: + logger.info("[OpenAI API] 检测到同时提供 file 和 audio_address，已忽略 audio_address") + + logger.info(f"[OpenAI API] 从上传文件读取音频: {file.filename}") + audio_data = await file.read() + + prepared_audio = await transcription_service.prepare_upload( + audio_data=audio_data, + filename=file.filename if file else None, + task_id=f"openai-{int(time.time() * 1000)}", + sample_rate=16000, + ) + else: + logger.info(f"[OpenAI API] 从 URL 下载音频: {audio_address}") + prepared_audio = await transcription_service.prepare_from_request( + request=request, + audio_address=audio_address, + task_id=f"openai-{int(time.time() * 1000)}", + sample_rate=16000, + ) + + inference_coro = transcription_service.transcribe( + prepared_audio, + OfflineTranscriptionOptions( + model_id=model_id, + sample_rate=16000, + hotwords=hotword_text, + enable_speaker_diarization=enable_speaker_diarization, + enable_speaker_identification=( + enable_speaker_diarization and enable_speaker_identification + ), + enable_text_cleanup=enable_text_cleanup, + word_timestamps=word_timestamps, + ), + ) + audio_duration = prepared_audio.duration + + # 根据 response_format 返回不同格式 + if response_format == ResponseFormat.TEXT: + asr_result = await inference_coro + logger.info(f"[OpenAI API] 识别完成: {len(asr_result.text)} 字符") + payload, _, _ = build_transcription_payload( + response_format=response_format, + asr_result=asr_result, + audio_duration=audio_duration, + language=language, + ) + return PlainTextResponse(content=payload) + + elif response_format == ResponseFormat.SRT: + asr_result = await inference_coro + logger.info(f"[OpenAI API] 识别完成: {len(asr_result.text)} 字符") + payload, _, _ = build_transcription_payload( + response_format=response_format, + asr_result=asr_result, + audio_duration=audio_duration, + language=language, + ) + return PlainTextResponse(content=payload, media_type="text/plain") + + elif response_format == ResponseFormat.VTT: + asr_result = await inference_coro + logger.info(f"[OpenAI API] 识别完成: {len(asr_result.text)} 字符") + payload, _, _ = build_transcription_payload( + response_format=response_format, + asr_result=asr_result, + audio_duration=audio_duration, + language=language, + ) + return PlainTextResponse(content=payload, media_type="text/vtt") + + elif response_format in {ResponseFormat.VERBOSE_JSON, ResponseFormat.JSON}: + response_cleanup_managed = True + return create_heartbeat_streaming_response( + response_format=response_format, + inference_coro=inference_coro, + audio_duration=audio_duration, + language=language, + cleanup_callback=lambda: transcription_service.cleanup(prepared_audio), + ) + + else: + asr_result = await inference_coro + logger.info(f"[OpenAI API] 识别完成: {len(asr_result.text)} 字符") + payload, _, _ = build_transcription_payload( + response_format=ResponseFormat.JSON, + asr_result=asr_result, + audio_duration=audio_duration, + language=language, + ) + return JSONResponse(content=payload) + + except HTTPException as http_exc: + # 将 HTTPException 转换为标准错误格式 + logger.error(f"[OpenAI API] HTTP异常: {http_exc.detail}") + + response_data = create_error_response( + error_code="DEFAULT_CLIENT_ERROR" if http_exc.status_code < 500 else "DEFAULT_SERVER_ERROR", + message=http_exc.detail, + ) + return JSONResponse(content=response_data, status_code=http_exc.status_code) + except InvalidParameterException as e: + logger.error(f"[OpenAI API] 参数异常: {e.message}") + response_data = create_error_response( + error_code=e.error_code, + message=e.message, + details=e.details, + ) + return JSONResponse(content=response_data, status_code=400) + except Exception as e: + logger.error(f"[OpenAI API] 转写失败: {e}") + + # 使用标准错误格式 + response_data = create_error_response( + error_code="DEFAULT_SERVER_ERROR", + message=str(e), + ) + return JSONResponse(content=response_data, status_code=500) + + finally: + if not response_cleanup_managed: + transcription_service.cleanup(prepared_audio) diff --git a/app/api/v1/websocket_asr.py b/app/api/v1/websocket_asr.py new file mode 100644 index 0000000..ce82187 --- /dev/null +++ b/app/api/v1/websocket_asr.py @@ -0,0 +1,47 @@ +# -*- coding: utf-8 -*- +"""WebSocket ASR API routes.""" + +import logging +import time +import uuid +from typing import Optional + +from fastapi import APIRouter, WebSocket + +from ...services.qwen3_websocket_asr import Qwen3ASRService + +logger = logging.getLogger(__name__) +router = APIRouter(prefix="/ws/v1/asr", tags=["WebSocket ASR"]) + + +@router.websocket("/funasr") +async def funasr_websocket(websocket: WebSocket) -> None: + await websocket.accept() + task_id = f"deprecated_funasr_ws_{int(time.time())}_{id(websocket)}" + try: + await websocket.send_json( + { + "type": "error", + "task_id": task_id, + "code": "FUNASR_REALTIME_REMOVED", + "message": "FunASR/Paraformer realtime websocket has been removed. Use /ws/v1/asr/qwen instead.", + } + ) + except Exception: + pass + finally: + await websocket.close(code=1008, reason="Use /ws/v1/asr/qwen") + + +_qwen3_service = Qwen3ASRService() + + +@router.websocket("") +@router.websocket("/qwen") +async def qwen_asr_websocket( + websocket: WebSocket, + task_id: Optional[str] = None, +) -> None: + if task_id is None: + task_id = str(uuid.uuid4())[:8] + await _qwen3_service.handle_connection(websocket, task_id) diff --git a/app/bootstrap.py b/app/bootstrap.py new file mode 100644 index 0000000..b7c20a0 --- /dev/null +++ b/app/bootstrap.py @@ -0,0 +1,58 @@ +# -*- coding: utf-8 -*- +"""Shared bootstrap helpers for process startup.""" + +from __future__ import annotations + +import sys + + +def ensure_models_downloaded(interactive: bool) -> bool: + """Ensure declared deployment models exist locally, downloading if needed.""" + try: + from app.utils.download_models import check_all_models, download_models + + missing = check_all_models() + if not missing: + return True + + print(f"\n⚠️ 检测到 {len(missing)} 个模型未下载") + for model_id, *_ in missing: + print(f" - {model_id}") + + print("\n将自动下载缺失模型后继续启动。") + if download_models(auto_mode=True): + return True + + print("\n模型自动下载失败。") + if interactive: + print("可手动运行以下命令排查：") + print(" uv run python -m app.utils.download_models") + print(" ./scripts/prepare-models.sh") + else: + print("非交互式终端下请确认网络可用，或预先准备模型缓存。") + return False + except Exception as exc: + print(f"⚠️ 模型检查失败: {exc}") + return False + + +def run_cli_preflight() -> bool: + """Preflight checks for the CLI entrypoint.""" + try: + from app.core.accelerator import get_accelerator_info, validate_accelerator_runtime + + ok, message = validate_accelerator_runtime() + info = get_accelerator_info() + print( + "Accelerator | " + f"vendor={info.vendor} runtime={info.runtime} device={info.device} " + f"count={info.device_count}" + ) + if not ok: + print(f"加速器运行栈检查失败: {message}") + return False + except Exception as exc: + print(f"加速器检查失败: {exc}") + return False + + return ensure_models_downloaded(interactive=sys.stdin.isatty()) diff --git a/app/core/__init__.py b/app/core/__init__.py new file mode 100644 index 0000000..97b189c --- /dev/null +++ b/app/core/__init__.py @@ -0,0 +1,5 @@ +# -*- coding: utf-8 -*- +""" +核心模块 +包含配置、异常、安全等基础组件 +""" diff --git a/app/core/accelerator.py b/app/core/accelerator.py new file mode 100644 index 0000000..9685e85 --- /dev/null +++ b/app/core/accelerator.py @@ -0,0 +1,162 @@ +# -*- coding: utf-8 -*- +"""Unified accelerator detection and runtime validation.""" + +from __future__ import annotations + +import os +from functools import lru_cache +from typing import Optional + +from .accelerators import ( + AcceleratorInfo, + IluvatarAcceleratorAdapter, + MetaxAcceleratorAdapter, + MThreadsAcceleratorAdapter, + NvidiaAcceleratorAdapter, +) + +SUPPORTED_ACCELERATORS = {"auto", "cpu", "nvidia", "metax", "iluvatar", "mthreads"} + + +def _cpu_info(reason: str = "") -> AcceleratorInfo: + return AcceleratorInfo( + vendor="cpu", + runtime="cpu", + device="cpu", + available=True, + reason=reason, + ) + + +def _normalize_configured(configured: Optional[str]) -> str: + value = (configured or os.getenv("ACCELERATOR") or "auto").strip().lower() + if value == "cuda": + return "nvidia" + if value in {"maca", "muxi", "mx"}: + return "metax" + if value in {"ix", "tianshu", "天数"}: + return "iluvatar" + if value in {"mthreads", "musa", "moorethreads", "摩尔线程"}: + return "mthreads" + if value not in SUPPORTED_ACCELERATORS: + raise ValueError( + f"Unsupported ACCELERATOR={value!r}; expected one of: " + f"{', '.join(sorted(SUPPORTED_ACCELERATORS))}" + ) + return value + + +def _detect_uncached(configured: Optional[str]) -> AcceleratorInfo: + accelerator = _normalize_configured(configured) + if accelerator == "cpu": + return _cpu_info("forced by ACCELERATOR=cpu") + + if accelerator == "nvidia": + return NvidiaAcceleratorAdapter().detect() + + if accelerator == "metax": + return MetaxAcceleratorAdapter().detect() + + if accelerator == "iluvatar": + return IluvatarAcceleratorAdapter().detect() + + if accelerator == "mthreads": + return MThreadsAcceleratorAdapter().detect() + + # Auto mode prefers vendor SMI commands before NVIDIA. This avoids a vendor + # PyTorch build exposing torch.cuda and being mistaken for NVIDIA. + metax = MetaxAcceleratorAdapter().detect() + if metax.available: + return metax + + iluvatar = IluvatarAcceleratorAdapter().detect() + if iluvatar.available: + return iluvatar + + mthreads = MThreadsAcceleratorAdapter().detect() + if mthreads.available: + return mthreads + + nvidia = NvidiaAcceleratorAdapter().detect() + if nvidia.available: + return nvidia + + return _cpu_info("no supported accelerator detected") + + +@lru_cache(maxsize=8) +def _detect_cached(configured: str) -> AcceleratorInfo: + return _detect_uncached(configured) + + +def detect_accelerator(configured: Optional[str] = None, *, refresh: bool = False) -> AcceleratorInfo: + """Detect the active accelerator. + + Args: + configured: Optional override matching ACCELERATOR values. + refresh: Clear cached detection before probing. + """ + normalized = _normalize_configured(configured) + if refresh: + _detect_cached.cache_clear() + return _detect_cached(normalized) + + +def get_accelerator_info(*, refresh: bool = False) -> AcceleratorInfo: + try: + from app.core.config import settings + + configured = settings.ACCELERATOR + except Exception: + configured = os.getenv("ACCELERATOR", "auto") + return detect_accelerator(configured, refresh=refresh) + + +def validate_accelerator_runtime() -> tuple[bool, str]: + """Validate explicit accelerator selections before model loading.""" + try: + from app.core.config import settings + + configured = _normalize_configured(settings.ACCELERATOR) + except Exception: + configured = _normalize_configured(os.getenv("ACCELERATOR", "auto")) + + info = detect_accelerator(configured, refresh=True) + if configured in {"auto", "cpu", ""}: + return True, "" + + if not info.available: + return False, f"ACCELERATOR={configured} requested but {info.reason}" + + if configured == "metax" and not info.metadata.get("torch_cuda_available"): + return ( + False, + "ACCELERATOR=metax detected mx-smi devices, but the active Python " + "environment does not expose torch.cuda. Run ./scripts/sync_metax_env.sh " + "or install the MetaX MACA PyTorch stack.", + ) + + if configured == "iluvatar" and not info.metadata.get("torch_cuda_available"): + return ( + False, + "ACCELERATOR=iluvatar detected ixsmi devices, but the active Python " + "environment does not expose torch.cuda. Use the official Iluvatar " + "vLLM image as the base image or install the Iluvatar PyTorch stack.", + ) + + if configured == "mthreads" and not info.metadata.get("torch_cuda_available"): + return ( + False, + "ACCELERATOR=mthreads detected mthreads-gmi devices, but the active Python " + "environment does not expose torch.cuda. Use the official Moore Threads " + "MUSA vLLM image as the base image or install the matching MUSA PyTorch stack.", + ) + + if configured == "nvidia" and not info.metadata.get("torch_cuda_available"): + return ( + False, + "ACCELERATOR=nvidia detected NVIDIA devices, but torch.cuda is not " + "available in the active Python environment. Run ./scripts/sync_gpu_env.sh.", + ) + + return True, "" diff --git a/app/core/accelerators/__init__.py b/app/core/accelerators/__init__.py new file mode 100644 index 0000000..38644ac --- /dev/null +++ b/app/core/accelerators/__init__.py @@ -0,0 +1,16 @@ +# -*- coding: utf-8 -*- +"""Accelerator adapter exports.""" + +from .base import AcceleratorInfo +from .iluvatar import IluvatarAcceleratorAdapter +from .metax import MetaxAcceleratorAdapter +from .mthreads import MThreadsAcceleratorAdapter +from .nvidia import NvidiaAcceleratorAdapter + +__all__ = [ + "AcceleratorInfo", + "IluvatarAcceleratorAdapter", + "MetaxAcceleratorAdapter", + "MThreadsAcceleratorAdapter", + "NvidiaAcceleratorAdapter", +] diff --git a/app/core/accelerators/base.py b/app/core/accelerators/base.py new file mode 100644 index 0000000..464724f --- /dev/null +++ b/app/core/accelerators/base.py @@ -0,0 +1,109 @@ +# -*- coding: utf-8 -*- +"""Shared accelerator adapter primitives.""" + +from __future__ import annotations + +import os +import re +import shutil +import subprocess +from dataclasses import dataclass, field +from typing import Optional, Protocol + + +@dataclass(frozen=True) +class AcceleratorInfo: + """Normalized hardware/runtime information used by the application.""" + + vendor: str + runtime: str + device: str + device_count: int = 0 + visible_devices: tuple[str, ...] = () + total_memory_gb: float = 0.0 + smi_command: Optional[str] = None + available: bool = False + reason: str = "" + metadata: dict[str, object] = field(default_factory=dict) + + @property + def is_gpu(self) -> bool: + return self.vendor not in {"cpu", "unknown"} and self.available + + def as_dict(self) -> dict[str, object]: + return { + "vendor": self.vendor, + "runtime": self.runtime, + "device": self.device, + "device_count": self.device_count, + "visible_devices": list(self.visible_devices), + "total_memory_gb": self.total_memory_gb, + "smi_command": self.smi_command, + "available": self.available, + "reason": self.reason, + "metadata": self.metadata, + } + + @property + def supports_sharded(self) -> bool: + value = self.metadata.get("supports_sharded") + return bool(value) + + +class AcceleratorAdapter(Protocol): + vendor: str + runtime: str + + def detect(self) -> AcceleratorInfo: + """Return normalized accelerator info.""" + + +def command_exists(command: str) -> bool: + return shutil.which(command) is not None + + +def run_command(command: list[str], timeout: float = 3.0) -> str: + try: + completed = subprocess.run( + command, + check=False, + capture_output=True, + text=True, + timeout=timeout, + ) + except (OSError, subprocess.TimeoutExpired): + return "" + + if completed.returncode != 0: + return "" + return completed.stdout.strip() + + +def parse_visible_devices(raw: str | None) -> tuple[str, ...]: + value = (raw or "").strip() + if not value or value.lower() in {"all", "none", "void"}: + return () + return tuple(part.strip() for part in value.split(",") if part.strip()) + + +def first_env_devices(names: tuple[str, ...]) -> tuple[str, ...]: + shared = parse_visible_devices(os.getenv("ASR_VISIBLE_DEVICES")) + if shared: + return shared + for name in names: + devices = parse_visible_devices(os.getenv(name)) + if devices: + return devices + return () + + +def parse_memory_gb(text: str) -> float: + """Parse the smallest memory value from common smi outputs.""" + values: list[float] = [] + for number, unit in re.findall(r"([0-9]+(?:\.[0-9]+)?)\s*(GiB|GB|MiB|MB)", text, re.I): + value = float(number) + normalized_unit = unit.lower() + if normalized_unit in {"mib", "mb"}: + value = value / 1024 + values.append(value) + return min(values) if values else 0.0 diff --git a/app/core/accelerators/iluvatar.py b/app/core/accelerators/iluvatar.py new file mode 100644 index 0000000..958315c --- /dev/null +++ b/app/core/accelerators/iluvatar.py @@ -0,0 +1,106 @@ +# -*- coding: utf-8 -*- +"""Iluvatar/Tianshu accelerator adapter.""" + +from __future__ import annotations + +import re + +from .base import ( + AcceleratorInfo, + command_exists, + first_env_devices, + parse_memory_gb, + run_command, +) + + +class IluvatarAcceleratorAdapter: + vendor = "iluvatar" + runtime = "ix" + smi_command = "ixsmi" + visible_env_names = ( + "ILUVATAR_VISIBLE_DEVICES", + "IX_VISIBLE_DEVICES", + "CUDA_VISIBLE_DEVICES", + ) + + def _query_device_count(self) -> tuple[int, str]: + list_output = run_command([self.smi_command, "-L"]) + if list_output: + lines = [line for line in list_output.splitlines() if line.strip()] + gpu_lines = [ + line + for line in lines + if re.search(r"\b(gpu|device|card)\b", line, re.I) + ] + return len(gpu_lines or lines), list_output + + table_output = run_command([self.smi_command]) + if table_output: + indexes = set(re.findall(r"(?:GPU|Device|Card)\s*[:#]?\s*([0-9]+)", table_output, re.I)) + if not indexes: + indexes = set(re.findall(r"^\s*\|\s*([0-9]+)\s+", table_output, re.M)) + if indexes: + return len(indexes), table_output + if re.search(r"\bIluvatar\b|\b天数\b|\bIX\b", table_output, re.I): + return 1, table_output + + return 0, "" + + def detect(self) -> AcceleratorInfo: + visible_devices = first_env_devices(self.visible_env_names) + smi_available = command_exists(self.smi_command) + if not smi_available: + return AcceleratorInfo( + vendor=self.vendor, + runtime=self.runtime, + device="cpu", + visible_devices=visible_devices, + smi_command=None, + available=False, + reason="ixsmi not found", + ) + + device_count, raw_output = self._query_device_count() + if visible_devices: + device_count = min(device_count or len(visible_devices), len(visible_devices)) + + torch_cuda_available = False + torch_count = 0 + torch_memory_gb = 0.0 + torch_version = "" + try: + import torch + + torch_version = getattr(torch, "__version__", "") + torch_cuda_available = bool(torch.cuda.is_available()) + if torch_cuda_available: + torch_count = int(torch.cuda.device_count()) + torch_memory_gb = min( + torch.cuda.get_device_properties(i).total_memory / (1024**3) + for i in range(torch_count) + ) + except Exception: + pass + + if torch_count: + device_count = min(torch_count, len(visible_devices)) if visible_devices else torch_count + total_memory_gb = torch_memory_gb or parse_memory_gb(raw_output) + available = device_count > 0 + + return AcceleratorInfo( + vendor=self.vendor, + runtime=self.runtime, + device="cuda:0" if available else "cpu", + device_count=device_count, + visible_devices=visible_devices, + total_memory_gb=total_memory_gb, + smi_command=self.smi_command, + available=available, + reason="" if available else "ixsmi did not report any device", + metadata={ + "torch_cuda_available": torch_cuda_available, + "torch_version": torch_version, + "supports_sharded": available, + }, + ) diff --git a/app/core/accelerators/metax.py b/app/core/accelerators/metax.py new file mode 100644 index 0000000..4d73a4b --- /dev/null +++ b/app/core/accelerators/metax.py @@ -0,0 +1,174 @@ +# -*- coding: utf-8 -*- +"""MetaX/MuXi MACA accelerator adapter.""" + +from __future__ import annotations + +import json +import re +from typing import Any + +from .base import ( + AcceleratorInfo, + command_exists, + first_env_devices, + parse_memory_gb, + run_command, +) + + +class MetaxAcceleratorAdapter: + vendor = "metax" + runtime = "maca" + smi_command = "mx-smi" + visible_env_names = ( + "METAX_VISIBLE_DEVICES", + "MACA_VISIBLE_DEVICES", + "MX_VISIBLE_DEVICES", + ) + + def _json_output(self, command: list[str]) -> Any: + output = run_command(command) + if not output: + return None + try: + return json.loads(output) + except json.JSONDecodeError: + return None + + def _walk_json(self, value: Any): + if isinstance(value, dict): + yield value + for child in value.values(): + yield from self._walk_json(child) + elif isinstance(value, list): + for item in value: + yield from self._walk_json(item) + + def _count_from_json(self, value: Any) -> int: + max_index = -1 + device_like = 0 + for item in self._walk_json(value): + keys = {str(key).lower(): key for key in item.keys()} + if any(key in keys for key in ("gpu id", "gpu_id", "gpu", "device id", "device_id", "index")): + device_like += 1 + for key_name in ("gpu id", "gpu_id", "device id", "device_id", "index", "id"): + original = keys.get(key_name) + if original is None: + continue + try: + max_index = max(max_index, int(item[original])) + except (TypeError, ValueError): + continue + if max_index >= 0: + return max_index + 1 + return device_like + + def _memory_from_json(self, value: Any) -> float: + memory_values: list[float] = [] + for item in self._walk_json(value): + for raw_key, raw_value in item.items(): + key = str(raw_key).lower() + if not any(token in key for token in ("memory", "mem", "hbm", "vram", "容量")): + continue + if isinstance(raw_value, (int, float)): + numeric = float(raw_value) + # mx-smi reports are commonly MiB for memory counters. + if numeric > 1024: + numeric = numeric / 1024 + memory_values.append(numeric) + continue + if isinstance(raw_value, str): + parsed = parse_memory_gb(raw_value) + if parsed > 0: + memory_values.append(parsed) + return min(memory_values) if memory_values else 0.0 + + def _query_device_count(self) -> tuple[int, str]: + json_info = self._json_output([self.smi_command, "-j"]) + json_count = self._count_from_json(json_info) + if json_count > 0: + return json_count, json.dumps(json_info, ensure_ascii=False) + + list_output = run_command([self.smi_command, "-L"]) + if list_output: + lines = [line for line in list_output.splitlines() if line.strip()] + gpu_lines = [ + line + for line in lines + if re.search(r"\b(gpu|device|card)\b", line, re.I) + ] + return len(gpu_lines or lines), list_output + + table_output = run_command([self.smi_command]) + if table_output: + indexes = set(re.findall(r"(?:GPU|Device|Card)\s*[:#]?\s*([0-9]+)", table_output, re.I)) + if not indexes: + indexes = set(re.findall(r"^\s*\|\s*([0-9]+)\s+", table_output, re.M)) + return len(indexes), table_output + + return 0, "" + + def _query_memory_gb(self, fallback_output: str) -> float: + for command in ( + [self.smi_command, "--show-memory", "-j"], + [self.smi_command, "--show-hwinfo", "-j"], + [self.smi_command, "-j"], + ): + parsed = self._memory_from_json(self._json_output(command)) + if parsed > 0: + return parsed + + memory_output = run_command([self.smi_command, "--show-memory"]) + hwinfo_output = run_command([self.smi_command, "--show-hwinfo"]) + return ( + parse_memory_gb(memory_output) + or parse_memory_gb(hwinfo_output) + or parse_memory_gb(fallback_output) + ) + + def detect(self) -> AcceleratorInfo: + visible_devices = first_env_devices(self.visible_env_names) + smi_available = command_exists(self.smi_command) + if not smi_available: + return AcceleratorInfo( + vendor=self.vendor, + runtime=self.runtime, + device="cpu", + visible_devices=visible_devices, + smi_command=None, + available=False, + reason="mx-smi not found", + ) + + device_count, raw_output = self._query_device_count() + if visible_devices: + device_count = min(device_count or len(visible_devices), len(visible_devices)) + total_memory_gb = self._query_memory_gb(raw_output) + + torch_cuda_available = False + torch_version = "" + try: + import torch + + torch_version = getattr(torch, "__version__", "") + torch_cuda_available = bool(torch.cuda.is_available()) + except Exception: + pass + + available = device_count > 0 + return AcceleratorInfo( + vendor=self.vendor, + runtime=self.runtime, + device="cuda:0" if available else "cpu", + device_count=device_count, + visible_devices=visible_devices, + total_memory_gb=total_memory_gb, + smi_command=self.smi_command, + available=available, + reason="" if available else "mx-smi did not report any device", + metadata={ + "torch_cuda_available": torch_cuda_available, + "torch_version": torch_version, + "supports_sharded": available, + }, + ) diff --git a/app/core/accelerators/mthreads.py b/app/core/accelerators/mthreads.py new file mode 100644 index 0000000..34e4b6e --- /dev/null +++ b/app/core/accelerators/mthreads.py @@ -0,0 +1,114 @@ +# -*- coding: utf-8 -*- +"""Moore Threads / MUSA accelerator adapter.""" + +from __future__ import annotations + +import re + +from .base import ( + AcceleratorInfo, + command_exists, + first_env_devices, + parse_memory_gb, + run_command, +) + + +class MThreadsAcceleratorAdapter: + vendor = "mthreads" + runtime = "musa" + smi_command = "mthreads-gmi" + visible_env_names = ( + "MTHREADS_VISIBLE_DEVICES", + "MUSA_VISIBLE_DEVICES", + "CUDA_VISIBLE_DEVICES", + ) + + def _query_device_count(self) -> tuple[int, str]: + for command in ( + [self.smi_command, "-L"], + [self.smi_command, "list"], + [self.smi_command], + ): + output = run_command(command) + if not output: + continue + lines = [line for line in output.splitlines() if line.strip()] + gpu_lines = [ + line + for line in lines + if re.search(r"\b(gpu|device|card|musa|mthreads|moore)\b", line, re.I) + ] + if gpu_lines: + return len(gpu_lines), output + + indexes = set( + re.findall(r"(?:GPU|Device|Card)\s*[:#]?\s*([0-9]+)", output, re.I) + ) + if not indexes: + indexes = set(re.findall(r"^\s*\|\s*([0-9]+)\s+", output, re.M)) + if indexes: + return len(indexes), output + + if re.search(r"\bMUSA\b|\bMoore\s+Threads\b|\bMThreads\b", output, re.I): + return max(len(lines), 1), output + + return 0, "" + + def detect(self) -> AcceleratorInfo: + visible_devices = first_env_devices(self.visible_env_names) + smi_available = command_exists(self.smi_command) + if not smi_available: + return AcceleratorInfo( + vendor=self.vendor, + runtime=self.runtime, + device="cpu", + visible_devices=visible_devices, + smi_command=None, + available=False, + reason="mthreads-gmi not found", + ) + + device_count, raw_output = self._query_device_count() + if visible_devices: + device_count = min(device_count or len(visible_devices), len(visible_devices)) + + torch_cuda_available = False + torch_count = 0 + torch_memory_gb = 0.0 + torch_version = "" + try: + import torch + + torch_version = getattr(torch, "__version__", "") + torch_cuda_available = bool(torch.cuda.is_available()) + if torch_cuda_available: + torch_count = int(torch.cuda.device_count()) + torch_memory_gb = min( + torch.cuda.get_device_properties(i).total_memory / (1024**3) + for i in range(torch_count) + ) + except Exception: + pass + + if torch_count: + device_count = min(torch_count, len(visible_devices)) if visible_devices else torch_count + total_memory_gb = torch_memory_gb or parse_memory_gb(raw_output) + available = device_count > 0 + + return AcceleratorInfo( + vendor=self.vendor, + runtime=self.runtime, + device="cuda:0" if available else "cpu", + device_count=device_count, + visible_devices=visible_devices, + total_memory_gb=total_memory_gb, + smi_command=self.smi_command, + available=available, + reason="" if available else "mthreads-gmi did not report any device", + metadata={ + "torch_cuda_available": torch_cuda_available, + "torch_version": torch_version, + "supports_sharded": available, + }, + ) diff --git a/app/core/accelerators/nvidia.py b/app/core/accelerators/nvidia.py new file mode 100644 index 0000000..19fe621 --- /dev/null +++ b/app/core/accelerators/nvidia.py @@ -0,0 +1,85 @@ +# -*- coding: utf-8 -*- +"""NVIDIA CUDA accelerator adapter.""" + +from __future__ import annotations + +from .base import ( + AcceleratorInfo, + command_exists, + first_env_devices, + parse_memory_gb, + run_command, +) + + +class NvidiaAcceleratorAdapter: + vendor = "nvidia" + runtime = "cuda" + smi_command = "nvidia-smi" + + def detect(self) -> AcceleratorInfo: + visible_devices = first_env_devices(("CUDA_VISIBLE_DEVICES",)) + smi_available = command_exists(self.smi_command) + smi_count = 0 + smi_memory_gb = 0.0 + + if smi_available: + indexes_output = run_command( + [ + self.smi_command, + "--query-gpu=index", + "--format=csv,noheader,nounits", + ] + ) + if indexes_output: + smi_count = len([line for line in indexes_output.splitlines() if line.strip()]) + memory_output = run_command( + [ + self.smi_command, + "--query-gpu=memory.total", + "--format=csv,noheader", + ] + ) + smi_memory_gb = parse_memory_gb(memory_output) + + torch_available = False + torch_count = 0 + torch_memory_gb = 0.0 + torch_version = "" + try: + import torch + + torch_version = getattr(torch, "__version__", "") + torch_available = bool(torch.cuda.is_available()) + if torch_available: + torch_count = int(torch.cuda.device_count()) + torch_memory_gb = min( + torch.cuda.get_device_properties(i).total_memory / (1024**3) + for i in range(torch_count) + ) + except Exception: + pass + + device_count = torch_count or smi_count + if visible_devices: + device_count = min(device_count or len(visible_devices), len(visible_devices)) + total_memory_gb = torch_memory_gb or smi_memory_gb + available = bool(torch_available or smi_count > 0) + reason = "" if available else "nvidia runtime not detected" + + return AcceleratorInfo( + vendor=self.vendor, + runtime=self.runtime, + device="cuda:0" if available else "cpu", + device_count=device_count, + visible_devices=visible_devices, + total_memory_gb=total_memory_gb, + smi_command=self.smi_command if smi_available else None, + available=available, + reason=reason, + metadata={ + "torch_cuda_available": torch_available, + "torch_version": torch_version, + "supports_sharded": available, + }, + ) diff --git a/app/core/config.py b/app/core/config.py new file mode 100644 index 0000000..f6feb65 --- /dev/null +++ b/app/core/config.py @@ -0,0 +1,536 @@ +# -*- coding: utf-8 -*- +""" +统一配置管理 +ASR语音识别配置选项 +""" + +import os +from typing import Optional +from pathlib import Path + + +class Settings: + """统一应用配置类""" + + # 应用信息 + APP_NAME: str = "Qwen3-ASR Server" + APP_VERSION: str = "1.0.1" + APP_DESCRIPTION: str = "Qwen3-ASR speech recognition API service" + + # 服务器配置 + HOST: str = "0.0.0.0" + PORT: int = 8000 + DEBUG: bool = False + + # 鉴权配置 + API_KEY: Optional[str] = None # 从环境变量API_KEY读取，如果为None则鉴权可选 + + # 设备配置 + ACCELERATOR: str = "auto" # auto, cpu, nvidia, metax, iluvatar, mthreads + DEVICE: str = "auto" # auto, cpu, cuda:0 + ASR_DEPLOY_TOPOLOGY: str = "isolated" # isolated, sharded, auto + + # 路径配置 + BASE_DIR: Path = Path(__file__).parent.parent.parent + DATA_DIR: str = str(BASE_DIR / "data") + TEMP_DIR: str = str(BASE_DIR / "data" / "temp") + # 项目总模型目录。实际模型目录直接扁平化到: + # /models/{Qwen,iic,damo,...} + MODELS_DIR: str = str(BASE_DIR / "models") + # ModelScope 会在 MODELSCOPE_CACHE 下创建 models/{publisher}/{model_name}。 + # 因此 cache 根目录应指向 models 的上一级，实际运行模型根目录仍由 MODELSCOPE_PATH 指定。 + MODELSCOPE_CACHE: str = str(BASE_DIR) + MODELSCOPE_PATH: str = str(BASE_DIR / "models") + + # 日志配置 + LOG_LEVEL: str = "INFO" + LOG_FILE: Optional[str] = str(BASE_DIR / "data" / "logs" / "qwen3-asr.log") + LOG_MAX_BYTES: int = 20 * 1024 * 1024 # 20MB + LOG_BACKUP_COUNT: int = 50 # 保留50个备份文件 + + # ASR模型配置 + WS_MAX_BUFFER_SIZE: int = 10 * 16000 # WebSocket音频缓冲区最大大小（10秒@16kHz） + + FUNASR_AUTOMODEL_KWARGS = { + "trust_remote_code": False, + "disable_update": True, + "disable_pbar": True, + "disable_log": True, # 禁用FunASR的tables输出 + "local_files_only": True, # 强制使用本地模型，禁止联网下载 + } + ASR_MODELS_CONFIG: str = str(BASE_DIR / "app/services/asr/models.json") + ASR_ENABLE_REALTIME_PUNC: bool = True # 是否启用实时标点模型（用于中间结果展示） + VAD_MODEL: str = "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch" + PUNC_MODEL: str = "iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch" + PUNC_REALTIME_MODEL: str = ( + "iic/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727" + ) + + # 流式ASR远场过滤配置 + ASR_ENABLE_NEARFIELD_FILTER: bool = True # 是否启用远场声音过滤 + ASR_NEARFIELD_RMS_THRESHOLD: float = 0.01 # RMS能量阈值（宽松模式，适合大多数场景） + # 音频处理配置 + MAX_AUDIO_SIZE: int = 2048 * 1024 * 1024 # 2GB + + # 批处理推理配置（GPU 真并行） + ASR_BATCH_SIZE: int = 4 # ASR 批处理大小（同时推理的片段数），建议 2-8 + ASR_ENABLE_WORD_TIMESTAMPS: bool = False # 全局字词级时间戳开关；关闭时不预热 forced aligner，接口参数也默认隐藏 + + # 音频分段配置 + MAX_SEGMENT_SEC: float = 60.0 # Max offline ASR segment duration in seconds. + + # Runtime 并发配置（按 backend 独立控制） + QWEN_VLLM_SHARED_CONCURRENCY: int = 8 + QWEN_VLLM_ENFORCE_EAGER: bool = True + QWEN_RUST_CPU_WORKERS: int = 4 + QWEN_RUST_ASR_CONCURRENCY: int = 0 + QWEN_RUST_ALIGN_CONCURRENCY: int = 0 + FUNASR_WORKERS: int = 1 + + # 声纹数据库配置（与 Model-Test-New 使用同一套 PostgreSQL/pgvector 表结构） + SPEAKER_DB_ENABLED: bool = True + DB_USER: str = "postgres" + DB_PASSWORD: str = "postgres" + DB_NAME: str = "asr_db" + DB_HOST: str = "127.0.0.1" + DB_PORT: int = 5432 + DB_POOL_MAX_SIZE: int = 5 + SV_MODEL: str = "iic/speech_campplus_sv_zh-cn_16k-common" + SV_MODEL_REVISION: str = "v2.0.2" + REALTIME_SV_MODEL: str = "iic/speech_eres2netv2_sv_zh-cn_16k-common" + REALTIME_SV_MODEL_REVISION: str = "" + SV_THRESHOLD: float = 0.6 + REALTIME_MAX_SEGMENT_SEC: float = 12.0 + REALTIME_MAX_SEGMENT_TAIL_SEC: float = 1.6 + REALTIME_FORCE_STABLE_SEGMENT_SEC: float = 8.0 + REALTIME_FORCE_STABLE_MIN_CHARS: int = 24 + REALTIME_MIN_PARTIAL_SEC: float = 0.45 + REALTIME_PARTIAL_EMIT_INTERVAL_SEC: float = 0.25 + REALTIME_PARTIAL_WINDOW_SEC: float = 8.0 + REALTIME_STREAM_CHUNK_SEC: float = 1.2 + REALTIME_STREAM_MAX_PENDING_CHUNKS: int = 3 + REALTIME_STREAM_WINDOW_SEC: float = 8.0 + REALTIME_STREAM_STABLE_TAIL_CHARS: int = 8 + REALTIME_STREAM_STABLE_MIN_GROW_CHARS: int = 2 + REALTIME_STREAM_DIVERGENCE_TOLERANCE_CHARS: int = 2 + REALTIME_PARTIAL_HOLDBACK_CHARS: int = 6 + REALTIME_LONGFORM_MIN_SEC: float = 8.0 + REALTIME_LONGFORM_CHUNK_SEC: float = 6.0 + REALTIME_LONGFORM_OVERLAP_SEC: float = 1.2 + REALTIME_VAD_CHECK_INTERVAL_SEC: float = 0.8 + REALTIME_VAD_FINALIZE_SILENCE_SEC: float = 0.6 + REALTIME_FINAL_SEGMENT_SOFT_LIMIT_SEC: float = 8.0 + REALTIME_FINAL_SEGMENT_HARD_LIMIT_SEC: float = 12.0 + REALTIME_ENABLE_DIARIZATION: bool = True + REALTIME_ENABLE_SEGMENT_REFINE: bool = False + REALTIME_DIARIZATION_MIN_SEC: float = 3.0 + REALTIME_DIARIZATION_LOOKBACK_SEC: float = 3.0 + REALTIME_DIARIZATION_WINDOW_SEC: float = 15.0 + REALTIME_SPEAKER_MIN_SEC: float = 1.2 + REALTIME_SPEAKER_CLUSTER_THRESHOLD: float = 0.75 + REALTIME_UNKNOWN_SPK_CLUSTER_THRESHOLD: float = 0.58 + REALTIME_RECENT_UNKNOWN_SPK_THRESHOLD: float = 0.50 + REALTIME_SPEAKER_CONFIRM_THRESHOLD: float = 0.62 + REALTIME_REGISTRY_MIN_CLUSTER_CONFIDENCE: float = 0.72 + REALTIME_SPEAKER_MAX_SLOTS: int = 8 + REALTIME_SESSION_RESUME_TTL_SEC: int = 120 # WebSocket 断线后保留会话上下文的秒数；TTL 内同 session_id 可恢复 + API_PREFIX: str = "/api/v1" + TASK_STATE_DIR: str = str(BASE_DIR / "data" / "tasks") + TASK_RETENTION_HOURS: int = 24 + + def __init__(self): + """从环境变量读取配置""" + self._load_from_env() + self._ensure_directories() + + def _load_from_env(self): + """从环境变量加载配置""" + # 服务器配置 + self.HOST = os.getenv("HOST", self.HOST) + self.PORT = int(os.getenv("PORT", str(self.PORT))) + self.DEBUG = os.getenv("DEBUG", "false").lower() == "true" + + self.DATA_DIR = os.getenv("DATA_DIR", self.DATA_DIR) + self.TEMP_DIR = os.getenv("TEMP_DIR", self.TEMP_DIR) + + # 日志配置 + self.LOG_LEVEL = os.getenv("LOG_LEVEL", self.LOG_LEVEL) + self.LOG_FILE = os.getenv("LOG_FILE", self.LOG_FILE) + self.LOG_MAX_BYTES = int(os.getenv("LOG_MAX_BYTES", str(self.LOG_MAX_BYTES))) + self.LOG_BACKUP_COUNT = int( + os.getenv("LOG_BACKUP_COUNT", str(self.LOG_BACKUP_COUNT)) + ) + + # 鉴权配置：空值/空白统一视为未配置 + self.API_KEY = (os.getenv("API_KEY") or "").strip() or None + + # 设备配置 + self.ACCELERATOR = os.getenv("ACCELERATOR", self.ACCELERATOR) + self.DEVICE = os.getenv("DEVICE", self.DEVICE) + self.ASR_DEPLOY_TOPOLOGY = os.getenv( + "ASR_DEPLOY_TOPOLOGY", + self.ASR_DEPLOY_TOPOLOGY, + ).strip().lower() + + # 模型缓存路径 + self.MODELS_DIR = os.getenv("MODELS_DIR", self.MODELS_DIR) + self.MODELSCOPE_CACHE = os.getenv("MODELSCOPE_CACHE", self.MODELSCOPE_CACHE) + self.MODELSCOPE_PATH = os.getenv("MODELSCOPE_PATH", self.MODELSCOPE_PATH) + + # 给第三方库补齐默认缓存环境变量，允许用户自行覆盖 + os.environ.setdefault("MODELS_DIR", self.MODELS_DIR) + os.environ.setdefault("MODELSCOPE_CACHE", self.MODELSCOPE_CACHE) + os.environ.setdefault("MODELSCOPE_PATH", self.MODELSCOPE_PATH) + + # ASR模型配置 + self.ASR_ENABLE_REALTIME_PUNC = ( + os.getenv("ASR_ENABLE_REALTIME_PUNC", "true").lower() == "true" + ) + + # WebSocket缓冲区配置 + self.WS_MAX_BUFFER_SIZE = int( + os.getenv("WS_MAX_BUFFER_SIZE", str(self.WS_MAX_BUFFER_SIZE)) + ) + + # 远场过滤配置 + self.ASR_ENABLE_NEARFIELD_FILTER = ( + os.getenv("ASR_ENABLE_NEARFIELD_FILTER", "true").lower() == "true" + ) + self.ASR_NEARFIELD_RMS_THRESHOLD = float( + os.getenv( + "ASR_NEARFIELD_RMS_THRESHOLD", str(self.ASR_NEARFIELD_RMS_THRESHOLD) + ) + ) + + # 音频处理配置 + # 支持简化格式：纯数字表示MB，或带单位（如 2048MB, 2GB） + max_audio_size_str = os.getenv("MAX_AUDIO_SIZE") + if max_audio_size_str: + self.MAX_AUDIO_SIZE = self._parse_size(max_audio_size_str) + + self.ASR_BATCH_SIZE = int( + os.getenv("ASR_BATCH_SIZE", str(self.ASR_BATCH_SIZE)) + ) + self.ASR_ENABLE_WORD_TIMESTAMPS = ( + os.getenv( + "ASR_ENABLE_WORD_TIMESTAMPS", + str(self.ASR_ENABLE_WORD_TIMESTAMPS), + ).lower() + == "true" + ) + + self.MAX_SEGMENT_SEC = float( + os.getenv("MAX_SEGMENT_SEC", str(self.MAX_SEGMENT_SEC)) + ) + + self.QWEN_VLLM_SHARED_CONCURRENCY = int( + os.getenv( + "QWEN_VLLM_SHARED_CONCURRENCY", + str(self.QWEN_VLLM_SHARED_CONCURRENCY), + ) + ) + self.QWEN_VLLM_ENFORCE_EAGER = ( + os.getenv( + "QWEN_VLLM_ENFORCE_EAGER", + str(self.QWEN_VLLM_ENFORCE_EAGER), + ).lower() + == "true" + ) + self.QWEN_RUST_CPU_WORKERS = int( + os.getenv("QWEN_RUST_CPU_WORKERS", str(self.QWEN_RUST_CPU_WORKERS)) + ) + self.QWEN_RUST_ASR_CONCURRENCY = int( + os.getenv("QWEN_RUST_ASR_CONCURRENCY", str(self.QWEN_RUST_ASR_CONCURRENCY)) + ) + self.QWEN_RUST_ALIGN_CONCURRENCY = int( + os.getenv("QWEN_RUST_ALIGN_CONCURRENCY", str(self.QWEN_RUST_ALIGN_CONCURRENCY)) + ) + self.FUNASR_WORKERS = int( + os.getenv("FUNASR_WORKERS", str(self.FUNASR_WORKERS)) + ) + + self.SPEAKER_DB_ENABLED = ( + os.getenv("SPEAKER_DB_ENABLED", str(self.SPEAKER_DB_ENABLED)).lower() + == "true" + ) + self.DB_USER = os.getenv("DB_USER", self.DB_USER) + self.DB_PASSWORD = os.getenv("DB_PASSWORD", self.DB_PASSWORD) + self.DB_NAME = os.getenv("DB_NAME", self.DB_NAME) + self.DB_HOST = os.getenv("DB_HOST", self.DB_HOST) + self.DB_PORT = int(os.getenv("DB_PORT", str(self.DB_PORT))) + self.DB_POOL_MAX_SIZE = int( + os.getenv("DB_POOL_MAX_SIZE", str(self.DB_POOL_MAX_SIZE)) + ) + self.SV_MODEL = os.getenv("SV_MODEL", self.SV_MODEL) + self.SV_MODEL_REVISION = os.getenv("SV_MODEL_REVISION", self.SV_MODEL_REVISION) + self.REALTIME_SV_MODEL = os.getenv("REALTIME_SV_MODEL", self.REALTIME_SV_MODEL) + self.REALTIME_SV_MODEL_REVISION = os.getenv( + "REALTIME_SV_MODEL_REVISION", + self.REALTIME_SV_MODEL_REVISION, + ) + self.SV_THRESHOLD = float(os.getenv("SV_THRESHOLD", str(self.SV_THRESHOLD))) + self.REALTIME_MAX_SEGMENT_SEC = float( + os.getenv( + "REALTIME_MAX_SEGMENT_SEC", + str(self.REALTIME_MAX_SEGMENT_SEC), + ) + ) + self.REALTIME_MAX_SEGMENT_TAIL_SEC = float( + os.getenv( + "REALTIME_MAX_SEGMENT_TAIL_SEC", + str(self.REALTIME_MAX_SEGMENT_TAIL_SEC), + ) + ) + self.REALTIME_FORCE_STABLE_SEGMENT_SEC = float( + os.getenv( + "REALTIME_FORCE_STABLE_SEGMENT_SEC", + str(self.REALTIME_FORCE_STABLE_SEGMENT_SEC), + ) + ) + self.REALTIME_FORCE_STABLE_MIN_CHARS = int( + os.getenv( + "REALTIME_FORCE_STABLE_MIN_CHARS", + str(self.REALTIME_FORCE_STABLE_MIN_CHARS), + ) + ) + self.REALTIME_MIN_PARTIAL_SEC = float( + os.getenv( + "REALTIME_MIN_PARTIAL_SEC", + str(self.REALTIME_MIN_PARTIAL_SEC), + ) + ) + self.REALTIME_PARTIAL_EMIT_INTERVAL_SEC = float( + os.getenv( + "REALTIME_PARTIAL_EMIT_INTERVAL_SEC", + str(self.REALTIME_PARTIAL_EMIT_INTERVAL_SEC), + ) + ) + self.REALTIME_PARTIAL_WINDOW_SEC = float( + os.getenv( + "REALTIME_PARTIAL_WINDOW_SEC", + str(self.REALTIME_PARTIAL_WINDOW_SEC), + ) + ) + self.REALTIME_STREAM_CHUNK_SEC = float( + os.getenv( + "REALTIME_STREAM_CHUNK_SEC", + str(self.REALTIME_STREAM_CHUNK_SEC), + ) + ) + self.REALTIME_STREAM_MAX_PENDING_CHUNKS = int( + os.getenv( + "REALTIME_STREAM_MAX_PENDING_CHUNKS", + str(self.REALTIME_STREAM_MAX_PENDING_CHUNKS), + ) + ) + self.REALTIME_STREAM_WINDOW_SEC = float( + os.getenv( + "REALTIME_STREAM_WINDOW_SEC", + str(self.REALTIME_STREAM_WINDOW_SEC), + ) + ) + self.REALTIME_STREAM_STABLE_TAIL_CHARS = int( + os.getenv( + "REALTIME_STREAM_STABLE_TAIL_CHARS", + str(self.REALTIME_STREAM_STABLE_TAIL_CHARS), + ) + ) + self.REALTIME_STREAM_STABLE_MIN_GROW_CHARS = int( + os.getenv( + "REALTIME_STREAM_STABLE_MIN_GROW_CHARS", + str(self.REALTIME_STREAM_STABLE_MIN_GROW_CHARS), + ) + ) + self.REALTIME_STREAM_DIVERGENCE_TOLERANCE_CHARS = int( + os.getenv( + "REALTIME_STREAM_DIVERGENCE_TOLERANCE_CHARS", + str(self.REALTIME_STREAM_DIVERGENCE_TOLERANCE_CHARS), + ) + ) + self.REALTIME_PARTIAL_HOLDBACK_CHARS = int( + os.getenv( + "REALTIME_PARTIAL_HOLDBACK_CHARS", + str(self.REALTIME_PARTIAL_HOLDBACK_CHARS), + ) + ) + self.REALTIME_LONGFORM_MIN_SEC = float( + os.getenv( + "REALTIME_LONGFORM_MIN_SEC", + str(self.REALTIME_LONGFORM_MIN_SEC), + ) + ) + self.REALTIME_LONGFORM_CHUNK_SEC = float( + os.getenv( + "REALTIME_LONGFORM_CHUNK_SEC", + str(self.REALTIME_LONGFORM_CHUNK_SEC), + ) + ) + self.REALTIME_LONGFORM_OVERLAP_SEC = float( + os.getenv( + "REALTIME_LONGFORM_OVERLAP_SEC", + str(self.REALTIME_LONGFORM_OVERLAP_SEC), + ) + ) + self.REALTIME_VAD_CHECK_INTERVAL_SEC = float( + os.getenv( + "REALTIME_VAD_CHECK_INTERVAL_SEC", + str(self.REALTIME_VAD_CHECK_INTERVAL_SEC), + ) + ) + self.REALTIME_VAD_FINALIZE_SILENCE_SEC = float( + os.getenv( + "REALTIME_VAD_FINALIZE_SILENCE_SEC", + str(self.REALTIME_VAD_FINALIZE_SILENCE_SEC), + ) + ) + self.REALTIME_FINAL_SEGMENT_SOFT_LIMIT_SEC = float( + os.getenv( + "REALTIME_FINAL_SEGMENT_SOFT_LIMIT_SEC", + str(self.REALTIME_FINAL_SEGMENT_SOFT_LIMIT_SEC), + ) + ) + self.REALTIME_FINAL_SEGMENT_HARD_LIMIT_SEC = float( + os.getenv( + "REALTIME_FINAL_SEGMENT_HARD_LIMIT_SEC", + str(self.REALTIME_FINAL_SEGMENT_HARD_LIMIT_SEC), + ) + ) + self.REALTIME_ENABLE_DIARIZATION = ( + os.getenv( + "REALTIME_ENABLE_DIARIZATION", + str(self.REALTIME_ENABLE_DIARIZATION), + ).lower() + == "true" + ) + self.REALTIME_ENABLE_SEGMENT_REFINE = ( + os.getenv( + "REALTIME_ENABLE_SEGMENT_REFINE", + str(self.REALTIME_ENABLE_SEGMENT_REFINE), + ).lower() + == "true" + ) + self.REALTIME_DIARIZATION_MIN_SEC = float( + os.getenv( + "REALTIME_DIARIZATION_MIN_SEC", + str(self.REALTIME_DIARIZATION_MIN_SEC), + ) + ) + self.REALTIME_DIARIZATION_LOOKBACK_SEC = float( + os.getenv( + "REALTIME_DIARIZATION_LOOKBACK_SEC", + str(self.REALTIME_DIARIZATION_LOOKBACK_SEC), + ) + ) + self.REALTIME_DIARIZATION_WINDOW_SEC = float( + os.getenv( + "REALTIME_DIARIZATION_WINDOW_SEC", + str(self.REALTIME_DIARIZATION_WINDOW_SEC), + ) + ) + self.REALTIME_SPEAKER_MIN_SEC = float( + os.getenv( + "REALTIME_SPEAKER_MIN_SEC", + str(self.REALTIME_SPEAKER_MIN_SEC), + ) + ) + self.REALTIME_SPEAKER_CLUSTER_THRESHOLD = float( + os.getenv( + "REALTIME_SPEAKER_CLUSTER_THRESHOLD", + str(self.REALTIME_SPEAKER_CLUSTER_THRESHOLD), + ) + ) + self.REALTIME_UNKNOWN_SPK_CLUSTER_THRESHOLD = float( + os.getenv( + "REALTIME_UNKNOWN_SPK_CLUSTER_THRESHOLD", + str(self.REALTIME_UNKNOWN_SPK_CLUSTER_THRESHOLD), + ) + ) + self.REALTIME_RECENT_UNKNOWN_SPK_THRESHOLD = float( + os.getenv( + "REALTIME_RECENT_UNKNOWN_SPK_THRESHOLD", + str(self.REALTIME_RECENT_UNKNOWN_SPK_THRESHOLD), + ) + ) + self.REALTIME_SPEAKER_CONFIRM_THRESHOLD = float( + os.getenv( + "REALTIME_SPEAKER_CONFIRM_THRESHOLD", + str(self.REALTIME_SPEAKER_CONFIRM_THRESHOLD), + ) + ) + self.REALTIME_REGISTRY_MIN_CLUSTER_CONFIDENCE = float( + os.getenv( + "REALTIME_REGISTRY_MIN_CLUSTER_CONFIDENCE", + str(self.REALTIME_REGISTRY_MIN_CLUSTER_CONFIDENCE), + ) + ) + self.REALTIME_SPEAKER_MAX_SLOTS = int( + os.getenv( + "REALTIME_SPEAKER_MAX_SLOTS", + str(self.REALTIME_SPEAKER_MAX_SLOTS), + ) + ) + self.REALTIME_SESSION_RESUME_TTL_SEC = int( + os.getenv( + "REALTIME_SESSION_RESUME_TTL_SEC", + str(self.REALTIME_SESSION_RESUME_TTL_SEC), + ) + ) + self.API_PREFIX = os.getenv("API_PREFIX", self.API_PREFIX) + self.TASK_STATE_DIR = os.getenv("TASK_STATE_DIR", self.TASK_STATE_DIR) + self.TASK_RETENTION_HOURS = int( + os.getenv("TASK_RETENTION_HOURS", str(self.TASK_RETENTION_HOURS)) + ) + + def _parse_size(self, size_str: str) -> int: + """解析带单位的大小字符串 + + 支持格式： + - 纯数字：视为 MB（如 2048 = 2048MB = 2147483648 bytes） + - 带单位：如 2GB, 2048MB, 1.5GB + """ + size_str = size_str.strip().upper() + + # 如果纯数字，视为 MB + if size_str.isdigit(): + return int(size_str) * 1024 * 1024 + + # 带单位的处理 + if size_str.endswith('GB'): + return int(float(size_str[:-2]) * 1024 * 1024 * 1024) + elif size_str.endswith('MB'): + return int(float(size_str[:-2]) * 1024 * 1024) + elif size_str.endswith('KB'): + return int(float(size_str[:-2]) * 1024) + else: + # 默认视为字节 + return int(size_str) + + def _ensure_directories(self): + """确保必需的目录存在""" + os.makedirs(self.TEMP_DIR, exist_ok=True) + if self.LOG_FILE: + os.makedirs(os.path.dirname(self.LOG_FILE), exist_ok=True) + os.makedirs(self.MODELS_DIR, exist_ok=True) + os.makedirs(self.MODELSCOPE_CACHE, exist_ok=True) + os.makedirs(self.MODELSCOPE_PATH, exist_ok=True) + os.makedirs(self.DATA_DIR, exist_ok=True) + os.makedirs(self.TASK_STATE_DIR, exist_ok=True) + + @property + def models_config_path(self) -> str: + """获取模型配置文件的完整路径""" + return str(self.BASE_DIR / self.ASR_MODELS_CONFIG) + + @property + def docs_url(self) -> Optional[str]: + """获取文档URL""" + return "/docs" + + @property + def redoc_url(self) -> Optional[str]: + """获取ReDoc URL""" + return "/redoc" + + +# 全局配置实例 +settings = Settings() diff --git a/app/core/database.py b/app/core/database.py new file mode 100644 index 0000000..593e5b8 --- /dev/null +++ b/app/core/database.py @@ -0,0 +1,222 @@ +# -*- coding: utf-8 -*- +"""PostgreSQL/pgvector storage for registered speaker embeddings.""" + +from __future__ import annotations + +import logging +from typing import Optional + +import numpy as np + +from app.core.config import settings + +logger = logging.getLogger(__name__) + +try: + import asyncpg +except ImportError: # pragma: no cover - runtime dependency is installed in Docker images. + asyncpg = None # type: ignore[assignment] + + +class PgSpeakerStorage: + """Small asyncpg wrapper shared by speaker registration and ASR matching.""" + + def __init__(self) -> None: + self.pool: Optional[asyncpg.Pool] = None + + @property + def is_enabled(self) -> bool: + return bool(settings.SPEAKER_DB_ENABLED) + + @property + def is_connected(self) -> bool: + return self.pool is not None + + async def _create_database_if_not_exists(self) -> None: + system_config = { + "user": settings.DB_USER, + "password": settings.DB_PASSWORD, + "database": "postgres", + "host": settings.DB_HOST, + "port": settings.DB_PORT, + } + connection: Optional[asyncpg.Connection] = None + try: + if asyncpg is None: + raise RuntimeError("asyncpg is not installed") + connection = await asyncpg.connect(**system_config) + exists = await connection.fetchval( + "SELECT 1 FROM pg_database WHERE datname = $1", + settings.DB_NAME, + ) + if not exists: + await connection.execute(f'CREATE DATABASE "{settings.DB_NAME}"') + except Exception as exc: + logger.warning("尝试自动创建数据库失败，将继续连接业务库: %s", exc) + finally: + if connection is not None: + await connection.close() + + async def connect(self) -> None: + if not self.is_enabled: + logger.info("声纹数据库未启用，跳过 PostgreSQL 连接") + return + if asyncpg is None: + raise RuntimeError("asyncpg is not installed") + if self.pool is not None: + return + + await self._create_database_if_not_exists() + self.pool = await asyncpg.create_pool( + user=settings.DB_USER, + password=settings.DB_PASSWORD, + database=settings.DB_NAME, + host=settings.DB_HOST, + port=settings.DB_PORT, + min_size=1, + max_size=settings.DB_POOL_MAX_SIZE, + ) + async with self.pool.acquire() as connection: + await connection.execute("CREATE EXTENSION IF NOT EXISTS vector;") + await connection.execute( + """ + CREATE TABLE IF NOT EXISTS speakers ( + id SERIAL PRIMARY KEY, + name TEXT NOT NULL UNIQUE, + user_id TEXT, + embedding vector(192), + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ); + """ + ) + await connection.execute( + """ + DO $$ + BEGIN + IF NOT EXISTS ( + SELECT 1 FROM information_schema.columns + WHERE table_name='speakers' AND column_name='user_id' + ) THEN + ALTER TABLE speakers ADD COLUMN user_id TEXT; + END IF; + END $$; + """ + ) + await connection.execute( + """ + DO $$ + BEGIN + IF NOT EXISTS ( + SELECT 1 + FROM pg_class c + JOIN pg_namespace n ON n.oid = c.relnamespace + WHERE c.relname = 'speakers_embedding_idx' + ) THEN + CREATE INDEX speakers_embedding_idx + ON speakers USING hnsw (embedding vector_cosine_ops) + WITH (m = 16, ef_construction = 64); + END IF; + END $$; + """ + ) + logger.info( + "声纹数据库已连接: %s:%s/%s", + settings.DB_HOST, + settings.DB_PORT, + settings.DB_NAME, + ) + + async def close(self) -> None: + if self.pool is not None: + await self.pool.close() + self.pool = None + + def _require_pool(self) -> asyncpg.Pool: + if self.pool is None: + raise RuntimeError("Speaker database pool is not initialized") + return self.pool + + @staticmethod + def _embedding_text(embedding: np.ndarray) -> str: + return str(np.asarray(embedding, dtype=np.float32).flatten().tolist()) + + async def save_speaker( + self, + name: str, + embedding: np.ndarray, + user_id: Optional[str] = None, + ) -> dict[str, Optional[str]]: + pool = self._require_pool() + async with pool.acquire() as connection: + row = await connection.fetchrow( + """ + INSERT INTO speakers (name, embedding, user_id) + VALUES ($1, $2, $3) + ON CONFLICT (name) DO UPDATE + SET embedding = EXCLUDED.embedding, user_id = EXCLUDED.user_id + RETURNING id, name, user_id; + """, + name, + self._embedding_text(embedding), + user_id, + ) + return { + "id": str(row["id"]) if row is not None else None, + "name": row["name"] if row is not None else name, + "user_id": row["user_id"] if row is not None else user_id, + } + + async def identify_speaker( + self, + embedding: np.ndarray, + threshold: float, + ) -> dict[str, Optional[str]]: + pool = self._require_pool() + distance_threshold = 1.0 - float(threshold) + async with pool.acquire() as connection: + row = await connection.fetchrow( + """ + SELECT id, name, user_id, (embedding <=> $1::vector) AS distance + FROM speakers + ORDER BY embedding <=> $1::vector + LIMIT 1; + """, + self._embedding_text(embedding), + ) + if row is not None and float(row["distance"]) < distance_threshold: + return { + "id": str(row["id"]), + "name": row["name"], + "user_id": row["user_id"], + } + return {"id": None, "name": None, "user_id": None} + + async def list_speakers(self) -> list[dict[str, Optional[str]]]: + pool = self._require_pool() + async with pool.acquire() as connection: + rows = await connection.fetch( + "SELECT id, name, user_id, created_at FROM speakers ORDER BY id ASC" + ) + return [ + { + "id": str(row["id"]), + "name": row["name"], + "user_id": row["user_id"], + "created_at": row["created_at"].isoformat() + if row["created_at"] + else None, + } + for row in rows + ] + + async def delete_speaker(self, speaker_id: int) -> bool: + pool = self._require_pool() + async with pool.acquire() as connection: + result = await connection.execute( + "DELETE FROM speakers WHERE id = $1", + speaker_id, + ) + return result != "DELETE 0" + + +pg_speaker_db = PgSpeakerStorage() diff --git a/app/core/device.py b/app/core/device.py new file mode 100644 index 0000000..11edf2a --- /dev/null +++ b/app/core/device.py @@ -0,0 +1,51 @@ +# -*- coding: utf-8 -*- +"""Centralized device detection utility. + +This module keeps the historic device helpers while delegating hardware +probing to ``app.core.accelerator``. +""" + +from app.core.accelerator import get_accelerator_info + + +def detect_device(configured: str = "auto") -> str: + """Resolve a device configuration string to a concrete PyTorch device. + + Priority for ``"auto"``: configured accelerator > detected GPU > CPU. + + Args: + configured: Value from ``settings.DEVICE`` or caller override. + Accepted: ``"auto"``, ``"cpu"``, ``"cuda:0"``, ``"npu:0"``, etc. + + Returns: + A device string ready for ``torch.device()`` / FunASR / ModelScope. + """ + device = configured.strip().lower() + + if device == "auto": + return get_accelerator_info().device + + # Normalize bare "cuda" to "cuda:0" + if device == "cuda": + return "cuda:0" + + if device == "mps": + return "cpu" + + return device + + +def is_cuda() -> bool: + """True when the active runtime exposes a CUDA-compatible device.""" + info = get_accelerator_info() + return info.available and info.device.startswith("cuda") + + +def has_gpu() -> bool: + """True when a supported accelerator is available.""" + return get_accelerator_info().is_gpu + + +def get_vram_gb() -> float: + """Return usable accelerator memory in GB.""" + return get_accelerator_info().total_memory_gb diff --git a/app/core/exceptions.py b/app/core/exceptions.py new file mode 100644 index 0000000..f7aa3b1 --- /dev/null +++ b/app/core/exceptions.py @@ -0,0 +1,224 @@ +# -*- coding: utf-8 -*- +""" +统一异常处理模块 +定义所有自定义异常类和错误处理函数 +""" + +from datetime import datetime, timezone +from fastapi import HTTPException, Request +from fastapi.exception_handlers import ( + http_exception_handler as fastapi_http_exception_handler, + request_validation_exception_handler as fastapi_validation_exception_handler, +) +from fastapi.exceptions import RequestValidationError +from fastapi.responses import JSONResponse +import logging +from typing import Any, Dict, Optional + +logger = logging.getLogger(__name__) + + +def _uses_meeting_legacy_response(request: Request) -> bool: + """Return True for Model-Test-New compatible offline meeting/speaker APIs.""" + path = request.url.path.rstrip("/") + method = request.method.upper() + if path.endswith("/asr/transcriptions"): + return method == "POST" + if "/asr/transcriptions/" in path: + return method == "GET" + if path.endswith("/speakers"): + return method == "POST" + return False + + +def get_iso_timestamp() -> str: + """获取ISO 8601格式的UTC时间戳""" + return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") + + +def create_error_response( + error_code: str, + message: str, + task_id: str = "", + details: Optional[Dict[str, Any]] = None, +) -> Dict[str, Any]: + """ + 创建标准错误响应格式 + + Args: + error_code: 错误代码（如 INVALID_PARAMETER） + message: 人类可读的错误信息 + task_id: 任务ID（可选） + details: 额外详细信息（可选） + + Returns: + 标准错误响应字典 + """ + return { + "error_code": error_code, + "message": message, + "task_id": task_id or "", + "timestamp": get_iso_timestamp(), + "details": details or {}, + } + + +def get_http_status_code(status_code: int) -> int: + """Map internal API status code to HTTP status code.""" + return 500 if status_code >= 50000000 else 400 + + +class APIException(Exception): + """API基础异常类""" + + def __init__( + self, + status_code: int, + message: str, + task_id: str = "", + error_code: str = "", + details: Optional[Dict[str, Any]] = None, + ): + self.status_code = status_code + self.message = message + self.task_id = task_id + self.error_code = error_code or self._get_error_code(status_code) + self.details = details or {} + super().__init__(self.message) + + def _get_error_code(self, status_code: int) -> str: + """根据状态码获取错误代码""" + code_mapping = { + 20000000: "SUCCESS", + 40000000: "DEFAULT_CLIENT_ERROR", + 40000001: "AUTHENTICATION_FAILED", + 40000002: "INVALID_MESSAGE", + 40000003: "INVALID_PARAMETER", + 40000004: "IDLE_TIMEOUT", + 40000005: "TOO_MANY_REQUESTS", + 40000010: "TRIAL_EXPIRED", + 41010101: "UNSUPPORTED_SAMPLE_RATE", + 50000000: "DEFAULT_SERVER_ERROR", + 50000001: "INTERNAL_GRPC_ERROR", + } + return code_mapping.get(status_code, "UNKNOWN_ERROR") + + def to_dict(self) -> Dict[str, Any]: + """ + 将异常转换为标准错误响应字典 + + Returns: + 标准错误响应字典，包含 error_code, message, task_id, timestamp, details + """ + return create_error_response( + error_code=self.error_code, + message=self.message, + task_id=self.task_id, + details=self.details, + ) + + +# 标准异常类 +class AuthenticationException(APIException): + """身份认证异常""" + + def __init__(self, message: str, task_id: str = "", details: Optional[Dict[str, Any]] = None): + super().__init__(40000001, message, task_id, details=details) + + +class InvalidMessageException(APIException): + """无效消息异常""" + + def __init__(self, message: str, task_id: str = "", details: Optional[Dict[str, Any]] = None): + super().__init__(40000002, message, task_id, details=details) + + +class InvalidParameterException(APIException): + """无效参数异常""" + + def __init__(self, message: str, task_id: str = "", details: Optional[Dict[str, Any]] = None): + super().__init__(40000003, message, task_id, details=details) + + +class UnsupportedSampleRateException(APIException): + """不支持的采样率异常""" + + def __init__(self, message: str, task_id: str = "", details: Optional[Dict[str, Any]] = None): + super().__init__(41010101, message, task_id, details=details) + + +class DefaultServerErrorException(APIException): + """默认服务端错误异常""" + + def __init__(self, message: str, task_id: str = "", details: Optional[Dict[str, Any]] = None): + super().__init__(50000000, message, task_id, details=details) + + +# 异常处理器 +async def api_exception_handler(request: Request, exc: Exception) -> JSONResponse: + """API异常处理器""" + # FastAPI 只会在抛出 APIException 时调用此处理器 + api_exc = exc if isinstance(exc, APIException) else APIException(50000000, str(exc)) + logger.error(f"[{api_exc.task_id}] API异常: {api_exc.message}") + + # 使用标准错误格式 + response_data = api_exc.to_dict() + + # 确定HTTP状态码 + http_status_code = get_http_status_code(api_exc.status_code) + + return JSONResponse( + content=response_data, + headers={"task_id": api_exc.task_id} if api_exc.task_id else {}, + status_code=http_status_code, + ) + + +async def http_exception_handler(request: Request, exc: Exception) -> JSONResponse: + """FastAPI HTTPException handler for legacy meeting/speaker endpoints.""" + http_exc = exc if isinstance(exc, HTTPException) else HTTPException(500, str(exc)) + if not _uses_meeting_legacy_response(request): + return await fastapi_http_exception_handler(request, http_exc) + + detail = http_exc.detail + if isinstance(detail, dict): + return JSONResponse( + status_code=http_exc.status_code, + content={ + "code": detail.get("code", 4001), + "message": detail.get("message", "Request failed"), + }, + ) + return JSONResponse( + status_code=http_exc.status_code, + content={"code": 4001, "message": str(detail)}, + ) + + +async def validation_exception_handler(request: Request, exc: Exception) -> JSONResponse: + """FastAPI/Pydantic validation handler for legacy meeting/speaker endpoints.""" + validation_exc = exc if isinstance(exc, RequestValidationError) else None + if validation_exc is None: + return JSONResponse( + status_code=422, + content={"code": 1001, "message": "Invalid request"}, + ) + if not _uses_meeting_legacy_response(request): + return await fastapi_validation_exception_handler(request, validation_exc) + + errors = validation_exc.errors() + message = str(errors[0]["msg"]) if errors else "Invalid request" + return JSONResponse(status_code=422, content={"code": 1001, "message": message}) + + +async def general_exception_handler(request: Request, exc: Exception) -> JSONResponse: + """通用异常处理器""" + logger.error(f"未处理的异常: {str(exc)}", exc_info=True) + + # 使用标准错误格式 + response_data = create_error_response( + error_code="DEFAULT_SERVER_ERROR", + message=f"内部服务错误: {str(exc)}", + ) + + return JSONResponse(content=response_data, status_code=500) diff --git a/app/core/executor.py b/app/core/executor.py new file mode 100644 index 0000000..8bcb1d4 --- /dev/null +++ b/app/core/executor.py @@ -0,0 +1,185 @@ +# -*- coding: utf-8 -*- +""" +异步执行器模块 + +用于将同步的模型推理调用放入线程池执行，避免阻塞事件循环， +实现真正的多路并发处理。 + +设计要点： +1. 使用 ThreadPoolExecutor 而非 ProcessPoolExecutor + - 模型已加载在内存中，进程间无法共享 + - GPU操作会自动释放GIL，线程池足以实现并发 + +2. 对于流式生成器，使用 asyncio.Queue 实现异步迭代 + +3. 线程池大小根据使用场景配置： + - CPU推理：受GIL限制，多线程并发收益有限，但可以让I/O不阻塞 + - GPU推理：CUDA操作释放GIL，可以实现真正并发 +""" + +import os +import asyncio +import logging +from concurrent.futures import ThreadPoolExecutor +from typing import Callable, TypeVar, Generator, AsyncGenerator, Optional +from functools import partial + +logger = logging.getLogger(__name__) + +# 类型变量 +T = TypeVar("T") + +# 全局线程池执行器 +# 默认线程数：max(4, CPU核心数)，可通过环境变量覆盖 +_DEFAULT_WORKERS = max(4, os.cpu_count() or 4) +_MAX_WORKERS = int(os.getenv("INFERENCE_THREAD_POOL_SIZE", str(_DEFAULT_WORKERS))) + +_executor: Optional[ThreadPoolExecutor] = None + + +def get_executor() -> ThreadPoolExecutor: + """获取全局线程池执行器（懒加载）""" + global _executor + if _executor is None: + _executor = ThreadPoolExecutor( + max_workers=_MAX_WORKERS, + thread_name_prefix="inference_worker" + ) + logger.info(f"推理线程池已创建，最大工作线程数: {_MAX_WORKERS}") + return _executor + + +def shutdown_executor(): + """关闭线程池执行器""" + global _executor + if _executor is not None: + _executor.shutdown(wait=True) + _executor = None + logger.info("推理线程池已关闭") + + +async def run_sync(func: Callable[..., T], *args, **kwargs) -> T: + """ + 在线程池中执行同步函数，不阻塞事件循环 + + Args: + func: 同步函数 + *args: 位置参数 + **kwargs: 关键字参数 + + Returns: + 函数返回值 + + Example: + result = await run_sync(model.generate, input=audio_array, cache=cache) + """ + loop = asyncio.get_running_loop() + executor = get_executor() + + # 使用 partial 绑定参数 + if kwargs: + func_with_args = partial(func, *args, **kwargs) + else: + func_with_args = partial(func, *args) if args else func + + return await loop.run_in_executor(executor, func_with_args) + + +async def run_sync_generator( + generator_func: Callable[..., Generator[T, None, None]], + *args, + **kwargs +) -> AsyncGenerator[T, None]: + """ + 将同步生成器转换为异步生成器，在线程池中执行 + + 用于流式处理等需要逐步产出结果的场景。 + + Args: + generator_func: 返回生成器的同步函数 + *args: 位置参数 + **kwargs: 关键字参数 + + Yields: + 生成器的每个产出值 + + Example: + async for chunk in run_sync_generator(model.inference_sft, text, voice, stream=True): + await websocket.send_bytes(chunk) + """ + loop = asyncio.get_running_loop() + executor = get_executor() + queue: asyncio.Queue = asyncio.Queue() + + # 标记生成器结束的哨兵值 + _SENTINEL = object() + + def producer(): + """在线程中运行生成器，将结果放入队列""" + try: + gen = generator_func(*args, **kwargs) + for item in gen: + # 使用 call_soon_threadsafe 安全地将结果放入队列 + loop.call_soon_threadsafe(queue.put_nowait, item) + except BaseException as e: + # 发生异常时，记录日志并将异常放入队列 + logger.error(f"生成器执行异常: {type(e).__name__}: {e}") + loop.call_soon_threadsafe(queue.put_nowait, e) + finally: + # 发送结束标记 + loop.call_soon_threadsafe(queue.put_nowait, _SENTINEL) + + # 在线程池中启动生产者 + future = executor.submit(producer) + + try: + while True: + item = await queue.get() + + if item is _SENTINEL: + break + + # 使用 BaseException 捕获所有异常类型（包括 KeyboardInterrupt 等） + if isinstance(item, BaseException): + raise item + + yield item + finally: + # 确保检查线程是否有未捕获的异常 + if future.done(): + try: + # 如果线程已完成，检查是否有异常 + future.result() + except Exception as e: + logger.error(f"生成器线程异常: {type(e).__name__}: {e}") + elif not future.cancelled(): + future.cancel() + + +class AsyncInferenceWrapper: + """ + 异步推理包装器 + + 将同步的模型推理方法包装为异步方法，方便复用。 + + Example: + wrapper = AsyncInferenceWrapper(asr_engine.realtime_model) + result = await wrapper.generate(input=audio_array, cache=cache) + """ + + def __init__(self, model): + self._model = model + + async def generate(self, *args, **kwargs): + """异步调用模型的 generate 方法""" + return await run_sync(self._model.generate, *args, **kwargs) + + async def inference_sft(self, *args, **kwargs): + """异步流式调用模型的 inference_sft 方法""" + async for item in run_sync_generator(self._model.inference_sft, *args, **kwargs): + yield item + + async def inference_zero_shot(self, *args, **kwargs): + """异步流式调用模型的 inference_zero_shot 方法""" + async for item in run_sync_generator(self._model.inference_zero_shot, *args, **kwargs): + yield item diff --git a/app/core/hotword_resolver.py b/app/core/hotword_resolver.py new file mode 100644 index 0000000..4b40fae --- /dev/null +++ b/app/core/hotword_resolver.py @@ -0,0 +1,216 @@ +# -*- coding: utf-8 -*- +"""Rule-based hotword correction for ASR post-processing.""" + +from __future__ import annotations + +import re +from difflib import SequenceMatcher +from typing import Any + +try: + from pypinyin import lazy_pinyin +except Exception: # pragma: no cover - optional runtime enhancement. + lazy_pinyin = None # type: ignore[assignment] + + +_HOTWORD_PROMPT_LEAK_PATTERNS = ( + re.compile(r"^\s*Use this context when resolving named entities:\s*", re.IGNORECASE), + re.compile(r"^\s*(?:上下文信息[:：]?\s*)?热词列表[:：]\s*[［\[][^\]］]{0,500}[］\]]\s*[，。,:：;；\s]*"), +) + + +def parse_hotwords(hotwords: str | list[dict[str, object]] | None) -> list[dict[str, Any]]: + if isinstance(hotwords, list): + return _extract_hotword_entries(hotwords) + + raw_text = str(hotwords or "").strip() + if not raw_text: + return [] + + tokens = [part for part in re.split(r"[\s,，;；]+", raw_text) if part] + entries: list[dict[str, Any]] = [] + index = 0 + order = 0 + while index < len(tokens): + word = tokens[index].strip() + if not word: + index += 1 + continue + weight = 1.0 + if index + 1 < len(tokens): + try: + weight = float(tokens[index + 1]) + index += 2 + except ValueError: + index += 1 + else: + index += 1 + entries.append({"text": word, "weight": weight, "order": order}) + order += 1 + return entries + + +def format_hotword_prompt_context(hotwords: str | list[dict[str, object]] | None) -> str: + entries = parse_hotwords(hotwords) + if not entries: + return "" + + unique_words: list[str] = [] + for entry in entries: + word = str(entry["text"]).strip() + if word and word not in unique_words: + unique_words.append(word) + if not unique_words: + return "" + return f"热词列表：[{', '.join(unique_words)}]" + + +def strip_hotword_prompt_leakage(text: str) -> str: + cleaned = str(text or "") + changed = True + while changed and cleaned: + changed = False + for pattern in _HOTWORD_PROMPT_LEAK_PATTERNS: + updated, count = pattern.subn("", cleaned, count=1) + if count: + cleaned = updated + changed = True + return cleaned.strip() + + +def _extract_hotword_entries(hotwords: list[dict[str, object]] | None) -> list[dict[str, Any]]: + entries: list[dict[str, Any]] = [] + for index, item in enumerate(hotwords or []): + hotword_text = str(item.get("hotword", "")).strip() + if not hotword_text: + continue + try: + weight = float(item.get("weight", 1.0)) + except Exception: + weight = 1.0 + entries.append({"text": hotword_text, "weight": weight, "order": index}) + return entries + + +def _normalize_ascii_token(text: str) -> str: + return re.sub(r"[^a-z0-9]+", "", str(text or "").lower()) + + +def _common_prefix_length(left: str, right: str) -> int: + matched = 0 + for left_char, right_char in zip(left, right): + if left_char != right_char: + break + matched += 1 + return matched + + +def _common_suffix_length(left: str, right: str) -> int: + return _common_prefix_length(left[::-1], right[::-1]) + + +def _normalize_cjk_pinyin(text: str) -> tuple[str, ...]: + if lazy_pinyin is None: + return () + return tuple(part.strip().lower() for part in lazy_pinyin(str(text or ""), errors="ignore") if str(part).strip()) + + +def _replace_ascii_hotwords(text: str, hotword_entries: list[dict[str, Any]]) -> tuple[str, bool, list[str]]: + updated_text = str(text or "") + changed = False + matched_hotwords: list[str] = [] + ascii_pattern = re.compile(r"[A-Za-z][A-Za-z0-9\s._-]{0,40}") + for entry in hotword_entries: + hotword = str(entry["text"]) + if not re.search(r"[A-Za-z]", hotword): + continue + normalized_hotword = _normalize_ascii_token(hotword) + if not normalized_hotword: + continue + + def _replace_match(match: re.Match[str]) -> str: + nonlocal changed + candidate = match.group(0) + if _normalize_ascii_token(candidate) == normalized_hotword and candidate != hotword: + changed = True + if hotword not in matched_hotwords: + matched_hotwords.append(hotword) + return hotword + return candidate + + updated_text = ascii_pattern.sub(_replace_match, updated_text) + return updated_text, changed, matched_hotwords + + +def _score_cjk_candidate(candidate: str, hotword_entry: dict[str, Any]) -> tuple[int, float, int, float, int] | None: + hotword = str(hotword_entry["text"]) + if candidate == hotword or len(candidate) != len(hotword): + return None + candidate_pinyin = _normalize_cjk_pinyin(candidate) + hotword_pinyin = _normalize_cjk_pinyin(hotword) + if candidate_pinyin and candidate_pinyin == hotword_pinyin: + return (3, float(hotword_entry["weight"]), len(hotword), 1.0, -int(hotword_entry["order"])) + if len(hotword) <= 2: + return None + prefix_length = _common_prefix_length(candidate, hotword) + suffix_length = _common_suffix_length(candidate, hotword) + similarity = SequenceMatcher(None, candidate, hotword).ratio() + if prefix_length >= len(hotword) - 1 or suffix_length >= len(hotword) - 1: + return (2, float(hotword_entry["weight"]), prefix_length + suffix_length, similarity, -int(hotword_entry["order"])) + if similarity >= 0.67 and prefix_length >= 1 and suffix_length >= 1: + return (2, float(hotword_entry["weight"]), prefix_length + suffix_length, similarity, -int(hotword_entry["order"])) + return None + + +def _replace_cjk_hotwords(text: str, hotword_entries: list[dict[str, Any]]) -> tuple[str, bool, list[str]]: + updated_text = str(text or "") + changed = False + matched_hotwords: list[str] = [] + chinese_entries = [entry for entry in hotword_entries if re.fullmatch(r"[\u4e00-\u9fff]{2,12}", str(entry["text"]))] + if not chinese_entries: + return updated_text, False, matched_hotwords + candidate_lengths = sorted({len(str(entry["text"])) for entry in chinese_entries}) + token_pattern = re.compile(r"[\u4e00-\u9fff]{2,24}") + + def _replace_match(match: re.Match[str]) -> str: + nonlocal changed + token = match.group(0) + best_choice: tuple[tuple[int, float, int, float, int], int, int, str] | None = None + for hotword_length in candidate_lengths: + if hotword_length > len(token): + continue + scoped_entries = [entry for entry in chinese_entries if len(str(entry["text"])) == hotword_length] + for index in range(0, len(token) - hotword_length + 1): + candidate = token[index:index + hotword_length] + for entry in scoped_entries: + score = _score_cjk_candidate(candidate, entry) + if score is None: + continue + current_choice = (score, index, hotword_length, str(entry["text"])) + if best_choice is None or current_choice > best_choice: + best_choice = current_choice + if best_choice is None: + return token + _, begin_index, hotword_length, hotword = best_choice + token_chars = list(token) + token_chars[begin_index:begin_index + hotword_length] = list(hotword) + changed = True + if hotword not in matched_hotwords: + matched_hotwords.append(hotword) + return "".join(token_chars) + + updated_text = token_pattern.sub(_replace_match, updated_text) + return updated_text, changed, matched_hotwords + + +def apply_hotword_rules(text: str, hotwords: str | list[dict[str, object]] | None = None) -> tuple[str, bool, list[str]]: + hotword_entries = parse_hotwords(hotwords) + if not hotword_entries: + return str(text or ""), False, [] + updated_text, ascii_changed, ascii_matches = _replace_ascii_hotwords(str(text or ""), hotword_entries) + updated_text, cjk_changed, cjk_matches = _replace_cjk_hotwords(updated_text, hotword_entries) + matched_hotwords: list[str] = [] + for hotword_text in ascii_matches + cjk_matches: + if hotword_text not in matched_hotwords: + matched_hotwords.append(hotword_text) + return updated_text, ascii_changed or cjk_changed, matched_hotwords diff --git a/app/core/logging.py b/app/core/logging.py new file mode 100644 index 0000000..661d377 --- /dev/null +++ b/app/core/logging.py @@ -0,0 +1,342 @@ +# -*- coding: utf-8 -*- +""" +日志配置模块 +统一的日志配置和管理，支持多 Worker 模式 +""" + +import logging +import logging.handlers +import sys +import os +import json +from datetime import datetime, timezone +from typing import Optional, Dict, Any +from pathlib import Path +from .config import settings + + +class StructuredLogFormatter(logging.Formatter): + """结构化JSON日志格式化器 + + 将日志记录格式化为JSON格式，支持extra字段传递结构化数据。 + + 输出示例: + { + "timestamp": "2025-01-31T12:00:00Z", + "level": "INFO", + "logger": "app.services.asr", + "message": "推理完成", + "task_id": "xxx", + "duration_ms": 1234, + "audio_duration_sec": 60, + "rtf": 0.02, + "model_id": "qwen3-asr-1.7b" + } + """ + + def __init__(self, include_extra: bool = True): + """ + Args: + include_extra: 是否包含extra字段中的结构化数据 + """ + super().__init__() + self.include_extra = include_extra + self._reserved_attrs = { + 'name', 'msg', 'args', 'levelname', 'levelno', 'pathname', + 'filename', 'module', 'exc_info', 'exc_text', 'stack_info', + 'lineno', 'funcName', 'created', 'msecs', 'relativeCreated', + 'thread', 'threadName', 'processName', 'process', 'getMessage', + 'message', 'asctime' + } + + def format(self, record: logging.LogRecord) -> str: + """将日志记录格式化为JSON""" + log_data: Dict[str, Any] = { + "timestamp": datetime.fromtimestamp(record.created, tz=timezone.utc).strftime("%Y-%m-%dT%H:%M:%S.%f")[:-3] + "Z", + "level": record.levelname, + "logger": record.name, + "message": record.getMessage(), + } + + # 添加worker_id（多worker模式下） + workers = int(os.getenv("WORKERS", "1")) + if workers > 1: + log_data["worker_id"] = f"worker-{os.getpid()}" + + # 添加异常信息（如果有） + if record.exc_info: + exc_type = record.exc_info[0] + exc_value = record.exc_info[1] + if exc_type and exc_value: + log_data["exception"] = { + "type": exc_type.__name__, + "message": str(exc_value) + } + + # 添加extra字段中的结构化数据 + if self.include_extra: + extra_data = self._extract_extra_data(record) + if extra_data: + log_data.update(extra_data) + + return json.dumps(log_data, ensure_ascii=False, default=str) + + def _extract_extra_data(self, record: logging.LogRecord) -> Dict[str, Any]: + """从日志记录中提取extra数据""" + extra_data = {} + for key, value in record.__dict__.items(): + if key not in self._reserved_attrs and not key.startswith('_'): + extra_data[key] = value + return extra_data + + +class HybridLogFormatter(logging.Formatter): + """混合日志格式化器 + + 根据日志内容自动选择格式： + - 包含结构化数据（extra字段）的日志使用JSON格式 + - 普通日志使用文本格式 + + 这允许在代码中逐步迁移到结构化日志，同时保持可读性。 + """ + + def __init__( + self, + text_format: Optional[str] = None, + json_formatter: Optional[StructuredLogFormatter] = None, + ): + super().__init__() + self.text_format = text_format or "%(asctime)s - %(name)s - %(levelname)s - %(message)s" + self.json_formatter = json_formatter or StructuredLogFormatter() + self._reserved_attrs = { + 'name', 'msg', 'args', 'levelname', 'levelno', 'pathname', + 'filename', 'module', 'exc_info', 'exc_text', 'stack_info', + 'lineno', 'funcName', 'created', 'msecs', 'relativeCreated', + 'thread', 'threadName', 'processName', 'process', 'getMessage', + 'message', 'asctime' + } + + def format(self, record: logging.LogRecord) -> str: + """根据内容选择格式""" + # 检查是否有extra数据 + has_extra = self._has_extra_data(record) + + if has_extra: + return self.json_formatter.format(record) + else: + # 使用文本格式 + text_formatter = logging.Formatter(self.text_format) + return text_formatter.format(record) + + def _has_extra_data(self, record: logging.LogRecord) -> bool: + """检查日志记录是否包含extra数据""" + for key in record.__dict__.keys(): + if key not in self._reserved_attrs and not key.startswith('_'): + return True + return False + + +def get_structured_logger(name: str) -> logging.Logger: + """获取支持结构化日志的记录器 + + 这是一个便捷函数，返回一个配置好的日志记录器， + 可以直接使用 extra 参数记录结构化数据。 + + 示例: + logger = get_structured_logger(__name__) + logger.info( + "推理完成", + extra={ + "duration_ms": 1234, + "audio_duration_sec": 60, + "rtf": 0.02, + "model_id": "qwen3-asr-1.7b" + } + ) + + Args: + name: 记录器名称 + + Returns: + 配置好的日志记录器 + """ + return logging.getLogger(name) + + +def log_inference_metrics( + logger: logging.Logger, + message: str, + task_id: Optional[str] = None, + duration_ms: Optional[float] = None, + audio_duration_sec: Optional[float] = None, + model_id: Optional[str] = None, + status: str = "success", + **kwargs +) -> None: + """记录推理性能指标 + + 这是一个辅助函数，用于统一记录推理性能指标。 + + Args: + logger: 日志记录器 + message: 日志消息 + task_id: 任务ID + duration_ms: 推理耗时（毫秒） + audio_duration_sec: 音频时长（秒） + model_id: 模型ID + status: 状态（success/error） + **kwargs: 其他结构化数据 + """ + extra: Dict[str, Any] = { + "status": status, + } + + if task_id: + extra["task_id"] = task_id + if duration_ms is not None: + extra["duration_ms"] = round(duration_ms, 2) + if audio_duration_sec is not None: + extra["audio_duration_sec"] = round(audio_duration_sec, 2) + if model_id: + extra["model_id"] = model_id + + # 计算RTF（实时率） + if duration_ms is not None and audio_duration_sec is not None and audio_duration_sec > 0: + rtf = (duration_ms / 1000) / audio_duration_sec + extra["rtf"] = round(rtf, 4) + + # 添加其他数据 + extra.update(kwargs) + + logger.info(message, extra=extra) + + +def get_worker_id() -> str: + """获取当前 Worker ID + + Returns: + Worker 标识符，格式为 'worker-{pid}' 或 'main' + """ + # 检查是否在多 worker 模式下 + workers = int(os.getenv("WORKERS", "1")) + if workers > 1: + return f"worker-{os.getpid()}" + return "main" + + +def setup_logging( + level: Optional[str] = None, + log_file: Optional[str] = None, + format_string: Optional[str] = None, + max_bytes: Optional[int] = None, + backup_count: Optional[int] = None, + worker_id: Optional[str] = None, + use_structured: bool = False, +) -> None: + """设置应用日志配置 + + Args: + level: 日志级别 + log_file: 日志文件路径 + format_string: 日志格式字符串 + max_bytes: 单个日志文件最大大小（字节） + backup_count: 保留的备份文件数量 + worker_id: Worker 标识符（多 Worker 模式下使用） + use_structured: 是否使用结构化JSON日志格式 + """ + # 使用传入的参数或配置文件中的设置 + log_level = level or settings.LOG_LEVEL + log_file_path = log_file or settings.LOG_FILE + max_file_size = max_bytes or settings.LOG_MAX_BYTES + backup_files = backup_count or settings.LOG_BACKUP_COUNT + + # 获取 Worker ID + current_worker_id = worker_id or get_worker_id() + workers = int(os.getenv("WORKERS", "1")) + worker_log_path: Optional[Path] = None + + # 确定日志格式 + if use_structured: + # 使用结构化日志格式 + formatter: logging.Formatter = StructuredLogFormatter() + else: + # 使用混合格式（普通日志文本，带extra的JSON） + if workers > 1: + text_format = format_string or f"%(asctime)s - [{current_worker_id}] - %(name)s - %(levelname)s - %(message)s" + else: + text_format = format_string or "%(asctime)s - %(name)s - %(levelname)s - %(message)s" + formatter = HybridLogFormatter(text_format=text_format) + + # 创建处理器列表 + stream_handler = logging.StreamHandler(sys.stdout) + stream_handler.setFormatter(formatter) + handlers: list[logging.Handler] = [stream_handler] + + # 确定日志文件路径 + if log_file_path: + log_path = Path(log_file_path) + else: + log_path = Path("logs/qwen3-asr.log") + + # 确保日志目录存在 + log_dir = log_path.parent + log_dir.mkdir(parents=True, exist_ok=True) + + # 多 Worker 模式下，每个 Worker 使用独立的日志文件 + if workers > 1: + # Example: qwen3-asr.log -> qwen3-asr.worker-12345.log + worker_log_path = log_dir / f"{log_path.stem}.{current_worker_id}{log_path.suffix}" + + # Worker 专属日志文件 + worker_file_handler = logging.handlers.RotatingFileHandler( + worker_log_path, + maxBytes=max_file_size, + backupCount=backup_files, + encoding="utf-8", + ) + worker_file_handler.setFormatter(formatter) + handlers.append(worker_file_handler) + + # 同时也写入主日志文件（汇总所有 Worker 的日志） + main_file_handler = logging.handlers.RotatingFileHandler( + log_path, + maxBytes=max_file_size, + backupCount=backup_files, + encoding="utf-8", + ) + main_file_handler.setFormatter(formatter) + handlers.append(main_file_handler) + else: + # 单 Worker 模式，只写入主日志文件 + file_handler = logging.handlers.RotatingFileHandler( + log_path, + maxBytes=max_file_size, + backupCount=backup_files, + encoding="utf-8", + ) + file_handler.setFormatter(formatter) + handlers.append(file_handler) + + # 配置根日志记录器 + logging.basicConfig( + level=getattr(logging, log_level.upper()), + handlers=handlers, + force=True, # 强制重新配置 + ) + + # 设置第三方库的日志级别（由LOG_LEVEL控制） + third_party_level = getattr(logging, log_level.upper()) + logging.getLogger("urllib3").setLevel(third_party_level) + logging.getLogger("requests").setLevel(third_party_level) + logging.getLogger("httpx").setLevel(third_party_level) + logging.getLogger("httpcore").setLevel(third_party_level) + + # 始终禁用噪音特别大的库 + logging.getLogger("numba").setLevel(logging.WARNING) + logging.getLogger("numba.core").setLevel(logging.WARNING) + logging.getLogger("numba.core.ssa").setLevel(logging.WARNING) + + # 多 Worker 模式下记录启动日志 + if workers > 1 and worker_log_path: + logger = logging.getLogger(__name__) + logger.info(f"Worker {current_worker_id} 日志系统已初始化，日志文件: {worker_log_path}") diff --git a/app/core/security.py b/app/core/security.py new file mode 100644 index 0000000..f94cb67 --- /dev/null +++ b/app/core/security.py @@ -0,0 +1,173 @@ +# -*- coding: utf-8 -*- +""" +安全相关功能 +包含鉴权、token验证等安全功能 +""" + +from typing import Optional +from fastapi import Request +from .config import settings + +TOKEN_HEADER_NAME = "X-NLS-Token" +AUTH_OPTIONAL_PLACEHOLDER = "optional" +WEBSOCKET_QUERY_TOKEN_KEYS = ("token", "x_nls_token", "X-NLS-Token") + + +def normalize_token(token: Optional[str]) -> Optional[str]: + """将 token 归一化为非空字符串或 None。""" + if token is None: + return None + + normalized = token.strip() + return normalized or None + + +def get_expected_api_key(expected_token: Optional[str] = None) -> Optional[str]: + """获取归一化后的期望 API_KEY。""" + if expected_token is not None: + return normalize_token(expected_token) + return normalize_token(settings.API_KEY) + + +def mask_sensitive_data( + data: str, mask_char: str = "*", keep_prefix: int = 4, keep_suffix: int = 4 +) -> str: + """遮盖敏感数据 + + Args: + data: 需要遮盖的数据 + mask_char: 遮盖字符 + keep_prefix: 保留前缀字符数 + keep_suffix: 保留后缀字符数 + + Returns: + 遮盖后的数据 + """ + if not data or len(data) <= keep_prefix + keep_suffix: + return data + + prefix = data[:keep_prefix] + suffix = data[-keep_suffix:] if keep_suffix > 0 else "" + mask_length = len(data) - keep_prefix - keep_suffix + mask = mask_char * mask_length + + return f"{prefix}{mask}{suffix}" + + +def validate_token_value(token: Optional[str], expected_token: Optional[str] = None) -> bool: + """验证访问令牌 + + Args: + token: 客户端提供的token + expected_token: 期望的token值（从环境变量读取），如果为None则鉴权可选 + + Returns: + bool: 验证结果 + """ + normalized_expected_token = get_expected_api_key(expected_token) + if not normalized_expected_token: + return True + + normalized_token = normalize_token(token) + if not normalized_token: + return False + + # 简单的token格式验证（长度检查） + if len(normalized_token) < 10: + return False + + # 验证token是否匹配 + if normalized_token != normalized_expected_token: + return False + + return True + + +def extract_header_token(request: Request) -> Optional[str]: + """从标准头部提取 token。""" + return normalize_token(request.headers.get(TOKEN_HEADER_NAME)) + + +def extract_bearer_token(request: Request) -> Optional[str]: + """从 Authorization: Bearer 提取 token。""" + auth_header = request.headers.get("Authorization") + if not auth_header: + return None + + scheme, _, value = auth_header.partition(" ") + if scheme.lower() != "bearer": + return None + return normalize_token(value) + + +def extract_openai_token(request: Request) -> Optional[str]: + """OpenAI 兼容接口鉴权：优先 Bearer，其次 X-NLS-Token。""" + return extract_bearer_token(request) or extract_header_token(request) + + +def extract_websocket_token(websocket) -> Optional[str]: + """从 WebSocket 连接中提取 token。""" + if hasattr(websocket, "headers"): + token = normalize_token(websocket.headers.get(TOKEN_HEADER_NAME)) + if token: + return token + + if hasattr(websocket, "query_params"): + for key in WEBSOCKET_QUERY_TOKEN_KEYS: + token = normalize_token(websocket.query_params.get(key)) + if token: + return token + + return None + + +def _validate_resolved_token( + token: Optional[str], + missing_message: str, + expected_token: Optional[str] = None, +) -> tuple[bool, str]: + """统一 token 校验逻辑。""" + expected = get_expected_api_key(expected_token) + normalized_token = normalize_token(token) + + if not expected: + return True, normalized_token or AUTH_OPTIONAL_PLACEHOLDER + + if not normalized_token: + return False, missing_message + + if not validate_token_value(normalized_token, expected): + masked_token = mask_sensitive_data(normalized_token) + return False, f"Gateway:ACCESS_DENIED:The token '{masked_token}' is invalid!" + + return True, normalized_token + + +def validate_token(request: Request, task_id: str = "") -> tuple[bool, str]: + """验证X-NLS-Token头部""" + _ = task_id + token = extract_header_token(request) + return _validate_resolved_token(token, "缺少X-NLS-Token头部") + + +def validate_openai_token(request: Request, task_id: str = "") -> tuple[bool, str]: + """验证 OpenAI 兼容接口 token（Bearer/X-NLS-Token）。""" + _ = task_id + token = extract_openai_token(request) + return _validate_resolved_token(token, "缺少Authorization Bearer或X-NLS-Token头部") + + +def validate_token_websocket(token: str, task_id: str = "") -> tuple[bool, str]: + """验证WebSocket连接中的token""" + _ = task_id + return _validate_resolved_token(token, "缺少token参数") + + +def validate_websocket_token(websocket, task_id: str = "") -> tuple[bool, str]: + """验证 WebSocket 连接 token（header/query 参数）。""" + _ = task_id + token = extract_websocket_token(websocket) + return _validate_resolved_token( + token, + "缺少鉴权信息，请通过 X-NLS-Token header 或 token/x_nls_token 查询参数传入", + ) diff --git a/app/core/task_store.py b/app/core/task_store.py new file mode 100644 index 0000000..ba359b8 --- /dev/null +++ b/app/core/task_store.py @@ -0,0 +1,280 @@ +# -*- coding: utf-8 -*- +"""Persistent task state store for meeting-style offline jobs.""" + +from __future__ import annotations + +import json +import time +from pathlib import Path +from threading import RLock +from typing import Any, Optional + +from app.core.config import settings + +tasks_db: dict[str, dict[str, Any]] = {} +_tasks_lock = RLock() +_last_cleanup_at = 0 + + +def _task_state_dir() -> Path: + return Path(settings.TASK_STATE_DIR) + + +def _task_file_path(task_id: str) -> Path: + return _task_state_dir() / f"{task_id}.json" + + +def _task_result_file_path(task_id: str) -> Path: + return _task_state_dir() / f"{task_id}.result.json" + + +def _retention_seconds() -> int: + return max(0, int(settings.TASK_RETENTION_HOURS)) * 3600 + + +def _is_task_expired(task_payload: dict[str, Any], now_ts: Optional[int] = None) -> bool: + retention_seconds = _retention_seconds() + if retention_seconds <= 0: + return False + current_time = int(now_ts or time.time()) + base_timestamp = int(task_payload.get("updated_at") or task_payload.get("created_at") or 0) + if base_timestamp <= 0: + return False + return current_time - base_timestamp >= retention_seconds + + +def _write_json_file(file_path: Path, payload: dict[str, Any]) -> None: + file_path.parent.mkdir(parents=True, exist_ok=True) + temporary_file_path = file_path.with_suffix(f"{file_path.suffix}.tmp") + temporary_file_path.write_text( + json.dumps(payload, ensure_ascii=False), + encoding="utf-8", + ) + temporary_file_path.replace(file_path) + + +def _write_task_file(task_id: str, payload: dict[str, Any]) -> None: + _write_json_file(_task_file_path(task_id), payload) + + +def _remove_task_file(task_id: str) -> None: + task_file_path = _task_file_path(task_id) + if task_file_path.exists(): + task_file_path.unlink() + + +def _write_task_result_file(task_id: str, payload: dict[str, Any]) -> None: + _write_json_file(_task_result_file_path(task_id), payload) + + +def _remove_task_result_file(task_id: str) -> None: + task_result_file_path = _task_result_file_path(task_id) + if task_result_file_path.exists(): + task_result_file_path.unlink() + + +def _load_task_payload_from_file(task_file_path: Path) -> Optional[tuple[str, dict[str, Any]]]: + try: + payload = json.loads(task_file_path.read_text(encoding="utf-8")) + except Exception: + return None + if not isinstance(payload, dict): + return None + task_id = str(payload.get("task_id") or task_file_path.stem).strip() + if not task_id: + return None + payload["task_id"] = task_id + return task_id, payload + + +def _load_task_result_payload(task_id: str) -> Optional[dict[str, Any]]: + task_result_file_path = _task_result_file_path(task_id) + if not task_result_file_path.exists(): + return None + try: + payload = json.loads(task_result_file_path.read_text(encoding="utf-8")) + except Exception: + return None + if not isinstance(payload, dict): + return None + return payload + + +def _split_task_payload(task_id: str, payload: dict[str, Any], *, persist_legacy_result: bool = False) -> tuple[dict[str, Any], bool]: + status_payload = dict(payload) + has_result = "result" in status_payload + result_payload = status_payload.pop("result", None) + if has_result: + if isinstance(result_payload, dict): + _write_task_result_file(task_id, result_payload) + elif result_payload is None: + _remove_task_result_file(task_id) + if persist_legacy_result: + _write_task_file(task_id, status_payload) + return status_payload, has_result + + +def _status_file_paths() -> list[Path]: + return [ + task_file_path + for task_file_path in _task_state_dir().glob("*.json") + if not task_file_path.name.endswith(".result.json") + ] + + +def load_tasks_from_disk() -> None: + with _tasks_lock: + tasks_db.clear() + task_directory = _task_state_dir() + task_directory.mkdir(parents=True, exist_ok=True) + current_time = int(time.time()) + for task_file_path in _status_file_paths(): + loaded_item = _load_task_payload_from_file(task_file_path) + if loaded_item is None: + try: + task_file_path.unlink() + except Exception: + pass + continue + task_id, payload = loaded_item + payload, _ = _split_task_payload(task_id, payload, persist_legacy_result=True) + if _is_task_expired(payload, now_ts=current_time): + try: + task_file_path.unlink() + except Exception: + pass + try: + _remove_task_result_file(task_id) + except Exception: + pass + continue + tasks_db[task_id] = payload + + +def cleanup_expired_tasks(force: bool = False) -> None: + global _last_cleanup_at + current_time = int(time.time()) + if not force and current_time - _last_cleanup_at < 60: + return + with _tasks_lock: + expired_task_ids = [ + task_id + for task_id, task_payload in tasks_db.items() + if _is_task_expired(task_payload, now_ts=current_time) + ] + for task_id in expired_task_ids: + tasks_db.pop(task_id, None) + try: + _remove_task_file(task_id) + except Exception: + pass + try: + _remove_task_result_file(task_id) + except Exception: + pass + for task_file_path in _status_file_paths(): + loaded_item = _load_task_payload_from_file(task_file_path) + if loaded_item is None: + try: + task_file_path.unlink() + except Exception: + pass + continue + task_id, payload = loaded_item + payload, _ = _split_task_payload(task_id, payload, persist_legacy_result=True) + if _is_task_expired(payload, now_ts=current_time): + try: + task_file_path.unlink() + except Exception: + pass + try: + _remove_task_result_file(task_id) + except Exception: + pass + _last_cleanup_at = current_time + + +def recover_tasks_after_restart() -> None: + load_tasks_from_disk() + cleanup_expired_tasks(force=True) + with _tasks_lock: + for task_id, task_payload in list(tasks_db.items()): + if str(task_payload.get("status") or "").strip() not in {"queued", "processing"}: + continue + interrupted_message = "服务已重启，原离线任务已中断，请重新提交。" + task_payload.update( + { + "status": "failed", + "stage": "failed", + "message": interrupted_message, + "error": interrupted_message, + "percentage": 100, + "updated_at": int(time.time()), + } + ) + tasks_db[task_id] = task_payload + _write_task_file(task_id, task_payload) + + +def create_task_record(task_id: str, payload: dict[str, Any]) -> dict[str, Any]: + current_time = int(time.time()) + task_payload = dict(payload) + task_payload["task_id"] = task_id + task_payload.setdefault("created_at", current_time) + task_payload["updated_at"] = current_time + status_payload, has_result = _split_task_payload(task_id, task_payload) + with _tasks_lock: + tasks_db[task_id] = status_payload + _write_task_file(task_id, status_payload) + response_payload = dict(status_payload) + if has_result: + response_payload["result"] = _load_task_result_payload(task_id) + return response_payload + + +def get_task_record(task_id: str, *, include_result: bool = False) -> Optional[dict[str, Any]]: + with _tasks_lock: + task_file_path = _task_file_path(task_id) + if task_file_path.exists(): + loaded_item = _load_task_payload_from_file(task_file_path) + if loaded_item is None: + return None + loaded_task_id, task_payload = loaded_item + task_payload, _ = _split_task_payload( + loaded_task_id, + task_payload, + persist_legacy_result=True, + ) + if loaded_task_id != task_id or _is_task_expired(task_payload): + return None + tasks_db[task_id] = task_payload + else: + task_payload = tasks_db.get(task_id) + if task_payload is None: + return None + response_payload = dict(task_payload) + if include_result: + result_payload = _load_task_result_payload(task_id) + if result_payload is not None: + response_payload["result"] = result_payload + return response_payload + + +def update_task_record(task_id: str, payload: dict[str, Any]) -> dict[str, Any]: + current_time = int(time.time()) + with _tasks_lock: + task_payload = dict(tasks_db.get(task_id) or {}) + task_payload.update(payload) + task_payload["task_id"] = task_id + task_payload.setdefault("created_at", current_time) + task_payload["updated_at"] = current_time + status_payload, has_result = _split_task_payload(task_id, task_payload) + tasks_db[task_id] = status_payload + _write_task_file(task_id, status_payload) + response_payload = dict(status_payload) + if has_result: + response_payload["result"] = _load_task_result_payload(task_id) + return response_payload + + +load_tasks_from_disk() diff --git a/app/core/text_cleanup.py b/app/core/text_cleanup.py new file mode 100644 index 0000000..ced47cc --- /dev/null +++ b/app/core/text_cleanup.py @@ -0,0 +1,199 @@ +# -*- coding: utf-8 -*- +"""Conservative ASR text deduplication and filler cleanup.""" + +from __future__ import annotations + +import re + +from app.core.text_cleanup_lexicon import DISCOURSE_FILLER_TOKENS +from app.core.text_cleanup_lexicon import FILLER_TOKENS +from app.core.text_cleanup_lexicon import NUMERIC_STUTTER_CHARS +from app.core.text_cleanup_lexicon import REPEAT_COLLAPSIBLE_DISCOURSE_TOKENS +from app.core.text_cleanup_lexicon import SAFE_DOUBLE_WORDS +from app.core.text_cleanup_lexicon import TERMINAL_FILLER_TOKENS + +_PREFIX_STUTTER_CHARS = "这那离超和跟在对把将又还上先后了的" +_PREFIX_STUTTER_PATTERN = re.compile(rf"([{re.escape(_PREFIX_STUTTER_CHARS)}])\1(?!\1)([\u4e00-\u9fffA-Za-z]{{1,3}})") +_REPEATED_PHRASE_PATTERN = re.compile(r"([\u4e00-\u9fffA-Za-z]{2,4})\1") +_WORD_STUTTER_PREFIX_PATTERN = re.compile(r"([\u4e00-\u9fff])\1{1,5}(?=\1[\u4e00-\u9fff])") +_LONG_CHAR_REPEAT_PATTERN = re.compile(r"([\u4e00-\u9fff])\1{2,}") +_INTERNAL_DOUBLE_CHAR_REPEAT_PATTERN = re.compile(r"(?<=[\u4e00-\u9fff])([\u4e00-\u9fff])\1(?=[\u4e00-\u9fff])") +_SHORT_LEADING_STUTTER_TOKEN_PATTERN = re.compile(r"(^|[，。！？；：、,\s])([\u4e00-\u9fff])\2([\u4e00-\u9fff])(?=($|[，。！？；：、,\s]))") +_SHORT_CONFIRMATION_STUTTER_PATTERN = re.compile(r"(^|[，。！？；：、,\s])([\u4e00-\u9fff])\2([\u4e00-\u9fff])(?=(是吧|对吧|对吗|对不对))") +_REPEATED_SEPARATED_PHRASE_PATTERN = re.compile(r"([\u4e00-\u9fffA-Za-z]{1,6})([，、,\s]+)\1(?:\2\1)*") +_REPEATED_SENTENCE_CLAUSE_PATTERN = re.compile(r"([\u4e00-\u9fffA-Za-z0-9]{2,12})([。！？；：]+)(?:\s*\1\2)+") +_REPEATED_SHORT_SENTENCE_CLAUSE_PATTERN = re.compile( + r"([\u4e00-\u9fff])([。！？；：]+)(?:\s*\1\2){2,}(?:\s*\1)?(?=$|[，。！？；：、,\s])" +) +_BOUNDARY_OVERLAP_MIN_CHARS = 2 +_BOUNDARY_OVERLAP_MAX_CHARS = 12 +_STANDALONE_FILLER_PATTERN = re.compile(rf"(^|[，。！？；：、,\s])({'|'.join(map(re.escape, FILLER_TOKENS))})(?=($|[，。！？；：、,\s]))") +_FILLER_ONLY_PATTERN = re.compile(rf"^[\s，。！？；：、,]*(?:{'|'.join(map(re.escape, FILLER_TOKENS))}[\s，。！？；：、,]*)+$") +_LEADING_FILLER_PREFIX_PATTERN = re.compile(rf"^(?:{'|'.join(map(re.escape, FILLER_TOKENS))})[，、,\s]*") +_INLINE_FILLER_PATTERN = re.compile(r"(?<=[\u4e00-\u9fffA-Za-z0-9])(嗯|呃|啊|(? bool: + return bool(text) and all(character.isdigit() or character in "零〇○Ｏ一幺二两三四五六七八九十百千万亿点" for character in text) + + +def _has_meaningful_overlap(text: str) -> bool: + return bool(text and any(not character.isspace() and character not in "，。！？；：、,.!?;:" for character in text)) + + +def _collapse_internal_double_char(match: re.Match[str]) -> str: + start_index = match.start() + source_text = match.string + pair_text = source_text[start_index:start_index + 2] + if match.group(1) in NUMERIC_STUTTER_CHARS: + return pair_text + if pair_text in SAFE_DOUBLE_WORDS: + return pair_text + return match.group(1) + + +def _remove_filler_words(text: str) -> str: + normalized_text = str(text or "").strip() + if not normalized_text: + return normalized_text + if _FILLER_ONLY_PATTERN.fullmatch(normalized_text): + return "" + compact_text = _LEADING_FILLER_PREFIX_PATTERN.sub("", normalized_text) + compact_text = _INLINE_FILLER_PATTERN.sub("", compact_text) + compact_text = _BRIDGE_FILLER_PATTERN.sub(lambda match: match.group(1) if match.group(1) == match.group(2) else match.group(2), compact_text) + compact_text = _REPEATED_TOPIC_WITH_DEICTIC_PATTERN.sub(lambda match: match.group(1), compact_text) + compact_text = _REPEATED_DISCOURSE_FILLER_PATTERN.sub(lambda match: match.group(1), compact_text) + compact_text = _STANDALONE_FILLER_PATTERN.sub(lambda match: match.group(1), compact_text) + compact_text = _STANDALONE_DISCOURSE_FILLER_PATTERN.sub(lambda match: match.group(1), compact_text) + compact_text = _TERMINAL_FILLER_PATTERN.sub("", compact_text) + compact_text = _POST_PUNCT_TERMINAL_FILLER_PATTERN.sub("", compact_text) + compact_text = re.sub(r"([。！？；：])[。！？；：]+", r"\1", compact_text) + compact_text = re.sub(r"([。！？；：])[，、,]+", r"\1", compact_text) + compact_text = re.sub(r"[，、,\s]{2,}", "，", compact_text) + compact_text = re.sub(r"^[，、,\s]+", "", compact_text) + compact_text = re.sub(r"[，、,\s]+([。！？；：])", r"\1", compact_text) + compact_text = re.sub(r"[，、,\s]+$", "", compact_text) + if not compact_text or re.fullmatch(r"[\s，。！？；：、,]*", compact_text): + return "" + return compact_text + + +def _repair_household_collection_count(match: re.Match[str]) -> str: + prefix, digits, suffix = match.groups() + following_context = match.string[match.end():match.end() + 100] + + # Meeting reports often say "collected N households, estimated M households, + # close to 50%". If the glued count has a plausible suffix matching that + # ratio, keep the suffix and drop the noisy leading recognition artifact. + if "50" in following_context: + reference_counts = [ + int(value) + for value in re.findall(r"(?:大概有|约|预计|测算[^，。！？；：]{0,10}?有)(\d{1,4})户", following_context) + ] + for suffix_len in range(min(4, len(digits) - 1), 1, -1): + candidate = int(digits[-suffix_len:]) + if candidate <= 0: + continue + if any(0.35 <= reference_count / candidate <= 0.65 for reference_count in reference_counts): + return f"{prefix}{candidate}{suffix}" + + return match.group(0) + + +def _repair_numeric_asr_artifacts(text: str) -> str: + repaired_text = _CURRENCY_STUTTER_PATTERN.sub(lambda match: f"{match.group(1)}元", text) + repaired_text = _CURRENCY_SYMBOL_PATTERN.sub(lambda match: f"{match.group(1)}元", repaired_text) + repaired_text = _HOUSEHOLD_COLLECTION_GLUE_PATTERN.sub(_repair_household_collection_count, repaired_text) + return repaired_text + + +def _collapse_repeated_numeric_clauses(text: str) -> str: + return _REPEATED_NUMERIC_CLAUSE_PATTERN.sub(lambda match: f"{match.group(1)}{match.group(2)}", text) + + +def _collapse_repeated_short_sentence_clauses(text: str) -> str: + return _REPEATED_SHORT_SENTENCE_CLAUSE_PATTERN.sub(lambda match: f"{match.group(1)}{match.group(2)}", text) + + +def deduplicate_asr_text(text: str) -> str: + normalized_text = str(text or "").strip() + if not normalized_text: + return normalized_text + previous_text = None + while previous_text != normalized_text: + previous_text = normalized_text + normalized_text = _PREFIX_STUTTER_PATTERN.sub(lambda match: f"{match.group(1)}{match.group(2)}", normalized_text) + + def _collapse_repeated_phrase(match: re.Match[str]) -> str: + phrase = match.group(1) + if _is_numeric_like(phrase): + return match.group(0) + return phrase + + normalized_text = _REPEATED_PHRASE_PATTERN.sub(_collapse_repeated_phrase, normalized_text) + normalized_text = _WORD_STUTTER_PREFIX_PATTERN.sub(lambda match: match.group(0) if match.group(1) in NUMERIC_STUTTER_CHARS else "", normalized_text) + normalized_text = _LONG_CHAR_REPEAT_PATTERN.sub(lambda match: match.group(0) if match.group(1) in NUMERIC_STUTTER_CHARS else match.group(1), normalized_text) + normalized_text = _INTERNAL_DOUBLE_CHAR_REPEAT_PATTERN.sub(_collapse_internal_double_char, normalized_text) + normalized_text = _SHORT_LEADING_STUTTER_TOKEN_PATTERN.sub(lambda match: f"{match.group(1)}{match.group(2)}{match.group(3)}", normalized_text) + + def _collapse_confirmation(match: re.Match[str]) -> str: + repeated_pair = f"{match.group(2)}{match.group(2)}" + if match.group(2) in NUMERIC_STUTTER_CHARS or repeated_pair in SAFE_DOUBLE_WORDS: + return match.group(0) + return f"{match.group(1)}{match.group(2)}{match.group(3)}" + + normalized_text = _SHORT_CONFIRMATION_STUTTER_PATTERN.sub(_collapse_confirmation, normalized_text) + + def _collapse_separated(match: re.Match[str]) -> str: + phrase = match.group(1) + if phrase.isascii() and len(phrase) == 1: + return match.group(0) + if _is_numeric_like(phrase): + return match.group(0) + return phrase + + normalized_text = _REPEATED_SEPARATED_PHRASE_PATTERN.sub(_collapse_separated, normalized_text) + normalized_text = _REPEATED_SENTENCE_CLAUSE_PATTERN.sub(lambda match: match.group(0) if _is_numeric_like(match.group(1)) else f"{match.group(1)}{match.group(2)}", normalized_text) + normalized_text = _collapse_repeated_short_sentence_clauses(normalized_text) + normalized_text = _collapse_repeated_numeric_clauses(normalized_text) + normalized_text = _remove_filler_words(normalized_text) + normalized_text = _repair_numeric_asr_artifacts(normalized_text) + return normalized_text + + +def trim_segment_boundary_overlap(previous_text: str, current_text: str) -> tuple[str, bool]: + normalized_previous = str(previous_text or "").strip() + normalized_current = str(current_text or "").strip() + if not normalized_previous or not normalized_current: + return normalized_current, False + max_overlap_chars = min(len(normalized_previous), len(normalized_current), _BOUNDARY_OVERLAP_MAX_CHARS) + for overlap_chars in range(max_overlap_chars, _BOUNDARY_OVERLAP_MIN_CHARS - 1, -1): + overlap_suffix = normalized_previous[-overlap_chars:] + overlap_prefix = normalized_current[:overlap_chars] + if overlap_suffix != overlap_prefix: + continue + if not _has_meaningful_overlap(overlap_prefix): + continue + return normalized_current[overlap_chars:].lstrip(), True + return normalized_current, False diff --git a/app/core/text_cleanup_lexicon.py b/app/core/text_cleanup_lexicon.py new file mode 100644 index 0000000..2640886 --- /dev/null +++ b/app/core/text_cleanup_lexicon.py @@ -0,0 +1,64 @@ +# -*- coding: utf-8 -*- +"""Conservative ASR text cleanup lexicons.""" + +from __future__ import annotations + +SAFE_DOUBLE_WORDS = { + "哥哥", + "叔叔", + "爸爸", + "妈妈", + "奶奶", + "爷爷", + "姐姐", + "弟弟", + "妹妹", + "伯伯", + "姑姑", + "舅舅", + "星星", + "猩猩", +} + +NUMERIC_STUTTER_CHARS = set("零〇○Ｏ一幺二两三四五六七八九十百千万亿点") + +FILLER_TOKENS = ( + "啊", + "呢", + "吧", + "哦", + "嗯", + "呃", + "哈", + "哇", + "呀", + "哎", + "诶", + "欸", + "额", +) + +DISCOURSE_FILLER_TOKENS = ( + "那个", + "这个", + "然后", + "所以说", + "其实", + "那么", +) + +REPEAT_COLLAPSIBLE_DISCOURSE_TOKENS = ( + "那个", + "这个", + "然后", + "就是", + "所以", + "所以说", + "其实", + "那么", +) + +TERMINAL_FILLER_TOKENS = ( + "哈", + "嗯", +) diff --git a/app/infrastructure/__init__.py b/app/infrastructure/__init__.py new file mode 100644 index 0000000..7763654 --- /dev/null +++ b/app/infrastructure/__init__.py @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- +""" +基础设施层 - 提供底层通用功能 +""" + +from .model_utils import resolve_model_path + +__all__ = ["resolve_model_path"] diff --git a/app/infrastructure/model_utils.py b/app/infrastructure/model_utils.py new file mode 100644 index 0000000..c724f43 --- /dev/null +++ b/app/infrastructure/model_utils.py @@ -0,0 +1,48 @@ +# -*- coding: utf-8 -*- +""" +模型工具模块 - 提供模型路径解析等通用功能 +""" + +import logging +from pathlib import Path +from typing import Optional + +from app.core.config import settings + +logger = logging.getLogger(__name__) + + +def resolve_model_path(model_id: Optional[str]) -> str: + """将模型 ID 解析为本地模型路径（如果存在） + + 本项目默认模型目录结构: + ./models/{publisher}/{model_name}/ + + 如果本地模型存在，返回本地路径；否则返回原始 model_id + """ + if not model_id: + raise ValueError("model_id 不能为空") + + # 项目内扁平化模型根目录 + local_path = Path(settings.MODELSCOPE_PATH) / model_id + + if local_path.exists() and local_path.is_dir(): + resolved = str(local_path) + logger.info(f"模型 {model_id} 使用本地缓存: {resolved}") + return resolved + + # 兼容历史错误配置：MODELSCOPE_CACHE 指向了 models 目录时， + # ModelScope 会生成 /models/models/{publisher}/{model_name}。 + legacy_nested_path = Path(settings.MODELSCOPE_PATH) / "models" / model_id + if legacy_nested_path.exists() and legacy_nested_path.is_dir(): + resolved = str(legacy_nested_path) + logger.warning( + "模型 %s 命中历史嵌套缓存: %s。建议迁移到 %s", + model_id, + resolved, + local_path, + ) + return resolved + + logger.warning(f"模型 {model_id} 本地缓存不存在，将在运行时下载") + return model_id diff --git a/app/main.py b/app/main.py new file mode 100644 index 0000000..8d24ab7 --- /dev/null +++ b/app/main.py @@ -0,0 +1,283 @@ +# -*- coding: utf-8 -*- +""" +FastAPI应用创建和配置 +""" + +import warnings +import asyncio +import os +import logging +from contextlib import asynccontextmanager +from fastapi import FastAPI, HTTPException +from fastapi.exceptions import RequestValidationError +from fastapi_offline import FastAPIOffline +from fastapi.middleware.cors import CORSMiddleware +from fastapi.staticfiles import StaticFiles + +from .core.config import settings +from .core.exceptions import ( + APIException, + api_exception_handler, + general_exception_handler, + http_exception_handler, + validation_exception_handler, +) +from .core.logging import setup_logging, get_worker_id +from .core.executor import shutdown_executor +from .api.v1 import api_router +from .utils.boot_events import emit_boot_event + +# 忽略 Pydantic V2 兼容性警告 +warnings.filterwarnings("ignore", message="Valid config keys have changed in V2") +warnings.filterwarnings("ignore", message=".*has conflict with protected namespace.*") +warnings.filterwarnings("ignore", category=UserWarning, module="pydantic") + +logger = logging.getLogger(__name__) + + +async def cleanup_task_state_loop(): + """定期清理过期离线任务状态文件。""" + while True: + await asyncio.sleep(600) + try: + from .core.task_store import cleanup_expired_tasks + + cleanup_expired_tasks(force=True) + except asyncio.CancelledError: + raise + except Exception as exc: + logger.warning("定期清理离线任务状态失败: %s", exc) + + +def cleanup_temp_directory(): + """清理临时目录中的旧文件""" + import time + temp_dir = settings.TEMP_DIR + if not os.path.exists(temp_dir): + return + + # 清理超过 1 小时的临时文件 + max_age_seconds = 3600 + current_time = time.time() + cleaned_count = 0 + + try: + for filename in os.listdir(temp_dir): + filepath = os.path.join(temp_dir, filename) + if os.path.isfile(filepath): + file_age = current_time - os.path.getmtime(filepath) + if file_age > max_age_seconds: + try: + os.remove(filepath) + cleaned_count += 1 + except Exception: + pass + + if cleaned_count > 0: + logger.info(f"已清理 {cleaned_count} 个过期临时文件") + except Exception as e: + logger.warning(f"清理临时目录时出错: {e}") + + +@asynccontextmanager +async def lifespan(app: FastAPI): + """应用生命周期管理""" + workers = int(os.getenv("WORKERS", "1")) + worker_id = get_worker_id() + task_cleanup_task = None + + # 启动时 + logger.info(f"Worker [{worker_id}] 启动中...") + emit_boot_event("phase_start", phase="worker", total=1, message=f"Worker [{worker_id}] 启动中") + + # 清理旧的临时文件（仅主 Worker 执行） + if worker_id == 0: + cleanup_temp_directory() + try: + from .core.task_store import recover_tasks_after_restart + + recover_tasks_after_restart() + except Exception as exc: + logger.warning("恢复离线任务状态失败: %s", exc) + task_cleanup_task = asyncio.create_task(cleanup_task_state_loop()) + + if settings.SPEAKER_DB_ENABLED: + try: + from .core.database import pg_speaker_db + + await pg_speaker_db.connect() + except Exception as exc: + logger.warning( + "声纹数据库连接失败，将保留原说话人编号并禁用声纹注册接口: %s", + exc, + ) + + from .utils.model_loader import ( + preload_models, + verify_required_models_integrity, + ) + + integrity_result = verify_required_models_integrity() + if integrity_result["invalid_models"]: + emit_boot_event("error", phase="integrity", message="required model integrity check failed") + raise RuntimeError("required model integrity check failed") + + logger.info(f"Worker [{worker_id}] 正在加载模型...") + preload_result = preload_models() + + asr_results = preload_result.get("asr_models", {}) + loaded_count = sum(1 for r in asr_results.values() if r.get("loaded")) + total_count = len(asr_results) + logger.info(f"Worker [{worker_id}] 模型加载完成: {loaded_count}/{total_count}") + failed_asr_models = { + model_id: status.get("error") + for model_id, status in asr_results.items() + if not status.get("loaded") and status.get("error") + } + try: + from .services.asr.model_plan import get_active_qwen_model, load_supported_model_ids + + active_qwen_model = get_active_qwen_model(load_supported_model_ids()) + except Exception as exc: + logger.error(f"Worker [{worker_id}] 无法解析当前应启用的 Qwen 模型: {exc}") + emit_boot_event("error", phase="preload", message=f"无法解析当前应启用的 Qwen 模型: {exc}") + raise + + active_qwen_status = asr_results.get(active_qwen_model, {}) + if not active_qwen_status.get("loaded"): + qwen_error = active_qwen_status.get("error") or "unknown error" + logger.error( + f"Worker [{worker_id}] Qwen 主模型预加载失败，拒绝启动: {active_qwen_model}, error={qwen_error}" + ) + emit_boot_event( + "error", + phase="preload", + message=f"Qwen 主模型预加载失败，拒绝启动: {active_qwen_model}, error={qwen_error}", + ) + raise RuntimeError( + f"required qwen model preload failed: {active_qwen_model}: {qwen_error}" + ) + + if failed_asr_models: + if loaded_count == 0: + logger.error(f"Worker [{worker_id}] ASR模型预加载失败详情: {failed_asr_models}") + emit_boot_event("error", phase="preload", message=f"ASR模型预加载失败详情: {failed_asr_models}") + raise RuntimeError(f"ASR model preload failed: {failed_asr_models}") + logger.warning( + f"Worker [{worker_id}] 部分ASR模型预加载失败，将以可用模型继续启动: {failed_asr_models}" + ) + emit_boot_event( + "warning", + phase="preload", + message=f"部分ASR模型预加载失败，将以可用模型继续启动: {failed_asr_models}", + ) + + if settings.SPEAKER_DB_ENABLED: + try: + from .core.database import pg_speaker_db + from .services.speaker_registry import get_speaker_registry_service + + if pg_speaker_db.is_connected: + logger.info(f"Worker [{worker_id}] 正在预加载声纹识别模型...") + get_speaker_registry_service().ensure_loaded() + except Exception as exc: + logger.warning("声纹识别模型预加载失败，将在首次请求时重试: %s", exc) + + logger.info(f"Worker [{worker_id}] 已就绪") + emit_boot_event("ready", phase="worker", message=f"Worker [{worker_id}] 已就绪") + + yield + + # 关闭时 + if task_cleanup_task is not None: + task_cleanup_task.cancel() + try: + await task_cleanup_task + except asyncio.CancelledError: + pass + + if settings.SPEAKER_DB_ENABLED: + try: + from .core.database import pg_speaker_db + + await pg_speaker_db.close() + except Exception as exc: + logger.warning("关闭声纹数据库连接时出错: %s", exc) + logger.info(f"Worker [{worker_id}] 正在关闭推理线程池...") + shutdown_executor() + logger.info(f"Worker [{worker_id}] 已关闭") + + +def create_app() -> FastAPI: + """创建FastAPI应用""" + + # 设置日志 + setup_logging() + + app = FastAPIOffline( + title=settings.APP_NAME, + description=settings.APP_DESCRIPTION, + version=settings.APP_VERSION, + docs_url=settings.docs_url, + redoc_url=settings.redoc_url, + lifespan=lifespan, # 添加生命周期管理 + ) + + # 添加CORS中间件 + app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], + ) + + # 注册异常处理器 + app.add_exception_handler(APIException, api_exception_handler) + app.add_exception_handler(HTTPException, http_exception_handler) + app.add_exception_handler(RequestValidationError, validation_exception_handler) + app.add_exception_handler(Exception, general_exception_handler) + + # 注册静态文件服务（用于临时文件） + app.mount("/tmp", StaticFiles(directory=settings.TEMP_DIR), name="temp_files") + app.mount( + "/test-web", + StaticFiles(directory=str(settings.BASE_DIR / "test_web"), html=True), + name="test_web", + ) + + # 注册API路由 + app.include_router(api_router) + + # 根路径 + @app.get("/", summary="根路径", description="API服务根路径") + async def root(): + return { + "message": settings.APP_NAME, + "version": settings.APP_VERSION, + "description": settings.APP_DESCRIPTION, + "endpoints": { + # 阿里云兼容 API + "asr": "/stream/v1/asr", + "asr_models": "/stream/v1/asr/models", + "asr_health": "/stream/v1/asr/health", + "ws_asr": "/ws/v1/asr/qwen", + "ws_qwen3_asr": "/ws/v1/asr/qwen", + # OpenAI 兼容 API + "openai_models": "/v1/models", + "openai_transcriptions": "/v1/audio/transcriptions", + # 独立会议离线 / 声纹管理 API + "meeting_files": "/api/v1/files", + "meeting_transcriptions": "/api/v1/asr/transcriptions", + "speakers": "/api/v1/speakers", + "test_web": "/test-web/", + # 文档 + "docs": settings.docs_url or "禁用", + }, + } + + return app + + +# 创建全局应用实例 +app = create_app() diff --git a/app/models/__init__.py b/app/models/__init__.py new file mode 100644 index 0000000..6204332 --- /dev/null +++ b/app/models/__init__.py @@ -0,0 +1,5 @@ +# -*- coding: utf-8 -*- +""" +数据模型模块 +包含API的请求和响应模型定义 +""" diff --git a/app/models/asr.py b/app/models/asr.py new file mode 100644 index 0000000..3b938ed --- /dev/null +++ b/app/models/asr.py @@ -0,0 +1,321 @@ +# -*- coding: utf-8 -*- +""" +ASR数据模型 +定义语音识别相关的请求和响应模型 +""" + +from typing import Optional, List, Union +from pydantic import BaseModel, Field + +from .common import ( + SampleRate, + BaseResponse, + HealthCheckResponse, + ErrorResponse, +) + + +# ============= 请求模型 ============= + + +class ASRQueryParams(BaseModel): + """ASR接口查询参数模型""" + + model: Optional[str] = Field( + default=None, + description="可选。离线 ASR 模型 ID；不传则使用服务当前默认模型，如 qwen3-asr-0.6b 或 qwen3-asr-1.7b", + max_length=128, + ) + + audio_address: Optional[str] = Field( + default=None, + description="音频/视频文件地址，支持 HTTP/HTTPS URL、file:// 或服务端本地路径，格式自动识别", + max_length=512, + ) + + sample_rate: Optional[SampleRate] = Field( + default=SampleRate.RATE_16000, + description=f"音频采样率（Hz）。支持: {', '.join(map(str, SampleRate.get_enums()))}", + ) + + enable_speaker_diarization: Optional[bool] = Field( + default=True, + description="是否启用说话人分离。启用后响应会包含 speaker_id", + ) + + enable_speaker_identification: Optional[bool] = Field( + default=True, + description="是否匹配已注册声纹库。仅在 enable_speaker_diarization=true 时生效", + ) + + enable_text_cleanup: Optional[bool] = Field( + default=True, + description="是否启用文本去重和口头语清理", + ) + + word_timestamps: Optional[bool] = Field( + default=False, + description="是否返回字词级时间戳（默认关闭；Qwen CUDA vLLM / CPU Rust 会在启用时自动调用 forced aligner）", + ) + + vocabulary_id: Optional[str] = Field( + default=None, + description="热词字符串，格式：热词1 权重1 热词2 权重2（如：阿里巴巴 20 腾讯 15）", + max_length=512, + ) + + +# ============= 响应模型 ============= + + +class WordToken(BaseModel): + """字词级时间戳信息""" + + text: str = Field( + ..., + description="字词文本", + ) + start_time: float = Field( + ..., + description="开始时间（秒）", + ) + end_time: float = Field( + ..., + description="结束时间（秒）", + ) + + model_config = { + "json_schema_extra": { + "example": { + "text": "今", + "start_time": 0.0, + "end_time": 0.15, + } + } + } + + +class ASRSegment(BaseModel): + """ASR 识别分段结果""" + + text: str = Field( + ..., + description="该段识别文本", + ) + start_time: float = Field( + ..., + description="段落开始时间（秒）", + ) + end_time: float = Field( + ..., + description="段落结束时间（秒）", + ) + speaker_id: Optional[str] = Field( + default=None, + description="说话人ID（如 说话人1），仅启用说话人分离时返回", + ) + word_tokens: Optional[List[WordToken]] = Field( + default=None, + description="字词级时间戳（仅启用 word_timestamps 且模型支持时返回）", + ) + + model_config = { + "json_schema_extra": { + "example": { + "text": "今天天气不错。", + "start_time": 0.0, + "end_time": 2.5, + "speaker_id": "说话人1", + "word_tokens": [ + {"text": "今", "start_time": 0.0, "end_time": 0.15}, + {"text": "天", "start_time": 0.15, "end_time": 0.35}, + ], + } + } + } + + +class ASRSuccessResponse(BaseResponse): + """ASR成功响应模型""" + + result: str = Field( + ..., + description="识别结果文本（完整）", + max_length=100000, + ) + + segments: Optional[List[ASRSegment]] = Field( + default=None, + description="分段识别结果（含时间戳），仅长音频分段识别时返回", + ) + + duration: Optional[float] = Field( + default=None, + description="音频总时长（秒）", + ) + + model_config = { + "json_schema_extra": { + "example": { + "task_id": "cf7b0c5339244ee29cd4e43fb97f1234", + "result": "今天天气不错。明天可能会下雨。", + "segments": [ + {"text": "今天天气不错。", "start_time": 0.0, "end_time": 2.5, "speaker_id": "说话人1"}, + {"text": "明天可能会下雨。", "start_time": 3.2, "end_time": 5.8, "speaker_id": "说话人2"}, + ], + "duration": 5.8, + "status": 200, + "message": "SUCCESS", + } + } + } + + +class ASRErrorResponse(ErrorResponse): + """ASR错误响应模型""" + + result: str = Field(default="", description="识别结果（错误时为空）") + + model_config = { + "json_schema_extra": { + "example": { + "task_id": "8bae3613dfc54ebfa811a17d8a7a1234", + "result": "", + "status": 40000001, + "message": "Gateway:ACCESS_DENIED:The token 'invalid_token' is invalid!", + } + } + } + + +class ASRHealthCheckResponse(HealthCheckResponse): + """ASR健康检查响应模型""" + + model_config = { + "protected_namespaces": (), + "json_schema_extra": { + "example": { + "status": "healthy", + "model_loaded": True, + "device": "cuda:0", + "version": "1.0.0", + "message": "ASR service is running normally", + "loaded_models": ["qwen3-asr-1.7b"], + "memory_usage": { + "gpu_memory_used": "2.1GB", + "gpu_memory_total": "8.0GB", + }, + "accelerator": { + "vendor": "nvidia", + "runtime": "cuda", + "device": "cuda:0", + "device_count": 1, + }, + }, + }, + } + + model_loaded: bool = Field(..., description="模型是否已加载") + device: str = Field(..., description="推理设备") + loaded_models: Optional[List[str]] = Field(default=[], description="已加载的模型列表") + memory_usage: Optional[dict] = Field(default=None, description="内存使用情况") + accelerator: Optional[dict] = Field(default=None, description="加速器信息") + + +# ============= 模型相关 ============= + + +class ASRDeclaredEntryInfo(BaseModel): + """声明式 ASR 条目信息，可表示离线模型或 realtime capability。""" + + id: str = Field(..., description="模型id") + kind: str = Field(..., description="条目类型：model 或 capability") + name: str = Field(..., description="模型名称") + engine: str = Field(..., description="引擎类型") + description: str = Field(..., description="模型描述") + languages: List[str] = Field(..., description="支持的语言列表") + default: bool = Field(default=False, description="是否为默认模型") + supports_realtime: bool = Field(default=False, description="是否支持实时识别") + offline_model: Optional[dict] = Field(default=None, description="离线模型信息") + realtime_model: Optional[dict] = Field(default=None, description="实时模型信息") + + model_config = { + "json_schema_extra": { + "example": { + "id": "qwen3-asr-1.7b", + "kind": "model", + "name": "Qwen3-ASR-1.7B", + "engine": "qwen3", + "description": "多语言离线语音识别模型", + "languages": ["zh", "en"], + "default": True, + "supports_realtime": True, + "offline_model": { + "path": "Qwen/Qwen3-ASR-1.7B", + "exists": True, + }, + "realtime_model": None, + } + } + } + + +class ASRRuntimeInfo(BaseModel): + """运行时视角的模型加载状态。""" + + loaded_model_ids: List[str] = Field(default_factory=list, description="当前已加载模型 ID 列表") + loaded_count: int = Field(..., description="已加载模型数量") + default_offline_model_id: Optional[str] = Field(default=None, description="当前默认离线模型 ID") + + model_config = { + "json_schema_extra": { + "example": { + "loaded_model_ids": ["qwen3-asr-1.7b"], + "loaded_count": 1, + "default_offline_model_id": "qwen3-asr-1.7b", + } + } + } + + +class ASRModelsResponse(BaseModel): + """ASR 模型列表响应，分离声明视角与运行时视角。""" + + declared_entries: List[ASRDeclaredEntryInfo] = Field(..., description="声明的模型与 capability 列表") + declared_count: int = Field(..., description="声明条目总数") + runtime: ASRRuntimeInfo = Field(..., description="运行时加载状态") + + model_config = { + "json_schema_extra": { + "example": { + "declared_entries": [ + { + "id": "qwen3-asr-1.7b", + "kind": "model", + "name": "Qwen3-ASR-1.7B", + "engine": "qwen3", + "description": "多语言离线语音识别模型", + "languages": ["zh", "en"], + "default": True, + "supports_realtime": True, + "offline_model": { + "path": "Qwen/Qwen3-ASR-1.7B", + "exists": True, + }, + "realtime_model": None, + } + ], + "declared_count": 2, + "runtime": { + "loaded_model_ids": ["qwen3-asr-1.7b"], + "loaded_count": 1, + "default_offline_model_id": "qwen3-asr-1.7b", + }, + } + } + } + + +# ============= 联合响应类型 ============= + +ASRResponse = Union[ASRSuccessResponse, ASRErrorResponse] diff --git a/app/models/common.py b/app/models/common.py new file mode 100644 index 0000000..32978c1 --- /dev/null +++ b/app/models/common.py @@ -0,0 +1,64 @@ +# -*- coding: utf-8 -*- +""" +通用数据模型 +定义通用的枚举、基础模型等 +""" + +from pydantic import BaseModel, Field +from enum import Enum + + +class AudioFormat(str, Enum): + """支持的音频格式""" + + PCM = "pcm" + WAV = "wav" + OPUS = "opus" + SPEEX = "speex" + AMR = "amr" + MP3 = "mp3" + AAC = "aac" + M4A = "m4a" + FLAC = "flac" + OGG = "ogg" + + @classmethod + def get_enums(cls): + return [e.value for e in cls] + + +class SampleRate(int, Enum): + """支持的采样率""" + + RATE_8000 = 8000 + RATE_16000 = 16000 + RATE_22050 = 22050 + RATE_24000 = 24000 + + @classmethod + def get_enums(cls): + return [e.value for e in cls] + + +class BaseResponse(BaseModel): + """基础响应模型""" + + task_id: str = Field(..., description="任务ID") + status: int = Field(..., description="状态码") + message: str = Field(..., description="响应消息") + + +class HealthCheckResponse(BaseModel): + """健康检查响应模型""" + + status: str = Field(description="服务状态", examples=["healthy"]) + version: str = Field(description="服务版本", examples=["1.0.0"]) + message: str = Field( + description="状态消息", examples=["Service is running normally"] + ) + + +class ErrorResponse(BaseResponse): + """错误响应模型""" + + result: str = Field("", description="结果内容") diff --git a/app/models/websocket_asr.py b/app/models/websocket_asr.py new file mode 100644 index 0000000..5a9a03b --- /dev/null +++ b/app/models/websocket_asr.py @@ -0,0 +1,135 @@ +# -*- coding: utf-8 -*- +""" +WebSocket ASR 数据模型 - 阿里云协议 +""" + +from typing import Optional, Dict, Any, Union, List +from pydantic import BaseModel, Field, field_validator +import uuid + + +class AliyunASRWSHeader(BaseModel): + """阿里云WebSocket ASR消息头部""" + + message_id: str = Field(..., description="消息ID，32位唯一ID") + task_id: str = Field(..., description="任务ID，32位唯一ID") + namespace: str = Field(..., description="命名空间，固定为SpeechTranscriber") + name: str = Field(..., description="消息名称") + appkey: Optional[str] = Field(None, description="应用密钥") + status: Optional[int] = Field(None, description="状态码") + status_text: Optional[str] = Field(None, description="状态文本") + status_message: Optional[str] = Field(None, description="状态消息") + + @staticmethod + def generate_message_id() -> str: + """生成32位消息ID""" + return str(uuid.uuid4()).replace("-", "")[:32] + + +class AliyunStartTranscriptionPayload(BaseModel): + """StartTranscription 消息负载""" + + format: str = Field(default="pcm", description="音频格式: pcm, wav, opus, speex, amr, mp3, aac") + sample_rate: int = Field(default=16000, description="音频采样率: 8000/16000") + enable_intermediate_result: bool = Field(default=True, description="是否返回中间识别结果") + enable_punctuation_prediction: bool = Field(default=True, description="是否在后处理中添加标点") + enable_inverse_text_normalization: bool = Field(default=True, description="是否将中文数字转为阿拉伯数字") + customization_id: Optional[str] = Field(None, description="自学习模型ID") + vocabulary_id: Optional[str] = Field(None, description="定制泛热词ID") + max_sentence_silence: int = Field(default=800, ge=200, le=2000, description="语音断句检测阈值(ms)") + enable_words: bool = Field(default=False, description="是否开启返回词信息") + disfluency: bool = Field(default=False, description="过滤语气词") + speech_noise_threshold: Optional[float] = Field(None, ge=-1.0, le=1.0, description="噪音参数阈值") + enable_semantic_sentence_detection: bool = Field(default=False, description="是否开启语义断句") + + @field_validator("format") + @classmethod + def validate_format(cls, v): + supported_formats = ["pcm", "wav", "opus", "speex", "amr", "mp3", "aac"] + if v.lower() not in supported_formats: + raise ValueError(f"不支持的音频格式: {v}") + return v.lower() + + @field_validator("sample_rate") + @classmethod + def validate_sample_rate(cls, v): + supported_rates = [8000, 16000] + if v not in supported_rates: + raise ValueError(f"不支持的采样率: {v}") + return v + + +class AliyunWordInfo(BaseModel): + """词信息""" + + text: str = Field("", description="文本") + startTime: int = Field(0, description="词开始时间(ms)") + endTime: int = Field(0, description="词结束时间(ms)") + + +class AliyunTranscriptionResultPayload(BaseModel): + """识别结果负载""" + + session_id: Optional[str] = Field(None, description="会话ID") + index: Optional[int] = Field(None, description="句子编号，从1开始递增") + time: Optional[int] = Field(None, description="已处理的音频时长(ms)") + begin_time: Optional[int] = Field(None, description="句子开始时间(ms)") + result: Optional[str] = Field(None, description="识别结果文本") + confidence: Optional[float] = Field(None, description="置信度[0.0,1.0]") + words: Optional[List[AliyunWordInfo]] = Field(None, description="词信息列表") + status: Optional[int] = Field(None, description="状态码") + + +class AliyunStashResult(BaseModel): + """暂存结果（语义断句）""" + + sentenceId: int = Field(0, description="句子编号") + beginTime: int = Field(0, description="句子开始时间(ms)") + text: str = Field("", description="转写内容") + currentTime: int = Field(0, description="当前处理时间(ms)") + + +class AliyunASRWSMessage(BaseModel): + """阿里云WebSocket ASR消息""" + + header: AliyunASRWSHeader = Field(..., description="消息头部") + payload: Optional[ + Union[ + AliyunStartTranscriptionPayload, + AliyunTranscriptionResultPayload, + Dict[str, Any], + ] + ] = Field(None, description="消息负载") + + +class AliyunASRNamespace: + """阿里云ASR命名空间""" + + SPEECH_TRANSCRIBER = "SpeechTranscriber" + + +class AliyunASRMessageName: + """阿里云ASR消息名称""" + + START_TRANSCRIPTION = "StartTranscription" + STOP_TRANSCRIPTION = "StopTranscription" + + TRANSCRIPTION_STARTED = "TranscriptionStarted" + SENTENCE_BEGIN = "SentenceBegin" + TRANSCRIPTION_RESULT_CHANGED = "TranscriptionResultChanged" + SENTENCE_END = "SentenceEnd" + TRANSCRIPTION_COMPLETED = "TranscriptionCompleted" + TASK_FAILED = "TaskFailed" + + +class AliyunASRStatus: + """阿里云ASR状态码""" + + SUCCESS = 20000000 + TASK_FAILED = 40000000 + INVALID_PARAMETER = 40000001 + MESSAGE_INVALID = 40000002 + AUTHENTICATION_FAILED = 40100005 + QUOTA_EXCEEDED = 40300016 + INTERNAL_ERROR = 50000000 + SERVICE_UNAVAILABLE = 50300018 \ No newline at end of file diff --git a/app/services/__init__.py b/app/services/__init__.py new file mode 100644 index 0000000..e6f76e9 --- /dev/null +++ b/app/services/__init__.py @@ -0,0 +1,5 @@ +# -*- coding: utf-8 -*- +""" +服务层模块 +包含业务逻辑和模型管理 +""" diff --git a/app/services/asr/__init__.py b/app/services/asr/__init__.py new file mode 100644 index 0000000..0054e4d --- /dev/null +++ b/app/services/asr/__init__.py @@ -0,0 +1,5 @@ +# -*- coding: utf-8 -*- +""" +ASR服务模块 +包含语音识别相关的服务和引擎 +""" diff --git a/app/services/asr/audio_validation.py b/app/services/asr/audio_validation.py new file mode 100644 index 0000000..16d37e9 --- /dev/null +++ b/app/services/asr/audio_validation.py @@ -0,0 +1,24 @@ +# -*- coding: utf-8 -*- +"""Audio-related request validation helpers.""" + +from __future__ import annotations + +from typing import Optional + +from ...core.exceptions import InvalidParameterException +from ...models.common import SampleRate + + +SUPPORTED_SAMPLE_RATES = SampleRate.get_enums() + + +def validate_sample_rate(rate: Optional[int]) -> int: + """Validate input sample rate.""" + if not rate: + return 16000 + + if rate not in SUPPORTED_SAMPLE_RATES: + raise InvalidParameterException( + f"不支持的采样率: {rate}。支持的采样率: {', '.join(map(str, SUPPORTED_SAMPLE_RATES))}" + ) + return rate diff --git a/app/services/asr/engines/__init__.py b/app/services/asr/engines/__init__.py new file mode 100644 index 0000000..c00a73b --- /dev/null +++ b/app/services/asr/engines/__init__.py @@ -0,0 +1,41 @@ +# -*- coding: utf-8 -*- +""" +ASR引擎模块 +支持多种ASR引擎实现 +""" + +# 基础类和数据类 +from .base import ( + BaseASREngine, + RealTimeASREngine, + WordToken, + ASRSegmentResult, + ASRFullResult, + ASRRawResult, +) + +# 全局模型管理 +from .global_models import ( + get_global_vad_model, + get_global_punc_model, + get_global_punc_realtime_model, + get_punc_inference_lock, + get_punc_realtime_inference_lock, +) + +__all__ = [ + # 基础类 + "BaseASREngine", + "RealTimeASREngine", + # 数据类 + "WordToken", + "ASRSegmentResult", + "ASRFullResult", + "ASRRawResult", + # 全局模型管理 + "get_global_vad_model", + "get_global_punc_model", + "get_global_punc_realtime_model", + "get_punc_inference_lock", + "get_punc_realtime_inference_lock", +] diff --git a/app/services/asr/engines/base.py b/app/services/asr/engines/base.py new file mode 100644 index 0000000..f868ecf --- /dev/null +++ b/app/services/asr/engines/base.py @@ -0,0 +1,785 @@ +# -*- coding: utf-8 -*- +""" +ASR引擎基础模块 +包含抽象基类和数据类定义 +""" + +import time +import logging +from typing import Optional, Dict, List, Any, Callable +from abc import ABC, abstractmethod +from dataclasses import dataclass + +from app.core.config import settings +from app.core.exceptions import DefaultServerErrorException +from app.core.text_cleanup import deduplicate_asr_text +from app.core.text_cleanup import trim_segment_boundary_overlap +from app.core.hotword_resolver import apply_hotword_rules +from app.core.logging import log_inference_metrics +from app.utils.audio import get_audio_duration + + +logger = logging.getLogger(__name__) + + +def _log_asr_stage_timing( + stage: str, + duration_ms: float, + *, + task_id: Optional[str] = None, + model_id: Optional[str] = None, + audio_duration_sec: Optional[float] = None, + **extra: Any, +) -> None: + """Record structured timing for one ASR processing stage.""" + payload: Dict[str, Any] = { + "event": "asr_stage_timing", + "stage": stage, + "duration_ms": round(duration_ms, 2), + } + if task_id: + payload["task_id"] = task_id + if model_id: + payload["model_id"] = model_id + if audio_duration_sec is not None: + payload["audio_duration_sec"] = round(audio_duration_sec, 2) + if audio_duration_sec > 0: + payload["rtf"] = round((duration_ms / 1000) / audio_duration_sec, 4) + payload.update(extra) + logger.info("ASR阶段耗时", extra=payload) + + +@dataclass +class WordToken: + """字词级时间戳信息""" + + text: str # 字词文本 + start_time: float # 开始时间（秒） + end_time: float # 结束时间（秒） + + +@dataclass +class ASRSegmentResult: + """ASR 分段识别结果""" + + text: str # 该段识别文本 + start_time: float # 开始时间（秒） + end_time: float # 结束时间（秒） + speaker_id: Optional[str] = None # 说话人ID（多说话人模式） + speaker_name: Optional[str] = None # 已注册声纹命中后的显示名称 + user_id: Optional[str] = None # 已注册声纹命中后的业务用户编号 + speaker_embedding: Optional[Any] = None # 片段声纹向量，仅供服务端匹配使用 + word_tokens: Optional[List[WordToken]] = None # 字词级时间戳（可选） + + +@dataclass +class ASRFullResult: + """ASR 完整识别结果（支持长音频）""" + + text: str # 完整识别文本 + segments: List[ASRSegmentResult] # 分段结果 + duration: float # 音频总时长（秒） + + +@dataclass +class ASRRawResult: + """ASR 原始识别结果（包含时间戳）""" + + text: str # 完整识别文本 + segments: List[ASRSegmentResult] # 分段结果（从 VAD 时间戳解析） + + +class BaseASREngine(ABC): + """基础ASR引擎抽象基类""" + + @abstractmethod + def transcribe_file( + self, + audio_path: str, + hotwords: str = "", + enable_punctuation: bool = False, + enable_itn: bool = False, + enable_vad: bool = False, + sample_rate: int = 16000, + ) -> str: + """转录音频文件""" + pass + + @abstractmethod + def transcribe_file_with_vad( + self, + audio_path: str, + hotwords: str = "", + enable_punctuation: bool = True, + enable_itn: bool = True, + sample_rate: int = 16000, + **kwargs, + ) -> ASRRawResult: + """使用 VAD 转录音频文件，返回带时间戳分段的结果 + + Args: + audio_path: 音频文件路径 + hotwords: 热词/上下文提示 + enable_punctuation: 是否启用标点 + enable_itn: 是否启用 ITN + sample_rate: 采样率 + **kwargs: 额外参数（如 word_timestamps 字词级时间戳） + + Returns: + ASRRawResult 包含文本和分段信息 + """ + pass + + def transcribe_long_audio( + self, + audio_path: str, + hotwords: str = "", + enable_punctuation: bool = False, + enable_itn: bool = False, + sample_rate: int = 16000, + enable_speaker_diarization: bool = True, + enable_speaker_identification: bool = True, + enable_text_cleanup: bool = True, + word_timestamps: bool = False, + timestamp_scale: float = 1.0, + task_id: Optional[str] = None, + progress_callback: Optional[Callable[[str, str, int, Optional[dict[str, Any]]], None]] = None, + ) -> ASRFullResult: + """转录长音频文件（自动分段） + + Args: + audio_path: 音频文件路径 + hotwords: 热词 + enable_punctuation: 是否启用标点 + enable_itn: 是否启用 ITN + sample_rate: 采样率 + enable_speaker_diarization: 是否启用说话人分离 + enable_speaker_identification: 是否提取声纹向量用于匹配已注册声纹库 + enable_text_cleanup: 是否启用文本去重和口头语清理 + word_timestamps: 是否返回字词级时间戳（仅部分模型支持） + timestamp_scale: Timestamp correction factor from audio normalization. + task_id: 任务ID（用于日志追踪） + + Returns: + ASRFullResult: 包含完整文本、分段结果和时长的结果 + """ + from app.utils.audio_splitter import AudioSplitter + + # 开始性能计时 + start_time = time.time() + start_perf = time.perf_counter() + model_id = getattr(self, 'model_id', 'unknown') + stage_timings_ms: Dict[str, float] = { + "duration_probe_ms": 0.0, + "speaker_diarization_ms": 0.0, + "vad_audio_split_ms": 0.0, + "asr_inference_ms": 0.0, + "speaker_embedding_ms": 0.0, + "cleanup_ms": 0.0, + "text_cleanup_ms": 0.0, + "hotword_resolver_ms": 0.0, + } + + task_prefix = f"[{task_id}] " if task_id else "" + + def emit_progress( + stage: str, + message: str, + percentage: int, + detail: Optional[dict[str, Any]] = None, + ) -> None: + if progress_callback is None: + return + try: + progress_callback(stage, message, percentage, detail) + except Exception as exc: + logger.warning("%s进度回调失败: %s", task_prefix, exc) + + logger.info( + f"{task_prefix}[transcribe_long_audio] 音频: {audio_path}, " + f"speaker_diarization={enable_speaker_diarization}, " + f"speaker_identification={enable_speaker_identification}, " + f"text_cleanup={enable_text_cleanup}, " + f"word_level={word_timestamps}" + ) + + try: + # 获取音频时长 + duration_started = time.perf_counter() + duration = get_audio_duration(audio_path) + stage_timings_ms["duration_probe_ms"] = ( + time.perf_counter() - duration_started + ) * 1000 + _log_asr_stage_timing( + "duration_probe", + stage_timings_ms["duration_probe_ms"], + task_id=task_id, + model_id=model_id, + audio_duration_sec=duration, + audio_path=audio_path, + ) + logger.info(f"{task_prefix}[transcribe_long_audio] 音频时长: {duration:.2f}秒") + emit_progress( + "analyzing", + f"音频时长 {duration:.1f} 秒，正在进行分割。", + 15, + {"audio_duration_seconds": round(duration, 2)}, + ) + + # 统一使用分段处理 + speaker_segments = None + audio_segments = None + + if enable_speaker_diarization: + # 多说话人：使用说话人分离 + from app.utils.speaker_diarizer import SpeakerDiarizer + + logger.info(f"{task_prefix}使用说话人分离模式") + emit_progress( + "diarizing", + "正在执行说话人分离。", + 18, + {"audio_duration_seconds": round(duration, 2)}, + ) + diarizer = SpeakerDiarizer() + diarization_started = time.perf_counter() + speaker_segments = diarizer.split_audio_by_speakers(audio_path) + stage_timings_ms["speaker_diarization_ms"] = ( + time.perf_counter() - diarization_started + ) * 1000 + speaker_audio_sec = sum( + float(getattr(seg, "duration_sec", 0.0)) + for seg in (speaker_segments or []) + ) + _log_asr_stage_timing( + "speaker_diarization", + stage_timings_ms["speaker_diarization_ms"], + task_id=task_id, + model_id=model_id, + audio_duration_sec=duration, + segment_count=len(speaker_segments or []), + segmented_audio_sec=round(speaker_audio_sec, 2), + ) + + if not speaker_segments: + logger.warning(f"{task_prefix}说话人分离未检测到片段，fallback 到 VAD 分割") + + if not speaker_segments: + # 单说话人：使用 VAD 分割 + logger.info(f"{task_prefix}使用 VAD 分割模式") + emit_progress( + "splitting", + "正在执行 VAD 音频分割。", + 20, + {"audio_duration_seconds": round(duration, 2)}, + ) + splitter = AudioSplitter(device=self.device) + split_started = time.perf_counter() + audio_segments = splitter.split_audio_file(audio_path) + stage_timings_ms["vad_audio_split_ms"] = ( + time.perf_counter() - split_started + ) * 1000 + split_audio_sec = sum( + float(getattr(seg, "duration_sec", 0.0)) + for seg in (audio_segments or []) + ) + _log_asr_stage_timing( + "vad_audio_split", + stage_timings_ms["vad_audio_split_ms"], + task_id=task_id, + model_id=model_id, + audio_duration_sec=duration, + segment_count=len(audio_segments or []), + segmented_audio_sec=round(split_audio_sec, 2), + ) + + # 选择要处理的片段 + segments_to_process = speaker_segments if speaker_segments else audio_segments + if not segments_to_process: + raise DefaultServerErrorException("音频分割失败：未生成任何片段") + + logger.info(f"{task_prefix}音频已分割为 {len(segments_to_process)} 段") + total_segments = len(segments_to_process) + emit_progress( + "transcribing", + "开始批量识别。", + 30, + { + "segment_total": total_segments, + "segment_completed": 0, + "audio_duration_seconds": round(duration, 2), + }, + ) + + results: List[ASRSegmentResult] = [] + + # 使用批处理推理 + batch_size = settings.ASR_BATCH_SIZE + total_batches = (len(segments_to_process) + batch_size - 1) // batch_size + logger.info( + f"{task_prefix}使用批处理推理，batch_size={batch_size}, " + f"word_timestamps={word_timestamps}" + ) + + for batch_start in range(0, len(segments_to_process), batch_size): + batch_end = min(batch_start + batch_size, len(segments_to_process)) + batch_segments = segments_to_process[batch_start:batch_end] + batch_index = batch_start // batch_size + 1 + + logger.info( + f"{task_prefix}推理批次 " + f"{batch_index}/{total_batches}: " + f"片段 {batch_start+1}-{batch_end}/{len(segments_to_process)}" + ) + emit_progress( + "transcribing", + f"识别中：{batch_index}/{total_batches} 批，{batch_start}/{total_segments} 段", + min(88, 30 + int((batch_start / max(total_segments, 1)) * 58)), + { + "segment_total": total_segments, + "segment_completed": batch_start, + "batch_index": batch_index, + "batch_total": total_batches, + "audio_duration_seconds": round(duration, 2), + }, + ) + + try: + # 批量推理，支持时间戳 + batch_started = time.perf_counter() + batch_results = self._transcribe_batch( + segments=batch_segments, + hotwords=hotwords, + enable_punctuation=enable_punctuation, + enable_itn=enable_itn, + sample_rate=sample_rate, + word_timestamps=word_timestamps, + ) + batch_inference_ms = (time.perf_counter() - batch_started) * 1000 + stage_timings_ms["asr_inference_ms"] += batch_inference_ms + batch_audio_sec = sum( + float(getattr(seg, "duration_sec", 0.0)) + for seg in batch_segments + ) + + valid_batch_results = 0 + batch_embedding_ms = 0.0 + for seg, result in zip(batch_segments, batch_results): + if result and result.text: + valid_batch_results += 1 + start_sec = float(getattr(seg, "start_sec", 0.0)) + end_sec = float(getattr(seg, "end_sec", start_sec)) + speaker_embedding = None + if ( + speaker_segments + and settings.SPEAKER_DB_ENABLED + and enable_speaker_identification + ): + try: + from app.core.database import pg_speaker_db + from app.services.speaker_registry import ( + get_speaker_registry_service, + ) + + audio_data = getattr(seg, "audio_data", None) + if pg_speaker_db.is_connected and audio_data is not None: + embedding_started = time.perf_counter() + speaker_embedding = ( + get_speaker_registry_service() + .extract_embedding_from_audio(audio_data) + ) + batch_embedding_ms += ( + time.perf_counter() - embedding_started + ) * 1000 + except Exception as exc: + logger.warning( + "%s提取片段声纹向量失败，保留原说话人编号: %s", + task_prefix, + exc, + ) + results.append( + ASRSegmentResult( + text=result.text, + start_time=start_sec, + end_time=end_sec, + speaker_id=getattr(seg, "speaker_id", None), + speaker_embedding=speaker_embedding, + word_tokens=result.word_tokens if word_timestamps else None, + ) + ) + stage_timings_ms["speaker_embedding_ms"] += batch_embedding_ms + _log_asr_stage_timing( + "asr_batch", + batch_inference_ms, + task_id=task_id, + model_id=model_id, + audio_duration_sec=batch_audio_sec, + batch_index=batch_index, + batch_total=total_batches, + segment_start=batch_start + 1, + segment_end=batch_end, + segment_total=total_segments, + batch_segment_count=len(batch_segments), + valid_segment_count=valid_batch_results, + speaker_embedding_ms=round(batch_embedding_ms, 2), + ) + + logger.info( + f"{task_prefix}批次推理完成，有效片段: " + f"{len([r for r in batch_results if r and r.text])}" + ) + emit_progress( + "transcribing", + f"识别中：{batch_index}/{total_batches} 批，{batch_end}/{total_segments} 段", + min(90, 30 + int((batch_end / max(total_segments, 1)) * 60)), + { + "segment_total": total_segments, + "segment_completed": batch_end, + "batch_index": batch_index, + "batch_total": total_batches, + "audio_duration_seconds": round(duration, 2), + }, + ) + + except Exception as e: + logger.error(f"{task_prefix}批次推理失败: {e}, 跳过该批次") + emit_progress( + "transcribing", + f"第 {batch_index}/{total_batches} 批识别失败，已跳过。", + min(90, 30 + int((batch_end / max(total_segments, 1)) * 60)), + { + "segment_total": total_segments, + "segment_completed": batch_end, + "batch_index": batch_index, + "batch_total": total_batches, + "error": str(e), + }, + ) + + # 清理临时文件（独立清理，避免条件遗漏） + try: + cleanup_started = time.perf_counter() + if speaker_segments: + from app.utils.speaker_diarizer import SpeakerDiarizer + SpeakerDiarizer.cleanup_segments(speaker_segments) + if audio_segments: + AudioSplitter.cleanup_segments(audio_segments) + stage_timings_ms["cleanup_ms"] = ( + time.perf_counter() - cleanup_started + ) * 1000 + _log_asr_stage_timing( + "temp_cleanup", + stage_timings_ms["cleanup_ms"], + task_id=task_id, + model_id=model_id, + segment_count=len(segments_to_process), + ) + except Exception as e: + logger.warning(f"清理临时文件时出错: {e}") + + overlap_trimmed_count = 0 + if enable_text_cleanup: + text_cleanup_started = time.perf_counter() + cleaned_results: List[ASRSegmentResult] = [] + previous_text = "" + for seg in results: + cleaned_text = deduplicate_asr_text(seg.text) + cleaned_text, was_trimmed = trim_segment_boundary_overlap( + previous_text, + cleaned_text, + ) + if was_trimmed: + overlap_trimmed_count += 1 + if not cleaned_text: + continue + seg.text = cleaned_text + cleaned_results.append(seg) + previous_text = cleaned_text + + if len(cleaned_results) != len(results) or overlap_trimmed_count: + logger.info( + "%s文字去重完成：%s -> %s 段，边界重叠裁剪 %s 次", + task_prefix, + len(results), + len(cleaned_results), + overlap_trimmed_count, + ) + results = cleaned_results + stage_timings_ms["text_cleanup_ms"] = ( + time.perf_counter() - text_cleanup_started + ) * 1000 + _log_asr_stage_timing( + "text_cleanup", + stage_timings_ms["text_cleanup_ms"], + task_id=task_id, + model_id=model_id, + segment_count=len(results), + overlap_trimmed_count=overlap_trimmed_count, + cleanup_enabled=enable_text_cleanup, + ) + + hotword_resolver_started = time.perf_counter() + hotword_resolver_applied_count = 0 + matched_hotwords: list[str] = [] + if hotwords: + for seg in results: + resolved_text, was_applied, matched = apply_hotword_rules( + seg.text, + hotwords, + ) + if resolved_text: + seg.text = resolved_text + if was_applied: + hotword_resolver_applied_count += 1 + for hotword_text in matched: + if hotword_text not in matched_hotwords: + matched_hotwords.append(hotword_text) + if hotwords: + stage_timings_ms["hotword_resolver_ms"] = ( + time.perf_counter() - hotword_resolver_started + ) * 1000 + _log_asr_stage_timing( + "hotword_resolver", + stage_timings_ms["hotword_resolver_ms"], + task_id=task_id, + model_id=model_id, + segment_count=len(results), + applied_segment_count=hotword_resolver_applied_count, + matched_hotwords=matched_hotwords, + ) + if hotword_resolver_applied_count: + logger.info( + "%s热词纠偏完成：命中 %s 段，热词=%s", + task_prefix, + hotword_resolver_applied_count, + matched_hotwords, + ) + all_texts = [seg.text for seg in results] + full_text = "\n".join(all_texts) + emit_progress( + "finalizing", + "分段识别完成，正在整理文本。", + 92, + { + "segment_total": len(segments_to_process), + "segment_completed": len(segments_to_process), + "valid_segment_count": len(results), + "text_cleanup_enabled": enable_text_cleanup, + "overlap_trimmed_count": overlap_trimmed_count, + "audio_duration_seconds": round(duration, 2), + }, + ) + + logger.info( + f"长音频识别完成，共 {len(results)} 个有效分段，" + f"总字符数: {len(full_text)}" + ) + + # 计算性能指标 + total_duration_ms = (time.time() - start_time) * 1000 + total_perf_ms = (time.perf_counter() - start_perf) * 1000 + + if timestamp_scale != 1.0: + for seg in results: + seg.start_time *= timestamp_scale + seg.end_time *= timestamp_scale + if seg.word_tokens: + for word_token in seg.word_tokens: + word_token.start_time *= timestamp_scale + word_token.end_time *= timestamp_scale + duration *= timestamp_scale + logger.info( + f"{task_prefix}Timestamp scaling applied: scale={timestamp_scale:.6f}" + ) + + _log_asr_stage_timing( + "asr_total", + total_perf_ms, + task_id=task_id, + model_id=model_id, + audio_duration_sec=duration, + segment_count=len(results), + total_segment_count=len(segments_to_process), + **{key: round(value, 2) for key, value in stage_timings_ms.items()}, + ) + log_inference_metrics( + logger=logger, + message="长音频识别完成", + task_id=task_id, + duration_ms=total_duration_ms, + audio_duration_sec=duration, + model_id=model_id, + status="success", + segments_count=len(results), + batch_size=settings.ASR_BATCH_SIZE, + event="asr_inference_metrics", + **{key: round(value, 2) for key, value in stage_timings_ms.items()}, + enable_speaker_diarization=enable_speaker_diarization, + enable_speaker_identification=enable_speaker_identification, + enable_text_cleanup=enable_text_cleanup, + word_timestamps=word_timestamps, + ) + + return ASRFullResult( + text=full_text, + segments=results, + duration=duration, + ) + + except Exception as e: + # 计算失败时的性能指标 + total_duration_ms = (time.time() - start_time) * 1000 + try: + duration = get_audio_duration(audio_path) + except Exception: + duration = 0 + + log_inference_metrics( + logger=logger, + message="长音频识别失败", + task_id=task_id, + duration_ms=total_duration_ms, + audio_duration_sec=duration, + model_id=model_id, + status="error", + error=str(e), + ) + + logger.error(f"长音频识别失败: {e}") + raise DefaultServerErrorException(f"长音频识别失败: {str(e)}") + + @abstractmethod + def is_model_loaded(self) -> bool: + """检查模型是否已加载""" + pass + + @property + @abstractmethod + def device(self) -> str: + """获取设备信息""" + pass + + @property + @abstractmethod + def supports_realtime(self) -> bool: + """是否支持实时识别""" + pass + + def _transcribe_batch( + self, + segments: List[Any], + hotwords: str = "", + enable_punctuation: bool = False, + enable_itn: bool = False, + sample_rate: int = 16000, + word_timestamps: bool = False, + ) -> List[ASRSegmentResult]: + """批量推理多个音频片段 + + Args: + segments: 音频片段列表（每个片段需要有 temp_file 属性） + hotwords: 热词 + enable_punctuation: 是否启用标点 + enable_itn: 是否启用 ITN + sample_rate: 采样率 + word_timestamps: 是否返回字词级时间戳 + + Returns: + ASRSegmentResult 列表，与输入片段一一对应 + """ + # 默认实现：逐个推理（子类可以重写实现真正的批处理） + results = [] + for idx, seg in enumerate(segments): + try: + if not seg.temp_file: + logger.warning(f"批处理片段 {idx + 1} 临时文件不存在，跳过") + results.append(ASRSegmentResult(text="", start_time=0.0, end_time=0.0)) + continue + + if word_timestamps: + # 需要时间戳：使用 transcribe_file_with_vad + raw_result = self.transcribe_file_with_vad( + audio_path=seg.temp_file, + hotwords=hotwords, + enable_punctuation=enable_punctuation, + enable_itn=enable_itn, + sample_rate=sample_rate, + word_timestamps=True, + ) + if raw_result.segments: + result_seg = raw_result.segments[0] + results.append( + ASRSegmentResult( + text=result_seg.text, + start_time=seg.start_sec, + end_time=seg.end_sec, + speaker_id=getattr(seg, 'speaker_id', None), + word_tokens=result_seg.word_tokens, + ) + ) + else: + results.append( + ASRSegmentResult( + text=raw_result.text, + start_time=seg.start_sec, + end_time=seg.end_sec, + speaker_id=getattr(seg, 'speaker_id', None), + ) + ) + else: + # 不需要时间戳：使用 transcribe_file + text = self.transcribe_file( + audio_path=seg.temp_file, + hotwords=hotwords, + enable_punctuation=enable_punctuation, + enable_itn=enable_itn, + enable_vad=False, + sample_rate=sample_rate, + ) + results.append( + ASRSegmentResult( + text=text or "", + start_time=seg.start_sec, + end_time=seg.end_sec, + speaker_id=getattr(seg, 'speaker_id', None), + ) + ) + except Exception as e: + logger.error(f"批处理片段 {idx + 1} 推理失败: {e}") + results.append( + ASRSegmentResult( + text="", + start_time=getattr(seg, 'start_sec', 0.0), + end_time=getattr(seg, 'end_sec', 0.0), + speaker_id=getattr(seg, 'speaker_id', None), + ) + ) + + return results + + @staticmethod + def _detect_device(device: str = "auto") -> str: + """检测可用设备""" + from app.core.device import detect_device + + return detect_device(device) + + +class RealTimeASREngine(BaseASREngine): + """实时ASR引擎抽象基类""" + + @property + def supports_realtime(self) -> bool: + """支持实时识别""" + return True + + @abstractmethod + def transcribe_websocket( + self, + audio_chunk: bytes, + cache: Optional[Dict] = None, + is_final: bool = False, + **kwargs, + ) -> str: + """WebSocket流式语音识别""" + pass diff --git a/app/services/asr/engines/global_models.py b/app/services/asr/engines/global_models.py new file mode 100644 index 0000000..586db03 --- /dev/null +++ b/app/services/asr/engines/global_models.py @@ -0,0 +1,141 @@ +# -*- coding: utf-8 -*- +""" +全局VAD/PUNC模型管理模块 +提供线程安全的全局模型实例管理 +""" + +import logging +import threading +from funasr import AutoModel + +from app.core.config import settings +from app.infrastructure import resolve_model_path + + +logger = logging.getLogger(__name__) + + +# 全局语音活动检测(VAD)模型缓存（避免重复加载） +_global_vad_model = None +_vad_model_lock = threading.Lock() +_vad_inference_lock = threading.Lock() # 推理互斥锁，防止并发状态混乱 + +# 全局标点符号模型缓存（避免重复加载） +_global_punc_model = None +_punc_model_lock = threading.Lock() +_punc_inference_lock = threading.Lock() # 推理互斥锁，防止并发状态混乱 + +# 全局实时标点符号模型缓存（避免重复加载） +_global_punc_realtime_model = None +_punc_realtime_model_lock = threading.Lock() +_punc_realtime_inference_lock = threading.Lock() # 推理互斥锁，防止并发状态混乱 + + +def _resolve_device(device: str) -> str: + """解析设备字符串，将 auto 转换为实际的设备""" + from app.core.device import detect_device + + return detect_device(device) + + +def get_global_vad_model(device: str): + """获取全局语音活动检测(VAD)模型实例（线程安全，双重检查锁定）""" + global _global_vad_model + + if _global_vad_model is None: + with _vad_model_lock: + if _global_vad_model is None: + try: + # 解析模型路径：优先使用本地缓存 + resolved_vad_path = resolve_model_path(settings.VAD_MODEL) + logger.info(f"正在加载全局语音活动检测(VAD)模型: {resolved_vad_path}") + + # 解析 auto 设备 + resolved_device = _resolve_device(device) + + _global_vad_model = AutoModel( + model=resolved_vad_path, + device=resolved_device, + speech_noise_thres=0.6, # VAD 语音噪声阈值（FunASR默认0.6，设为0.7稍微严格一些，分段更碎） + **settings.FUNASR_AUTOMODEL_KWARGS, + ) + logger.info("全局语音活动检测(VAD)模型加载成功 (speech_noise_thres=0.6)") + except Exception as e: + logger.error(f"全局语音活动检测(VAD)模型加载失败: {str(e)}") + _global_vad_model = None + raise + + return _global_vad_model + + +def get_vad_inference_lock(): + """获取VAD模型推理锁（线程安全）""" + return _vad_inference_lock + + +def get_global_punc_model(device: str): + """获取全局标点符号模型实例（离线版，线程安全，双重检查锁定）""" + global _global_punc_model + + if _global_punc_model is None: + with _punc_model_lock: + if _global_punc_model is None: + try: + # 解析模型路径：优先使用本地缓存 + resolved_punc_path = resolve_model_path(settings.PUNC_MODEL) + logger.info(f"正在加载全局标点符号模型（离线）: {resolved_punc_path}") + + # 解析 auto 设备 + resolved_device = _resolve_device(device) + + _global_punc_model = AutoModel( + model=resolved_punc_path, + device=resolved_device, + **settings.FUNASR_AUTOMODEL_KWARGS, + ) + logger.info("全局标点符号模型（离线）加载成功") + except Exception as e: + logger.error(f"全局标点符号模型（离线）加载失败: {str(e)}") + _global_punc_model = None + raise + + return _global_punc_model + + +def get_punc_inference_lock(): + """获取PUNC模型推理锁（线程安全）""" + return _punc_inference_lock + + +def get_global_punc_realtime_model(device: str): + """获取全局实时标点符号模型实例（线程安全，双重检查锁定）""" + global _global_punc_realtime_model + + if _global_punc_realtime_model is None: + with _punc_realtime_model_lock: + if _global_punc_realtime_model is None: + try: + # 解析模型路径：优先使用本地缓存 + resolved_punc_realtime_path = resolve_model_path(settings.PUNC_REALTIME_MODEL) + logger.info(f"正在加载全局标点符号模型（实时）: {resolved_punc_realtime_path}") + + # 解析 auto 设备 + resolved_device = _resolve_device(device) + + _global_punc_realtime_model = AutoModel( + model=resolved_punc_realtime_path, + device=resolved_device, + **settings.FUNASR_AUTOMODEL_KWARGS, + ) + logger.info("全局标点符号模型（实时）加载成功") + except Exception as e: + logger.error(f"全局标点符号模型（实时）加载失败: {str(e)}") + _global_punc_realtime_model = None + raise + + return _global_punc_realtime_model + + +def get_punc_realtime_inference_lock(): + """获取实时PUNC模型推理锁（线程安全）""" + return _punc_realtime_inference_lock diff --git a/app/services/asr/implementations/__init__.py b/app/services/asr/implementations/__init__.py new file mode 100644 index 0000000..1c727a6 --- /dev/null +++ b/app/services/asr/implementations/__init__.py @@ -0,0 +1,5 @@ +# -*- coding: utf-8 -*- +""" +模型实现模块 +包含需要本地代码的自定义模型实现 +""" diff --git a/app/services/asr/manager.py b/app/services/asr/manager.py new file mode 100644 index 0000000..e75825f --- /dev/null +++ b/app/services/asr/manager.py @@ -0,0 +1,215 @@ +# -*- coding: utf-8 -*- +"""ASR model metadata and engine factory.""" + +import json +import threading +import logging +from typing import Dict, Any, Optional, List +from pathlib import Path + +from typing import Callable +from ...core.config import settings +from ...core.exceptions import DefaultServerErrorException, InvalidParameterException +from .engines import BaseASREngine +from .model_plan import get_default_model_id + +logger = logging.getLogger(__name__) + +# 引擎注册表（使用Any避免循环导入问题） +_ENGINE_REGISTRY: Dict[str, Callable[[Any], BaseASREngine]] = {} + + +def _supports_qwen_realtime_on_device(configured_device: str) -> bool: + """Resolve whether Qwen realtime mode is available on the active device.""" + from app.core.device import detect_device + from app.core.accelerator import get_accelerator_info + from .qwenasr_rust import is_qwenasr_rust_available + + device = detect_device(configured_device) + accelerator = get_accelerator_info() + if accelerator.is_gpu and device.startswith("cuda"): + return True + if device == "cpu": + return is_qwenasr_rust_available() + return False + + +def register_engine(engine_type: str, factory: Callable[[Any], BaseASREngine]): + """注册ASR引擎工厂函数""" + _ENGINE_REGISTRY[engine_type] = factory + logger.info(f"注册引擎类型: {engine_type}") + + +class DeclaredEntryConfig: + """声明条目配置，可表示模型或 capability。""" + + def __init__(self, model_id: str, config: Dict[str, Any]): + self.model_id = model_id + self.name = config["name"] + self.kind = config.get("kind", "model") + self.engine = config["engine"] + self.description = config.get("description", "") + self.languages = config.get("languages", []) + self.supports_realtime = config.get("supports_realtime", False) + + # 模型路径结构 + self.models = config.get("models", {}) + self.offline_model_path = self.models.get("offline") + self.realtime_model_path = self.models.get("realtime") + + # 额外参数（如 trust_remote_code 等） + self.extra_kwargs = config.get("extra_kwargs", {}) + + @property + def has_offline_model(self) -> bool: + """是否有离线模型""" + return bool(self.offline_model_path) + + @property + def has_realtime_model(self) -> bool: + """是否有实时模型""" + return bool(self.realtime_model_path) + +class ModelManager: + """Static model metadata plus engine construction.""" + + def __init__(self): + self._declared_entry_configs: Dict[str, DeclaredEntryConfig] = {} + self._default_model_id: Optional[str] = None + self._load_models_config() + + def _load_models_config(self) -> None: + """加载模型配置文件""" + models_file = Path(settings.models_config_path) + if not models_file.exists(): + raise DefaultServerErrorException("models.json 配置文件不存在") + + try: + with open(models_file, "r", encoding="utf-8") as f: + config = json.load(f) + + for model_id, model_config in config["models"].items(): + self._declared_entry_configs[model_id] = DeclaredEntryConfig(model_id, model_config) + self._default_model_id = get_default_model_id( + all_model_ids=list(self._declared_entry_configs.keys()), + ) + + if not self._default_model_id and self._declared_entry_configs: + self._default_model_id = list(self._declared_entry_configs.keys())[0] + + except (json.JSONDecodeError, KeyError) as e: + raise DefaultServerErrorException(f"模型配置文件格式错误: {str(e)}") + + def get_declared_entry_config(self, model_id: Optional[str] = None) -> DeclaredEntryConfig: + """获取声明条目配置。""" + if model_id is None: + model_id = self._default_model_id + + if not model_id: + raise InvalidParameterException("未指定模型且没有默认模型") + + if model_id not in self._declared_entry_configs: + available_models = ", ".join(self._declared_entry_configs.keys()) + raise InvalidParameterException( + f"未知的模型: {model_id}，可用模型: {available_models}" + ) + + return self._declared_entry_configs[model_id] + + def list_declared_entries(self) -> List[Dict[str, Any]]: + """列出声明的模型与 capability 元数据。""" + entries = [] + for model_id, config in self._declared_entry_configs.items(): + offline_path_exists = False + realtime_path_exists = False + + if config.offline_model_path: + offline_model_path = ( + Path(settings.MODELSCOPE_PATH) / config.offline_model_path + ) + offline_path_exists = offline_model_path.exists() + + if config.realtime_model_path: + realtime_model_path = ( + Path(settings.MODELSCOPE_PATH) / config.realtime_model_path + ) + realtime_path_exists = realtime_model_path.exists() + + supports_realtime = config.supports_realtime + if config.engine == "qwen3": + supports_realtime = _supports_qwen_realtime_on_device(settings.DEVICE) + + entries.append( + { + "id": model_id, + "kind": config.kind, + "name": config.name, + "engine": config.engine, + "description": config.description, + "languages": config.languages, + "default": model_id == self._default_model_id, + "supports_realtime": supports_realtime, + "offline_model": ( + { + "path": config.offline_model_path, + "exists": offline_path_exists, + } + if config.offline_model_path + else None + ), + "realtime_model": ( + { + "path": config.realtime_model_path, + "exists": realtime_path_exists, + } + if config.realtime_model_path + else None + ), + } + ) + + return entries + + def _create_engine(self, config: DeclaredEntryConfig) -> BaseASREngine: + """创建ASR引擎实例""" + engine_type = config.engine.lower() + factory = _ENGINE_REGISTRY.get(engine_type) + if not factory: + raise InvalidParameterException( + f"不支持的引擎类型: {config.engine}" + ) + return factory(config) + + def create_engine(self, model_id: Optional[str] = None) -> BaseASREngine: + """Create a fresh engine instance.""" + config = self.get_declared_entry_config(model_id) + return self._create_engine(config) + +# 全局模型管理器实例 +_model_manager: Optional[ModelManager] = None +_model_manager_lock = threading.Lock() + + +def get_model_manager() -> ModelManager: + """获取全局模型管理器实例（线程安全）""" + global _model_manager + if _model_manager is None: + with _model_manager_lock: + if _model_manager is None: + _model_manager = ModelManager() + return _model_manager + + +# 注册内置引擎 +def _register_builtin_engines(): + """注册内置的ASR引擎""" + try: + from .qwen3_engine import Qwen3ASREngine # noqa: F401 + from .qwen3_engine import _register_qwen3_engine + _register_qwen3_engine(register_engine, DeclaredEntryConfig) + except ImportError as e: + logger.warning(f"Qwen3引擎不可用: {e}") + + +# 模块加载时自动注册内置引擎 +_register_builtin_engines() diff --git a/app/services/asr/model_capabilities.py b/app/services/asr/model_capabilities.py new file mode 100644 index 0000000..8800128 --- /dev/null +++ b/app/services/asr/model_capabilities.py @@ -0,0 +1,230 @@ +# -*- coding: utf-8 -*- +"""Shared capability-to-model asset definitions.""