Split FunASR frontend and backend services

.env.example

@@ -1,50 +1,28 @@
-# 选择已经下载到 demo/models 目录中的 ASR 模型；必须与实际模型目录保持一致。
-# 默认使用轻量的 0.6B 模型。如下载的是 1.7B，请改为 1.7b，不能混用。
-QWEN3_ASR_MODEL=0.6b
-# 本地模型根目录。下载脚本、VLLM 启动脚本和辅助服务都会从这里查找模型。
-MODEL_DIR=D:/github-project/ASR/Qwen-Asr/demo/models
+# Local model assets; the backend never downloads models at startup.
+MODEL_DIR=models
+# Set this if CAM++ is not at a model_manifest.json path.
+# CAM_MODEL_PATH=iic/speech_campplus_sv_zh-cn_16k-common
 
-# VLLM 宿主机服务绑定地址；0.0.0.0 表示允许服务器网卡接收外部请求。
-VLLM_HOST=0.0.0.0
-# VLLM 服务端口；启动器、健康检查和前端连接地址必须使用同一个端口。
-VLLM_PORT=9950
-# VLLM 可执行文件名称；新版环境通常为 vllm，启动器会自动执行 vllm serve。
-VLLM_EXECUTABLE=vllm
-# 启动成功提示中显示的地址，只影响日志和使用说明，不改变实际监听地址。
-VLLM_DISPLAY_HOST=127.0.0.1
-# 启动轮询使用的地址；如果 VLLM 部署在本机，通常保持 127.0.0.1 即可。
-VLLM_PROBE_HOST=127.0.0.1
-# VLLM 启动阶段最多检查多少次健康状态，超过次数仍未就绪则启动失败。
-VLLM_STARTUP_CHECK_LOOPS=60
-# 两次健康检查之间的等待秒数；模型加载较慢时可以适当增大检查次数。
-VLLM_STARTUP_CHECK_INTERVAL_SECONDS=2
-# VLLM 使用的显存比例。显存还要留给 VAD、聚类和声纹辅助模型时，建议预留余量。
-VLLM_GPU_MEMORY_UTILIZATION=0.3
-# VLLM 的最大上下文长度；数值越大通常占用越多显存，请结合显卡容量调整。
-VLLM_MAX_MODEL_LEN=16384
-# VLLM 同时处理的最大序列数；实时单路验证可保持默认值，多路并发时再调大。
-VLLM_MAX_NUM_SEQS=16
-# 张量并行 GPU 数量；单卡部署为 1，多卡部署时填写参与并行的 GPU 数量。
-VLLM_TENSOR_PARALLEL_SIZE=1
-# 是否启用 eager 模式。true 通常更容易启动和排查，false 可能获得更高性能。
-VLLM_ENFORCE_EAGER=true
+# FunASR realtime engine
+FUNASR_ASR_MODEL=paraformer-zh-streaming
+FUNASR_VAD_MODEL=fsmn-vad
+FUNASR_DEVICE=cuda:0
+FUNASR_VAD_DEVICE=cpu
+FUNASR_CHUNK_SIZE=0,10,5
+FUNASR_ENCODER_LOOK_BACK=4
+FUNASR_DECODER_LOOK_BACK=1
+FUNASR_VAD_CHUNK_MS=200
+FUNASR_MAX_SEGMENT_SEC=30
 
-# GB10 需要使用 CUDA 13 工具链中的 ptxas；serve.py 会自动加载该配置并传给 VLLM。
-TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas
-
-# 可选：为外部客户端设置稳定的公开模型名称。留空时默认使用完整 ModelScope ID。
-# VLLM_SERVED_MODEL_NAME=Qwen/Qwen3-ASR-0.6B
-
-# 辅助模型服务地址；它独立加载 VAD、CAM++ 聚类和声纹模型，WebSocket
-# 服务只通过 HTTP 调用，不会把这些 GPU 模型重复加载到 WebSocket 进程。
+# CAM++ is required and started by the backend launcher.
 AUXILIARY_SERVICE_URL=http://127.0.0.1:8010
-# WebSocket 调用已独立部署的 vLLM；跨服务器时改成模型服务器的实际地址。
-MODEL_SERVICE_URL=http://127.0.0.1:9950/v1
-# 辅助服务监听的 GPU；单卡服务器保持 cuda:0，多卡时可改成指定卡号。
-AUXILIARY_DEVICE=cuda:0
-# 仅保留兼容旧配置；VAD/CAM++ 核心模型缺失时始终拒绝启动，不会静默降级。
-# 可选 diarization/aligner 缺失不会阻断实时服务。
-AUXILIARY_ALLOW_MISSING=false
-# 启动时强制加载 VAD + CAM++ speaker_verification；完整 diarization 首次调用时按需加载。
-# 如确实需要启动时额外预加载，可追加：vad,speaker_verification,diarization
-AUXILIARY_PRELOAD_KINDS=vad,speaker_verification
+
+# Standalone frontend and browser-facing backend origin.
+FRONTEND_HOST=127.0.0.1
+FRONTEND_PORT=8080
+FRONTEND_ORIGIN=http://127.0.0.1:8080
+BACKEND_PUBLIC_URL=http://127.0.0.1:8082
+
+WEB_HOST=0.0.0.0
+WEB_PORT=8082
+WEB_DISPLAY_HOST=127.0.0.1