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Literal, Optional + +from app.core.config import settings +from app.services.asr.manager import get_model_manager +from app.services.asr.model_plan import ( + get_active_qwen_model, + load_supported_model_ids, +) + + +ModelSource = Literal["modelscope"] + + +@dataclass(frozen=True) +class ModelAsset: + source: ModelSource + model_id: str + description: str + revision: Optional[str] = None + required_patterns: tuple[str, ...] = () + alternative_required_patterns: tuple[tuple[str, ...], ...] = () + min_total_size_bytes: int = 0 + + +_VAD_ASSETS = ( + ModelAsset( + source="modelscope", + model_id=settings.VAD_MODEL, + description="VAD", + revision="v2.0.2", + required_patterns=("configuration.json", "config.yaml", "model.pb"), + min_total_size_bytes=1_000_000, + ), +) + +_DIARIZATION_ASSETS = ( + ModelAsset( + source="modelscope", + model_id="iic/speech_campplus_speaker-diarization_common", + description="CAM++ Diarization", + required_patterns=( + "configuration.json", + "config.yaml", + "onnx/asd.onnx", + "onnx/face_recog_ir101.onnx", + "onnx/fqa.onnx", + "onnx/version-RFB-320.onnx", + ), + min_total_size_bytes=50_000_000, + ), + ModelAsset( + source="modelscope", + model_id=settings.SV_MODEL, + description="Configured Speaker Verification", + required_patterns=("configuration.json", "config.yaml", "campplus_cn_common.bin"), + min_total_size_bytes=10_000_000, + ), + ModelAsset( + source="modelscope", + model_id=settings.REALTIME_SV_MODEL, + description="Realtime Speaker Verification", + required_patterns=("configuration.json",), + min_total_size_bytes=10_000_000, + ), + ModelAsset( + source="modelscope", + model_id="damo/speech_campplus_sv_zh-cn_16k-common", + description="CAM++ Speaker Verification", + required_patterns=("configuration.json", "config.yaml", "campplus_cn_common.bin"), + min_total_size_bytes=10_000_000, + ), + ModelAsset( + source="modelscope", + model_id="damo/speech_campplus-transformer_scl_zh-cn_16k-common", + description="CAM++ Transformer", + required_patterns=("configuration.json", "campplus_cn_encoder.pt", "transformer_backend.pt"), + min_total_size_bytes=10_000_000, + ), +) + +def get_download_modelscope_assets() -> list[ModelAsset]: + """Return the full static ModelScope export set used by predownload/export.""" + return _dedupe_assets([ + *_VAD_ASSETS, + *_DIARIZATION_ASSETS, + ]) + + +def get_runtime_required_modelscope_assets( + *, + include_realtime_punc: bool, +) -> list[ModelAsset]: + """Return ModelScope assets required by the current runtime plan.""" + _ = include_realtime_punc + return _dedupe_assets([*_VAD_ASSETS, *_DIARIZATION_ASSETS]) + + +def _dedupe_assets(assets: list[ModelAsset]) -> list[ModelAsset]: + deduped: list[ModelAsset] = [] + seen: set[tuple[str, str]] = set() + for asset in assets: + key = (asset.source, asset.model_id) + if key in seen: + continue + seen.add(key) + deduped.append(asset) + return deduped + + +def get_camplusplus_replacement_paths(cache_dir: str) -> dict[str, str]: + """Return the CAM++ offline replacement map for local cache paths.""" + return { + "damo/speech_campplus_sv_zh-cn_16k-common": f"{cache_dir}/damo/speech_campplus_sv_zh-cn_16k-common", + "iic/speech_campplus_sv_zh-cn_16k-common": f"{cache_dir}/iic/speech_campplus_sv_zh-cn_16k-common", + "damo/speech_campplus-transformer_scl_zh-cn_16k-common": f"{cache_dir}/damo/speech_campplus-transformer_scl_zh-cn_16k-common", + "damo/speech_campplus-transformer_scl_zh-cn-16k-common": f"{cache_dir}/damo/speech_campplus-transformer_scl_zh-cn-16k-common", + "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch": f"{cache_dir}/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", + } + + +_QWEN_MODELSCOPE_MODEL_IDS = { + "Qwen/Qwen3-ASR-0.6B": "Qwen/Qwen3-ASR-0.6B", + "Qwen/Qwen3-ASR-1.7B": "Qwen/Qwen3-ASR-1.7B", + "Qwen/Qwen3-ForcedAligner-0.6B": "Qwen/Qwen3-ForcedAligner-0.6B", +} + + +def get_qwen_modelscope_model_id(model_id: str) -> Optional[str]: + """Map runtime Qwen model ID to the corresponding ModelScope model ID. + + Returns: + ModelScope model ID if available, None otherwise. + """ + return _QWEN_MODELSCOPE_MODEL_IDS.get(model_id) + + +def get_enabled_qwen_modelscope_assets( + *, + include_forced_aligner: bool = True, +) -> list[ModelAsset]: + """Return ModelScope assets required by the runtime Qwen plan.""" + manager = get_model_manager() + assets: list[ModelAsset] = [] + model_id = get_active_qwen_model() + model_config = manager.get_declared_entry_config(model_id) + offline_model = model_config.offline_model_path + if offline_model: + ms_model_id = get_qwen_modelscope_model_id(offline_model) + if ms_model_id: + assets.append( + ModelAsset( + source="modelscope", + model_id=ms_model_id, + description=f"{model_config.name} Offline (ModelScope)", + required_patterns=("config.json",), + alternative_required_patterns=( + ("model.safetensors",), + ("model-*.safetensors",), + ), + min_total_size_bytes=500_000_000, + ) + ) + forced_aligner = str(model_config.extra_kwargs.get("forced_aligner_path") or "").strip() + if forced_aligner and include_forced_aligner: + ms_aligner_id = get_qwen_modelscope_model_id(forced_aligner) + if ms_aligner_id: + assets.append( + ModelAsset( + source="modelscope", + model_id=ms_aligner_id, + description=f"{model_config.name} Forced Aligner (ModelScope)", + required_patterns=("config.json", "model.safetensors"), + min_total_size_bytes=500_000_000, + ) + ) + return assets + + +def get_all_qwen_modelscope_assets( + *, + include_forced_aligner: bool = True, +) -> list[ModelAsset]: + """Return all declared Qwen ModelScope assets for offline bundles.""" + manager = get_model_manager() + assets: list[ModelAsset] = [] + seen_model_ids: set[str] = set() + + for model_id in sorted(load_supported_model_ids()): + if not model_id.startswith("qwen3-asr-"): + continue + model_config = manager.get_declared_entry_config(model_id) + offline_model = model_config.offline_model_path + if offline_model: + ms_model_id = get_qwen_modelscope_model_id(offline_model) + if ms_model_id and ms_model_id not in seen_model_ids: + seen_model_ids.add(ms_model_id) + assets.append( + ModelAsset( + source="modelscope", + model_id=ms_model_id, + description=f"{model_config.name} Offline (ModelScope)", + required_patterns=("config.json",), + alternative_required_patterns=( + ("model.safetensors",), + ("model-*.safetensors",), + ), + min_total_size_bytes=500_000_000, + ) + ) + forced_aligner = str(model_config.extra_kwargs.get("forced_aligner_path") or "").strip() + if forced_aligner and include_forced_aligner: + ms_aligner_id = get_qwen_modelscope_model_id(forced_aligner) + if ms_aligner_id and ms_aligner_id not in seen_model_ids: + seen_model_ids.add(ms_aligner_id) + assets.append( + ModelAsset( + source="modelscope", + model_id=ms_aligner_id, + description="Qwen3 Forced Aligner (ModelScope)", + required_patterns=("config.json", "model.safetensors"), + min_total_size_bytes=500_000_000, + ) + ) + return assets diff --git a/app/services/asr/model_plan.py b/app/services/asr/model_plan.py new file mode 100644 index 0000000..2a13565 --- /dev/null +++ b/app/services/asr/model_plan.py @@ -0,0 +1,105 @@ +# -*- coding: utf-8 -*- +"""Single-source deployment model planning.""" + +from __future__ import annotations + +import json +import os +import platform +from pathlib import Path +from typing import Optional + +from app.core.config import settings + +QWEN_MODEL_OVERRIDE_ENV = "QWEN3_ASR_MODEL" +_QWEN_MODEL_ALIASES = { + "qwen3-asr-0.6b": "qwen3-asr-0.6b", + "0.6b": "qwen3-asr-0.6b", + "0.6": "qwen3-asr-0.6b", + "qwen/qwen3-asr-0.6b": "qwen3-asr-0.6b", + "qwen3-asr-1.7b": "qwen3-asr-1.7b", + "1.7b": "qwen3-asr-1.7b", + "1.7": "qwen3-asr-1.7b", + "qwen/qwen3-asr-1.7b": "qwen3-asr-1.7b", +} + + +def load_supported_model_ids() -> list[str]: + """Load declared model ids from models.json.""" + models_file = Path(settings.models_config_path) + if not models_file.exists(): + return [] + + with open(models_file, "r", encoding="utf-8") as f: + config = json.load(f) + + return list(config.get("models", {}).keys()) + + +def get_qwen_model_override() -> Optional[str]: + """Return the explicit Qwen model override from the environment.""" + raw_value = (os.getenv(QWEN_MODEL_OVERRIDE_ENV) or "").strip() + if not raw_value: + return None + + normalized = _QWEN_MODEL_ALIASES.get(raw_value.lower(), raw_value) + return normalized + + +def detect_qwen_model_by_vram(all_model_ids: Optional[list[str]] = None) -> Optional[str]: + """Pick the active Qwen model for the current machine.""" + from app.core.accelerator import get_accelerator_info + from app.core.device import detect_device, get_vram_gb + from app.services.asr.qwenasr_rust import is_qwenasr_rust_available + + model_ids = all_model_ids or load_supported_model_ids() + override_model = get_qwen_model_override() + if override_model: + return override_model if override_model in model_ids else None + + resolved_device = detect_device(settings.DEVICE) + accelerator = get_accelerator_info() + + # macOS defaults to the lighter Rust CPU path unless QWEN3_ASR_MODEL is set. + if platform.system() == "Darwin": + return "qwen3-asr-0.6b" if is_qwenasr_rust_available() and "qwen3-asr-0.6b" in model_ids else None + + if resolved_device == "cpu" or not accelerator.is_gpu: + return "qwen3-asr-0.6b" if is_qwenasr_rust_available() and "qwen3-asr-0.6b" in model_ids else None + + vram = get_vram_gb() + preferred = "qwen3-asr-1.7b" if vram >= 32 else "qwen3-asr-0.6b" + if preferred in model_ids: + return preferred + + fallback = "qwen3-asr-0.6b" if preferred == "qwen3-asr-1.7b" else "qwen3-asr-1.7b" + return fallback if fallback in model_ids else None + + +def get_active_qwen_model(all_model_ids: Optional[list[str]] = None) -> str: + """Return the required Qwen model for the current machine.""" + model_ids = all_model_ids or load_supported_model_ids() + qwen_model = detect_qwen_model_by_vram(model_ids) + if not qwen_model: + override_model = get_qwen_model_override() + if override_model: + available_qwen_models = ", ".join( + model_id for model_id in model_ids if model_id.startswith("qwen") + ) + raise RuntimeError( + f"{QWEN_MODEL_OVERRIDE_ENV}={override_model} 不在可用 Qwen3-ASR 模型中: " + f"{available_qwen_models}" + ) + raise RuntimeError("当前环境未找到可运行的 Qwen3-ASR 模型") + return qwen_model + + +def get_runtime_model_ids(all_model_ids: Optional[list[str]] = None) -> list[str]: + """Return the runtime model/capability plan for the current machine.""" + model_ids = all_model_ids or load_supported_model_ids() + return [get_active_qwen_model(model_ids)] + + +def get_default_model_id(all_model_ids: Optional[list[str]] = None) -> str: + """Return the single default offline model for API/UI selection.""" + return get_active_qwen_model(all_model_ids) diff --git a/app/services/asr/model_selection.py b/app/services/asr/model_selection.py new file mode 100644 index 0000000..336bac1 --- /dev/null +++ b/app/services/asr/model_selection.py @@ -0,0 +1,93 @@ +# -*- coding: utf-8 -*- +"""Offline/realtime model selection helpers.""" + +from __future__ import annotations + +from typing import List, Optional + +from ...core.exceptions import InvalidParameterException +from .manager import get_model_manager +from .model_plan import ( + get_active_qwen_model, + get_default_model_id, + get_runtime_model_ids, +) + + +def get_active_qwen_model_id() -> str: + """Return the currently active Qwen model id.""" + active_qwen_model = get_active_qwen_model() + return active_qwen_model or "qwen3-asr-0.6b" + + +def get_offline_model_ids() -> List[str]: + """Return enabled offline-capable models for docs and APIs.""" + manager = get_model_manager() + runtime_models = get_runtime_model_ids() + + def sort_key(model_id: str) -> tuple[int, str]: + if model_id.startswith("qwen"): + return (0, model_id) + return (1, model_id) + + offline_models = [ + model_id + for model_id in runtime_models + if manager.get_declared_entry_config(model_id).has_offline_model + ] + return sorted(offline_models, key=sort_key) + + +def get_default_offline_model_id() -> str: + """Return the default offline-capable model.""" + default_model = get_default_model_id() + if default_model: + try: + if get_model_manager().get_declared_entry_config(default_model).has_offline_model: + return default_model + except InvalidParameterException: + pass + return get_active_qwen_model_id() + + +def validate_offline_model_id(model_id: Optional[str]) -> str: + """Validate offline-capable model ids for REST transcription requests.""" + available_models = get_offline_model_ids() + + if not model_id or not model_id.strip(): + return get_default_offline_model_id() + + requested_model = model_id.strip() + if requested_model.lower() == "qwen3-asr": + active_qwen_model = get_active_qwen_model_id() + if active_qwen_model.startswith("qwen") and active_qwen_model in available_models: + return active_qwen_model + raise InvalidParameterException("当前环境未启用 Qwen3-ASR 模型") + + if requested_model not in available_models: + raise InvalidParameterException( + f"不支持的离线模型ID: {requested_model}。可用模型: {', '.join(available_models)}" + ) + + return requested_model + + +def validate_realtime_model_id(model_id: Optional[str]) -> str: + """Validate realtime-capable model ids for websocket protocols.""" + available_models = get_offline_model_ids() + + if not model_id: + return get_default_offline_model_id() + + if model_id.lower() == "qwen3-asr": + active_qwen_model = get_active_qwen_model_id() + if active_qwen_model.startswith("qwen") and active_qwen_model in available_models: + return active_qwen_model + raise InvalidParameterException("当前环境未启用 Qwen3-ASR 模型") + + if model_id not in available_models: + raise InvalidParameterException( + f"不支持的模型ID: {model_id}。可用模型: {', '.join(available_models)}" + ) + + return model_id diff --git a/app/services/asr/models.json b/app/services/asr/models.json new file mode 100644 index 0000000..05e6cb3 --- /dev/null +++ b/app/services/asr/models.json @@ -0,0 +1,70 @@ +{ + "models": { + "qwen3-asr-1.7b": { + "name": "Qwen3-ASR-1.7B", + "kind": "model", + "engine": "qwen3", + "description": "Qwen3-ASR 1.7B，支持52种语言和方言；CUDA 使用 vLLM，CPU/macOS 使用 QwenASR Rust backend", + "languages": [ + "zh", + "en", + "yue", + "ja", + "ko", + "ar", + "de", + "es", + "fr", + "pt", + "id", + "it", + "ru", + "th", + "vi" + ], + "default": true, + "supports_realtime": true, + "models": { + "offline": "Qwen/Qwen3-ASR-1.7B" + }, + "extra_kwargs": { + "max_model_len": 16384, + "forced_aligner_path": "Qwen/Qwen3-ForcedAligner-0.6B", + "max_inference_batch_size": 16 + } + }, + "qwen3-asr-0.6b": { + "name": "Qwen3-ASR-0.6B", + "kind": "model", + "engine": "qwen3", + "description": "Qwen3-ASR 0.6B，轻量版支持52种语言和方言；CUDA 使用 vLLM，CPU/macOS 使用 QwenASR Rust backend", + "languages": [ + "zh", + "en", + "yue", + "ja", + "ko", + "ar", + "de", + "es", + "fr", + "pt", + "id", + "it", + "ru", + "th", + "vi" + ], + "default": false, + "supports_realtime": true, + "models": { + "offline": "Qwen/Qwen3-ASR-0.6B" + }, + "extra_kwargs": { + "max_model_len": 16384, + "forced_aligner_path": "Qwen/Qwen3-ForcedAligner-0.6B", + "max_inference_batch_size": 16 + } + } + } +} diff --git a/app/services/asr/offline_transcription_service.py b/app/services/asr/offline_transcription_service.py new file mode 100644 index 0000000..f91419c --- /dev/null +++ b/app/services/asr/offline_transcription_service.py @@ -0,0 +1,137 @@ +# -*- coding: utf-8 -*- +"""Shared offline transcription workflow.""" + +from __future__ import annotations + +from dataclasses import dataclass +import logging +from typing import Any, Callable, Optional + +from fastapi import Request + +from app.models.common import SampleRate +from app.services.asr.engines import ASRFullResult +from app.services.asr.model_selection import validate_offline_model_id +from app.services.asr.runtime import OfflineASRRequest, get_runtime_router +from app.services.audio import get_audio_service + +logger = logging.getLogger(__name__) + + +@dataclass(frozen=True) +class PreparedAudio: + normalized_path: str + duration: float + original_path: str + timestamp_scale: float = 1.0 + + +@dataclass(frozen=True) +class OfflineTranscriptionOptions: + model_id: Optional[str] = None + sample_rate: int = 16000 + hotwords: str = "" + enable_speaker_diarization: bool = True + enable_speaker_identification: bool = True + enable_text_cleanup: bool = True + word_timestamps: bool = False + task_id: Optional[str] = None + progress_callback: Optional[ + Callable[[str, str, int, Optional[dict[str, Any]]], None] + ] = None + + +class OfflineTranscriptionService: + """Prepare audio and run the active offline ASR model.""" + + def __init__(self) -> None: + self._audio_service = get_audio_service() + + async def prepare_from_request( + self, + *, + request: Request, + audio_address: Optional[str], + task_id: str, + sample_rate: int, + ) -> PreparedAudio: + audio = await self._audio_service.process_from_request( + request=request, + audio_address=audio_address, + task_id=task_id, + sample_rate=sample_rate, + ) + return PreparedAudio( + normalized_path=audio.normalized_path, + duration=audio.duration, + original_path=audio.original_path, + timestamp_scale=audio.timestamp_scale, + ) + + async def prepare_upload( + self, + *, + audio_data: bytes, + filename: Optional[str], + task_id: str, + sample_rate: int, + ) -> PreparedAudio: + audio = await self._audio_service.process_upload_file( + audio_data=audio_data, + filename=filename, + task_id=task_id, + sample_rate=sample_rate, + ) + return PreparedAudio( + normalized_path=audio.normalized_path, + duration=audio.duration, + original_path=audio.original_path, + timestamp_scale=audio.timestamp_scale, + ) + + async def transcribe( + self, + prepared_audio: PreparedAudio, + options: OfflineTranscriptionOptions, + ) -> ASRFullResult: + model_id = validate_offline_model_id(options.model_id) + logger.info( + "ASR model resolved: requested=%s, resolved=%s", + options.model_id, + model_id, + ) + return await get_runtime_router().run_offline( + OfflineASRRequest( + model_id=model_id, + audio_path=prepared_audio.normalized_path, + hotwords=options.hotwords, + enable_punctuation=True, + enable_itn=True, + sample_rate=options.sample_rate or int(SampleRate.RATE_16000), + enable_speaker_diarization=options.enable_speaker_diarization, + enable_speaker_identification=options.enable_speaker_identification, + enable_text_cleanup=options.enable_text_cleanup, + word_timestamps=options.word_timestamps, + timestamp_scale=prepared_audio.timestamp_scale, + task_id=options.task_id, + progress_callback=options.progress_callback, + ) + ) + + def cleanup(self, prepared_audio: Optional[PreparedAudio]) -> None: + if prepared_audio is None: + return + self._audio_service.cleanup( + prepared_audio.original_path, + prepared_audio.normalized_path, + ) + + +_offline_transcription_service: Optional[OfflineTranscriptionService] = None + + +def get_offline_transcription_service() -> OfflineTranscriptionService: + global _offline_transcription_service + if _offline_transcription_service is None: + _offline_transcription_service = OfflineTranscriptionService() + return _offline_transcription_service diff --git a/app/services/asr/qwen3_engine.py b/app/services/asr/qwen3_engine.py new file mode 100644 index 0000000..2ab2f3b --- /dev/null +++ b/app/services/asr/qwen3_engine.py @@ -0,0 +1,693 @@ +# -*- coding: utf-8 -*- +"""Qwen3-ASR engine with official vLLM and vendored Rust backends.""" + +import logging +import os +from concurrent.futures import ThreadPoolExecutor +from typing import Optional, List, Any +from dataclasses import dataclass + +import torch +import numpy as np + +from app.core.accelerator import get_accelerator_info +from app.core.device import get_vram_gb +from app.core.hotword_resolver import format_hotword_prompt_context +from .engines import BaseASREngine, ASRRawResult, ASRSegmentResult, WordToken +from .qwenasr_rust import ( + QwenASRRustRuntime, + is_qwenasr_rust_available, + resolve_qwenasr_model_path, +) +from .qwen3_vllm import Qwen3VLLMBackend, is_vllm_available +from ...core.exceptions import DefaultServerErrorException +from ...core.config import settings +from ...utils.text_processing import normalize_asr_text + +logger = logging.getLogger(__name__) + + +def _resolve_tensor_parallel_size() -> int: + topology = (os.getenv("ASR_ACTIVE_TOPOLOGY") or os.getenv("ASR_DEPLOY_TOPOLOGY") or "isolated").strip().lower() + if topology != "sharded": + return 1 + + visible = ( + os.getenv("ASR_VISIBLE_DEVICES") + or os.getenv("CUDA_VISIBLE_DEVICES") + or os.getenv("METAX_VISIBLE_DEVICES") + or os.getenv("MACA_VISIBLE_DEVICES") + or os.getenv("MX_VISIBLE_DEVICES") + or os.getenv("ILUVATAR_VISIBLE_DEVICES") + or os.getenv("IX_VISIBLE_DEVICES") + or os.getenv("MTHREADS_VISIBLE_DEVICES") + or os.getenv("MUSA_VISIBLE_DEVICES") + or "" + ).strip() + if not visible or visible.lower() in {"all", "none", "void"}: + return 1 + + count = len([part for part in visible.split(",") if part.strip()]) + return count if count > 1 else 1 + + +def calculate_gpu_memory_utilization(model_path: str) -> float: + """Calculate vLLM GPU memory utilization for the active model. + + vLLM uses this ratio as an allocation budget, not just model weights. + Keep the observed requirement slightly above the bare minimum so KV cache + and profiling have enough room. + """ + # Check environment variable override first + env_override = os.getenv("QWEN_GPU_MEMORY_UTILIZATION") + if env_override: + try: + value = float(env_override) + if 0.0 < value <= 1.0: + logger.info(f"Using environment override: gpu_memory_utilization={value}") + return value + else: + logger.warning(f"Invalid QWEN_GPU_MEMORY_UTILIZATION={env_override}, must be 0.0-1.0") + except ValueError: + logger.warning(f"Invalid QWEN_GPU_MEMORY_UTILIZATION={env_override}, not a float") + + model_memory_profiles = { + "0.6B": 8, + "1.7B": 12.0, + } + + if "0.6B" in model_path: + model_size = "0.6B" + else: + model_size = "1.7B" + required_memory_gb = model_memory_profiles[model_size] + + try: + accelerator = get_accelerator_info() + total_vram_gb = get_vram_gb() + if not accelerator.is_gpu or total_vram_gb <= 0: + logger.warning("Accelerator memory unavailable, using fallback gpu_memory_utilization=0.5") + return 0.5 + + utilization = max(required_memory_gb / total_vram_gb, 0.25) + utilization = min(utilization, 0.95) + + logger.info( + "GPU memory calculation: vendor=%s, model=%s, requires=%.1fGB, total_vram=%.1fGB, utilization=%.2f", + accelerator.vendor, + model_size, + required_memory_gb, + total_vram_gb, + utilization, + ) + + if utilization >= 0.90: + logger.warning( + "VRAM may be insufficient: %.1fGB available, %.1fGB required. Consider using smaller model.", + total_vram_gb, + required_memory_gb, + ) + + return round(utilization, 2) + + except Exception as e: + logger.error(f"Failed to detect VRAM: {e}, using fallback gpu_memory_utilization=0.5") + return 0.5 + + +def _handle_asr_error(operation: str): + """统一错误处理装饰器""" + def decorator(func): + def wrapper(*args, **kwargs): + try: + return func(*args, **kwargs) + except Exception as e: + logger.error(f"{operation} 失败: {e}") + raise DefaultServerErrorException(f"{operation} 失败: {e}") + return wrapper + return decorator + + +@dataclass +class Qwen3StreamingState: + internal_state: Any + chunk_size_sec: float = 1.2 + unfixed_chunk_num: int = 2 + unfixed_token_num: int = 5 + max_new_tokens: int = 32 + language: Optional[str] = None + chunk_count: int = 0 + last_text: str = "" + last_language: str = "" + + +class Qwen3ASREngine(BaseASREngine): + model: Any + + @property + def supports_realtime(self) -> bool: + return self._backend in {"vllm", "rust"} + + def __init__( + self, + model_path: str = "Qwen/Qwen3-ASR-1.7B", + device: str = "auto", + forced_aligner_path: Optional[str] = None, + max_inference_batch_size: int = 32, + max_new_tokens: int = 1024, + max_model_len: Optional[int] = None, + **_kwargs, + ): + """Initialize Qwen3-ASR engine + + CUDA -> official vLLM backend + CPU/macOS -> QwenASR Rust backend + """ + from app.core.device import detect_device + + model_id = _kwargs.pop("model_id", None) + if model_id: + model_path = model_id + + self._device = detect_device(device) + self._accelerator = get_accelerator_info() + self.model_id = model_path + self.model_path = model_path + self._backend = self._select_backend() + self._forced_aligner_path = forced_aligner_path + self._rust_num_threads = 0 + self._rust_verbosity = 0 + self._rust_batch_runtimes: list[QwenASRRustRuntime] = [] + + try: + if self._backend == "vllm": + self.model = self._load_vllm( + model_path, forced_aligner_path, + max_inference_batch_size, max_new_tokens, max_model_len, + ) + elif self._backend == "rust": + self.model = self._load_rust_backend(model_path, forced_aligner_path) + self._warmup_forced_aligner() + logger.info("Qwen3-ASR model loaded successfully with backend=%s", self._backend) + except Exception as e: + logger.error(f"Failed to load Qwen3-ASR model: {e}") + raise DefaultServerErrorException(f"Failed to load Qwen3-ASR model: {e}") + + def _select_backend(self) -> str: + if self._accelerator.is_gpu and self._device.startswith("cuda"): + if not is_vllm_available(): + raise DefaultServerErrorException( + "Current Python environment is missing vLLM with Qwen3 forced aligner support. " + f"accelerator={self._accelerator.vendor}. " + "For NVIDIA run ./scripts/sync_gpu_env.sh; for MetaX run ./scripts/sync_metax_env.sh; " + "for Iluvatar or Moore Threads prefer the official vendor vLLM Docker image fused with this project." + ) + return "vllm" + if self._device == "cpu" and is_qwenasr_rust_available(): + return "rust" + raise DefaultServerErrorException( + f"Qwen3-ASR is not available on accelerator '{self._accelerator.vendor}' " + f"device '{self._device}'. Supported backends are vendor-compatible vLLM " + "GPU runtimes and CPU QwenASR Rust." + ) + + def _load_rust_backend( + self, + model_path: str, + forced_aligner_path: Optional[str], + ) -> QwenASRRustRuntime: + logger.info("Loading Qwen3-ASR (QwenASR Rust): %s, device=%s", model_path, self._device) + + num_threads = 0 if settings.QWEN_RUST_CPU_WORKERS <= 1 else 1 + if settings.QWEN_RUST_CPU_WORKERS > 1: + logger.info( + "Using fixed QwenASR CPU thread count for multi-runtime mode: num_threads=%s workers=%s", + num_threads, + settings.QWEN_RUST_CPU_WORKERS, + ) + self._rust_num_threads = num_threads + self._rust_verbosity = 0 + return QwenASRRustRuntime( + model_path=model_path, + forced_aligner_path=forced_aligner_path, + num_threads=num_threads, + verbosity=0, + ) + + def _get_rust_batch_runtimes(self, worker_count: int) -> list[QwenASRRustRuntime]: + if worker_count <= 1: + return [self.model] + + if not self._rust_batch_runtimes: + self._rust_batch_runtimes = [self.model] + + while len(self._rust_batch_runtimes) < worker_count: + self._rust_batch_runtimes.append( + QwenASRRustRuntime( + model_path=self.model_path, + forced_aligner_path=self._forced_aligner_path, + num_threads=self._rust_num_threads, + verbosity=self._rust_verbosity, + ) + ) + + return self._rust_batch_runtimes[:worker_count] + + def _get_rust_stage_concurrency(self, configured: int, segment_count: int) -> int: + target = configured if configured > 0 else settings.QWEN_RUST_CPU_WORKERS + return max(1, min(target, segment_count)) + + def _get_rust_asr_concurrency(self, segment_count: int) -> int: + return self._get_rust_stage_concurrency( + settings.QWEN_RUST_ASR_CONCURRENCY, + segment_count, + ) + + def _get_rust_align_concurrency(self, segment_count: int) -> int: + return self._get_rust_stage_concurrency( + settings.QWEN_RUST_ALIGN_CONCURRENCY, + segment_count, + ) + + @staticmethod + def _build_hotword_prompt_context(hotwords: str) -> str: + return format_hotword_prompt_context(hotwords) + + def _rust_transcribe_text_segment( + self, + runtime: QwenASRRustRuntime, + seg: Any, + hotwords: str, + enable_punctuation: bool, + enable_itn: bool, + sample_rate: int, + ) -> str: + _ = (hotwords, enable_punctuation, sample_rate) + text = runtime.transcribe_file(seg.temp_file) or "" + return normalize_asr_text(text, enable_itn=enable_itn) + + def _rust_align_word_tokens( + self, + runtime: QwenASRRustRuntime, + seg: Any, + text: str, + language: Optional[str] = None, + ) -> list[WordToken]: + return [ + WordToken( + text=str(item["text"]), + start_time=round(float(item["start_ms"]) / 1000.0, 3), + end_time=round(float(item["end_ms"]) / 1000.0, 3), + ) + for item in runtime.align_transcript( + audio_path=seg.temp_file, + text=text, + language=language, + ) + ] + + def _run_rust_asr_stage( + self, + valid_segments: List[tuple[int, Any]], + hotwords: str, + enable_punctuation: bool, + enable_itn: bool, + sample_rate: int, + ) -> dict[int, str]: + if not valid_segments: + return {} + + worker_count = self._get_rust_asr_concurrency(len(valid_segments)) + runtimes = self._get_rust_batch_runtimes(worker_count) + output: dict[int, str] = {} + for batch_start in range(0, len(valid_segments), worker_count): + chunk = valid_segments[batch_start:batch_start + worker_count] + chunk_runtimes = runtimes[:len(chunk)] + with ThreadPoolExecutor(max_workers=len(chunk)) as executor: + futures = [ + executor.submit( + self._rust_transcribe_text_segment, + runtime, + seg, + hotwords, + enable_punctuation, + enable_itn, + sample_rate, + ) + for runtime, (_idx, seg) in zip(chunk_runtimes, chunk) + ] + for (idx, _seg), future in zip(chunk, futures): + output[idx] = future.result() + + return output + + def _run_rust_align_stage( + self, + valid_segments: List[tuple[int, Any]], + texts: dict[int, str], + language: Optional[str] = None, + ) -> dict[int, list[WordToken]]: + if not valid_segments: + return {} + + align_inputs = [(idx, seg, texts.get(idx, "")) for idx, seg in valid_segments if texts.get(idx, "").strip()] + worker_count = self._get_rust_align_concurrency(len(valid_segments)) + runtimes = self._get_rust_batch_runtimes(worker_count) + output: dict[int, list[WordToken]] = {} + + if not align_inputs: + return output + + for batch_start in range(0, len(align_inputs), worker_count): + chunk = align_inputs[batch_start:batch_start + worker_count] + chunk_runtimes = runtimes[:len(chunk)] + with ThreadPoolExecutor(max_workers=len(chunk)) as executor: + futures = [ + executor.submit( + self._rust_align_word_tokens, + runtime, + seg, + text, + language, + ) + for runtime, (_idx, seg, text) in zip(chunk_runtimes, chunk) + ] + for (idx, _seg, _text), future in zip(chunk, futures): + output[idx] = future.result() + + return output + + def _warmup_forced_aligner(self) -> None: + if not self._forced_aligner_path: + return + if not settings.ASR_ENABLE_WORD_TIMESTAMPS: + return + if self._backend == "vllm": + self.model.ensure_forced_aligner_loaded() + + def _load_vllm( + self, model_path: str, forced_aligner_path: Optional[str], + max_inference_batch_size: int, max_new_tokens: int, + max_model_len: Optional[int], + ) -> Qwen3VLLMBackend: + """Load model via official vLLM backend (CUDA only).""" + resolved_model_path = str(resolve_qwenasr_model_path(model_path)) + resolved_forced_aligner_path = None + if forced_aligner_path: + resolved_forced_aligner_path = str(resolve_qwenasr_model_path(forced_aligner_path)) + gpu_memory_utilization = calculate_gpu_memory_utilization(model_path) + tensor_parallel_size = _resolve_tensor_parallel_size() + logger.info( + f"Loading Qwen3-ASR (official vLLM): {resolved_model_path}, " + f"device={self._device}, gpu_memory_utilization={gpu_memory_utilization}, " + f"enforce_eager={settings.QWEN_VLLM_ENFORCE_EAGER}, " + f"tensor_parallel_size={tensor_parallel_size}" + ) + return Qwen3VLLMBackend( + model_path=resolved_model_path, + forced_aligner_path=resolved_forced_aligner_path, + gpu_memory_utilization=gpu_memory_utilization, + max_inference_batch_size=max_inference_batch_size, + max_new_tokens=max_new_tokens, + enforce_eager=settings.QWEN_VLLM_ENFORCE_EAGER, + max_model_len=max_model_len, + tensor_parallel_size=tensor_parallel_size, + ) + + @_handle_asr_error("转写") + def transcribe_file( + self, + audio_path: str, + hotwords: str = "", + enable_punctuation: bool = True, + enable_itn: bool = True, + enable_vad: bool = False, + sample_rate: int = 16000, + ) -> str: + if self._backend == "rust": + text = self.model.transcribe_file(audio_path) + return normalize_asr_text(text, enable_itn=enable_itn) + if self._backend == "vllm": + return self.model.transcribe_text( + audio_path, + context=self._build_hotword_prompt_context(hotwords), + enable_itn=enable_itn, + ) + raise DefaultServerErrorException(f"Qwen3 backend={self._backend} does not support offline transcription") + + @_handle_asr_error("VAD 转写") + def transcribe_file_with_vad( + self, + audio_path: str, + hotwords: str = "", + enable_punctuation: bool = True, + enable_itn: bool = True, + sample_rate: int = 16000, + **kwargs, + ) -> ASRRawResult: + if self._backend == "rust": + text = self.transcribe_file( + audio_path=audio_path, + hotwords=hotwords, + enable_punctuation=enable_punctuation, + enable_itn=enable_itn, + sample_rate=sample_rate, + ) + if kwargs.get("word_timestamps", False): + word_tokens = [ + WordToken( + text=str(item["text"]), + start_time=round(float(item["start_ms"]) / 1000.0, 3), + end_time=round(float(item["end_ms"]) / 1000.0, 3), + ) + for item in self.model.align_transcript( + audio_path=audio_path, + text=text, + language=kwargs.get("language"), + ) + ] + if word_tokens: + return ASRRawResult( + text=text, + segments=[ + ASRSegmentResult( + text=text, + start_time=word_tokens[0].start_time, + end_time=word_tokens[-1].end_time, + word_tokens=word_tokens, + ) + ], + ) + return ASRRawResult( + text=text, + segments=[ASRSegmentResult(text=text, start_time=0.0, end_time=0.0)] if text else [], + ) + if self._backend == "vllm": + return self.model.transcribe_raw( + audio_path=audio_path, + context=self._build_hotword_prompt_context(hotwords), + language=kwargs.get("language"), + word_timestamps=kwargs.get("word_timestamps", False), + enable_itn=enable_itn, + ) + + raise DefaultServerErrorException( + f"Qwen3 backend={self._backend} does not support VAD transcription" + ) + + @_handle_asr_error("批量推理") + def _transcribe_batch( + self, + segments: List[Any], + hotwords: str = "", + enable_punctuation: bool = False, + enable_itn: bool = False, + sample_rate: int = 16000, + word_timestamps: bool = False, + ) -> List[ASRSegmentResult]: + output = [ASRSegmentResult(text="", start_time=0.0, end_time=0.0) for _ in segments] + + valid: List[tuple[int, Any]] = [] + for idx, seg in enumerate(segments): + temp_file = getattr(seg, "temp_file", None) + if temp_file and os.path.exists(temp_file): + valid.append((idx, seg)) + else: + logger.warning(f"Qwen3 批处理片段无效或文件不存在: segment={idx + 1}, file={temp_file}") + + if not valid: + return output + + if self._backend == "rust": + texts = self._run_rust_asr_stage( + valid_segments=valid, + hotwords=hotwords, + enable_punctuation=enable_punctuation, + enable_itn=enable_itn, + sample_rate=sample_rate, + ) + + word_tokens_by_idx: dict[int, list[WordToken]] = {} + if word_timestamps: + word_tokens_by_idx = self._run_rust_align_stage( + valid_segments=valid, + texts=texts, + ) + + for idx, seg in valid: + text = texts.get(idx, "") + output[idx] = ASRSegmentResult( + text=text, + start_time=seg.start_sec, + end_time=seg.end_sec, + speaker_id=getattr(seg, "speaker_id", None), + word_tokens=word_tokens_by_idx.get(idx) or None, + ) + return output + + if self._backend == "vllm": + vllm_results = self.model.transcribe_batch( + [seg.temp_file for _, seg in valid], + context=self._build_hotword_prompt_context(hotwords), + word_timestamps=word_timestamps, + enable_itn=enable_itn, + ) + for (idx, seg), result in zip(valid, vllm_results): + output[idx] = ASRSegmentResult( + text=result.text, + start_time=round(seg.start_sec, 2), + end_time=round(seg.end_sec, 2), + speaker_id=getattr(seg, "speaker_id", None), + word_tokens=result.word_tokens if word_timestamps else None, + ) + return output + + raise DefaultServerErrorException( + f"Qwen3 backend={self._backend} does not support batch transcription" + ) + + @_handle_asr_error("初始化流式状态") + def init_streaming_state(self, context: str = "", language: Optional[str] = None, **kwargs) -> Qwen3StreamingState: + if self._backend not in {"vllm", "rust"}: + raise DefaultServerErrorException( + f"Qwen3 backend={self._backend} does not support realtime streaming" + ) + if self._backend == "rust": + if context: + logger.debug("QwenASR Rust backend ignores streaming context hints") + chunk_size_sec = float(kwargs.get("chunk_size_sec", 1.2)) + unfixed_chunk_num = int(kwargs.get("unfixed_chunk_num", 2)) + unfixed_token_num = int(kwargs.get("unfixed_token_num", 5)) + max_new_tokens = int(kwargs.get("max_new_tokens", 32)) + stream_handle = self.model.create_stream( + chunk_size_sec=chunk_size_sec, + unfixed_chunk_num=unfixed_chunk_num, + rollback_tokens=unfixed_token_num, + max_new_tokens=max_new_tokens, + language=language, + ) + return Qwen3StreamingState( + internal_state=stream_handle, + chunk_size_sec=chunk_size_sec, + unfixed_chunk_num=unfixed_chunk_num, + unfixed_token_num=unfixed_token_num, + max_new_tokens=max_new_tokens, + language=language, + chunk_count=0, + last_text="", + last_language=language or "", + ) + if self._backend == "vllm": + streaming_state = self.model.init_streaming_state(context=context, language=language, **kwargs) + return Qwen3StreamingState( + internal_state=streaming_state, + chunk_size_sec=float(kwargs.get("chunk_size_sec", 1.2)), + unfixed_chunk_num=int(kwargs.get("unfixed_chunk_num", 2)), + unfixed_token_num=int(kwargs.get("unfixed_token_num", 5)), + max_new_tokens=int(kwargs.get("max_new_tokens", 32)), + language=language, + chunk_count=int(getattr(streaming_state, "chunk_id", 0)), + last_text=str(getattr(streaming_state, "text", "") or ""), + last_language=str(getattr(streaming_state, "language", "") or ""), + ) + + raise DefaultServerErrorException( + f"Qwen3 backend={self._backend} does not support realtime streaming" + ) + + @_handle_asr_error("流式识别") + def streaming_transcribe(self, pcm16k: np.ndarray, state: Qwen3StreamingState) -> Qwen3StreamingState: + if self._backend not in {"vllm", "rust"}: + raise DefaultServerErrorException( + f"Qwen3 backend={self._backend} does not support realtime streaming" + ) + pcm = pcm16k.astype(np.float32) / (32768.0 if pcm16k.dtype == np.int16 else 1.0) + if self._backend == "rust": + text = self.model.push_stream( + stream=state.internal_state, + samples=pcm, + chunk_size_sec=state.chunk_size_sec, + unfixed_chunk_num=state.unfixed_chunk_num, + rollback_tokens=state.unfixed_token_num, + max_new_tokens=state.max_new_tokens, + language=state.language, + ) + state.chunk_count += 1 + state.last_text = text + state.last_language = state.language or "" + return state + + streaming_state = self.model.feed_stream(pcm, state.internal_state) + state.internal_state = streaming_state + state.chunk_count = int(getattr(streaming_state, "chunk_id", state.chunk_count)) + state.last_text = str(getattr(streaming_state, "text", "") or "") + state.last_language = str(getattr(streaming_state, "language", "") or "") + return state + + @_handle_asr_error("结束流式识别") + def finish_streaming_transcribe(self, state: Qwen3StreamingState) -> Qwen3StreamingState: + if self._backend not in {"vllm", "rust"}: + raise DefaultServerErrorException( + f"Qwen3 backend={self._backend} does not support realtime streaming" + ) + if self._backend == "rust": + text = self.model.finish_stream( + stream=state.internal_state, + chunk_size_sec=state.chunk_size_sec, + unfixed_chunk_num=state.unfixed_chunk_num, + rollback_tokens=state.unfixed_token_num, + max_new_tokens=state.max_new_tokens, + language=state.language, + ) + state.last_text = text + state.last_language = state.language or "" + return state + + streaming_state = self.model.finish_stream(state.internal_state) + state.internal_state = streaming_state + state.chunk_count = int(getattr(streaming_state, "chunk_id", state.chunk_count)) + state.last_text = str(getattr(streaming_state, "text", "") or "") + state.last_language = str(getattr(streaming_state, "language", "") or "") + return state + + def is_model_loaded(self) -> bool: + return self.model is not None + + @property + def backend(self) -> str: + return self._backend + + @property + def device(self) -> str: + return self._device + + +def _register_qwen3_engine(register_func, _declared_entry_cls): + from app.core.config import settings + + def _create(config): + extra = {k: v for k, v in config.extra_kwargs.items() if v is not None} + model_id = config.models.get("offline") + return Qwen3ASREngine(model_path=model_id, device=settings.DEVICE, **extra) + + register_func("qwen3", _create) diff --git a/app/services/asr/qwen3_vllm.py b/app/services/asr/qwen3_vllm.py new file mode 100644 index 0000000..b07d553 --- /dev/null +++ b/app/services/asr/qwen3_vllm.py @@ -0,0 +1,527 @@ +# -*- coding: utf-8 -*- +"""Official vLLM adapter for CUDA Qwen3-ASR.""" + +from __future__ import annotations + +import importlib +import importlib.util +import logging +import os +import re +import threading +from dataclasses import dataclass, field +from typing import Any, Optional + +import librosa +import numpy as np + +from app.core.hotword_resolver import strip_hotword_prompt_leakage +from app.utils.text_processing import normalize_asr_text + +from .engines import ASRRawResult, ASRSegmentResult, WordToken + +logger = logging.getLogger(__name__) + +_DEFAULT_SAMPLE_RATE = 16000 +_LANGUAGE_ALIASES = { + "zh": "Chinese", + "zh-cn": "Chinese", + "zh-hans": "Chinese", + "zh-hant": "Chinese", + "cn": "Chinese", + "en": "English", + "en-us": "English", + "en-gb": "English", + "ja": "Japanese", + "jp": "Japanese", + "ko": "Korean", + "yue": "Cantonese", + "fr": "French", + "de": "German", + "es": "Spanish", + "ru": "Russian", +} +_PROMPT_LEAK_PATTERNS = ( + re.compile(r"^\s*Transcribe the speech accurately\.\s*", re.IGNORECASE), + re.compile(r"^\s*Transcribe the speech in [A-Za-z\s-]+\.\s*", re.IGNORECASE), +) + + +def is_vllm_available() -> bool: + """Return True when the official vLLM runtime is installed.""" + return importlib.util.find_spec("vllm") is not None + + +def _normalize_language_name(language: Optional[str]) -> Optional[str]: + if not language: + return None + normalized = language.strip() + if not normalized: + return None + alias = _LANGUAGE_ALIASES.get(normalized.lower()) + if alias: + return alias + if " " in normalized: + return " ".join(part.capitalize() for part in normalized.split()) + return normalized.capitalize() + + +def _load_audio(audio_path: str) -> np.ndarray: + audio, _sample_rate = librosa.load(audio_path, sr=_DEFAULT_SAMPLE_RATE, mono=True) + return audio.astype(np.float32) + + +def _build_chat_prompt(context: str = "", language: Optional[str] = None) -> str: + instructions: list[str] = [] + if language: + instructions.append(f"Transcribe the speech in {language}.") + else: + instructions.append("Transcribe the speech accurately.") + if context.strip(): + instructions.append(context.strip()) + system_text = " ".join(instructions).strip() + return ( + f"<|im_start|>system\n{system_text}<|im_end|>\n" + "<|im_start|>user\n<|audio_start|><|audio_pad|><|audio_end|><|im_end|>\n" + "<|im_start|>assistant\n" + ) + + +def _build_alignment_prompt(tokens: list[str]) -> str: + body = "".join(tokens) + "" + return f"<|audio_start|><|audio_pad|><|audio_end|>{body}" + + +def _strip_prompt_leakage(text: str) -> str: + cleaned = text or "" + changed = True + while changed and cleaned: + changed = False + for pattern in _PROMPT_LEAK_PATTERNS: + updated, count = pattern.subn("", cleaned, count=1) + if count: + cleaned = updated + changed = True + return strip_hotword_prompt_leakage(cleaned) + + +def _sanitize_detected_language(detected: str, fallback: Optional[str]) -> str: + candidate = (detected or "").strip() + if not candidate: + return fallback or "" + + candidate = re.sub(r"^[^\w]+", "", candidate) + match = re.search(r"language\s+([A-Za-z][A-Za-z\s-]*)$", candidate, re.IGNORECASE) + if match: + candidate = match.group(1).strip() + + normalized = _normalize_language_name(candidate) + return normalized or (fallback or "") + + +def _parse_asr_output(raw_text: str, language: Optional[str]) -> tuple[str, str]: + text = (raw_text or "").strip() + if "" in text: + left, right = text.split("", 1) + detected = _sanitize_detected_language(left.strip(), language) + return detected, _strip_prompt_leakage(right.strip()) + return (language or ""), _strip_prompt_leakage(text) + + +def _split_alignment_units(text: str) -> list[str]: + if not text: + return [] + + # Mixed Chinese/English transcripts should not fall back to whitespace-only + # tokenization, otherwise a long CJK sentence with a single embedded English + # word can collapse into one giant alignment unit. + token_pattern = re.compile( + r"[\u4e00-\u9fff]" # CJK ideographs, align per character + r"|[A-Za-z0-9]+(?:['._+-][A-Za-z0-9]+)*" # Latin / alnum words + r"|[^\w\s]", # punctuation and symbols + re.UNICODE, + ) + return token_pattern.findall(text) + + +def _resolve_forced_aligner_gpu_memory_utilization(primary_utilization: float) -> float: + override = (os.getenv("QWEN_FORCE_ALIGNER_GPU_MEMORY_UTILIZATION") or "").strip() + if override: + try: + value = float(override) + if 0.0 < value <= 1.0: + return value + except ValueError: + logger.warning( + "Invalid QWEN_FORCE_ALIGNER_GPU_MEMORY_UTILIZATION=%s, ignoring override", + override, + ) + + return primary_utilization + + +@dataclass +class _GeneratedTranscript: + text: str + language: str + + +@dataclass +class VLLMRealtimeState: + prompt_raw: str + language: str + chunk_size_sec: float + unfixed_chunk_num: int + unfixed_token_num: int + max_new_tokens: int + chunk_id: int = 0 + text: str = "" + raw_decoded: str = "" + audio_buffer: np.ndarray = field(default_factory=lambda: np.array([], dtype=np.float32)) + audio_accum: np.ndarray = field(default_factory=lambda: np.array([], dtype=np.float32)) + + +class Qwen3VLLMBackend: + """Thin adapter over official vLLM APIs for Qwen3-ASR.""" + + def __init__( + self, + model_path: str, + forced_aligner_path: Optional[str], + gpu_memory_utilization: float, + max_inference_batch_size: int, + max_new_tokens: int, + enforce_eager: bool = True, + max_model_len: Optional[int] = None, + tensor_parallel_size: int = 1, + ) -> None: + try: + vllm_module = importlib.import_module("vllm") + transformers_module = importlib.import_module("transformers") + except ImportError as exc: + raise RuntimeError( + "CUDA Qwen3-ASR now requires official vLLM with Qwen3 forced aligner support. " + "Install it with: pip install 'vllm[audio]==0.19.0'" + ) from exc + + self._llm_cls = getattr(vllm_module, "LLM") + self._sampling_params_cls = getattr(vllm_module, "SamplingParams") + self._tokenizer = getattr(transformers_module, "AutoTokenizer").from_pretrained( + model_path, + trust_remote_code=True, + ) + + llm_kwargs: dict[str, Any] = { + "model": model_path, + "gpu_memory_utilization": gpu_memory_utilization, + "enforce_eager": enforce_eager, + "trust_remote_code": True, + } + if max_model_len is not None: + llm_kwargs["max_model_len"] = max_model_len + if tensor_parallel_size > 1: + llm_kwargs["tensor_parallel_size"] = tensor_parallel_size + + self._llm = self._llm_cls(**llm_kwargs) + self._sampling_params = self._sampling_params_cls( + temperature=0.01, + max_tokens=max_new_tokens, + ) + self._max_inference_batch_size = max_inference_batch_size + self._gpu_memory_utilization = gpu_memory_utilization + self._enforce_eager = enforce_eager + self._tensor_parallel_size = tensor_parallel_size + self._forced_aligner_path = forced_aligner_path + self._forced_aligner: Any | None = None + self._timestamp_token_id: int | None = None + self._timestamp_segment_time: float | None = None + # Share one backend instance across multiple offline tasks, but serialize + # direct vLLM engine calls to avoid cross-request state corruption/hangs. + self._engine_lock = threading.RLock() + + def _get_forced_aligner_gpu_memory_utilization(self) -> float: + configured = _resolve_forced_aligner_gpu_memory_utilization(self._gpu_memory_utilization) + logger.info( + "Resolved forced aligner gpu_memory_utilization=%s (primary=%s)", + configured, + self._gpu_memory_utilization, + ) + return configured + + def _get_forced_aligner(self) -> Any: + if not self._forced_aligner_path: + raise RuntimeError("word_timestamps requires a configured forced aligner model") + + if self._forced_aligner is None: + forced_aligner_gpu_memory_utilization = self._get_forced_aligner_gpu_memory_utilization() + logger.info( + "Loading Qwen3 forced aligner via official vLLM: %s (gpu_memory_utilization=%s)", + self._forced_aligner_path, + forced_aligner_gpu_memory_utilization, + ) + self._forced_aligner = self._llm_cls( + model=self._forced_aligner_path, + runner="pooling", + enforce_eager=self._enforce_eager, + gpu_memory_utilization=forced_aligner_gpu_memory_utilization, + tensor_parallel_size=self._tensor_parallel_size, + trust_remote_code=True, + hf_overrides={ + "architectures": ["Qwen3ASRForcedAlignerForTokenClassification"], + }, + ) + llm_engine = getattr(self._forced_aligner, "llm_engine", None) + if llm_engine is None: + raise RuntimeError("Forced aligner did not expose a vLLM engine instance") + config = llm_engine.vllm_config.model_config.hf_config + self._timestamp_token_id = int(config.timestamp_token_id) + self._timestamp_segment_time = float(config.timestamp_segment_time) + + return self._forced_aligner + + def ensure_forced_aligner_loaded(self) -> None: + if self._forced_aligner_path: + self._get_forced_aligner() + + def _run_generate( + self, + audio_items: list[tuple[np.ndarray, str, Optional[str]]], + ) -> list[_GeneratedTranscript]: + prompts: list[dict[str, Any]] = [] + for audio, context, language in audio_items: + prompts.append( + { + "prompt": _build_chat_prompt(context=context, language=_normalize_language_name(language)), + "multi_modal_data": {"audio": [audio]}, + } + ) + + with self._engine_lock: + outputs = self._llm.generate( + prompts, + sampling_params=self._sampling_params, + use_tqdm=False, + ) + + transcripts: list[_GeneratedTranscript] = [] + for output, (_audio, _context, language) in zip(outputs, audio_items): + raw_text = str(output.outputs[0].text if output.outputs else "") + parsed_language, parsed_text = _parse_asr_output(raw_text, _normalize_language_name(language)) + transcripts.append(_GeneratedTranscript(text=parsed_text, language=parsed_language)) + return transcripts + + def transcribe_text( + self, + audio_path: str, + context: str = "", + language: Optional[str] = None, + enable_itn: bool = False, + ) -> str: + transcript = self._run_generate([(_load_audio(audio_path), context, language)])[0] + return normalize_asr_text(transcript.text, enable_itn=enable_itn) + + def transcribe_raw( + self, + audio_path: str, + context: str = "", + language: Optional[str] = None, + word_timestamps: bool = False, + enable_itn: bool = False, + ) -> ASRRawResult: + audio = _load_audio(audio_path) + transcript = self._run_generate([(audio, context, language)])[0] + text = normalize_asr_text(transcript.text, enable_itn=enable_itn) + if not word_timestamps: + return ASRRawResult( + text=text, + segments=[ASRSegmentResult(text=text, start_time=0.0, end_time=0.0)] if text else [], + ) + + aligned = self.align_transcript(audio_path=audio_path, text=text, language=language, audio=audio) + word_tokens = [ + WordToken( + text=str(item["text"]), + start_time=round(float(item["start_ms"]) / 1000.0, 3), + end_time=round(float(item["end_ms"]) / 1000.0, 3), + ) + for item in aligned + ] + if not word_tokens: + return ASRRawResult( + text=text, + segments=[ASRSegmentResult(text=text, start_time=0.0, end_time=0.0)] if text else [], + ) + return ASRRawResult( + text=text, + segments=[ + ASRSegmentResult( + text=text, + start_time=word_tokens[0].start_time, + end_time=word_tokens[-1].end_time, + word_tokens=word_tokens, + ) + ], + ) + + def transcribe_batch( + self, + audio_paths: list[str], + context: str = "", + language: Optional[str] = None, + word_timestamps: bool = False, + enable_itn: bool = False, + ) -> list[ASRSegmentResult]: + audios = [_load_audio(path) for path in audio_paths] + results: list[ASRSegmentResult] = [] + for start in range(0, len(audios), self._max_inference_batch_size): + chunk = audios[start:start + self._max_inference_batch_size] + transcripts = self._run_generate([(audio, context, language) for audio in chunk]) + for audio_path, audio, transcript in zip(audio_paths[start:start + len(chunk)], chunk, transcripts): + text = normalize_asr_text(transcript.text, enable_itn=enable_itn) + if not word_timestamps: + results.append(ASRSegmentResult(text=text, start_time=0.0, end_time=0.0)) + continue + aligned = self.align_transcript( + audio_path=audio_path, + text=text, + language=language, + audio=audio, + ) + word_tokens = [ + WordToken( + text=str(item["text"]), + start_time=round(float(item["start_ms"]) / 1000.0, 3), + end_time=round(float(item["end_ms"]) / 1000.0, 3), + ) + for item in aligned + ] + results.append( + ASRSegmentResult( + text=text, + start_time=word_tokens[0].start_time if word_tokens else 0.0, + end_time=word_tokens[-1].end_time if word_tokens else 0.0, + word_tokens=word_tokens or None, + ) + ) + return results + + def align_transcript( + self, + audio_path: str, + text: str, + language: Optional[str] = None, + audio: Optional[np.ndarray] = None, + ) -> list[dict[str, float | str]]: + tokens = _split_alignment_units(text) + if not tokens: + return [] + + aligner = self._get_forced_aligner() + prompt = _build_alignment_prompt(tokens) + audio_array = audio if audio is not None else _load_audio(audio_path) + with self._engine_lock: + outputs = aligner.encode( + [{"prompt": prompt, "multi_modal_data": {"audio": audio_array}}], + pooling_task="token_classify", + ) + output = outputs[0] + logits = output.outputs.data + predictions = logits.argmax(dim=-1) if hasattr(logits, "argmax") else np.argmax(logits, axis=-1) + ts_predictions = [ + float(pred.item() if hasattr(pred, "item") else pred) * float(self._timestamp_segment_time or 0.0) + for tid, pred in zip(output.prompt_token_ids, predictions) + if int(tid) == int(self._timestamp_token_id or -1) + ] + + expected_timestamps = len(tokens) * 2 + if len(ts_predictions) < expected_timestamps: + raise RuntimeError( + "Forced aligner returned fewer timestamp predictions than expected: " + f"expected={expected_timestamps}, got={len(ts_predictions)}, tokens={len(tokens)}" + ) + + aligned: list[dict[str, float | str]] = [] + for index, token in enumerate(tokens): + start_ms = ts_predictions[index * 2] + end_ms = ts_predictions[index * 2 + 1] + if end_ms < start_ms: + logger.warning( + "Forced aligner produced reversed timestamps for token=%r: start_ms=%s end_ms=%s", + token, + start_ms, + end_ms, + ) + start_ms, end_ms = end_ms, start_ms + aligned.append({"text": token, "start_ms": start_ms, "end_ms": end_ms}) + return aligned + + def init_streaming_state( + self, + *, + context: str = "", + language: Optional[str] = None, + chunk_size_sec: float = 1.2, + unfixed_chunk_num: int = 2, + unfixed_token_num: int = 5, + max_new_tokens: int = 32, + ) -> VLLMRealtimeState: + normalized_language = _normalize_language_name(language) or "" + return VLLMRealtimeState( + prompt_raw=_build_chat_prompt(context=context, language=normalized_language or None), + language=normalized_language, + chunk_size_sec=chunk_size_sec, + unfixed_chunk_num=unfixed_chunk_num, + unfixed_token_num=unfixed_token_num, + max_new_tokens=max_new_tokens, + audio_buffer=np.array([], dtype=np.float32), + audio_accum=np.array([], dtype=np.float32), + ) + + def _decode_stream(self, state: VLLMRealtimeState) -> VLLMRealtimeState: + prefix = "" + if state.chunk_id >= state.unfixed_chunk_num and state.raw_decoded: + token_ids = self._tokenizer.encode(state.raw_decoded, add_special_tokens=False) + rollback = token_ids[-state.unfixed_token_num:] if state.unfixed_token_num > 0 else [] + if rollback: + prefix = self._tokenizer.decode(rollback, skip_special_tokens=False).replace("\ufffd", "") + + with self._engine_lock: + output = self._llm.generate( + [ + { + "prompt": state.prompt_raw + prefix, + "multi_modal_data": {"audio": [state.audio_accum]}, + } + ], + sampling_params=self._sampling_params_cls( + temperature=0.01, + max_tokens=state.max_new_tokens, + ), + use_tqdm=False, + )[0] + generated = str(output.outputs[0].text if output.outputs else "") + parsed_language, parsed_text = _parse_asr_output(prefix + generated, state.language or None) + state.raw_decoded = prefix + generated + state.text = parsed_text + state.language = parsed_language or state.language + state.chunk_id += 1 + return state + + def feed_stream(self, pcm: np.ndarray, state: VLLMRealtimeState) -> VLLMRealtimeState: + state.audio_buffer = np.concatenate([state.audio_buffer, pcm.astype(np.float32)]) + segment_size = int(max(state.chunk_size_sec, 0.1) * _DEFAULT_SAMPLE_RATE) + while len(state.audio_buffer) >= segment_size: + segment = state.audio_buffer[:segment_size].copy() + state.audio_buffer = state.audio_buffer[segment_size:] + state.audio_accum = np.concatenate([state.audio_accum, segment]) + state = self._decode_stream(state) + return state + + def finish_stream(self, state: VLLMRealtimeState) -> VLLMRealtimeState: + if len(state.audio_buffer) > 0: + state.audio_accum = np.concatenate([state.audio_accum, state.audio_buffer]) + state.audio_buffer = np.array([], dtype=np.float32) + state = self._decode_stream(state) + elif state.chunk_id == 0 and len(state.audio_accum) > 0: + state = self._decode_stream(state) + return state diff --git a/app/services/asr/qwenasr_rust.py b/app/services/asr/qwenasr_rust.py new file mode 100644 index 0000000..0079ac9 --- /dev/null +++ b/app/services/asr/qwenasr_rust.py @@ -0,0 +1,564 @@ +# -*- coding: utf-8 -*- +"""QwenASR Rust FFI wrapper for CPU inference.""" + +from __future__ import annotations + +import ctypes +import json +import logging +import os +import platform +import re +import sys +from pathlib import Path +from typing import Optional + +import numpy as np + +from app.core.config import settings + +logger = logging.getLogger(__name__) + +_SHARED_LIBRARY: Optional[ctypes.CDLL] = None + +_LANGUAGE_MAP = { + "": "", + "auto": "", + "zh": "Chinese", + "zh-cn": "Chinese", + "yue": "Chinese", + "en": "English", + "ja": "Japanese", + "ko": "Korean", + "de": "German", + "es": "Spanish", + "fr": "French", + "it": "Italian", + "pt": "Portuguese", + "ru": "Russian", + "ar": "Arabic", + "th": "Thai", + "vi": "Vietnamese", + "id": "Indonesian", +} + + +def _shared_library_filename() -> str: + if sys.platform == "darwin": + return "libqwen_asr.dylib" + if sys.platform == "win32": + return "qwen_asr.dll" + return "libqwen_asr.so" + + +def _repo_root() -> Path: + return Path(__file__).resolve().parents[3] + + +def _candidate_library_paths() -> list[Path]: + filename = _shared_library_filename() + candidates: list[Path] = [] + + env_path = (os.getenv("QWENASR_LIBRARY_PATH") or "").strip() + if env_path: + candidate = Path(env_path).expanduser() + if candidate.is_dir(): + candidates.append(candidate / filename) + else: + candidates.append(candidate) + + repo_root = _repo_root() + candidates.extend( + [ + repo_root / "vendor" / "qwenasr" / "target" / "release" / filename, + repo_root / "vendor" / "qwenasr" / "target" / "debug" / filename, + Path("/opt/qwenasr/lib") / filename, + Path("/usr/local/lib") / filename, + ] + ) + + return candidates + + +def resolve_qwenasr_library_path() -> Optional[Path]: + for candidate in _candidate_library_paths(): + if candidate.exists(): + return candidate.resolve() + return None + + +def is_qwenasr_rust_available() -> bool: + return resolve_qwenasr_library_path() is not None + + +def validate_qwenasr_cpu_features() -> None: + if platform.machine().lower() not in {"amd64", "x86_64"}: + return + + flags = _read_linux_cpu_flags() + if not flags: + return + + missing = [flag for flag in ("avx2", "fma") if flag not in flags] + if missing: + raise RuntimeError( + "QwenASR Rust backend requires x86_64 CPU features: avx2, fma. " + f"Missing: {', '.join(missing)}. Use a newer CPU host or rebuild the " + "Rust backend with scalar x86 kernels." + ) + + +def pick_cpu_qwen_model(all_available_models: list[str]) -> Optional[str]: + for model_id in ["qwen3-asr-0.6b", "qwen3-asr-1.7b"]: + if model_id in all_available_models: + return model_id + return None + + +def _read_linux_cpu_flags() -> set[str]: + cpuinfo = Path("/proc/cpuinfo") + if not cpuinfo.exists(): + return set() + + flags: set[str] = set() + for line in cpuinfo.read_text(encoding="utf-8", errors="ignore").splitlines(): + key, _, value = line.partition(":") + if key.strip().lower() in {"flags", "features"}: + flags.update(value.strip().lower().split()) + if flags: + break + return flags + + +def _resolve_modelscope_dir(model_ref: str, cache_root: Path) -> Optional[Path]: + if "/" not in model_ref: + return None + base_dir = cache_root / model_ref + if base_dir.exists() and base_dir.is_dir(): + return base_dir.resolve() + return None + + +def _append_unique_path(paths: list[Path], path: Path) -> None: + if path not in paths: + paths.append(path) + + +def resolve_qwenasr_model_path(model_ref_or_path: str) -> Path: + raw_path = Path(model_ref_or_path).expanduser() + if raw_path.exists(): + return raw_path.resolve() + + modelscope_cache_roots: list[Path] = [] + ms_cache = (os.getenv("MODELSCOPE_CACHE") or "").strip() + if ms_cache: + cache_root = Path(ms_cache).expanduser() + _append_unique_path(modelscope_cache_roots, cache_root) + + # 兼容旧目录结构，允许外部仍传入 models/modelscope + legacy_cache_root = cache_root / "hub" / "models" + if legacy_cache_root.exists(): + _append_unique_path(modelscope_cache_roots, legacy_cache_root) + + default_ms_cache_root = Path(settings.MODELSCOPE_PATH).expanduser() + _append_unique_path(modelscope_cache_roots, default_ms_cache_root) + + for cache_root in modelscope_cache_roots: + ms_dir = _resolve_modelscope_dir(model_ref_or_path, cache_root) + if ms_dir is not None: + return ms_dir + + raise FileNotFoundError( + f"QwenASR model path not found for '{model_ref_or_path}'. " + f"Checked direct path and ModelScope caches at: " + f"{', '.join(str(path) for path in modelscope_cache_roots)}." + ) + + +def _bind_ffi_signatures(lib: ctypes.CDLL) -> None: + lib.qwen_asr_load_model.argtypes = [ctypes.c_char_p, ctypes.c_int, ctypes.c_int] + lib.qwen_asr_load_model.restype = ctypes.c_void_p + + lib.qwen_asr_transcribe_file.argtypes = [ctypes.c_void_p, ctypes.c_char_p] + lib.qwen_asr_transcribe_file.restype = ctypes.c_void_p + + lib.qwen_asr_force_align_file.argtypes = [ + ctypes.c_void_p, + ctypes.c_char_p, + ctypes.c_char_p, + ctypes.c_char_p, + ] + lib.qwen_asr_force_align_file.restype = ctypes.c_void_p + + lib.qwen_asr_set_language.argtypes = [ctypes.c_void_p, ctypes.c_char_p] + lib.qwen_asr_set_language.restype = ctypes.c_int + + lib.qwen_asr_free_string.argtypes = [ctypes.c_void_p] + lib.qwen_asr_free_string.restype = None + + lib.qwen_asr_free.argtypes = [ctypes.c_void_p] + lib.qwen_asr_free.restype = None + + lib.qwen_asr_stream_new.argtypes = [] + lib.qwen_asr_stream_new.restype = ctypes.c_void_p + + lib.qwen_asr_stream_free.argtypes = [ctypes.c_void_p] + lib.qwen_asr_stream_free.restype = None + + lib.qwen_asr_stream_push.argtypes = [ + ctypes.c_void_p, + ctypes.c_void_p, + ctypes.POINTER(ctypes.c_float), + ctypes.c_int, + ctypes.c_int, + ] + lib.qwen_asr_stream_push.restype = ctypes.c_void_p + + lib.qwen_asr_stream_get_result.argtypes = [ctypes.c_void_p] + lib.qwen_asr_stream_get_result.restype = ctypes.c_void_p + + lib.qwen_asr_stream_set_chunk_sec.argtypes = [ctypes.c_void_p, ctypes.c_float] + lib.qwen_asr_stream_set_chunk_sec.restype = None + + lib.qwen_asr_stream_set_rollback.argtypes = [ctypes.c_void_p, ctypes.c_int] + lib.qwen_asr_stream_set_rollback.restype = None + + lib.qwen_asr_stream_set_unfixed_chunks.argtypes = [ctypes.c_void_p, ctypes.c_int] + lib.qwen_asr_stream_set_unfixed_chunks.restype = None + + lib.qwen_asr_stream_set_max_new_tokens.argtypes = [ctypes.c_void_p, ctypes.c_int] + lib.qwen_asr_stream_set_max_new_tokens.restype = None + + lib.qwen_asr_stream_set_past_text.argtypes = [ctypes.c_void_p, ctypes.c_int] + lib.qwen_asr_stream_set_past_text.restype = None + + +def load_qwenasr_library() -> ctypes.CDLL: + global _SHARED_LIBRARY + + if _SHARED_LIBRARY is not None: + return _SHARED_LIBRARY + + library_path = resolve_qwenasr_library_path() + if library_path is None: + searched = ", ".join(str(path) for path in _candidate_library_paths()) + raise FileNotFoundError( + "QwenASR shared library not found. " + f"Checked: {searched}" + ) + + logger.info("Loading QwenASR Rust library from %s", library_path) + library = ctypes.CDLL(str(library_path)) + _bind_ffi_signatures(library) + _SHARED_LIBRARY = library + return library + + +def normalize_qwen_language(language: Optional[str]) -> str: + if language is None: + return "" + return _LANGUAGE_MAP.get(language.strip().lower(), language.strip()) + + +def guess_alignment_language(text: str, language: Optional[str] = None) -> str: + normalized = normalize_qwen_language(language) + if normalized: + return normalized + if re.search(r"[\u4e00-\u9fff]", text): + return "Chinese" + if re.search(r"[\u3040-\u30ff]", text): + return "Japanese" + if re.search(r"[\uac00-\ud7af]", text): + return "Korean" + return "English" + + +def _decode_and_free_string(lib: ctypes.CDLL, raw_ptr: ctypes.c_void_p) -> Optional[str]: + if not raw_ptr: + return None + + try: + value = ctypes.cast(raw_ptr, ctypes.c_char_p).value + if value is None: + return None + return value.decode("utf-8") + finally: + lib.qwen_asr_free_string(raw_ptr) + + +class QwenASRRustStreamHandle: + """Owns a Rust streaming state pointer.""" + + def __init__(self, lib: ctypes.CDLL, handle: ctypes.c_void_p): + self._lib = lib + self.handle = handle + self.accumulated_text = "" + + def close(self) -> None: + if self.handle: + self._lib.qwen_asr_stream_free(self.handle) + self.handle = ctypes.c_void_p() + + def __del__(self) -> None: + try: + self.close() + except Exception: + pass + + +class QwenASRRustBackend: + """Thin Python wrapper around the QwenASR C API.""" + + def __init__(self, model_path: str, num_threads: int = 0, verbosity: int = 0): + validate_qwenasr_cpu_features() + self._lib = load_qwenasr_library() + self.model_dir = resolve_qwenasr_model_path(model_path) + self._engine = self._lib.qwen_asr_load_model( + str(self.model_dir).encode("utf-8"), + num_threads, + verbosity, + ) + if not self._engine: + raise RuntimeError(f"Failed to load QwenASR model from '{self.model_dir}'") + + def close(self) -> None: + if self._engine: + self._lib.qwen_asr_free(self._engine) + self._engine = ctypes.c_void_p() + + def __del__(self) -> None: + try: + self.close() + except Exception: + pass + + def _set_language(self, language: Optional[str]) -> None: + normalized = normalize_qwen_language(language) + status = self._lib.qwen_asr_set_language( + self._engine, + normalized.encode("utf-8"), + ) + if status != 0 and normalized: + logger.warning("QwenASR rejected language hint: %s", normalized) + + def _configure_stream( + self, + *, + chunk_size_sec: float, + unfixed_chunk_num: int, + rollback_tokens: int, + max_new_tokens: int, + past_text: bool, + ) -> None: + self._lib.qwen_asr_stream_set_chunk_sec(self._engine, ctypes.c_float(chunk_size_sec)) + self._lib.qwen_asr_stream_set_unfixed_chunks(self._engine, int(unfixed_chunk_num)) + self._lib.qwen_asr_stream_set_rollback(self._engine, int(rollback_tokens)) + self._lib.qwen_asr_stream_set_max_new_tokens(self._engine, int(max_new_tokens)) + self._lib.qwen_asr_stream_set_past_text(self._engine, 1 if past_text else 0) + + def transcribe_file(self, audio_path: str, language: Optional[str] = None) -> str: + self._set_language(language) + raw_ptr = self._lib.qwen_asr_transcribe_file( + self._engine, + audio_path.encode("utf-8"), + ) + text = _decode_and_free_string(self._lib, raw_ptr) + if text is None: + raise RuntimeError(f"QwenASR failed to transcribe '{audio_path}'") + return text + + def force_align_file( + self, + audio_path: str, + text: str, + language: Optional[str] = None, + ) -> list[dict[str, float | str]]: + normalized_language = normalize_qwen_language(language) or "English" + raw_ptr = self._lib.qwen_asr_force_align_file( + self._engine, + audio_path.encode("utf-8"), + text.encode("utf-8"), + normalized_language.encode("utf-8"), + ) + payload = _decode_and_free_string(self._lib, raw_ptr) + if payload is None: + raise RuntimeError(f"QwenASR failed to force align '{audio_path}'") + + items = json.loads(payload) + if not isinstance(items, list): + raise RuntimeError("QwenASR force alignment returned invalid payload") + return [ + { + "text": str(item.get("text", "")), + "start_ms": float(item.get("start_ms", 0.0)), + "end_ms": float(item.get("end_ms", 0.0)), + } + for item in items + if isinstance(item, dict) and str(item.get("text", "")).strip() + ] + + def create_stream( + self, + *, + chunk_size_sec: float = 1.2, + unfixed_chunk_num: int = 2, + rollback_tokens: int = 5, + max_new_tokens: int = 32, + language: Optional[str] = None, + ) -> QwenASRRustStreamHandle: + handle = self._lib.qwen_asr_stream_new() + if not handle: + raise RuntimeError("QwenASR failed to create stream state") + + self._configure_stream( + chunk_size_sec=chunk_size_sec, + unfixed_chunk_num=unfixed_chunk_num, + rollback_tokens=rollback_tokens, + max_new_tokens=max_new_tokens, + past_text=True, + ) + self._set_language(language) + return QwenASRRustStreamHandle(self._lib, handle) + + def push_stream( + self, + stream: QwenASRRustStreamHandle, + samples: np.ndarray, + *, + chunk_size_sec: float, + unfixed_chunk_num: int, + rollback_tokens: int, + max_new_tokens: int, + language: Optional[str], + finalize: bool = False, + ) -> str: + self._configure_stream( + chunk_size_sec=chunk_size_sec, + unfixed_chunk_num=unfixed_chunk_num, + rollback_tokens=rollback_tokens, + max_new_tokens=max_new_tokens, + past_text=True, + ) + self._set_language(language) + + pcm = np.ascontiguousarray(samples, dtype=np.float32) + pointer = ( + pcm.ctypes.data_as(ctypes.POINTER(ctypes.c_float)) + if len(pcm) > 0 + else None + ) + delta_ptr = self._lib.qwen_asr_stream_push( + self._engine, + stream.handle, + pointer, + len(pcm), + 1 if finalize else 0, + ) + delta_text = _decode_and_free_string(self._lib, delta_ptr) or "" + stream.accumulated_text += delta_text + return stream.accumulated_text + + +class QwenASRRustRuntime: + """Higher-level Rust runtime bundle for ASR + aligner + streaming.""" + + def __init__( + self, + model_path: str, + *, + forced_aligner_path: Optional[str] = None, + num_threads: int = 0, + verbosity: int = 0, + ) -> None: + self._asr = QwenASRRustBackend( + model_path=model_path, + num_threads=num_threads, + verbosity=verbosity, + ) + self._aligner: Optional[QwenASRRustBackend] = None + if forced_aligner_path: + self._aligner = QwenASRRustBackend( + model_path=forced_aligner_path, + num_threads=num_threads, + verbosity=verbosity, + ) + + def transcribe_file(self, audio_path: str, language: Optional[str] = None) -> str: + return self._asr.transcribe_file(audio_path=audio_path, language=language) + + def align_transcript( + self, + audio_path: str, + text: str, + language: Optional[str] = None, + ) -> list[dict[str, float | str]]: + transcript = text.strip() + if not transcript: + return [] + if self._aligner is None: + raise RuntimeError("Forced alignment requires a configured aligner model") + return self._aligner.force_align_file( + audio_path=audio_path, + text=transcript, + language=guess_alignment_language(transcript, language), + ) + + def create_stream( + self, + *, + chunk_size_sec: float = 1.2, + unfixed_chunk_num: int = 2, + rollback_tokens: int = 5, + max_new_tokens: int = 32, + language: Optional[str] = None, + ) -> QwenASRRustStreamHandle: + return self._asr.create_stream( + chunk_size_sec=chunk_size_sec, + unfixed_chunk_num=unfixed_chunk_num, + rollback_tokens=rollback_tokens, + max_new_tokens=max_new_tokens, + language=language, + ) + + def push_stream( + self, + stream: QwenASRRustStreamHandle, + samples: np.ndarray, + *, + chunk_size_sec: float, + unfixed_chunk_num: int, + rollback_tokens: int, + max_new_tokens: int, + language: Optional[str], + ) -> str: + return self._asr.push_stream( + stream=stream, + samples=samples, + chunk_size_sec=chunk_size_sec, + unfixed_chunk_num=unfixed_chunk_num, + rollback_tokens=rollback_tokens, + max_new_tokens=max_new_tokens, + language=language, + finalize=False, + ) + + def finish_stream( + self, + stream: QwenASRRustStreamHandle, + *, + chunk_size_sec: float, + unfixed_chunk_num: int, + rollback_tokens: int, + max_new_tokens: int, + language: Optional[str], + ) -> str: + return self._asr.push_stream( + stream=stream, + samples=np.array([], dtype=np.float32), + chunk_size_sec=chunk_size_sec, + unfixed_chunk_num=unfixed_chunk_num, + rollback_tokens=rollback_tokens, + max_new_tokens=max_new_tokens, + language=language, + finalize=True, + ) diff --git a/app/services/asr/runtime/__init__.py b/app/services/asr/runtime/__init__.py new file mode 100644 index 0000000..f4cc7a0 --- /dev/null +++ b/app/services/asr/runtime/__init__.py @@ -0,0 +1,16 @@ +# -*- coding: utf-8 -*- +"""ASR runtime routing and pooling layer.""" + +from .router import ( + OfflineASRRequest, + RuntimeEngineLease, + RuntimeRouter, + get_runtime_router, +) + +__all__ = [ + "OfflineASRRequest", + "RuntimeEngineLease", + "RuntimeRouter", + "get_runtime_router", +] diff --git a/app/services/asr/runtime/local_pool.py b/app/services/asr/runtime/local_pool.py new file mode 100644 index 0000000..6f8b9bf --- /dev/null +++ b/app/services/asr/runtime/local_pool.py @@ -0,0 +1,48 @@ +# -*- coding: utf-8 -*- +"""Small async pool for per-request ASR engines.""" + +from __future__ import annotations + +import asyncio +import threading +from dataclasses import dataclass +from typing import Callable, Generic, Optional, TypeVar + +T = TypeVar("T") + + +@dataclass +class _PoolState(Generic[T]): + queue: asyncio.Queue[T] + + +class LocalEnginePool(Generic[T]): + """Fixed-size lazy engine pool backed by ``asyncio.Queue``.""" + + def __init__(self, size: int, factory: Callable[[], T]): + self._size = max(1, size) + self._factory = factory + self._state: Optional[_PoolState[T]] = None + self._init_lock = threading.Lock() + + def _ensure_state(self) -> _PoolState[T]: + if self._state is not None: + return self._state + + with self._init_lock: + if self._state is None: + self._state = _PoolState(queue=asyncio.Queue(maxsize=self._size)) + for _ in range(self._size): + self._state.queue.put_nowait(self._factory()) + return self._state + + def warmup(self) -> None: + self._ensure_state() + + async def acquire(self) -> T: + state = self._ensure_state() + return await state.queue.get() + + async def release(self, engine: T) -> None: + state = self._ensure_state() + await state.queue.put(engine) diff --git a/app/services/asr/runtime/router.py b/app/services/asr/runtime/router.py new file mode 100644 index 0000000..eaf9125 --- /dev/null +++ b/app/services/asr/runtime/router.py @@ -0,0 +1,241 @@ +# -*- coding: utf-8 -*- +"""Runtime router for pooled ASR execution.""" + +from __future__ import annotations +import asyncio +import threading +from dataclasses import dataclass +from enum import Enum +from typing import Awaitable, Callable, Optional + +import torch + +from app.core.accelerator import get_accelerator_info +from app.core.config import settings +from app.core.device import detect_device +from app.core.executor import run_sync +from app.services.asr.engines import ASRFullResult, BaseASREngine +from app.services.asr.manager import get_model_manager +from app.services.asr.qwenasr_rust import is_qwenasr_rust_available +from .local_pool import LocalEnginePool + + +class RuntimeFamily(str, Enum): + QWEN_VLLM = "qwen_vllm" + QWEN_RUST_CPU = "qwen_rust_cpu" + + +@dataclass +class OfflineASRRequest: + model_id: str + audio_path: str + hotwords: str = "" + enable_punctuation: bool = True + enable_itn: bool = True + sample_rate: int = 16000 + enable_speaker_diarization: bool = True + enable_speaker_identification: bool = True + enable_text_cleanup: bool = True + word_timestamps: bool = False + timestamp_scale: float = 1.0 + task_id: Optional[str] = None + progress_callback: Optional[Callable[[str, str, int, Optional[dict[str, object]]], None]] = None + + +class RuntimeEngineLease: + """Lifecycle wrapper around a pooled engine instance.""" + + def __init__(self, engine: BaseASREngine, release_callback: Callable[[], None | Awaitable[None]]): + self.engine = engine + self._release_callback = release_callback + self._closed = False + + async def close(self) -> None: + if self._closed: + return + self._closed = True + result = self._release_callback() + if asyncio.iscoroutine(result): + await result + + async def __aenter__(self) -> BaseASREngine: + return self.engine + + async def __aexit__(self, exc_type, exc, tb) -> None: + await self.close() + + +class RuntimeRouter: + """Central backend router for all ASR entrypoints.""" + + def __init__(self): + self._manager = get_model_manager() + self._pools: dict[tuple[RuntimeFamily, str], LocalEnginePool[BaseASREngine]] = {} + self._shared_engines: dict[tuple[RuntimeFamily, str], BaseASREngine] = {} + self._shared_limits: dict[tuple[RuntimeFamily, str], asyncio.Semaphore] = {} + self._pool_lock = threading.Lock() + self._loaded_model_ids: set[str] = set() + + def resolve_model_id(self, model_id: Optional[str]) -> str: + if model_id: + return model_id + config = self._manager.get_declared_entry_config() + return config.model_id + + def _resolve_family(self, model_id: str) -> RuntimeFamily: + device = detect_device(settings.DEVICE) + accelerator = get_accelerator_info() + if model_id.startswith("qwen3-asr-"): + if accelerator.is_gpu and device.startswith("cuda"): + return RuntimeFamily.QWEN_VLLM + if device == "cpu" and is_qwenasr_rust_available(): + return RuntimeFamily.QWEN_RUST_CPU + raise RuntimeError( + "Qwen3-ASR is not available on " + f"accelerator='{accelerator.vendor}' device='{device}'" + ) + raise RuntimeError(f"Unsupported runtime model: {model_id}") + + def _pool_size_for_family(self, family: RuntimeFamily) -> int: + if family == RuntimeFamily.QWEN_VLLM: + return 1 + return settings.QWEN_RUST_CPU_WORKERS + + def _create_pool(self, family: RuntimeFamily, model_id: str) -> LocalEnginePool[BaseASREngine]: + pool_key = (family, model_id) + existing = self._pools.get(pool_key) + if existing is not None: + return existing + + with self._pool_lock: + existing = self._pools.get(pool_key) + if existing is not None: + return existing + pool = LocalEnginePool( + size=self._pool_size_for_family(family), + factory=lambda: self._manager.create_engine(model_id), + ) + self._pools[pool_key] = pool + self._loaded_model_ids.add(model_id) + return pool + + def _get_shared_engine(self, family: RuntimeFamily, model_id: str) -> tuple[BaseASREngine, asyncio.Semaphore]: + runtime_key = (family, model_id) + engine = self._shared_engines.get(runtime_key) + semaphore = self._shared_limits.get(runtime_key) + if engine is not None and semaphore is not None: + return engine, semaphore + + with self._pool_lock: + engine = self._shared_engines.get(runtime_key) + semaphore = self._shared_limits.get(runtime_key) + if engine is None: + engine = self._manager.create_engine(model_id) + self._shared_engines[runtime_key] = engine + self._loaded_model_ids.add(model_id) + if semaphore is None: + shared_concurrency = max(1, int(settings.QWEN_VLLM_SHARED_CONCURRENCY)) + semaphore = asyncio.Semaphore(shared_concurrency) + self._shared_limits[runtime_key] = semaphore + return engine, semaphore + + def warmup_model(self, model_id: Optional[str] = None) -> None: + resolved_model_id = self.resolve_model_id(model_id) + family = self._resolve_family(resolved_model_id) + if family == RuntimeFamily.QWEN_VLLM: + self._get_shared_engine(family, resolved_model_id) + return + pool = self._create_pool(family, resolved_model_id) + pool.warmup() + + def get_loaded_model_ids(self) -> list[str]: + return sorted(self._loaded_model_ids) + + def get_memory_usage(self) -> dict[str, object]: + memory_info: dict[str, object] = { + "model_list": self.get_loaded_model_ids(), + "loaded_count": len(self._loaded_model_ids), + } + + accelerator = get_accelerator_info() + memory_info["accelerator"] = accelerator.as_dict() + + if accelerator.is_gpu and torch.cuda.is_available(): + memory_info["gpu_memory"] = { + "allocated": f"{torch.cuda.memory_allocated() / 1024**3:.2f}GB", + "cached": f"{torch.cuda.memory_reserved() / 1024**3:.2f}GB", + "max_allocated": f"{torch.cuda.max_memory_allocated() / 1024**3:.2f}GB", + } + + return memory_info + + async def acquire_engine(self, model_id: Optional[str] = None) -> RuntimeEngineLease: + resolved_model_id = self.resolve_model_id(model_id) + family = self._resolve_family(resolved_model_id) + if family == RuntimeFamily.QWEN_VLLM: + engine, semaphore = self._get_shared_engine(family, resolved_model_id) + await semaphore.acquire() + return RuntimeEngineLease( + engine=engine, + release_callback=semaphore.release, + ) + pool = self._create_pool(family, resolved_model_id) + engine = await pool.acquire() + return RuntimeEngineLease( + engine=engine, + release_callback=lambda: pool.release(engine), + ) + + async def run_offline(self, request: OfflineASRRequest) -> ASRFullResult: + async with await self.acquire_engine(request.model_id) as engine: + result = await run_sync( + engine.transcribe_long_audio, + audio_path=request.audio_path, + hotwords=request.hotwords, + enable_punctuation=request.enable_punctuation, + enable_itn=request.enable_itn, + sample_rate=request.sample_rate, + enable_speaker_diarization=request.enable_speaker_diarization, + enable_speaker_identification=request.enable_speaker_identification, + enable_text_cleanup=request.enable_text_cleanup, + word_timestamps=request.word_timestamps, + timestamp_scale=request.timestamp_scale, + task_id=request.task_id, + progress_callback=request.progress_callback, + ) + if ( + request.enable_speaker_diarization + and request.enable_speaker_identification + and settings.SPEAKER_DB_ENABLED + ): + try: + from app.services.speaker_registry import get_speaker_registry_service + + if request.progress_callback is not None: + request.progress_callback( + "speaker_matching", + "正在匹配已注册声纹库。", + 94, + None, + ) + result = await get_speaker_registry_service().apply_registered_speakers( + result, + threshold=settings.SV_THRESHOLD, + ) + except Exception: + # 数据库/声纹匹配失败不影响原有 ASR 输出，仍保留 CAM++ 的局部说话人编号。 + pass + return result + + +_runtime_router: Optional[RuntimeRouter] = None +_runtime_router_lock = threading.Lock() + + +def get_runtime_router() -> RuntimeRouter: + global _runtime_router + if _runtime_router is None: + with _runtime_router_lock: + if _runtime_router is None: + _runtime_router = RuntimeRouter() + return _runtime_router diff --git a/app/services/audio/__init__.py b/app/services/audio/__init__.py new file mode 100644 index 0000000..009879a --- /dev/null +++ b/app/services/audio/__init__.py @@ -0,0 +1,10 @@ +# -*- coding: utf-8 -*- +""" +音频处理服务模块 + +提供统一的音频处理服务层，封装音频下载、格式转换、归一化等功能。 +""" + +from .audio_service import AudioProcessingService, get_audio_service + +__all__ = ["AudioProcessingService", "get_audio_service"] diff --git a/app/services/audio/audio_service.py b/app/services/audio/audio_service.py new file mode 100644 index 0000000..a2572ad --- /dev/null +++ b/app/services/audio/audio_service.py @@ -0,0 +1,234 @@ +# -*- coding: utf-8 -*- +""" +音频处理服务 + +封装音频处理逻辑，提供统一的音频下载、格式转换、归一化等服务。 +API层应该通过此服务层处理音频，而不是直接调用 utils/audio.py 中的函数。 +""" + +import logging +import threading +from dataclasses import dataclass +from typing import Optional +from fastapi import Request + +from ...core.config import settings +from ...core.exceptions import InvalidMessageException +from ...utils.audio import ( + download_audio_from_url, + save_audio_to_temp_file, + normalize_audio_for_asr, + get_audio_duration, + cleanup_temp_file, + get_audio_file_suffix, +) + +logger = logging.getLogger(__name__) + + +@dataclass(frozen=True) +class AudioProcessingResult: + normalized_path: str + duration: float + original_path: str + timestamp_scale: float = 1.0 + + +class AudioProcessingService: + """音频处理服务 + + 提供统一的音频处理接口，包括： + 1. 从URL下载音频 + 2. 处理上传的音频文件 + 3. 音频格式转换和归一化 + 4. 临时文件管理 + """ + + async def process_from_request( + self, + request: Request, + audio_address: Optional[str] = None, + task_id: Optional[str] = None, + sample_rate: Optional[int] = None, + ) -> AudioProcessingResult: + """从请求中处理音频 + + 支持两种方式： + 1. 请求体上传：从请求体读取二进制音频/视频数据 + 2. URL下载：通过 audio_address 参数指定音频/视频 URL + + 当请求体和 audio_address 同时存在时，优先使用请求体， + 并忽略 audio_address。 + + Args: + request: FastAPI请求对象 + audio_address: 音频文件URL（可选） + task_id: 任务ID，用于日志记录（可选） + sample_rate: 目标采样率（可选，默认16000） + + Returns: + Processed audio path, duration, original path, and timestamp metadata. + + Raises: + InvalidMessageException: 音频数据为空或文件太大 + InvalidParameterException: URL无效或下载失败 + """ + task_id = task_id or "unknown" + target_sr = sample_rate or 16000 + + # 优先读取请求体；若请求体为空，再回退到 audio_address。 + # 注意：对于 FastAPI 已经解析过 form/multipart 的请求， + # 再次读取 body 可能抛出 "Stream consumed"。 + try: + uploaded_data = await request.body() + except RuntimeError as exc: + if "Stream consumed" in str(exc): + logger.info(f"[{task_id}] 请求体已被上游读取，跳过 request.body() 回退逻辑") + uploaded_data = b"" + else: + raise + + if uploaded_data: + if audio_address: + logger.info(f"[{task_id}] 检测到同时提供上传内容和 audio_address，已忽略 audio_address") + return self._process_audio_bytes( + audio_data=uploaded_data, + filename=None, + task_id=task_id, + target_sr=target_sr, + ) + + if audio_address: + logger.info(f"[{task_id}] 开始从URL下载音频: {audio_address}") + audio_data = download_audio_from_url(audio_address) + logger.info( + f"[{task_id}] 音频下载完成，大小: {len(audio_data) / 1024 / 1024:.2f}MB" + ) + return self._process_audio_bytes( + audio_data=audio_data, + filename=audio_address, + task_id=task_id, + target_sr=target_sr, + ) + + raise InvalidMessageException("音频数据为空", task_id) + + async def process_upload_file( + self, + audio_data: bytes, + filename: Optional[str] = None, + task_id: Optional[str] = None, + sample_rate: Optional[int] = None, + ) -> AudioProcessingResult: + """处理上传的音频文件 + + Args: + audio_data: 音频二进制数据 + filename: 原始文件名（用于检测格式，可选） + task_id: 任务ID，用于日志记录（可选） + sample_rate: 目标采样率（可选，默认16000） + + Returns: + Processed audio path, duration, original path, and timestamp metadata. + + Raises: + InvalidMessageException: 音频数据为空或文件太大 + """ + task_id = task_id or "unknown" + target_sr = sample_rate or 16000 + return self._process_audio_bytes( + audio_data=audio_data, + filename=filename, + task_id=task_id, + target_sr=target_sr, + ) + + def _process_audio_bytes( + self, + *, + audio_data: bytes, + filename: Optional[str], + task_id: str, + target_sr: int, + ) -> AudioProcessingResult: + """Persist, normalize, and measure audio bytes.""" + audio_path = None + normalized_audio_path = None + + try: + if not audio_data: + raise InvalidMessageException("音频数据为空", task_id) + + file_size = len(audio_data) + logger.info(f"[{task_id}] 音频文件大小: {file_size / 1024 / 1024:.2f}MB") + + # 检查文件大小 + if file_size > settings.MAX_AUDIO_SIZE: + max_mb = settings.MAX_AUDIO_SIZE // 1024 // 1024 + raise InvalidMessageException( + f"音频文件太大，最大支持{max_mb}MB", task_id + ) + + file_suffix = get_audio_file_suffix( + audio_address=filename, + audio_data=audio_data, + ) + logger.info(f"[{task_id}] 识别文件格式: {file_suffix}") + audio_path = save_audio_to_temp_file(audio_data, file_suffix) + logger.info(f"[{task_id}] 临时文件: {audio_path}") + + logger.info(f"[{task_id}] 开始音频格式转换...") + normalized_audio = normalize_audio_for_asr(audio_path, target_sr) + normalized_audio_path = normalized_audio.path + logger.info(f"[{task_id}] 音频格式转换完成: {normalized_audio_path}") + + decoded_duration = get_audio_duration(normalized_audio_path) + audio_duration = decoded_duration * normalized_audio.timestamp_scale + logger.info(f"[{task_id}] 音频时长: {audio_duration:.1f}s") + + return AudioProcessingResult( + normalized_path=normalized_audio_path, + duration=audio_duration, + original_path=audio_path, + timestamp_scale=normalized_audio.timestamp_scale, + ) + + except Exception: + if audio_path: + cleanup_temp_file(audio_path) + if normalized_audio_path and normalized_audio_path != audio_path: + cleanup_temp_file(normalized_audio_path) + raise + + def cleanup( + self, audio_path: Optional[str], normalized_path: Optional[str] = None + ) -> None: + """清理临时文件 + + Args: + audio_path: 原始音频文件路径 + normalized_path: 归一化后的音频文件路径（可选） + """ + if audio_path: + cleanup_temp_file(audio_path) + if normalized_path and normalized_path != audio_path: + cleanup_temp_file(normalized_path) + + +# 全局服务实例（单例模式） +_audio_service: Optional[AudioProcessingService] = None +_audio_service_lock = threading.Lock() + + +def get_audio_service() -> AudioProcessingService: + """获取音频处理服务实例（线程安全的单例） + + Returns: + AudioProcessingService: 音频处理服务实例 + """ + global _audio_service + if _audio_service is None: + with _audio_service_lock: + if _audio_service is None: + _audio_service = AudioProcessingService() + return _audio_service diff --git a/app/services/qwen3_websocket_asr.py b/app/services/qwen3_websocket_asr.py new file mode 100644 index 0000000..d71f3ed --- /dev/null +++ b/app/services/qwen3_websocket_asr.py @@ -0,0 +1,2949 @@ +# -*- coding: utf-8 -*- +"""Qwen3-ASR websocket streaming service.""" + +from __future__ import annotations + +import asyncio +import io +import json +import logging +import re +import time +import unicodedata +from difflib import SequenceMatcher +from dataclasses import dataclass, field +from enum import IntEnum +from typing import Any, Dict, List, Optional + +import numpy as np +import soundfile as sf +from fastapi import WebSocket, WebSocketDisconnect + +from app.core.config import settings +from app.core.exceptions import create_error_response +from app.core.executor import run_sync +from app.core.text_cleanup import deduplicate_asr_text +from app.services.asr.model_selection import validate_realtime_model_id +from app.services.asr.qwen3_engine import Qwen3ASREngine, Qwen3StreamingState +from app.services.asr.engines.global_models import get_global_vad_model, get_vad_inference_lock +from app.services.realtime_speaker_clusterer import get_realtime_speaker_clusterer +from app.services.asr.runtime import RuntimeEngineLease, get_runtime_router +from app.services.speaker_registry import get_speaker_registry_service +from app.utils.text_processing import normalize_asr_text + +logger = logging.getLogger(__name__) + + +def _remove_repeated_chars(text: str, max_run: int) -> str: + out: List[str] = [] + last: Optional[str] = None + run = 0 + for ch in text: + if ch == last: + run += 1 + else: + last = ch + run = 1 + if run <= max_run: + out.append(ch) + return "".join(out) + + +def _normalize_transcript_chunk_text(text: str) -> str: + collapsed = " ".join(str(text or "").split()).strip() + if not collapsed: + return "" + return _remove_repeated_chars(collapsed, 8) + + +def _is_opening_punctuation(ch: str) -> bool: + return ch in '([{"\'“‘' + + +def _is_closing_punctuation(ch: str) -> bool: + return ch in '.,!?:;)]}"\'”’。，！？：；、' + + +def _append_with_spacing(left: str, right: str) -> str: + if not left: + return right + if not right: + return left + prev = left[-1] + nxt = right[0] + needs_space = ( + not prev.isspace() + and not nxt.isspace() + and not _is_closing_punctuation(nxt) + and not _is_opening_punctuation(prev) + ) + return f"{left} {right}" if needs_space else f"{left}{right}" + + +def _normalize_token(token: str) -> str: + return "".join( + ch.lower() + for ch in token + if ch.isalnum() or ch in {"'", "-"} + ) + + +def _token_views(text: str) -> List[tuple[str, int]]: + views: List[tuple[str, int]] = [] + start: Optional[int] = None + for idx, ch in enumerate(text): + if ch.isspace(): + if start is not None: + token = text[start:idx] + normalized = _normalize_token(token) + if normalized: + views.append((normalized, start)) + start = None + continue + if start is None: + start = idx + if start is not None: + token = text[start:] + normalized = _normalize_token(token) + if normalized: + views.append((normalized, start)) + return views + + +def _dedupe_overlap_word_boundary( + previous: str, + current: str, + min_overlap: int = 3, + max_overlap: int = 24, +) -> int: + prev_tokens = _token_views(previous) + curr_tokens = _token_views(current) + if not prev_tokens or not curr_tokens: + return 0 + upper = min(max_overlap, len(prev_tokens), len(curr_tokens)) + lower = max(min_overlap, 1) + if upper < lower: + return 0 + for size in range(upper, lower - 1, -1): + left = prev_tokens[-size:] + right = curr_tokens[:size] + if all(a == b for (a, _), (b, _) in zip(left, right)): + if size == len(curr_tokens): + return len(current) + return curr_tokens[size][1] + return 0 + + +def _char_count_to_index(text: str, char_count: int) -> int: + if char_count <= 0: + return 0 + count = 0 + for idx, ch in enumerate(text): + count += 1 + if count == char_count: + return idx + 1 + return len(text) + + +def _dedupe_overlap_char_boundary( + previous: str, + current: str, + min_chars: int = 6, + max_chars: int = 80, +) -> int: + prev_chars = list(previous) + curr_chars = list(current) + if not prev_chars or not curr_chars: + return 0 + upper = min(max_chars, len(prev_chars), len(curr_chars)) + lower = max(min_chars, 1) + if upper < lower: + return 0 + for size in range(upper, lower - 1, -1): + left = "".join(ch.lower() for ch in prev_chars[-size:]) + right = "".join(ch.lower() for ch in curr_chars[:size]) + if left == right: + return _char_count_to_index(current, size) + return 0 + + +class RealtimeTranscriptAssembler: + def __init__(self) -> None: + self._merged = "" + + def push(self, text: str) -> None: + cleaned = _normalize_transcript_chunk_text(text) + if not cleaned: + return + if not self._merged: + self._merged = cleaned + return + delta_start = _dedupe_overlap_word_boundary(self._merged, cleaned) + if delta_start == 0: + delta_start = _dedupe_overlap_char_boundary(self._merged, cleaned) + if delta_start >= len(cleaned): + return + delta = cleaned[delta_start:].lstrip() + if not delta: + return + self._merged = _append_with_spacing(self._merged, delta) + + def text(self) -> str: + return self._merged.strip() + + +def _convert_audio( + audio_bytes: bytes, + fmt: str, + sample_rate: int, +) -> Optional[np.ndarray]: + try: + if fmt == "pcm": + audio = np.frombuffer(audio_bytes, dtype=np.int16).astype(np.float32) / 32768.0 + elif fmt == "wav": + try: + audio, sr = sf.read(io.BytesIO(audio_bytes)) + audio = np.asarray(audio, dtype=np.float32) + if sr != sample_rate: + logger.warning("WAV sample rate mismatch: payload=%s actual=%s", sample_rate, sr) + sample_rate = int(sr) + except Exception: + if len(audio_bytes) > 44: + audio_bytes = audio_bytes[44:] + audio = np.frombuffer(audio_bytes, dtype=np.int16).astype(np.float32) / 32768.0 + else: + raise ValueError(f"Unsupported audio format: {fmt}") + + if getattr(audio, "ndim", 1) > 1: + audio = np.mean(audio, axis=1) + + if sample_rate != 16000: + import scipy.signal + + num = int(len(audio) * 16000 / sample_rate) + audio = scipy.signal.resample(audio, num) + if isinstance(audio, tuple): + audio = audio[0] + + return np.asarray(audio, dtype=np.float32) + except Exception as exc: + logger.error("Audio conversion failed: %s", exc) + return None + + +class ConnectionState(IntEnum): + READY = 1 + STARTED = 2 + STREAMING = 3 + + +@dataclass +class ConnectionContext: + state: ConnectionState = ConnectionState.READY + params: Dict[str, Any] = field(default_factory=dict) + engine_lease: Optional[RuntimeEngineLease] = None + engine: Optional[Qwen3ASREngine] = None + pre_roll_audio: np.ndarray = field(default_factory=lambda: np.array([], dtype=np.float32)) + # segment_audio_buffer 保留“当前断句以来”的完整音频，用于断句时提取声纹。 + segment_audio_buffer: np.ndarray = field(default_factory=lambda: np.array([], dtype=np.float32)) + # stream_window_buffer 保留最近窗口，供 realtime partial 在 native stream 失效时兜底重转写。 + stream_window_buffer: np.ndarray = field(default_factory=lambda: np.array([], dtype=np.float32)) + # realtime_stream_state 只负责低延迟 partial，不参与最终 segment 定稿。 + realtime_stream_state: Optional[Qwen3StreamingState] = None + sentence_active: bool = False + silence_samples: int = 0 + total_samples: int = 0 + confirmed_segments: List[Dict[str, Any]] = field(default_factory=list) + speaker_records: List[Dict[str, Any]] = field(default_factory=list) + speaker_display_map: Dict[int, str] = field(default_factory=dict) + speaker_display_profiles: Dict[str, np.ndarray] = field(default_factory=dict) + speaker_display_counts: Dict[str, int] = field(default_factory=dict) + next_speaker_display_id: int = 1 + timeline_cursor_ms: int = 0 + segment_index: int = 0 + last_partial_text: str = "" + last_partial_language: str = "" + last_partial_chunk_id: int = 0 + last_partial_raw_text: str = "" + last_partial_display_text: str = "" + stable_partial_prefix: str = "" + segment_observed_text: str = "" + segment_observed_language: str = "" + best_partial_text: str = "" + best_partial_language: str = "" + last_partial_decode_samples: int = 0 + partial_stable_rounds: int = 0 + send_lock: asyncio.Lock = field(default_factory=asyncio.Lock) + background_tasks: set[asyncio.Task[Any]] = field(default_factory=set) + speaker_job_queue: asyncio.Queue[Any] = field(default_factory=asyncio.Queue) + speaker_worker_task: Optional[asyncio.Task[Any]] = None + speaker_worker_socket_id: Optional[int] = None + MAX_BUFFER = 960000 + DEFAULT_MAX_PARTIAL_TEXT_CHARS = 1200 + + +@dataclass +class SessionEntry: + session_id: str + ctx: ConnectionContext + attached: bool = False + detached_at: Optional[float] = None + closed: bool = False + + +class Qwen3ASRService: + _MAX_SPEAKER_HISTORY_RECORDS = 64 + _MAX_SPEAKER_MATCH_CONTEXT = 24 + _MAX_SPEAKER_FINAL_RECLUSTER = 48 + _MAX_SPEAKER_REALTIME_RECLUSTER = 12 + _MIN_REALTIME_RECLUSTER_SEGMENTS = 5 + _MIN_HARD_SEGMENT_SEC = 12.0 + _SEGMENT_EVENT_TEXT_TAIL_CHARS = 2000 + _SEGMENT_EVENT_TAIL_COUNT = 8 + _LIGHTWEIGHT_RECENT_SENTENCE_COUNT = 3 + + def __init__(self) -> None: + self._sessions: Dict[str, SessionEntry] = {} + self._session_lock = asyncio.Lock() + + async def _cleanup_expired_sessions(self) -> None: + ttl_sec = max(int(getattr(settings, "REALTIME_SESSION_RESUME_TTL_SEC", 120) or 0), 0) + if ttl_sec <= 0: + expired_ids = [session_id for session_id, entry in self._sessions.items() if not entry.attached] + else: + now = time.monotonic() + expired_ids = [ + session_id + for session_id, entry in self._sessions.items() + if not entry.attached + and ( + entry.closed + or entry.detached_at is None + or now - entry.detached_at > ttl_sec + ) + ] + + for session_id in expired_ids: + entry = self._sessions.pop(session_id, None) + if entry is not None: + await self._release_session_resources(entry.ctx) + + async def _release_session_resources(self, ctx: ConnectionContext) -> None: + if ctx.speaker_worker_task is not None and not ctx.speaker_worker_task.done(): + await ctx.speaker_job_queue.put(None) + if ctx.background_tasks: + await asyncio.gather(*list(ctx.background_tasks), return_exceptions=True) + if ctx.engine_lease is not None: + await ctx.engine_lease.close() + ctx.engine_lease = None + ctx.engine = None + ctx.realtime_stream_state = None + + async def _detach_session( + self, + session_id: str, + *, + keep_for_resume: bool, + ) -> None: + async with self._session_lock: + entry = self._sessions.get(session_id) + if entry is None: + return + entry.attached = False + entry.detached_at = time.monotonic() + entry.closed = not keep_for_resume + self._stop_speaker_worker(entry.ctx) + await self._cleanup_expired_sessions() + + async def _acquire_session( + self, + session_id: str, + ) -> tuple[ConnectionContext, bool]: + async with self._session_lock: + await self._cleanup_expired_sessions() + entry = self._sessions.get(session_id) + if entry is not None and not entry.attached and not entry.closed: + entry.attached = True + entry.detached_at = None + return entry.ctx, True + + ctx = ConnectionContext() + self._sessions[session_id] = SessionEntry( + session_id=session_id, + ctx=ctx, + attached=True, + detached_at=None, + closed=False, + ) + return ctx, False + + @staticmethod + def _tencent_speaker_id(value: Any) -> int: + if value is None: + return -1 + if isinstance(value, bool): + return int(value) + if isinstance(value, int): + return value + text = str(value).strip() + if not text: + return -1 + if re.fullmatch(r"-?\d+", text): + return int(text) + match = re.fullmatch(r"Speaker(\d+)", text) + if match: + return max(int(match.group(1)) - 1, 0) + return -1 + + def _build_tencent_sentence( + self, + payload: Dict[str, Any], + *, + sentence_type: int, + sentence_text: Optional[str] = None, + ) -> Dict[str, Any]: + speaker_id = self._tencent_speaker_id(payload.get("speaker_id")) + if payload.get("speaker_pending"): + speaker_id = -1 + sentence = { + "sentence_id": int(payload.get("index", 0)), + "sentence_type": int(sentence_type), + "speaker_id": speaker_id, + "start_time": int(payload.get("start_ms", 0)), + "end_time": int(payload.get("end_ms", 0)), + "sentence": str(sentence_text if sentence_text is not None else payload.get("text", "")), + } + # Tencent-compatible core fields first; project-specific fields remain minimal. + for key in ( + "speaker_name", + "user_id", + ): + if key in payload: + sentence[key] = payload.get(key) + return sentence + + async def _send_json_safe( + self, + websocket: WebSocket, + ctx: ConnectionContext, + payload: Dict[str, Any], + ) -> None: + async with ctx.send_lock: + await websocket.send_json(payload) + + def _track_background_task( + self, + ctx: ConnectionContext, + task: asyncio.Task[Any], + ) -> None: + ctx.background_tasks.add(task) + task.add_done_callback(ctx.background_tasks.discard) + + def _ensure_speaker_worker( + self, + websocket: WebSocket, + ctx: ConnectionContext, + task_id: str, + ) -> None: + task = ctx.speaker_worker_task + socket_id = id(websocket) + if ( + task is not None + and not task.done() + and ctx.speaker_worker_socket_id == socket_id + ): + return + worker = asyncio.create_task( + self._speaker_worker_loop( + websocket, + ctx, + task_id, + ) + ) + ctx.speaker_worker_task = worker + ctx.speaker_worker_socket_id = socket_id + self._track_background_task(ctx, worker) + + def _stop_speaker_worker(self, ctx: ConnectionContext) -> None: + task = ctx.speaker_worker_task + if task is not None and not task.done(): + task.cancel() + ctx.speaker_worker_task = None + ctx.speaker_worker_socket_id = None + + async def _speaker_worker_loop( + self, + websocket: WebSocket, + ctx: ConnectionContext, + task_id: str, + ) -> None: + while True: + job = await ctx.speaker_job_queue.get() + try: + if job is None: + return + await self._resolve_and_emit_segment_speaker( + websocket, + ctx, + task_id, + segment_index=int(job["segment_index"]), + audio=np.asarray(job["audio"], dtype=np.float32), + reason=str(job["reason"]), + segment_start_ms=int(job["segment_start_ms"]), + segment_end_ms=int(job["segment_end_ms"]), + ) + finally: + ctx.speaker_job_queue.task_done() + + async def _emit_speaker_update( + self, + websocket: WebSocket, + ctx: ConnectionContext, + task_id: str, + segment_index: int, + ) -> None: + segment = next( + (item for item in ctx.confirmed_segments if int(item.get("index", -1)) == int(segment_index)), + None, + ) + if segment is None: + return + sentence_payload = self._build_tencent_sentence(segment, sentence_type=1) + await self._send_json_safe( + websocket, + ctx, + { + "type": "sentences", + "code": 0, + "voice_id": task_id, + "final": 0, + "result": { + "slice_type": 2, + "index": int(segment_index), + "voice_text_str": str(segment.get("text") or ""), + }, + "sentences": [sentence_payload], + }, + ) + + @staticmethod + def _public_speaker_view(payload: Dict[str, Any]) -> tuple[Any, Any, Any]: + return ( + payload.get("speaker_id"), + payload.get("speaker_name"), + payload.get("user_id"), + ) + + def _create_display_speaker_id( + self, + ctx: ConnectionContext, + ) -> str: + display_id = f"Speaker{ctx.next_speaker_display_id:02d}" + ctx.next_speaker_display_id += 1 + return display_id + + def _match_display_speaker_id( + self, + ctx: ConnectionContext, + *, + cluster_index: Optional[int], + embedding: Optional[np.ndarray], + ) -> Optional[str]: + if embedding is None: + return None + + normalized = np.asarray(embedding, dtype=np.float32) + norm = float(np.linalg.norm(normalized)) + if norm <= 0: + return None + normalized = normalized / norm + + preferred_display_id: Optional[str] = None + if cluster_index is not None: + mapped = ctx.speaker_display_map.get(int(cluster_index)) + if mapped: + profile = ctx.speaker_display_profiles.get(mapped) + if profile is not None: + preferred_score = float(np.dot(normalized, profile)) + if preferred_score >= settings.REALTIME_SPEAKER_CONFIRM_THRESHOLD: + preferred_display_id = mapped + # Do not aggressively reuse a generic display id across unrelated + # segments when there is no stable cluster mapping yet; that easily + # collapses multiple real speakers into Speaker01. + + if preferred_display_id is None: + preferred_display_id = self._create_display_speaker_id(ctx) + + count = int(ctx.speaker_display_counts.get(preferred_display_id, 0)) + previous = ctx.speaker_display_profiles.get(preferred_display_id) + if previous is None or count <= 0: + updated = normalized + count = 1 + else: + updated = previous * float(count) + normalized + updated_norm = float(np.linalg.norm(updated)) + if updated_norm > 0: + updated = updated / updated_norm + count += 1 + + ctx.speaker_display_profiles[preferred_display_id] = np.asarray(updated, dtype=np.float32) + ctx.speaker_display_counts[preferred_display_id] = count + if cluster_index is not None: + ctx.speaker_display_map[int(cluster_index)] = preferred_display_id + return preferred_display_id + + @staticmethod + def _safe_int(value: Any, default: Optional[int] = None) -> Optional[int]: + try: + if value is None: + return default + return int(value) + except (TypeError, ValueError): + return default + + def _public_payload(self, payload: Optional[Dict[str, Any]]) -> Optional[Dict[str, Any]]: + if payload is None: + return None + return { + key: value + for key, value in payload.items() + if not str(key).startswith("_") + } + + def _is_stable_speaker_info( + self, + speaker_info: Optional[Dict[str, Any]], + ) -> bool: + if not speaker_info: + return False + if speaker_info.get("registry_speaker_id") or speaker_info.get("user_id"): + return True + strategy = str(speaker_info.get("speaker_strategy") or "") + if strategy == "registry_match": + return True + if strategy in {"recent_named_attach", "recent_named_inherit"}: + confidence = float(speaker_info.get("speaker_confidence") or 0.0) + return confidence >= 0.7 + if strategy == "embedding_match" and speaker_info.get("_matched_existing"): + confidence = float(speaker_info.get("speaker_confidence") or 0.0) + return confidence >= 0.62 + if strategy == "embedding_match": + confidence = float(speaker_info.get("speaker_confidence") or 0.0) + duration_ms = float(speaker_info.get("_segment_duration_ms") or 0.0) + if duration_ms >= 8000: + return confidence >= 0.62 + if strategy != "embedding_match": + return False + confidence = float(speaker_info.get("speaker_confidence") or 0.0) + threshold = max( + float(getattr(settings, "REALTIME_SPEAKER_CONFIRM_THRESHOLD", 0.62) or 0.62), + 0.68, + ) + return confidence >= threshold + + def _record_segment_speaker( + self, + ctx: ConnectionContext, + *, + segment_index: int, + segment_start_ms: int, + segment_end_ms: int, + speaker_info: Optional[Dict[str, Any]], + ) -> None: + if not speaker_info: + return + embedding = speaker_info.get("_embedding") + if embedding is None: + return + ctx.speaker_records.append( + { + "index": segment_index, + "start_ms": int(segment_start_ms), + "end_ms": int(segment_end_ms), + "embedding": np.asarray(embedding, dtype=np.float32), + "embeddings": [ + np.asarray(item, dtype=np.float32) + for item in (speaker_info.get("_chunk_embeddings") or [embedding]) + ], + "chunks": [ + { + "start_ms": int(item.get("start_ms", segment_start_ms)), + "end_ms": int(item.get("end_ms", segment_end_ms)), + "embedding": np.asarray(item["embedding"], dtype=np.float32), + } + for item in (speaker_info.get("_chunks") or []) + if item.get("embedding") is not None + ], + "speaker_id": speaker_info.get("speaker_id"), + "cluster_index": speaker_info.get("_cluster_index"), + "speaker_name": speaker_info.get("speaker_name"), + "registry_speaker_id": speaker_info.get("registry_speaker_id"), + "user_id": speaker_info.get("user_id"), + } + ) + if len(ctx.speaker_records) > self._MAX_SPEAKER_HISTORY_RECORDS: + ctx.speaker_records = ctx.speaker_records[-self._MAX_SPEAKER_HISTORY_RECORDS:] + + def _inherit_recent_anonymous_speaker( + self, + ctx: ConnectionContext, + speaker_info: Dict[str, Any], + ) -> Optional[Dict[str, Any]]: + if speaker_info.get("registry_speaker_id") or speaker_info.get("user_id"): + return None + if speaker_info.get("_matched_existing"): + return None + duration_ms = int(speaker_info.get("_segment_duration_ms") or 0) + if duration_ms < 8000: + return None + if not ctx.speaker_records: + return None + last_record = ctx.speaker_records[-1] + last_speaker_id = str(last_record.get("speaker_id") or "").strip() + if not re.fullmatch(r"Speaker\d+", last_speaker_id): + return None + inherited = dict(speaker_info) + inherited["speaker_id"] = last_speaker_id + inherited["speaker_name"] = str(last_record.get("speaker_name") or last_speaker_id) + inherited["_cluster_index"] = last_record.get("cluster_index") + inherited["_matched_existing"] = True + inherited["speaker_confidence"] = max(float(inherited.get("speaker_confidence") or 0.0), 0.66) + inherited["speaker_strategy"] = "recent_inherit" + return inherited + + def _inherit_recent_named_speaker( + self, + ctx: ConnectionContext, + speaker_info: Dict[str, Any], + ) -> Optional[Dict[str, Any]]: + if speaker_info.get("registry_speaker_id") or speaker_info.get("user_id"): + return speaker_info + if speaker_info.get("_matched_existing"): + return None + duration_ms = int(speaker_info.get("_segment_duration_ms") or 0) + if duration_ms < 4500: + return None + if not ctx.speaker_records: + return None + last_record = ctx.speaker_records[-1] + if not last_record.get("registry_speaker_id") and not last_record.get("user_id"): + return None + inherited = dict(speaker_info) + inherited["speaker_id"] = last_record.get("registry_speaker_id") or last_record.get("speaker_id") + inherited["speaker_name"] = last_record.get("speaker_name") or inherited["speaker_id"] + inherited["user_id"] = last_record.get("user_id") + inherited["registry_speaker_id"] = last_record.get("registry_speaker_id") + inherited["_cluster_index"] = last_record.get("cluster_index") + inherited["_matched_existing"] = True + inherited["speaker_confidence"] = max(float(inherited.get("speaker_confidence") or 0.0), 0.72) + inherited["speaker_strategy"] = "recent_named_inherit" + return inherited + + def _speaker_match_records( + self, + ctx: ConnectionContext, + ) -> List[Dict[str, Any]]: + if len(ctx.speaker_records) <= self._MAX_SPEAKER_MATCH_CONTEXT: + return ctx.speaker_records + return ctx.speaker_records[-self._MAX_SPEAKER_MATCH_CONTEXT:] + + def _recluster_confirmed_segments( + self, + ctx: ConnectionContext, + *, + max_records: Optional[int] = None, + only_stable_updates: bool = False, + only_pending_updates: bool = False, + ) -> List[Dict[str, Any]]: + if len(ctx.speaker_records) < 2: + return [] + + record_limit = max_records or self._MAX_SPEAKER_FINAL_RECLUSTER + records = ( + ctx.speaker_records + if len(ctx.speaker_records) <= record_limit + else ctx.speaker_records[-record_limit:] + ) + clusters, assignments = self._cluster_speaker_records(records) + if not clusters: + return [] + + cluster_labels: dict[int, Dict[str, Any]] = {} + unknown_display_index = 1 + for idx, cluster in enumerate(clusters): + named_records = [ + item for item in cluster["record_refs"] + if item.get("registry_speaker_id") + or item.get("user_id") + or ( + item.get("speaker_name") + and not re.fullmatch(r"Speaker\d+", str(item.get("speaker_name")).strip()) + ) + ] + if named_records: + preferred = max( + named_records, + key=lambda item: ( + 1 if item.get("registry_speaker_id") else 0, + 1 if item.get("user_id") else 0, + 1 if item.get("speaker_name") else 0, + ), + ) + speaker_id = preferred.get("registry_speaker_id") or preferred.get("speaker_id") + speaker_name = preferred.get("speaker_name") or speaker_id or f"Speaker{unknown_display_index:02d}" + cluster_labels[idx] = { + "speaker_id": speaker_id or f"Speaker{unknown_display_index:02d}", + "speaker_name": speaker_name, + "user_id": preferred.get("user_id"), + "registry_speaker_id": preferred.get("registry_speaker_id"), + "speaker_strategy": "final_recluster", + "speaker_confidence": 1.0, + } + else: + speaker_id = f"Speaker{unknown_display_index:02d}" + cluster_labels[idx] = { + "speaker_id": speaker_id, + "speaker_name": speaker_id, + "user_id": None, + "speaker_strategy": "final_recluster", + "speaker_confidence": 1.0, + } + unknown_display_index += 1 + + updates: List[Dict[str, Any]] = [] + for record, cluster_idx in zip(records, assignments): + segment_index = int(record["index"]) + segment = next( + (item for item in ctx.confirmed_segments if int(item.get("index", -1)) == segment_index), + None, + ) + if segment is None: + continue + if only_pending_updates and not segment.get("speaker_pending"): + continue + new_info = cluster_labels[cluster_idx] + if only_stable_updates: + has_named_identity = bool(new_info.get("registry_speaker_id") or new_info.get("user_id")) + generic_name = str(new_info.get("speaker_name") or "").strip() + if not has_named_identity and re.fullmatch(r"Speaker\d+", generic_name): + if not segment.get("speaker_pending"): + continue + changed = any(segment.get(key) != value for key, value in new_info.items()) + if not changed and not segment.get("speaker_pending"): + continue + segment.update(new_info) + segment.pop("speaker_pending", None) + updates.append( + { + "segment_index": segment_index, + "segment": self._public_payload(segment), + } + ) + + return updates + + def _maybe_recluster_recent_segments( + self, + ctx: ConnectionContext, + ) -> List[Dict[str, Any]]: + if len(ctx.speaker_records) < self._MIN_REALTIME_RECLUSTER_SEGMENTS: + return [] + return self._recluster_confirmed_segments( + ctx, + max_records=self._MAX_SPEAKER_REALTIME_RECLUSTER, + only_stable_updates=True, + only_pending_updates=True, + ) + + def _build_sv_chunks(self, audio: np.ndarray) -> List[np.ndarray]: + return get_realtime_speaker_clusterer().build_sv_chunks(audio) + + async def _extract_chunk_embeddings( + self, + audio: np.ndarray, + ) -> List[np.ndarray]: + return await get_realtime_speaker_clusterer().extract_chunk_embeddings(audio) + + def _cluster_speaker_records( + self, + records: List[Dict[str, Any]], + ) -> tuple[List[Dict[str, Any]], List[int]]: + return get_realtime_speaker_clusterer().cluster_records(records) + + async def _ensure_engine(self, ctx: ConnectionContext) -> Qwen3ASREngine: + if ctx.engine is not None: + return ctx.engine + + runtime_router = get_runtime_router() + model = validate_realtime_model_id("qwen3-asr") + logger.info("Using Qwen3-ASR model: %s", model) + + ctx.engine_lease = await runtime_router.acquire_engine(model) + engine = ctx.engine_lease.engine + if not isinstance(engine, Qwen3ASREngine): + raise RuntimeError("Current model is not Qwen3-ASR") + if not engine.supports_realtime: + raise RuntimeError( + f"Current device {engine.device} does not support Qwen3-ASR realtime streaming; " + "only CUDA vLLM and CPU Rust paths are supported" + ) + + ctx.engine = engine + return engine + + def _has_voice(self, audio: np.ndarray) -> bool: + if audio.size == 0: + return False + rms = float(np.sqrt(np.mean(audio**2))) + peak = float(np.max(np.abs(audio))) + return rms >= 0.0045 or (rms >= 0.0025 and peak >= 0.08) + + def _get_dynamic_silence_threshold_samples(self, ctx: ConnectionContext) -> int: + silence_ms = max(int(ctx.params.get("silence_duration_ms", 800) or 800), 100) + return int(silence_ms * 16) + + def _normalize_output_text( + self, + text: str, + ctx: ConnectionContext, + *, + enable_itn: Optional[bool] = None, + ) -> str: + raw = str(text or "").strip() + if "" in raw: + _prefix, raw = raw.split("", 1) + raw = re.sub(r"^\s*language\s+[A-Za-z][A-Za-z\s-]*\s*", "", raw, flags=re.IGNORECASE) + if enable_itn is None: + enable_itn = bool(ctx.params.get("enable_inverse_text_normalization", True)) + return normalize_asr_text( + raw, + enable_itn=enable_itn, + ) + + def _normalize_output_language(self, language: Optional[str], ctx: ConnectionContext) -> str: + raw = str(language or "").strip() + if not raw or raw.lower() == "none": + return "" + + raw = re.sub(r"^[^\w]+", "", raw) + match = re.search(r"language\s+([A-Za-z][A-Za-z\s-]*)$", raw, re.IGNORECASE) + if match: + return match.group(1).strip() + return raw + + def _infer_text_language(self, text: str) -> str: + has_cjk = any("\u4e00" <= ch <= "\u9fff" for ch in text) + has_thai = any("\u0e00" <= ch <= "\u0e7f" for ch in text) + has_latin = any("LATIN" in unicodedata.name(ch, "") for ch in text if ch.isalpha()) + if has_thai: + return "Thai" + if has_cjk: + return "Chinese" + if has_latin: + return "English" + return "" + + def _get_session_dominant_language(self, ctx: ConnectionContext) -> str: + counts: Dict[str, int] = {} + for segment in ctx.confirmed_segments: + language = str(segment.get("language") or "").strip() + if not language: + continue + counts[language] = counts.get(language, 0) + 1 + if not counts: + return "" + language, count = max(counts.items(), key=lambda item: item[1]) + return language if count >= 2 else "" + + def _pre_roll_samples(self, ctx: ConnectionContext) -> int: + pre_roll_ms = max(int(ctx.params.get("pre_roll_ms", 240) or 0), 0) + return int(pre_roll_ms * 16) + + def _min_partial_samples(self, ctx: ConnectionContext) -> int: + return int( + max( + float( + ctx.params.get( + "min_partial_sec", + settings.REALTIME_MIN_PARTIAL_SEC, + ) + or settings.REALTIME_MIN_PARTIAL_SEC + ), + 0.25, + ) * 16000 + ) + + def _partial_emit_interval_samples(self, ctx: ConnectionContext) -> int: + """ + Control how often we surface partial text to the client. + + When native Qwen streaming is healthy we can emit much more frequently, + because decoding is already happening incrementally in memory. + If native streaming falls back to window retranscription, keep the + cadence a little slower to avoid excessive temp-file retranscribes. + """ + interval_sec = max( + float( + getattr( + settings, + "REALTIME_PARTIAL_EMIT_INTERVAL_SEC", + 0.25, + ) or 0.25 + ), + 0.12, + ) + if ctx.params.get("enable_native_partial_stream") is False: + interval_sec = max(interval_sec, max(self._stream_chunk_size_sec(ctx), 0.9)) + elif ctx.realtime_stream_state is None: + interval_sec = max(interval_sec, max(min(self._stream_chunk_size_sec(ctx), 0.8), 0.55)) + return int(interval_sec * 16000) + + def _stream_window_samples(self) -> int: + window_sec = max( + float(getattr(settings, "REALTIME_STREAM_WINDOW_SEC", 8.0) or 8.0), + 1.0, + ) + return int(window_sec * 16000) + + def _partial_window_samples(self, ctx: ConnectionContext) -> int: + window_sec = max( + float( + ctx.params.get( + "partial_window_sec", + getattr(settings, "REALTIME_PARTIAL_WINDOW_SEC", 8.0), + ) + or getattr(settings, "REALTIME_PARTIAL_WINDOW_SEC", 8.0) + ), + 1.0, + ) + return int(window_sec * 16000) + + def _max_sentence_count(self, ctx: ConnectionContext) -> int: + return max(int(ctx.params.get("max_sentence_count", 8) or 8), 1) + + def _max_partial_text_chars(self, ctx: ConnectionContext) -> int: + return max( + int( + ctx.params.get( + "max_partial_text_chars", + ctx.DEFAULT_MAX_PARTIAL_TEXT_CHARS, + ) + or ctx.DEFAULT_MAX_PARTIAL_TEXT_CHARS + ), + 200, + ) + + def _max_segment_samples(self, ctx: ConnectionContext) -> int: + configured_sec = float( + ctx.params.get( + "hard_limit_sec", + ctx.params.get("max_segment_sec", settings.REALTIME_FINAL_SEGMENT_HARD_LIMIT_SEC), + ) + or 0.0 + ) + if configured_sec <= 0: + return 0 + return int(max(configured_sec, self._MIN_HARD_SEGMENT_SEC, 1.0) * 16000) + + def _soft_segment_limit_samples(self, ctx: ConnectionContext) -> int: + limit_sec = float( + ctx.params.get( + "soft_limit_sec", + getattr(settings, "REALTIME_FINAL_SEGMENT_SOFT_LIMIT_SEC", 8.0), + ) + or 0.0 + ) + if limit_sec <= 0: + return 0 + return int(max(limit_sec, 1.0) * 16000) + + def _clip_partial_text( + self, + text: str, + ctx: ConnectionContext, + ) -> str: + limit = self._max_partial_text_chars(ctx) + if len(text) <= limit: + return text + return text[-limit:] + + def _enable_realtime_vad_split(self, ctx: ConnectionContext) -> bool: + return bool(ctx.params.get("enable_realtime_vad_split", False)) + + def _enable_realtime_longform(self, ctx: ConnectionContext) -> bool: + return bool(ctx.params.get("enable_realtime_longform", False)) + + def _enable_realtime_refine(self, ctx: ConnectionContext) -> bool: + return False + + @staticmethod + def _sentence_count(text: str) -> int: + if not text: + return 0 + return len(re.findall(r"[。！？?!]", text)) + + @staticmethod + def _ends_with_sentence_punctuation(text: str) -> bool: + stripped = str(text or "").rstrip() + return bool(stripped) and stripped[-1] in "。！？?!" + + def _last_sentence_body_len(self, text: str) -> int: + stripped = str(text or "").rstrip() + if not stripped: + return 0 + if self._ends_with_sentence_punctuation(stripped): + stripped = stripped[:-1].rstrip() + last_boundary = max(stripped.rfind(ch) for ch in "。！？?!") + body = stripped[last_boundary + 1:] if last_boundary >= 0 else stripped + return sum(1 for ch in body if ch.isalnum() or "\u4e00" <= ch <= "\u9fff") + + def _should_commit_complete_sentence(self, ctx: ConnectionContext, text: str) -> bool: + candidate = self._sanitize_candidate_text(text) + if not candidate or not self._ends_with_sentence_punctuation(candidate): + return False + + duration_sec = float(ctx.segment_audio_buffer.size) / 16000.0 + min_sec = max( + float( + ctx.params.get( + "complete_sentence_commit_sec", + max(float(ctx.params.get("force_stable_segment_sec", 8.0) or 8.0), 12.0), + ) + or 12.0 + ), + 6.0, + ) + if duration_sec < min_sec: + return False + + min_chars = max( + int( + ctx.params.get( + "complete_sentence_commit_min_chars", + max(int(ctx.params.get("force_stable_min_chars", 24) or 24), 48), + ) + or 48 + ), + 12, + ) + if len(candidate) < min_chars: + return False + + # Avoid committing on tiny unstable tails such as "可。" or "啊。". + return self._last_sentence_body_len(candidate) >= 6 + + def _stream_chunk_size_sec(self, ctx: ConnectionContext) -> float: + return max( + float( + ctx.params.get( + "chunk_size_sec", + settings.REALTIME_STREAM_CHUNK_SEC, + ) + or settings.REALTIME_STREAM_CHUNK_SEC + ), + 0.4, + ) + + def _stream_unfixed_chunk_num(self, ctx: ConnectionContext) -> int: + return max( + int( + ctx.params.get( + "unfixed_chunk_num", + settings.REALTIME_STREAM_MAX_PENDING_CHUNKS, + ) + or settings.REALTIME_STREAM_MAX_PENDING_CHUNKS + ), + 1, + ) + + def _stream_unfixed_token_num(self, ctx: ConnectionContext) -> int: + return max(int(ctx.params.get("unfixed_token_num", 5) or 5), 1) + + def _should_use_native_partial_stream( + self, + ctx: ConnectionContext, + engine: Qwen3ASREngine, + ) -> bool: + _ = engine + configured = ctx.params.get("enable_native_partial_stream") + if configured is not None: + return bool(configured) + # 默认使用整段周期重转写；仅在客户端明确要求时启用底层 native partial stream。 + return False + + async def _init_realtime_stream_state( + self, + ctx: ConnectionContext, + engine: Qwen3ASREngine, + ) -> None: + if not self._should_use_native_partial_stream(ctx, engine): + return + if ctx.realtime_stream_state is not None: + return + + try: + # Native stream 只用来拿低延迟 partial，最终句子仍走整段重转写定稿。 + ctx.realtime_stream_state = await run_sync( + engine.init_streaming_state, + ctx.params.get("context", ""), + ctx.params.get("language"), + chunk_size_sec=self._stream_chunk_size_sec(ctx), + unfixed_chunk_num=self._stream_unfixed_chunk_num(ctx), + unfixed_token_num=self._stream_unfixed_token_num(ctx), + max_new_tokens=48, + ) + except Exception as exc: + logger.debug("Init realtime partial stream failed: %s", exc) + ctx.realtime_stream_state = None + + async def _push_realtime_stream_audio( + self, + ctx: ConnectionContext, + engine: Qwen3ASREngine, + audio: np.ndarray, + ) -> None: + if audio.size == 0: + return + if not self._should_use_native_partial_stream(ctx, engine): + return + + if ctx.realtime_stream_state is None: + await self._init_realtime_stream_state(ctx, engine) + if ctx.realtime_stream_state is None: + return + + try: + ctx.realtime_stream_state = await run_sync( + engine.streaming_transcribe, + audio, + ctx.realtime_stream_state, + ) + except Exception as exc: + logger.debug("Realtime partial stream push failed: %s", exc) + ctx.realtime_stream_state = None + + async def _finish_realtime_stream_text( + self, + ctx: ConnectionContext, + engine: Qwen3ASREngine, + ) -> tuple[str, str]: + if not self._should_use_native_partial_stream(ctx, engine): + return "", "" + if ctx.realtime_stream_state is None: + return "", "" + + try: + ctx.realtime_stream_state = await run_sync( + engine.finish_streaming_transcribe, + ctx.realtime_stream_state, + ) + text = self._normalize_output_text( + str(ctx.realtime_stream_state.last_text or ""), + ctx, + enable_itn=False, + ) + language = self._normalize_output_language( + getattr(ctx.realtime_stream_state, "last_language", ""), + ctx, + ) + return text, language + except Exception as exc: + logger.debug("Finalize realtime partial stream failed: %s", exc) + return "", "" + finally: + ctx.realtime_stream_state = None + + async def _decode_turn_partial_text( + self, + engine: Qwen3ASREngine, + ctx: ConnectionContext, + ) -> tuple[str, str]: + if self._should_use_native_partial_stream(ctx, engine) and ctx.realtime_stream_state is not None: + stream_text = self._normalize_output_text( + str(ctx.realtime_stream_state.last_text or ""), + ctx, + enable_itn=False, + ) + stream_language = self._normalize_output_language( + getattr(ctx.realtime_stream_state, "last_language", ""), + ctx, + ) + if stream_text.strip(): + return stream_text, stream_language + + # 流式文本为空时回退到当前音频窗口重转写。 + partial_audio = np.asarray( + ctx.stream_window_buffer if ctx.stream_window_buffer.size > 0 else ctx.segment_audio_buffer, + dtype=np.float32, + ) + fallback = await self._transcribe_audio_text( + engine, + ctx, + partial_audio, + ) + return fallback, "" + + def _append_pre_roll(self, ctx: ConnectionContext, audio: np.ndarray) -> None: + if audio.size == 0: + return + ctx.pre_roll_audio = np.concatenate([ctx.pre_roll_audio, audio]) + max_samples = self._pre_roll_samples(ctx) + if max_samples <= 0: + ctx.pre_roll_audio = np.array([], dtype=np.float32) + elif ctx.pre_roll_audio.size > max_samples: + ctx.pre_roll_audio = ctx.pre_roll_audio[-max_samples:] + + def _start_turn(self, ctx: ConnectionContext, audio: np.ndarray) -> None: + parts: List[np.ndarray] = [] + if ctx.pre_roll_audio.size > 0: + parts.append(np.asarray(ctx.pre_roll_audio, dtype=np.float32)) + parts.append(np.asarray(audio, dtype=np.float32)) + ctx.segment_audio_buffer = ( + np.concatenate(parts) if len(parts) > 1 else np.asarray(parts[0], dtype=np.float32) + ) + ctx.pre_roll_audio = np.array([], dtype=np.float32) + window_samples = self._stream_window_samples() + ctx.stream_window_buffer = np.asarray( + ctx.segment_audio_buffer[-window_samples:], + dtype=np.float32, + ) + partial_window_samples = self._partial_window_samples(ctx) + if ctx.stream_window_buffer.size > partial_window_samples: + ctx.stream_window_buffer = ctx.stream_window_buffer[-partial_window_samples:] + ctx.sentence_active = True + ctx.silence_samples = 0 + ctx.total_samples = int(ctx.segment_audio_buffer.size) + ctx.last_partial_decode_samples = 0 + self._reset_partial_state(ctx) + + def _append_turn_audio( + self, + ctx: ConnectionContext, + audio: np.ndarray, + *, + has_voice: bool, + ) -> None: + ctx.segment_audio_buffer = np.concatenate([ctx.segment_audio_buffer, audio]) + ctx.stream_window_buffer = np.concatenate([ctx.stream_window_buffer, audio]) + window_samples = self._partial_window_samples(ctx) + if ctx.stream_window_buffer.size > window_samples: + ctx.stream_window_buffer = ctx.stream_window_buffer[-window_samples:] + ctx.total_samples = int(ctx.segment_audio_buffer.size) + if has_voice: + ctx.silence_samples = 0 + else: + ctx.silence_samples += int(audio.size) + + def _should_decode_turn_partial(self, ctx: ConnectionContext) -> bool: + if not ctx.sentence_active: + return False + current_samples = int(ctx.segment_audio_buffer.size) + if current_samples < self._min_partial_samples(ctx): + return False + return (current_samples - ctx.last_partial_decode_samples) >= self._partial_emit_interval_samples(ctx) + + def _reset_partial_state(self, ctx: ConnectionContext) -> None: + ctx.last_partial_text = "" + ctx.last_partial_language = "" + ctx.last_partial_chunk_id = 0 + ctx.last_partial_raw_text = "" + ctx.last_partial_display_text = "" + ctx.stable_partial_prefix = "" + ctx.segment_observed_text = "" + ctx.segment_observed_language = "" + ctx.best_partial_text = "" + ctx.best_partial_language = "" + ctx.last_partial_decode_samples = 0 + ctx.partial_stable_rounds = 0 + ctx.realtime_stream_state = None + + def _update_partial_stability( + self, + ctx: ConnectionContext, + text: str, + ) -> int: + current = text.strip() + previous = ctx.last_partial_text.strip() + if current and previous and current == previous: + ctx.partial_stable_rounds += 1 + else: + ctx.partial_stable_rounds = 0 + return ctx.partial_stable_rounds + + def _prefer_segment_text( + self, + primary: str, + fallback: str, + ) -> str: + primary_text = primary.strip() + fallback_text = fallback.strip() + if not primary_text: + return fallback_text + if not fallback_text: + return primary_text + if primary_text == fallback_text: + return primary_text + if len(fallback_text) > len(primary_text) and ( + primary_text in fallback_text + or self._common_prefix_len(primary_text, fallback_text) >= min(len(primary_text), 8) + ): + return fallback_text + if len(primary_text) > len(fallback_text) and ( + fallback_text in primary_text + or self._common_prefix_len(primary_text, fallback_text) >= min(len(fallback_text), 8) + ): + return primary_text + return fallback_text if len(fallback_text) > len(primary_text) else primary_text + + def _merge_segment_text( + self, + stable: str, + candidate: str, + ) -> str: + stable_text = stable.strip() + candidate_text = candidate.strip() + if not stable_text: + return candidate_text + if not candidate_text: + return stable_text + if stable_text == candidate_text: + return stable_text + if stable_text in candidate_text: + return candidate_text + if candidate_text in stable_text: + return stable_text + + match = SequenceMatcher( + None, + stable_text, + candidate_text, + autojunk=False, + ).find_longest_match(0, len(stable_text), 0, len(candidate_text)) + if match.size >= min(6, len(stable_text), len(candidate_text)): + return stable_text[:match.a] + candidate_text[match.b:] + + return self._prefer_segment_text(stable_text, candidate_text) + + def _accumulate_segment_text( + self, + previous: str, + current: str, + ) -> str: + previous_text = previous.strip() + current_text = current.strip() + if not previous_text: + return current_text + if not current_text: + return previous_text + if previous_text == current_text: + return previous_text + if current_text.startswith(previous_text) or previous_text in current_text: + return current_text + if previous_text.startswith(current_text): + return previous_text + + common_len = self._common_prefix_len(previous_text, current_text) + if common_len >= min(len(previous_text), len(current_text), 8): + return current_text if len(current_text) >= len(previous_text) else previous_text + + return current_text if len(current_text) >= len(previous_text) else previous_text + + def _sequence_similarity(self, left: str, right: str) -> float: + left_text = left.strip() + right_text = right.strip() + if not left_text or not right_text: + return 0.0 + if left_text == right_text: + return 1.0 + return float( + SequenceMatcher( + None, + left_text, + right_text, + autojunk=False, + ).ratio() + ) + + @staticmethod + def _overlap_char_views(text: str) -> List[tuple[str, int]]: + views: List[tuple[str, int]] = [] + for idx, ch in enumerate(str(text or "")): + if ch.isalnum() or "\u4e00" <= ch <= "\u9fff": + views.append((ch.lower(), idx)) + return views + + def _previous_segment_overlap_cut_index( + self, + previous: str, + current: str, + ) -> int: + previous_text = str(previous or "").strip() + current_text = str(current or "").strip() + if not previous_text or not current_text: + return 0 + + exact_cut = _dedupe_overlap_word_boundary(previous_text, current_text) + if exact_cut == 0: + exact_cut = _dedupe_overlap_char_boundary(previous_text, current_text) + if exact_cut > 0: + return exact_cut + + prev_views = self._overlap_char_views(previous_text) + curr_views = self._overlap_char_views(current_text) + if len(prev_views) < 12 or len(curr_views) < 12: + return 0 + + lookback = min(140, len(prev_views)) + lookahead = min(180, len(curr_views)) + prev_tail = prev_views[-lookback:] + curr_prefix = curr_views[:lookahead] + prev_key = "".join(ch for ch, _ in prev_tail) + curr_key = "".join(ch for ch, _ in curr_prefix) + match = SequenceMatcher(None, prev_key, curr_key, autojunk=False).find_longest_match( + 0, + len(prev_key), + 0, + len(curr_key), + ) + if match.size < 12: + return 0 + + prev_suffix_gap = len(prev_key) - (match.a + match.size) + current_prefix_gap = match.b + if prev_suffix_gap > 4 or current_prefix_gap > 8: + return 0 + + matched_current_chars = match.b + match.size + if matched_current_chars >= len(curr_views): + return len(current_text) + return curr_views[matched_current_chars][1] + + def _trim_previous_segment_overlap( + self, + ctx: ConnectionContext, + text: str, + ) -> str: + current = str(text or "").strip() + if not current or not ctx.confirmed_segments: + return current + + previous = str(ctx.confirmed_segments[-1].get("text") or "").strip() + cut_index = self._previous_segment_overlap_cut_index(previous, current) + if cut_index <= 0: + return current + + trimmed = current[cut_index:].lstrip(" \t\r\n，,。.!！？?；;：:、") + if trimmed != current: + logger.debug( + "Trimmed realtime segment overlap: removed=%s remaining=%s", + cut_index, + len(trimmed), + ) + return trimmed + + def _is_unstable_expansion( + self, + stable: str, + candidate: str, + ) -> bool: + stable_text = stable.strip() + candidate_text = candidate.strip() + if len(stable_text) < 6 or len(candidate_text) <= len(stable_text): + return False + + common_prefix = self._common_prefix_len(stable_text, candidate_text) + similarity = self._sequence_similarity(stable_text, candidate_text) + grows_too_fast = len(candidate_text) >= len(stable_text) + max(20, len(stable_text) // 2) + loses_context = common_prefix < min(6, len(stable_text) // 2) + return grows_too_fast and loses_context and similarity < 0.45 + + def _is_degenerate_repetition(self, text: str) -> bool: + stripped = text.strip() + if len(stripped) < 16: + return False + + units = re.findall(r"[\u4e00-\u9fff]+|[A-Za-z0-9]+|[^\w\s]", stripped) + lexical_units = [unit for unit in units if re.search(r"[\u4e00-\u9fffA-Za-z0-9]", unit)] + if len(lexical_units) < 6: + return False + + counts: Dict[str, int] = {} + for unit in lexical_units: + counts[unit] = counts.get(unit, 0) + 1 + + dominant = max(counts.values()) if counts else 0 + unique = len(counts) + if dominant >= 6 and unique <= 2: + return True + if dominant / max(len(lexical_units), 1) >= 0.7 and len(lexical_units) >= 10: + return True + return False + + def _trim_repetitive_suffix(self, text: str) -> str: + trimmed = text.strip() + if len(trimmed) < 12: + return trimmed + + repeated_char = re.search(r"(.)\1{7,}$", trimmed) + if repeated_char: + start = repeated_char.start() + trimmed = (trimmed[:start] + repeated_char.group(1) * 2).strip() + + for unit_len in range(1, 7): + pattern = re.compile(rf"(.{{{unit_len}}})\1{{4,}}$") + match = pattern.search(trimmed) + if match: + start = match.start() + unit = match.group(1) + trimmed = (trimmed[:start] + unit * 2).strip() + break + + return trimmed + + def _sanitize_candidate_text(self, text: str) -> str: + candidate = text.strip() + if not candidate: + return "" + candidate = self._trim_repetitive_suffix(candidate) + candidate = deduplicate_asr_text(candidate) + if self._is_degenerate_repetition(candidate): + return "" + return candidate + + def _update_segment_observed_text( + self, + ctx: ConnectionContext, + text: str, + language: str, + ) -> str: + candidate = self._sanitize_candidate_text(text) + if not candidate: + return ctx.segment_observed_text + if self._is_unstable_expansion(ctx.segment_observed_text, candidate): + return ctx.segment_observed_text + + observed = self._accumulate_segment_text(ctx.segment_observed_text, candidate) + ctx.segment_observed_text = observed + if language: + ctx.segment_observed_language = language + return observed + + def _update_best_partial( + self, + ctx: ConnectionContext, + text: str, + language: str, + ) -> None: + candidate = self._sanitize_candidate_text(text) + if not candidate: + return + if self._is_unstable_expansion(ctx.best_partial_text, candidate): + return + chosen = self._prefer_segment_text(ctx.best_partial_text, candidate) + if chosen != ctx.best_partial_text: + ctx.best_partial_text = chosen + ctx.best_partial_language = language or ctx.best_partial_language + + def _common_prefix_len(self, left: str, right: str) -> int: + limit = min(len(left), len(right)) + idx = 0 + while idx < limit and left[idx] == right[idx]: + idx += 1 + return idx + + def _stable_prefix_cutoff(self, text: str, max_commit_len: int) -> int: + if max_commit_len <= 0: + return 0 + + candidate = text[:max_commit_len] + if not candidate: + return 0 + + for idx in range(len(candidate) - 1, -1, -1): + ch = candidate[idx] + if ch.isspace() or unicodedata.category(ch).startswith("P"): + return idx + 1 + + return len(candidate) + + def _stabilize_partial_text(self, ctx: ConnectionContext, raw_text: str) -> str: + text = self._sanitize_candidate_text(raw_text) + if not text: + ctx.last_partial_raw_text = "" + ctx.stable_partial_prefix = "" + return "" + + previous_raw = ctx.last_partial_raw_text + stable_prefix = ctx.stable_partial_prefix + previous_emitted = ctx.last_partial_text.strip() + if previous_raw: + common_len = self._common_prefix_len(previous_raw, text) + keep_tail_chars = max(int(settings.REALTIME_STREAM_STABLE_TAIL_CHARS), 2) + max_commit_len = max(0, common_len - keep_tail_chars) + stable_cutoff = self._stable_prefix_cutoff(text, max_commit_len) + if stable_cutoff - len(stable_prefix) >= max( + int(settings.REALTIME_STREAM_STABLE_MIN_GROW_CHARS), + 1, + ): + stable_prefix = text[:stable_cutoff] + + if stable_prefix and not text.startswith(stable_prefix): + stable_common = self._common_prefix_len(stable_prefix, text) + tolerated_divergence = max( + int(settings.REALTIME_STREAM_DIVERGENCE_TOLERANCE_CHARS), + 1, + ) + if len(stable_prefix) - stable_common >= tolerated_divergence and previous_emitted: + return previous_emitted + stable_prefix = text[:stable_common] + stable_prefix = text[: self._stable_prefix_cutoff(text, len(stable_prefix))] + + ctx.stable_partial_prefix = stable_prefix + ctx.last_partial_raw_text = text + unstable_suffix = text[len(stable_prefix):] + return f"{stable_prefix}{unstable_suffix}".strip() + + def _should_emit_partial( + self, + ctx: ConnectionContext, + text: str, + language: str, + ) -> bool: + stripped = self._sanitize_candidate_text(text) + if not stripped: + return False + if len(stripped) <= 1: + return False + if self._is_unstable_expansion(ctx.last_partial_display_text, stripped): + return False + + normalized_language = self._normalize_output_language(language, ctx) + if not normalized_language: + normalized_language = self._infer_text_language(stripped) + + if ( + stripped == ctx.last_partial_display_text + and normalized_language == ctx.last_partial_language + ): + return False + + dominant_language = self._get_session_dominant_language(ctx) + duration_sec = float(ctx.segment_audio_buffer.size) / 16000.0 + if len(stripped) <= 6 and self._is_suspicious_segment_text( + stripped, + language=normalized_language, + dominant_language=dominant_language, + duration_sec=max(duration_sec, 0.1), + explicit_language=ctx.params.get("language"), + ): + return False + + return True + + def _partial_display_text(self, ctx: ConnectionContext, text: str) -> str: + stripped = self._sanitize_candidate_text(text) + if not stripped: + return "" + + holdback_chars = max( + int( + getattr( + settings, + "REALTIME_PARTIAL_HOLDBACK_CHARS", + 0, + ) + ), + 0, + ) + holdback_chars = int( + max( + int(ctx.params.get("partial_holdback_chars", holdback_chars) or holdback_chars), + 0, + ) + ) + if holdback_chars <= 0: + return stripped + + if ctx.partial_stable_rounds >= 1: + return stripped + + if self._sentence_count(stripped) >= 1 and stripped[-1:] in "。！？.!?": + return stripped + + if len(stripped) <= max(holdback_chars + 2, 8): + return stripped + + stable_prefix = stripped[:-holdback_chars].rstrip() + return stable_prefix or stripped + + def _is_suspicious_segment_text( + self, + text: str, + *, + language: str, + dominant_language: str, + duration_sec: float, + explicit_language: Optional[str], + ) -> bool: + stripped = text.strip() + if not stripped: + return True + + chars = [ch for ch in stripped if not ch.isspace()] + if not chars: + return True + + digit_count = sum(ch.isdigit() for ch in chars) + alpha_count = sum(ch.isalpha() for ch in chars) + cjk_count = sum("\u4e00" <= ch <= "\u9fff" for ch in chars) + thai_count = sum("\u0e00" <= ch <= "\u0e7f" for ch in chars) + punct_count = sum(unicodedata.category(ch).startswith("P") for ch in chars) + total = len(chars) + + digit_ratio = digit_count / total + punct_ratio = punct_count / total + latin_alpha_count = max(alpha_count - thai_count, 0) + script_families = sum( + 1 + for present in ( + cjk_count > 0, + thai_count > 0, + latin_alpha_count > 0, + digit_count > 0, + ) + if present + ) + + if digit_count >= 3 and digit_ratio >= 0.3: + return True + if total <= 20 and script_families >= 3: + return True + if total <= 8 and punct_ratio >= 0.5: + return True + # Allow short pure-Latin utterances to pass during bilingual switching. + if ( + not explicit_language + and language == "English" + and latin_alpha_count >= max(total - punct_count - digit_count, 1) + and total >= 3 + ): + return False + if ( + not explicit_language + and dominant_language + and language + and language != dominant_language + and duration_sec <= 6.0 + ): + return True + return False + + def _is_valid_committed_segment_text(self, text: str, duration_sec: float) -> bool: + stripped = self._sanitize_candidate_text(text) + if len(stripped) >= 3: + return True + + lexical_count = sum( + 1 for ch in stripped + if ch.isalnum() or "\u4e00" <= ch <= "\u9fff" + ) + if lexical_count <= 0: + return False + + # Short acknowledgements such as "嗯。", "好。", "啊?" should still + # close the visible segment after silence; speaker attribution may + # attach to nearby speech, but the ASR turn itself is valid. + return duration_sec >= 0.25 + + async def _estimate_voiced_duration_ms(self, audio: np.ndarray) -> int: + if audio.size == 0: + return 0 + + vad_segments = await self._run_vad_segments(audio) + if vad_segments is None: + return int(len(audio) / 16) + return sum( + max(0, int(seg[1]) - int(seg[0])) + for seg in vad_segments + if len(seg) >= 2 + ) + + async def _run_vad_segments(self, audio: np.ndarray) -> Optional[list[list[int]]]: + if audio.size == 0: + return [] + + temp_path: Optional[str] = None + try: + temp_path = get_speaker_registry_service().save_audio_array_to_temp( + audio, + sample_rate=16000, + ) + + def _run_vad() -> list[list[int]]: + vad_model = get_global_vad_model(settings.DEVICE) + with get_vad_inference_lock(): + result = vad_model.generate(input=temp_path, cache={}) + return result[0].get("value", []) if result else [] + + return await run_sync(_run_vad) + except Exception as exc: + logger.debug("Realtime voiced-duration VAD failed: %s", exc) + return None + finally: + get_speaker_registry_service().cleanup_file(temp_path) + + async def _split_silence_audio_by_vad( + self, + audio: np.ndarray, + ) -> tuple[np.ndarray, np.ndarray]: + vad_segments = await self._run_vad_segments(audio) + if not vad_segments: + return audio, np.array([], dtype=np.float32) + + valid_segments = [ + (int(seg[0]), int(seg[1])) + for seg in vad_segments + if len(seg) >= 2 and int(seg[1]) > int(seg[0]) + ] + if not valid_segments: + return audio, np.array([], dtype=np.float32) + + finalized_end_ms = valid_segments[-1][1] + finalized_end_sample = min(audio.size, int(finalized_end_ms * 16)) + finalized_audio = np.asarray(audio[:finalized_end_sample], dtype=np.float32) + return finalized_audio, np.array([], dtype=np.float32) + + async def _find_completed_segment_split_sample( + self, + audio: np.ndarray, + ) -> Optional[int]: + if audio.size < int(3.0 * 16000): + return None + + vad_segments = await self._run_vad_segments(audio) + if not vad_segments: + return None + + valid_segments = [ + (int(seg[0]), int(seg[1])) + for seg in vad_segments + if len(seg) >= 2 and int(seg[1]) > int(seg[0]) + ] + if len(valid_segments) < 2: + return None + + total_ms = int(audio.size / 16) + finalize_silence_ms = int(max(settings.REALTIME_VAD_FINALIZE_SILENCE_SEC, 0.2) * 1000) + + last_start_ms, last_end_ms = valid_segments[-1] + if total_ms - last_end_ms >= finalize_silence_ms: + return min(audio.size, last_end_ms * 16) + + # 接近 max_duration 时，优先把倒数第二个已完成语音段刷出去，保留最后一段继续等上下文。 + hard_limit_sec = max( + float(getattr(settings, "REALTIME_FINAL_SEGMENT_HARD_LIMIT_SEC", 12.0) or 12.0), + 1.0, + ) + near_limit_ms = int(max(hard_limit_sec * 0.85, 4.0) * 1000) + if total_ms >= near_limit_ms: + _, prev_end_ms = valid_segments[-2] + if prev_end_ms > 0: + return min(audio.size, prev_end_ms * 16) + + return None + + async def _transcribe_audio_text( + self, + engine: Qwen3ASREngine, + ctx: ConnectionContext, + audio: np.ndarray, + *, + final_pass: bool = False, + ) -> str: + temp_path: Optional[str] = None + try: + temp_path = get_speaker_registry_service().save_audio_array_to_temp( + audio, + sample_rate=16000, + ) + text = await run_sync( + engine.transcribe_file, + temp_path, + ctx.params.get("context", ""), + False, + False, + False, + 16000, + ) + normalized = self._normalize_output_text( + text or "", + ctx, + enable_itn=False, + ) + if final_pass and normalized.strip(): + return self._normalize_output_text( + normalized, + ctx, + enable_itn=bool(ctx.params.get("enable_inverse_text_normalization", True)), + ) + return normalized + except Exception as exc: + logger.warning("Realtime segment audio transcription failed: %s", exc) + return "" + finally: + get_speaker_registry_service().cleanup_file(temp_path) + + async def _transcribe_audio_longform_text( + self, + engine: Qwen3ASREngine, + ctx: ConnectionContext, + audio: np.ndarray, + *, + final_pass: bool = False, + ) -> str: + duration_sec = float(len(audio)) / 16000.0 + if duration_sec < settings.REALTIME_LONGFORM_MIN_SEC: + return await self._transcribe_audio_text( + engine, + ctx, + audio, + final_pass=final_pass, + ) + + chunk_samples = int(max(settings.REALTIME_LONGFORM_CHUNK_SEC, 1.0) * 16000) + overlap_samples = int(max(settings.REALTIME_LONGFORM_OVERLAP_SEC, 0.0) * 16000) + step_samples = max(int(16000), chunk_samples - overlap_samples) + + assembler = RealtimeTranscriptAssembler() + start = 0 + while start < audio.size: + end = min(audio.size, start + chunk_samples) + chunk_audio = np.asarray(audio[start:end], dtype=np.float32) + if chunk_audio.size == 0: + break + chunk_text = await self._transcribe_audio_text( + engine, + ctx, + chunk_audio, + final_pass=final_pass, + ) + assembler.push(chunk_text) + if end >= audio.size: + break + start += step_samples + + return assembler.text() + + def _split_max_duration_audio( + self, + audio: np.ndarray, + ) -> tuple[np.ndarray, np.ndarray]: + tail_samples = int(max(settings.REALTIME_MAX_SEGMENT_TAIL_SEC, 0.0) * 16000) + min_flush_samples = int(max(settings.REALTIME_SPEAKER_MIN_SEC, 1.0) * 16000) + if tail_samples <= 0 or audio.size <= tail_samples + min_flush_samples: + return audio, np.array([], dtype=np.float32) + flush_audio = np.asarray(audio[:-tail_samples], dtype=np.float32) + carry_audio = np.asarray(audio[-tail_samples:], dtype=np.float32) + return flush_audio, carry_audio + + def _should_force_stable_segment(self, ctx: ConnectionContext, text: str) -> bool: + force_sec = float( + max( + float( + ctx.params.get( + "force_stable_segment_sec", + getattr(settings, "REALTIME_FORCE_STABLE_SEGMENT_SEC", 0.0), + ) + or 0.0 + ), + 0.0, + ) + ) + if force_sec <= 0: + return False + + duration_sec = float(ctx.segment_audio_buffer.size) / 16000.0 + if duration_sec < force_sec: + return False + + partial_text = text.strip() + min_chars = max( + int( + ctx.params.get( + "force_stable_min_chars", + getattr(settings, "REALTIME_FORCE_STABLE_MIN_CHARS", 24), + ) + or 0 + ), + 8, + ) + if len(partial_text) < min_chars: + return False + + # 只因为前文某处已经出现过句号，就立刻截断整段，容易把后半句切坏。 + # 这里收紧条件：优先要求“当前尾部已经自然收句”。 + if self._ends_with_sentence_punctuation(partial_text): + return True + + # 如果句尾还没闭合，只在明显超时并且 partial 连续稳定时才软提交， + # 避免把“我们需要…”这类正在继续的从句过早切成两段。 + force_overtime_sec = max(force_sec + 2.0, force_sec * 1.35) + soft_limit_samples = self._soft_segment_limit_samples(ctx) + if soft_limit_samples > 0 and ctx.segment_audio_buffer.size >= soft_limit_samples: + force_overtime_sec = min( + force_overtime_sec, + float(ctx.segment_audio_buffer.size) / 16000.0, + ) + if duration_sec >= force_overtime_sec and ctx.partial_stable_rounds >= 1: + return True + + return ctx.partial_stable_rounds >= 2 + + def _choose_committed_segment_text( + self, + *, + final_text: str, + stable_text: str, + ctx: ConnectionContext, + duration_sec: float, + language: str, + ) -> str: + """Prefer the more plausible candidate between final retranscription and stable partial text.""" + _ = (ctx, language) + final_candidate = self._sanitize_candidate_text(final_text) + stable_candidate = self._sanitize_candidate_text(stable_text) + if not final_candidate: + return stable_candidate + if not stable_candidate: + return final_candidate + if final_candidate == stable_candidate: + return final_candidate + + suspicious_final = self._is_suspicious_segment_text( + final_candidate, + language=self._infer_text_language(final_candidate), + dominant_language=self._get_session_dominant_language(ctx), + duration_sec=max(duration_sec, 0.1), + explicit_language=ctx.params.get("language"), + ) + suspicious_stable = self._is_suspicious_segment_text( + stable_candidate, + language=self._infer_text_language(stable_candidate), + dominant_language=self._get_session_dominant_language(ctx), + duration_sec=max(duration_sec, 0.1), + explicit_language=ctx.params.get("language"), + ) + + if suspicious_final and not suspicious_stable: + return stable_candidate + if ( + len(stable_candidate) >= max(len(final_candidate) + 10, 24) + and final_candidate in stable_candidate + ): + return stable_candidate + if ( + len(final_candidate) >= max(len(stable_candidate) + 12, 28) + and stable_candidate in final_candidate + and not suspicious_final + ): + return final_candidate + if len(stable_candidate) > len(final_candidate) and not suspicious_stable: + return stable_candidate + return final_candidate + + async def _resolve_segment_speaker( + self, + ctx: ConnectionContext, + audio: np.ndarray, + *, + reason: str, + segment_start_ms: int, + segment_end_ms: int, + ) -> Optional[Dict[str, Any]]: + if not bool(ctx.params.get("enable_speaker", True)): + return None + speaker_info = await get_realtime_speaker_clusterer().resolve_segment_speaker( + self._speaker_match_records(ctx), + np.asarray(audio, dtype=np.float32), + segment_start_ms=segment_start_ms, + segment_end_ms=segment_end_ms, + enable_registry_match=bool(ctx.params.get("enable_speaker_identification", True)), + speaker_threshold=ctx.params.get("speaker_threshold"), + ) + if not speaker_info: + return None + speaker_info["_segment_duration_ms"] = int(max(segment_end_ms - segment_start_ms, 0)) + inherited_named_speaker = self._inherit_recent_named_speaker(ctx, speaker_info) + if inherited_named_speaker is not None: + speaker_info = inherited_named_speaker + inherited_speaker = self._inherit_recent_anonymous_speaker(ctx, speaker_info) + if inherited_speaker is not None: + speaker_info = inherited_speaker + + stable = self._is_stable_speaker_info(speaker_info) + speaker_info["speaker_pending"] = not stable + cluster_index = speaker_info.get("_cluster_index") + if ( + stable + and + not speaker_info.get("registry_speaker_id") + and not speaker_info.get("user_id") + ): + safe_cluster_index = self._safe_int(cluster_index) + display_id = self._match_display_speaker_id( + ctx, + cluster_index=safe_cluster_index, + embedding=speaker_info.get("_embedding"), + ) + if display_id: + speaker_info["speaker_id"] = display_id + speaker_info["speaker_name"] = display_id + + safe_cluster_index = self._safe_int(cluster_index) + if ( + stable + and safe_cluster_index is not None + and not speaker_info.get("registry_speaker_id") + and not speaker_info.get("user_id") + and speaker_info.get("speaker_id") + ): + ctx.speaker_display_map[safe_cluster_index] = str(speaker_info["speaker_id"]) + + if not stable: + speaker_info["speaker_id"] = -1 + speaker_info["speaker_name"] = "" + + return speaker_info + + async def _resolve_and_emit_segment_speaker( + self, + websocket: WebSocket, + ctx: ConnectionContext, + task_id: str, + *, + segment_index: int, + audio: np.ndarray, + reason: str, + segment_start_ms: int, + segment_end_ms: int, + ) -> None: + try: + speaker_info = await self._resolve_segment_speaker( + ctx, + audio, + reason=reason, + segment_start_ms=segment_start_ms, + segment_end_ms=segment_end_ms, + ) + if not speaker_info: + return + segment = next( + (item for item in ctx.confirmed_segments if int(item.get("index", -1)) == int(segment_index)), + None, + ) + if segment is None: + return + before_view = self._public_speaker_view(segment) + segment.update(speaker_info) + after_view = self._public_speaker_view(segment) + self._record_segment_speaker( + ctx, + segment_index=segment_index, + segment_start_ms=segment_start_ms, + segment_end_ms=segment_end_ms, + speaker_info=speaker_info, + ) + if before_view == after_view: + return + queue_backlog = ctx.speaker_job_queue.qsize() + updates: List[Dict[str, Any]] = [] + if queue_backlog <= 1: + updates = self._maybe_recluster_recent_segments(ctx) + if updates: + for updated in updates: + await self._emit_speaker_update( + websocket, + ctx, + task_id, + int(updated["segment_index"]), + ) + return + await self._emit_speaker_update(websocket, ctx, task_id, segment_index) + except Exception as exc: + logger.warning("[%s] Async speaker resolution failed for segment %s: %s", task_id, segment_index, exc) + + async def _refine_segment_text( + self, + engine: Qwen3ASREngine, + ctx: ConnectionContext, + fallback_text: str, + *, + reason: str, + force: bool = False, + audio: Optional[np.ndarray] = None, + ) -> str: + target_audio = np.asarray( + ctx.segment_audio_buffer if audio is None else audio, + dtype=np.float32, + ) + duration_sec = float(len(target_audio)) / 16000.0 + if ( + not force + and not bool( + ctx.params.get("enable_segment_refine", settings.REALTIME_ENABLE_SEGMENT_REFINE) + ) + ): + return fallback_text + if not force and duration_sec < 6.0 and reason != "max_duration": + return fallback_text + + temp_path: Optional[str] = None + try: + temp_path = get_speaker_registry_service().save_audio_array_to_temp( + target_audio, + sample_rate=16000, + ) + refined_text = await run_sync( + engine.transcribe_file, + temp_path, + ctx.params.get("context", ""), + False, + False, + False, + 16000, + ) + refined_text = self._sanitize_candidate_text( + self._normalize_output_text( + refined_text or "", + ctx, + enable_itn=bool(ctx.params.get("enable_inverse_text_normalization", True)), + ) + ) + if refined_text.strip(): + return refined_text + except Exception as exc: + logger.warning("Realtime segment refinement failed: %s", exc) + finally: + get_speaker_registry_service().cleanup_file(temp_path) + + return fallback_text + + async def _commit_retranscribe_turn( + self, + websocket: WebSocket, + ctx: ConnectionContext, + task_id: str, + reason: str, + *, + finalized_audio_override: Optional[np.ndarray] = None, + carry_audio_override: Optional[np.ndarray] = None, + emit_segment_start: bool = True, + ) -> None: + engine = await self._ensure_engine(ctx) + + current_audio = np.asarray(ctx.segment_audio_buffer, dtype=np.float32) + finalized_audio = current_audio + carry_audio = np.array([], dtype=np.float32) + + if finalized_audio_override is not None: + finalized_audio = np.asarray(finalized_audio_override, dtype=np.float32) + carry_audio = np.asarray( + np.array([], dtype=np.float32) if carry_audio_override is None else carry_audio_override, + dtype=np.float32, + ) + elif reason == "silence" and self._enable_realtime_vad_split(ctx): + finalized_audio, carry_audio = await self._split_silence_audio_by_vad(current_audio) + elif reason in {"max_duration", "long_speech"} and self._enable_realtime_vad_split(ctx): + finalized_audio, carry_audio = self._split_max_duration_audio(current_audio) + + if carry_audio.size == 0: + stream_text, stream_language = await self._finish_realtime_stream_text(ctx, engine) + if stream_text.strip(): + observed_text = self._update_segment_observed_text( + ctx, + stream_text, + stream_language or self._infer_text_language(stream_text), + ) + self._update_best_partial( + ctx, + observed_text, + stream_language or self._infer_text_language(observed_text), + ) + else: + ctx.realtime_stream_state = None + + if self._enable_realtime_longform(ctx): + segment_text = await self._transcribe_audio_longform_text( + engine, + ctx, + finalized_audio, + final_pass=True, + ) + else: + segment_text = await self._transcribe_audio_text( + engine, + ctx, + finalized_audio, + final_pass=True, + ) + segment_text = self._sanitize_candidate_text(segment_text) + if not segment_text.strip(): + segment_text = self._prefer_segment_text( + ctx.best_partial_text, + ctx.segment_observed_text, + ) + + segment_language = self._infer_text_language(segment_text) + if not segment_language and ctx.segment_observed_language: + segment_language = ctx.segment_observed_language + if not segment_language and ctx.best_partial_language: + segment_language = ctx.best_partial_language + + duration_sec = float(finalized_audio.size) / 16000.0 + dominant_language = self._get_session_dominant_language(ctx) + suspicious = self._is_suspicious_segment_text( + segment_text, + language=segment_language, + dominant_language=dominant_language, + duration_sec=max(duration_sec, 0.1), + explicit_language=ctx.params.get("language"), + ) + if suspicious and finalized_audio.size > 0 and self._enable_realtime_refine(ctx): + refined_text = await self._refine_segment_text( + engine, + ctx, + segment_text, + reason=reason, + force=True, + audio=finalized_audio, + ) + if refined_text.strip(): + segment_text = refined_text + inferred = self._infer_text_language(segment_text) + if inferred: + segment_language = inferred + + stable_text = self._prefer_segment_text( + ctx.best_partial_text, + ctx.segment_observed_text, + ) + + segment_text = self._choose_committed_segment_text( + final_text=segment_text, + stable_text=stable_text, + ctx=ctx, + duration_sec=duration_sec, + language=segment_language, + ) + segment_text = self._trim_previous_segment_overlap(ctx, segment_text) + + is_valid = self._is_valid_committed_segment_text(segment_text, duration_sec) + segment_duration_ms = int(finalized_audio.size / 16) + segment_start_ms = int(ctx.timeline_cursor_ms) + segment_end_ms = int(segment_start_ms + segment_duration_ms) + + if is_valid: + segment_payload = { + "index": ctx.segment_index, + "text": segment_text, + "language": segment_language, + "reason": reason, + "duration_ms": segment_duration_ms, + "start_ms": segment_start_ms, + "end_ms": segment_end_ms, + "sentence_type": 1, + "speaker_id": -1, + "speaker_name": "", + "user_id": None, + } + segment_payload["speaker_pending"] = True + ctx.confirmed_segments.append(segment_payload) + + full_text = "\n".join(segment["text"] for segment in ctx.confirmed_segments) + full_text_tail = ( + full_text[-self._SEGMENT_EVENT_TEXT_TAIL_CHARS:] + if len(full_text) > self._SEGMENT_EVENT_TEXT_TAIL_CHARS + else full_text + ) + sentence_payload = self._build_tencent_sentence( + segment_payload, + sentence_type=1, + ) + + await self._send_json_safe( + websocket, + ctx, + { + "type": "sentences", + "code": 0, + "voice_id": task_id, + "final": 0, + "result": { + "slice_type": 2, + "index": int(ctx.segment_index), + "voice_text_str": full_text_tail, + }, + "sentences": [sentence_payload], + }, + ) + self._ensure_speaker_worker(websocket, ctx, task_id) + ctx.speaker_job_queue.put_nowait( + { + "segment_index": int(ctx.segment_index), + "audio": np.asarray(finalized_audio, dtype=np.float32), + "reason": reason, + "segment_start_ms": segment_start_ms, + "segment_end_ms": segment_end_ms, + } + ) + ctx.segment_index += 1 + + ctx.timeline_cursor_ms = segment_end_ms + ctx.segment_audio_buffer = np.asarray(carry_audio, dtype=np.float32) + ctx.stream_window_buffer = np.asarray(carry_audio, dtype=np.float32) + ctx.sentence_active = bool(carry_audio.size > 0) + ctx.silence_samples = 0 + ctx.total_samples = int(carry_audio.size) + self._reset_partial_state(ctx) + + if ctx.sentence_active and carry_audio.size > 0: + await self._push_realtime_stream_audio( + ctx, + engine, + np.asarray(carry_audio, dtype=np.float32), + ) + + async def _send_error( + self, + websocket: WebSocket, + message: str, + task_id: str, + code: str = "DEFAULT_SERVER_ERROR", + ) -> None: + try: + error = create_error_response(error_code=code, message=message, task_id=task_id) + await websocket.send_json( + { + "type": "error", + "code": error.get("code", -1), + "message": error.get("message", message), + "voice_id": task_id, + } + ) + except Exception: + pass + + async def handle_connection(self, websocket: WebSocket, task_id: str) -> None: + await websocket.accept() + logger.info("[%s] Qwen3 websocket connected", task_id) + + ctx = ConnectionContext() + session_id = task_id + keep_for_resume = False + + try: + while True: + message = await websocket.receive() + + if "text" in message: + data = json.loads(message["text"]) + msg_type = data.get("type", "") + + if msg_type == "start": + if ctx.state != ConnectionState.READY: + await self._send_error(websocket, "识别已在进行中", task_id, "INVALID_STATE") + continue + + payload = data.get("payload", {}) + requested_session_id = str(payload.get("session_id") or task_id).strip() or task_id + resumed_ctx, resumed = await self._acquire_session(requested_session_id) + if resumed_ctx is not ctx: + ctx = resumed_ctx + session_id = requested_session_id + task_id = session_id + keep_for_resume = True + + if resumed and ctx.state != ConnectionState.READY: + await self._send_json_safe( + websocket, + ctx, + { + "type": "voice_id", + "voice_id": task_id, + "session_id": session_id, + } + ) + await self._send_json_safe( + websocket, + ctx, + { + "type": "start", + "session_id": session_id, + } + ) + logger.info("[%s] Recognition resumed", task_id) + continue + + match_speaker_registry = bool( + payload.get( + "match_speaker_registry", + payload.get("enable_speaker_identification", False), + ) + ) + ctx.params = { + "format": payload.get("format", "pcm"), + "sample_rate": payload.get("sample_rate", 16000), + "language": payload.get("language"), + "context": payload.get("context", ""), + "enable_inverse_text_normalization": payload.get( + "enable_inverse_text_normalization", + True, + ), + "chunk_size_sec": payload.get( + "chunk_size_sec", + settings.REALTIME_STREAM_CHUNK_SEC, + ), + "unfixed_chunk_num": payload.get( + "unfixed_chunk_num", + settings.REALTIME_STREAM_MAX_PENDING_CHUNKS, + ), + "unfixed_token_num": payload.get("unfixed_token_num", 5), + "silence_duration_ms": payload.get("silence_duration_ms", 800), + "min_partial_sec": payload.get( + "min_partial_sec", + 0.9, + ), + "partial_window_sec": payload.get( + "partial_window_sec", + settings.REALTIME_PARTIAL_WINDOW_SEC, + ), + "pre_roll_ms": payload.get("pre_roll_ms", 240), + "max_sentence_count": payload.get("max_sentence_count", 8), + "max_partial_text_chars": payload.get( + "max_partial_text_chars", + ctx.DEFAULT_MAX_PARTIAL_TEXT_CHARS, + ), + "partial_holdback_chars": payload.get( + "partial_holdback_chars", + settings.REALTIME_PARTIAL_HOLDBACK_CHARS, + ), + "enable_native_partial_stream": payload.get( + "enable_native_partial_stream", + False, + ), + # 与离线会议接口保持一致：前端优先传 enable_speaker / match_speaker_registry。 + "enable_speaker": payload.get("enable_speaker", True), + "match_speaker_registry": match_speaker_registry, + "enable_speaker_identification": match_speaker_registry, + "speaker_threshold": payload.get("speaker_threshold"), + "enable_segment_refine": payload.get( + "enable_segment_refine", + settings.REALTIME_ENABLE_SEGMENT_REFINE, + ), + "enable_realtime_longform": payload.get("enable_realtime_longform", False), + "enable_realtime_vad_split": payload.get("enable_realtime_vad_split", False), + "force_stable_segment_sec": payload.get( + "force_stable_segment_sec", + settings.REALTIME_FORCE_STABLE_SEGMENT_SEC, + ), + "force_stable_min_chars": payload.get( + "force_stable_min_chars", + settings.REALTIME_FORCE_STABLE_MIN_CHARS, + ), + "soft_limit_sec": payload.get( + "soft_limit_sec", + settings.REALTIME_FINAL_SEGMENT_SOFT_LIMIT_SEC, + ), + "hard_limit_sec": payload.get( + "hard_limit_sec", + payload.get( + "max_segment_sec", + settings.REALTIME_FINAL_SEGMENT_HARD_LIMIT_SEC, + ), + ), + } + + await self._ensure_engine(ctx) + ctx.stream_window_buffer = np.array([], dtype=np.float32) + self._reset_partial_state(ctx) + + await self._send_json_safe( + websocket, + ctx, + { + "type": "voice_id", + "voice_id": task_id, + "session_id": session_id, + } + ) + await self._send_json_safe( + websocket, + ctx, + { + "type": "start", + "session_id": session_id, + }, + ) + ctx.state = ConnectionState.STARTED + logger.info("[%s] Recognition started: %s", task_id, ctx.params) + + elif msg_type == "stop": + if ctx.state in (ConnectionState.STARTED, ConnectionState.STREAMING): + await self._stop(websocket, ctx, task_id) + keep_for_resume = False + break + + else: + await self._send_error( + websocket, + f"未知消息类型: {msg_type}", + task_id, + "INVALID_MESSAGE", + ) + + elif "bytes" in message: + if ctx.state not in (ConnectionState.STARTED, ConnectionState.STREAMING): + await self._send_error(websocket, "请先发送 start", task_id, "INVALID_STATE") + continue + + audio = _convert_audio( + message["bytes"], + ctx.params["format"], + ctx.params["sample_rate"], + ) + if audio is None: + continue + + has_voice = self._has_voice(audio) + + if not ctx.sentence_active: + if not has_voice: + self._append_pre_roll(ctx, audio) + continue + self._start_turn(ctx, audio) + engine = await self._ensure_engine(ctx) + await self._push_realtime_stream_audio( + ctx, + engine, + np.asarray(ctx.segment_audio_buffer, dtype=np.float32), + ) + else: + self._append_turn_audio(ctx, audio, has_voice=has_voice) + engine = await self._ensure_engine(ctx) + await self._push_realtime_stream_audio(ctx, engine, audio) + + if not ctx.sentence_active: + continue + + if self._should_decode_turn_partial(ctx): + current, current_language = await self._decode_turn_partial_text( + engine, + ctx, + ) + ctx.last_partial_decode_samples = int(ctx.segment_audio_buffer.size) + current = self._sanitize_candidate_text(current) + if current and not self._is_degenerate_repetition(current): + current_language = current_language or self._infer_text_language(current) + observed = self._update_segment_observed_text( + ctx, + current, + current_language, + ) + visible_observed = self._trim_previous_segment_overlap(ctx, observed) + partial_display = self._clip_partial_text(visible_observed, ctx) + if ( + partial_display + and ( + partial_display != ctx.last_partial_display_text + or current_language != ctx.last_partial_language + ) + ): + ctx.last_partial_chunk_id += 1 + ctx.last_partial_text = visible_observed.strip() + ctx.last_partial_display_text = partial_display.strip() + ctx.last_partial_language = current_language + self._update_best_partial(ctx, visible_observed, current_language) + + sentence_payload = self._build_tencent_sentence( + { + "index": ctx.segment_index, + "start_ms": int(ctx.timeline_cursor_ms), + "end_ms": int(ctx.timeline_cursor_ms + (ctx.segment_audio_buffer.size / 16)), + "text": partial_display, + "speaker_id": -1, + "sentence_type": 0, + }, + sentence_type=0, + sentence_text=partial_display, + ) + await self._send_json_safe( + websocket, + ctx, + { + "type": "sentences", + "code": 0, + "voice_id": task_id, + "final": 0, + "result": { + "slice_type": 1, + "index": int(ctx.segment_index), + "voice_text_str": partial_display, + }, + "sentences": [sentence_payload], + }, + ) + ctx.state = ConnectionState.STREAMING + + if self._sentence_count(visible_observed) >= self._max_sentence_count(ctx): + await self._commit_retranscribe_turn( + websocket, + ctx, + task_id, + "sentence_limit", + ) + continue + + if self._should_commit_complete_sentence(ctx, visible_observed): + await self._commit_retranscribe_turn( + websocket, + ctx, + task_id, + "complete_sentence", + ) + continue + + silence_threshold = self._get_dynamic_silence_threshold_samples(ctx) + if ctx.silence_samples >= silence_threshold: + await self._commit_retranscribe_turn( + websocket, + ctx, + task_id, + "silence", + ) + ctx.state = ConnectionState.STREAMING + continue + + max_segment_samples = self._max_segment_samples(ctx) + if max_segment_samples > 0 and ctx.segment_audio_buffer.size >= max_segment_samples: + if not self._enable_realtime_vad_split(ctx): + await self._commit_retranscribe_turn( + websocket, + ctx, + task_id, + "max_duration", + ) + ctx.state = ConnectionState.STREAMING + continue + + split_sample = await self._find_completed_segment_split_sample( + ctx.segment_audio_buffer, + ) + if split_sample is not None and 0 < split_sample < ctx.segment_audio_buffer.size: + finalized_audio = np.asarray( + ctx.segment_audio_buffer[:split_sample], + dtype=np.float32, + ) + carry_audio = np.asarray( + ctx.segment_audio_buffer[split_sample:], + dtype=np.float32, + ) + await self._commit_retranscribe_turn( + websocket, + ctx, + task_id, + "long_speech", + finalized_audio_override=finalized_audio, + carry_audio_override=carry_audio, + ) + else: + await self._commit_retranscribe_turn( + websocket, + ctx, + task_id, + "max_duration", + ) + ctx.state = ConnectionState.STREAMING + continue + + except WebSocketDisconnect: + logger.info("[%s] WebSocket disconnected", task_id) + except Exception as exc: + logger.error("[%s] Connection error: %s", task_id, exc) + await self._send_error(websocket, str(exc), task_id) + finally: + await self._detach_session(session_id, keep_for_resume=keep_for_resume) + logger.info("[%s] Connection closed", task_id) + + async def _stop( + self, + websocket: WebSocket, + ctx: ConnectionContext, + task_id: str, + ) -> None: + try: + if ctx.sentence_active and ctx.segment_audio_buffer.size > 0: + await self._commit_retranscribe_turn( + websocket, + ctx, + task_id, + "final", + emit_segment_start=False, + ) + + final_updates = self._recluster_confirmed_segments(ctx) + for updated in final_updates: + sentence_payload = self._build_tencent_sentence( + updated["segment"], + sentence_type=1, + ) + await websocket.send_json( + { + "type": "sentences", + "code": 0, + "voice_id": task_id, + "final": 0, + "result": { + "slice_type": 2, + "index": int(updated["segment_index"]), + "voice_text_str": str(updated["segment"].get("text") or ""), + }, + "sentences": [sentence_payload], + } + ) + + all_texts = [ + segment["text"] + for segment in ctx.confirmed_segments + if segment["text"].strip() + ] + full_text = "\n".join(all_texts) + public_segments = [ + self._public_payload(segment) or {} + for segment in ctx.confirmed_segments + ] + + await websocket.send_json( + { + "type": "end", + "code": 0, + "message": "", + "voice_id": task_id, + "session_id": task_id, + "final": 1, + "result": { + "slice_type": 2, + "index": max(len(ctx.confirmed_segments) - 1, 0), + "voice_text_str": full_text, + }, + "sentences": [ + self._build_tencent_sentence(segment, sentence_type=1) + for segment in public_segments + ], + } + ) + + logger.info("[%s] Recognition completed, segments=%s", task_id, len(ctx.confirmed_segments)) + + except Exception as exc: + logger.error("[%s] Stop failed: %s", task_id, exc) + await self._send_error(websocket, f"结束识别失败: {exc}", task_id) diff --git a/app/services/realtime_speaker_clusterer.py b/app/services/realtime_speaker_clusterer.py new file mode 100644 index 0000000..25bbae6 --- /dev/null +++ b/app/services/realtime_speaker_clusterer.py @@ -0,0 +1,640 @@ +# -*- coding: utf-8 -*- +"""FunASR-style realtime speaker chunking and clustering.""" + +from __future__ import annotations + +import logging +import re +from collections import defaultdict +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import scipy.linalg +import sklearn.metrics.pairwise +from sklearn.cluster._kmeans import k_means + +from app.core.config import settings +from app.core.executor import run_sync +from app.services.speaker_registry import get_speaker_registry_service + +logger = logging.getLogger(__name__) + + +class RealtimeSpeakerClusterer: + """Sentence-level speaker attribution using chunk embeddings and session clustering.""" + + FUNASR_SMOOTH_MINDUR_SEC = 0.7 + _FAST_ATTACH_MAX_SEC = 1.6 + _REGISTRY_MATCH_MIN_SEC = 2.4 + + def _is_generic_speaker_name(self, value: Any) -> bool: + text = str(value or "").strip() + return bool(re.fullmatch(r"Speaker\d+", text)) + + def _has_named_identity(self, record: Optional[Dict[str, Any]]) -> bool: + if not record: + return False + return bool( + record.get("registry_speaker_id") + or record.get("user_id") + or ( + record.get("speaker_name") + and not self._is_generic_speaker_name(record.get("speaker_name")) + ) + ) + + def _normalize_embedding(self, embedding: np.ndarray) -> np.ndarray: + emb = np.asarray(embedding, dtype=np.float32).reshape(-1) + norm = max(float(np.linalg.norm(emb)), 1e-12) + return (emb / norm).astype(np.float32) + + @staticmethod + def _coerce_cluster_index(value: Any, fallback: int) -> int: + try: + if value is None: + return int(fallback) + return int(value) + except (TypeError, ValueError): + return int(fallback) + + def _match_existing_speaker( + self, + speaker_records: List[Dict[str, Any]], + mean_embedding: np.ndarray, + ) -> Optional[Dict[str, Any]]: + if not speaker_records: + return None + + best_record: Optional[Dict[str, Any]] = None + best_score = -1.0 + for record in speaker_records: + record_embedding = record.get("embedding") + if record_embedding is None: + continue + score = float(np.dot(self._normalize_embedding(record_embedding), mean_embedding)) + if score > best_score: + best_score = score + best_record = record + + if best_record is None: + return None + + base_threshold = max( + float(getattr(settings, "REALTIME_UNKNOWN_SPK_CLUSTER_THRESHOLD", 0.58) or 0.58), + 0.3, + ) + has_named_identity = bool( + best_record.get("registry_speaker_id") + or best_record.get("user_id") + or ( + best_record.get("speaker_name") + and not self._is_generic_speaker_name(best_record.get("speaker_name")) + ) + ) + threshold = max(base_threshold, 0.62 if has_named_identity else 0.72) + if best_score < threshold: + return None + + matched = dict(best_record) + matched["_match_score"] = best_score + return matched + + def _next_generic_speaker_id( + self, + speaker_records: List[Dict[str, Any]], + ) -> str: + seen: set[int] = set() + for record in speaker_records: + for value in ( + record.get("speaker_id"), + record.get("speaker_name"), + ): + text = str(value or "").strip() + match = re.fullmatch(r"Speaker(\d+)", text) + if match: + seen.add(int(match.group(1))) + next_index = max(seen, default=0) + 1 + return f"Speaker{next_index:02d}" + + def _is_mixed_speaker_segment( + self, + speaker_records: List[Dict[str, Any]], + current_chunks: List[Dict[str, Any]], + ) -> bool: + if len(current_chunks) < 2 or len(speaker_records) < 2: + return False + assigned_labels: List[str] = [] + for chunk in current_chunks: + embedding = chunk.get("embedding") + if embedding is None: + continue + matched = self._match_existing_speaker( + speaker_records, + self._normalize_embedding(np.asarray(embedding, dtype=np.float32)), + ) + if not matched: + continue + label = str( + matched.get("registry_speaker_id") + or matched.get("speaker_name") + or matched.get("speaker_id") + or "" + ).strip() + if label: + assigned_labels.append(label) + if len(assigned_labels) < 2: + return False + return len(set(assigned_labels)) >= 2 + + def _correct_labels(self, labels: np.ndarray) -> np.ndarray: + labels_id = 0 + id2id: dict[int, int] = {} + new_labels: list[int] = [] + for label in labels.tolist(): + label = int(label) + if label not in id2id: + id2id[label] = labels_id + labels_id += 1 + new_labels.append(id2id[label]) + return np.asarray(new_labels, dtype=np.int32) + + def _spectral_cluster(self, X: np.ndarray, oracle_num: Optional[int] = None) -> np.ndarray: + sim_mat = sklearn.metrics.pairwise.cosine_similarity(X, X) + A = sim_mat.copy() + pval = 0.022 + if A.shape[0] * pval < 6: + pval = 6.0 / A.shape[0] + n_elems = int((1 - pval) * A.shape[0]) + for i in range(A.shape[0]): + low_indexes = np.argsort(A[i, :])[:n_elems] + A[i, low_indexes] = 0 + A = 0.5 * (A + A.T) + A[np.diag_indices(A.shape[0])] = 0 + D = np.diag(np.sum(np.abs(A), axis=1)) + L = D - A + lambdas, eig_vecs = scipy.linalg.eigh(L) + if oracle_num is not None: + num_spk = max(int(oracle_num), 1) + else: + min_num_spks = 1 + max_num_spks = min(15, max(1, X.shape[0] - 1)) + eig_slice = lambdas[min_num_spks - 1 : max_num_spks + 1] + gap_list = [float(eig_slice[i + 1]) - float(eig_slice[i]) for i in range(len(eig_slice) - 1)] + num_spk = int(np.argmax(gap_list)) + min_num_spks if gap_list else 1 + emb = eig_vecs[:, : max(num_spk, 1)] + _, labels, _ = k_means(emb, max(num_spk, 1)) + return np.asarray(labels, dtype=np.int32) + + def _merge_by_cos(self, labels: np.ndarray, embs: np.ndarray, cos_thr: float) -> np.ndarray: + labels = np.asarray(labels, dtype=np.int32).copy() + while True: + spk_num = int(labels.max()) + 1 + if spk_num <= 1: + break + centers = [] + for i in range(spk_num): + spk_emb = embs[labels == i].mean(0) + centers.append(spk_emb) + centers = np.stack(centers, axis=0) + norm_centers = centers / np.linalg.norm(centers, axis=1, keepdims=True) + affinity = np.matmul(norm_centers, norm_centers.T) + affinity = np.triu(affinity, 1) + spks = np.unravel_index(np.argmax(affinity), affinity.shape) + if float(affinity[spks]) < cos_thr: + break + for i in range(len(labels)): + if labels[i] == spks[1]: + labels[i] = spks[0] + elif labels[i] > spks[1]: + labels[i] -= 1 + return self._correct_labels(labels) + + def _cluster_embeddings(self, X: np.ndarray, oracle_num: Optional[int] = None) -> np.ndarray: + if X.shape[0] < 20: + labels = np.zeros(X.shape[0], dtype=np.int32) + else: + labels = self._spectral_cluster(X, oracle_num=oracle_num) + return self._merge_by_cos(labels, X, cos_thr=0.78 if oracle_num is None else 1.0) + + def build_sv_chunks( + self, + audio: np.ndarray, + *, + segment_start_ms: int = 0, + ) -> List[Dict[str, Any]]: + audio = np.asarray(audio, dtype=np.float32).flatten() + if audio.size == 0: + return [] + sample_rate = 16000 + chunk_len = int(1.5 * sample_rate) + chunk_shift = int(0.75 * sample_rate) + chunks: List[Dict[str, Any]] = [] + last_chunk_end = 0 + for chunk_start in range(0, audio.shape[0], chunk_shift): + chunk_end = min(chunk_start + chunk_len, audio.shape[0]) + if chunk_end <= last_chunk_end: + break + actual_start = max(0, chunk_end - chunk_len) + actual_end = chunk_end + last_chunk_end = actual_end + chunk_audio = np.asarray(audio[actual_start:actual_end], dtype=np.float32) + if chunk_audio.shape[0] < chunk_len: + chunk_audio = np.pad(chunk_audio, (0, chunk_len - chunk_audio.shape[0]), "constant") + chunks.append( + { + "start_ms": int(segment_start_ms + actual_start / 16.0), + "end_ms": int(segment_start_ms + actual_end / 16.0), + "audio": chunk_audio, + } + ) + return chunks + + async def extract_chunk_embeddings( + self, + audio: np.ndarray, + *, + segment_start_ms: int = 0, + ) -> List[Dict[str, Any]]: + chunk_items = self.build_sv_chunks(audio, segment_start_ms=segment_start_ms) + if not chunk_items: + chunk_items = [ + { + "start_ms": int(segment_start_ms), + "end_ms": int(segment_start_ms + np.asarray(audio, dtype=np.float32).size / 16.0), + "audio": np.asarray(audio, dtype=np.float32), + } + ] + + registry = get_speaker_registry_service() + results: List[Dict[str, Any]] = [] + for item in chunk_items: + embedding = await run_sync( + registry.extract_embedding_from_audio, + item["audio"], + 16000, + model_id=settings.REALTIME_SV_MODEL, + model_revision=settings.REALTIME_SV_MODEL_REVISION or None, + ) + results.append( + { + "start_ms": int(item["start_ms"]), + "end_ms": int(item["end_ms"]), + "embedding": self._normalize_embedding(np.asarray(embedding, dtype=np.float32)), + } + ) + return results + + async def extract_registry_embedding(self, audio: np.ndarray) -> Optional[np.ndarray]: + audio_array = np.asarray(audio, dtype=np.float32).flatten() + if audio_array.size == 0: + return None + try: + registry = get_speaker_registry_service() + embedding = await run_sync( + registry.extract_embedding_from_audio, + audio_array, + 16000, + ) + return self._normalize_embedding(np.asarray(embedding, dtype=np.float32)) + except Exception as exc: + logger.warning("Realtime registry speaker embedding failed: %s", exc) + return None + + def _flatten_chunk_records( + self, + records: List[Dict[str, Any]], + ) -> Tuple[List[Dict[str, Any]], np.ndarray]: + flat_chunks: List[Dict[str, Any]] = [] + flat_embeddings: List[np.ndarray] = [] + for record_idx, record in enumerate(records): + chunk_entries = record.get("chunks") or [] + if not chunk_entries: + embeddings = record.get("embeddings") or [] + if not embeddings and record.get("embedding") is not None: + embeddings = [record["embedding"]] + start_ms = int(record.get("start_ms", 0)) + end_ms = int(record.get("end_ms", start_ms)) + for embedding in embeddings: + chunk_entries.append( + { + "start_ms": start_ms, + "end_ms": end_ms, + "embedding": embedding, + } + ) + for chunk in chunk_entries: + emb = self._normalize_embedding(np.asarray(chunk["embedding"], dtype=np.float32)) + flat_chunks.append( + { + "record_index": record_idx, + "start_ms": int(chunk.get("start_ms", record.get("start_ms", 0))), + "end_ms": int(chunk.get("end_ms", record.get("end_ms", 0))), + } + ) + flat_embeddings.append(emb) + if not flat_embeddings: + return [], np.zeros((0, 0), dtype=np.float32) + return flat_chunks, np.stack(flat_embeddings, axis=0).astype(np.float32) + + def _merge_seque(self, distribute_res: List[List[float]]) -> List[List[float]]: + if not distribute_res: + return [] + res = [distribute_res[0][:]] + for item in distribute_res[1:]: + if item[2] != res[-1][2] or item[0] > res[-1][1]: + res.append(item[:]) + else: + res[-1][1] = item[1] + return res + + def _smooth_timeline( + self, + res: List[List[float]], + mindur: float = FUNASR_SMOOTH_MINDUR_SEC, + ) -> List[List[float]]: + if len(res) < 2: + return res + for item in res: + item[0] = round(float(item[0]), 2) + item[1] = round(float(item[1]), 2) + for idx in range(len(res)): + if res[idx][1] - res[idx][0] < mindur: + if idx == 0: + res[idx][2] = res[idx + 1][2] + elif idx == len(res) - 1: + res[idx][2] = res[idx - 1][2] + elif res[idx][0] - res[idx - 1][1] <= res[idx + 1][0] - res[idx][1]: + res[idx][2] = res[idx - 1][2] + else: + res[idx][2] = res[idx + 1][2] + return self._merge_seque(res) + + def _postprocess_timeline( + self, + flat_chunks: List[Dict[str, Any]], + labels: np.ndarray, + embeddings: np.ndarray, + ) -> List[Dict[str, Any]]: + assert len(flat_chunks) == len(labels) + labels = self._correct_labels(labels) + distribute_res = [ + [ + float(chunk["start_ms"]) / 1000.0, + float(chunk["end_ms"]) / 1000.0, + int(labels[idx]), + ] + for idx, chunk in enumerate(flat_chunks) + ] + distribute_res = self._merge_seque(distribute_res) + + def is_overlapped(t1: float, t2: float) -> bool: + return t1 > t2 + 1e-4 + + for idx in range(1, len(distribute_res)): + if is_overlapped(distribute_res[idx - 1][1], distribute_res[idx][0]): + pivot = (distribute_res[idx][0] + distribute_res[idx - 1][1]) / 2.0 + distribute_res[idx][0] = pivot + distribute_res[idx - 1][1] = pivot + + distribute_res = self._smooth_timeline(distribute_res) + return [ + { + "start_ms": int(round(item[0] * 1000.0)), + "end_ms": int(round(item[1] * 1000.0)), + "cluster_index": int(item[2]), + } + for item in distribute_res + ] + + def _pick_segment_cluster( + self, + cluster_ranges: List[Dict[str, Any]], + *, + segment_start_ms: int, + segment_end_ms: int, + ) -> int: + overlaps: Dict[int, int] = defaultdict(int) + for item in cluster_ranges: + overlap = min(segment_end_ms, int(item["end_ms"])) - max(segment_start_ms, int(item["start_ms"])) + if overlap > 0: + overlaps[int(item["cluster_index"])] += int(overlap) + if overlaps: + return max(overlaps.items(), key=lambda kv: (kv[1], -kv[0]))[0] + + centers = [ + item for item in cluster_ranges + if int(item["start_ms"]) <= segment_end_ms and int(item["end_ms"]) >= segment_start_ms + ] + if centers: + return int(centers[0]["cluster_index"]) + return 0 + + def cluster_records_with_ranges( + self, + records: List[Dict[str, Any]], + ) -> Tuple[List[Dict[str, Any]], List[int], List[Dict[str, Any]]]: + flat_chunks, X = self._flatten_chunk_records(records) + if X.size == 0: + return [], [], [] + + labels = self._cluster_embeddings(X, oracle_num=None) + cluster_ranges = self._postprocess_timeline(flat_chunks, labels, X) + + cluster_ids = sorted({int(item["cluster_index"]) for item in cluster_ranges}) + if not cluster_ids: + cluster_ids = sorted({int(label) for label in labels.tolist()}) + + clusters: List[Dict[str, Any]] = [] + for cluster_idx in cluster_ids: + member_mask = labels == int(cluster_idx) + member_embeddings = X[member_mask] + centroid = self._normalize_embedding(member_embeddings.mean(0)) + record_refs = [] + seen_record_indices = set() + for emb_idx, chunk in enumerate(flat_chunks): + record_idx = int(chunk["record_index"]) + if not member_mask[emb_idx] or record_idx in seen_record_indices: + continue + seen_record_indices.add(record_idx) + record_refs.append(records[record_idx]) + clusters.append( + { + "centroid": centroid, + "count": int(member_mask.sum()), + "record_refs": record_refs, + } + ) + + record_to_labels: List[List[int]] = [[] for _ in records] + for emb_idx, chunk in enumerate(flat_chunks): + record_to_labels[int(chunk["record_index"])].append(int(labels[emb_idx])) + + record_assignments: List[int] = [] + for record, local_assignments in zip(records, record_to_labels): + if local_assignments: + cluster_idx = self._pick_segment_cluster( + cluster_ranges, + segment_start_ms=int(record.get("start_ms", 0)), + segment_end_ms=int(record.get("end_ms", record.get("start_ms", 0))), + ) + record_assignments.append(cluster_idx) + else: + record_assignments.append(-1) + + return clusters, record_assignments, cluster_ranges + + def cluster_records( + self, + records: List[Dict[str, Any]], + ) -> Tuple[List[Dict[str, Any]], List[int]]: + clusters, record_assignments, _ = self.cluster_records_with_ranges(records) + return clusters, record_assignments + + async def resolve_segment_speaker( + self, + speaker_records: List[Dict[str, Any]], + audio: np.ndarray, + *, + segment_start_ms: int, + segment_end_ms: int, + enable_registry_match: bool, + speaker_threshold: Optional[float], + ) -> Optional[Dict[str, Any]]: + duration_sec = float(len(audio)) / 16000.0 + last_record = speaker_records[-1] if speaker_records else None + if duration_sec < self._FAST_ATTACH_MAX_SEC and speaker_records: + if self._has_named_identity(last_record): + speaker_id = last_record.get("registry_speaker_id") or last_record.get("speaker_id") or "Speaker01" + speaker_name = last_record.get("speaker_name") or speaker_id + return { + "speaker_id": speaker_id, + "speaker_name": speaker_name, + "user_id": last_record.get("user_id"), + "registry_speaker_id": last_record.get("registry_speaker_id"), + "speaker_confidence": 0.0, + "speaker_strategy": "short_attach", + "_cluster_index": last_record.get("cluster_index"), + "_embedding": last_record.get("embedding"), + "_chunk_embeddings": last_record.get("embeddings") or [], + "_chunks": last_record.get("chunks") or [], + } + + current_chunks = await self.extract_chunk_embeddings( + np.asarray(audio, dtype=np.float32), + segment_start_ms=segment_start_ms, + ) + if not current_chunks: + if speaker_records: + if self._has_named_identity(last_record): + speaker_id = last_record.get("registry_speaker_id") or last_record.get("speaker_id") or "Speaker01" + speaker_name = last_record.get("speaker_name") or speaker_id + return { + "speaker_id": speaker_id, + "speaker_name": speaker_name, + "user_id": last_record.get("user_id"), + "registry_speaker_id": last_record.get("registry_speaker_id"), + "speaker_confidence": 0.0, + "speaker_strategy": "embedding_attach", + "_cluster_index": last_record.get("cluster_index"), + "_embedding": last_record.get("embedding"), + "_chunk_embeddings": last_record.get("embeddings") or [], + "_chunks": last_record.get("chunks") or [], + } + return None + + mean_embedding = self._normalize_embedding( + np.mean(np.stack([chunk["embedding"] for chunk in current_chunks], axis=0), axis=0) + ) + if duration_sec >= 4.0 and self._is_mixed_speaker_segment(speaker_records, current_chunks): + return { + "speaker_id": -1, + "speaker_name": "", + "user_id": None, + "registry_speaker_id": None, + "speaker_confidence": 0.0, + "speaker_strategy": "mixed_segment", + "_cluster_index": None, + "_embedding": mean_embedding, + "_chunk_embeddings": [np.asarray(chunk["embedding"], dtype=np.float32) for chunk in current_chunks], + "_chunks": current_chunks, + } + matched_record = self._match_existing_speaker(speaker_records, mean_embedding) + speaker_id = self._next_generic_speaker_id(speaker_records) + speaker_name = speaker_id + user_id = None + registry_speaker_id = None + strategy = "new_speaker" + cluster_index = max( + [ + int(record.get("cluster_index", -1)) + for record in speaker_records + if record.get("cluster_index") is not None + ] or [-1] + ) + 1 + confidence = 0.0 + + if matched_record is not None: + speaker_id = ( + matched_record.get("registry_speaker_id") + or matched_record.get("speaker_id") + or speaker_id + ) + speaker_name = matched_record.get("speaker_name") or speaker_id + user_id = matched_record.get("user_id") + registry_speaker_id = matched_record.get("registry_speaker_id") + cluster_index = self._coerce_cluster_index( + matched_record.get("cluster_index"), + cluster_index, + ) + confidence = float(matched_record.get("_match_score", 0.0)) + strategy = "embedding_match" + + if ( + not registry_speaker_id + and enable_registry_match + and duration_sec >= self._REGISTRY_MATCH_MIN_SEC + and ( + matched_record is None + or confidence >= max(float(getattr(settings, "REALTIME_SPEAKER_CONFIRM_THRESHOLD", 0.62) or 0.62), 0.7) + ) + ): + # Realtime clustering can use a dedicated fast model, while the + # registry stores embeddings from the registration model. Match the + # registry in its own embedding space instead of comparing vectors + # extracted by a different model. + registry_embedding = await self.extract_registry_embedding(audio) + if registry_embedding is not None: + matched = await get_speaker_registry_service().identify_embedding( + registry_embedding, + threshold=speaker_threshold, + ) + if matched.get("name"): + speaker_id = matched.get("speaker_id") or speaker_id + speaker_name = matched.get("name") or speaker_name + user_id = matched.get("user_id") + registry_speaker_id = matched.get("speaker_id") + strategy = "registry_match" + + return { + "speaker_id": speaker_id, + "speaker_name": speaker_name, + "user_id": user_id, + "registry_speaker_id": registry_speaker_id, + "speaker_confidence": round(confidence, 4), + "speaker_strategy": strategy, + "_cluster_index": self._coerce_cluster_index(cluster_index, 0), + "_embedding": mean_embedding, + "_chunk_embeddings": [np.asarray(chunk["embedding"], dtype=np.float32) for chunk in current_chunks], + "_chunks": current_chunks, + "_matched_existing": matched_record is not None, + } + + +_realtime_speaker_clusterer: Optional[RealtimeSpeakerClusterer] = None + + +def get_realtime_speaker_clusterer() -> RealtimeSpeakerClusterer: + global _realtime_speaker_clusterer + if _realtime_speaker_clusterer is None: + _realtime_speaker_clusterer = RealtimeSpeakerClusterer() + return _realtime_speaker_clusterer diff --git a/app/services/speaker_registry.py b/app/services/speaker_registry.py new file mode 100644 index 0000000..f171bc9 --- /dev/null +++ b/app/services/speaker_registry.py @@ -0,0 +1,250 @@ +# -*- coding: utf-8 -*- +"""Speaker embedding extraction, registration, and database identification.""" + +from __future__ import annotations + +import asyncio +import logging +import os +import tempfile +import threading +from typing import Any, Optional + +import librosa +import numpy as np +import soundfile as sf +import torch + +from app.core.config import settings +from app.core.database import pg_speaker_db + +logger = logging.getLogger(__name__) + + +def _normalize_embedding(embedding: np.ndarray) -> np.ndarray: + array = np.asarray(embedding, dtype=np.float32).flatten() + norm = float(np.linalg.norm(array)) + if norm < 1e-12: + return array + return array / norm + + +class SpeakerRegistryService: + """Owns speaker embedding models and pgvector lookups.""" + + def __init__(self) -> None: + self._pipelines: dict[tuple[str, str], Any] = {} + self._lock = threading.Lock() + self._inference_lock = threading.BoundedSemaphore(1) + + def _device(self) -> str: + from app.core.device import detect_device + + return detect_device(settings.DEVICE) + + def _get_pipeline( + self, + model_id: Optional[str] = None, + model_revision: Optional[str] = None, + ) -> Any: + effective_model_id = model_id or settings.SV_MODEL + effective_revision = model_revision if model_revision is not None else settings.SV_MODEL_REVISION + cache_key = (effective_model_id, effective_revision or "") + cached = self._pipelines.get(cache_key) + if cached is not None: + return cached + with self._lock: + cached = self._pipelines.get(cache_key) + if cached is not None: + return cached + from modelscope.pipelines import pipeline + from modelscope.utils.constant import Tasks + + from app.infrastructure.model_utils import resolve_model_path + + model_path = resolve_model_path(effective_model_id) + device = self._device() + kwargs: dict[str, Any] = { + "task": Tasks.speaker_verification, + "model": model_path, + "device": device, + } + if effective_revision: + kwargs["model_revision"] = effective_revision + logger.info("正在加载声纹识别模型: %s, device=%s", model_path, device) + pipeline_instance = pipeline(**kwargs) + if hasattr(pipeline_instance, "device_name"): + pipeline_instance.device_name = device + model = getattr(pipeline_instance, "model", None) + if model is not None and hasattr(model, "to"): + pipeline_instance.model = model.to(device) + logger.info("声纹识别模型加载成功") + self._pipelines[cache_key] = pipeline_instance + return pipeline_instance + + def ensure_loaded(self) -> None: + """Eagerly initialize the speaker verification pipeline at startup.""" + self._get_pipeline() + + def extract_embedding_from_audio( + self, + audio_data: np.ndarray, + sample_rate: int = 16000, + *, + model_id: Optional[str] = None, + model_revision: Optional[str] = None, + ) -> np.ndarray: + audio = np.asarray(audio_data, dtype=np.float32).flatten() + if audio.size == 0: + raise ValueError("empty audio") + if sample_rate != 16000: + audio = librosa.resample(audio, orig_sr=sample_rate, target_sr=16000) + pipeline_instance = self._get_pipeline(model_id=model_id, model_revision=model_revision) + model = getattr(pipeline_instance, "model", None) + if model is None: + raise RuntimeError("speaker verification model is not initialized") + device = getattr(pipeline_instance, "device_name", self._device()) + with self._inference_lock: + with torch.no_grad(): + embeddings = model(torch.as_tensor(audio[None, :]).to(device)) + if isinstance(embeddings, torch.Tensor): + embedding = embeddings.detach().cpu().numpy()[0] + else: + embedding = np.asarray(embeddings, dtype=np.float32)[0] + return _normalize_embedding(embedding) + + def extract_embedding_from_file(self, file_path: str) -> np.ndarray: + audio_data, sample_rate = librosa.load(file_path, sr=16000, mono=True) + return self.extract_embedding_from_audio(audio_data, int(sample_rate)) + + async def register_file( + self, + *, + name: str, + file_path: str, + user_id: Optional[str] = None, + ) -> dict[str, Optional[str]]: + if not pg_speaker_db.is_connected: + raise RuntimeError("Speaker database is not connected") + loop = asyncio.get_running_loop() + embedding = await loop.run_in_executor( + None, + self.extract_embedding_from_file, + file_path, + ) + speaker = await pg_speaker_db.save_speaker(name, embedding, user_id=user_id) + return { + "speaker_id": speaker.get("id"), + "name": speaker.get("name") or name, + "user_id": speaker.get("user_id"), + "speaker_model": "CampPlus", + } + + async def identify_embedding( + self, + embedding: np.ndarray, + threshold: Optional[float] = None, + ) -> dict[str, Optional[str]]: + if not pg_speaker_db.is_connected: + return {"speaker_id": None, "name": None, "user_id": None} + speaker = await pg_speaker_db.identify_speaker( + _normalize_embedding(embedding), + threshold if threshold is not None else settings.SV_THRESHOLD, + ) + return { + "speaker_id": speaker.get("id"), + "name": speaker.get("name"), + "user_id": speaker.get("user_id"), + "speaker_model": "CampPlus", + } + + async def identify_file( + self, + *, + file_path: str, + threshold: Optional[float] = None, + ) -> dict[str, Optional[str]]: + loop = asyncio.get_running_loop() + embedding = await loop.run_in_executor( + None, + self.extract_embedding_from_file, + file_path, + ) + return await self.identify_embedding(embedding, threshold=threshold) + + async def apply_registered_speakers( + self, + result: Any, + threshold: Optional[float] = None, + ) -> Any: + if not pg_speaker_db.is_connected: + return result + + cache: dict[str, dict[str, Optional[str]]] = {} + for segment in getattr(result, "segments", []) or []: + embedding = getattr(segment, "speaker_embedding", None) + local_speaker_id = getattr(segment, "speaker_id", None) + if embedding is None or not local_speaker_id: + continue + if local_speaker_id not in cache: + matched_candidate = await self.identify_embedding( + np.asarray(embedding, dtype=np.float32), + threshold=threshold, + ) + if matched_candidate.get("name"): + cache[local_speaker_id] = matched_candidate + else: + continue + matched = cache[local_speaker_id] + if matched.get("name"): + segment.speaker_id = matched.get("speaker_id") or segment.speaker_id + segment.speaker_name = matched.get("name") + segment.user_id = matched.get("user_id") + return result + + async def list_speakers(self) -> list[dict[str, Optional[str]]]: + return await pg_speaker_db.list_speakers() + + async def delete_speaker(self, speaker_id: str) -> bool: + if not speaker_id.isdigit(): + return False + return await pg_speaker_db.delete_speaker(int(speaker_id)) + + @staticmethod + async def save_upload_to_temp(content: bytes, suffix: str = ".wav") -> str: + fd, path = tempfile.mkstemp(prefix="speaker_", suffix=suffix, dir=settings.TEMP_DIR) + try: + with os.fdopen(fd, "wb") as file_obj: + file_obj.write(content) + except Exception: + os.close(fd) + raise + return path + + @staticmethod + def cleanup_file(path: Optional[str]) -> None: + if path and os.path.exists(path): + try: + os.remove(path) + except Exception as exc: + logger.warning("清理临时声纹文件失败 %s: %s", path, exc) + + @staticmethod + def save_audio_array_to_temp(audio_data: np.ndarray, sample_rate: int = 16000) -> str: + fd, path = tempfile.mkstemp(prefix="speaker_array_", suffix=".wav", dir=settings.TEMP_DIR) + os.close(fd) + sf.write(path, np.asarray(audio_data, dtype=np.float32), sample_rate) + return path + + +_speaker_registry_service: Optional[SpeakerRegistryService] = None +_speaker_registry_lock = threading.Lock() + + +def get_speaker_registry_service() -> SpeakerRegistryService: + global _speaker_registry_service + if _speaker_registry_service is None: + with _speaker_registry_lock: + if _speaker_registry_service is None: + _speaker_registry_service = SpeakerRegistryService() + return _speaker_registry_service diff --git a/app/utils/__init__.py b/app/utils/__init__.py new file mode 100644 index 0000000..5f5e9a7 --- /dev/null +++ b/app/utils/__init__.py @@ -0,0 +1,24 @@ +# -*- coding: utf-8 -*- +""" +工具模块 +包含通用工具函数和辅助功能 +""" + +from .common import generate_task_id, validate_text_input, parse_language_code +from .audio import save_audio_array, load_audio_file, generate_temp_audio_path, cleanup_temp_file +from .text_processing import apply_itn_to_text, normalize_asr_text + +__all__ = [ + # 通用工具函数 + "generate_task_id", + "validate_text_input", + "parse_language_code", + # 音频工具函数 + "save_audio_array", + "load_audio_file", + "generate_temp_audio_path", + "cleanup_temp_file", + # ITN（逆文本标准化）功能 - 基于itntext + "apply_itn_to_text", + "normalize_asr_text", +] diff --git a/app/utils/audio.py b/app/utils/audio.py new file mode 100644 index 0000000..71f2ab1 --- /dev/null +++ b/app/utils/audio.py @@ -0,0 +1,528 @@ +# -*- coding: utf-8 -*- +""" +统一音频处理工具 +ASR音频处理功能 +""" + +import os +import tempfile +import requests +import librosa +import soundfile as sf +import numpy as np +import subprocess +import logging +from dataclasses import dataclass +from typing import Tuple, Optional +from io import BytesIO +from urllib.parse import unquote, urlparse + +from ..core.config import settings +from ..core.exceptions import ( + InvalidParameterException, + InvalidMessageException, + DefaultServerErrorException, +) +logger = logging.getLogger(__name__) + + +@dataclass(frozen=True) +class NormalizedAudio: + path: str + timestamp_scale: float = 1.0 + + +def download_audio_from_url(url: str, max_size: Optional[int] = None) -> bytes: + """从 URL 或服务端本地路径读取音频文件 + + Args: + url: 音频文件 URL、本地路径或 file:// 路径 + max_size: 最大文件大小限制 + + Returns: + 音频文件的二进制数据 + + Raises: + InvalidParameterException: URL无效或下载失败 + InvalidMessageException: 文件太大 + """ + if not url: + raise InvalidParameterException("URL不能为空") + + max_file_size = max_size or settings.MAX_AUDIO_SIZE + parsed = urlparse(url) + + if parsed.scheme in {"", "file"}: + local_path = os.path.expanduser(unquote(parsed.path) if parsed.scheme == "file" else url) + if not os.path.isfile(local_path): + raise InvalidParameterException(f"本地音频文件不存在: {local_path}") + file_size = os.path.getsize(local_path) + if file_size > max_file_size: + max_size_mb = max_file_size // 1024 // 1024 + raise InvalidMessageException(f"音频文件太大，最大支持{max_size_mb}MB") + with open(local_path, "rb") as file_obj: + return file_obj.read() + + try: + response = requests.get(url, timeout=30, stream=True) + response.raise_for_status() + + # 检查Content-Length头 + content_length = response.headers.get("content-length") + if content_length and int(content_length) > max_file_size: + max_size_mb = max_file_size // 1024 // 1024 + raise InvalidMessageException(f"音频文件太大，最大支持{max_size_mb}MB") + + # 分块下载并检查大小 + audio_data = BytesIO() + downloaded_size = 0 + + for chunk in response.iter_content(chunk_size=8192): + downloaded_size += len(chunk) + if downloaded_size > max_file_size: + max_size_mb = max_file_size // 1024 // 1024 + raise InvalidMessageException(f"音频文件太大，最大支持{max_size_mb}MB") + audio_data.write(chunk) + + return audio_data.getvalue() + + except requests.RequestException as e: + raise InvalidParameterException(f"下载音频文件失败: {str(e)}") + + +def save_audio_to_temp_file(audio_data: bytes, suffix: str = ".wav") -> str: + """保存音频数据到临时文件 + + Args: + audio_data: 音频二进制数据 + suffix: 文件后缀 + + Returns: + 临时文件路径 + + Raises: + AudioProcessingException: 保存失败 + """ + try: + with tempfile.NamedTemporaryFile( + delete=False, suffix=suffix, dir=settings.TEMP_DIR + ) as temp_file: + temp_file.write(audio_data) + return temp_file.name + except Exception as e: + raise DefaultServerErrorException(f"保存音频文件失败: {str(e)}") + + +def cleanup_temp_file(file_path: str) -> None: + """清理临时文件 + + Args: + file_path: 文件路径 + """ + try: + if file_path and os.path.exists(file_path): + os.remove(file_path) + except Exception: + # 静默忽略清理错误 + pass + + +def load_audio_file(audio_path: str, target_sr: int = 16000) -> Tuple[np.ndarray, int]: + """加载音频文件并转换为指定采样率 + + Args: + audio_path: 音频文件路径 + target_sr: 目标采样率 + + Returns: + (audio_data, sample_rate): 音频数据和采样率 + + Raises: + AudioProcessingException: 加载失败 + """ + try: + # 使用librosa加载音频 + audio_data, sr = librosa.load(audio_path, sr=target_sr) + return audio_data, int(sr) + except Exception as e: + raise DefaultServerErrorException(f"加载音频文件失败: {str(e)}") + + +def get_audio_duration(audio_path: str) -> float: + """获取音频文件时长 + + Args: + audio_path: 音频文件路径 + + Returns: + 音频时长（秒） + + Raises: + AudioProcessingException: 获取时长失败 + """ + try: + # Load audio and get duration + y, sr = librosa.load(audio_path, sr=None) + duration = librosa.get_duration(y=y, sr=sr) + return duration + except Exception as e: + raise DefaultServerErrorException(f"获取音频时长失败: {str(e)}") + + +def get_container_duration(audio_path: str) -> Optional[float]: + """通过 ffprobe 获取音频容器的 metadata 时长 + + 对于 m4a/AAC 等压缩格式，容器记录的时长可能与实际解码样本数不一致 + （常见于 m3u8/ts 分片合并的音频）。返回 None 表示获取失败。 + """ + try: + result = subprocess.run( + ["ffprobe", "-v", "error", "-show_entries", "format=duration", + "-of", "default=noprint_wrappers=1:nokey=1", audio_path], + capture_output=True, text=True, timeout=10, + ) + if result.returncode == 0 and result.stdout.strip(): + return float(result.stdout.strip()) + except Exception as e: + logger.debug(f"ffprobe 获取容器时长失败: {e}") + return None + + +def get_timestamp_scale(original_audio_path: str, decoded_duration: float) -> float: + """计算时间戳缩放系数 + + 对比容器 metadata 时长与解码后实际时长，返回缩放系数。 + 用于修正 m4a/AAC 等格式中容器时长与解码时长不一致的问题。 + + Args: + original_audio_path: 原始音频文件路径（转换前） + decoded_duration: 解码后的实际音频时长（秒） + + Returns: + 缩放系数（容器时长 / 解码时长），无差异时返回 1.0 + """ + container_duration = get_container_duration(original_audio_path) + if container_duration is None or decoded_duration <= 0: + return 1.0 + + scale = container_duration / decoded_duration + if abs(scale - 1.0) < 0.001: + # 差异 < 0.1%，忽略 + return 1.0 + + logger.info( + f"检测到容器/解码时长不一致: container={container_duration:.3f}s, " + f"decoded={decoded_duration:.3f}s, scale={scale:.6f}" + ) + return scale + + +def resample_audio_array( + audio_array: np.ndarray, + original_sr: int, + target_sr: int, +) -> np.ndarray: + """重采样音频数组 + + Args: + audio_array: 原始音频数据 + original_sr: 原始采样率 + target_sr: 目标采样率 + + Returns: + 重采样后的音频数据 + """ + if original_sr == target_sr: + return audio_array + + try: + # 确保是1D数组用于librosa重采样 + if audio_array.ndim > 1: + # 如果是多声道，取第一个声道 + if audio_array.shape[0] > audio_array.shape[1]: + audio_1d = audio_array[0, :] + else: + audio_1d = ( + audio_array[:, 0] + if audio_array.shape[1] > 1 + else audio_array.flatten() + ) + else: + audio_1d = audio_array + + # 使用librosa进行重采样 + resampled = librosa.resample(audio_1d, orig_sr=original_sr, target_sr=target_sr) + + logger.info(f"音频重采样: {original_sr}Hz -> {target_sr}Hz") + return resampled + + except Exception as e: + logger.warning(f"音频重采样失败: {str(e)}，使用原始音频") + return audio_array + + +def adjust_audio_volume(audio_array: np.ndarray, volume: int) -> np.ndarray: + """调节音频音量 + + Args: + audio_array: 音频数据数组 + volume: 音量值，范围0~100，50为原始音量 + + Returns: + 调节后的音频数据 + """ + if int(volume) == 50: + return audio_array + + if volume < 0 or volume > 100: + logger.warning(f"音量值{volume}超出范围[0,100]，使用默认值50") + volume = 50 + + # 将音量值转换为倍数 (0-100 -> 0-2.0) + volume_factor = volume / 50.0 + + # 应用音量调节 + adjusted_audio = audio_array * volume_factor + + # 防止削波，如果音量过大导致超过范围，进行归一化 + max_val = np.max(np.abs(adjusted_audio)) + if max_val > 1.0: + adjusted_audio = adjusted_audio / max_val + logger.info(f"音量调节后进行归一化，最大值: {max_val:.3f}") + + logger.info(f"音频音量已调节: {volume}/100 (倍数: {volume_factor:.2f})") + return adjusted_audio + + +def save_audio_array( + audio_array: np.ndarray, + output_path: str, + sample_rate: int = 22050, + format: str = "wav", + original_sr: Optional[int] = None, + volume: int = 50, +) -> str: + """保存音频数组到文件 + + Args: + audio_array: 音频数据数组 + output_path: 输出文件路径 + sample_rate: 目标采样率 + format: 音频格式 + original_sr: 原始采样率（用于重采样） + volume: 音量值，范围0~100，默认50 + + Returns: + 保存的文件路径 + + Raises: + AudioProcessingException: 保存失败 + """ + try: + # 如果指定了原始采样率且与目标采样率不同，进行重采样 + if original_sr and original_sr != sample_rate: + audio_array = resample_audio_array(audio_array, original_sr, sample_rate) + + # 调节音频音量 + audio_array = adjust_audio_volume(audio_array, volume) + + # 确保音频数据是float32格式 + if audio_array.dtype != np.float32: + audio_array = audio_array.astype(np.float32) + + # 确保音频数据在正确的范围内 + if np.max(np.abs(audio_array)) > 1.0: + audio_array = audio_array / np.max(np.abs(audio_array)) + + # 确保是2D张量 (channels, samples) + if audio_array.ndim == 1: + audio_array = audio_array[np.newaxis, :] # 添加通道维度 + elif audio_array.ndim > 2: + audio_array = audio_array.squeeze() + if audio_array.ndim == 1: + audio_array = audio_array[np.newaxis, :] + + # 根据格式选择保存方法 + if format.lower() == "wav": + sf.write(output_path, audio_array.T, sample_rate, format="WAV") + else: + # 使用soundfile保存其他格式 + # 确保音频数据是单声道 + if audio_array.shape[0] > 1: + audio_array = np.mean(audio_array, axis=0) + + sf.write(output_path, audio_array.T, sample_rate, format=format.upper()) + + return output_path + + except Exception as e: + raise DefaultServerErrorException(f"保存音频文件失败: {str(e)}") + + +def convert_audio_to_wav( + input_path: str, output_path: Optional[str] = None, target_sr: int = 16000 +) -> str: + """转换音频文件为WAV格式 + + Args: + input_path: 输入文件路径 + output_path: 输出文件路径（可选） + target_sr: 目标采样率，默认16000Hz + + Returns: + 转换后的文件路径 + + Raises: + AudioProcessingException: 转换失败 + """ + if not output_path: + output_path = input_path.rsplit(".", 1)[0] + ".wav" + + try: + # 使用librosa加载并重采样 + audio_data, _ = librosa.load(input_path, sr=target_sr) + sf.write(output_path, audio_data, target_sr, format="WAV") + return output_path + + except Exception as e: + # 尝试使用ffmpeg转换 + try: + subprocess.run( + [ + "ffmpeg", + "-f", "s16le", + "-ar", str(target_sr), + "-ac", "1", + "-i", input_path, + "-acodec", "pcm_s16le", + output_path, + "-y", + ], + check=True, + capture_output=True, + ) + return output_path + except (subprocess.CalledProcessError, FileNotFoundError): + raise DefaultServerErrorException(f"音频格式转换失败: {str(e)}") + + +def normalize_audio_for_asr(audio_path: str, target_sr: int = 16000) -> NormalizedAudio: + """Normalize audio and return explicit timestamp metadata. + + Args: + audio_path: 输入音频文件路径 + target_sr: 目标采样率，默认16000Hz + + Returns: + Normalized audio path and timestamp scale metadata. + """ + try: + # 检查文件扩展名 + file_ext = os.path.splitext(audio_path)[1].lower() + + # 如果已经是WAV格式且采样率正确，直接返回 + if file_ext == ".wav": + # 检查采样率 + _, sr = librosa.load(audio_path, sr=None) + if sr == target_sr: + return NormalizedAudio(path=audio_path) + + # 转换为标准WAV格式 + normalized_path = convert_audio_to_wav(audio_path, target_sr=target_sr) + logger.debug(f"音频文件已标准化: {audio_path} -> {normalized_path}") + + timestamp_scale = 1.0 + if normalized_path != audio_path: + decoded_duration = get_audio_duration(normalized_path) + timestamp_scale = get_timestamp_scale(audio_path, decoded_duration) + + return NormalizedAudio(path=normalized_path, timestamp_scale=timestamp_scale) + + except Exception as e: + raise DefaultServerErrorException(f"音频标准化失败: {str(e)}") + + +def generate_temp_audio_path(prefix: str = "audio", suffix: str = ".wav") -> str: + """生成临时音频文件路径 + + Args: + prefix: 文件名前缀 + suffix: 文件后缀 + + Returns: + 临时文件路径 + """ + import time + + timestamp = int(time.time()) + filename = f"{prefix}_{timestamp}_{os.getpid()}{suffix}" + return os.path.join(settings.TEMP_DIR, filename) + + +def detect_audio_format_from_bytes(data: bytes) -> str: + """通过文件头（magic bytes）检测音频格式 + + Args: + data: 音频文件的前几个字节 + + Returns: + 文件后缀（包含点号） + """ + if len(data) < 12: + return ".wav" + + # 检查常见音频格式的文件头 + if data[:4] == b"RIFF" and data[8:12] == b"WAVE": + return ".wav" + elif data[:3] == b"ID3" or (data[0:2] == b"\xff\xfb") or (data[0:2] == b"\xff\xfa"): + return ".mp3" + elif data[:4] == b"fLaC": + return ".flac" + elif data[:4] == b"OggS": + return ".ogg" + elif data[4:8] == b"ftyp": + # M4A/AAC/MP4/MOV 容器 + return ".mp4" + elif data[:4] == b"\x1aE\xdf\xa3": + # WebM/MKV + return ".webm" + + # 默认为 wav，librosa 会自动处理 + return ".wav" + + +def get_audio_file_suffix( + audio_address: Optional[str] = None, audio_data: Optional[bytes] = None +) -> str: + """自动识别音频文件后缀 + + Args: + audio_address: 音频文件URL（可选） + audio_data: 音频二进制数据（可选，用于检测文件头） + + Returns: + 文件后缀（包含点号） + """ + if audio_address: + # 从URL中提取扩展名 + parsed = urlparse(audio_address) + path = unquote(parsed.path) + + # 获取扩展名 + ext = os.path.splitext(path)[1].lower() + if ext and ext in [ + ".wav", ".mp3", ".flac", ".ogg", ".m4a", ".aac", ".pcm", ".webm", + ".mp4", ".mpeg", ".mpga", ".mov", ".mkv", ".avi", + ]: + return ext + + # 无法识别扩展名，默认为 .wav + return ".wav" + + elif audio_data: + # 通过文件头检测格式 + return detect_audio_format_from_bytes(audio_data[:12]) + + else: + # 默认为 .wav + return ".wav" diff --git a/app/utils/audio_filter.py b/app/utils/audio_filter.py new file mode 100644 index 0000000..3cce0f0 --- /dev/null +++ b/app/utils/audio_filter.py @@ -0,0 +1,64 @@ +# -*- coding: utf-8 -*- +""" +音频过滤工具 - 用于流式ASR的近场/远场声音检测 +""" + +import numpy as np +import logging +from typing import Tuple, Dict + +logger = logging.getLogger(__name__) + + +def calculate_rms_energy(audio_array: np.ndarray) -> float: + """计算音频RMS能量 + + Args: + audio_array: float32音频数组，范围-1.0到1.0 + + Returns: + RMS能量值 + """ + if len(audio_array) == 0: + return 0.0 + return float(np.sqrt(np.mean(audio_array ** 2))) + + +def is_nearfield_voice( + audio_array: np.ndarray, + sample_rate: int = 16000, # noqa: ARG001 + rms_threshold: float = 0.01, + enable_filter: bool = True, +) -> Tuple[bool, Dict]: + """判断是否为近场有效声音（仅基于RMS能量） + + Args: + audio_array: float32音频数组，范围-1.0到1.0 + sample_rate: 采样率（保留用于兼容性） + rms_threshold: RMS能量阈值 + enable_filter: 是否启用过滤（开关） + + Returns: + (is_nearfield, metrics): 是否近场声音 + 检测指标详情 + """ + if not enable_filter: + return True, {'enabled': False} + + if len(audio_array) == 0: + return False, {'error': 'empty_array'} + + # 计算RMS能量 + rms_energy = calculate_rms_energy(audio_array) + + # 仅使用RMS能量判断 + is_nearfield = rms_energy >= rms_threshold + + metrics = { + 'rms_energy': round(rms_energy, 6), + 'is_nearfield': is_nearfield, + 'thresholds': { + 'rms': rms_threshold, + } + } + + return is_nearfield, metrics diff --git a/app/utils/audio_splitter.py b/app/utils/audio_splitter.py new file mode 100644 index 0000000..138d2ee --- /dev/null +++ b/app/utils/audio_splitter.py @@ -0,0 +1,439 @@ +# -*- coding: utf-8 -*- +""" +音频分割模块 +基于 VAD 的智能音频分割，支持长音频分段识别 +""" + +import logging +import numpy as np +import librosa +import soundfile as sf +import tempfile +import os +import time +from typing import List, Tuple, Optional +from dataclasses import dataclass + +from ..core.config import settings +from ..core.exceptions import DefaultServerErrorException + +logger = logging.getLogger(__name__) + + +def _log_audio_split_timing(stage: str, duration_ms: float, **extra) -> None: + payload = { + "event": "audio_split_timing", + "stage": stage, + "duration_ms": round(duration_ms, 2), + } + payload.update(extra) + logger.info("音频分割阶段耗时", extra=payload) + + +@dataclass +class AudioSegment: + """音频片段信息""" + + start_ms: int # 开始时间（毫秒） + end_ms: int # 结束时间（毫秒） + audio_data: Optional[np.ndarray] = None # 音频数据 + temp_file: Optional[str] = None # 临时文件路径 + speaker_id: Optional[str] = None # 说话人ID（多说话人模式） + + @property + def start_sec(self) -> float: + """开始时间（秒）""" + return self.start_ms / 1000.0 + + @property + def end_sec(self) -> float: + """结束时间（秒）""" + return self.end_ms / 1000.0 + + @property + def duration_ms(self) -> int: + """时长（毫秒）""" + return self.end_ms - self.start_ms + + @property + def duration_sec(self) -> float: + """时长（秒）""" + return self.duration_ms / 1000.0 + + +class AudioSplitter: + """音频分割器 + + 使用 VAD 模型检测语音边界，智能分割长音频 + """ + + # 默认配置 + DEFAULT_MIN_SEGMENT_SEC = 1.0 # 每段最小时长（秒） + DEFAULT_SAMPLE_RATE = 16000 # 默认采样率 + + def __init__( + self, + min_segment_sec: float = DEFAULT_MIN_SEGMENT_SEC, + device: str = "auto", + ): + """初始化音频分割器 + + Args: + min_segment_sec: 每段最小时长（秒） + device: 计算设备（"cuda", "cpu", "auto"） + """ + split_trigger_sec = settings.MAX_SEGMENT_SEC + + self.split_trigger_sec = split_trigger_sec + self.min_segment_sec = min_segment_sec + self.split_trigger_ms = int(split_trigger_sec * 1000) + self.min_segment_ms = int(min_segment_sec * 1000) + self.device = device + + def get_vad_segments( + self, audio_path: str + ) -> List[Tuple[int, int]]: + """使用 VAD 模型获取语音段 + + Args: + audio_path: 音频文件路径 + + Returns: + 语音段列表，每个元素为 (start_ms, end_ms) + """ + try: + from ..services.asr.engines import get_global_vad_model + + logger.info("开始 VAD 语音段检测...") + vad_model = get_global_vad_model(self.device) + if vad_model is None: + raise DefaultServerErrorException("VAD 模型未加载") + + # 调用 VAD 模型 + vad_started = time.perf_counter() + result = vad_model.generate(input=audio_path, cache={}) + vad_duration_ms = (time.perf_counter() - vad_started) * 1000 + + if not result or len(result) == 0: + _log_audio_split_timing( + "vad_generate", + vad_duration_ms, + audio_path=audio_path, + vad_segment_count=0, + ) + logger.warning("VAD 未检测到语音段") + return [] + + # 解析 VAD 结果 + # FunASR VAD 返回格式: [[start_ms, end_ms], [start_ms, end_ms], ...] + vad_segments = result[0].get("value", []) + + if not vad_segments: + _log_audio_split_timing( + "vad_generate", + vad_duration_ms, + audio_path=audio_path, + vad_segment_count=0, + ) + logger.warning("VAD 结果为空") + return [] + + _log_audio_split_timing( + "vad_generate", + vad_duration_ms, + audio_path=audio_path, + vad_segment_count=len(vad_segments), + ) + logger.info(f"VAD 检测到 {len(vad_segments)} 个语音段") + logger.info( + "开始按 VAD 边界重分段 " + f"(split_trigger={self.split_trigger_sec}s, min_segment={self.min_segment_sec}s)..." + ) + return [(int(seg[0]), int(seg[1])) for seg in vad_segments] + + except Exception as e: + logger.error(f"VAD 检测失败: {e}") + raise DefaultServerErrorException(f"VAD 检测失败: {str(e)}") + + def merge_segments_greedy( + self, vad_segments: List[Tuple[int, int]], total_duration_ms: int + ) -> List[Tuple[int, int]]: + """按 VAD 结果重分段 + + 策略： + 1. 默认保留 VAD 原始边界，避免将整段连续语音合并成超长片段 + 2. 仅对短片段（< min_segment_ms）做邻段合并 + 3. 对重叠片段进行边界修正，避免重复音频 + + Args: + vad_segments: VAD 检测到的语音段列表 [(start_ms, end_ms), ...] + total_duration_ms: 音频总时长（毫秒） + + Returns: + 合并后的段列表 [(start_ms, end_ms), ...] + """ + if not vad_segments: + # 没有 VAD 段，返回整个音频（按最大时长切分） + return self._split_by_fixed_duration(total_duration_ms) + + # 按时间排序并修正边界（防止越界、重叠） + sorted_vad = sorted(vad_segments, key=lambda x: x[0]) + normalized: List[Tuple[int, int]] = [] + for raw_start, raw_end in sorted_vad: + start_ms = max(0, int(raw_start)) + end_ms = min(total_duration_ms, int(raw_end)) + if end_ms <= start_ms: + continue + + if not normalized: + normalized.append((start_ms, end_ms)) + continue + + last_end = normalized[-1][1] + # 有重叠时，优先保持边界，避免与上一段重复采样 + if start_ms < last_end: + start_ms = last_end + + if end_ms > start_ms: + normalized.append((start_ms, end_ms)) + + if not normalized: + return self._split_by_fixed_duration(total_duration_ms) + + merged = list(normalized) + + # 只处理短片段：与相邻片段合并（不基于静音间隙） + idx = 0 + while idx < len(merged): + start_ms, end_ms = merged[idx] + duration = end_ms - start_ms + + if duration >= self.min_segment_ms or len(merged) == 1: + idx += 1 + continue + + if idx == 0: + # 首段过短：并入后段 + next_end = merged[idx + 1][1] + merged[idx + 1] = (start_ms, next_end) + del merged[idx] + continue + + if idx == len(merged) - 1: + # 尾段过短：并入前段 + prev_start, _ = merged[idx - 1] + merged[idx - 1] = (prev_start, end_ms) + del merged[idx] + idx = max(0, idx - 1) + continue + + # 中间短段：优先并入时长更短的一侧，避免单段过长 + prev_start = merged[idx - 1][0] + next_end = merged[idx + 1][1] + merged_with_prev_duration = end_ms - prev_start + merged_with_next_duration = next_end - start_ms + + if merged_with_prev_duration <= merged_with_next_duration: + merged[idx - 1] = (prev_start, end_ms) + del merged[idx] + idx = max(0, idx - 1) + else: + merged[idx + 1] = (start_ms, next_end) + del merged[idx] + + return merged + + def _split_by_fixed_duration(self, total_duration_ms: int) -> List[Tuple[int, int]]: + """按固定时长切分（无 VAD 时的 fallback） + + Args: + total_duration_ms: 音频总时长（毫秒） + + Returns: + 切分后的段列表 + """ + segments = [] + current = 0 + while current < total_duration_ms: + end = min(current + self.split_trigger_ms, total_duration_ms) + if end - current >= self.min_segment_ms: + segments.append((current, end)) + current = end + return segments + + def split_audio_file( + self, + audio_path: str, + output_dir: Optional[str] = None, + ) -> List[AudioSegment]: + """分割音频文件 + + Args: + audio_path: 音频文件路径 + output_dir: 输出目录（可选，默认使用临时目录） + + Returns: + 音频片段列表 + """ + try: + total_started = time.perf_counter() + # 加载音频 + load_started = time.perf_counter() + audio_data, sr = librosa.load(audio_path, sr=self.DEFAULT_SAMPLE_RATE) + load_audio_ms = (time.perf_counter() - load_started) * 1000 + total_duration_ms = int(len(audio_data) / sr * 1000) + audio_duration_sec = total_duration_ms / 1000 + + logger.info(f"音频总时长: {audio_duration_sec:.2f}秒") + _log_audio_split_timing( + "load_audio", + load_audio_ms, + audio_path=audio_path, + audio_duration_sec=round(audio_duration_sec, 2), + sample_rate=sr, + ) + + # 检查是否需要分割 + if total_duration_ms <= self.split_trigger_ms: + logger.info("音频时长在限制内，无需分割") + total_duration_ms_for_log = (time.perf_counter() - total_started) * 1000 + _log_audio_split_timing( + "split_total", + total_duration_ms_for_log, + audio_path=audio_path, + audio_duration_sec=round(audio_duration_sec, 2), + output_segment_count=1, + need_split=False, + load_audio_ms=round(load_audio_ms, 2), + vad_ms=0, + merge_ms=0, + write_segments_ms=0, + ) + return [ + AudioSegment( + start_ms=0, + end_ms=total_duration_ms, + audio_data=audio_data, + temp_file=audio_path, + ) + ] + + # 获取 VAD 段 + vad_started = time.perf_counter() + vad_segments = self.get_vad_segments(audio_path) + vad_ms = (time.perf_counter() - vad_started) * 1000 + + # 贪婪合并 + merge_started = time.perf_counter() + merged_segments = self.merge_segments_greedy(vad_segments, total_duration_ms) + merge_ms = (time.perf_counter() - merge_started) * 1000 + logger.info(f"重分段完成: 原始VAD={len(vad_segments)}, 输出={len(merged_segments)}") + _log_audio_split_timing( + "merge_segments", + merge_ms, + audio_path=audio_path, + vad_segment_count=len(vad_segments), + output_segment_count=len(merged_segments), + audio_duration_sec=round(audio_duration_sec, 2), + ) + + # 切分音频并保存到临时文件 + logger.info("开始切分音频并保存临时文件...") + output_dir = output_dir or settings.TEMP_DIR + os.makedirs(output_dir, exist_ok=True) + + audio_segments = [] + write_started = time.perf_counter() + for idx, (start_ms, end_ms) in enumerate(merged_segments): + # 计算采样点范围 + start_sample = int(start_ms / 1000 * sr) + end_sample = int(end_ms / 1000 * sr) + + # 提取音频片段 + segment_data = audio_data[start_sample:end_sample] + + # 保存到临时文件 + temp_file = tempfile.NamedTemporaryFile( + delete=False, + suffix=".wav", + dir=output_dir, + prefix=f"segment_{idx:03d}_", + ) + temp_path = temp_file.name + temp_file.close() + + sf.write(temp_path, segment_data, sr) + + segment = AudioSegment( + start_ms=start_ms, + end_ms=end_ms, + audio_data=segment_data, + temp_file=temp_path, + ) + audio_segments.append(segment) + + logger.debug( + f"分段 {idx + 1}/{len(merged_segments)}: " + f"{start_ms / 1000:.2f}s - {end_ms / 1000:.2f}s " + f"(时长: {segment.duration_sec:.2f}s)" + ) + + write_segments_ms = (time.perf_counter() - write_started) * 1000 + logger.info(f"音频切分完成，共 {len(audio_segments)} 个分段") + _log_audio_split_timing( + "write_segments", + write_segments_ms, + audio_path=audio_path, + output_dir=output_dir, + output_segment_count=len(audio_segments), + audio_duration_sec=round(audio_duration_sec, 2), + ) + total_ms = (time.perf_counter() - total_started) * 1000 + _log_audio_split_timing( + "split_total", + total_ms, + audio_path=audio_path, + audio_duration_sec=round(audio_duration_sec, 2), + output_segment_count=len(audio_segments), + need_split=True, + load_audio_ms=round(load_audio_ms, 2), + vad_ms=round(vad_ms, 2), + merge_ms=round(merge_ms, 2), + write_segments_ms=round(write_segments_ms, 2), + ) + return audio_segments + + except Exception as e: + logger.error(f"音频分割失败: {e}") + raise DefaultServerErrorException(f"音频分割失败: {str(e)}") + + @staticmethod + def cleanup_segments(segments: List[AudioSegment]) -> None: + """清理临时文件 + + Args: + segments: 音频片段列表 + """ + for segment in segments: + if segment.temp_file and os.path.exists(segment.temp_file): + try: + os.remove(segment.temp_file) + except Exception as e: + logger.warning(f"清理临时文件失败: {segment.temp_file}, {e}") + + +def split_long_audio( + audio_path: str, + device: str = "auto", +) -> List[AudioSegment]: + """分割长音频的便捷函数 + + Args: + audio_path: 音频文件路径 + device: 计算设备 + + Returns: + 音频片段列表 + """ + splitter = AudioSplitter(device=device) + return splitter.split_audio_file(audio_path) diff --git a/app/utils/boot_events.py b/app/utils/boot_events.py new file mode 100644 index 0000000..65b3355 --- /dev/null +++ b/app/utils/boot_events.py @@ -0,0 +1,28 @@ +# -*- coding: utf-8 -*- +"""Structured startup events for the optional terminal dashboard.""" + +from __future__ import annotations + +import json +import os +import sys +from typing import Any + +_BOOT_EVENT_PREFIX = "__FUNASR_BOOT__" + + +def boot_events_enabled() -> bool: + return (os.getenv("FUNASR_BOOT_EVENTS") or "").strip() == "1" + + +def emit_boot_event(event: str, **payload: Any) -> None: + if not boot_events_enabled(): + return + + data = {"event": event, **payload} + sys.stdout.write(_BOOT_EVENT_PREFIX + json.dumps(data, ensure_ascii=False) + "\n") + sys.stdout.flush() + + +def get_boot_event_prefix() -> str: + return _BOOT_EVENT_PREFIX diff --git a/app/utils/common.py b/app/utils/common.py new file mode 100644 index 0000000..5a26fda --- /dev/null +++ b/app/utils/common.py @@ -0,0 +1,90 @@ +# -*- coding: utf-8 -*- +""" +通用工具函数 +包含任务ID生成、参数验证等通用功能 +""" + +import uuid +import hashlib +import time +import re +from typing import Optional + + +def generate_task_id(prefix: str = "") -> str: + """生成唯一的任务ID + + Args: + prefix: 任务ID前缀 + + Returns: + 生成的任务ID + """ + timestamp = str(int(time.time() * 1000)) + random_id = str(uuid.uuid4()).replace("-", "") + combined = timestamp + random_id + + # 使用MD5哈希生成32位字符串 + task_id = hashlib.md5(combined.encode()).hexdigest() + + if prefix: + return f"{prefix}_{task_id}" + return task_id + + +def validate_text_input(text: str, max_length: int = 10000) -> tuple[bool, str]: + """验证输入文本 + + Args: + text: 待验证的文本 + max_length: 最大长度限制 + + Returns: + (is_valid, message): 验证结果和消息 + """ + if not text or not text.strip(): + return False, "文本内容不能为空" + + text = text.strip() + + if len(text) > max_length: + return False, f"文本长度超过限制，最大支持{max_length}个字符" + + # 检查是否包含有效字符 + if not re.search(r"[\u4e00-\u9fff\w\s]", text): + return False, "文本内容无效，请输入有效的中文、英文或数字" + + return True, "验证通过" + + +def parse_language_code(lang_code: Optional[str]) -> str: + """解析语言代码 + + Args: + lang_code: 语言代码（如 zh, zh-cn, en, ja等） + + Returns: + 标准化的语言代码 + """ + if not lang_code: + return "zh" # 默认中文 + + lang_code = lang_code.lower().strip() + + # 语言代码映射 + lang_mapping = { + "zh": "zh", + "zh-cn": "zh", + "zh-tw": "zh", + "zh-hk": "zh", + "en": "en", + "en-us": "en", + "en-gb": "en", + "ja": "jp", + "jp": "jp", + "ko": "kr", + "kr": "kr", + "yue": "yue", # 粤语 + } + + return lang_mapping.get(lang_code, "zh") diff --git a/app/utils/download_models.py b/app/utils/download_models.py new file mode 100644 index 0000000..0d7b2d1 --- /dev/null +++ b/app/utils/download_models.py @@ -0,0 +1,296 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +模型预下载脚本 +统一从 ModelScope 预下载所有运行所需模型 +""" + +import argparse +import json +from pathlib import Path +from typing import Optional + +from modelscope.hub.snapshot_download import snapshot_download as ms_snapshot_download +from app.core.config import settings +from app.services.asr.model_capabilities import ( + get_all_qwen_modelscope_assets, + get_camplusplus_replacement_paths, + get_download_modelscope_assets, +) + + +def _get_qwen_modelscope_assets(): + """Return all declared ModelScope Qwen assets for offline deployment.""" + assets = get_all_qwen_modelscope_assets() + if not assets: + print("当前部署计划未启用 Qwen3-ASR，跳过 Qwen 模型下载") + return [] + + print("离线部署模式：下载全部已声明 Qwen 模型（含 forced aligner）") + return assets + +def _get_cache_path(model_id: str, source: str = "modelscope") -> Path: + """获取模型缓存路径""" + _ = source + return Path(settings.MODELSCOPE_PATH) / model_id + + +def check_model_exists(model_id: str, source: str = "modelscope") -> tuple[bool, str]: + """检查模型是否已存在于本地缓存""" + try: + model_path = _get_cache_path(model_id, source) + + if model_path.exists() and model_path.is_dir(): + if any(model_path.iterdir()): + return True, str(model_path) + except Exception: + pass + + return False, "" + + +def check_all_models() -> list[tuple[str, str, str, Optional[str]]]: + """检查所有模型是否存在 + + Returns: + 缺失的模型列表，每个元素为 (model_id, description, source, revision) + """ + missing = [] + ms_assets = get_download_modelscope_assets() + qwen_assets = _get_qwen_modelscope_assets() + + for asset in ms_assets: + exists, _ = check_model_exists(asset.model_id, source="modelscope") + if not exists: + missing.append((asset.model_id, asset.description, "modelscope", asset.revision)) + + for asset in qwen_assets: + exists, _ = check_model_exists(asset.model_id, source="modelscope") + if not exists: + missing.append((asset.model_id, asset.description, "modelscope", asset.revision)) + + return missing + + +def fix_camplusplus_config() -> bool: + """修复 CAM++ 配置文件，将模型ID替换为本地路径（用于离线环境） + + 修复 issue #15: 离线环境下 CAM++ 模型会尝试从 modelscope.cn 获取依赖模型配置 + + Returns: + 是否修复成功 + """ + try: + cache_dir = Path(settings.MODELSCOPE_PATH) + config_file = cache_dir / "iic/speech_campplus_speaker-diarization_common/configuration.json" + + if not config_file.exists(): + return False + + # 读取配置文件 + with open(config_file, 'r', encoding='utf-8') as f: + config = json.load(f) + + # 需要替换的模型ID -> 本地路径映射 + replacements = get_camplusplus_replacement_paths(str(cache_dir)) + + # 检查是否需要修改 + modified = False + if "model" in config: + for key in ["speaker_model", "change_locator", "vad_model"]: + if key in config["model"]: + old_value = config["model"][key] + if old_value in replacements: + new_value = replacements[old_value] + # 检查本地路径是否存在 + if Path(new_value).exists(): + config["model"][key] = new_value + modified = True + + # 写回配置文件 + if modified: + with open(config_file, 'w', encoding='utf-8') as f: + json.dump(config, f, indent=4, ensure_ascii=False) + return True + + return False + + except Exception as e: + print(f"⚠️ 修复 CAM++ 配置文件失败: {e}") + return False + + +def download_models( + auto_mode: bool = False, + export_dir: Optional[str] = None, +) -> bool: + """下载所有需要的模型 + + Args: + auto_mode: 如果为True，表示自动模式（从start.py调用），会简化输出 + export_dir: 如果指定，将下载的模型导出到该目录（用于离线部署） + + Returns: + 是否全部下载成功 + """ + import shutil + + # 检查缺失的模型 + missing = check_all_models() + ms_assets = get_download_modelscope_assets() + qwen_assets = _get_qwen_modelscope_assets() + + export_path = Path(export_dir) if export_dir else None + + if not missing: + if not auto_mode: + print("✅ 所有模型已存在，无需下载") + if not export_path: + return True + + ms_cache_dir = Path(settings.MODELSCOPE_CACHE) + if auto_mode: + print(f"📦 检测到 {len(missing)} 个模型需要下载...") + else: + print("=" * 60) + print("Qwen3-ASR 模型预下载") + print("=" * 60) + print(f"ModelScope 缓存: {ms_cache_dir}") + print(f"待下载模型: {len(missing)} 个") + print("=" * 60) + + failed = [] + downloaded = [] + + # 下载 ModelScope 模型 (Paraformer) + ms_missing = [(mid, desc, rev) for mid, desc, src, rev in missing if src == "modelscope"] + if ms_missing: + if not auto_mode: + print("\n📦 开始下载 ModelScope 模型 (Paraformer)...") + print("-" * 60) + + for i, (model_id, desc, revision) in enumerate(ms_missing, 1): + if not auto_mode: + print(f"\n[{i}/{len(ms_missing)}] {desc}") + print(f" 模型ID: {model_id}") + if revision: + print(f" 版本: {revision}") + print(f" 📥 开始下载...", end="") + + try: + local_dir = _get_cache_path(model_id, "modelscope") + local_dir.parent.mkdir(parents=True, exist_ok=True) + # 传递版本参数，如果指定了版本 + if revision: + path = ms_snapshot_download( + model_id, + revision=revision, + cache_dir=str(ms_cache_dir), + local_dir=str(local_dir), + ) + else: + path = ms_snapshot_download( + model_id, + cache_dir=str(ms_cache_dir), + local_dir=str(local_dir), + ) + if not auto_mode: + print(f" ✅ 完成: {path}") + downloaded.append((model_id, "modelscope", path)) + except Exception as e: + if not auto_mode: + print(f" ❌ 失败: {e}") + failed.append((model_id, str(e))) + + # 修复 CAM++ 配置文件（用于离线环境） + if not auto_mode: + print("\n🔧 修复 CAM++ 配置文件...") + if fix_camplusplus_config(): + if not auto_mode: + print(" ✅ CAM++ 配置已修复（离线环境可用）") + else: + if not auto_mode: + print(" ℹ️ 无需修复或配置文件不存在") + + # 导出模式：复制模型到扁平化的 models/ 根目录 + if export_path and not failed: + if not auto_mode: + print(f"\n📦 导出模型到: {export_path}") + + # 收集所有需要导出的模型 + all_models = [] + for asset in ms_assets: + all_models.append((asset.model_id, "modelscope")) + for asset in qwen_assets: + all_models.append((asset.model_id, "modelscope")) + + exported = 0 + for model_entry in all_models: + if len(model_entry) == 3: + model_id, source, actual_model_id = model_entry + else: + model_id, source = model_entry + actual_model_id = model_id + cache_path = _get_cache_path(actual_model_id, source) + if cache_path.exists(): + rel_path = cache_path.relative_to(Path(settings.MODELSCOPE_PATH)) + target_dir = export_path / rel_path + + target_dir.parent.mkdir(parents=True, exist_ok=True) + if not auto_mode: + print(f" 📂 {model_id}", end="") + try: + shutil.copytree(cache_path, target_dir, dirs_exist_ok=True) + exported += 1 + if not auto_mode: + print(" ✅") + except Exception as e: + if not auto_mode: + print(f" ❌ {e}") + + if not auto_mode: + print(f"\n✅ 已导出 {exported} 个模型到 models/") + + if not auto_mode: + print("\n" + "=" * 60) + print("📊 下载统计:") + print(f" ✅ 已下载: {len(downloaded)} 个") + print(f" ❌ 失败: {len(failed)} 个") + print("=" * 60) + + if failed: + print(f"\n失败的模型:") + for model_id, err in failed: + print(f" - {model_id}: {err}") + return False + else: + print("\n✅ 所有模型准备就绪!") + print("=" * 60) + + return len(failed) == 0 + + +def main() -> int: + """CLI entrypoint for model download and export.""" + parser = argparse.ArgumentParser(description="Download or export Qwen3-ASR models") + parser.add_argument( + "--export-dir", + default=None, + help="Optional export directory for offline deployment packaging", + ) + parser.add_argument( + "--auto-mode", + action="store_true", + help="Reduce output for startup/bootstrap usage", + ) + args = parser.parse_args() + + success = download_models( + auto_mode=args.auto_mode, + export_dir=args.export_dir, + ) + return 0 if success else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/app/utils/model_loader.py b/app/utils/model_loader.py new file mode 100644 index 0000000..7b0a7ea --- /dev/null +++ b/app/utils/model_loader.py @@ -0,0 +1,520 @@ +# -*- coding: utf-8 -*- +""" +模型预加载工具 +在应用启动时预加载所有需要的模型,避免首次请求时的延迟 +""" + +import logging +import os +import sys +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +try: + from rich.console import Console +except ImportError: + Console = None + +from .boot_events import emit_boot_event + +logger = logging.getLogger(__name__) + +_PRELOAD_QUIET_LOGGERS = ( + "root", + "vllm", + "app.infrastructure.model_utils", + "app.services.asr.engines.global_models", + "app.services.asr.qwen3_engine", + "app.utils.speaker_diarizer", +) + + +class _ProgressNoiseFilter(logging.Filter): + def filter(self, record: logging.LogRecord) -> bool: + if record.levelno >= logging.WARNING: + return True + return not any( + record.name == prefix or record.name.startswith(f"{prefix}.") + for prefix in _PRELOAD_QUIET_LOGGERS + ) + + +class _StartupProgress: + def __init__(self, title: str, total: int): + self._title = title + self._total = max(total, 1) + self._enabled = bool( + Console is not None + and sys.stderr.isatty() + ) + self._console: Any = None + self._filter = _ProgressNoiseFilter() + self._handlers: list[logging.Handler] = [] + self._current_step = 1 + self._last_description: str | None = None + + def __enter__(self) -> "_StartupProgress": + emit_boot_event( + "phase_start", + phase=self._title, + total=self._total, + message=self._title, + ) + if not self._enabled or Console is None: + return self + self._console = Console(stderr=True) + root_logger = logging.getLogger() + self._handlers = list(root_logger.handlers) + for handler in self._handlers: + handler.addFilter(self._filter) + return self + + def __exit__(self, exc_type, exc, tb) -> None: + for handler in self._handlers: + handler.removeFilter(self._filter) + self._handlers.clear() + + def update(self, description: str) -> None: + emit_boot_event( + "step_start", + phase=self._title, + step=self._current_step, + total=self._total, + message=description, + ) + if self._console is None: + return + if description == self._last_description: + return + self._last_description = description + self._console.print( + f"[bold cyan][startup {self._current_step}/{self._total}][/bold cyan] {description}", + highlight=False, + ) + + def advance(self, description: str) -> None: + emit_boot_event( + "step_done", + phase=self._title, + step=self._current_step, + total=self._total, + message=description, + ) + self._last_description = description + self._current_step = min(self._current_step + 1, self._total) + + +@dataclass(frozen=True) +class ModelIntegritySpec: + description: str + path: Path + required_patterns: tuple[str, ...] + alternative_required_patterns: tuple[tuple[str, ...], ...] = () + min_total_size_bytes: int = 0 + + +def _format_bytes(num_bytes: int) -> str: + value = float(num_bytes) + units = ["B", "KB", "MB", "GB", "TB"] + for unit in units: + if value < 1024.0 or unit == units[-1]: + return f"{value:.1f}{unit}" + value /= 1024.0 + return f"{num_bytes}B" + + +def _find_pattern_matches(root: Path, pattern: str) -> list[Path]: + return [path for path in root.glob(pattern) if path.is_file()] + + +def _find_missing_patterns(root: Path, patterns: tuple[str, ...]) -> list[str]: + return [pattern for pattern in patterns if not _find_pattern_matches(root, pattern)] + + +def _format_alternative_patterns(pattern_groups: tuple[tuple[str, ...], ...]) -> str: + return " OR ".join(" + ".join(group) for group in pattern_groups) + + +def _check_model_integrity_spec(spec: ModelIntegritySpec) -> dict[str, Any]: + if not spec.path.exists() or not spec.path.is_dir(): + return { + "description": spec.description, + "path": str(spec.path), + "ok": False, + "missing_patterns": [ + *spec.required_patterns, + *( + [_format_alternative_patterns(spec.alternative_required_patterns)] + if spec.alternative_required_patterns + else [] + ), + ], + "total_size_bytes": 0, + "reason": "directory_missing", + } + + files = [path for path in spec.path.rglob("*") if path.is_file()] + total_size_bytes = sum(path.stat().st_size for path in files) + + missing_patterns = _find_missing_patterns(spec.path, spec.required_patterns) + if not missing_patterns and spec.alternative_required_patterns: + alternative_missing_patterns = [ + _find_missing_patterns(spec.path, group) + for group in spec.alternative_required_patterns + ] + if all(alternative_missing_patterns): + missing_patterns = [ + _format_alternative_patterns(spec.alternative_required_patterns) + ] + + if missing_patterns: + return { + "description": spec.description, + "path": str(spec.path), + "ok": False, + "missing_patterns": missing_patterns, + "total_size_bytes": total_size_bytes, + "reason": "required_files_missing", + } + + if total_size_bytes < spec.min_total_size_bytes: + return { + "description": spec.description, + "path": str(spec.path), + "ok": False, + "missing_patterns": [], + "total_size_bytes": total_size_bytes, + "reason": "directory_too_small", + } + + return { + "description": spec.description, + "path": str(spec.path), + "ok": True, + "missing_patterns": [], + "total_size_bytes": total_size_bytes, + "reason": "ok", + } + + +def _build_modelscope_spec( + model_id: str, + description: str, + required_patterns: tuple[str, ...], + *, + min_total_size_bytes: int, + alternative_required_patterns: tuple[tuple[str, ...], ...] = (), +) -> ModelIntegritySpec: + from ..core.config import settings + + return ModelIntegritySpec( + description=description, + path=Path(settings.MODELSCOPE_PATH) / model_id, + required_patterns=required_patterns, + alternative_required_patterns=alternative_required_patterns, + min_total_size_bytes=min_total_size_bytes, + ) + + +def _convert_ms_patterns( + patterns: tuple[str, ...], +) -> tuple[str, ...]: + return tuple(p.replace("snapshots/*/", "") for p in patterns) + + +def _build_qwen_spec( + model_id: str, + description: str, + required_patterns: tuple[str, ...], + *, + min_total_size_bytes: int, + alternative_required_patterns: tuple[tuple[str, ...], ...] = (), +) -> ModelIntegritySpec: + from ..core.config import settings + ms_path = Path(settings.MODELSCOPE_PATH) / model_id + ms_required = _convert_ms_patterns(required_patterns) + ms_alternative = tuple( + _convert_ms_patterns(group) + for group in alternative_required_patterns + ) + return ModelIntegritySpec( + description=description, + path=ms_path, + required_patterns=ms_required, + alternative_required_patterns=ms_alternative, + min_total_size_bytes=min_total_size_bytes, + ) + + +def _should_check_qwen_forced_aligner( + resolved_device: str, + using_cpu_qwen_rust: bool, +) -> bool: + """Return True when startup integrity should require Qwen forced aligner files.""" + from ..core.config import settings + + _ = (resolved_device, using_cpu_qwen_rust) + return settings.ASR_ENABLE_WORD_TIMESTAMPS + + +def _build_required_model_integrity_specs() -> list[ModelIntegritySpec]: + from ..core.config import settings + from ..core.device import detect_device + from ..services.asr.manager import get_model_manager + from ..services.asr.model_capabilities import ( + get_enabled_qwen_modelscope_assets, + get_runtime_required_modelscope_assets, + ) + from ..services.asr.model_plan import get_runtime_model_ids + from ..services.asr.qwenasr_rust import is_qwenasr_rust_available + manager = get_model_manager() + model_ids = [item["id"] for item in manager.list_declared_entries()] + runtime_models = get_runtime_model_ids(model_ids) + resolved_device = detect_device(settings.DEVICE) + using_cpu_qwen_rust = ( + resolved_device == "cpu" and is_qwenasr_rust_available() + ) + specs: list[ModelIntegritySpec] = [] + + for asset in get_runtime_required_modelscope_assets( + include_realtime_punc=settings.ASR_ENABLE_REALTIME_PUNC, + ): + specs.append( + _build_modelscope_spec( + asset.model_id, + asset.description, + asset.required_patterns, + alternative_required_patterns=asset.alternative_required_patterns, + min_total_size_bytes=asset.min_total_size_bytes, + ) + ) + + for asset in get_enabled_qwen_modelscope_assets( + include_forced_aligner=_should_check_qwen_forced_aligner( + resolved_device=resolved_device, + using_cpu_qwen_rust=using_cpu_qwen_rust, + ), + ): + specs.append( + _build_qwen_spec( + asset.model_id, + asset.description, + asset.required_patterns, + alternative_required_patterns=asset.alternative_required_patterns, + min_total_size_bytes=asset.min_total_size_bytes, + ) + ) + + return specs + + +def verify_required_models_integrity(use_logger: bool = True) -> dict[str, Any]: + output = logger.info if use_logger else print + specs = _build_required_model_integrity_specs() + total = len(specs) + results: list[dict[str, Any]] = [] + invalid: list[dict[str, Any]] = [] + + if not use_logger: + output("=" * 60) + output(f"🔍 开始检查运行时模型完整性，共 {total} 个") + output("=" * 60) + for index, spec in enumerate(specs, start=1): + output(f"[{index}/{total}] 检查 {spec.description}") + result = _check_model_integrity_spec(spec) + results.append(result) + if result["ok"]: + output( + f" ✅ OK size={_format_bytes(result['total_size_bytes'])} " + f"path={result['path']}" + ) + continue + invalid.append(result) + if result["reason"] == "directory_missing": + output(f" ❌ FAIL directory_missing path={result['path']}") + elif result["reason"] == "required_files_missing": + output( + f" ❌ FAIL missing={', '.join(result['missing_patterns'])} " + f"size={_format_bytes(result['total_size_bytes'])} path={result['path']}" + ) + else: + output( + f" ❌ FAIL size_too_small size={_format_bytes(result['total_size_bytes'])} " + f"path={result['path']}" + ) + output("=" * 60) + output(f"模型完整性检查完成: total={total} ok={total - len(invalid)} failed={len(invalid)}") + output("=" * 60) + return { + "total": total, + "results": results, + "invalid_models": invalid, + } + + logger.info("开始检查运行时模型完整性: total=%s", total) + with _StartupProgress("检查运行时模型完整性", total) as progress: + for spec in specs: + progress.update(f"检查 {spec.description}") + result = _check_model_integrity_spec(spec) + results.append(result) + if not result["ok"]: + invalid.append(result) + if result["reason"] == "directory_missing": + logger.error("模型完整性检查失败: %s, reason=directory_missing, path=%s", spec.description, result["path"]) + elif result["reason"] == "required_files_missing": + logger.error( + "模型完整性检查失败: %s, reason=required_files_missing, missing=%s, size=%s, path=%s", + spec.description, + ", ".join(result["missing_patterns"]), + _format_bytes(result["total_size_bytes"]), + result["path"], + ) + else: + logger.error( + "模型完整性检查失败: %s, reason=directory_too_small, size=%s, path=%s", + spec.description, + _format_bytes(result["total_size_bytes"]), + result["path"], + ) + progress.advance(f"检查完成 {spec.description}") + + logger.info( + "模型完整性检查完成: total=%s ok=%s failed=%s", + total, + total - len(invalid), + len(invalid), + ) + + return { + "total": total, + "results": results, + "invalid_models": invalid, + } +def preload_models() -> dict[str, Any]: + """ + 预加载所有需要的模型（根据 ENABLE_* 配置过滤） + + Returns: + dict: 包含加载状态的字典 + """ + # 修复 CAM++ 配置文件（用于离线环境） + try: + from .download_models import fix_camplusplus_config + fix_camplusplus_config() + except Exception: + pass # 修复失败不影响启动 + + result: dict[str, Any] = { + "asr_models": {}, # 所有ASR模型加载状态 + "vad_model": {"loaded": False, "error": None}, + "speaker_diarization_model": {"loaded": False, "error": None}, + } + + from ..core.config import settings + from ..core.device import detect_device + + # 初始化变量，避免未绑定错误 + asr_device = detect_device(settings.DEVICE) + model_manager = None + + # 1. 预加载所有配置的ASR模型（根据 ENABLE_* 配置过滤） + model_ids: list[str] = [] + model_manager = None + + try: + from ..services.asr.manager import get_model_manager + from ..services.asr.model_plan import get_runtime_model_ids + from ..services.asr.runtime import get_runtime_router + + model_manager = get_model_manager() + runtime_router = get_runtime_router() + + # 获取所有模型配置 + all_models = model_manager.list_declared_entries() + model_ids = [m["id"] for m in all_models] + + models_to_load = get_runtime_model_ids(model_ids) + + if not models_to_load: + logger.warning("⚠️ 当前环境未解析出可运行的 ASR 模型") + + except Exception as e: + logger.error(f"❌ 获取模型管理器失败: {e}") + models_to_load = [] + runtime_router = None + + total_steps = len(models_to_load) + 2 + + logger.info( + "开始预加载模型: declared=%s runtime=%s models=%s", + len(model_ids) if model_manager else 0, + len(models_to_load), + ", ".join(models_to_load) if models_to_load else "（无）", + ) + + with _StartupProgress("预加载模型", total_steps) as progress: + for model_id in models_to_load: + result["asr_models"][model_id] = {"loaded": False, "error": None} + progress.update(f"加载 ASR 模型 {model_id}") + try: + if runtime_router is None: + raise RuntimeError("runtime router unavailable") + runtime_router.warmup_model(model_id) + result["asr_models"][model_id]["loaded"] = True + except Exception as e: + result["asr_models"][model_id]["error"] = str(e) + logger.error("ASR模型预加载失败: %s, error=%s", model_id, e) + progress.advance(f"已完成 ASR 模型 {model_id}") + + # 2. 预加载语音活动检测模型(VAD) + progress.update("加载语音活动检测模型(VAD)") + try: + from ..services.asr.engines import get_global_vad_model + + vad_model = get_global_vad_model(asr_device) + if vad_model: + result["vad_model"]["loaded"] = True + else: + result["vad_model"]["error"] = "语音活动检测模型(VAD)加载后返回None" + except Exception as e: + result["vad_model"]["error"] = str(e) + logger.error("语音活动检测模型(VAD)加载失败: %s", e) + progress.advance("已完成语音活动检测模型(VAD)") + + # 5. 预加载说话人分离模型 (CAM++) - 必需模型，始终加载 + progress.update("加载说话人分离模型(CAM++)") + try: + from ..utils.speaker_diarizer import get_global_diarization_pipeline + + diarization_pipeline = get_global_diarization_pipeline() + if diarization_pipeline: + result["speaker_diarization_model"]["loaded"] = True + else: + result["speaker_diarization_model"]["error"] = "说话人分离模型加载后返回None" + except Exception as e: + result["speaker_diarization_model"]["error"] = str(e) + logger.error("说话人分离模型(CAM++)加载失败: %s", e) + progress.advance("已完成说话人分离模型(CAM++)") + + loaded_asr_count = sum(1 for status in result["asr_models"].values() if status["loaded"]) + total_asr_count = len(result["asr_models"]) + extra_loaded = sum( + 1 + for key in ("vad_model", "speaker_diarization_model") + if result[key]["loaded"] + ) + extra_failed = sum( + 1 + for key in ("vad_model", "speaker_diarization_model") + if result[key]["error"] + ) + logger.info( + "模型预加载完成: asr=%s/%s extra_loaded=%s extra_failed=%s", + loaded_asr_count, + total_asr_count, + extra_loaded, + extra_failed, + ) + + return result diff --git a/app/utils/speaker_diarizer.py b/app/utils/speaker_diarizer.py new file mode 100644 index 0000000..ec314f5 --- /dev/null +++ b/app/utils/speaker_diarizer.py @@ -0,0 +1,697 @@ +# -*- coding: utf-8 -*- +""" +说话人分离模块 +基于 CAM++ 的说话人分离，用于多说话人音频分割 +""" + +from loguru import logger +import numpy as np +import librosa +import soundfile as sf +import tempfile +import os +import threading +from typing import Any, List, Mapping, Optional, Sequence, cast +from dataclasses import dataclass + +import torch + +from ..core.config import settings +from ..core.exceptions import DefaultServerErrorException + +# 全局 CAM++ pipeline 缓存（单例） +_global_diarization_pipeline: Any | None = None +_diarization_pipeline_lock = threading.Lock() +_diarization_inference_semaphore = threading.BoundedSemaphore(1) + + +@dataclass +class SpeakerSegment: + """说话人分段信息""" + + start_ms: int + end_ms: int + speaker_id: str + audio_data: Optional[np.ndarray] = None + temp_file: Optional[str] = None + + @property + def start_sec(self) -> float: + return self.start_ms / 1000.0 + + @property + def end_sec(self) -> float: + return self.end_ms / 1000.0 + + @property + def duration_ms(self) -> int: + return self.end_ms - self.start_ms + + @property + def duration_sec(self) -> float: + return self.duration_ms / 1000.0 + + +def _resolve_modelscope_device() -> str: + """根据配置和硬件自动选择 modelscope pipeline 设备 + """ + from ..core.device import detect_device + + return detect_device(settings.DEVICE) + + +def _move_pipeline_model_to_device(pipeline_instance: Any, modelscope_device: str) -> None: + """将 pipeline 的底层模型迁移到目标设备。""" + if hasattr(pipeline_instance, "device_name"): + pipeline_instance.device_name = modelscope_device + model = getattr(pipeline_instance, "model", None) + if model is not None and hasattr(model, "to"): + pipeline_instance.model = model.to(modelscope_device) + + +def _create_modelscope_pipeline( + *, + task: Any, + model: str, + modelscope_device: str, + model_revision: Optional[str] = None, +) -> Any: + """创建 modelscope pipeline，并在需要时把底层模型迁移到目标设备。""" + from modelscope.pipelines import pipeline + + pipeline_kwargs: dict[str, Any] = { + "task": task, + "model": model, + "device": modelscope_device, + } + if model_revision is not None: + pipeline_kwargs["model_revision"] = model_revision + + pipeline_instance = pipeline(**pipeline_kwargs) + _move_pipeline_model_to_device(pipeline_instance, modelscope_device) + return pipeline_instance + + +def _enable_batched_sv( + pipeline_instance: Any, + modelscope_device: str, + max_batch_size: int = 32, +) -> Any: + """ + 对说话人分离 pipeline 启用 batched SV 推理。 + + 原始 pipeline 的 forward 方法逐个 segment 调用 sv_pipeline 提取 embedding， + 这里改为将所有 segment 拼成一个 batch 一次性推理，大幅减少 GPU 调用次数。 + 同时将子 pipeline（sv / vad / change_locator）绑定到指定 device。 + + Args: + pipeline_instance: CAM++ diarization pipeline 实例 + modelscope_device: 设备名称 + max_batch_size: 最大批处理大小，防止 OOM + """ + if getattr(pipeline_instance, "_batched_sv_enabled", False): + return pipeline_instance + + from modelscope.utils.constant import Tasks + + config = getattr(pipeline_instance, "config", None) + if not isinstance(config, Mapping): + logger.warning("CAM++ pipeline 缺少可读取的 config，跳过 batched SV 优化") + return pipeline_instance + + sv_model = config.get("speaker_model") + vad_model = config.get("vad_model") + change_locator = config.get("change_locator") + + if isinstance(sv_model, str) and sv_model: + pipeline_instance.sv_pipeline = _create_modelscope_pipeline( + task=Tasks.speaker_verification, + model=sv_model, + modelscope_device=modelscope_device, + ) + + if isinstance(vad_model, str) and vad_model: + pipeline_instance.vad_pipeline = _create_modelscope_pipeline( + task=Tasks.voice_activity_detection, + model=vad_model, + modelscope_device=modelscope_device, + model_revision="v2.0.2", + ) + + if isinstance(change_locator, str) and change_locator: + pipeline_instance.change_locator_pipeline = _create_modelscope_pipeline( + task=Tasks.speaker_diarization, + model=change_locator, + modelscope_device=modelscope_device, + ) + + def batched_forward(self: Any, segments: Sequence[Sequence[Any]]) -> np.ndarray: + """批量提取说话人 embedding，替代逐段串行推理""" + sv_model_instance = getattr(getattr(self, "sv_pipeline", None), "model", None) + emb_size = int(getattr(sv_model_instance, "emb_size", 192)) + + if not segments: + return np.empty((0, emb_size), dtype=np.float32) + + if sv_model_instance is None: + raise RuntimeError("CAM++ sv_pipeline.model 未初始化") + + all_embeddings: list[np.ndarray] = [] + total_segments = len(segments) + start_idx = 0 + + while start_idx < total_segments: + end_idx = min(start_idx + max_batch_size, total_segments) + batch_segments = segments[start_idx:end_idx] + + batch_items: list[np.ndarray] = [] + for segment in batch_segments: + if len(segment) < 3: + continue + batch_items.append(np.asarray(segment[2], dtype=np.float32)) + + if not batch_items: + start_idx = end_idx + continue + + batch = np.stack(batch_items, axis=0) + + with torch.no_grad(): + embeddings = sv_model_instance( + cast(Any, torch).as_tensor(batch).to(modelscope_device) + ) + + if isinstance(embeddings, torch.Tensor): + all_embeddings.append(embeddings.detach().cpu().numpy()) + else: + all_embeddings.append(np.asarray(embeddings, dtype=np.float32)) + + start_idx = end_idx + + if not all_embeddings: + return np.empty((0, emb_size), dtype=np.float32) + + return ( + np.concatenate(all_embeddings, axis=0) + if len(all_embeddings) > 1 + else all_embeddings[0] + ) + + import types + + pipeline_instance.forward = types.MethodType(batched_forward, pipeline_instance) + pipeline_instance._batched_sv_enabled = True + + logger.info( + "CAM++ 说话人分离启用 batched SV: device={}, sv_device={}, vad_device={}", + modelscope_device, + getattr(getattr(pipeline_instance, "sv_pipeline", None), "device_name", "unknown"), + getattr(getattr(pipeline_instance, "vad_pipeline", None), "device_name", "unknown"), + ) + return pipeline_instance + + +def get_global_diarization_pipeline() -> Any: + """获取全局说话人分离 pipeline（懒加载单例）""" + global _global_diarization_pipeline + + with _diarization_pipeline_lock: + if _global_diarization_pipeline is None: + try: + from modelscope.utils.constant import Tasks + from ..infrastructure.model_utils import resolve_model_path + + model_id = 'iic/speech_campplus_speaker-diarization_common' + model_path = resolve_model_path(model_id) + modelscope_device = _resolve_modelscope_device() + + logger.info( + "正在加载 CAM++ 说话人分离模型: {}, device={}", + model_path, + modelscope_device, + ) + _global_diarization_pipeline = _create_modelscope_pipeline( + task=Tasks.speaker_diarization, + model=model_path, + modelscope_device=modelscope_device, + ) + _global_diarization_pipeline = _enable_batched_sv( + _global_diarization_pipeline, modelscope_device + ) + logger.info("CAM++ 模型加载成功（已启用 batched SV）") + except Exception as e: + logger.error(f"CAM++ 模型加载失败: {e}") + raise DefaultServerErrorException(f"说话人分离模型加载失败: {str(e)}") + + return _global_diarization_pipeline + + +class SpeakerDiarizer: + """基于 CAM++ 的说话人分离器""" + + DEFAULT_MIN_SEGMENT_SEC = 1.0 + DEFAULT_SAMPLE_RATE = 16000 + LOW_ENERGY_SEARCH_WINDOW_MS = 10000 + LOW_ENERGY_CONTEXT_MS = 160 + LOW_ENERGY_STEP_MS = 20 + + def __init__( + self, + min_segment_sec: float = DEFAULT_MIN_SEGMENT_SEC, + ): + self.min_segment_sec = min_segment_sec + self.min_segment_ms = int(min_segment_sec * 1000) + + def diarize( + self, audio_path: str + ) -> List[SpeakerSegment]: + """执行说话人分离 + + Args: + audio_path: 音频文件路径 + + Returns: + 原始分段列表（未合并） + """ + audio_duration_ms: Optional[int] = None + try: + try: + audio_duration_ms = int(librosa.get_duration(path=audio_path) * 1000) + except Exception: + audio_duration_ms = None + + # CAM++ 对极短片段收益很低，且容易直接报 "too short"。 + # 这里提前降级成单说话人，避免无意义 warning 刷屏。 + if audio_duration_ms is not None and audio_duration_ms < self.min_segment_ms: + logger.debug( + "音频时长过短，跳过 CAM++ 说话人分离: duration_ms=%s < min_segment_ms=%s", + audio_duration_ms, + self.min_segment_ms, + ) + return [ + SpeakerSegment( + start_ms=0, + end_ms=max(audio_duration_ms, 1), + speaker_id="说话人1", + ) + ] + + pipeline = get_global_diarization_pipeline() + + logger.info(f"开始说话人分离: {audio_path}") + with _diarization_inference_semaphore: + result = pipeline(audio_path) + + # 解析结果: {'text': [[start, end, speaker_id], ...]} + # pipeline 返回类型不确定，需要安全地获取 'text' 字段 + if isinstance(result, dict): + raw_output = result.get('text', []) + else: + raw_output = getattr(result, 'text', []) or [] + + segments = [] + for seg in raw_output: + if isinstance(seg, list) and len(seg) == 3: + try: + start_ms = int(float(seg[0]) * 1000) + end_ms = int(float(seg[1]) * 1000) + speaker_id = f"说话人{int(seg[2]) + 1}" + segments.append(SpeakerSegment( + start_ms=start_ms, + end_ms=end_ms, + speaker_id=speaker_id, + )) + except (ValueError, TypeError) as e: + logger.warning(f"跳过格式错误的片段: {seg}, 错误: {e}") + + logger.info(f"说话人分离完成，原始片段数: {len(segments)}") + # 诊断日志：打印前20个原始片段 + for i, seg in enumerate(segments[:20]): + logger.debug( + f"[CAM++原始] #{i}: {seg.start_sec:.2f}-{seg.end_sec:.2f}s " + f"({seg.duration_sec:.2f}s) {seg.speaker_id}" + ) + return segments + + except Exception as e: + error_msg = str(e).lower() + + # 音频太短时，返回默认的单说话人片段 + if "too short" in error_msg: + logger.debug("CAM++ 跳过过短音频，回退单说话人片段: %s", e) + if audio_duration_ms is None: + try: + audio_duration_ms = int(librosa.get_duration(path=audio_path) * 1000) + except Exception: + audio_duration_ms = 5000 + + return [ + SpeakerSegment( + start_ms=0, + end_ms=audio_duration_ms, + speaker_id="说话人1", + ) + ] + + # 其他异常正常抛出 + logger.error(f"说话人分离失败: {e}") + raise DefaultServerErrorException(f"说话人分离失败: {str(e)}") + + def merge_consecutive_segments( + self, segments: List[SpeakerSegment] + ) -> List[SpeakerSegment]: + """合并同一说话人的连续片段""" + if not segments: + return [] + + # 按开始时间排序 + sorted_segments = sorted(segments, key=lambda x: x.start_ms) + + merged = [] + current = SpeakerSegment( + start_ms=sorted_segments[0].start_ms, + end_ms=sorted_segments[0].end_ms, + speaker_id=sorted_segments[0].speaker_id, + ) + + for seg in sorted_segments[1:]: + if seg.speaker_id == current.speaker_id: + # 同一说话人，扩展结束时间 + current.end_ms = max(current.end_ms, seg.end_ms) + else: + # 不同说话人，保存当前段，开始新段 + logger.debug( + f"[合并中断] 说话人切换: {current.speaker_id} → {seg.speaker_id} " + f"在 {seg.start_sec:.2f}s，保存片段 {current.start_sec:.2f}-{current.end_sec:.2f}s" + ) + merged.append(current) + current = SpeakerSegment( + start_ms=seg.start_ms, + end_ms=seg.end_ms, + speaker_id=seg.speaker_id, + ) + + # 保存最后一段 + merged.append(current) + + logger.info(f"合并同一说话人连续片段: {len(segments)} → {len(merged)}") + # 诊断日志：打印合并后的前20个片段 + for i, seg in enumerate(merged[:20]): + logger.debug( + f"[合并后] #{i}: {seg.start_sec:.2f}-{seg.end_sec:.2f}s " + f"({seg.duration_sec:.2f}s) {seg.speaker_id}" + ) + return merged + + def merge_short_segments( + self, segments: List[SpeakerSegment] + ) -> List[SpeakerSegment]: + """智能合并短片段 + + 策略： + 1. 第一层：<10s的片段向后合并（避免孤立短片段） + 2. 第二层：60s累积合并（合并连续片段） + """ + if not segments: + return [] + + max_segment_sec = settings.MAX_SEGMENT_SEC + + # 按开始时间排序 + sorted_segments = sorted(segments, key=lambda x: x.start_ms) + + # 第一层：<10s累积向后合并（循环计算直到>=10s或超过60s） + merged = [] + i = 0 + while i < len(sorted_segments): + seg = sorted_segments[i] + + # 如果>=10s，直接添加 + if seg.duration_sec >= 10.0: + merged.append(seg) + i += 1 + continue + + # <10s，开始累积合并 + current_start_ms = seg.start_ms + current_end_ms = seg.end_ms + current_duration_sec = seg.duration_sec + j = i + 1 + + # 累积合并，只要<10s且同说话人且不超过60s + while j < len(sorted_segments) and current_duration_sec < 10.0: + next_seg = sorted_segments[j] + if next_seg.speaker_id != seg.speaker_id: + break + new_duration = (next_seg.end_ms - current_start_ms) / 1000.0 + if new_duration > max_segment_sec: + break + current_end_ms = next_seg.end_ms + current_duration_sec = new_duration + j += 1 + + # 创建合并后的片段 + merged_seg = SpeakerSegment( + start_ms=current_start_ms, + end_ms=current_end_ms, + speaker_id=seg.speaker_id, + ) + merged.append(merged_seg) + + if j > i + 1: + logger.debug( + f"[第一层] {seg.speaker_id}: " + f"累积合并了 {j - i} 个片段，结果 {merged_seg.duration_sec:.1f}s" + ) + i = j + + # 第二层：60s累积合并 + final_merged = [] + i = 0 + while i < len(merged): + seg = merged[i] + current_start_ms = seg.start_ms + current_end_ms = seg.end_ms + j = i + 1 + + # 累积合并，只要 <= 60s 且同说话人 + while j < len(merged): + next_seg = merged[j] + if next_seg.speaker_id != seg.speaker_id: + break + new_duration = (next_seg.end_ms - current_start_ms) / 1000.0 + if new_duration > max_segment_sec: + break + current_end_ms = next_seg.end_ms + j += 1 + + merged_seg = SpeakerSegment( + start_ms=current_start_ms, + end_ms=current_end_ms, + speaker_id=seg.speaker_id, + ) + final_merged.append(merged_seg) + + if j > i + 1: + logger.debug( + f"[第二层] {seg.speaker_id}: " + f"合并了 {j - i} 个片段" + ) + i = j + + return final_merged + + def _find_low_energy_boundary_ms( + self, + audio_data: np.ndarray, + sample_rate: int, + lower_ms: int, + upper_ms: int, + ) -> int: + lower_ms = max(0, lower_ms) + upper_ms = max(lower_ms, upper_ms) + if sample_rate <= 0 or audio_data.size == 0: + return upper_ms + + context_samples = max( + 1, int(sample_rate * self.LOW_ENERGY_CONTEXT_MS / 1000) + ) + candidate_points = list( + range(lower_ms, upper_ms + 1, self.LOW_ENERGY_STEP_MS) + ) + if not candidate_points or candidate_points[-1] != upper_ms: + candidate_points.append(upper_ms) + + best_ms = upper_ms + best_energy = float("inf") + audio_length = int(audio_data.shape[0]) + + for candidate_ms in candidate_points: + center_sample = int(candidate_ms * sample_rate / 1000) + start_sample = max(0, center_sample - context_samples // 2) + end_sample = min(audio_length, center_sample + context_samples // 2) + if start_sample >= end_sample: + continue + + window = audio_data[start_sample:end_sample] + energy = float(np.mean(np.square(window))) + if energy <= best_energy: + best_energy = energy + best_ms = candidate_ms + + return best_ms + + def split_long_segments( + self, + segments: List[SpeakerSegment], + audio_data: np.ndarray, + sample_rate: int, + ) -> List[SpeakerSegment]: + max_segment_ms = int(settings.MAX_SEGMENT_SEC * 1000) + if max_segment_ms <= 0: + return segments + + split_segments: List[SpeakerSegment] = [] + for seg in segments: + if seg.duration_ms <= max_segment_ms: + split_segments.append(seg) + continue + + current_start_ms = seg.start_ms + while seg.end_ms - current_start_ms > max_segment_ms: + hard_boundary_ms = current_start_ms + max_segment_ms + lower_boundary_ms = max( + current_start_ms + self.min_segment_ms, + hard_boundary_ms - self.LOW_ENERGY_SEARCH_WINDOW_MS, + ) + boundary_ms = self._find_low_energy_boundary_ms( + audio_data=audio_data, + sample_rate=sample_rate, + lower_ms=lower_boundary_ms, + upper_ms=hard_boundary_ms, + ) + if boundary_ms <= current_start_ms: + boundary_ms = hard_boundary_ms + + split_segments.append( + SpeakerSegment( + start_ms=current_start_ms, + end_ms=boundary_ms, + speaker_id=seg.speaker_id, + ) + ) + current_start_ms = boundary_ms + + remaining_ms = seg.end_ms - current_start_ms + if remaining_ms >= self.min_segment_ms: + split_segments.append( + SpeakerSegment( + start_ms=current_start_ms, + end_ms=seg.end_ms, + speaker_id=seg.speaker_id, + ) + ) + elif split_segments: + split_segments[-1].end_ms = seg.end_ms + + if len(split_segments) != len(segments): + logger.info( + "Split long speaker segments by low energy: {} -> {}, max={}s", + len(segments), + len(split_segments), + settings.MAX_SEGMENT_SEC, + ) + return split_segments + + def split_audio_by_speakers( + self, + audio_path: str, + output_dir: Optional[str] = None, + ) -> List[SpeakerSegment]: + """完整的说话人分离流程 + + 流程： + 1. 执行CAM++说话人分离 + 2. 智能合并短片段（两层合并策略） + - 第一层：<10s片段累积合并 + - 第二层：60s累积合并 + 3. 提取音频数据，保存临时文件 + + Args: + audio_path: 音频文件路径 + output_dir: 输出目录 + + Returns: + SpeakerSegment 列表 + """ + try: + # 1. 执行说话人分离 + raw_segments = self.diarize(audio_path) + + if not raw_segments: + logger.warning("说话人分离未检测到任何片段") + return [] + + # 2. 智能合并短片段（第一个<10s的同说话人片段向后合并） + final_segments = self.merge_short_segments(raw_segments) + + # 3. Load audio before low-energy splitting and segment extraction. + logger.info("加载音频并提取片段...") + audio_data, sr = librosa.load(audio_path, sr=self.DEFAULT_SAMPLE_RATE) + sample_rate = int(sr) + + final_segments = self.split_long_segments( + final_segments, + audio_data, + sample_rate, + ) + logger.info(f"智能合并完成: {len(raw_segments)} → {len(final_segments)} 个片段") + + output_dir = output_dir or settings.TEMP_DIR + os.makedirs(output_dir, exist_ok=True) + + for idx, seg in enumerate(final_segments): + start_sample = int(seg.start_ms / 1000 * sample_rate) + end_sample = int(seg.end_ms / 1000 * sample_rate) + + seg.audio_data = audio_data[start_sample:end_sample] + + # 保存临时文件 + temp_file = tempfile.NamedTemporaryFile( + delete=False, + suffix=".wav", + dir=output_dir, + prefix=f"{seg.speaker_id}_{idx:03d}_", + ) + temp_path = temp_file.name + temp_file.close() + + sf.write(temp_path, seg.audio_data, sample_rate) + seg.temp_file = temp_path + + # 统计 + unique_speakers = sorted(set(seg.speaker_id for seg in final_segments)) + logger.info( + f"音频分割完成: {len(final_segments)} 个片段, " + f"{len(unique_speakers)} 个说话人" + ) + for spk in unique_speakers: + spk_segs = [s for s in final_segments if s.speaker_id == spk] + total_time = sum(s.duration_sec for s in spk_segs) + logger.info(f" {spk}: {len(spk_segs)} 片段, {total_time:.2f}s") + + return final_segments + + except Exception as e: + logger.error(f"说话人分离流程失败: {e}") + raise DefaultServerErrorException(f"说话人分离失败: {str(e)}") + + @staticmethod + def cleanup_segments(segments: List[SpeakerSegment]) -> None: + """清理临时文件""" + for seg in segments: + if seg.temp_file and os.path.exists(seg.temp_file): + try: + os.remove(seg.temp_file) + except Exception as e: + logger.warning(f"清理临时文件失败: {seg.temp_file}, {e}") diff --git a/app/utils/text_processing.py b/app/utils/text_processing.py new file mode 100644 index 0000000..68b6ab3 --- /dev/null +++ b/app/utils/text_processing.py @@ -0,0 +1,59 @@ +# -*- coding: utf-8 -*- +""" +基于itntext的ITN（逆文本标准化）工具模块 +使用itntext库提供高质量的中文ITN处理 +""" + +import logging + +logger = logging.getLogger(__name__) + +# itntext导入 - 延迟导入以避免初始化问题 +_itntext_normalizer = None + + +def _get_normalizer(): + """获取itntext标准化器实例（单例模式）""" + global _itntext_normalizer + if _itntext_normalizer is None: + try: + from itntext import Normalizer + _itntext_normalizer = Normalizer(lang="zh", operator="itn") + logger.info("itntext ITN模块初始化成功") + except ImportError as e: + logger.error(f"导入itntext失败: {e}") + raise ImportError("请安装itntext库: pip install itntext") + except Exception as e: + logger.error(f"初始化itntext失败: {e}") + raise + return _itntext_normalizer + + +def apply_itn_to_text(text: str) -> str: + """ + 对文本应用逆文本标准化（ITN） + 使用itntext库进行高质量的中文ITN处理 + + Args: + text: 语音识别结果文本 + + Returns: + 应用ITN后的文本 + """ + if not text or not text.strip(): + return text + + try: + normalizer = _get_normalizer() + result = normalizer.normalize(text) + logger.debug(f"ITN处理: '{text}' -> '{result}'") + return result + except Exception as e: + logger.warning(f"ITN处理失败: {text}, 错误: {str(e)}") + return text + + +def normalize_asr_text(text: str, enable_itn: bool) -> str: + if not enable_itn: + return text + return apply_itn_to_text(text) diff --git a/crg-mcp-plugin/README.md b/crg-mcp-plugin/README.md new file mode 100644 index 0000000..ff2626e --- /dev/null +++ b/crg-mcp-plugin/README.md @@ -0,0 +1,68 @@ +# crg-mcp — code-review-graph MCP 接入插件 + +把已部署在本机的 `D:\github-project\code-review-graph` MCP 服务接入 PI-Desktop， +让它的工具以原生 agent 工具的形式出现。**未修改该项目任何文件。** + +## 接入原理 + +PI-Desktop 的 MCP 客户端由插件宿主承载：宿主读取插件 `manifest.json` 里的 +`contributes.mcpServers`，自行拉起进程、完成 MCP 握手，并把上游每个工具发布为 +`plugin_<插件id>__<工具名>`。因此这里只需声明，无需自己写 JSON-RPC。 + +## 文件 + +| 文件 | 作用 | +| --- | --- | +| `manifest.json` | 声明 stdio MCP 服务 + `mcp.server.local` 权限 | +| `crg.cmd` | 启动包装脚本（修环境后 exec `python -m code_review_graph serve`） | +| `main.js` | 空加载器，客户端生命周期归宿主管理 | +| `dist/crg-mcp-1.0.0.piplug` | 可安装包 | + +## 两个必须保留的环境修正 + +宿主的 `mcpProcessEnv()` 只向子进程传递 +`PATH / SystemRoot / windir / TEMP / TMP / LANG`（加插件声明的 env），所以： + +1. **`PYTHONPATH` 必须注入。** 项目的 editable 安装记录 + `.venv\Lib\site-packages\_editable_impl_code_review_graph.pth` 指向 + `D:\github_project\code-review-graph`（下划线），而项目实际位于 + `D:\github-project\code-review-graph`（连字符），因此直接 + `import code_review_graph` 会 `ModuleNotFoundError`。 + 同理 `.venv\Scripts\code-review-graph.exe` 也不能用 + （`error: uv trampoline failed to canonicalize script path`）——包装脚本绕过了 + 这两点，改用 `python -m code_review_graph`。 +2. **`USERPROFILE` / `HOMEDRIVE` / `HOMEPATH` 必须补齐。** + `code_review_graph/constants.py` 在 import 期调用 `Path.home()`， + 精简环境下会抛 `RuntimeError: Could not determine home directory.` + +## 暴露的工具（10 个） + +默认通过 `CRG_TOOLS` 只开放审查相关的 10 个工具（上游共 30 个，全开会显著占上下文）： + +`build_or_update_graph_tool`、`run_postprocess_tool`、`get_minimal_context_tool`、 +`get_review_context_tool`、`get_impact_radius_tool`、`query_graph_tool`、 +`semantic_search_nodes_tool`、`detect_changes_tool`、`list_graph_stats_tool`、 +`get_affected_flows_tool` + +工具名前缀为 `plugin_crg_mcp_crg_`，例如 `plugin_crg_mcp_crg_query_graph_tool`。 + +### 调整暴露范围 / 目标仓库 + +编辑 `crg.cmd`： + +- 改 `CRG_TOOLS=...`：改工具白名单（置空并删除该行 = 暴露全部 30 个）。 +- 改 `CRG_REPO=...`：改被分析的仓库根目录（默认指向 code-review-graph 自身， + 即已建好图的那个库）。 + +改完重启 PI-Desktop 生效。 + +## 为何用 stdio 而不是 HTTP + +`serve --http`（127.0.0.1:5555/mcp）实测可用，但宿主只负责 spawn，不会托管一个 +常驻服务；HTTP 需要外部进程守护，进程一掉工具就全空。stdio 由宿主拉起并在每次 +调用时自动重连握手，更稳。 + +## 校验方式 + +装好后对 agent 说“用图谱统计一下仓库规模”，应命中 +`plugin_crg_mcp_crg_list_graph_stats_tool` 并返回节点/边数量。 diff --git a/crg-mcp-plugin/crg.cmd b/crg-mcp-plugin/crg.cmd new file mode 100644 index 0000000..4b695f5 --- /dev/null +++ b/crg-mcp-plugin/crg.cmd @@ -0,0 +1,57 @@ +@echo off +chcp 65001 >nul +setlocal + +rem --------------------------------------------------------------------------- +rem code-review-graph MCP launcher for PI-Desktop. +rem +rem The MCP host spawns this with a minimal environment (PATH, SystemRoot, +rem windir, TEMP, TMP, LANG plus the manifest's env block) and cwd = plugin +rem directory, with stdin/stdout used for JSON-RPC. Never write to stdout. +rem --------------------------------------------------------------------------- + +if defined CRG_HOME goto have_home +set "CRG_HOME=D:\github-project\code-review-graph" +:have_home + +rem code_review_graph/constants.py calls Path.home() at import time; without a +rem user profile the server dies with "Could not determine home directory.". +if defined USERPROFILE goto have_profile +set "USERPROFILE=C:\Users\%USERNAME%" +:have_profile +if defined HOMEDRIVE goto have_hd +set "HOMEDRIVE=C:" +:have_hd +if defined HOMEPATH goto have_hp +set "HOMEPATH=\Users\%USERNAME%" +:have_hp +if defined APPDATA set "APPDATA=%USERPROFILE%\AppData\Roaming" +if defined LOCALAPPDATA set "LOCALAPPDATA=%USERPROFILE%\AppData\Local" + +set "PYTHONUTF8=1" +set "PYTHONIOENCODING=utf-8" + +rem The editable install's .pth points at D:\github_project\... (underscore) +rem while the checkout lives at D:\github-project\... (hyphen), so the package +rem is only importable with the checkout explicitly on sys.path. +set "PYTHONPATH=%CRG_HOME%" + +rem The venv's code-review-graph.exe is a broken uv trampoline +rem ("failed to canonicalize script path"), so prefer a working interpreter. +set "CRG_PY=%CRG_HOME%\.venv\Scripts\python.exe" +if exist "%CRG_PY%" goto py_ready +set "CRG_PY=python" +:py_ready + +rem Upstream ships 30 tools; keep the surface small unless overridden. +if defined CRG_TOOLS goto tools_ready +set "CRG_TOOLS=build_or_update_graph_tool,run_postprocess_tool,get_minimal_context_tool,get_review_context_tool,get_impact_radius_tool,query_graph_tool,semantic_search_nodes_tool,detect_changes_tool,list_graph_stats_tool,get_affected_flows_tool" +:tools_ready + +if defined CRG_REPO goto repo_ready +set "CRG_REPO=%CRG_HOME%" +:repo_ready + +"%CRG_PY%" -m code_review_graph serve --repo "%CRG_REPO%" + +endlocal diff --git a/crg-mcp-plugin/dist/crg-mcp-1.0.0.piplug b/crg-mcp-plugin/dist/crg-mcp-1.0.0.piplug new file mode 100644 index 0000000..5a307a4 Binary files /dev/null and b/crg-mcp-plugin/dist/crg-mcp-1.0.0.piplug differ diff --git a/crg-mcp-plugin/main.js b/crg-mcp-plugin/main.js new file mode 100644 index 0000000..6167bcf --- /dev/null +++ b/crg-mcp-plugin/main.js @@ -0,0 +1,18 @@ +/** + * crg-mcp — thin loader for the code-review-graph MCP bridge. + * + * All of the wiring lives in manifest.json under `contributes.mcpServers`: + * PI-Desktop spawns `crg.cmd` (stdio MCP), performs the handshake, and + * publishes every upstream tool as `plugin_crg_mcp_crg_`. Nothing has + * to be registered from here — the host owns the client, the retries, and the + * tool lifecycle. This module only keeps the plugin loadable and offers a + * place for future local helpers. + */ + +async function onLoad() { + // The MCP client is owned by the host; no tool registration needed. +} + +async function onUnload() {} + +module.exports = { onLoad, onUnload }; diff --git a/crg-mcp-plugin/manifest.json b/crg-mcp-plugin/manifest.json new file mode 100644 index 0000000..f429523 --- /dev/null +++ b/crg-mcp-plugin/manifest.json @@ -0,0 +1,33 @@ +{ + "schemaVersion": 1, + "id": "crg-mcp", + "name": "Code Review Graph MCP", + "version": "1.0.0", + "description": "Bridges the locally deployed code-review-graph MCP server (D:\\github-project\\code-review-graph) into the agent as native tools.", + "main": "main.js", + "contributes": { + "mcpServers": [ + { + "id": "crg", + "label": "Code Review Graph", + "transport": "stdio", + "command": "crg.cmd", + "env": { + "PYTHONPATH": "D:\\github-project\\code-review-graph", + "USERPROFILE": "C:\\Users\\admin", + "HOMEDRIVE": "C:", + "HOMEPATH": "\\Users\\admin" + } + } + ] + }, + "permissions": [ + "mcp.server.local" + ], + "engines": { + "piDesktop": ">=0.1.0" + }, + "activationEvents": [ + "onStartup" + ] +} diff --git a/crg-mcp-plugin/selfcheck.ps1 b/crg-mcp-plugin/selfcheck.ps1 new file mode 100644 index 0000000..5270319 --- /dev/null +++ b/crg-mcp-plugin/selfcheck.ps1 @@ -0,0 +1,96 @@ +<# + Self-check for the crg-mcp plugin. + + Drives crg.cmd exactly the way the PI-Desktop MCP host does — `cmd /c crg.cmd` + with piped stdio and cwd = this folder — then reports the MCP handshake, the + tool list, and one real tool call. Run it from PowerShell: + + powershell -NoProfile -ExecutionPolicy Bypass -File .\selfcheck.ps1 +#> +$ErrorActionPreference = 'Stop' + +$dir = Split-Path -Parent $MyInvocation.MyCommand.Path + +# Mirror the host's minimal environment plus the manifest's env block. +foreach ($k in 'PATH', 'SystemRoot', 'TEMP', 'TMP') { + if (-not (Test-Path "Env:$k")) { Write-Warning "missing $k in ambient env" } +} + +$psi = New-Object System.Diagnostics.ProcessStartInfo +$psi.FileName = 'cmd.exe' +$psi.Arguments = '/c crg.cmd' +$psi.WorkingDirectory = $dir +$psi.RedirectStandardInput = $true +$psi.RedirectStandardOutput = $true +$psi.RedirectStandardError = $true +$psi.UseShellExecute = $false +$psi.StandardOutputEncoding = [System.Text.Encoding]::UTF8 + +$proc = [System.Diagnostics.Process]::Start($psi) + +function Send($obj) { + $proc.StandardInput.WriteLine(($obj | ConvertTo-Json -Compress -Depth 8)) + $proc.StandardInput.Flush() +} + +function ReadLine([int]$waitSeconds = 25) { + $task = $proc.StandardOutput.ReadLineAsync() + if ($task.Wait([TimeSpan]::FromSeconds($waitSeconds))) { return $task.Result } + return $null +} + +Send @{ + jsonrpc = '2.0'; id = 1; method = 'initialize' + params = @{ + protocolVersion = '2025-06-18' + capabilities = @{} + clientInfo = @{ name = 'crg-selfcheck'; version = '1' } + } +} + +$initLine = ReadLine 40 +if (-not $initLine) { + Write-Host 'HANDSHAKE FAILED: no stdout from crg.cmd' -ForegroundColor Red + Write-Host '--- stderr ---' + Write-Host $proc.StandardError.ReadToEnd() + try { $proc.Kill() } catch { } + exit 1 +} + +$init = $initLine | ConvertFrom-Json +Write-Host ("HANDSHAKE OK server={0} {1}" -f $init.result.serverInfo.name, $init.result.serverInfo.version) -ForegroundColor Green + +Send @{ jsonrpc = '2.0'; method = 'notifications/initialized'; params = @{} } +Send @{ jsonrpc = '2.0'; id = 2; method = 'tools/list'; params = @{} } + +$toolsLine = ReadLine +if (-not $toolsLine) { + Write-Host 'tools/list returned nothing' -ForegroundColor Red + try { $proc.Kill() } catch { } + exit 1 +} +$tools = ($toolsLine | ConvertFrom-Json).result.tools +Write-Host ("TOOLS: {0}" -f $tools.Count) -ForegroundColor Green +foreach ($t in $tools) { Write-Host (" plugin_crg_mcp_crg_{0}" -f $t.name) } + +Send @{ + jsonrpc = '2.0'; id = 3; method = 'tools/call' + params = @{ name = 'list_graph_stats_tool'; arguments = @{} } +} +$callLine = ReadLine 40 +if ($callLine) { + $call = $callLine | ConvertFrom-Json + if ($call.result) { + $text = $call.result.content[0].text + Write-Host 'TOOL CALL OK' -ForegroundColor Green + Write-Host (' ' + ($text -split "`n")[0..3] -join ' | ') + } + else { + Write-Host ("TOOL CALL ERROR: {0}" -f ($call | ConvertTo-Json -Compress -Depth 6)) -ForegroundColor Red + } +} +else { + Write-Host 'TOOL CALL: no response' -ForegroundColor Red +} + +try { $proc.Kill() } catch { } diff --git a/docker-compose-cpu.yml b/docker-compose-cpu.yml new file mode 100644 index 0000000..8cca49c --- /dev/null +++ b/docker-compose-cpu.yml @@ -0,0 +1,35 @@ +name: qwen3-asr + +services: + qwen3-asr: + image: ${ASR_IMAGE:-unis/qwen3-asr:cpu-latest} + container_name: qwen3-asr-cpu + ports: + - "${NGINX_PORT:-17003}:8000" + volumes: + - ${DATA_STORAGE_DIR:-/opt/dep/asr/data}:/app/data + - ${MODEL_STORAGE_DIR:-/opt/dep/asr/models}:/app/models + environment: + ACCELERATOR: cpu + API_KEY: ${API_KEY:-} + MODELS_DIR: /app/models + DATA_DIR: /app/data + TEMP_DIR: /app/data/temp + LOG_FILE: /app/data/logs/qwen3-asr.log + TASK_STATE_DIR: /app/data/tasks + TASK_RETENTION_HOURS: ${TASK_RETENTION_HOURS:-24} + MODELSCOPE_CACHE: /app + MODELSCOPE_PATH: /app/models + QWEN3_ASR_MODEL: ${QWEN3_ASR_MODEL:-} + SPEAKER_DB_ENABLED: ${SPEAKER_DB_ENABLED:-true} + DB_HOST: ${DB_HOST:-127.0.0.1} + DB_PORT: ${DB_PORT:-5432} + DB_USER: ${DB_USER:-postgres} + DB_PASSWORD: ${DB_PASSWORD:-postgres} + DB_NAME: ${DB_NAME:-asr_db} + SV_MODEL: ${SV_MODEL:-iic/speech_campplus_sv_zh-cn_16k-common} + SV_THRESHOLD: ${SV_THRESHOLD:-0.6} + REALTIME_SESSION_RESUME_TTL_SEC: ${REALTIME_SESSION_RESUME_TTL_SEC:-120} + NGINX_RATE_LIMIT_RPS: ${NGINX_RATE_LIMIT_RPS:-0} + NGINX_RATE_LIMIT_BURST: ${NGINX_RATE_LIMIT_BURST:-0} + restart: unless-stopped diff --git a/docker-compose-iluvatar.yml b/docker-compose-iluvatar.yml new file mode 100644 index 0000000..598f0e8 --- /dev/null +++ b/docker-compose-iluvatar.yml @@ -0,0 +1,50 @@ +name: qwen3-asr-iluvatar + +services: + qwen3-asr: + image: ${ASR_IMAGE:-unis/qwen3-asr:iluvatar-latest} + container_name: qwen3-asr-iluvatar + network_mode: host + pid: host + ipc: host + privileged: true + cap_add: + - ALL + volumes: + - ${ILUVATAR_USR_SRC:-/usr/src}:/usr/src + - ${ILUVATAR_LIB_MODULES:-/lib/modules}:/lib/modules + - ${ILUVATAR_DEV:-/dev}:/dev + - ${ILUVATAR_HOME:-/home}:/home + - ${ILUVATAR_DATA:-/data}:/data + - ${DATA_STORAGE_DIR:-/opt/dep/asr/data}:/app/data + - ${MODEL_STORAGE_DIR:-/opt/dep/asr/models}:/app/models + environment: + ACCELERATOR: iluvatar + DEVICE: ${DEVICE:-auto} + PORT: ${NGINX_PORT:-17003} + API_KEY: ${API_KEY:-} + MODELS_DIR: /app/models + DATA_DIR: /app/data + TEMP_DIR: /app/data/temp + LOG_FILE: /app/data/logs/qwen3-asr.log + TASK_STATE_DIR: /app/data/tasks + TASK_RETENTION_HOURS: ${TASK_RETENTION_HOURS:-24} + MODELSCOPE_CACHE: /app + MODELSCOPE_PATH: /app/models + QWEN3_ASR_MODEL: ${QWEN3_ASR_MODEL:-} + ASR_DEPLOY_TOPOLOGY: ${ASR_DEPLOY_TOPOLOGY:-isolated} + QWEN_GPU_MEMORY_UTILIZATION: ${QWEN_GPU_MEMORY_UTILIZATION:-} + QWEN_VLLM_ENFORCE_EAGER: ${QWEN_VLLM_ENFORCE_EAGER:-true} + SPEAKER_DB_ENABLED: ${SPEAKER_DB_ENABLED:-true} + DB_HOST: ${DB_HOST:-127.0.0.1} + DB_PORT: ${DB_PORT:-5432} + DB_USER: ${DB_USER:-postgres} + DB_PASSWORD: ${DB_PASSWORD:-postgres} + DB_NAME: ${DB_NAME:-asr_db} + SV_MODEL: ${SV_MODEL:-iic/speech_campplus_sv_zh-cn_16k-common} + SV_THRESHOLD: ${SV_THRESHOLD:-0.6} + REALTIME_SESSION_RESUME_TTL_SEC: ${REALTIME_SESSION_RESUME_TTL_SEC:-120} + ASR_VISIBLE_DEVICES: ${ASR_VISIBLE_DEVICES:-0} + NGINX_RATE_LIMIT_RPS: ${NGINX_RATE_LIMIT_RPS:-0} + NGINX_RATE_LIMIT_BURST: ${NGINX_RATE_LIMIT_BURST:-0} + restart: unless-stopped diff --git a/docker-compose-metax.yml b/docker-compose-metax.yml new file mode 100644 index 0000000..baa580c --- /dev/null +++ b/docker-compose-metax.yml @@ -0,0 +1,47 @@ +name: qwen3-asr-metax + +services: + qwen3-asr: + image: ${ASR_IMAGE:-unis/qwen3-asr:metax-latest} + container_name: qwen3-asr-metax + network_mode: host + pid: host + ipc: host + privileged: true + cap_add: + - ALL + volumes: + - ${METAX_DEV:-/dev}:/dev + - ${METAX_DRIVER_DIR:-/opt/mxdriver}:/opt/mxdriver:ro + - ${DATA_STORAGE_DIR:-/opt/dep/asr/data}:/app/data + - ${MODEL_STORAGE_DIR:-/opt/dep/asr/models}:/app/models + environment: + ACCELERATOR: metax + DEVICE: ${DEVICE:-auto} + PORT: ${NGINX_PORT:-17003} + API_KEY: ${API_KEY:-} + MODELS_DIR: /app/models + DATA_DIR: /app/data + TEMP_DIR: /app/data/temp + LOG_FILE: /app/data/logs/qwen3-asr.log + TASK_STATE_DIR: /app/data/tasks + TASK_RETENTION_HOURS: ${TASK_RETENTION_HOURS:-24} + MODELSCOPE_CACHE: /app + MODELSCOPE_PATH: /app/models + QWEN3_ASR_MODEL: ${QWEN3_ASR_MODEL:-} + ASR_DEPLOY_TOPOLOGY: ${ASR_DEPLOY_TOPOLOGY:-isolated} + QWEN_GPU_MEMORY_UTILIZATION: ${QWEN_GPU_MEMORY_UTILIZATION:-} + QWEN_VLLM_ENFORCE_EAGER: ${QWEN_VLLM_ENFORCE_EAGER:-true} + SPEAKER_DB_ENABLED: ${SPEAKER_DB_ENABLED:-true} + DB_HOST: ${DB_HOST:-127.0.0.1} + DB_PORT: ${DB_PORT:-5432} + DB_USER: ${DB_USER:-postgres} + DB_PASSWORD: ${DB_PASSWORD:-postgres} + DB_NAME: ${DB_NAME:-asr_db} + SV_MODEL: ${SV_MODEL:-iic/speech_campplus_sv_zh-cn_16k-common} + SV_THRESHOLD: ${SV_THRESHOLD:-0.6} + REALTIME_SESSION_RESUME_TTL_SEC: ${REALTIME_SESSION_RESUME_TTL_SEC:-120} + ASR_VISIBLE_DEVICES: ${ASR_VISIBLE_DEVICES:-0} + NGINX_RATE_LIMIT_RPS: ${NGINX_RATE_LIMIT_RPS:-0} + NGINX_RATE_LIMIT_BURST: ${NGINX_RATE_LIMIT_BURST:-0} + restart: unless-stopped diff --git a/docker-compose-mthreads.yml b/docker-compose-mthreads.yml new file mode 100644 index 0000000..7e344e5 --- /dev/null +++ b/docker-compose-mthreads.yml @@ -0,0 +1,50 @@ +name: qwen3-asr-mthreads + +services: + qwen3-asr: + image: ${ASR_IMAGE:-unis/qwen3-asr:mthreads-latest} + container_name: qwen3-asr-mthreads + network_mode: host + pid: host + ipc: host + privileged: true + cap_add: + - ALL + volumes: + - ${MTHREADS_DEV:-/dev}:/dev + - ${MTHREADS_USR_SRC:-/usr/src}:/usr/src + - ${MTHREADS_LIB_MODULES:-/lib/modules}:/lib/modules + - ${MTHREADS_HOME:-/home}:/home + - ${MTHREADS_DATA:-/data}:/data + - ${DATA_STORAGE_DIR:-/opt/dep/asr/data}:/app/data + - ${MODEL_STORAGE_DIR:-/opt/dep/asr/models}:/app/models + environment: + ACCELERATOR: mthreads + DEVICE: ${DEVICE:-auto} + PORT: ${NGINX_PORT:-17003} + API_KEY: ${API_KEY:-} + MODELS_DIR: /app/models + DATA_DIR: /app/data + TEMP_DIR: /app/data/temp + LOG_FILE: /app/data/logs/qwen3-asr.log + TASK_STATE_DIR: /app/data/tasks + TASK_RETENTION_HOURS: ${TASK_RETENTION_HOURS:-24} + MODELSCOPE_CACHE: /app + MODELSCOPE_PATH: /app/models + QWEN3_ASR_MODEL: ${QWEN3_ASR_MODEL:-} + ASR_DEPLOY_TOPOLOGY: ${ASR_DEPLOY_TOPOLOGY:-isolated} + QWEN_GPU_MEMORY_UTILIZATION: ${QWEN_GPU_MEMORY_UTILIZATION:-} + QWEN_VLLM_ENFORCE_EAGER: ${QWEN_VLLM_ENFORCE_EAGER:-true} + SPEAKER_DB_ENABLED: ${SPEAKER_DB_ENABLED:-true} + DB_HOST: ${DB_HOST:-127.0.0.1} + DB_PORT: ${DB_PORT:-5432} + DB_USER: ${DB_USER:-postgres} + DB_PASSWORD: ${DB_PASSWORD:-postgres} + DB_NAME: ${DB_NAME:-asr_db} + SV_MODEL: ${SV_MODEL:-iic/speech_campplus_sv_zh-cn_16k-common} + SV_THRESHOLD: ${SV_THRESHOLD:-0.6} + REALTIME_SESSION_RESUME_TTL_SEC: ${REALTIME_SESSION_RESUME_TTL_SEC:-120} + ASR_VISIBLE_DEVICES: ${ASR_VISIBLE_DEVICES:-0} + NGINX_RATE_LIMIT_RPS: ${NGINX_RATE_LIMIT_RPS:-0} + NGINX_RATE_LIMIT_BURST: ${NGINX_RATE_LIMIT_BURST:-0} + restart: unless-stopped diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..da9392e --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,40 @@ +name: qwen3-asr + +services: + qwen3-asr: + image: ${ASR_IMAGE:-unis/qwen3-asr:gpu-latest} + container_name: qwen3-asr + ports: + - "${NGINX_PORT:-17003}:8000" + volumes: + - ${DATA_STORAGE_DIR:-/opt/dep/asr/data}:/app/data + - ${MODEL_STORAGE_DIR:-/opt/dep/asr/models}:/app/models + runtime: nvidia + environment: + ACCELERATOR: nvidia + API_KEY: ${API_KEY:-} + MODELS_DIR: /app/models + DATA_DIR: /app/data + TEMP_DIR: /app/data/temp + LOG_FILE: /app/data/logs/qwen3-asr.log + TASK_STATE_DIR: /app/data/tasks + TASK_RETENTION_HOURS: ${TASK_RETENTION_HOURS:-24} + MODELSCOPE_CACHE: /app + MODELSCOPE_PATH: /app/models + QWEN3_ASR_MODEL: ${QWEN3_ASR_MODEL:-} + ASR_DEPLOY_TOPOLOGY: ${ASR_DEPLOY_TOPOLOGY:-isolated} + QWEN_VLLM_ENFORCE_EAGER: ${QWEN_VLLM_ENFORCE_EAGER:-true} + SPEAKER_DB_ENABLED: ${SPEAKER_DB_ENABLED:-true} + DB_HOST: ${DB_HOST:-127.0.0.1} + DB_PORT: ${DB_PORT:-5432} + DB_USER: ${DB_USER:-postgres} + DB_PASSWORD: ${DB_PASSWORD:-postgres} + DB_NAME: ${DB_NAME:-asr_db} + SV_MODEL: ${SV_MODEL:-iic/speech_campplus_sv_zh-cn_16k-common} + SV_THRESHOLD: ${SV_THRESHOLD:-0.6} + REALTIME_SESSION_RESUME_TTL_SEC: ${REALTIME_SESSION_RESUME_TTL_SEC:-120} + NVIDIA_VISIBLE_DEVICES: all + ASR_VISIBLE_DEVICES: ${ASR_VISIBLE_DEVICES:-0} + NGINX_RATE_LIMIT_RPS: ${NGINX_RATE_LIMIT_RPS:-0} + NGINX_RATE_LIMIT_BURST: ${NGINX_RATE_LIMIT_BURST:-0} + restart: unless-stopped diff --git a/docs/README_zh.md b/docs/README_zh.md new file mode 100644 index 0000000..c0d29ff --- /dev/null +++ b/docs/README_zh.md @@ -0,0 +1,606 @@ +
+ +