.env.funasr.example

@@ -0,0 +1,31 @@
+# Local model assets; the backend never downloads models at startup.
+MODEL_DIR=models
+# Set this if CAM++ is not at a model_manifest.json path.
+# CAM_MODEL_PATH=iic/speech_campplus_sv_zh-cn_16k-common
+
+# FunASR streaming ASR model. The backend launcher requires it to exist locally.
+FUNASR_ASR_MODEL=paraformer-zh-streaming
+FUNASR_VAD_MODEL=fsmn-vad
+
+# ASR and VAD may use different devices to limit peak GPU memory.
+FUNASR_DEVICE=cuda:0
+FUNASR_VAD_DEVICE=cpu
+
+# [left context, current chunk, right lookahead]; 10 * 60ms = 600ms.
+FUNASR_CHUNK_SIZE=0,10,5
+FUNASR_ENCODER_LOOK_BACK=4
+FUNASR_DECODER_LOOK_BACK=1
+FUNASR_VAD_CHUNK_MS=200
+FUNASR_MAX_SEGMENT_SEC=30
+
+# CAM++ is required. The backend launcher starts this service itself.
+AUXILIARY_SERVICE_URL=http://127.0.0.1:8010
+
+# Standalone frontend and browser-facing backend origin.
+FRONTEND_HOST=127.0.0.1
+FRONTEND_PORT=8080
+FRONTEND_ORIGIN=http://127.0.0.1:8080
+BACKEND_PUBLIC_URL=http://127.0.0.1:8082
+WEB_HOST=0.0.0.0
+WEB_PORT=8082
+WEB_DISPLAY_HOST=127.0.0.1

.env.qwen_legacy.example

@@ -0,0 +1,50 @@
+# 选择已经下载到 demo/models 目录中的 ASR 模型；必须与实际模型目录保持一致。
+# 默认使用轻量的 0.6B 模型。如下载的是 1.7B，请改为 1.7b，不能混用。
+QWEN3_ASR_MODEL=0.6b
+# 本地模型根目录。下载脚本、VLLM 启动脚本和辅助服务都会从这里查找模型。
+MODEL_DIR=D:/github-project/ASR/Qwen-Asr/demo/models
+
+# VLLM 宿主机服务绑定地址；0.0.0.0 表示允许服务器网卡接收外部请求。
+VLLM_HOST=0.0.0.0
+# VLLM 服务端口；启动器、健康检查和前端连接地址必须使用同一个端口。
+VLLM_PORT=9950
+# VLLM 可执行文件名称；新版环境通常为 vllm，启动器会自动执行 vllm serve。
+VLLM_EXECUTABLE=vllm
+# 启动成功提示中显示的地址，只影响日志和使用说明，不改变实际监听地址。
+VLLM_DISPLAY_HOST=127.0.0.1
+# 启动轮询使用的地址；如果 VLLM 部署在本机，通常保持 127.0.0.1 即可。
+VLLM_PROBE_HOST=127.0.0.1
+# VLLM 启动阶段最多检查多少次健康状态，超过次数仍未就绪则启动失败。
+VLLM_STARTUP_CHECK_LOOPS=60
+# 两次健康检查之间的等待秒数；模型加载较慢时可以适当增大检查次数。
+VLLM_STARTUP_CHECK_INTERVAL_SECONDS=2
+# VLLM 使用的显存比例。显存还要留给 VAD、聚类和声纹辅助模型时，建议预留余量。
+VLLM_GPU_MEMORY_UTILIZATION=0.3
+# VLLM 的最大上下文长度；数值越大通常占用越多显存，请结合显卡容量调整。
+VLLM_MAX_MODEL_LEN=16384
+# VLLM 同时处理的最大序列数；实时单路验证可保持默认值，多路并发时再调大。
+VLLM_MAX_NUM_SEQS=16
+# 张量并行 GPU 数量；单卡部署为 1，多卡部署时填写参与并行的 GPU 数量。
+VLLM_TENSOR_PARALLEL_SIZE=1
+# 是否启用 eager 模式。true 通常更容易启动和排查，false 可能获得更高性能。
+VLLM_ENFORCE_EAGER=true
+
+# GB10 需要使用 CUDA 13 工具链中的 ptxas；serve.py 会自动加载该配置并传给 VLLM。
+TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas
+
+# 可选：为外部客户端设置稳定的公开模型名称。留空时默认使用完整 ModelScope ID。
+# VLLM_SERVED_MODEL_NAME=Qwen/Qwen3-ASR-0.6B
+
+# 辅助模型服务地址；它独立加载 VAD、CAM++ 聚类和声纹模型，WebSocket
+# 服务只通过 HTTP 调用，不会把这些 GPU 模型重复加载到 WebSocket 进程。
+AUXILIARY_SERVICE_URL=http://127.0.0.1:8010
+# WebSocket 调用已独立部署的 vLLM；跨服务器时改成模型服务器的实际地址。
+MODEL_SERVICE_URL=http://127.0.0.1:9950/v1
+# 辅助服务监听的 GPU；单卡服务器保持 cuda:0，多卡时可改成指定卡号。
+AUXILIARY_DEVICE=cuda:0
+# 仅保留兼容旧配置；VAD/CAM++ 核心模型缺失时始终拒绝启动，不会静默降级。
+# 可选 diarization/aligner 缺失不会阻断实时服务。
+AUXILIARY_ALLOW_MISSING=false
+# 启动时强制加载 VAD + CAM++ speaker_verification；完整 diarization 首次调用时按需加载。
+# 如确实需要启动时额外预加载，可追加：vad,speaker_verification,diarization
+AUXILIARY_PRELOAD_KINDS=vad,speaker_verification