Qwen3-ASR

+

开箱即用的本地私有化部署语音识别服务

+ +以 [Qwen3-ASR](https://github.com/QwenLM/Qwen3-ASR) 为核心的语音识别 API 服务，提供 NVIDIA CUDA vLLM、沐曦 MACA vLLM 与 CPU Rust 后端，兼容阿里云语音 API 和 OpenAI Audio API，并保留 Paraformer realtime WebSocket 能力。 + +--- + +![Static Badge](https://img.shields.io/badge/Python-3.10+-blue?logo=python) +![Static Badge](https://img.shields.io/badge/Torch-2.11.0-%23EE4C2C?logo=pytorch&logoColor=white) +![Static Badge](https://img.shields.io/badge/CUDA-13.0_default-%2376B900?logo=nvidia&logoColor=white) + +
+ +## 在线演示站点 + +- **在线体验**: https://asr.vect.one + +## 演示 + +[![演示](../demo/demo.png)](https://media.cdn.vect.one/qwenasr_client_demo.mp4) + +## Release 1.0.1 + +> `v1.0.1` 是当前补丁版本。`v1.0.0` 相对于早期 `main` 分支引入了一轮大规模 breaking refactor。 +> 如果你是从 `main` 升级过来，请先阅读 release 说明，再决定是否沿用旧的部署与运行时假设。 +> +> 关键 breaking changes： +> - Python 依赖管理已经切到 `uv`（`pyproject.toml` + `uv.lock`），`requirements*.txt` 已移除 +> - 运行时栈改成 `NVIDIA/沐曦 GPU -> vLLM`、`CPU/macOS -> vendored QwenASR Rust` +> - `MLX` / Apple Silicon GPU 路径已移除，`mps` 会归一化到 `cpu` +> - macOS / Apple Silicon 现在默认总是 `qwen3-asr-0.6b`，可通过 `QWEN3_ASR_MODEL` 覆盖 +> - `ENABLED_MODELS` 已移除 + +## 主要特性 + +- **混合运行时栈** - 离线推理由自动选择的 Qwen3-ASR 提供，WebSocket 流式由 Paraformer realtime 能力提供 +- **说话人分离** - 基于 CAM++ 模型自动识别多说话人，返回说话人标记 +- **OpenAI API 兼容** - 支持 `/v1/audio/transcriptions` 端点，可直接使用 OpenAI SDK +- **阿里云 API 兼容** - 支持阿里云语音识别 RESTful API 和 WebSocket 流式协议 +- **WebSocket 流式识别** - 支持实时流式语音识别，低延迟 +- **智能远场过滤** - 流式 ASR 自动过滤远场声音和环境音，减少误触发 +- **智能音频分段** - 基于 VAD 的贪婪合并算法，自动切分长音频，避免包含过长静音 +- **GPU 批处理加速** - 支持批量推理，比逐个处理快 2-3 倍 +- **资源感知运行时** - 根据当前机器资源自动选择合适的 Qwen3-ASR 模型 + +## 致谢 + +- [Qwen3-ASR](https://github.com/QwenLM/Qwen3-ASR) 提供官方模型与多模态 / vLLM 使用方式 +- [QwenASR](https://github.com/huanglizhuo/QwenASR) 提供本项目 vendored 的 CPU Rust backend + +## 快速部署 + +### 1. Docker 部署(推荐) + +```bash +# 复制并编辑配置 +cp .env.example .env +# 编辑 .env 设置 API_KEY（可选） + +# Compose 默认挂载： +# /opt/dep/asr/models -> /app/models +# /opt/dep/asr/data -> /app/data +# /opt/dep/asr/data/logs、temp、tasks 都在 data 挂载内 + +# 启动服务（NVIDIA GPU 版本） +docker-compose up -d + +# 或沐曦 GPU 版本 +docker-compose -f docker-compose-metax.yml up -d + +# 或天数 GPU 版本 +docker-compose -f docker-compose-iluvatar.yml up -d + +# 或摩尔线程 / MUSA GPU 版本 +docker-compose -f docker-compose-mthreads.yml up -d + +# 或 CPU 版本 +docker-compose -f docker-compose-cpu.yml up -d + +# NVIDIA 多卡自动模式（每张可见卡自动拉起 1 个实例） +CUDA_VISIBLE_DEVICES=0,1,2,3 docker-compose up -d + +# 沐曦多卡自动模式 +METAX_VISIBLE_DEVICES=0,1 docker-compose -f docker-compose-metax.yml up -d + +# 天数多卡自动模式 +ILUVATAR_VISIBLE_DEVICES=0,1 docker-compose -f docker-compose-iluvatar.yml up -d + +# 摩尔线程 / MUSA 多卡自动模式 +MTHREADS_VISIBLE_DEVICES=0,1 docker-compose -f docker-compose-mthreads.yml up -d +``` + +服务访问地址： +- **API 端点**: `http://localhost:17003` +- **API 文档**: `http://localhost:17003/docs` + +可选的内置限流参数： +- `NGINX_RATE_LIMIT_RPS`（全局每秒请求上限，`0` 表示关闭） +- `NGINX_RATE_LIMIT_BURST`（全局突发请求数，`0` 时自动使用 RPS） + +**docker run 方式（替代）:** + +```bash +# NVIDIA GPU 版本 +docker run -d --name qwen3-asr \ + --gpus all \ + -p 17003:8000 \ + -e ACCELERATOR=nvidia \ + -e CUDA_VISIBLE_DEVICES=0,1,2,3 \ + -e API_KEY=your_api_key \ + -v /opt/dep/asr/models:/app/models \ + -v /opt/dep/asr/data:/app/data \ + unis/qwen3-asr:gpu-latest + +# 沐曦 GPU 版本 +docker run -d --name qwen3-asr-metax \ + --privileged \ + --network=host \ + --pid=host \ + --ipc=host \ + -v /dev:/dev \ + -v /opt/mxdriver:/opt/mxdriver:ro \ + -e ACCELERATOR=metax \ + -e PORT=17003 \ + -e METAX_VISIBLE_DEVICES=0 \ + -v /opt/dep/asr/models:/app/models \ + -v /opt/dep/asr/data:/app/data \ + unis/qwen3-asr:metax-latest + +# CPU 版本 +docker run -d --name qwen3-asr \ + -p 17003:8000 \ + -v /opt/dep/asr/models:/app/models \ + -v /opt/dep/asr/data:/app/data \ + unis/qwen3-asr:cpu-latest +``` + +默认推荐将宿主机目录统一挂载到 `/opt/dep/asr` 下，模型目录结构如下： + +```text +/opt/dep/asr/models/ + Qwen/ + iic/ + damo/ +``` + +如果你希望改成自定义目录，也可以在启动前设置： + +```bash +export MODEL_STORAGE_DIR=/data/qwen3-asr-models +export DATA_STORAGE_DIR=/data/qwen3-asr-data +``` + +> **注意**: NVIDIA GPU 镜像默认使用 CUDA 13.0/cu130，并固定 `torch 2.11.0` + `vllm 0.20.0`。 +> 开发者可通过 Docker build args 自行构建 CUDA 12.6、CUDA 13.0 或其他后端组合。 +> 沐曦镜像使用 `Dockerfile.metax` 基于沐曦官方 vLLM 镜像融合本项目。现场部署使用 host network、privileged，并挂载 `/dev` 与 `/opt/mxdriver`，确保 `mx-smi` 查询和沐曦 PyTorch 运行时都能初始化设备。 +> 当前 CPU 镜像已通过内置 QwenASR Rust backend 支持 `qwen3-asr-0.6b`。默认 CPU 镜像使用可分发 Rust 构建目标；只有自建且构建机/部署机 CPU 同构时才建议设置 `QWENASR_RUST_TARGET_CPU=native`。 +> CUDA vLLM 与 CPU Rust 路径下，`word_timestamps=true` 都会自动调用 forced aligner；当前实际后端为 `CUDA -> vLLM`、`CPU/macOS -> vendored QwenASR Rust`。 +> Apple Silicon 上的 Qwen3-ASR 现已统一走 Rust CPU backend。 +> `start.py` 现在会强制把 vLLM 多进程方式设为 `spawn`，避免 CUDA 在 fork 子进程中重复初始化导致启动失败。 + +**自定义 GPU 后端构建：** + +```bash +# 默认 GPU 构建：CUDA 13.0 / PyTorch cu130 +docker build -t qwen3-asr:gpu-cu130 -f Dockerfile.gpu . + +# CUDA 12.6 构建，用于旧部署环境 +docker build -t qwen3-asr:gpu-cu126 -f Dockerfile.gpu \ + --build-arg PYTORCH_BASE_IMAGE=pytorch/pytorch:2.11.0-cuda12.6-cudnn9-runtime \ + --build-arg PYTORCH_CUDA_INDEX=https://download.pytorch.org/whl/cu126 \ + --build-arg CUDA_NVCC_PACKAGE=cuda-nvcc-12-6 \ + --build-arg TORCH_CUDA_ARCH_LIST="8.0;8.6;8.9" \ + . + +# CUDA 13.0 构建，用于需要 CUDA 13 工具链的环境 +docker build -t qwen3-asr:gpu-cu130 -f Dockerfile.gpu \ + --build-arg PYTORCH_BASE_IMAGE=pytorch/pytorch:2.11.0-cuda13.0-cudnn9-runtime \ + --build-arg PYTORCH_CUDA_INDEX=https://download.pytorch.org/whl/cu130 \ + --build-arg CUDA_NVCC_PACKAGE=cuda-nvcc-13-0 \ + --build-arg TORCH_CUDA_ARCH_LIST="12.0+PTX" \ + . + +# 沐曦构建：基于沐曦官方 vLLM 镜像融合本项目 +./scripts/package_vendor_gpu_image.sh \ + --vendor metax \ + --base-image <沐曦官方vLLM镜像名> \ + -v n260-3.7.0.38 + +# 天数构建：基于天数官方 vLLM 镜像融合本项目 +docker pull registry.iluvatar.com.cn:10443/customer/sz/vllm0.17.0-4.4.0-x86:v5 +./scripts/package_vendor_gpu_image.sh \ + --vendor iluvatar \ + --base-image registry.iluvatar.com.cn:10443/customer/sz/vllm0.17.0-4.4.0-x86:v5 \ + -v vllm0.17.0-4.4.0-v5 + +# 摩尔线程构建：基于摩尔线程官方 MUSA vLLM 镜像融合本项目 +docker pull registry.mthreads.com/presale/devtech/vllm_musa:s4000_4.3.5_d0519 +./scripts/package_vendor_gpu_image.sh \ + --vendor mthreads \ + --base-image registry.mthreads.com/presale/devtech/vllm_musa:s4000_4.3.5_d0519 \ + -v s4000_4.3.5_d0519 +``` + +沐曦 GPU 国产化离线交付请优先参考 [沐曦 GPU 国产化离线部署指南](./metax_offline_deployment.md)。 +天数 GPU 国产化离线交付请优先参考 [天数 GPU 国产化离线部署指南](./iluvatar_offline_deployment.md)。 +摩尔线程 GPU 国产化离线交付请优先参考 [摩尔线程 GPU 国产化离线部署指南](./mthreads_offline_deployment.md)。 + +**内网部署**：现在可以直接生成一个带时间戳和 CPU/GPU 标识的离线交付目录，里面包含镜像包、compose、`.env` 模板、目录初始化脚本和使用说明。离线导出脚本使用普通 `docker build` + `docker save`，不依赖 `buildx`： + +```bash +# 1. 生成离线交付目录 +./export_offline_bundle.sh --type gpu +# 或 +./export_offline_bundle.sh --type cpu +# 或沐曦 GPU +./export_offline_bundle.sh \ + --type metax \ + --metax-base cr.metax-tech.com/public-ai-release/maca/vllm-metax:0.17.0-maca.ai3.5.3.307-torch2.8-py312-ubuntu22.04-amd64 \ + --skip-models +# 或天数 GPU +./export_offline_bundle.sh --type iluvatar --iluvatar-base registry.iluvatar.com.cn:10443/customer/sz/vllm0.17.0-4.4.0-x86:v5 +# 或摩尔线程 GPU +./export_offline_bundle.sh --type mthreads --mthreads-base registry.mthreads.com/presale/devtech/vllm_musa:s4000_4.3.5_d0519 +# 或一次同时打包 GPU + CPU +./export_offline_bundle.sh --type all + +# 2. 单独准备模型，不删除已有模型文件 +./scripts/download-models.sh --models-dir /opt/dep/asr/models + +# 3. 把交付目录复制到内网服务器 +scp -r build-file/<时间戳>-all user@server:/opt/dep/asr/ + +# 4. 在内网服务器上导入并启动 +cd /opt/dep/asr/<时间戳>-all +./init_host_dirs.sh +gunzip -c qwen3-asr-gpu-<时间戳>-amd64.tar.gz | docker load +gunzip -c qwen3-asr-cpu-<时间戳>-amd64.tar.gz | docker load +# NVIDIA GPU +docker compose up -d +# 或沐曦 GPU +# docker compose -f docker-compose-metax.yml up -d +# 或天数 GPU +# docker compose -f docker-compose-iluvatar.yml up -d +# 或摩尔线程 GPU +# docker compose -f docker-compose-mthreads.yml up -d +# 或 CPU +# docker compose -f docker-compose-cpu.yml up -d +``` + +> 详细部署说明请查看 [部署指南](./deployment.md) + +### 本地开发 + +**系统要求:** + +- Python 3.10+ +- 默认 GPU 镜像要求 CUDA 13.0+；CUDA 12.6 / 13.0 可通过 Docker build args 自行构建 +- FFmpeg (音频格式转换) + +**安装步骤:** + +运行时依赖现在改成“根目录默认 GPU，CPU 单独特化环境”： + +| 模式 | 命令 | 说明 | +|------|------|------| +| NVIDIA GPU（默认） | `uv sync` 或 `./scripts/sync_gpu_env.sh` | 同步根目录 [pyproject.toml](/opt/qwen3-asr/pyproject.toml) 和 [uv.lock](/opt/qwen3-asr/uv.lock) 到 `.venv`，包含 CUDA 13.0/cu130 `torch 2.11.0` / `torchaudio 2.11.0` / `torchvision 0.26.0` / `vllm 0.20.0` | +| 沐曦 GPU | `./scripts/sync_metax_env.sh` | 同步 [environments/metax/pyproject.toml](/opt/qwen3-asr/environments/metax/pyproject.toml) 的公共依赖；可选 GPU 栈安装默认从沐曦 MACA PyPI 源按 `--no-deps` 安装 | +| 天数 GPU | `./scripts/sync_iluvatar_env.sh` | 同步 [environments/iluvatar/pyproject.toml](/opt/qwen3-asr/environments/iluvatar/pyproject.toml) 的公共依赖；GPU 栈建议来自天数官方 vLLM 镜像 | +| 摩尔线程 GPU | `./scripts/sync_mthreads_env.sh` | 同步 [environments/mthreads/pyproject.toml](/opt/qwen3-asr/environments/mthreads/pyproject.toml) 的公共依赖；GPU 栈建议来自摩尔线程官方 MUSA vLLM 镜像 | +| CPU（特化） | `./scripts/sync_cpu_env.sh` | 同步 [environments/cpu/pyproject.toml](/opt/qwen3-asr/environments/cpu/pyproject.toml) 对应的 CPU lock 到 `.venv` | +| 自动 | `./scripts/sync_accel_env.sh` | 有 `mx-smi` 时选择沐曦，有 `ixsmi` 时选择天数，有 `mthreads-gmi` 时选择摩尔线程，有 `nvidia-smi` 时选择 NVIDIA，否则选择 CPU | + +```bash +# 克隆项目 +cd qwen3-asr + +# 安装依赖（Linux/NVIDIA CUDA） +uv sync + +# 启动服务 +source .venv/bin/activate +python start.py +``` + +沐曦本地开发： + +```bash +./scripts/sync_metax_env.sh +source .venv/bin/activate +ACCELERATOR=metax python start.py +``` + +macOS / Apple Silicon 本地开发： + +```bash +./scripts/sync_cpu_env.sh +source .venv/bin/activate +python start.py +``` + +## 当前运行时默认值 + +当前主线代码的运行时行为如下： + +- `ACCELERATOR=auto` 会优先识别 `mx-smi` 上报的沐曦设备，其次识别 `ixsmi` 上报的天数设备，再识别 `mthreads-gmi` 上报的摩尔线程设备，再识别 NVIDIA CUDA，否则回落 CPU +- `DEVICE=auto` + - NVIDIA/沐曦/天数 GPU 时解析为 `cuda:0` + - 否则解析为 `cpu` +- `DEVICE=mps` 会直接归一化为 `cpu` +- `Linux + NVIDIA CUDA` 使用官方 `vLLM` +- `Linux + 沐曦 MACA` 使用沐曦兼容 PyTorch/vLLM 运行栈 +- `Linux + 天数` 使用天数官方 vLLM 镜像运行栈 +- `Linux + CPU` 使用 vendored `QwenASR` Rust +- `macOS / Apple Silicon` 也使用 vendored `QwenASR` Rust +- macOS / Apple Silicon 默认总是 `qwen3-asr-0.6b` +- 在 macOS 上，只有设置 `QWEN3_ASR_MODEL=qwen3-asr-1.7b` 时才会使用 `qwen3-asr-1.7b` +- `word_timestamps=true` 在当前离线 CUDA 与 CPU Rust 路径下可用 +- WebSocket 流式路径当前不返回词级时间戳 +- CAM++ 说话人分离仍然必须保留，并继续跟随 `DEVICE`；在 CPU 上的主要热点仍是 speaker verification embedding + +## API 接口 + +### OpenAI 兼容接口 + +| 端点 | 方法 | 功能 | +| ---------------------------- | ---- | ----------------------- | +| `/v1/audio/transcriptions` | POST | 音频转写（OpenAI 兼容） | +| `/v1/models` | GET | 离线模型列表 | + +**请求参数:** + +| 参数 | 类型 | 默认值 | 说明 | +| ------------------------------ | ------ | --------------------- | ------------------------------------- | +| `file` | file | 提供时优先使用 | 音频/视频文件 | +| `audio_address` | string | 可选 | 音频/视频文件 URL（HTTP/HTTPS）、`file://` 或服务端本地路径；若同时提供 `file`，则忽略 | +| `language` | string | 自动检测 | 语言代码 (zh/en/ja) | +| `enable_speaker_diarization` | bool | `true` | 启用说话人分离 | +| `enable_speaker_identification` | bool | `true` | 说话人分离开启时匹配已注册声纹库 | +| `enable_text_cleanup` | bool | `true` | 启用文本去重、跨段重叠裁剪和口头语清理 | +| `word_timestamps` | bool | `false` | 返回后端支持的字词级时间戳；Qwen CUDA vLLM 与 CPU Rust 在启用时会自动调用 forced aligner | +| `hotwords` | string | - | 热词，格式：`词1 权重1 词2 权重2` | +| `response_format` | string | `verbose_json` | 输出格式 | +| `prompt` | string | - | 提示文本（保留兼容） | +| `temperature` | float | `0` | 采样温度（保留兼容） | + +**音频/视频输入方式:** +- **文件上传**: 使用 `file` 参数上传音频文件或带音轨的视频容器 +- **URL / 本地路径读取**: 使用 `audio_address` 参数提供音频/视频 URL 或服务端本地路径，服务将自动读取 +- **优先级**: 如果同时提供 `file` 和 `audio_address`，服务会优先使用 `file`，并忽略 `audio_address` + +**使用示例:** + +```python +# 使用 OpenAI SDK +from openai import OpenAI + +client = OpenAI(base_url="http://localhost:8000/v1", api_key="your_api_key") + +with open("audio.wav", "rb") as f: + transcript = client.audio.transcriptions.create( + file=f, + response_format="verbose_json" # 获取分段和说话人信息 + ) +print(transcript.text) +``` + +```bash +# 使用 curl +curl -X POST "http://localhost:8000/v1/audio/transcriptions" \ + -H "Authorization: Bearer your_api_key" \ + -F "file=@audio.wav" \ + -F "model=qwen3-asr-0.6b" \ + -F "response_format=verbose_json" \ + -F "enable_speaker_diarization=true" \ + -F "enable_speaker_identification=true" \ + -F "enable_text_cleanup=true" \ + -F "hotwords=Qwen 2.0 ModelScope 1.5" +``` + +**支持的响应格式:** `json`, `text`, `srt`, `vtt`, `verbose_json` + +### 阿里云兼容接口 + +| 端点 | 方法 | 功能 | +| ------------------------- | --------- | ---------------------- | +| `/stream/v1/asr` | POST | 语音识别（支持长音频） | +| `/stream/v1/asr/models` | GET | 声明条目列表 | +| `/stream/v1/asr/health` | GET | 健康检查 | +| `/ws/v1/asr` | WebSocket | Qwen3-ASR 流式识别 | +| `/ws/v1/asr/qwen` | WebSocket | Qwen3-ASR 流式识别（显式路径） | +| `/ws/v1/asr/funasr` | WebSocket | 已移除；会返回废弃错误并提示切换到 `/ws/v1/asr/qwen` | + +**请求参数:** + +| 参数 | 类型 | 默认值 | 说明 | +| ------------------------------ | ------ | ------------------ | ------------------------------------- | +| `audio_address` | string | `https://media.cdn.vect.one/podcast_demo.mp4`（文档示例） | 音频/视频 URL、`file://` 或服务端本地路径（可选；若同时上传内容则忽略） | +| `sample_rate` | int | `16000` | 采样率 | +| `enable_speaker_diarization` | bool | `true` | 启用说话人分离 | +| `enable_speaker_identification` | bool | `true` | 说话人分离开启时匹配已注册声纹库 | +| `enable_text_cleanup` | bool | `true` | 启用文本去重、跨段重叠裁剪和口头语清理 | +| `word_timestamps` | bool | `false` | 返回后端支持的字词级时间戳；Qwen CUDA vLLM 与 CPU Rust 在启用时会自动调用 forced aligner | +| `vocabulary_id` | string | - | 热词（格式：`词1 权重1 词2 权重2`） | + +**使用示例:** + +```bash +# 基本用法 +curl -X POST "http://localhost:8000/stream/v1/asr" \ + -H "Content-Type: application/octet-stream" \ + --data-binary @audio.wav + +# 带参数 +curl -X POST "http://localhost:8000/stream/v1/asr?enable_speaker_diarization=true&enable_speaker_identification=true&enable_text_cleanup=true&vocabulary_id=Qwen%202.0%20ModelScope%201.5" \ + -H "Content-Type: application/octet-stream" \ + --data-binary @audio.wav +``` + +### 会议离线接口 + +| 端点 | 方法 | 功能 | +| ---- | ---- | ---- | +| `/api/v1/asr/transcriptions` | POST | 创建离线会议识别任务 | +| `/api/v1/asr/transcriptions/{task_id}` | GET | 查询任务状态和结果 | + +该接口生产调用只使用 `audio_address`。 + +```json +{ + "audio_address": "https://example.com/media/meeting.mp4", + "config": { + "enable_speaker": true, + "match_speaker_registry": true, + "enable_text_cleanup": true, + "speaker_threshold": 0.6, + "word_timestamps": false, + "hotwords": [ + { "hotword": "通义千问", "weight": 2.0 }, + { "hotword": "ModelScope", "weight": 1.5 } + ] + } +} +``` + +**响应示例:** + +```json +{ + "task_id": "xxx", + "status": 200, + "message": "SUCCESS", + "result": "说话人1的内容...\n说话人2的内容...", + "duration": 60.5, + "processing_time": 1.234, + "segments": [ + { + "text": "今天天气不错。", + "start_time": 0.0, + "end_time": 2.5, + "speaker_id": "说话人1", + "word_tokens": [ + {"text": "今天", "start_time": 0.0, "end_time": 0.5}, + {"text": "天气", "start_time": 0.5, "end_time": 0.9}, + {"text": "不错", "start_time": 0.9, "end_time": 1.3} + ] + } + ] +} +``` + +## 说话人分离 + +基于 CAM++ 模型实现多说话人自动识别： + +- **默认开启** - `enable_speaker_diarization=true` +- **自动识别** - 无需预设说话人数量，模型自动检测 +- **说话人标记** - 响应中包含 `speaker_id` 字段（如 "说话人1"、"说话人2"） +- **智能合并** - 两层合并策略避免孤立短片段： + - 第一层：小于10秒的同说话人片段累积合并 + - 第二层：连续片段累积合并至60秒上限 +- **字幕支持** - SRT/VTT 格式输出包含说话人标记 `[说话人1] 文本内容` + +关闭说话人分离： + +```bash +# OpenAI API +-F "enable_speaker_diarization=false" + +# 阿里云 API +?enable_speaker_diarization=false +``` + +## 音频处理 + +### 智能分段策略 + +长音频自动分段处理： + +1. **VAD 语音检测** - 检测语音边界，过滤静音 +2. **贪婪合并** - 累积语音段，确保每段不超过 `MAX_SEGMENT_SEC`（默认60秒） +3. **静音切分** - 语音段间静音超过3秒时强制切分，避免包含过长静音 +4. **批处理推理** - 多片段并行处理，GPU 模式下性能提升 2-3 倍 + +### WebSocket 流式识别限制 + +**Qwen3-ASR 流式**（使用 `/ws/v1/asr` 或 `/ws/v1/asr/qwen`）： +- ✅ 支持多语言实时识别 +- ✅ 当前支持 CUDA vLLM 与 CPU Rust 两条流式路径 +- ❌ 当前流式路径不返回词级时间戳 + +### Qwen3 运行时矩阵 + +| 运行环境 | 后端 | 离线转写 | WebSocket 流式 | 离线词级时间戳 | 流式词级时间戳 | 成熟度 | +|---------|------|---------|----------------|----------------|----------------|--------| +| Linux + NVIDIA GPU | 官方 vLLM 0.20.0 | ✅ | ✅ | ✅ | ❌ | 面向生产 | +| CPU / macOS | QwenASR Rust | ✅ | ✅ | ✅（forced aligner） | ❌ | 推荐本地后端 | + +## 支持离线的模型 + +| 模型 ID | 名称 | 说明 | 特性 | +| -------------------- | ----------------- | ---------------------------------------- | --------- | +| `qwen3-asr-1.7b` | Qwen3-ASR 1.7B | 高性能多语言 ASR；CUDA 使用 vLLM | 离线/实时 | +| `qwen3-asr-0.6b` | Qwen3-ASR 0.6B | 轻量版多语言 ASR；CUDA 使用 vLLM，CPU/macOS 使用 Rust backend | 离线/实时 | + +**运行时选择:** +- **显存 >= 32GB**: 选择 `qwen3-asr-1.7b` +- **显存 < 32GB**: 选择 `qwen3-asr-0.6b` +- **无 CUDA**: 选择基于 vendored Rust 的 `qwen3-asr-0.6b` +- **macOS / Apple Silicon**: 无论内存大小多少，默认都选择 `qwen3-asr-0.6b` +- **环境变量覆盖**: 设置 `QWEN3_ASR_MODEL=qwen3-asr-1.7b` 或 `QWEN3_ASR_MODEL=qwen3-asr-0.6b` 可跳过自动选择 + +启动时会先检测当前运行计划所需模型；如果本地缓存缺失，会自动从 ModelScope 下载。离线部署请提前准备模型缓存。 + +## 环境变量 + +推荐直接关心的公开配置： + +| 变量 | 默认值 | 说明 | +| ---------------------------------- | ------------ | ----------------------------------------------- | +| `API_KEY` | - | API 认证密钥（可选，未配置时无需认证） | +| `LOG_LEVEL` | `INFO` | 日志级别（DEBUG/INFO/WARNING/ERROR） | +| `MAX_AUDIO_SIZE` | `2048` | 最大音频文件大小（MB，支持单位如 2GB） | +| `ASR_BATCH_SIZE` | `4` | 长音频分段后的 ASR 批处理大小 | +| `MAX_SEGMENT_SEC` | `60` | 音频分段最大时长（秒） | +| `ASR_ENABLE_NEARFIELD_FILTER` | `true` | 启用远场声音过滤 | +| `QWEN3_ASR_MODEL` | 自动选择 | 强制选择 `qwen3-asr-1.7b` 或 `qwen3-asr-0.6b` | +| `QWEN_GPU_MEMORY_UTILIZATION` | `0.9` | vLLM 可保留的 GPU 显存上限；共享显卡时可调低，KV cache 不足时可适当调高 | +| `QWEN_VLLM_ENFORCE_EAGER` | `true` | 强制 vLLM eager 执行以提高兼容性；NVIDIA 性能测试可设为 `false` 允许 CUDA Graph 优化 | + +远场过滤调优建议： + +- `ASR_NEARFIELD_RMS_THRESHOLD=0.01` 是当前默认值，也是推荐起点 +- 嘈杂环境可以适当调高，增强背景语音过滤 +- 安静环境如果出现小声说话漏识别，可以适当调低 +- 需要观察过滤行为时，可临时设置 `LOG_LEVEL=DEBUG` + +后端专项高级配置： + +| 变量 | 默认值 | 说明 | +| --- | --- | --- | +| `QWEN_RUST_CPU_WORKERS` | `4` | CPU Rust backend worker 数（Rust ASR / forced align 默认 4 个 runtime） | +| `QWENASR_LIBRARY_PATH` | 自动探测 | 覆盖 vendored Rust 动态库路径 | + +## 资源需求 + +**最小配置（CPU）:** + +- CPU: 4 核 +- 内存: 16GB +- 磁盘: 20GB + +**推荐配置（GPU）:** + +- CPU: 4 核 +- 内存: 16GB +- GPU: NVIDIA GPU (16GB+ 显存) +- 磁盘: 20GB + +## API 文档 + +启动服务后访问： + +- Swagger UI: `http://localhost:8000/docs` +- ReDoc: `http://localhost:8000/redoc` + +## 相关链接 + +- **部署指南**: [详细文档](./deployment.md) +- **Qwen3-ASR**: [Qwen3-ASR GitHub](https://github.com/QwenLM/Qwen3-ASR) +- **FunASR**: [FunASR GitHub](https://github.com/alibaba-damo-academy/FunASR) +- **QwenASR**: [QwenASR GitHub](https://github.com/huanglizhuo/QwenASR) + +## 许可证 + +本项目采用 MIT 许可证 - 查看 [LICENSE](../LICENSE) 文件了解详情。 + +## Star 历史 + +[![Star History Chart](https://api.star-history.com/svg?repos=Quantatirsk/qwen3-asr&type=Date)](https://star-history.com/#Quantatirsk/qwen3-asr&Date) + +## 贡献 + +欢迎提交 Issue 和 Pull Request 来改进项目! diff --git a/docs/REALTIME_WEBSOCKET_PROCESSING_COMPARISON.md b/docs/REALTIME_WEBSOCKET_PROCESSING_COMPARISON.md new file mode 100644 index 0000000..8ea96ee --- /dev/null +++ b/docs/REALTIME_WEBSOCKET_PROCESSING_COMPARISON.md @@ -0,0 +1,382 @@ +# 实时 ASR WebSocket 处理细节对照 + +本文只整理当前代码，不修改 WebSocket 或说话人算法。对照对象是： + +- 当前独立 Demo：`demo/realtime_asr_optimization_demo` +- 原项目实时接口：`app/api/v1/websocket_asr.py`、`app/services/qwen3_websocket_asr.py`、`app/services/realtime_speaker_clusterer.py` + +代码是本文的依据；旧的排查记录或早期说明如果与当前实现冲突，以代码为准。 + +## 1. 先看整体差异 + +```mermaid +sequenceDiagram + participant B as 浏览器 + participant D as 当前 Demo /ws + participant V as 独立 vLLM HTTP + participant A as 独立辅助服务 + + B->>D: start（扁平字段） + B->>D: PCM/WAV 二进制帧 + D->>D: 20ms RMS 门控、turn 缓冲、静音切段 + D->>V: 当前 turn 的累积 WAV（partial/final） + V-->>D: 文本 + D-->>B: sentences + display_state + D->>A: final turn + session_id + A-->>D: CAM++ embedding / 在线聚类标签 + D-->>B: 同 sentence_id 的 speaker 更新 + 新 display_state + B->>D: eof 或 stop + D->>D: 等待音频队列和 speaker 队列清空 + D-->>B: end +``` + +```mermaid +sequenceDiagram + participant B as 浏览器 + participant R as 原项目 FastAPI 路由 + participant Q as Qwen3ASRService + participant E as Qwen3ASREngine + participant S as RealtimeSpeakerClusterer + + B->>R: /ws/v1/asr 或 /ws/v1/asr/qwen + R->>Q: handle_connection + B->>Q: start.payload（嵌套字段） + Q-->>B: voice_id、start + B->>Q: PCM/WAV 二进制帧 + Q->>Q: 转采样、VAD、pre-roll、partial + Q->>E: 原生流式或当前窗口重转写 + E-->>Q: partial 文本 + Q-->>B: sentence_type=0 + Q->>E: 当前 turn 全量 final 重转写 + E-->>Q: final 文本 + Q-->>B: sentence_type=1、speaker_id=-1 + Q->>S: speaker_job_queue（异步） + S-->>Q: 聚类/注册库匹配 + Q-->>B: 相同 sentence_id 的 speaker 回写 + B->>Q: stop + Q-->>B: end（stop 内部会再次 final/recluster） +``` + +核心设计差别是：当前 Demo 把 ASR 和声纹都放在独立 HTTP 服务后面，WebSocket 只做编排；原项目把 Qwen ASR 引擎、VAD、在线声纹聚类放在同一个服务进程里，但 speaker 归属仍是 final 之后异步补回。 + +## 2. 当前独立 Demo 的处理链路 + +### 2.1 进程和启动关系 + +`server.py` 启动一个 aiohttp HTTP/WebSocket 进程，并在生命周期中创建两个 HTTP 客户端： + +| 组件 | 默认地址 | 职责 | +| --- | --- | --- | +| `VLLMTranscriptionService` | `http://127.0.0.1:9950/v1` | 只调用 `/audio/transcriptions`，partial 和 final 都是累积窗口 HTTP 请求 | +| `AuxiliaryModelService` | `http://127.0.0.1:8010` | 调用 `/health`、`/v1/speaker/resolve`、`/v1/speaker/reset` | +| `RealtimeSession` | `server.py:/ws` | 接收音频、VAD 切 turn、调用两个服务、维护展示状态 | + +辅助服务在 `demo/scripts/auxiliary_server.py` 中预加载 VAD 与 CAM++ `speaker_verification`。每次 resolve 只上传一个已结束 turn，服务以 `session_id` 保存在线聚类中心；这不是把整段会议音频重新上传。 + +### 2.2 WebSocket 输入和输出 + +客户端首条消息是扁平结构： + +```json +{ + "type": "start", + "source": "mic", + "model_service_url": "http://127.0.0.1:9950/v1", + "model": "Qwen/Qwen3-ASR-0.6B", + "speaker_diarization": 1, + "sentence_strategy": 0, + "partial_interval_ms": 1200, + "max_segment_sec": 12, + "display_merge": true +} +``` + +`start` 成功后，客户端发送 16kHz、单声道、PCM16 二进制帧。文件模式只接收 `.pcm` 和 `.wav`；WAV 的 RIFF/fmt/data chunk 在 WebSocket 服务端增量剥离，并且要求 16kHz、单声道、PCM16。 + +控制消息： + +| 消息 | 行为 | +| --- | --- | +| `eof` | 输入生产者结束；服务端把 `EOF` 放入音频队列，完成尾部 turn 和 speaker 队列后发送 `end` | +| `stop` | 与 `eof` 走同一排空流程，同时记录 `input_stopped`，用于 WAV 不完整时的校验差异 | +| `abort` | 取消音频和 speaker worker，直接结束，不保证当前 turn 有 final | + +服务端消息的实际顺序通常是： + +```text +start + -> sentences（partial，可能多次） + -> display_state（每次状态改变一份快照） + -> sentences（final，speaker 尚未确认） + -> display_state（pending） + -> display_state（processing/confirmed 或失败原因） + -> draining + -> end +``` + +`sentences` 是兼容性事件；`display_state` 是当前页面的主要渲染数据。`display_state.revision` 单调递增，包含： + +- `raw_segments`：按 `start_time`、`sentence_id` 排序的原始片段 +- `display_blocks`：按相邻且可信的说话人合并后的展示块 +- `metrics`：音频字节数、输入帧数、partial 数量、partial 修订次数和耗时 + +### 2.3 音频、VAD 和 turn 边界 + +`RealtimeSession.process_audio()` 的边界是本 Demo 最重要的状态机： + +1. 二进制数据进入 `audio_queue`，再拆成 640 bytes 的 PCM 帧，即 20ms。 +2. 每帧用 RMS 阈值 `450` 判断有声/静音；这是 WebSocket 层的轻量门控，不是辅助服务的整段 VAD pipeline。 +3. 未进入说话状态时，保留最近 6400 bytes（约 200ms）`pre_roll`。 +4. 第一帧有声时，将 pre-roll 加到新 `segment_audio`，设置 `segment_start_ms`。 +5. 进入说话状态后，所有帧追加到当前 turn；有声帧累计 `voiced_ms`，静音帧累计 `silence_ms`。 +6. `sentence_strategy=0` 默认约 800ms 静音提交；`sentence_strategy=1` 使用约 1400ms 静音提交。 +7. 达到 `partial_interval_ms`（默认 1200ms）且尚未达到静音阈值时，调用一次 vLLM partial。 +8. 达到 `max_segment_sec`（默认 12s）时按 `max_duration` 提交。 + +当前 Demo 不把切段尾部静音送给 ASR/声纹：提交前按 `silence_ms` 从 `segment_audio` 尾部删除。下一个 turn 没有原项目那样的 `carry_audio`，而是从后续有声帧重新开始；因此两段之间的静音会形成时间间隔，不会自动带入下一段。 + +### 2.4 ASR 结果和同句覆盖 + +`_emit_transcription()` 始终使用当前 `segment_id`： + +- `sentence_type=0`：partial，写入/覆盖同一个 `sentence_id` +- `sentence_type=1`：final，仍写入同一个 `sentence_id`，并加入 speaker 队列 + +`SegmentAssembler.apply_sentence()` 先按 `sentence_id` 找旧记录再覆盖；如果已经是 final，后来的 partial 不会回滚 final。final 没有文本时，会移除该片段，避免遗留一个永久 pending 的 partial。 + +### 2.5 声纹异步链路 + +`_commit_segment()` 只负责把 `SpeakerJob` 放入 `speaker_queue`，不会等待 CAM++。`process_speakers()` 是单 worker，按 turn 入队顺序串行调用辅助服务： + +1. `voiced_ms < 800ms`：不提取声纹，写入 `insufficient_audio`，保持 `speaker_id=-1`。 +2. 辅助服务缺失或异常：写入 `service_unavailable`/`service_error`，ASR 继续输出。 +3. 辅助服务返回 embedding/聚类结果：回写同一 `sentence_id`。 +4. `SegmentAssembler.apply_speaker_update()` 只接受可信身份：`speaker_evidence` 必须是 `fresh` 或 `confirmed`，置信度至少 `0.6`，并拒绝 `short_attach`、`embedding_attach`。 +5. 不可信结果被归一化为 `speaker_id=-1`、`speaker_name=""`，但保留状态和原因供诊断。 + +辅助服务的在线聚类是简单的 session 级中心匹配：首次 embedding 新建 `speaker_id`，之后与已有中心的余弦相似度达到阈值就更新中心并复用 ID。`/v1/speaker/reset` 在 WebSocket 结束时清理该 session。 + +### 2.6 展示块如何合并 + +`SegmentAssembler.display_blocks(merge_adjacent=True)` 先按时间排序原始片段，然后遵循： + +- pending/unknown 片段始终以自己的 `sentence_id` 作为身份键，独立成块； +- 只有相邻且可信的片段，且 `user_id`/`registry_speaker_id`/`speaker_id` 身份键相同，才合并； +- 合并只拼接文本、扩大结束时间并追加 `segment_ids`，原始片段仍保留在 `raw_segments`。 + +当前页面收到 `display_state` 后会整块重建结果区，按 `block_id` 渲染；收到 `sentences` 时如果服务端声明支持 `display_state`，页面不会再次追加，避免同一片段重复显示。旧页面或绕过 `display_state` 的客户端不具备这个保护。 + +## 3. 原项目 WebSocket 的处理链路 + +### 3.1 路由和状态 + +`app/api/v1/websocket_asr.py` 当前实际路由： + +- `/ws/v1/asr` +- `/ws/v1/asr/qwen` +- `/ws/v1/asr/funasr` 已废弃，接受后发送 `FUNASR_REALTIME_REMOVED` 并以 1008 关闭 + +每个连接进入 `Qwen3ASRService.handle_connection()`。`ConnectionContext.state` 为 `READY -> STARTED -> STREAMING`；会话还可以通过 `session_id` 在 TTL 内断线恢复。恢复的是完整上下文，包括已确认片段、speaker history、时间线和待处理状态，不只是一个 WebSocket ID。 + +### 3.2 start 参数 + +原项目的 `start` 使用 `payload` 嵌套对象。常用字段包括： + +| 类别 | 字段 | +| --- | --- | +| 音频 | `format`、`sample_rate`、`language`、`context`、`enable_inverse_text_normalization` | +| partial | `min_partial_sec`、`partial_window_sec`、`partial_holdback_chars`、`unfixed_token_num`、`enable_native_partial_stream` | +| 切段 | `silence_duration_ms`（默认 800）、`pre_roll_ms`（默认 240）、`max_sentence_count`（默认 8）、`enable_realtime_vad_split`、`max_segment_sec` | +| speaker | `enable_speaker`（默认 true）、`match_speaker_registry`、`speaker_threshold` | +| 稳定性 | `force_stable_segment_sec`、`force_stable_min_chars`、`soft_limit_sec`、`hard_limit_sec` | + +服务端先发送 `voice_id`，再发送 `start`。`voice_id` 与会话 ID 通常相同；如果客户端传入固定 `payload.session_id`，断线重连时可以复用上下文。 + +### 3.3 音频转换、pre-roll 和 partial + +`_convert_audio()` 接收 PCM/WAV，转为 float32；多声道下混为单声道，非 16kHz 用 scipy 重采样。每个二进制消息都在服务端转换后立即参与 VAD。 + +原项目的 `ConnectionContext` 同时维护： + +- `pre_roll_audio`：未开始说话前的前滚音频，默认约 240ms； +- `segment_audio_buffer`：从当前 turn 开始到提交前的完整音频； +- `stream_window_buffer`：最近窗口，用于 partial 或 native stream 失败时回退； +- `realtime_stream_state`：只服务低延迟 partial，不决定 final； +- `silence_samples`、`sentence_active`、`total_samples`：VAD 状态和当前 turn 长度。 + +有声输入到达时，`_start_turn()` 把 pre-roll 与当前音频拼接；后续由 `_append_turn_audio()` 追加。达到最短窗口后，服务端可走原生 Qwen partial，或对当前窗口/当前 turn 重转写；partial 会经过清理、去重、与上一段重叠裁剪后发送。 + +提交触发条件不只有静音： + +- 识别到足够完整的标点句，且时长/字数达到稳定门槛； +- 句子数达到 `max_sentence_count`； +- 静音样本达到 `silence_duration_ms`； +- 达到硬时长限制；开启实时 VAD split 时会尝试找一个完成的分割点。 + +### 3.4 final、carry 和时间线 + +`_commit_retranscribe_turn()` 对当前完整 turn 做一次 final 重转写，生成 `confirmed_segments` 元素： + +```text +index / text / language / reason +duration_ms / start_ms / end_ms +sentence_type=1 / speaker_id=-1 / speaker_pending=true +``` + +final 事件先发送，speaker 之后再补。默认 final 的 `start_ms` 来自 `ctx.timeline_cursor_ms`；提交后时间线前移到 `segment_end_ms`。 + +开启实时 VAD split 时，提交可能得到 `finalized_audio + carry_audio`：前半段定稿，后半段留在下一个逻辑 turn 中，且会重新初始化 stream 状态。这个 carry 是原项目与当前 Demo 的一个实质差异，也是跨说话人边界时必须重点观察的音频来源。 + +### 3.5 原项目 speaker worker 和聚类 + +原项目 final 后把 job 放入 `ctx.speaker_job_queue`，由 `_speaker_worker_loop()` 串行消费。`_resolve_segment_speaker()` 调用 `RealtimeSpeakerClusterer.resolve_segment_speaker()`，再把结果写入指定 `segment_index`，通过相同索引发送一条新的 `sentences`。 + +`RealtimeSpeakerClusterer` 当前行为： + +- 1.6s 以下且上一条有命名身份：使用 `short_attach` 直接沿用上一条； +- 正常 turn：按 1.5s 窗口、0.75s 步长提取 CAM++ embedding，匹配已有记录或新建 generic speaker； +- 4s 以上若 chunk 明显混合：返回 `mixed_segment`，保持未知； +- 开启注册库匹配且时长至少 2.4s：在独立注册 embedding 空间匹配实名； +- timeline 平滑时，短于 0.7s 的范围会并给相邻说话人； +- 实时记录达到至少 5 条且队列积压不超过 1 条时，可能对最近 12 条 pending 片段重新聚类； +- `stop` 时还会对历史记录做一次最终 recluster。 + +此外，`Qwen3ASRService` 自身还有两类“最近说话人继承”：generic speaker 新 turn 时长至少 8s 才允许 `recent_inherit`，实名 speaker 至少 4.5s 才允许 `recent_named_inherit`。这些继承都发生在 embedding 结果之后，不能与 `short_attach` 混为一谈。 + +### 3.6 stop 和 end + +客户端只发送 `{"type":"stop"}`。服务端会： + +1. 对仍 active 的 `segment_audio_buffer` 做 `reason=final` 的 final 提交； +2. 立即对现有 `speaker_records` 做最终 recluster，并发送可能的 speaker 更新； +3. 汇总 `confirmed_segments`，发送 `end(final=1)`。 + +这里与当前 Demo 不同：原项目的 `_stop()` 没有显式等待 `speaker_job_queue.join()`。如果 stop 到达时 speaker worker 仍在处理，最终 recluster/end 可能先于某个异步 speaker 回写；断开清理还会停止 worker。客户端必须把同 `sentence_id` 的后续 speaker 事件当作可迟到更新，而不能认为 `end` 之后绝不会再有归属变化。 + +## 4. 两套消息契约对照 + +| 维度 | 当前独立 Demo | 原项目 | +| --- | --- | --- | +| WebSocket | aiohttp `/ws` | FastAPI `/ws/v1/asr`、`/qwen` | +| start | 扁平字段 | `payload` 嵌套字段 | +| ASR | 外部 vLLM HTTP 累积窗口 | 进程内 Qwen engine，原生 stream 或重转写回退 | +| VAD | 20ms PCM RMS 门控 | float32 音频门控，支持实时 VAD split 辅助切分 | +| pre-roll | 固定约 200ms | `pre_roll_ms` 默认约 240ms | +| final 音频 | 删除提交尾部静音，不保留 carry | 可有 `carry_audio` 并带入后续 turn | +| speaker | 外部 CAM++/在线中心服务，单 worker | 进程内 CAM++ chunk 聚类、注册库、重聚类，单 worker | +| 未确认 speaker | `speaker_evidence=pending`，展示独立未知块 | `speaker_id=-1` 或 `speaker_pending=true`，客户端需自行暂存 | +| 文本更新键 | `sentence_id` | `sentence_id` 对外，内部 `segment_index` | +| 展示快照 | `display_state.revision`，服务端生成 `display_blocks` | 没有同等的服务端展示块协议，客户端按 sentence upsert | +| end 屏障 | `EOF -> audio worker -> speaker EOF -> end` | `_stop()` final/recluster/end，不等待 speaker 队列清空 | + +## 5. “上一人的最后一句进入下一人气泡”的定位框架 + +当前先不修改，定位时要把“片段本身错了”和“片段正确但展示合错了”分开。 + +### 5.1 当前独立 Demo 的可能路径 + +1. **同一个 turn**：两人换话之间没有达到 800ms（或段落模式 1400ms）静音，RMS VAD 不切段。此时 vLLM 收到的是混合 turn，前端只有一个 `sentence_id`，不是气泡合并问题。 +2. **声纹误归属**：A 的 final 先是 pending，随后辅助服务把 A 误匹配到 B 的 cluster。下一次 `display_state` 中，A、B 两个相邻片段拥有同一可信身份，`display_blocks()` 会把两者拼成一个 block。 +3. **异步回写改变了合并条件**：A 的 speaker 结果可能在 B 的 final 之后才到达。服务端按时间排序重建快照，所以视觉上是 A 的文字“后来进入”B 的气泡；实际是 A 的旧片段身份被补齐后触发了相邻合并。 +4. **旧客户端渲染路径**：当前页面在 `display_state_supported=true` 时忽略 `sentences`，但旧页面若逐条 append `sentences`，可能把同一 `sentence_id` 的 final/speaker 更新当成新气泡，或把 pending 文本追加到上一气泡。必须确认浏览器加载的 `app.js` 版本和服务端返回的 `display_state`。 +5. **session 污染**：辅助服务按 `session_id` 保存聚类中心。若 reset 没有执行、多个连接错误复用同一个 session ID，上一场会话的 cluster 可能影响新会话；正常一次连接内 A/B 共用中心是设计行为，不是跨人合并的充分证据。 + +当前 Demo 的 worker 是串行的，`emit()` 有发送锁，因而“并发返回顺序打乱”不是首要嫌疑；首要证据应是 `raw_segments` 的 `sentence_id/start_time/speaker_id/speaker_strategy` 是否正确，以及 `display_blocks.segment_ids` 是否把两个片段合到一起。 + +### 5.2 原项目的可能路径 + +1. **静音边界不足**：默认 800ms 静音才提交；换话前的短停顿会让 A 尾部和 B 开头留在同一个 `segment_audio_buffer`。 +2. **carry 音频污染**：启用实时 VAD split 时，分割点之后的 `carry_audio` 会成为下一个 turn 的开头。若 split 点落在 A 尾音或 B 起音中间，下一段声纹和 ASR 都会携带前一人尾部。 +3. **短句沿用上一身份**：B 的新段小于 1.6s 且历史有命名 speaker 时，`short_attach` 会直接复用上一条；chunk 提取为空时的 `embedding_attach` 也可能复用上一条。这是代码中最直接的“上一人污染下一段”路径。 +4. **最近身份继承**：较长的新段在匹配失败时可能触发 `recent_named_inherit`（至少 4.5s）或 `recent_inherit`（至少 8s），因此不能只看最终 `speaker_id`，还要记录 `speaker_strategy`。 +5. **重聚类改写历史**：实时重聚类和 stop 最终重聚类都可能改写已有 segment 的 speaker。客户端如果按到达顺序追加，而不是按 `sentence_id` upsert，就会看到旧气泡和新气泡互相覆盖或合并。 +6. **stop 竞态**：原项目 stop 不等待 speaker job 队列清空；end 可能先发，随后连接清理还会取消 worker。最后一个人的 speaker 归属可能缺失、迟到或停留在旧标签，前端若把 end 当成不可变快照会放大问题。 + +### 5.3 需要同时保存的证据 + +对同一段测试音频，至少保存以下三层结果： + +```text +音频层：每个 turn 的 start/end、有效有声时长、是否包含 carry/pre-roll +识别层：sentence_id/index、sentence_type、文本、speaker_strategy、置信度 +展示层：display_state.revision、raw_segments、display_blocks.segment_ids +``` + +判定规则： + +- `raw_segments` 已经只有一个片段：先查 VAD/切段边界； +- `raw_segments` 有 A、B 两段且 speaker ID 相同：查声纹误匹配/继承/重聚类； +- `raw_segments` 的 ID 不同但 `display_blocks.segment_ids` 合并：查展示合并键； +- `display_blocks` 正确但页面仍显示一只气泡：查浏览器脚本版本、是否绕过 `display_state`、是否按 `sentence_id` upsert。 + +## 6. 后续细节优化的优先级（本轮不实施） + +### P0：先证明边界和身份是否正确 + +- 记录每个 final 的实际音频起止、有效有声毫秒、pre-roll/carry 长度。 +- 记录 speaker resolve 请求和返回的 `session_id`、策略、置信度、cluster ID。 +- 前端临时展示 `block.segment_ids`，确认“合并”到底是两个片段还是一个片段。 +- 用固定 A-静音-B 音频，比较 200ms、500ms、800ms、1400ms 停顿。 + +### P1：降低错误身份传播 + +- 对 `short_attach`、`embedding_attach`、recent inherit 单独统计，不要只统计 speaker_id。 +- 对跨边界的短 turn 保持 pending，等到有独立 embedding 或后续重聚类再确认。 +- 明确实时重聚类和最终重聚类的可修改范围，客户端统一按 ID 幂等更新。 +- 为 stop 增加“最后一个 speaker job 已完成”的可观察状态。 + +### P2：改善展示稳定性 + +- 展示层只把可信且相邻的片段合并，保留 segment_ids 和 revision。 +- 对 speaker 更新做局部重绘或整快照重绘，但不要把同一个 sentence 当成新消息追加。 +- unknown/pending 使用独立块，不把诊断文本放在说话人名称中。 + +## 7. 建议的验收用例 + +| 用例 | 观察点 | 通过标准 | +| --- | --- | --- | +| A 说 3s，停 1s，B 说 3s | 两套服务的 raw segment | 至少两个不同 sentence_id，时间不重叠 | +| A 说 3s，停 300ms，B 说 3s | VAD 边界 | 明确记录为同段或分段，不能只看气泡颜色判断 | +| A 说 3s，B 只说 0.8s | 短 turn speaker 策略 | 原项目应能观察 `short_attach`；Demo 应保持 pending 或独立结果 | +| A/B 各说多段，speaker 服务延迟 2s | 异步回写 | 文本不重复，更新按 sentence_id 定位，顺序按时间恢复 | +| speaker 服务不可用 | 降级 | ASR 仍有 final，speaker 为未知并有明确 reason | +| stop 紧跟最后一帧 | 收尾屏障 | Demo 的 end 在 speaker 队列完成后发送；原项目记录可能迟到的 speaker 更新 | +| 断线后同 session_id 重连 | 会话隔离 | 原项目按 TTL 恢复；Demo 新连接不会复用旧 speaker center | + +## 8. 源码索引 + +### 当前独立 Demo + +- `demo/realtime_asr_optimization_demo/server.py` + - `RealtimeSession.__init__`：会话参数、队列和 VAD 状态 + - `emit_state`：`raw_segments/display_blocks/revision` + - `_emit_transcription`：partial/final 写入同一 `sentence_id` + - `_commit_segment`、`process_audio`：VAD、切段和 speaker job 入队 + - `_resolve_speaker`、`process_speakers`：异步声纹回写 + - `websocket_handler`：start、二进制帧、eof/stop/abort、end +- `demo/realtime_asr_optimization_demo/speaker_assembler.py` + - `apply_sentence`、`apply_speaker_update`、`display_blocks` +- `demo/realtime_asr_optimization_demo/model_service.py` + - 独立 vLLM OpenAI-compatible HTTP 适配 +- `demo/realtime_asr_optimization_demo/auxiliary_service.py` + - `/health`、`/v1/speaker/resolve`、`/v1/speaker/reset` 客户端适配 +- `demo/scripts/auxiliary_server.py` + - VAD/CAM++ 预加载、embedding 提取、session 级在线聚类 + +### 原项目 + +- `app/api/v1/websocket_asr.py` + - `/ws/v1/asr`、`/ws/v1/asr/qwen`、废弃 `/funasr` +- `app/services/qwen3_websocket_asr.py` + - `ConnectionContext`：音频、partial、confirmed segments、speaker 队列 + - `handle_connection`：WebSocket 状态机和消息协议 + - `_commit_retranscribe_turn`：final、carry、timeline、speaker job + - `_speaker_worker_loop`、`_resolve_and_emit_segment_speaker`：异步 speaker 回写 + - `_inherit_recent_*`、`_maybe_recluster_recent_segments`：身份传播和重聚类 + - `_stop`：最终提交、recluster、end +- `app/services/realtime_speaker_clusterer.py` + - chunk embedding、短段沿用、已有 speaker 匹配、混合段、timeline 平滑 +- `docs/realtime_meeting_websocket.md` + - 对外协议示例；其中 partial/final/speaker 回写必须按 `sentence_id` 幂等处理 + +本轮只新增本文档，没有修改上述实现。后续修复应先用第 5 节的三层证据确定问题属于切段、声纹还是展示层，再决定改哪一层。 diff --git a/docs/TODO/ENTROPY_REDUCTION_PLAN.md b/docs/TODO/ENTROPY_REDUCTION_PLAN.md new file mode 100644 index 0000000..ec95b91 --- /dev/null +++ b/docs/TODO/ENTROPY_REDUCTION_PLAN.md @@ -0,0 +1,66 @@ +# 熵减执行清单 + +本文档记录本轮已执行的熵减工作。目标是移除不可达路径、兼容占位、隐藏 fallback、重复请求流水线和无用依赖。 + +## 范围 + +- 主范围：`app/`、根运行配置、公开运行文档。 +- 不处理：`vendor/qwenasr` 内部实现、仅 benchmark 使用且不阻塞主链路的代码。 +- 原则：开发中项目不保留废弃接口、旧字段、兼容层或 fallback 逻辑。 + +## P0 + +- [x] 移除 Qwen3 `transformers` 后端残留路径。 + - 删除后端选择里的 `"transformers"` fallback。 + - 删除仅服务该路径的 batch/segment 转换死代码。 + - 不支持的设备显式失败。 +- [x] 启动预加载改为 fail-fast。 + - 模型完整性检查或预加载失败时停止 worker。 + - 不再静默降级到首次请求加载。 +- [x] 移除被忽略的离线模型兼容参数。 + - 删除 REST `model_id` 兼容处理。 + - 删除 OpenAI transcription `model` 兼容处理。 + - 运行时模型选择统一由部署计划和 `QWEN3_ASR_MODEL` 控制。 + +## P1 + +- [x] 抽出共享离线转写服务。 + - API 层只处理协议输入和响应格式。 + - 音频准备、`OfflineASRRequest`、runtime 调用和清理边界集中到服务层。 +- [x] 合并音频字节处理逻辑。 + - `process_from_request` 和 `process_upload_file` 共享私有 byte 处理 helper。 + +## P2 + +- [x] 将 `.tsscale` sidecar 隐式耦合改为显式结构化元数据。 +- [x] 拆分 WebSocket 路由和 Qwen3 协议服务。 + - Qwen3 websocket 状态机移出 API route。 + - route 模块仅保留端点注册和 service delegation。 +- [x] 删除首轮发现的无用 helper。 +- [x] 审计并移除无用直接依赖。 + - 根环境和 CPU 环境移除直接依赖 `pydub`、`httpx`。 + - 保留 ModelScope/FunASR 动态 runtime 依赖。 +- [x] 继续压缩阿里协议 WebSocket service。 + - 删除大段注释、未使用状态、未使用参数和死函数。 + - 合并重复响应构造。 + - 删除重复音频转换。 + +## 验证 + +- [x] `uv run python -m py_compile $(find app -name '*.py' -not -path '*/__pycache__/*') start.py` +- [x] `uvx pyright` +- [x] 变更模块 import smoke check。 +- [x] 手工 API smoke plan 已记录： + - `/stream/v1/asr` + - `/v1/audio/transcriptions` + - `/ws/v1/asr/funasr` + - `/ws/v1/asr/qwen` + +## 手工 Smoke Plan + +启动服务并准备模型后执行： + +1. `POST /stream/v1/asr`，使用小 WAV request body，确认返回 `result`、`segments`、`duration`、`processing_time`。 +2. `POST /v1/audio/transcriptions`，使用 multipart `file` 和 `response_format=verbose_json`，确认返回 OpenAI 风格 `text` 和 `segments`。 +3. 连接 `/ws/v1/asr/funasr`，发送阿里兼容 start/audio/stop 消息，确认 sentence 事件仍正常返回。 +4. 连接 `/ws/v1/asr/qwen`，发送 start/audio/stop 消息，确认 partial/final 事件仍正常返回。 diff --git a/docs/TODO/X86_RUST_ALIGN_OPT_PLAN.md b/docs/TODO/X86_RUST_ALIGN_OPT_PLAN.md new file mode 100644 index 0000000..845d37e --- /dev/null +++ b/docs/TODO/X86_RUST_ALIGN_OPT_PLAN.md @@ -0,0 +1,457 @@ +# x86 Rust Align Optimization Plan + +## 当前结论 + +- 当前工作区的 vendored Rust backend 已经收敛到更接近 upstream `huanglizhuo/QwenASR` 的 Linux/x86_64 路径： + - `release` + - `RUSTFLAGS="-C target-cpu=native"` + - `BLAS/OpenBLAS` + - x86_64 默认 `BF16` decode +- 保留的有意偏离只有两类： + - `SharedQwenModel` / shared model cache + - 中性的 `ffi` feature（`macos-ffi` 仅作为兼容别名保留） + +## upstream 参考 + +- upstream repo: `https://github.com/huanglizhuo/QwenASR` +- inspected commit: `4e85a19b05f034e106a345d279c68f50df718ab8` + +## 本机环境 + +- CPU: `Intel Core i5-13600KF` +- visible CPUs: `14` +- memory: user reported `G.SKILL DDR5-6400` + +## 已验证 benchmark + +音频： + +- `/opt/qwen3-asr/temp/test_assets/podcast_demo_2min_16k.wav` +- duration: `120s` + +### decode 路径对照（runtime concurrency = 4） + +`INT8 decode` + +- total: `173.42s` +- asr: `54.69s` +- align: `118.74s` +- rtf: `1.4452` + +来源： + +- `/opt/qwen3-asr/temp/test_assets/qwen_rust_runtime_concurrency_2min_int8.json` + +`BF16 decode` + +- total: `125.87s` +- asr: `46.40s` +- align: `79.47s` +- rtf: `1.0489` + +来源： + +- `/opt/qwen3-asr/temp/test_assets/qwen_rust_runtime_concurrency_2min_bf16.json` + +结论： + +- 在当前这台 x86_64 机器上，`BF16 decode` 明显优于 `INT8 decode` +- 因此 x86_64 默认 decode 路径应保持 `BF16` + +### 收敛后的默认路径 benchmark + +来源： + +- `/opt/qwen3-asr/temp/test_assets/qwen_rust_runtime_concurrency_2min_default_after_converge.json` + +结果： + +- `runtime concurrency = 4` + - total: `131.77s` + - asr: `51.54s` + - align: `80.23s` + - rtf: `1.0981` +- `runtime concurrency = 14` + - total: `128.25s` + - asr: `57.12s` + - align: `71.13s` + - rtf: `1.0688` + +结论： + +- 当前主瓶颈仍然在 `align` +- `align_sec` 明显大于或接近 `asr_sec` +- `runtime concurrency` 的最优值并不稳定，说明问题不在单纯线程数，而在具体阶段的访问模式和 kernel 行为 + +## 为什么不继续默认走 INT8 + +- upstream README 对 Linux/x86_64 的主路径描述是 `BLAS + AVX2/FMA` +- 当前本机实测中，`INT8 decode` 明显慢于 `BF16 decode` +- 说明这台机器上的主瓶颈不只是权重带宽，更多是： + - x86_64 上 INT8 kernel 的有效带宽利用率 + - cache / 数据布局 + - 实现成熟度差异 + +## 当前仍需保留的偏离 + +### 1. Shared model cache + +文件： + +- `/opt/qwen3-asr/vendor/qwenasr/crates/qwen-asr/src/context.rs` + +目的： + +- 多 runtime / 多 worker 场景下复用只读模型权重 +- 避免每个 runtime 重复 mmap / 持有整套权重 + +### 2. ffi feature + +文件： + +- `/opt/qwen3-asr/vendor/qwenasr/crates/qwen-asr/Cargo.toml` +- `/opt/qwen3-asr/vendor/qwenasr/crates/qwen-asr/src/lib.rs` +- `/opt/qwen3-asr/Dockerfile.cpu` + +目的： + +- 让 Linux CPU 集成不再依赖命名不准确的 `macos-ffi` +- 同时保留兼容别名，避免已有脚本立即失效 + +## align 热点拆解计划 + +### Phase 1: 阶段级 profiling + +状态：已完成 + +目标： + +- 先确认 `align` 的主耗时究竟在哪一段 + +位置： + +- `/opt/qwen3-asr/vendor/qwenasr/crates/qwen-asr/src/align.rs` +- `/opt/qwen3-asr/vendor/qwenasr/crates/qwen-asr/src/decoder.rs` + +需要拆出的阶段： + +- `mel_spectrogram` +- `encoder.forward` +- `input_embeds build` +- `decoder_prefill_logits` +- `timestamp argmax extract` +- `fix_timestamps` + +验收： + +- 2 分钟样本上输出稳定的阶段级耗时表 + +实际结果： + +- 产物： + - `/opt/qwen3-asr/temp/test_assets/qwen_rust_runtime_concurrency_2min_align_profile.json` + - `/opt/qwen3-asr/temp/test_logs/qwen_rust_runtime_concurrency_2min_align_profile.stderr` +- 结论： + - `align` 的主热点明确落在 `decoder_prefill_logits` + - `final rms_norm` 和 `lm_head projection` 不是主矛盾 + +### Phase 2: decoder_prefill_logits 内部分解 + +状态：已完成 + +如果 `decoder_prefill_logits` 是主热点，则继续拆分： + +- `decoder_prefill` +- final `rms_norm` +- `lm_head projection` + +目的： + +- 判断到底是 decoder prefill 慢，还是最后分类头 projection 慢 + +实际结果： + +- 产物： + - `/opt/qwen3-asr/temp/test_assets/qwen_rust_runtime_concurrency_2min_align_breakdown.json` + - `/opt/qwen3-asr/temp/test_logs/qwen_rust_runtime_concurrency_2min_align_breakdown.stderr` +- 结论： + - 真正的大头是 `decoder_prefill` + - 长段（`seq_len=1801`）时，`attention_ms` 占 `decoder_prefill` 的绝大部分 + - 关键样本： + - `decoder_prefill total_ms=79280.78` + - `attention_ms=73769.91` + - `qkv_ms=1336.33` + - `gate_up_ms=1866.68` + - `down_proj_ms=955.94` + +### Phase 2.5: 失败尝试记录 + +状态：已完成并回退 + +尝试： + +- 针对 x86_64 预先物化 prefill 用 F32 权重，避免每次 `align` 反复做 `BF16 -> F32` + +结果： + +- 长段 `decoder_prefill` 没有稳定收益，反而出现回归 +- 这条路径已经回退，不保留在主线代码里 + +结论： + +- 当前瓶颈不是简单的 BF16 权重转换 +- 更直接的问题是 multi-token causal attention 的算法路径 + +### Phase 3: 对热点段做针对性优化 + +状态：第一轮已完成 + +根据 profiling 结果，按优先级选一个方向： + +1. 如果热点在 `input_embeds build` + - 复用固定 prefix/suffix embeddings + - 减少逐 token 小块 copy + - 降低每段 align 的重复构造开销 + +2. 如果热点在 `decoder_prefill` + - 检查 `BF16 matvec / attention / swiglu` 的实际热点 + - 优化并行粒度或数据布局 + +3. 如果热点在 `lm_head projection` + - 优先优化 `BF16` classify head 路径 + - 避免无价值的整块 materialize + - 但只有在 profiling 证明是主热点后才动 + +4. 如果热点在后处理 + - 精简 `fix_timestamps` + - 减少 `Vec/String` 分配 + +### Phase 3 实施结果 + +本轮实际落地的是： + +- 文件： + - `/opt/qwen3-asr/vendor/qwenasr/crates/qwen-asr/src/kernels/mod.rs` +- 改动： + - 对 BLAS multi-token causal attention 增加长序列专用 batched 路径 + - 仅在 `seq_q >= 256` 时启用 + - 从“每个 head、每一行 2 次小 GEMM”改为： + - 每个 head 1 次 `Q @ K^T` + - 行级 causal softmax + - 每个 head 1 次 `softmax @ V` + +### Phase 3 回归结果 + +来源： + +- 优化后 benchmark： + - `/opt/qwen3-asr/temp/test_assets/qwen_rust_runtime_concurrency_2min_after_attention_opt.json` +- 优化后 profiling： + - `/opt/qwen3-asr/temp/test_assets/qwen_rust_runtime_concurrency_2min_attention_opt_profile.json` + - `/opt/qwen3-asr/temp/test_logs/qwen_rust_runtime_concurrency_2min_attention_opt_profile.stderr` + +关键对比： + +- 之前默认路径（2 分钟样本，基线文件）： + - `total=128.25s` + - `asr=57.12s` + - `align=71.13s` +- 优化后： + - `total=76.46s` + - `asr=45.32s` + - `align=31.14s` + - `rtf=0.6372` + +attention 热点变化： + +- 长段 `seq_len=1801` + - 优化前： + - `decoder_prefill total_ms=79280.78` + - `attention_ms=73769.91` + - 优化后： + - `decoder_prefill total_ms=16625.88` + - `attention_ms=9981.88` + +结论： + +- 当前这台 i5 上，`align` 的主矛盾已经从“attention 明显失控”收敛到了“attention 仍是第一热点，但已降到可接受量级” +- 这一轮优化是有效的，应该保留 + +## Phase 4: FFN 路径继续收敛 + +状态：已完成一轮，并保留有效部分 + +本轮动作： + +- 文件： + - `/opt/qwen3-asr/vendor/qwenasr/crates/qwen-asr/src/decoder.rs` +- 改动： + - 仅对 x86_64 `BF16 prefill` 的 FFN 路径物化共享 F32 权重 + - 范围只包括： + - `gate_up_fused` + - `down_weight` + - `QKV` 和 `O-proj` 暂不纳入 + +回归数据： + +- 不带 profiling： + - `/opt/qwen3-asr/temp/test_assets/qwen_rust_runtime_concurrency_2min_after_ffn_opt.json` + - `total=58.61s` + - `asr=29.50s` + - `align=29.11s` +- 带 profiling： + - `/opt/qwen3-asr/temp/test_assets/qwen_rust_runtime_concurrency_2min_ffn_opt_profile.json` + - `total=62.07s` + - `asr=32.15s` + - `align=29.92s` + +与上一轮 attention-only profiling 对比： + +- attention-only： + - `total=64.58s` + - `align=30.86s` +- FFN-opt： + - `total=62.07s` + - `align=29.92s` + +长段热点对比（`seq_len=1801`）： + +- FFN-opt 之前： + - `attention_ms=9981.88` + - `gate_up_ms=2080.10` + - `down_proj_ms=1183.64` +- FFN-opt 之后： + - `attention_ms=9660.15` + - `gate_up_ms=2210.39` + - `down_proj_ms=1166.96` + +结论： + +- FFN 这轮不是“大收益”，但 `align_sec` 仍然有小幅下降 +- 收益不像 attention 优化那样压倒性，更像是小幅收敛 +- 当前可以保留，但不值得继续在同一方向上扩大复杂度 + +## Phase 5: QKV / O-proj 试验与回退 + +状态：已完成并回退 + +尝试： + +- 在 prefill 中进一步物化 `QKV` 和 `O-proj` 的共享 F32 权重 + +回归数据： + +- 试验版本： + - `/opt/qwen3-asr/temp/test_assets/qwen_rust_runtime_concurrency_2min_after_qkv_opt.json` + - `total=63.12s` + - `align=30.07s` + +结论： + +- 相比 FFN-opt 版本，没有形成净收益 +- 因此这条路径已回退，不保留在主线代码里 + +## Phase 6: attention 继续细化的两次试验 + +状态：已完成并回退 + +### 试验 A：query-block batched attention + +尝试： + +- 将长序列 batched causal attention 从“整段一次性 `QK^T` / `SV`”改成按 query block 分块执行 + +结果： + +- `/opt/qwen3-asr/temp/test_assets/qwen_rust_runtime_concurrency_2min_after_attention_block_opt.json` +- `total=79.18s` +- `align=33.40s` + +结论： + +- 这条路径在当前 i5 + OpenBLAS 组合下没有收益 +- 增加 GEMM 次数带来的额外调度开销，超过了小块缓存收益 +- 已回退 + +### 试验 B：提高 batched 切换阈值到 512 + +尝试： + +- 只改 `BATCHED_CAUSAL_ATTENTION_THRESHOLD` +- 让中等长度序列继续走 row-wise 路径，只把更长的序列交给 batched 路径 + +结果： + +- `/opt/qwen3-asr/temp/test_assets/qwen_rust_runtime_concurrency_2min_after_attention_threshold512.json` +- `total=74.49s` +- `align=30.42s` + +结论： + +- 相比当前主线最优版本也没有收益 +- 说明当前阈值 `256` 不是主要问题 +- 已恢复回 `256` + +## Phase 7: K/V block + online softmax 试验 + +状态：已完成并回退 + +尝试： + +- 仅替换长序列 batched attention 路径 +- 改为按 `K/V` block 流式累积的 online softmax +- 短序列和单 token 路径完全不动 + +目标： + +- 不再一次性物化整块 `scores` +- 降低大矩阵内存压力 +- 观察是否能进一步压低长段 `attention_ms` + +结果： + +- `/opt/qwen3-asr/temp/test_assets/qwen_rust_runtime_concurrency_2min_after_kvblock_online_softmax.json` +- `total=74.11s` +- `align=30.33s` + +结论： + +- 当前实现下没有优于主线最优版本 +- 在这台机器上，额外的 block 循环和 online softmax 合并开销，超过了减少大 `scores` 矩阵带来的收益 +- 已回退 + +## 当前判断 + +- 当前最值钱、且已验证有效的优化仍然是： + - 长序列 batched causal attention + - FFN selective F32 物化 +- 继续扩大到 `QKV/O-proj` 这一步暂时不划算 +- query-block 化和 batched 阈值调优目前也不划算 +- `K/V block + online softmax` 在当前实现形态下也不划算 +- 后续判断应优先看： + - `align_sec` + - `decoder_prefill` profiling + - 尤其是长段 `seq_len` 下的热点变化 + +补充： + +- `asr_sec` 在多次回归中波动明显大于 `align_sec` +- 因此后续评估优化效果时，不应只盯总耗时，应优先以 `align` profiling 为准 + +## 暂不做的事 + +- 不再把 x86_64 默认路径改回 `INT8 decode` +- 不继续做没有 profiling 支撑的 `align` 结构性改写 +- 不围绕 `runtime concurrency` 数量盲调 + +## 下一步执行顺序 + +1. 保留当前 batched causal attention 路径，继续观察不同长段下的稳定性 +2. 保留 FFN selective F32 物化，继续观察其稳定收益 +3. 如需继续优化，优先看： + - 更激进的 `attention` 算法级改动，例如按 `K/V` block 的 online softmax + - 再其次才是 `qkv_ms + gate_up_ms + down_proj_ms` +4. 如果后续继续深挖，再考虑： + - batched attention 的 block 化，降低大 score matrix 的瞬时内存 + - `decoder_prefill` 内的投影层进一步收敛 +5. 保持同一份 2 分钟样本持续回归，避免再次把回归误当成优化 diff --git a/docs/deployment.md b/docs/deployment.md new file mode 100644 index 0000000..fddb2eb --- /dev/null +++ b/docs/deployment.md @@ -0,0 +1,699 @@ +# Qwen3-ASR 部署指南 + +快速部署 Qwen3-ASR 语音识别服务，支持 CPU/macOS、NVIDIA GPU、沐曦 GPU、天数 GPU 与摩尔线程 GPU 运行形态。 + +如果你正在继续验证本轮 CUDA 官方 vLLM 迁移，请同时参考： + +- [PENDING_CUDA_VLLM_HANDOFF.md](./TODO/PENDING_CUDA_VLLM_HANDOFF.md) + +依赖安装现在改成根目录默认 NVIDIA GPU，CPU、沐曦、天数与摩尔线程为单独特化环境： + +| 模式 | 命令 | 说明 | +|------|------|------| +| NVIDIA GPU | `uv sync` 或 `./scripts/sync_gpu_env.sh` | Linux/NVIDIA 运行时，默认锁定 CUDA 13.0/cu130 `torch 2.11.0` / `torchaudio 2.11.0` / `torchvision 0.26.0` + `vllm 0.20.0` | +| 沐曦 GPU | `./scripts/sync_metax_env.sh` | 同步公共依赖；可选 GPU 栈安装默认从沐曦 MACA PyPI 源按 `--no-deps` 安装 | +| 天数 GPU | `./scripts/sync_iluvatar_env.sh` | 同步公共依赖；GPU 栈使用天数官方 vLLM 镜像 | +| 摩尔线程 GPU | `./scripts/sync_mthreads_env.sh` | 同步公共依赖；GPU 栈使用摩尔线程官方 MUSA vLLM 镜像 | +| CPU | `./scripts/sync_cpu_env.sh` | Linux/CPU 运行时 | +| 自动 | `./scripts/sync_accel_env.sh` | 根据 `mx-smi` / `ixsmi` / `mthreads-gmi` / `nvidia-smi` 自动选择沐曦、天数、摩尔线程、NVIDIA 或 CPU 环境 | + +## 快速部署 + +### NVIDIA GPU 版本部署（推荐） + +适用于生产环境，提供更快的推理速度： + +**前置要求：** +- NVIDIA GPU（默认镜像面向 CUDA 13.0+；CUDA 12.6 / 13.0 可通过构建参数覆盖） +- 已安装 NVIDIA Container Toolkit +- 显存 12GB+（推荐 16GB+ 以支持 Qwen3-ASR 1.7B） + +```bash +# 使用 docker run（带模型挂载） +docker run -d --name qwen3-asr \ + --gpus all \ + -p 17003:8000 \ + -v /opt/dep/asr/models:/app/models \ + -v /opt/dep/asr/data:/app/data \ + -e ACCELERATOR=nvidia \ + -e DEVICE=auto \ + -e QWEN_GPU_MEMORY_UTILIZATION=0.3 \ + -e QWEN_VLLM_ENFORCE_EAGER=true \ + unis/qwen3-asr:gpu-latest + +# 或使用 docker-compose（推荐） +docker-compose up -d +``` + +### 沐曦 GPU 版本部署 + +适用于已安装沐曦驱动与容器运行栈的机器： + +```bash +docker run -d --name qwen3-asr-metax \ + --privileged \ + --network=host \ + --pid=host \ + --ipc=host \ + -v /dev:/dev \ + -v /opt/mxdriver:/opt/mxdriver:ro \ + -v /opt/dep/asr/models:/app/models \ + -v /opt/dep/asr/data:/app/data \ + -e ACCELERATOR=metax \ + -e PORT=17003 \ + -e METAX_VISIBLE_DEVICES=0 \ + unis/qwen3-asr:metax-latest + +# 或使用 docker-compose-metax.yml +docker compose -f docker-compose-metax.yml up -d +``` + +构建沐曦镜像时，`Dockerfile.metax` 会基于沐曦官方 vLLM 镜像融合本项目代码与通用依赖： + +```bash +./scripts/package_vendor_gpu_image.sh \ + --vendor metax \ + --base-image <沐曦官方vLLM镜像名> \ + -v n260-3.7.0.38 +``` + +沐曦等国产 GPU 的生产推荐路径是“厂商官方 vLLM 镜像 + 本项目代码/通用依赖”。不要在项目 Dockerfile 中重新 `pip install vllm`，避免解析到 PyPI/NVIDIA CUDA 依赖。 + +沐曦 GPU 的完整编译、模型准备和离线交付流程见 [沐曦 GPU 国产化离线部署指南](./metax_offline_deployment.md)。 + +### 天数 GPU 版本部署 + +适用于已安装天数驱动与容器运行栈的机器。按天数官方镜像运行建议，本 compose 使用 host network、host pid/ipc、privileged、`/dev`、`/usr/src`、`/lib/modules` 等挂载，并额外挂载本项目模型与数据目录： + +```bash +docker pull registry.iluvatar.com.cn:10443/customer/sz/vllm0.17.0-4.4.0-x86:v5 + +./scripts/package_vendor_gpu_image.sh \ + --vendor iluvatar \ + --base-image registry.iluvatar.com.cn:10443/customer/sz/vllm0.17.0-4.4.0-x86:v5 \ + -v vllm0.17.0-4.4.0-v5 + +ASR_IMAGE=unis/qwen3-asr:iluvatar-vllm0.17.0-4.4.0-v5 \ +docker compose -f docker-compose-iluvatar.yml up -d +``` + +天数 GPU 的完整编译、模型准备和离线交付流程见 [天数 GPU 国产化离线部署指南](./iluvatar_offline_deployment.md)。 + +### 摩尔线程 GPU 版本部署 + +适用于已安装摩尔线程驱动与 MUSA 容器运行栈的机器： + +```bash +docker pull registry.mthreads.com/presale/devtech/vllm_musa:s4000_4.3.5_d0519 + +./scripts/package_vendor_gpu_image.sh \ + --vendor mthreads \ + --base-image registry.mthreads.com/presale/devtech/vllm_musa:s4000_4.3.5_d0519 \ + -v s4000_4.3.5_d0519 + +ASR_IMAGE=unis/qwen3-asr:mthreads-s4000_4.3.5_d0519 \ +docker compose -f docker-compose-mthreads.yml up -d +``` + +摩尔线程 GPU 的完整编译、模型准备和离线交付流程见 [摩尔线程 GPU 国产化离线部署指南](./mthreads_offline_deployment.md)。 + +默认推荐将宿主机目录统一挂载到 `/opt/dep/asr` 下，目录结构如下： + +```text +/opt/dep/asr/models/ + Qwen/ + iic/ + damo/ +``` + +如果你希望挂载到自定义目录，可统一设置： + +```bash +export MODEL_STORAGE_DIR=/data/qwen3-asr-models +export DATA_STORAGE_DIR=/data/qwen3-asr-data +mkdir -p "$MODEL_STORAGE_DIR" "$DATA_STORAGE_DIR" +docker-compose up -d +``` + +### 多 GPU 拓扑模式 + +项目现在支持统一的多 GPU 拓扑开关： + +- `ASR_DEPLOY_TOPOLOGY=isolated` + - 默认模式 + - 每张卡启动 1 个 backend 实例 + - 容器内使用 Nginx 负载均衡到多个实例 +- `ASR_DEPLOY_TOPOLOGY=sharded` + - 单个 backend 进程占用多张卡 + - 由 vLLM 在进程内部做多卡分片 +- `ASR_DEPLOY_TOPOLOGY=auto` + - 优先尝试 `sharded` + - 如果当前平台、可见设备或 shard 数不满足条件，则自动回退到 `isolated` + +### NVIDIA 多 GPU 自动并行部署（推荐） + +适用于并发量较高场景。该方案通过容器 entrypoint 自动完成： +- 根据 `ASR_VISIBLE_DEVICES` 拉起多个 ASR 实例（每张卡 1 个实例） +- 容器内自动生成 Nginx upstream 并负载均衡到各实例 +- 对外仍只暴露一个服务端口（默认 `8000`） + +你不需要手工维护多个 `docker-compose` 服务块或手工维护 nginx upstream。 + +```bash +# 4 卡示例：GPU0,1,2,3 各启动 1 个实例 +ASR_VISIBLE_DEVICES=0,1,2,3 docker-compose up -d +``` + +常用组合： +- 单卡（保持默认）：`ASR_VISIBLE_DEVICES=0` +- 双卡：`ASR_VISIBLE_DEVICES=0,1` +- 四卡：`ASR_VISIBLE_DEVICES=0,1,2,3` + +强制单实例多卡分片： + +```bash +ASR_DEPLOY_TOPOLOGY=sharded \ +ASR_VISIBLE_DEVICES=0,1 \ +docker-compose up -d +``` + +自动选择模式： + +```bash +ASR_DEPLOY_TOPOLOGY=auto \ +ASR_VISIBLE_DEVICES=0,1 \ +docker-compose up -d +``` + +**服务访问地址：** +- API 服务: `http://localhost:17003` +- API 文档: `http://localhost:17003/docs` + +### 沐曦多 GPU 自动并行部署 + +```bash +ASR_VISIBLE_DEVICES=0,1 docker compose -f docker-compose-metax.yml up -d +``` + +### 摩尔线程多 GPU 自动并行部署 + +```bash +ASR_VISIBLE_DEVICES=0,1 docker compose -f docker-compose-mthreads.yml up -d +``` + +### CPU 版本部署 + +适用于开发测试或无 GPU 环境： + +```bash +docker run -d --name qwen3-asr \ + -p 17003:8000 \ + -v /opt/dep/asr/models:/app/models \ + -v /opt/dep/asr/data:/app/data \ + -e DEVICE=cpu \ + unis/qwen3-asr:cpu-latest +``` + +**注意：** CPU 版本不使用 GPU/vLLM 路径。 +当前 CPU 镜像已集成 QwenASR Rust backend，会自动选择 `qwen3-asr-0.6b`。 +CPU 镜像默认使用可分发的 `x86-64-v2` Rust 构建目标，避免把构建机的 native CPU 指令带入通用镜像。 +如果你确认构建机与部署机 CPU 指令集一致，可在自建镜像时设置 `QWENASR_RUST_TARGET_CPU=native` 换取更激进优化。 +当前 Rust backend 的 x86 kernel 需要 `avx2` 与 `fma`，不满足时启动会给出明确错误。镜像默认限制 +`OPENBLAS_NUM_THREADS=1` / `OMP_NUM_THREADS=1` / `GOTO_NUM_THREADS=1`，以减少多 runtime 并发时的线程争抢。 +CUDA vLLM 与 CPU Rust 路径下，`word_timestamps=true` 会自动调用 forced aligner 返回字词级时间戳。 + +### 离线交付目录导出 + +如果你需要把镜像交付到不能联网的机器，推荐直接生成一个完整的离线交付目录。该脚本使用普通 `docker build` + `docker save`，不依赖 `buildx`： + +```bash +# 生成 GPU 离线交付目录 +./export_offline_bundle.sh --type gpu + +# 或生成 CPU 离线交付目录 +./export_offline_bundle.sh --type cpu + +# 或生成沐曦 GPU 离线交付目录 +./export_offline_bundle.sh \ + --type metax \ + --metax-base cr.metax-tech.com/public-ai-release/maca/vllm-metax:0.17.0-maca.ai3.5.3.307-torch2.8-py312-ubuntu22.04-amd64 \ + --skip-models +./export_offline_bundle.sh --type iluvatar --iluvatar-base registry.iluvatar.com.cn:10443/customer/sz/vllm0.17.0-4.4.0-x86:v5 +./export_offline_bundle.sh --type mthreads --mthreads-base registry.mthreads.com/presale/devtech/vllm_musa:s4000_4.3.5_d0519 + +# 或一次同时生成 GPU + CPU 离线交付目录 +./export_offline_bundle.sh --type all +``` + +生成后的目录形如： + +```text +build-file/ + 20260520_153000-all/ + qwen3-asr-cpu-20260520_153000-amd64.tar.gz + qwen3-asr-gpu-20260520_153000-amd64.tar.gz + docker-compose.yml + docker-compose-cpu.yml + docker-compose-metax.yml + .env.example + init_host_dirs.sh + README.md + DEPLOYMENT.md +``` + +其中会自动包含： + +- 对应类型的一份或两份镜像压缩包 +- 对应类型的 compose 文件；天数包只包含 `docker-compose-iluvatar.yml` +- 摩尔线程包只包含 `docker-compose-mthreads.yml` +- `.env` 模板 +- 宿主机目录初始化脚本 +- 离线部署说明文档 + +模型文件建议使用 `./scripts/download-models.sh --models-dir /opt/dep/asr/models` 单独准备；该脚本增量补齐缺失模型，不删除已有目录，也不依赖 uv。 + +当前运行时 / 设备默认值以主 README 为准： + +- `README.md` +- `docs/README_zh.md` +设计背景与实现思路可参考： + +- 当前 Qwen3 后端：`NVIDIA/沐曦 GPU -> vLLM`、`CPU/macOS -> vendored QwenASR Rust` +- 引用项目 [QwenASR](https://github.com/huanglizhuo/QwenASR) + +### macOS / Apple Silicon 本地部署 + +适用于 M1/M2/M3/M4 机器上的本地 Qwen3-ASR 推理。当前 macOS 已统一走 vendored QwenASR Rust CPU backend。 + +```bash +./scripts/sync_cpu_env.sh +source .venv/bin/activate +python start.py +``` + +### 验证部署 + +```bash +# 健康检查 +curl http://localhost:17003/stream/v1/asr/health + +# 查看可用模型 +curl http://localhost:17003/stream/v1/asr/models + +# 测试语音识别（阿里云协议） +curl -X POST "http://localhost:17003/stream/v1/asr" \ + -H "Content-Type: application/octet-stream" \ + --data-binary @test.wav + +# 测试 OpenAI 兼容接口 +curl -X POST "http://localhost:17003/v1/audio/transcriptions" \ + -H "Authorization: Bearer any" \ + -F "file=@test.wav" \ + -F "model=qwen3-asr-1.7b" +``` + +## 从源码构建镜像 + +### 使用构建脚本 + +项目提供了一个更薄的 `scripts/build_docker.sh` 包装层，用于统一 `docker buildx` 参数： + +```bash +# 构建所有版本（CPU + GPU） +./scripts/build_docker.sh + +# 仅构建 GPU 版本 +./scripts/build_docker.sh -t gpu + +# 构建指定版本并推送 +./scripts/build_docker.sh -t all -v 1.0.1 -p + +# 查看帮助 +./scripts/build_docker.sh -h +``` + +**构建脚本参数：** + +| 参数 | 说明 | 默认值 | +|------|------|--------| +| `-a, --arch` | 目标架构: `amd64`, `arm64`, `multi` | `amd64` | +| `-t, --type` | 构建类型: `cpu`, `gpu`, `all` | `all` | +| `-v, --version` | 版本标签 | `latest` | +| `-p, --push` | 构建后推送到 Docker Hub | 否 | +| `-e, --export` | 导出单架构镜像为 tar.gz | 否 | +| `-o, --output` | 导出目录 | `.` | +| `-r, --registry` | 镜像仓库 | `unis` | +| `-n, --no-cache` | 禁用 Docker 构建缓存 | 否 | + +### 手动构建 + +```bash +# 构建 CPU 版本 +docker build -t qwen3-asr:cpu-latest -f Dockerfile.cpu . + +# 构建绑定当前机器指令集的 CPU 版本（仅适合同构部署） +docker build -t qwen3-asr:cpu-native -f Dockerfile.cpu \ + --build-arg QWENASR_RUST_TARGET_CPU=native \ + . + +# 构建默认 GPU 版本（CUDA 13.0 / PyTorch cu130） +docker build -t qwen3-asr:gpu-cu130 -f Dockerfile.gpu . + +# 构建 CUDA 12.6 版本 +docker build -t qwen3-asr:gpu-cu126 -f Dockerfile.gpu \ + --build-arg PYTORCH_BASE_IMAGE=pytorch/pytorch:2.11.0-cuda12.6-cudnn9-runtime \ + --build-arg PYTORCH_CUDA_INDEX=https://download.pytorch.org/whl/cu126 \ + --build-arg CUDA_NVCC_PACKAGE=cuda-nvcc-12-6 \ + --build-arg TORCH_CUDA_ARCH_LIST="8.0;8.6;8.9" \ + . + +# 构建 CUDA 13.0 版本 +docker build -t qwen3-asr:gpu-cu130 -f Dockerfile.gpu \ + --build-arg PYTORCH_BASE_IMAGE=pytorch/pytorch:2.11.0-cuda13.0-cudnn9-runtime \ + --build-arg PYTORCH_CUDA_INDEX=https://download.pytorch.org/whl/cu130 \ + --build-arg CUDA_NVCC_PACKAGE=cuda-nvcc-13-0 \ + --build-arg TORCH_CUDA_ARCH_LIST="12.0+PTX" \ + . +``` + +`Dockerfile.cpu` 可覆盖的 CPU 构建参数： + +| 参数 | 默认值 | 说明 | +|------|--------|------| +| `QWENASR_RUST_TARGET_CPU` | `x86-64-v2` | amd64 Rust backend 编译目标；可设为 `native` 构建绑定当前 CPU 的镜像 | + +`Dockerfile.gpu` 可覆盖的 GPU 构建参数： + +| 参数 | 默认值 | 用途 | +|------|--------|------| +| `PYTORCH_BASE_IMAGE` | `pytorch/pytorch:2.11.0-cuda13.0-cudnn9-runtime` | 选择 PyTorch/CUDA 基础镜像 | +| `PYTORCH_CUDA_INDEX` | `https://download.pytorch.org/whl/cu130` | 选择 PyTorch wheel CUDA 后端 | +| `CUDA_NVCC_PACKAGE` | `cuda-nvcc-13-0` | 安装匹配的 nvcc，用于 vLLM/FlashInfer JIT | +| `TORCH_CUDA_ARCH_LIST` | `12.0+PTX` | 指定 JIT 编译目标架构 | +| `VLLM_PACKAGE` | `vllm==0.20.0` | 覆盖 vLLM 包版本或来源 | + +### 模型说明 + +服务支持以下 ASR 模型： + +| 模型 | 说明 | 适用场景 | +|------|------|----------| +| Qwen3-ASR-1.7B ⭐ | 多语言 ASR（52种语言+方言，字级时间戳） | CUDA | +| Qwen3-ASR-0.6B | 轻量版多语言 ASR | CUDA / CPU Rust / macOS | + +**运行时模型选择：** + +系统根据机器资源自动选择合适的 Qwen3-ASR 模型： +- **显存 >= 32GB**: 自动加载 `qwen3-asr-1.7b` +- **显存 < 32GB**: 自动加载 `qwen3-asr-0.6b` +- **无 CUDA**: 自动加载基于 vendored Rust 的 `qwen3-asr-0.6b` +- **macOS / Apple Silicon**: 无论内存大小多少，默认都加载 `qwen3-asr-0.6b` +- **环境变量覆盖**: 设置 `QWEN3_ASR_MODEL=qwen3-asr-1.7b` 或 `QWEN3_ASR_MODEL=qwen3-asr-0.6b` 可硬覆盖自动选择 + +### 模型下载 + +启动时会先检测当前运行计划所需模型；如果本地缓存缺失，会自动从 ModelScope 下载。离线部署请提前准备模型缓存。 +手动准备方式： + +```bash +# 增量补齐离线部署所需模型，不删除已有文件 +./scripts/download-models.sh --models-dir /opt/dep/asr/models + +# 如果本机缺少 Python 依赖，也可以使用已构建镜像下载 +ASR_IMAGE=unis/qwen3-asr:iluvatar-vllm0.17.0-4.4.0-v5 \ +./scripts/download-models.sh --mode docker --models-dir /opt/dep/asr/models +``` + +离线部署时，推荐目录结构： + +```text +/opt/dep/asr/models/ + Qwen/ + iic/ + damo/ +``` + +然后保持与 compose 文件一致的挂载： + +```yaml +volumes: + - ${MODEL_STORAGE_DIR:-/opt/dep/asr/models}:/app/models + - ${DATA_STORAGE_DIR:-/opt/dep/asr/data}:/app/data +``` + +## 环境变量配置 + +### 基础配置 + +| 环境变量 | 默认值 | 说明 | +|----------|--------|------| +| `HOST` | `0.0.0.0` | 服务绑定地址 | +| `PORT` | `8000` | 服务端口 | +| `DEBUG` | `false` | 调试模式（启用后可访问 /docs） | +| `LOG_LEVEL` | `INFO` | 日志级别：DEBUG, INFO, WARNING, ERROR | +| `WORKERS` | `1` | 工作进程数（多进程会复制模型，显存成倍增加） | +| `MAX_AUDIO_SIZE` | `2048` | 最大音频文件大小（MB，支持单位如 2GB） | +| `API_KEY` | - | 服务端统一鉴权密钥 | + +### 设备配置 + +| 环境变量 | 默认值 | 说明 | +|----------|--------|------| +| `DEVICE` | `auto` | 设备选择：`auto`, `cpu`, `cuda:0` | +| `ASR_VISIBLE_DEVICES` | `0` | 统一可见 GPU 设备配置，程序会按当前 accelerator 自动映射到底层变量 | +| `ASR_DEPLOY_TOPOLOGY` | `isolated` | 部署拓扑：`isolated`, `sharded`, `auto` | + +### 内置 Nginx 与限流配置 + +| 环境变量 | 默认值 | 说明 | +|----------|--------|------| +| `NGINX_RATE_LIMIT_RPS` | `0` | 全局每秒请求上限，`0` 表示关闭 | +| `NGINX_RATE_LIMIT_BURST` | `0` | 全局突发请求数，`0` 时自动取 `NGINX_RATE_LIMIT_RPS` | + +### ASR 模型配置 + +| 环境变量 | 默认值 | 说明 | +|----------|--------|------| +| `ASR_ENABLE_REALTIME_PUNC` | `true` | 是否启用实时标点模型 | + +### 性能优化配置 + +| 环境变量 | 默认值 | 说明 | +|----------|--------|------| +| `ASR_BATCH_SIZE` | `4` | 长音频分段后的 ASR 批处理大小 | +| `INFERENCE_THREAD_POOL_SIZE` | 自动 | 推理线程池大小；默认按 CPU 核数自动设置 | +| `MAX_SEGMENT_SEC` | `60` | 音频分段最大时长（秒） | +| `QWEN_GPU_MEMORY_UTILIZATION` | `0.9` | vLLM 可保留的 GPU 显存上限，KV cache 不足时可适当调高 | +| `QWEN_VLLM_ENFORCE_EAGER` | `true` | 强制 vLLM eager 执行以提高兼容性；NVIDIA 性能测试可设为 `false` 允许 CUDA Graph 优化 | +| `WS_MAX_BUFFER_SIZE` | `160000` | WebSocket 音频缓冲区大小（样本数） | +| `QWEN_RUST_CPU_WORKERS` | `4` | CPU Rust backend worker 数；Rust ASR / forced align 默认按该数量并行 | +| `QWEN_RUST_ASR_CONCURRENCY` | `0` | Rust ASR 阶段批内并行度；`0` 表示跟随 `QWEN_RUST_CPU_WORKERS` | +| `QWEN_RUST_ALIGN_CONCURRENCY` | `0` | Rust forced align 阶段批内并行度；`0` 表示跟随 `QWEN_RUST_CPU_WORKERS` | +| `QWENASR_LIBRARY_PATH` | 自动探测 | 覆盖 vendored Rust 动态库路径 | + +### 远场过滤配置 + +流式 ASR 远场声音过滤功能，自动过滤远场声音和环境音： + +| 环境变量 | 默认值 | 说明 | +|----------|--------|------| +| `ASR_ENABLE_NEARFIELD_FILTER` | `true` | 启用远场声音过滤 | +| `ASR_NEARFIELD_RMS_THRESHOLD` | `0.01` | RMS 能量阈值 | +| `LOG_LEVEL=DEBUG` | - | 需要观察过滤细节时打开调试日志 | + +调优建议： + +- `ASR_NEARFIELD_RMS_THRESHOLD=0.01` 是当前默认值，也是推荐起点 +- 嘈杂环境可以适当调高，增强背景语音过滤 +- 安静环境如果出现小声说话漏识别，可以适当调低 +- 需要观察过滤行为时，可临时设置 `LOG_LEVEL=DEBUG` + +### 鉴权配置 + +| 环境变量 | 默认值 | 说明 | +|----------|--------|------| +| `API_KEY` | - | 服务端统一鉴权密钥；同时兼容 `Authorization: Bearer` 和 `X-NLS-Token` | + +**使用示例：** + +```bash +# 使用 Token +curl -H "X-NLS-Token: your_token" http://localhost:8000/stream/v1/asr/health + +# 使用 Bearer Token（OpenAI 兼容） +curl -H "Authorization: Bearer your_token" http://localhost:8000/v1/models +``` + +### 日志配置 + +| 环境变量 | 默认值 | 说明 | +|----------|--------|------| +| `LOG_LEVEL` | `INFO` | 日志级别：`DEBUG`, `INFO`, `WARNING` | +| `LOG_FILE` | `data/logs/qwen3-asr.log` | 日志文件路径 | +| `LOG_MAX_BYTES` | `20971520` | 单个日志文件最大大小（20MB） | +| `LOG_BACKUP_COUNT` | `50` | 日志备份文件数量 | + +## Docker Compose 配置 + +### 基础配置（GPU） + +```yaml +services: + qwen3-asr: + image: unis/qwen3-asr:gpu-latest + container_name: qwen3-asr + ports: + - "17003:8000" + volumes: + - /opt/dep/asr/models:/app/models + - /opt/dep/asr/data:/app/data + environment: + - DEBUG=false + - LOG_LEVEL=INFO + - DEVICE=auto + - QWEN_GPU_MEMORY_UTILIZATION=0.3 + - QWEN_VLLM_ENFORCE_EAGER=true + - ASR_BATCH_SIZE=4 + - WORKERS=1 + - INFERENCE_THREAD_POOL_SIZE=4 + restart: unless-stopped + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [gpu] +``` + +### CPU 版本配置 + +```yaml +services: + qwen3-asr: + image: unis/qwen3-asr:cpu-latest + container_name: qwen3-asr + ports: + - "17003:8000" + volumes: + - /opt/dep/asr/models:/app/models + - /opt/dep/asr/data:/app/data + environment: + - DEBUG=false + - LOG_LEVEL=INFO + - DEVICE=cpu + - WORKERS=1 + - INFERENCE_THREAD_POOL_SIZE=1 + restart: unless-stopped +``` + +### 生产环境配置（内置 Nginx，推荐） + +```yaml +services: + qwen3-asr: + image: unis/qwen3-asr:gpu-latest + container_name: qwen3-asr + ports: + - "17003:8000" + volumes: + - /opt/dep/asr/models:/app/models + - /opt/dep/asr/data:/app/data + environment: + - DEBUG=false + - LOG_LEVEL=INFO + - DEVICE=auto + - CUDA_VISIBLE_DEVICES=0,1 + - QWEN_GPU_MEMORY_UTILIZATION=0.3 + - QWEN_VLLM_ENFORCE_EAGER=true + - NGINX_RATE_LIMIT_RPS=20 + - NGINX_RATE_LIMIT_BURST=40 + - WORKERS=1 + - INFERENCE_THREAD_POOL_SIZE=4 + - ASR_BATCH_SIZE=4 + restart: unless-stopped + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [gpu] +``` + +## 服务监控 + +### 健康检查 + +```bash +curl http://localhost:17003/stream/v1/asr/health +``` + +### 日志监控 + +```bash +# 实时查看日志 +docker logs -f qwen3-asr + +# 查看错误日志 +docker logs qwen3-asr 2>&1 | grep -i error +``` + +### 资源监控 + +```bash +# 容器资源使用 +docker stats qwen3-asr + +# GPU 使用情况 +docker exec -it qwen3-asr nvidia-smi +``` + +## 资源需求 + +### 最小配置（CPU 版本） + +- CPU: 4 核 +- 内存: 8GB +- 磁盘: 10GB + +### 推荐配置（GPU 版本） + +- CPU: 8 核 +- 内存: 16GB +- GPU: NVIDIA GPU (12GB+ 显存，含说话人分离模型) +- 磁盘: 25GB + +## 故障排除 + +### 常见问题 + +| 问题 | 症状 | 解决方案 | +|------|------|----------| +| GPU 内存不足 | CUDA OOM 错误 | 设置 `DEVICE=cpu` 或使用更大显存的 GPU | +| 模型加载失败 / 缓慢 | 本地模型缓存缺失 | 先运行 `./scripts/download-models.sh --models-dir /opt/dep/asr/models` 预准备模型 | +| 端口被占用 | 端口冲突错误 | 修改端口映射：`"8080:8000"` | +| 说话人分离失败 | CAM++ 模型错误 | 检查模型是否完整下载，显存是否充足 | + +### 调试模式 + +```bash +# 启用调试模式 +docker run -e DEBUG=true -e LOG_LEVEL=DEBUG ... + +# 进入容器调试 +docker exec -it qwen3-asr /bin/bash +``` + +## 更新服务 + +```bash +# 拉取最新镜像（GPU 版本） +docker pull unis/qwen3-asr:gpu-latest + +# 拉取最新镜像（CPU 版本） +docker pull unis/qwen3-asr:cpu-latest + +# 重启服务 +docker-compose down && docker-compose up -d +``` diff --git a/docs/iluvatar_offline_deployment.md b/docs/iluvatar_offline_deployment.md new file mode 100644 index 0000000..4f9b4d6 --- /dev/null +++ b/docs/iluvatar_offline_deployment.md @@ -0,0 +1,242 @@ +# 天数 GPU 国产化离线部署指南 + +本文档用于天数/Iluvatar GPU 环境的编译、模型准备、离线包导出与目标机部署。 + +## 推荐基础镜像 + +天数默认推荐使用以下官方 vLLM 镜像作为基础镜像： + +```bash +registry.iluvatar.com.cn:10443/customer/sz/vllm0.17.0-4.4.0-x86:v5 +``` + +该镜像负责提供天数 IX 运行时、PyTorch、vLLM 与相关内核。本项目的 `Dockerfile.iluvatar` 只叠加通用 Python 依赖和业务代码，不在 Dockerfile 内重新安装 vLLM，也不使用 uv 虚拟环境。 + +## 目录约定 + +目标机推荐统一使用以下宿主机目录： + +```text +/opt/dep/asr/ + models/ + data/ + logs/ + temp/ + tasks/ +``` + +容器内默认挂载为： + +```text +/app/models +/app/data +``` + +## 在线编译融合镜像 + +在可访问天数镜像仓库和 Python 包源的构建机上执行： + +```bash +docker pull registry.iluvatar.com.cn:10443/customer/sz/vllm0.17.0-4.4.0-x86:v5 + +./scripts/package_vendor_gpu_image.sh \ + --vendor iluvatar \ + --base-image registry.iluvatar.com.cn:10443/customer/sz/vllm0.17.0-4.4.0-x86:v5 \ + -v vllm0.17.0-4.4.0-v5 +``` + +脚本会生成融合镜像： + +```text +unis/qwen3-asr:iluvatar-vllm0.17.0-4.4.0-v5 +``` + +并导出镜像归档到： + +```text +build-file/qwen3-asr-iluvatar-vllm0.17.0-4.4.0-v5-amd64.tar.gz +``` + +如果只需要本机镜像，不需要导出 tar 包，可增加 `--no-export`。 + +## 单独准备模型 + +模型下载建议独立于业务服务执行。`download-models.sh` 是增量下载脚本，不会删除已有模型目录，也不依赖 uv。 + +在项目根目录或离线包目录执行： + +```bash +./scripts/download-models.sh --models-dir /opt/dep/asr/models +``` + +如果本机没有 Python 依赖，但已经有融合镜像，可以用镜像内环境下载： + +```bash +ASR_IMAGE=unis/qwen3-asr:iluvatar-vllm0.17.0-4.4.0-v5 \ +./scripts/download-models.sh \ + --mode docker \ + --models-dir /opt/dep/asr/models +``` + +模型目录最终应至少包含： + +```text +/opt/dep/asr/models/ + Qwen/ + Qwen3-ASR-0.6B/ + Qwen3-ForcedAligner-0.6B/ + damo/ + iic/ +``` + +## 导出完整离线交付包 + +如果要交付给不能联网的目标机，推荐直接导出天数专用离线包： + +```bash +./export_offline_bundle.sh \ + --type iluvatar \ + --iluvatar-base registry.iluvatar.com.cn:10443/customer/sz/vllm0.17.0-4.4.0-x86:v5 \ + -v vllm0.17.0-4.4.0-v5 +``` + +如果模型要单独准备，不希望离线包包含模型压缩包： + +```bash +./export_offline_bundle.sh \ + --type iluvatar \ + --iluvatar-base registry.iluvatar.com.cn:10443/customer/sz/vllm0.17.0-4.4.0-x86:v5 \ + -v vllm0.17.0-4.4.0-v5 \ + --skip-models +``` + +天数离线包只会包含天数专用启动文件： + +```text +docker-compose-iluvatar.yml +.env +.env.example +init_host_dirs.sh +download-models.sh +download_models_standalone.py +README.md +DEPLOYMENT.md +BUNDLE_INFO.txt +qwen3-asr-iluvatar-*-amd64.tar.gz +``` + +不会再要求使用通用 `docker-compose.yml`。 + +## 目标机离线部署 + +将整个离线包复制到目标机后，进入离线包目录： + +```bash +chmod +x init_host_dirs.sh download-models.sh +./init_host_dirs.sh +``` + +导入镜像： + +```bash +gunzip -c qwen3-asr-iluvatar-vllm0.17.0-4.4.0-v5-amd64.tar.gz | docker load +``` + +确认 `.env` 中的镜像名与导入镜像一致： + +```env +ASR_IMAGE=unis/qwen3-asr:iluvatar-vllm0.17.0-4.4.0-v5 +``` + +按需设置显卡与 vLLM 显存比例： + +```env +ILUVATAR_VISIBLE_DEVICES=0 +IX_VISIBLE_DEVICES=0 +CUDA_VISIBLE_DEVICES=0 +QWEN_GPU_MEMORY_UTILIZATION=0.25 +QWEN_VLLM_ENFORCE_EAGER=true +``` + +启动服务： + +```bash +docker compose -f docker-compose-iluvatar.yml up -d +``` + +查看状态与日志： + +```bash +docker compose -f docker-compose-iluvatar.yml ps +docker compose -f docker-compose-iluvatar.yml logs -f +``` + +服务默认监听 host 网络端口： + +```text +http://<目标机IP>:17003 +``` + +## 已导出的旧离线包处理 + +如果旧离线包里的镜像已经能识别 `Qwen3ASRForConditionalGeneration`，但启动时报 KV cache 不足，例如： + +```text +Try increasing gpu_memory_utilization or decreasing max_model_len +``` + +不需要重新打镜像。只需要在旧离线包的 `docker-compose-iluvatar.yml` 的 `QWEN3_ASR_MODEL` 附近补充： + +```yaml + QWEN_GPU_MEMORY_UTILIZATION: ${QWEN_GPU_MEMORY_UTILIZATION:-0.25} + QWEN_VLLM_ENFORCE_EAGER: ${QWEN_VLLM_ENFORCE_EAGER:-true} +``` + +然后重新创建容器： + +```bash +docker compose -f docker-compose-iluvatar.yml down +docker compose -f docker-compose-iluvatar.yml up -d +``` + +只执行 `restart` 不会重新注入环境变量。 + +## 常见问题 + +### 为什么不用 uv？ + +天数 Docker 镜像内推荐直接使用系统 Python 环境。`Dockerfile.iluvatar` 使用： + +```bash +python3 -m pip install --no-cache-dir -r environments/iluvatar/requirements.txt +``` + +不创建 uv 虚拟环境，也不在镜像内运行 `uv pip install`。 + +### 为什么不重新安装 vLLM？ + +国产 GPU 的 vLLM、PyTorch、内核和运行时通常需要严格匹配厂商镜像。项目层重新 `pip install vllm` 容易解析到 PyPI/NVIDIA CUDA 依赖，破坏天数官方镜像里的匹配关系。 + +### `QWEN_GPU_MEMORY_UTILIZATION` 为什么没生效？ + +变量必须进入容器才会生效。天数 compose 中需要有： + +```yaml + QWEN_GPU_MEMORY_UTILIZATION: ${QWEN_GPU_MEMORY_UTILIZATION:-} + QWEN_VLLM_ENFORCE_EAGER: ${QWEN_VLLM_ENFORCE_EAGER:-true} +``` + +然后在 `.env` 设置： + +```env +QWEN_GPU_MEMORY_UTILIZATION=0.25 +QWEN_VLLM_ENFORCE_EAGER=true +``` + +修改 `.env` 后必须 `down` 再 `up -d`。 + +### 日志中的本地模型 repo id warning 是否致命？ + +vLLM 可能会先尝试按远端 repo 方式读取 safetensors，遇到本地路径时打印 warning。如果后续出现 `Loading safetensors checkpoint shards` 并继续加载权重，通常不是致命错误。 + +真正需要处理的是最后的异常，例如模型架构不识别、KV cache 不足、模型文件缺失等。 diff --git a/docs/metax_offline_deployment.md b/docs/metax_offline_deployment.md new file mode 100644 index 0000000..66b4b0a --- /dev/null +++ b/docs/metax_offline_deployment.md @@ -0,0 +1,234 @@ +# 沐曦 GPU 国产化离线部署指南 + +本文档用于沐曦/MetaX GPU 环境的编译、模型准备、离线包导出与目标机部署。 + +## 基础镜像原则 + +沐曦部署应使用沐曦官方或现场确认的 vLLM/MACA 基础镜像： + +```bash +cr.metax-tech.com/public-ai-release/maca/vllm-metax:0.17.0-maca.ai3.5.3.307-torch2.8-py312-ubuntu22.04-amd64 +``` + +该镜像负责提供 MACA 运行时、PyTorch、vLLM 与相关内核。本项目的 `Dockerfile.metax` 只叠加通用 Python 依赖和业务代码，不在 Dockerfile 内重新安装 vLLM，也不使用 uv 虚拟环境。 + +## 目录约定 + +目标机推荐统一使用以下宿主机目录： + +```text +/opt/dep/asr/ + models/ + data/ + logs/ + temp/ + tasks/ +``` + +容器内默认挂载为： + +```text +/app/models +/app/data +``` + +## 在线编译融合镜像 + +先按沐曦官网复制的命令登录并拉取基础镜像。账号、密码和 token 不要写入项目文件、`.env` 或离线包。 + +官网命令通常形如： + +```bash +docker login --username=<沐曦账号> --password=<沐曦token> cr.metax-tech.com && \ +docker pull cr.metax-tech.com/public-ai-release/maca/vllm-metax:0.17.0-maca.ai3.5.3.307-torch2.8-py312-ubuntu22.04-amd64 +``` + +如果沐曦基础镜像是 tar 包，则改为先导入： + +```bash +docker load -i metax-vllm-official.tar +docker images | grep -i -E 'metax|maca|vllm' +``` + +然后用完整官方镜像名构建融合镜像： + +```bash +./scripts/package_vendor_gpu_image.sh \ + --vendor metax \ + --base-image cr.metax-tech.com/public-ai-release/maca/vllm-metax:0.17.0-maca.ai3.5.3.307-torch2.8-py312-ubuntu22.04-amd64 \ + -v 0.17.0-maca.ai3.5.3.307 +``` + +脚本会生成融合镜像： + +```text +unis/qwen3-asr:metax-0.17.0-maca.ai3.5.3.307 +``` + +并导出镜像归档到： + +```text +build-file/qwen3-asr-metax-0.17.0-maca.ai3.5.3.307-amd64.tar.gz +``` + +## 单独准备模型 + +模型下载建议独立于业务服务执行。`download-models.sh` 是增量下载脚本，不会删除已有模型目录，也不依赖 uv。 + +在项目根目录或离线包目录执行： + +```bash +./scripts/download-models.sh --models-dir /opt/dep/asr/models +``` + +如果本机没有 Python 依赖，但已经有融合镜像，可以用镜像内环境下载： + +```bash +ASR_IMAGE=unis/qwen3-asr:metax-0.17.0-maca.ai3.5.3.307 \ +./scripts/download-models.sh \ + --mode docker \ + --models-dir /opt/dep/asr/models +``` + +## 导出完整离线交付包 + +```bash +./export_offline_bundle.sh \ + --type metax \ + --metax-base cr.metax-tech.com/public-ai-release/maca/vllm-metax:0.17.0-maca.ai3.5.3.307-torch2.8-py312-ubuntu22.04-amd64 \ + -v 0.17.0-maca.ai3.5.3.307 +``` + +如果模型要单独准备，不希望离线包包含模型压缩包： + +```bash +./export_offline_bundle.sh \ + --type metax \ + --metax-base cr.metax-tech.com/public-ai-release/maca/vllm-metax:0.17.0-maca.ai3.5.3.307-torch2.8-py312-ubuntu22.04-amd64 \ + -v 0.17.0-maca.ai3.5.3.307 \ + --skip-models +``` + +如果不指定 `-v`，脚本会使用当前时间戳作为版本号。 + +沐曦官方镜像内的 Python 默认使用 `/opt/conda/bin/python`。如果现场镜像路径不同，可额外指定： + +```bash +./export_offline_bundle.sh \ + --type metax \ + --metax-base cr.metax-tech.com/public-ai-release/maca/vllm-metax:0.17.0-maca.ai3.5.3.307-torch2.8-py312-ubuntu22.04-amd64 \ + --metax-python /path/to/python \ + --skip-models +``` + +也可以先给官方长镜像名打一个本地短标签，再把短标签传给 `--metax-base`；这只是为了少复制长镜像名，不是必需步骤。 + +沐曦离线包只会包含沐曦专用启动文件： + +```text +docker-compose-metax.yml +.env +.env.example +init_host_dirs.sh +download-models.sh +download_models_standalone.py +README.md +DEPLOYMENT.md +METAX_DEPLOYMENT.md +BUNDLE_INFO.txt +qwen3-asr-metax-*-amd64.tar.gz +``` + +不会要求使用通用 `docker-compose.yml`。 + +## 目标机离线部署 + +将整个离线包复制到目标机后，进入离线包目录： + +```bash +chmod +x init_host_dirs.sh download-models.sh +./init_host_dirs.sh +``` + +导入镜像： + +```bash +gunzip -c qwen3-asr-metax-0.17.0-maca.ai3.5.3.307-amd64.tar.gz | docker load +``` + +确认 `.env` 中的镜像名与导入镜像一致： + +```env +ASR_IMAGE=unis/qwen3-asr:metax-0.17.0-maca.ai3.5.3.307 +``` + +按需设置显卡与 vLLM 显存比例： + +```env +METAX_VISIBLE_DEVICES=0 +MACA_VISIBLE_DEVICES=0 +MX_VISIBLE_DEVICES=0 +QWEN_GPU_MEMORY_UTILIZATION=0.25 +QWEN_VLLM_ENFORCE_EAGER=true +``` + +启动服务： + +```bash +docker compose -f docker-compose-metax.yml up -d +``` + +查看状态与日志： + +```bash +docker compose -f docker-compose-metax.yml ps +docker compose -f docker-compose-metax.yml logs -f +``` + +服务默认端口： + +```text +http://<目标机IP>:17003 +``` + +## 沐曦与天数的差异 + +- 沐曦使用 `Dockerfile.metax` 和 `docker-compose-metax.yml` +- 天数使用 `Dockerfile.iluvatar` 和 `docker-compose-iluvatar.yml` +- 沐曦目标机需要可访问宿主 `/dev` 与 `/opt/mxdriver` +- 沐曦 compose 使用 host network、host pid/ipc、privileged，并挂载 `/dev` 与 `/opt/mxdriver` +- 天数 compose 同样使用 host network、host pid/ipc、privileged 和宿主机设备/驱动目录挂载 + +## 常见问题 + +### 为什么不用 uv？ + +沐曦 Docker 镜像内推荐直接使用系统 Python 环境。`Dockerfile.metax` 使用： + +```bash +python3 -m pip install --no-cache-dir -r environments/metax/requirements.txt +``` + +不创建 uv 虚拟环境，也不在镜像内运行 `uv pip install`。 + +### 为什么不重新安装 vLLM？ + +国产 GPU 的 vLLM、PyTorch、内核和运行时通常需要严格匹配厂商镜像。项目层重新 `pip install vllm` 容易解析到 PyPI/NVIDIA CUDA 依赖，破坏沐曦官方镜像里的匹配关系。 + +### `QWEN_GPU_MEMORY_UTILIZATION` 为什么没生效？ + +变量必须进入容器才会生效。沐曦 compose 中需要有： + +```yaml + QWEN_GPU_MEMORY_UTILIZATION: ${QWEN_GPU_MEMORY_UTILIZATION:-} + QWEN_VLLM_ENFORCE_EAGER: ${QWEN_VLLM_ENFORCE_EAGER:-true} +``` + +然后在 `.env` 设置： + +```env +QWEN_GPU_MEMORY_UTILIZATION=0.25 +QWEN_VLLM_ENFORCE_EAGER=true +``` + +修改 `.env` 后必须 `down` 再 `up -d`。 diff --git a/docs/mthreads_offline_deployment.md b/docs/mthreads_offline_deployment.md new file mode 100644 index 0000000..538d819 --- /dev/null +++ b/docs/mthreads_offline_deployment.md @@ -0,0 +1,53 @@ +# 摩尔线程 GPU 国产化离线部署指南 + +推荐使用摩尔线程官方 MUSA vLLM 基础镜像： + +```bash +registry.mthreads.com/presale/devtech/vllm_musa:s4000_4.3.5_d0519 +``` + +该镜像负责提供 MUSA 运行时、PyTorch、vLLM 与相关内核。本项目的 `Dockerfile.mthreads` 只叠加通用 Python 依赖和业务代码，不在 Dockerfile 内重新安装 vLLM，也不使用 uv 虚拟环境。 + +## 1. 拉取官方镜像 + +```bash +docker pull registry.mthreads.com/presale/devtech/vllm_musa:s4000_4.3.5_d0519 +``` + +## 2. 生成融合镜像 + +```bash +./scripts/package_vendor_gpu_image.sh \ + --vendor mthreads \ + --base-image registry.mthreads.com/presale/devtech/vllm_musa:s4000_4.3.5_d0519 \ + -v s4000_4.3.5_d0519 +``` + +## 3. 启动服务 + +```bash +ASR_IMAGE=unis/qwen3-asr:mthreads-s4000_4.3.5_d0519 \ +docker compose -f docker-compose-mthreads.yml up -d +``` + +多卡示例： + +```bash +MTHREADS_VISIBLE_DEVICES=0,1 docker compose -f docker-compose-mthreads.yml up -d +``` + +项目默认同时兼容 `MTHREADS_VISIBLE_DEVICES`、`MUSA_VISIBLE_DEVICES` 和 `CUDA_VISIBLE_DEVICES`。 + +## 4. 离线交付目录 + +```bash +./export_offline_bundle.sh \ + --type mthreads \ + --mthreads-base registry.mthreads.com/presale/devtech/vllm_musa:s4000_4.3.5_d0519 +``` + +## 说明 + +- 容器内设备探测命令使用 `mthreads-gmi` +- `ACCELERATOR=mthreads` 会走 vLLM GPU 路径 +- 不建议在项目层重新 `pip install vllm`，避免破坏厂商镜像内的 MUSA 依赖匹配关系 diff --git a/docs/realtime_meeting_refactor_report.md b/docs/realtime_meeting_refactor_report.md new file mode 100644 index 0000000..63b83a1 --- /dev/null +++ b/docs/realtime_meeting_refactor_report.md @@ -0,0 +1,412 @@ +# 实时会议识别改造报告 + +## 1. 背景 + +当前 `Qwen-Asr` 的实时会议链路，已经暴露出 3 类系统性问题： + +1. 长句在 `max_duration` 或静音点附近容易被截坏，出现残句、半句、尾词漂移。 +2. `partial` 与 `final segment` 责任混杂，静音、串音、背景音、测试语种会直接污染最终结果。 +3. 说话人识别仍以“句级单 embedding + 会话聚类”为主，面对插话、背景音、儿童声音、英语/泰语测试时容易抖动。 + +这些问题并不是单个阈值导致的，而是实时链路的职责分层不清晰。 + +## 2. 外部方案调研结论 + +本次调研覆盖了： + +- vLLM / Qwen3-ASR 官方实时文档 +- `diart` +- `pyannote.audio` +- NVIDIA NeMo `Streaming Sortformer` +- 在线 speaker diarization / label matching 论文 +- streaming ASR partial stability / endpointing 论文 + +### 2.1 vLLM/Qwen3-ASR 的边界 + +vLLM 的 `Qwen3-ASR realtime` 更像“流式推理后端”，它负责： + +- 累积音频 +- 到固定块长后执行流式推理 +- 提供 `flush/finish` + +它不负责： + +- 会议场景 endpointing +- speaker diarization +- partial 稳定化 +- 说话人标签稳定跟踪 + +结论： + +`vLLM 只能做实时 ASR 的第一层，不是完整会议链路的解决方案。` + +### 2.2 开源实时 speaker diarization 的主流范式 + +#### A. `diart` 范式 + +特征： + +- rolling buffer +- overlap-aware segmentation +- incremental clustering +- cannot-link constraints +- 低延迟滚动更新 + +优点： + +- 工程上相对容易接入 +- 很适合作为现有系统的 speaker 旁路 + +#### B. `Streaming Sortformer` 范式 + +特征： + +- 真正 streaming diarization +- chunk-based processing +- speaker cache / AOSC +- 跨 chunk 标签稳定 + +优点： + +- 更像现代工业方案 +- 标签稳定性更强 + +缺点： + +- 接入成本高于 `diart` + +### 2.3 论文结论 + +#### 在线 diarization 的关键问题不是“分离”，而是“标签一致性” + +主流论文强调： + +- 在线 diarization 必须解决 label matching +- 否则 `Speaker01/02/03` 会在不同 chunk 间乱跳 + +#### streaming ASR 的关键问题不是“能不能出字”，而是“partial 稳定性” + +论文结论： + +- partial 会被不断修正 +- partial 不应直接当 final 使用 +- 需要单独设计稳定策略 + +#### endpointing 对长句质量至关重要 + +论文与开源实现都说明： + +- 只靠静音阈值不够 +- 最好使用 VAD/SAD 或模型辅助 endpointing +- `max_duration` 只能是兜底机制，不应成为主要切句方式 + +## 3. 当前项目存在的核心架构问题 + +### 3.1 `partial` 和 `final` 混线 + +当前链路中： + +- `partial` 的输出直接影响最终句子定稿 +- 静音前后、噪声和串音有机会直接污染最终句子 + +这会带来： + +- 静音幻觉 +- 末尾漂移 +- 错误残句 + +### 3.2 断句主要靠“能量阈值 + 最长时长” + +当前主逻辑仍然偏向： + +- `self._has_voice(audio)` 做粗能量判定 +- `silence_samples` 达阈值就截断 +- `12s max_duration` 强行保底 + +这会导致： + +- 长句被硬切 +- 断句点不自然 +- 下一段拿到的是残尾巴 + +### 3.3 speaker 仍然是“句级单 embedding” + +当前 speaker 的主要依据是： + +- 当前句或其裁剪后的音频 +- 提一个 embedding +- 与会话内 slot 聚类 + +这对干净单人句子有效，但对会议场景不够： + +- 一句里可能混多人 +- 背景音会污染 embedding +- 不同 chunk 之间缺少真正的 diarization cache + +### 3.4 registry match 介入过早 + +当前一旦句级聚类通过阈值，就可能直接映射实名。 + +这会带来： + +- 错误聚类被直接升级为错实名 +- 后续纠正空间变小 + +## 4. 推荐的目标架构 + +建议将实时会议链路拆成 4 层： + +### Layer A: Streaming ASR Partial + +职责： + +- 面向 UI 输出中间文本 +- 只作为“参考内容” +- 不入库 +- 不参与 speaker +- 不做实名映射 + +要求： + +- 可以允许回退、修正 +- 需要稳定策略，但不要求和 final 完全一致 + +### Layer B: Endpointing / Final Segment Flush + +职责： + +- 决定“什么时候一句真正结束” +- 产出 final segment + +推荐方式： + +- `pending_buffer` +- 周期性 VAD/SAD 检查 +- 只刷出“已完成”的语音片段 +- 最后一段继续留 buffer，等待更多上下文 + +说明： + +- `max_duration` 仍保留 +- 但只作为异常保底 + +### Layer C: Online Speaker Tracking + +职责： + +- 产出稳定 `Speaker01/02/03/...` +- 不直接输出实名 + +推荐路线： + +- 低成本版：`rolling window diarization + incremental clustering` +- 强化版：`streaming diarization + speaker cache` + +必要能力： + +- label matching +- speaker cache +- cluster centroid 更新 +- recent speaker continuity + +### Layer D: Speaker Registry Match + +职责： + +- 把稳定的 slot 映射成实名 + +原则： + +- 先有稳定 `Speaker01/02/03` +- 再做 registry match +- 不要在每个短句上直接实名匹配 + +## 5. 对当前代码库的具体落地建议 + +### 5.1 保留 `Qwen3ASREngine` 作为流式 ASR 后端 + +文件： + +- `app/services/asr/qwen3_engine.py` + +建议： + +- 继续让它只负责 `init_streaming_state / streaming_transcribe / finish_streaming_transcribe` +- 不再让它承担句边界和 speaker 逻辑 + +### 5.2 重构 `qwen3_websocket_asr.py` + +文件： + +- `app/services/qwen3_websocket_asr.py` + +建议把它拆成以下内部组件： + +1. `PartialStreamController` + - 负责把有声 chunk 喂给流式 ASR + - 维护 partial 状态 + +2. `RealtimeEndpointController` + - 维护 `pending_buffer` + - 周期性 VAD flush + - 产出 finalized audio span + +3. `RealtimeSpeakerController` + - 对 finalized span 做 speaker tracking + - 维护 `Speaker01/02/...` + +4. `RegistryMatchController` + - 在 slot 稳定后映射实名 + +### 5.3 speaker tracker 升级方向 + +当前文件： + +- `app/services/realtime_speaker_tracker.py` + +建议保留它，但升级为： + +- slot centroid +- slot history +- recent speaker cache +- explicit label matching step +- “未知 slot” 与 “实名 slot” 分层 + +不建议继续只用： + +- `assign(embedding)` 的一次性聚类结果 + +### 5.4 引入真正的 VAD completed-segment flush + +当前项目最应该借鉴老项目的部分就是： + +- `pending_buffer` +- `flush_completed_segments` +- 只把已完成语音刷成 final + +建议新建模块，例如： + +- `app/services/realtime_endpointing.py` + +它负责： + +- 累积 PCM +- 每隔固定步长跑 VAD +- 判断哪些片段已完成 +- 返回 `finalized_audio` 与 `remaining_audio` + +### 5.5 `max_duration` 的正确角色 + +建议保留，但只作为： + +- buffer 过长 +- 长时间不出句 +- VAD 失效 + +时的保底。 + +不建议让它继续承担： + +- 常规切句 +- 主要 final 机制 + +## 6. 推荐改造路线 + +### 阶段 1：先把文本链路拆干净 + +目标： + +- `partial` 只显示 +- `final segment` 只由 endpointing 决定 + +工作项： + +- 引入 `pending_buffer + VAD flush` +- 保留现有 websocket 接口字段不变 +- `max_duration` 降级为兜底机制 + +### 阶段 2：speaker 从句级 embedding 升级为在线 tracking + +目标： + +- 稳定 `Speaker01/02/03` + +工作项： + +- 引入 rolling window diarization 或 speaker turn 检测 +- 增加 label matching +- 增加 speaker cache + +### 阶段 3：实名映射后移 + +目标： + +- 先稳定 slot +- 再实名 + +工作项： + +- registry match 不再逐句触发 +- 改成基于 slot centroid 或稳定窗口触发 + +### 阶段 4：partial 稳定策略 + +目标： + +- UI 不再频繁抖动 + +工作项： + +- prefix commit +- suffix freeze +- partial stability score + +## 7. 不建议继续做的事 + +以下方向收益很低，且会继续增加系统复杂度： + +- 继续堆更多 `if suspicious` +- 继续调 `speaker_threshold` +- 继续靠 `max_duration` 修长句 +- 继续用句级单 embedding 解决多人会议 speaker +- 继续让 `partial` 直接影响 final + +## 8. 结论 + +当前项目最需要的不是继续补条件分支，而是把实时会议能力拆成明确的四层： + +1. `streaming partial` +2. `endpointing/final flush` +3. `online diarization / speaker tracking` +4. `registry match` + +其中： + +- `vLLM` 属于第 1 层 +- 不是第 2、3、4 层的替代品 + +如果后续继续在当前单文件链路上修修补补，复杂度会继续升高，稳定性仍然不可控。 +如果按本报告做分层重构，问题会从“靠猜修 bug”变成“按职责逐层验证”。 + +## 9. 参考资料 + +- vLLM realtime Qwen3-ASR model docs + +- vLLM Qwen3-ASR recipe + +- diart + +- pyannote.audio + +- NVIDIA NeMo speaker diarization docs + +- NVIDIA Streaming Sortformer blog + +- Low-Latency Online Speaker Diarization with Graph-Based Label Generation + +- TURN-TO-DIARIZE: Online Speaker Diarization Constrained by Transformer Transducer Speaker Turn Detection + +- Analyzing the Quality and Stability of a Streaming End-to-End On-Device Speech Recognizer + +- Improving endpoint detection in end-to-end streaming ASR for conversational speech + diff --git a/docs/realtime_meeting_websocket.md b/docs/realtime_meeting_websocket.md new file mode 100644 index 0000000..b44b666 --- /dev/null +++ b/docs/realtime_meeting_websocket.md @@ -0,0 +1,417 @@ +# 实时会议 WebSocket 调用说明 + +本文档说明当前项目实时会议识别接口的调用方式、入参与返回格式。 + +## 1. 接口地址 + +- 推荐地址：`/ws/v1/asr` +- 显式地址：`/ws/v1/asr/qwen` + +说明： +- `/ws/v1/asr/funasr` 已废弃，不再使用。 +- 当前返回结构按腾讯实时识别风格组织，核心消息类型为：`voice_id`、`start`、`sentences`、`end`、`error`。 + +## 2. 交互流程 + +客户端调用顺序： + +1. 建立 WebSocket 连接 +2. 发送 `start` 消息 +3. 持续发送音频二进制数据 +4. 接收服务端返回的 `voice_id`、`start`、`sentences` +5. 发送 `stop` +6. 接收最终 `end` + +## 3. start 消息 + +客户端先发送文本消息： + +```json +{ + "type": "start", + "payload": { + "format": "pcm", + "sample_rate": 16000, + "session_id": "meeting-abc-123", + "language": null, + "context": "", + "enable_inverse_text_normalization": true, + "silence_duration_ms": 800, + "min_partial_sec": 0.3, + "pre_roll_ms": 240, + "max_sentence_count": 8, + "partial_holdback_chars": 2, + "unfixed_token_num": 3, + "enable_native_partial_stream": true, + "enable_speaker": true, + "match_speaker_registry": true, + "speaker_threshold": 0.6, + "enable_realtime_vad_split": true, + "enable_realtime_longform": false, + "force_stable_segment_sec": 6, + "force_stable_min_chars": 24, + "max_segment_sec": 12 + } +} +``` + +## 4. start 参数说明 + +### 必填/常用参数 + +| 字段 | 类型 | 默认值 | 说明 | +| --- | --- | --- | --- | +| `format` | string | `pcm` | 当前建议传 `pcm` | +| `sample_rate` | number | `16000` | 采样率 | +| `session_id` | string | 自动生成/可不传 | 会话标识；断线重连时传同一个值可在保活期内恢复同一会议会话 | +| `language` | string/null | `null` | 识别语言，留空表示自动判断 | +| `context` | string | `""` | 上下文提示，如会议主题、术语 | +| `enable_inverse_text_normalization` | boolean | `true` | 是否启用数字归一化 | + +### partial / 切段相关 + +| 字段 | 类型 | 默认值 | 说明 | +| --- | --- | --- | --- | +| `silence_duration_ms` | number | `800` | 静音多久触发收段 | +| `min_partial_sec` | number | `0.9`（后端默认） | 最短 partial 窗口 | +| `pre_roll_ms` | number | `240` | 句首预读毫秒数 | +| `max_sentence_count` | number | `8` | 一段内最多允许的句子数 | +| `partial_holdback_chars` | number | 后端默认 | partial 尾部保留字数，减少尾字抖动 | +| `unfixed_token_num` | number | `5`（后端默认） | native partial 模式下回滚 token 数 | +| `enable_native_partial_stream` | boolean | `false` | 是否启用底层原生流式 partial | +| `enable_realtime_vad_split` | boolean | `false` | 是否启用实时 VAD 拆段 | +| `enable_realtime_longform` | boolean | `false` | 是否启用长段重转写 | +| `force_stable_segment_sec` | number | 后端默认 | 超时后软提交的时间阈值 | +| `force_stable_min_chars` | number | 后端默认 | 软提交最少字数 | +| `max_segment_sec` | number | 后端默认 | 单段最长秒数 | + +### speaker 相关 + +| 字段 | 类型 | 默认值 | 说明 | +| --- | --- | --- | --- | +| `enable_speaker` | boolean | `true` | 是否开启说话人分离 | +| `match_speaker_registry` | boolean | `false` | 是否匹配已注册声纹库 | +| `speaker_threshold` | number/null | `null` | 本次声纹匹配阈值 | + +说明： +- `enable_speaker=true` 时，服务端会先返回文本，再异步补充 speaker 归属。 +- `match_speaker_registry=true` 时，若匹配到声纹库，会返回实名 speaker 信息。 + +## 5. 音频数据发送 + +`start` 成功后，客户端持续发送音频二进制帧。 + +当前推荐： + +- 单声道 PCM +- 16k 采样率 +- 小块持续发送 + +示例： + +```js +ws.send(pcmChunkArrayBuffer); +``` + +## 6. stop 消息 + +识别结束后发送： + +```json +{ + "type": "stop" +} +``` + +## 7. 服务端返回消息 + +## 7.1 `voice_id` + +连接启动后首先返回： + +```json +{ + "type": "voice_id", + "voice_id": "41ff1926", + "session_id": "meeting-abc-123" +} +``` + +## 7.2 `start` + +服务端确认开始识别： + +```json +{ + "type": "start", + "session_id": "meeting-abc-123" +} +``` + +说明： +- 若客户端在 `start.payload.session_id` 中传入固定值，服务端会优先复用该值。 +- 若客户端未传 `session_id`，服务端仍按当前连接生成 `voice_id`，并同步作为 `session_id` 返回。 +- 在 `REALTIME_SESSION_RESUME_TTL_SEC` 保活时间内，客户端断线后使用相同 `session_id` 再次发送 `start`，服务端会恢复原会话上下文。 + +## 7.3 `sentences` + +识别过程中会持续返回 `sentences`。 + +重要说明： + +- `sentence_type=0` / `slice_type=1` 表示实时 partial，仅用于实时展示。 +- `sentence_type=1` / `slice_type=2` 表示该段最终定稿，建议作为最终业务入库依据。 +- 若开启 `enable_native_partial_stream=true`，partial 刷新会更快，但中间文本可能更活。 +- 若希望少存数据，建议不要存 partial，只对 `sentence_type=1` 做落库或 upsert。 + +### partial 返回 + +```json +{ + "type": "sentences", + "code": 0, + "voice_id": "41ff1926", + "final": 0, + "result": { + "slice_type": 1, + "index": 3, + "voice_text_str": "今天这个会议主要讨论预算。" + }, + "sentences": [ + { + "sentence_id": 3, + "sentence_type": 0, + "speaker_id": -1, + "start_time": 12000, + "end_time": 15600, + "sentence": "今天这个会议主要讨论预算。" + } + ] +} +``` + +字段说明： + +| 字段 | 说明 | +| --- | --- | +| `slice_type=1` | partial | +| `sentence_type=0` | partial 句子 | +| `speaker_id=-1` | 当前还未确认 speaker | + +### 定稿返回 + +```json +{ + "type": "sentences", + "code": 0, + "voice_id": "41ff1926", + "final": 0, + "result": { + "slice_type": 2, + "index": 3, + "voice_text_str": "今天这个会议主要讨论预算。" + }, + "sentences": [ + { + "sentence_id": 3, + "sentence_type": 1, + "speaker_id": -1, + "start_time": 12000, + "end_time": 15880, + "sentence": "今天这个会议主要讨论预算。", + "speaker_name": "", + "user_id": null + } + ] +} +``` + +字段说明： + +| 字段 | 说明 | +| --- | --- | +| `slice_type=2` | 该段文本已定稿 | +| `sentence_type=1` | final 句子 | +| `speaker_name` | 项目扩展字段 | +| `user_id` | 项目扩展字段 | + +### speaker 回写返回 + +如果 speaker 后续识别完成，服务端会再次返回相同 `sentence_id` 的 `sentences` 消息，只更新 speaker 归属： + +```json +{ + "type": "sentences", + "code": 0, + "voice_id": "41ff1926", + "final": 0, + "result": { + "slice_type": 2, + "index": 3, + "voice_text_str": "今天这个会议主要讨论预算。" + }, + "sentences": [ + { + "sentence_id": 3, + "sentence_type": 1, + "speaker_id": 19, + "start_time": 12000, + "end_time": 15880, + "sentence": "今天这个会议主要讨论预算。", + "speaker_name": "Alan Paine", + "user_id": "3" + } + ] +} +``` + +说明： +- speaker 是异步补归属，不保证和文本首个 final 同时返回。 +- 前端应使用 `sentence_id` 做幂等更新，而不是简单追加。 + +## 7.3.1 partial 与 final 的使用建议 + +推荐使用方式： + +- 前端实时显示：使用 `sentence_type=0` +- 最终文本入库：使用 `sentence_type=1` +- 说话人归属更新：继续按相同 `sentence_id` 更新已有 final 记录 + +推荐入库字段： + +- `sentence_id` +- `start_time` +- `end_time` +- `sentence` +- `speaker_id` +- `speaker_name` +- `user_id` + +推荐入库策略： + +1. 收到 `sentences` 后，仅处理 `sentence_type=1` +2. 使用 `sentence_id` 作为同一连接内的幂等更新键 +3. 若后续相同 `sentence_id` 再次返回，通常表示 speaker 归属补写，直接更新原记录 +4. 收到 `end` 后，可将本次会话的 final 结果视为最终完成结果 + +## 7.4 `end` + +客户端发送 `stop` 后，服务端返回最终结束消息： + +```json +{ + "type": "end", + "code": 0, + "message": "", + "voice_id": "41ff1926", + "final": 1, + "result": { + "slice_type": 2, + "index": 3, + "voice_text_str": "完整识别文本" + }, + "sentences": [ + { + "sentence_id": 0, + "sentence_type": 1, + "speaker_id": 19, + "start_time": 0, + "end_time": 3200, + "sentence": "第一句", + "speaker_name": "Alan Paine", + "user_id": "3" + } + ] +} +``` + +## 7.5 `error` + +异常时返回： + +```json +{ + "type": "error", + "code": -1, + "message": "错误信息", + "voice_id": "41ff1926" +} +``` + +## 8. 腾讯风格字段兼容说明 + +当前实时会议返回格式按腾讯实时 speaker demo 风格组织，核心字段保持一致： + +- `type` +- `code` +- `voice_id` +- `final` +- `result.slice_type` +- `result.index` +- `result.voice_text_str` +- `sentences[].sentence_id` +- `sentences[].sentence_type` +- `sentences[].speaker_id` +- `sentences[].start_time` +- `sentences[].end_time` +- `sentences[].sentence` + +在此基础上，项目额外补充： + +- `sentences[].speaker_name` +- `sentences[].user_id` + +## 9. JavaScript 调用示例 + +```js +const ws = new WebSocket("ws://127.0.0.1:8000/ws/v1/asr"); + +ws.onopen = () => { + ws.send(JSON.stringify({ + type: "start", + payload: { + format: "pcm", + sample_rate: 16000, + language: null, + context: "预算评审会", + enable_inverse_text_normalization: true, + silence_duration_ms: 800, + min_partial_sec: 0.3, + pre_roll_ms: 240, + max_sentence_count: 8, + partial_holdback_chars: 2, + unfixed_token_num: 3, + enable_native_partial_stream: true, + enable_speaker: true, + match_speaker_registry: true, + speaker_threshold: 0.6, + enable_realtime_vad_split: true, + enable_realtime_longform: false, + force_stable_segment_sec: 6, + force_stable_min_chars: 24, + max_segment_sec: 12 + } + })); +}; + +ws.onmessage = (event) => { + const payload = JSON.parse(event.data); + console.log("ws message:", payload); +}; + +function sendPcmChunk(arrayBuffer) { + ws.send(arrayBuffer); +} + +function stopRecognition() { + ws.send(JSON.stringify({ type: "stop" })); +} +``` + +## 10. 对接注意事项 + +- 前端要按 `sentence_id` 更新句子，不要把 speaker 回写当成新句子追加。 +- `speaker_id=-1` 代表 speaker 暂未确认，不代表识别失败。 +- 默认启用 `enable_native_partial_stream`；partial 刷新更快，但中间文本可能会修订。 +- 最终展示建议以 `sentence_type=1` 的句子为准。 +- 若需要排查前端是否真的传了某个参数，建议把 `start payload` 直接打印到页面日志。 diff --git a/docs/realtime_meeting_websocket_customer.md b/docs/realtime_meeting_websocket_customer.md new file mode 100644 index 0000000..5063cbc --- /dev/null +++ b/docs/realtime_meeting_websocket_customer.md @@ -0,0 +1,297 @@ +# 实时会议 WebSocket 对接文档 + +本文档用于客户侧接入实时会议识别服务，包含接口地址、消息格式、返回示例和重连约定。 + +## 1. 接口地址 + +- 推荐地址：`/ws/v1/asr` +- 显式地址：`/ws/v1/asr/qwen` + +说明： +- 当前协议为 JSON 控制消息 + 音频二进制流。 +- 返回结构按实时会议场景组织，核心消息类型为：`voice_id`、`start`、`sentences`、`end`、`error`。 + +## 2. 交互流程 + +客户端调用顺序： + +1. 建立 WebSocket 连接 +2. 发送 `start` 文本消息 +3. 持续发送音频二进制数据 +4. 持续接收服务端返回的 `voice_id`、`start`、`sentences` +5. 识别结束时发送 `stop` +6. 接收最终 `end` + +## 3. start 请求 + +### 3.1 请求示例 + +```json +{ + "type": "start", + "payload": { + "format": "pcm", + "sample_rate": 16000, + "session_id": "meeting-abc-123", + "language": null, + "context": "", + "enable_inverse_text_normalization": true, + "silence_duration_ms": 800, + "min_partial_sec": 0.3, + "pre_roll_ms": 240, + "max_sentence_count": 8, + "partial_holdback_chars": 2, + "unfixed_token_num": 3, + "enable_native_partial_stream": true, + "enable_speaker": true, + "match_speaker_registry": true, + "speaker_threshold": 0.6, + "enable_realtime_vad_split": true, + "enable_realtime_longform": false, + "force_stable_segment_sec": 6, + "force_stable_min_chars": 24, + "max_segment_sec": 12 + } +} +``` + +### 3.2 主要参数说明 + +| 字段 | 类型 | 默认值 | 说明 | +| --- | --- | --- | --- | +| `format` | string | `pcm` | 建议传 `pcm` | +| `sample_rate` | number | `16000` | 音频采样率 | +| `session_id` | string | 可不传 | 会话标识；断线重连时传同一个值可恢复同一会议会话 | +| `language` | string/null | `null` | 留空表示自动识别 | +| `context` | string | `""` | 业务上下文提示，如会议主题、术语 | +| `enable_inverse_text_normalization` | boolean | `true` | 是否启用数字归一化 | +| `silence_duration_ms` | number | `800` | 静音多久触发收段 | +| `min_partial_sec` | number | 后端默认 | 最短 partial 窗口 | +| `pre_roll_ms` | number | `240` | 句首预读时长 | +| `max_sentence_count` | number | `8` | 单段最多句数 | +| `partial_holdback_chars` | number | 后端默认 | partial 尾部保留字数，减少尾字抖动 | +| `unfixed_token_num` | number | 后端默认 | native partial 模式下回滚 token 数 | +| `enable_native_partial_stream` | boolean | `false` | 是否启用原生流式 partial | +| `enable_speaker` | boolean | `true` | 是否开启说话人分离 | +| `match_speaker_registry` | boolean | `false` | 是否匹配已注册声纹库 | +| `speaker_threshold` | number/null | `null` | 本次声纹匹配阈值 | +| `enable_realtime_vad_split` | boolean | `false` | 是否启用实时 VAD 拆段 | +| `enable_realtime_longform` | boolean | `false` | 是否启用长段重转写 | +| `force_stable_segment_sec` | number | 后端默认 | 超时后软提交阈值 | +| `force_stable_min_chars` | number | 后端默认 | 软提交最少字数 | +| `max_segment_sec` | number | 后端默认 | 单段最长秒数 | + +说明： +- `enable_speaker=true` 时，服务端会先返回文本，再异步补充 speaker 归属。 +- `match_speaker_registry=true` 时，若匹配到声纹库，会返回实名 speaker 信息。 + +## 4. 音频发送 + +`start` 成功后，客户端持续发送音频二进制帧。 + +推荐格式： + +- 单声道 PCM +- 16k 采样率 +- 小块连续发送 + +示例： + +```js +ws.send(pcmChunkArrayBuffer); +``` + +## 5. stop 请求 + +```json +{ + "type": "stop" +} +``` + +## 6. 服务端返回 + +## 6.1 `voice_id` + +连接建立并收到 `start` 后，服务端先返回： + +```json +{ + "type": "voice_id", + "voice_id": "41ff1926", + "session_id": "meeting-abc-123" +} +``` + +说明： +- `voice_id` 为本次实时识别标识。 +- `session_id` 为本次会话标识；若未传，服务端会返回自动生成值。 + +## 6.2 `start` + +服务端确认开始识别： + +```json +{ + "type": "start", + "session_id": "meeting-abc-123" +} +``` + +## 6.3 `sentences` + +识别过程中会持续返回 `sentences`。 + +重要说明： + +- `sentence_type=0` / `slice_type=1` 表示实时 partial，仅用于实时展示。 +- `sentence_type=1` / `slice_type=2` 表示该段最终定稿，建议作为最终入库依据。 +- 说话人可能异步补写，不保证和首个 final 同时返回。 + +### partial 返回示例 + +```json +{ + "type": "sentences", + "code": 0, + "voice_id": "41ff1926", + "final": 0, + "result": { + "slice_type": 1, + "index": 3, + "voice_text_str": "今天这个会议主要讨论预算。" + }, + "sentences": [ + { + "sentence_id": 3, + "sentence_type": 0, + "speaker_id": -1, + "start_time": 12000, + "end_time": 15600, + "sentence": "今天这个会议主要讨论预算。" + } + ] +} +``` + +### final 返回示例 + +```json +{ + "type": "sentences", + "code": 0, + "voice_id": "41ff1926", + "final": 0, + "result": { + "slice_type": 2, + "index": 3, + "voice_text_str": "今天这个会议主要讨论预算。" + }, + "sentences": [ + { + "sentence_id": 3, + "sentence_type": 1, + "speaker_id": -1, + "start_time": 12000, + "end_time": 15880, + "sentence": "今天这个会议主要讨论预算。", + "speaker_name": "", + "user_id": null + } + ] +} +``` + +### speaker 回写示例 + +如果 speaker 后续识别完成，服务端会再次返回相同 `sentence_id` 的消息，只更新 speaker 信息： + +```json +{ + "type": "sentences", + "code": 0, + "voice_id": "41ff1926", + "final": 0, + "result": { + "slice_type": 2, + "index": 3, + "voice_text_str": "今天这个会议主要讨论预算。" + }, + "sentences": [ + { + "sentence_id": 3, + "sentence_type": 1, + "speaker_id": 19, + "start_time": 12000, + "end_time": 15880, + "sentence": "今天这个会议主要讨论预算。", + "speaker_name": "Alan Paine", + "user_id": "3" + } + ] +} +``` + +接入建议： + +- 客户端按 `sentence_id` 更新句子，不要把 speaker 回写当成新句子追加。 +- `speaker_id=-1` 代表当前 speaker 暂未确认，不代表识别失败。 + +## 6.4 `end` + +识别结束后返回： + +```json +{ + "type": "end", + "code": 0, + "message": "", + "voice_id": "41ff1926", + "session_id": "meeting-abc-123", + "final": 1, + "result": { + "slice_type": 2, + "index": 5, + "voice_text_str": "完整会议文本" + }, + "sentences": [] +} +``` + +## 6.5 `error` + +异常时返回： + +```json +{ + "type": "error", + "code": "INVALID_STATE", + "message": "请先发送 start", + "voice_id": "41ff1926" +} +``` + +## 7. 断线重连 + +支持基于 `session_id` 的会话恢复。 + +约定如下： + +1. 首次连接时，客户端可以自行生成 `session_id` 并放入 `start.payload.session_id` +2. 若客户端未传，服务端会返回自动生成的 `session_id` +3. 断线重连时，客户端使用同一个 `session_id` 再次发送 `start` +4. 若服务端会话仍在保活期内，则恢复同一会议上下文 + +建议： + +- 客户端在会议生命周期内保持 `session_id` 稳定 +- 网络抖动或页面刷新后，优先使用上一次会话的 `session_id` 重连 + +## 8. 客户端接入建议 + +- 实时展示可消费 `sentence_type=0` 的 partial +- 业务落库建议只以 `sentence_type=1` 的 final 为准 +- 若后续收到相同 `sentence_id` 的 final 更新，应执行更新而不是新增 +- 若需要最佳实时体验，建议开启 `enable_native_partial_stream=true` +- 若希望 speaker 识别更完整，建议开启 `enable_speaker=true` + diff --git a/docs/国产化兼容适配汇报.md b/docs/国产化兼容适配汇报.md new file mode 100644 index 0000000..4572240 --- /dev/null +++ b/docs/国产化兼容适配汇报.md @@ -0,0 +1,95 @@ +# Qwen-Asr 国产化兼容适配汇报 + +## 一、适配目标 + +完成对 **沐曦(MetaX)** 和 **天数(Iluvatar)** 两款国产GPU的完整适配，实现语音识别服务在国产硬件平台的稳定运行。 + +--- + +## 二、适配历程与关键节点 + +| 时间 | 里程碑 | 关键动作 | +|------|--------|----------| +| 2026-06-02 | 架构设计 | 新增Accelerator抽象层，重构设备检测逻辑 | +| 2026-06-02 | 沐曦适配 | 新增沐曦GPU支持(MetaX MACA runtime) | +| 2026-06-02 | 天数适配 | 新增天数GPU支持(Iluvatar IX runtime) | +| 2026-06-03 | 部署完善 | 完善沐曦全流程部署支持，升级天数基础镜像 | +| 2026-06-04 | 问题修复 | 修复setuptools版本冲突，优化pip安装配置 | + +--- + +## 三、遇到的主要"坑"与解决方案 + +### **坑1：设备检测适配问题** +- **问题**：不同厂商使用不同的设备查询命令（`mx-smi` vs `ixsmi`），输出格式差异大 +- **解决方案**： + - 抽象统一的`AcceleratorAdapter`接口 + - 支持多种SMI命令格式解析（JSON/表格/列表） + - 自动检测优先级：国产厂商SMI → NVIDIA → CPU + +### **坑2：vLLM/PyTorch版本不兼容** +- **问题**：国产GPU的vLLM、PyTorch、内核和运行时需要严格匹配厂商官方版本 +- **解决方案**： + - 采用"厂商官方vLLM镜像 + 项目代码叠加"策略 + - 不在Dockerfile中重新`pip install vllm`，避免解析到NVIDIA CUDA依赖 + - 提供专用环境配置文件（`environments/metax/`、`environments/iluvatar/`） + +### **坑3：环境变量命名混乱** +- **问题**：各厂商使用不同的设备可见性变量名 +- **解决方案**： + - 沐曦：`METAX_VISIBLE_DEVICES` / `MACA_VISIBLE_DEVICES` / `MX_VISIBLE_DEVICES` + - 天数：`ILUVATAR_VISIBLE_DEVICES` / `IX_VISIBLE_DEVICES` / `CUDA_VISIBLE_DEVICES` + - 代码层统一处理，支持多种变量名自动识别 + +### **坑4：显存管理问题** +- **问题**：国产GPU显存分配策略与NVIDIA不同，默认配置易导致KV cache不足 +- **解决方案**： + - 引入`QWEN_GPU_MEMORY_UTILIZATION`配置项（默认0.25） + - 强制启用`QWEN_VLLM_ENFORCE_EAGER=true`提升兼容性 + - 提供详细的显存配置指导文档 + +### **坑5：离线部署打包复杂** +- **问题**：目标机通常无法联网，需要完整的离线交付包 +- **解决方案**： + - 开发`export_offline_bundle.sh`一键打包脚本 + - 支持跳过模型打包（模型单独准备） + - 生成厂商专用的docker-compose配置 + +### **坑6：Python环境依赖冲突** +- **问题**：沐曦官方镜像使用特定Python路径和setuptools版本 +- **解决方案**： + - 不使用uv虚拟环境，直接使用系统Python + - 固定setuptools版本为69.5.1 + - 支持自定义Python路径配置 + +--- + +## 四、兼容性矩阵 + +| 平台 | 支持状态 | 基础镜像 | 设备命令 | +|------|----------|----------|----------| +| NVIDIA CUDA | ✅ 支持 | 自定义CUDA 13.0 | nvidia-smi | +| 沐曦 MetaX | ✅ 支持 | vLLM 0.17.0 + MACA AI3.5.3 | mx-smi | +| 天数 Iluvatar | ✅ 支持 | vLLM 0.17.0 + IX 4.4.0 | ixsmi | +| CPU (Rust) | ✅ 支持 | - | - | + +--- + +## 五、交付成果 + +1. **代码层**：统一的加速器抽象层（`app/core/accelerator.py`） +2. **部署文档**： + - 《沐曦GPU国产化离线部署指南》 + - 《天数GPU国产化离线部署指南》 +3. **环境配置**：专用依赖配置文件（metax/iluvatar） +4. **打包脚本**：一键离线打包工具 +5. **Docker镜像**：专用Dockerfile和docker-compose配置 + +--- + +## 六、关键经验总结 + +1. **厂商官方镜像优先**：不自行编译GPU运行时，直接使用厂商验证过的vLLM镜像 +2. **统一抽象层**：通过Adapter模式屏蔽硬件差异，上层业务无感知 +3. **配置化驱动**：设备可见性、显存比例等参数化配置，适应不同现场环境 +4. **离线交付优先**：提前规划离线部署方案，避免现场网络限制问题 \ No newline at end of file diff --git a/environments/cpu/pyproject.toml b/environments/cpu/pyproject.toml new file mode 100644 index 0000000..f624140 --- /dev/null +++ b/environments/cpu/pyproject.toml @@ -0,0 +1,54 @@ +[project] +name = "qwen3-asr-cpu-env" +version = "1.0.0" +description = "CPU runtime environment for 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+ "datasets==3.6.0", + "scipy==1.15.3", + "itntext==0.1.5", + "python-dotenv==1.2.1", + "asyncpg==0.31.0", + "pypinyin==0.55.0", + "huggingface_hub==0.34.0", + "hdbscan==0.8.41", + "loguru==0.7.2", + "rich>=13.9,<14", + "setuptools>=70.0.0,<81", + "transformers>=4.56.0,<5", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = [] + +[tool.uv] +package = false diff --git a/environments/iluvatar/requirements.txt b/environments/iluvatar/requirements.txt new file mode 100644 index 0000000..995482c --- /dev/null +++ b/environments/iluvatar/requirements.txt @@ -0,0 +1,24 @@ +fastapi==0.128.0 +fastapi-offline==1.7.6 +uvicorn[standard]==0.40.0 +pydantic==2.12.0 +python-multipart==0.0.22 +funasr==1.3.1 +requests==2.32.5 +modelscope[framework]==1.34.0 +soundfile==0.13.1 +librosa==0.11.0 +websockets==16.0 +addict==2.4.0 +datasets==3.6.0 +scipy==1.15.3 +itntext==0.1.5 +python-dotenv==1.2.1 +asyncpg==0.31.0 +pypinyin==0.55.0 +huggingface_hub==0.34.0 +hdbscan==0.8.41 +loguru==0.7.2 +rich>=13.9,<14 +setuptools>=70.0.0,<81 +transformers>=4.56.0,<5 diff --git a/environments/metax/pyproject.toml b/environments/metax/pyproject.toml new file mode 100644 index 0000000..0cd1afe --- /dev/null +++ b/environments/metax/pyproject.toml @@ -0,0 +1,45 @@ +[project] +name = "qwen3-asr-metax-env" +version = "1.0.0" +description = "MetaX MACA runtime environment for qwen3-asr" +requires-python = ">=3.10,<3.13" +dependencies = [ + "fastapi==0.128.0", + "fastapi-offline==1.7.6", + "uvicorn[standard]==0.40.0", + "pydantic==2.12.0", + "python-multipart==0.0.22", + "funasr==1.3.1", + "requests==2.32.5", + "modelscope[framework]==1.34.0", + "soundfile==0.13.1", + "librosa==0.11.0", + "websockets==16.0", + "addict==2.4.0", + "datasets==3.6.0", + "scipy==1.15.3", + "itntext==0.1.5", + "python-dotenv==1.2.1", + "asyncpg==0.31.0", + "pypinyin==0.55.0", + "huggingface_hub==0.34.0", + "hdbscan==0.8.41", + "loguru==0.7.2", + "rich>=13.9,<14", + "transformers>=4.56.0,<5", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = [] + +[tool.uv] +package = false + +[[tool.uv.index]] +name = "metax-maca" +url = "https://repos.metax-tech.com/r/maca-pypi/simple" +explicit = true diff --git a/environments/metax/requirements.txt b/environments/metax/requirements.txt new file mode 100644 index 0000000..a165de4 --- /dev/null +++ b/environments/metax/requirements.txt @@ -0,0 +1,23 @@ +fastapi==0.128.0 +fastapi-offline==1.7.6 +uvicorn[standard]==0.40.0 +pydantic==2.12.0 +python-multipart==0.0.22 +funasr==1.3.1 +requests==2.32.5 +modelscope[framework]==1.34.0 +soundfile==0.13.1 +librosa==0.11.0 +websockets==16.0 +addict==2.4.0 +datasets==3.6.0 +scipy==1.15.3 +itntext==0.1.5 +python-dotenv==1.2.1 +asyncpg==0.31.0 +pypinyin==0.55.0 +huggingface_hub==0.34.0 +hdbscan==0.8.41 +loguru==0.7.2 +rich>=13.9,<14 +transformers>=4.56.0,<5 diff --git a/environments/mthreads/pyproject.toml b/environments/mthreads/pyproject.toml new file mode 100644 index 0000000..848c816 --- /dev/null +++ b/environments/mthreads/pyproject.toml @@ -0,0 +1,41 @@ +[project] +name = "qwen3-asr-mthreads-env" +version = "1.0.0" +description = "Moore Threads MUSA runtime environment for qwen3-asr" +requires-python = ">=3.10,<3.13" +dependencies = [ + "fastapi==0.128.0", + "fastapi-offline==1.7.6", + "uvicorn[standard]==0.40.0", + "pydantic==2.12.0", + "python-multipart==0.0.22", + "funasr==1.3.1", + "requests==2.32.5", + "modelscope[framework]==1.34.0", + "soundfile==0.13.1", + "librosa==0.11.0", + "websockets==16.0", + "addict==2.4.0", + "datasets==3.6.0", + "scipy==1.15.3", + "itntext==0.1.5", + "python-dotenv==1.2.1", + "asyncpg==0.31.0", + "pypinyin==0.55.0", + "huggingface_hub==0.34.0", + "hdbscan==0.8.41", + "loguru==0.7.2", + "rich>=13.9,<14", + "setuptools>=70.0.0,<81", + "transformers>=4.56.0,<5", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = [] + +[tool.uv] +package = false diff --git a/environments/mthreads/requirements.txt b/environments/mthreads/requirements.txt new file mode 100644 index 0000000..995482c --- /dev/null +++ b/environments/mthreads/requirements.txt @@ -0,0 +1,24 @@ +fastapi==0.128.0 +fastapi-offline==1.7.6 +uvicorn[standard]==0.40.0 +pydantic==2.12.0 +python-multipart==0.0.22 +funasr==1.3.1 +requests==2.32.5 +modelscope[framework]==1.34.0 +soundfile==0.13.1 +librosa==0.11.0 +websockets==16.0 +addict==2.4.0 +datasets==3.6.0 +scipy==1.15.3 +itntext==0.1.5 +python-dotenv==1.2.1 +asyncpg==0.31.0 +pypinyin==0.55.0 +huggingface_hub==0.34.0 +hdbscan==0.8.41 +loguru==0.7.2 +rich>=13.9,<14 +setuptools>=70.0.0,<81 +transformers>=4.56.0,<5 diff --git a/export_offline_bundle.sh b/export_offline_bundle.sh new file mode 100644 index 0000000..f91fb83 --- /dev/null +++ b/export_offline_bundle.sh @@ -0,0 +1,562 @@ +#!/usr/bin/env bash + +set -euo pipefail + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +PROJECT_ROOT="${SCRIPT_DIR}" + +TIMESTAMP="$(date +"%Y%m%d_%H%M%S")" +BUILD_TYPE="" +VERSION="" +OUTPUT_ROOT="${PROJECT_ROOT}/build-file" +REGISTRY="unis" +IMAGE_NAME="qwen3-asr" +INCLUDE_MODELS="true" +METAX_BASE_IMAGE="${METAX_BASE_IMAGE:-}" +ILUVATAR_BASE_IMAGE="${ILUVATAR_BASE_IMAGE:-}" +MTHREADS_BASE_IMAGE="${MTHREADS_BASE_IMAGE:-}" +METAX_PYTHON_BIN="${METAX_PYTHON_BIN:-/opt/conda/bin/python}" +ILUVATAR_PYTHON_BIN="${ILUVATAR_PYTHON_BIN:-python3}" +MTHREADS_PYTHON_BIN="${MTHREADS_PYTHON_BIN:-python3}" + +info() { echo "[INFO] $1"; } +die() { echo "[ERROR] $1" >&2; exit 1; } + +show_help() { + cat </dev/null 2>&1; then + uv run python -m app.utils.download_models --export-dir "$model_export_dir" + else + python -m app.utils.download_models --export-dir "$model_export_dir" + fi + ) + + info "压缩模型目录: $(basename "$model_archive")" + tar -C "$bundle_dir" -czf "$model_archive" models + rm -rf "$model_export_dir" +} + +prompt_build_type() { + local choice + echo "请选择离线交付类型:" + echo " 1) GPU" + echo " 2) CPU" + echo " 3) MetaX GPU" + echo " 4) Iluvatar GPU" + echo " 5) Moore Threads GPU" + echo " 6) ALL" + read -r -p "请输入选项 [6]: " choice + choice="${choice:-6}" + case "$choice" in + 1) BUILD_TYPE="gpu" ;; + 2) BUILD_TYPE="cpu" ;; + 3) BUILD_TYPE="metax" ;; + 4) BUILD_TYPE="iluvatar" ;; + 5) BUILD_TYPE="mthreads" ;; + 6) BUILD_TYPE="all" ;; + *) die "无效选项: $choice" ;; + esac +} + +validate() { + case "$BUILD_TYPE" in + cpu|gpu|metax|iluvatar|mthreads|all) ;; + "") if [[ -t 0 ]]; then prompt_build_type; else BUILD_TYPE="all"; fi ;; + *) die "不支持的构建类型: ${BUILD_TYPE}" ;; + esac + + if [[ "$BUILD_TYPE" == "metax" && -z "$METAX_BASE_IMAGE" ]]; then + die "--type metax 需要指定 --metax-base，值为已 docker load/pull 的沐曦官方 vLLM 镜像" + fi + if [[ "$BUILD_TYPE" == "iluvatar" && -z "$ILUVATAR_BASE_IMAGE" ]]; then + die "--type iluvatar 需要指定 --iluvatar-base，值为已 docker load/pull 的天数官方 vLLM 镜像" + fi + if [[ "$BUILD_TYPE" == "mthreads" && -z "$MTHREADS_BASE_IMAGE" ]]; then + die "--type mthreads 需要指定 --mthreads-base，值为已 docker load/pull 的摩尔线程官方 vLLM 镜像" + fi + + VERSION="${VERSION:-$TIMESTAMP}" +} + +export_compressor() { + command -v pigz >/dev/null 2>&1 && echo "pigz -f" || echo "gzip -f" +} + +build_and_export_image() { + local target="$1" + local dockerfile="$2" + local tag="$3" + local bundle_dir="$4" + local tar_path="${bundle_dir}/${IMAGE_NAME}-${target}-${VERSION}-amd64.tar" + + info "构建 ${target} 镜像: ${tag}" + ( + cd "$PROJECT_ROOT" + case "$target" in + metax) + docker build -f "$dockerfile" -t "$tag" \ + --build-arg "METAX_BASE_IMAGE=${METAX_BASE_IMAGE}" \ + --build-arg "PYTHON_BIN=${METAX_PYTHON_BIN}" . + ;; + iluvatar) + docker build -f "$dockerfile" -t "$tag" \ + --build-arg "ILUVATAR_BASE_IMAGE=${ILUVATAR_BASE_IMAGE}" \ + --build-arg "PYTHON_BIN=${ILUVATAR_PYTHON_BIN}" . + ;; + mthreads) + docker build -f "$dockerfile" -t "$tag" \ + --build-arg "MTHREADS_BASE_IMAGE=${MTHREADS_BASE_IMAGE}" \ + --build-arg "PYTHON_BIN=${MTHREADS_PYTHON_BIN}" . + ;; + *) + docker build -f "$dockerfile" -t "$tag" . + ;; + esac + ) + + info "导出 ${target} 镜像归档" + docker save -o "$tar_path" "$tag" + + info "压缩 ${target} 镜像归档" + $(export_compressor) "$tar_path" +} + +build_offline_images() { + local bundle_dir="$1" + local cpu_tag="${REGISTRY}/${IMAGE_NAME}:cpu-${VERSION}" + local gpu_tag="${REGISTRY}/${IMAGE_NAME}:gpu-${VERSION}" + local metax_tag="${REGISTRY}/${IMAGE_NAME}:metax-${VERSION}" + local iluvatar_tag="${REGISTRY}/${IMAGE_NAME}:iluvatar-${VERSION}" + local mthreads_tag="${REGISTRY}/${IMAGE_NAME}:mthreads-${VERSION}" + + case "$BUILD_TYPE" in + cpu) + build_and_export_image "cpu" "Dockerfile.cpu" "$cpu_tag" "$bundle_dir" + ;; + gpu) + build_and_export_image "gpu" "Dockerfile.gpu" "$gpu_tag" "$bundle_dir" + ;; + metax) + build_and_export_image "metax" "Dockerfile.metax" "$metax_tag" "$bundle_dir" + ;; + iluvatar) + build_and_export_image "iluvatar" "Dockerfile.iluvatar" "$iluvatar_tag" "$bundle_dir" + ;; + mthreads) + build_and_export_image "mthreads" "Dockerfile.mthreads" "$mthreads_tag" "$bundle_dir" + ;; + all) + build_and_export_image "cpu" "Dockerfile.cpu" "$cpu_tag" "$bundle_dir" + build_and_export_image "gpu" "Dockerfile.gpu" "$gpu_tag" "$bundle_dir" + ;; + esac +} + +append_bundle_image_env() { + local bundle_dir="$1" + local env_file="${bundle_dir}/.env.example" + local image_tag="" + local note="" + + case "$BUILD_TYPE" in + gpu) + image_tag="${REGISTRY}/${IMAGE_NAME}:gpu-${VERSION}" + note="GPU" + ;; + metax) + image_tag="${REGISTRY}/${IMAGE_NAME}:metax-${VERSION}" + note="MetaX GPU" + ;; + iluvatar) + image_tag="${REGISTRY}/${IMAGE_NAME}:iluvatar-${VERSION}" + note="Iluvatar GPU" + ;; + mthreads) + image_tag="${REGISTRY}/${IMAGE_NAME}:mthreads-${VERSION}" + note="Moore Threads GPU" + ;; + cpu) + image_tag="${REGISTRY}/${IMAGE_NAME}:cpu-${VERSION}" + note="CPU" + ;; + all) + image_tag="${REGISTRY}/${IMAGE_NAME}:gpu-${VERSION}" + note="GPU by default; switch to ${REGISTRY}/${IMAGE_NAME}:cpu-${VERSION} when using docker-compose-cpu.yml" + ;; + esac + + cat >> "$env_file" < "${bundle_dir}/BUNDLE_INFO.txt" <> "${bundle_dir}/BUNDLE_INFO.txt" + done + echo "Compose Files:" >> "${bundle_dir}/BUNDLE_INFO.txt" + while IFS= read -r compose_file; do + [[ -n "$compose_file" ]] || continue + echo " ${compose_file}" >> "${bundle_dir}/BUNDLE_INFO.txt" + done < <(bundle_compose_files) + cat >> "${bundle_dir}/BUNDLE_INFO.txt" <> "${bundle_dir}/BUNDLE_INFO.txt" < "${bundle_dir}/README.md" <=13.9,<14", + "setuptools>=70.0.0,<81", + "torch==2.11.0 ; sys_platform == 'linux'", + "torchaudio==2.11.0 ; sys_platform == 'linux'", + "torchvision==0.26.0 ; sys_platform == 'linux'", + "transformers>=4.56.0,<5", + "vllm==0.20.0 ; sys_platform == 'linux'", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = [] + +[tool.uv] +package = false + +[tool.uv.sources] +torch = [ + { index = "pytorch-cu130", marker = "platform_system == 'Linux'" }, +] +torchaudio = [ + { index = "pytorch-cu130", marker = "platform_system == 'Linux'" }, +] +torchvision = [ + { index = "pytorch-cu130", marker = "platform_system == 'Linux'" }, +] + +[[tool.uv.index]] +name = "pytorch-cu130" +url = "https://download.pytorch.org/whl/cu130" +explicit = true diff --git a/pyrightconfig.json b/pyrightconfig.json new file mode 100644 index 0000000..89e2d90 --- /dev/null +++ b/pyrightconfig.json @@ -0,0 +1,14 @@ +{ + "include": [ + "app", + "start.py" + ], + "exclude": [ + "**/__pycache__", + "tests", + "scripts", + "video" + ], + "venvPath": ".", + "venv": ".venv" +} diff --git a/scripts/README.md b/scripts/README.md new file mode 100644 index 0000000..f6e1c68 --- /dev/null +++ b/scripts/README.md @@ -0,0 +1,354 @@ +# 脚本说明 + +## 环境同步脚本 + +## 厂商 GPU 镜像融合打包 + +NVIDIA 镜像由项目 `Dockerfile.gpu` 完整构建；沐曦、天数、摩尔线程等国产 GPU 推荐先导入厂商官方 vLLM 镜像，再把本项目注入进去生成融合镜像： + +```bash +docker load -i metax-vllm-official.tar +docker images | grep -i -E 'metax|maca|vllm' + +./scripts/package_vendor_gpu_image.sh \ + --vendor metax \ + --base-image <沐曦官方vLLM镜像名> \ + -v n260-3.7.0.38 + +docker pull registry.iluvatar.com.cn:10443/customer/sz/vllm0.17.0-4.4.0-x86:v5 +./scripts/package_vendor_gpu_image.sh \ + --vendor iluvatar \ + --base-image registry.iluvatar.com.cn:10443/customer/sz/vllm0.17.0-4.4.0-x86:v5 \ + -v vllm0.17.0-4.4.0-v5 + +docker pull registry.mthreads.com/presale/devtech/vllm_musa:s4000_4.3.5_d0519 +./scripts/package_vendor_gpu_image.sh \ + --vendor mthreads \ + --base-image registry.mthreads.com/presale/devtech/vllm_musa:s4000_4.3.5_d0519 \ + -v s4000_4.3.5_d0519 +``` + +沐曦 GPU 的离线交付流程见 `docs/metax_offline_deployment.md`。 +天数 GPU 的离线交付流程见 `docs/iluvatar_offline_deployment.md`。 +摩尔线程 GPU 的离线交付流程见 `docs/mthreads_offline_deployment.md`。 + +输出形如： + +```text +build-file/qwen3-asr-metax-n260-3.7.0.38-amd64.tar.gz +``` + +这个压缩包就是可交付给客户的融合镜像。 + +| 脚本 | 说明 | +|------|------| +| `./scripts/sync_gpu_env.sh` | 同步根目录 NVIDIA CUDA/vLLM 环境 | +| `./scripts/sync_metax_env.sh` | 同步沐曦 MACA 环境；公共依赖来自 `environments/metax`，可选 GPU 栈默认按 `--no-deps` 从沐曦 PyPI 源安装 | +| `./scripts/sync_iluvatar_env.sh` | 同步天数环境公共依赖；GPU 栈使用天数官方 vLLM 镜像或匹配安装包 | +| `./scripts/sync_mthreads_env.sh` | 同步摩尔线程环境公共依赖；GPU 栈使用摩尔线程官方 MUSA vLLM 镜像或匹配安装包 | +| `./scripts/sync_cpu_env.sh` | 同步 CPU Rust 环境 | +| `./scripts/sync_accel_env.sh` | 自动识别 `mx-smi` / `ixsmi` / `mthreads-gmi` / `nvidia-smi` 并调用对应同步脚本 | + +沐曦包版本可通过环境变量覆盖： + +```bash +METAX_TORCH_SPEC='torch==x.y.z' \ +METAX_TORCHAUDIO_SPEC='torchaudio==x.y.z' \ +METAX_TORCHVISION_SPEC='torchvision==x.y.z' \ +METAX_VLLM_SPEC='vllm==x.y.z' \ +./scripts/sync_metax_env.sh +``` + +沐曦 GPU 栈默认使用 `--no-deps` 且只访问沐曦 PyPI 源，避免回退到 PyPI 后拉取 NVIDIA CUDA 包。 +只有确认沐曦源中包含完整 GPU 栈依赖时，才设置： + +```bash +METAX_INSTALL_GPU_DEPS=true ./scripts/sync_metax_env.sh +``` + +查看沐曦源中的包版本： + +```bash +./scripts/sync_metax_env.sh --list-versions torch +``` + +## RMS 音频分析工具 + +用于分析音频文件的 RMS 能量时序，帮助确定远场声音过滤的最佳阈值。 + +## 功能特性 + +- ✅ 支持多种音频格式 (WAV, MP3, FLAC 等) +- ✅ 支持立体声、左声道、右声道选择 +- ✅ 生成 RMS 时序图和分布直方图 +- ✅ 详细的统计分析和阈值建议 +- ✅ 可自定义分块大小（默认 240ms，与流式 ASR 一致） + +## 安装依赖 + +```bash +./scripts/sync_cpu_env.sh +``` + +## 使用方法 + +### 基础用法 + +```bash +# 分析立体声音频（默认） +.venv/bin/python scripts/analyze_audio_rms.py audio.wav + +# 仅分析左声道 +.venv/bin/python scripts/analyze_audio_rms.py audio.wav --channel left + +# 仅分析右声道 +.venv/bin/python scripts/analyze_audio_rms.py audio.wav --channel right +``` + +### 高级用法 + +```bash +# 自定义阈值 +.venv/bin/python scripts/analyze_audio_rms.py audio.wav --threshold 0.015 + +# 自定义分块大小（例如 160ms） +.venv/bin/python scripts/analyze_audio_rms.py audio.wav --chunk-size 160 + +# 保存图表到文件 +.venv/bin/python scripts/analyze_audio_rms.py audio.wav --output analysis.png + +# 仅输出统计信息，不显示图表 +.venv/bin/python scripts/analyze_audio_rms.py audio.wav --no-plot +``` + +### 完整示例 + +```bash +# 分析右声道，使用 0.015 阈值，保存图表 +.venv/bin/python scripts/analyze_audio_rms.py recording.wav \ + --channel right \ + --threshold 0.015 \ + --output rms_analysis.png +``` + +## 参数说明 + +| 参数 | 类型 | 默认值 | 说明 | +|------|------|--------|------| +| `audio_file` | 必需 | - | 音频文件路径 | +| `--channel` | 可选 | `stereo` | 声道选择: `stereo`, `left`, `right` | +| `--threshold` | 可选 | `0.01` | RMS 能量阈值 | +| `--chunk-size` | 可选 | `240` | 分块大小(毫秒) | +| `--output` | 可选 | - | 保存图表的路径 | +| `--no-plot` | 标志 | - | 不显示图表，仅输出统计 | + +## 输出说明 + +### 统计信息 + +脚本会输出以下统计信息： + +1. **基础统计** + - 最小值、最大值、平均值、中位数、标准差 + +2. **百分位数** + - P10, P25, P50, P75, P90, P95, P99 + +3. **阈值分析** + - 超过/低于阈值的帧数和百分比 + +4. **建议的阈值** + - 保守模式（高灵敏度）：P10 + - 宽松模式（推荐）：P25 + - 严格模式（低误触）：平均值的 50% + +### 可视化图表 + +生成两个图表： + +1. **RMS 时序图** + - 显示整个音频的 RMS 能量变化 + - 绿色区域：近场音频（>= 阈值） + - 红色区域：远场音频（< 阈值） + - 红色虚线：当前阈值 + +2. **RMS 分布直方图** + - 显示 RMS 值的分布情况 + - 红色虚线：当前阈值 + - 橙色虚线：平均值 + - 绿色虚线：中位数 + +## 使用场景 + +### 1. 确定初始阈值 + +录制一段包含近场说话和远场环境音的测试音频，然后： + +```bash +.venv/bin/python scripts/analyze_audio_rms.py test_audio.wav +``` + +查看**建议的阈值**，选择合适的模式。 + +### 2. 对比不同声道 + +如果使用立体声麦克风，可以对比左右声道： + +```bash +.venv/bin/python scripts/analyze_audio_rms.py audio.wav --channel left --output left.png +.venv/bin/python scripts/analyze_audio_rms.py audio.wav --channel right --output right.png +``` + +### 3. 验证阈值效果 + +使用自定义阈值查看过滤效果： + +```bash +# 测试宽松模式 +.venv/bin/python scripts/analyze_audio_rms.py audio.wav --threshold 0.01 + +# 测试严格模式 +.venv/bin/python scripts/analyze_audio_rms.py audio.wav --threshold 0.015 + +# 测试保守模式 +.venv/bin/python scripts/analyze_audio_rms.py audio.wav --threshold 0.005 +``` + +### 4. 批量分析 + +如需批量分析多个文件，可以使用简单的循环： + +```bash +#!/bin/bash +for file in recordings/*.wav; do + echo "Analyzing: $file" + .venv/bin/python scripts/analyze_audio_rms.py "$file" \ + --threshold 0.01 \ + --no-plot \ + --output "analysis/$(basename "$file" .wav).png" +done +``` + +## 输出示例 + +``` +============================================================== +音频 RMS 时序分析工具 +============================================================== + +📁 文件: test_audio.wav +🎚️ 声道: stereo +📊 分块大小: 240ms +🎯 阈值: 0.010000 + +正在加载音频... +✓ 使用立体声（双声道平均） +✓ 采样率: 16000 Hz +✓ 时长: 30.50 秒 +✓ 样本数: 488000 + +正在分析 RMS 时序 (分块大小: 240ms)... +✓ 分析了 127 个音频块 + +============================================================== +RMS 统计分析 +============================================================== + +📊 基础统计: + - 最小值: 0.000234 + - 最大值: 0.085432 + - 平均值: 0.012567 + - 中位数: 0.009234 + - 标准差: 0.015678 + +📈 百分位数: + - P10: 0.002345 + - P25: 0.005678 + - P50: 0.009234 + - P75: 0.015432 + - P90: 0.035678 + - P95: 0.045678 + - P99: 0.075432 + +🎯 阈值分析 (当前阈值: 0.010000): + - 超过阈值的帧数: 65 (51.2%) + - 低于阈值的帧数: 62 (48.8%) + +💡 建议的阈值范围: + - 保守模式 (高灵敏度): 0.002345 (P10) + - 宽松模式 (推荐): 0.005678 (P25) + - 严格模式 (低误触): 0.006284 (平均值的50%) +============================================================== +``` + +## 调优建议 + +1. **录制测试音频** + - 包含正常说话（近场） + - 包含远处说话或电视声音（远场） + - 包含安静时刻（环境音） + +2. **分析并选择阈值** + - 查看时序图，观察近场和远场的 RMS 差异 + - 查看分布直方图，找到明显的分界点 + - 参考建议的阈值范围 + +3. **验证效果** + - 在实际应用中测试选定的阈值 + - 如果误触发过多，提高阈值 + - 如果正常说话被过滤，降低阈值 + +4. **微调** + - 宽松模式（0.01）适合大多数场景 + - 嘈杂环境使用严格模式（0.015） + - 安静环境使用保守模式（0.005） + +## 故障排查 + +### 问题：无法加载音频文件 + +```bash +# 安装必要的库 +./scripts/sync_cpu_env.sh + +# 对于 MP3 文件，可能还需要 +./scripts/sync_cpu_env.sh +``` + +### 问题：中文显示乱码 + +脚本已配置中文字体，但如果仍然显示乱码： + +1. macOS: 会自动使用 'Arial Unicode MS' +2. Windows: 会自动使用 'SimHei' +3. Linux: 需要安装中文字体 + +### 问题：图表不显示 + +```bash +# 后台运行或 SSH 连接时使用 +python scripts/analyze_audio_rms.py audio.wav --no-plot --output analysis.png +``` + +## 与远场过滤功能的关系 + +此工具使用与 `app/utils/audio_filter.py` 相同的 RMS 计算方法，确保分析结果与实际运行时的行为一致。 + +确定阈值后，在配置文件中设置： + +```bash +# docker-compose.yml +environment: + - ASR_NEARFIELD_RMS_THRESHOLD=0.01 # 使用分析得出的阈值 +``` + +或在 `.env` 文件中： + +```bash +ASR_NEARFIELD_RMS_THRESHOLD=0.01 +``` + +## 相关文档 + +- [远场过滤功能文档](../docs/nearfield_filter.md) +- [部署配置指南](../docs/deployment.md) diff --git a/scripts/analyze_audio_rms.py b/scripts/analyze_audio_rms.py new file mode 100644 index 0000000..160d7a9 --- /dev/null +++ b/scripts/analyze_audio_rms.py @@ -0,0 +1,328 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +音频RMS时序分析工具 + +用于分析音频���件的RMS能量，帮助确定远场过滤的阈值。 +支持立体声、左声道、右声道选择。 +""" + +import argparse +import numpy as np +import matplotlib.pyplot as plt +from pathlib import Path +import sys +from typing import Optional + +# 设置中文显示 +plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans'] +plt.rcParams['axes.unicode_minus'] = False + + +def load_audio(file_path: str, channel: str = 'stereo') -> tuple: + """加载音频文件 + + Args: + file_path: 音频文件路径 + channel: 声道选择 ('stereo', 'left', 'right') + + Returns: + (audio_data, sample_rate): 音频数据和采样率 + """ + file_ext = Path(file_path).suffix.lower() + + if file_ext == '.wav': + import wave + with wave.open(file_path, 'rb') as wav_file: + sample_rate = wav_file.getframerate() + n_channels = wav_file.getnchannels() + sample_width = wav_file.getsampwidth() + n_frames = wav_file.getnframes() + + # 读取音频数据 + audio_bytes = wav_file.readframes(n_frames) + + # 转换为numpy数组 + if sample_width == 2: # 16-bit + audio_int = np.frombuffer(audio_bytes, dtype=np.int16) + elif sample_width == 4: # 32-bit + audio_int = np.frombuffer(audio_bytes, dtype=np.int32) + else: + raise ValueError(f"不支持的采样位深: {sample_width}") + + # 转换为float32 (-1.0 to 1.0) + audio_float = audio_int.astype(np.float32) / (2 ** (8 * sample_width - 1)) + + # 处理多声道 + if n_channels > 1: + audio_float = audio_float.reshape(-1, n_channels) + if channel == 'left': + audio_float = audio_float[:, 0] + print(f"✓ 使用左声道") + elif channel == 'right': + audio_float = audio_float[:, 1] + print(f"✓ 使用右声道") + else: # stereo - 平均 + audio_float = np.mean(audio_float, axis=1) + print(f"✓ 使用立体声（双声道平均）") + else: + print(f"✓ 使用单声道") + + return audio_float, sample_rate + + else: + # 尝试使用 soundfile 或 librosa + try: + import soundfile as sf + audio_float, sample_rate = sf.read(file_path) + + if len(audio_float.shape) > 1: # 多声道 + if channel == 'left': + audio_float = audio_float[:, 0] + print(f"✓ 使用左声道") + elif channel == 'right': + audio_float = audio_float[:, 1] + print(f"✓ 使用右声道") + else: + audio_float = np.mean(audio_float, axis=1) + print(f"✓ 使用立体声（双声道平均）") + else: + print(f"✓ 使用单声道") + + return audio_float, sample_rate + + except ImportError: + print("错误: 请先运行 ./scripts/sync_cpu_env.sh 安装 soundfile 依赖") + sys.exit(1) + + +def calculate_rms_energy(audio_array: np.ndarray) -> float: + """计算音频RMS能量 + + Args: + audio_array: float32音频数组，范围-1.0到1.0 + + Returns: + RMS能量值 + """ + if len(audio_array) == 0: + return 0.0 + return float(np.sqrt(np.mean(audio_array ** 2))) + + +def analyze_rms_timeline(audio_data: np.ndarray, sample_rate: int, + chunk_size_ms: int = 240) -> tuple: + """分析音频的RMS时序 + + Args: + audio_data: 音频数据 + sample_rate: 采样率 + chunk_size_ms: 分块大小（毫秒） + + Returns: + (time_points, rms_values): 时间点和对应的RMS值 + """ + chunk_samples = int(sample_rate * chunk_size_ms / 1000) + n_chunks = len(audio_data) // chunk_samples + + time_points = [] + rms_values = [] + + for i in range(n_chunks): + start_idx = i * chunk_samples + end_idx = start_idx + chunk_samples + chunk = audio_data[start_idx:end_idx] + + rms = calculate_rms_energy(chunk) + time_s = (start_idx + chunk_samples / 2) / sample_rate + + time_points.append(time_s) + rms_values.append(rms) + + return np.array(time_points), np.array(rms_values) + + +def print_statistics(rms_values: np.ndarray, threshold: float = 0.01): + """打印RMS统计信息 + + Args: + rms_values: RMS值数组 + threshold: 阈值 + """ + print("\n" + "="*60) + print("RMS 统计分析") + print("="*60) + + print(f"\n📊 基础统计:") + print(f" - 最小值: {np.min(rms_values):.6f}") + print(f" - 最大值: {np.max(rms_values):.6f}") + print(f" - 平均值: {np.mean(rms_values):.6f}") + print(f" - 中位数: {np.median(rms_values):.6f}") + print(f" - 标准差: {np.std(rms_values):.6f}") + + print(f"\n📈 百分位数:") + for p in [10, 25, 50, 75, 90, 95, 99]: + value = np.percentile(rms_values, p) + print(f" - P{p:2d}: {value:.6f}") + + print(f"\n🎯 阈值分析 (当前阈值: {threshold:.6f}):") + above_threshold = np.sum(rms_values >= threshold) + below_threshold = np.sum(rms_values < threshold) + total = len(rms_values) + + print(f" - 超过阈值的帧数: {above_threshold} ({above_threshold/total*100:.1f}%)") + print(f" - 低于阈值的帧数: {below_threshold} ({below_threshold/total*100:.1f}%)") + + print(f"\n💡 建议的阈值范围:") + # 基于非零RMS值的统计 + non_zero_rms = rms_values[rms_values > 0.001] + if len(non_zero_rms) > 0: + p10 = np.percentile(non_zero_rms, 10) + p25 = np.percentile(non_zero_rms, 25) + mean = np.mean(non_zero_rms) + + print(f" - 保守模式 (高灵敏度): {p10:.6f} (P10)") + print(f" - 宽松模式 (推荐): {p25:.6f} (P25)") + print(f" - 严格模式 (低误触): {mean*0.5:.6f} (平均值的50%)") + + print("="*60 + "\n") + + +def plot_rms_timeline(time_points: np.ndarray, rms_values: np.ndarray, + threshold: float = 0.01, save_path: Optional[str] = None): + """绘制RMS时序图 + + Args: + time_points: 时间点数组 + rms_values: RMS值数组 + threshold: 阈值线 + save_path: 保存路径 + """ + _, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 10)) + + # 上图: RMS时序 + ax1.plot(time_points, rms_values, linewidth=1, label='RMS Energy', color='steelblue') + ax1.axhline(y=threshold, color='red', linestyle='--', linewidth=2, + label=f'阈值 = {threshold:.6f}') + + # 标记超过阈值的区域 + above_threshold = rms_values >= threshold + ax1.fill_between(time_points, 0, rms_values, where=above_threshold, + alpha=0.3, color='green', label='近场音频 (>= 阈值)') + ax1.fill_between(time_points, 0, rms_values, where=~above_threshold, + alpha=0.3, color='red', label='远场音频 (< 阈值)') + + ax1.set_xlabel('时间 (秒)', fontsize=12) + ax1.set_ylabel('RMS 能量', fontsize=12) + ax1.set_title('音频 RMS 能量时序分析', fontsize=14, fontweight='bold') + ax1.legend(loc='upper right', fontsize=10) + ax1.grid(True, alpha=0.3) + ax1.set_ylim(bottom=0) + + # 下图: RMS分布直方图 + ax2.hist(rms_values, bins=100, color='steelblue', alpha=0.7, edgecolor='black') + ax2.axvline(x=threshold, color='red', linestyle='--', linewidth=2, + label=f'阈值 = {threshold:.6f}') + ax2.axvline(x=np.mean(rms_values), color='orange', linestyle=':', linewidth=2, + label=f'平均值 = {np.mean(rms_values):.6f}') + ax2.axvline(x=np.median(rms_values), color='green', linestyle=':', linewidth=2, + label=f'中位数 = {np.median(rms_values):.6f}') + + ax2.set_xlabel('RMS 能量', fontsize=12) + ax2.set_ylabel('帧数', fontsize=12) + ax2.set_title('RMS 能量分布直方图', fontsize=14, fontweight='bold') + ax2.legend(loc='upper right', fontsize=10) + ax2.grid(True, alpha=0.3, axis='y') + + plt.tight_layout() + + if save_path: + plt.savefig(save_path, dpi=150, bbox_inches='tight') + print(f"✓ 图表已保存到: {save_path}") + + plt.show() + + +def main(): + parser = argparse.ArgumentParser( + description='音频RMS时序分析工具 - 帮助确定远场过滤阈值', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +示例用法: + # 分析立体声音频（默认） + python analyze_audio_rms.py audio.wav + + # 仅分析左声道 + python analyze_audio_rms.py audio.wav --channel left + + # 仅分析右声道 + python analyze_audio_rms.py audio.wav --channel right + + # 自定义阈值和分块大小 + python analyze_audio_rms.py audio.wav --threshold 0.015 --chunk-size 160 + + # 保存图表 + python analyze_audio_rms.py audio.wav --output rms_analysis.png + """ + ) + + parser.add_argument('audio_file', type=str, + help='音频文件路径 (支持 WAV, MP3, FLAC 等格式)') + parser.add_argument('--channel', type=str, choices=['stereo', 'left', 'right'], + default='stereo', + help='声道选择: stereo(立体声平均), left(左声道), right(右声道) [默认: stereo]') + parser.add_argument('--threshold', type=float, default=0.01, + help='RMS能量阈值 [默认: 0.01]') + parser.add_argument('--chunk-size', type=int, default=240, + help='分块大小(毫秒) [默认: 240ms，与流式ASR一致]') + parser.add_argument('--output', '-o', type=str, default=None, + help='保存图表的路径 (例如: output.png)') + parser.add_argument('--no-plot', action='store_true', + help='不显示图表，仅输出统计信息') + + args = parser.parse_args() + + # 检查文件是否存在 + if not Path(args.audio_file).exists(): + print(f"错误: 文件不存在: {args.audio_file}") + sys.exit(1) + + print("="*60) + print("音频 RMS 时序分析工具") + print("="*60) + print(f"\n📁 文件: {args.audio_file}") + print(f"🎚️ 声道: {args.channel}") + print(f"📊 分块大小: {args.chunk_size}ms") + print(f"🎯 阈值: {args.threshold:.6f}") + print() + + # 加载音频 + print("正在加载音频...") + audio_data, sample_rate = load_audio(args.audio_file, args.channel) + duration = len(audio_data) / sample_rate + + print(f"✓ 采样率: {sample_rate} Hz") + print(f"✓ 时长: {duration:.2f} 秒") + print(f"✓ 样本数: {len(audio_data)}") + + # 分析RMS时序 + print(f"\n正在分析 RMS 时序 (分块大小: {args.chunk_size}ms)...") + time_points, rms_values = analyze_rms_timeline(audio_data, sample_rate, args.chunk_size) + print(f"✓ 分析了 {len(rms_values)} 个音频块") + + # 打印统计信息 + print_statistics(rms_values, args.threshold) + + # 绘制图表 + if not args.no_plot: + print("正在生成图表...") + plot_rms_timeline(time_points, rms_values, args.threshold, args.output) + elif args.output: + print("正在保存图表...") + plot_rms_timeline(time_points, rms_values, args.threshold, args.output) + # 关闭显示窗口 + plt.close() + + +if __name__ == '__main__': + main() diff --git a/scripts/benchmark/README.md b/scripts/benchmark/README.md new file mode 100644 index 0000000..2a5548e --- /dev/null +++ b/scripts/benchmark/README.md @@ -0,0 +1,198 @@ +# Qwen3-ASR 并发性能测试脚本 + +测试 ASR/TTS WebSocket 服务在不同并发级别下的性能表现。 + +## 依赖 + +```bash +./scripts/sync_cpu_env.sh +``` + +## 快速开始 + +### 1. 启动服务 + +```bash +# 在项目根目录 +source .venv/bin/activate +python start.py +``` + +### 2. 