FUNASR_README.md

@@ -0,0 +1,53 @@
+# FunASR realtime browser demo
+
+The frontend and backend start separately. The backend launcher loads three
+required local assets: streaming ASR, FSMN VAD, and CAM++ speaker verification.
+It starts the CAM++ model service and the WebSocket service and stops both
+together. No model is downloaded by the launcher.
+
+## Model directories
+
+Put the assets under models/ or set MODEL_DIR in .env. The default
+FunASR names resolve to these local directories:
+
+- models/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online
+- models/iic/speech_fsmn_vad_zh-cn-16k-common-pytorch (or the damo/ variant)
+- models/iic/speech_campplus_sv_zh-cn_16k-common (or the damo/ variant)
+
+If your directories have different names, set FUNASR_ASR_MODEL and
+FUNASR_VAD_MODEL to their paths. The CAM++ directories follow
+model_manifest.json; set CAM_MODEL_PATH for another location. Backend startup reports every checked path when a
+required asset is missing.
+
+## Start
+
+First install a torch/torchaudio build suitable for the host CPU or CUDA,
+then install the project dependencies:
+
+~~~powershell
+cd D:\github-project\ASR\Asr-demo
+python -m pip install -r requirements-funasr.txt
+python -m pip install -r requirements-auxiliary.txt
+if (-not (Test-Path .env)) { Copy-Item .env.funasr.example .env }
+~~~
+
+In one terminal start the backend:
+
+~~~powershell
+python scripts\run_backend.py
+~~~
+
+In another terminal start the frontend:
+
+~~~powershell
+python scripts\run_frontend.py
+~~~
+
+Open http://127.0.0.1:8080/. The backend WebSocket listens on port 8082 and
+the CAM++ service on port 8010. BACKEND_PUBLIC_URL must be reachable from
+the browser. Set FRONTEND_ORIGIN to the exact frontend origin if it differs
+from the default.
+
+The browser sends start, PCM16 frames, and stop/eof over WebSocket. Speaker
+labels are required. Short or unusable speech may still receive an unknown
+speaker label, but missing CAM++ prevents backend startup.

README.md

@@ -1,151 +1,25 @@
-# Qwen3-ASR VLLM 独立部署项目
+# FunASR realtime ASR demo
 
-本目录是后续实时 ASR 功能验证使用的独立模型服务项目。
+This branch runs streaming ASR, VAD, and CAM++ from local model assets.
+The browser UI starts as a separate service.
 
-它不导入、不启动、也不调用仓库根目录下原项目的 `app/` 代码。模型下载、VLLM 启动、配置和服务验证都在本目录内完成。后续验证 demo 只需要调用这里提供的 VLLM OpenAI 兼容接口。
+## Start
 
-## 当前下载范围
+Install a torch/torchaudio build for the host, then install
+requirements-funasr.txt and requirements-auxiliary.txt. Copy
+.env.funasr.example to .env if no .env exists, and check MODEL_DIR.
 
-默认下载一个 ASR 模型和独立辅助模型运行服务所需的全部模型资产：
+Start the backend (CAM++ model service plus WebSocket) in one terminal:
 
-```text
-Qwen/Qwen3-ASR-0.6B
-```
+~~~powershell
+python scripts\run_backend.py
+~~~
 
-ASR 如需使用大模型，可显式选择 1.7B：
+Start the frontend in another terminal:
 
-```text
-Qwen/Qwen3-ASR-1.7B
-```
+~~~powershell
+python scripts\run_frontend.py
+~~~
 
-辅助资产包括 VAD、CAM++ 分离、配置声纹、实时声纹、CAM++ Transformer 和 Qwen3 ForcedAligner。它们不会由 `qwen-asr-serve` 启动，而是由独立 Python 辅助模型服务加载。
-
-不会下载另一个未选择的 ASR 模型；辅助模型资产会随默认部署包下载，供独立 Python 运行服务预加载。
-
-## 1. 下载模型
-
-模型直接下载到宿主机的 `demo/models`。ASR 由 VLLM 启动，辅助模型由独立 Python 运行服务启动。
-
-```powershell
-cd D:\github-project\ASR\Qwen-Asr\demo
-python -m venv .venv
-.\.venv\Scripts\Activate.ps1
-pip install -r requirements-download.txt
-python scripts\download_models.py
-```
-
-上述命令会下载默认 `0.6B` ASR 以及全部辅助模型，并在下载完成后把 CAM++ 配置中的依赖模型 ID 改为 `demo/models` 下的本地路径，保证辅助服务可以离线启动。只下载 ASR 时使用：
-
-```powershell
-python scripts\download_models.py --skip-auxiliary
-```
-
-只下载辅助模型时使用：
-
-```powershell
-python scripts\download_models.py --auxiliary-only
-```
-
-选择 1.7B：
-
-```powershell
-python scripts\download_models.py --model 1.7b
-```
-
-检查模型是否完整但不下载：
-
-```powershell
-python scripts\download_models.py --check-only
-```
-
-ModelScope 下载也可以通过环境变量调整缓存目录：
-
-```powershell
-$env:MODELSCOPE_CACHE = 'D:\modelscope-cache'
-python scripts\download_models.py
-```
-
-## 2. 宿主机启动 VLLM 服务
-
-需要宿主机具备与 VLLM 兼容的 Python、CUDA 和 NVIDIA 驱动环境。安装部署依赖：
-
-```bash
-python -m pip install -r requirements-deploy.txt
-```
-
-先复制并按服务器实际路径修改 `.env`，启动器会自动读取该文件：
-
-```bash
-cp .env.example .env
-```
-
-默认启动 `Qwen/Qwen3-ASR-0.6B`，监听地址为 `0.0.0.0:9950`：
-
-```bash
-python scripts/serve.py
-```
-
-模型和启动检查循环可以通过命令行或环境变量传入；端口统一在 `scripts/serve.py` 的 `SERVER_PORT` 变量中维护：
-
-```bash
-QWEN3_ASR_MODEL=0.6b VLLM_STARTUP_CHECK_LOOPS=120 \
-VLLM_STARTUP_CHECK_INTERVAL_SECONDS=2 python scripts/serve.py
-```
-
-如果使用 `1.7B`，下载和启动必须指定同一个模型：
-
-```powershell
-python scripts\download_models.py --model 1.7b
-python -m scripts.serve --model 1.7b
-```
-
-启动器会按 `VLLM_STARTUP_CHECK_LOOPS` 次数轮询 `/health`，每次间隔由 `VLLM_STARTUP_CHECK_INTERVAL_SECONDS` 指定。服务端口、健康检查端口和就绪提示统一使用 `scripts/serve.py` 中的 `SERVER_PORT`。
-
-服务启动后可检查：
-
-```bash
-curl "http://${VLLM_DISPLAY_HOST:-127.0.0.1}:9950/health"
-curl "http://${VLLM_DISPLAY_HOST:-127.0.0.1}:9950/v1/models"
-```
-
-## 3. 调用转写接口
-
-VLLM 服务提供 OpenAI 兼容的音频转写接口：
-
-```bash
-curl "http://${VLLM_DISPLAY_HOST:-127.0.0.1}:9950/v1/audio/transcriptions" \
-  -H "Authorization: Bearer EMPTY" \
-  -F "file=@./audio/sample.wav" \
-  -F "model=Qwen/Qwen3-ASR-0.6B"
-```
-
-## 4. 启动辅助模型服务
-
-另开一个终端，在同一台服务器启动 VAD、CAM++ 和声纹模型运行服务：
-
-```powershell
-cd D:\github-project\ASR\Qwen-Asr\demo
-pip install -r requirements-auxiliary.txt
-python scripts\auxiliary_server.py
-```
-
-辅助服务默认监听 `0.0.0.0:8010`。实时链路启动时严格加载 VAD 和 CAM++ `speaker_verification` 声纹模型，用于每个 turn 的特征提取与在线聚类；完整 CAM++ 分离、Transformer 和 ForcedAligner 不阻断核心服务，完整分离模型会在调用 `/v1/diarization` 时按需加载。检查状态：
-
-```bash
-curl http://127.0.0.1:8010/health
-```
-
-WebSocket demo 默认连接 `9950` 的 ASR VLLM，辅助服务使用 `8010`。`/health` 的 `ready` 要求 `vad_ready` 与 `speaker_embedding_ready` 同时为 true；完整 diarization 资产缺失不会影响实时 `/v1/speaker/resolve`。
-
-实时链路中的职责是：WebSocket 用 RMS 帧门控快速检测停顿；辅助服务用 FunASR VAD 提供 `/v1/vad`，并加载 CAM++ `speaker_verification` 提取 turn embedding，再由服务端在线聚类。`speech_campplus_speaker-diarization_common` 是完整音频分离接口的额外 pipeline，不是实时 turn 聚类的唯一入口。
-
-也可以使用多模态 Chat Completions 接口，后续实时验证项目将以此服务边界为准。
-
-## 项目边界
-
-- `scripts/download_models.py`：下载选定 ASR 和全部辅助模型资产。
-- `scripts/serve.py`：读取 `.env`，解析模型选择、宿主机参数并启动新版 `vllm serve`。
-- `requirements-deploy.txt`：安装宿主机部署所需的官方 Qwen3-ASR VLLM 依赖。
-- `tests/`：只验证本项目自己的模型清单和选择逻辑，不依赖原项目。
-
-模型服务就绪后，新的实时 ASR demo 放在同级 `demo` 项目中继续开发，但不得通过 Python import 或 HTTP/WebSocket 调用原项目服务。
+Open http://127.0.0.1:8080/. Model directory layout and address settings
+are described in FUNASR_README.md.