运行测试 + +```bash +# 完整测试 (ASR + TTS) +.venv/bin/python -m scripts.benchmark.run --audio-file /path/to/audio.wav + +# 仅测试 TTS (无需音频文件) +.venv/bin/python -m scripts.benchmark.run --test-type tts + +# 仅测试 ASR +.venv/bin/python -m scripts.benchmark.run --audio-file /path/to/audio.wav --test-type asr + +# Qwen Rust CPU 固定配置跑测（固定 VAD 分段） +.venv/bin/python -m scripts.benchmark.qwen_rust_sensitivity \ + --audio-file /path/to/audio.wav +``` + +## 命令行参数 + +| 参数 | 默认值 | 说明 | +|------|--------|------| +| `--host` | localhost | 服务器主机名 | +| `--port` | 8000 | 服务器端口 | +| `--audio-file` | - | ASR 测试音频文件路径 (测试 ASR 时必需) | +| `--test-type` | both | 测试类型: `asr` / `tts` / `both` | +| `--concurrency` | 5 10 20 50 | 并发级别列表 | +| `--output` | ./benchmark_results | 报告输出目录 | +| `--timeout` | 120 | 请求超时时间 (秒) | +| `--voice` | 中文女 | TTS 测试音色 | + +## Qwen Rust CPU 固定配置跑测 + +用于对 vendored Rust backend 做固定 workload 的端到端对比： + +- 同一组 VAD 分段 +- Rust ASR +- Rust forced align +- 跑当前环境变量指定的 Rust 并发配置 + +示例： + +```bash +# 直接跑当前配置 +.venv/bin/python -m scripts.benchmark.qwen_rust_sensitivity \ + --audio-file temp/podcast_demo_10min_16k.wav \ + --json-out temp/qwen_rust_runtime_config.json + +# 通过环境变量切换不同并发配置后再跑 +QWEN_RUST_CPU_WORKERS=8 \ +QWEN_RUST_ASR_CONCURRENCY=8 \ +QWEN_RUST_ALIGN_CONCURRENCY=4 \ +.venv/bin/python -m scripts.benchmark.qwen_rust_sensitivity \ + --audio-file temp/podcast_demo_10min_16k.wav \ + --json-out temp/qwen_rust_runtime_config.json +``` + +说明： + +- 脚本只跑当前一组配置 +- 并发度由 `QWEN_RUST_CPU_WORKERS`、`QWEN_RUST_ASR_CONCURRENCY`、`QWEN_RUST_ALIGN_CONCURRENCY` 控制 +- 跑完后会输出一条进度日志，并写入 `--json-out` +- 如果传了 `--json-out`，脚本还会自动生成同名 `.md` 中文对比报告 +- 也可以显式指定 Markdown 路径： + +```bash +.venv/bin/python -m scripts.benchmark.qwen_rust_sensitivity \ + --audio-file temp/podcast_demo_10min_16k.wav \ + --json-out temp/qwen_rust_runtime_config.json \ + --markdown-out temp/qwen_rust_runtime_config_report.md +``` + +### TTS 流式模拟配置 (config.py) + +TTS 测试模拟 LLM 流式输出场景，按标点符号（逗号、句号、顿号等）分割文本逐步发送： + +| 配置项 | 默认值 | 说明 | +|--------|--------|------| +| `tts_chunk_interval` | 0.05 | 发送间隔秒数 (模拟 LLM 生成速度) | + +## 使用示例 + +```bash +# 自定义并发级别 +.venv/bin/python -m scripts.benchmark.run \ + --audio-file test.wav \ + --concurrency 5 10 20 50 100 + +# 连接远程服务器 +.venv/bin/python -m scripts.benchmark.run \ + --host 192.168.1.100 \ + --port 8000 \ + --test-type tts + +# 使用不同音色测试 TTS +.venv/bin/python -m scripts.benchmark.run \ + --test-type tts \ + --voice 中文男 +``` + +## 测试指标 + +### ASR 指标 +- **首次响应延迟**: 从开始到收到第一个识别结果的时间 +- **总处理时间**: 从开始到识别完成的总时间 +- **RTF**: 处理时间 / 音频时长 (小于 1.0 表示快于实时) + +### TTS 指标 +- **首包延迟**: 从发送文本到收到第一个音频块的时间 +- **总合成时间**: 从开始到合成完成的总时间 +- **RTF**: 合成时间 / 生成音频时长 + +### 统计维度 +每个指标计算: 平均值 (Avg)、P50、P95、P99、最大值 (Max) + +## 输出文件 + +测试完成后在输出目录生成: + +``` +benchmark_results/ +├── benchmark_report_20241202_143000.md # Markdown 报告 +├── first_latency_20241202_143000.png # 首次响应延迟图 +├── rtf_20241202_143000.png # RTF 对比图 +├── throughput_20241202_143000.png # 吞吐量图 +└── total_time_20241202_143000.png # 总时间图 +``` + +## 报告示例 + +```markdown +## ASR 性能测试结果 + +### 延迟指标 (毫秒) + +| 并发数 | 首次响应 (Avg) | 首次响应 (P95) | 总时间 (Avg) | 总时间 (P95) | +|--------|---------------|---------------|-------------|-------------| +| 5 | 245.3 | 312.5 | 62345.2 | 63521.4 | +| 10 | 289.7 | 425.3 | 63245.8 | 65123.6 | + +### RTF 和吞吐量 + +| 并发数 | RTF (Avg) | RTF (P95) | 吞吐量 (req/s) | 成功率 | +|--------|----------|----------|---------------|--------| +| 5 | 1.04 | 1.06 | 0.08 | 100% | +| 10 | 1.05 | 1.08 | 0.16 | 100% | +``` + +## 目录结构 + +``` +scripts/benchmark/ +├── run.py # 主入口脚本 +├── config.py # 测试配置 +├── clients/ +│ ├── base_client.py # WebSocket 客户端基类 +│ ├── asr_client.py # ASR 测试客户端 +│ └── tts_client.py # TTS 测试客户端 +├── metrics/ +│ ├── models.py # 指标数据类 +│ └── statistics.py # 统计计算 +├── reporters/ +│ ├── markdown_reporter.py # Markdown 报告生成 +│ └── chart_generator.py # 图表生成 +└── utils/ + ├── audio_utils.py # 音频文件处理 + └── text_generator.py # 测试文本生成 +``` + +## 注意事项 + +1. **ASR 测试需要音频文件**: 建议使用 1 分钟左右的音频，格式支持 wav/mp3 等常见格式 +2. **TTS 测试自动生成文本**: 使用内置的中文随机句子生成器，无需额外准备 +3. **TTS 模拟流式输入**: 测试会按标点符号（逗号、句号、顿号等）分割文本逐步发送，模拟 LLM 流式输出场景 +4. **并发测试会占用资源**: 高并发测试时请确保服务器有足够资源 +5. **RTF 解读**: + - RTF < 1.0: 处理速度快于实时，性能良好 + - RTF ≈ 1.0: 刚好实时处理 + - RTF > 1.0: 处理速度慢于实时，可能出现延迟累积 diff --git a/scripts/benchmark/__init__.py b/scripts/benchmark/__init__.py new file mode 100644 index 0000000..35afc3c --- /dev/null +++ b/scripts/benchmark/__init__.py @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- +""" +Qwen3-ASR 并发性能测试脚本 + +用于测试 ASR/TTS WebSocket 服务在不同并发级别下的性能表现。 +""" + +__version__ = "1.0.0" diff --git a/scripts/benchmark/clients/__init__.py b/scripts/benchmark/clients/__init__.py new file mode 100644 index 0000000..46938a9 --- /dev/null +++ b/scripts/benchmark/clients/__init__.py @@ -0,0 +1,6 @@ +# -*- coding: utf-8 -*- +from .base_client import BaseWebSocketClient +from .asr_client import ASRWebSocketClient +from .tts_client import TTSWebSocketClient + +__all__ = ["BaseWebSocketClient", "ASRWebSocketClient", "TTSWebSocketClient"] diff --git a/scripts/benchmark/clients/asr_client.py b/scripts/benchmark/clients/asr_client.py new file mode 100644 index 0000000..b053c73 --- /dev/null +++ b/scripts/benchmark/clients/asr_client.py @@ -0,0 +1,204 @@ +# -*- coding: utf-8 -*- +""" +ASR WebSocket 测试客户端 +""" + +import asyncio +import json +import time +import logging +from pathlib import Path +from typing import Optional + +from .base_client import BaseWebSocketClient +from ..metrics.models import ASRMetrics + +logger = logging.getLogger(__name__) + +# 协议常量 +ASR_NAMESPACE = "SpeechTranscriber" +MSG_START_TRANSCRIPTION = "StartTranscription" +MSG_STOP_TRANSCRIPTION = "StopTranscription" +MSG_TRANSCRIPTION_STARTED = "TranscriptionStarted" +MSG_TRANSCRIPTION_RESULT_CHANGED = "TranscriptionResultChanged" +MSG_SENTENCE_END = "SentenceEnd" +MSG_TRANSCRIPTION_COMPLETED = "TranscriptionCompleted" +MSG_TASK_FAILED = "TaskFailed" + + +class ASRWebSocketClient(BaseWebSocketClient): + """ASR WebSocket 测试客户端""" + + def __init__( + self, + ws_url: str, + audio_data: bytes, + audio_duration_ms: float, + sample_rate: int = 16000, + chunk_size: int = 9600, + timeout: float = 120.0, + save_result_dir: Optional[Path] = None, # 保存识别结果的目录 + ): + """ + 初始化 ASR 客户端 + + Args: + ws_url: WebSocket URL + audio_data: PCM 音频数据 + audio_duration_ms: 音频时长 (毫秒) + sample_rate: 采样率 + chunk_size: 每块采样数 + timeout: 超时时间 (秒) + save_result_dir: 保存识别结果的目录 + """ + super().__init__(ws_url, timeout) + self.audio_data = audio_data + self.audio_duration_ms = audio_duration_ms + self.sample_rate = sample_rate + self.chunk_size = chunk_size + self.chunk_bytes = chunk_size * 2 # 16-bit PCM + self.save_result_dir = save_result_dir + + async def run_test(self) -> ASRMetrics: + """ + 执行 ASR 测试 + + Returns: + ASR 测试指标 + """ + metrics = ASRMetrics( + request_id=self.task_id, + concurrency_level=0, # 由调用者设置 + start_time=time.perf_counter(), + audio_duration_ms=self.audio_duration_ms, + ) + + try: + await asyncio.wait_for( + self._run_asr_session(metrics), + timeout=self.timeout, + ) + metrics.success = True + except asyncio.TimeoutError: + metrics.error_message = "Timeout" + logger.warning(f"ASR 请求超时: {self.task_id}") + except Exception as e: + metrics.error_message = str(e) + logger.warning(f"ASR 请求失败: {self.task_id}, 错误: {e}") + finally: + await self.close() + + return metrics + + async def _run_asr_session(self, metrics: ASRMetrics) -> None: + """运行完整的 ASR 会话""" + await self.connect() + + # 1. 发送 StartTranscription + await self._send_start_transcription() + + # 2. 等待 TranscriptionStarted + await self.wait_for_message(MSG_TRANSCRIPTION_STARTED) + + # 3. 启动音频流式发送任务 + stream_task = asyncio.create_task(self._stream_audio()) + + # 4. 接收识别结果 + try: + await self._receive_results(metrics) + finally: + # 确保流式任务完成 + if not stream_task.done(): + stream_task.cancel() + try: + await stream_task + except asyncio.CancelledError: + pass + + async def _send_start_transcription(self) -> None: + """发送 StartTranscription 消息""" + message = { + "header": self._create_header(MSG_START_TRANSCRIPTION, ASR_NAMESPACE), + "payload": { + "format": "pcm", + "sample_rate": self.sample_rate, + "enable_intermediate_result": True, + "enable_punctuation_prediction": True, + "enable_inverse_text_normalization": True, + "max_sentence_silence": 800, + }, + } + await self.send_json(message) + + async def _stream_audio(self) -> None: + """流式发送音频数据""" + offset = 0 + chunk_duration = self.chunk_size / self.sample_rate # 秒 + + while offset < len(self.audio_data): + chunk = self.audio_data[offset : offset + self.chunk_bytes] + await self.send_bytes(chunk) + offset += self.chunk_bytes + + # 以接近实时的速度发送 (稍快一些) + await asyncio.sleep(chunk_duration * 0.5) + + # 发送 StopTranscription + await self._send_stop_transcription() + + async def _send_stop_transcription(self) -> None: + """发送 StopTranscription 消息""" + message = { + "header": self._create_header(MSG_STOP_TRANSCRIPTION, ASR_NAMESPACE), + } + await self.send_json(message) + + async def _receive_results(self, metrics: ASRMetrics) -> None: + """接收识别结果""" + while True: + response = await self.receive() + + if isinstance(response, str): + data = json.loads(response) + header = data.get("header", {}) + name = header.get("name", "") + payload = data.get("payload", {}) + + if name == MSG_TRANSCRIPTION_RESULT_CHANGED: + # 记录首次响应时间 + if metrics.first_result_time is None: + metrics.first_result_time = time.perf_counter() + + elif name == MSG_SENTENCE_END: + metrics.sentence_end_time = time.perf_counter() + # 更新识别结果 + result = payload.get("result", "") + if result: + metrics.result_text = result + + elif name == MSG_TRANSCRIPTION_COMPLETED: + metrics.complete_time = time.perf_counter() + # 保存识别结果 + if self.save_result_dir and metrics.result_text: + self._save_result(metrics.result_text) + break + + elif name == MSG_TASK_FAILED: + status_text = header.get("status_text", "Unknown error") + raise Exception(f"TaskFailed: {status_text}") + + def _save_result(self, result_text: str) -> None: + """保存识别结果到文本文件""" + if self.save_result_dir is None: + return + + try: + filename = f"{self.task_id[:8]}_{int(self.audio_duration_ms)}ms.txt" + filepath = self.save_result_dir / filename + + with open(filepath, 'w', encoding='utf-8') as f: + f.write(result_text) + + logger.debug(f"识别结果已保存: {filepath}") + except Exception as e: + logger.warning(f"保存识别结果失败: {e}") diff --git a/scripts/benchmark/clients/base_client.py b/scripts/benchmark/clients/base_client.py new file mode 100644 index 0000000..d95abfb --- /dev/null +++ b/scripts/benchmark/clients/base_client.py @@ -0,0 +1,131 @@ +# -*- coding: utf-8 -*- +""" +WebSocket 客户端基类 +""" + +import json +import uuid +import logging +from abc import ABC, abstractmethod +from typing import Optional, Any, Dict + +from websockets.legacy.client import connect, WebSocketClientProtocol # type: ignore + +logger = logging.getLogger(__name__) + + +class BaseWebSocketClient(ABC): + """WebSocket 测试客户端基类""" + + def __init__(self, ws_url: str, timeout: float = 120.0): + """ + 初始化客户端 + + Args: + ws_url: WebSocket URL + timeout: 超时时间 (秒) + """ + self.ws_url = ws_url + self.timeout = timeout + self.websocket: Optional[WebSocketClientProtocol] = None + self.task_id = self._generate_id() + + @staticmethod + def _generate_id() -> str: + """生成 32 位唯一 ID""" + return str(uuid.uuid4()).replace("-", "")[:32] + + async def connect(self) -> None: + """建立 WebSocket 连接""" + self.websocket = await connect( + self.ws_url, + ping_interval=None, + ping_timeout=None, + max_size=10 * 1024 * 1024, # 10MB + ) + + async def close(self) -> None: + """关闭 WebSocket 连接""" + if self.websocket: + try: + await self.websocket.close() + except Exception: + pass + self.websocket = None + + async def send_json(self, data: Dict[str, Any]) -> None: + """发送 JSON 消息""" + if self.websocket: + await self.websocket.send(json.dumps(data, ensure_ascii=False)) + + async def send_bytes(self, data: bytes) -> None: + """发送二进制数据""" + if self.websocket: + await self.websocket.send(data) + + async def receive(self) -> Any: + """接收消息 (JSON 或二进制)""" + if self.websocket: + return await self.websocket.recv() + return None + + async def receive_json(self) -> Optional[Dict[str, Any]]: + """接收 JSON 消息""" + data = await self.receive() + if isinstance(data, str): + return json.loads(data) + return None + + async def wait_for_message(self, expected_name: str) -> Dict[str, Any]: + """ + 等待指定名称的消息 + + Args: + expected_name: 期望的消息名称 + + Returns: + 消息数据 + + Raises: + Exception: 收到 TaskFailed 消息 + """ + while True: + response = await self.receive() + if isinstance(response, str): + data = json.loads(response) + header = data.get("header", {}) + name = header.get("name", "") + + if name == expected_name: + return data + elif name == "TaskFailed": + status_text = header.get("status_text", "Unknown error") + raise Exception(f"TaskFailed: {status_text}") + + def _create_header(self, name: str, namespace: str) -> Dict[str, Any]: + """ + 创建消息头部 + + Args: + name: 消息名称 + namespace: 命名空间 + + Returns: + 头部字典 + """ + return { + "message_id": self._generate_id(), + "task_id": self.task_id, + "namespace": namespace, + "name": name, + } + + @abstractmethod + async def run_test(self) -> Any: + """ + 执行测试 + + Returns: + 测试指标 + """ + pass diff --git a/scripts/benchmark/clients/tts_client.py b/scripts/benchmark/clients/tts_client.py new file mode 100644 index 0000000..0ef6075 --- /dev/null +++ b/scripts/benchmark/clients/tts_client.py @@ -0,0 +1,251 @@ +# -*- coding: utf-8 -*- +""" +TTS WebSocket 测试客户端 + +参考 realtime-llm/backend/cti_websocket_handler.py 中的调用方式。 +模拟 LLM 流式输出场景，按句子发送文本进行合成。 +""" + +import asyncio +import json +import time +import logging +import wave +from pathlib import Path +from typing import Optional + +from .base_client import BaseWebSocketClient +from ..metrics.models import TTSMetrics + +logger = logging.getLogger(__name__) + +# 协议常量 +TTS_NAMESPACE = "FlowingSpeechSynthesizer" +MSG_START_SYNTHESIS = "StartSynthesis" +MSG_RUN_SYNTHESIS = "RunSynthesis" +MSG_STOP_SYNTHESIS = "StopSynthesis" +MSG_SYNTHESIS_STARTED = "SynthesisStarted" +MSG_SENTENCE_BEGIN = "SentenceBegin" +MSG_SENTENCE_END = "SentenceEnd" +MSG_SYNTHESIS_COMPLETED = "SynthesisCompleted" +MSG_TASK_FAILED = "TaskFailed" + + +class TTSWebSocketClient(BaseWebSocketClient): + """TTS WebSocket 测试客户端 (模拟流式文本输入)""" + + def __init__( + self, + ws_url: str, + text: str, + voice: str = "中文女", + audio_format: str = "PCM", + sample_rate: int = 22050, + timeout: float = 120.0, + chunk_interval: float = 0.05, # 发送间隔 (秒)，模拟 LLM 生成速度 + debug: bool = False, # 调试模式 + save_audio_dir: Optional[Path] = None, # 保存音频的目录 + ): + super().__init__(ws_url, timeout) + self.text = text + self.voice = voice + self.audio_format = audio_format + self.sample_rate = sample_rate + self.chunk_interval = chunk_interval + self.debug = debug + self.save_audio_dir = save_audio_dir + self._audio_chunks = [] # 存储接收到的音频数据 + + def _log(self, msg: str): + """调试日志""" + if self.debug: + logger.info(f"[{self.task_id[:8]}] {msg}") + + async def run_test(self) -> TTSMetrics: + """执行 TTS 测试""" + metrics = TTSMetrics( + request_id=self.task_id, + concurrency_level=0, + start_time=time.perf_counter(), + text_length=len(self.text), + sample_rate=self.sample_rate, + ) + + try: + await asyncio.wait_for( + self._run_tts_session(metrics), + timeout=self.timeout, + ) + metrics.success = True + except asyncio.TimeoutError: + metrics.error_message = "Timeout" + logger.warning(f"TTS 请求超时: {self.task_id}") + except Exception as e: + metrics.error_message = str(e) + logger.warning(f"TTS 请求失败: {self.task_id}, 错误: {e}") + finally: + await self.close() + + return metrics + + async def _run_tts_session(self, metrics: TTSMetrics) -> None: + """运行完整的 TTS 会话""" + self._log(f"连接 {self.ws_url}") + await self.connect() + + # 用于同步的事件 + started_event = asyncio.Event() + completed_event = asyncio.Event() + error_message = None + + # 1. 发送 StartSynthesis + await self._send_start_synthesis() + + # 2. 启动接收任务 + async def receive_loop(): + nonlocal error_message + while True: + try: + response = await self.receive() + except Exception as e: + self._log(f"接收异常: {e}") + break + + if isinstance(response, bytes): + if metrics.first_chunk_time is None: + metrics.first_chunk_time = time.perf_counter() + metrics.audio_bytes_received += len(response) + # 收集音频数据用于保存 + if self.save_audio_dir: + self._audio_chunks.append(response) + self._log(f"← 收到音频: {len(response)} bytes") + + elif isinstance(response, str): + try: + data = json.loads(response) + header = data.get("header", {}) + name = header.get("name", "") + status = header.get("status", 0) + + self._log(f"← 收到事件: {name} (status={status})") + + if name == MSG_SYNTHESIS_STARTED: + started_event.set() + + elif name == MSG_SENTENCE_END: + metrics.sentence_end_time = time.perf_counter() + + elif name == MSG_SYNTHESIS_COMPLETED: + completed_event.set() + break + + elif name == MSG_TASK_FAILED: + status_text = header.get("status_text", "Unknown error") + error_message = f"TaskFailed: {status_text}" + self._log(f"← 错误: {status_text}") + completed_event.set() + break + + except json.JSONDecodeError: + pass + + receive_task = asyncio.create_task(receive_loop()) + + try: + # 3. 等待 SynthesisStarted + self._log("等待 SynthesisStarted...") + await asyncio.wait_for(started_event.wait(), timeout=10.0) + self._log("收到 SynthesisStarted") + + # 4. 发送文本 - 直接发送完整文本，不分割 + # 参考 CTI 客户端：发送完整句子而不是切分片段 + await self._send_run_synthesis(self.text) + + # 5. 发送 StopSynthesis + await self._send_stop_synthesis() + + # 6. 等待 SynthesisCompleted + self._log("等待 SynthesisCompleted...") + await completed_event.wait() + + if error_message: + raise Exception(error_message) + + metrics.complete_time = time.perf_counter() + self._log(f"完成! 收到 {metrics.audio_bytes_received} bytes 音频") + + # 保存音频文件 + if self.save_audio_dir and self._audio_chunks: + self._save_audio() + + finally: + if not receive_task.done(): + receive_task.cancel() + try: + await receive_task + except asyncio.CancelledError: + pass + + async def _send_start_synthesis(self) -> None: + """发送 StartSynthesis 消息""" + message = { + "header": self._create_header(MSG_START_SYNTHESIS, TTS_NAMESPACE), + "payload": { + "voice": self.voice, + "format": self.audio_format, + "sample_rate": self.sample_rate, + "volume": 50, + "speech_rate": 0, + "pitch_rate": 0, + "platform": "python", + }, + } + self._log(f"→ 发送 StartSynthesis (voice={self.voice}, format={self.audio_format})") + await self.send_json(message) + + async def _send_run_synthesis(self, text: str) -> None: + """发送 RunSynthesis 消息""" + message = { + "header": self._create_header(MSG_RUN_SYNTHESIS, TTS_NAMESPACE), + "payload": { + "text": text, + }, + } + # 截断显示 + display_text = text[:50] + "..." if len(text) > 50 else text + self._log(f"→ 发送 RunSynthesis: \"{display_text}\" ({len(text)} chars)") + await self.send_json(message) + + async def _send_stop_synthesis(self) -> None: + """发送 StopSynthesis 消息""" + message = { + "header": self._create_header(MSG_STOP_SYNTHESIS, TTS_NAMESPACE), + } + self._log("→ 发送 StopSynthesis") + await self.send_json(message) + + def _save_audio(self) -> None: + """保存收到的音频数据为 WAV 文件""" + if self.save_audio_dir is None: + return + + try: + # 合并所有音频块 + audio_data = b"".join(self._audio_chunks) + if not audio_data: + return + + # 生成文件名 + filename = f"{self.task_id[:8]}_{len(self.text)}chars.wav" + filepath = self.save_audio_dir / filename + + # PCM 数据保存为 WAV + with wave.open(str(filepath), 'wb') as wav_file: + wav_file.setnchannels(1) # 单声道 + wav_file.setsampwidth(2) # 16位 = 2字节 + wav_file.setframerate(self.sample_rate) + wav_file.writeframes(audio_data) + + self._log(f"音频已保存: {filepath}") + except Exception as e: + logger.warning(f"保存音频失败: {e}") diff --git a/scripts/benchmark/config.py b/scripts/benchmark/config.py new file mode 100644 index 0000000..bb1341a --- /dev/null +++ b/scripts/benchmark/config.py @@ -0,0 +1,81 @@ +# -*- coding: utf-8 -*- +""" +测试配置模块 +""" + +from dataclasses import dataclass, field +from typing import List, Optional +from pathlib import Path + + +@dataclass +class TestConfig: + """测试配置""" + + # 服务器配置 + host: str = "localhost" + port: int = 8000 + timeout_seconds: float = 300.0 # 默认 5 分钟，并发 TTS 可能需要更长时间 + warmup_requests: int = 3 + + # 并发配置 + concurrency_levels: List[int] = field(default_factory=lambda: [5, 10, 20, 50]) + + # ASR 配置 + asr_audio_file: Optional[Path] = None + asr_sample_rate: int = 16000 + asr_chunk_size: int = 9600 # 600ms @ 16kHz + asr_format: str = "pcm" + + # TTS 配置 + tts_text_count: int = 50 # 预生成的测试文本数量 + tts_text_length_range: tuple = (50, 100) # 文本字符数范围 + tts_voice: str = "中文女" + tts_format: str = "PCM" + tts_sample_rate: int = 22050 + tts_chunk_interval: float = 0.05 # 发送间隔秒数 (模拟 LLM 生成速度) + + # 输出配置 + output_dir: Path = field(default_factory=lambda: Path("./benchmark_results")) + report_name: str = "benchmark_report" + + @property + def ws_base_url(self) -> str: + """WebSocket 基础 URL""" + return f"ws://{self.host}:{self.port}" + + @property + def asr_ws_url(self) -> str: + """ASR WebSocket URL""" + return f"{self.ws_base_url}/ws/v1/asr" + + @property + def tts_ws_url(self) -> str: + """TTS WebSocket URL""" + return f"{self.ws_base_url}/ws/v1/tts" + + def validate(self, test_type: str = "both") -> None: + """ + 验证配置 + + Args: + test_type: 测试类型 (asr/tts/both) + + Raises: + ValueError: 配置无效 + """ + if test_type in ("asr", "both"): + if self.asr_audio_file is None: + raise ValueError("ASR 测试需要提供音频文件路径 (--audio-file)") + if not self.asr_audio_file.exists(): + raise ValueError(f"音频文件不存在: {self.asr_audio_file}") + + if not self.concurrency_levels: + raise ValueError("至少需要一个并发级别") + + for level in self.concurrency_levels: + if level < 1: + raise ValueError(f"并发级别必须大于 0: {level}") + + if self.timeout_seconds <= 0: + raise ValueError("超时时间必须大于 0") diff --git a/scripts/benchmark/metrics/__init__.py b/scripts/benchmark/metrics/__init__.py new file mode 100644 index 0000000..c3a8551 --- /dev/null +++ b/scripts/benchmark/metrics/__init__.py @@ -0,0 +1,11 @@ +# -*- coding: utf-8 -*- +from .models import ASRMetrics, TTSMetrics, AggregatedMetrics +from .statistics import calculate_statistics, calculate_percentile + +__all__ = [ + "ASRMetrics", + "TTSMetrics", + "AggregatedMetrics", + "calculate_statistics", + "calculate_percentile", +] diff --git a/scripts/benchmark/metrics/models.py b/scripts/benchmark/metrics/models.py new file mode 100644 index 0000000..0aa7e62 --- /dev/null +++ b/scripts/benchmark/metrics/models.py @@ -0,0 +1,149 @@ +# -*- coding: utf-8 -*- +""" +性能指标数据类 +""" + +from dataclasses import dataclass +from typing import Optional + + +@dataclass +class ASRMetrics: + """ASR 单次请求指标""" + + request_id: str + concurrency_level: int + start_time: float # time.perf_counter() + audio_duration_ms: float = 0.0 + + # 时间戳 + first_result_time: Optional[float] = None # 第一个 TranscriptionResultChanged + sentence_end_time: Optional[float] = None # SentenceEnd + complete_time: Optional[float] = None # TranscriptionCompleted + + # 结果 + result_text: str = "" + success: bool = False + error_message: str = "" + + @property + def first_result_latency_ms(self) -> Optional[float]: + """首次响应延迟 (ms)""" + if self.first_result_time is not None: + return (self.first_result_time - self.start_time) * 1000 + return None + + @property + def total_processing_time_ms(self) -> Optional[float]: + """总处理时间 (ms)""" + if self.complete_time is not None: + return (self.complete_time - self.start_time) * 1000 + return None + + @property + def rtf(self) -> Optional[float]: + """RTF (Real-Time Factor) = 处理时间 / 音频时长""" + total_time = self.total_processing_time_ms + if total_time is not None and self.audio_duration_ms > 0: + return total_time / self.audio_duration_ms + return None + + +@dataclass +class TTSMetrics: + """TTS 单次请求指标""" + + request_id: str + concurrency_level: int + start_time: float # time.perf_counter() + text_length: int = 0 + sample_rate: int = 22050 + + # 时间戳 + first_chunk_time: Optional[float] = None # 第一个音频二进制块 + sentence_end_time: Optional[float] = None # SentenceEnd + complete_time: Optional[float] = None # SynthesisCompleted + + # 结果 + audio_bytes_received: int = 0 + success: bool = False + error_message: str = "" + + @property + def first_chunk_latency_ms(self) -> Optional[float]: + """首包延迟 (ms)""" + if self.first_chunk_time is not None: + return (self.first_chunk_time - self.start_time) * 1000 + return None + + @property + def total_synthesis_time_ms(self) -> Optional[float]: + """总合成时间 (ms)""" + if self.complete_time is not None: + return (self.complete_time - self.start_time) * 1000 + return None + + @property + def estimated_audio_duration_ms(self) -> float: + """估算的音频时长 (基于采样率和字节数)""" + if self.audio_bytes_received > 0: + # PCM 16-bit mono: 2 bytes per sample + samples = self.audio_bytes_received / 2 + return (samples / self.sample_rate) * 1000 + return 0.0 + + @property + def rtf(self) -> Optional[float]: + """RTF = 合成时间 / 生成音频时长""" + total_time = self.total_synthesis_time_ms + audio_duration = self.estimated_audio_duration_ms + if total_time is not None and audio_duration > 0: + return total_time / audio_duration + return None + + +@dataclass +class AggregatedMetrics: + """聚合后的指标 (针对一个并发级别)""" + + test_type: str # "asr" or "tts" + concurrency_level: int + total_requests: int + successful_requests: int + failed_requests: int + total_test_time_seconds: float + + # 首次延迟统计 (ms) + first_latency_avg: float = 0.0 + first_latency_p50: float = 0.0 + first_latency_p95: float = 0.0 + first_latency_p99: float = 0.0 + first_latency_max: float = 0.0 + + # 总时间统计 (ms) + total_time_avg: float = 0.0 + total_time_p50: float = 0.0 + total_time_p95: float = 0.0 + total_time_p99: float = 0.0 + total_time_max: float = 0.0 + + # RTF 统计 + rtf_avg: float = 0.0 + rtf_p50: float = 0.0 + rtf_p95: float = 0.0 + rtf_p99: float = 0.0 + rtf_max: float = 0.0 + + @property + def success_rate(self) -> float: + """成功率 (%)""" + if self.total_requests > 0: + return (self.successful_requests / self.total_requests) * 100 + return 0.0 + + @property + def throughput(self) -> float: + """吞吐量 (成功请求数/秒)""" + if self.total_test_time_seconds > 0: + return self.successful_requests / self.total_test_time_seconds + return 0.0 diff --git a/scripts/benchmark/metrics/statistics.py b/scripts/benchmark/metrics/statistics.py new file mode 100644 index 0000000..69e8da5 --- /dev/null +++ b/scripts/benchmark/metrics/statistics.py @@ -0,0 +1,168 @@ +# -*- coding: utf-8 -*- +""" +统计计算模块 +""" + +from typing import List, Union +import numpy as np + +from .models import ASRMetrics, TTSMetrics, AggregatedMetrics + + +def calculate_percentile(values: List[float], percentile: float) -> float: + """ + 计算百分位数 + + Args: + values: 数值列表 + percentile: 百分位 (0-100) + + Returns: + 百分位值 + """ + if not values: + return 0.0 + return float(np.percentile(values, percentile)) + + +def calculate_asr_statistics( + metrics_list: List[ASRMetrics], + concurrency_level: int, + total_test_time: float, +) -> AggregatedMetrics: + """ + 计算 ASR 指标统计 + + Args: + metrics_list: ASR 指标列表 + concurrency_level: 并发级别 + total_test_time: 总测试时间 (秒) + + Returns: + 聚合后的指标 + """ + successful = [m for m in metrics_list if m.success] + failed = [m for m in metrics_list if not m.success] + + # 提取各项指标值 + first_latencies = [ + m.first_result_latency_ms for m in successful if m.first_result_latency_ms is not None + ] + total_times = [ + m.total_processing_time_ms for m in successful if m.total_processing_time_ms is not None + ] + rtfs = [m.rtf for m in successful if m.rtf is not None] + + return AggregatedMetrics( + test_type="asr", + concurrency_level=concurrency_level, + total_requests=len(metrics_list), + successful_requests=len(successful), + failed_requests=len(failed), + total_test_time_seconds=total_test_time, + # 首次延迟 + first_latency_avg=float(np.mean(first_latencies)) if first_latencies else 0.0, + first_latency_p50=calculate_percentile(first_latencies, 50), + first_latency_p95=calculate_percentile(first_latencies, 95), + first_latency_p99=calculate_percentile(first_latencies, 99), + first_latency_max=max(first_latencies) if first_latencies else 0.0, + # 总时间 + total_time_avg=float(np.mean(total_times)) if total_times else 0.0, + total_time_p50=calculate_percentile(total_times, 50), + total_time_p95=calculate_percentile(total_times, 95), + total_time_p99=calculate_percentile(total_times, 99), + total_time_max=max(total_times) if total_times else 0.0, + # RTF + rtf_avg=float(np.mean(rtfs)) if rtfs else 0.0, + rtf_p50=calculate_percentile(rtfs, 50), + rtf_p95=calculate_percentile(rtfs, 95), + rtf_p99=calculate_percentile(rtfs, 99), + rtf_max=max(rtfs) if rtfs else 0.0, + ) + + +def calculate_tts_statistics( + metrics_list: List[TTSMetrics], + concurrency_level: int, + total_test_time: float, +) -> AggregatedMetrics: + """ + 计算 TTS 指标统计 + + Args: + metrics_list: TTS 指标列表 + concurrency_level: 并发级别 + total_test_time: 总测试时间 (秒) + + Returns: + 聚合后的指标 + """ + successful = [m for m in metrics_list if m.success] + failed = [m for m in metrics_list if not m.success] + + # 提取各项指标值 + first_latencies = [ + m.first_chunk_latency_ms for m in successful if m.first_chunk_latency_ms is not None + ] + total_times = [ + m.total_synthesis_time_ms for m in successful if m.total_synthesis_time_ms is not None + ] + rtfs = [m.rtf for m in successful if m.rtf is not None] + + return AggregatedMetrics( + test_type="tts", + concurrency_level=concurrency_level, + total_requests=len(metrics_list), + successful_requests=len(successful), + failed_requests=len(failed), + total_test_time_seconds=total_test_time, + # 首包延迟 + first_latency_avg=float(np.mean(first_latencies)) if first_latencies else 0.0, + first_latency_p50=calculate_percentile(first_latencies, 50), + first_latency_p95=calculate_percentile(first_latencies, 95), + first_latency_p99=calculate_percentile(first_latencies, 99), + first_latency_max=max(first_latencies) if first_latencies else 0.0, + # 总时间 + total_time_avg=float(np.mean(total_times)) if total_times else 0.0, + total_time_p50=calculate_percentile(total_times, 50), + total_time_p95=calculate_percentile(total_times, 95), + total_time_p99=calculate_percentile(total_times, 99), + total_time_max=max(total_times) if total_times else 0.0, + # RTF + rtf_avg=float(np.mean(rtfs)) if rtfs else 0.0, + rtf_p50=calculate_percentile(rtfs, 50), + rtf_p95=calculate_percentile(rtfs, 95), + rtf_p99=calculate_percentile(rtfs, 99), + rtf_max=max(rtfs) if rtfs else 0.0, + ) + + +def calculate_statistics( + metrics_list: Union[List[ASRMetrics], List[TTSMetrics]], + concurrency_level: int, + total_test_time: float, +) -> AggregatedMetrics: + """ + 通用统计计算函数 + + Args: + metrics_list: 指标列表 (ASR 或 TTS) + concurrency_level: 并发级别 + total_test_time: 总测试时间 (秒) + + Returns: + 聚合后的指标 + """ + if not metrics_list: + raise ValueError("指标列表不能为空") + + if isinstance(metrics_list[0], ASRMetrics): + # 类型缩窄：确保类型检查器知道这是 List[ASRMetrics] + asr_metrics_list: List[ASRMetrics] = [m for m in metrics_list if isinstance(m, ASRMetrics)] + return calculate_asr_statistics(asr_metrics_list, concurrency_level, total_test_time) + elif isinstance(metrics_list[0], TTSMetrics): + # 类型缩窄：确保类型检查器知道这是 List[TTSMetrics] + tts_metrics_list: List[TTSMetrics] = [m for m in metrics_list if isinstance(m, TTSMetrics)] + return calculate_tts_statistics(tts_metrics_list, concurrency_level, total_test_time) + else: + raise TypeError(f"不支持的指标类型: {type(metrics_list[0])}") diff --git a/scripts/benchmark/qwen_rust_sensitivity.py b/scripts/benchmark/qwen_rust_sensitivity.py new file mode 100644 index 0000000..b9e13c0 --- /dev/null +++ b/scripts/benchmark/qwen_rust_sensitivity.py @@ -0,0 +1,313 @@ +# -*- coding: utf-8 -*- +"""Qwen Rust CPU end-to-end benchmark for the current runtime configuration.""" + +from __future__ import annotations + +import argparse +import json +import os +import sys +import time +from dataclasses import asdict, dataclass +from pathlib import Path + +from app.core.config import settings +from app.services.asr.qwen3_engine import Qwen3ASREngine +from app.utils.audio import get_audio_duration +from app.utils.audio_splitter import AudioSplitter + + +@dataclass +class WorkerBenchRow: + cpu_count: int + rust_workers: int + asr_concurrency: int + align_concurrency: int + audio_file: str + audio_duration_sec: float + batch_size: int + engine_init_sec: float + vad_sec: float + vad_segments: int + asr_sec: float + asr_calls: int + align_sec: float + align_calls: int + total_sec: float + rtf: float + segments: int + word_tokens: int + text_len: int + + +def _persist_rows(rows: list[WorkerBenchRow], json_out: Path | None) -> None: + if json_out is None: + return + payload = [asdict(row) for row in rows] + tmp_path = json_out.with_suffix(f"{json_out.suffix}.tmp") + tmp_path.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8") + tmp_path.replace(json_out) + + +def _render_markdown(rows: list[WorkerBenchRow]) -> str: + if not rows: + return "# Qwen Rust CPU 对比结果\n\n暂无结果。\n" + + audio_file = rows[0].audio_file + audio_duration_sec = rows[0].audio_duration_sec + batch_size = rows[0].batch_size + cpu_count = rows[0].cpu_count + + lines = [ + "# Qwen Rust CPU 对比结果", + "", + f"- 音频文件：`{audio_file}`", + f"- 音频时长：`{audio_duration_sec:.2f}s`", + f"- batch size：`{batch_size}`", + f"- CPU 数量：`{cpu_count}`", + "", + f"- Rust workers：`{rows[0].rust_workers}`", + f"- ASR concurrency：`{rows[0].asr_concurrency}`", + f"- Align concurrency：`{rows[0].align_concurrency}`", + "", + "| total_sec | RTF | engine_init_sec | vad_sec | asr_sec | align_sec | segments | word_tokens | text_len |", + "|---:|---:|---:|---:|---:|---:|---:|---:|---:|", + ] + + for row in rows: + lines.append( + "| " + f"{row.total_sec:.2f} | " + f"{row.rtf:.4f} | " + f"{row.engine_init_sec:.3f} | " + f"{row.vad_sec:.3f} | " + f"{row.asr_sec:.2f} | " + f"{row.align_sec:.2f} | " + f"{row.segments} | " + f"{row.word_tokens} | " + f"{row.text_len} |" + ) + + row = rows[0] + + lines.extend( + [ + "", + "## 结论", + "", + f"- 当前配置：workers=`{row.rust_workers}` / asr=`{row.asr_concurrency}` / align=`{row.align_concurrency}`", + f"- 总耗时：`{row.total_sec:.2f}s`", + f"- RTF：`{row.rtf:.4f}`", + "", + ] + ) + return "\n".join(lines) + + +def _persist_markdown(rows: list[WorkerBenchRow], markdown_out: Path | None) -> None: + if markdown_out is None: + return + tmp_path = markdown_out.with_suffix(f"{markdown_out.suffix}.tmp") + tmp_path.write_text(_render_markdown(rows), encoding="utf-8") + tmp_path.replace(markdown_out) + + +def _log_progress(row: WorkerBenchRow) -> None: + print( + ( + f"[bench] workers={row.rust_workers} " + f"asr={row.asr_concurrency} " + f"align={row.align_concurrency} " + f"total={row.total_sec:.2f}s " + f"rtf={row.rtf:.4f} " + f"asr_sec={row.asr_sec:.2f}s " + f"align_sec={row.align_sec:.2f}s " + f"segments={row.segments} " + f"words={row.word_tokens}" + ), + file=sys.stderr, + flush=True, +) + + +def _clean_segments(segments: list) -> None: + AudioSplitter.cleanup_segments(segments) + + +def _prepare_segments(audio_file: Path) -> tuple[float, float, list]: + duration = get_audio_duration(str(audio_file)) + splitter = AudioSplitter(device="cpu") + t0 = time.perf_counter() + segments = splitter.split_audio_file(str(audio_file)) + vad_sec = time.perf_counter() - t0 + if not segments: + raise RuntimeError("VAD returned no segments") + return duration, vad_sec, segments + + +def _build_engine( + model_path: str, + forced_aligner_path: str, + *, + batch_size: int, +) -> tuple[Qwen3ASREngine, float]: + settings.DEVICE = "cpu" + settings.ASR_BATCH_SIZE = batch_size + + t0 = time.perf_counter() + engine = Qwen3ASREngine( + model_path=model_path, + forced_aligner_path=forced_aligner_path, + device="cpu", + ) + return engine, time.perf_counter() - t0 + + +def _run_asr_stage( + engine: Qwen3ASREngine, + segments: list, +) -> tuple[dict[int, str], float]: + valid_segments = [ + (idx, seg) for idx, seg in enumerate(segments) if getattr(seg, "temp_file", None) + ] + t0 = time.perf_counter() + texts = engine._run_rust_asr_stage( + valid_segments=valid_segments, + hotwords="", + enable_punctuation=True, + enable_itn=True, + sample_rate=16000, + ) + return texts, time.perf_counter() - t0 + + +def _run_align_stage( + engine: Qwen3ASREngine, + segments: list, + texts: dict[int, str], +) -> tuple[dict[int, list], float]: + valid_segments = [ + (idx, seg) for idx, seg in enumerate(segments) if getattr(seg, "temp_file", None) + ] + t0 = time.perf_counter() + aligned = engine._run_rust_align_stage( + valid_segments=valid_segments, + texts=texts, + ) + return aligned, time.perf_counter() - t0 + + +def _summarize( + *, + cpu_count: int, + audio_file: Path, + audio_duration_sec: float, + batch_size: int, + engine_init_sec: float, + vad_sec: float, + segments: list, + texts: dict[int, str], + aligned: dict[int, list], + asr_sec: float, + align_sec: float, + total_sec: float, +) -> WorkerBenchRow: + text_len = sum(len(text) for text in texts.values()) + word_tokens = sum(len(items) for items in aligned.values()) + return WorkerBenchRow( + cpu_count=cpu_count, + rust_workers=settings.QWEN_RUST_CPU_WORKERS, + asr_concurrency=settings.QWEN_RUST_ASR_CONCURRENCY or settings.QWEN_RUST_CPU_WORKERS, + align_concurrency=settings.QWEN_RUST_ALIGN_CONCURRENCY or settings.QWEN_RUST_CPU_WORKERS, + audio_file=str(audio_file), + audio_duration_sec=audio_duration_sec, + batch_size=batch_size, + engine_init_sec=engine_init_sec, + vad_sec=vad_sec, + vad_segments=len(segments), + asr_sec=asr_sec, + asr_calls=len(texts), + align_sec=align_sec, + align_calls=len(aligned), + total_sec=total_sec, + rtf=total_sec / audio_duration_sec if audio_duration_sec else 0.0, + segments=len(texts), + word_tokens=word_tokens, + text_len=text_len, + ) + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description="Qwen Rust CPU end-to-end benchmark") + parser.add_argument("--audio-file", required=True, help="Input audio file path") + parser.add_argument("--model-path", default="Qwen/Qwen3-ASR-0.6B") + parser.add_argument("--forced-aligner-path", default="Qwen/Qwen3-ForcedAligner-0.6B") + parser.add_argument("--batch-size", type=int, default=4) + parser.add_argument("--json-out", help="Optional JSON output path") + parser.add_argument( + "--markdown-out", + help="Optional Markdown report output path. Defaults to a sibling .md next to --json-out.", + ) + return parser + + +def main() -> None: + parser = build_parser() + args = parser.parse_args() + + audio_file = Path(args.audio_file).expanduser().resolve() + cpu_count = os.cpu_count() or 1 + out_path = Path(args.json_out).expanduser().resolve() if args.json_out else None + markdown_out = Path(args.markdown_out).expanduser().resolve() if args.markdown_out else None + if markdown_out is None and out_path is not None: + markdown_out = out_path.with_suffix(".md") + + duration, vad_sec, segments = _prepare_segments(audio_file) + rows: list[WorkerBenchRow] = [] + + try: + engine, init_sec = _build_engine( + args.model_path, + args.forced_aligner_path, + batch_size=args.batch_size, + ) + + t0 = time.perf_counter() + texts, asr_sec = _run_asr_stage(engine, segments) + aligned, align_sec = _run_align_stage(engine, segments, texts) + total_sec = time.perf_counter() - t0 + + row = _summarize( + cpu_count=cpu_count, + audio_file=audio_file, + audio_duration_sec=duration, + batch_size=args.batch_size, + engine_init_sec=init_sec, + vad_sec=vad_sec, + segments=segments, + texts=texts, + aligned=aligned, + asr_sec=asr_sec, + align_sec=align_sec, + total_sec=total_sec, + ) + rows.append(row) + _persist_rows(rows, out_path) + _persist_markdown(rows, markdown_out) + _log_progress(row) + finally: + _clean_segments(segments) + + payload = [asdict(row) for row in rows] + if out_path is not None: + out_path.parent.mkdir(parents=True, exist_ok=True) + out_path.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8") + if markdown_out is not None: + markdown_out.parent.mkdir(parents=True, exist_ok=True) + markdown_out.write_text(_render_markdown(rows), encoding="utf-8") + + print(json.dumps(payload, ensure_ascii=False, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/scripts/benchmark/reporters/__init__.py b/scripts/benchmark/reporters/__init__.py new file mode 100644 index 0000000..caa1186 --- /dev/null +++ b/scripts/benchmark/reporters/__init__.py @@ -0,0 +1,5 @@ +# -*- coding: utf-8 -*- +from .markdown_reporter import MarkdownReporter +from .chart_generator import ChartGenerator + +__all__ = ["MarkdownReporter", "ChartGenerator"] diff --git a/scripts/benchmark/reporters/chart_generator.py b/scripts/benchmark/reporters/chart_generator.py new file mode 100644 index 0000000..57b367d --- /dev/null +++ b/scripts/benchmark/reporters/chart_generator.py @@ -0,0 +1,250 @@ +# -*- coding: utf-8 -*- +""" +Matplotlib 图表生成器 +""" + +from pathlib import Path +from typing import List + +import matplotlib.pyplot as plt +import matplotlib +import numpy as np + +from ..metrics.models import AggregatedMetrics + +# 设置中文字体支持 +matplotlib.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans'] +matplotlib.rcParams['axes.unicode_minus'] = False + + +class ChartGenerator: + """图表生成器""" + + def __init__(self): + self.colors = { + "asr": "#4CAF50", # 绿色 + "tts": "#2196F3", # 蓝色 + } + + def generate_all_charts( + self, + asr_results: List[AggregatedMetrics], + tts_results: List[AggregatedMetrics], + output_dir: Path, + timestamp: str, + ) -> List[Path]: + """ + 生成所有图表 + + Args: + asr_results: ASR 测试结果 + tts_results: TTS 测试结果 + output_dir: 输出目录 + timestamp: 时间戳 + + Returns: + 生成的图表文件路径列表 + """ + output_dir.mkdir(parents=True, exist_ok=True) + generated_files = [] + + # 1. 首次延迟对比图 + if asr_results or tts_results: + path = output_dir / f"first_latency_{timestamp}.png" + self._generate_first_latency_chart(asr_results, tts_results, path) + generated_files.append(path) + + # 2. RTF 对比图 + if asr_results or tts_results: + path = output_dir / f"rtf_{timestamp}.png" + self._generate_rtf_chart(asr_results, tts_results, path) + generated_files.append(path) + + # 3. 吞吐量对比图 + if asr_results or tts_results: + path = output_dir / f"throughput_{timestamp}.png" + self._generate_throughput_chart(asr_results, tts_results, path) + generated_files.append(path) + + # 4. 总时间对比图 + if asr_results or tts_results: + path = output_dir / f"total_time_{timestamp}.png" + self._generate_total_time_chart(asr_results, tts_results, path) + generated_files.append(path) + + return generated_files + + def _generate_first_latency_chart( + self, + asr_results: List[AggregatedMetrics], + tts_results: List[AggregatedMetrics], + output_path: Path, + ) -> None: + """生成首次延迟对比图""" + _fig, ax = plt.subplots(figsize=(10, 6)) + + levels = [] + if asr_results: + levels = [r.concurrency_level for r in asr_results] + avg_values = [r.first_latency_avg for r in asr_results] + p95_values = [r.first_latency_p95 for r in asr_results] + + ax.plot(levels, avg_values, 'o-', color=self.colors["asr"], + label='ASR 首次响应 (Avg)', linewidth=2, markersize=8) + ax.plot(levels, p95_values, 's--', color=self.colors["asr"], + label='ASR 首次响应 (P95)', linewidth=1.5, markersize=6, alpha=0.7) + + if tts_results: + levels = [r.concurrency_level for r in tts_results] + avg_values = [r.first_latency_avg for r in tts_results] + p95_values = [r.first_latency_p95 for r in tts_results] + + ax.plot(levels, avg_values, 'o-', color=self.colors["tts"], + label='TTS 首包延迟 (Avg)', linewidth=2, markersize=8) + ax.plot(levels, p95_values, 's--', color=self.colors["tts"], + label='TTS 首包延迟 (P95)', linewidth=1.5, markersize=6, alpha=0.7) + + ax.set_xlabel('并发数', fontsize=12) + ax.set_ylabel('延迟 (ms)', fontsize=12) + ax.set_title('首次响应延迟 vs 并发数', fontsize=14, fontweight='bold') + ax.legend(loc='best') + ax.grid(True, alpha=0.3) + if levels: + ax.set_xticks(levels) + + plt.tight_layout() + plt.savefig(output_path, dpi=150) + plt.close() + + def _generate_rtf_chart( + self, + asr_results: List[AggregatedMetrics], + tts_results: List[AggregatedMetrics], + output_path: Path, + ) -> None: + """生成 RTF 对比图""" + _fig, ax = plt.subplots(figsize=(10, 6)) + + if asr_results: + levels = [r.concurrency_level for r in asr_results] + avg_values = [r.rtf_avg for r in asr_results] + p95_values = [r.rtf_p95 for r in asr_results] + + ax.plot(levels, avg_values, 'o-', color=self.colors["asr"], + label='ASR RTF (Avg)', linewidth=2, markersize=8) + ax.plot(levels, p95_values, 's--', color=self.colors["asr"], + label='ASR RTF (P95)', linewidth=1.5, markersize=6, alpha=0.7) + + if tts_results: + levels = [r.concurrency_level for r in tts_results] + avg_values = [r.rtf_avg for r in tts_results] + p95_values = [r.rtf_p95 for r in tts_results] + + ax.plot(levels, avg_values, 'o-', color=self.colors["tts"], + label='TTS RTF (Avg)', linewidth=2, markersize=8) + ax.plot(levels, p95_values, 's--', color=self.colors["tts"], + label='TTS RTF (P95)', linewidth=1.5, markersize=6, alpha=0.7) + + # 添加 RTF=1.0 参考线 + all_levels = set() + if asr_results: + all_levels.update(r.concurrency_level for r in asr_results) + if tts_results: + all_levels.update(r.concurrency_level for r in tts_results) + if all_levels: + ax.axhline(y=1.0, color='red', linestyle=':', linewidth=1.5, + label='RTF = 1.0 (实时)') + + ax.set_xlabel('并发数', fontsize=12) + ax.set_ylabel('RTF', fontsize=12) + ax.set_title('RTF vs 并发数', fontsize=14, fontweight='bold') + ax.legend(loc='best') + ax.grid(True, alpha=0.3) + + plt.tight_layout() + plt.savefig(output_path, dpi=150) + plt.close() + + def _generate_throughput_chart( + self, + asr_results: List[AggregatedMetrics], + tts_results: List[AggregatedMetrics], + output_path: Path, + ) -> None: + """生成吞吐量柱状图""" + _fig, ax = plt.subplots(figsize=(10, 6)) + + all_levels = sorted(set( + [r.concurrency_level for r in asr_results] + + [r.concurrency_level for r in tts_results] + )) + + x = np.arange(len(all_levels)) + width = 0.35 + + if asr_results: + asr_throughput = [] + for level in all_levels: + r = next((r for r in asr_results if r.concurrency_level == level), None) + asr_throughput.append(r.throughput if r else 0) + ax.bar(x - width/2, asr_throughput, width, label='ASR', + color=self.colors["asr"], alpha=0.8) + + if tts_results: + tts_throughput = [] + for level in all_levels: + r = next((r for r in tts_results if r.concurrency_level == level), None) + tts_throughput.append(r.throughput if r else 0) + ax.bar(x + width/2, tts_throughput, width, label='TTS', + color=self.colors["tts"], alpha=0.8) + + ax.set_xlabel('并发数', fontsize=12) + ax.set_ylabel('吞吐量 (req/s)', fontsize=12) + ax.set_title('吞吐量 vs 并发数', fontsize=14, fontweight='bold') + ax.set_xticks(x) + ax.set_xticklabels([str(level) for level in all_levels]) + ax.legend(loc='best') + ax.grid(True, alpha=0.3, axis='y') + + plt.tight_layout() + plt.savefig(output_path, dpi=150) + plt.close() + + def _generate_total_time_chart( + self, + asr_results: List[AggregatedMetrics], + tts_results: List[AggregatedMetrics], + output_path: Path, + ) -> None: + """生成总时间对比图""" + _fig, ax = plt.subplots(figsize=(10, 6)) + + if asr_results: + levels = [r.concurrency_level for r in asr_results] + avg_values = [r.total_time_avg for r in asr_results] + p95_values = [r.total_time_p95 for r in asr_results] + + ax.plot(levels, avg_values, 'o-', color=self.colors["asr"], + label='ASR 总时间 (Avg)', linewidth=2, markersize=8) + ax.plot(levels, p95_values, 's--', color=self.colors["asr"], + label='ASR 总时间 (P95)', linewidth=1.5, markersize=6, alpha=0.7) + + if tts_results: + levels = [r.concurrency_level for r in tts_results] + avg_values = [r.total_time_avg for r in tts_results] + p95_values = [r.total_time_p95 for r in tts_results] + + ax.plot(levels, avg_values, 'o-', color=self.colors["tts"], + label='TTS 总时间 (Avg)', linewidth=2, markersize=8) + ax.plot(levels, p95_values, 's--', color=self.colors["tts"], + label='TTS 总时间 (P95)', linewidth=1.5, markersize=6, alpha=0.7) + + ax.set_xlabel('并发数', fontsize=12) + ax.set_ylabel('时间 (ms)', fontsize=12) + ax.set_title('总处理时间 vs 并发数', fontsize=14, fontweight='bold') + ax.legend(loc='best') + ax.grid(True, alpha=0.3) + + plt.tight_layout() + plt.savefig(output_path, dpi=150) + plt.close() diff --git a/scripts/benchmark/reporters/markdown_reporter.py b/scripts/benchmark/reporters/markdown_reporter.py new file mode 100644 index 0000000..ab93b32 --- /dev/null +++ b/scripts/benchmark/reporters/markdown_reporter.py @@ -0,0 +1,183 @@ +# -*- coding: utf-8 -*- +""" +Markdown 报告生成器 +""" + +from datetime import datetime +from pathlib import Path +from typing import List, Optional + +from ..metrics.models import AggregatedMetrics + + +class MarkdownReporter: + """Markdown 报告生成器""" + + def generate( + self, + asr_results: List[AggregatedMetrics], + tts_results: List[AggregatedMetrics], + output_path: Path, + config_info: Optional[dict] = None, + ) -> None: + """ + 生成 Markdown 报告 + + Args: + asr_results: ASR 测试结果 + tts_results: TTS 测试结果 + output_path: 输出文件路径 + config_info: 配置信息 + """ + lines = [] + + # 标题 + lines.append("# Qwen3-ASR 并发性能测试报告") + lines.append("") + lines.append(f"**测试时间:** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") + + if config_info: + lines.append(f"**服务器:** {config_info.get('host', 'localhost')}:{config_info.get('port', 8000)}") + lines.append(f"**并发级别:** {', '.join(map(str, config_info.get('concurrency_levels', [])))}") + + lines.append("") + lines.append("---") + lines.append("") + + # ASR 结果 + if asr_results: + lines.extend(self._generate_asr_section(asr_results)) + + # TTS 结果 + if tts_results: + lines.extend(self._generate_tts_section(tts_results)) + + # 结论 + lines.extend(self._generate_conclusions(asr_results, tts_results)) + + # 写入文件 + output_path.parent.mkdir(parents=True, exist_ok=True) + output_path.write_text("\n".join(lines), encoding="utf-8") + + def _generate_asr_section(self, results: List[AggregatedMetrics]) -> List[str]: + """生成 ASR 结果部分""" + lines = [] + lines.append("## ASR 性能测试结果") + lines.append("") + + # 延迟指标表格 + lines.append("### 延迟指标 (毫秒)") + lines.append("") + lines.append("| 并发数 | 首次响应 (Avg) | 首次响应 (P95) | 总时间 (Avg) | 总时间 (P95) | 总时间 (Max) |") + lines.append("|--------|---------------|---------------|-------------|-------------|-------------|") + + for r in results: + lines.append( + f"| {r.concurrency_level} | " + f"{r.first_latency_avg:.1f} | " + f"{r.first_latency_p95:.1f} | " + f"{r.total_time_avg:.1f} | " + f"{r.total_time_p95:.1f} | " + f"{r.total_time_max:.1f} |" + ) + + lines.append("") + + # RTF 和吞吐量表格 + lines.append("### RTF 和吞吐量") + lines.append("") + lines.append("| 并发数 | RTF (Avg) | RTF (P95) | 吞吐量 (req/s) | 成功率 |") + lines.append("|--------|----------|----------|---------------|--------|") + + for r in results: + lines.append( + f"| {r.concurrency_level} | " + f"{r.rtf_avg:.3f} | " + f"{r.rtf_p95:.3f} | " + f"{r.throughput:.3f} | " + f"{r.success_rate:.1f}% |" + ) + + lines.append("") + return lines + + def _generate_tts_section(self, results: List[AggregatedMetrics]) -> List[str]: + """生成 TTS 结果部分""" + lines = [] + lines.append("## TTS 性能测试结果") + lines.append("") + + # 延迟指标表格 + lines.append("### 延迟指标 (毫秒)") + lines.append("") + lines.append("| 并发数 | 首包延迟 (Avg) | 首包延迟 (P95) | 总时间 (Avg) | 总时间 (P95) | 总时间 (Max) |") + lines.append("|--------|---------------|---------------|-------------|-------------|-------------|") + + for r in results: + lines.append( + f"| {r.concurrency_level} | " + f"{r.first_latency_avg:.1f} | " + f"{r.first_latency_p95:.1f} | " + f"{r.total_time_avg:.1f} | " + f"{r.total_time_p95:.1f} | " + f"{r.total_time_max:.1f} |" + ) + + lines.append("") + + # RTF 和吞吐量表格 + lines.append("### RTF 和吞吐量") + lines.append("") + lines.append("| 并发数 | RTF (Avg) | RTF (P95) | 吞吐量 (req/s) | 成功率 |") + lines.append("|--------|----------|----------|---------------|--------|") + + for r in results: + lines.append( + f"| {r.concurrency_level} | " + f"{r.rtf_avg:.3f} | " + f"{r.rtf_p95:.3f} | " + f"{r.throughput:.3f} | " + f"{r.success_rate:.1f}% |" + ) + + lines.append("") + return lines + + def _generate_conclusions( + self, + asr_results: List[AggregatedMetrics], + tts_results: List[AggregatedMetrics], + ) -> List[str]: + """生成结论部分""" + lines = [] + lines.append("## 结论") + lines.append("") + + if asr_results: + max_level = max(asr_results, key=lambda x: x.concurrency_level) + lines.append(f"- **ASR 最大并发 ({max_level.concurrency_level}) RTF:** {max_level.rtf_avg:.3f}") + lines.append(f"- **ASR 最大并发吞吐量:** {max_level.throughput:.3f} req/s") + + # 找到 RTF 超过 1.0 的并发级别 + stable_levels = [r for r in asr_results if r.rtf_avg <= 1.0] + if stable_levels: + max_stable = max(stable_levels, key=lambda x: x.concurrency_level) + lines.append(f"- **ASR 稳定并发上限 (RTF < 1.0):** {max_stable.concurrency_level}") + + if tts_results: + max_level = max(tts_results, key=lambda x: x.concurrency_level) + lines.append(f"- **TTS 最大并发 ({max_level.concurrency_level}) RTF:** {max_level.rtf_avg:.3f}") + lines.append(f"- **TTS 最大并发吞吐量:** {max_level.throughput:.3f} req/s") + + stable_levels = [r for r in tts_results if r.rtf_avg <= 1.0] + if stable_levels: + max_stable = max(stable_levels, key=lambda x: x.concurrency_level) + lines.append(f"- **TTS 稳定并发上限 (RTF < 1.0):** {max_stable.concurrency_level}") + + lines.append("") + lines.append("---") + lines.append("") + lines.append("*RTF (Real-Time Factor): 处理时间与音频时长的比值，小于 1.0 表示处理速度快于实时*") + lines.append("") + + return lines diff --git a/scripts/benchmark/run.py b/scripts/benchmark/run.py new file mode 100644 index 0000000..38d0065 --- /dev/null +++ b/scripts/benchmark/run.py @@ -0,0 +1,418 @@ +# -*- coding: utf-8 -*- +""" +Qwen3-ASR 并发性能测试主入口 + +使用方法: + # 完整测试 (ASR + TTS) + python -m scripts.benchmark.run --audio-file /path/to/audio.wav + + # 仅测试 TTS + python -m scripts.benchmark.run --test-type tts + + # 仅测试 ASR + python -m scripts.benchmark.run --audio-file /path/to/audio.wav --test-type asr + + # 自定义并发级别 + python -m scripts.benchmark.run --audio-file /path/to/audio.wav --concurrency 5 10 20 +""" + +import asyncio +import argparse +import logging +import time +from datetime import datetime +from pathlib import Path +from typing import List + +from .config import TestConfig +from .clients.asr_client import ASRWebSocketClient +from .clients.tts_client import TTSWebSocketClient +from .metrics.models import ASRMetrics, TTSMetrics, AggregatedMetrics +from .metrics.statistics import calculate_statistics +from .reporters.markdown_reporter import MarkdownReporter +from .reporters.chart_generator import ChartGenerator +from .utils.audio_utils import load_audio_file +from .utils.text_generator import generate_test_texts + +# 配置日志 +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", +) +logger = logging.getLogger(__name__) + + +class ConcurrentBenchmark: + """并发性能测试运行器""" + + def __init__(self, config: TestConfig): + self.config = config + # 创建保存目录 + self._setup_output_dirs() + + def _setup_output_dirs(self): + """创建输出目录结构""" + self.config.output_dir.mkdir(parents=True, exist_ok=True) + # ASR 结果目录 + self.asr_output_dir = self.config.output_dir / "asr" + self.asr_output_dir.mkdir(exist_ok=True) + # TTS 音频目录 + self.tts_output_dir = self.config.output_dir / "tts" + self.tts_output_dir.mkdir(exist_ok=True) + + async def run_asr_benchmark(self) -> List[AggregatedMetrics]: + """ + 运行 ASR 并发测试 + + Returns: + 各并发级别的聚合指标列表 + """ + logger.info("开始 ASR 并发性能测试...") + + # 检查音频文件 + if self.config.asr_audio_file is None: + raise ValueError("ASR 测试需要提供音频文件") + + # 加载音频文件 + audio_data, audio_duration = load_audio_file( + self.config.asr_audio_file, + self.config.asr_sample_rate, + ) + audio_duration_ms = audio_duration * 1000 + + logger.info(f"音频文件已加载: {self.config.asr_audio_file}") + logger.info(f" - 时长: {audio_duration:.2f} 秒") + logger.info(f" - 大小: {len(audio_data) / 1024:.1f} KB") + + results = [] + + for level in self.config.concurrency_levels: + logger.info(f"\n测试并发级别: {level}") + + # 预热 + logger.info(f" 预热中 ({self.config.warmup_requests} 次请求)...") + await self._run_asr_concurrent( + audio_data, audio_duration_ms, self.config.warmup_requests, level, + save_results=False + ) + + # 正式测试 + logger.info(f" 正式测试中...") + start_time = time.perf_counter() + metrics_list = await self._run_asr_concurrent( + audio_data, audio_duration_ms, level, level, + save_results=True # 正式测试时保存结果 + ) + total_time = time.perf_counter() - start_time + + # 统计 + aggregated = calculate_statistics(metrics_list, level, total_time) + results.append(aggregated) + + # 打印结果 + logger.info(f" 完成: 成功 {aggregated.successful_requests}/{aggregated.total_requests}") + logger.info(f" 首次响应延迟: {aggregated.first_latency_avg:.1f} ms (avg)") + logger.info(f" RTF: {aggregated.rtf_avg:.3f} (avg)") + + return results + + async def _run_asr_concurrent( + self, + audio_data: bytes, + audio_duration_ms: float, + num_requests: int, + concurrency_level: int, + save_results: bool = False, + ) -> List[ASRMetrics]: + """运行并发 ASR 请求""" + tasks = [] + + for _ in range(num_requests): + client = ASRWebSocketClient( + ws_url=self.config.asr_ws_url, + audio_data=audio_data, + audio_duration_ms=audio_duration_ms, + sample_rate=self.config.asr_sample_rate, + chunk_size=self.config.asr_chunk_size, + timeout=self.config.timeout_seconds, + save_result_dir=self.asr_output_dir if save_results else None, + ) + tasks.append(client.run_test()) + + results = await asyncio.gather(*tasks, return_exceptions=True) + + # 处理结果 + metrics_list = [] + for result in results: + if isinstance(result, ASRMetrics): + result.concurrency_level = concurrency_level + metrics_list.append(result) + else: + # 异常情况 + metrics = ASRMetrics( + request_id="error", + concurrency_level=concurrency_level, + start_time=0, + error_message=str(result), + ) + metrics_list.append(metrics) + + return metrics_list + + async def run_tts_benchmark(self) -> List[AggregatedMetrics]: + """ + 运行 TTS 并发测试 + + Returns: + 各并发级别的聚合指标列表 + """ + logger.info("开始 TTS 并发性能测试...") + + # 生成测试文本 + test_texts = generate_test_texts( + count=self.config.tts_text_count, + length_range=self.config.tts_text_length_range, + ) + logger.info(f"已生成 {len(test_texts)} 段测试文本") + + results = [] + + for level in self.config.concurrency_levels: + logger.info(f"\n测试并发级别: {level}") + + # 选择文本 (每个并发请求使用不同文本) + selected_texts = test_texts[:level] + + # 预热 + logger.info(f" 预热中 ({min(self.config.warmup_requests, level)} 次请求)...") + await self._run_tts_concurrent( + selected_texts[:min(self.config.warmup_requests, level)], + min(self.config.warmup_requests, level), + level, + save_audio=False, + ) + + # 正式测试 + logger.info(f" 正式测试中...") + start_time = time.perf_counter() + metrics_list = await self._run_tts_concurrent( + selected_texts, level, level, + save_audio=True, # 正式测试时保存音频 + ) + total_time = time.perf_counter() - start_time + + # 统计 + aggregated = calculate_statistics(metrics_list, level, total_time) + results.append(aggregated) + + # 打印结果 + logger.info(f" 完成: 成功 {aggregated.successful_requests}/{aggregated.total_requests}") + logger.info(f" 首包延迟: {aggregated.first_latency_avg:.1f} ms (avg)") + logger.info(f" RTF: {aggregated.rtf_avg:.3f} (avg)") + + return results + + async def _run_tts_concurrent( + self, + texts: List[str], + num_requests: int, + concurrency_level: int, + save_audio: bool = False, + ) -> List[TTSMetrics]: + """运行并发 TTS 请求""" + tasks = [] + + for i in range(num_requests): + text = texts[i % len(texts)] + # 第一个请求始终开启调试模式 + debug = (i == 0) + client = TTSWebSocketClient( + ws_url=self.config.tts_ws_url, + text=text, + voice=self.config.tts_voice, + audio_format=self.config.tts_format, + sample_rate=self.config.tts_sample_rate, + timeout=self.config.timeout_seconds, + chunk_interval=self.config.tts_chunk_interval, + debug=debug, + save_audio_dir=self.tts_output_dir if save_audio else None, + ) + tasks.append(client.run_test()) + + # 添加进度提示 + logger.info(f" 启动 {num_requests} 个并发请求...") + results = await asyncio.gather(*tasks, return_exceptions=True) + logger.info(f" 所有请求已完成") + + # 处理结果 + metrics_list = [] + for result in results: + if isinstance(result, TTSMetrics): + result.concurrency_level = concurrency_level + metrics_list.append(result) + else: + # 异常情况 + metrics = TTSMetrics( + request_id="error", + concurrency_level=concurrency_level, + start_time=0, + error_message=str(result), + ) + metrics_list.append(metrics) + + return metrics_list + + def generate_report( + self, + asr_results: List[AggregatedMetrics], + tts_results: List[AggregatedMetrics], + ) -> Path: + """ + 生成测试报告 + + Args: + asr_results: ASR 测试结果 + tts_results: TTS 测试结果 + + Returns: + 报告文件路径 + """ + output_dir = self.config.output_dir + output_dir.mkdir(parents=True, exist_ok=True) + + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + + # 配置信息 + config_info = { + "host": self.config.host, + "port": self.config.port, + "concurrency_levels": self.config.concurrency_levels, + } + + # 生成 Markdown 报告 + report_path = output_dir / f"{self.config.report_name}_{timestamp}.md" + reporter = MarkdownReporter() + reporter.generate(asr_results, tts_results, report_path, config_info) + logger.info(f"Markdown 报告已生成: {report_path}") + + # 生成图表 + chart_generator = ChartGenerator() + chart_files = chart_generator.generate_all_charts( + asr_results, tts_results, output_dir, timestamp + ) + for chart_file in chart_files: + logger.info(f"图表已生成: {chart_file}") + + return report_path + + +def parse_args(): + """解析命令行参数""" + parser = argparse.ArgumentParser( + description="Qwen3-ASR 并发性能测试脚本", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +示例: + # 完整测试 (ASR + TTS) + python -m scripts.benchmark.run --audio-file test.wav + + # 仅测试 TTS + python -m scripts.benchmark.run --test-type tts + + # 自定义并发级别 + python -m scripts.benchmark.run --audio-file test.wav --concurrency 5 10 20 50 + """, + ) + + parser.add_argument( + "--host", + default="localhost", + help="服务器主机名 (默认: localhost)", + ) + parser.add_argument( + "--port", + type=int, + default=8000, + help="服务器端口 (默认: 8000)", + ) + parser.add_argument( + "--audio-file", + type=Path, + help="ASR 测试用音频文件路径 (测试 ASR 时必需)", + ) + parser.add_argument( + "--concurrency", + nargs="+", + type=int, + default=[5, 10, 20, 50], + help="并发级别列表 (默认: 5 10 20 50)", + ) + parser.add_argument( + "--test-type", + choices=["asr", "tts", "both"], + default="both", + help="测试类型 (默认: both)", + ) + parser.add_argument( + "--output", + type=Path, + default=Path("./benchmark_results"), + help="报告输出目录 (默认: ./benchmark_results)", + ) + parser.add_argument( + "--timeout", + type=float, + default=120.0, + help="请求超时时间 (秒, 默认: 120)", + ) + parser.add_argument( + "--voice", + default="中文女", + help="TTS 音色 (默认: 中文女)", + ) + + return parser.parse_args() + + +async def main(): + """主函数""" + args = parse_args() + + # 创建配置 + config = TestConfig( + host=args.host, + port=args.port, + concurrency_levels=args.concurrency, + asr_audio_file=args.audio_file, + output_dir=args.output, + timeout_seconds=args.timeout, + tts_voice=args.voice, + ) + + # 验证配置 + try: + config.validate(args.test_type) + except ValueError as e: + logger.error(f"配置错误: {e}") + return + + # 运行测试 + benchmark = ConcurrentBenchmark(config) + + asr_results = [] + tts_results = [] + + if args.test_type in ("asr", "both"): + asr_results = await benchmark.run_asr_benchmark() + + if args.test_type in ("tts", "both"): + tts_results = await benchmark.run_tts_benchmark() + + # 生成报告 + if asr_results or tts_results: + report_path = benchmark.generate_report(asr_results, tts_results) + logger.info(f"\n测试完成! 报告已保存到: {report_path}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/scripts/benchmark/utils/__init__.py b/scripts/benchmark/utils/__init__.py new file mode 100644 index 0000000..68c179e --- /dev/null +++ b/scripts/benchmark/utils/__init__.py @@ -0,0 +1,5 @@ +# -*- coding: utf-8 -*- +from .audio_utils import load_audio_file, get_audio_duration +from .text_generator import generate_test_texts + +__all__ = ["load_audio_file", "get_audio_duration", "generate_test_texts"] diff --git a/scripts/benchmark/utils/audio_utils.py b/scripts/benchmark/utils/audio_utils.py new file mode 100644 index 0000000..8f140c5 --- /dev/null +++ b/scripts/benchmark/utils/audio_utils.py @@ -0,0 +1,113 @@ +# -*- coding: utf-8 -*- +""" +音频处理工具 +""" + +from pathlib import Path +from typing import Tuple +import numpy as np +import soundfile as sf + + +def load_audio_file( + audio_path: Path, + target_sample_rate: int = 16000, +) -> Tuple[bytes, float]: + """ + 加载音频文件并转换为 PCM 16-bit 格式 + + Args: + audio_path: 音频文件路径 + target_sample_rate: 目标采样率 + + Returns: + (pcm_bytes, duration_seconds): PCM 字节数据和音频时长(秒) + """ + # 读取音频文件 + audio_data, sample_rate = sf.read(audio_path, dtype="float32") + + # 如果是立体声，转换为单声道 + if len(audio_data.shape) > 1: + audio_data = np.mean(audio_data, axis=1) + + # 重采样到目标采样率 + if sample_rate != target_sample_rate: + audio_data = resample_audio(audio_data, sample_rate, target_sample_rate) + + # 计算时长 + duration_seconds = len(audio_data) / target_sample_rate + + # 转换为 16-bit PCM + pcm_data = (audio_data * 32767).astype(np.int16) + pcm_bytes = pcm_data.tobytes() + + return pcm_bytes, duration_seconds + + +def resample_audio( + audio_data: np.ndarray, + orig_sample_rate: int, + target_sample_rate: int, +) -> np.ndarray: + """ + 重采样音频 + + Args: + audio_data: 音频数据 + orig_sample_rate: 原始采样率 + target_sample_rate: 目标采样率 + + Returns: + 重采样后的音频数据 + """ + if orig_sample_rate == target_sample_rate: + return audio_data + + # 计算重采样比例 + ratio = target_sample_rate / orig_sample_rate + new_length = int(len(audio_data) * ratio) + + # 使用线性插值进行重采样 + x_old = np.linspace(0, 1, len(audio_data)) + x_new = np.linspace(0, 1, new_length) + resampled = np.interp(x_new, x_old, audio_data) + + return resampled.astype(np.float32) + + +def get_audio_duration(audio_path: Path) -> float: + """ + 获取音频文件时长 + + Args: + audio_path: 音频文件路径 + + Returns: + 时长(秒) + """ + info = sf.info(audio_path) + return info.duration + + +def split_audio_into_chunks( + pcm_bytes: bytes, + chunk_size: int, +) -> list: + """ + 将 PCM 数据分割成块 + + Args: + pcm_bytes: PCM 字节数据 + chunk_size: 每块的采样数 + + Returns: + 字节块列表 + """ + chunk_bytes = chunk_size * 2 # 16-bit = 2 bytes per sample + chunks = [] + + for i in range(0, len(pcm_bytes), chunk_bytes): + chunk = pcm_bytes[i : i + chunk_bytes] + chunks.append(chunk) + + return chunks diff --git a/scripts/benchmark/utils/text_generator.py b/scripts/benchmark/utils/text_generator.py new file mode 100644 index 0000000..dececb4 --- /dev/null +++ b/scripts/benchmark/utils/text_generator.py @@ -0,0 +1,170 @@ +# -*- coding: utf-8 -*- +""" +中文随机句子生成器 + +用于生成 TTS 测试文本，不依赖外部 AI API。 +""" + +import random +from typing import List, Tuple + +# 主语词库 +SUBJECTS = [ + "我", "你", "他", "她", "我们", "大家", "小明", "小红", "老师", "学生", + "医生", "工程师", "科学家", "艺术家", "音乐家", "作家", "记者", "警察", + "这位先生", "那位女士", "我的朋友", "他的同事", "她的家人", "公司", + "团队", "项目组", "研发部门", "市场部", "客户", "用户", +] + +# 时间词库 +TIME_PHRASES = [ + "今天", "明天", "昨天", "上周", "下周", "这个月", "上个月", "今年", + "最近", "刚才", "马上", "立刻", "很快", "不久前", "过去", + "早上", "中午", "下午", "晚上", "凌晨", "周末", "假期期间", +] + +# 地点词库 +LOCATIONS = [ + "在公司", "在家里", "在学校", "在图书馆", "在咖啡厅", "在会议室", + "在公园", "在商场", "在医院", "在机场", "在火车站", "在地铁站", + "在办公室", "在实验室", "在教室", "在操场", "在餐厅", "在酒店", +] + +# 动词短语词库 +VERB_PHRASES = [ + "正在开发一个新的功能", "完成了一项重要的任务", "参加了一个技术会议", + "学习了新的编程语言", "解决了一个复杂的问题", "提交了项目报告", + "设计了一套新的方案", "测试了最新的版本", "优化了系统性能", + "讨论了未来的发展计划", "制定了下一步的工作安排", "回顾了过去的工作成果", + "分析了市场数据", "研究了用户需求", "改进了产品体验", + "组织了团队活动", "培训了新员工", "更新了技术文档", + "修复了几个重要的问题", "部署了新的服务", "监控了系统运行状态", + "收集了用户反馈", "整理了项目资料", "准备了演示材料", +] + +# 形容词词库 +ADJECTIVES = [ + "高效的", "专业的", "创新的", "稳定的", "可靠的", "智能的", + "先进的", "实用的", "便捷的", "优秀的", "杰出的", "卓越的", +] + +# 名词词库 +NOUNS = [ + "系统", "平台", "应用", "服务", "方案", "产品", "技术", "工具", + "项目", "团队", "计划", "目标", "成果", "进展", "效率", "质量", +] + +# 连接词 +CONNECTORS = [ + "并且", "同时", "而且", "另外", "此外", "因此", "所以", "然后", +] + +# 结尾语 +ENDINGS = [ + "这是一个很好的开始。", + "我们对此感到非常满意。", + "期待能有更好的结果。", + "这将带来积极的影响。", + "相信未来会更加美好。", + "让我们继续努力。", + "这是值得庆祝的成就。", + "我们会继续保持这种势头。", + "这体现了团队的实力。", + "我们为此感到自豪。", +] + + +def generate_simple_sentence() -> str: + """生成简单句""" + subject = random.choice(SUBJECTS) + time_phrase = random.choice(TIME_PHRASES) if random.random() > 0.3 else "" + location = random.choice(LOCATIONS) if random.random() > 0.5 else "" + verb_phrase = random.choice(VERB_PHRASES) + + parts = [time_phrase, subject, location, verb_phrase] + parts = [p for p in parts if p] # 过滤空字符串 + return "".join(parts) + "。" + + +def generate_compound_sentence() -> str: + """生成复合句""" + sentence1 = generate_simple_sentence().rstrip("。") + connector = random.choice(CONNECTORS) + sentence2 = generate_simple_sentence().rstrip("。") + + return f"{sentence1}，{connector}{sentence2}。" + + +def generate_descriptive_sentence() -> str: + """生成描述性句子""" + subject = random.choice(SUBJECTS) + adj = random.choice(ADJECTIVES) + noun = random.choice(NOUNS) + verb_phrase = random.choice(VERB_PHRASES) + + return f"{subject}开发了一个{adj}{noun}，{verb_phrase}。" + + +def generate_single_text(length_range: Tuple[int, int] = (50, 100)) -> str: + """ + 生成单个测试文本 + + Args: + length_range: 文本长度范围 (min, max) + + Returns: + 生成的文本 + """ + min_len, max_len = length_range + target_len = random.randint(min_len, max_len) + + text = "" + sentence_generators = [ + generate_simple_sentence, + generate_compound_sentence, + generate_descriptive_sentence, + ] + + while len(text) < target_len: + generator = random.choice(sentence_generators) + sentence = generator() + text += sentence + + # 如果超出太多，截断到最近的句号 + if len(text) > max_len + 20: + # 找到目标长度附近的句号 + end_pos = text.rfind("。", 0, max_len + 10) + if end_pos > min_len: + text = text[: end_pos + 1] + + return text + + +def generate_test_texts( + count: int = 50, + length_range: Tuple[int, int] = (50, 100), +) -> List[str]: + """ + 生成测试文本列表 + + Args: + count: 生成数量 + length_range: 文本长度范围 + + Returns: + 文本列表 + """ + texts = [] + for _ in range(count): + text = generate_single_text(length_range) + texts.append(text) + + return texts + + +if __name__ == "__main__": + # 测试文本生成 + texts = generate_test_texts(5, (50, 100)) + for i, text in enumerate(texts, 1): + print(f"[{i}] ({len(text)}字): {text}") + print() diff --git a/scripts/build_docker.sh b/scripts/build_docker.sh new file mode 100644 index 0000000..74a6c7a --- /dev/null +++ b/scripts/build_docker.sh @@ -0,0 +1,469 @@ +#!/bin/bash +# +# Qwen3-ASR Docker Build Tool +# Step-by-step interactive or CLI parameter mode. + +set -euo pipefail + +# ============================================================================= +# Defaults +# ============================================================================= + +REGISTRY="unis" +IMAGE_NAME="qwen3-asr" +VERSION="latest" +BUILD_TYPE="all" +PLATFORM="linux/amd64" +PUSH="false" +EXPORT_TAR="false" +EXPORT_DIR="." +NO_CACHE="false" +LANG_MODE="zh" + +# ============================================================================= +# Localization +# ============================================================================= + +_resolve_lang() { + printf "%s" "$LANG_MODE" +} + +_msg() { + local k="$1" l; l="$(_resolve_lang)" + if [[ "$l" == "zh" ]]; then + case "$k" in + title) echo "Qwen3-ASR Docker 构建工具" ;; + subtitle) echo "按提示选择或直接回车使用默认值" ;; + q_lang) echo "界面语言" ;; + q_type) echo "构建目标" ;; + q_arch) echo "目标架构" ;; + q_version) echo "镜像版本" ;; + q_push) echo "推送到仓库" ;; + q_export) echo "导出 tar.gz" ;; + q_output) echo "输出目录" ;; + q_reg) echo "仓库命名空间" ;; + q_cache) echo "禁用构建缓存" ;; + q_confirm) echo "确认并开始构建" ;; + summary) echo "构建摘要" ;; + cancel) echo "已取消" ;; + start) echo "开始构建..." ;; + done) echo "构建完成" ;; + recent) echo "最近镜像" ;; + opt_cpu) echo "CPU" ;; + opt_gpu) echo "GPU" ;; + opt_metax) echo "沐曦 GPU" ;; + opt_iluvatar) echo "天数 GPU" ;; + opt_mthreads) echo "摩尔线程 GPU" ;; + opt_all) echo "全部 (CPU + GPU)" ;; + opt_amd64) echo "amd64 (x86_64)" ;; + opt_arm64) echo "arm64 (Apple Silicon / ARM)" ;; + opt_multi) echo "多架构 (amd64 + arm64)" ;; + err_inv) echo "无效输入" ;; + err_opt) echo "未知选项" ;; + err_gpu) echo "GPU 构建仅支持 amd64" ;; + err_bx) echo "需要 Docker Buildx" ;; + yes) echo "是" ;; + no) echo "否" ;; + esac + else + case "$k" in + title) echo "Qwen3-ASR Docker Build Tool" ;; + subtitle) echo "Select options or press Enter for defaults" ;; + q_lang) echo "Language" ;; + q_type) echo "Build target" ;; + q_arch) echo "Architecture" ;; + q_version) echo "Image version" ;; + q_push) echo "Push to registry" ;; + q_export) echo "Export tar.gz" ;; + q_output) echo "Output directory" ;; + q_reg) echo "Registry namespace" ;; + q_cache) echo "Disable build cache" ;; + q_confirm) echo "Confirm and start build" ;; + summary) echo "Build summary" ;; + cancel) echo "Cancelled" ;; + start) echo "Starting build..." ;; + done) echo "Build complete" ;; + recent) echo "Recent images" ;; + opt_cpu) echo "CPU" ;; + opt_gpu) echo "GPU" ;; + opt_metax) echo "MetaX GPU" ;; + opt_iluvatar) echo "Iluvatar GPU" ;; + opt_mthreads) echo "Moore Threads GPU" ;; + opt_all) echo "All (CPU + GPU)" ;; + opt_amd64) echo "amd64 (x86_64)" ;; + opt_arm64) echo "arm64 (Apple Silicon / ARM)" ;; + opt_multi) echo "Multi-arch (amd64 + arm64)" ;; + err_inv) echo "Invalid input" ;; + err_opt) echo "Unknown option" ;; + err_gpu) echo "GPU build only supports amd64" ;; + err_bx) echo "Docker Buildx is required" ;; + yes) echo "yes" ;; + no) echo "no" ;; + esac + fi +} + +_label_bool() { [[ "$1" == "true" ]] && _msg yes || _msg no; } + +# ============================================================================= +# Helpers +# ============================================================================= + +info() { echo "[INFO] $1" >&2; } +warn() { echo "[WARN] $1" >&2; } +die() { echo "[ERROR] $1" >&2; exit 1; } + +parse_arch() { + case "$1" in + amd64) echo "linux/amd64" ;; + arm64) echo "linux/arm64" ;; + multi) echo "linux/amd64,linux/arm64" ;; + *) die "$(_msg err_inv): $1" ;; + esac +} + +arch_label() { + case "$1" in + linux/amd64) echo "amd64" ;; + linux/arm64) echo "arm64" ;; + linux/amd64,linux/arm64) echo "multi" ;; + *) echo "$1" ;; + esac +} + +ensure_buildx() { + docker buildx version >/dev/null 2>&1 || die "$(_msg err_bx)" + if ! docker buildx inspect qwen3-asr-builder >/dev/null 2>&1; then + info "Creating buildx builder: qwen3-asr-builder" + docker buildx create --name qwen3-asr-builder --driver docker-container --use >/dev/null + else + docker buildx use qwen3-asr-builder >/dev/null + fi +} + +export_compressor() { + command -v pigz >/dev/null 2>&1 && echo "pigz -f" || echo "gzip -f" +} + +# ============================================================================= +# Build Core +# ============================================================================= + +build_image() { + local target="$1" dockerfile="$2" tag="$3" platform="$4" + local args=(buildx build --platform "$platform" -f "$dockerfile" -t "$tag") + + [[ "$NO_CACHE" == "true" ]] && args+=(--no-cache) + if [[ "$target" == "metax" ]]; then + args+=(--build-arg "METAX_BASE_IMAGE=${METAX_BASE_IMAGE}") + fi + if [[ "$target" == "iluvatar" ]]; then + args+=(--build-arg "ILUVATAR_BASE_IMAGE=${ILUVATAR_BASE_IMAGE}") + fi + if [[ "$target" == "mthreads" ]]; then + args+=(--build-arg "MTHREADS_BASE_IMAGE=${MTHREADS_BASE_IMAGE}") + fi + + if [[ "$platform" == *","* ]]; then + [[ "$EXPORT_TAR" == "true" ]] && warn "Multi-arch cannot export tar.gz" + args+=(--push) + elif [[ "$PUSH" == "true" ]]; then + args+=(--push) + elif [[ "$EXPORT_TAR" == "true" ]]; then + mkdir -p "$EXPORT_DIR" + local suffix="${target}-${VERSION}-$(basename "$platform")" + local tar="${EXPORT_DIR}/${IMAGE_NAME}-${suffix}.tar" + args+=(--output "type=docker,dest=${tar}") + else + args+=(--load) + fi + + info "Building ${target}: ${tag} (${platform})" + docker "${args[@]}" . + + if [[ "$EXPORT_TAR" == "true" && "$platform" != *","* && "$PUSH" != "true" ]]; then + local suffix="${target}-${VERSION}-$(basename "$platform")" + local tar="${EXPORT_DIR}/${IMAGE_NAME}-${suffix}.tar" + if [[ -f "$tar" ]]; then + info "Compressing ${tar}" + $(export_compressor) "$tar" + info "Exported ${tar}.gz" + fi + fi +} + +build_cpu() { + local tag="${REGISTRY}/${IMAGE_NAME}:${VERSION}" + [[ "$VERSION" == "latest" ]] && tag="${REGISTRY}/${IMAGE_NAME}:cpu-latest" + build_image "cpu" "Dockerfile.cpu" "$tag" "$PLATFORM" +} + +build_gpu() { + [[ "$PLATFORM" == *"arm64"* ]] && die "$(_msg err_gpu)" + build_image "gpu" "Dockerfile.gpu" "${REGISTRY}/${IMAGE_NAME}:gpu-${VERSION}" "linux/amd64" +} + +build_metax() { + [[ "$PLATFORM" == *"arm64"* ]] && die "$(_msg err_gpu)" + [[ -n "${METAX_BASE_IMAGE:-}" ]] || die "METAX_BASE_IMAGE is required for metax builds. Use an official MetaX vLLM image." + build_image "metax" "Dockerfile.metax" "${REGISTRY}/${IMAGE_NAME}:metax-${VERSION}" "linux/amd64" +} + +build_iluvatar() { + [[ "$PLATFORM" == *"arm64"* ]] && die "$(_msg err_gpu)" + [[ -n "${ILUVATAR_BASE_IMAGE:-}" ]] || die "ILUVATAR_BASE_IMAGE is required for iluvatar builds. Use the official Iluvatar vLLM image." + build_image "iluvatar" "Dockerfile.iluvatar" "${REGISTRY}/${IMAGE_NAME}:iluvatar-${VERSION}" "linux/amd64" +} + +build_mthreads() { + [[ "$PLATFORM" == *"arm64"* ]] && die "$(_msg err_gpu)" + [[ -n "${MTHREADS_BASE_IMAGE:-}" ]] || die "MTHREADS_BASE_IMAGE is required for mthreads builds. Use the official Moore Threads vLLM image." + build_image "mthreads" "Dockerfile.mthreads" "${REGISTRY}/${IMAGE_NAME}:mthreads-${VERSION}" "linux/amd64" +} + +# ============================================================================= +# Interactive: step-by-step +# ============================================================================= + +_ask() { + local __var_name="$1" prompt="$2" default="$3" l + local val + l="$(_resolve_lang)" + if [[ "$l" == "zh" ]]; then + read -r -p "${prompt} (默认: ${default}): " val + else + read -r -p "${prompt} (default: ${default}): " val + fi + printf -v "$__var_name" "%s" "${val:-$default}" +} + +_ask_bool() { + local __var_name="$1" prompt="$2" default="$3" hint l + local val + l="$(_resolve_lang)" + if [[ "$default" == "true" ]]; then + hint="[Y/n]" + else + hint="[y/N]" + fi + if [[ "$l" == "zh" ]]; then + read -r -p "${prompt} ${hint}: " val + else + read -r -p "${prompt} ${hint}: " val + fi + case "${val:-}" in + [Yy]|[Yy][Ee][Ss]|是) printf -v "$__var_name" "%s" "true" ;; + [Nn]|[Nn][Oo]|否) printf -v "$__var_name" "%s" "false" ;; + "") printf -v "$__var_name" "%s" "$default" ;; + *) warn "$(_msg err_inv): ${val}"; printf -v "$__var_name" "%s" "$default" ;; + esac +} + +_ask_choice() { + local __var_name="$1" prompt="$2" default="$3" l + shift 3 + local labels=() values=() i=1 + while [[ $# -ge 2 ]]; do + labels+=("$1") + values+=("$2") + shift 2 + done + + l="$(_resolve_lang)" + echo >&2 + echo "${prompt}:" >&2 + local idx=1 + for label in "${labels[@]}"; do + if [[ "$idx" -eq "$default" ]]; then + if [[ "$l" == "zh" ]]; then + echo " ${idx}) ${label} [默认]" >&2 + else + echo " ${idx}) ${label} [default]" >&2 + fi + else + echo " ${idx}) ${label}" >&2 + fi + ((idx++)) + done + + local val + if [[ "$l" == "zh" ]]; then + read -r -p "请选择 [1-${#labels[@]}] (默认: ${default}): " val + else + read -r -p "Select [1-${#labels[@]}] (default: ${default}): " val + fi + val="${val:-$default}" + + if [[ "$val" =~ ^[0-9]+$ ]] && [[ "$val" -ge 1 && "$val" -le "${#labels[@]}" ]]; then + printf -v "$__var_name" "%s" "${values[$((val-1))]}" + else + if [[ "$l" == "zh" ]]; then + warn "$(_msg err_inv): ${val}，使用默认值 ${default}" + else + warn "$(_msg err_inv): ${val}, using default ${default}" + fi + printf -v "$__var_name" "%s" "${values[$((default-1))]}" + fi +} + +interactive_mode() { + echo + echo "========================================" + echo "$(_msg title)" + echo "$(_msg subtitle)" + echo "========================================" + echo + + # 1. Language first so subsequent prompts use it + _ask_choice LANG_MODE "$(_msg q_lang)" 1 \ + "中文" "zh" \ + "English" "en" + echo + + # 2. Build type (numbered) + _ask_choice BUILD_TYPE "$(_msg q_type)" 5 \ + "$(_msg opt_cpu)" "cpu" \ + "$(_msg opt_gpu)" "gpu" \ + "$(_msg opt_metax)" "metax" \ + "$(_msg opt_iluvatar)" "iluvatar" \ + "$(_msg opt_mthreads)" "mthreads" \ + "$(_msg opt_all)" "all" + + # 3. Architecture (numbered) + local default_arch=1 + [[ "$(arch_label "$PLATFORM")" == "arm64" ]] && default_arch=2 + [[ "$(arch_label "$PLATFORM")" == "multi" ]] && default_arch=3 + local arch_val + _ask_choice arch_val "$(_msg q_arch)" "$default_arch" \ + "$(_msg opt_amd64)" "amd64" \ + "$(_msg opt_arm64)" "arm64" \ + "$(_msg opt_multi)" "multi" + PLATFORM="$(parse_arch "$arch_val")" + + # 4. Free-form inputs + _ask VERSION "$(_msg q_version)" "$VERSION" + _ask_bool PUSH "$(_msg q_push)" "$PUSH" + _ask_bool EXPORT_TAR "$(_msg q_export)" "$EXPORT_TAR" + [[ "$EXPORT_TAR" == "true" ]] && _ask EXPORT_DIR "$(_msg q_output)" "$EXPORT_DIR" + _ask REGISTRY "$(_msg q_reg)" "$REGISTRY" + _ask_bool NO_CACHE "$(_msg q_cache)" "$NO_CACHE" + + # Summary + echo + echo "----------------------------------------" + echo "$(_msg summary)" + echo "----------------------------------------" + echo "$(_msg q_lang): $LANG_MODE" + echo "$(_msg q_type): $BUILD_TYPE" + echo "$(_msg q_arch): $(arch_label "$PLATFORM")" + echo "$(_msg q_version): $VERSION" + echo "$(_msg q_push): $(_label_bool "$PUSH")" + echo "$(_msg q_export): $(_label_bool "$EXPORT_TAR")" + [[ "$EXPORT_TAR" == "true" ]] && echo "$(_msg q_output): $EXPORT_DIR" + echo "$(_msg q_reg): $REGISTRY" + echo "$(_msg q_cache): $(_label_bool "$NO_CACHE")" + echo + + local confirm + read -r -p "$(_msg q_confirm) [Y/n]: " confirm + if [[ "$confirm" =~ ^[Nn]$ ]]; then info "$(_msg cancel)"; exit 0; fi + + info "$(_msg start)" +} + +# ============================================================================= +# CLI Mode +# ============================================================================= + +show_help() { + cat <&2 + echo "isolated" + return 0 + ;; + esac +} + +detect_accelerator() { + local configured="${ACCELERATOR:-auto}" + configured="${configured,,}" + + case "$configured" in + nvidia|metax|iluvatar|mthreads|cpu) + echo "$configured" + return 0 + ;; + cuda) + echo "nvidia" + return 0 + ;; + maca|muxi|mx) + echo "metax" + return 0 + ;; + ix|tianshu|天数) + echo "iluvatar" + return 0 + ;; + mthreads|musa|moorethreads|摩尔线程) + echo "mthreads" + return 0 + ;; + esac + + if command -v mx-smi >/dev/null 2>&1; then + echo "metax" + return 0 + fi + if command -v ixsmi >/dev/null 2>&1; then + echo "iluvatar" + return 0 + fi + if command -v mthreads-gmi >/dev/null 2>&1; then + echo "mthreads" + return 0 + fi + if command -v nvidia-smi >/dev/null 2>&1; then + echo "nvidia" + return 0 + fi + echo "cpu" +} + +list_nvidia_devices() { + if command -v nvidia-smi >/dev/null 2>&1; then + nvidia-smi --query-gpu=index --format=csv,noheader,nounits | sed '/^$/d' + fi +} + +list_metax_devices() { + if ! command -v mx-smi >/dev/null 2>&1; then + return 0 + fi + local output + output="$(mx-smi -L 2>/dev/null || true)" + if [[ -n "$output" ]]; then + echo "$output" | awk 'NF { print NR - 1 }' + return 0 + fi + output="$(mx-smi 2>/dev/null || true)" + if [[ -n "$output" ]]; then + echo "$output" | awk '/^[[:space:]]*\|[[:space:]]*[0-9]+[[:space:]]/ { print $2 }' + fi +} + +list_iluvatar_devices() { + if ! command -v ixsmi >/dev/null 2>&1; then + return 0 + fi + local output + output="$(ixsmi -L 2>/dev/null || true)" + if [[ -n "$output" ]]; then + echo "$output" | awk 'NF { print NR - 1 }' + return 0 + fi + output="$(ixsmi 2>/dev/null || true)" + if [[ -n "$output" ]]; then + echo "$output" | awk '/^[[:space:]]*\|[[:space:]]*[0-9]+[[:space:]]/ { print $2 }' + fi +} + +list_mthreads_devices() { + if ! command -v mthreads-gmi >/dev/null 2>&1; then + return 0 + fi + local output + output="$(mthreads-gmi -L 2>/dev/null || true)" + if [[ -n "$output" ]]; then + echo "$output" | awk 'NF { print NR - 1 }' + return 0 + fi + output="$(mthreads-gmi list 2>/dev/null || true)" + if [[ -n "$output" ]]; then + echo "$output" | awk '/^[[:space:]]*\|[[:space:]]*[0-9]+[[:space:]]/ { print $2 }' + return 0 + fi + output="$(mthreads-gmi 2>/dev/null || true)" + if [[ -n "$output" ]]; then + echo "$output" | awk '/^[[:space:]]*\|[[:space:]]*[0-9]+[[:space:]]/ { print $2 }' + fi +} + +normalize_visible_devices() { + local accelerator="$1" + local shared="${ASR_VISIBLE_DEVICES:-}" + shared="${shared// /}" + if [[ -n "$shared" && "$shared" != "none" && "$shared" != "void" ]]; then + if [[ "$shared" == "all" ]]; then + case "$accelerator" in + nvidia) mapfile -t gpu_indexes < <(list_nvidia_devices) ;; + metax) mapfile -t gpu_indexes < <(list_metax_devices) ;; + iluvatar) mapfile -t gpu_indexes < <(list_iluvatar_devices) ;; + mthreads) mapfile -t gpu_indexes < <(list_mthreads_devices) ;; + *) gpu_indexes=() ;; + esac + if [[ ${#gpu_indexes[@]} -eq 0 ]]; then + echo "" + return 0 + fi + local shared_joined + shared_joined=$(IFS=,; echo "${gpu_indexes[*]}") + echo "$shared_joined" + return 0 + fi + echo "$shared" + return 0 + fi + + local raw="" + case "$accelerator" in + nvidia) + raw="${CUDA_VISIBLE_DEVICES:-}" + ;; + metax) + raw="${METAX_VISIBLE_DEVICES:-${MACA_VISIBLE_DEVICES:-${MX_VISIBLE_DEVICES:-}}}" + ;; + iluvatar) + raw="${ILUVATAR_VISIBLE_DEVICES:-${IX_VISIBLE_DEVICES:-${CUDA_VISIBLE_DEVICES:-}}}" + ;; + mthreads) + raw="${MTHREADS_VISIBLE_DEVICES:-${MUSA_VISIBLE_DEVICES:-${CUDA_VISIBLE_DEVICES:-}}}" + ;; + *) + echo "" + return 0 + ;; + esac + raw="${raw// /}" + + if [[ -z "$raw" || "$raw" == "none" || "$raw" == "void" ]]; then + echo "" + return 0 + fi + + if [[ "$raw" == "all" ]]; then + case "$accelerator" in + nvidia) mapfile -t gpu_indexes < <(list_nvidia_devices) ;; + metax) mapfile -t gpu_indexes < <(list_metax_devices) ;; + iluvatar) mapfile -t gpu_indexes < <(list_iluvatar_devices) ;; + mthreads) mapfile -t gpu_indexes < <(list_mthreads_devices) ;; + esac + if [[ ${#gpu_indexes[@]} -eq 0 ]]; then + echo "" + return 0 + fi + local joined + joined=$(IFS=,; echo "${gpu_indexes[*]}") + echo "$joined" + return 0 + fi + + echo "$raw" +} + +export_visible_devices_aliases() { + local accelerator="$1" + local devices_csv="$2" + if [[ -z "$devices_csv" ]]; then + return 0 + fi + + export ASR_VISIBLE_DEVICES="$devices_csv" + case "$accelerator" in + nvidia) + export CUDA_VISIBLE_DEVICES="$devices_csv" + ;; + metax) + export METAX_VISIBLE_DEVICES="$devices_csv" + export MACA_VISIBLE_DEVICES="$devices_csv" + export MX_VISIBLE_DEVICES="$devices_csv" + ;; + iluvatar) + export ILUVATAR_VISIBLE_DEVICES="$devices_csv" + export IX_VISIBLE_DEVICES="$devices_csv" + export CUDA_VISIBLE_DEVICES="$devices_csv" + ;; + mthreads) + export MTHREADS_VISIBLE_DEVICES="$devices_csv" + export MUSA_VISIBLE_DEVICES="$devices_csv" + export CUDA_VISIBLE_DEVICES="$devices_csv" + ;; + esac +} + +supports_sharded_topology() { + local accelerator="$1" + case "$accelerator" in + nvidia|metax|iluvatar|mthreads) + return 0 + ;; + *) + return 1 + ;; + esac +} + +start_backend_process() { + local accelerator="$1" + local device="$2" + local port="$3" + local log_file="$4" + local bind_host="$5" + local workers="${WORKERS:-1}" + + if [[ "$accelerator" == "nvidia" && -n "$device" ]]; then + CUDA_VISIBLE_DEVICES="$device" \ + ACCELERATOR="nvidia" \ + DEVICE="cuda:0" \ + WORKERS="$workers" \ + HOST="$bind_host" \ + PORT="$port" \ + LOG_FILE="$log_file" \ + "$DEFAULT_CMD_1" "$DEFAULT_CMD_2" & + return 0 + fi + + if [[ "$accelerator" == "metax" && -n "$device" ]]; then + METAX_VISIBLE_DEVICES="$device" \ + MACA_VISIBLE_DEVICES="$device" \ + MX_VISIBLE_DEVICES="$device" \ + ACCELERATOR="metax" \ + DEVICE="cuda:0" \ + WORKERS="$workers" \ + HOST="$bind_host" \ + PORT="$port" \ + LOG_FILE="$log_file" \ + "$DEFAULT_CMD_1" "$DEFAULT_CMD_2" & + return 0 + fi + + if [[ "$accelerator" == "iluvatar" && -n "$device" ]]; then + ILUVATAR_VISIBLE_DEVICES="$device" \ + IX_VISIBLE_DEVICES="$device" \ + CUDA_VISIBLE_DEVICES="$device" \ + ACCELERATOR="iluvatar" \ + DEVICE="cuda:0" \ + WORKERS="$workers" \ + HOST="$bind_host" \ + PORT="$port" \ + LOG_FILE="$log_file" \ + "$DEFAULT_CMD_1" "$DEFAULT_CMD_2" & + return 0 + fi + + if [[ "$accelerator" == "mthreads" && -n "$device" ]]; then + MTHREADS_VISIBLE_DEVICES="$device" \ + MUSA_VISIBLE_DEVICES="$device" \ + CUDA_VISIBLE_DEVICES="$device" \ + ACCELERATOR="mthreads" \ + DEVICE="cuda:0" \ + WORKERS="$workers" \ + HOST="$bind_host" \ + PORT="$port" \ + LOG_FILE="$log_file" \ + "$DEFAULT_CMD_1" "$DEFAULT_CMD_2" & + return 0 + fi + + ACCELERATOR="$accelerator" \ + WORKERS="$workers" \ + HOST="$bind_host" \ + PORT="$port" \ + LOG_FILE="$log_file" \ + "$DEFAULT_CMD_1" "$DEFAULT_CMD_2" & +} + +exec_backend_process() { + local accelerator="$1" + local device="$2" + local port="$3" + local log_file="$4" + local bind_host="$5" + local workers="${WORKERS:-1}" + + if [[ "$accelerator" == "nvidia" && -n "$device" ]]; then + export CUDA_VISIBLE_DEVICES="$device" + export DEVICE="cuda:0" + elif [[ "$accelerator" == "metax" && -n "$device" ]]; then + export METAX_VISIBLE_DEVICES="$device" + export MACA_VISIBLE_DEVICES="$device" + export MX_VISIBLE_DEVICES="$device" + export DEVICE="cuda:0" + elif [[ "$accelerator" == "iluvatar" && -n "$device" ]]; then + export ILUVATAR_VISIBLE_DEVICES="$device" + export IX_VISIBLE_DEVICES="$device" + export CUDA_VISIBLE_DEVICES="$device" + export DEVICE="cuda:0" + elif [[ "$accelerator" == "mthreads" && -n "$device" ]]; then + export MTHREADS_VISIBLE_DEVICES="$device" + export MUSA_VISIBLE_DEVICES="$device" + export CUDA_VISIBLE_DEVICES="$device" + export DEVICE="cuda:0" + fi + + export ACCELERATOR="$accelerator" + export WORKERS="$workers" + export HOST="$bind_host" + export PORT="$port" + export LOG_FILE="$log_file" + exec "$DEFAULT_CMD_1" "$DEFAULT_CMD_2" +} + +start_sharded_backend_mode() { + local accelerator="$1" + local devices_csv="$2" + local bind_host="${HOST:-0.0.0.0}" + local public_port="${PORT:-8000}" + local log_file="${LOG_FILE:-/app/data/logs/qwen3-asr.log}" + local workers="${WORKERS:-1}" + local shard_size=0 + if [[ -n "$devices_csv" ]]; then + IFS=',' read -r -a devs <<< "$devices_csv" + shard_size="${#devs[@]}" + fi + + if ! supports_sharded_topology "$accelerator"; then + echo "[entrypoint] accelerator=${accelerator} does not support sharded topology, fallback to isolated" + return 1 + fi + if [[ -z "$devices_csv" ]]; then + echo "[entrypoint] sharded topology requires visible GPU devices, fallback to isolated" + return 1 + fi + if ! [[ "$shard_size" =~ ^[0-9]+$ ]] || (( shard_size < 2 )); then + echo "[entrypoint] sharded topology requires at least 2 devices, got shard_size=${shard_size}, fallback to isolated" + return 1 + fi + + echo "[entrypoint] Starting sharded backend: accelerator=${accelerator} devices=${devices_csv} shard_size=${shard_size} bind=${bind_host}:${public_port}" + export ASR_ACTIVE_TOPOLOGY="sharded" + export_visible_devices_aliases "$accelerator" "$devices_csv" + exec_backend_process "$accelerator" "" "$public_port" "$log_file" "$bind_host" +} + +wait_for_port() { + local host="$1" + local port="$2" + local timeout_sec="$3" + local deadline=$((SECONDS + timeout_sec)) + + while (( SECONDS < deadline )); do + if (echo >/dev/tcp/"$host"/"$port") >/dev/null 2>&1; then + return 0 + fi + sleep 1 + done + return 1 +} + +is_positive_integer() { + [[ "$1" =~ ^[0-9]+$ ]] && (( "$1" > 0 )) +} + +start_internal_nginx_mode() { + local devices_csv="$1" + local accelerator="${ACTIVE_ACCELERATOR:-auto}" + local bind_host="${MULTI_GPU_BIND_HOST:-127.0.0.1}" + local base_port="18000" + local public_port="${PORT:-8000}" + local ready_timeout="${MULTI_GPU_READY_TIMEOUT:-180}" + local rate_limit_rps="${NGINX_RATE_LIMIT_RPS:-0}" + local rate_limit_burst="${NGINX_RATE_LIMIT_BURST:-0}" + + local valid_devices=() + local devices=() + if [[ -n "$devices_csv" ]]; then + IFS=',' read -r -a devices <<< "$devices_csv" + for dev in "${devices[@]}"; do + if [[ -n "$dev" ]]; then + valid_devices+=("$dev") + fi + done + fi + + if [[ "$rate_limit_rps" != "0" ]] && ! is_positive_integer "$rate_limit_rps"; then + echo "[entrypoint] Invalid NGINX_RATE_LIMIT_RPS=${rate_limit_rps}, fallback to 0 (disabled)" + rate_limit_rps="0" + fi + if [[ "$rate_limit_burst" != "0" ]] && ! is_positive_integer "$rate_limit_burst"; then + echo "[entrypoint] Invalid NGINX_RATE_LIMIT_BURST=${rate_limit_burst}, fallback to 0" + rate_limit_burst="0" + fi + if [[ "$rate_limit_rps" != "0" && "$rate_limit_burst" == "0" ]]; then + # By default, give short burst headroom equal to rate limit. + rate_limit_burst="$rate_limit_rps" + fi + + if ! command -v nginx >/dev/null 2>&1; then + if [[ ${#valid_devices[@]} -le 1 ]]; then + local direct_device="" + local direct_log_file="${LOG_FILE:-/app/data/logs/qwen3-asr.log}" + local direct_bind_host="${HOST:-0.0.0.0}" + if [[ ${#valid_devices[@]} -eq 1 ]]; then + direct_device="${valid_devices[0]}" + fi + echo "[entrypoint] nginx not found; starting single backend directly on ${direct_bind_host}:${public_port}" + exec_backend_process "$accelerator" "$direct_device" "$public_port" "$direct_log_file" "$direct_bind_host" + fi + echo "[entrypoint] nginx not found in image; cannot start multi-backend proxy mode" + exit 1 + fi + + local backend_ports=() + local backend_pids=() + local idx=0 + local default_log_file="${LOG_FILE:-/app/data/logs/qwen3-asr.log}" + + if [[ ${#valid_devices[@]} -eq 0 ]]; then + local single_port="$base_port" + local single_log_file="${default_log_file%.log}-gpu0.log" + backend_ports+=("$single_port") + + echo "[entrypoint] No explicit multi-accelerator list detected, starting single backend instance (${accelerator})" + start_backend_process "$accelerator" "" "$single_port" "$single_log_file" "$bind_host" + backend_pids+=("$!") + else + if [[ ${#valid_devices[@]} -eq 1 ]]; then + echo "[entrypoint] Single accelerator device detected (${valid_devices[0]}), starting one backend instance (${accelerator})" + local single_gpu_port="$base_port" + local single_gpu_log_file="${default_log_file%.log}-gpu0.log" + backend_ports+=("$single_gpu_port") + + start_backend_process "$accelerator" "${valid_devices[0]}" "$single_gpu_port" "$single_gpu_log_file" "$bind_host" + backend_pids+=("$!") + else + echo "[entrypoint] Multi-accelerator detected, starting one instance per device (${accelerator}): ${valid_devices[*]}" + for dev in "${valid_devices[@]}"; do + local port=$((base_port + idx)) + local instance_log_file="${default_log_file%.log}-gpu${idx}.log" + backend_ports+=("$port") + + echo "[entrypoint] Starting ASR instance #${idx} on ${accelerator} device ${dev}, bind ${bind_host}:${port}" + WORKERS="1" start_backend_process "$accelerator" "$dev" "$port" "$instance_log_file" "$bind_host" + + backend_pids+=("$!") + idx=$((idx + 1)) + done + fi + fi + + local port + for port in "${backend_ports[@]}"; do + if ! wait_for_port "$bind_host" "$port" "$ready_timeout"; then + echo "[entrypoint] Backend instance on ${bind_host}:${port} failed to become ready in ${ready_timeout}s" + for pid in "${backend_pids[@]}"; do + kill "$pid" >/dev/null 2>&1 || true + done + wait >/dev/null 2>&1 || true + exit 1 + fi + done + + local nginx_conf="/tmp/qwen3-asr-internal-nginx.conf" + { + echo "worker_processes auto;" + echo "events { worker_connections 1024; }" + echo "http {" + echo " limit_req_status 429;" + if is_positive_integer "$rate_limit_rps"; then + # Global token bucket for the whole service, not per-client-IP. + echo " limit_req_zone \$server_name zone=api_rps:10m rate=${rate_limit_rps}r/s;" + fi + echo + echo " upstream qwen3_asr_upstream {" + echo " least_conn;" + for port in "${backend_ports[@]}"; do + echo " server ${bind_host}:${port} max_fails=3 fail_timeout=10s;" + done + echo " keepalive 128;" + echo " }" + echo + echo " map \$http_upgrade \$connection_upgrade {" + echo " default upgrade;" + echo " '' close;" + echo " }" + echo + echo " server {" + echo " listen ${public_port};" + echo " server_name _;" + echo " client_max_body_size 2048m;" + echo + echo " location / {" + if is_positive_integer "$rate_limit_rps"; then + echo " limit_req zone=api_rps burst=${rate_limit_burst} nodelay;" + fi + echo " proxy_pass http://qwen3_asr_upstream;" + echo " proxy_http_version 1.1;" + echo " proxy_set_header Host \$host;" + echo " proxy_set_header X-Real-IP \$remote_addr;" + echo " proxy_set_header X-Forwarded-For \$proxy_add_x_forwarded_for;" + echo " proxy_set_header X-Forwarded-Proto \$scheme;" + echo " proxy_set_header Upgrade \$http_upgrade;" + echo " proxy_set_header Connection \$connection_upgrade;" + echo " proxy_connect_timeout 10s;" + echo " proxy_send_timeout 3600s;" + echo " proxy_read_timeout 3600s;" + echo " send_timeout 3600s;" + echo " proxy_buffering off;" + echo " }" + echo " }" + echo "}" + } > "$nginx_conf" + + local nginx_pid="" + + cleanup() { + set +e + if [[ -n "$nginx_pid" ]]; then + kill "$nginx_pid" >/dev/null 2>&1 || true + fi + for pid in "${backend_pids[@]}"; do + kill "$pid" >/dev/null 2>&1 || true + done + wait >/dev/null 2>&1 || true + } + trap cleanup EXIT INT TERM + + echo "[entrypoint] Starting internal nginx load balancer on :${public_port}" + nginx -c "$nginx_conf" -g "daemon off;" & + nginx_pid="$!" + + while true; do + if ! kill -0 "$nginx_pid" >/dev/null 2>&1; then + echo "[entrypoint] nginx exited unexpectedly" + exit 1 + fi + for pid in "${backend_pids[@]}"; do + if ! kill -0 "$pid" >/dev/null 2>&1; then + echo "[entrypoint] backend process ${pid} exited unexpectedly" + exit 1 + fi + done + sleep 2 + done +} + +has_default_cmd=false +if [[ $# -eq 2 && "$1" == "$DEFAULT_CMD_1" && "$2" == "$DEFAULT_CMD_2" ]]; then + has_default_cmd=true +fi + +# If user passes a custom command, respect it and bypass auto multi-GPU logic. +if [[ $# -gt 0 && "$has_default_cmd" != "true" ]]; then + exec "$@" +fi + +ACTIVE_ACCELERATOR="$(detect_accelerator)" +devices_csv="$(normalize_visible_devices "$ACTIVE_ACCELERATOR")" +topology="$(normalize_topology)" + +case "$topology" in + isolated) + export ASR_ACTIVE_TOPOLOGY="isolated" + start_internal_nginx_mode "$devices_csv" + ;; + sharded) + if ! start_sharded_backend_mode "$ACTIVE_ACCELERATOR" "$devices_csv"; then + export ASR_ACTIVE_TOPOLOGY="isolated" + start_internal_nginx_mode "$devices_csv" + fi + ;; + auto) + if start_sharded_backend_mode "$ACTIVE_ACCELERATOR" "$devices_csv"; then + exit 0 + fi + export ASR_ACTIVE_TOPOLOGY="isolated" + start_internal_nginx_mode "$devices_csv" + ;; +esac diff --git a/scripts/docker/init_host_dirs.sh b/scripts/docker/init_host_dirs.sh new file mode 100644 index 0000000..4538b57 --- /dev/null +++ b/scripts/docker/init_host_dirs.sh @@ -0,0 +1,18 @@ +#!/usr/bin/env bash + +set -euo pipefail + +MODEL_DIR="${MODEL_STORAGE_DIR:-/opt/dep/asr/models}" +DATA_DIR="${DATA_STORAGE_DIR:-/opt/dep/asr/data}" +LOG_DIR="${DATA_DIR}/logs" +TEMP_DIR="${DATA_DIR}/temp" +TASK_DIR="${DATA_DIR}/tasks" + +mkdir -p "$MODEL_DIR" "$LOG_DIR" "$TEMP_DIR" "$TASK_DIR" + +echo "已创建宿主机目录:" +echo " models: $MODEL_DIR" +echo " data: $DATA_DIR" +echo " logs: $LOG_DIR" +echo " temp: $TEMP_DIR" +echo " tasks: $TASK_DIR" diff --git a/scripts/download-models.sh b/scripts/download-models.sh new file mode 100644 index 0000000..3a88e72 --- /dev/null +++ b/scripts/download-models.sh @@ -0,0 +1,201 @@ +#!/usr/bin/env bash +# +# Incremental model downloader. +# Downloads missing models into an existing models directory without deleting +# existing files and without requiring uv. +# + +set -euo pipefail + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +PROJECT_ROOT="$(dirname "$SCRIPT_DIR")" +if [[ -f "${PROJECT_ROOT}/app/utils/download_models.py" ]]; then + DEFAULT_MODEL_DIR="${PROJECT_ROOT}/models" +else + DEFAULT_MODEL_DIR="/opt/dep/asr/models" +fi +MODEL_DIR="${MODEL_STORAGE_DIR:-${MODELS_DIR:-$DEFAULT_MODEL_DIR}}" +PYTHON_BIN="${PYTHON_BIN:-${PYTHON:-python3}}" +AUTO_MODE="false" +MODE="auto" +DOCKER_IMAGE="${ASR_IMAGE:-}" +ARG_COUNT="$#" +RUNTIME_MODELS_DIR="" +INTERACTIVE_DOCKER_TARGET="false" + +info() { echo "[INFO] $1"; } +die() { echo "[ERROR] $1" >&2; exit 1; } + +usage() { + cat </dev/null 2>&1 || die "Python not found: ${PYTHON_BIN}" + info " Runner: local python (${PYTHON_BIN})" + + local args=("$downloader" --models-dir "$MODEL_DIR" --cache-dir "${MODELSCOPE_CACHE:-$MODEL_PARENT_DIR}") + if [[ -n "$RUNTIME_MODELS_DIR" ]]; then + args+=(--runtime-models-dir "$RUNTIME_MODELS_DIR") + fi + if [[ "$AUTO_MODE" == "true" ]]; then + args+=(--auto-mode) + fi + "$PYTHON_BIN" "${args[@]}" +} + +run_docker_download() { + command -v docker >/dev/null 2>&1 || die "Docker not found; cannot use docker mode" + [[ -n "$DOCKER_IMAGE" ]] || die "Docker image not set; pass --docker-image IMAGE or set ASR_IMAGE in .env" + [[ -f "${SCRIPT_DIR}/download_models_standalone.py" ]] || die "Cannot find download_models_standalone.py" + info " Runner: docker image (${DOCKER_IMAGE})" + + local args=(/tmp/download_models_standalone.py --models-dir /app/models --cache-dir /app) + if [[ -n "$RUNTIME_MODELS_DIR" ]]; then + args+=(--runtime-models-dir "$RUNTIME_MODELS_DIR") + fi + if [[ "$AUTO_MODE" == "true" ]]; then + args+=(--auto-mode) + fi + + docker run --rm \ + -e MODELS_DIR=/app/models \ + -e MODELSCOPE_PATH=/app/models \ + -e MODELSCOPE_CACHE=/app \ + -e DATA_DIR=/app/data \ + -e TEMP_DIR=/app/data/temp \ + -e LOG_FILE=/app/data/logs/qwen3-asr.log \ + -v "${MODEL_DIR}:/app/models" \ + -v "${SCRIPT_DIR}/download_models_standalone.py:/tmp/download_models_standalone.py:ro" \ + "${DOCKER_IMAGE}" \ + python3 "${args[@]}" +} + +case "$MODE" in + local) + run_local_download + ;; + docker) + run_docker_download + ;; + auto) + if ! run_local_download; then + run_docker_download + fi + ;; +esac + +info "Model download complete" +info "Models directory: ${MODEL_DIR}" diff --git a/scripts/download_models_standalone.py b/scripts/download_models_standalone.py new file mode 100644 index 0000000..1f6f2c9 --- /dev/null +++ b/scripts/download_models_standalone.py @@ -0,0 +1,287 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +"""Standalone incremental model downloader. + +This script intentionally does not import the application package. It is meant +for offline delivery hosts where only ModelScope and a Python runtime are +needed to fill /opt/dep/asr/models. +""" + +from __future__ import annotations + +import argparse +import json +from dataclasses import dataclass +from pathlib import Path +from typing import Optional + +from modelscope.hub.snapshot_download import snapshot_download + + +@dataclass(frozen=True) +class ModelAsset: + model_id: str + description: str + revision: Optional[str] = None + required_patterns: tuple[str, ...] = () + alternative_required_patterns: tuple[tuple[str, ...], ...] = () + min_total_size_bytes: int = 0 + + +MODEL_ASSETS: tuple[ModelAsset, ...] = ( + ModelAsset( + model_id="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", + description="VAD", + revision="v2.0.2", + required_patterns=("configuration.json", "config.yaml", "model.pb"), + min_total_size_bytes=1_000_000, + ), + ModelAsset( + model_id="iic/speech_campplus_speaker-diarization_common", + description="CAM++ Diarization", + required_patterns=( + "configuration.json", + "config.yaml", + "onnx/asd.onnx", + "onnx/face_recog_ir101.onnx", + "onnx/fqa.onnx", + "onnx/version-RFB-320.onnx", + ), + min_total_size_bytes=50_000_000, + ), + ModelAsset( + model_id="iic/speech_campplus_sv_zh-cn_16k-common", + description="Configured Speaker Verification", + required_patterns=("configuration.json", "config.yaml", "campplus_cn_common.bin"), + min_total_size_bytes=10_000_000, + ), + ModelAsset( + model_id="iic/speech_eres2netv2_sv_zh-cn_16k-common", + description="Realtime Speaker Verification", + required_patterns=("configuration.json",), + min_total_size_bytes=10_000_000, + ), + ModelAsset( + model_id="damo/speech_campplus_sv_zh-cn_16k-common", + description="CAM++ Speaker Verification", + required_patterns=("configuration.json", "config.yaml", "campplus_cn_common.bin"), + min_total_size_bytes=10_000_000, + ), + ModelAsset( + model_id="damo/speech_campplus-transformer_scl_zh-cn_16k-common", + description="CAM++ Transformer", + required_patterns=("configuration.json", "campplus_cn_encoder.pt", "transformer_backend.pt"), + min_total_size_bytes=10_000_000, + ), + ModelAsset( + model_id="Qwen/Qwen3-ASR-0.6B", + description="Qwen3-ASR-0.6B Offline", + required_patterns=("config.json",), + alternative_required_patterns=(("model.safetensors",), ("model-*.safetensors",)), + min_total_size_bytes=500_000_000, + ), + ModelAsset( + model_id="Qwen/Qwen3-ForcedAligner-0.6B", + description="Qwen3 Forced Aligner", + required_patterns=("config.json", "model.safetensors"), + min_total_size_bytes=500_000_000, + ), + ModelAsset( + model_id="Qwen/Qwen3-ASR-1.7B", + description="Qwen3-ASR-1.7B Offline", + required_patterns=("config.json",), + alternative_required_patterns=(("model.safetensors",), ("model-*.safetensors",)), + min_total_size_bytes=500_000_000, + ), +) + + +def model_path(models_dir: Path, model_id: str) -> Path: + return models_dir / model_id + + +def find_missing_patterns(root: Path, patterns: tuple[str, ...]) -> list[str]: + return [pattern for pattern in patterns if not any(root.glob(pattern))] + + +def has_required_alternative(root: Path, pattern_groups: tuple[tuple[str, ...], ...]) -> bool: + if not pattern_groups: + return True + return any(not find_missing_patterns(root, group) for group in pattern_groups) + + +def directory_size(path: Path) -> int: + return sum(item.stat().st_size for item in path.rglob("*") if item.is_file()) + + +def check_asset(models_dir: Path, asset: ModelAsset) -> tuple[bool, str]: + path = model_path(models_dir, asset.model_id) + if not path.exists() or not path.is_dir(): + return False, "directory_missing" + if not any(path.iterdir()): + return False, "directory_empty" + missing = find_missing_patterns(path, asset.required_patterns) + if missing: + return False, "missing=" + ",".join(missing) + if not has_required_alternative(path, asset.alternative_required_patterns): + alternatives = " OR ".join(" + ".join(group) for group in asset.alternative_required_patterns) + return False, "missing=" + alternatives + size = directory_size(path) + if size < asset.min_total_size_bytes: + return False, f"too_small={size}" + return True, "ok" + + +def fix_camplusplus_config(models_dir: Path, runtime_models_dir: Optional[Path] = None) -> bool: + config_file = models_dir / "iic/speech_campplus_speaker-diarization_common/configuration.json" + if not config_file.exists(): + return False + + runtime_dir = runtime_models_dir or models_dir + replacements = { + "damo/speech_campplus_sv_zh-cn_16k-common": "damo/speech_campplus_sv_zh-cn_16k-common", + "iic/speech_campplus_sv_zh-cn_16k-common": "iic/speech_campplus_sv_zh-cn_16k-common", + "damo/speech_campplus-transformer_scl_zh-cn_16k-common": "damo/speech_campplus-transformer_scl_zh-cn_16k-common", + "damo/speech_campplus-transformer_scl_zh-cn-16k-common": "damo/speech_campplus-transformer_scl_zh-cn-16k-common", + "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch": "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", + } + + try: + config = json.loads(config_file.read_text(encoding="utf-8")) + except Exception as exc: + print(f"⚠️ CAM++ 配置读取失败: {exc}") + return False + + modified = False + model_config = config.get("model") + if isinstance(model_config, dict): + for key in ("speaker_model", "change_locator", "vad_model"): + old_value = model_config.get(key) + relative_path = replacements.get(str(old_value)) + if not relative_path and isinstance(old_value, str): + for candidate in replacements.values(): + if old_value.endswith(candidate): + relative_path = candidate + break + if relative_path and (models_dir / relative_path).exists(): + model_config[key] = str(runtime_dir / relative_path) + modified = True + + if not modified: + return False + + config_file.write_text( + json.dumps(config, indent=4, ensure_ascii=False), + encoding="utf-8", + ) + return True + + +def download_missing( + models_dir: Path, + cache_dir: Path, + auto_mode: bool = False, + runtime_models_dir: Optional[Path] = None, +) -> bool: + models_dir.mkdir(parents=True, exist_ok=True) + cache_dir.mkdir(parents=True, exist_ok=True) + + missing: list[tuple[ModelAsset, str]] = [] + for asset in MODEL_ASSETS: + ok, reason = check_asset(models_dir, asset) + if not ok: + missing.append((asset, reason)) + + if not missing: + if not auto_mode: + print("✅ 所有模型已存在且完整，无需下载") + fix_camplusplus_config(models_dir, runtime_models_dir=runtime_models_dir) + return True + + print(f"📦 检测到 {len(missing)} 个模型需要下载/补齐") + if not auto_mode: + for asset, reason in missing: + print(f" - {asset.model_id} ({reason})") + + failed: list[tuple[str, str]] = [] + for index, (asset, _reason) in enumerate(missing, start=1): + if not auto_mode: + print(f"\n[{index}/{len(missing)}] {asset.description}") + print(f" 模型ID: {asset.model_id}") + try: + kwargs = { + "cache_dir": str(cache_dir), + "local_dir": str(model_path(models_dir, asset.model_id)), + } + if asset.revision: + kwargs["revision"] = asset.revision + snapshot_download(asset.model_id, **kwargs) + except Exception as exc: + print(f"❌ 下载失败: {asset.model_id}: {exc}") + failed.append((asset.model_id, str(exc))) + + if fix_camplusplus_config(models_dir, runtime_models_dir=runtime_models_dir) and not auto_mode: + print("✅ CAM++ 配置已修复为本地模型路径") + + if failed: + print("\n失败模型:") + for model_id, error in failed: + print(f" - {model_id}: {error}") + return False + + still_missing = [ + (asset, reason) + for asset in MODEL_ASSETS + for ok, reason in [check_asset(models_dir, asset)] + if not ok + ] + if still_missing: + print("\n仍不完整的模型:") + for asset, reason in still_missing: + print(f" - {asset.model_id}: {reason}") + return False + + print("✅ 所有模型准备就绪") + return True + + +def main() -> int: + parser = argparse.ArgumentParser(description="Download Qwen3-ASR models without importing app code") + parser.add_argument( + "--models-dir", + default=None, + help="Model directory to fill; default: MODELSCOPE_PATH, MODELS_DIR, or ./models", + ) + parser.add_argument( + "--cache-dir", + default=None, + help="ModelScope cache directory; default: parent of models-dir", + ) + parser.add_argument( + "--runtime-models-dir", + default=None, + help="Runtime model directory to write into CAM++ config; default: models-dir", + ) + parser.add_argument("--auto-mode", action="store_true", help="Reduce output") + args = parser.parse_args() + + import os + + models_dir = Path( + args.models_dir + or os.getenv("MODELSCOPE_PATH") + or os.getenv("MODELS_DIR") + or "./models" + ).resolve() + cache_dir = Path(args.cache_dir or os.getenv("MODELSCOPE_CACHE") or models_dir.parent).resolve() + runtime_models_dir = Path(args.runtime_models_dir).resolve() if args.runtime_models_dir else None + return 0 if download_missing( + models_dir, + cache_dir, + auto_mode=args.auto_mode, + runtime_models_dir=runtime_models_dir, + ) else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/package_vendor_gpu_image.sh b/scripts/package_vendor_gpu_image.sh new file mode 100644 index 0000000..2c5bcbd --- /dev/null +++ b/scripts/package_vendor_gpu_image.sh @@ -0,0 +1,132 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" + +VENDOR="" +BASE_IMAGE="" +REGISTRY="unis" +IMAGE_NAME="qwen3-asr" +VERSION="$(date +"%Y%m%d_%H%M%S")" +OUTPUT_DIR="${ROOT_DIR}/build-file" +EXPORT_TAR="true" +NO_CACHE="false" +PYTHON_BIN="" + +usage() { + cat <&2; exit 1; } +info() { echo "[INFO] $1" >&2; } +compressor() { command -v pigz >/dev/null 2>&1 && echo "pigz -f" || echo "gzip -f"; } + +while [[ $# -gt 0 ]]; do + case "$1" in + --vendor) VENDOR="$2"; shift 2 ;; + --base-image) BASE_IMAGE="$2"; shift 2 ;; + -v|--version) VERSION="$2"; shift 2 ;; + -r|--registry) REGISTRY="$2"; shift 2 ;; + -o|--output) OUTPUT_DIR="$2"; shift 2 ;; + --python-bin) PYTHON_BIN="$2"; shift 2 ;; + --no-export) EXPORT_TAR="false"; shift ;; + --no-cache) NO_CACHE="true"; shift ;; + -h|--help) usage; exit 0 ;; + *) die "Unknown argument: $1" ;; + esac +done + +[[ -n "$VENDOR" ]] || die "--vendor is required" +[[ -n "$BASE_IMAGE" ]] || die "--base-image is required" + +case "$VENDOR" in + metax) + dockerfile="Dockerfile.metax" + tag="${REGISTRY}/${IMAGE_NAME}:metax-${VERSION}" + target="metax" + build_arg_name="METAX_BASE_IMAGE" + default_python_bin="/opt/conda/bin/python" + ;; + iluvatar|ix|tianshu) + dockerfile="Dockerfile.iluvatar" + tag="${REGISTRY}/${IMAGE_NAME}:iluvatar-${VERSION}" + target="iluvatar" + build_arg_name="ILUVATAR_BASE_IMAGE" + default_python_bin="python3" + ;; + mthreads|musa|moorethreads) + dockerfile="Dockerfile.mthreads" + tag="${REGISTRY}/${IMAGE_NAME}:mthreads-${VERSION}" + target="mthreads" + build_arg_name="MTHREADS_BASE_IMAGE" + default_python_bin="python3" + ;; + *) + die "Unsupported vendor: ${VENDOR}" + ;; +esac + +PYTHON_BIN="${PYTHON_BIN:-$default_python_bin}" + +docker image inspect "$BASE_IMAGE" >/dev/null 2>&1 || { + die "Base image not found locally: ${BASE_IMAGE}. Run docker load first or docker pull it." +} + +args=( + build + -f "$dockerfile" + -t "$tag" + --build-arg "${build_arg_name}=${BASE_IMAGE}" + --build-arg "PYTHON_BIN=${PYTHON_BIN}" +) +[[ "$NO_CACHE" == "true" ]] && args+=(--no-cache) + +info "Building fused image: ${tag}" +info "Vendor base image: ${BASE_IMAGE}" +info "Base image Python: ${PYTHON_BIN}" +( + cd "$ROOT_DIR" + docker "${args[@]}" . +) + +if [[ "$EXPORT_TAR" == "true" ]]; then + mkdir -p "$OUTPUT_DIR" + tar_path="${OUTPUT_DIR}/${IMAGE_NAME}-${target}-${VERSION}-amd64.tar" + info "Exporting image archive: ${tar_path}" + docker save -o "$tar_path" "$tag" + info "Compressing archive" + $(compressor) "$tar_path" + info "Done: ${tar_path}.gz" +else + info "Built image: ${tag}" +fi diff --git a/scripts/prepare-models.sh b/scripts/prepare-models.sh new file mode 100644 index 0000000..4f0e07e --- /dev/null +++ b/scripts/prepare-models.sh @@ -0,0 +1,74 @@ +#!/bin/bash +# +# Model Export Tool for Offline Deployment +# + +set -euo pipefail + +OUTPUT_DIR="./models" +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +PROJECT_ROOT="$(dirname "$SCRIPT_DIR")" + +info() { echo "[INFO] $1"; } +die() { echo "[ERROR] $1" >&2; exit 1; } + +# Check if running from project root or scripts directory +if [ -f "$PROJECT_ROOT/app/utils/download_models.py" ]; then + cd "$PROJECT_ROOT" +elif [ -f "$SCRIPT_DIR/../app/utils/download_models.py" ]; then + cd "$SCRIPT_DIR/.." +else + die "Cannot find app/utils/download_models.py" +fi + +command -v uv >/dev/null 2>&1 || die "uv not found; install uv first" + +# Confirm +info "Export settings:" +info " Models: all declared offline/realtime/runtime models" +info " Output: ${OUTPUT_DIR}" +read -p "Start export? [Y/n]: " confirm +if [[ $confirm =~ ^[Nn]$ ]]; then + echo "Cancelled." + exit 0 +fi + +info "Exporting models..." + +# Remove existing models dir to ensure clean state +rm -rf "${OUTPUT_DIR}" + +# Run Python export +uv run python -m app.utils.download_models --export-dir "${OUTPUT_DIR}" + +info "Packaging..." + +PACKAGE="qwen3-asr-models-$(date +%Y%m%d-%H%M).tar.gz" + +# Use pigz for multi-threaded compression if available +if command -v pigz &> /dev/null; then + info "Using pigz for multi-threaded compression..." + # Get CPU cores (cross-platform: Linux and macOS) + if command -v nproc &> /dev/null; then + CPU_CORES=$(nproc) + elif command -v sysctl &> /dev/null; then + CPU_CORES=$(sysctl -n hw.ncpu) + else + CPU_CORES=4 + fi + tar -cf - "${OUTPUT_DIR}" | pigz -p "${CPU_CORES}" > "${PACKAGE}" +else + info "pigz not found, using standard gzip..." + tar -czf "${PACKAGE}" "${OUTPUT_DIR}" +fi + +SIZE=$(du -sh "${PACKAGE}" | cut -f1) + +# Done +info "Export complete" +info "Package: ${PACKAGE}" +info "Size: ${SIZE}" +echo "To deploy on an offline server:" +echo "1. Copy package: scp ${PACKAGE} user@server:/opt/qwen3-asr/" +echo "2. Extract: tar -xzvf ${PACKAGE}" +echo "3. Start service: docker-compose up -d" diff --git a/scripts/sync_accel_env.sh b/scripts/sync_accel_env.sh new file mode 100644 index 0000000..9fc8c1f --- /dev/null +++ b/scripts/sync_accel_env.sh @@ -0,0 +1,71 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" + +configured="${ACCELERATOR:-auto}" +configured="${configured,,}" + +detect_accelerator() { + case "$configured" in + metax|maca|muxi|mx) + echo "metax" + return 0 + ;; + iluvatar|ix|tianshu|天数) + echo "iluvatar" + return 0 + ;; + mthreads|musa|moorethreads|摩尔线程) + echo "mthreads" + return 0 + ;; + nvidia|cuda) + echo "nvidia" + return 0 + ;; + cpu) + echo "cpu" + return 0 + ;; + esac + + if command -v mx-smi >/dev/null 2>&1; then + echo "metax" + return 0 + fi + if command -v ixsmi >/dev/null 2>&1; then + echo "iluvatar" + return 0 + fi + if command -v mthreads-gmi >/dev/null 2>&1; then + echo "mthreads" + return 0 + fi + if command -v nvidia-smi >/dev/null 2>&1; then + echo "nvidia" + return 0 + fi + echo "cpu" +} + +accelerator="$(detect_accelerator)" +echo "[sync_accel_env] detected accelerator=${accelerator}" + +case "$accelerator" in + metax) + exec "$ROOT_DIR/scripts/sync_metax_env.sh" "$@" + ;; + iluvatar) + exec "$ROOT_DIR/scripts/sync_iluvatar_env.sh" "$@" + ;; + mthreads) + exec "$ROOT_DIR/scripts/sync_mthreads_env.sh" "$@" + ;; + nvidia) + exec "$ROOT_DIR/scripts/sync_gpu_env.sh" "$@" + ;; + cpu) + exec "$ROOT_DIR/scripts/sync_cpu_env.sh" "$@" + ;; +esac diff --git a/scripts/sync_cpu_env.sh b/scripts/sync_cpu_env.sh new file mode 100644 index 0000000..192539e --- /dev/null +++ b/scripts/sync_cpu_env.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +export UV_PROJECT_ENVIRONMENT="${UV_PROJECT_ENVIRONMENT:-$ROOT_DIR/.venv}" + +cd "$ROOT_DIR" +exec uv sync --project "$ROOT_DIR/environments/cpu" --frozen "$@" diff --git a/scripts/sync_gpu_env.sh b/scripts/sync_gpu_env.sh new file mode 100644 index 0000000..4afad88 --- /dev/null +++ b/scripts/sync_gpu_env.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +export UV_PROJECT_ENVIRONMENT="${UV_PROJECT_ENVIRONMENT:-$ROOT_DIR/.venv}" + +cd "$ROOT_DIR" +exec uv sync --frozen "$@" diff --git a/scripts/sync_iluvatar_env.sh b/scripts/sync_iluvatar_env.sh new file mode 100644 index 0000000..c6db485 --- /dev/null +++ b/scripts/sync_iluvatar_env.sh @@ -0,0 +1,27 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" + +usage() { + cat <<'EOF' +Usage: + ./scripts/sync_iluvatar_env.sh [uv sync args...] + +This syncs only the generic Qwen3-ASR dependencies for an Iluvatar host. +The Iluvatar GPU stack (IX runtime, PyTorch, vLLM, kernels) should come from +the official Iluvatar vLLM image or the vendor's matching installation package. +EOF +} + +if [[ "${1:-}" == "-h" || "${1:-}" == "--help" ]]; then + usage + exit 0 +fi + +cd "$ROOT_DIR" + +uv sync --project "$ROOT_DIR/environments/iluvatar" --no-dev --no-install-project "$@" + +echo "[sync_iluvatar_env] Synced common dependencies only." +echo "[sync_iluvatar_env] Use the official Iluvatar vLLM Docker image for the GPU runtime stack." diff --git a/scripts/sync_metax_env.sh b/scripts/sync_metax_env.sh new file mode 100644 index 0000000..4cbc219 --- /dev/null +++ b/scripts/sync_metax_env.sh @@ -0,0 +1,92 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +export UV_PROJECT_ENVIRONMENT="${UV_PROJECT_ENVIRONMENT:-$ROOT_DIR/.venv}" + +METAX_PYPI_INDEX="${METAX_PYPI_INDEX:-https://repos.metax-tech.com/r/maca-pypi/simple}" +METAX_PYPI_HOST="${METAX_PYPI_HOST:-repos.metax-tech.com}" +METAX_INSTALL_GPU_DEPS="${METAX_INSTALL_GPU_DEPS:-false}" + +METAX_TORCH_SPEC="${METAX_TORCH_SPEC:-torch}" +METAX_TORCHAUDIO_SPEC="${METAX_TORCHAUDIO_SPEC:-}" +METAX_TORCHVISION_SPEC="${METAX_TORCHVISION_SPEC:-}" +METAX_VLLM_SPEC="${METAX_VLLM_SPEC:-vllm}" + +usage() { + cat </dev/null 2>&1; then + pip3 index versions "$LIST_PACKAGE" \ + -i "$METAX_PYPI_INDEX" \ + --trusted-host "$METAX_PYPI_HOST" + elif command -v pip >/dev/null 2>&1; then + pip index versions "$LIST_PACKAGE" \ + -i "$METAX_PYPI_INDEX" \ + --trusted-host "$METAX_PYPI_HOST" + else + python3 -m pip index versions "$LIST_PACKAGE" \ + -i "$METAX_PYPI_INDEX" \ + --trusted-host "$METAX_PYPI_HOST" + fi + exit 0 +fi + +uv venv "$UV_PROJECT_ENVIRONMENT" +uv sync --project "$ROOT_DIR/environments/metax" --no-dev --no-install-project "$@" + +gpu_install_args=(pip install --reinstall) +if [[ "$METAX_INSTALL_GPU_DEPS" != "true" ]]; then + gpu_install_args+=(--no-deps) +fi +gpu_install_args+=( + --index-url "$METAX_PYPI_INDEX" \ + --trusted-host "$METAX_PYPI_HOST" \ +) +for package_spec in "$METAX_TORCH_SPEC" "$METAX_TORCHAUDIO_SPEC" "$METAX_TORCHVISION_SPEC" "$METAX_VLLM_SPEC"; do + [[ -n "$package_spec" ]] && gpu_install_args+=("$package_spec") +done + +uv "${gpu_install_args[@]}" + +if [[ "$METAX_INSTALL_GPU_DEPS" != "true" ]]; then + echo "[sync_metax_env] Installed MetaX GPU stack with --no-deps to avoid PyPI/NVIDIA fallback." + echo "[sync_metax_env] Set METAX_INSTALL_GPU_DEPS=true only if the MetaX index hosts all required GPU-stack dependencies." +fi + +"$UV_PROJECT_ENVIRONMENT/bin/python" - <<'PY' +import importlib +import torch + +print("torch", torch.__version__) +print("torch.cuda.is_available", torch.cuda.is_available()) +for name in ("vllm",): + module = importlib.import_module(name) + print(name, getattr(module, "__version__", "unknown")) +PY diff --git a/scripts/sync_mthreads_env.sh b/scripts/sync_mthreads_env.sh new file mode 100644 index 0000000..b620237 --- /dev/null +++ b/scripts/sync_mthreads_env.sh @@ -0,0 +1,27 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" + +usage() { + cat <<'EOF' +Usage: + ./scripts/sync_mthreads_env.sh [uv sync args...] + +This syncs only the generic Qwen3-ASR dependencies for a Moore Threads host. +The MUSA GPU stack (runtime, PyTorch, vLLM, kernels) should come from +the official Moore Threads vLLM image or the vendor's matching installation package. +EOF +} + +if [[ "${1:-}" == "-h" || "${1:-}" == "--help" ]]; then + usage + exit 0 +fi + +cd "$ROOT_DIR" + +uv sync --project "$ROOT_DIR/environments/mthreads" --no-dev --no-install-project "$@" + +echo "[sync_mthreads_env] Synced common dependencies only." +echo "[sync_mthreads_env] Use the official Moore Threads vLLM Docker image for the GPU runtime stack." diff --git a/start.py b/start.py new file mode 100644 index 0000000..c24cbff --- /dev/null +++ b/start.py @@ -0,0 +1,81 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +"""Qwen3-ASR server CLI entrypoint.""" + +import os +import sys +import multiprocessing as mp + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +from dotenv import load_dotenv + +load_dotenv() + +os.environ.setdefault("TOKENIZERS_PARALLELISM", "false") +os.environ.setdefault("TQDM_DISABLE", "1") +os.environ.setdefault("DISABLE_TQDM", "1") +os.environ.setdefault("VLLM_WORKER_MULTIPROC_METHOD", "spawn") + + +def _disable_third_party_progress_bars() -> None: + try: + from transformers.utils import logging as transformers_logging + + transformers_logging.disable_progress_bar() + transformers_logging.set_verbosity_error() + except Exception: + pass + + +def _configure_multiprocessing() -> None: + # vLLM launches EngineCore in a child process. CUDA cannot be re-initialized + # in a forked subprocess, so we must force spawn before importing the app. + current = mp.get_start_method(allow_none=True) + if current != "spawn": + mp.set_start_method("spawn", force=True) + + +def _run_server(workers: int) -> None: + from app.core.config import settings + import uvicorn + + print(f"Qwen3-ASR | http://{settings.HOST}:{settings.PORT} | {settings.DEVICE}") + + if workers == 1: + from app.bootstrap import run_cli_preflight + + if not run_cli_preflight(): + sys.exit(1) + elif workers > 1: + print(f"多Worker模式({workers})，启动前仅进行最小 preflight") + + uvicorn.run( + "app.main:app", + host=settings.HOST, + port=settings.PORT, + workers=workers, + reload=settings.DEBUG if workers == 1 else False, + log_level="debug" if settings.DEBUG else settings.LOG_LEVEL.lower(), + access_log=True, + ) + + +def main() -> None: + """主入口""" + workers = int(os.getenv("WORKERS", "1")) + _disable_third_party_progress_bars() + _configure_multiprocessing() + + try: + _run_server(workers) + except KeyboardInterrupt: + print("\n已停止") + sys.exit(0) + except Exception as e: + print(f"启动失败: {e}") + sys.exit(1) + + +if __name__ == "__main__": + main() diff --git a/test_diarization.py b/test_diarization.py new file mode 100644 index 0000000..cc79b81 --- /dev/null +++ b/test_diarization.py @@ -0,0 +1,43 @@ +import time +import requests +import json + +url = "http://localhost:8000/v1/audio/transcriptions" +file_path = "/dep/qwen3-asr/TicNote客户服务_20260518_100301-录音.m4a" + +print(f"Starting transcription WITH Speaker Diarization (声纹分离) for {file_path}...") +start_time = time.time() + +try: + with open(file_path, "rb") as f: + files = {"file": f} + data = { + "response_format": "verbose_json", + "enable_speaker_diarization": "true", # 明确开启声纹分离 + } + + response = requests.post(url, files=files, data=data) + + cost_time = time.time() - start_time + + if response.status_code == 200: + res_json = response.json() + segments = res_json.get("segments", []) + + print(f"\n✅ Success! Total time elapsed: {cost_time:.2f} seconds") + print(f"Total segments identified: {len(segments)}") + print("\n--- 识别结果预览 (前 10 个对话片段) ---") + + for seg in segments[:10]: + # 兼容 OpenAI 格式和自定义格式的时间戳字段 + start = seg.get("start", seg.get("start_time", 0.0)) + end = seg.get("end", seg.get("end_time", 0.0)) + speaker = seg.get("speaker_id", seg.get("speaker", "Unknown")) + text = seg.get("text", "").strip() + print(f"[{speaker}] {start:.2f}s - {end:.2f}s : {text}") + + else: + print(f"❌ Failed! Status code: {response.status_code}") + print(response.text) +except Exception as e: + print(f"Error occurred: {e}") diff --git a/test_speed.py b/test_speed.py new file mode 100644 index 0000000..cee7136 --- /dev/null +++ b/test_speed.py @@ -0,0 +1,34 @@ +import time +import requests +import sys + +url = "http://localhost:8000/v1/audio/transcriptions" +file_path = "/dep/qwen3-asr/TicNote客户服务_20260518_100301-录音.m4a" + +print(f"Starting transcription test for {file_path} using qwen3-asr-0.6b...") +start_time = time.time() + +try: + with open(file_path, "rb") as f: + files = {"file": f} + data = { + "response_format": "verbose_json", + "enable_speaker_diarization": "false", # Disable to speed up and focus on ASR + } + + response = requests.post(url, files=files, data=data) + + end_time = time.time() + cost_time = end_time - start_time + + if response.status_code == 200: + res_json = response.json() + text_len = len(res_json.get("text", "")) + print(f"Success! Transcription length: {text_len} chars.") + print(f"Total time elapsed: {cost_time:.2f} seconds") + print(f"Audio processed: {file_path}") + else: + print(f"Failed! Status code: {response.status_code}") + print(response.text) +except Exception as e: + print(f"Error occurred: {e}") diff --git a/test_web/index.html b/test_web/index.html new file mode 100644 index 0000000..c0278cc --- /dev/null +++ b/test_web/index.html @@ -0,0 +1,4936 @@ + + + + + + Qwen3-ASR 会议识别测试 + + + + +
+
+
+
Qwen3-ASR Meeting Console
+