README_QWEN_LEGACY.md

@@ -0,0 +1,151 @@
+# Qwen3-ASR VLLM 独立部署项目
+
+本目录是后续实时 ASR 功能验证使用的独立模型服务项目。
+
+它不导入、不启动、也不调用仓库根目录下原项目的 `app/` 代码。模型下载、VLLM 启动、配置和服务验证都在本目录内完成。后续验证 demo 只需要调用这里提供的 VLLM OpenAI 兼容接口。
+
+## 当前下载范围
+
+默认下载一个 ASR 模型和独立辅助模型运行服务所需的全部模型资产：
+
+```text
+Qwen/Qwen3-ASR-0.6B
+```
+
+ASR 如需使用大模型，可显式选择 1.7B：
+
+```text
+Qwen/Qwen3-ASR-1.7B
+```
+
+辅助资产包括 VAD、CAM++ 分离、配置声纹、实时声纹、CAM++ Transformer 和 Qwen3 ForcedAligner。它们不会由 `qwen-asr-serve` 启动，而是由独立 Python 辅助模型服务加载。
+
+不会下载另一个未选择的 ASR 模型；辅助模型资产会随默认部署包下载，供独立 Python 运行服务预加载。
+
+## 1. 下载模型
+
+模型直接下载到宿主机的 `demo/models`。ASR 由 VLLM 启动，辅助模型由独立 Python 运行服务启动。
+
+```powershell
+cd D:\github-project\ASR\Qwen-Asr\demo
+python -m venv .venv
+.\.venv\Scripts\Activate.ps1
+pip install -r requirements-download.txt
+python scripts\download_models.py
+```
+
+上述命令会下载默认 `0.6B` ASR 以及全部辅助模型，并在下载完成后把 CAM++ 配置中的依赖模型 ID 改为 `demo/models` 下的本地路径，保证辅助服务可以离线启动。只下载 ASR 时使用：
+
+```powershell
+python scripts\download_models.py --skip-auxiliary
+```
+
+只下载辅助模型时使用：
+
+```powershell
+python scripts\download_models.py --auxiliary-only
+```
+
+选择 1.7B：
+
+```powershell
+python scripts\download_models.py --model 1.7b
+```
+
+检查模型是否完整但不下载：
+
+```powershell
+python scripts\download_models.py --check-only
+```
+
+ModelScope 下载也可以通过环境变量调整缓存目录：
+
+```powershell
+$env:MODELSCOPE_CACHE = 'D:\modelscope-cache'
+python scripts\download_models.py
+```
+
+## 2. 宿主机启动 VLLM 服务
+
+需要宿主机具备与 VLLM 兼容的 Python、CUDA 和 NVIDIA 驱动环境。安装部署依赖：
+
+```bash
+python -m pip install -r requirements-deploy.txt
+```
+
+先复制并按服务器实际路径修改 `.env`，启动器会自动读取该文件：
+
+```bash
+cp .env.example .env
+```
+
+默认启动 `Qwen/Qwen3-ASR-0.6B`，监听地址为 `0.0.0.0:9950`：
+
+```bash
+python scripts/serve.py
+```
+
+模型和启动检查循环可以通过命令行或环境变量传入；端口统一在 `scripts/serve.py` 的 `SERVER_PORT` 变量中维护：
+
+```bash
+QWEN3_ASR_MODEL=0.6b VLLM_STARTUP_CHECK_LOOPS=120 \
+VLLM_STARTUP_CHECK_INTERVAL_SECONDS=2 python scripts/serve.py
+```
+
+如果使用 `1.7B`，下载和启动必须指定同一个模型：
+
+```powershell
+python scripts\download_models.py --model 1.7b
+python -m scripts.serve --model 1.7b
+```
+
+启动器会按 `VLLM_STARTUP_CHECK_LOOPS` 次数轮询 `/health`，每次间隔由 `VLLM_STARTUP_CHECK_INTERVAL_SECONDS` 指定。服务端口、健康检查端口和就绪提示统一使用 `scripts/serve.py` 中的 `SERVER_PORT`。
+
+服务启动后可检查：
+
+```bash
+curl "http://${VLLM_DISPLAY_HOST:-127.0.0.1}:9950/health"
+curl "http://${VLLM_DISPLAY_HOST:-127.0.0.1}:9950/v1/models"
+```
+
+## 3. 调用转写接口
+
+VLLM 服务提供 OpenAI 兼容的音频转写接口：
+
+```bash
+curl "http://${VLLM_DISPLAY_HOST:-127.0.0.1}:9950/v1/audio/transcriptions" \
+  -H "Authorization: Bearer EMPTY" \
+  -F "file=@./audio/sample.wav" \
+  -F "model=Qwen/Qwen3-ASR-0.6B"
+```
+
+## 4. 启动辅助模型服务
+
+另开一个终端，在同一台服务器启动 VAD、CAM++ 和声纹模型运行服务：
+
+```powershell
+cd D:\github-project\ASR\Qwen-Asr\demo
+pip install -r requirements-auxiliary.txt
+python scripts\auxiliary_server.py
+```
+
+辅助服务默认监听 `0.0.0.0:8010`。实时链路启动时严格加载 VAD 和 CAM++ `speaker_verification` 声纹模型，用于每个 turn 的特征提取与在线聚类；完整 CAM++ 分离、Transformer 和 ForcedAligner 不阻断核心服务，完整分离模型会在调用 `/v1/diarization` 时按需加载。检查状态：
+
+```bash
+curl http://127.0.0.1:8010/health
+```
+
+WebSocket demo 默认连接 `9950` 的 ASR VLLM，辅助服务使用 `8010`。`/health` 的 `ready` 要求 `vad_ready` 与 `speaker_embedding_ready` 同时为 true；完整 diarization 资产缺失不会影响实时 `/v1/speaker/resolve`。
+
+实时链路中的职责是：WebSocket 用 RMS 帧门控快速检测停顿；辅助服务用 FunASR VAD 提供 `/v1/vad`，并加载 CAM++ `speaker_verification` 提取 turn embedding，再由服务端在线聚类。`speech_campplus_speaker-diarization_common` 是完整音频分离接口的额外 pipeline，不是实时 turn 聚类的唯一入口。
+
+也可以使用多模态 Chat Completions 接口，后续实时验证项目将以此服务边界为准。
+
+## 项目边界
+
+- `scripts/download_models.py`：下载选定 ASR 和全部辅助模型资产。
+- `scripts/serve.py`：读取 `.env`，解析模型选择、宿主机参数并启动新版 `vllm serve`。
+- `requirements-deploy.txt`：安装宿主机部署所需的官方 Qwen3-ASR VLLM 依赖。
+- `tests/`：只验证本项目自己的模型清单和选择逻辑，不依赖原项目。
+
+模型服务就绪后，新的实时 ASR demo 放在同级 `demo` 项目中继续开发，但不得通过 Python import 或 HTTP/WebSocket 调用原项目服务。