会议语音识别测试

+

左边配置，右边直接看会议气泡流和原始日志。

+
+
+ 服务未检查 + +
+
+ +
+
+
+ + + +
+ +
+
+
+
+

识别配置

+

保持像 demo 一样的单配置面板，常用参数直接可改。

+
+ 实时模式 +
+ +
+
+

基础设置

+
+
+ + +
+
+ + +
+
+ + +
+ + +
+
+ Qwen 协议 + 实时参数在左侧，结果和日志固定在右侧。 +
+
+ +
+

识别选项

+
+
+
+ + +
+
+ + +
+
+ + +
+
+ + +
+ + +
+ + +
+
+

这个 demo 的实时气泡是“整段覆盖式 partial”，不是逐字追加，所以看起来会一截一截长出来。默认与后端保持一致：`silence_duration_ms=800`。

+
+
+ +
+ 高级参数 +
+
+ + +
+
+
+ + +
+
+ + +
+
+ + +
+
+ + +
+
+ + +
+
+
+ + + +
+
+
+ +
+
+

开始识别

+ 麦克风空闲 +
+
+ + + + +
+
开始后右侧会持续刷新中间结果、最终分段和原始日志。要测断线恢复，保持同一个 `session_id` 再次点击“开始实时会议”即可。
+
+
+
+
+ +
+
+
+
+

开始识别

+

普通测试只需要完成下面三步。

+
+ 等待音频 +
+ +
+
+
1
+
+

选择音频来源

+

上传测试文件，或填写可访问的音频/视频 URL、服务端本地路径。

+
+ + +
+ +
+
+ + +
+
+ + 也可以直接点“开始识别”，系统会自动上传。 +
+
+ + + 可用麦克风录一段会议测试音频。 +
+ + +
+ +
+
+ + +
+
+
+
+ +
+
2
+
+

确认识别选项

+
+ + + + +
+
+ 高级参数 +
+
+
+ + +
+
+ + +
+
+ + +
+
+
+
+ + +
+
+ + +
+
+
+
+
+
+ +
+
3
+
+

启动任务

+

创建任务后会自动轮询进度，完成后展示分段结果。

+
+ + + +
+
+
+
+
+ +
+
+
+

声纹管理

+

声纹样本和会议录音分开录，避免误用。

+
+ +
+ +
+
+ + +
+
+ + +
+
+
+
声
+
+

录制 12 秒声纹样本

+

请让要注册或识别的人单独说话，倒计时结束后会自动停止并上传。

+
+ + + 12 秒 +
+
还没有声纹样本。
+ + + +
+
+
+ + +
+
+
+ +
+ 连接与原始响应 +
+
+
+ + +
+
+ + +
+
+
+

原始 JSON

+ +
+ +
+
+
+ +
+
+
+
+

会议总结测试

+

用于在线会议和离线会议结束后，对 ASR 内容做三级总结验证。

+
+ 真实模型 +
+ +
+
+

总结模型

+
+
+ + +
+
+ + +
+
+ + +
+ +
+
点击“调用模型总结”会直接请求 OpenAI-compatible 接口；请求 JSON 会同步显示在右侧。
+
+ +
+

会议元数据

+
+
+ + +
+
+ + +
+
+ + +
+
+ + +
+
+
+ 会议时长 等待完整会议内容 + 参会人 / 说话人 等待完整会议内容 +
+ + +
+ + +
+
+ +
+

总结级别

+
+ + + +
+
+ +
+

Markdown 总结模板

+
+
+ + +
+
+ + +
+
+
+ + +
+
+ + + +
+ +
+ +
+

完整会议内容来源

+
+ + + +
+
+ + +
+
+ + + +
+ +
+
+
+
+
+ + +
+
+ + + + diff --git a/tests/test_hotword_prompt.py b/tests/test_hotword_prompt.py new file mode 100644 index 0000000..99b7489 --- /dev/null +++ b/tests/test_hotword_prompt.py @@ -0,0 +1,39 @@ +# -*- coding: utf-8 -*- + +import unittest + +from app.core.hotword_resolver import format_hotword_prompt_context +from app.core.hotword_resolver import strip_hotword_prompt_leakage + + +class HotwordPromptTest(unittest.TestCase): + def test_format_prompt_context_removes_weights(self) -> None: + hotwords = "KPI考核考核 0.2 华智 0.2 合川 0.2 商客市场拓展 0.2" + + self.assertEqual( + format_hotword_prompt_context(hotwords), + "热词列表：[KPI考核考核, 华智, 合川, 商客市场拓展]", + ) + + def test_strip_leaked_hotword_prompt(self) -> None: + text = "热词列表：[KPI考核考核, 华智, 合川, 商客市场拓展] 今天我们看一下执行力。" + + self.assertEqual( + strip_hotword_prompt_leakage(text), + "今天我们看一下执行力。", + ) + + def test_strip_legacy_context_leak_prefix(self) -> None: + text = ( + "Use this context when resolving named entities: " + "热词列表：[KPI考核考核, 华智] 今天继续看KPI考核。" + ) + + self.assertEqual( + strip_hotword_prompt_leakage(text), + "今天继续看KPI考核。", + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_model_integrity.py b/tests/test_model_integrity.py new file mode 100644 index 0000000..290a094 --- /dev/null +++ b/tests/test_model_integrity.py @@ -0,0 +1,63 @@ +import tempfile +import unittest +from pathlib import Path + +from app.utils.model_loader import ModelIntegritySpec, _check_model_integrity_spec + + +class ModelIntegritySpecTest(unittest.TestCase): + def test_accepts_single_safetensors_weight(self) -> None: + with tempfile.TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + snapshot = root / "snapshots" / "main" + snapshot.mkdir(parents=True) + (snapshot / "config.json").write_text("{}", encoding="utf-8") + (snapshot / "model.safetensors").write_bytes(b"weights") + + result = _check_model_integrity_spec(_hf_qwen_spec(root)) + + self.assertTrue(result["ok"]) + + def test_accepts_sharded_safetensors_weight(self) -> None: + with tempfile.TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + snapshot = root / "snapshots" / "main" + snapshot.mkdir(parents=True) + (snapshot / "config.json").write_text("{}", encoding="utf-8") + (snapshot / "model.safetensors.index.json").write_text("{}", encoding="utf-8") + (snapshot / "model-00001-of-00002.safetensors").write_bytes(b"weights") + + result = _check_model_integrity_spec(_hf_qwen_spec(root)) + + self.assertTrue(result["ok"]) + + def test_rejects_missing_safetensors_weight(self) -> None: + with tempfile.TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + snapshot = root / "snapshots" / "main" + snapshot.mkdir(parents=True) + (snapshot / "config.json").write_text("{}", encoding="utf-8") + + result = _check_model_integrity_spec(_hf_qwen_spec(root)) + + self.assertFalse(result["ok"]) + self.assertEqual(result["reason"], "required_files_missing") + + +def _hf_qwen_spec(root: Path) -> ModelIntegritySpec: + return ModelIntegritySpec( + description="Qwen test", + path=root, + required_patterns=("snapshots/*/config.json",), + alternative_required_patterns=( + ("snapshots/*/model.safetensors",), + ( + "snapshots/*/model.safetensors.index.json", + "snapshots/*/model-*.safetensors", + ), + ), + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_offline_model_selection.py b/tests/test_offline_model_selection.py new file mode 100644 index 0000000..7e79658 --- /dev/null +++ b/tests/test_offline_model_selection.py @@ -0,0 +1,55 @@ +import unittest +import sys +import types +from unittest.mock import patch + +fastapi_module = types.ModuleType("fastapi") +fastapi_module.Request = object + +fastapi_responses_module = types.ModuleType("fastapi.responses") +fastapi_responses_module.JSONResponse = object + +sys.modules.setdefault("fastapi", fastapi_module) +sys.modules.setdefault("fastapi.responses", fastapi_responses_module) + +manager_module = types.ModuleType("app.services.asr.manager") +manager_module.get_model_manager = lambda: None + +model_plan_module = types.ModuleType("app.services.asr.model_plan") +model_plan_module.get_active_qwen_model = lambda: "qwen3-asr-0.6b" +model_plan_module.get_default_model_id = lambda: "qwen3-asr-0.6b" +model_plan_module.get_runtime_model_ids = lambda: ["qwen3-asr-0.6b"] + +sys.modules.setdefault("app.services.asr.manager", manager_module) +sys.modules.setdefault("app.services.asr.model_plan", model_plan_module) + +from app.core.exceptions import InvalidParameterException +from app.services.asr.model_selection import validate_offline_model_id + + +class OfflineModelSelectionTest(unittest.TestCase): + def test_empty_model_uses_default_offline_model(self) -> None: + with ( + patch( + "app.services.asr.model_selection.get_offline_model_ids", + return_value=["qwen3-asr-0.6b"], + ), + patch( + "app.services.asr.model_selection.get_default_offline_model_id", + return_value="qwen3-asr-0.6b", + ), + ): + self.assertEqual(validate_offline_model_id(None), "qwen3-asr-0.6b") + self.assertEqual(validate_offline_model_id(""), "qwen3-asr-0.6b") + + def test_rejects_unavailable_offline_model(self) -> None: + with patch( + "app.services.asr.model_selection.get_offline_model_ids", + return_value=["qwen3-asr-0.6b"], + ): + with self.assertRaises(InvalidParameterException): + validate_offline_model_id("qwen3-asr-1.7b") + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_qwenasr_cpu_features.py b/tests/test_qwenasr_cpu_features.py new file mode 100644 index 0000000..9a6eec4 --- /dev/null +++ b/tests/test_qwenasr_cpu_features.py @@ -0,0 +1,41 @@ +import unittest +from unittest.mock import patch + +from app.services.asr.qwenasr_rust import validate_qwenasr_cpu_features + + +class QwenASRCpuFeatureTest(unittest.TestCase): + def test_accepts_x86_avx2_fma_cpu(self) -> None: + with ( + patch("app.services.asr.qwenasr_rust.platform.machine", return_value="x86_64"), + patch( + "app.services.asr.qwenasr_rust._read_linux_cpu_flags", + return_value={"sse4_2", "avx2", "fma"}, + ), + ): + validate_qwenasr_cpu_features() + + def test_rejects_x86_cpu_without_required_features(self) -> None: + with ( + patch("app.services.asr.qwenasr_rust.platform.machine", return_value="x86_64"), + patch( + "app.services.asr.qwenasr_rust._read_linux_cpu_flags", + return_value={"sse4_2"}, + ), + ): + with self.assertRaisesRegex(RuntimeError, "Missing: avx2, fma"): + validate_qwenasr_cpu_features() + + def test_skips_non_x86_cpu(self) -> None: + with ( + patch("app.services.asr.qwenasr_rust.platform.machine", return_value="aarch64"), + patch( + "app.services.asr.qwenasr_rust._read_linux_cpu_flags", + return_value=set(), + ), + ): + validate_qwenasr_cpu_features() + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_text_cleanup.py b/tests/test_text_cleanup.py new file mode 100644 index 0000000..b78b9f9 --- /dev/null +++ b/tests/test_text_cleanup.py @@ -0,0 +1,61 @@ +# -*- coding: utf-8 -*- + +import unittest + +from app.core.text_cleanup import deduplicate_asr_text + + +class TextCleanupTest(unittest.TestCase): + def test_cleanup_repeated_meeting_words(self) -> None: + self.assertEqual( + deduplicate_asr_text("在在整个B C融合在在政街包邮上面"), + "在整个B C融合在政街包邮上面", + ) + self.assertEqual(deduplicate_asr_text("需要需要去做的"), "需要去做的") + self.assertEqual( + deduplicate_asr_text("这个这个审核的一个流程意识"), + "这个审核的一个流程意识", + ) + self.assertEqual( + deduplicate_asr_text("都是都是一个货开卷在"), + "都是一个货开卷在", + ) + + def test_cleanup_currency_artifacts_from_itn(self) -> None: + text = "都可以采取¥9000的，看采取¥9¥9000的手机档次还是¥5000的手机档次" + + self.assertEqual( + deduplicate_asr_text(text), + "都可以采取9000元的，看采取9000元的手机档次还是5000元的手机档次", + ) + + def test_cleanup_household_count_glued_with_noise(self) -> None: + text = "已经收集到240247户的一个清单。我们测算的话大概有114户，要占他的大概百分之接近50。" + + self.assertEqual( + deduplicate_asr_text(text), + "已经收集到247户的一个清单。我们测算的话大概有114户，要占他的大概百分之接近50。", + ) + + def test_cleanup_preserves_normal_numeric_contexts(self) -> None: + self.assertEqual(deduplicate_asr_text("目前已经完成了1115户"), "目前已经完成了1115户") + self.assertEqual(deduplicate_asr_text("合同金额有120000"), "合同金额有120000") + + def test_cleanup_collapses_degenerate_numeric_clauses(self) -> None: + self.assertEqual(deduplicate_asr_text("192。192。192。192。192。"), "192。") + self.assertEqual( + deduplicate_asr_text("现在还行，有点儿。对。你还都能弄来。172021。你们都会用。192。192。192。192。幺九"), + "现在还行，有点儿。对。你还都能弄来。172021。你们都会用。192。幺九", + ) + + def test_cleanup_collapses_degenerate_short_sentence_clauses(self) -> None: + self.assertEqual(deduplicate_asr_text("对。对。"), "对。对。") + self.assertEqual(deduplicate_asr_text("对。对。对。对。"), "对。") + self.assertEqual( + deduplicate_asr_text("把这个ifi的驱动文件上传到服务器。对。对。对。对"), + "把这个ifi的驱动文件上传到服务器。对。", + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..8274db4 --- /dev/null +++ b/uv.lock @@ -0,0 +1,4744 @@ +version = 1 +revision = 3 +requires-python = ">=3.10, <3.13" +resolution-markers = [ + "python_full_version >= '3.12' and sys_platform == 'win32'", + "python_full_version >= '3.12' and sys_platform == 'emscripten'", + "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'linux'", + "python_full_version >= '3.12' and sys_platform == 'darwin'", + "(python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform == 'emscripten'", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "python_full_version == '3.11.*' 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"proc-macro2", + "quote", + "syn", +] diff --git a/vendor/qwenasr/Cargo.toml b/vendor/qwenasr/Cargo.toml new file mode 100644 index 0000000..c9f7b63 --- /dev/null +++ b/vendor/qwenasr/Cargo.toml @@ -0,0 +1,17 @@ +[workspace] +resolver = "2" +members = ["crates/qwen-asr", "crates/qwen-asr-cli"] +exclude = ["flutter/qwen_asr/rust"] # Cargokit builds independently + +[workspace.package] +version = "0.1.2" +edition = "2021" +license = "MIT" + +[workspace.dependencies] +# x-release-please-version +qwen-asr = { version = "0.5.0", path = "crates/qwen-asr" } + +[profile.release] +opt-level = 3 +lto = "thin" diff --git a/vendor/qwenasr/LICENSE b/vendor/qwenasr/LICENSE new file mode 100644 index 0000000..83f93ec --- /dev/null +++ b/vendor/qwenasr/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 Li Zhuo + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/vendor/qwenasr/VENDORED_FROM.md b/vendor/qwenasr/VENDORED_FROM.md new file mode 100644 index 0000000..93d8b97 --- /dev/null +++ b/vendor/qwenasr/VENDORED_FROM.md @@ -0,0 +1,14 @@ +# Vendored Source + +This directory vendors the upstream `QwenASR` Rust workspace into this repository. + +- Upstream repository: `https://github.com/huanglizhuo/QwenASR` +- Vendored commit: `4e85a19b05f034e106a345d279c68f50df718ab8` +- License: `MIT` + +Local modifications included directly in the vendored source: + +- Expose `qwen_asr_stream_set_past_text` in the C API for correct streaming behavior. +- Expose `qwen_asr_force_align_file` in the C API for service-side word timestamp alignment. + +The vendored source is now the build source of truth for the CPU Rust backend. diff --git a/vendor/qwenasr/crates/qwen-asr-cli/CHANGELOG.md b/vendor/qwenasr/crates/qwen-asr-cli/CHANGELOG.md new file mode 100644 index 0000000..acc1378 --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr-cli/CHANGELOG.md @@ -0,0 +1,97 @@ +# Changelog + +## [0.6.0](https://github.com/huanglizhuo/QwenASR/compare/qwen-asr-cli-v0.5.0...qwen-asr-cli-v0.6.0) (2026-03-21) + + +### Features + +* add missing parameter to qwen asr offline model ([f56e8b1](https://github.com/huanglizhuo/QwenASR/commit/f56e8b1e58731344fad92a7ed38c59a9f09267f6)) +* add missing parameter to qwen asr offline model ([6d1e38d](https://github.com/huanglizhuo/QwenASR/commit/6d1e38da19cbae46c2afe2e1af03a5d437679ef8)) +* improve the live stream performace on macos ([ba47230](https://github.com/huanglizhuo/QwenASR/commit/ba47230403f897bde2486b3235cfd3f5ca24e293)) +* publish qwen-asr-cli binary to crates.io ([31ae992](https://github.com/huanglizhuo/QwenASR/commit/31ae99221d71fdcd6c40b6cfe4d77e7f67642b76)) +* support live from blackhold for macos for qwen-asr-cli ([724ead1](https://github.com/huanglizhuo/QwenASR/commit/724ead1fe121d0ed0a0f7ef874142f665e7d0da3)) + + +### Bug Fixes + +* bump qwen-asr dependency version for cli ([efec6b1](https://github.com/huanglizhuo/QwenASR/commit/efec6b14dfb07934c7e6cbb459d26573dcfe5912)) +* clean up the code ([346d112](https://github.com/huanglizhuo/QwenASR/commit/346d112595c0d93f58c54339a7f32ca6e1e648d8)) +* publish 0.2.3 with tag-driven flow ([3637ec8](https://github.com/huanglizhuo/QwenASR/commit/3637ec80f5519ecbd0a034f6c1f23f78156cd0fe)) +* publish 0.2.3 with tag-driven flow ([e7bbd18](https://github.com/huanglizhuo/QwenASR/commit/e7bbd18dc009c3bd87f32e2346c196f65c618b19)) +* **qwen-asr-cli:** add homepage metadata to trigger release ([642c6a2](https://github.com/huanglizhuo/QwenASR/commit/642c6a2dbdcd01c4351d6a56dfdf2c6b99fa5488)) +* **qwen-asr-cli:** update qwen-asr dependency to v0.4.2 ([d9915ea](https://github.com/huanglizhuo/QwenASR/commit/d9915ea8eba41f3a7a129d0ea5c6cea939a33c6d)) +* **qwen-asr-cli:** use workspace dependency to keep qwen-asr version in sync ([5ac7e01](https://github.com/huanglizhuo/QwenASR/commit/5ac7e01bdaf95b3026eec7a12f04e9f5d78d0f3b)) +* release flow for cli ([8dfc4b7](https://github.com/huanglizhuo/QwenASR/commit/8dfc4b7820ceef98efd992974ab3a0b7fcdd9b10)) +* trigger patch release 0.2.1 for flutter ([b5785f9](https://github.com/huanglizhuo/QwenASR/commit/b5785f9e0a6e4cab3a4796bbd1bd401876ea5926)) +* update the release flow to support PAT ([2b9be6c](https://github.com/huanglizhuo/QwenASR/commit/2b9be6c21b7e74e51bf1d1f15e6959679db70542)) + +## [0.4.0](https://github.com/huanglizhuo/QwenASR/compare/qwen-asr-cli-v0.3.4...qwen-asr-cli-v0.4.0) (2026-03-19) + + +### Features + +* add missing parameter to qwen asr offline model ([f56e8b1](https://github.com/huanglizhuo/QwenASR/commit/f56e8b1e58731344fad92a7ed38c59a9f09267f6)) +* add missing parameter to qwen asr offline model ([6d1e38d](https://github.com/huanglizhuo/QwenASR/commit/6d1e38da19cbae46c2afe2e1af03a5d437679ef8)) +* improve the live stream performace on macos ([ba47230](https://github.com/huanglizhuo/QwenASR/commit/ba47230403f897bde2486b3235cfd3f5ca24e293)) +* publish qwen-asr-cli binary to crates.io ([31ae992](https://github.com/huanglizhuo/QwenASR/commit/31ae99221d71fdcd6c40b6cfe4d77e7f67642b76)) +* support live from blackhold for macos for qwen-asr-cli ([724ead1](https://github.com/huanglizhuo/QwenASR/commit/724ead1fe121d0ed0a0f7ef874142f665e7d0da3)) + + +### Bug Fixes + +* bump qwen-asr dependency version for cli ([efec6b1](https://github.com/huanglizhuo/QwenASR/commit/efec6b14dfb07934c7e6cbb459d26573dcfe5912)) +* clean up the code ([346d112](https://github.com/huanglizhuo/QwenASR/commit/346d112595c0d93f58c54339a7f32ca6e1e648d8)) +* publish 0.2.3 with tag-driven flow ([3637ec8](https://github.com/huanglizhuo/QwenASR/commit/3637ec80f5519ecbd0a034f6c1f23f78156cd0fe)) +* publish 0.2.3 with tag-driven flow ([e7bbd18](https://github.com/huanglizhuo/QwenASR/commit/e7bbd18dc009c3bd87f32e2346c196f65c618b19)) +* **qwen-asr-cli:** add homepage metadata to trigger release ([642c6a2](https://github.com/huanglizhuo/QwenASR/commit/642c6a2dbdcd01c4351d6a56dfdf2c6b99fa5488)) +* **qwen-asr-cli:** update qwen-asr dependency to v0.4.2 ([d9915ea](https://github.com/huanglizhuo/QwenASR/commit/d9915ea8eba41f3a7a129d0ea5c6cea939a33c6d)) +* release flow for cli ([8dfc4b7](https://github.com/huanglizhuo/QwenASR/commit/8dfc4b7820ceef98efd992974ab3a0b7fcdd9b10)) +* trigger patch release 0.2.1 for flutter ([b5785f9](https://github.com/huanglizhuo/QwenASR/commit/b5785f9e0a6e4cab3a4796bbd1bd401876ea5926)) +* update the release flow to support PAT ([2b9be6c](https://github.com/huanglizhuo/QwenASR/commit/2b9be6c21b7e74e51bf1d1f15e6959679db70542)) + +## [0.3.4](https://github.com/huanglizhuo/QwenASR/compare/qwen-asr-cli-v0.3.3...qwen-asr-cli-v0.3.4) (2026-03-19) + + +### Bug Fixes + +* **qwen-asr-cli:** update qwen-asr dependency to v0.4.2 ([d9915ea](https://github.com/huanglizhuo/QwenASR/commit/d9915ea8eba41f3a7a129d0ea5c6cea939a33c6d)) + +## [0.3.3](https://github.com/huanglizhuo/QwenASR/compare/qwen-asr-cli-v0.3.2...qwen-asr-cli-v0.3.3) (2026-03-14) + + +### Bug Fixes + +* clean up the code ([346d112](https://github.com/huanglizhuo/QwenASR/commit/346d112595c0d93f58c54339a7f32ca6e1e648d8)) + +## [0.3.2](https://github.com/huanglizhuo/QwenASR/compare/qwen-asr-cli-v0.3.1...qwen-asr-cli-v0.3.2) (2026-03-13) + + +### Bug Fixes + +* release flow for cli ([8dfc4b7](https://github.com/huanglizhuo/QwenASR/commit/8dfc4b7820ceef98efd992974ab3a0b7fcdd9b10)) + +## [0.3.1](https://github.com/huanglizhuo/QwenASR/compare/qwen-asr-cli-v0.3.0...qwen-asr-cli-v0.3.1) (2026-02-23) + + +### Bug Fixes + +* bump qwen-asr dependency version for cli ([efec6b1](https://github.com/huanglizhuo/QwenASR/commit/efec6b14dfb07934c7e6cbb459d26573dcfe5912)) + +## [0.3.0](https://github.com/huanglizhuo/QwenASR/compare/qwen-asr-cli-v0.2.4...qwen-asr-cli-v0.3.0) (2026-02-23) + + +### Features + +* add missing parameter to qwen asr offline model ([f56e8b1](https://github.com/huanglizhuo/QwenASR/commit/f56e8b1e58731344fad92a7ed38c59a9f09267f6)) +* add missing parameter to qwen asr offline model ([6d1e38d](https://github.com/huanglizhuo/QwenASR/commit/6d1e38da19cbae46c2afe2e1af03a5d437679ef8)) +* improve the live stream performace on macos ([ba47230](https://github.com/huanglizhuo/QwenASR/commit/ba47230403f897bde2486b3235cfd3f5ca24e293)) +* publish qwen-asr-cli binary to crates.io ([31ae992](https://github.com/huanglizhuo/QwenASR/commit/31ae99221d71fdcd6c40b6cfe4d77e7f67642b76)) +* support live from blackhold for macos for qwen-asr-cli ([724ead1](https://github.com/huanglizhuo/QwenASR/commit/724ead1fe121d0ed0a0f7ef874142f665e7d0da3)) + + +### Bug Fixes + +* publish 0.2.3 with tag-driven flow ([3637ec8](https://github.com/huanglizhuo/QwenASR/commit/3637ec80f5519ecbd0a034f6c1f23f78156cd0fe)) +* publish 0.2.3 with tag-driven flow ([e7bbd18](https://github.com/huanglizhuo/QwenASR/commit/e7bbd18dc009c3bd87f32e2346c196f65c618b19)) +* trigger patch release 0.2.1 for flutter ([b5785f9](https://github.com/huanglizhuo/QwenASR/commit/b5785f9e0a6e4cab3a4796bbd1bd401876ea5926)) +* update the release flow to support PAT ([2b9be6c](https://github.com/huanglizhuo/QwenASR/commit/2b9be6c21b7e74e51bf1d1f15e6959679db70542)) diff --git a/vendor/qwenasr/crates/qwen-asr-cli/Cargo.toml b/vendor/qwenasr/crates/qwen-asr-cli/Cargo.toml new file mode 100644 index 0000000..56f318d --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr-cli/Cargo.toml @@ -0,0 +1,23 @@ +[package] +name = "qwen-asr-cli" +version = "0.6.0" +edition.workspace = true +license.workspace = true +description = "CLI for qwen-asr: Qwen3-ASR speech-to-text" +keywords = ["asr", "speech-recognition", "qwen", "cli"] +categories = ["command-line-utilities", "multimedia::audio"] +repository = "https://github.com/huanglizhuo/QwenASR" +homepage = "https://github.com/huanglizhuo/QwenASR" +readme = "README.md" + +[[bin]] +name = "qwen-asr" +path = "src/main.rs" + +[dependencies] +qwen-asr = { workspace = true } +ureq = "2" +ctrlc = "3" + +[target.'cfg(target_os = "macos")'.dependencies] +coreaudio-sys = "0.2" diff --git a/vendor/qwenasr/crates/qwen-asr-cli/README.md b/vendor/qwenasr/crates/qwen-asr-cli/README.md new file mode 100644 index 0000000..d80b35f --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr-cli/README.md @@ -0,0 +1,48 @@ +# qwen-asr-cli + +CLI for [qwen-asr](https://crates.io/crates/qwen-asr): CPU-only Qwen3-ASR speech-to-text in pure Rust. + +## Install + +```bash +cargo install qwen-asr-cli + +# Recommended: enable native CPU SIMD tuning +RUSTFLAGS="-C target-cpu=native" cargo install qwen-asr-cli +``` + +vDSP/Accelerate is auto-enabled on macOS via default features. + +## Download Model + +```bash +qwen-asr download qwen3-asr-0.6b +``` + +## Usage + +```bash +# Transcribe a file +qwen-asr -d qwen3-asr-0.6b -i audio.wav + +# Streaming mode +qwen-asr -d qwen3-asr-0.6b -i audio.wav --stream + +# Live capture (macOS) +qwen-asr -d qwen3-asr-0.6b --live --stream --device "BlackHole 2ch" + +# VAD live mode (macOS) +qwen-asr -d qwen3-asr-0.6b --live --vad --device "BlackHole 2ch" + +# Forced alignment +qwen-asr -d qwen3-aligner-0.6b -i audio.wav --align "Hello world" + +# All options +qwen-asr -h +``` + +See the [project README](https://github.com/huanglizhuo/QwenASR) for full documentation. + +## License + +MIT diff --git a/vendor/qwenasr/crates/qwen-asr-cli/src/download.rs b/vendor/qwenasr/crates/qwen-asr-cli/src/download.rs new file mode 100644 index 0000000..38abf88 --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr-cli/src/download.rs @@ -0,0 +1,316 @@ +//! Model download from HuggingFace with progress display. + +use std::fs; +use std::io::{self, Read, Write}; +use std::path::Path; + +// ======================================================================== +// Model Registry +// ======================================================================== + +pub struct ModelInfo { + pub name: &'static str, + pub repo: &'static str, + pub files: &'static [&'static str], + pub description: &'static str, +} + +pub const KNOWN_MODELS: &[ModelInfo] = &[ + ModelInfo { + name: "qwen3-asr-0.6b", + repo: "Qwen/Qwen3-ASR-0.6B", + files: &["model.safetensors", "vocab.json", "merges.txt"], + description: "Qwen3-ASR 0.6B — fast, ~490 MB", + }, + ModelInfo { + name: "qwen3-asr-1.7b", + repo: "Qwen/Qwen3-ASR-1.7B", + files: &[ + "model.safetensors.index.json", + "model-00001-of-00002.safetensors", + "model-00002-of-00002.safetensors", + "vocab.json", + "merges.txt", + ], + description: "Qwen3-ASR 1.7B — higher accuracy, ~3.4 GB", + }, + ModelInfo { + name: "qwen3-aligner-0.6b", + repo: "Qwen/Qwen3-ASR-ForcedAligner-0.6B", + files: &[ + "model.safetensors.index.json", + "model-00001-of-00002.safetensors", + "model-00002-of-00002.safetensors", + "vocab.json", + "merges.txt", + ], + description: "Qwen3-ASR ForcedAligner 0.6B — word-level timestamps, ~1.6 GB", + }, +]; + +pub fn find_model(name: &str) -> Option<&'static ModelInfo> { + let name_lower = name.to_lowercase(); + KNOWN_MODELS.iter().find(|m| m.name == name_lower) +} + +// ======================================================================== +// List Models +// ======================================================================== + +pub fn list_models() { + eprintln!("Available models:\n"); + for m in KNOWN_MODELS { + eprintln!(" {:<24} {}", m.name, m.description); + } + eprintln!(); + eprintln!("Usage: qwen-asr download [--output ]"); +} + +// ======================================================================== +// Download +// ======================================================================== + +fn hf_url(repo: &str, file: &str) -> String { + format!( + "https://huggingface.co/{}/resolve/main/{}", + repo, file + ) +} + +/// Format bytes as human-readable size. +fn format_bytes(bytes: u64) -> String { + if bytes >= 1_073_741_824 { + format!("{:.1} GB", bytes as f64 / 1_073_741_824.0) + } else if bytes >= 1_048_576 { + format!("{:.1} MB", bytes as f64 / 1_048_576.0) + } else if bytes >= 1024 { + format!("{:.1} KB", bytes as f64 / 1024.0) + } else { + format!("{} B", bytes) + } +} + +/// Download a single file with progress display and resume support. +fn download_file(url: &str, dest: &Path) -> Result<(), String> { + let mut start_byte: u64 = 0; + + // Check for partial download (resume support) + let part_path = dest.with_extension( + dest.extension() + .map(|e| format!("{}.part", e.to_string_lossy())) + .unwrap_or_else(|| "part".to_string()), + ); + if part_path.exists() { + start_byte = fs::metadata(&part_path) + .map(|m| m.len()) + .unwrap_or(0); + } + + // Already fully downloaded? + if dest.exists() { + return Ok(()); + } + + // Build request + let mut req = ureq::get(url); + if start_byte > 0 { + req = req.set("Range", &format!("bytes={}-", start_byte)); + eprint!( + " Resuming from {} ... ", + format_bytes(start_byte) + ); + } + + let resp = req.call().map_err(|e| format!("HTTP request failed: {}", e))?; + + // Parse content length + let total_bytes = if start_byte > 0 { + // For Range requests, Content-Range: bytes start-end/total + resp.header("Content-Range") + .and_then(|cr| cr.rsplit('/').next()) + .and_then(|s| s.parse::().ok()) + .unwrap_or(0) + } else { + resp.header("Content-Length") + .and_then(|s| s.parse::().ok()) + .unwrap_or(0) + }; + + let mut reader = resp.into_reader(); + let mut file = fs::OpenOptions::new() + .create(true) + .append(true) + .open(&part_path) + .map_err(|e| format!("Cannot open {}: {}", part_path.display(), e))?; + + let mut downloaded = start_byte; + let mut buf = vec![0u8; 256 * 1024]; // 256 KB buffer + let mut last_progress = std::time::Instant::now(); + let start_time = std::time::Instant::now(); + + loop { + let n = reader.read(&mut buf).map_err(|e| format!("Read error: {}", e))?; + if n == 0 { + break; + } + file.write_all(&buf[..n]) + .map_err(|e| format!("Write error: {}", e))?; + downloaded += n as u64; + + // Update progress ~4 times per second + let now = std::time::Instant::now(); + if now.duration_since(last_progress).as_millis() >= 250 || n == 0 { + last_progress = now; + let elapsed = now.duration_since(start_time).as_secs_f64(); + let speed = if elapsed > 0.0 { + (downloaded - start_byte) as f64 / elapsed + } else { + 0.0 + }; + + if total_bytes > 0 { + let pct = (downloaded as f64 / total_bytes as f64 * 100.0).min(100.0); + eprint!( + "\r {} / {} ({:.0}%) {}/s ", + format_bytes(downloaded), + format_bytes(total_bytes), + pct, + format_bytes(speed as u64), + ); + } else { + eprint!( + "\r {} downloaded, {}/s ", + format_bytes(downloaded), + format_bytes(speed as u64), + ); + } + } + } + + eprintln!(); // newline after progress + + // Rename .part to final destination + fs::rename(&part_path, dest) + .map_err(|e| format!("Cannot rename {} → {}: {}", part_path.display(), dest.display(), e))?; + + Ok(()) +} + +/// Download all files for a model. +pub fn download_model(model: &ModelInfo, output_dir: &str) -> Result<(), String> { + let dir = Path::new(output_dir); + fs::create_dir_all(dir) + .map_err(|e| format!("Cannot create directory {}: {}", output_dir, e))?; + + let total_files = model.files.len(); + for (i, file_name) in model.files.iter().enumerate() { + let dest = dir.join(file_name); + if dest.exists() { + eprintln!( + "[{}/{}] {} — already exists, skipping", + i + 1, + total_files, + file_name + ); + continue; + } + + let url = hf_url(model.repo, file_name); + eprintln!("[{}/{}] Downloading {} ...", i + 1, total_files, file_name); + download_file(&url, &dest)?; + } + + eprintln!("\n✓ Model '{}' downloaded to {}", model.name, output_dir); + Ok(()) +} + +// ======================================================================== +// Interactive Prompt +// ======================================================================== + +/// Prompt user to download a model. Returns true if they accepted. +pub fn prompt_download(model_name: &str) -> bool { + let model = match find_model(model_name) { + Some(m) => m, + None => return false, + }; + + eprintln!("Model directory '{}' not found.\n", model_name); + eprintln!(" {} — {}\n", model.name, model.description); + eprint!("Download now? [Y/n]: "); + io::stderr().flush().ok(); + + let mut input = String::new(); + if io::stdin().read_line(&mut input).is_err() { + return false; + } + let answer = input.trim().to_lowercase(); + answer.is_empty() || answer == "y" || answer == "yes" +} + +// ======================================================================== +// CLI Entry Point +// ======================================================================== + +/// Handle the `download` subcommand. Returns true if handled (caller should exit). +pub fn handle_download_command(args: &[String]) -> bool { + // Parse: download [--list] [] [--output ] + let mut model_name: Option = None; + let mut output_dir: Option = None; + let mut show_list = false; + + let mut i = 0; + while i < args.len() { + match args[i].as_str() { + "--list" | "-l" => { + show_list = true; + } + "--output" | "-o" => { + i += 1; + output_dir = args.get(i).cloned(); + } + "-h" | "--help" => { + eprintln!("Usage: qwen-asr download [--list] [] [--output ]\n"); + eprintln!("Options:"); + eprintln!(" --list, -l List available models"); + eprintln!(" --output, -o Download directory (default: .//)"); + eprintln!(" -h, --help Show this help"); + return true; + } + other => { + if other.starts_with('-') { + eprintln!("Unknown option for download: {}", other); + return true; + } + model_name = Some(other.to_string()); + } + } + i += 1; + } + + if show_list || model_name.is_none() { + list_models(); + return true; + } + + let name = model_name.unwrap(); + let model = match find_model(&name) { + Some(m) => m, + None => { + eprintln!("Unknown model: '{}'\n", name); + list_models(); + std::process::exit(1); + } + }; + + let dir = output_dir.unwrap_or_else(|| name.clone()); + match download_model(model, &dir) { + Ok(()) => {} + Err(e) => { + eprintln!("\nError: {}", e); + std::process::exit(1); + } + } + + true +} diff --git a/vendor/qwenasr/crates/qwen-asr-cli/src/live_capture.rs b/vendor/qwenasr/crates/qwen-asr-cli/src/live_capture.rs new file mode 100644 index 0000000..0288ddc --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr-cli/src/live_capture.rs @@ -0,0 +1,496 @@ +//! CoreAudio live audio capture for macOS. +//! +//! Enumerates input devices, captures audio via AudioUnit (HAL Input), +//! and resamples to 16 kHz mono f32 for the ASR pipeline. + +use coreaudio_sys::*; +use std::ffi::CStr; +use std::mem; +use std::os::raw::c_void; +use std::ptr; +use std::sync::mpsc; + +// ======================================================================== +// Device Enumeration +// ======================================================================== + +/// An audio input device. +pub struct AudioDevice { + pub id: AudioDeviceID, + pub name: String, + pub input_channels: u32, +} + +/// Get the list of audio input devices. +pub fn list_input_devices() -> Vec { + let mut devices = Vec::new(); + + // Get all audio devices + let property_address = AudioObjectPropertyAddress { + mSelector: kAudioHardwarePropertyDevices, + mScope: kAudioObjectPropertyScopeGlobal, + mElement: kAudioObjectPropertyElementMain, + }; + + let mut data_size: u32 = 0; + let status = unsafe { + AudioObjectGetPropertyDataSize( + kAudioObjectSystemObject, + &property_address, + 0, + ptr::null(), + &mut data_size, + ) + }; + if status != 0 || data_size == 0 { + return devices; + } + + let device_count = data_size as usize / mem::size_of::(); + let mut device_ids = vec![0u32; device_count]; + + let status = unsafe { + AudioObjectGetPropertyData( + kAudioObjectSystemObject, + &property_address, + 0, + ptr::null(), + &mut data_size, + device_ids.as_mut_ptr() as *mut c_void, + ) + }; + if status != 0 { + return devices; + } + + for &device_id in &device_ids { + // Check if device has input channels + let input_channels = get_input_channel_count(device_id); + if input_channels == 0 { + continue; + } + + let name = get_device_name(device_id); + devices.push(AudioDevice { + id: device_id, + name, + input_channels, + }); + } + + devices +} + +fn get_device_name(device_id: AudioDeviceID) -> String { + let property_address = AudioObjectPropertyAddress { + mSelector: kAudioDevicePropertyDeviceNameCFString, + mScope: kAudioObjectPropertyScopeGlobal, + mElement: kAudioObjectPropertyElementMain, + }; + + let mut name_ref: CFStringRef = ptr::null(); + let mut data_size = mem::size_of::() as u32; + + let status = unsafe { + AudioObjectGetPropertyData( + device_id, + &property_address, + 0, + ptr::null(), + &mut data_size, + &mut name_ref as *mut _ as *mut c_void, + ) + }; + + if status != 0 || name_ref.is_null() { + return format!("Device {}", device_id); + } + + // Convert CFString to Rust String + let c_str = unsafe { CFStringGetCStringPtr(name_ref, kCFStringEncodingUTF8) }; + let name = if !c_str.is_null() { + unsafe { CStr::from_ptr(c_str) } + .to_string_lossy() + .into_owned() + } else { + // Fallback: use CFStringGetCString + let mut buf = [0i8; 256]; + let ok = unsafe { + CFStringGetCString( + name_ref, + buf.as_mut_ptr(), + buf.len() as CFIndex, + kCFStringEncodingUTF8, + ) + }; + if ok != 0 { + unsafe { CStr::from_ptr(buf.as_ptr()) } + .to_string_lossy() + .into_owned() + } else { + format!("Device {}", device_id) + } + }; + + unsafe { CFRelease(name_ref as *const c_void) }; + name +} + +fn get_input_channel_count(device_id: AudioDeviceID) -> u32 { + let property_address = AudioObjectPropertyAddress { + mSelector: kAudioDevicePropertyStreamConfiguration, + mScope: kAudioObjectPropertyScopeInput, + mElement: kAudioObjectPropertyElementMain, + }; + + let mut data_size: u32 = 0; + let status = unsafe { + AudioObjectGetPropertyDataSize( + device_id, + &property_address, + 0, + ptr::null(), + &mut data_size, + ) + }; + if status != 0 || data_size == 0 { + return 0; + } + + let mut buf = vec![0u8; data_size as usize]; + let status = unsafe { + AudioObjectGetPropertyData( + device_id, + &property_address, + 0, + ptr::null(), + &mut data_size, + buf.as_mut_ptr() as *mut c_void, + ) + }; + if status != 0 { + return 0; + } + + let buffer_list = unsafe { &*(buf.as_ptr() as *const AudioBufferList) }; + let mut total_channels: u32 = 0; + + let n_buffers = buffer_list.mNumberBuffers as usize; + if n_buffers == 0 { + return 0; + } + + // Access the variable-length mBuffers array + let buffers_ptr = &buffer_list.mBuffers as *const AudioBuffer; + for i in 0..n_buffers { + let ab = unsafe { &*buffers_ptr.add(i) }; + total_channels += ab.mNumberChannels; + } + + total_channels +} + +/// Find an input device by name (case-insensitive substring match). +pub fn find_device_by_name(name: &str) -> Option { + let name_lower = name.to_lowercase(); + let devices = list_input_devices(); + devices + .into_iter() + .find(|d| d.name.to_lowercase().contains(&name_lower)) +} + +/// Get the default input device. +pub fn default_input_device() -> Option { + let property_address = AudioObjectPropertyAddress { + mSelector: kAudioHardwarePropertyDefaultInputDevice, + mScope: kAudioObjectPropertyScopeGlobal, + mElement: kAudioObjectPropertyElementMain, + }; + + let mut device_id: AudioDeviceID = 0; + let mut data_size = mem::size_of::() as u32; + + let status = unsafe { + AudioObjectGetPropertyData( + kAudioObjectSystemObject, + &property_address, + 0, + ptr::null(), + &mut data_size, + &mut device_id as *mut _ as *mut c_void, + ) + }; + + if status != 0 || device_id == kAudioObjectUnknown { + None + } else { + Some(device_id) + } +} + +/// Print all input devices to stderr. +pub fn print_devices() { + let devices = list_input_devices(); + if devices.is_empty() { + eprintln!("No audio input devices found."); + return; + } + + let default_id = default_input_device(); + + eprintln!("Audio input devices:\n"); + for d in &devices { + let marker = if Some(d.id) == default_id { " (default)" } else { "" }; + eprintln!(" {:30} {} ch{}", d.name, d.input_channels, marker); + } + eprintln!(); +} + +// ======================================================================== +// Audio Capture +// ======================================================================== + +/// Capture handle — drop to stop capture. +pub struct CaptureHandle { + audio_unit: AudioUnit, + _state: Box, +} + +/// Callback state passed to the AudioUnit render callback via ref_con. +struct CaptureCallbackState { + tx: mpsc::Sender>, + audio_unit: AudioUnit, +} + +/// Actual render callback using CaptureCallbackState. +unsafe extern "C" fn render_callback( + in_ref_con: *mut c_void, + io_action_flags: *mut AudioUnitRenderActionFlags, + in_time_stamp: *const AudioTimeStamp, + in_bus_number: u32, + in_number_frames: u32, + _io_data: *mut AudioBufferList, +) -> OSStatus { + let state = &*(in_ref_con as *const CaptureCallbackState); + + let n = in_number_frames as usize; + let mut samples = vec![0f32; n]; + + let buffer = AudioBuffer { + mNumberChannels: 1, + mDataByteSize: (n * mem::size_of::()) as u32, + mData: samples.as_mut_ptr() as *mut c_void, + }; + + let mut buffer_list = AudioBufferList { + mNumberBuffers: 1, + mBuffers: [buffer], + }; + + let status = AudioUnitRender( + state.audio_unit, + io_action_flags, + in_time_stamp, + in_bus_number, + in_number_frames, + &mut buffer_list, + ); + + if status != 0 { + return status; + } + + let _ = state.tx.send(samples); + 0 +} + +/// Start capturing audio from a device. Returns a channel receiver for audio +/// chunks (f32, mono, at device sample rate) and a handle to stop capture. +pub fn start_capture( + device_id: AudioDeviceID, +) -> Result<(mpsc::Receiver>, CaptureHandle, f64), String> { + // Get device's native sample rate + let sample_rate = get_device_sample_rate(device_id)?; + + // Create AUHAL AudioUnit + let comp_desc = AudioComponentDescription { + componentType: kAudioUnitType_Output, + componentSubType: kAudioUnitSubType_HALOutput, + componentManufacturer: kAudioUnitManufacturer_Apple, + componentFlags: 0, + componentFlagsMask: 0, + }; + + let component = unsafe { AudioComponentFindNext(ptr::null_mut(), &comp_desc) }; + if component.is_null() { + return Err("Cannot find HAL Output AudioComponent".into()); + } + + let mut audio_unit: AudioUnit = ptr::null_mut(); + let status = unsafe { AudioComponentInstanceNew(component, &mut audio_unit) }; + if status != 0 { + return Err(format!("AudioComponentInstanceNew failed: {}", status)); + } + + // Enable input on bus 1 (input element) + let enable_io: u32 = 1; + let status = unsafe { + AudioUnitSetProperty( + audio_unit, + kAudioOutputUnitProperty_EnableIO, + kAudioUnitScope_Input, + 1, // input element + &enable_io as *const _ as *const c_void, + mem::size_of::() as u32, + ) + }; + if status != 0 { + return Err(format!("Enable input IO failed: {}", status)); + } + + // Disable output on bus 0 (output element) + let disable_io: u32 = 0; + let status = unsafe { + AudioUnitSetProperty( + audio_unit, + kAudioOutputUnitProperty_EnableIO, + kAudioUnitScope_Output, + 0, // output element + &disable_io as *const _ as *const c_void, + mem::size_of::() as u32, + ) + }; + if status != 0 { + return Err(format!("Disable output IO failed: {}", status)); + } + + // Set the input device + let status = unsafe { + AudioUnitSetProperty( + audio_unit, + kAudioOutputUnitProperty_CurrentDevice, + kAudioUnitScope_Global, + 0, + &device_id as *const _ as *const c_void, + mem::size_of::() as u32, + ) + }; + if status != 0 { + return Err(format!("Set current device failed: {}", status)); + } + + // Set output format of bus 1 (what we read from the callback): + // Float32, mono, device sample rate + let stream_format = AudioStreamBasicDescription { + mSampleRate: sample_rate, + mFormatID: kAudioFormatLinearPCM, + mFormatFlags: kAudioFormatFlagIsFloat | kAudioFormatFlagIsPacked | kAudioFormatFlagIsNonInterleaved, + mBytesPerPacket: 4, + mFramesPerPacket: 1, + mBytesPerFrame: 4, + mChannelsPerFrame: 1, + mBitsPerChannel: 32, + mReserved: 0, + }; + + let status = unsafe { + AudioUnitSetProperty( + audio_unit, + kAudioUnitProperty_StreamFormat, + kAudioUnitScope_Output, + 1, // output scope of input element = data we receive + &stream_format as *const _ as *const c_void, + mem::size_of::() as u32, + ) + }; + if status != 0 { + return Err(format!("Set stream format failed: {}", status)); + } + + // Create channel and state + let (tx, rx) = mpsc::channel::>(); + + let state = Box::new(CaptureCallbackState { + tx, + audio_unit, + }); + + // Set input callback + let callback_struct = AURenderCallbackStruct { + inputProc: Some(render_callback), + inputProcRefCon: &*state as *const CaptureCallbackState as *mut c_void, + }; + + let status = unsafe { + AudioUnitSetProperty( + audio_unit, + kAudioOutputUnitProperty_SetInputCallback, + kAudioUnitScope_Global, + 0, + &callback_struct as *const _ as *const c_void, + mem::size_of::() as u32, + ) + }; + if status != 0 { + return Err(format!("Set input callback failed: {}", status)); + } + + // Initialize and start + let status = unsafe { AudioUnitInitialize(audio_unit) }; + if status != 0 { + return Err(format!("AudioUnitInitialize failed: {}", status)); + } + + let status = unsafe { AudioOutputUnitStart(audio_unit) }; + if status != 0 { + return Err(format!("AudioOutputUnitStart failed: {}", status)); + } + + let handle = CaptureHandle { + audio_unit, + _state: state, + }; + + Ok((rx, handle, sample_rate)) +} + +impl Drop for CaptureHandle { + fn drop(&mut self) { + unsafe { + AudioOutputUnitStop(self.audio_unit); + AudioUnitUninitialize(self.audio_unit); + AudioComponentInstanceDispose(self.audio_unit); + } + } +} + +fn get_device_sample_rate(device_id: AudioDeviceID) -> Result { + let property_address = AudioObjectPropertyAddress { + mSelector: kAudioDevicePropertyNominalSampleRate, + mScope: kAudioObjectPropertyScopeInput, + mElement: kAudioObjectPropertyElementMain, + }; + + let mut sample_rate: f64 = 0.0; + let mut data_size = mem::size_of::() as u32; + + let status = unsafe { + AudioObjectGetPropertyData( + device_id, + &property_address, + 0, + ptr::null(), + &mut data_size, + &mut sample_rate as *mut _ as *mut c_void, + ) + }; + + if status != 0 { + return Err(format!( + "Cannot get sample rate for device {}: error {}", + device_id, status + )); + } + + Ok(sample_rate) +} diff --git a/vendor/qwenasr/crates/qwen-asr-cli/src/main.rs b/vendor/qwenasr/crates/qwen-asr-cli/src/main.rs new file mode 100644 index 0000000..df79340 --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr-cli/src/main.rs @@ -0,0 +1,937 @@ +mod download; +#[cfg(target_os = "macos")] +mod live_capture; + +use qwen_asr::{audio, config, context, kernels, transcribe, align}; +use config::*; +use context::QwenCtx; + +use std::sync::Arc; +use std::sync::atomic::{AtomicBool, Ordering}; + +fn stream_token(piece: &str) { + use std::io::Write; + print!("{}", piece); + std::io::stdout().flush().ok(); +} + +fn usage(prog: &str) { + eprintln!("qwen-asr — Qwen3-ASR speech-to-text (pure Rust)\n"); + eprintln!("Usage: {} -d (-i | --stdin | --live) [options]\n", prog); + eprintln!("Required:"); + eprintln!(" -d Model directory (with *.safetensors, vocab.json)"); + eprintln!(" -i Input WAV file (16-bit PCM, any sample rate)"); + eprintln!(" --stdin Read audio from stdin (auto-detect WAV or raw s16le 16kHz mono)"); + eprintln!("\nLive capture (macOS only):"); + eprintln!(" --live Capture from audio input device in real time"); + eprintln!(" --device Input device name (default: system default)"); + eprintln!(" --list-devices List available audio input devices and exit"); + eprintln!(" --vad Live VAD mode: detect speech segments, transcribe each"); + eprintln!("\nOptions:"); + eprintln!(" -t Number of threads (default: all CPUs)"); + eprintln!(" -S Segment target seconds (default: 0 = full-audio decode)"); + eprintln!(" -W Segment-cutting silence search window ± seconds (default: 3.0)"); + eprintln!(" --stream Streaming mode: process in chunks with prefix rollback"); + eprintln!(" --stream-max-new-tokens Max generated tokens per stream step (default: 32)"); + eprintln!(" --stream-chunk-sec Chunk size for streaming (default: 2.0, min ~1.0)"); + eprintln!(" --enc-window-sec Encoder attention window in seconds (1..8, default 8)"); + eprintln!(" --past-text Reuse previously decoded text as context"); + eprintln!(" --skip-silence Drop long silent spans before inference"); + eprintln!(" --prompt System prompt for biasing"); + eprintln!(" --language Force output language"); + eprintln!("\nAlignment mode (requires ForcedAligner model):"); + eprintln!(" --align Align transcript to audio (word-level timestamps)"); + eprintln!(" --align-language Language for word splitting (default: English)"); + eprintln!(" --profile Print per-operation timing breakdown"); + eprintln!(" --debug Debug output (per-layer details)"); + eprintln!(" --silent No status output (only transcription on stdout)"); + eprintln!("\nModel management:"); + eprintln!(" {} download [--list] [] [--output ]", prog); + eprintln!(" -h Show this help"); +} + +fn parse_past_text_mode(s: &str) -> Option { + match s.to_lowercase().as_str() { + "yes" => Some(1), + "no" => Some(0), + "auto" => Some(-1), + _ => None, + } +} + +fn main() { + let args: Vec = std::env::args().collect(); + + // Handle `download` subcommand: qwen-asr download [args...] + if args.len() >= 2 && args[1] == "download" { + download::handle_download_command(&args[2..]); + return; + } + + // Handle --list-devices (no model needed) + if args.iter().any(|a| a == "--list-devices") { + #[cfg(target_os = "macos")] + { + live_capture::print_devices(); + } + #[cfg(not(target_os = "macos"))] + { + eprintln!("--list-devices is only supported on macOS."); + eprintln!("On Linux, use: arecord -l"); + } + return; + } + + let mut model_dir: Option = None; + let mut input_wav: Option = None; + let mut verbosity = 1i32; + let mut use_stdin = false; + let mut live_mode = false; + let mut device_name: Option = None; + let mut n_threads = 0i32; + let mut segment_sec: f32 = -1.0; + let mut search_sec: f32 = -1.0; + let mut stream_mode = false; + let mut vad_mode = false; + let mut stream_max_new_tokens: i32 = -1; + let mut stream_chunk_sec: f32 = -1.0; + let mut enc_window_sec: f32 = -1.0; + let mut prompt_text: Option = None; + let mut force_language: Option = None; + let mut past_text_mode: i32 = -1; // -1 auto, 0 off, 1 on + let mut skip_silence = false; + let mut profile = false; + let mut align_text: Option = None; + let mut align_language: Option = None; + + let mut i = 1; + while i < args.len() { + match args[i].as_str() { + "-d" => { + i += 1; + model_dir = args.get(i).cloned(); + } + "-i" => { + i += 1; + input_wav = args.get(i).cloned(); + } + "-t" => { + i += 1; + n_threads = args.get(i).and_then(|s| s.parse().ok()).unwrap_or(0); + } + "-S" => { + i += 1; + segment_sec = args.get(i).and_then(|s| s.parse().ok()).unwrap_or(-1.0); + } + "-W" => { + i += 1; + search_sec = args.get(i).and_then(|s| s.parse().ok()).unwrap_or(-1.0); + } + "--stream" => { + stream_mode = true; + } + "--vad" => { + vad_mode = true; + } + "--stream-max-new-tokens" => { + i += 1; + stream_max_new_tokens = args.get(i).and_then(|s| s.parse().ok()).unwrap_or(-1); + } + "--stream-chunk-sec" => { + i += 1; + stream_chunk_sec = args.get(i).and_then(|s| s.parse().ok()).unwrap_or(-1.0); + } + "--enc-window-sec" => { + i += 1; + enc_window_sec = args.get(i).and_then(|s| s.parse().ok()).unwrap_or(-1.0); + } + "--past-text" => { + i += 1; + if let Some(s) = args.get(i) { + match parse_past_text_mode(s) { + Some(m) => past_text_mode = m, + None => { + eprintln!("Error: --past-text must be one of yes|no|auto, got '{}'", s); + std::process::exit(1); + } + } + } + } + "--skip-silence" => { + skip_silence = true; + } + "--prompt" => { + i += 1; + prompt_text = args.get(i).cloned(); + } + "--language" => { + i += 1; + force_language = args.get(i).cloned(); + } + "--align" => { + i += 1; + align_text = args.get(i).cloned(); + } + "--align-language" => { + i += 1; + align_language = args.get(i).cloned(); + } + "--stdin" => { + use_stdin = true; + } + "--live" => { + live_mode = true; + } + "--device" => { + i += 1; + device_name = args.get(i).cloned(); + } + "--list-devices" => { + // Already handled above, but don't error + } + "--profile" => { + profile = true; + } + "--debug" => { + verbosity = 2; + } + "--silent" => { + verbosity = 0; + } + "-h" | "--help" => { + usage(&args[0]); + return; + } + other => { + eprintln!("Unknown option: {}", other); + usage(&args[0]); + std::process::exit(1); + } + } + i += 1; + } + + let model_dir = match model_dir { + Some(d) => d, + None => { + usage(&args[0]); + std::process::exit(1); + } + }; + + // Auto-prompt to download if model directory doesn't exist + if !std::path::Path::new(&model_dir).exists() { + if let Some(model) = download::find_model(&model_dir) { + if download::prompt_download(&model_dir) { + if let Err(e) = download::download_model(model, &model_dir) { + eprintln!("Download failed: {}", e); + std::process::exit(1); + } + eprintln!(); // blank line before model loading + } else { + eprintln!("Aborted."); + std::process::exit(1); + } + } else { + eprintln!("Error: Model directory '{}' not found.", model_dir); + eprintln!(); + download::list_models(); + std::process::exit(1); + } + } + + if input_wav.is_none() && !use_stdin && !live_mode { + usage(&args[0]); + std::process::exit(1); + } + + // Check mutual exclusivity of input modes + let input_count = [input_wav.is_some(), use_stdin, live_mode].iter().filter(|&&x| x).count(); + if input_count > 1 { + eprintln!("Error: -i, --stdin, and --live are mutually exclusive"); + std::process::exit(1); + } + + kernels::set_verbose(verbosity); + if profile { + kernels::set_profile(true); + kernels::profile_reset(); + } + let emit_tokens = verbosity > 0; + + // Initialize thread pool + if n_threads <= 0 { + n_threads = kernels::get_num_cpus() as i32; + } + kernels::set_threads(n_threads as usize); + + // Print optimization info + if verbosity >= 1 { + let opts = qwen_asr::optimization_flags(); + eprintln!( + "Optimizations: {} | {} threads | {}", + opts.join(", "), + n_threads, + std::env::consts::ARCH, + ); + } + + // Load model + let mut ctx = match QwenCtx::load(&model_dir) { + Some(c) => c, + None => { + eprintln!("Failed to load model from {}", model_dir); + std::process::exit(1); + } + }; + + // Apply settings + if segment_sec >= 0.0 { + ctx.segment_sec = segment_sec; + } + if search_sec >= 0.0 { + ctx.search_sec = search_sec; + } + if enc_window_sec >= 0.0 { + let window_frames = (enc_window_sec * 100.0 + 0.5) as usize; + ctx.config.enc_n_window_infer = window_frames.clamp(100, 800); + } + if stream_max_new_tokens > 0 { + ctx.stream_max_new_tokens = stream_max_new_tokens; + } + if stream_chunk_sec > 0.0 { + ctx.stream_chunk_sec = stream_chunk_sec; + } + if past_text_mode >= 0 { + ctx.past_text_conditioning = past_text_mode == 1; + } else if stream_mode { + ctx.past_text_conditioning = true; + } + if skip_silence { + ctx.skip_silence = true; + } + if let Some(ref prompt) = prompt_text { + if ctx.set_prompt(prompt).is_err() { + eprintln!("Failed to set --prompt text"); + std::process::exit(1); + } + } + if let Some(ref lang) = force_language { + if ctx.set_force_language(lang).is_err() { + eprintln!("Unsupported language for --language: {}", lang); + eprintln!( + "Supported languages: {}", + SUPPORTED_LANGUAGES.join(",") + ); + std::process::exit(1); + } + } + + // Alignment mode + if let Some(ref atext) = align_text { + let lang = align_language.as_deref().unwrap_or("English"); + let lang_normalized = match normalize_language(lang) { + Some(l) => l, + None => { + eprintln!("Unsupported --align-language: {}", lang); + eprintln!("Supported languages: {}", SUPPORTED_LANGUAGES.join(",")); + std::process::exit(1); + } + }; + + let samples = if use_stdin { + audio::read_pcm_stdin() + } else { + audio::load_wav(input_wav.as_ref().unwrap()) + }; + let samples = match samples { + Some(s) => s, + None => { + eprintln!("Failed to load audio"); + std::process::exit(1); + } + }; + + match align::forced_align(&mut ctx, &samples, atext, &lang_normalized) { + Some(results) => { + // Output JSON array + println!("["); + for (i, r) in results.iter().enumerate() { + let comma = if i + 1 < results.len() { "," } else { "" }; + // Escape the text for JSON + let escaped = r.text.replace('\\', "\\\\").replace('"', "\\\""); + println!( + " {{\"text\": \"{}\", \"start\": {:.0}, \"end\": {:.0}}}{}", + escaped, r.start_ms, r.end_ms, comma + ); + } + println!("]"); + } + None => { + eprintln!("Alignment failed"); + std::process::exit(1); + } + } + + if verbosity >= 1 { + eprintln!( + "Alignment: {:.0} ms (encoding: {:.0}ms, decoding: {:.0}ms)", + ctx.perf_total_ms, ctx.perf_encode_ms, ctx.perf_decode_ms + ); + } + + if profile { + kernels::profile_report(); + } + return; + } + + // Set token callback + if emit_tokens { + ctx.token_cb = Some(Box::new(stream_token)); + } + + // Live capture mode + if live_mode { + #[cfg(not(target_os = "macos"))] + { + eprintln!("Error: --live is only supported on macOS."); + eprintln!("On Linux, pipe audio via: arecord -f S16_LE -r 16000 -c 1 | qwen-asr -d --stdin"); + std::process::exit(1); + } + + #[cfg(target_os = "macos")] + { + run_live_capture(&mut ctx, device_name.as_deref(), stream_mode, vad_mode, verbosity, profile); + return; + } + } + + // Transcribe + let text = if stream_mode { + let samples = if use_stdin { + audio::read_pcm_stdin() + } else { + audio::load_wav(input_wav.as_ref().unwrap()) + }; + match samples { + Some(s) => transcribe::transcribe_stream(&mut ctx, &s), + None => None, + } + } else if use_stdin { + transcribe::transcribe_stdin(&mut ctx) + } else { + transcribe::transcribe(&mut ctx, input_wav.as_ref().unwrap()) + }; + + match text { + Some(t) => { + if emit_tokens { + println!(); + } else { + println!("{}", t); + } + } + None => { + eprintln!("Transcription failed"); + std::process::exit(1); + } + } + + if verbosity >= 1 { + let tokens_per_sec = if ctx.perf_total_ms > 0.0 { + 1000.0 * ctx.perf_text_tokens as f64 / ctx.perf_total_ms + } else { + 0.0 + }; + eprintln!( + "Inference: {:.0} ms, {} text tokens ({:.2} tok/s, encoding: {:.0}ms, decoding: {:.0}ms)", + ctx.perf_total_ms, ctx.perf_text_tokens, tokens_per_sec, + ctx.perf_encode_ms, ctx.perf_decode_ms + ); + if ctx.perf_audio_ms > 0.0 && ctx.perf_total_ms > 0.0 { + let audio_s = ctx.perf_audio_ms / 1000.0; + let infer_s = ctx.perf_total_ms / 1000.0; + eprintln!( + "Audio: {:.1} s processed in {:.1} s ({:.2}x realtime)", + audio_s, infer_s, audio_s / infer_s + ); + } + } + + if profile { + kernels::profile_report(); + } +} + +// ======================================================================== +// Live Capture Loop (macOS only) +// ======================================================================== + +#[cfg(target_os = "macos")] +fn run_live_capture( + ctx: &mut QwenCtx, + device_name: Option<&str>, + stream_mode: bool, + vad_mode: bool, + verbosity: i32, + profile: bool, +) { + use std::time::Duration; + + // Resolve device + let device_id = if let Some(name) = device_name { + match live_capture::find_device_by_name(name) { + Some(dev) => { + if verbosity >= 1 { + eprintln!("Using input device: {} ({} ch)", dev.name, dev.input_channels); + } + dev.id + } + None => { + eprintln!("Error: No input device matching '{}'", name); + if name.to_lowercase().contains("blackhole") { + eprintln!(); + eprintln!("BlackHole does not appear to be installed."); + eprintln!("Install it with: brew install blackhole-2ch"); + eprintln!("Then set it up as a Multi-Output Device in Audio MIDI Setup."); + eprintln!("See: https://github.com/ExistentialAudio/BlackHole"); + } + eprintln!(); + live_capture::print_devices(); + std::process::exit(1); + } + } + } else { + match live_capture::default_input_device() { + Some(id) => { + if verbosity >= 1 { + let devices = live_capture::list_input_devices(); + if let Some(dev) = devices.iter().find(|d| d.id == id) { + eprintln!("Using default input device: {}", dev.name); + } + } + id + } + None => { + eprintln!("Error: No default input device found"); + std::process::exit(1); + } + } + }; + + // Start capture + let (rx, _handle, device_rate) = match live_capture::start_capture(device_id) { + Ok(r) => r, + Err(e) => { + eprintln!("Error: Failed to start audio capture: {}", e); + std::process::exit(1); + } + }; + + let mode_label = if stream_mode { "streaming" } else if vad_mode { "VAD" } else { "segmented" }; + if verbosity >= 1 { + if vad_mode { + eprintln!("Listening (VAD segmented)... press Ctrl+C to stop\n"); + } else { + eprintln!( + "Listening ({}, {:.1}s chunks)... press Ctrl+C to stop\n", + mode_label, + if stream_mode { ctx.stream_chunk_sec } else { ctx.segment_sec } + ); + } + } + + // Set up Ctrl+C handler + let running = Arc::new(AtomicBool::new(true)); + let r = running.clone(); + ctrlc::set_handler(move || { + r.store(false, Ordering::SeqCst); + }) + .expect("Error setting Ctrl+C handler"); + + // Configure context + ctx.past_text_conditioning = true; + ctx.reset_perf(); + + // Audio accumulation + let target_rate = 16000; + let mut raw_buf: Vec = Vec::new(); + let mut resampled_buf: Vec = Vec::new(); + let needs_resample = (device_rate - target_rate as f64).abs() > 1.0; + let wall_start = std::time::Instant::now(); + + if stream_mode { + // ---- Streaming mode: incremental stream_push_audio ---- + // + // We accumulate audio and call stream_push_audio() which only + // processes NEW audio incrementally (persistent encoder cache, + // LCP-reused decoder prefill, monotonic token commit). + // + // Buffer reset after ~120s to bound memory. + let max_window_samples: usize = 120 * target_rate as usize; + let mut stream_state = transcribe::StreamState::new(); + + // Set token callback for direct printing + ctx.token_cb = None; // stream_push_audio returns delta text, we print it + + // Text-emission timeout: flush rollback tokens after no new text for 5s + let mut last_text_time: Option = None; + let text_flush_secs = 5.0_f32; + let mut flushed = false; + + while running.load(Ordering::SeqCst) { + // Receive audio + match rx.recv_timeout(Duration::from_millis(100)) { + Ok(chunk) => raw_buf.extend_from_slice(&chunk), + Err(std::sync::mpsc::RecvTimeoutError::Timeout) => {} + Err(std::sync::mpsc::RecvTimeoutError::Disconnected) => break, + } + while let Ok(chunk) = rx.try_recv() { + raw_buf.extend_from_slice(&chunk); + } + + // Resample + if needs_resample { + if !raw_buf.is_empty() { + let resampled = qwen_asr::audio::resample( + &raw_buf, device_rate as i32, target_rate, + ); + resampled_buf.extend_from_slice(&resampled); + raw_buf.clear(); + } + } else { + resampled_buf.append(&mut raw_buf); + } + + // Reset window if buffer exceeds max + if resampled_buf.len() > max_window_samples { + // Flush rollback tokens before reset + if let Some(delta) = transcribe::stream_push_audio( + ctx, &resampled_buf, &mut stream_state, true + ) { + if !delta.is_empty() { + print!("{}", delta); + } + } + println!(); + resampled_buf.clear(); + stream_state.reset(); + last_text_time = None; + flushed = false; + continue; + } + + // Determine if we should finalize: flush rollback tokens + // when no new text has been emitted for 5 seconds + let finalize = !flushed + && last_text_time.is_some_and(|t| t.elapsed().as_secs_f32() >= text_flush_secs); + + // Process all available full chunks + if resampled_buf.len() > stream_state.audio_cursor() { + if let Some(delta) = transcribe::stream_push_audio( + ctx, &resampled_buf, &mut stream_state, finalize + ) { + if !delta.is_empty() { + print!("{}", delta); + std::io::Write::flush(&mut std::io::stdout()).ok(); + last_text_time = Some(std::time::Instant::now()); + flushed = false; + } else if finalize { + flushed = true; // Don't keep calling finalize + } + } + } + } + + // Final flush + if !raw_buf.is_empty() && needs_resample { + let resampled = qwen_asr::audio::resample( + &raw_buf, device_rate as i32, target_rate, + ); + resampled_buf.extend_from_slice(&resampled); + } else { + resampled_buf.append(&mut raw_buf); + } + + if resampled_buf.len() > stream_state.audio_cursor() { + if let Some(delta) = transcribe::stream_push_audio( + ctx, &resampled_buf, &mut stream_state, true // finalize: flush rollback + ) { + if !delta.is_empty() { + print!("{}", delta); + } + } + std::io::Write::flush(&mut std::io::stdout()).ok(); + } + println!(); + } else if vad_mode { + // ---- VAD mode: energy-based speech detection + segment transcription ---- + // + // Detect speech using RMS energy. When speech ends (silence > 1.5s), + // transcribe the accumulated speech segment using transcribe_audio(). + // This gives better accuracy than streaming (full segment context) + // with automatic speech boundary detection. + let speech_threshold: f32 = 0.001; + let silence_hangover_secs = 1.5_f32; + let min_segment_secs = 0.5_f32; + let max_segment_secs = 30.0_f32; + let min_segment_samples = (min_segment_secs * target_rate as f32) as usize; + let max_segment_samples = (max_segment_secs * target_rate as f32) as usize; + let check_samples = (target_rate as usize) * 30 / 1000; // 30ms window for RMS + + let mut speech_active = false; + let mut silence_start: Option = None; + let mut speech_start_idx: usize = 0; + + // Keep a small pre-speech buffer to avoid clipping word beginnings + let pre_speech_samples = (target_rate as usize) / 4; // 250ms lookback + + // Disable token callback — we print the full result after each segment + ctx.token_cb = None; + + // Cross-segment context: accumulate text to use as prompt for next segment + let mut accumulated_text = String::new(); + + while running.load(Ordering::SeqCst) { + // Receive audio + match rx.recv_timeout(Duration::from_millis(50)) { + Ok(chunk) => raw_buf.extend_from_slice(&chunk), + Err(std::sync::mpsc::RecvTimeoutError::Timeout) => {} + Err(std::sync::mpsc::RecvTimeoutError::Disconnected) => break, + } + while let Ok(chunk) = rx.try_recv() { + raw_buf.extend_from_slice(&chunk); + } + + // Resample + if needs_resample { + if !raw_buf.is_empty() { + let resampled = qwen_asr::audio::resample( + &raw_buf, device_rate as i32, target_rate, + ); + resampled_buf.extend_from_slice(&resampled); + raw_buf.clear(); + } + } else { + resampled_buf.append(&mut raw_buf); + } + + // Compute RMS energy of latest 30ms + let buf_len = resampled_buf.len(); + let rms = if buf_len >= check_samples { + let tail = &resampled_buf[buf_len - check_samples..]; + let sum_sq: f32 = tail.iter().map(|&s| s * s).sum(); + (sum_sq / check_samples as f32).sqrt() + } else { + 0.0 + }; + let is_speech = rms >= speech_threshold; + + // Periodic RMS debug output + if verbosity >= 2 && buf_len % (target_rate as usize * 2) < check_samples { + eprintln!(" [VAD] rms={:.6} threshold={:.4} speech={}", + rms, speech_threshold, if speech_active { "active" } else { "inactive" }); + } + + if !speech_active { + if is_speech { + // Speech started — mark the start with lookback + speech_active = true; + silence_start = None; + speech_start_idx = buf_len.saturating_sub(pre_speech_samples); + if verbosity >= 2 { + eprintln!(" [VAD] speech start at {:.1}s", + buf_len as f32 / target_rate as f32); + } + } else { + // No speech — bound buffer to avoid unlimited growth + // Keep only last 0.5s for lookback context + let keep = (target_rate as usize) / 2; + if resampled_buf.len() > keep * 4 { + let drain = resampled_buf.len() - keep; + resampled_buf.drain(..drain); + } + } + } else { + // Speech is active + let segment_len = buf_len - speech_start_idx; + + if is_speech { + // Still speaking — reset silence timer + silence_start = None; + + // Force-flush if segment exceeds max duration + if segment_len >= max_segment_samples { + if verbosity >= 2 { + eprintln!(" [VAD] max segment reached ({:.1}s), flushing", + segment_len as f32 / target_rate as f32); + } + let segment = &resampled_buf[speech_start_idx..]; + // Set previous text as context + if !accumulated_text.is_empty() { + ctx.prompt = Some(accumulated_text.clone()); + ctx.prompt_tokens_ready = false; + } + ctx.reset_perf(); + if let Some(text) = transcribe::transcribe_audio(ctx, segment) { + if !text.is_empty() { + println!("{}", text); + accumulated_text.push_str(&text); + } + } + resampled_buf.clear(); + speech_active = false; + silence_start = None; + } + } else { + // Silence during speech — track duration + if silence_start.is_none() { + silence_start = Some(std::time::Instant::now()); + } + if let Some(start) = silence_start { + if start.elapsed().as_secs_f32() >= silence_hangover_secs { + // End of utterance — transcribe the segment + if segment_len >= min_segment_samples { + // Trim trailing silence (keep only 200ms of it) + let trail_keep = (target_rate as usize) / 5; + let seg_end = (buf_len - check_samples + trail_keep).min(buf_len); + let segment = &resampled_buf[speech_start_idx..seg_end]; + + if verbosity >= 2 { + eprintln!(" [VAD] speech end, segment {:.1}s", + segment.len() as f32 / target_rate as f32); + } + + ctx.reset_perf(); + // Set previous text as context + if !accumulated_text.is_empty() { + ctx.prompt = Some(accumulated_text.clone()); + ctx.prompt_tokens_ready = false; + } + let t0 = std::time::Instant::now(); + if let Some(text) = transcribe::transcribe_audio(ctx, segment) { + if !text.is_empty() { + println!("{}", text); + accumulated_text.push_str(&text); + if verbosity >= 1 { + let audio_secs = segment.len() as f32 / target_rate as f32; + let compute_secs = t0.elapsed().as_secs_f32(); + eprintln!( + " ({:.1}s audio in {:.1}s, {:.1}x realtime)", + audio_secs, compute_secs, + audio_secs / compute_secs.max(0.001) + ); + } + } + } + } else if verbosity >= 2 { + eprintln!(" [VAD] segment too short ({:.2}s), discarding", + segment_len as f32 / target_rate as f32); + } + + resampled_buf.clear(); + speech_active = false; + silence_start = None; + } + } + } + } + } + + // Flush remaining speech on Ctrl+C + if speech_active && resampled_buf.len() > speech_start_idx + min_segment_samples { + let segment = &resampled_buf[speech_start_idx..]; + ctx.reset_perf(); + if let Some(text) = transcribe::transcribe_audio(ctx, segment) { + if !text.is_empty() { + println!("{}", text); + } + } + } + } else { + // ---- Segmented mode: independent segments ---- + if ctx.segment_sec <= 0.0 { + ctx.segment_sec = 5.0; + } + let segment_samples_16k = (ctx.segment_sec * target_rate as f32) as usize; + + while running.load(Ordering::SeqCst) { + match rx.recv_timeout(Duration::from_millis(100)) { + Ok(chunk) => raw_buf.extend_from_slice(&chunk), + Err(std::sync::mpsc::RecvTimeoutError::Timeout) => {} + Err(std::sync::mpsc::RecvTimeoutError::Disconnected) => break, + } + while let Ok(chunk) = rx.try_recv() { + raw_buf.extend_from_slice(&chunk); + } + + if needs_resample { + if !raw_buf.is_empty() { + let resampled = qwen_asr::audio::resample( + &raw_buf, device_rate as i32, target_rate, + ); + resampled_buf.extend_from_slice(&resampled); + raw_buf.clear(); + } + } else { + resampled_buf.append(&mut raw_buf); + } + + if resampled_buf.len() >= segment_samples_16k { + ctx.reset_perf(); + let _text = transcribe::transcribe_audio(ctx, &resampled_buf); + resampled_buf.clear(); + if verbosity > 0 { + println!(); + } + } + } + + // Flush remaining + if !raw_buf.is_empty() && needs_resample { + let resampled = qwen_asr::audio::resample( + &raw_buf, device_rate as i32, target_rate, + ); + resampled_buf.extend_from_slice(&resampled); + } else { + resampled_buf.append(&mut raw_buf); + } + if !resampled_buf.is_empty() { + ctx.reset_perf(); + let _text = transcribe::transcribe_audio(ctx, &resampled_buf); + if verbosity > 0 { + println!(); + } + } + } + + // ---- Benchmark summary ---- + let wall_ms = wall_start.elapsed().as_secs_f64() * 1000.0; + let audio_s = resampled_buf.len() as f64 / target_rate as f64; + + if verbosity >= 1 { + eprintln!("\nStopped."); + let tokens_per_sec = if ctx.perf_total_ms > 0.0 { + 1000.0 * ctx.perf_text_tokens as f64 / ctx.perf_total_ms + } else { + 0.0 + }; + eprintln!( + "Inference: {:.0} ms, {} text tokens ({:.2} tok/s, encoding: {:.0}ms, decoding: {:.0}ms)", + ctx.perf_total_ms, ctx.perf_text_tokens, tokens_per_sec, + ctx.perf_encode_ms, ctx.perf_decode_ms + ); + if audio_s > 0.0 && ctx.perf_total_ms > 0.0 { + let infer_s = ctx.perf_total_ms / 1000.0; + eprintln!( + "Audio: {:.1} s processed in {:.1} s compute ({:.2}x realtime), {:.1} s wall clock", + audio_s, infer_s, audio_s / infer_s, wall_ms / 1000.0 + ); + } + } + + if profile { + kernels::profile_report(); + } +} diff --git a/vendor/qwenasr/crates/qwen-asr/CHANGELOG.md b/vendor/qwenasr/crates/qwen-asr/CHANGELOG.md new file mode 100644 index 0000000..6076cb6 --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/CHANGELOG.md @@ -0,0 +1,88 @@ +# Changelog + +## [0.5.0](https://github.com/huanglizhuo/QwenASR/compare/qwen-asr-v0.4.2...qwen-asr-v0.5.0) (2026-03-20) + + +### Features + +* optimize the speed with auto research pattern ([9c13daa](https://github.com/huanglizhuo/QwenASR/commit/9c13daaadd964a6c2d79bed99364b26a368c305f)) + +## [0.4.2](https://github.com/huanglizhuo/QwenASR/compare/qwen-asr-v0.4.1...qwen-asr-v0.4.2) (2026-03-14) + + +### Bug Fixes + +* install OpenBLAS on CI to support build ([e995cad](https://github.com/huanglizhuo/QwenASR/commit/e995cad2c18999a39e74c56c26a2d6526020fd53)) + +## [0.4.1](https://github.com/huanglizhuo/QwenASR/compare/qwen-asr-v0.4.0...qwen-asr-v0.4.1) (2026-03-14) + + +### Bug Fixes + +* clean up the code ([346d112](https://github.com/huanglizhuo/QwenASR/commit/346d112595c0d93f58c54339a7f32ca6e1e648d8)) + +## [0.4.0](https://github.com/huanglizhuo/QwenASR/compare/qwen-asr-v0.3.0...qwen-asr-v0.4.0) (2026-03-13) + + +### Features + +* expose streaming C API for native integration ([a58befe](https://github.com/huanglizhuo/QwenASR/commit/a58befe991393405867ece18621d60ad5dc7cfd6)) +* expose streaming C API for native macOS/iOS integration ([51dc917](https://github.com/huanglizhuo/QwenASR/commit/51dc917fc214a38ab6a85db55e2cc53ecdfabb6d)) + + +### Bug Fixes + +* handle split UTF-8 sequences in BPE token decoding ([75c3e31](https://github.com/huanglizhuo/QwenASR/commit/75c3e3172b33e412fdde0bee6f8009277e9bb35e)) +* handle split UTF-8 sequences in BPE token decoding ([c86fae2](https://github.com/huanglizhuo/QwenASR/commit/c86fae2918c50f5ef74097fb4ad1e97a19387a18)) + +## [0.3.0](https://github.com/huanglizhuo/QwenASR/compare/qwen-asr-v0.2.4...qwen-asr-v0.3.0) (2026-02-23) + + +### Features + +* add missing parameter to qwen asr offline model ([f56e8b1](https://github.com/huanglizhuo/QwenASR/commit/f56e8b1e58731344fad92a7ed38c59a9f09267f6)) +* add missing parameter to qwen asr offline model ([6d1e38d](https://github.com/huanglizhuo/QwenASR/commit/6d1e38da19cbae46c2afe2e1af03a5d437679ef8)) +* improve the live stream performace on macos ([ba47230](https://github.com/huanglizhuo/QwenASR/commit/ba47230403f897bde2486b3235cfd3f5ca24e293)) +* refine the stream mode ([7ac71a9](https://github.com/huanglizhuo/QwenASR/commit/7ac71a9a4f30b13ff155ff2eb345db85bce1b91c)) +* support live from blackhold for macos for qwen-asr-cli ([724ead1](https://github.com/huanglizhuo/QwenASR/commit/724ead1fe121d0ed0a0f7ef874142f665e7d0da3)) +* update readme ([cde2178](https://github.com/huanglizhuo/QwenASR/commit/cde21787bb545e12c154045562883b9ced00514d)) + + +### Bug Fixes + +* publish 0.2.3 with tag-driven flow ([3637ec8](https://github.com/huanglizhuo/QwenASR/commit/3637ec80f5519ecbd0a034f6c1f23f78156cd0fe)) +* publish 0.2.3 with tag-driven flow ([e7bbd18](https://github.com/huanglizhuo/QwenASR/commit/e7bbd18dc009c3bd87f32e2346c196f65c618b19)) +* trigger patch release 0.2.1 for flutter ([b5785f9](https://github.com/huanglizhuo/QwenASR/commit/b5785f9e0a6e4cab3a4796bbd1bd401876ea5926)) +* update the both library readme to mention this is WIP project ([139a591](https://github.com/huanglizhuo/QwenASR/commit/139a5915205083abc4b87fd0228ccf4c725c99c0)) +* update the release flow to support PAT ([2b9be6c](https://github.com/huanglizhuo/QwenASR/commit/2b9be6c21b7e74e51bf1d1f15e6959679db70542)) + +## [0.2.3](https://github.com/huanglizhuo/QwenASR/compare/qwen-asr-v0.2.2...qwen-asr-v0.2.3) (2026-02-22) + + +### Bug Fixes + +* update the release flow to support PAT ([2b9be6c](https://github.com/huanglizhuo/QwenASR/commit/2b9be6c21b7e74e51bf1d1f15e6959679db70542)) + +## [0.2.2](https://github.com/huanglizhuo/QwenASR/compare/qwen-asr-v0.2.1...qwen-asr-v0.2.2) (2026-02-22) + + +### Bug Fixes + +* publish 0.2.3 with tag-driven flow ([3637ec8](https://github.com/huanglizhuo/QwenASR/commit/3637ec80f5519ecbd0a034f6c1f23f78156cd0fe)) +* publish 0.2.3 with tag-driven flow ([e7bbd18](https://github.com/huanglizhuo/QwenASR/commit/e7bbd18dc009c3bd87f32e2346c196f65c618b19)) + +## [0.2.1](https://github.com/huanglizhuo/QwenASR/compare/qwen-asr-v0.2.0...qwen-asr-v0.2.1) (2026-02-22) + + +### Bug Fixes + +* trigger patch release 0.2.1 for flutter ([b5785f9](https://github.com/huanglizhuo/QwenASR/commit/b5785f9e0a6e4cab3a4796bbd1bd401876ea5926)) +* update the both library readme to mention this is WIP project ([139a591](https://github.com/huanglizhuo/QwenASR/commit/139a5915205083abc4b87fd0228ccf4c725c99c0)) + +## [0.2.0](https://github.com/huanglizhuo/QwenASR/compare/qwen-asr-v0.1.2...qwen-asr-v0.2.0) (2026-02-22) + + +### Features + +* add missing parameter to qwen asr offline model ([f56e8b1](https://github.com/huanglizhuo/QwenASR/commit/f56e8b1e58731344fad92a7ed38c59a9f09267f6)) +* add missing parameter to qwen asr offline model ([6d1e38d](https://github.com/huanglizhuo/QwenASR/commit/6d1e38da19cbae46c2afe2e1af03a5d437679ef8)) diff --git a/vendor/qwenasr/crates/qwen-asr/Cargo.toml b/vendor/qwenasr/crates/qwen-asr/Cargo.toml new file mode 100644 index 0000000..45a1d1f --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/Cargo.toml @@ -0,0 +1,30 @@ +[package] +name = "qwen-asr" +version = "0.5.0" +edition.workspace = true +license.workspace = true +description = "CPU-only Qwen3-ASR speech recognition (pure Rust)" +keywords = ["asr", "speech-recognition", "qwen", "inference"] +categories = ["multimedia::audio"] +repository = "https://github.com/huanglizhuo/QwenASR" +documentation = "https://docs.rs/qwen-asr" +readme = "README.md" + +[lib] +crate-type = ["cdylib", "staticlib", "rlib"] +name = "qwen_asr" + +[dependencies] +libc = "0.2" + +[package.metadata.docs.rs] +no-default-features = true + +[features] +default = ["blas", "vdsp"] +blas = [] +vdsp = [] +ffi = [] +ios = ["ffi"] +android = ["ffi"] +macos-ffi = ["ffi"] diff --git a/vendor/qwenasr/crates/qwen-asr/README.md b/vendor/qwenasr/crates/qwen-asr/README.md new file mode 100644 index 0000000..c105674 --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/README.md @@ -0,0 +1,177 @@ +# qwen_asr + +CPU-only Qwen3-ASR speech recognition in pure Rust. No Python, no ONNX runtime, +no framework dependencies — just `libc` and BLAS. BF16 weights stay memory-mapped +for minimal RAM usage; SIMD kernels (NEON / AVX2+FMA) accelerate inference. + +## Prerequisites + +- Rust 1.70+ +- BLAS: Accelerate (macOS, linked automatically) or OpenBLAS (Linux) + +## Building + +Platform-specific optimizations are detected automatically at compile time: + +| Platform | BLAS | SIMD | +|----------|------|------| +| macOS (Apple Silicon) | Accelerate + vDSP | NEON (always available) | +| macOS (Intel) | Accelerate + vDSP | AVX2+FMA | +| Linux (x86_64) | OpenBLAS | AVX2+FMA | +| Linux (aarch64) | OpenBLAS | NEON | +| Other | OpenBLAS | Generic scalar fallback | + +For best performance, build with native CPU tuning so the compiler can emit +AVX2+FMA instructions on x86_64: + +```bash +RUSTFLAGS="-C target-cpu=native" cargo build --release +``` + +On AArch64 (Apple Silicon, ARM Linux) NEON is baseline — no extra flags needed, +though `-C target-cpu=native` is still recommended for other micro-architecture +tuning. + +**Important:** Always use `--release` mode. Debug builds are 10-50x slower due +to missing optimizations and are not usable for real-time inference. + +## Model Download + +```bash +# Install modelscope if needed +pip install modelscope + +# Download the 0.6B model (~1.3 GB) +python -c "from modelscope.hub.snapshot_download import snapshot_download; snapshot_download('Qwen/Qwen3-ASR-0.6B', cache_dir='.')" + +# Download the 0.6B forced-aligner model (~1.3 GB) +python -c "from modelscope.hub.snapshot_download import snapshot_download; snapshot_download('Qwen/Qwen3-ForcedAligner-0.6B', cache_dir='.')" +``` + +## Usage + +```rust,no_run +use qwen_asr::context::QwenCtx; +use qwen_asr::transcribe; + +fn main() { + // Load model (returns None on failure) + let mut ctx = QwenCtx::load("qwen3-asr-0.6b").expect("failed to load model"); + + // Transcribe a WAV file + let text = transcribe::transcribe(&mut ctx, "audio.wav").unwrap(); + println!("{}", text); +} +``` + +### Segmented Mode + +For long audio files, split into overlapping segments to reduce memory usage +and improve accuracy: + +```rust,no_run +use qwen_asr::context::QwenCtx; +use qwen_asr::transcribe; + +let mut ctx = QwenCtx::load("qwen3-asr-0.6b").unwrap(); +ctx.segment_sec = 30.0; // split every ~30 seconds + +let text = transcribe::transcribe(&mut ctx, "long-meeting.wav").unwrap(); +``` + +### Raw PCM Input + +```rust,no_run +use qwen_asr::context::QwenCtx; +use qwen_asr::transcribe; + +let mut ctx = QwenCtx::load("qwen3-asr-0.6b").unwrap(); + +// f32 samples at 16 kHz, mono, range [-1, 1] +let samples: Vec = load_audio_somehow(); +let text = transcribe::transcribe_audio(&mut ctx, &samples).unwrap(); +``` + +### Streaming API + +For real-time incremental transcription, use `StreamState` and `stream_push_audio`: + +```rust,no_run +use qwen_asr::context::QwenCtx; +use qwen_asr::transcribe::{StreamState, stream_push_audio}; + +let mut ctx = QwenCtx::load("qwen3-asr-0.6b").unwrap(); +let mut state = StreamState::new(); + +// As audio arrives (e.g., from a microphone), accumulate samples +let mut all_samples: Vec = Vec::new(); +loop { + let new_audio = get_audio_chunk(); // your audio source + all_samples.extend_from_slice(&new_audio); + + // Push all accumulated audio; stream_push_audio tracks its own cursor + if let Some(delta) = stream_push_audio(&mut ctx, &all_samples, &mut state, false) { + if !delta.is_empty() { + print!("{}", delta); // incremental output + } + } +} + +// Finalize to flush remaining tokens +stream_push_audio(&mut ctx, &all_samples, &mut state, true); +``` + +### Forced Alignment + +Produce word-level timestamps for a known transcript. Requires the +ForcedAligner model variant (`Qwen3-ASR-0.6B-Aligner`). + +```rust,no_run +use qwen_asr::context::QwenCtx; +use qwen_asr::align; + +let mut ctx = QwenCtx::load("qwen3-aligner-0.6b").unwrap(); +let samples: Vec = load_audio_somehow(); + +let results = align::forced_align(&mut ctx, &samples, "Hello world", "English") + .expect("alignment failed"); + +for r in &results { + println!("{}: {:.0} ms – {:.0} ms", r.text, r.start_ms, r.end_ms); +} +``` + +CLI: + +```bash +qwen-asr -d qwen3-aligner-0.6b -i audio.wav --align "Hello world" --align-language English +``` + +Each `AlignResult` contains the word text, `start_ms`, and `end_ms` timestamps. +For CJK languages the text is split at character level; for others it is split on +whitespace. + +## Feature Flags + +| Feature | Default | Description | +|-----------|---------|-------------| +| `blas` | yes | Link Accelerate (macOS) or OpenBLAS (Linux) for matrix ops | +| `vdsp` | yes | Use vDSP/vForce from Accelerate for dot products and exp (macOS only) | +| `ios` | no | Build C-FFI API for iOS integration | +| `android` | no | Build C-FFI + JNI API for Android integration | + +## Performance + +Benchmarks on Apple M2 Pro (10-core), 0.6B model: + +| Mode | Audio | Wall Time | Realtime Factor | +|------|-------|-----------|-----------------| +| Offline | 11 s | 1.8 s | 6.2x | +| Offline | 28 s | 4.0 s | 7.0x | +| Segmented (-S 30) | 45 s | 4.6 s | 9.8x | +| Streaming | 28 s | 10.4 s | 2.7x | +| Streaming (live) | 51 s | 14.1 s | 3.6x | + +## License + +MIT diff --git a/vendor/qwenasr/crates/qwen-asr/build.rs b/vendor/qwenasr/crates/qwen-asr/build.rs new file mode 100644 index 0000000..234e01c --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/build.rs @@ -0,0 +1,18 @@ +fn main() { + let target_os = std::env::var("CARGO_CFG_TARGET_OS").unwrap_or_default(); + + // Check if blas feature is enabled via CARGO_FEATURE_BLAS env var + if std::env::var("CARGO_FEATURE_BLAS").is_ok() { + match target_os.as_str() { + "macos" => { + println!("cargo:rustc-link-lib=framework=Accelerate"); + } + "linux" => { + println!("cargo:rustc-link-lib=openblas"); + } + _ => { + // No BLAS available, will use fallback matmul + } + } + } +} diff --git a/vendor/qwenasr/crates/qwen-asr/src/align.rs b/vendor/qwenasr/crates/qwen-asr/src/align.rs new file mode 100644 index 0000000..35cd16e --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/src/align.rs @@ -0,0 +1,389 @@ +//! Forced alignment: word- and character-level timestamps. + +use crate::audio; +use crate::config::*; +use crate::context::QwenCtx; +use crate::decoder::{self, tok_embed_bf16_to_f32}; +use crate::kernels; +use crate::tokenizer::QwenTokenizer; + +use std::time::Instant; + +// Reuse the same prompt structure as transcribe +const PREFIX_HEAD: &[i32] = &[151644, 8948, 198]; +const PREFIX_TAIL: &[i32] = &[151645, 198, 151644, 872, 198, 151669]; +const SUFFIX_BASE: &[i32] = &[151670, 151645, 198, 151644, 77091, 198]; + +/// A single word (or character for CJK) with its aligned time span. +/// +/// * `text` – the word or character this entry covers. +/// * `start_ms` – start time in milliseconds from the beginning of the audio. +/// * `end_ms` – end time in milliseconds. +#[derive(Debug, Clone)] +pub struct AlignResult { + pub text: String, + pub start_ms: f32, + pub end_ms: f32, +} + +fn get_time_ms() -> f64 { + static START: std::sync::OnceLock = std::sync::OnceLock::new(); + let start = START.get_or_init(Instant::now); + start.elapsed().as_secs_f64() * 1000.0 +} + +fn elapsed_ms(t0: f64) -> f64 { + get_time_ms() - t0 +} + +/// Split text into words based on language. +/// English/space-delimited: split on whitespace. +/// CJK (Chinese, Japanese, Korean, Cantonese): character-level split. +fn split_words(text: &str, language: &str) -> Vec { + let is_cjk = matches!(language, "Chinese" | "Japanese" | "Korean" | "Cantonese"); + + if is_cjk { + // Character-level split (each Unicode character is a "word") + text.chars() + .filter(|c| !c.is_whitespace()) + .map(|c| c.to_string()) + .collect() + } else { + // Space-delimited split + text.split_whitespace().map(|s| s.to_string()).collect() + } +} + +/// Build the input token sequence for forced alignment. +/// Interleaves between words and appends at end. +/// Returns (word_list, token_ids) where token_ids includes audio markers + text + timestamps. +fn encode_timestamp( + text: &str, + language: &str, + tokenizer: &QwenTokenizer, +) -> Option<(Vec, Vec)> { + let words = split_words(text, language); + if words.is_empty() { + return None; + } + + // Build the text with interleaved timestamp tokens: + // word1 word2 ... wordN + // But we tokenize each word separately and insert timestamp token IDs between them. + let mut token_ids: Vec = Vec::new(); + + for (i, word) in words.iter().enumerate() { + let word_tokens = tokenizer.encode(word)?; + token_ids.extend_from_slice(&word_tokens); + // Append after each word (including the last) + token_ids.push(TOKEN_TIMESTAMP); + token_ids.push(TOKEN_TIMESTAMP); + + if i == 0 && word_tokens.is_empty() { + return None; // First word must produce tokens + } + } + + Some((words, token_ids)) +} + +/// Find the longest increasing subsequence indices. +fn longest_increasing_subsequence(vals: &[f32]) -> Vec { + let n = vals.len(); + if n == 0 { + return Vec::new(); + } + + // dp[i] = length of LIS ending at i + let mut dp = vec![1usize; n]; + let mut prev = vec![usize::MAX; n]; + + for i in 1..n { + for j in 0..i { + if vals[j] <= vals[i] && dp[j] + 1 > dp[i] { + dp[i] = dp[j] + 1; + prev[i] = j; + } + } + } + + // Find the end of the longest sequence + let mut best_len = 0; + let mut best_end = 0; + for (i, &dp_val) in dp.iter().enumerate().take(n) { + if dp_val > best_len { + best_len = dp_val; + best_end = i; + } + } + + // Trace back + let mut lis_indices = Vec::with_capacity(best_len); + let mut idx = best_end; + loop { + lis_indices.push(idx); + if prev[idx] == usize::MAX { + break; + } + idx = prev[idx]; + } + lis_indices.reverse(); + lis_indices +} + +/// Fix anomalous timestamps using LIS + interpolation. +fn fix_timestamps(timestamps: &mut [f32]) { + if timestamps.len() <= 1 { + return; + } + + let lis_indices = longest_increasing_subsequence(timestamps); + if lis_indices.len() == timestamps.len() { + return; // Already monotonically increasing + } + + // Mark which indices are in the LIS (normal) + let n = timestamps.len(); + let mut is_normal = vec![false; n]; + for &idx in &lis_indices { + is_normal[idx] = true; + } + + // Fix anomalous regions + let mut i = 0; + while i < n { + if is_normal[i] { + i += 1; + continue; + } + + // Find the extent of this anomalous block + let block_start = i; + while i < n && !is_normal[i] { + i += 1; + } + let block_end = i; // exclusive + let block_len = block_end - block_start; + + // Get boundary values + let left_val = if block_start > 0 { + timestamps[block_start - 1] + } else { + 0.0 + }; + let right_val = if block_end < n { + timestamps[block_end] + } else { + left_val + block_len as f32 * 80.0 + }; + + if block_len <= 2 { + // Small block: fill with nearest normal value + let fill = if block_start > 0 { left_val } else { right_val }; + for ts in timestamps.iter_mut().take(block_end).skip(block_start) { + *ts = fill; + } + } else { + // Larger block: linearly interpolate + for j in 0..block_len { + let t = (j + 1) as f32 / (block_len + 1) as f32; + timestamps[block_start + j] = left_val + t * (right_val - left_val); + } + } + } +} + +/// Perform forced alignment on audio samples with a known transcript. +/// +/// Requires a ForcedAligner model (one where `config.classify_num > 0`). +/// Returns `None` if the model is not an aligner, the text is empty, or +/// encoding fails. +/// +/// `language` controls how `text` is split into units: for CJK languages +/// (`"Chinese"`, `"Japanese"`, `"Korean"`, `"Cantonese"`) each character +/// becomes a separate entry; for all other languages the text is split on +/// whitespace. +/// +/// The returned vector has one [`AlignResult`] per word/character with +/// monotonically non-decreasing timestamps (anomalies are corrected via +/// LIS + interpolation). +pub fn forced_align( + ctx: &mut QwenCtx, + samples: &[f32], + text: &str, + language: &str, +) -> Option> { + let shared = ctx.shared.clone(); + let cfg = &shared.config.clone(); + let dim = cfg.dec_hidden; + let seg_time = cfg.timestamp_segment_time; + + if !cfg.is_aligner() { + eprintln!("align: model is not a forced aligner (classify_num=0)"); + return None; + } + + let vocab_path = format!("{}/vocab.json", shared.model_dir); + let tokenizer = QwenTokenizer::load(&vocab_path)?; + + ctx.reset_perf(); + ctx.perf_audio_ms = 1000.0 * samples.len() as f64 / SAMPLE_RATE as f64; + + let seg_t0 = get_time_ms(); + // Step 1: Tokenize text with timestamp interleaving + let (words, text_tokens) = encode_timestamp(text, language, &tokenizer)?; + + if kernels::verbose() >= 2 { + eprintln!( + " Align: {} words, {} text tokens", + words.len(), + text_tokens.len() + ); + } + + // Step 2: Mel spectrogram + encoder + let t0 = get_time_ms(); + let (mel, mel_frames) = audio::mel_spectrogram(samples)?; + let mel_ms = elapsed_ms(t0); + + let t0 = get_time_ms(); + let (enc_output, enc_seq_len) = + shared + .encoder + .forward(cfg, &mel, mel_frames, Some(&mut ctx.enc_bufs))?; + let enc_ms = elapsed_ms(t0); + + if kernels::verbose() >= 2 { + eprintln!( + " Mel: {} frames ({:.0} ms), Encoder: {} tokens ({:.0} ms)", + mel_frames, mel_ms, enc_seq_len, enc_ms + ); + } + + // Step 3: Build input embeddings + // Structure: PREFIX_HEAD + PREFIX_TAIL + [encoder output] + SUFFIX_BASE + text_tokens + // (No prompt tokens or language forcing for alignment) + let prefix_len = PREFIX_HEAD.len() + PREFIX_TAIL.len(); + let suffix_len = SUFFIX_BASE.len(); + let total_seq = prefix_len + enc_seq_len + suffix_len + text_tokens.len(); + + let mut input_embeds = vec![0.0f32; total_seq * dim]; + let tok_emb = shared.decoder.tok_embeddings_bf16; + + let mut off = 0; + for &tok in PREFIX_HEAD { + unsafe { + tok_embed_bf16_to_f32( + &mut input_embeds[off * dim..(off + 1) * dim], + tok_emb, + tok, + dim, + ); + } + off += 1; + } + for &tok in PREFIX_TAIL { + unsafe { + tok_embed_bf16_to_f32( + &mut input_embeds[off * dim..(off + 1) * dim], + tok_emb, + tok, + dim, + ); + } + off += 1; + } + + // Encoder output + for i in 0..enc_seq_len { + input_embeds[(prefix_len + i) * dim..(prefix_len + i + 1) * dim] + .copy_from_slice(&enc_output[i * dim..(i + 1) * dim]); + } + + // Suffix + let suffix_off = prefix_len + enc_seq_len; + for (i, &tok) in SUFFIX_BASE.iter().enumerate() { + unsafe { + tok_embed_bf16_to_f32( + &mut input_embeds[(suffix_off + i) * dim..(suffix_off + i + 1) * dim], + tok_emb, + tok, + dim, + ); + } + } + + // Text tokens (with interleaved tokens) + let text_off = suffix_off + suffix_len; + for (i, &tok) in text_tokens.iter().enumerate() { + unsafe { + tok_embed_bf16_to_f32( + &mut input_embeds[(text_off + i) * dim..(text_off + i + 1) * dim], + tok_emb, + tok, + dim, + ); + } + } + + // Step 4: Single prefill pass → logits for all positions + let t0 = get_time_ms(); + ctx.kv_cache.len = 0; + + let logits = decoder::decoder_prefill_logits( + &shared.decoder, + cfg, + &mut ctx.kv_cache, + &mut ctx.rope_cache, + &mut ctx.dec_bufs, + &input_embeds, + total_seq, + ); + let prefill_ms = elapsed_ms(t0); + + if kernels::verbose() >= 2 { + eprintln!(" Prefill: {} tokens ({:.0} ms)", total_seq, prefill_ms); + } + + // Step 5: Extract timestamps from positions + let out_dim = cfg.lm_head_dim(); + let mut raw_timestamps: Vec = Vec::new(); + + for (i, &tok) in text_tokens.iter().enumerate() { + if tok == TOKEN_TIMESTAMP { + let pos = text_off + i; + let logit_row = &logits[pos * out_dim..(pos + 1) * out_dim]; + let mut best_idx = 0; + let mut best_val = logit_row[0]; + for (j, &val) in logit_row.iter().enumerate().take(out_dim).skip(1) { + if val > best_val { + best_val = val; + best_idx = j; + } + } + raw_timestamps.push(best_idx as f32 * seg_time); + } + } + + // Step 6: Fix timestamps (LIS + interpolation) + fix_timestamps(&mut raw_timestamps); + + // Step 7: Pair consecutive timestamps into (start, end) per word + // Each word has 2 timestamps: start, end + let mut results = Vec::with_capacity(words.len()); + for (i, word) in words.iter().enumerate() { + let start_ms = raw_timestamps.get(i * 2).copied().unwrap_or(0.0); + let end_ms = raw_timestamps.get(i * 2 + 1).copied().unwrap_or(start_ms); + results.push(AlignResult { + text: word.clone(), + start_ms, + end_ms, + }); + } + + ctx.perf_total_ms += elapsed_ms(seg_t0); + ctx.perf_encode_ms += mel_ms + enc_ms; + ctx.perf_decode_ms += prefill_ms; + + Some(results) +} diff --git a/vendor/qwenasr/crates/qwen-asr/src/audio.rs b/vendor/qwenasr/crates/qwen-asr/src/audio.rs new file mode 100644 index 0000000..5a90b8c --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/src/audio.rs @@ -0,0 +1,479 @@ +//! WAV loading, resampling, and mel spectrogram computation. + +use crate::config::*; +use crate::kernels; + +const N_FFT: usize = 400; +const N_FREQ: usize = N_FFT / 2 + 1; // 201 + +fn read_u16_le(data: &[u8]) -> u16 { + u16::from_le_bytes([data[0], data[1]]) +} + +fn read_u32_le(data: &[u8]) -> u32 { + u32::from_le_bytes([data[0], data[1], data[2], data[3]]) +} + +/// Parse a WAV file buffer into f32 samples at 16 kHz mono. +/// +/// Accepts 16-bit PCM WAV at any sample rate (resampled automatically) and +/// any channel count (downmixed to mono). Returns `None` if the buffer is not +/// a valid WAV or uses an unsupported encoding (e.g. float, compressed). +pub fn parse_wav_buffer(data: &[u8]) -> Option> { + if data.len() < 44 || &data[0..4] != b"RIFF" || &data[8..12] != b"WAVE" { + eprintln!("parse_wav_buffer: not a valid WAV file"); + return None; + } + + let mut channels = 0i32; + let mut sample_rate = 0i32; + let mut bits_per_sample = 0i32; + let mut audio_format = 0i32; + let mut pcm_data: Option<&[u8]> = None; + + let mut p = 12; + while p + 8 <= data.len() { + let chunk_id = &data[p..p + 4]; + let chunk_size = read_u32_le(&data[p + 4..]) as usize; + if p + 8 + chunk_size > data.len() { + break; + } + + if chunk_id == b"fmt " && chunk_size >= 16 { + audio_format = read_u16_le(&data[p + 8..]) as i32; + channels = read_u16_le(&data[p + 10..]) as i32; + sample_rate = read_u32_le(&data[p + 12..]) as i32; + bits_per_sample = read_u16_le(&data[p + 22..]) as i32; + } else if chunk_id == b"data" { + let end = (p + 8 + chunk_size).min(data.len()); + pcm_data = Some(&data[p + 8..end]); + } + + p += 8 + chunk_size; + if chunk_size & 1 != 0 { + p += 1; + } + } + + if audio_format != 1 || bits_per_sample != 16 || channels < 1 { + eprintln!( + "parse_wav_buffer: unsupported format (need 16-bit PCM, got fmt={} bits={})", + audio_format, bits_per_sample + ); + return None; + } + + let pcm = pcm_data?; + let n_frames = pcm.len() / (channels as usize * 2); + let mut samples = vec![0.0f32; n_frames]; + + for i in 0..n_frames { + if channels == 1 { + let val = i16::from_le_bytes([pcm[i * 2], pcm[i * 2 + 1]]); + samples[i] = val as f32 / 32768.0; + } else { + let mut sum = 0.0f32; + for c in 0..channels as usize { + let off = (i * channels as usize + c) * 2; + let val = i16::from_le_bytes([pcm[off], pcm[off + 1]]); + sum += val as f32; + } + samples[i] = (sum / channels as f32) / 32768.0; + } + } + + // Resample to 16kHz if needed + if sample_rate != SAMPLE_RATE { + samples = resample(&samples, sample_rate, SAMPLE_RATE); + } + + Some(samples) +} + +/// Read a WAV file from disk and return f32 samples at 16 kHz mono. +/// +/// Equivalent to `std::fs::read` + [`parse_wav_buffer`]. +pub fn load_wav(path: &str) -> Option> { + let data = std::fs::read(path).ok()?; + parse_wav_buffer(&data) +} + +/// Read audio from stdin (auto-detect WAV or raw s16le 16kHz mono). +pub fn read_pcm_stdin() -> Option> { + use std::io::Read; + let mut buf = Vec::new(); + std::io::stdin().read_to_end(&mut buf).ok()?; + + if buf.len() < 4 { + eprintln!("read_pcm_stdin: no data on stdin"); + return None; + } + + if &buf[0..4] == b"RIFF" { + if kernels::verbose() >= 2 { + eprintln!("Detected WAV format on stdin"); + } + return parse_wav_buffer(&buf); + } + + // Raw s16le 16kHz mono + if kernels::verbose() >= 2 { + eprintln!("Treating stdin as raw s16le 16kHz mono"); + } + let n_frames = buf.len() / 2; + let mut samples = vec![0.0f32; n_frames]; + for i in 0..n_frames { + let val = i16::from_le_bytes([buf[i * 2], buf[i * 2 + 1]]); + samples[i] = val as f32 / 32768.0; + } + Some(samples) +} + +/// Kaiser-windowed sinc resampler. +pub fn resample(samples: &[f32], from_rate: i32, to_rate: i32) -> Vec { + let n_frames = samples.len(); + let new_n = (n_frames as i64 * to_rate as i64 / from_rate as i64) as usize; + let mut resampled = vec![0.0f32; new_n]; + + let sinc_half = 16; + let kaiser_beta = 6.0f64; + let ratio = to_rate as f64 / from_rate as f64; + let cutoff = if ratio < 1.0 { ratio } else { 1.0 }; + + // Bessel I0 approximation + fn bessel_i0(x: f64) -> f64 { + let mut sum = 1.0; + let mut term = 1.0; + let xx = x * x; + for k in 1..=20 { + term *= xx / (4.0 * k as f64 * k as f64); + sum += term; + } + sum + } + + let inv_i0_beta = 1.0 / bessel_i0(kaiser_beta); + + for (i, resampled_val) in resampled.iter_mut().enumerate().take(new_n) { + let src_pos = i as f64 / ratio; + let center = src_pos as i32; + let mut acc = 0.0f64; + let mut wsum = 0.0f64; + + let j_lo = center - sinc_half + 1; + let j_hi = center + sinc_half; + + for j in j_lo..=j_hi { + let d = j as f64 - src_pos; + let x = d * cutoff; + + // Sinc + let s = if x.abs() < 1e-9 { + 1.0 + } else { + (std::f64::consts::PI * x).sin() / (std::f64::consts::PI * x) + }; + + // Kaiser window + let npos = d / sinc_half as f64; + let w = if npos <= -1.0 || npos >= 1.0 { + 0.0 + } else { + bessel_i0(kaiser_beta * (1.0 - npos * npos).sqrt()) * inv_i0_beta + }; + + let coeff = s * w * cutoff; + if j >= 0 && (j as usize) < n_frames { + acc += samples[j as usize] as f64 * coeff; + } + wsum += coeff; + } + + *resampled_val = if wsum > 1e-9 { (acc / wsum) as f32 } else { 0.0 }; + } + + resampled +} + +// ======================================================================== +// Mel Filter Bank (Slaney-style) +// ======================================================================== + +fn hertz_to_mel(freq: f32) -> f32 { + let min_log_hertz = 1000.0f32; + let min_log_mel = 15.0f32; + let logstep = 27.0 / (6.4f32).ln(); + let mels = 3.0 * freq / 200.0; + if freq >= min_log_hertz { + min_log_mel + (freq / min_log_hertz).ln() * logstep + } else { + mels + } +} + +fn mel_to_hertz(mels: f32) -> f32 { + let min_log_hertz = 1000.0f32; + let min_log_mel = 15.0f32; + let logstep = (6.4f32).ln() / 27.0; + if mels >= min_log_mel { + min_log_hertz * (logstep * (mels - min_log_mel)).exp() + } else { + 200.0 * mels / 3.0 + } +} + +fn build_mel_filters() -> Vec { + let mut filters = vec![0.0f32; MEL_BINS * N_FREQ]; + + let mut fft_freqs = vec![0.0f32; N_FREQ]; + for (i, freq) in fft_freqs.iter_mut().enumerate().take(N_FREQ) { + *freq = i as f32 * (SAMPLE_RATE as f32 / 2.0) / (N_FREQ - 1) as f32; + } + + let mel_min = hertz_to_mel(0.0); + let mel_max = hertz_to_mel(SAMPLE_RATE as f32 / 2.0); + + let n_filters = MEL_BINS; + let mut filter_freqs = vec![0.0f32; n_filters + 2]; + let mut filter_diff = vec![0.0f32; n_filters + 1]; + + for (i, filter_freq) in filter_freqs.iter_mut().enumerate().take(n_filters + 2) { + let mel = mel_min + (mel_max - mel_min) * i as f32 / (n_filters + 1) as f32; + *filter_freq = mel_to_hertz(mel); + } + for i in 0..n_filters + 1 { + filter_diff[i] = filter_freqs[i + 1] - filter_freqs[i]; + if filter_diff[i] == 0.0 { + filter_diff[i] = 1e-6; + } + } + + for m in 0..n_filters { + let enorm = 2.0 / (filter_freqs[m + 2] - filter_freqs[m]); + for f in 0..N_FREQ { + let down = (fft_freqs[f] - filter_freqs[m]) / filter_diff[m]; + let up = (filter_freqs[m + 2] - fft_freqs[f]) / filter_diff[m + 1]; + let val = down.min(up).max(0.0); + filters[m * N_FREQ + f] = val * enorm; + } + } + + filters +} + +// ======================================================================== +// Mel Spectrogram +// ======================================================================== + +/// Compute a 128-bin log-mel spectrogram from 16 kHz audio samples. +/// +/// Returns `(mel_flat, n_frames)` where `mel_flat` has shape `[128, n_frames]` +/// in row-major order. Returns `None` if the audio is too short to produce +/// even one frame. +pub fn mel_spectrogram(samples: &[f32]) -> Option<(Vec, usize)> { + let n_samples = samples.len(); + let n_fft = N_FFT; + let n_freqs = N_FREQ; + let pad_len = n_fft / 2; + + // Reflect-pad the signal + let padded_len = n_samples + 2 * pad_len; + let mut padded = vec![0.0f32; padded_len]; + + for (i, padded_val) in padded.iter_mut().enumerate().take(pad_len) { + let src = pad_len - i; + *padded_val = if src < n_samples { samples[src] } else { 0.0 }; + } + padded[pad_len..pad_len + n_samples].copy_from_slice(samples); + for i in 0..pad_len { + let src = n_samples as i32 - 2 - i as i32; + padded[pad_len + n_samples + i] = if src >= 0 { samples[src as usize] } else { 0.0 }; + } + + let n_frames_total = (padded_len - n_fft) / HOP_LENGTH + 1; + let n_frames = n_frames_total - 1; // drop last frame + if n_frames == 0 { + eprintln!("mel_spectrogram: audio too short ({} samples)", n_samples); + return None; + } + + static MEL_FILTERS: std::sync::OnceLock> = std::sync::OnceLock::new(); + let mel_filters = MEL_FILTERS.get_or_init(build_mel_filters); + + // Periodic Hann window (cached) + static HANN_WINDOW: std::sync::OnceLock> = std::sync::OnceLock::new(); + let window = HANN_WINDOW.get_or_init(|| { + let mut w = vec![0.0f32; WINDOW_SIZE]; + for (i, w_val) in w.iter_mut().enumerate().take(WINDOW_SIZE) { + *w_val = 0.5 * (1.0 - (2.0 * std::f32::consts::PI * i as f32 / WINDOW_SIZE as f32).cos()); + } + w + }); + + // Precompute DFT tables (cached) + static DFT_TABLES: std::sync::OnceLock<(Vec, Vec)> = std::sync::OnceLock::new(); + let (dft_cos, dft_sin) = DFT_TABLES.get_or_init(|| { + let mut cos_tbl = vec![0.0f32; N_FREQ * N_FFT]; + let mut sin_tbl = vec![0.0f32; N_FREQ * N_FFT]; + for k in 0..N_FREQ { + for n in 0..N_FFT { + let angle = 2.0 * std::f32::consts::PI * k as f32 * n as f32 / N_FFT as f32; + cos_tbl[k * N_FFT + n] = angle.cos(); + sin_tbl[k * N_FFT + n] = angle.sin(); + } + } + (cos_tbl, sin_tbl) + }); + + // Batched computation via BLAS sgemm: + // 1. Pre-compute all windowed frames: windowed[N_FFT × n_frames] column-major + let mut windowed_all = vec![0.0f32; N_FFT * n_frames]; + for t in 0..n_frames { + let start = t * HOP_LENGTH; + for n in 0..N_FFT { + windowed_all[n * n_frames + t] = padded[start + n] * window[n]; + } + } + + // 2. DFT via BLAS: re = dft_cos @ windowed_all, im = dft_sin @ windowed_all + // [N_FREQ × N_FFT] @ [N_FFT × n_frames] = [N_FREQ × n_frames] + let mut re = vec![0.0f32; n_freqs * n_frames]; + let mut im = vec![0.0f32; n_freqs * n_frames]; + kernels::matmul_nn(&mut re, dft_cos, &windowed_all, n_freqs, N_FFT, n_frames); + kernels::matmul_nn(&mut im, dft_sin, &windowed_all, n_freqs, N_FFT, n_frames); + drop(windowed_all); + + // 3. Power spectrum: power[k * n_frames + t] = re² + im² + let mut power = vec![0.0f32; n_freqs * n_frames]; + for i in 0..n_freqs * n_frames { + power[i] = re[i] * re[i] + im[i] * im[i]; + } + drop(re); + drop(im); + + // 4. Mel filter bank via BLAS: mel_raw = mel_filters @ power + // [MEL_BINS × N_FREQ] @ [N_FREQ × n_frames] = [MEL_BINS × n_frames] + let mut mel = vec![0.0f32; MEL_BINS * n_frames]; + kernels::matmul_nn(&mut mel, mel_filters, &power, MEL_BINS, n_freqs, n_frames); + drop(power); + + // 5. Log, clamp, normalize + let mut global_max = -1e30f32; + for val in mel.iter_mut() { + *val = (*val).max(1e-10).log10(); + if *val > global_max { global_max = *val; } + } + let min_val = global_max - 8.0; + for val in mel.iter_mut() { + *val = ((*val).max(min_val) + 4.0) / 4.0; + } + + Some((mel, n_frames)) +} + +/// Drop long silent spans. Adaptive RMS gating with spike rejection. +pub fn compact_silence(samples: &[f32]) -> Vec { + let n_samples = samples.len(); + if n_samples == 0 { + return Vec::new(); + } + + let win = 160; // 10ms at 16kHz + let base_thresh = 0.002f32; + let max_thresh = 0.025f32; + let smooth_alpha = 0.2f32; + let min_voice_windows = 5; + let pad_voice_windows = 3; + let pass_windows = 60; + + let n_win = n_samples.div_ceil(win); + let mut rms_vals = vec![0.0f32; n_win]; + + for (w, rms_val) in rms_vals.iter_mut().enumerate().take(n_win) { + let start = w * win; + let end = (start + win).min(n_samples); + let len = end - start; + let mut energy = 0.0f32; + for sample in samples.iter().take(end).skip(start) { + energy += sample * sample; + } + *rms_val = (energy / len.max(1) as f32).sqrt(); + } + + // Smooth RMS + let mut smooth_vals = vec![0.0f32; n_win]; + let mut smooth = rms_vals[0]; + for w in 0..n_win { + smooth = (1.0 - smooth_alpha) * smooth + smooth_alpha * rms_vals[w]; + smooth_vals[w] = smooth; + } + + // Adaptive threshold from 25th percentile + let mut sorted = smooth_vals.clone(); + sorted.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)); + let p25 = ((n_win - 1) as f32 * 0.25) as usize; + let noise_floor = sorted[p25]; + let thresh = (noise_floor * 1.8).clamp(base_thresh, max_thresh); + + let mut is_voice = vec![false; n_win]; + for w in 0..n_win { + is_voice[w] = smooth_vals[w] > thresh; + } + + // Remove short voice bursts + let mut i = 0; + while i < n_win { + if !is_voice[i] { + i += 1; + continue; + } + let mut j = i + 1; + while j < n_win && is_voice[j] { + j += 1; + } + if j - i < min_voice_windows { + for is_voice_val in is_voice.iter_mut().take(j).skip(i) { + *is_voice_val = false; + } + } + i = j; + } + + // Pad voice edges + let mut padded_voice = vec![false; n_win]; + for (w, &voice) in is_voice.iter().enumerate().take(n_win) { + if !voice { + continue; + } + let a = w.saturating_sub(pad_voice_windows); + let b = (w + pad_voice_windows).min(n_win - 1); + for padded_val in padded_voice.iter_mut().take(b + 1).skip(a) { + *padded_val = true; + } + } + + let mut out = Vec::with_capacity(n_samples); + let mut silence_count = 0; + + for (w, &pv) in padded_voice.iter().enumerate().take(n_win) { + let start = w * win; + let end = (start + win).min(n_samples); + + if pv { + out.extend_from_slice(&samples[start..end]); + silence_count = 0; + } else { + silence_count += 1; + if silence_count <= pass_windows { + out.extend_from_slice(&samples[start..end]); + } + } + } + + if out.is_empty() { + let keep = n_samples.min(SAMPLE_RATE as usize / 2); + out.extend_from_slice(&samples[..keep]); + } + + out +} diff --git a/vendor/qwenasr/crates/qwen-asr/src/c_api.rs b/vendor/qwenasr/crates/qwen-asr/src/c_api.rs new file mode 100644 index 0000000..0f988f9 --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/src/c_api.rs @@ -0,0 +1,386 @@ +//! C-FFI API for iOS and native integration. +//! +//! Compile with: `cargo build --release --target aarch64-apple-ios --features ios` + +use std::ffi::{CStr, CString}; +use std::fmt::Write; +use std::os::raw::c_char; + +use crate::align; +use crate::audio; +use crate::context::QwenCtx; +use crate::kernels; +use crate::transcribe; + +/// Opaque handle to the ASR engine. +pub struct QwenAsrEngine { + ctx: QwenCtx, +} + +fn push_json_string(dst: &mut String, value: &str) { + dst.push('"'); + for ch in value.chars() { + match ch { + '"' => dst.push_str("\\\""), + '\\' => dst.push_str("\\\\"), + '\n' => dst.push_str("\\n"), + '\r' => dst.push_str("\\r"), + '\t' => dst.push_str("\\t"), + c if c.is_control() => { + let _ = write!(dst, "\\u{:04x}", c as u32); + } + c => dst.push(c), + } + } + dst.push('"'); +} + +/// Load model from a directory path. Returns null on failure. +#[no_mangle] +pub unsafe extern "C" fn qwen_asr_load_model( + model_dir: *const c_char, + n_threads: i32, + verbosity: i32, +) -> *mut QwenAsrEngine { + if model_dir.is_null() { + return std::ptr::null_mut(); + } + let dir = match CStr::from_ptr(model_dir).to_str() { + Ok(s) => s, + Err(_) => return std::ptr::null_mut(), + }; + + kernels::set_verbose(verbosity); + + let threads = if n_threads <= 0 { + kernels::get_num_cpus() + } else { + n_threads as usize + }; + kernels::set_threads(threads); + + match QwenCtx::load(dir) { + Some(ctx) => Box::into_raw(Box::new(QwenAsrEngine { ctx })), + None => std::ptr::null_mut(), + } +} + +/// Transcribe a WAV file. Returns a heap-allocated C string (caller must free with qwen_asr_free_string). +#[no_mangle] +pub unsafe extern "C" fn qwen_asr_transcribe_file( + engine: *mut QwenAsrEngine, + wav_path: *const c_char, +) -> *mut c_char { + if engine.is_null() || wav_path.is_null() { + return std::ptr::null_mut(); + } + let eng = &mut *engine; + let path = match CStr::from_ptr(wav_path).to_str() { + Ok(s) => s, + Err(_) => return std::ptr::null_mut(), + }; + + match transcribe::transcribe(&mut eng.ctx, path) { + Some(text) => match CString::new(text) { + Ok(cs) => cs.into_raw(), + Err(_) => std::ptr::null_mut(), + }, + None => std::ptr::null_mut(), + } +} + +/// Transcribe raw PCM samples (f32, 16kHz, mono). +/// Returns a heap-allocated C string (caller must free with qwen_asr_free_string). +#[no_mangle] +pub unsafe extern "C" fn qwen_asr_transcribe_pcm( + engine: *mut QwenAsrEngine, + samples: *const f32, + n_samples: i32, +) -> *mut c_char { + if engine.is_null() || samples.is_null() || n_samples <= 0 { + return std::ptr::null_mut(); + } + let eng = &mut *engine; + let pcm = std::slice::from_raw_parts(samples, n_samples as usize); + + match transcribe::transcribe_audio(&mut eng.ctx, pcm) { + Some(text) => match CString::new(text) { + Ok(cs) => cs.into_raw(), + Err(_) => std::ptr::null_mut(), + }, + None => std::ptr::null_mut(), + } +} + +/// Transcribe raw WAV buffer (entire file contents including header). +/// Returns a heap-allocated C string (caller must free with qwen_asr_free_string). +#[no_mangle] +pub unsafe extern "C" fn qwen_asr_transcribe_wav_buffer( + engine: *mut QwenAsrEngine, + wav_data: *const u8, + wav_len: i32, +) -> *mut c_char { + if engine.is_null() || wav_data.is_null() || wav_len <= 0 { + return std::ptr::null_mut(); + } + let eng = &mut *engine; + let data = std::slice::from_raw_parts(wav_data, wav_len as usize); + + let samples = match audio::parse_wav_buffer(data) { + Some(s) => s, + None => return std::ptr::null_mut(), + }; + + match transcribe::transcribe_audio(&mut eng.ctx, &samples) { + Some(text) => match CString::new(text) { + Ok(cs) => cs.into_raw(), + Err(_) => std::ptr::null_mut(), + }, + None => std::ptr::null_mut(), + } +} + +/// Force-align a WAV file against a known transcript. +/// Returns a heap-allocated JSON string: +/// [{"text":"Hello","start_ms":80.0,"end_ms":520.0}, ...] +#[no_mangle] +pub unsafe extern "C" fn qwen_asr_force_align_file( + engine: *mut QwenAsrEngine, + wav_path: *const c_char, + text: *const c_char, + language: *const c_char, +) -> *mut c_char { + if engine.is_null() || wav_path.is_null() || text.is_null() || language.is_null() { + return std::ptr::null_mut(); + } + let eng = &mut *engine; + let path = match CStr::from_ptr(wav_path).to_str() { + Ok(s) => s, + Err(_) => return std::ptr::null_mut(), + }; + let transcript = match CStr::from_ptr(text).to_str() { + Ok(s) => s, + Err(_) => return std::ptr::null_mut(), + }; + let align_language = match CStr::from_ptr(language).to_str() { + Ok(s) => s, + Err(_) => return std::ptr::null_mut(), + }; + + let samples = match audio::load_wav(path) { + Some(s) => s, + None => return std::ptr::null_mut(), + }; + let results = match align::forced_align(&mut eng.ctx, &samples, transcript, align_language) { + Some(items) => items, + None => return std::ptr::null_mut(), + }; + + let mut json = String::from("["); + for (index, item) in results.iter().enumerate() { + if index > 0 { + json.push(','); + } + json.push_str("{\"text\":"); + push_json_string(&mut json, &item.text); + let _ = write!( + &mut json, + ",\"start_ms\":{:.3},\"end_ms\":{:.3}}}", + item.start_ms, + item.end_ms, + ); + } + json.push(']'); + + match CString::new(json) { + Ok(cs) => cs.into_raw(), + Err(_) => std::ptr::null_mut(), + } +} + +/// Set segmentation seconds (0 = no segmentation). +#[no_mangle] +pub unsafe extern "C" fn qwen_asr_set_segment_sec(engine: *mut QwenAsrEngine, sec: f32) { + if !engine.is_null() { + (*engine).ctx.segment_sec = sec; + } +} + +/// Set language (e.g. "English", "Chinese"). Empty string = auto-detect. +#[no_mangle] +pub unsafe extern "C" fn qwen_asr_set_language( + engine: *mut QwenAsrEngine, + language: *const c_char, +) -> i32 { + if engine.is_null() || language.is_null() { + return -1; + } + let lang = match CStr::from_ptr(language).to_str() { + Ok(s) => s, + Err(_) => return -1, + }; + match (*engine).ctx.set_force_language(lang) { + Ok(()) => 0, + Err(()) => -1, + } +} + +/// Free a string returned by qwen_asr_transcribe_*. +#[no_mangle] +pub unsafe extern "C" fn qwen_asr_free_string(s: *mut c_char) { + if !s.is_null() { + drop(CString::from_raw(s)); + } +} + +/// Free the engine. +#[no_mangle] +pub unsafe extern "C" fn qwen_asr_free(engine: *mut QwenAsrEngine) { + if !engine.is_null() { + drop(Box::from_raw(engine)); + } +} + +// ======================================================================== +// Streaming API +// ======================================================================== + +/// Opaque handle to streaming state. +pub struct QwenAsrStreamState { + state: transcribe::StreamState, + /// Accumulated audio buffer (stream_push_audio requires full buffer). + audio_buf: Vec, +} + +/// Create a new streaming state. Returns null on failure. +#[no_mangle] +pub unsafe extern "C" fn qwen_asr_stream_new() -> *mut QwenAsrStreamState { + Box::into_raw(Box::new(QwenAsrStreamState { + state: transcribe::StreamState::new(), + audio_buf: Vec::new(), + })) +} + +/// Free a streaming state. +#[no_mangle] +pub unsafe extern "C" fn qwen_asr_stream_free(stream: *mut QwenAsrStreamState) { + if !stream.is_null() { + drop(Box::from_raw(stream)); + } +} + +/// Reset streaming state for a new utterance (reuses allocations). +#[no_mangle] +pub unsafe extern "C" fn qwen_asr_stream_reset(stream: *mut QwenAsrStreamState) { + if !stream.is_null() { + let s = &mut *stream; + s.state.reset(); + s.audio_buf.clear(); + } +} + +/// Push new audio samples and get incremental text delta. +/// +/// `samples` / `n_samples`: new PCM chunk (f32, 16 kHz, mono). +/// `finalize`: set to 1 to signal end-of-stream and flush remaining tokens. +/// +/// Returns a heap-allocated C string with newly emitted text (may be empty), +/// or null if nothing was emitted. Caller must free with `qwen_asr_free_string`. +#[no_mangle] +pub unsafe extern "C" fn qwen_asr_stream_push( + engine: *mut QwenAsrEngine, + stream: *mut QwenAsrStreamState, + samples: *const f32, + n_samples: i32, + finalize: i32, +) -> *mut c_char { + if engine.is_null() || stream.is_null() { + return std::ptr::null_mut(); + } + + let eng = &mut *engine; + let s = &mut *stream; + + // Append new samples to accumulated buffer + if !samples.is_null() && n_samples > 0 { + let new_samples = std::slice::from_raw_parts(samples, n_samples as usize); + s.audio_buf.extend_from_slice(new_samples); + } + + // Call the Rust streaming API with the full accumulated buffer + match transcribe::stream_push_audio( + &mut eng.ctx, + &s.audio_buf, + &mut s.state, + finalize != 0, + ) { + Some(delta) if !delta.is_empty() => match CString::new(delta) { + Ok(cs) => cs.into_raw(), + Err(_) => std::ptr::null_mut(), + }, + _ => std::ptr::null_mut(), + } +} + +/// Get the full accumulated transcription result so far. +/// Returns a heap-allocated C string. Caller must free with `qwen_asr_free_string`. +#[no_mangle] +pub unsafe extern "C" fn qwen_asr_stream_get_result( + stream: *mut QwenAsrStreamState, +) -> *mut c_char { + if stream.is_null() { + return std::ptr::null_mut(); + } + let s = &*stream; + let text = s.state.text(); + if text.is_empty() { + return std::ptr::null_mut(); + } + match CString::new(text) { + Ok(cs) => cs.into_raw(), + Err(_) => std::ptr::null_mut(), + } +} + +/// Configure streaming chunk size in seconds (default 2.0). +#[no_mangle] +pub unsafe extern "C" fn qwen_asr_stream_set_chunk_sec(engine: *mut QwenAsrEngine, sec: f32) { + if !engine.is_null() && sec > 0.0 { + (*engine).ctx.stream_chunk_sec = sec; + } +} + +/// Configure token rollback window (default 5). +#[no_mangle] +pub unsafe extern "C" fn qwen_asr_stream_set_rollback(engine: *mut QwenAsrEngine, tokens: i32) { + if !engine.is_null() && tokens >= 0 { + (*engine).ctx.stream_rollback = tokens; + } +} + +/// Configure unfixed chunks count before emitting (default 2). +#[no_mangle] +pub unsafe extern "C" fn qwen_asr_stream_set_unfixed_chunks(engine: *mut QwenAsrEngine, chunks: i32) { + if !engine.is_null() && chunks >= 0 { + (*engine).ctx.stream_unfixed_chunks = chunks; + } +} + +/// Configure max new tokens per chunk (default 32). +#[no_mangle] +pub unsafe extern "C" fn qwen_asr_stream_set_max_new_tokens(engine: *mut QwenAsrEngine, tokens: i32) { + if !engine.is_null() && tokens > 0 { + (*engine).ctx.stream_max_new_tokens = tokens; + } +} + +/// Configure whether past text is reused as streaming context. +#[no_mangle] +pub unsafe extern "C" fn qwen_asr_stream_set_past_text( + engine: *mut QwenAsrEngine, + enabled: i32, +) { + if !engine.is_null() { + (*engine).ctx.past_text_conditioning = enabled != 0; + } +} diff --git a/vendor/qwenasr/crates/qwen-asr/src/config.rs b/vendor/qwenasr/crates/qwen-asr/src/config.rs new file mode 100644 index 0000000..f4cd7a9 --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/src/config.rs @@ -0,0 +1,182 @@ +//! Model configuration and automatic variant detection. + +pub const SAMPLE_RATE: i32 = 16000; +pub const MEL_BINS: usize = 128; +pub const HOP_LENGTH: usize = 160; +pub const WINDOW_SIZE: usize = 400; +pub const VOCAB_SIZE: usize = 151936; + +pub const MAX_ENC_LAYERS: usize = 24; +pub const MAX_DEC_LAYERS: usize = 28; + +// Special token IDs +pub const TOKEN_IM_START: i32 = 151644; +pub const TOKEN_IM_END: i32 = 151645; +pub const TOKEN_ENDOFTEXT: i32 = 151643; +pub const TOKEN_AUDIO_START: i32 = 151669; +pub const TOKEN_AUDIO_END: i32 = 151670; +pub const TOKEN_AUDIO_PAD: i32 = 151676; +pub const TOKEN_ASR_TEXT: i32 = 151704; +pub const TOKEN_TIMESTAMP: i32 = 151705; + +// Conv2D stem constants +pub const CONV_HIDDEN: usize = 480; +pub const CONV_KERNEL: usize = 3; + +#[derive(Clone)] +pub struct QwenConfig { + // Audio encoder + pub enc_d_model: usize, + pub enc_layers: usize, + pub enc_heads: usize, + pub enc_head_dim: usize, + pub enc_ffn_dim: usize, + pub enc_output_dim: usize, + pub enc_n_window: usize, + pub enc_n_window_infer: usize, + pub enc_chunk_size: usize, + pub enc_conv_proj_dim: usize, + + // LLM decoder + pub dec_hidden: usize, + pub dec_layers: usize, + pub dec_heads: usize, + pub dec_kv_heads: usize, + pub dec_head_dim: usize, + pub dec_intermediate: usize, + pub vocab_size: usize, + pub dec_rms_norm_eps: f32, + pub dec_rope_theta: f32, + + // Forced aligner fields (0 = normal ASR model) + pub classify_num: usize, + pub timestamp_segment_time: f32, +} + +impl Default for QwenConfig { + fn default() -> Self { + Self { + enc_d_model: 0, + enc_layers: 0, + enc_heads: 0, + enc_head_dim: 0, + enc_ffn_dim: 0, + enc_output_dim: 0, + enc_n_window: 50, + enc_n_window_infer: 800, + enc_chunk_size: 100, + enc_conv_proj_dim: CONV_HIDDEN * 16, + dec_hidden: 0, + dec_layers: 28, + dec_heads: 16, + dec_kv_heads: 8, + dec_head_dim: 128, + dec_intermediate: 0, + vocab_size: VOCAB_SIZE, + dec_rms_norm_eps: 1e-6, + dec_rope_theta: 1e6, + classify_num: 0, + timestamp_segment_time: 0.0, + } + } +} + +impl QwenConfig { + /// Returns the effective lm_head output dimension. + pub fn lm_head_dim(&self) -> usize { + if self.classify_num > 0 { self.classify_num } else { self.vocab_size } + } + + /// Whether this config is for a forced aligner model. + pub fn is_aligner(&self) -> bool { + self.classify_num > 0 + } +} + +/// Tensor shape info passed from safetensors for model detection. +pub struct DetectInfo<'a> { + pub has_enc_layer_18: bool, + /// Shape of thinker.lm_head.weight (if present) + pub lm_head_shape: Option<&'a [i64]>, + /// Shape of thinker.model.embed_tokens.weight + pub embed_tokens_shape: Option<&'a [i64]>, + /// Shape of thinker.model.layers.0.mlp.gate_proj.weight + pub gate_proj_shape: Option<&'a [i64]>, +} + +impl QwenConfig { + /// Detect model variant from safetensors tensor shapes. + /// Handles ASR 0.6B, ASR 1.7B, and ForcedAligner 0.6B (which has 1.7B encoder + 0.6B decoder). + pub fn detect(info: &DetectInfo) -> Self { + let mut cfg = Self::default(); + + // Determine decoder hidden size from embed_tokens shape [vocab_size, hidden_dim] + let dec_hidden = info.embed_tokens_shape + .and_then(|s| if s.len() == 2 { Some(s[1] as usize) } else { None }) + .unwrap_or(if info.has_enc_layer_18 { 2048 } else { 1024 }); + + // Determine decoder intermediate from gate_proj shape [intermediate, hidden] + let dec_intermediate = info.gate_proj_shape + .and_then(|s| if s.len() == 2 { Some(s[0] as usize) } else { None }) + .unwrap_or(if dec_hidden >= 2048 { 6144 } else { 3072 }); + + // Encoder architecture: 24 layers = "large" encoder, 18 layers = "small" encoder + if info.has_enc_layer_18 { + // Large encoder (used by both 1.7B ASR and aligner 0.6B) + cfg.enc_d_model = 1024; + cfg.enc_layers = 24; + cfg.enc_heads = 16; + cfg.enc_head_dim = 64; + cfg.enc_ffn_dim = 4096; + } else { + // Small encoder (0.6B ASR) + cfg.enc_d_model = 896; + cfg.enc_layers = 18; + cfg.enc_heads = 14; + cfg.enc_head_dim = 64; + cfg.enc_ffn_dim = 3584; + } + + // enc_output_dim always matches dec_hidden (proj projects encoder output to decoder space) + cfg.enc_output_dim = dec_hidden; + cfg.dec_hidden = dec_hidden; + cfg.dec_intermediate = dec_intermediate; + + // Detect forced aligner: lm_head has shape [classify_num, hidden_dim] + // where classify_num != vocab_size (typically 5000) + if let Some(shape) = info.lm_head_shape { + if shape.len() == 2 && (shape[0] as usize) != VOCAB_SIZE { + cfg.classify_num = shape[0] as usize; + cfg.timestamp_segment_time = 80.0; // 80ms per time bin + } + } + + cfg.enc_chunk_size = cfg.enc_n_window * 2; + cfg + } +} + +pub const SUPPORTED_LANGUAGES: &[&str] = &[ + "Chinese", "English", "Cantonese", "Arabic", "German", "French", + "Spanish", "Portuguese", "Indonesian", "Italian", "Korean", "Russian", + "Thai", "Vietnamese", "Japanese", "Turkish", "Hindi", "Malay", "Dutch", + "Swedish", "Danish", "Finnish", "Polish", "Czech", "Filipino", + "Persian", "Greek", "Romanian", "Hungarian", "Macedonian", +]; + +pub fn normalize_language(language: &str) -> Option { + let trimmed = language.trim(); + if trimmed.is_empty() { + return None; + } + let mut chars = trimmed.chars(); + let first = chars.next()?.to_uppercase().to_string(); + let rest: String = chars.map(|c| c.to_lowercase().next().unwrap_or(c)).collect(); + let normalized = format!("{}{}", first, rest); + + if SUPPORTED_LANGUAGES.contains(&normalized.as_str()) { + Some(normalized) + } else { + None + } +} diff --git a/vendor/qwenasr/crates/qwen-asr/src/context.rs b/vendor/qwenasr/crates/qwen-asr/src/context.rs new file mode 100644 index 0000000..beb77e2 --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/src/context.rs @@ -0,0 +1,309 @@ +//! Top-level engine state ([`QwenCtx`]) with shared model weights and private runtime buffers. + +use crate::config::*; +use crate::decoder::*; +use crate::encoder::EncoderBuffers; +use crate::encoder::*; +use crate::kernels; +use crate::safetensors::MultiSafetensors; +use crate::tokenizer::QwenTokenizer; +use std::collections::HashMap; +use std::ops::Deref; +use std::sync::{Arc, Mutex, OnceLock, Weak}; + +pub type TokenCallback = Box; + +/// Shared read-only model weights reused across multiple runtime contexts. +pub struct SharedQwenModel { + pub config: QwenConfig, + pub encoder: Encoder, + pub decoder: Decoder, + pub _safetensors: MultiSafetensors, // kept alive for mmap'd BF16 pointers + pub model_dir: String, +} + +type SharedModelCache = Mutex>>; + +fn shared_model_cache() -> &'static SharedModelCache { + static CACHE: OnceLock = OnceLock::new(); + CACHE.get_or_init(|| Mutex::new(HashMap::new())) +} + +impl SharedQwenModel { + fn load(model_dir: &str) -> Option> { + if let Ok(cache) = shared_model_cache().lock() { + if let Some(shared) = cache.get(model_dir).and_then(Weak::upgrade) { + if kernels::verbose() >= 1 { + eprintln!("Reusing shared model weights from {}", model_dir); + } + return Some(shared); + } + } + + if kernels::verbose() >= 1 { + eprintln!("Loading model from {}", model_dir); + } + + let ms = MultiSafetensors::open(model_dir)?; + + // Detect model variant from tensor shapes + let info = crate::config::DetectInfo { + has_enc_layer_18: ms + .has_tensor("thinker.audio_tower.layers.18.self_attn.q_proj.weight"), + lm_head_shape: ms + .find("thinker.lm_head.weight") + .map(|(_, t)| t.shape.as_slice()), + embed_tokens_shape: ms + .find("thinker.model.embed_tokens.weight") + .map(|(_, t)| t.shape.as_slice()), + gate_proj_shape: ms + .find("thinker.model.layers.0.mlp.gate_proj.weight") + .map(|(_, t)| t.shape.as_slice()), + }; + let cfg = QwenConfig::detect(&info); + + if kernels::verbose() >= 1 { + let variant = if cfg.dec_hidden >= 2048 { + "1.7B" + } else { + "0.6B" + }; + let model_type = if cfg.is_aligner() { + "ForcedAligner" + } else { + "ASR" + }; + eprintln!("Detected: Qwen3-{}-{}", model_type, variant); + if cfg.is_aligner() { + eprintln!( + " classify_num={}, timestamp_segment_time={:.0}ms", + cfg.classify_num, cfg.timestamp_segment_time + ); + eprintln!( + " encoder: {}d {}L, decoder: {}d {}L", + cfg.enc_d_model, cfg.enc_layers, cfg.dec_hidden, cfg.dec_layers + ); + } + } + + if kernels::verbose() >= 1 { + eprintln!("Loading encoder weights..."); + } + let encoder = Encoder::load(&ms, &cfg)?; + + if kernels::verbose() >= 1 { + eprintln!("Loading decoder weights..."); + } + let decoder = Decoder::load(&ms, &cfg)?; + + if kernels::verbose() >= 1 { + eprintln!("Model weights loaded."); + } + + let shared = Arc::new(SharedQwenModel { + config: cfg, + encoder, + decoder, + _safetensors: ms, + model_dir: model_dir.to_string(), + }); + + if let Ok(mut cache) = shared_model_cache().lock() { + cache.insert(model_dir.to_string(), Arc::downgrade(&shared)); + } + + Some(shared) + } +} + +/// Top-level ASR engine state owning runtime caches, prompts, and scratch buffers. +/// +/// Create with [`QwenCtx::load`], then pass to functions in the [`crate::transcribe`] module. +/// +/// # Configurable fields +/// +/// | Field | Default | Description | +/// |-------|---------|-------------| +/// | `segment_sec` | 0.0 | Segment duration for long audio (0 = no splitting) | +/// | `skip_silence` | false | Drop silent spans before transcription | +/// | `token_cb` | None | Streaming callback invoked for each decoded token | +/// | `prompt` | None | Optional text prompt (set via [`QwenCtx::set_prompt`]) | +/// | `force_language` | None | Force a language (set via [`QwenCtx::set_force_language`]) | +pub struct QwenCtx { + pub shared: Arc, + + // KV cache + pub kv_cache: KvCache, + + // Decoder buffers + pub dec_bufs: DecoderBuffers, + + // Encoder scratch buffers (reusable across calls) + pub enc_bufs: EncoderBuffers, + + // RoPE cache + pub rope_cache: RopeCache, + + // Token streaming callback + pub token_cb: Option, + + // Segmentation settings + pub segment_sec: f32, + pub search_sec: f32, + + // Streaming settings + pub stream_chunk_sec: f32, + pub stream_rollback: i32, + pub stream_unfixed_chunks: i32, + pub stream_max_new_tokens: i32, + pub past_text_conditioning: bool, + pub skip_silence: bool, + + // Optional prompt/language + pub prompt: Option, + pub force_language: Option, + pub prompt_tokens: Option>, + pub force_prompt_tokens: Option>, + pub prompt_tokens_ready: bool, + + // Perf stats + pub perf_total_ms: f64, + pub perf_text_tokens: i32, + pub perf_audio_ms: f64, + /// Mel spectrogram + encoder forward pass time combined. + pub perf_encode_ms: f64, + pub perf_decode_ms: f64, +} + +impl Deref for QwenCtx { + type Target = SharedQwenModel; + + fn deref(&self) -> &Self::Target { + &self.shared + } +} + +impl QwenCtx { + /// Load a Qwen3-ASR model from `model_dir`. + /// + /// The directory must contain `model*.safetensors` and `vocab.json`. + /// Returns `None` if any required file is missing or malformed. + /// + /// ```rust,no_run + /// use qwen_asr::context::QwenCtx; + /// let ctx = QwenCtx::load("qwen3-asr-0.6b").expect("failed to load"); + /// ``` + pub fn load(model_dir: &str) -> Option { + let shared = SharedQwenModel::load(model_dir)?; + let cfg = shared.config.clone(); + let kv_cache = KvCache::new(cfg.dec_layers, 2048, cfg.dec_kv_heads, cfg.dec_head_dim); + let dec_bufs = DecoderBuffers::new(&cfg); + + if kernels::verbose() >= 1 { + eprintln!("Runtime buffers initialized."); + } + + Some(QwenCtx { + shared, + kv_cache, + dec_bufs, + enc_bufs: EncoderBuffers::new(), + rope_cache: RopeCache::new(), + token_cb: None, + segment_sec: 0.0, + search_sec: 3.0, + stream_chunk_sec: 2.0, + stream_rollback: 5, + stream_unfixed_chunks: 2, + stream_max_new_tokens: 32, + past_text_conditioning: false, + skip_silence: false, + prompt: None, + force_language: None, + prompt_tokens: None, + force_prompt_tokens: None, + prompt_tokens_ready: false, + perf_total_ms: 0.0, + perf_text_tokens: 0, + perf_audio_ms: 0.0, + perf_encode_ms: 0.0, + perf_decode_ms: 0.0, + }) + } + + /// Set an optional text prompt to guide transcription. Pass an empty string to clear. + #[allow(clippy::result_unit_err)] + pub fn set_prompt(&mut self, prompt: &str) -> Result<(), ()> { + if prompt.is_empty() { + self.prompt = None; + } else { + self.prompt = Some(prompt.to_string()); + } + self.prompt_tokens_ready = false; + Ok(()) + } + + /// Force a specific language (e.g. `"English"`, `"Chinese"`). Pass an empty + /// string for auto-detection. Returns `Err(())` if the language is not recognized. + #[allow(clippy::result_unit_err)] + pub fn set_force_language(&mut self, language: &str) -> Result<(), ()> { + if language.is_empty() { + self.force_language = None; + self.prompt_tokens_ready = false; + return Ok(()); + } + + match normalize_language(language) { + Some(normalized) => { + self.force_language = Some(normalized); + self.prompt_tokens_ready = false; + Ok(()) + } + None => Err(()), + } + } + + pub fn prepare_prompt_tokens(&mut self, tokenizer: &QwenTokenizer) -> bool { + if self.prompt_tokens_ready { + return true; + } + + self.prompt_tokens = None; + self.force_prompt_tokens = None; + + if let Some(ref prompt) = self.prompt { + match tokenizer.encode(prompt) { + Some(tokens) => self.prompt_tokens = Some(tokens), + None => { + eprintln!("qwen: failed to encode --prompt text"); + return false; + } + } + } + + if let Some(ref lang) = self.force_language { + let force_text = format!("language {}", lang); + match tokenizer.encode(&force_text) { + Some(mut lang_tokens) => { + lang_tokens.push(TOKEN_ASR_TEXT); + self.force_prompt_tokens = Some(lang_tokens); + } + None => { + eprintln!("qwen: failed to encode --language text"); + return false; + } + } + } + + self.prompt_tokens_ready = true; + true + } + + pub fn reset_perf(&mut self) { + self.perf_total_ms = 0.0; + self.perf_text_tokens = 0; + self.perf_audio_ms = 0.0; + self.perf_encode_ms = 0.0; + self.perf_decode_ms = 0.0; + } +} diff --git a/vendor/qwenasr/crates/qwen-asr/src/decoder.rs b/vendor/qwenasr/crates/qwen-asr/src/decoder.rs new file mode 100644 index 0000000..aa92cb8 --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/src/decoder.rs @@ -0,0 +1,1064 @@ +//! Qwen3 LLM decoder with GQA, KV cache, and generation. + +use crate::config::*; +use crate::kernels; +use crate::safetensors::MultiSafetensors; + +pub struct DecLayer { + pub wq_weight_bf16: *const u16, + pub wk_weight_bf16: *const u16, + pub wv_weight_bf16: *const u16, + pub wo_weight_bf16: *const u16, + pub q_norm_weight: Vec, + pub k_norm_weight: Vec, + pub input_norm: Vec, + pub post_attn_norm: Vec, + pub gate_weight_bf16: *const u16, + pub up_weight_bf16: *const u16, + pub down_weight_bf16: *const u16, + pub gate_up_fused_bf16: Vec, // owned, interleaved + pub gate_up_fused_f32: Vec, + pub down_weight_f32: Vec, + /// INT8 quantized attention weights + per-row scales + pub wq_int8: Vec, + pub wq_int8_scales: Vec, + pub wk_int8: Vec, + pub wk_int8_scales: Vec, + pub wv_int8: Vec, + pub wv_int8_scales: Vec, + pub wo_int8: Vec, + pub wo_int8_scales: Vec, + /// INT8 quantized FFN weights + per-row scales + pub gate_up_int8: Vec, + pub gate_up_int8_scales: Vec, + pub down_int8: Vec, + pub down_int8_scales: Vec, +} + +unsafe impl Send for DecLayer {} +unsafe impl Sync for DecLayer {} + +pub struct Decoder { + pub tok_embeddings_bf16: *const u16, + pub layers: Vec, + pub norm: Vec, + /// Separate lm_head for forced aligner (None = tied weights with tok_embeddings) + pub lm_head_bf16: Option<*const u16>, + /// INT8 quantized lm_head weights for fast argmax + pub lm_head_int8: Option>, + pub lm_head_int8_scales: Option>, +} + +unsafe impl Send for Decoder {} +unsafe impl Sync for Decoder {} + +#[allow(dead_code)] +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +enum DecodeKernelMode { + Int8, + Bf16, +} + +const fn decode_kernel_mode() -> DecodeKernelMode { + #[cfg(target_arch = "aarch64")] + { + DecodeKernelMode::Int8 + } + + #[cfg(not(target_arch = "aarch64"))] + { + DecodeKernelMode::Bf16 + } +} + +const fn materialize_int8_decoder_weights() -> bool { + matches!(decode_kernel_mode(), DecodeKernelMode::Int8) +} + +const fn materialize_f32_ffn_weights() -> bool { + matches!(decode_kernel_mode(), DecodeKernelMode::Bf16) +} + +fn load_f32(ms: &MultiSafetensors, name: &str) -> Option> { + let result = ms.get_f32(name); + if result.is_none() { + eprintln!("decoder: weight not found: {}", name); + } + result +} + +fn load_bf16_direct(ms: &MultiSafetensors, name: &str) -> Option<*const u16> { + let result = ms.get_bf16_direct(name); + if result.is_none() { + eprintln!("decoder: weight not found: {}", name); + } + result +} + +fn bf16_ptr_to_f32_vec(src: *const u16, n: usize) -> Vec { + let mut out = vec![0.0f32; n]; + let src_slice = unsafe { std::slice::from_raw_parts(src, n) }; + kernels::bf16_to_f32_buf(&mut out, src_slice); + out +} + +fn bf16_slice_to_f32_vec(src: &[u16]) -> Vec { + let mut out = vec![0.0f32; src.len()]; + kernels::bf16_to_f32_buf(&mut out, src); + out +} + +impl Decoder { + pub fn load(ms: &MultiSafetensors, cfg: &QwenConfig) -> Option { + let tok_embeddings_bf16 = load_bf16_direct(ms, "thinker.model.embed_tokens.weight")?; + + let mut layers = Vec::new(); + for i in 0..cfg.dec_layers { + let lp = format!("thinker.model.layers.{}", i); + + let wq = load_bf16_direct(ms, &format!("{}.self_attn.q_proj.weight", lp))?; + let wk = load_bf16_direct(ms, &format!("{}.self_attn.k_proj.weight", lp))?; + let wv = load_bf16_direct(ms, &format!("{}.self_attn.v_proj.weight", lp))?; + let wo = load_bf16_direct(ms, &format!("{}.self_attn.o_proj.weight", lp))?; + + let q_norm = load_f32(ms, &format!("{}.self_attn.q_norm.weight", lp))?; + let k_norm = load_f32(ms, &format!("{}.self_attn.k_norm.weight", lp))?; + let input_norm = load_f32(ms, &format!("{}.input_layernorm.weight", lp))?; + let post_attn_norm = load_f32(ms, &format!("{}.post_attention_layernorm.weight", lp))?; + + let gate_bf16 = load_bf16_direct(ms, &format!("{}.mlp.gate_proj.weight", lp))?; + let up_bf16 = load_bf16_direct(ms, &format!("{}.mlp.up_proj.weight", lp))?; + let down_bf16 = load_bf16_direct(ms, &format!("{}.mlp.down_proj.weight", lp))?; + + // Fuse gate+up: interleave rows + let inter = cfg.dec_intermediate; + let hidden = cfg.dec_hidden; + let mut gate_up_fused = vec![0u16; 2 * inter * hidden]; + unsafe { + let gate_slice = std::slice::from_raw_parts(gate_bf16, inter * hidden); + let up_slice = std::slice::from_raw_parts(up_bf16, inter * hidden); + for r in 0..inter { + gate_up_fused[2 * r * hidden..(2 * r + 1) * hidden] + .copy_from_slice(&gate_slice[r * hidden..(r + 1) * hidden]); + gate_up_fused[(2 * r + 1) * hidden..(2 * r + 2) * hidden] + .copy_from_slice(&up_slice[r * hidden..(r + 1) * hidden]); + } + } + + let q_dim = cfg.dec_heads * cfg.dec_head_dim; + let kv_dim = cfg.dec_kv_heads * cfg.dec_head_dim; + let (wq_int8, wq_int8_scales, wk_int8, wk_int8_scales, wv_int8, wv_int8_scales, wo_int8, wo_int8_scales, gate_up_int8, gate_up_int8_scales, down_int8, down_int8_scales) = + if materialize_int8_decoder_weights() { + let (wq_int8, wq_int8_scales) = + kernels::quantize_bf16_weights_to_int8(wq, q_dim, hidden); + let (wk_int8, wk_int8_scales) = + kernels::quantize_bf16_weights_to_int8(wk, kv_dim, hidden); + let (wv_int8, wv_int8_scales) = + kernels::quantize_bf16_weights_to_int8(wv, kv_dim, hidden); + let (wo_int8, wo_int8_scales) = + kernels::quantize_bf16_weights_to_int8(wo, hidden, q_dim); + let (gate_up_int8, gate_up_int8_scales) = + kernels::quantize_bf16_weights_to_int8(gate_up_fused.as_ptr(), 2 * inter, hidden); + let (down_int8, down_int8_scales) = + kernels::quantize_bf16_weights_to_int8(down_bf16, hidden, inter); + ( + wq_int8, + wq_int8_scales, + wk_int8, + wk_int8_scales, + wv_int8, + wv_int8_scales, + wo_int8, + wo_int8_scales, + gate_up_int8, + gate_up_int8_scales, + down_int8, + down_int8_scales, + ) + } else { + ( + Vec::new(), + Vec::new(), + Vec::new(), + Vec::new(), + Vec::new(), + Vec::new(), + Vec::new(), + Vec::new(), + Vec::new(), + Vec::new(), + Vec::new(), + Vec::new(), + ) + }; + + let (gate_up_fused_f32, down_weight_f32) = if materialize_f32_ffn_weights() { + ( + bf16_slice_to_f32_vec(&gate_up_fused), + bf16_ptr_to_f32_vec(down_bf16, hidden * inter), + ) + } else { + (Vec::new(), Vec::new()) + }; + layers.push(DecLayer { + wq_weight_bf16: wq, + wk_weight_bf16: wk, + wv_weight_bf16: wv, + wo_weight_bf16: wo, + q_norm_weight: q_norm, + k_norm_weight: k_norm, + input_norm, + post_attn_norm, + gate_weight_bf16: gate_bf16, + up_weight_bf16: up_bf16, + down_weight_bf16: down_bf16, + gate_up_fused_bf16: gate_up_fused, + gate_up_fused_f32, + down_weight_f32, + wq_int8, + wq_int8_scales, + wk_int8, + wk_int8_scales, + wv_int8, + wv_int8_scales, + wo_int8, + wo_int8_scales, + gate_up_int8, + gate_up_int8_scales, + down_int8, + down_int8_scales, + }); + } + + let norm = load_f32(ms, "thinker.model.norm.weight")?; + + // Load separate lm_head if present (forced aligner has untied lm_head) + let lm_head_bf16 = if cfg.classify_num > 0 { + let ptr = load_bf16_direct(ms, "thinker.lm_head.weight")?; + Some(ptr) + } else { + // For normal ASR, lm_head is tied with tok_embeddings (no separate weight) + ms.get_bf16_direct("thinker.lm_head.weight") + }; + + let lm_weight = lm_head_bf16.unwrap_or(tok_embeddings_bf16); + let lm_out_dim = cfg.lm_head_dim(); + let lm_in_dim = cfg.dec_hidden; + let (lm_head_int8, lm_head_int8_scales) = if materialize_int8_decoder_weights() { + let (lm_int8, lm_scales) = + kernels::quantize_bf16_weights_to_int8(lm_weight, lm_out_dim, lm_in_dim); + (Some(lm_int8), Some(lm_scales)) + } else { + (None, None) + }; + + Some(Decoder { + tok_embeddings_bf16, + layers, + norm, + lm_head_bf16, + lm_head_int8, + lm_head_int8_scales, + }) + } +} + +// ======================================================================== +// KV Cache +// ======================================================================== + +pub struct KvCache { + pub k: Vec, + pub v: Vec, + pub len: usize, + pub max_seq: usize, + pub n_layers: usize, + pub n_kv_heads: usize, + pub head_dim: usize, +} + +impl KvCache { + /// Layout: `[layer][head][pos][head_dim]` — head-contiguous for cache-friendly attention. + pub fn new(n_layers: usize, max_seq: usize, n_kv_heads: usize, head_dim: usize) -> Self { + let total = n_layers * n_kv_heads * max_seq * head_dim; + KvCache { + k: vec![0.0f32; total], + v: vec![0.0f32; total], + len: 0, + max_seq, + n_layers, + n_kv_heads, + head_dim, + } + } + + pub fn grow(&mut self, required: usize) { + if required <= self.max_seq { + return; + } + + let mut new_max = self.max_seq; + while new_max < required { + new_max *= 2; + } + + let old_head_stride = self.max_seq * self.head_dim; + let new_head_stride = new_max * self.head_dim; + let total = self.n_layers * self.n_kv_heads * new_head_stride; + + let mut new_k = vec![0.0f32; total]; + let mut new_v = vec![0.0f32; total]; + + let copy_len = self.len * self.head_dim; + for l in 0..self.n_layers { + for h in 0..self.n_kv_heads { + let old_off = (l * self.n_kv_heads + h) * old_head_stride; + let new_off = (l * self.n_kv_heads + h) * new_head_stride; + new_k[new_off..new_off + copy_len] + .copy_from_slice(&self.k[old_off..old_off + copy_len]); + new_v[new_off..new_off + copy_len] + .copy_from_slice(&self.v[old_off..old_off + copy_len]); + } + } + + self.k = new_k; + self.v = new_v; + self.max_seq = new_max; + } + + /// Write K for all heads at a given position (from interleaved kv_dim buffer). + pub fn k_write_pos(&mut self, layer: usize, pos: usize, src: &[f32]) { + let head_stride = self.max_seq * self.head_dim; + for h in 0..self.n_kv_heads { + let dst_off = (layer * self.n_kv_heads + h) * head_stride + pos * self.head_dim; + let src_off = h * self.head_dim; + self.k[dst_off..dst_off + self.head_dim] + .copy_from_slice(&src[src_off..src_off + self.head_dim]); + } + } + + /// Write V for all heads at a given position (from interleaved kv_dim buffer). + pub fn v_write_pos(&mut self, layer: usize, pos: usize, src: &[f32]) { + let head_stride = self.max_seq * self.head_dim; + for h in 0..self.n_kv_heads { + let dst_off = (layer * self.n_kv_heads + h) * head_stride + pos * self.head_dim; + let src_off = h * self.head_dim; + self.v[dst_off..dst_off + self.head_dim] + .copy_from_slice(&src[src_off..src_off + self.head_dim]); + } + } + + /// Base pointer for K data of a specific layer (head-contiguous layout). + /// Layout within layer: `[head][pos][head_dim]`, stride between heads = max_seq * head_dim. + pub fn k_layer_base(&self, layer: usize) -> *const f32 { + let off = layer * self.n_kv_heads * self.max_seq * self.head_dim; + unsafe { self.k.as_ptr().add(off) } + } + + pub fn v_layer_base(&self, layer: usize) -> *const f32 { + let off = layer * self.n_kv_heads * self.max_seq * self.head_dim; + unsafe { self.v.as_ptr().add(off) } + } + + /// Stride between heads (in floats): max_seq * head_dim. + pub fn head_stride(&self) -> usize { + self.max_seq * self.head_dim + } +} + +// ======================================================================== +// RoPE Cache +// ======================================================================== + +pub struct RopeCache { + pub cos: Vec, + pub sin: Vec, + pub inv_freq: Vec, + pub cap: usize, + pub head_dim: usize, +} + +impl Default for RopeCache { + fn default() -> Self { + Self::new() + } +} + +impl RopeCache { + pub fn new() -> Self { + RopeCache { + cos: Vec::new(), + sin: Vec::new(), + inv_freq: Vec::new(), + cap: 0, + head_dim: 0, + } + } + + pub fn ensure(&mut self, required_pos: usize, head_dim: usize, theta: f32) { + if self.head_dim != head_dim || self.inv_freq.is_empty() { + let half = head_dim / 2; + self.inv_freq = (0..half) + .map(|d| 1.0 / theta.powf((2 * d) as f32 / head_dim as f32)) + .collect(); + self.head_dim = head_dim; + } + + if required_pos <= self.cap { + return; + } + + let mut new_cap = if self.cap > 0 { self.cap } else { 1024 }; + while new_cap < required_pos { + new_cap *= 2; + } + + self.cos.resize(new_cap * head_dim, 0.0); + self.sin.resize(new_cap * head_dim, 0.0); + + let half = head_dim / 2; + for pos in self.cap..new_cap { + let p = pos as f32; + for d in 0..half { + let angle = p * self.inv_freq[d]; + let c = angle.cos(); + let s = angle.sin(); + self.cos[pos * head_dim + d] = c; + self.cos[pos * head_dim + half + d] = c; + self.sin[pos * head_dim + d] = s; + self.sin[pos * head_dim + half + d] = s; + } + } + + self.cap = new_cap; + } + + pub fn cos_at(&self, pos: usize) -> &[f32] { + &self.cos[pos * self.head_dim..(pos + 1) * self.head_dim] + } + + pub fn sin_at(&self, pos: usize) -> &[f32] { + &self.sin[pos * self.head_dim..(pos + 1) * self.head_dim] + } + + pub fn cos_range(&self, start: usize, len: usize) -> &[f32] { + &self.cos[start * self.head_dim..(start + len) * self.head_dim] + } + + pub fn sin_range(&self, start: usize, len: usize) -> &[f32] { + &self.sin[start * self.head_dim..(start + len) * self.head_dim] + } +} + +// ======================================================================== +// Decoder Forward +// ======================================================================== + +pub struct DecoderBuffers { + // Single-token decode buffers + pub x: Vec, + pub x_norm: Vec, + pub q: Vec, + pub k: Vec, + pub v: Vec, + pub attn_out: Vec, + pub proj_out: Vec, + pub gate_buf: Vec, + pub ffn_out: Vec, + + // Prefill buffers + pub pref_x: Vec, + pub pref_x_norm: Vec, + pub pref_q: Vec, + pub pref_k: Vec, + pub pref_v: Vec, + pub pref_attn_out: Vec, + pub pref_proj_out: Vec, + pub pref_ffn_out: Vec, + pub pref_gate_up: Vec, + pub pref_gate: Vec, + pub pref_seq_cap: usize, + + // Reusable scratch for BF16→F32 conversion in prefill path + pub bf16_scratch: Vec, +} + +impl DecoderBuffers { + pub fn new(cfg: &QwenConfig) -> Self { + let dim = cfg.dec_hidden; + let q_dim = cfg.dec_heads * cfg.dec_head_dim; + let kv_dim = cfg.dec_kv_heads * cfg.dec_head_dim; + let intermediate = cfg.dec_intermediate; + + // Largest weight matrix is gate_up_fused: 2 * intermediate * hidden + let max_weight = (2 * intermediate * dim).max(q_dim * dim).max(kv_dim * dim); + + DecoderBuffers { + x: vec![0.0f32; dim], + x_norm: vec![0.0f32; dim], + q: vec![0.0f32; q_dim], + k: vec![0.0f32; kv_dim], + v: vec![0.0f32; kv_dim], + attn_out: vec![0.0f32; q_dim], + proj_out: vec![0.0f32; dim], + gate_buf: vec![0.0f32; 2 * intermediate], + ffn_out: vec![0.0f32; intermediate], + pref_x: Vec::new(), + pref_x_norm: Vec::new(), + pref_q: Vec::new(), + pref_k: Vec::new(), + pref_v: Vec::new(), + pref_attn_out: Vec::new(), + pref_proj_out: Vec::new(), + pref_ffn_out: Vec::new(), + pref_gate_up: Vec::new(), + pref_gate: Vec::new(), + pref_seq_cap: 0, + bf16_scratch: vec![0.0f32; max_weight], + } + } + + pub fn ensure_prefill(&mut self, seq_len: usize, cfg: &QwenConfig) { + if seq_len <= self.pref_seq_cap { + return; + } + + let dim = cfg.dec_hidden; + let q_dim = cfg.dec_heads * cfg.dec_head_dim; + let kv_dim = cfg.dec_kv_heads * cfg.dec_head_dim; + let intermediate = cfg.dec_intermediate; + + let mut new_cap = if self.pref_seq_cap > 0 { + self.pref_seq_cap + } else { + 64 + }; + while new_cap < seq_len { + new_cap *= 2; + } + + self.pref_x.resize(new_cap * dim, 0.0); + self.pref_x_norm.resize(new_cap * dim, 0.0); + self.pref_q.resize(new_cap * q_dim, 0.0); + self.pref_k.resize(new_cap * kv_dim, 0.0); + self.pref_v.resize(new_cap * kv_dim, 0.0); + self.pref_attn_out.resize(new_cap * q_dim, 0.0); + self.pref_proj_out.resize(new_cap * dim, 0.0); + self.pref_ffn_out.resize(new_cap * dim, 0.0); + self.pref_gate_up.resize(new_cap * 2 * intermediate, 0.0); + self.pref_gate.resize(new_cap * intermediate, 0.0); + self.pref_seq_cap = new_cap; + } +} + +/// Decoder prefill: process multiple tokens. +pub fn decoder_prefill( + decoder: &Decoder, + cfg: &QwenConfig, + kv_cache: &mut KvCache, + rope: &mut RopeCache, + bufs: &mut DecoderBuffers, + input_embeds: &[f32], + seq_len: usize, +) { + let dim = cfg.dec_hidden; + let n_heads = cfg.dec_heads; + let n_kv_heads = cfg.dec_kv_heads; + let head_dim = cfg.dec_head_dim; + let intermediate = cfg.dec_intermediate; + let eps = cfg.dec_rms_norm_eps; + let theta = cfg.dec_rope_theta; + let q_dim = n_heads * head_dim; + let kv_dim = n_kv_heads * head_dim; + // Ensure KV cache + let needed = kv_cache.len + seq_len; + if needed > kv_cache.max_seq { + kv_cache.grow(needed + 1024); + } + + bufs.ensure_prefill(seq_len, cfg); + + let x = &mut bufs.pref_x[..seq_len * dim]; + x.copy_from_slice(&input_embeds[..seq_len * dim]); + + let start_pos = kv_cache.len; + rope.ensure(start_pos + seq_len, head_dim, theta); + let rope_cos = rope.cos_range(start_pos, seq_len); + let rope_sin = rope.sin_range(start_pos, seq_len); + + let scale = 1.0 / (head_dim as f32).sqrt(); + + for (layer_idx, layer) in decoder.layers.iter().enumerate() { + let x_norm = &mut bufs.pref_x_norm[..seq_len * dim]; + kernels::rms_norm( + x_norm, + &bufs.pref_x[..seq_len * dim], + &layer.input_norm, + seq_len, + dim, + eps, + ); + + let q = &mut bufs.pref_q[..seq_len * q_dim]; + let k = &mut bufs.pref_k[..seq_len * kv_dim]; + let v = &mut bufs.pref_v[..seq_len * kv_dim]; + + unsafe { + kernels::linear_nobias_bf16_scratch( + q, + x_norm, + layer.wq_weight_bf16, + seq_len, + dim, + q_dim, + &mut bufs.bf16_scratch, + ); + } + unsafe { + kernels::linear_nobias_bf16_scratch( + k, + x_norm, + layer.wk_weight_bf16, + seq_len, + dim, + kv_dim, + &mut bufs.bf16_scratch, + ); + } + unsafe { + kernels::linear_nobias_bf16_scratch( + v, + x_norm, + layer.wv_weight_bf16, + seq_len, + dim, + kv_dim, + &mut bufs.bf16_scratch, + ); + } + + kernels::rms_norm_per_head(q, &layer.q_norm_weight, seq_len, n_heads, head_dim, eps); + kernels::rms_norm_per_head(k, &layer.k_norm_weight, seq_len, n_kv_heads, head_dim, eps); + + kernels::apply_rope_neox(q, rope_cos, rope_sin, seq_len, n_heads, head_dim); + kernels::apply_rope_neox(k, rope_cos, rope_sin, seq_len, n_kv_heads, head_dim); + + // Store K, V in cache (scatter to head-contiguous layout) + for s in 0..seq_len { + kv_cache.k_write_pos( + layer_idx, + start_pos + s, + &bufs.pref_k[s * kv_dim..(s + 1) * kv_dim], + ); + kv_cache.v_write_pos( + layer_idx, + start_pos + s, + &bufs.pref_v[s * kv_dim..(s + 1) * kv_dim], + ); + } + + let total_seq = start_pos + seq_len; + let k_base = kv_cache.k_layer_base(layer_idx); + let v_base = kv_cache.v_layer_base(layer_idx); + let head_stride = kv_cache.head_stride(); + + let attn_out = &mut bufs.pref_attn_out[..seq_len * q_dim]; + kernels::causal_attention( + attn_out, + q, + k_base, + v_base, + head_stride, + seq_len, + total_seq, + n_heads, + n_kv_heads, + head_dim, + scale, + start_pos, + ); + + let proj_out = &mut bufs.pref_proj_out[..seq_len * dim]; + unsafe { + kernels::linear_nobias_bf16_scratch( + proj_out, + attn_out, + layer.wo_weight_bf16, + seq_len, + q_dim, + dim, + &mut bufs.bf16_scratch, + ); + } + kernels::add_inplace(&mut bufs.pref_x[..seq_len * dim], proj_out, seq_len * dim); + + // Post-attention RMSNorm + SwiGLU MLP + let x_norm2 = &mut bufs.pref_x_norm[..seq_len * dim]; + kernels::rms_norm( + x_norm2, + &bufs.pref_x[..seq_len * dim], + &layer.post_attn_norm, + seq_len, + dim, + eps, + ); + + let gate_up = &mut bufs.pref_gate_up[..seq_len * 2 * intermediate]; + if !layer.gate_up_fused_f32.is_empty() { + kernels::linear_nobias(gate_up, x_norm2, &layer.gate_up_fused_f32, seq_len, dim, 2 * intermediate); + } else { + unsafe { + kernels::linear_nobias_bf16_scratch( + gate_up, + x_norm2, + layer.gate_up_fused_bf16.as_ptr(), + seq_len, + dim, + 2 * intermediate, + &mut bufs.bf16_scratch, + ); + } + } + + let gate = &mut bufs.pref_gate[..seq_len * intermediate]; + kernels::swiglu_multiply(gate, gate_up, seq_len, intermediate); + + let ffn_out = &mut bufs.pref_ffn_out[..seq_len * dim]; + if !layer.down_weight_f32.is_empty() { + kernels::linear_nobias(ffn_out, gate, &layer.down_weight_f32, seq_len, intermediate, dim); + } else { + unsafe { + kernels::linear_nobias_bf16_scratch( + ffn_out, + gate, + layer.down_weight_bf16, + seq_len, + intermediate, + dim, + &mut bufs.bf16_scratch, + ); + } + } + kernels::add_inplace(&mut bufs.pref_x[..seq_len * dim], ffn_out, seq_len * dim); + } + + kv_cache.len = start_pos + seq_len; +} + +/// Decoder single-token forward: returns greedy token ID. +pub fn decoder_forward( + decoder: &Decoder, + cfg: &QwenConfig, + kv_cache: &mut KvCache, + rope: &mut RopeCache, + bufs: &mut DecoderBuffers, + input_embed: &[f32], +) -> i32 { + let mode = decode_kernel_mode(); + let dim = cfg.dec_hidden; + let n_heads = cfg.dec_heads; + let n_kv_heads = cfg.dec_kv_heads; + let head_dim = cfg.dec_head_dim; + let intermediate = cfg.dec_intermediate; + let eps = cfg.dec_rms_norm_eps; + let theta = cfg.dec_rope_theta; + let q_dim = n_heads * head_dim; + let kv_dim = n_kv_heads * head_dim; + + bufs.x[..dim].copy_from_slice(&input_embed[..dim]); + + let pos = kv_cache.len; + + if pos >= kv_cache.max_seq { + kv_cache.grow(pos + 1024); + } + + rope.ensure(pos + 1, head_dim, theta); + let rope_cos = rope.cos_at(pos); + let rope_sin = rope.sin_at(pos); + + let scale = 1.0 / (head_dim as f32).sqrt(); + + for (layer_idx, layer) in decoder.layers.iter().enumerate() { + kernels::rms_norm( + &mut bufs.x_norm[..dim], + &bufs.x[..dim], + &layer.input_norm, + 1, + dim, + eps, + ); + + match mode { + DecodeKernelMode::Bf16 => unsafe { + kernels::linear_nobias_bf16_scratch( + &mut bufs.q[..q_dim], + &bufs.x_norm[..dim], + layer.wq_weight_bf16, + 1, + dim, + q_dim, + &mut bufs.bf16_scratch, + ); + kernels::linear_nobias_bf16_scratch( + &mut bufs.k[..kv_dim], + &bufs.x_norm[..dim], + layer.wk_weight_bf16, + 1, + dim, + kv_dim, + &mut bufs.bf16_scratch, + ); + kernels::linear_nobias_bf16_scratch( + &mut bufs.v[..kv_dim], + &bufs.x_norm[..dim], + layer.wv_weight_bf16, + 1, + dim, + kv_dim, + &mut bufs.bf16_scratch, + ); + }, + DecodeKernelMode::Int8 => { + kernels::linear_nobias_int8_qkv( + &mut bufs.q[..q_dim], + &mut bufs.k[..kv_dim], + &mut bufs.v[..kv_dim], + &bufs.x_norm[..dim], + &layer.wq_int8, + &layer.wq_int8_scales, + &layer.wk_int8, + &layer.wk_int8_scales, + &layer.wv_int8, + &layer.wv_int8_scales, + dim, + q_dim, + kv_dim, + ); + } + } + + kernels::rms_norm_per_head( + &mut bufs.q[..q_dim], + &layer.q_norm_weight, + 1, + n_heads, + head_dim, + eps, + ); + kernels::rms_norm_per_head( + &mut bufs.k[..kv_dim], + &layer.k_norm_weight, + 1, + n_kv_heads, + head_dim, + eps, + ); + + kernels::apply_rope_neox( + &mut bufs.q[..q_dim], + rope_cos, + rope_sin, + 1, + n_heads, + head_dim, + ); + kernels::apply_rope_neox( + &mut bufs.k[..kv_dim], + rope_cos, + rope_sin, + 1, + n_kv_heads, + head_dim, + ); + + kv_cache.k_write_pos(layer_idx, pos, &bufs.k[..kv_dim]); + kv_cache.v_write_pos(layer_idx, pos, &bufs.v[..kv_dim]); + + let total_seq = pos + 1; + let k_base = kv_cache.k_layer_base(layer_idx); + let v_base = kv_cache.v_layer_base(layer_idx); + let head_stride = kv_cache.head_stride(); + + kernels::causal_attention( + &mut bufs.attn_out[..q_dim], + &bufs.q[..q_dim], + k_base, + v_base, + head_stride, + 1, + total_seq, + n_heads, + n_kv_heads, + head_dim, + scale, + pos, + ); + + match mode { + DecodeKernelMode::Bf16 => unsafe { + kernels::linear_nobias_bf16_scratch( + &mut bufs.x_norm[..dim], + &bufs.attn_out[..q_dim], + layer.wo_weight_bf16, + 1, + q_dim, + dim, + &mut bufs.bf16_scratch, + ); + kernels::add_inplace(&mut bufs.x[..dim], &bufs.x_norm[..dim], dim); + }, + DecodeKernelMode::Int8 => { + kernels::linear_nobias_int8_addto( + &mut bufs.x[..dim], + &bufs.attn_out[..q_dim], + &layer.wo_int8, + &layer.wo_int8_scales, + q_dim, + dim, + ); + } + } + + kernels::rms_norm( + &mut bufs.x_norm[..dim], + &bufs.x[..dim], + &layer.post_attn_norm, + 1, + dim, + eps, + ); + + match mode { + DecodeKernelMode::Bf16 => unsafe { + kernels::linear_nobias_bf16_scratch( + &mut bufs.gate_buf[..2 * intermediate], + &bufs.x_norm[..dim], + layer.gate_up_fused_bf16.as_ptr(), + 1, + dim, + 2 * intermediate, + &mut bufs.bf16_scratch, + ); + kernels::swiglu_multiply( + &mut bufs.ffn_out[..intermediate], + &bufs.gate_buf[..2 * intermediate], + 1, + intermediate, + ); + kernels::linear_nobias_bf16_scratch( + &mut bufs.x_norm[..dim], + &bufs.ffn_out[..intermediate], + layer.down_weight_bf16, + 1, + intermediate, + dim, + &mut bufs.bf16_scratch, + ); + kernels::add_inplace(&mut bufs.x[..dim], &bufs.x_norm[..dim], dim); + }, + DecodeKernelMode::Int8 => { + kernels::linear_nobias_int8_swiglu( + &mut bufs.ffn_out[..intermediate], + &bufs.x_norm[..dim], + &layer.gate_up_int8, + &layer.gate_up_int8_scales, + dim, + intermediate, + ); + kernels::linear_nobias_int8_addto( + &mut bufs.x[..dim], + &bufs.ffn_out[..intermediate], + &layer.down_int8, + &layer.down_int8_scales, + intermediate, + dim, + ); + } + } + } + + kv_cache.len = pos + 1; + + // Final norm + streaming argmax (use x_norm as temp to avoid heap allocation) + kernels::rms_norm( + &mut bufs.x_norm[..dim], + &bufs.x[..dim], + &decoder.norm, + 1, + dim, + eps, + ); + bufs.x[..dim].copy_from_slice(&bufs.x_norm[..dim]); + let lm_out_dim = cfg.lm_head_dim(); + + if matches!(mode, DecodeKernelMode::Int8) { + if let (Some(ref int8_data), Some(ref scales)) = + (&decoder.lm_head_int8, &decoder.lm_head_int8_scales) + { + return kernels::argmax_matvec_int8(&bufs.x[..dim], int8_data, scales, dim, lm_out_dim) + as i32; + } + } + + let lm_weight = decoder.lm_head_bf16.unwrap_or(decoder.tok_embeddings_bf16); + kernels::argmax_matvec_bf16(&bufs.x[..dim], lm_weight, dim, lm_out_dim) as i32 +} + +/// Decoder prefill that returns per-position logits (for forced aligner). +/// Returns `[seq_len × out_dim]` logits where out_dim = classify_num. +pub fn decoder_prefill_logits( + decoder: &Decoder, + cfg: &QwenConfig, + kv_cache: &mut KvCache, + rope: &mut RopeCache, + bufs: &mut DecoderBuffers, + input_embeds: &[f32], + seq_len: usize, +) -> Vec { + let dim = cfg.dec_hidden; + let eps = cfg.dec_rms_norm_eps; + let out_dim = cfg.lm_head_dim(); + + // Run the standard prefill to get hidden states + decoder_prefill(decoder, cfg, kv_cache, rope, bufs, input_embeds, seq_len); + + // After prefill, pref_x contains the final hidden states for all positions. + // Apply final RMS norm and lm_head projection. + let x = &bufs.pref_x[..seq_len * dim]; + let mut x_norm = vec![0.0f32; seq_len * dim]; + kernels::rms_norm(&mut x_norm, x, &decoder.norm, seq_len, dim, eps); + + let lm_weight = decoder.lm_head_bf16.unwrap_or(decoder.tok_embeddings_bf16); + + // Project each position through lm_head: [seq_len × dim] × [out_dim × dim]^T → [seq_len × out_dim] + let mut logits = vec![0.0f32; seq_len * out_dim]; + unsafe { + kernels::linear_nobias_bf16_scratch( + &mut logits, + &x_norm, + lm_weight, + seq_len, + dim, + out_dim, + &mut bufs.bf16_scratch, + ); + } + + logits +} + + +/// Convert a token embedding from bf16 to f32. +/// +/// # Safety +/// tok_emb_bf16 must point to valid memory for at least (token_id + 1) * dim bf16 values. +pub unsafe fn tok_embed_bf16_to_f32( + dst: &mut [f32], + tok_emb_bf16: *const u16, + token_id: i32, + dim: usize, +) { + let src = unsafe { std::slice::from_raw_parts(tok_emb_bf16.add(token_id as usize * dim), dim) }; + kernels::bf16_to_f32_buf(dst, src); +} diff --git a/vendor/qwenasr/crates/qwen-asr/src/encoder.rs b/vendor/qwenasr/crates/qwen-asr/src/encoder.rs new file mode 100644 index 0000000..a55f797 --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/src/encoder.rs @@ -0,0 +1,359 @@ +//! Audio encoder: Conv2D stem + windowed transformer + projection cascade. + +use crate::config::*; +use crate::kernels; +use crate::safetensors::MultiSafetensors; + +pub struct EncLayer { + pub wq_weight: Vec, + pub wq_bias: Vec, + pub wk_weight: Vec, + pub wk_bias: Vec, + pub wv_weight: Vec, + pub wv_bias: Vec, + pub wo_weight: Vec, + pub wo_bias: Vec, + pub attn_norm_weight: Vec, + pub attn_norm_bias: Vec, + pub fc1_weight: Vec, + pub fc1_bias: Vec, + pub fc2_weight: Vec, + pub fc2_bias: Vec, + pub ffn_norm_weight: Vec, + pub ffn_norm_bias: Vec, +} + +pub struct EncoderBuffers { + pub x_norm: Vec, + pub q: Vec, + pub k: Vec, + pub v: Vec, + pub attn_out: Vec, + pub proj_out: Vec, + pub ffn_mid: Vec, + pub ffn_out: Vec, + pub cap_tokens: usize, +} + +impl Default for EncoderBuffers { + fn default() -> Self { + Self::new() + } +} + +impl EncoderBuffers { + pub fn new() -> Self { + EncoderBuffers { + x_norm: Vec::new(), + q: Vec::new(), + k: Vec::new(), + v: Vec::new(), + attn_out: Vec::new(), + proj_out: Vec::new(), + ffn_mid: Vec::new(), + ffn_out: Vec::new(), + cap_tokens: 0, + } + } + + pub fn ensure(&mut self, total_tokens: usize, d_model: usize, ffn_dim: usize) { + if total_tokens <= self.cap_tokens { + return; + } + let mut new_cap = if self.cap_tokens > 0 { self.cap_tokens } else { 256 }; + while new_cap < total_tokens { + new_cap *= 2; + } + self.x_norm.resize(new_cap * d_model, 0.0); + self.q.resize(new_cap * d_model, 0.0); + self.k.resize(new_cap * d_model, 0.0); + self.v.resize(new_cap * d_model, 0.0); + self.attn_out.resize(new_cap * d_model, 0.0); + self.proj_out.resize(new_cap * d_model, 0.0); + self.ffn_mid.resize(new_cap * ffn_dim, 0.0); + self.ffn_out.resize(new_cap * d_model, 0.0); + self.cap_tokens = new_cap; + } +} + +pub struct Encoder { + pub conv1_weight: Vec, + pub conv1_bias: Vec, + pub conv2_weight: Vec, + pub conv2_bias: Vec, + pub conv3_weight: Vec, + pub conv3_bias: Vec, + pub conv_out_weight: Vec, + pub layers: Vec, + pub ln_post_weight: Vec, + pub ln_post_bias: Vec, + pub proj1_weight: Vec, + pub proj1_bias: Vec, + pub proj2_weight: Vec, + pub proj2_bias: Vec, +} + +const ENC_PREFIX: &str = "thinker.audio_tower."; + +fn load_f32(ms: &MultiSafetensors, name: &str) -> Option> { + let result = ms.get_f32(name); + if result.is_none() { + eprintln!("encoder: weight not found: {}", name); + } + result +} + +fn load_bf16_as_f32(ms: &MultiSafetensors, name: &str) -> Option> { + let (si, t) = ms.find(name).or_else(|| { + eprintln!("encoder: weight not found: {}", name); + None + })?; + + let bf16_ptr = ms.shards[si].get_bf16_direct(t)?; + let n = t.numel(); + let mut f32_data = vec![0.0f32; n]; + let src = unsafe { std::slice::from_raw_parts(bf16_ptr, n) }; + for i in 0..n { + f32_data[i] = f32::from_bits((src[i] as u32) << 16); + } + Some(f32_data) +} + +impl Encoder { + pub fn load(ms: &MultiSafetensors, cfg: &QwenConfig) -> Option { + let p = ENC_PREFIX; + + let conv1_weight = load_f32(ms, &format!("{}conv2d1.weight", p))?; + let conv1_bias = load_f32(ms, &format!("{}conv2d1.bias", p))?; + let conv2_weight = load_f32(ms, &format!("{}conv2d2.weight", p))?; + let conv2_bias = load_f32(ms, &format!("{}conv2d2.bias", p))?; + let conv3_weight = load_f32(ms, &format!("{}conv2d3.weight", p))?; + let conv3_bias = load_f32(ms, &format!("{}conv2d3.bias", p))?; + let conv_out_weight = load_bf16_as_f32(ms, &format!("{}conv_out.weight", p))?; + + let mut layers = Vec::new(); + for i in 0..cfg.enc_layers { + let lp = format!("{}layers.{}", p, i); + + let layer = EncLayer { + wq_weight: load_bf16_as_f32(ms, &format!("{}.self_attn.q_proj.weight", lp))?, + wq_bias: load_f32(ms, &format!("{}.self_attn.q_proj.bias", lp))?, + wk_weight: load_bf16_as_f32(ms, &format!("{}.self_attn.k_proj.weight", lp))?, + wk_bias: load_f32(ms, &format!("{}.self_attn.k_proj.bias", lp))?, + wv_weight: load_bf16_as_f32(ms, &format!("{}.self_attn.v_proj.weight", lp))?, + wv_bias: load_f32(ms, &format!("{}.self_attn.v_proj.bias", lp))?, + wo_weight: load_bf16_as_f32(ms, &format!("{}.self_attn.out_proj.weight", lp))?, + wo_bias: load_f32(ms, &format!("{}.self_attn.out_proj.bias", lp))?, + attn_norm_weight: load_f32(ms, &format!("{}.self_attn_layer_norm.weight", lp))?, + attn_norm_bias: load_f32(ms, &format!("{}.self_attn_layer_norm.bias", lp))?, + fc1_weight: load_bf16_as_f32(ms, &format!("{}.fc1.weight", lp))?, + fc1_bias: load_f32(ms, &format!("{}.fc1.bias", lp))?, + fc2_weight: load_bf16_as_f32(ms, &format!("{}.fc2.weight", lp))?, + fc2_bias: load_f32(ms, &format!("{}.fc2.bias", lp))?, + ffn_norm_weight: load_f32(ms, &format!("{}.final_layer_norm.weight", lp))?, + ffn_norm_bias: load_f32(ms, &format!("{}.final_layer_norm.bias", lp))?, + }; + layers.push(layer); + } + + let ln_post_weight = load_f32(ms, &format!("{}ln_post.weight", p))?; + let ln_post_bias = load_f32(ms, &format!("{}ln_post.bias", p))?; + let proj1_weight = load_bf16_as_f32(ms, &format!("{}proj1.weight", p))?; + let proj1_bias = load_f32(ms, &format!("{}proj1.bias", p))?; + let proj2_weight = load_bf16_as_f32(ms, &format!("{}proj2.weight", p))?; + let proj2_bias = load_f32(ms, &format!("{}proj2.bias", p))?; + + Some(Encoder { + conv1_weight, + conv1_bias, + conv2_weight, + conv2_bias, + conv3_weight, + conv3_bias, + conv_out_weight, + layers, + ln_post_weight, + ln_post_bias, + proj1_weight, + proj1_bias, + proj2_weight, + proj2_bias, + }) + } + + /// Run encoder forward pass on mel spectrogram. + /// mel: [128, mel_frames], returns [total_tokens, output_dim]. + pub fn forward(&self, cfg: &QwenConfig, mel: &[f32], mel_frames: usize, enc_bufs: Option<&mut EncoderBuffers>) -> Option<(Vec, usize)> { + let d_model = cfg.enc_d_model; + let n_heads = cfg.enc_heads; + let head_dim = cfg.enc_head_dim; + let ffn_dim = cfg.enc_ffn_dim; + let output_dim = cfg.enc_output_dim; + let chunk_size = cfg.enc_chunk_size; + let n_window_infer = cfg.enc_n_window_infer; + + // Determine tokens per full chunk + let tokens_per_chunk = { + let w = chunk_size; + let w1 = (w + 2 - 3) / 2 + 1; + let w2 = (w1 + 2 - 3) / 2 + 1; + (w2 + 2 - 3) / 2 + 1 + }; + + let n_chunks = mel_frames.div_ceil(chunk_size); + + // Pre-calculate total tokens + let mut total_tokens = 0; + let mut chunk_sizes = Vec::new(); + for c in 0..n_chunks { + let start = c * chunk_size; + let end = (start + chunk_size).min(mel_frames); + let chunk_w = end - start; + let w1 = (chunk_w + 2 - 3) / 2 + 1; + let w2 = (w1 + 2 - 3) / 2 + 1; + let w3 = (w2 + 2 - 3) / 2 + 1; + total_tokens += w3; + chunk_sizes.push((start, end, w3)); + } + + // Main sequence buffer: [total_tokens, d_model] + let mut x = vec![0.0f32; total_tokens * d_model]; + let mut token_offset = 0; + + // Process each chunk through Conv2D + reshape + project + sinusoidal PE + for &(start, end, w3) in &chunk_sizes { + let chunk_w = end - start; + + // Extract chunk mel: [128, chunk_w] + let mut chunk_mel = vec![0.0f32; 128 * chunk_w]; + for m in 0..128 { + chunk_mel[m * chunk_w..(m + 1) * chunk_w] + .copy_from_slice(&mel[m * mel_frames + start..m * mel_frames + end]); + } + + // Conv2D layer 1: [1, 128, chunk_w] -> [480, h1, w1] + let h1 = (128 + 2 - 3) / 2 + 1; // 64 + let w1 = (chunk_w + 2 - 3) / 2 + 1; + let mut c1 = vec![0.0f32; CONV_HIDDEN * h1 * w1]; + kernels::conv2d( + &mut c1, &chunk_mel, &self.conv1_weight, Some(&self.conv1_bias), + 1, CONV_HIDDEN, 128, chunk_w, 3, 3, 2, 1, + ); + kernels::gelu(&mut c1, CONV_HIDDEN * h1 * w1); + + // Conv2D layer 2: [480, h1, w1] -> [480, h2, w2] + let h2 = (h1 + 2 - 3) / 2 + 1; // 32 + let w2 = (w1 + 2 - 3) / 2 + 1; + let mut c2 = vec![0.0f32; CONV_HIDDEN * h2 * w2]; + kernels::conv2d( + &mut c2, &c1, &self.conv2_weight, Some(&self.conv2_bias), + CONV_HIDDEN, CONV_HIDDEN, h1, w1, 3, 3, 2, 1, + ); + kernels::gelu(&mut c2, CONV_HIDDEN * h2 * w2); + + // Conv2D layer 3: [480, h2, w2] -> [480, h3, w3] + let h3 = (h2 + 2 - 3) / 2 + 1; // 16 + let _w3_calc = (w2 + 2 - 3) / 2 + 1; + debug_assert_eq!(_w3_calc, w3); + let mut c3 = vec![0.0f32; CONV_HIDDEN * h3 * w3]; + kernels::conv2d( + &mut c3, &c2, &self.conv3_weight, Some(&self.conv3_bias), + CONV_HIDDEN, CONV_HIDDEN, h2, w2, 3, 3, 2, 1, + ); + kernels::gelu(&mut c3, CONV_HIDDEN * h3 * w3); + + // Reshape [480, h3, w3] -> [w3, 480*h3] + // Loop order: ch → f → t for sequential reads from c3 + let conv_proj_dim = CONV_HIDDEN * h3; + let mut reshaped = vec![0.0f32; w3 * conv_proj_dim]; + for ch in 0..CONV_HIDDEN { + for f in 0..h3 { + let src_off = ch * h3 * w3 + f * w3; + let dst_col = ch * h3 + f; + for t in 0..w3 { + reshaped[t * conv_proj_dim + dst_col] = c3[src_off + t]; + } + } + } + + // Project: [w3, 7680] -> [w3, d_model] + let projected = &mut x[token_offset * d_model..(token_offset + w3) * d_model]; + kernels::linear_nobias(projected, &reshaped, &self.conv_out_weight, w3, conv_proj_dim, d_model); + + // Add per-chunk sinusoidal PE + let mut pe = vec![0.0f32; w3 * d_model]; + kernels::sinusoidal_pe(&mut pe, w3, d_model); + kernels::add_inplace(projected, &pe, w3 * d_model); + + token_offset += w3; + } + + // Build attention window boundaries + let window_token_size = tokens_per_chunk * (n_window_infer / chunk_size); + let n_windows = total_tokens.div_ceil(window_token_size); + let mut window_starts = vec![0i32; n_windows + 1]; + for (w, ws) in window_starts.iter_mut().enumerate().take(n_windows) { + *ws = (w * window_token_size) as i32; + } + window_starts[n_windows] = total_tokens as i32; + + // Transformer layer scratch buffers (reusable or fresh) + let mut _owned_bufs; + let bufs: &mut EncoderBuffers = match enc_bufs { + Some(b) => { b.ensure(total_tokens, d_model, ffn_dim); b } + None => { _owned_bufs = EncoderBuffers::new(); _owned_bufs.ensure(total_tokens, d_model, ffn_dim); &mut _owned_bufs } + }; + + let scale = 1.0 / (head_dim as f32).sqrt(); + let td = total_tokens * d_model; + let tf = total_tokens * ffn_dim; + + for layer in &self.layers { + // Self-attention + kernels::layer_norm(&mut bufs.x_norm[..td], &x, &layer.attn_norm_weight, &layer.attn_norm_bias, + total_tokens, d_model, 1e-5); + + kernels::linear(&mut bufs.q[..td], &bufs.x_norm[..td], &layer.wq_weight, Some(&layer.wq_bias), + total_tokens, d_model, d_model); + kernels::linear(&mut bufs.k[..td], &bufs.x_norm[..td], &layer.wk_weight, Some(&layer.wk_bias), + total_tokens, d_model, d_model); + kernels::linear(&mut bufs.v[..td], &bufs.x_norm[..td], &layer.wv_weight, Some(&layer.wv_bias), + total_tokens, d_model, d_model); + + kernels::bidirectional_attention(&mut bufs.attn_out[..td], &bufs.q[..td], &bufs.k[..td], &bufs.v[..td], + total_tokens, n_heads, head_dim, scale, + &window_starts, n_windows); + + // Fused: x += wo_bias + attn_out @ wo_weight.T + kernels::linear_accumulate(&mut x, &bufs.attn_out[..td], &layer.wo_weight, Some(&layer.wo_bias), + total_tokens, d_model, d_model); + + // FFN + kernels::layer_norm(&mut bufs.x_norm[..td], &x, &layer.ffn_norm_weight, &layer.ffn_norm_bias, + total_tokens, d_model, 1e-5); + + kernels::linear(&mut bufs.ffn_mid[..tf], &bufs.x_norm[..td], &layer.fc1_weight, Some(&layer.fc1_bias), + total_tokens, d_model, ffn_dim); + kernels::gelu(&mut bufs.ffn_mid[..tf], tf); + // Fused: x += fc2_bias + ffn_mid @ fc2_weight.T + kernels::linear_accumulate(&mut x, &bufs.ffn_mid[..tf], &layer.fc2_weight, Some(&layer.fc2_bias), + total_tokens, ffn_dim, d_model); + } + + // Final LayerNorm: use x_norm as temp, then swap into x + kernels::layer_norm(&mut bufs.x_norm[..td], &x, &self.ln_post_weight, &self.ln_post_bias, + total_tokens, d_model, 1e-5); + x[..td].copy_from_slice(&bufs.x_norm[..td]); + + // Projection: proj1 (GELU) -> proj2 (reuse scratch buffers) + kernels::linear(&mut bufs.q[..td], &x, &self.proj1_weight, Some(&self.proj1_bias), + total_tokens, d_model, d_model); + kernels::gelu(&mut bufs.q[..td], td); + + let mut enc_output = vec![0.0f32; total_tokens * output_dim]; + kernels::linear(&mut enc_output, &bufs.q[..td], &self.proj2_weight, Some(&self.proj2_bias), + total_tokens, d_model, output_dim); + + Some((enc_output, total_tokens)) + } +} diff --git a/vendor/qwenasr/crates/qwen-asr/src/jni_api.rs b/vendor/qwenasr/crates/qwen-asr/src/jni_api.rs new file mode 100644 index 0000000..0a31e20 --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/src/jni_api.rs @@ -0,0 +1,179 @@ +/// JNI API for Android integration. +/// Build with: cargo ndk -t arm64-v8a build --release --features android +/// +/// Java class: com.qwenasr.QAsrEngine +/// +/// ```java +/// public class QAsrEngine { +/// static { System.loadLibrary("qwen_asr"); } +/// private long nativeHandle; +/// public native boolean loadModel(String modelDir, int nThreads); +/// public native String transcribePcm(float[] samples); +/// public native String transcribeWav(byte[] wavData); +/// public native void setSegmentSec(float sec); +/// public native void setLanguage(String language); +/// public native void free(); +/// } +/// ``` + +use std::os::raw::{c_char, c_void}; + +// JNI types +type JNIEnv = *mut c_void; +type JClass = *mut c_void; +type JString = *mut c_void; +type JObject = *mut c_void; +type JFloatArray = *mut c_void; +type JByteArray = *mut c_void; +type JLong = i64; +type JInt = i32; +type JFloat = f32; +type JBoolean = u8; + +// JNI function signatures we need +extern "C" { + fn __android_log_write(prio: i32, tag: *const c_char, text: *const c_char) -> i32; +} + +// JNI string helpers (accessed via function pointers in JNIEnv) +// These are simplified - a real implementation would use jni-rs or manual vtable access. +// For now, we use the C-FFI API underneath. + +/// Load model. Returns native handle as jlong. +#[no_mangle] +pub unsafe extern "C" fn Java_com_qwenasr_QAsrEngine_nativeLoadModel( + env: JNIEnv, + _obj: JObject, + model_dir: JString, + n_threads: JInt, +) -> JLong { + // Use the C-FFI API for the actual implementation + let c_api_engine = crate::c_api::qwen_asr_load_model( + jstring_to_cstr(env, model_dir), + n_threads, + 0, // silent + ); + c_api_engine as JLong +} + +/// Transcribe PCM float array. +#[no_mangle] +pub unsafe extern "C" fn Java_com_qwenasr_QAsrEngine_nativeTranscribePcm( + env: JNIEnv, + _obj: JObject, + handle: JLong, + samples: JFloatArray, + n_samples: JInt, +) -> JString { + if handle == 0 { + return std::ptr::null_mut(); + } + + let samples_ptr = jfloat_array_get(env, samples); + if samples_ptr.is_null() { + return std::ptr::null_mut(); + } + + let result = crate::c_api::qwen_asr_transcribe_pcm( + handle as *mut crate::c_api::QwenAsrEngine, + samples_ptr, + n_samples, + ); + + jfloat_array_release(env, samples, samples_ptr); + + if result.is_null() { + return std::ptr::null_mut(); + } + + let jstr = new_jstring(env, result); + crate::c_api::qwen_asr_free_string(result); + jstr +} + +/// Transcribe WAV byte array. +#[no_mangle] +pub unsafe extern "C" fn Java_com_qwenasr_QAsrEngine_nativeTranscribeWav( + env: JNIEnv, + _obj: JObject, + handle: JLong, + wav_data: JByteArray, + wav_len: JInt, +) -> JString { + if handle == 0 { + return std::ptr::null_mut(); + } + + let data_ptr = jbyte_array_get(env, wav_data); + if data_ptr.is_null() { + return std::ptr::null_mut(); + } + + let result = crate::c_api::qwen_asr_transcribe_wav_buffer( + handle as *mut crate::c_api::QwenAsrEngine, + data_ptr as *const u8, + wav_len, + ); + + jbyte_array_release(env, wav_data, data_ptr); + + if result.is_null() { + return std::ptr::null_mut(); + } + + let jstr = new_jstring(env, result); + crate::c_api::qwen_asr_free_string(result); + jstr +} + +/// Free engine. +#[no_mangle] +pub unsafe extern "C" fn Java_com_qwenasr_QAsrEngine_nativeFree( + _env: JNIEnv, + _obj: JObject, + handle: JLong, +) { + if handle != 0 { + crate::c_api::qwen_asr_free(handle as *mut crate::c_api::QwenAsrEngine); + } +} + +/// Set segment seconds. +#[no_mangle] +pub unsafe extern "C" fn Java_com_qwenasr_QAsrEngine_nativeSetSegmentSec( + _env: JNIEnv, + _obj: JObject, + handle: JLong, + sec: JFloat, +) { + if handle != 0 { + crate::c_api::qwen_asr_set_segment_sec(handle as *mut crate::c_api::QwenAsrEngine, sec); + } +} + +// ======================================================================== +// JNI helper stubs - these use raw JNIEnv vtable access +// In production, use the `jni` crate. These are minimal stubs for compilation. +// ======================================================================== + +unsafe fn jstring_to_cstr(_env: JNIEnv, _jstr: JString) -> *const c_char { + // Real implementation: (*(*env)).GetStringUTFChars(env, jstr, null) + // Stub returns null - will need jni crate for real Android builds + std::ptr::null() +} + +unsafe fn jfloat_array_get(_env: JNIEnv, _arr: JFloatArray) -> *const f32 { + std::ptr::null() +} + +unsafe fn jfloat_array_release(_env: JNIEnv, _arr: JFloatArray, _ptr: *const f32) {} + +unsafe fn jbyte_array_get(_env: JNIEnv, _arr: JByteArray) -> *const i8 { + std::ptr::null() +} + +unsafe fn jbyte_array_release(_env: JNIEnv, _arr: JByteArray, _ptr: *const i8) {} + +unsafe fn new_jstring(_env: JNIEnv, _cstr: *const c_char) -> JString { + std::ptr::null_mut() +} diff --git a/vendor/qwenasr/crates/qwen-asr/src/kernels/avx.rs b/vendor/qwenasr/crates/qwen-asr/src/kernels/avx.rs new file mode 100644 index 0000000..55c53b4 --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/src/kernels/avx.rs @@ -0,0 +1,957 @@ +// x86 AVX2+FMA implementations of hot kernels. +#[cfg(target_arch = "x86_64")] +use core::arch::x86_64::*; + +#[cfg(target_arch = "x86_64")] +#[target_feature(enable = "avx2")] +pub unsafe fn bf16_to_f32_buf(dst: &mut [f32], src: &[u16]) { + let n = src.len(); + let mut i = 0usize; + + while i + 8 <= n { + let raw = _mm_loadu_si128(src.as_ptr().add(i) as *const __m128i); + let wide = _mm256_cvtepu16_epi32(raw); + let shifted = _mm256_slli_epi32(wide, 16); + _mm256_storeu_ps(dst.as_mut_ptr().add(i), _mm256_castsi256_ps(shifted)); + i += 8; + } + + while i < n { + dst[i] = f32::from_bits((src[i] as u32) << 16); + i += 1; + } +} + +/// Convert 8 BF16 values (in a __m128i) to 8 f32 values (in a __m256). +#[cfg(target_arch = "x86_64")] +#[target_feature(enable = "avx2")] +#[inline] +unsafe fn bf16x8_to_f32(raw: __m128i) -> __m256 { + // Zero-extend u16 -> u32, shift left 16 to put BF16 bits in f32 position + let wide = _mm256_cvtepu16_epi32(raw); + let shifted = _mm256_slli_epi32(wide, 16); + _mm256_castsi256_ps(shifted) +} + +/// Horizontal sum of __m256 -> f32 +#[cfg(target_arch = "x86_64")] +#[target_feature(enable = "avx2")] +#[inline] +unsafe fn hsum_ps(v: __m256) -> f32 { + // Add high 128 to low 128 + let hi = _mm256_extractf128_ps(v, 1); + let lo = _mm256_castps256_ps128(v); + let sum128 = _mm_add_ps(lo, hi); + // Horizontal add twice to reduce 4 -> 2 -> 1 + let shuf = _mm_movehdup_ps(sum128); // [1,1,3,3] + let sum64 = _mm_add_ps(sum128, shuf); // [0+1, _, 2+3, _] + let hi64 = _mm_movehl_ps(sum64, sum64); // [2+3, _, _, _] + let sum32 = _mm_add_ss(sum64, hi64); + _mm_cvtss_f32(sum32) +} + +#[cfg(target_arch = "x86_64")] +#[target_feature(enable = "avx2")] +#[inline] +unsafe fn hsum_epi32(v: __m256i) -> i32 { + let mut lanes = [0i32; 8]; + _mm256_storeu_si256(lanes.as_mut_ptr() as *mut __m256i, v); + lanes.into_iter().sum() +} + +#[cfg(target_arch = "x86_64")] +#[target_feature(enable = "avx2")] +#[inline] +unsafe fn dot_i8_i8(x: *const i8, w: *const i8, n: usize) -> i32 { + let mut k = 0usize; + let mut acc0 = _mm256_setzero_si256(); + let mut acc1 = _mm256_setzero_si256(); + let mut acc2 = _mm256_setzero_si256(); + let mut acc3 = _mm256_setzero_si256(); + + while k + 64 <= n { + let x0 = _mm_loadu_si128(x.add(k) as *const __m128i); + let w0 = _mm_loadu_si128(w.add(k) as *const __m128i); + let x1 = _mm_loadu_si128(x.add(k + 16) as *const __m128i); + let w1 = _mm_loadu_si128(w.add(k + 16) as *const __m128i); + let x2 = _mm_loadu_si128(x.add(k + 32) as *const __m128i); + let w2 = _mm_loadu_si128(w.add(k + 32) as *const __m128i); + let x3 = _mm_loadu_si128(x.add(k + 48) as *const __m128i); + let w3 = _mm_loadu_si128(w.add(k + 48) as *const __m128i); + + acc0 = _mm256_add_epi32( + acc0, + _mm256_madd_epi16(_mm256_cvtepi8_epi16(x0), _mm256_cvtepi8_epi16(w0)), + ); + acc1 = _mm256_add_epi32( + acc1, + _mm256_madd_epi16(_mm256_cvtepi8_epi16(x1), _mm256_cvtepi8_epi16(w1)), + ); + acc2 = _mm256_add_epi32( + acc2, + _mm256_madd_epi16(_mm256_cvtepi8_epi16(x2), _mm256_cvtepi8_epi16(w2)), + ); + acc3 = _mm256_add_epi32( + acc3, + _mm256_madd_epi16(_mm256_cvtepi8_epi16(x3), _mm256_cvtepi8_epi16(w3)), + ); + k += 64; + } + + while k + 16 <= n { + let xv = _mm_loadu_si128(x.add(k) as *const __m128i); + let wv = _mm_loadu_si128(w.add(k) as *const __m128i); + acc0 = _mm256_add_epi32( + acc0, + _mm256_madd_epi16(_mm256_cvtepi8_epi16(xv), _mm256_cvtepi8_epi16(wv)), + ); + k += 16; + } + + let mut sum = hsum_epi32(_mm256_add_epi32( + _mm256_add_epi32(acc0, acc1), + _mm256_add_epi32(acc2, acc3), + )); + + while k < n { + sum += (*x.add(k) as i32) * (*w.add(k) as i32); + k += 1; + } + sum +} + +#[cfg(target_arch = "x86_64")] +#[target_feature(enable = "avx2", enable = "fma")] +pub unsafe fn bf16_matvec_fused( + y: &mut [f32], + x: &[f32], + w_bf16: *const u16, + bias: Option<&[f32]>, + in_dim: usize, + out_dim: usize, +) { + let mut o = 0usize; + + // Process 2 output rows at a time + while o + 1 < out_dim { + let w0 = w_bf16.add(o * in_dim); + let w1 = w_bf16.add((o + 1) * in_dim); + let mut s0 = bias.map_or(0.0f32, |b| b[o]); + let mut s1 = bias.map_or(0.0f32, |b| b[o + 1]); + + let mut a0 = _mm256_setzero_ps(); + let mut a1 = _mm256_setzero_ps(); + let mut b0 = _mm256_setzero_ps(); + let mut b1 = _mm256_setzero_ps(); + let mut k = 0usize; + + // Main loop: 16 elements per iteration + while k + 16 <= in_dim { + let xlo = _mm256_loadu_ps(x.as_ptr().add(k)); + let xhi = _mm256_loadu_ps(x.as_ptr().add(k + 8)); + + // Row 0 + let raw0lo = _mm_loadu_si128(w0.add(k) as *const __m128i); + let raw0hi = _mm_loadu_si128(w0.add(k + 8) as *const __m128i); + let w0lo = bf16x8_to_f32(raw0lo); + let w0hi = bf16x8_to_f32(raw0hi); + a0 = _mm256_fmadd_ps(w0lo, xlo, a0); + a1 = _mm256_fmadd_ps(w0hi, xhi, a1); + + // Row 1 + let raw1lo = _mm_loadu_si128(w1.add(k) as *const __m128i); + let raw1hi = _mm_loadu_si128(w1.add(k + 8) as *const __m128i); + let w1lo = bf16x8_to_f32(raw1lo); + let w1hi = bf16x8_to_f32(raw1hi); + b0 = _mm256_fmadd_ps(w1lo, xlo, b0); + b1 = _mm256_fmadd_ps(w1hi, xhi, b1); + + k += 16; + } + + // 8-element cleanup + while k + 8 <= in_dim { + let xv = _mm256_loadu_ps(x.as_ptr().add(k)); + let r0 = bf16x8_to_f32(_mm_loadu_si128(w0.add(k) as *const __m128i)); + let r1 = bf16x8_to_f32(_mm_loadu_si128(w1.add(k) as *const __m128i)); + a0 = _mm256_fmadd_ps(r0, xv, a0); + b0 = _mm256_fmadd_ps(r1, xv, b0); + k += 8; + } + + s0 += hsum_ps(_mm256_add_ps(a0, a1)); + s1 += hsum_ps(_mm256_add_ps(b0, b1)); + + // Scalar tail + while k < in_dim { + let wv0 = f32::from_bits((*w0.add(k) as u32) << 16); + let wv1 = f32::from_bits((*w1.add(k) as u32) << 16); + s0 += wv0 * x[k]; + s1 += wv1 * x[k]; + k += 1; + } + + y[o] = s0; + y[o + 1] = s1; + o += 2; + } + + // Handle remaining odd row + while o < out_dim { + let w_row = w_bf16.add(o * in_dim); + let mut sum = bias.map_or(0.0f32, |b| b[o]); + let mut k = 0usize; + + let mut acc0 = _mm256_setzero_ps(); + let mut acc1 = _mm256_setzero_ps(); + + while k + 16 <= in_dim { + let xlo = _mm256_loadu_ps(x.as_ptr().add(k)); + let xhi = _mm256_loadu_ps(x.as_ptr().add(k + 8)); + let wlo = bf16x8_to_f32(_mm_loadu_si128(w_row.add(k) as *const __m128i)); + let whi = bf16x8_to_f32(_mm_loadu_si128(w_row.add(k + 8) as *const __m128i)); + acc0 = _mm256_fmadd_ps(wlo, xlo, acc0); + acc1 = _mm256_fmadd_ps(whi, xhi, acc1); + k += 16; + } + + while k + 8 <= in_dim { + let xv = _mm256_loadu_ps(x.as_ptr().add(k)); + let wv = bf16x8_to_f32(_mm_loadu_si128(w_row.add(k) as *const __m128i)); + acc0 = _mm256_fmadd_ps(wv, xv, acc0); + k += 8; + } + + sum += hsum_ps(_mm256_add_ps(acc0, acc1)); + + while k < in_dim { + let w_val = f32::from_bits((*w_row.add(k) as u32) << 16); + sum += w_val * x[k]; + k += 1; + } + y[o] = sum; + o += 1; + } +} + +#[cfg(target_arch = "x86_64")] +#[target_feature(enable = "avx2", enable = "fma")] +pub unsafe fn argmax_bf16_range( + x: &[f32], + w_bf16: *const u16, + in_dim: usize, + start: usize, + end: usize, +) -> (usize, f32) { + let mut best = start; + let mut best_val = -1e30f32; + let mut o = start; + + // Process 2 rows at a time + while o + 1 < end { + let w0 = w_bf16.add(o * in_dim); + let w1 = w_bf16.add((o + 1) * in_dim); + let mut a0 = _mm256_setzero_ps(); + let mut a1 = _mm256_setzero_ps(); + let mut b0 = _mm256_setzero_ps(); + let mut b1 = _mm256_setzero_ps(); + let mut k = 0usize; + + while k + 16 <= in_dim { + let xlo = _mm256_loadu_ps(x.as_ptr().add(k)); + let xhi = _mm256_loadu_ps(x.as_ptr().add(k + 8)); + + let r0lo = bf16x8_to_f32(_mm_loadu_si128(w0.add(k) as *const __m128i)); + let r0hi = bf16x8_to_f32(_mm_loadu_si128(w0.add(k + 8) as *const __m128i)); + a0 = _mm256_fmadd_ps(r0lo, xlo, a0); + a1 = _mm256_fmadd_ps(r0hi, xhi, a1); + + let r1lo = bf16x8_to_f32(_mm_loadu_si128(w1.add(k) as *const __m128i)); + let r1hi = bf16x8_to_f32(_mm_loadu_si128(w1.add(k + 8) as *const __m128i)); + b0 = _mm256_fmadd_ps(r1lo, xlo, b0); + b1 = _mm256_fmadd_ps(r1hi, xhi, b1); + + k += 16; + } + + let mut s0 = hsum_ps(_mm256_add_ps(a0, a1)); + let mut s1 = hsum_ps(_mm256_add_ps(b0, b1)); + + while k < in_dim { + let wv0 = f32::from_bits((*w0.add(k) as u32) << 16); + let wv1 = f32::from_bits((*w1.add(k) as u32) << 16); + s0 += wv0 * x[k]; + s1 += wv1 * x[k]; + k += 1; + } + + if s0 > best_val { + best_val = s0; + best = o; + } + if s1 > best_val { + best_val = s1; + best = o + 1; + } + o += 2; + } + + while o < end { + let w_row = w_bf16.add(o * in_dim); + let mut sum = 0.0f32; + let mut k = 0usize; + + let mut acc0 = _mm256_setzero_ps(); + let mut acc1 = _mm256_setzero_ps(); + while k + 16 <= in_dim { + let xlo = _mm256_loadu_ps(x.as_ptr().add(k)); + let xhi = _mm256_loadu_ps(x.as_ptr().add(k + 8)); + let wlo = bf16x8_to_f32(_mm_loadu_si128(w_row.add(k) as *const __m128i)); + let whi = bf16x8_to_f32(_mm_loadu_si128(w_row.add(k + 8) as *const __m128i)); + acc0 = _mm256_fmadd_ps(wlo, xlo, acc0); + acc1 = _mm256_fmadd_ps(whi, xhi, acc1); + k += 16; + } + sum += hsum_ps(_mm256_add_ps(acc0, acc1)); + + while k < in_dim { + let w_val = f32::from_bits((*w_row.add(k) as u32) << 16); + sum += w_val * x[k]; + k += 1; + } + if sum > best_val { + best_val = sum; + best = o; + } + o += 1; + } + + (best, best_val) +} + +#[cfg(target_arch = "x86_64")] +#[target_feature(enable = "avx2")] +pub unsafe fn matvec_int8( + y: &mut [f32], + x_int8: *const i8, + x_scale: f32, + w_int8: *const i8, + w_scales: &[f32], + bias: Option<&[f32]>, + in_dim: usize, + out_dim: usize, +) { + let mut row = 0usize; + while row < out_dim { + let weights = w_int8.add(row * in_dim); + let acc = dot_i8_i8(x_int8, weights, in_dim); + let mut value = (acc as f32) * x_scale * w_scales[row]; + if let Some(bias_values) = bias { + value += bias_values[row]; + } + y[row] = value; + row += 1; + } +} + +#[cfg(target_arch = "x86_64")] +#[target_feature(enable = "avx2")] +pub unsafe fn argmax_int8_range( + x_int8: *const i8, + x_scale: f32, + w_int8: *const i8, + w_scales: &[f32], + in_dim: usize, + start: usize, + end: usize, +) -> (usize, f32) { + let mut best = start; + let mut best_val = f32::NEG_INFINITY; + + for row in start..end { + let acc = dot_i8_i8(x_int8, w_int8.add(row * in_dim), in_dim); + let value = (acc as f32) * x_scale * w_scales[row]; + if value > best_val { + best = row; + best_val = value; + } + } + + (best, best_val) +} + +#[cfg(target_arch = "x86_64")] +#[target_feature(enable = "avx2", enable = "fma")] +pub unsafe fn dot_f32(a: &[f32], b: &[f32], n: usize) -> f32 { + let mut i = 0usize; + let mut acc0 = _mm256_setzero_ps(); + let mut acc1 = _mm256_setzero_ps(); + let mut acc2 = _mm256_setzero_ps(); + let mut acc3 = _mm256_setzero_ps(); + + while i + 32 <= n { + acc0 = _mm256_fmadd_ps( + _mm256_loadu_ps(a.as_ptr().add(i)), + _mm256_loadu_ps(b.as_ptr().add(i)), + acc0, + ); + acc1 = _mm256_fmadd_ps( + _mm256_loadu_ps(a.as_ptr().add(i + 8)), + _mm256_loadu_ps(b.as_ptr().add(i + 8)), + acc1, + ); + acc2 = _mm256_fmadd_ps( + _mm256_loadu_ps(a.as_ptr().add(i + 16)), + _mm256_loadu_ps(b.as_ptr().add(i + 16)), + acc2, + ); + acc3 = _mm256_fmadd_ps( + _mm256_loadu_ps(a.as_ptr().add(i + 24)), + _mm256_loadu_ps(b.as_ptr().add(i + 24)), + acc3, + ); + i += 32; + } + + while i + 8 <= n { + acc0 = _mm256_fmadd_ps( + _mm256_loadu_ps(a.as_ptr().add(i)), + _mm256_loadu_ps(b.as_ptr().add(i)), + acc0, + ); + i += 8; + } + + let mut sum = hsum_ps(_mm256_add_ps( + _mm256_add_ps(acc0, acc1), + _mm256_add_ps(acc2, acc3), + )); + + while i < n { + sum += a[i] * b[i]; + i += 1; + } + sum +} + +#[cfg(target_arch = "x86_64")] +#[target_feature(enable = "avx2")] +pub unsafe fn vec_scale_inplace(dst: &mut [f32], scale: f32, n: usize) { + let mut i = 0usize; + let s = _mm256_set1_ps(scale); + + while i + 32 <= n { + _mm256_storeu_ps( + dst.as_mut_ptr().add(i), + _mm256_mul_ps(_mm256_loadu_ps(dst.as_ptr().add(i)), s), + ); + _mm256_storeu_ps( + dst.as_mut_ptr().add(i + 8), + _mm256_mul_ps(_mm256_loadu_ps(dst.as_ptr().add(i + 8)), s), + ); + _mm256_storeu_ps( + dst.as_mut_ptr().add(i + 16), + _mm256_mul_ps(_mm256_loadu_ps(dst.as_ptr().add(i + 16)), s), + ); + _mm256_storeu_ps( + dst.as_mut_ptr().add(i + 24), + _mm256_mul_ps(_mm256_loadu_ps(dst.as_ptr().add(i + 24)), s), + ); + i += 32; + } + + while i + 8 <= n { + _mm256_storeu_ps( + dst.as_mut_ptr().add(i), + _mm256_mul_ps(_mm256_loadu_ps(dst.as_ptr().add(i)), s), + ); + i += 8; + } + + while i < n { + dst[i] *= scale; + i += 1; + } +} + +#[cfg(target_arch = "x86_64")] +#[target_feature(enable = "avx2", enable = "fma")] +pub unsafe fn vec_axpy_inplace(dst: &mut [f32], src: &[f32], alpha: f32, n: usize) { + let mut i = 0usize; + let a = _mm256_set1_ps(alpha); + + while i + 32 <= n { + _mm256_storeu_ps( + dst.as_mut_ptr().add(i), + _mm256_fmadd_ps( + _mm256_loadu_ps(src.as_ptr().add(i)), + a, + _mm256_loadu_ps(dst.as_ptr().add(i)), + ), + ); + _mm256_storeu_ps( + dst.as_mut_ptr().add(i + 8), + _mm256_fmadd_ps( + _mm256_loadu_ps(src.as_ptr().add(i + 8)), + a, + _mm256_loadu_ps(dst.as_ptr().add(i + 8)), + ), + ); + _mm256_storeu_ps( + dst.as_mut_ptr().add(i + 16), + _mm256_fmadd_ps( + _mm256_loadu_ps(src.as_ptr().add(i + 16)), + a, + _mm256_loadu_ps(dst.as_ptr().add(i + 16)), + ), + ); + _mm256_storeu_ps( + dst.as_mut_ptr().add(i + 24), + _mm256_fmadd_ps( + _mm256_loadu_ps(src.as_ptr().add(i + 24)), + a, + _mm256_loadu_ps(dst.as_ptr().add(i + 24)), + ), + ); + i += 32; + } + + while i + 8 <= n { + _mm256_storeu_ps( + dst.as_mut_ptr().add(i), + _mm256_fmadd_ps( + _mm256_loadu_ps(src.as_ptr().add(i)), + a, + _mm256_loadu_ps(dst.as_ptr().add(i)), + ), + ); + i += 8; + } + + while i < n { + dst[i] += alpha * src[i]; + i += 1; + } +} + +#[cfg(target_arch = "x86_64")] +#[target_feature(enable = "avx2", enable = "fma")] +pub unsafe fn vec_scale_add(dst: &mut [f32], src: &[f32], correction: f32, n: usize) { + let mut i = 0usize; + let c = _mm256_set1_ps(correction); + + while i + 32 <= n { + _mm256_storeu_ps( + dst.as_mut_ptr().add(i), + _mm256_fmadd_ps( + _mm256_loadu_ps(dst.as_ptr().add(i)), + c, + _mm256_loadu_ps(src.as_ptr().add(i)), + ), + ); + _mm256_storeu_ps( + dst.as_mut_ptr().add(i + 8), + _mm256_fmadd_ps( + _mm256_loadu_ps(dst.as_ptr().add(i + 8)), + c, + _mm256_loadu_ps(src.as_ptr().add(i + 8)), + ), + ); + _mm256_storeu_ps( + dst.as_mut_ptr().add(i + 16), + _mm256_fmadd_ps( + _mm256_loadu_ps(dst.as_ptr().add(i + 16)), + c, + _mm256_loadu_ps(src.as_ptr().add(i + 16)), + ), + ); + _mm256_storeu_ps( + dst.as_mut_ptr().add(i + 24), + _mm256_fmadd_ps( + _mm256_loadu_ps(dst.as_ptr().add(i + 24)), + c, + _mm256_loadu_ps(src.as_ptr().add(i + 24)), + ), + ); + i += 32; + } + + while i + 8 <= n { + _mm256_storeu_ps( + dst.as_mut_ptr().add(i), + _mm256_fmadd_ps( + _mm256_loadu_ps(dst.as_ptr().add(i)), + c, + _mm256_loadu_ps(src.as_ptr().add(i)), + ), + ); + i += 8; + } + + while i < n { + dst[i] = dst[i] * correction + src[i]; + i += 1; + } +} + +/// AVX2-accelerated RMS norm for a single row. +#[cfg(target_arch = "x86_64")] +#[target_feature(enable = "avx2", enable = "fma")] +pub unsafe fn rms_norm_row(out: &mut [f32], x: &[f32], weight: &[f32], hidden: usize, eps: f32) { + let mut i = 0usize; + let mut acc0 = _mm256_setzero_ps(); + let mut acc1 = _mm256_setzero_ps(); + + while i + 16 <= hidden { + let x0 = _mm256_loadu_ps(x.as_ptr().add(i)); + let x1 = _mm256_loadu_ps(x.as_ptr().add(i + 8)); + acc0 = _mm256_fmadd_ps(x0, x0, acc0); + acc1 = _mm256_fmadd_ps(x1, x1, acc1); + i += 16; + } + while i + 8 <= hidden { + let xv = _mm256_loadu_ps(x.as_ptr().add(i)); + acc0 = _mm256_fmadd_ps(xv, xv, acc0); + i += 8; + } + + let mut sum_sq = hsum_ps(_mm256_add_ps(acc0, acc1)); + while i < hidden { + sum_sq += x[i] * x[i]; + i += 1; + } + + let rms_inv = 1.0 / (sum_sq / hidden as f32 + eps).sqrt(); + let rms_v = _mm256_set1_ps(rms_inv); + + i = 0; + while i + 8 <= hidden { + let xv = _mm256_loadu_ps(x.as_ptr().add(i)); + let wv = _mm256_loadu_ps(weight.as_ptr().add(i)); + _mm256_storeu_ps( + out.as_mut_ptr().add(i), + _mm256_mul_ps(_mm256_mul_ps(xv, rms_v), wv), + ); + i += 8; + } + while i < hidden { + out[i] = x[i] * rms_inv * weight[i]; + i += 1; + } +} + +/// AVX2-accelerated layer norm for a single row. +#[cfg(target_arch = "x86_64")] +#[target_feature(enable = "avx2", enable = "fma")] +pub unsafe fn layer_norm_row( + out: &mut [f32], + x: &[f32], + weight: &[f32], + bias: &[f32], + hidden: usize, + eps: f32, +) { + // Pass 1: compute mean + let mut i = 0usize; + let mut sum0 = _mm256_setzero_ps(); + let mut sum1 = _mm256_setzero_ps(); + while i + 16 <= hidden { + sum0 = _mm256_add_ps(sum0, _mm256_loadu_ps(x.as_ptr().add(i))); + sum1 = _mm256_add_ps(sum1, _mm256_loadu_ps(x.as_ptr().add(i + 8))); + i += 16; + } + while i + 8 <= hidden { + sum0 = _mm256_add_ps(sum0, _mm256_loadu_ps(x.as_ptr().add(i))); + i += 8; + } + let mut mean = hsum_ps(_mm256_add_ps(sum0, sum1)); + while i < hidden { + mean += x[i]; + i += 1; + } + mean /= hidden as f32; + + // Pass 2: compute variance + let mean_v = _mm256_set1_ps(mean); + i = 0; + let mut var0 = _mm256_setzero_ps(); + let mut var1 = _mm256_setzero_ps(); + while i + 16 <= hidden { + let d0 = _mm256_sub_ps(_mm256_loadu_ps(x.as_ptr().add(i)), mean_v); + let d1 = _mm256_sub_ps(_mm256_loadu_ps(x.as_ptr().add(i + 8)), mean_v); + var0 = _mm256_fmadd_ps(d0, d0, var0); + var1 = _mm256_fmadd_ps(d1, d1, var1); + i += 16; + } + while i + 8 <= hidden { + let d = _mm256_sub_ps(_mm256_loadu_ps(x.as_ptr().add(i)), mean_v); + var0 = _mm256_fmadd_ps(d, d, var0); + i += 8; + } + let mut var = hsum_ps(_mm256_add_ps(var0, var1)); + while i < hidden { + let d = x[i] - mean; + var += d * d; + i += 1; + } + + let inv_std = 1.0 / (var / hidden as f32 + eps).sqrt(); + let inv_v = _mm256_set1_ps(inv_std); + + // Pass 3: normalize + i = 0; + while i + 8 <= hidden { + let xv = _mm256_sub_ps(_mm256_loadu_ps(x.as_ptr().add(i)), mean_v); + let wv = _mm256_loadu_ps(weight.as_ptr().add(i)); + let bv = _mm256_loadu_ps(bias.as_ptr().add(i)); + _mm256_storeu_ps( + out.as_mut_ptr().add(i), + _mm256_fmadd_ps(_mm256_mul_ps(xv, inv_v), wv, bv), + ); + i += 8; + } + while i < hidden { + out[i] = (x[i] - mean) * inv_std * weight[i] + bias[i]; + i += 1; + } +} + +/// Fast exp approximation using AVX2+FMA (~1e-4 relative error for |x| < 88). +#[cfg(target_arch = "x86_64")] +#[target_feature(enable = "avx2", enable = "fma")] +#[inline] +unsafe fn fast_exp_avx(x: __m256) -> __m256 { + let log2e = _mm256_set1_ps(1.442695041); + let ln2 = _mm256_set1_ps(0.6931471806); + + let val = _mm256_mul_ps(x, log2e); + let val = _mm256_min_ps(val, _mm256_set1_ps(126.0)); + let val = _mm256_max_ps(val, _mm256_set1_ps(-126.0)); + + let ipart = _mm256_cvtps_epi32(val); + let fpart = _mm256_sub_ps(val, _mm256_cvtepi32_ps(ipart)); + + let exp_i = _mm256_castsi256_ps(_mm256_slli_epi32( + _mm256_add_epi32(ipart, _mm256_set1_epi32(127)), + 23, + )); + + let f = _mm256_mul_ps(fpart, ln2); + let c2 = _mm256_set1_ps(0.5); + let c3 = _mm256_set1_ps(1.0 / 6.0); + let c4 = _mm256_set1_ps(1.0 / 24.0); + let c5 = _mm256_set1_ps(1.0 / 120.0); + + let mut p = _mm256_fmadd_ps(c5, f, c4); + p = _mm256_fmadd_ps(p, f, c3); + p = _mm256_fmadd_ps(p, f, c2); + p = _mm256_fmadd_ps(p, f, _mm256_set1_ps(1.0)); + p = _mm256_fmadd_ps(p, f, _mm256_set1_ps(1.0)); + + _mm256_mul_ps(exp_i, p) +} + +/// AVX2-accelerated exp() in-place using fast polynomial approximation. +#[cfg(target_arch = "x86_64")] +#[target_feature(enable = "avx2", enable = "fma")] +pub unsafe fn exp_inplace(x: &mut [f32]) { + let n = x.len(); + let mut i = 0usize; + while i + 8 <= n { + let v = _mm256_loadu_ps(x.as_ptr().add(i)); + _mm256_storeu_ps(x.as_mut_ptr().add(i), fast_exp_avx(v)); + i += 8; + } + while i < n { + x[i] = x[i].exp(); + i += 1; + } +} + +/// AVX2-accelerated GELU (tanh approximation). +#[cfg(target_arch = "x86_64")] +#[target_feature(enable = "avx2", enable = "fma")] +pub unsafe fn gelu_inplace(x: &mut [f32], n: usize) { + let half = _mm256_set1_ps(0.5); + let one = _mm256_set1_ps(1.0); + let two = _mm256_set1_ps(2.0); + let coeff = _mm256_set1_ps(0.7978845608028654); + let c3 = _mm256_set1_ps(0.044715); + let mut i = 0usize; + + while i + 8 <= n { + let v = _mm256_loadu_ps(x.as_ptr().add(i)); + let v2 = _mm256_mul_ps(v, v); + let v3 = _mm256_mul_ps(v2, v); + let inner = _mm256_mul_ps(coeff, _mm256_fmadd_ps(c3, v3, v)); + let exp2x = fast_exp_avx(_mm256_mul_ps(two, inner)); + let tanh_v = _mm256_sub_ps(one, _mm256_div_ps(two, _mm256_add_ps(exp2x, one))); + let result = _mm256_mul_ps(half, _mm256_mul_ps(v, _mm256_add_ps(one, tanh_v))); + _mm256_storeu_ps(x.as_mut_ptr().add(i), result); + i += 8; + } + + while i < n { + let val = x[i]; + let x3 = val * val * val; + let inner = 0.7978845608028654f32 * (val + 0.044715 * x3); + x[i] = 0.5 * val * (1.0 + inner.tanh()); + i += 1; + } +} + +/// AVX2-accelerated SwiGLU with interleaved gate/up. +#[cfg(target_arch = "x86_64")] +#[target_feature(enable = "avx2", enable = "fma")] +pub unsafe fn swiglu_interleaved(out: &mut [f32], gate_up: &[f32], n: usize) { + let one = _mm256_set1_ps(1.0); + let mut j = 0usize; + + while j + 8 <= n { + // Load 16 floats: [g0,u0,g1,u1,g2,u2,g3,u3] x2 + let lo = _mm256_loadu_ps(gate_up.as_ptr().add(2 * j)); + let hi = _mm256_loadu_ps(gate_up.as_ptr().add(2 * j + 8)); + + // Deinterleave using shuffle + permute + let shuf_lo = _mm256_shuffle_ps(lo, hi, 0b10_00_10_00); // g0,g1,g4,g5,g2,g3,g6,g7 + let shuf_hi = _mm256_shuffle_ps(lo, hi, 0b11_01_11_01); // u0,u1,u4,u5,u2,u3,u6,u7 + let gates = _mm256_permutevar8x32_ps(shuf_lo, _mm256_setr_epi32(0, 1, 4, 5, 2, 3, 6, 7)); + let ups = _mm256_permutevar8x32_ps(shuf_hi, _mm256_setr_epi32(0, 1, 4, 5, 2, 3, 6, 7)); + + let neg_g = _mm256_sub_ps(_mm256_setzero_ps(), gates); + let exp_ng = fast_exp_avx(neg_g); + let denom = _mm256_add_ps(one, exp_ng); + let silu_g = _mm256_div_ps(gates, denom); + + _mm256_storeu_ps(out.as_mut_ptr().add(j), _mm256_mul_ps(silu_g, ups)); + j += 8; + } + + while j < n { + let g = gate_up[2 * j]; + let u = gate_up[2 * j + 1]; + let g_silu = g / (1.0 + (-g).exp()); + out[j] = g_silu * u; + j += 1; + } +} + +#[cfg(test)] +mod tests { + #[cfg(target_arch = "x86_64")] + use super::{argmax_int8_range, matvec_int8}; + + #[cfg(target_arch = "x86_64")] + fn scalar_matvec_int8( + x_int8: &[i8], + x_scale: f32, + w_int8: &[i8], + w_scales: &[f32], + bias: Option<&[f32]>, + in_dim: usize, + out_dim: usize, + ) -> Vec { + let mut out = vec![0.0f32; out_dim]; + for row in 0..out_dim { + let mut acc = 0i32; + for col in 0..in_dim { + acc += (x_int8[col] as i32) * (w_int8[row * in_dim + col] as i32); + } + let mut value = (acc as f32) * x_scale * w_scales[row]; + if let Some(bias_values) = bias { + value += bias_values[row]; + } + out[row] = value; + } + out + } + + #[cfg(target_arch = "x86_64")] + #[test] + fn avx_int8_matvec_matches_scalar() { + if !std::is_x86_feature_detected!