pyproject.toml

@@ -3,18 +3,22 @@ requires = ["setuptools>=68", "wheel"]
 build-backend = "setuptools.build_meta"
 
 [project]
-name = "qwen3-asr-vllm-deployment"
+name = "funasr-realtime-asr-demo"
 version = "0.1.0"
-description = "Standalone Qwen3-ASR model downloader and VLLM deployment"
+description = "FunASR streaming ASR browser demo"
 requires-python = ">=3.10,<3.14"
 dependencies = [
-  "modelscope==1.34.0",
-  "qwen-asr[vllm]==0.0.6",
+  "aiohttp==3.11.11",
+  "python-dotenv>=1.0",
+  "funasr==1.4.16",
+  "modelscope[framework]==1.34.0",
+  "soundfile==0.13.1",
+  "librosa==0.11.0",
+  "numpy>=1.24",
 ]
 
 [project.scripts]
-qwen3-asr-download = "scripts.download_models:main"
-qwen3-asr-serve = "scripts.serve:main"
+funasr-realtime-demo = "scripts.run_funasr_demo:main"
 
 [tool.setuptools]
-packages = ["scripts"]
+packages = ["scripts", "realtime_websocket"]

pyproject_qwen_legacy.toml

@@ -0,0 +1,20 @@
+[build-system]
+requires = ["setuptools>=68", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "qwen3-asr-vllm-deployment"
+version = "0.1.0"
+description = "Standalone Qwen3-ASR model downloader and VLLM deployment"
+requires-python = ">=3.10,<3.14"
+dependencies = [
+  "modelscope==1.34.0",
+  "qwen-asr[vllm]==0.0.6",
+]
+
+[project.scripts]
+qwen3-asr-download = "scripts.download_models:main"
+qwen3-asr-serve = "scripts.serve:main"
+
+[tool.setuptools]
+packages = ["scripts"]

realtime_websocket/__init__.py

@@ -0,0 +1 @@
+"""FunASR realtime browser demo package."""