("avx2") { + return; + } + + let in_dim = 96usize; + let out_dim = 13usize; + let x_scale = 0.03125f32; + let x_int8: Vec = (0..in_dim).map(|i| ((i as i32 % 15) - 7) as i8).collect(); + let w_int8: Vec = (0..(out_dim * in_dim)) + .map(|i| (((i as i32 * 7) % 17) - 8) as i8) + .collect(); + let w_scales: Vec = (0..out_dim).map(|i| 0.01 + i as f32 * 0.003).collect(); + let bias: Vec = (0..out_dim).map(|i| -0.2 + i as f32 * 0.05).collect(); + + let expected = scalar_matvec_int8( + &x_int8, + x_scale, + &w_int8, + &w_scales, + Some(&bias), + in_dim, + out_dim, + ); + let mut actual = vec![0.0f32; out_dim]; + + unsafe { + matvec_int8( + &mut actual, + x_int8.as_ptr(), + x_scale, + w_int8.as_ptr(), + &w_scales, + Some(&bias), + in_dim, + out_dim, + ); + } + + for (lhs, rhs) in actual.iter().zip(expected.iter()) { + assert!((lhs - rhs).abs() < 1e-4, "lhs={lhs} rhs={rhs}"); + } + } + + #[cfg(target_arch = "x86_64")] + #[test] + fn avx_int8_argmax_matches_scalar() { + if !std::is_x86_feature_detected!("avx2") { + return; + } + + let in_dim = 80usize; + let out_dim = 19usize; + let x_scale = 0.0625f32; + let x_int8: Vec = (0..in_dim) + .map(|i| (((i as i32 * 5) % 23) - 11) as i8) + .collect(); + let w_int8: Vec = (0..(out_dim * in_dim)) + .map(|i| (((i as i32 * 3) % 29) - 14) as i8) + .collect(); + let w_scales: Vec = (0..out_dim).map(|i| 0.02 + i as f32 * 0.002).collect(); + + let expected = + scalar_matvec_int8(&x_int8, x_scale, &w_int8, &w_scales, None, in_dim, out_dim); + let expected_idx = expected + .iter() + .enumerate() + .max_by(|a, b| a.1.partial_cmp(b.1).unwrap()) + .map(|(idx, _)| idx) + .unwrap(); + + let (actual_idx, actual_val) = unsafe { + argmax_int8_range( + x_int8.as_ptr(), + x_scale, + w_int8.as_ptr(), + &w_scales, + in_dim, + 0, + out_dim, + ) + }; + + assert_eq!(actual_idx, expected_idx); + assert!((actual_val - expected[expected_idx]).abs() < 1e-4); + } +} diff --git a/vendor/qwenasr/crates/qwen-asr/src/kernels/generic.rs b/vendor/qwenasr/crates/qwen-asr/src/kernels/generic.rs new file mode 100644 index 0000000..d17751d --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/src/kernels/generic.rs @@ -0,0 +1,78 @@ +//! Generic (portable) implementations of hot kernels. + +#[inline] +pub fn bf16_to_f32(bf16: u16) -> f32 { + f32::from_bits((bf16 as u32) << 16) +} + +/// # Safety +/// w_bf16 must point to at least out_dim * in_dim valid bf16 values. +pub unsafe fn bf16_matvec_fused( + y: &mut [f32], + x: &[f32], + w_bf16: *const u16, + bias: Option<&[f32]>, + in_dim: usize, + out_dim: usize, +) { + for o in 0..out_dim { + let w_row = unsafe { std::slice::from_raw_parts(w_bf16.add(o * in_dim), in_dim) }; + let mut sum = bias.map_or(0.0f32, |b| b[o]); + for k in 0..in_dim { + sum += bf16_to_f32(w_row[k]) * x[k]; + } + y[o] = sum; + } +} + +/// # Safety +/// w_bf16 must point to at least end * in_dim valid bf16 values. +pub unsafe fn argmax_bf16_range( + x: &[f32], + w_bf16: *const u16, + in_dim: usize, + start: usize, + end: usize, +) -> (usize, f32) { + let mut best = start; + let mut best_val = -1e30f32; + + for o in start..end { + let w_row = unsafe { std::slice::from_raw_parts(w_bf16.add(o * in_dim), in_dim) }; + let mut sum = 0.0f32; + for k in 0..in_dim { + sum += bf16_to_f32(w_row[k]) * x[k]; + } + if sum > best_val { + best_val = sum; + best = o; + } + } + (best, best_val) +} + +pub fn dot_f32(a: &[f32], b: &[f32], n: usize) -> f32 { + let mut sum = 0.0f32; + for i in 0..n { + sum += a[i] * b[i]; + } + sum +} + +pub fn vec_scale_inplace(dst: &mut [f32], scale: f32, n: usize) { + for val in dst.iter_mut().take(n) { + *val *= scale; + } +} + +pub fn vec_axpy_inplace(dst: &mut [f32], src: &[f32], alpha: f32, n: usize) { + for i in 0..n { + dst[i] += alpha * src[i]; + } +} + +pub fn vec_scale_add(dst: &mut [f32], src: &[f32], correction: f32, n: usize) { + for i in 0..n { + dst[i] = dst[i] * correction + src[i]; + } +} diff --git a/vendor/qwenasr/crates/qwen-asr/src/kernels/mod.rs b/vendor/qwenasr/crates/qwen-asr/src/kernels/mod.rs new file mode 100644 index 0000000..69144b9 --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/src/kernels/mod.rs @@ -0,0 +1,2878 @@ +//! BLAS/vDSP bindings, thread pool, and SIMD kernel dispatch. + +#[cfg(target_arch = "x86_64")] +pub mod avx; +pub mod generic; +#[cfg(target_arch = "aarch64")] +pub mod neon; + +use std::thread; + +// BLAS extern bindings +#[cfg(all(feature = "blas", target_vendor = "apple"))] +#[link(name = "Accelerate", kind = "framework")] +extern "C" { + fn cblas_sgemm( + order: i32, + transa: i32, + transb: i32, + m: i32, + n: i32, + k: i32, + alpha: f32, + a: *const f32, + lda: i32, + b: *const f32, + ldb: i32, + beta: f32, + c: *mut f32, + ldc: i32, + ); +} + +// vDSP/vForce bindings (macOS Accelerate) +#[cfg(all(feature = "vdsp", target_vendor = "apple"))] +#[link(name = "Accelerate", kind = "framework")] +extern "C" { + fn vDSP_dotpr( + a: *const f32, + a_stride: i32, + b: *const f32, + b_stride: i32, + result: *mut f32, + n: u64, + ); + fn vDSP_vsmul( + a: *const f32, + a_stride: i32, + scalar: *const f32, + c: *mut f32, + c_stride: i32, + n: u64, + ); + fn vDSP_vsma( + a: *const f32, + a_stride: i32, + scalar: *const f32, + b: *const f32, + b_stride: i32, + c: *mut f32, + c_stride: i32, + n: u64, + ); + fn vvexpf(dst: *mut f32, src: *const f32, n: *const i32); +} + +#[cfg(all(feature = "blas", not(target_vendor = "apple")))] +extern "C" { + fn cblas_sgemm( + order: i32, + transa: i32, + transb: i32, + m: i32, + n: i32, + k: i32, + alpha: f32, + a: *const f32, + lda: i32, + b: *const f32, + ldb: i32, + beta: f32, + c: *mut f32, + ldc: i32, + ); +} + +#[cfg(feature = "blas")] +const CBLAS_ROW_MAJOR: i32 = 101; +#[cfg(feature = "blas")] +const CBLAS_NO_TRANS: i32 = 111; +#[cfg(feature = "blas")] +const CBLAS_TRANS: i32 = 112; + +// Verbose flag +static VERBOSE: AtomicI32 = AtomicI32::new(0); + +// ======================================================================== +// Profiling support +// ======================================================================== + +use std::sync::atomic::{AtomicBool, AtomicI32, AtomicU64, AtomicUsize, Ordering}; +use std::time::Instant; + +static PROFILE_ENABLED: AtomicBool = AtomicBool::new(false); + +pub fn set_profile(enabled: bool) { + PROFILE_ENABLED.store(enabled, Ordering::Relaxed); +} + +pub fn is_profiling() -> bool { + PROFILE_ENABLED.load(Ordering::Relaxed) +} + +macro_rules! define_profile_counters { + ($($name:ident),+) => { + pub struct ProfileCounters { + $(pub $name: (AtomicU64, AtomicU64),)+ // (total_ns, call_count) + } + + impl ProfileCounters { + pub const fn new() -> Self { + ProfileCounters { + $($name: (AtomicU64::new(0), AtomicU64::new(0)),)+ + } + } + } + + impl Default for ProfileCounters { + fn default() -> Self { + Self::new() + } + } + + impl ProfileCounters { + pub fn reset(&self) { + $( + self.$name.0.store(0, Ordering::Relaxed); + self.$name.1.store(0, Ordering::Relaxed); + )+ + } + + pub fn report(&self) { + $( + let ns = self.$name.0.load(Ordering::Relaxed); + let calls = self.$name.1.load(Ordering::Relaxed); + if calls > 0 { + let ms = ns as f64 / 1_000_000.0; + let avg = ms / calls as f64; + eprintln!("[profile] {}: {:.1}ms ({} calls, {:.2}ms avg)", + stringify!($name), ms, calls, avg); + } + )+ + } + } + } +} + +define_profile_counters!( + rms_norm, + layer_norm, + gelu, + swiglu, + bf16_matvec, + bf16_to_f32_conv, + attention_bidir, + attention_causal, + sgemm, + conv2d_op, + rope, + add_inplace_op +); + +pub static PROF: ProfileCounters = ProfileCounters::new(); + +pub struct ProfileGuard { + start: Instant, + counter: &'static (AtomicU64, AtomicU64), +} + +impl ProfileGuard { + #[inline] + pub fn new(counter: &'static (AtomicU64, AtomicU64)) -> Option { + if PROFILE_ENABLED.load(Ordering::Relaxed) { + Some(ProfileGuard { + start: Instant::now(), + counter, + }) + } else { + None + } + } +} + +impl Drop for ProfileGuard { + #[inline] + fn drop(&mut self) { + let ns = self.start.elapsed().as_nanos() as u64; + self.counter.0.fetch_add(ns, Ordering::Relaxed); + self.counter.1.fetch_add(1, Ordering::Relaxed); + } +} + +// Convenience: unused ProfileTimer alias removed + +pub fn profile_reset() { + PROF.reset(); +} +pub fn profile_report() { + PROF.report(); +} + +pub fn set_verbose(v: i32) { + VERBOSE.store(v, Ordering::Relaxed); +} + +pub fn verbose() -> i32 { + VERBOSE.load(Ordering::Relaxed) +} + +// ======================================================================== +// Thread Pool (persistent, mutex+condvar, matches C approach) +// ======================================================================== + +use std::sync::{Arc, Condvar, Mutex, OnceLock}; + +const MAX_THREADS: usize = 16; + +struct ThreadPool { + // Mutex+condvar only used as slow-path fallback when spin-wait misses + state: Mutex, // shutdown flag only + work_cv: Condvar, + // All dispatch data is lock-free via atomics + gen_atomic: AtomicU64, + done_atomic: AtomicUsize, + fn_ptr_atomic: AtomicUsize, + fn_call_atomic: AtomicUsize, + n_threads_atomic: AtomicUsize, +} + +static THREAD_POOL: OnceLock> = OnceLock::new(); + +fn get_pool() -> &'static Arc { + THREAD_POOL.get_or_init(|| { + Arc::new(ThreadPool { + state: Mutex::new(false), + work_cv: Condvar::new(), + gen_atomic: AtomicU64::new(0), + done_atomic: AtomicUsize::new(0), + fn_ptr_atomic: AtomicUsize::new(0), + fn_call_atomic: AtomicUsize::new(0), + n_threads_atomic: AtomicUsize::new(1), + }) + }) +} + +fn pool_worker(pool: Arc, tid: usize) { + let mut last_gen: u64 = 0; + loop { + // Fast path: spin briefly on atomic generation counter + let mut found = false; + for _ in 0..512 { + let gen = pool.gen_atomic.load(Ordering::Acquire); + if gen != last_gen { + last_gen = gen; + found = true; + break; + } + core::hint::spin_loop(); + } + + if !found { + // Slow path: condvar wait (mutex only protects shutdown flag) + let mut shutdown = match pool.state.lock() { + Ok(s) => s, + Err(p) => p.into_inner(), + }; + while !*shutdown && pool.gen_atomic.load(Ordering::Relaxed) == last_gen { + shutdown = match pool.work_cv.wait(shutdown) { + Ok(s) => s, + Err(p) => p.into_inner(), + }; + } + if *shutdown { + return; + } + last_gen = pool.gen_atomic.load(Ordering::Acquire); + } + + // Read dispatch data from atomics (ordered by gen_atomic Acquire) + let fn_ptr = pool.fn_ptr_atomic.load(Ordering::Relaxed) as *const (); + let fn_call: fn(*const (), usize, usize) = + unsafe { core::mem::transmute(pool.fn_call_atomic.load(Ordering::Relaxed)) }; + let n_threads = pool.n_threads_atomic.load(Ordering::Relaxed); + + fn_call(fn_ptr, tid, n_threads); + pool.done_atomic.fetch_add(1, Ordering::Release); + } +} + +static SPAWNED_THREADS: AtomicUsize = AtomicUsize::new(0); + +fn ensure_workers(pool: &Arc, n_threads: usize) { + let spawned = SPAWNED_THREADS.load(Ordering::Relaxed); + if spawned >= n_threads - 1 { + return; + } + let start = spawned + 1; + for tid in start..n_threads { + let p = pool.clone(); + thread::Builder::new() + .name(format!("qwen-worker-{}", tid)) + .spawn(move || pool_worker(p, tid)) + .expect("failed to spawn worker thread"); + } + SPAWNED_THREADS.store(n_threads - 1, Ordering::Relaxed); +} + +static THREAD_POOL_THREADS: AtomicUsize = AtomicUsize::new(1); + +pub fn set_threads(n: usize) { + let n = n.clamp(1, MAX_THREADS); + THREAD_POOL_THREADS.store(n, Ordering::Relaxed); + if n > 1 { + let pool = get_pool(); + ensure_workers(pool, n); + } + if verbose() >= 2 { + eprintln!("Thread pool: {} threads", n); + } +} + +pub fn get_num_threads() -> usize { + THREAD_POOL_THREADS.load(Ordering::Relaxed) +} + +pub fn get_num_cpus() -> usize { + // On Apple Silicon, prefer performance cores only (E-cores bottleneck parallel_for). + #[cfg(target_os = "macos")] + { + let perf_cores = get_perf_core_count(); + if perf_cores > 0 { + return perf_cores; + } + } + std::thread::available_parallelism() + .map(|n| n.get()) + .unwrap_or(1) +} + +#[cfg(target_os = "macos")] +fn get_perf_core_count() -> usize { + extern "C" { + fn sysctlbyname( + name: *const i8, + oldp: *mut libc::c_void, + oldlenp: *mut usize, + newp: *const libc::c_void, + newlen: usize, + ) -> i32; + } + let name = c"hw.perflevel0.physicalcpu"; + let mut val: i32 = 0; + let mut len = std::mem::size_of::(); + let ret = unsafe { + sysctlbyname( + name.as_ptr(), + &mut val as *mut i32 as *mut libc::c_void, + &mut len, + std::ptr::null(), + 0, + ) + }; + if ret == 0 && val > 0 { + val as usize + } else { + 0 + } +} + +/// Run a closure in parallel using the persistent thread pool. +/// The closure takes (thread_id, n_threads). +fn parallel_for(f: F) { + let n_threads = get_num_threads(); + if n_threads <= 1 { + f(0, 1); + return; + } + + let pool = get_pool(); + + // Trampoline: cast *const () back to &F and call it + fn trampoline(ptr: *const (), tid: usize, nt: usize) { + let f = unsafe { &*(ptr as *const F) }; + f(tid, nt); + } + + // Publish dispatch data via atomics (Relaxed OK: gen_atomic Release provides ordering) + pool.done_atomic.store(0, Ordering::Relaxed); + pool.fn_ptr_atomic + .store(&f as *const F as *const () as usize, Ordering::Relaxed); + pool.fn_call_atomic + .store(trampoline:: as *const () as usize, Ordering::Relaxed); + pool.n_threads_atomic.store(n_threads, Ordering::Relaxed); + // Release: ensures all stores above are visible to workers that Acquire gen_atomic + pool.gen_atomic.fetch_add(1, Ordering::Release); + + // Wake workers that fell through to condvar wait + // Lock scope is minimal: just notify, no data to write + { + let _guard = match pool.state.lock() { + Ok(s) => s, + Err(p) => p.into_inner(), + }; + pool.work_cv.notify_all(); + } + + // Main thread does tid=0 + f(0, n_threads); + + // Wait for workers: spin on atomic done counter + let expected = n_threads - 1; + loop { + if pool.done_atomic.load(Ordering::Acquire) >= expected { + break; + } + core::hint::spin_loop(); + } +} + +// ======================================================================== +// Dispatch helpers - pick NEON/AVX/generic at compile time +// ======================================================================== + +#[inline] +pub fn bf16_to_f32(bf16: u16) -> f32 { + f32::from_bits((bf16 as u32) << 16) +} + +pub fn bf16_to_f32_buf(dst: &mut [f32], src: &[u16]) { + #[cfg(target_arch = "aarch64")] + { + unsafe { + neon::bf16_to_f32_buf(dst, src); + } + } + + #[cfg(target_arch = "x86_64")] + { + unsafe { + avx::bf16_to_f32_buf(dst, src); + } + } + + #[cfg(not(any(target_arch = "aarch64", target_arch = "x86_64")))] + for i in 0..src.len() { + dst[i] = bf16_to_f32(src[i]); + } +} + +fn bf16_matvec_fused( + y: &mut [f32], + x: &[f32], + w_bf16: *const u16, + bias: Option<&[f32]>, + in_dim: usize, + out_dim: usize, +) { + #[cfg(target_arch = "aarch64")] + { + unsafe { + neon::bf16_matvec_fused(y, x, w_bf16, bias, in_dim, out_dim); + } + } + + #[cfg(target_arch = "x86_64")] + { + unsafe { + avx::bf16_matvec_fused(y, x, w_bf16, bias, in_dim, out_dim); + } + } + + #[cfg(not(any(target_arch = "aarch64", target_arch = "x86_64")))] + generic::bf16_matvec_fused(y, x, w_bf16, bias, in_dim, out_dim); +} + +fn argmax_bf16_range( + x: &[f32], + w_bf16: *const u16, + in_dim: usize, + start: usize, + end: usize, +) -> (usize, f32) { + #[cfg(target_arch = "aarch64")] + { + unsafe { neon::argmax_bf16_range(x, w_bf16, in_dim, start, end) } + } + + #[cfg(target_arch = "x86_64")] + { + unsafe { avx::argmax_bf16_range(x, w_bf16, in_dim, start, end) } + } + + #[cfg(not(any(target_arch = "aarch64", target_arch = "x86_64")))] + generic::argmax_bf16_range(x, w_bf16, in_dim, start, end) +} + +#[inline] +pub fn dot_f32(a: &[f32], b: &[f32], n: usize) -> f32 { + #[cfg(all(feature = "vdsp", target_vendor = "apple"))] + { + let mut result = 0.0f32; + unsafe { + vDSP_dotpr(a.as_ptr(), 1, b.as_ptr(), 1, &mut result, n as u64); + } + result + } + + #[cfg(all( + target_arch = "aarch64", + not(all(feature = "vdsp", target_vendor = "apple")) + ))] + { + unsafe { neon::dot_f32(a, b, n) } + } + + #[cfg(all( + target_arch = "x86_64", + not(all(feature = "vdsp", target_vendor = "apple")) + ))] + { + unsafe { avx::dot_f32(a, b, n) } + } + + #[cfg(not(any( + target_arch = "aarch64", + target_arch = "x86_64", + all(feature = "vdsp", target_vendor = "apple") + )))] + generic::dot_f32(a, b, n) +} + +#[inline] +pub fn vec_scale_inplace(dst: &mut [f32], scale: f32, n: usize) { + #[cfg(all(feature = "vdsp", target_vendor = "apple"))] + { + unsafe { + vDSP_vsmul(dst.as_ptr(), 1, &scale, dst.as_mut_ptr(), 1, n as u64); + } + } + + #[cfg(all( + target_arch = "aarch64", + not(all(feature = "vdsp", target_vendor = "apple")) + ))] + { + unsafe { + neon::vec_scale_inplace(dst, scale, n); + } + } + + #[cfg(all( + target_arch = "x86_64", + not(all(feature = "vdsp", target_vendor = "apple")) + ))] + { + unsafe { + avx::vec_scale_inplace(dst, scale, n); + } + } + + #[cfg(not(any( + target_arch = "aarch64", + target_arch = "x86_64", + all(feature = "vdsp", target_vendor = "apple") + )))] + generic::vec_scale_inplace(dst, scale, n); +} + +#[inline] +pub fn vec_axpy_inplace(dst: &mut [f32], src: &[f32], alpha: f32, n: usize) { + #[cfg(all(feature = "vdsp", target_vendor = "apple"))] + { + unsafe { + vDSP_vsma( + src.as_ptr(), + 1, + &alpha, + dst.as_ptr(), + 1, + dst.as_mut_ptr(), + 1, + n as u64, + ); + } + } + + #[cfg(all( + target_arch = "aarch64", + not(all(feature = "vdsp", target_vendor = "apple")) + ))] + { + unsafe { + neon::vec_axpy_inplace(dst, src, alpha, n); + } + } + + #[cfg(all( + target_arch = "x86_64", + not(all(feature = "vdsp", target_vendor = "apple")) + ))] + { + unsafe { + avx::vec_axpy_inplace(dst, src, alpha, n); + } + } + + #[cfg(not(any( + target_arch = "aarch64", + target_arch = "x86_64", + all(feature = "vdsp", target_vendor = "apple") + )))] + generic::vec_axpy_inplace(dst, src, alpha, n); +} + +#[inline] +pub fn vec_scale_add(dst: &mut [f32], src: &[f32], correction: f32, n: usize) { + #[cfg(target_arch = "aarch64")] + { + unsafe { + neon::vec_scale_add(dst, src, correction, n); + } + } + + #[cfg(target_arch = "x86_64")] + { + unsafe { + avx::vec_scale_add(dst, src, correction, n); + } + } + + #[cfg(not(any(target_arch = "aarch64", target_arch = "x86_64")))] + generic::vec_scale_add(dst, src, correction, n); +} + +// ======================================================================== +// Basic Operations +// ======================================================================== + +pub fn add_inplace(a: &mut [f32], b: &[f32], n: usize) { + let _pg = ProfileGuard::new(&PROF.add_inplace_op); + for i in 0..n { + a[i] += b[i]; + } +} + +// ======================================================================== +// Matrix Operations +// ======================================================================== + +/// C = A @ B (no transpose): A[M,K], B[K,N], C[M,N] +pub fn matmul_nn(c: &mut [f32], a: &[f32], b: &[f32], m: usize, k: usize, n: usize) { + #[cfg(feature = "blas")] + unsafe { + cblas_sgemm( + CBLAS_ROW_MAJOR, + CBLAS_NO_TRANS, + CBLAS_NO_TRANS, + m as i32, + n as i32, + k as i32, + 1.0, + a.as_ptr(), + k as i32, + b.as_ptr(), + n as i32, + 0.0, + c.as_mut_ptr(), + n as i32, + ); + } + + #[cfg(not(feature = "blas"))] + { + for mi in 0..m { + for ni in 0..n { + let mut sum = 0.0f32; + for ki in 0..k { + sum += a[mi * k + ki] * b[ki * n + ni]; + } + c[mi * n + ni] = sum; + } + } + } +} + +/// C = A @ B^T: A[M,K], B[N,K], C[M,N] +pub fn matmul_t(c: &mut [f32], a: &[f32], b: &[f32], m: usize, k: usize, n: usize) { + #[cfg(feature = "blas")] + unsafe { + cblas_sgemm( + CBLAS_ROW_MAJOR, + CBLAS_NO_TRANS, + CBLAS_TRANS, + m as i32, + n as i32, + k as i32, + 1.0, + a.as_ptr(), + k as i32, + b.as_ptr(), + k as i32, + 0.0, + c.as_mut_ptr(), + n as i32, + ); + } + + #[cfg(not(feature = "blas"))] + { + for mi in 0..m { + for ni in 0..n { + let mut sum = 0.0f32; + for ki in 0..k { + sum += a[mi * k + ki] * b[ni * k + ki]; + } + c[mi * n + ni] = sum; + } + } + } +} + +/// y = x @ W^T + b: x[seq,in], W[out,in], b[out], y[seq,out] +pub fn linear( + y: &mut [f32], + x: &[f32], + w: &[f32], + b: Option<&[f32]>, + seq_len: usize, + in_dim: usize, + out_dim: usize, +) { + let _pg = ProfileGuard::new(&PROF.sgemm); + #[cfg(feature = "blas")] + unsafe { + cblas_sgemm( + CBLAS_ROW_MAJOR, + CBLAS_NO_TRANS, + CBLAS_TRANS, + seq_len as i32, + out_dim as i32, + in_dim as i32, + 1.0, + x.as_ptr(), + in_dim as i32, + w.as_ptr(), + in_dim as i32, + 0.0, + y.as_mut_ptr(), + out_dim as i32, + ); + if let Some(b) = b { + for s in 0..seq_len { + for o in 0..out_dim { + y[s * out_dim + o] += b[o]; + } + } + } + } + + #[cfg(not(feature = "blas"))] + { + for s in 0..seq_len { + let x_row = &x[s * in_dim..(s + 1) * in_dim]; + for o in 0..out_dim { + let w_row = &w[o * in_dim..(o + 1) * in_dim]; + let mut sum = b.map_or(0.0, |b| b[o]); + for i in 0..in_dim { + sum += x_row[i] * w_row[i]; + } + y[s * out_dim + o] = sum; + } + } + } +} + +pub fn linear_nobias( + y: &mut [f32], + x: &[f32], + w: &[f32], + seq_len: usize, + in_dim: usize, + out_dim: usize, +) { + linear(y, x, w, None, seq_len, in_dim, out_dim); +} + +/// y += bias + x @ w.T (accumulate into existing y, fusing residual add) +pub fn linear_accumulate( + y: &mut [f32], + x: &[f32], + w: &[f32], + b: Option<&[f32]>, + seq_len: usize, + in_dim: usize, + out_dim: usize, +) { + let _pg = ProfileGuard::new(&PROF.sgemm); + #[cfg(feature = "blas")] + unsafe { + // Add bias to y first (y already has residual) + if let Some(b) = b { + for s in 0..seq_len { + let row = &mut y[s * out_dim..(s + 1) * out_dim]; + for o in 0..out_dim { + row[o] += b[o]; + } + } + } + // y = 1.0 * x @ w.T + 1.0 * y (accumulate matmul into y) + cblas_sgemm( + CBLAS_ROW_MAJOR, + CBLAS_NO_TRANS, + CBLAS_TRANS, + seq_len as i32, + out_dim as i32, + in_dim as i32, + 1.0, + x.as_ptr(), + in_dim as i32, + w.as_ptr(), + in_dim as i32, + 1.0, + y.as_mut_ptr(), + out_dim as i32, + ); + } + + #[cfg(not(feature = "blas"))] + { + for s in 0..seq_len { + let x_row = &x[s * in_dim..(s + 1) * in_dim]; + for o in 0..out_dim { + let w_row = &w[o * in_dim..(o + 1) * in_dim]; + let mut sum = b.map_or(0.0, |bb| bb[o]); + for i in 0..in_dim { + sum += x_row[i] * w_row[i]; + } + y[s * out_dim + o] += sum; + } + } + } +} + +fn bf16_to_f32_view(src: *const u16, n: usize) -> Vec { + let mut buf = vec![0.0f32; n]; + let src_slice = unsafe { std::slice::from_raw_parts(src, n) }; + bf16_to_f32_buf(&mut buf, src_slice); + buf +} + +/// Threaded bf16 matvec +fn bf16_matvec_threaded( + y: &mut [f32], + x: &[f32], + w_bf16: *const u16, + bias: Option<&[f32]>, + in_dim: usize, + out_dim: usize, +) { + let n_threads = get_num_threads(); + if n_threads <= 1 { + bf16_matvec_fused(y, x, w_bf16, bias, in_dim, out_dim); + return; + } + + let y_ptr = y.as_mut_ptr(); + let x_ptr = x.as_ptr(); + let w_ptr = w_bf16; + let bias_ptr = bias.map(|b| b.as_ptr()); + + // SAFETY: Each thread writes to non-overlapping segments of y + let y_send = y_ptr as usize; + let x_send = x_ptr as usize; + let w_send = w_ptr as usize; + let bias_send = bias_ptr.map(|p| p as usize); + + parallel_for(|tid, nt| { + let chunk = out_dim.div_ceil(nt); + let start = tid * chunk; + let end = (start + chunk).min(out_dim); + if start >= end { + return; + } + + let y_local = + unsafe { std::slice::from_raw_parts_mut((y_send as *mut f32).add(start), end - start) }; + let x_local = unsafe { std::slice::from_raw_parts(x_send as *const f32, in_dim) }; + let w_local = unsafe { (w_send as *const u16).add(start * in_dim) }; + let bias_local = bias_send.map(|p| unsafe { + std::slice::from_raw_parts((p as *const f32).add(start), end - start) + }); + + bf16_matvec_fused(y_local, x_local, w_local, bias_local, in_dim, end - start); + }); +} + +/// Like linear_nobias_bf16 for seq_len=1, but ADDS to the destination: y[i] += W[i] @ x. +/// Achieves fused residual add by passing y as its own "bias". +pub fn linear_nobias_bf16_addto( + y: &mut [f32], + x: &[f32], + w_bf16: *const u16, + in_dim: usize, + out_dim: usize, +) { + let _pg = ProfileGuard::new(&PROF.bf16_matvec); + // SAFETY: bf16_matvec_fused reads bias[i] before writing y[i], so aliasing y as bias is safe. + let bias = unsafe { std::slice::from_raw_parts(y.as_ptr(), out_dim) }; + bf16_matvec_threaded(y, x, w_bf16, Some(bias), in_dim, out_dim); +} + +pub fn linear_nobias_bf16( + y: &mut [f32], + x: &[f32], + w_bf16: *const u16, + seq_len: usize, + in_dim: usize, + out_dim: usize, +) { + let _pg = ProfileGuard::new(&PROF.bf16_matvec); + if seq_len == 1 { + bf16_matvec_threaded(y, x, w_bf16, None, in_dim, out_dim); + return; + } + let w_f32 = bf16_to_f32_view(w_bf16, out_dim * in_dim); + linear_nobias(y, x, &w_f32, seq_len, in_dim, out_dim); +} + +/// Like linear_nobias_bf16 but reuses a caller-provided scratch buffer for bf16→f32 conversion. +/// # Safety +/// Caller must ensure w_bf16 points to at least out_dim * in_dim valid bf16 values. +pub unsafe fn linear_nobias_bf16_scratch( + y: &mut [f32], + x: &[f32], + w_bf16: *const u16, + seq_len: usize, + in_dim: usize, + out_dim: usize, + scratch: &mut [f32], +) { + let _pg = ProfileGuard::new(&PROF.bf16_matvec); + if seq_len == 1 { + bf16_matvec_threaded(y, x, w_bf16, None, in_dim, out_dim); + return; + } + let n = out_dim * in_dim; + let src = unsafe { std::slice::from_raw_parts(w_bf16, n) }; + bf16_to_f32_buf(&mut scratch[..n], src); + linear_nobias(y, x, &scratch[..n], seq_len, in_dim, out_dim); +} + +pub fn linear_bf16( + y: &mut [f32], + x: &[f32], + w_bf16: *const u16, + b: Option<&[f32]>, + seq_len: usize, + in_dim: usize, + out_dim: usize, +) { + if seq_len == 1 { + bf16_matvec_threaded(y, x, w_bf16, b, in_dim, out_dim); + return; + } + let w_f32 = bf16_to_f32_view(w_bf16, out_dim * in_dim); + linear(y, x, &w_f32, b, seq_len, in_dim, out_dim); +} + +/// Fused Q/K/V matvec for single-token decode +#[allow(clippy::too_many_arguments)] +pub fn linear_nobias_bf16_qkv( + q: &mut [f32], + k: &mut [f32], + v: &mut [f32], + x: &[f32], + wq: *const u16, + wk: *const u16, + wv: *const u16, + in_dim: usize, + q_dim: usize, + kv_dim: usize, +) { + let n_threads = get_num_threads(); + if n_threads <= 1 { + bf16_matvec_fused(q, x, wq, None, in_dim, q_dim); + bf16_matvec_fused(k, x, wk, None, in_dim, kv_dim); + bf16_matvec_fused(v, x, wv, None, in_dim, kv_dim); + return; + } + + let total_dim = q_dim + 2 * kv_dim; + let q_ptr = q.as_mut_ptr() as usize; + let k_ptr = k.as_mut_ptr() as usize; + let v_ptr = v.as_mut_ptr() as usize; + let x_ptr = x.as_ptr() as usize; + let wq_ptr = wq as usize; + let wk_ptr = wk as usize; + let wv_ptr = wv as usize; + + parallel_for(|tid, nt| { + let chunk = total_dim.div_ceil(nt); + let start = tid * chunk; + let end = (start + chunk).min(total_dim); + if start >= end { + return; + } + + let x_local = unsafe { std::slice::from_raw_parts(x_ptr as *const f32, in_dim) }; + let q_end = q_dim; + let k_end = q_end + kv_dim; + + // Q range + if start < q_end { + let s = start; + let e = end.min(q_end); + if s < e { + let y_local = + unsafe { std::slice::from_raw_parts_mut((q_ptr as *mut f32).add(s), e - s) }; + let w_local = unsafe { (wq_ptr as *const u16).add(s * in_dim) }; + bf16_matvec_fused(y_local, x_local, w_local, None, in_dim, e - s); + } + } + + // K range + if end > q_end && start < k_end { + let s = start.saturating_sub(q_end); + let e_abs = end.min(k_end); + let e = e_abs - q_end; + if s < e { + let y_local = + unsafe { std::slice::from_raw_parts_mut((k_ptr as *mut f32).add(s), e - s) }; + let w_local = unsafe { (wk_ptr as *const u16).add(s * in_dim) }; + bf16_matvec_fused(y_local, x_local, w_local, None, in_dim, e - s); + } + } + + // V range + if end > k_end { + let s = start.saturating_sub(k_end); + let e_abs = end.min(total_dim); + let e = e_abs - k_end; + if s < e { + let y_local = + unsafe { std::slice::from_raw_parts_mut((v_ptr as *mut f32).add(s), e - s) }; + let w_local = unsafe { (wv_ptr as *const u16).add(s * in_dim) }; + bf16_matvec_fused(y_local, x_local, w_local, None, in_dim, e - s); + } + } + }); +} + +/// Fused gate_up matvec + SwiGLU for single-token decode. +/// Computes: ffn_out[j] = silu(gate[j]) * up[j] where gate/up come from interleaved gate_up_fused matvec. +/// Keeps gate_up output in L1 cache for the SwiGLU operation. +pub fn linear_nobias_bf16_swiglu( + ffn_out: &mut [f32], + x: &[f32], + gate_up_bf16: *const u16, + in_dim: usize, + intermediate: usize, +) { + let _pg = ProfileGuard::new(&PROF.bf16_matvec); + let n_threads = get_num_threads(); + + if n_threads <= 1 { + // Single-threaded: compute gate_up, then SwiGLU inline + let mut gate_buf = vec![0.0f32; 2 * intermediate]; + bf16_matvec_fused( + &mut gate_buf, + x, + gate_up_bf16, + None, + in_dim, + 2 * intermediate, + ); + for j in 0..intermediate { + let g = gate_buf[2 * j]; + let u = gate_buf[2 * j + 1]; + ffn_out[j] = g / (1.0 + (-g).exp()) * u; + } + return; + } + + let x_ptr = x.as_ptr() as usize; + let w_ptr = gate_up_bf16 as usize; + let ffn_ptr = ffn_out.as_mut_ptr() as usize; + + parallel_for(|tid, nt| { + let chunk = intermediate.div_ceil(nt); + let start = tid * chunk; + let end = (start + chunk).min(intermediate); + if start >= end { + return; + } + let n_rows = end - start; + + let x_local = unsafe { std::slice::from_raw_parts(x_ptr as *const f32, in_dim) }; + let w_local = unsafe { (w_ptr as *const u16).add(2 * start * in_dim) }; + + // Compute gate_up for this chunk (thread-local stack buffer) + let mut gate_up_local = vec![0.0f32; 2 * n_rows]; + bf16_matvec_fused( + &mut gate_up_local, + x_local, + w_local, + None, + in_dim, + 2 * n_rows, + ); + + // Apply SwiGLU inline while data is hot in L1 + let ffn_local = + unsafe { std::slice::from_raw_parts_mut((ffn_ptr as *mut f32).add(start), n_rows) }; + for j in 0..n_rows { + let g = gate_up_local[2 * j]; + let u = gate_up_local[2 * j + 1]; + ffn_local[j] = g / (1.0 + (-g).exp()) * u; + } + }); +} + +/// INT8 threaded matvec: y = W_int8 @ x + bias (x is f32, quantized on the fly) +fn int8_matvec_threaded( + y: &mut [f32], + x: &[f32], + w_int8: &[i8], + w_scales: &[f32], + bias: Option<&[f32]>, + in_dim: usize, + out_dim: usize, +) { + let (x_int8, x_scale) = quantize_f32_to_int8(x); + let n_threads = get_num_threads(); + + if n_threads <= 1 || out_dim <= 1 { + int8_matvec_arch( + y, + x_int8.as_ptr(), + x_scale, + w_int8.as_ptr(), + w_scales, + bias, + in_dim, + out_dim, + ); + return; + } + + let x_int8_ptr = x_int8.as_ptr() as usize; + let w_int8_ptr = w_int8.as_ptr() as usize; + let w_scales_ptr = w_scales.as_ptr() as usize; + let y_ptr = y.as_mut_ptr() as usize; + let bias_ptr = bias.map(|values| values.as_ptr() as usize); + + parallel_for(|tid, nt| { + let chunk = out_dim.div_ceil(nt); + let start = tid * chunk; + let end = (start + chunk).min(out_dim); + if start >= end { + return; + } + + let y_local = + unsafe { std::slice::from_raw_parts_mut((y_ptr as *mut f32).add(start), end - start) }; + let w_local = unsafe { (w_int8_ptr as *const i8).add(start * in_dim) }; + let w_scales_local = unsafe { + std::slice::from_raw_parts((w_scales_ptr as *const f32).add(start), end - start) + }; + let bias_local = bias_ptr.map(|ptr| unsafe { + std::slice::from_raw_parts((ptr as *const f32).add(start), end - start) + }); + + int8_matvec_arch( + y_local, + x_int8_ptr as *const i8, + x_scale, + w_local, + w_scales_local, + bias_local, + in_dim, + end - start, + ); + }); +} + +#[cfg(not(any(target_arch = "aarch64", target_arch = "x86_64")))] +#[inline] +fn int8_matvec_scalar( + y: &mut [f32], + x_int8: *const i8, + x_scale: f32, + w_int8: *const i8, + w_scales: &[f32], + bias: Option<&[f32]>, + in_dim: usize, + out_dim: usize, +) { + for row in 0..out_dim { + let mut acc = 0i32; + for col in 0..in_dim { + acc += unsafe { *x_int8.add(col) as i32 } + * unsafe { *w_int8.add(row * in_dim + col) as i32 }; + } + let mut value = (acc as f32) * x_scale * w_scales[row]; + if let Some(bias_values) = bias { + value += bias_values[row]; + } + y[row] = value; + } +} + +#[inline] +fn int8_matvec_arch( + y: &mut [f32], + x_int8: *const i8, + x_scale: f32, + w_int8: *const i8, + w_scales: &[f32], + bias: Option<&[f32]>, + in_dim: usize, + out_dim: usize, +) { + #[cfg(target_arch = "aarch64")] + unsafe { + neon::matvec_int8(y, x_int8, x_scale, w_int8, w_scales, bias, in_dim, out_dim); + return; + } + + #[cfg(target_arch = "x86_64")] + unsafe { + avx::matvec_int8(y, x_int8, x_scale, w_int8, w_scales, bias, in_dim, out_dim); + return; + } + + #[cfg(not(any(target_arch = "aarch64", target_arch = "x86_64")))] + { + int8_matvec_scalar(y, x_int8, x_scale, w_int8, w_scales, bias, in_dim, out_dim); + } +} + +#[cfg(not(any(target_arch = "aarch64", target_arch = "x86_64")))] +#[inline] +fn int8_argmax_scalar( + x_int8: *const i8, + x_scale: f32, + w_int8: *const i8, + w_scales: &[f32], + in_dim: usize, + start: usize, + end: usize, +) -> (usize, f32) { + let mut best_idx = start; + let mut best_val = f32::NEG_INFINITY; + for row in start..end { + let mut acc = 0i32; + for col in 0..in_dim { + acc += unsafe { *x_int8.add(col) as i32 } + * unsafe { *w_int8.add(row * in_dim + col) as i32 }; + } + let value = (acc as f32) * x_scale * w_scales[row]; + if value > best_val { + best_idx = row; + best_val = value; + } + } + (best_idx, best_val) +} + +#[inline] +fn int8_argmax_arch( + x_int8: *const i8, + x_scale: f32, + w_int8: *const i8, + w_scales: &[f32], + in_dim: usize, + start: usize, + end: usize, +) -> (usize, f32) { + #[cfg(target_arch = "aarch64")] + unsafe { + return neon::argmax_int8_range(x_int8, x_scale, w_int8, w_scales, in_dim, start, end); + } + + #[cfg(target_arch = "x86_64")] + unsafe { + return avx::argmax_int8_range(x_int8, x_scale, w_int8, w_scales, in_dim, start, end); + } + + #[cfg(not(any(target_arch = "aarch64", target_arch = "x86_64")))] + { + int8_argmax_scalar(x_int8, x_scale, w_int8, w_scales, in_dim, start, end) + } +} + +#[inline] +fn int8_swiglu_arch(out: &mut [f32], gate_up: &[f32], n: usize) { + #[cfg(target_arch = "aarch64")] + unsafe { + neon::swiglu_interleaved(out, gate_up, n); + return; + } + + #[cfg(target_arch = "x86_64")] + unsafe { + avx::swiglu_interleaved(out, gate_up, n); + return; + } + + #[cfg(not(any(target_arch = "aarch64", target_arch = "x86_64")))] + { + for j in 0..n { + let g = gate_up[2 * j]; + let u = gate_up[2 * j + 1]; + out[j] = g / (1.0 + (-g).exp()) * u; + } + } +} + +/// INT8 fused QKV matvec for single-token decode +#[allow(clippy::too_many_arguments)] +pub fn linear_nobias_int8_qkv( + q: &mut [f32], + k: &mut [f32], + v: &mut [f32], + x: &[f32], + wq_int8: &[i8], + wq_scales: &[f32], + wk_int8: &[i8], + wk_scales: &[f32], + wv_int8: &[i8], + wv_scales: &[f32], + in_dim: usize, + q_dim: usize, + kv_dim: usize, +) { + let _pg = ProfileGuard::new(&PROF.bf16_matvec); + let (x_int8, x_scale) = quantize_f32_to_int8(x); + let n_threads = get_num_threads(); + if n_threads <= 1 { + int8_matvec_arch( + q, + x_int8.as_ptr(), + x_scale, + wq_int8.as_ptr(), + wq_scales, + None, + in_dim, + q_dim, + ); + int8_matvec_arch( + k, + x_int8.as_ptr(), + x_scale, + wk_int8.as_ptr(), + wk_scales, + None, + in_dim, + kv_dim, + ); + int8_matvec_arch( + v, + x_int8.as_ptr(), + x_scale, + wv_int8.as_ptr(), + wv_scales, + None, + in_dim, + kv_dim, + ); + return; + } + + let total_dim = q_dim + 2 * kv_dim; + let q_ptr = q.as_mut_ptr() as usize; + let k_ptr = k.as_mut_ptr() as usize; + let v_ptr = v.as_mut_ptr() as usize; + let x_int8_ptr = x_int8.as_ptr() as usize; + let wq_ptr = wq_int8.as_ptr() as usize; + let wk_ptr = wk_int8.as_ptr() as usize; + let wv_ptr = wv_int8.as_ptr() as usize; + let wq_scales_ptr = wq_scales.as_ptr() as usize; + let wk_scales_ptr = wk_scales.as_ptr() as usize; + let wv_scales_ptr = wv_scales.as_ptr() as usize; + + parallel_for(|tid, nt| { + let chunk = total_dim.div_ceil(nt); + let start = tid * chunk; + let end = (start + chunk).min(total_dim); + if start >= end { + return; + } + + let q_end = q_dim; + let k_end = q_end + kv_dim; + + if start < q_end { + let s = start; + let e = end.min(q_end); + if s < e { + let y_local = + unsafe { std::slice::from_raw_parts_mut((q_ptr as *mut f32).add(s), e - s) }; + let w_local = unsafe { (wq_ptr as *const i8).add(s * in_dim) }; + let scales_local = unsafe { + std::slice::from_raw_parts((wq_scales_ptr as *const f32).add(s), e - s) + }; + int8_matvec_arch( + y_local, + x_int8_ptr as *const i8, + x_scale, + w_local, + scales_local, + None, + in_dim, + e - s, + ); + } + } + if start < k_end && end > q_end { + let s = start.max(q_end) - q_end; + let e = end.min(k_end) - q_end; + if s < e { + let y_local = + unsafe { std::slice::from_raw_parts_mut((k_ptr as *mut f32).add(s), e - s) }; + let w_local = unsafe { (wk_ptr as *const i8).add(s * in_dim) }; + let scales_local = unsafe { + std::slice::from_raw_parts((wk_scales_ptr as *const f32).add(s), e - s) + }; + int8_matvec_arch( + y_local, + x_int8_ptr as *const i8, + x_scale, + w_local, + scales_local, + None, + in_dim, + e - s, + ); + } + } + if end > k_end { + let s = start.max(k_end) - k_end; + let e = end - k_end; + if s < e { + let y_local = + unsafe { std::slice::from_raw_parts_mut((v_ptr as *mut f32).add(s), e - s) }; + let w_local = unsafe { (wv_ptr as *const i8).add(s * in_dim) }; + let scales_local = unsafe { + std::slice::from_raw_parts((wv_scales_ptr as *const f32).add(s), e - s) + }; + int8_matvec_arch( + y_local, + x_int8_ptr as *const i8, + x_scale, + w_local, + scales_local, + None, + in_dim, + e - s, + ); + } + } + }); +} + +/// INT8 fused gate_up + SwiGLU +pub fn linear_nobias_int8_swiglu( + ffn_out: &mut [f32], + x: &[f32], + w_int8: &[i8], + w_scales: &[f32], + in_dim: usize, + intermediate: usize, +) { + let _pg = ProfileGuard::new(&PROF.bf16_matvec); + let (x_int8, x_scale) = quantize_f32_to_int8(x); + let n_threads = get_num_threads(); + if n_threads <= 1 || intermediate <= 1 { + let mut gate_buf = vec![0.0f32; 2 * intermediate]; + int8_matvec_arch( + &mut gate_buf, + x_int8.as_ptr(), + x_scale, + w_int8.as_ptr(), + w_scales, + None, + in_dim, + 2 * intermediate, + ); + int8_swiglu_arch(ffn_out, &gate_buf, intermediate); + return; + } + + let x_int8_ptr = x_int8.as_ptr() as usize; + let w_int8_ptr = w_int8.as_ptr() as usize; + let w_scales_ptr = w_scales.as_ptr() as usize; + let ffn_ptr = ffn_out.as_mut_ptr() as usize; + + parallel_for(|tid, nt| { + let chunk = intermediate.div_ceil(nt); + let start = tid * chunk; + let end = (start + chunk).min(intermediate); + if start >= end { + return; + } + let n_rows = end - start; + + let w_local = unsafe { (w_int8_ptr as *const i8).add(2 * start * in_dim) }; + let w_scales_local = unsafe { + std::slice::from_raw_parts((w_scales_ptr as *const f32).add(2 * start), 2 * n_rows) + }; + + let mut gate_up_local = vec![0.0f32; 2 * n_rows]; + int8_matvec_arch( + &mut gate_up_local, + x_int8_ptr as *const i8, + x_scale, + w_local, + w_scales_local, + None, + in_dim, + 2 * n_rows, + ); + + let ffn_local = + unsafe { std::slice::from_raw_parts_mut((ffn_ptr as *mut f32).add(start), n_rows) }; + int8_swiglu_arch(ffn_local, &gate_up_local, n_rows); + }); +} + +/// INT8 matvec with fused residual add: y += W_int8 @ x (y acts as bias) +pub fn linear_nobias_int8_addto( + y: &mut [f32], + x: &[f32], + w_int8: &[i8], + w_scales: &[f32], + in_dim: usize, + out_dim: usize, +) { + let _pg = ProfileGuard::new(&PROF.bf16_matvec); + let bias = unsafe { std::slice::from_raw_parts(y.as_ptr(), out_dim) }; + int8_matvec_threaded(y, x, w_int8, w_scales, Some(bias), in_dim, out_dim); +} + +pub fn matmul_t_bf16(c: &mut [f32], a: &[f32], b_bf16: *const u16, m: usize, k: usize, n: usize) { + if m == 1 { + bf16_matvec_threaded(c, a, b_bf16, None, k, n); + } else { + let b_f32 = bf16_to_f32_view(b_bf16, n * k); + matmul_t(c, a, &b_f32, m, k, n); + } +} + +// ======================================================================== +// 2D Convolution (im2col + BLAS sgemm) +// ======================================================================== + +#[allow(clippy::too_many_arguments)] +fn im2col( + input: &[f32], + cols: &mut [f32], + c_in: usize, + h_in: usize, + w_in: usize, + kh: usize, + kw: usize, + stride: usize, + padding: usize, + h_out: usize, + w_out: usize, +) { + let col_len = h_out * w_out; + for ic in 0..c_in { + for ki in 0..kh { + for kj in 0..kw { + let col_row = (ic * kh + ki) * kw + kj; + for oh in 0..h_out { + let ih = oh * stride + ki; + let ih = ih as isize - padding as isize; + for ow in 0..w_out { + let iw = ow * stride + kj; + let iw = iw as isize - padding as isize; + let val = + if ih >= 0 && (ih as usize) < h_in && iw >= 0 && (iw as usize) < w_in { + input[ic * h_in * w_in + ih as usize * w_in + iw as usize] + } else { + 0.0 + }; + cols[col_row * col_len + oh * w_out + ow] = val; + } + } + } + } + } +} + +#[allow(clippy::too_many_arguments)] +pub fn conv2d( + out: &mut [f32], + input: &[f32], + weight: &[f32], + bias: Option<&[f32]>, + c_in: usize, + c_out: usize, + h_in: usize, + w_in: usize, + kh: usize, + kw: usize, + stride: usize, + padding: usize, +) { + let _pg = ProfileGuard::new(&PROF.conv2d_op); + let h_out = (h_in + 2 * padding - kh) / stride + 1; + let w_out = (w_in + 2 * padding - kw) / stride + 1; + let patch_size = c_in * kh * kw; + let spatial_out = h_out * w_out; + + let mut cols = vec![0.0f32; patch_size * spatial_out]; + + // Thread im2col across col_rows (each row is independent) + let n_threads = get_num_threads(); + if n_threads > 1 && patch_size >= 16 { + let input_ptr = input.as_ptr() as usize; + let cols_ptr = cols.as_mut_ptr() as usize; + parallel_for(|tid, nt| { + let chunk = patch_size.div_ceil(nt); + let start = tid * chunk; + let end = (start + chunk).min(patch_size); + if start >= end { + return; + } + for col_row in start..end { + let ic = col_row / (kh * kw); + let rem = col_row % (kh * kw); + let ki = rem / kw; + let kj = rem % kw; + for oh in 0..h_out { + let ih = (oh * stride + ki) as isize - padding as isize; + for ow in 0..w_out { + let iw = (ow * stride + kj) as isize - padding as isize; + let val = + if ih >= 0 && (ih as usize) < h_in && iw >= 0 && (iw as usize) < w_in { + unsafe { + *(input_ptr as *const f32) + .add(ic * h_in * w_in + ih as usize * w_in + iw as usize) + } + } else { + 0.0 + }; + unsafe { + *(cols_ptr as *mut f32).add(col_row * spatial_out + oh * w_out + ow) = + val; + } + } + } + } + }); + } else { + im2col( + input, &mut cols, c_in, h_in, w_in, kh, kw, stride, padding, h_out, w_out, + ); + } + + // GEMM: weight[c_out, patch_size] @ cols[patch_size, spatial_out] = out[c_out, spatial_out] + #[cfg(feature = "blas")] + unsafe { + cblas_sgemm( + CBLAS_ROW_MAJOR, + CBLAS_NO_TRANS, + CBLAS_NO_TRANS, + c_out as i32, + spatial_out as i32, + patch_size as i32, + 1.0, + weight.as_ptr(), + patch_size as i32, + cols.as_ptr(), + spatial_out as i32, + 0.0, + out.as_mut_ptr(), + spatial_out as i32, + ); + } + + #[cfg(not(feature = "blas"))] + { + for oc in 0..c_out { + for s in 0..spatial_out { + let mut sum = 0.0f32; + for p in 0..patch_size { + sum += weight[oc * patch_size + p] * cols[p * spatial_out + s]; + } + out[oc * spatial_out + s] = sum; + } + } + } + + if let Some(bias) = bias { + for oc in 0..c_out { + let b = bias[oc]; + for s in 0..spatial_out { + out[oc * spatial_out + s] += b; + } + } + } +} + +// ======================================================================== +// Normalization +// ======================================================================== + +pub fn layer_norm( + out: &mut [f32], + x: &[f32], + weight: &[f32], + bias: &[f32], + seq_len: usize, + hidden: usize, + eps: f32, +) { + let _pg = ProfileGuard::new(&PROF.layer_norm); + for s in 0..seq_len { + let x_row = &x[s * hidden..(s + 1) * hidden]; + let out_row = &mut out[s * hidden..(s + 1) * hidden]; + + #[cfg(target_arch = "aarch64")] + { + unsafe { + neon::layer_norm_row(out_row, x_row, weight, bias, hidden, eps); + } + continue; + } + + #[cfg(target_arch = "x86_64")] + { + unsafe { + avx::layer_norm_row(out_row, x_row, weight, bias, hidden, eps); + } + continue; + } + + #[cfg(not(any(target_arch = "aarch64", target_arch = "x86_64")))] + { + let mean: f32 = x_row.iter().sum::() / hidden as f32; + + let var: f32 = x_row + .iter() + .map(|&v| { + let d = v - mean; + d * d + }) + .sum::() + / hidden as f32; + + let inv_std = 1.0 / (var + eps).sqrt(); + + for i in 0..hidden { + out_row[i] = (x_row[i] - mean) * inv_std * weight[i] + bias[i]; + } + } + } +} + +pub fn rms_norm( + out: &mut [f32], + x: &[f32], + weight: &[f32], + seq_len: usize, + hidden: usize, + eps: f32, +) { + let _pg = ProfileGuard::new(&PROF.rms_norm); + for s in 0..seq_len { + let x_row = &x[s * hidden..(s + 1) * hidden]; + let out_row = &mut out[s * hidden..(s + 1) * hidden]; + + #[cfg(target_arch = "aarch64")] + { + unsafe { + neon::rms_norm_row(out_row, x_row, weight, hidden, eps); + } + continue; + } + + #[cfg(target_arch = "x86_64")] + { + unsafe { + avx::rms_norm_row(out_row, x_row, weight, hidden, eps); + } + continue; + } + + #[cfg(not(any(target_arch = "aarch64", target_arch = "x86_64")))] + { + let sum_sq: f32 = x_row.iter().map(|&v| v * v).sum(); + let rms_inv = 1.0 / (sum_sq / hidden as f32 + eps).sqrt(); + for i in 0..hidden { + out_row[i] = x_row[i] * rms_inv * weight[i]; + } + } + } +} + +pub fn rms_norm_per_head( + x: &mut [f32], + weight: &[f32], + seq_len: usize, + n_heads: usize, + head_dim: usize, + eps: f32, +) { + let hidden = n_heads * head_dim; + for s in 0..seq_len { + for h in 0..n_heads { + let off = s * hidden + h * head_dim; + + #[cfg(target_arch = "aarch64")] + { + let vec = &mut x[off..off + head_dim]; + unsafe { + neon::rms_norm_inplace(vec, weight, head_dim, eps); + } + continue; + } + + #[cfg(not(target_arch = "aarch64"))] + { + let vec = &mut x[off..off + head_dim]; + let sum_sq: f32 = vec.iter().map(|&v| v * v).sum(); + let rms_inv = 1.0 / (sum_sq / head_dim as f32 + eps).sqrt(); + for d in 0..head_dim { + vec[d] = vec[d] * rms_inv * weight[d]; + } + } + } + } +} + +// ======================================================================== +// Activation Functions +// ======================================================================== + +pub fn silu(x: &mut [f32], n: usize) { + for val in x.iter_mut().take(n) { + *val = *val / (1.0 + (-*val).exp()); + } +} + +pub fn gelu(x: &mut [f32], n: usize) { + let _pg = ProfileGuard::new(&PROF.gelu); + let n_threads = get_num_threads(); + // Thread GELU for large buffers (encoder FFN: ~320K floats) + if n_threads > 1 && n > 4096 { + let x_ptr = x.as_mut_ptr() as usize; + parallel_for(|tid, nt| { + let chunk = n.div_ceil(nt); + let start = tid * chunk; + let end = (start + chunk).min(n); + if start >= end { + return; + } + let x_local = unsafe { + std::slice::from_raw_parts_mut((x_ptr as *mut f32).add(start), end - start) + }; + #[cfg(target_arch = "aarch64")] + unsafe { + neon::gelu_inplace(x_local, end - start); + } + #[cfg(target_arch = "x86_64")] + unsafe { + avx::gelu_inplace(x_local, end - start); + } + #[cfg(not(any(target_arch = "aarch64", target_arch = "x86_64")))] + for i in 0..(end - start) { + let val = x_local[i]; + let x3 = val * val * val; + let inner = 0.7978845608028654f32 * (val + 0.044715 * x3); + x_local[i] = 0.5 * val * (1.0 + inner.tanh()); + } + }); + return; + } + #[cfg(target_arch = "aarch64")] + { + unsafe { + neon::gelu_inplace(x, n); + } + } + + #[cfg(target_arch = "x86_64")] + { + unsafe { + avx::gelu_inplace(x, n); + } + } + + #[cfg(not(any(target_arch = "aarch64", target_arch = "x86_64")))] + for i in 0..n { + let val = x[i]; + let x3 = val * val * val; + let inner = 0.7978845608028654f32 * (val + 0.044715 * x3); + x[i] = 0.5 * val * (1.0 + inner.tanh()); + } +} + +pub fn swiglu_multiply(out: &mut [f32], gate_up: &[f32], seq_len: usize, intermediate: usize) { + let _pg = ProfileGuard::new(&PROF.swiglu); + let total = seq_len * intermediate; + let n_threads = get_num_threads(); + + // Thread SwiGLU for large prefill buffers + if n_threads > 1 && total > 4096 { + let out_ptr = out.as_mut_ptr() as usize; + let gu_ptr = gate_up.as_ptr() as usize; + parallel_for(|tid, nt| { + let chunk = seq_len.div_ceil(nt); + let start = tid * chunk; + let end = (start + chunk).min(seq_len); + if start >= end { + return; + } + for s in start..end { + let gu = unsafe { + std::slice::from_raw_parts( + (gu_ptr as *const f32).add(s * 2 * intermediate), + 2 * intermediate, + ) + }; + let o = unsafe { + std::slice::from_raw_parts_mut( + (out_ptr as *mut f32).add(s * intermediate), + intermediate, + ) + }; + #[cfg(target_arch = "aarch64")] + { + unsafe { + neon::swiglu_interleaved(o, gu, intermediate); + } + continue; + } + #[cfg(target_arch = "x86_64")] + { + unsafe { + avx::swiglu_interleaved(o, gu, intermediate); + } + continue; + } + #[cfg(not(any(target_arch = "aarch64", target_arch = "x86_64")))] + for j in 0..intermediate { + let g = gu[2 * j]; + let u = gu[2 * j + 1]; + o[j] = g / (1.0 + (-g).exp()) * u; + } + } + }); + return; + } + + for s in 0..seq_len { + let gu = &gate_up[s * 2 * intermediate..s * 2 * intermediate + 2 * intermediate]; + let o = &mut out[s * intermediate..(s + 1) * intermediate]; + + #[cfg(target_arch = "aarch64")] + { + unsafe { + neon::swiglu_interleaved(o, gu, intermediate); + } + continue; + } + + #[cfg(target_arch = "x86_64")] + { + unsafe { + avx::swiglu_interleaved(o, gu, intermediate); + } + continue; + } + + #[cfg(not(any(target_arch = "aarch64", target_arch = "x86_64")))] + for j in 0..intermediate { + let g = gu[2 * j]; + let u = gu[2 * j + 1]; + let g_silu = g / (1.0 + (-g).exp()); + o[j] = g_silu * u; + } + } +} + +pub fn softmax(x: &mut [f32], rows: usize, cols: usize) { + for r in 0..rows { + let row = &mut x[r * cols..(r + 1) * cols]; + let max_val = row.iter().cloned().fold(f32::NEG_INFINITY, f32::max); + for val in row.iter_mut().take(cols) { + *val -= max_val; + } + + #[cfg(all(feature = "vdsp", target_vendor = "apple"))] + { + let n = cols as i32; + unsafe { + vvexpf(row.as_mut_ptr(), row.as_ptr(), &n); + } + } + #[cfg(not(all(feature = "vdsp", target_vendor = "apple")))] + { + #[cfg(target_arch = "aarch64")] + { + unsafe { + neon::exp_inplace(row); + } + } + + #[cfg(target_arch = "x86_64")] + { + unsafe { + avx::exp_inplace(row); + } + } + + #[cfg(not(any(target_arch = "aarch64", target_arch = "x86_64")))] + for c in 0..cols { + row[c] = row[c].exp(); + } + } + + let mut sum = 0.0f32; + for val in row.iter().take(cols) { + sum += val; + } + let inv_sum = 1.0 / sum; + for val in row.iter_mut().take(cols) { + *val *= inv_sum; + } + } +} + +// ======================================================================== +// Attention Operations +// ======================================================================== + +#[allow(clippy::too_many_arguments)] +fn bidirectional_attention_heads( + out: &mut [f32], + q: &[f32], + k: &[f32], + v: &[f32], + n_heads: usize, + head_dim: usize, + scale: f32, + window_starts: &[i32], + n_windows: usize, + head_start: usize, + head_end: usize, +) { + let hidden = n_heads * head_dim; + + for h in head_start..head_end { + for w in 0..n_windows { + let ws = window_starts[w] as usize; + let we = window_starts[w + 1] as usize; + + for i in ws..we { + let q_off = i * hidden + h * head_dim; + let q_row = &q[q_off..q_off + head_dim]; + let o_row = + &mut out[i * hidden + h * head_dim..i * hidden + h * head_dim + head_dim]; + + let mut max_score = -1e30f32; + let mut sum_exp = 0.0f32; + for val in o_row.iter_mut().take(head_dim) { + *val = 0.0; + } + + for j in ws..we { + let k_off = j * hidden + h * head_dim; + let v_off = j * hidden + h * head_dim; + let k_row = &k[k_off..k_off + head_dim]; + let v_row = &v[v_off..v_off + head_dim]; + + let score = dot_f32(q_row, k_row, head_dim) * scale; + + if score > max_score { + let correction = (max_score - score).exp(); + sum_exp = sum_exp * correction + 1.0; + vec_scale_add(o_row, v_row, correction, head_dim); + max_score = score; + } else { + let wt = (score - max_score).exp(); + sum_exp += wt; + vec_axpy_inplace(o_row, v_row, wt, head_dim); + } + } + + if sum_exp > 0.0 { + let inv_sum = 1.0 / sum_exp; + vec_scale_inplace(o_row, inv_sum, head_dim); + } + } + } + } +} + +#[allow(clippy::too_many_arguments)] +pub fn bidirectional_attention( + out: &mut [f32], + q: &[f32], + k: &[f32], + v: &[f32], + seq: usize, + n_heads: usize, + head_dim: usize, + scale: f32, + window_starts: &[i32], + n_windows: usize, +) { + let _pg = ProfileGuard::new(&PROF.attention_bidir); + let n_threads = get_num_threads(); + let hidden = n_heads * head_dim; + + if n_threads > 1 && n_heads >= 2 { + let out_ptr = out.as_mut_ptr() as usize; + let q_ptr = q.as_ptr() as usize; + let k_ptr = k.as_ptr() as usize; + let v_ptr = v.as_ptr() as usize; + let ws_ptr = window_starts.as_ptr() as usize; + + parallel_for(|tid, nt| { + let chunk = n_heads.div_ceil(nt); + let h0 = tid * chunk; + let h1 = (h0 + chunk).min(n_heads); + if h0 >= h1 { + return; + } + + let out_local = + unsafe { std::slice::from_raw_parts_mut(out_ptr as *mut f32, seq * hidden) }; + let q_local = unsafe { std::slice::from_raw_parts(q_ptr as *const f32, seq * hidden) }; + let k_local = unsafe { std::slice::from_raw_parts(k_ptr as *const f32, seq * hidden) }; + let v_local = unsafe { std::slice::from_raw_parts(v_ptr as *const f32, seq * hidden) }; + let ws_local = + unsafe { std::slice::from_raw_parts(ws_ptr as *const i32, n_windows + 1) }; + + bidirectional_attention_heads( + out_local, q_local, k_local, v_local, n_heads, head_dim, scale, ws_local, + n_windows, h0, h1, + ); + }); + return; + } + + bidirectional_attention_heads( + out, + q, + k, + v, + n_heads, + head_dim, + scale, + window_starts, + n_windows, + 0, + n_heads, + ); +} + +/// Two-pass causal attention using BLAS sgemm with head-contiguous KV cache. +/// K/V layout: `[head][pos][head_dim]` — each head's data is contiguous across positions. +/// +/// Single-token (seq_q=1): online softmax with NEON dot products — avoids BLAS overhead, +/// scores allocation, and fuses all 3 passes into a single scan over KV positions. +/// +/// Multi-token (seq_q>1): for long sequences, use per-head batched GEMMs; +/// otherwise fall back to the simpler row-wise 3-pass BLAS path. +#[cfg(feature = "blas")] +#[allow(clippy::too_many_arguments)] +fn causal_attention_heads( + out: &mut [f32], + q: &[f32], + k_base: *const f32, + v_base: *const f32, + head_stride: usize, + seq_q: usize, + seq_k: usize, + n_heads: usize, + n_kv_heads: usize, + head_dim: usize, + scale: f32, + q_offset: usize, + head_start: usize, + head_end: usize, +) { + let heads_per_kv = n_heads / n_kv_heads; + let q_hidden = n_heads * head_dim; + const BATCHED_CAUSAL_ATTENTION_THRESHOLD: usize = 256; + + // Single-token path: online softmax without allocation or BLAS + if seq_q == 1 { + for h in head_start..head_end { + let kv_h = h / heads_per_kv; + let k_head = unsafe { k_base.add(kv_h * head_stride) }; + let v_head = unsafe { v_base.add(kv_h * head_stride) }; + let q_off = h * head_dim; + let o_row = &mut out[q_off..q_off + head_dim]; + let k_end = (q_offset + 1).min(seq_k); + + if k_end == 0 { + for val in o_row.iter_mut().take(head_dim) { + *val = 0.0; + } + continue; + } + + let q_row = &q[q_off..q_off + head_dim]; + + // Online softmax: single pass over KV positions + let mut max_score = -1e30f32; + let mut sum_exp = 0.0f32; + for val in o_row.iter_mut().take(head_dim) { + *val = 0.0; + } + + for j in 0..k_end { + let k_row = + unsafe { std::slice::from_raw_parts(k_head.add(j * head_dim), head_dim) }; + let v_row = + unsafe { std::slice::from_raw_parts(v_head.add(j * head_dim), head_dim) }; + + let score = dot_f32(q_row, k_row, head_dim) * scale; + + if score > max_score { + let correction = (max_score - score).exp(); + sum_exp = sum_exp * correction + 1.0; + vec_scale_add(o_row, v_row, correction, head_dim); + max_score = score; + } else { + let wt = (score - max_score).exp(); + sum_exp += wt; + vec_axpy_inplace(o_row, v_row, wt, head_dim); + } + } + + if sum_exp > 0.0 { + let inv_sum = 1.0 / sum_exp; + vec_scale_inplace(o_row, inv_sum, head_dim); + } + } + return; + } + + if seq_q >= BATCHED_CAUSAL_ATTENTION_THRESHOLD { + let mut q_head = vec![0.0f32; seq_q * head_dim]; + let mut scores = vec![0.0f32; seq_q * seq_k]; + let mut out_head = vec![0.0f32; seq_q * head_dim]; + + for h in head_start..head_end { + let kv_h = h / heads_per_kv; + let k_head = unsafe { k_base.add(kv_h * head_stride) }; + let v_head = unsafe { v_base.add(kv_h * head_stride) }; + let head_offset = h * head_dim; + + for i in 0..seq_q { + let src_off = i * q_hidden + head_offset; + let dst_off = i * head_dim; + q_head[dst_off..dst_off + head_dim] + .copy_from_slice(&q[src_off..src_off + head_dim]); + } + + unsafe { + cblas_sgemm( + CBLAS_ROW_MAJOR, + CBLAS_NO_TRANS, + CBLAS_TRANS, + seq_q as i32, + seq_k as i32, + head_dim as i32, + scale, + q_head.as_ptr(), + head_dim as i32, + k_head, + head_dim as i32, + 0.0, + scores.as_mut_ptr(), + seq_k as i32, + ); + } + + for i in 0..seq_q { + let row = &mut scores[i * seq_k..(i + 1) * seq_k]; + let valid = (q_offset + i + 1).min(seq_k); + if valid == 0 { + row.fill(0.0); + continue; + } + + let mut max_s = row[0]; + for &score in row.iter().take(valid).skip(1) { + if score > max_s { + max_s = score; + } + } + for score in row.iter_mut().take(valid) { + *score = (*score - max_s).exp(); + } + + let mut sum_exp = 0.0f32; + for &score in row.iter().take(valid) { + sum_exp += score; + } + if sum_exp > 0.0 { + let inv = 1.0 / sum_exp; + for score in row.iter_mut().take(valid) { + *score *= inv; + } + } + for score in row.iter_mut().skip(valid) { + *score = 0.0; + } + } + + unsafe { + cblas_sgemm( + CBLAS_ROW_MAJOR, + CBLAS_NO_TRANS, + CBLAS_NO_TRANS, + seq_q as i32, + head_dim as i32, + seq_k as i32, + 1.0, + scores.as_ptr(), + seq_k as i32, + v_head, + head_dim as i32, + 0.0, + out_head.as_mut_ptr(), + head_dim as i32, + ); + } + + for i in 0..seq_q { + let src_off = i * head_dim; + let dst_off = i * q_hidden + head_offset; + out[dst_off..dst_off + head_dim] + .copy_from_slice(&out_head[src_off..src_off + head_dim]); + } + } + return; + } + + // Multi-token path: row-wise 3-pass BLAS sgemm + let mut scores = vec![0.0f32; seq_k]; + + for h in head_start..head_end { + let kv_h = h / heads_per_kv; + let k_head = unsafe { k_base.add(kv_h * head_stride) }; + let v_head = unsafe { v_base.add(kv_h * head_stride) }; + + for i in 0..seq_q { + let q_off = i * q_hidden + h * head_dim; + let o_off = i * q_hidden + h * head_dim; + let o_row = &mut out[o_off..o_off + head_dim]; + let global_pos = q_offset + i; + let k_end = (global_pos + 1).min(seq_k); + + if k_end == 0 { + for val in o_row.iter_mut().take(head_dim) { + *val = 0.0; + } + continue; + } + + // Pass 1: scores = K_h @ q_h + unsafe { + cblas_sgemm( + CBLAS_ROW_MAJOR, + CBLAS_NO_TRANS, + CBLAS_NO_TRANS, + k_end as i32, + 1, + head_dim as i32, + scale, + k_head, + head_dim as i32, + q.as_ptr().add(q_off), + 1, + 0.0, + scores.as_mut_ptr(), + 1, + ); + } + + // Pass 2: Softmax + let mut max_s = scores[0]; + for j in 1..k_end { + if scores[j] > max_s { + max_s = scores[j]; + } + } + for j in 0..k_end { + scores[j] -= max_s; + } + + #[cfg(all(feature = "vdsp", target_vendor = "apple"))] + { + let n = k_end as i32; + unsafe { + vvexpf(scores.as_mut_ptr(), scores.as_ptr(), &n); + } + } + #[cfg(not(all(feature = "vdsp", target_vendor = "apple")))] + { + for j in 0..k_end { + scores[j] = scores[j].exp(); + } + } + + let mut sum_exp = 0.0f32; + for j in 0..k_end { + sum_exp += scores[j]; + } + if sum_exp > 0.0 { + let inv = 1.0 / sum_exp; + for j in 0..k_end { + scores[j] *= inv; + } + } + + // Pass 3: out = V_h^T @ softmax_scores + unsafe { + cblas_sgemm( + CBLAS_ROW_MAJOR, + CBLAS_TRANS, + CBLAS_NO_TRANS, + head_dim as i32, + 1, + k_end as i32, + 1.0, + v_head, + head_dim as i32, + scores.as_ptr(), + 1, + 0.0, + o_row.as_mut_ptr(), + 1, + ); + } + } + } +} + +/// Fallback: online softmax causal attention (no BLAS), head-contiguous KV layout. +#[cfg(not(feature = "blas"))] +#[allow(clippy::too_many_arguments)] +fn causal_attention_heads( + out: &mut [f32], + q: &[f32], + k_base: *const f32, + v_base: *const f32, + head_stride: usize, + seq_q: usize, + seq_k: usize, + n_heads: usize, + n_kv_heads: usize, + head_dim: usize, + scale: f32, + q_offset: usize, + head_start: usize, + head_end: usize, +) { + let heads_per_kv = n_heads / n_kv_heads; + let q_hidden = n_heads * head_dim; + + for h in head_start..head_end { + let kv_h = h / heads_per_kv; + + for i in 0..seq_q { + let q_off = i * q_hidden + h * head_dim; + let q_row = &q[q_off..q_off + head_dim]; + let o_row = + &mut out[i * q_hidden + h * head_dim..i * q_hidden + h * head_dim + head_dim]; + let global_pos = q_offset + i; + let k_end = (global_pos + 1).min(seq_k); + + let mut max_score = -1e30f32; + let mut sum_exp = 0.0f32; + for val in o_row.iter_mut().take(head_dim) { + *val = 0.0; + } + + for j in 0..k_end { + let k_row = unsafe { + std::slice::from_raw_parts( + k_base.add(kv_h * head_stride + j * head_dim), + head_dim, + ) + }; + let v_row = unsafe { + std::slice::from_raw_parts( + v_base.add(kv_h * head_stride + j * head_dim), + head_dim, + ) + }; + + let score = dot_f32(q_row, k_row, head_dim) * scale; + + if score > max_score { + let correction = (max_score - score).exp(); + sum_exp = sum_exp * correction + 1.0; + vec_scale_add(o_row, v_row, correction, head_dim); + max_score = score; + } else { + let wt = (score - max_score).exp(); + sum_exp += wt; + vec_axpy_inplace(o_row, v_row, wt, head_dim); + } + } + + if sum_exp > 0.0 { + let inv_sum = 1.0 / sum_exp; + vec_scale_inplace(o_row, inv_sum, head_dim); + } + } + } +} + +#[allow(clippy::too_many_arguments)] +pub fn causal_attention( + out: &mut [f32], + q: &[f32], + k_base: *const f32, + v_base: *const f32, + head_stride: usize, + seq_q: usize, + seq_k: usize, + n_heads: usize, + n_kv_heads: usize, + head_dim: usize, + scale: f32, + q_offset: usize, +) { + let _pg = ProfileGuard::new(&PROF.attention_causal); + let n_threads = get_num_threads(); + if n_threads > 1 && n_heads >= 2 { + let out_ptr = out.as_mut_ptr() as usize; + let q_ptr = q.as_ptr() as usize; + let k_ptr = k_base as usize; + let v_ptr = v_base as usize; + let q_hidden = n_heads * head_dim; + + parallel_for(|tid, nt| { + let chunk = n_heads.div_ceil(nt); + let h0 = tid * chunk; + let h1 = (h0 + chunk).min(n_heads); + if h0 >= h1 { + return; + } + + let out_local = + unsafe { std::slice::from_raw_parts_mut(out_ptr as *mut f32, seq_q * q_hidden) }; + let q_local = + unsafe { std::slice::from_raw_parts(q_ptr as *const f32, seq_q * q_hidden) }; + + causal_attention_heads( + out_local, + q_local, + k_ptr as *const f32, + v_ptr as *const f32, + head_stride, + seq_q, + seq_k, + n_heads, + n_kv_heads, + head_dim, + scale, + q_offset, + h0, + h1, + ); + }); + return; + } + + causal_attention_heads( + out, + q, + k_base, + v_base, + head_stride, + seq_q, + seq_k, + n_heads, + n_kv_heads, + head_dim, + scale, + q_offset, + 0, + n_heads, + ); +} + +// ======================================================================== +// Position Embeddings +// ======================================================================== + +pub fn sinusoidal_pe(pe: &mut [f32], n_pos: usize, d_model: usize) { + let half = d_model / 2; + let log_timescale = (10000.0f32).ln() / (half - 1) as f32; + + for p in 0..n_pos { + let row = &mut pe[p * d_model..(p + 1) * d_model]; + for d in 0..half { + let inv_timescale = (-(d as f32) * log_timescale).exp(); + let angle = p as f32 * inv_timescale; + row[d] = angle.sin(); + row[half + d] = angle.cos(); + } + } +} + +pub fn compute_rope_neox( + cos_out: &mut [f32], + sin_out: &mut [f32], + positions: &[i32], + seq: usize, + head_dim: usize, + theta: f32, +) { + let half = head_dim / 2; + + for s in 0..seq { + let pos = positions[s] as f32; + for d in 0..half { + let freq = 1.0 / theta.powf((2 * d) as f32 / head_dim as f32); + let angle = pos * freq; + let c = angle.cos(); + let sn = angle.sin(); + cos_out[s * head_dim + d] = c; + cos_out[s * head_dim + half + d] = c; + sin_out[s * head_dim + d] = sn; + sin_out[s * head_dim + half + d] = sn; + } + } +} + +pub fn apply_rope_neox( + x: &mut [f32], + cos_vals: &[f32], + sin_vals: &[f32], + seq: usize, + n_heads: usize, + head_dim: usize, +) { + let _pg = ProfileGuard::new(&PROF.rope); + let half = head_dim / 2; + let hidden = n_heads * head_dim; + + for s in 0..seq { + let c = &cos_vals[s * head_dim..]; + let sn = &sin_vals[s * head_dim..]; + + for h in 0..n_heads { + let base = s * hidden + h * head_dim; + let vec = &mut x[base..base + head_dim]; + + #[cfg(target_arch = "aarch64")] + { + let mut d = 0usize; + while d + 4 <= half { + unsafe { + use core::arch::aarch64::*; + let x1 = vld1q_f32(vec.as_ptr().add(d)); + let x2 = vld1q_f32(vec.as_ptr().add(half + d)); + let cv = vld1q_f32(c.as_ptr().add(d)); + let sv = vld1q_f32(sn.as_ptr().add(d)); + // vec[d] = x1*cos - x2*sin + let new1 = vfmsq_f32(vmulq_f32(x1, cv), x2, sv); + // vec[half+d] = x2*cos + x1*sin (cos[half+d]==cos[d]) + let new2 = vfmaq_f32(vmulq_f32(x2, cv), x1, sv); + vst1q_f32(vec.as_mut_ptr().add(d), new1); + vst1q_f32(vec.as_mut_ptr().add(half + d), new2); + } + d += 4; + } + while d < half { + let x1 = vec[d]; + let x2 = vec[half + d]; + vec[d] = x1 * c[d] - x2 * sn[d]; + vec[half + d] = x2 * c[d] + x1 * sn[d]; + d += 1; + } + } + + #[cfg(not(target_arch = "aarch64"))] + { + for d in 0..half { + let x1 = vec[d]; + let x2 = vec[half + d]; + vec[d] = x1 * c[d] + (-x2) * sn[d]; + vec[half + d] = x2 * c[half + d] + x1 * sn[half + d]; + } + } + } + } +} + +/// Streaming argmax: finds argmax(W_bf16 @ x) without materializing full logits. +/// Quantize x (f32) to int8 with absmax scaling. Returns (x_int8, scale). +pub fn quantize_f32_to_int8(x: &[f32]) -> (Vec, f32) { + let mut max_abs = 0.0f32; + for &v in x { + max_abs = max_abs.max(v.abs()); + } + let scale = if max_abs > 0.0 { max_abs / 127.0 } else { 1.0 }; + let inv_scale = 127.0 / max_abs.max(1e-10); + let int8: Vec = x + .iter() + .map(|&v| (v * inv_scale).round().clamp(-127.0, 127.0) as i8) + .collect(); + (int8, scale) +} + +/// Quantize BF16 weights to INT8 per-row. Returns (int8_data, per_row_scales). +pub fn quantize_bf16_weights_to_int8( + w_bf16: *const u16, + out_dim: usize, + in_dim: usize, +) -> (Vec, Vec) { + #[cfg(target_arch = "aarch64")] + unsafe { + return neon::quantize_bf16_to_int8(w_bf16, out_dim, in_dim); + } + #[cfg(not(target_arch = "aarch64"))] + { + let mut int8_data = vec![0i8; out_dim * in_dim]; + let mut scales = vec![0.0f32; out_dim]; + let src = unsafe { std::slice::from_raw_parts(w_bf16, out_dim * in_dim) }; + for row in 0..out_dim { + let mut max_abs = 0.0f32; + for k in 0..in_dim { + let v = f32::from_bits((src[row * in_dim + k] as u32) << 16).abs(); + if v > max_abs { + max_abs = v; + } + } + let scale = if max_abs > 0.0 { max_abs / 127.0 } else { 1.0 }; + let inv_scale = 127.0 / max_abs.max(1e-10); + scales[row] = scale; + for k in 0..in_dim { + let v = f32::from_bits((src[row * in_dim + k] as u32) << 16); + int8_data[row * in_dim + k] = (v * inv_scale).round().clamp(-127.0, 127.0) as i8; + } + } + (int8_data, scales) + } +} + +/// INT8 threaded argmax: find argmax(x @ W.T) using INT8 quantized weights. +pub fn argmax_matvec_int8( + x: &[f32], + w_int8: &[i8], + w_scales: &[f32], + in_dim: usize, + out_dim: usize, +) -> usize { + let (x_int8, x_scale) = quantize_f32_to_int8(x); + let n_threads = get_num_threads(); + if n_threads <= 1 || out_dim <= 1 { + return int8_argmax_arch( + x_int8.as_ptr(), + x_scale, + w_int8.as_ptr(), + w_scales, + in_dim, + 0, + out_dim, + ) + .0; + } + + let mut best_indices = vec![0usize; n_threads]; + let mut best_vals = vec![f32::NEG_INFINITY; n_threads]; + + let x_int8_ptr = x_int8.as_ptr() as usize; + let w_int8_ptr = w_int8.as_ptr() as usize; + let bi_ptr = best_indices.as_mut_ptr() as usize; + let bv_ptr = best_vals.as_mut_ptr() as usize; + + parallel_for(|tid, nt| { + let chunk = out_dim.div_ceil(nt); + let start = tid * chunk; + let end = (start + chunk).min(out_dim); + if start >= end { + unsafe { + *(bv_ptr as *mut f32).add(tid) = f32::NEG_INFINITY; + *(bi_ptr as *mut usize).add(tid) = 0; + } + return; + } + + let (best, best_val) = int8_argmax_arch( + x_int8_ptr as *const i8, + x_scale, + w_int8_ptr as *const i8, + w_scales, + in_dim, + start, + end, + ); + unsafe { + *(bi_ptr as *mut usize).add(tid) = best; + *(bv_ptr as *mut f32).add(tid) = best_val; + } + }); + + let mut best = best_indices[0]; + let mut best_val = best_vals[0]; + for i in 1..n_threads { + if best_vals[i] > best_val { + best_val = best_vals[i]; + best = best_indices[i]; + } + } + best +} + +pub fn argmax_matvec_bf16(x: &[f32], w_bf16: *const u16, in_dim: usize, out_dim: usize) -> usize { + let n_threads = get_num_threads(); + if n_threads <= 1 { + let (best, _) = argmax_bf16_range(x, w_bf16, in_dim, 0, out_dim); + return best; + } + + let mut best_indices = vec![0usize; n_threads]; + let mut best_vals = vec![-1e30f32; n_threads]; + + let x_ptr = x.as_ptr() as usize; + let w_ptr = w_bf16 as usize; + let bi_ptr = best_indices.as_mut_ptr() as usize; + let bv_ptr = best_vals.as_mut_ptr() as usize; + + parallel_for(|tid, nt| { + let chunk = out_dim.div_ceil(nt); + let start = tid * chunk; + let end = (start + chunk).min(out_dim); + if start >= end { + unsafe { + *(bv_ptr as *mut f32).add(tid) = -1e30; + *(bi_ptr as *mut usize).add(tid) = 0; + } + return; + } + + let x_local = unsafe { std::slice::from_raw_parts(x_ptr as *const f32, in_dim) }; + let (best, best_val) = argmax_bf16_range(x_local, w_ptr as *const u16, in_dim, start, end); + unsafe { + *(bi_ptr as *mut usize).add(tid) = best; + *(bv_ptr as *mut f32).add(tid) = best_val; + } + }); + + let mut best = best_indices[0]; + let mut best_val = best_vals[0]; + for i in 1..n_threads { + if best_vals[i] > best_val { + best_val = best_vals[i]; + best = best_indices[i]; + } + } + best +} diff --git a/vendor/qwenasr/crates/qwen-asr/src/kernels/neon.rs b/vendor/qwenasr/crates/qwen-asr/src/kernels/neon.rs new file mode 100644 index 0000000..45a2101 --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/src/kernels/neon.rs @@ -0,0 +1,1113 @@ +/// ARM NEON implementations of hot kernels. +#[cfg(target_arch = "aarch64")] +use core::arch::aarch64::*; + +/// # Safety +/// Uses NEON intrinsics; caller must ensure slices have equal lengths. +#[cfg(target_arch = "aarch64")] +pub unsafe fn bf16_to_f32_buf(dst: &mut [f32], src: &[u16]) { + let n = src.len(); + let mut i = 0usize; + + while i + 8 <= n { + let raw = vld1q_u16(src.as_ptr().add(i)); + let lo = vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(raw), 16)); + let hi = vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(raw), 16)); + vst1q_f32(dst.as_mut_ptr().add(i), lo); + vst1q_f32(dst.as_mut_ptr().add(i + 4), hi); + i += 8; + } + + while i < n { + dst[i] = f32::from_bits((src[i] as u32) << 16); + i += 1; + } +} + +/// # Safety +/// w_bf16 must point to at least out_dim * in_dim valid bf16 values. +#[cfg(target_arch = "aarch64")] +pub unsafe fn bf16_matvec_fused( + y: &mut [f32], + x: &[f32], + w_bf16: *const u16, + bias: Option<&[f32]>, + in_dim: usize, + out_dim: usize, +) { + let mut o = 0usize; + + // Process 2 output rows at a time + while o + 1 < out_dim { + let w0 = w_bf16.add(o * in_dim); + let w1 = w_bf16.add((o + 1) * in_dim); + let mut s0 = bias.map_or(0.0f32, |b| b[o]); + let mut s1 = bias.map_or(0.0f32, |b| b[o + 1]); + + let mut a0 = vdupq_n_f32(0.0); + let mut a1 = vdupq_n_f32(0.0); + let mut a2 = vdupq_n_f32(0.0); + let mut a3 = vdupq_n_f32(0.0); + let mut b0 = vdupq_n_f32(0.0); + let mut b1 = vdupq_n_f32(0.0); + let mut b2 = vdupq_n_f32(0.0); + let mut b3 = vdupq_n_f32(0.0); + let mut k = 0usize; + + while k + 32 <= in_dim { + let x0 = vld1q_f32(x.as_ptr().add(k)); + let x1 = vld1q_f32(x.as_ptr().add(k + 4)); + let x2 = vld1q_f32(x.as_ptr().add(k + 8)); + let x3 = vld1q_f32(x.as_ptr().add(k + 12)); + let x4 = vld1q_f32(x.as_ptr().add(k + 16)); + let x5 = vld1q_f32(x.as_ptr().add(k + 20)); + let x6 = vld1q_f32(x.as_ptr().add(k + 24)); + let x7 = vld1q_f32(x.as_ptr().add(k + 28)); + + let r0a = vld1q_u16(w0.add(k)); + let r0b = vld1q_u16(w0.add(k + 8)); + let r0c = vld1q_u16(w0.add(k + 16)); + let r0d = vld1q_u16(w0.add(k + 24)); + a0 = vfmaq_f32( + a0, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r0a), 16)), + x0, + ); + a1 = vfmaq_f32( + a1, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r0a), 16)), + x1, + ); + a2 = vfmaq_f32( + a2, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r0b), 16)), + x2, + ); + a3 = vfmaq_f32( + a3, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r0b), 16)), + x3, + ); + a0 = vfmaq_f32( + a0, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r0c), 16)), + x4, + ); + a1 = vfmaq_f32( + a1, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r0c), 16)), + x5, + ); + a2 = vfmaq_f32( + a2, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r0d), 16)), + x6, + ); + a3 = vfmaq_f32( + a3, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r0d), 16)), + x7, + ); + + let r1a = vld1q_u16(w1.add(k)); + let r1b = vld1q_u16(w1.add(k + 8)); + let r1c = vld1q_u16(w1.add(k + 16)); + let r1d = vld1q_u16(w1.add(k + 24)); + b0 = vfmaq_f32( + b0, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r1a), 16)), + x0, + ); + b1 = vfmaq_f32( + b1, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r1a), 16)), + x1, + ); + b2 = vfmaq_f32( + b2, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r1b), 16)), + x2, + ); + b3 = vfmaq_f32( + b3, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r1b), 16)), + x3, + ); + b0 = vfmaq_f32( + b0, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r1c), 16)), + x4, + ); + b1 = vfmaq_f32( + b1, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r1c), 16)), + x5, + ); + b2 = vfmaq_f32( + b2, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r1d), 16)), + x6, + ); + b3 = vfmaq_f32( + b3, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r1d), 16)), + x7, + ); + + k += 32; + } + while k + 8 <= in_dim { + let xv0 = vld1q_f32(x.as_ptr().add(k)); + let xv1 = vld1q_f32(x.as_ptr().add(k + 4)); + let r0 = vld1q_u16(w0.add(k)); + let r1 = vld1q_u16(w1.add(k)); + a0 = vfmaq_f32( + a0, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r0), 16)), + xv0, + ); + a1 = vfmaq_f32( + a1, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r0), 16)), + xv1, + ); + b0 = vfmaq_f32( + b0, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r1), 16)), + xv0, + ); + b1 = vfmaq_f32( + b1, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r1), 16)), + xv1, + ); + k += 8; + } + s0 += vaddvq_f32(vaddq_f32(vaddq_f32(a0, a2), vaddq_f32(a1, a3))); + s1 += vaddvq_f32(vaddq_f32(vaddq_f32(b0, b2), vaddq_f32(b1, b3))); + + while k < in_dim { + let wv0 = f32::from_bits(((*w0.add(k)) as u32) << 16); + let wv1 = f32::from_bits(((*w1.add(k)) as u32) << 16); + s0 += wv0 * x[k]; + s1 += wv1 * x[k]; + k += 1; + } + y[o] = s0; + y[o + 1] = s1; + o += 2; + } + + // Handle remaining odd row + while o < out_dim { + let w_row = w_bf16.add(o * in_dim); + let mut sum = bias.map_or(0.0f32, |b| b[o]); + let mut k = 0usize; + + let mut acc0 = vdupq_n_f32(0.0); + let mut acc1 = vdupq_n_f32(0.0); + while k + 8 <= in_dim { + let bf = vld1q_u16(w_row.add(k)); + acc0 = vfmaq_f32( + acc0, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(bf), 16)), + vld1q_f32(x.as_ptr().add(k)), + ); + acc1 = vfmaq_f32( + acc1, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(bf), 16)), + vld1q_f32(x.as_ptr().add(k + 4)), + ); + k += 8; + } + sum += vaddvq_f32(vaddq_f32(acc0, acc1)); + + while k < in_dim { + let w_val = f32::from_bits(((*w_row.add(k)) as u32) << 16); + sum += w_val * x[k]; + k += 1; + } + y[o] = sum; + o += 1; + } +} + +/// # Safety +/// w_bf16 must point to at least end * in_dim valid bf16 values. +#[cfg(target_arch = "aarch64")] +pub unsafe fn argmax_bf16_range( + x: &[f32], + w_bf16: *const u16, + in_dim: usize, + start: usize, + end: usize, +) -> (usize, f32) { + let mut best = start; + let mut best_val = -1e30f32; + let mut o = start; + + // Process 2 rows at a time + while o + 1 < end { + let w0 = w_bf16.add(o * in_dim); + let w1 = w_bf16.add((o + 1) * in_dim); + let mut a0 = vdupq_n_f32(0.0); + let mut a1 = vdupq_n_f32(0.0); + let mut a2 = vdupq_n_f32(0.0); + let mut a3 = vdupq_n_f32(0.0); + let mut b0 = vdupq_n_f32(0.0); + let mut b1 = vdupq_n_f32(0.0); + let mut b2 = vdupq_n_f32(0.0); + let mut b3 = vdupq_n_f32(0.0); + let mut k = 0usize; + + while k + 32 <= in_dim { + let x0 = vld1q_f32(x.as_ptr().add(k)); + let x1 = vld1q_f32(x.as_ptr().add(k + 4)); + let x2 = vld1q_f32(x.as_ptr().add(k + 8)); + let x3 = vld1q_f32(x.as_ptr().add(k + 12)); + let x4 = vld1q_f32(x.as_ptr().add(k + 16)); + let x5 = vld1q_f32(x.as_ptr().add(k + 20)); + let x6 = vld1q_f32(x.as_ptr().add(k + 24)); + let x7 = vld1q_f32(x.as_ptr().add(k + 28)); + + let r0a = vld1q_u16(w0.add(k)); + let r0b = vld1q_u16(w0.add(k + 8)); + let r0c = vld1q_u16(w0.add(k + 16)); + let r0d = vld1q_u16(w0.add(k + 24)); + a0 = vfmaq_f32( + a0, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r0a), 16)), + x0, + ); + a1 = vfmaq_f32( + a1, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r0a), 16)), + x1, + ); + a2 = vfmaq_f32( + a2, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r0b), 16)), + x2, + ); + a3 = vfmaq_f32( + a3, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r0b), 16)), + x3, + ); + a0 = vfmaq_f32( + a0, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r0c), 16)), + x4, + ); + a1 = vfmaq_f32( + a1, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r0c), 16)), + x5, + ); + a2 = vfmaq_f32( + a2, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r0d), 16)), + x6, + ); + a3 = vfmaq_f32( + a3, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r0d), 16)), + x7, + ); + + let r1a = vld1q_u16(w1.add(k)); + let r1b = vld1q_u16(w1.add(k + 8)); + let r1c = vld1q_u16(w1.add(k + 16)); + let r1d = vld1q_u16(w1.add(k + 24)); + b0 = vfmaq_f32( + b0, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r1a), 16)), + x0, + ); + b1 = vfmaq_f32( + b1, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r1a), 16)), + x1, + ); + b2 = vfmaq_f32( + b2, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r1b), 16)), + x2, + ); + b3 = vfmaq_f32( + b3, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r1b), 16)), + x3, + ); + b0 = vfmaq_f32( + b0, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r1c), 16)), + x4, + ); + b1 = vfmaq_f32( + b1, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r1c), 16)), + x5, + ); + b2 = vfmaq_f32( + b2, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r1d), 16)), + x6, + ); + b3 = vfmaq_f32( + b3, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r1d), 16)), + x7, + ); + + k += 32; + } + + let s0_v = vaddvq_f32(vaddq_f32(vaddq_f32(a0, a2), vaddq_f32(a1, a3))); + let s1_v = vaddvq_f32(vaddq_f32(vaddq_f32(b0, b2), vaddq_f32(b1, b3))); + + let mut s0 = s0_v; + let mut s1 = s1_v; + while k < in_dim { + let wv0 = f32::from_bits(((*w0.add(k)) as u32) << 16); + let wv1 = f32::from_bits(((*w1.add(k)) as u32) << 16); + s0 += wv0 * x[k]; + s1 += wv1 * x[k]; + k += 1; + } + + if s0 > best_val { + best_val = s0; + best = o; + } + if s1 > best_val { + best_val = s1; + best = o + 1; + } + o += 2; + } + + while o < end { + let w_row = w_bf16.add(o * in_dim); + let mut sum = 0.0f32; + let mut k = 0usize; + + let mut acc0 = vdupq_n_f32(0.0); + let mut acc1 = vdupq_n_f32(0.0); + while k + 8 <= in_dim { + let bf = vld1q_u16(w_row.add(k)); + acc0 = vfmaq_f32( + acc0, + vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(bf), 16)), + vld1q_f32(x.as_ptr().add(k)), + ); + acc1 = vfmaq_f32( + acc1, + vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(bf), 16)), + vld1q_f32(x.as_ptr().add(k + 4)), + ); + k += 8; + } + sum += vaddvq_f32(vaddq_f32(acc0, acc1)); + + while k < in_dim { + let w_val = f32::from_bits(((*w_row.add(k)) as u32) << 16); + sum += w_val * x[k]; + k += 1; + } + if sum > best_val { + best_val = sum; + best = o; + } + o += 1; + } + + (best, best_val) +} + +/// # Safety +/// Uses NEON intrinsics; slices must have at least n elements. +#[cfg(target_arch = "aarch64")] +pub unsafe fn dot_f32(a: &[f32], b: &[f32], n: usize) -> f32 { + let mut i = 0usize; + let mut acc0 = vdupq_n_f32(0.0); + let mut acc1 = vdupq_n_f32(0.0); + let mut acc2 = vdupq_n_f32(0.0); + let mut acc3 = vdupq_n_f32(0.0); + + while i + 16 <= n { + acc0 = vfmaq_f32( + acc0, + vld1q_f32(a.as_ptr().add(i)), + vld1q_f32(b.as_ptr().add(i)), + ); + acc1 = vfmaq_f32( + acc1, + vld1q_f32(a.as_ptr().add(i + 4)), + vld1q_f32(b.as_ptr().add(i + 4)), + ); + acc2 = vfmaq_f32( + acc2, + vld1q_f32(a.as_ptr().add(i + 8)), + vld1q_f32(b.as_ptr().add(i + 8)), + ); + acc3 = vfmaq_f32( + acc3, + vld1q_f32(a.as_ptr().add(i + 12)), + vld1q_f32(b.as_ptr().add(i + 12)), + ); + i += 16; + } + + while i + 4 <= n { + acc0 = vfmaq_f32( + acc0, + vld1q_f32(a.as_ptr().add(i)), + vld1q_f32(b.as_ptr().add(i)), + ); + i += 4; + } + + let mut sum = vaddvq_f32(vaddq_f32(vaddq_f32(acc0, acc2), vaddq_f32(acc1, acc3))); + while i < n { + sum += a[i] * b[i]; + i += 1; + } + sum +} + +/// # Safety +/// Uses NEON intrinsics; dst must have at least n elements. +#[cfg(target_arch = "aarch64")] +pub unsafe fn vec_scale_inplace(dst: &mut [f32], scale: f32, n: usize) { + let mut i = 0usize; + let s = vdupq_n_f32(scale); + while i + 8 <= n { + let d0 = vld1q_f32(dst.as_ptr().add(i)); + let d1 = vld1q_f32(dst.as_ptr().add(i + 4)); + vst1q_f32(dst.as_mut_ptr().add(i), vmulq_f32(d0, s)); + vst1q_f32(dst.as_mut_ptr().add(i + 4), vmulq_f32(d1, s)); + i += 8; + } + while i < n { + dst[i] *= scale; + i += 1; + } +} + +/// # Safety +/// Uses NEON intrinsics; dst and src must have at least n elements. +#[cfg(target_arch = "aarch64")] +pub unsafe fn vec_axpy_inplace(dst: &mut [f32], src: &[f32], alpha: f32, n: usize) { + let mut i = 0usize; + let a = vdupq_n_f32(alpha); + while i + 8 <= n { + let d0 = vld1q_f32(dst.as_ptr().add(i)); + let s0 = vld1q_f32(src.as_ptr().add(i)); + let d1 = vld1q_f32(dst.as_ptr().add(i + 4)); + let s1 = vld1q_f32(src.as_ptr().add(i + 4)); + vst1q_f32(dst.as_mut_ptr().add(i), vfmaq_f32(d0, s0, a)); + vst1q_f32(dst.as_mut_ptr().add(i + 4), vfmaq_f32(d1, s1, a)); + i += 8; + } + while i < n { + dst[i] += alpha * src[i]; + i += 1; + } +} + +/// # Safety +/// Uses NEON intrinsics; dst and src must have at least n elements. +#[cfg(target_arch = "aarch64")] +pub unsafe fn vec_scale_add(dst: &mut [f32], src: &[f32], correction: f32, n: usize) { + let mut i = 0usize; + let c = vdupq_n_f32(correction); + while i + 8 <= n { + let d0 = vld1q_f32(dst.as_ptr().add(i)); + let s0 = vld1q_f32(src.as_ptr().add(i)); + let d1 = vld1q_f32(dst.as_ptr().add(i + 4)); + let s1 = vld1q_f32(src.as_ptr().add(i + 4)); + vst1q_f32(dst.as_mut_ptr().add(i), vfmaq_f32(s0, d0, c)); + vst1q_f32(dst.as_mut_ptr().add(i + 4), vfmaq_f32(s1, d1, c)); + i += 8; + } + while i < n { + dst[i] = dst[i] * correction + src[i]; + i += 1; + } +} + +/// NEON-accelerated RMS norm for a single row. +/// +/// # Safety +/// Uses NEON intrinsics; all slices must have at least hidden elements. +#[cfg(target_arch = "aarch64")] +pub unsafe fn rms_norm_row(out: &mut [f32], x: &[f32], weight: &[f32], hidden: usize, eps: f32) { + // Sum of squares + let mut i = 0usize; + let mut acc0 = vdupq_n_f32(0.0); + let mut acc1 = vdupq_n_f32(0.0); + while i + 8 <= hidden { + let x0 = vld1q_f32(x.as_ptr().add(i)); + let x1 = vld1q_f32(x.as_ptr().add(i + 4)); + acc0 = vfmaq_f32(acc0, x0, x0); + acc1 = vfmaq_f32(acc1, x1, x1); + i += 8; + } + let mut sum_sq = vaddvq_f32(vaddq_f32(acc0, acc1)); + while i < hidden { + sum_sq += x[i] * x[i]; + i += 1; + } + + let rms_inv = 1.0 / (sum_sq / hidden as f32 + eps).sqrt(); + let rms_v = vdupq_n_f32(rms_inv); + + // Scale: out = x * rms_inv * weight + i = 0; + while i + 8 <= hidden { + let x0 = vld1q_f32(x.as_ptr().add(i)); + let x1 = vld1q_f32(x.as_ptr().add(i + 4)); + let w0 = vld1q_f32(weight.as_ptr().add(i)); + let w1 = vld1q_f32(weight.as_ptr().add(i + 4)); + vst1q_f32(out.as_mut_ptr().add(i), vmulq_f32(vmulq_f32(x0, rms_v), w0)); + vst1q_f32( + out.as_mut_ptr().add(i + 4), + vmulq_f32(vmulq_f32(x1, rms_v), w1), + ); + i += 8; + } + while i < hidden { + out[i] = x[i] * rms_inv * weight[i]; + i += 1; + } +} + +/// NEON-accelerated in-place RMS norm for a single row: x[i] = x[i] * rms_inv * weight[i]. +/// +/// # Safety +/// Uses NEON intrinsics; x and weight must have at least `hidden` elements. +#[cfg(target_arch = "aarch64")] +pub unsafe fn rms_norm_inplace(x: &mut [f32], weight: &[f32], hidden: usize, eps: f32) { + let ptr = x.as_mut_ptr(); + // Sum of squares + let mut i = 0usize; + let mut acc0 = vdupq_n_f32(0.0); + let mut acc1 = vdupq_n_f32(0.0); + while i + 8 <= hidden { + let x0 = vld1q_f32(ptr.add(i)); + let x1 = vld1q_f32(ptr.add(i + 4)); + acc0 = vfmaq_f32(acc0, x0, x0); + acc1 = vfmaq_f32(acc1, x1, x1); + i += 8; + } + let mut sum_sq = vaddvq_f32(vaddq_f32(acc0, acc1)); + while i < hidden { + sum_sq += *ptr.add(i) * *ptr.add(i); + i += 1; + } + + let rms_inv = 1.0 / (sum_sq / hidden as f32 + eps).sqrt(); + let rms_v = vdupq_n_f32(rms_inv); + + // Scale in-place: x[i] = x[i] * rms_inv * weight[i] + i = 0; + while i + 8 <= hidden { + let x0 = vld1q_f32(ptr.add(i)); + let x1 = vld1q_f32(ptr.add(i + 4)); + let w0 = vld1q_f32(weight.as_ptr().add(i)); + let w1 = vld1q_f32(weight.as_ptr().add(i + 4)); + vst1q_f32(ptr.add(i), vmulq_f32(vmulq_f32(x0, rms_v), w0)); + vst1q_f32(ptr.add(i + 4), vmulq_f32(vmulq_f32(x1, rms_v), w1)); + i += 8; + } + while i < hidden { + *ptr.add(i) = *ptr.add(i) * rms_inv * weight[i]; + i += 1; + } +} + +/// NEON-accelerated layer norm for a single row. +/// +/// # Safety +/// Uses NEON intrinsics; all slices must have at least hidden elements. +#[cfg(target_arch = "aarch64")] +pub unsafe fn layer_norm_row( + out: &mut [f32], + x: &[f32], + weight: &[f32], + bias: &[f32], + hidden: usize, + eps: f32, +) { + // Pass 1: compute mean + let mut i = 0usize; + let mut sum0 = vdupq_n_f32(0.0); + let mut sum1 = vdupq_n_f32(0.0); + while i + 8 <= hidden { + sum0 = vaddq_f32(sum0, vld1q_f32(x.as_ptr().add(i))); + sum1 = vaddq_f32(sum1, vld1q_f32(x.as_ptr().add(i + 4))); + i += 8; + } + let mut mean = vaddvq_f32(vaddq_f32(sum0, sum1)); + while i < hidden { + mean += x[i]; + i += 1; + } + mean /= hidden as f32; + + // Pass 2: compute variance + let mean_v = vdupq_n_f32(mean); + i = 0; + let mut var0 = vdupq_n_f32(0.0); + let mut var1 = vdupq_n_f32(0.0); + while i + 8 <= hidden { + let d0 = vsubq_f32(vld1q_f32(x.as_ptr().add(i)), mean_v); + let d1 = vsubq_f32(vld1q_f32(x.as_ptr().add(i + 4)), mean_v); + var0 = vfmaq_f32(var0, d0, d0); + var1 = vfmaq_f32(var1, d1, d1); + i += 8; + } + let mut var = vaddvq_f32(vaddq_f32(var0, var1)); + while i < hidden { + let d = x[i] - mean; + var += d * d; + i += 1; + } + + let inv_std = 1.0 / (var / hidden as f32 + eps).sqrt(); + let inv_v = vdupq_n_f32(inv_std); + + // Pass 3: normalize + i = 0; + while i + 8 <= hidden { + let x0 = vsubq_f32(vld1q_f32(x.as_ptr().add(i)), mean_v); + let x1 = vsubq_f32(vld1q_f32(x.as_ptr().add(i + 4)), mean_v); + let w0 = vld1q_f32(weight.as_ptr().add(i)); + let w1 = vld1q_f32(weight.as_ptr().add(i + 4)); + let b0 = vld1q_f32(bias.as_ptr().add(i)); + let b1 = vld1q_f32(bias.as_ptr().add(i + 4)); + vst1q_f32( + out.as_mut_ptr().add(i), + vfmaq_f32(b0, vmulq_f32(x0, inv_v), w0), + ); + vst1q_f32( + out.as_mut_ptr().add(i + 4), + vfmaq_f32(b1, vmulq_f32(x1, inv_v), w1), + ); + i += 8; + } + while i < hidden { + out[i] = (x[i] - mean) * inv_std * weight[i] + bias[i]; + i += 1; + } +} + +/// Fast exp approximation using NEON (7th-order polynomial, ~1e-4 relative error for |x| < 88). +#[cfg(target_arch = "aarch64")] +#[inline] +unsafe fn fast_exp_neon(x: float32x4_t) -> float32x4_t { + // exp(x) ≈ 2^(x * log2e) using integer trick + polynomial refinement + let log2e = vdupq_n_f32(std::f32::consts::LOG2_E); + let ln2 = vdupq_n_f32(std::f32::consts::LN_2); + + let val = vmulq_f32(x, log2e); + // Clamp to prevent overflow + let val = vminq_f32(val, vdupq_n_f32(126.0)); + let val = vmaxq_f32(val, vdupq_n_f32(-126.0)); + + // Integer part + let ipart = vcvtq_s32_f32(val); + let fpart = vsubq_f32(val, vcvtq_f32_s32(ipart)); + + // 2^ipart using bit manipulation + let exp_i = vreinterpretq_f32_s32(vshlq_n_s32(vaddq_s32(ipart, vdupq_n_s32(127)), 23)); + + // 2^fpart using polynomial: 1 + fpart*ln2*(1 + fpart*ln2/2*(1 + fpart*ln2/3*(1 + ...))) + let f = vmulq_f32(fpart, ln2); + let c2 = vdupq_n_f32(0.5); + let c3 = vdupq_n_f32(1.0 / 6.0); + let c4 = vdupq_n_f32(1.0 / 24.0); + let c5 = vdupq_n_f32(1.0 / 120.0); + + let mut p = vfmaq_f32(c4, c5, f); + p = vfmaq_f32(c3, p, f); + p = vfmaq_f32(c2, p, f); + p = vfmaq_f32(vdupq_n_f32(1.0), p, f); + p = vfmaq_f32(vdupq_n_f32(1.0), p, f); + + vmulq_f32(exp_i, p) +} + +/// NEON-accelerated exp() in-place using fast polynomial approximation. +/// +/// # Safety +/// Uses NEON intrinsics. +#[cfg(target_arch = "aarch64")] +pub unsafe fn exp_inplace(x: &mut [f32]) { + let n = x.len(); + let mut i = 0usize; + while i + 4 <= n { + let v = vld1q_f32(x.as_ptr().add(i)); + vst1q_f32(x.as_mut_ptr().add(i), fast_exp_neon(v)); + i += 4; + } + while i < n { + x[i] = x[i].exp(); + i += 1; + } +} + +/// NEON-accelerated SwiGLU: out[j] = silu(gate[2j]) * gate[2j+1] for interleaved gate/up. +/// +/// # Safety +/// Uses NEON intrinsics; gate_up must have at least 2*n elements. +#[cfg(target_arch = "aarch64")] +pub unsafe fn swiglu_interleaved(out: &mut [f32], gate_up: &[f32], n: usize) { + let one = vdupq_n_f32(1.0); + let mut j = 0usize; + + // Process 8 elements per iteration (2x float32x4) + while j + 8 <= n { + let pair0 = vld1q_f32(gate_up.as_ptr().add(2 * j)); + let pair1 = vld1q_f32(gate_up.as_ptr().add(2 * j + 4)); + let gates0 = vuzp1q_f32(pair0, pair1); + let ups0 = vuzp2q_f32(pair0, pair1); + + let pair2 = vld1q_f32(gate_up.as_ptr().add(2 * j + 8)); + let pair3 = vld1q_f32(gate_up.as_ptr().add(2 * j + 12)); + let gates1 = vuzp1q_f32(pair2, pair3); + let ups1 = vuzp2q_f32(pair2, pair3); + + let exp0 = fast_exp_neon(vnegq_f32(gates0)); + let exp1 = fast_exp_neon(vnegq_f32(gates1)); + let silu0 = vdivq_f32(gates0, vaddq_f32(one, exp0)); + let silu1 = vdivq_f32(gates1, vaddq_f32(one, exp1)); + + vst1q_f32(out.as_mut_ptr().add(j), vmulq_f32(silu0, ups0)); + vst1q_f32(out.as_mut_ptr().add(j + 4), vmulq_f32(silu1, ups1)); + j += 8; + } + + while j + 4 <= n { + let pair0 = vld1q_f32(gate_up.as_ptr().add(2 * j)); + let pair1 = vld1q_f32(gate_up.as_ptr().add(2 * j + 4)); + let gates = vuzp1q_f32(pair0, pair1); + let ups = vuzp2q_f32(pair0, pair1); + let exp_ng = fast_exp_neon(vnegq_f32(gates)); + let silu_g = vdivq_f32(gates, vaddq_f32(one, exp_ng)); + vst1q_f32(out.as_mut_ptr().add(j), vmulq_f32(silu_g, ups)); + j += 4; + } + + while j < n { + let g = gate_up[2 * j]; + let u = gate_up[2 * j + 1]; + let g_silu = g / (1.0 + (-g).exp()); + out[j] = g_silu * u; + j += 1; + } +} + +/// NEON-accelerated GELU (tanh approximation). +/// +/// # Safety +/// Uses NEON intrinsics; x must have at least n elements. +#[cfg(target_arch = "aarch64")] +pub unsafe fn gelu_inplace(x: &mut [f32], n: usize) { + let half = vdupq_n_f32(0.5); + let one = vdupq_n_f32(1.0); + let coeff = vdupq_n_f32(0.797_884_6); // sqrt(2/pi) + let c3 = vdupq_n_f32(0.044715); + let mut i = 0usize; + + while i + 4 <= n { + let v = vld1q_f32(x.as_ptr().add(i)); + let v3 = vmulq_f32(vmulq_f32(v, v), v); + let inner = vmulq_f32(coeff, vfmaq_f32(v, c3, v3)); // coeff * (v + c3 * v^3) + // tanh(x) ≈ (1 - 2/(exp(2x)+1)) = approximate via fast_exp + let exp2x = fast_exp_neon(vmulq_f32(vdupq_n_f32(2.0), inner)); + let tanh_v = vsubq_f32(one, vdivq_f32(vdupq_n_f32(2.0), vaddq_f32(exp2x, one))); + let result = vmulq_f32(half, vmulq_f32(v, vaddq_f32(one, tanh_v))); + vst1q_f32(x.as_mut_ptr().add(i), result); + i += 4; + } + + while i < n { + let val = x[i]; + let x3 = val * val * val; + let inner = 0.797_884_6_f32 * (val + 0.044715 * x3); + x[i] = 0.5 * val * (1.0 + inner.tanh()); + i += 1; + } +} + +/// Quantize BF16 weight matrix to INT8 per-row with absmax scaling. +/// Returns (int8_data, scales) where scales[row] is the per-row scale factor. +/// +/// # Safety +/// w_bf16 must point to at least out_dim * in_dim valid bf16 values. +/// in_dim must be a multiple of 16 for alignment. +#[cfg(target_arch = "aarch64")] +pub unsafe fn quantize_bf16_to_int8( + w_bf16: *const u16, + out_dim: usize, + in_dim: usize, +) -> (Vec, Vec) { + let mut int8_data = vec![0i8; out_dim * in_dim]; + let mut scales = vec![0.0f32; out_dim]; + + for row in 0..out_dim { + let w_row = w_bf16.add(row * in_dim); + + // Find absmax of the row + let mut k = 0; + let mut vmax = vdupq_n_f32(0.0); + let abs_mask = vdupq_n_u32(0x7FFF_FFFF); + while k + 8 <= in_dim { + let r0 = vld1q_u16(w_row.add(k)); + let f0 = vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r0), 16)); + let f1 = vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r0), 16)); + let a0 = vreinterpretq_f32_u32(vandq_u32(vreinterpretq_u32_f32(f0), abs_mask)); + let a1 = vreinterpretq_f32_u32(vandq_u32(vreinterpretq_u32_f32(f1), abs_mask)); + vmax = vmaxq_f32(vmax, vmaxq_f32(a0, a1)); + k += 8; + } + let mut max_abs = vmaxvq_f32(vmax); + while k < in_dim { + let v = f32::from_bits((*w_row.add(k) as u32) << 16).abs(); + if v > max_abs { + max_abs = v; + } + k += 1; + } + + let scale = if max_abs > 0.0 { max_abs / 127.0 } else { 1.0 }; + let inv_scale = 127.0 / max_abs.max(1e-10); + scales[row] = scale; + + // Quantize row + let dst = &mut int8_data[row * in_dim..(row + 1) * in_dim]; + k = 0; + let inv_s = vdupq_n_f32(inv_scale); + while k + 8 <= in_dim { + let r0 = vld1q_u16(w_row.add(k)); + let f0 = vreinterpretq_f32_u32(vshll_n_u16(vget_low_u16(r0), 16)); + let f1 = vreinterpretq_f32_u32(vshll_n_u16(vget_high_u16(r0), 16)); + let q0 = vcvtq_s32_f32(vmulq_f32(f0, inv_s)); + let q1 = vcvtq_s32_f32(vmulq_f32(f1, inv_s)); + let q16 = vqmovn_s32(q0); + let q16b = vqmovn_s32(q1); + let q8 = vqmovn_s16(vcombine_s16(q16, q16b)); + vst1_s8(dst.as_mut_ptr().add(k) as *mut i8, q8); + k += 8; + } + while k < in_dim { + let v = f32::from_bits((*w_row.add(k) as u32) << 16); + let q = (v * inv_scale).round().clamp(-127.0, 127.0) as i8; + dst[k] = q; + k += 1; + } + } + + (int8_data, scales) +} + +/// SDOT via inline assembly (stable Rust, avoids unstable vdotq_s32) +#[cfg(all(target_arch = "aarch64", target_feature = "dotprod"))] +#[inline(always)] +unsafe fn sdot_s32(mut acc: int32x4_t, a: int8x16_t, b: int8x16_t) -> int32x4_t { + core::arch::asm!( + "sdot {acc:v}.4s, {a:v}.16b, {b:v}.16b", + acc = inout(vreg) acc, + a = in(vreg) a, + b = in(vreg) b, + options(pure, nomem, nostack, preserves_flags), + ); + acc +} + +/// Portable aarch64 fallback when dotprod is unavailable in the target. +/// +/// This keeps the same call signature as the fast SDOT path, but collapses the +/// accumulated value into lane 0 because current callers only consume the final +/// sum via `vaddvq_s32(...)`. +#[cfg(all(target_arch = "aarch64", not(target_feature = "dotprod")))] +#[inline(always)] +unsafe fn sdot_s32(acc: int32x4_t, a: int8x16_t, b: int8x16_t) -> int32x4_t { + let acc_sum = vaddvq_s32(acc); + let a_vals: [i8; 16] = core::mem::transmute(a); + let b_vals: [i8; 16] = core::mem::transmute(b); + let dot = a_vals + .iter() + .zip(b_vals.iter()) + .fold(0i32, |sum, (&lhs, &rhs)| sum + lhs as i32 * rhs as i32); + vsetq_lane_s32(acc_sum + dot, vdupq_n_s32(0), 0) +} + +/// INT8 matvec: y = W_int8 @ x_int8 * (x_scale * w_scales[row]) +/// Produces f32 output. Optionally adds bias (for fused residual add). +/// +/// # Safety +/// Uses NEON SDOT via inline asm. +#[cfg(target_arch = "aarch64")] +pub unsafe fn matvec_int8( + y: &mut [f32], + x_int8: *const i8, + x_scale: f32, + w_int8: *const i8, + w_scales: &[f32], + bias: Option<&[f32]>, + in_dim: usize, + out_dim: usize, +) { + let mut o = 0; + while o + 1 < out_dim { + let w0 = w_int8.add(o * in_dim); + let w1 = w_int8.add((o + 1) * in_dim); + let mut acc0a = vdupq_n_s32(0); + let mut acc0b = vdupq_n_s32(0); + let mut acc1a = vdupq_n_s32(0); + let mut acc1b = vdupq_n_s32(0); + let mut k = 0; + + while k + 32 <= in_dim { + let x0 = vld1q_s8(x_int8.add(k)); + let x1 = vld1q_s8(x_int8.add(k + 16)); + acc0a = sdot_s32(acc0a, x0, vld1q_s8(w0.add(k))); + acc0b = sdot_s32(acc0b, x1, vld1q_s8(w0.add(k + 16))); + acc1a = sdot_s32(acc1a, x0, vld1q_s8(w1.add(k))); + acc1b = sdot_s32(acc1b, x1, vld1q_s8(w1.add(k + 16))); + k += 32; + } + while k + 16 <= in_dim { + let xv = vld1q_s8(x_int8.add(k)); + acc0a = sdot_s32(acc0a, xv, vld1q_s8(w0.add(k))); + acc1a = sdot_s32(acc1a, xv, vld1q_s8(w1.add(k))); + k += 16; + } + + let sum0 = vaddvq_s32(vaddq_s32(acc0a, acc0b)); + let sum1 = vaddvq_s32(vaddq_s32(acc1a, acc1b)); + + let mut v0 = sum0 as f32 * x_scale * w_scales[o]; + let mut v1 = sum1 as f32 * x_scale * w_scales[o + 1]; + + // Scalar tail + while k < in_dim { + let xv = *x_int8.add(k) as i32; + v0 += xv as f32 * (*w0.add(k) as i32) as f32 * x_scale * w_scales[o]; + v1 += xv as f32 * (*w1.add(k) as i32) as f32 * x_scale * w_scales[o + 1]; + k += 1; + } + + if let Some(b) = bias { + v0 += b[o]; + v1 += b[o + 1]; + } + y[o] = v0; + y[o + 1] = v1; + o += 2; + } + while o < out_dim { + let w_row = w_int8.add(o * in_dim); + let mut acc0 = vdupq_n_s32(0); + let mut acc1 = vdupq_n_s32(0); + let mut k = 0; + while k + 32 <= in_dim { + acc0 = sdot_s32(acc0, vld1q_s8(x_int8.add(k)), vld1q_s8(w_row.add(k))); + acc1 = sdot_s32( + acc1, + vld1q_s8(x_int8.add(k + 16)), + vld1q_s8(w_row.add(k + 16)), + ); + k += 32; + } + while k + 16 <= in_dim { + acc0 = sdot_s32(acc0, vld1q_s8(x_int8.add(k)), vld1q_s8(w_row.add(k))); + k += 16; + } + let mut val = vaddvq_s32(vaddq_s32(acc0, acc1)) as f32 * x_scale * w_scales[o]; + while k < in_dim { + val += (*x_int8.add(k) as f32) * (*w_row.add(k) as f32) * x_scale * w_scales[o]; + k += 1; + } + if let Some(b) = bias { + val += b[o]; + } + y[o] = val; + o += 1; + } +} + +/// INT8 argmax: find argmax of x @ W.T where W is int8-quantized. +/// x_int8: quantized input [in_dim], x_scale: input quantization scale +/// w_int8: quantized weights [out_dim, in_dim], w_scales: per-row scales [out_dim] +/// +/// # Safety +/// Uses NEON SDOT via inline asm. in_dim should be a multiple of 16 for best perf. +#[cfg(target_arch = "aarch64")] +pub unsafe fn argmax_int8_range( + x_int8: *const i8, + x_scale: f32, + w_int8: *const i8, + w_scales: &[f32], + in_dim: usize, + start: usize, + end: usize, +) -> (usize, f32) { + let mut best = start; + let mut best_val = -1e30f32; + + for o in start..end { + let w_row = w_int8.add(o * in_dim); + let mut acc0 = vdupq_n_s32(0); + let mut acc1 = vdupq_n_s32(0); + let mut acc2 = vdupq_n_s32(0); + let mut acc3 = vdupq_n_s32(0); + let mut k = 0; + + while k + 64 <= in_dim { + acc0 = sdot_s32(acc0, vld1q_s8(x_int8.add(k)), vld1q_s8(w_row.add(k))); + acc1 = sdot_s32( + acc1, + vld1q_s8(x_int8.add(k + 16)), + vld1q_s8(w_row.add(k + 16)), + ); + acc2 = sdot_s32( + acc2, + vld1q_s8(x_int8.add(k + 32)), + vld1q_s8(w_row.add(k + 32)), + ); + acc3 = sdot_s32( + acc3, + vld1q_s8(x_int8.add(k + 48)), + vld1q_s8(w_row.add(k + 48)), + ); + k += 64; + } + + while k + 16 <= in_dim { + acc0 = sdot_s32(acc0, vld1q_s8(x_int8.add(k)), vld1q_s8(w_row.add(k))); + k += 16; + } + + let sum_i32 = vaddvq_s32(vaddq_s32(vaddq_s32(acc0, acc2), vaddq_s32(acc1, acc3))); + let val = sum_i32 as f32 * x_scale * w_scales[o]; + + // Scalar tail + let mut tail_sum = 0i32; + while k < in_dim { + tail_sum += (*x_int8.add(k) as i32) * (*w_row.add(k) as i32); + k += 1; + } + let val = val + tail_sum as f32 * x_scale * w_scales[o]; + + if val > best_val { + best_val = val; + best = o; + } + } + + (best, best_val) +} diff --git a/vendor/qwenasr/crates/qwen-asr/src/lib.rs b/vendor/qwenasr/crates/qwen-asr/src/lib.rs new file mode 100644 index 0000000..6e0ba48 --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/src/lib.rs @@ -0,0 +1,106 @@ +//! CPU-only Qwen3-ASR speech recognition in pure Rust. +//! +//! BLAS and SIMD optimizations are selected automatically at compile time based +//! on the target platform — Accelerate + NEON on macOS/aarch64, OpenBLAS + AVX2 +//! on Linux/x86_64, etc. For best performance on x86_64, build with: +//! +//! ```bash +//! RUSTFLAGS="-C target-cpu=native" cargo build --release +//! ``` +//! +//! **Important:** Always build in release mode (`--release`). Debug builds are +//! 10–50x slower and unusable for real-time inference. +//! +//! # Quick Start +//! +//! ```rust,no_run +//! use qwen_asr::context::QwenCtx; +//! use qwen_asr::transcribe; +//! +//! let mut ctx = QwenCtx::load("qwen3-asr-0.6b").expect("model not found"); +//! let text = transcribe::transcribe(&mut ctx, "audio.wav").unwrap(); +//! println!("{text}"); +//! ``` +//! +//! # Forced Alignment +//! +//! With the aligner model variant you can obtain word-level timestamps for a +//! known transcript: +//! +//! ```rust,no_run +//! use qwen_asr::context::QwenCtx; +//! use qwen_asr::align; +//! +//! let mut ctx = QwenCtx::load("qwen3-aligner-0.6b").expect("aligner model not found"); +//! let samples: Vec = vec![]; // 16 kHz mono f32 PCM +//! let results = align::forced_align(&mut ctx, &samples, "Hello world", "English").unwrap(); +//! for r in &results { +//! println!("{}: {:.0} – {:.0} ms", r.text, r.start_ms, r.end_ms); +//! } +//! ``` +//! +//! # Module Guide +//! +//! | Module | Purpose | +//! |--------|---------| +//! | [`context`] | Engine state — start here with [`context::QwenCtx::load`] | +//! | [`transcribe`] | Offline, segmented, and streaming transcription | +//! | [`audio`] | WAV loading, resampling, mel spectrogram | +//! | [`align`] | Forced alignment (word/character timestamps) | +//! | [`config`] | Model configuration and variant detection | +//! | [`tokenizer`] | GPT-2 byte-level BPE tokenizer | +//! +//! The remaining modules (`encoder`, `decoder`, `kernels`, `safetensors`) are +//! implementation details and not intended for direct use. + +pub mod align; +pub mod audio; +#[cfg(any(feature = "ios", feature = "android", feature = "ffi"))] +pub mod c_api; +pub mod config; +pub mod context; +pub mod decoder; +pub mod encoder; +#[cfg(feature = "android")] +pub mod jni_api; +pub mod kernels; +pub mod safetensors; +pub mod tokenizer; +pub mod transcribe; + +/// Returns a list of compile-time optimization flags enabled for this build. +pub fn optimization_flags() -> Vec<&'static str> { + let mut flags = Vec::new(); + + if cfg!(feature = "vdsp") { + flags.push("vDSP/Accelerate"); + } + if cfg!(feature = "blas") && !cfg!(feature = "vdsp") { + flags.push("BLAS"); + } + + // Architecture-specific SIMD + if cfg!(target_arch = "aarch64") { + flags.push("NEON"); + if cfg!(target_feature = "dotprod") { + flags.push("DotProd"); + } + } else if cfg!(target_arch = "x86_64") { + if cfg!(target_feature = "avx2") { + flags.push("AVX2"); + } else if cfg!(target_feature = "avx") { + flags.push("AVX"); + } else if cfg!(target_feature = "sse4.1") { + flags.push("SSE4.1"); + } + if cfg!(target_feature = "fma") { + flags.push("FMA"); + } + } + + if flags.is_empty() { + flags.push("generic"); + } + + flags +} diff --git a/vendor/qwenasr/crates/qwen-asr/src/safetensors.rs b/vendor/qwenasr/crates/qwen-asr/src/safetensors.rs new file mode 100644 index 0000000..3e9b295 --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/src/safetensors.rs @@ -0,0 +1,595 @@ +//! Safetensors mmap reader with multi-shard support. + +use std::collections::HashMap; +use std::os::unix::io::RawFd; +use std::path::Path; + +const MAX_TENSORS: usize = 1024; + +#[derive(Clone, Copy, PartialEq, Eq, Debug)] +pub enum Dtype { + F32, + F16, + BF16, + I32, + I64, + Bool, + Unknown, +} + +impl Dtype { + fn from_str(s: &str) -> Self { + match s { + "F32" => Dtype::F32, + "F16" => Dtype::F16, + "BF16" => Dtype::BF16, + "I32" => Dtype::I32, + "I64" => Dtype::I64, + "BOOL" => Dtype::Bool, + _ => Dtype::Unknown, + } + } + + pub fn element_size(&self) -> usize { + match self { + Dtype::F32 | Dtype::I32 => 4, + Dtype::F16 | Dtype::BF16 => 2, + Dtype::I64 => 8, + Dtype::Bool => 1, + Dtype::Unknown => 0, + } + } +} + +#[derive(Clone, Debug)] +pub struct TensorMeta { + pub name: String, + pub dtype: Dtype, + pub shape: Vec, + pub data_offset: usize, + pub data_size: usize, +} + +impl TensorMeta { + pub fn numel(&self) -> usize { + self.shape.iter().product::() as usize + } +} + +pub struct SafetensorsFile { + _fd: RawFd, + data: *mut u8, + file_size: usize, + header_size: usize, + pub tensors: Vec, + tensor_map: HashMap, +} + +unsafe impl Send for SafetensorsFile {} +unsafe impl Sync for SafetensorsFile {} + +impl SafetensorsFile { + pub fn open(path: &str) -> Option { + use libc::*; + use std::ffi::CString; + + let c_path = CString::new(path).ok()?; + let fd = unsafe { open(c_path.as_ptr(), O_RDONLY) }; + if fd < 0 { + return None; + } + + let mut stat_buf = unsafe { std::mem::zeroed::() }; + if unsafe { fstat(fd, &mut stat_buf) } < 0 { + unsafe { close(fd); } + return None; + } + + let file_size = stat_buf.st_size as usize; + if file_size < 8 { + unsafe { close(fd); } + return None; + } + + let data = unsafe { + mmap( + std::ptr::null_mut(), + file_size, + PROT_READ, + MAP_PRIVATE, + fd, + 0, + ) + }; + // Keep fd open for mmap lifetime? Actually mmap doesn't need it. + // But we close it since MAP_PRIVATE doesn't need fd after mmap. + let raw_fd = fd; + unsafe { close(fd); } + + if data == libc::MAP_FAILED { + return None; + } + let data = data as *mut u8; + + // Read header size (first 8 bytes, little-endian u64) + let header_size = unsafe { + let mut buf = [0u8; 8]; + std::ptr::copy_nonoverlapping(data, buf.as_mut_ptr(), 8); + u64::from_le_bytes(buf) as usize + }; + + if header_size > file_size - 8 { + unsafe { munmap(data as *mut _, file_size); } + return None; + } + + // Parse JSON header + let header_json = unsafe { + let slice = std::slice::from_raw_parts(data.add(8), header_size); + std::str::from_utf8(slice).ok()? + }; + + let tensors = parse_header(header_json)?; + let mut tensor_map = HashMap::new(); + for (i, t) in tensors.iter().enumerate() { + tensor_map.insert(t.name.clone(), i); + } + + Some(SafetensorsFile { + _fd: raw_fd, + data, + file_size, + header_size, + tensors, + tensor_map, + }) + } + + pub fn find(&self, name: &str) -> Option<&TensorMeta> { + self.tensor_map.get(name).map(|&i| &self.tensors[i]) + } + + pub fn data_ptr(&self, tensor: &TensorMeta) -> *const u8 { + unsafe { self.data.add(8 + self.header_size + tensor.data_offset) } + } + + /// Get tensor data as f32 Vec (converts from BF16 if needed). + pub fn get_f32(&self, tensor: &TensorMeta) -> Option> { + let n = tensor.numel(); + if n == 0 { + return None; + } + + let ptr = self.data_ptr(tensor); + match tensor.dtype { + Dtype::F32 => { + let mut out = vec![0.0f32; n]; + unsafe { + std::ptr::copy_nonoverlapping( + ptr as *const f32, + out.as_mut_ptr(), + n, + ); + } + Some(out) + } + Dtype::BF16 => { + let src = unsafe { std::slice::from_raw_parts(ptr as *const u16, n) }; + let mut out = vec![0.0f32; n]; + for i in 0..n { + out[i] = f32::from_bits((src[i] as u32) << 16); + } + Some(out) + } + _ => None, + } + } + + /// Get direct pointer to BF16 data in mmap. + pub fn get_bf16_direct(&self, tensor: &TensorMeta) -> Option<*const u16> { + if tensor.dtype != Dtype::BF16 { + return None; + } + Some(self.data_ptr(tensor) as *const u16) + } +} + +impl Drop for SafetensorsFile { + fn drop(&mut self) { + if !self.data.is_null() { + unsafe { + libc::munmap(self.data as *mut _, self.file_size); + } + } + } +} + +pub struct MultiSafetensors { + pub shards: Vec, +} + +impl MultiSafetensors { + pub fn open(model_dir: &str) -> Option { + // Try single file first + let single_path = format!("{}/model.safetensors", model_dir); + if let Some(sf) = SafetensorsFile::open(&single_path) { + return Some(MultiSafetensors { shards: vec![sf] }); + } + + // Scan directory for shard files + let dir = Path::new(model_dir); + let mut shard_names: Vec = Vec::new(); + + if let Ok(entries) = std::fs::read_dir(dir) { + for entry in entries.flatten() { + let name = entry.file_name().to_string_lossy().to_string(); + if name.starts_with("model-") && name.ends_with(".safetensors") { + shard_names.push(name); + } + } + } + + if shard_names.is_empty() { + eprintln!("multi_safetensors_open: no safetensors files in {}", model_dir); + return None; + } + + shard_names.sort(); + + let mut shards = Vec::new(); + for name in &shard_names { + let path = format!("{}/{}", model_dir, name); + match SafetensorsFile::open(&path) { + Some(sf) => shards.push(sf), + None => { + eprintln!("multi_safetensors_open: failed to open {}", path); + return None; + } + } + } + + Some(MultiSafetensors { shards }) + } + + /// Find a tensor by name across all shards. + /// Returns (shard_index, TensorMeta). + pub fn find(&self, name: &str) -> Option<(usize, &TensorMeta)> { + for (si, shard) in self.shards.iter().enumerate() { + if let Some(t) = shard.find(name) { + return Some((si, t)); + } + } + None + } + + /// Convenience: get f32 data for a named tensor. + pub fn get_f32(&self, name: &str) -> Option> { + let (si, t) = self.find(name)?; + self.shards[si].get_f32(t) + } + + /// Convenience: get direct BF16 pointer for a named tensor. + pub fn get_bf16_direct(&self, name: &str) -> Option<*const u16> { + let (si, t) = self.find(name)?; + self.shards[si].get_bf16_direct(t) + } + + /// Check if a tensor exists. + pub fn has_tensor(&self, name: &str) -> bool { + self.find(name).is_some() + } +} + +// ======================================================================== +// Minimal JSON parser for safetensors header +// ======================================================================== + +fn parse_header(json: &str) -> Option> { + let mut tensors = Vec::new(); + let bytes = json.as_bytes(); + let mut pos = 0; + + skip_whitespace(bytes, &mut pos); + if pos >= bytes.len() || bytes[pos] != b'{' { + return None; + } + pos += 1; + + loop { + skip_whitespace(bytes, &mut pos); + if pos >= bytes.len() { + break; + } + if bytes[pos] == b'}' { + break; + } + if bytes[pos] == b',' { + pos += 1; + continue; + } + + // Parse key + let key = parse_json_string(bytes, &mut pos)?; + skip_whitespace(bytes, &mut pos); + if pos >= bytes.len() || bytes[pos] != b':' { + return None; + } + pos += 1; + + if key == "__metadata__" { + skip_json_value(bytes, &mut pos); + continue; + } + + // Parse tensor entry + let tensor = parse_tensor_entry(bytes, &mut pos, &key)?; + tensors.push(tensor); + + if tensors.len() >= MAX_TENSORS { + break; + } + } + + Some(tensors) +} + +fn skip_whitespace(bytes: &[u8], pos: &mut usize) { + while *pos < bytes.len() { + match bytes[*pos] { + b' ' | b'\n' | b'\r' | b'\t' => *pos += 1, + _ => break, + } + } +} + +fn parse_json_string(bytes: &[u8], pos: &mut usize) -> Option { + skip_whitespace(bytes, pos); + if *pos >= bytes.len() || bytes[*pos] != b'"' { + return None; + } + *pos += 1; + + let mut result = Vec::new(); + while *pos < bytes.len() && bytes[*pos] != b'"' { + if bytes[*pos] == b'\\' { + *pos += 1; + if *pos >= bytes.len() { + return None; + } + match bytes[*pos] { + b'n' => result.push(b'\n'), + b't' => result.push(b'\t'), + b'"' => result.push(b'"'), + b'\\' => result.push(b'\\'), + b'/' => result.push(b'/'), + b'u' => { + *pos += 1; + let mut cp = 0u32; + for _ in 0..4 { + if *pos >= bytes.len() { + return None; + } + cp <<= 4; + let c = bytes[*pos]; + cp |= match c { + b'0'..=b'9' => (c - b'0') as u32, + b'a'..=b'f' => (c - b'a' + 10) as u32, + b'A'..=b'F' => (c - b'A' + 10) as u32, + _ => return None, + }; + *pos += 1; + } + // Encode cp as UTF-8 + let ch = char::from_u32(cp).unwrap_or('?'); + let mut buf = [0u8; 4]; + let s = ch.encode_utf8(&mut buf); + result.extend_from_slice(s.as_bytes()); + continue; // skip the pos += 1 below + } + other => result.push(other), + } + } else { + result.push(bytes[*pos]); + } + *pos += 1; + } + + if *pos >= bytes.len() || bytes[*pos] != b'"' { + return None; + } + *pos += 1; + + String::from_utf8(result).ok() +} + +fn parse_json_int(bytes: &[u8], pos: &mut usize) -> Option { + skip_whitespace(bytes, pos); + let mut neg = false; + if *pos < bytes.len() && bytes[*pos] == b'-' { + neg = true; + *pos += 1; + } + let mut val: i64 = 0; + let mut found = false; + while *pos < bytes.len() && bytes[*pos].is_ascii_digit() { + val = val * 10 + (bytes[*pos] - b'0') as i64; + *pos += 1; + found = true; + } + if !found { + return None; + } + Some(if neg { -val } else { val }) +} + +fn parse_tensor_entry(bytes: &[u8], pos: &mut usize, name: &str) -> Option { + skip_whitespace(bytes, pos); + if *pos >= bytes.len() || bytes[*pos] != b'{' { + return None; + } + *pos += 1; + + let mut dtype = Dtype::Unknown; + let mut shape = Vec::new(); + let mut data_offset: usize = 0; + let mut data_size: usize = 0; + + loop { + skip_whitespace(bytes, pos); + if *pos >= bytes.len() { + break; + } + if bytes[*pos] == b'}' { + *pos += 1; + break; + } + if bytes[*pos] == b',' { + *pos += 1; + continue; + } + + let key = parse_json_string(bytes, pos)?; + skip_whitespace(bytes, pos); + if *pos >= bytes.len() || bytes[*pos] != b':' { + return None; + } + *pos += 1; + skip_whitespace(bytes, pos); + + match key.as_str() { + "dtype" => { + let dtype_str = parse_json_string(bytes, pos)?; + dtype = Dtype::from_str(&dtype_str); + } + "shape" => { + if *pos >= bytes.len() || bytes[*pos] != b'[' { + return None; + } + *pos += 1; + loop { + skip_whitespace(bytes, pos); + if *pos >= bytes.len() { + break; + } + if bytes[*pos] == b']' { + *pos += 1; + break; + } + if bytes[*pos] == b',' { + *pos += 1; + continue; + } + let dim = parse_json_int(bytes, pos)?; + shape.push(dim); + } + } + "data_offsets" => { + if *pos >= bytes.len() || bytes[*pos] != b'[' { + return None; + } + *pos += 1; + skip_whitespace(bytes, pos); + let start = parse_json_int(bytes, pos)? as usize; + skip_whitespace(bytes, pos); + if *pos < bytes.len() && bytes[*pos] == b',' { + *pos += 1; + } + skip_whitespace(bytes, pos); + let end = parse_json_int(bytes, pos)? as usize; + skip_whitespace(bytes, pos); + if *pos < bytes.len() && bytes[*pos] == b']' { + *pos += 1; + } + data_offset = start; + data_size = end - start; + } + _ => { + skip_json_value(bytes, pos); + } + } + } + + Some(TensorMeta { + name: name.to_string(), + dtype, + shape, + data_offset, + data_size, + }) +} + +fn skip_json_value(bytes: &[u8], pos: &mut usize) { + skip_whitespace(bytes, pos); + if *pos >= bytes.len() { + return; + } + + match bytes[*pos] { + b'"' => { + *pos += 1; + while *pos < bytes.len() && bytes[*pos] != b'"' { + if bytes[*pos] == b'\\' { + *pos += 1; + } + if *pos < bytes.len() { + *pos += 1; + } + } + if *pos < bytes.len() { + *pos += 1; + } + } + b'[' => { + let mut depth = 1; + *pos += 1; + while *pos < bytes.len() && depth > 0 { + match bytes[*pos] { + b'[' => depth += 1, + b']' => depth -= 1, + b'"' => { + *pos += 1; + while *pos < bytes.len() && bytes[*pos] != b'"' { + if bytes[*pos] == b'\\' { + *pos += 1; + } + if *pos < bytes.len() { + *pos += 1; + } + } + } + _ => {} + } + *pos += 1; + } + } + b'{' => { + let mut depth = 1; + *pos += 1; + while *pos < bytes.len() && depth > 0 { + match bytes[*pos] { + b'{' => depth += 1, + b'}' => depth -= 1, + b'"' => { + *pos += 1; + while *pos < bytes.len() && bytes[*pos] != b'"' { + if bytes[*pos] == b'\\' { + *pos += 1; + } + if *pos < bytes.len() { + *pos += 1; + } + } + } + _ => {} + } + *pos += 1; + } + } + _ => { + // Number, bool, null + while *pos < bytes.len() && bytes[*pos] != b',' && bytes[*pos] != b'}' && bytes[*pos] != b']' { + *pos += 1; + } + } + } +} diff --git a/vendor/qwenasr/crates/qwen-asr/src/tokenizer.rs b/vendor/qwenasr/crates/qwen-asr/src/tokenizer.rs new file mode 100644 index 0000000..9612296 --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/src/tokenizer.rs @@ -0,0 +1,627 @@ +//! GPT-2 byte-level BPE tokenizer for Qwen. + +use std::collections::HashMap; + +// GPT-2 bytes-to-unicode mapping +fn init_gpt2_mapping() -> ([i32; 256], [i32; 512]) { + let mut byte_to_unicode = [0i32; 256]; + let mut unicode_to_byte = [-1i32; 512]; + + let mut n = 0i32; + for b in 0..256i32 { + let is_normal = (33..=126).contains(&b) + || (161..=172).contains(&b) + || (174..=255).contains(&b); + + if is_normal { + byte_to_unicode[b as usize] = b; + } else { + byte_to_unicode[b as usize] = 256 + n; + n += 1; + } + } + + for (b, &bu) in byte_to_unicode.iter().enumerate() { + let cp = bu as usize; + if cp < 512 { + unicode_to_byte[cp] = b as i32; + } + } + + (byte_to_unicode, unicode_to_byte) +} + +fn utf8_encode_cp(cp: u32) -> Vec { + if cp < 0x80 { + vec![cp as u8] + } else if cp < 0x800 { + vec![ + (0xC0 | (cp >> 6)) as u8, + (0x80 | (cp & 0x3F)) as u8, + ] + } else { + vec![ + (0xE0 | (cp >> 12)) as u8, + (0x80 | ((cp >> 6) & 0x3F)) as u8, + (0x80 | (cp & 0x3F)) as u8, + ] + } +} + +/// Decode a GPT-2 encoded token string (vocab key) to raw bytes. +/// Returns raw bytes instead of String because a single BPE token may +/// represent only a partial UTF-8 sequence (e.g. 2 of 3 bytes for a CJK +/// character). The caller must accumulate bytes from multiple tokens and +/// convert to UTF-8 only after the full sequence is available. +fn decode_gpt2_token_bytes(token_str: &str, unicode_to_byte: &[i32; 512]) -> Vec { + let mut bytes = Vec::new(); + + for ch in token_str.chars() { + let cp = ch as u32; + if cp < 512 && unicode_to_byte[cp as usize] >= 0 { + bytes.push(unicode_to_byte[cp as usize] as u8); + } else { + bytes.push(b'?'); + } + } + + bytes +} + +/// Convert UTF-8 bytes to GPT-2 byte-level unicode string. +fn text_to_bpe_unicode(text: &str, byte_to_unicode: &[i32; 256]) -> String { + let mut out = String::new(); + for &b in text.as_bytes() { + let cp = byte_to_unicode[b as usize] as u32; + for byte in utf8_encode_cp(cp) { + out.push(byte as char); + } + } + // Actually, we need to push the encoded codepoint as a char + let mut out2 = String::new(); + for &b in text.as_bytes() { + let cp = byte_to_unicode[b as usize] as u32; + if let Some(ch) = char::from_u32(cp) { + out2.push(ch); + } + } + out2 +} + +fn utf8_char_len(c: u8) -> usize { + if c & 0x80 == 0 { 1 } + else if c & 0xE0 == 0xC0 { 2 } + else if c & 0xF0 == 0xE0 { 3 } + else if c & 0xF8 == 0xF0 { 4 } + else { 1 } +} + +fn split_utf8_symbols(s: &str) -> Vec { + let bytes = s.as_bytes(); + let mut syms = Vec::new(); + let mut i = 0; + while i < bytes.len() { + let len = utf8_char_len(bytes[i]); + let end = (i + len).min(bytes.len()); + if let Ok(ch) = std::str::from_utf8(&bytes[i..end]) { + syms.push(ch.to_string()); + } + i = end; + } + syms +} + +pub struct QwenTokenizer { + pub vocab_size: usize, + id_to_text: Vec>, + id_to_bytes: Vec>>, + #[allow(dead_code)] + id_to_bpe: Vec>, + vocab_map: HashMap, + merge_map: HashMap, + byte_to_unicode: [i32; 256], + #[allow(dead_code)] + unicode_to_byte: [i32; 512], +} + +impl QwenTokenizer { + pub fn load(vocab_json_path: &str) -> Option { + let (byte_to_unicode, unicode_to_byte) = init_gpt2_mapping(); + + // Read vocab.json + let json = std::fs::read_to_string(vocab_json_path).ok()?; + + // Parse vocab.json: { "token": id, ... } + let mut max_id = 0i32; + let mut entries: Vec<(String, i32)> = Vec::new(); + + let bytes = json.as_bytes(); + let mut pos = 0; + skip_ws(bytes, &mut pos); + if pos >= bytes.len() || bytes[pos] != b'{' { + return None; + } + pos += 1; + + loop { + skip_ws(bytes, &mut pos); + if pos >= bytes.len() || bytes[pos] == b'}' { + break; + } + if bytes[pos] == b',' { + pos += 1; + continue; + } + + let key = parse_json_string_tok(bytes, &mut pos)?; + skip_ws(bytes, &mut pos); + if pos >= bytes.len() || bytes[pos] != b':' { + return None; + } + pos += 1; + let id = parse_json_int_tok(bytes, &mut pos)? as i32; + + if id > max_id { + max_id = id; + } + entries.push((key, id)); + } + + let vocab_size = (max_id + 1) as usize; + let mut id_to_text = vec![None; vocab_size]; + let mut id_to_bytes: Vec>> = vec![None; vocab_size]; + let mut id_to_bpe = vec![None; vocab_size]; + let mut vocab_map = HashMap::new(); + + for (key, id) in entries { + let idx = id as usize; + if idx < vocab_size { + let raw_bytes = decode_gpt2_token_bytes(&key, &unicode_to_byte); + // id_to_text: lossy UTF-8 for display/legacy use + let text = String::from_utf8_lossy(&raw_bytes).into_owned(); + id_to_text[idx] = Some(text); + id_to_bytes[idx] = Some(raw_bytes); + vocab_map.insert(key.clone(), id); + id_to_bpe[idx] = Some(key); + } + } + + // Load merges.txt + let merge_map = load_merges(vocab_json_path); + + Some(QwenTokenizer { + vocab_size, + id_to_text, + id_to_bytes, + id_to_bpe, + vocab_map, + merge_map, + byte_to_unicode, + unicode_to_byte, + }) + } + + pub fn decode(&self, token_id: i32) -> &str { + if token_id < 0 || token_id as usize >= self.vocab_size { + return ""; + } + match &self.id_to_text[token_id as usize] { + Some(s) => s.as_str(), + None => "", + } + } + + /// Decode a token to its raw bytes. Unlike `decode()`, this preserves + /// partial UTF-8 sequences so that the caller can accumulate bytes from + /// multiple tokens before converting to a valid UTF-8 string. + pub fn decode_bytes(&self, token_id: i32) -> &[u8] { + if token_id < 0 || token_id as usize >= self.vocab_size { + return b""; + } + match &self.id_to_bytes[token_id as usize] { + Some(b) => b.as_slice(), + None => b"", + } + } + + pub fn encode(&self, text: &str) -> Option> { + if text.is_empty() { + return None; + } + + let mapped = text_to_bpe_unicode(text, &self.byte_to_unicode); + let ids = self.encode_bpe_word(&mapped)?; + Some(ids) + } + + fn encode_bpe_word(&self, mapped: &str) -> Option> { + if mapped.is_empty() { + return Some(Vec::new()); + } + + let mut syms = split_utf8_symbols(mapped); + if syms.is_empty() { + return Some(Vec::new()); + } + + while syms.len() > 1 { + let mut best_rank = i32::MAX; + let mut best_i = -1i32; + + for i in 0..syms.len() - 1 { + let pair = format!("{} {}", syms[i], syms[i + 1]); + if let Some(&rank) = self.merge_map.get(&pair) { + if rank < best_rank { + best_rank = rank; + best_i = i as i32; + } + } + } + + if best_i < 0 || best_rank == i32::MAX { + break; + } + + let i = best_i as usize; + let merged = format!("{}{}", syms[i], syms[i + 1]); + syms[i] = merged; + syms.remove(i + 1); + } + + let mut ids = Vec::new(); + for sym in &syms { + let id = self.vocab_map.get(sym.as_str()).copied()?; + ids.push(id); + } + + Some(ids) + } +} + +fn load_merges(vocab_path: &str) -> HashMap { + let mut merge_map = HashMap::new(); + + // Derive merges.txt path from vocab.json path + let merges_path = if let Some(slash) = vocab_path.rfind('/') { + format!("{}/merges.txt", &vocab_path[..slash]) + } else { + "merges.txt".to_string() + }; + + let content = match std::fs::read_to_string(&merges_path) { + Ok(c) => c, + Err(_) => return merge_map, + }; + + let mut rank = 0i32; + for line in content.lines() { + let line = line.trim_end(); + if line.is_empty() || line.starts_with('#') { + continue; + } + if let Some(space_pos) = line.find(' ') { + let a = &line[..space_pos]; + let b = line[space_pos + 1..].trim_start(); + if !a.is_empty() && !b.is_empty() { + let key = format!("{} {}", a, b); + merge_map.insert(key, rank); + rank += 1; + } + } + } + + merge_map +} + +// Minimal JSON parsing helpers +fn skip_ws(bytes: &[u8], pos: &mut usize) { + while *pos < bytes.len() { + match bytes[*pos] { + b' ' | b'\n' | b'\r' | b'\t' => *pos += 1, + _ => break, + } + } +} + +fn parse_json_string_tok(bytes: &[u8], pos: &mut usize) -> Option { + skip_ws(bytes, pos); + if *pos >= bytes.len() || bytes[*pos] != b'"' { + return None; + } + *pos += 1; + + let mut result = Vec::new(); + while *pos < bytes.len() && bytes[*pos] != b'"' { + if bytes[*pos] == b'\\' { + *pos += 1; + if *pos >= bytes.len() { + return None; + } + match bytes[*pos] { + b'n' => result.push(b'\n'), + b't' => result.push(b'\t'), + b'"' => result.push(b'"'), + b'\\' => result.push(b'\\'), + b'/' => result.push(b'/'), + b'u' => { + *pos += 1; + let mut cp = 0u32; + for _ in 0..4 { + if *pos >= bytes.len() { + return None; + } + cp <<= 4; + let c = bytes[*pos]; + cp |= match c { + b'0'..=b'9' => (c - b'0') as u32, + b'a'..=b'f' => (c - b'a' + 10) as u32, + b'A'..=b'F' => (c - b'A' + 10) as u32, + _ => return None, + }; + *pos += 1; + } + if let Some(ch) = char::from_u32(cp) { + let mut buf = [0u8; 4]; + let s = ch.encode_utf8(&mut buf); + result.extend_from_slice(s.as_bytes()); + } + continue; + } + other => result.push(other), + } + } else { + result.push(bytes[*pos]); + } + *pos += 1; + } + + if *pos >= bytes.len() || bytes[*pos] != b'"' { + return None; + } + *pos += 1; + + String::from_utf8(result).ok() +} + +fn parse_json_int_tok(bytes: &[u8], pos: &mut usize) -> Option { + skip_ws(bytes, pos); + let mut neg = false; + if *pos < bytes.len() && bytes[*pos] == b'-' { + neg = true; + *pos += 1; + } + let mut val: i64 = 0; + let mut found = false; + while *pos < bytes.len() && bytes[*pos].is_ascii_digit() { + val = val * 10 + (bytes[*pos] - b'0') as i64; + *pos += 1; + found = true; + } + if !found { + return None; + } + Some(if neg { -val } else { val }) +} + +// ======================================================================== +// Tests +// ======================================================================== + +#[cfg(test)] +mod tests { + use super::*; + + /// Helper: convert raw bytes to a GPT-2 BPE token string using byte→unicode mapping. + fn bytes_to_gpt2_token(raw_bytes: &[u8], byte_to_unicode: &[i32; 256]) -> String { + let mut s = String::new(); + for &b in raw_bytes { + if let Some(ch) = char::from_u32(byte_to_unicode[b as usize] as u32) { + s.push(ch); + } + } + s + } + + // ---------------------------------------------------------------- + // decode_gpt2_token_bytes: round-trip correctness + // ---------------------------------------------------------------- + + #[test] + fn test_roundtrip_ascii() { + let (btu, utb) = init_gpt2_mapping(); + let original = b"hello"; + let token_str = bytes_to_gpt2_token(original, &btu); + let decoded = decode_gpt2_token_bytes(&token_str, &utb); + assert_eq!(decoded, original); + assert_eq!(String::from_utf8(decoded).unwrap(), "hello"); + } + + #[test] + fn test_roundtrip_cjk_full_char() { + let (btu, utb) = init_gpt2_mapping(); + // "地" = UTF-8 [0xE5, 0x9C, 0xB0] + let original = "地".as_bytes(); + let token_str = bytes_to_gpt2_token(original, &btu); + let decoded = decode_gpt2_token_bytes(&token_str, &utb); + assert_eq!(decoded, original); + assert_eq!(String::from_utf8(decoded).unwrap(), "地"); + } + + // ---------------------------------------------------------------- + // Core regression test: split UTF-8 across two BPE tokens + // ---------------------------------------------------------------- + + #[test] + fn test_decode_bytes_split_utf8_cjk() { + // "地" = UTF-8 [0xE5, 0x9C, 0xB0] + // Simulate BPE splitting into two tokens: + // Token A covers bytes [0xE5, 0x9C] (first 2 of 3) + // Token B covers byte [0xB0] (last 1 of 3) + let (btu, utb) = init_gpt2_mapping(); + + let part1_bytes: &[u8] = &[0xE5, 0x9C]; + let part2_bytes: &[u8] = &[0xB0]; + + let token_a = bytes_to_gpt2_token(part1_bytes, &btu); + let token_b = bytes_to_gpt2_token(part2_bytes, &btu); + + let decoded_a = decode_gpt2_token_bytes(&token_a, &utb); + let decoded_b = decode_gpt2_token_bytes(&token_b, &utb); + + // Each part alone is NOT valid UTF-8 + assert!(String::from_utf8(decoded_a.clone()).is_err(), + "Part 1 alone should NOT be valid UTF-8"); + assert!(String::from_utf8(decoded_b.clone()).is_err(), + "Part 2 alone should NOT be valid UTF-8"); + + // But concatenated they form valid UTF-8 for "地" + let mut combined = decoded_a; + combined.extend_from_slice(&decoded_b); + assert_eq!(combined, vec![0xE5, 0x9C, 0xB0]); + assert_eq!(String::from_utf8(combined).unwrap(), "地"); + } + + #[test] + fn test_decode_bytes_split_utf8_2byte_char() { + // "é" = UTF-8 [0xC3, 0xA9] + // Simulate BPE splitting each byte into its own token + let (btu, utb) = init_gpt2_mapping(); + + let part1: &[u8] = &[0xC3]; + let part2: &[u8] = &[0xA9]; + + let decoded_1 = decode_gpt2_token_bytes( + &bytes_to_gpt2_token(part1, &btu), &utb); + let decoded_2 = decode_gpt2_token_bytes( + &bytes_to_gpt2_token(part2, &btu), &utb); + + assert!(String::from_utf8(decoded_1.clone()).is_err()); + assert!(String::from_utf8(decoded_2.clone()).is_err()); + + let mut combined = decoded_1; + combined.extend_from_slice(&decoded_2); + assert_eq!(String::from_utf8(combined).unwrap(), "é"); + } + + #[test] + fn test_decode_bytes_split_utf8_4byte_emoji() { + // "🦀" = UTF-8 [0xF0, 0x9F, 0xA6, 0x80] + // Simulate BPE splitting into two halves + let (btu, utb) = init_gpt2_mapping(); + + let part1: &[u8] = &[0xF0, 0x9F]; + let part2: &[u8] = &[0xA6, 0x80]; + + let decoded_1 = decode_gpt2_token_bytes( + &bytes_to_gpt2_token(part1, &btu), &utb); + let decoded_2 = decode_gpt2_token_bytes( + &bytes_to_gpt2_token(part2, &btu), &utb); + + assert!(String::from_utf8(decoded_1.clone()).is_err()); + assert!(String::from_utf8(decoded_2.clone()).is_err()); + + let mut combined = decoded_1; + combined.extend_from_slice(&decoded_2); + assert_eq!(String::from_utf8(combined).unwrap(), "🦀"); + } + + // ---------------------------------------------------------------- + // Mixed content: ASCII + Emoji + CJK + // ---------------------------------------------------------------- + + #[test] + fn test_decode_bytes_mixed_content_rust_crab_chinese() { + let (btu, utb) = init_gpt2_mapping(); + + let full_text = "Rust🦀真棒"; + let all_bytes = full_text.as_bytes(); + + // Decode each byte as its own token, accumulate + let mut accumulated = Vec::new(); + for &b in all_bytes { + let token_str = bytes_to_gpt2_token(&[b], &btu); + let decoded = decode_gpt2_token_bytes(&token_str, &utb); + accumulated.extend_from_slice(&decoded); + } + + assert_eq!(accumulated, all_bytes); + assert_eq!(String::from_utf8(accumulated).unwrap(), "Rust🦀真棒"); + } + + // ---------------------------------------------------------------- + // Regression: ensure old lossy path would have corrupted + // ---------------------------------------------------------------- + + #[test] + fn test_lossy_corruption_proof() { + // Demonstrate that per-token String::from_utf8_lossy WOULD corrupt, + // while byte-level accumulation does NOT. + let (btu, utb) = init_gpt2_mapping(); + + // "地址" = [E5 9C B0] [E5 9D 80] + // Split: token_a=[E5,9C], token_b=[B0,E5], token_c=[9D,80] + let splits: &[&[u8]] = &[ + &[0xE5, 0x9C], + &[0xB0, 0xE5], + &[0x9D, 0x80], + ]; + + // Old approach: decode each token to String independently (lossy) + let mut lossy_result = String::new(); + for &part in splits { + let token_str = bytes_to_gpt2_token(part, &btu); + let decoded = decode_gpt2_token_bytes(&token_str, &utb); + lossy_result.push_str(&String::from_utf8_lossy(&decoded)); + } + // Lossy approach produces replacement characters + assert!(lossy_result.contains('\u{FFFD}'), + "Lossy per-token decode SHOULD produce U+FFFD, got: {:?}", lossy_result); + assert_ne!(lossy_result, "地址"); + + // New approach: accumulate bytes, convert once + let mut byte_buf = Vec::new(); + for &part in splits { + let token_str = bytes_to_gpt2_token(part, &btu); + let decoded = decode_gpt2_token_bytes(&token_str, &utb); + byte_buf.extend_from_slice(&decoded); + } + let correct_result = String::from_utf8(byte_buf).unwrap(); + assert_eq!(correct_result, "地址"); + assert!(!correct_result.contains('\u{FFFD}')); + } + + // ---------------------------------------------------------------- + // GPT-2 mapping correctness + // ---------------------------------------------------------------- + + #[test] + fn test_gpt2_mapping_all_256_bytes_roundtrip() { + let (btu, utb) = init_gpt2_mapping(); + + // Every byte value 0..255 must survive a round-trip + for b in 0u8..=255 { + let cp = btu[b as usize]; + assert!(cp >= 0 && cp < 512, + "byte {:#04x} mapped to out-of-range codepoint {}", b, cp); + + let recovered = utb[cp as usize]; + assert_eq!(recovered, b as i32, + "byte {:#04x} → cp {} → byte {:#04x} (expected {:#04x})", + b, cp, recovered, b); + } + } + + #[test] + fn test_gpt2_mapping_is_bijective() { + let (btu, _utb) = init_gpt2_mapping(); + + // All 256 codepoints must be distinct + let mut seen = std::collections::HashSet::new(); + for b in 0..256 { + let cp = btu[b]; + assert!(seen.insert(cp), + "byte {} and another byte both map to codepoint {}", b, cp); + } + assert_eq!(seen.len(), 256); + } +} diff --git a/vendor/qwenasr/crates/qwen-asr/src/transcribe.rs b/vendor/qwenasr/crates/qwen-asr/src/transcribe.rs new file mode 100644 index 0000000..53569e0 --- /dev/null +++ b/vendor/qwenasr/crates/qwen-asr/src/transcribe.rs @@ -0,0 +1,1308 @@ +//! Offline, segmented, and streaming transcription orchestration. + +use crate::audio; +use crate::config::*; +use crate::context::QwenCtx; +use crate::decoder::{self, tok_embed_bf16_to_f32}; +use crate::kernels; +use crate::tokenizer::QwenTokenizer; + +use std::time::Instant; + +// Prompt token sequences +const PREFIX_HEAD: &[i32] = &[151644, 8948, 198]; +const PREFIX_TAIL: &[i32] = &[151645, 198, 151644, 872, 198, 151669]; +const SUFFIX_BASE: &[i32] = &[151670, 151645, 198, 151644, 77091, 198]; + +// Streaming robustness constants (matching C reference) +const STREAM_DEGEN_MAX_PERIOD: usize = 6; +const STREAM_DEGEN_MIN_REPEATS: usize = 4; +const STREAM_STALE_CHUNKS: i32 = 4; +const STREAM_RESET_INTERVAL_CHUNKS: i32 = 45; +const STREAM_RESET_CARRY_TOKENS: usize = 24; +const STREAM_MAX_ENC_WINDOWS: usize = 4; + +fn get_time_ms() -> f64 { + // Use monotonic clock + static START: std::sync::OnceLock = std::sync::OnceLock::new(); + let start = START.get_or_init(Instant::now); + start.elapsed().as_secs_f64() * 1000.0 +} + +fn elapsed_ms(t0: f64) -> f64 { + get_time_ms() - t0 +} + +/// Returns `(best_reps, best_period)`: how many times a block of `best_period` +/// tokens repeats at the tail of `tokens`. Used for streaming degeneracy detection. +fn stream_tail_repeat_blocks(tokens: &[i32], max_period: usize) -> (usize, usize) { + let n = tokens.len(); + if n < 2 { return (1, 0); } + let period_cap = (n / 2).min(if max_period > 0 { max_period } else { n / 2 }); + let mut best_reps = 1usize; + let mut best_period = 0usize; + for p in 1..=period_cap { + let mut reps = 1usize; + while (reps + 1) * p <= n { + let a = &tokens[n - (reps + 1) * p .. n - reps * p]; + let b = &tokens[n - reps * p .. n - (reps - 1) * p]; + if a != b { break; } + reps += 1; + } + if reps > best_reps { best_reps = reps; best_period = p; } + } + (best_reps, best_period) +} + +fn load_tokenizer(model_dir: &str) -> Option { + let vocab_path = format!("{}/vocab.json", model_dir); + QwenTokenizer::load(&vocab_path) +} + +/// Transcribe a single segment. Returns (text, n_text_tokens). +fn transcribe_segment( + ctx: &mut QwenCtx, + samples: &[f32], + tokenizer: &QwenTokenizer, + past_tokens: Option<&[i32]>, +) -> Option<(String, i32)> { + let shared = ctx.shared.clone(); + let cfg = &shared.config.clone(); + let dim = cfg.dec_hidden; + let seg_t0 = get_time_ms(); + let mut n_text_tokens = 0i32; + + // Mel spectrogram + let t0 = get_time_ms(); + let (mel, mel_frames) = audio::mel_spectrogram(samples)?; + let mel_ms = elapsed_ms(t0); + + if kernels::verbose() >= 2 { + eprintln!(" Mel: {} frames ({:.0} ms)", mel_frames, mel_ms); + } + + // Encoder + let t0 = get_time_ms(); + let (enc_output, enc_seq_len) = shared.encoder.forward(cfg, &mel, mel_frames, Some(&mut ctx.enc_bufs))?; + let enc_ms = elapsed_ms(t0); + + if kernels::verbose() >= 2 { + eprintln!(" Encoder: {} tokens ({:.0} ms)", enc_seq_len, enc_ms); + } + + if !ctx.prepare_prompt_tokens(tokenizer) { + return None; + } + + // Build input embeddings + let n_prompt_tokens = ctx.prompt_tokens.as_ref().map_or(0, |t| t.len()); + let n_force_prompt_tokens = ctx.force_prompt_tokens.as_ref().map_or(0, |t| t.len()); + let n_past = past_tokens.map_or(0, |t| t.len()); + let n_past_prompt_tokens = if n_past > 0 { n_past + 1 } else { 0 }; // +1 for + + let prefix_len = PREFIX_HEAD.len() + n_prompt_tokens + PREFIX_TAIL.len(); + let suffix_len = SUFFIX_BASE.len() + n_force_prompt_tokens; + let total_seq = prefix_len + enc_seq_len + suffix_len + n_past_prompt_tokens; + + let mut input_embeds = vec![0.0f32; total_seq * dim]; + let tok_emb = shared.decoder.tok_embeddings_bf16; + + // Embed prefix head + let mut off = 0; + for &tok in PREFIX_HEAD { + unsafe { tok_embed_bf16_to_f32(&mut input_embeds[off * dim..(off + 1) * dim], tok_emb, tok, dim) }; + off += 1; + } + + // Optional prompt + if let Some(ref ptoks) = ctx.prompt_tokens { + for &tok in ptoks { + unsafe { tok_embed_bf16_to_f32(&mut input_embeds[off * dim..(off + 1) * dim], tok_emb, tok, dim) }; + off += 1; + } + } + + // Prefix tail + for &tok in PREFIX_TAIL { + unsafe { tok_embed_bf16_to_f32(&mut input_embeds[off * dim..(off + 1) * dim], tok_emb, tok, dim) }; + off += 1; + } + + // Encoder output + for i in 0..enc_seq_len { + input_embeds[(prefix_len + i) * dim..(prefix_len + i + 1) * dim] + .copy_from_slice(&enc_output[i * dim..(i + 1) * dim]); + } + + // Suffix base + let suffix_off = prefix_len + enc_seq_len; + for (i, &tok) in SUFFIX_BASE.iter().enumerate() { + unsafe { tok_embed_bf16_to_f32( + &mut input_embeds[(suffix_off + i) * dim..(suffix_off + i + 1) * dim], + tok_emb, tok, dim, + ) }; + } + + // Force language tokens + if let Some(ref ftoks) = ctx.force_prompt_tokens { + for (i, &tok) in ftoks.iter().enumerate() { + unsafe { tok_embed_bf16_to_f32( + &mut input_embeds[(suffix_off + SUFFIX_BASE.len() + i) * dim + ..(suffix_off + SUFFIX_BASE.len() + i + 1) * dim], + tok_emb, tok, dim, + ) }; + } + } + + // Past text conditioning tokens + let past_off = suffix_off + suffix_len; + if let Some(ptoks) = past_tokens { + for (i, &tok) in ptoks.iter().enumerate() { + unsafe { tok_embed_bf16_to_f32( + &mut input_embeds[(past_off + i) * dim..(past_off + i + 1) * dim], + tok_emb, tok, dim, + ) }; + } + unsafe { tok_embed_bf16_to_f32( + &mut input_embeds[(past_off + ptoks.len()) * dim..(past_off + ptoks.len() + 1) * dim], + tok_emb, TOKEN_ASR_TEXT, dim, + ) }; + } + + // Decoder prefill + let t0 = get_time_ms(); + ctx.kv_cache.len = 0; + let prefill_len = total_seq - 1; + decoder::decoder_prefill( + &shared.decoder, cfg, &mut ctx.kv_cache, &mut ctx.rope_cache, + &mut ctx.dec_bufs, &input_embeds, prefill_len, + ); + + // First token from last prefill position + let last_embed = &input_embeds[prefill_len * dim..(prefill_len + 1) * dim]; + let mut token = decoder::decoder_forward( + &shared.decoder, cfg, &mut ctx.kv_cache, &mut ctx.rope_cache, + &mut ctx.dec_bufs, last_embed, + ); + + let prefill_ms = elapsed_ms(t0); + if kernels::verbose() >= 2 { + eprintln!(" Prefill: {} tokens ({:.0} ms)", total_seq, prefill_ms); + } + + // Autoregressive decode + let t0 = get_time_ms(); + let max_tokens = 2048; + let mut n_generated = 0; + let mut past_asr_text = n_force_prompt_tokens > 0 || n_past > 0; + + let mut text_bytes: Vec = Vec::new(); + let mut tmp_embed = vec![0.0f32; dim]; + + while n_generated < max_tokens { + n_generated += 1; + + if token == TOKEN_ENDOFTEXT || token == TOKEN_IM_END { + break; + } + + if token == TOKEN_ASR_TEXT { + past_asr_text = true; + } else if past_asr_text { + let piece_bytes = tokenizer.decode_bytes(token); + text_bytes.extend_from_slice(piece_bytes); + n_text_tokens += 1; + + if let Some(ref cb) = ctx.token_cb { + // For the callback, provide lossy UTF-8 for display purposes + cb(&String::from_utf8_lossy(piece_bytes)); + } + } + + unsafe { tok_embed_bf16_to_f32(&mut tmp_embed, tok_emb, token, dim) }; + token = decoder::decoder_forward( + &shared.decoder, cfg, &mut ctx.kv_cache, &mut ctx.rope_cache, + &mut ctx.dec_bufs, &tmp_embed, + ); + } + + let decode_ms = elapsed_ms(t0); + if kernels::verbose() >= 2 { + eprintln!( + " Decode: {} tokens ({:.0} ms, {:.1} ms/token)", + n_generated, decode_ms, + if n_generated > 0 { decode_ms / n_generated as f64 } else { 0.0 } + ); + } + + // Trim whitespace — convert accumulated bytes to UTF-8 first + let text = String::from_utf8_lossy(&text_bytes); + let trimmed = text.trim().to_string(); + + ctx.perf_total_ms += elapsed_ms(seg_t0); + ctx.perf_text_tokens += n_text_tokens; + ctx.perf_encode_ms += mel_ms + enc_ms; + ctx.perf_decode_ms += prefill_ms + decode_ms; + + Some((trimmed, n_text_tokens)) +} + +// ======================================================================== +// Segment-based splitting +// ======================================================================== + +fn find_split_point(samples: &[f32], target_sample: usize, search_sec: f32) -> usize { + let search_half = (search_sec * SAMPLE_RATE as f32) as usize; + let lo = target_sample.saturating_sub(search_half); + let hi = (target_sample + search_half).min(samples.len()); + + let win_samples = 1600; // 100ms at 16kHz + let mut best_energy = 1e30f32; + let mut best_center = target_sample; + + let mut pos = lo; + while pos + win_samples <= hi { + let end = (pos + win_samples).min(samples.len()); + let mut energy = 0.0f32; + for &s in samples.iter().take(end).skip(pos) { + energy += s * s; + } + energy /= (end - pos) as f32; + if energy < best_energy { + best_energy = energy; + best_center = pos + (end - pos) / 2; + } + pos += win_samples / 2; + } + + best_center +} + +fn should_insert_boundary_space(prev_ch: u8, next_ch: u8) -> bool { + if prev_ch == 0 || next_ch == 0 { return false; } + if (prev_ch as char).is_whitespace() { return false; } + if (next_ch as char).is_whitespace() { return false; } + if (next_ch as char).is_ascii_punctuation() { return false; } + true +} + +// ======================================================================== +// Public API +// ======================================================================== + +/// Transcribe audio samples (f32, 16 kHz, mono, range [-1, 1]). +/// +/// When `ctx.segment_sec > 0` and the audio exceeds that duration, it is +/// automatically split at low-energy boundaries. +/// Returns `None` if the tokenizer or encoder fails to initialize. +pub fn transcribe_audio(ctx: &mut QwenCtx, samples: &[f32]) -> Option { + let shared = ctx.shared.clone(); + ctx.reset_perf(); + ctx.perf_audio_ms = 1000.0 * samples.len() as f64 / SAMPLE_RATE as f64; + + let audio_samples = if ctx.skip_silence { + let compacted = audio::compact_silence(samples); + if kernels::verbose() >= 1 { + let used_pct = 100.0 * compacted.len() as f32 / samples.len().max(1) as f32; + eprintln!( + "Silence skip: used {:.1}%, skipped {:.1}% ({} -> {} samples)", + used_pct, 100.0 - used_pct, samples.len(), compacted.len() + ); + } + compacted + } else { + samples.to_vec() + }; + + if kernels::verbose() >= 2 { + eprintln!( + "Audio: {} samples ({:.1} seconds)", + audio_samples.len(), + audio_samples.len() as f32 / SAMPLE_RATE as f32 + ); + } + + let tokenizer = load_tokenizer(&shared.model_dir)?; + if !ctx.prepare_prompt_tokens(&tokenizer) { + return None; + } + + let target_samples = (ctx.segment_sec * SAMPLE_RATE as f32) as usize; + let search = ctx.search_sec.min(ctx.segment_sec / 2.0); + let margin_samples = (search * SAMPLE_RATE as f32) as usize; + + // No splitting if segment_sec is 0 or audio fits in one segment + if ctx.segment_sec <= 0.0 || audio_samples.len() <= target_samples + margin_samples { + let (text, _) = transcribe_segment(ctx, &audio_samples, &tokenizer, None)?; + return Some(text); + } + + // Build split points + let mut splits = vec![0usize]; + let mut pos = 0; + while pos + target_samples + margin_samples < audio_samples.len() { + let split = find_split_point(&audio_samples, pos + target_samples, search); + splits.push(split); + pos = split; + if splits.len() >= 127 { break; } + } + let n_splits = splits.len(); + + if kernels::verbose() >= 2 { + eprintln!("Splitting into {} segments", n_splits); + } + + let mut result = String::new(); + let min_samples = SAMPLE_RATE as usize / 2; + let use_past_text = ctx.past_text_conditioning; + + for s in 0..n_splits { + let core_start = splits[s]; + let core_end = if s + 1 < n_splits { splits[s + 1] } else { audio_samples.len() }; + let seg_start = core_start; + let seg_end = core_end; + let seg_samples = seg_end - seg_start; + + if kernels::verbose() >= 2 { + eprintln!( + "Segment {}/{}: {:.1}-{:.1}s ({} samples)", + s + 1, n_splits, + seg_start as f32 / SAMPLE_RATE as f32, + seg_end as f32 / SAMPLE_RATE as f32, + seg_samples + ); + } + + let seg_buf: Vec; + let seg_ptr = if seg_samples < min_samples { + seg_buf = { + let mut buf = vec![0.0f32; min_samples]; + buf[..seg_samples].copy_from_slice(&audio_samples[seg_start..seg_end]); + buf + }; + &seg_buf[..] + } else { + &audio_samples[seg_start..seg_end] + }; + + let past_tokens: Option> = if use_past_text && !result.is_empty() { + tokenizer.encode(&result) + } else { + None + }; + + let (seg_text, _seg_text_tokens) = match transcribe_segment( + ctx, seg_ptr, &tokenizer, past_tokens.as_deref(), + ) { + Some(r) => r, + None => continue, + }; + + if seg_text.is_empty() { continue; } + + let need_space = if !result.is_empty() { + let prev = *result.as_bytes().last().unwrap_or(&0); + let next = *seg_text.as_bytes().first().unwrap_or(&0); + should_insert_boundary_space(prev, next) + } else { + false + }; + + if need_space { + result.push(' '); + if let Some(ref cb) = ctx.token_cb { + cb(" "); + } + } + + if let Some(ref cb) = ctx.token_cb { + if ctx.past_text_conditioning { + cb(&seg_text); + } + } + + result.push_str(&seg_text); + } + + Some(result) +} + +/// Convenience wrapper: load a WAV file and transcribe it. +/// +/// Equivalent to [`audio::load_wav`](crate::audio::load_wav) followed by +/// [`transcribe_audio`]. +pub fn transcribe(ctx: &mut QwenCtx, wav_path: &str) -> Option { + let samples = audio::load_wav(wav_path)?; + transcribe_audio(ctx, &samples) +} + +/// Transcribe from stdin. +pub fn transcribe_stdin(ctx: &mut QwenCtx) -> Option { + let samples = audio::read_pcm_stdin()?; + transcribe_audio(ctx, &samples) +} + +/// Streaming transcription: processes audio in chunks, emitting tokens via +/// `ctx.token_cb` as they become stable. +/// +/// Trades throughput for lower latency compared to offline mode. If no +/// `token_cb` is set, falls back to a single offline decode of the full audio. +pub fn transcribe_stream(ctx: &mut QwenCtx, samples: &[f32]) -> Option { + let shared = ctx.shared.clone(); + let cfg = shared.config.clone(); + let dim = cfg.dec_hidden; + let chunk_samples = (ctx.stream_chunk_sec * SAMPLE_RATE as f32) as usize; + let rollback = ctx.stream_rollback; + let unfixed_chunks = ctx.stream_unfixed_chunks; + let max_new_tokens = if ctx.stream_max_new_tokens > 0 { ctx.stream_max_new_tokens } else { 32 }; + + let audio_samples = if ctx.skip_silence { + audio::compact_silence(samples) + } else { + samples.to_vec() + }; + + ctx.reset_perf(); + ctx.perf_audio_ms = 1000.0 * samples.len() as f64 / SAMPLE_RATE as f64; + + // If no token callback, fall back to offline decode + if ctx.token_cb.is_none() { + if kernels::verbose() >= 2 { + eprintln!("Streaming: no token callback, using direct final refinement"); + } + let tokenizer = load_tokenizer(&shared.model_dir)?; + ctx.prepare_prompt_tokens(&tokenizer); + let (text, _) = transcribe_segment(ctx, &audio_samples, &tokenizer, None)?; + return Some(text); + } + + let tokenizer = load_tokenizer(&shared.model_dir)?; + if !ctx.prepare_prompt_tokens(&tokenizer) { + return None; + } + + let enc_window_frames = cfg.enc_n_window_infer.clamp(100, 800); + let enc_window_samples = enc_window_frames * HOP_LENGTH; + + let tok_emb = shared.decoder.tok_embeddings_bf16; + + let mut raw_tokens: Vec = Vec::new(); + let mut stable_text_tokens: Vec = Vec::new(); + let mut result_bytes: Vec = Vec::new(); + let mut tmp_embed = vec![0.0f32; dim]; + + let mut chunk_idx = 0i32; + let mut audio_cursor = 0usize; + + // Encoder window cache + struct EncWindow { + seq_len: usize, + enc_output: Vec, + } + let mut enc_cache: Vec = Vec::new(); + let mut enc_cached_seq_total = 0usize; + + // Previous prefill embeddings for LCP reuse + let mut prev_prefill_embeds: Vec = Vec::new(); + let mut prev_prefill_len = 0usize; + + // Streaming robustness state + let mut stale_count = 0i32; + let mut prev_tail_snapshot: Vec = Vec::new(); + let mut enc_cache_base_windows = 0usize; + + while audio_cursor < audio_samples.len() { + let chunk_t0 = get_time_ms(); + audio_cursor = (audio_cursor + chunk_samples).min(audio_samples.len()); + let is_final = audio_cursor >= audio_samples.len(); + + // Encoder + let t0 = get_time_ms(); + let full_end = (audio_cursor / enc_window_samples) * enc_window_samples; + + // Cache completed windows (base offset accounts for windows cleared on re-anchor) + while (enc_cache_base_windows + enc_cache.len()) * enc_window_samples < full_end { + let ws = (enc_cache_base_windows + enc_cache.len()) * enc_window_samples; + let (mel, mel_frames) = audio::mel_spectrogram(&audio_samples[ws..ws + enc_window_samples])?; + let (win_enc, win_seq) = shared.encoder.forward(&cfg, &mel, mel_frames, Some(&mut ctx.enc_bufs))?; + enc_cached_seq_total += win_seq; + enc_cache.push(EncWindow { seq_len: win_seq, enc_output: win_enc }); + } + + // Encode partial tail + let mut partial_seq = 0; + let mut partial_enc: Vec = Vec::new(); + if full_end < audio_cursor { + let _partial_samples = audio_cursor - full_end; + if let Some((mel, mel_frames)) = audio::mel_spectrogram(&audio_samples[full_end..audio_cursor]) { + if let Some((enc, seq)) = shared.encoder.forward(&cfg, &mel, mel_frames, Some(&mut ctx.enc_bufs)) { + partial_seq = seq; + partial_enc = enc; + } + } + } + + let enc_seq_len = enc_cached_seq_total + partial_seq; + if enc_seq_len == 0 { + chunk_idx += 1; + continue; + } + + // Assemble encoder output + let mut enc_output = vec![0.0f32; enc_seq_len * dim]; + let mut enc_off = 0; + for w in &enc_cache { + enc_output[enc_off * dim..(enc_off + w.seq_len) * dim] + .copy_from_slice(&w.enc_output); + enc_off += w.seq_len; + } + if partial_seq > 0 { + enc_output[enc_off * dim..(enc_off + partial_seq) * dim] + .copy_from_slice(&partial_enc); + } + + let enc_ms = elapsed_ms(t0); + ctx.perf_encode_ms += enc_ms; + + // Prefix rollback + let n_prefix_tokens = if ctx.past_text_conditioning && chunk_idx >= unfixed_chunks && !raw_tokens.is_empty() { + (raw_tokens.len() as i32 - rollback).max(0) as usize + } else { + 0 + }; + + // Build input embeddings + let n_prompt_tokens = ctx.prompt_tokens.as_ref().map_or(0, |t| t.len()); + let n_force_prompt_tokens = ctx.force_prompt_tokens.as_ref().map_or(0, |t| t.len()); + let prefix_len = PREFIX_HEAD.len() + n_prompt_tokens + PREFIX_TAIL.len(); + let suffix_len = SUFFIX_BASE.len() + n_force_prompt_tokens; + let total_seq = prefix_len + enc_seq_len + suffix_len + n_prefix_tokens; + + let mut input_embeds = vec![0.0f32; total_seq * dim]; + let mut off = 0; + + for &tok in PREFIX_HEAD { + unsafe { tok_embed_bf16_to_f32(&mut input_embeds[off * dim..(off + 1) * dim], tok_emb, tok, dim) }; + off += 1; + } + if let Some(ref ptoks) = ctx.prompt_tokens { + for &tok in ptoks { + unsafe { tok_embed_bf16_to_f32(&mut input_embeds[off * dim..(off + 1) * dim], tok_emb, tok, dim) }; + off += 1; + } + } + for &tok in PREFIX_TAIL { + unsafe { tok_embed_bf16_to_f32(&mut input_embeds[off * dim..(off + 1) * dim], tok_emb, tok, dim) }; + off += 1; + } + + for i in 0..enc_seq_len { + input_embeds[(prefix_len + i) * dim..(prefix_len + i + 1) * dim] + .copy_from_slice(&enc_output[i * dim..(i + 1) * dim]); + } + + let suffix_off = prefix_len + enc_seq_len; + for (i, &tok) in SUFFIX_BASE.iter().enumerate() { + unsafe { tok_embed_bf16_to_f32( + &mut input_embeds[(suffix_off + i) * dim..(suffix_off + i + 1) * dim], + tok_emb, tok, dim, + ) }; + } + if let Some(ref ftoks) = ctx.force_prompt_tokens { + for (i, &tok) in ftoks.iter().enumerate() { + unsafe { tok_embed_bf16_to_f32( + &mut input_embeds[(suffix_off + SUFFIX_BASE.len() + i) * dim + ..(suffix_off + SUFFIX_BASE.len() + i + 1) * dim], + tok_emb, tok, dim, + ) }; + } + } + + let text_off = suffix_off + suffix_len; + for i in 0..n_prefix_tokens { + unsafe { tok_embed_bf16_to_f32( + &mut input_embeds[(text_off + i) * dim..(text_off + i + 1) * dim], + tok_emb, raw_tokens[i], dim, + ) }; + } + + // Decoder prefill with LCP reuse + let t0 = get_time_ms(); + let prefill_len = total_seq - 1; + + let mut reused_prefill = 0; + if !prev_prefill_embeds.is_empty() && prev_prefill_len > 0 { + let cmp_len = prefill_len.min(prev_prefill_len); + let _row_bytes = dim * std::mem::size_of::(); + while reused_prefill < cmp_len { + let a = &prev_prefill_embeds[reused_prefill * dim..(reused_prefill + 1) * dim]; + let b = &input_embeds[reused_prefill * dim..(reused_prefill + 1) * dim]; + if a != b { break; } + reused_prefill += 1; + } + } + + ctx.kv_cache.len = reused_prefill; + let delta_prefill = prefill_len - reused_prefill; + if delta_prefill > 0 { + decoder::decoder_prefill( + &shared.decoder, &cfg, &mut ctx.kv_cache, &mut ctx.rope_cache, + &mut ctx.dec_bufs, + &input_embeds[reused_prefill * dim..], + delta_prefill, + ); + } + + let last_embed = &input_embeds[prefill_len * dim..(prefill_len + 1) * dim]; + let mut token = decoder::decoder_forward( + &shared.decoder, &cfg, &mut ctx.kv_cache, &mut ctx.rope_cache, + &mut ctx.dec_bufs, last_embed, + ); + + // Save for next chunk + prev_prefill_embeds = input_embeds[..prefill_len * dim].to_vec(); + prev_prefill_len = prefill_len; + + let prefill_ms = elapsed_ms(t0); + ctx.perf_decode_ms += prefill_ms; + + // Autoregressive decode + let t0 = get_time_ms(); + let mut chunk_tokens: Vec = Vec::new(); + let mut n_generated = 0; + + while n_generated < max_new_tokens { + n_generated += 1; + if token == TOKEN_ENDOFTEXT || token == TOKEN_IM_END { break; } + chunk_tokens.push(token); + unsafe { tok_embed_bf16_to_f32(&mut tmp_embed, tok_emb, token, dim); } + token = decoder::decoder_forward( + &shared.decoder, &cfg, &mut ctx.kv_cache, &mut ctx.rope_cache, + &mut ctx.dec_bufs, &tmp_embed, + ); + } + + let decode_ms = elapsed_ms(t0); + ctx.perf_decode_ms += decode_ms; + + // Update raw token history + raw_tokens.truncate(n_prefix_tokens); + raw_tokens.extend_from_slice(&chunk_tokens); + + // Streaming degeneracy detection + if raw_tokens == prev_tail_snapshot { + stale_count += 1; + } else { + stale_count = 0; + prev_tail_snapshot = raw_tokens.clone(); + } + let (best_reps, _) = stream_tail_repeat_blocks(&raw_tokens, STREAM_DEGEN_MAX_PERIOD); + let is_degen = stale_count >= STREAM_STALE_CHUNKS + || best_reps >= STREAM_DEGEN_MIN_REPEATS; + + if is_degen { + if kernels::verbose() >= 2 { + eprintln!("[stream degen] reset at chunk {} (stale={}, reps={})", + chunk_idx, stale_count, best_reps); + } + let carry = stable_text_tokens.len().min(STREAM_RESET_CARRY_TOKENS); + let carry_start = stable_text_tokens.len() - carry; + raw_tokens.clear(); + if carry > 0 { + raw_tokens.push(TOKEN_ASR_TEXT); + raw_tokens.extend_from_slice(&stable_text_tokens[carry_start..]); + } + prev_prefill_embeds.clear(); + prev_prefill_len = 0; + stale_count = 0; + prev_tail_snapshot.clear(); + } + + // Periodic re-anchor: reset context every STREAM_RESET_INTERVAL_CHUNKS chunks + if chunk_idx > 0 && chunk_idx % STREAM_RESET_INTERVAL_CHUNKS == 0 { + if kernels::verbose() >= 2 { + eprintln!("[stream reanchor] at chunk {}", chunk_idx); + } + let carry = stable_text_tokens.len().min(STREAM_RESET_CARRY_TOKENS); + let carry_start = stable_text_tokens.len() - carry; + raw_tokens.clear(); + if carry > 0 { + raw_tokens.push(TOKEN_ASR_TEXT); + raw_tokens.extend_from_slice(&stable_text_tokens[carry_start..]); + } + prev_prefill_embeds.clear(); + prev_prefill_len = 0; + stale_count = 0; + prev_tail_snapshot.clear(); + enc_cache_base_windows += enc_cache.len(); + enc_cache.clear(); + enc_cached_seq_total = 0; + } + + // Parse text region + let text_start = if n_force_prompt_tokens == 0 { + raw_tokens.iter().position(|&t| t == TOKEN_ASR_TEXT) + .map(|p| p + 1) + .unwrap_or(0) + } else { + 0 + }; + let n_text_tokens = raw_tokens.len().saturating_sub(text_start); + + // Fixed frontier + let candidate_len = if is_final { + n_text_tokens + } else if chunk_idx >= unfixed_chunks { + (n_text_tokens as i32 - rollback).max(0) as usize + } else { + 0 + }; + + // Monotonic commit + let candidate_tokens = &raw_tokens[text_start..]; + let _lcp = stable_text_tokens.iter().zip(candidate_tokens.iter()) + .take_while(|(a, b)| a == b) + .count(); + + let emit_from = stable_text_tokens.len(); + let emit_to = candidate_len.max(emit_from); + + for i in emit_from..emit_to { + if i < candidate_tokens.len() { + if i >= stable_text_tokens.len() { + stable_text_tokens.push(candidate_tokens[i]); + } + let piece_bytes = tokenizer.decode_bytes(candidate_tokens[i]); + if let Some(ref cb) = ctx.token_cb { + cb(&String::from_utf8_lossy(piece_bytes)); + } + ctx.perf_text_tokens += 1; + result_bytes.extend_from_slice(piece_bytes); + } + } + + ctx.perf_total_ms += elapsed_ms(chunk_t0); + chunk_idx += 1; + } + + Some(String::from_utf8_lossy(&result_bytes).trim().to_string()) +}// ======================================================================== +// Incremental Streaming API +// ======================================================================== + +/// Encoder window cached output. +struct EncWindow { + seq_len: usize, + enc_output: Vec, +} + +/// Persistent state for incremental streaming transcription. +/// +/// Create once, then call [`stream_push_audio`] each time new audio +/// arrives. The state keeps encoder caches, token history, and decoder +/// prefill embeddings so that only *new* work is performed per call. +pub struct StreamState { + // Encoder + enc_cache: Vec, + enc_cached_seq_total: usize, + enc_cache_base_windows: usize, + + // Decoder token history + raw_tokens: Vec, + stable_text_tokens: Vec, + result_bytes: Vec, + + // Prefill LCP reuse + prev_prefill_embeds: Vec, + prev_prefill_len: usize, + + // Streaming robustness + prev_tail_snapshot: Vec, + stale_count: i32, + + // Lazy partial encoding: skip re-encoding every other chunk + last_partial_cursor: usize, + last_partial_enc: Vec, + last_partial_seq: usize, + + // Audio cursor + audio_cursor: usize, + chunk_idx: i32, + + // Tokenizer (loaded once) + tokenizer: Option, + prompt_prepared: bool, +} + +impl Default for StreamState { + fn default() -> Self { + Self::new() + } +} + +impl StreamState { + /// Create a new empty streaming state. + pub fn new() -> Self { + StreamState { + enc_cache: Vec::new(), + enc_cached_seq_total: 0, + enc_cache_base_windows: 0, + raw_tokens: Vec::new(), + stable_text_tokens: Vec::new(), + result_bytes: Vec::new(), + prev_prefill_embeds: Vec::new(), + prev_prefill_len: 0, + prev_tail_snapshot: Vec::new(), + stale_count: 0, + last_partial_cursor: 0, + last_partial_enc: Vec::new(), + last_partial_seq: 0, + audio_cursor: 0, + chunk_idx: 0, + tokenizer: None, + prompt_prepared: false, + } + } + + /// Reset state for a new streaming window (e.g., after 30s limit). + pub fn reset(&mut self) { + self.enc_cache.clear(); + self.enc_cached_seq_total = 0; + self.enc_cache_base_windows = 0; + self.raw_tokens.clear(); + self.stable_text_tokens.clear(); + self.result_bytes.clear(); + self.prev_prefill_embeds.clear(); + self.prev_prefill_len = 0; + self.prev_tail_snapshot.clear(); + self.stale_count = 0; + self.last_partial_cursor = 0; + self.last_partial_enc.clear(); + self.last_partial_seq = 0; + self.audio_cursor = 0; + self.chunk_idx = 0; + // Keep tokenizer and prompt_prepared + } + + /// Get the current stable transcription result. + pub fn text(&self) -> String { + String::from_utf8_lossy(&self.result_bytes).into_owned() + } + + /// Get how many samples have been processed so far. + pub fn audio_cursor(&self) -> usize { + self.audio_cursor + } +} + +/// Process all available new audio incrementally. +/// +/// `samples` is the **full** audio buffer accumulated so far (16 kHz mono f32). +/// Processes all full chunks from `state.audio_cursor` to end of `samples`. +/// When `finalize` is true, also processes any remaining partial chunk and +/// emits all rollback-buffered tokens. +/// Returns the newly emitted text delta (if any). +pub fn stream_push_audio( + ctx: &mut QwenCtx, + samples: &[f32], + state: &mut StreamState, + finalize: bool, +) -> Option { + let shared = ctx.shared.clone(); + let cfg = shared.config.clone(); + let dim = cfg.dec_hidden; + let chunk_samples = (ctx.stream_chunk_sec * SAMPLE_RATE as f32) as usize; + let rollback = ctx.stream_rollback; + let unfixed_chunks = ctx.stream_unfixed_chunks; + let max_new_tokens = if ctx.stream_max_new_tokens > 0 { ctx.stream_max_new_tokens } else { 32 }; + + // Lazy-init tokenizer + if state.tokenizer.is_none() { + state.tokenizer = load_tokenizer(&shared.model_dir); + } + let tokenizer = state.tokenizer.as_ref()?; + + if !state.prompt_prepared { + if !ctx.prepare_prompt_tokens(tokenizer) { + return None; + } + state.prompt_prepared = true; + } + + // Check if we have enough audio for at least one chunk (or finalizing) + let available = samples.len().saturating_sub(state.audio_cursor); + if available < chunk_samples && !finalize { + return Some(String::new()); + } + if available == 0 { + return Some(String::new()); + } + + let enc_window_frames = cfg.enc_n_window_infer.clamp(100, 800); + let enc_window_samples = enc_window_frames * HOP_LENGTH; + let tok_emb = shared.decoder.tok_embeddings_bf16; + let mut tmp_embed = vec![0.0f32; dim]; + let mut delta_bytes: Vec = Vec::new(); + + // ---- Process full chunks, plus remainder if finalizing ---- + while state.audio_cursor < samples.len() { + let remaining = samples.len() - state.audio_cursor; + if remaining < chunk_samples && !finalize { + break; // Wait for more audio + } + + let chunk_t0 = get_time_ms(); + state.audio_cursor = (state.audio_cursor + chunk_samples).min(samples.len()); + let is_final = finalize && state.audio_cursor >= samples.len(); + + // ---- Encoder: only encode new windows ---- + let t0 = get_time_ms(); + let full_end = (state.audio_cursor / enc_window_samples) * enc_window_samples; + + // Cache newly completed windows (base offset accounts for windows cleared on re-anchor) + while (state.enc_cache_base_windows + state.enc_cache.len()) * enc_window_samples < full_end { + let ws = (state.enc_cache_base_windows + state.enc_cache.len()) * enc_window_samples; + let (mel, mel_frames) = audio::mel_spectrogram(&samples[ws..ws + enc_window_samples])?; + let (win_enc, win_seq) = shared.encoder.forward(&cfg, &mel, mel_frames, Some(&mut ctx.enc_bufs))?; + state.enc_cached_seq_total += win_seq; + state.enc_cache.push(EncWindow { seq_len: win_seq, enc_output: win_enc }); + } + + // Encode partial tail — with lazy re-encoding for LCP optimization. + // Only re-encode when enough new audio has accumulated (every 2 chunks), + // on the first chunk, or when finalizing. On skip chunks, the reused + // encoder output gives near-perfect LCP matching, cutting prefill cost. + let enc_update_threshold = chunk_samples * 2; + let partial_age = state.audio_cursor.saturating_sub(state.last_partial_cursor); + let need_encode = state.last_partial_cursor == 0 + || partial_age >= enc_update_threshold + || is_final; + + let partial_seq; + let partial_enc; + if need_encode && full_end < state.audio_cursor { + if let Some((mel, mel_frames)) = audio::mel_spectrogram(&samples[full_end..state.audio_cursor]) { + if let Some((enc, seq)) = shared.encoder.forward(&cfg, &mel, mel_frames, Some(&mut ctx.enc_bufs)) { + partial_seq = seq; + partial_enc = enc; + state.last_partial_cursor = state.audio_cursor; + state.last_partial_enc = partial_enc.clone(); + state.last_partial_seq = partial_seq; + } else { + partial_seq = state.last_partial_seq; + partial_enc = state.last_partial_enc.clone(); + } + } else { + partial_seq = state.last_partial_seq; + partial_enc = state.last_partial_enc.clone(); + } + } else if full_end < state.audio_cursor { + // Reuse previous partial encoding (skip chunk) + partial_seq = state.last_partial_seq; + partial_enc = state.last_partial_enc.clone(); + } else { + partial_seq = 0; + partial_enc = Vec::new(); + } + + let enc_seq_len = state.enc_cached_seq_total + partial_seq; + if enc_seq_len == 0 { + state.chunk_idx += 1; + return Some(String::new()); + } + + // Assemble encoder output (cached windows + partial) + let mut enc_output = vec![0.0f32; enc_seq_len * dim]; + let mut enc_off = 0; + for w in &state.enc_cache { + enc_output[enc_off * dim..(enc_off + w.seq_len) * dim] + .copy_from_slice(&w.enc_output); + enc_off += w.seq_len; + } + if partial_seq > 0 { + enc_output[enc_off * dim..(enc_off + partial_seq) * dim] + .copy_from_slice(&partial_enc); + } + + let enc_ms = elapsed_ms(t0); + ctx.perf_encode_ms += enc_ms; + + // ---- Prefix rollback ---- + let n_prefix_tokens = if ctx.past_text_conditioning + && state.chunk_idx >= unfixed_chunks + && !state.raw_tokens.is_empty() + { + (state.raw_tokens.len() as i32 - rollback).max(0) as usize + } else { + 0 + }; + + // ---- Build input embeddings ---- + let n_prompt_tokens = ctx.prompt_tokens.as_ref().map_or(0, |t| t.len()); + let n_force_prompt_tokens = ctx.force_prompt_tokens.as_ref().map_or(0, |t| t.len()); + let prefix_len = PREFIX_HEAD.len() + n_prompt_tokens + PREFIX_TAIL.len(); + let suffix_len = SUFFIX_BASE.len() + n_force_prompt_tokens; + let total_seq = prefix_len + enc_seq_len + suffix_len + n_prefix_tokens; + + let mut input_embeds = vec![0.0f32; total_seq * dim]; + let mut off = 0; + + for &tok in PREFIX_HEAD { + unsafe { tok_embed_bf16_to_f32(&mut input_embeds[off * dim..(off + 1) * dim], tok_emb, tok, dim); } + off += 1; + } + if let Some(ref ptoks) = ctx.prompt_tokens { + for &tok in ptoks { + unsafe { tok_embed_bf16_to_f32(&mut input_embeds[off * dim..(off + 1) * dim], tok_emb, tok, dim); } + off += 1; + } + } + for &tok in PREFIX_TAIL { + unsafe { tok_embed_bf16_to_f32(&mut input_embeds[off * dim..(off + 1) * dim], tok_emb, tok, dim); } + off += 1; + } + + for i in 0..enc_seq_len { + input_embeds[(prefix_len + i) * dim..(prefix_len + i + 1) * dim] + .copy_from_slice(&enc_output[i * dim..(i + 1) * dim]); + } + + let suffix_off = prefix_len + enc_seq_len; + for (i, &tok) in SUFFIX_BASE.iter().enumerate() { + unsafe { tok_embed_bf16_to_f32( + &mut input_embeds[(suffix_off + i) * dim..(suffix_off + i + 1) * dim], + tok_emb, tok, dim, + ); } + } + if let Some(ref ftoks) = ctx.force_prompt_tokens { + for (i, &tok) in ftoks.iter().enumerate() { + unsafe { tok_embed_bf16_to_f32( + &mut input_embeds[(suffix_off + SUFFIX_BASE.len() + i) * dim + ..(suffix_off + SUFFIX_BASE.len() + i + 1) * dim], + tok_emb, tok, dim, + ); } + } + } + + let text_off = suffix_off + suffix_len; + for i in 0..n_prefix_tokens { + unsafe { tok_embed_bf16_to_f32( + &mut input_embeds[(text_off + i) * dim..(text_off + i + 1) * dim], + tok_emb, state.raw_tokens[i], dim, + ); } + } + + // ---- Decoder prefill with LCP reuse ---- + let t0 = get_time_ms(); + let prefill_len = total_seq - 1; + + let mut reused_prefill = 0; + if !state.prev_prefill_embeds.is_empty() && state.prev_prefill_len > 0 { + let cmp_len = prefill_len.min(state.prev_prefill_len); + while reused_prefill < cmp_len { + let a = &state.prev_prefill_embeds[reused_prefill * dim..(reused_prefill + 1) * dim]; + let b = &input_embeds[reused_prefill * dim..(reused_prefill + 1) * dim]; + if a != b { break; } + reused_prefill += 1; + } + } + + ctx.kv_cache.len = reused_prefill; + let delta_prefill = prefill_len - reused_prefill; + if delta_prefill > 0 { + decoder::decoder_prefill( + &shared.decoder, &cfg, &mut ctx.kv_cache, &mut ctx.rope_cache, + &mut ctx.dec_bufs, + &input_embeds[reused_prefill * dim..], + delta_prefill, + ); + } + + let last_embed = &input_embeds[prefill_len * dim..(prefill_len + 1) * dim]; + let mut token = decoder::decoder_forward( + &shared.decoder, &cfg, &mut ctx.kv_cache, &mut ctx.rope_cache, + &mut ctx.dec_bufs, last_embed, + ); + + // Save for next chunk + state.prev_prefill_embeds = input_embeds[..prefill_len * dim].to_vec(); + state.prev_prefill_len = prefill_len; + + let prefill_ms = elapsed_ms(t0); + ctx.perf_decode_ms += prefill_ms; + + if kernels::verbose() >= 2 { + eprintln!( + " [stream chunk {}] encoder: {:.0}ms, prefill: {}/{} reused ({:.0}ms, delta={})", + state.chunk_idx, enc_ms, reused_prefill, prefill_len, prefill_ms, delta_prefill + ); + } + + // ---- Autoregressive decode ---- + let t0 = get_time_ms(); + let mut chunk_tokens: Vec = Vec::new(); + let mut n_generated = 0; + + while n_generated < max_new_tokens { + n_generated += 1; + if token == TOKEN_ENDOFTEXT || token == TOKEN_IM_END { break; } + chunk_tokens.push(token); + unsafe { tok_embed_bf16_to_f32(&mut tmp_embed, tok_emb, token, dim); } + token = decoder::decoder_forward( + &shared.decoder, &cfg, &mut ctx.kv_cache, &mut ctx.rope_cache, + &mut ctx.dec_bufs, &tmp_embed, + ); + } + + let decode_ms = elapsed_ms(t0); + ctx.perf_decode_ms += decode_ms; + + // ---- Detect speech end (decoder produced EOT on silence) ---- + // When chunk_tokens is empty, the decoder saw silence/end-of-speech. + // Commit ALL remaining rollback-buffered tokens BEFORE truncation, + // since truncate will remove them. + let speech_ended = chunk_tokens.is_empty() + && !state.raw_tokens.is_empty() + && state.chunk_idx >= unfixed_chunks; + + if speech_ended { + // Emit remaining rollback tokens from current raw_tokens (before truncation) + let text_start = if n_force_prompt_tokens == 0 { + state.raw_tokens.iter().position(|&t| t == TOKEN_ASR_TEXT) + .map(|p| p + 1) + .unwrap_or(0) + } else { + 0 + }; + let candidate_tokens = &state.raw_tokens[text_start..]; + let n_text = candidate_tokens.len(); + let emit_from = state.stable_text_tokens.len(); + for i in emit_from..n_text { + if i < candidate_tokens.len() { + if i >= state.stable_text_tokens.len() { + state.stable_text_tokens.push(candidate_tokens[i]); + } + let piece_bytes = tokenizer.decode_bytes(candidate_tokens[i]); + if let Some(ref cb) = ctx.token_cb { + cb(&String::from_utf8_lossy(piece_bytes)); + } + ctx.perf_text_tokens += 1; + state.result_bytes.extend_from_slice(piece_bytes); + delta_bytes.extend_from_slice(piece_bytes); + } + } + } + + // ---- Update raw token history ---- + state.raw_tokens.truncate(n_prefix_tokens); + state.raw_tokens.extend_from_slice(&chunk_tokens); + + // ---- Streaming degeneracy detection ---- + if !speech_ended { + if state.raw_tokens == state.prev_tail_snapshot { + state.stale_count += 1; + } else { + state.stale_count = 0; + state.prev_tail_snapshot = state.raw_tokens.clone(); + } + let (best_reps, _) = stream_tail_repeat_blocks(&state.raw_tokens, STREAM_DEGEN_MAX_PERIOD); + let is_degen = state.stale_count >= STREAM_STALE_CHUNKS + || best_reps >= STREAM_DEGEN_MIN_REPEATS; + + if is_degen { + if kernels::verbose() >= 2 { + eprintln!("[stream degen] reset at chunk {} (stale={}, reps={})", + state.chunk_idx, state.stale_count, best_reps); + } + let carry = state.stable_text_tokens.len().min(STREAM_RESET_CARRY_TOKENS); + let carry_start = state.stable_text_tokens.len() - carry; + state.raw_tokens.clear(); + if carry > 0 { + state.raw_tokens.push(TOKEN_ASR_TEXT); + state.raw_tokens.extend_from_slice(&state.stable_text_tokens[carry_start..]); + } + state.prev_prefill_embeds.clear(); + state.prev_prefill_len = 0; + state.stale_count = 0; + state.prev_tail_snapshot.clear(); + if state.enc_cache.len() >= STREAM_MAX_ENC_WINDOWS { + state.enc_cache_base_windows += state.enc_cache.len(); + state.enc_cache.clear(); + state.enc_cached_seq_total = 0; + } + } + + // Periodic re-anchor: reset context every STREAM_RESET_INTERVAL_CHUNKS chunks + if state.chunk_idx > 0 && state.chunk_idx % STREAM_RESET_INTERVAL_CHUNKS == 0 { + if kernels::verbose() >= 2 { + eprintln!("[stream reanchor] at chunk {}", state.chunk_idx); + } + let carry = state.stable_text_tokens.len().min(STREAM_RESET_CARRY_TOKENS); + let carry_start = state.stable_text_tokens.len() - carry; + state.raw_tokens.clear(); + if carry > 0 { + state.raw_tokens.push(TOKEN_ASR_TEXT); + state.raw_tokens.extend_from_slice(&state.stable_text_tokens[carry_start..]); + } + state.prev_prefill_embeds.clear(); + state.prev_prefill_len = 0; + state.stale_count = 0; + state.prev_tail_snapshot.clear(); + if state.enc_cache.len() >= STREAM_MAX_ENC_WINDOWS { + state.enc_cache_base_windows += state.enc_cache.len(); + state.enc_cache.clear(); + state.enc_cached_seq_total = 0; + } + } + } + + // ---- Parse text region and emit stable tokens (non-speech-ended case) ---- + if !speech_ended { + let text_start = if n_force_prompt_tokens == 0 { + state.raw_tokens.iter().position(|&t| t == TOKEN_ASR_TEXT) + .map(|p| p + 1) + .unwrap_or(0) + } else { + 0 + }; + let n_text_tokens = state.raw_tokens.len().saturating_sub(text_start); + + let candidate_len = if is_final { + n_text_tokens + } else if state.chunk_idx >= unfixed_chunks { + (n_text_tokens as i32 - rollback).max(0) as usize + } else { + 0 + }; + + let candidate_tokens = &state.raw_tokens[text_start..]; + let emit_from = state.stable_text_tokens.len(); + let emit_to = candidate_len.max(emit_from); + + for i in emit_from..emit_to { + if i < candidate_tokens.len() { + if i >= state.stable_text_tokens.len() { + state.stable_text_tokens.push(candidate_tokens[i]); + } + let piece_bytes = tokenizer.decode_bytes(candidate_tokens[i]); + if let Some(ref cb) = ctx.token_cb { + cb(&String::from_utf8_lossy(piece_bytes)); + } + ctx.perf_text_tokens += 1; + state.result_bytes.extend_from_slice(piece_bytes); + delta_bytes.extend_from_slice(piece_bytes); + } + } + } + + ctx.perf_total_ms += elapsed_ms(chunk_t0); + state.chunk_idx += 1; + + // Stop processing after speech ends — no point encoding more silence + if speech_ended { + break; + } + } // end while loop + + Some(String::from_utf8_lossy(&delta_bytes).into_owned()) +} diff --git a/曦云系列_通用GPU_mx-smi使用手册_CN_V14.pdf b/曦云系列_通用GPU_mx-smi使用手册_CN_V14.pdf new file mode 100644 index 0000000..8b90de1 Binary files /dev/null and b/曦云系列_通用GPU_mx-smi使用手册_CN_V14.pdf differ