realtime_websocket/funasr_engine.py

@@ -0,0 +1,393 @@
+"""FunASR realtime engine migrated into the demo project.
+
+This module keeps the browser-facing project independent from the original
+Qwen/VLLM service. It follows FunASR's streaming cache lifecycle:
+one cache per ASR session and is_final=True only for the last chunk.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import os
+from dataclasses import dataclass
+from typing import Any
+
+LOGGER = logging.getLogger(__name__)
+
+
+@dataclass(frozen=True)
+class FunASRServiceConfig:
+    """Configuration for the in-process FunASR models."""
+
+    model: str = "paraformer-zh-streaming"
+    vad_model: str = "fsmn-vad"
+    device: str = "cuda:0"
+    vad_device: str = "cpu"
+    sample_rate: int = 16000
+    vad_chunk_ms: int = 200
+    chunk_size: tuple[int, int, int] = (0, 10, 5)
+    encoder_chunk_look_back: int = 4
+    decoder_chunk_look_back: int = 1
+    max_segment_sec: float = 30.0
+
+    @classmethod
+    def from_env(cls) -> "FunASRServiceConfig":
+        """Read model selection from environment without changing WS fields."""
+        chunk_text = os.getenv("FUNASR_CHUNK_SIZE", "0,10,5")
+        try:
+            values = tuple(int(value.strip()) for value in chunk_text.split(","))
+            chunk_size = values if len(values) == 3 else cls.chunk_size
+        except ValueError:
+            chunk_size = cls.chunk_size
+        return cls(
+            model=os.getenv("FUNASR_ASR_MODEL", cls.model),
+            vad_model=os.getenv("FUNASR_VAD_MODEL", cls.vad_model),
+            device=os.getenv("FUNASR_DEVICE", cls.device),
+            vad_device=os.getenv("FUNASR_VAD_DEVICE", cls.vad_device),
+            vad_chunk_ms=max(50, int(os.getenv("FUNASR_VAD_CHUNK_MS", str(cls.vad_chunk_ms)))),
+            chunk_size=chunk_size,
+            encoder_chunk_look_back=max(
+                0, int(os.getenv("FUNASR_ENCODER_LOOK_BACK", str(cls.encoder_chunk_look_back)))
+            ),
+            decoder_chunk_look_back=max(
+                0, int(os.getenv("FUNASR_DECODER_LOOK_BACK", str(cls.decoder_chunk_look_back)))
+            ),
+            max_segment_sec=max(
+                2.0, float(os.getenv("FUNASR_MAX_SEGMENT_SEC", str(cls.max_segment_sec)))
+            ),
+        )
+
+
+@dataclass(frozen=True)
+class FunASRSegment:
+    """A partial or final event returned by one browser session."""
+
+    text: str
+    start_time_ms: float
+    end_time_ms: float
+    audio: bytes
+    voiced_ms: float
+    is_final: bool
+    sentence_id: int
+    reason: str | None = None
+
+
+def _result_text(result: Any) -> str:
+    """Extract text from the list/dict result shapes used by FunASR."""
+    if isinstance(result, list):
+        return _result_text(result[0]) if result else ""
+    if isinstance(result, dict):
+        value = result.get("text")
+        if value is None:
+            value = result.get("value")
+        return str(value or "").strip()
+    return str(result or "").strip()
+
+
+def _vad_events(result: Any) -> list[tuple[float, float]]:
+    """Normalize FunASR streaming VAD output to start/end milliseconds."""
+    if isinstance(result, list):
+        result = result[0] if result else {}
+    if isinstance(result, dict):
+        result = result.get("value") or result.get("segments") or []
+    if not isinstance(result, list):
+        return []
+    events: list[tuple[float, float]] = []
+    for item in result:
+        if not isinstance(item, (list, tuple)) or len(item) < 2:
+            continue
+        try:
+            events.append((float(item[0]), float(item[1])))
+        except (TypeError, ValueError):
+            continue
+    return events
+
+
+class FunASRModelService:
+    """Load FunASR once and create isolated streaming state per browser."""
+
+    native_partial_supported = True
+
+    def __init__(self, config: FunASRServiceConfig | None = None) -> None:
+        self.config = config or FunASRServiceConfig.from_env()
+        self.asr_model: Any | None = None
+        self.vad_model: Any | None = None
+        # Model objects are shared; each session owns its own cache. Serializing
+        # calls makes the first validation version predictable on one GPU.
+        self.inference_lock = asyncio.Lock()
+
+    async def start(self) -> None:
+        """Load streaming ASR and VAD outside the event loop."""
+        try:
+            from funasr import AutoModel
+        except ImportError as exc:  # pragma: no cover - deployment-only branch
+            raise RuntimeError(
+                "FunASR is not installed; run pip install -r requirements-deploy.txt"
+            ) from exc
+
+        def load_models() -> tuple[Any, Any]:
+            common = {"disable_pbar": True, "disable_log": True}
+            asr = AutoModel(model=self.config.model, device=self.config.device, **common)
+            vad = AutoModel(model=self.config.vad_model, device=self.config.vad_device, **common)
+            return asr, vad
+
+        self.asr_model, self.vad_model = await asyncio.to_thread(load_models)
+        LOGGER.info(
+            "FunASR ready: model=%s vad=%s device=%s vad_device=%s",
+            self.config.model,
+            self.config.vad_model,
+            self.config.device,
+            self.config.vad_device,
+        )
+
+    async def close(self) -> None:
+        """Release references so CUDA memory can be reclaimed on shutdown."""
+        self.asr_model = None
+        self.vad_model = None
+
+    def create_session(self) -> "FunASRRealtimeSession":
+        """Create a session with isolated VAD and ASR caches."""
+        if self.asr_model is None or self.vad_model is None:
+            raise RuntimeError("FunASR model service is not started")
+        return FunASRRealtimeSession(self)
+
+    async def generate_asr(self, audio: Any, status: dict[str, Any]) -> str:
+        """Run one blocking ASR chunk while preserving its mutable cache."""
+        if self.asr_model is None:
+            raise RuntimeError("FunASR ASR model is not loaded")
+
+        def generate() -> str:
+            return _result_text(self.asr_model.generate(input=audio, **status))
+
+        async with self.inference_lock:
+            return await asyncio.to_thread(generate)
+
+    async def generate_vad(
+        self,
+        audio: Any,
+        status: dict[str, Any],
+        chunk_ms: int,
+    ) -> list[tuple[float, float]]:
+        """Run one streaming VAD chunk and normalize endpoint events."""
+        if self.vad_model is None:
+            raise RuntimeError("FunASR VAD model is not loaded")
+
+        def generate() -> list[tuple[float, float]]:
+            result = self.vad_model.generate(input=audio, chunk_size=chunk_ms, **status)
+            return _vad_events(result)
+
+        async with self.inference_lock:
+            return await asyncio.to_thread(generate)
+
+
+class _StreamingASRTurn:
+    """One utterance using FunASR's ordered chunk/cache lifecycle."""
+
+    def __init__(self, service: FunASRModelService) -> None:
+        self.service = service
+        self.cache: dict[str, Any] = {}
+        self.pending = bytearray()
+        self.cumulative_text = ""
+        self.last_chunk_text = ""
+        self.pending_outputs: list[str] = []
+        self.chunk_samples = max(1, service.config.chunk_size[1] * 960)
+
+    @staticmethod
+    def _merge_chunk_text(current: str, chunk: str, previous_chunk: str) -> str:
+        """Merge chunk text while tolerating wrappers returning cumulative text."""
+        if not chunk or chunk == previous_chunk:
+            return current
+        if current and chunk.startswith(current):
+            return chunk
+        return current + chunk
+
+    async def append(self, pcm_bytes: bytes) -> list[str]:
+        """Decode complete chunks but hold one chunk for the final flush."""
+        if not pcm_bytes:
+            return []
+        self.pending.extend(pcm_bytes)
+        chunk_bytes = self.chunk_samples * 2
+        # Hold one full chunk so the real last chunk receives is_final=True.
+        while len(self.pending) >= chunk_bytes * 2:
+            chunk = bytes(self.pending[:chunk_bytes])
+            del self.pending[:chunk_bytes]
+            text = await self.service.generate_asr(
+                chunk,
+                {
+                    "cache": self.cache,
+                    "is_final": False,
+                    "chunk_size": self.service.config.chunk_size,
+                    "encoder_chunk_look_back": self.service.config.encoder_chunk_look_back,
+                    "decoder_chunk_look_back": self.service.config.decoder_chunk_look_back,
+                    "batch_size": 1,
+                },
+            )
+            self.cumulative_text = self._merge_chunk_text(
+                self.cumulative_text, text, self.last_chunk_text
+            )
+            self.last_chunk_text = text
+            if text:
+                self.pending_outputs.append(self.cumulative_text)
+        outputs = self.pending_outputs
+        self.pending_outputs = []
+        return outputs
+
+    async def finish(self) -> str:
+        """Flush the last buffered chunk and return cumulative text."""
+        if self.pending:
+            chunk = bytes(self.pending)
+            self.pending.clear()
+            text = await self.service.generate_asr(
+                chunk,
+                {
+                    "cache": self.cache,
+                    "is_final": True,
+                    "chunk_size": self.service.config.chunk_size,
+                    "encoder_chunk_look_back": self.service.config.encoder_chunk_look_back,
+                    "decoder_chunk_look_back": self.service.config.decoder_chunk_look_back,
+                    "batch_size": 1,
+                },
+            )
+            self.cumulative_text = self._merge_chunk_text(
+                self.cumulative_text, text, self.last_chunk_text
+            )
+            self.last_chunk_text = text
+        return self.cumulative_text.strip()
+
+
+class FunASRRealtimeSession:
+    """FunASR VAD + streaming ASR session used by one browser WebSocket."""
+
+    def __init__(self, service: FunASRModelService) -> None:
+        self.service = service
+        self.sample_rate = service.config.sample_rate
+        self.vad_chunk_bytes = service.config.vad_chunk_ms * self.sample_rate * 2 // 1000
+        self.vad_buffer = bytearray()
+        self.vad_cache: dict[str, Any] = {}
+        self.pre_roll = bytearray()
+        self.pre_roll_max_bytes = 300 * self.sample_rate * 2 // 1000
+        self.total_samples = 0
+        self.speech_started = False
+        self.segment_id = 0
+        self.segment_start_ms = 0.0
+        self.segment_audio = bytearray()
+        self.segment_voiced_ms = 0.0
+        self.asr_turn: _StreamingASRTurn | None = None
+
+    @staticmethod
+    def _to_float32(pcm_bytes: bytes) -> Any:
+        """Convert browser PCM16 to the float waveform expected by FunASR."""
+        import numpy as np
+
+        return np.frombuffer(pcm_bytes, dtype=np.int16).astype(np.float32) / 32768.0
+
+    def _start_segment(self, start_ms: float) -> None:
+        """Open a turn and retain a short pre-roll for initial phonemes."""
+        self.speech_started = True
+        self.segment_start_ms = max(
+            0.0,
+            start_ms - len(self.pre_roll) / (self.sample_rate * 2) * 1000,
+        )
+        self.segment_audio = bytearray(self.pre_roll)
+        self.segment_voiced_ms = 0.0
+        self.asr_turn = _StreamingASRTurn(self.service)
+        self.pre_roll.clear()
+
+    async def _feed_vad_chunk(self, chunk: bytes) -> list[FunASRSegment]:
+        """Feed VAD, then stream this audio into the active ASR turn."""
+        self.total_samples += len(chunk) // 2
+        events = await self.service.generate_vad(
+            self._to_float32(chunk),
+            {"cache": self.vad_cache, "is_final": False},
+            self.service.config.vad_chunk_ms,
+        )
+        starts = [start for start, _ in events if start >= 0]
+        ends = [end for _, end in events if end >= 0]
+        results: list[FunASRSegment] = []
+        if not self.speech_started and starts:
+            self._start_segment(starts[0])
+
+        if self.speech_started:
+            self.segment_audio.extend(chunk)
+            self.segment_voiced_ms += len(chunk) / (self.sample_rate * 2) * 1000
+            if self.asr_turn is not None:
+                for text in await self.asr_turn.append(chunk):
+                    results.append(self._partial(text))
+            if self._segment_duration_ms() >= self.service.config.max_segment_sec * 1000:
+                results.append(await self._finish_segment("max_duration"))
+        else:
+            self.pre_roll.extend(chunk)
+            del self.pre_roll[:-self.pre_roll_max_bytes]
+
+        if ends and self.speech_started:
+            results.append(await self._finish_segment("vad_end"))
+        return [event for event in results if event.text or event.is_final]
+
+    def _segment_duration_ms(self) -> float:
+        return len(self.segment_audio) / (self.sample_rate * 2) * 1000
+
+    def _partial(self, text: str) -> FunASRSegment:
+        """Create a partial event using the cumulative FunASR text."""
+        return FunASRSegment(
+            text=text,
+            start_time_ms=self.segment_start_ms,
+            end_time_ms=self.segment_start_ms + self._segment_duration_ms(),
+            audio=b"",
+            voiced_ms=self.segment_voiced_ms,
+            is_final=False,
+            sentence_id=self.segment_id,
+        )
+
+    async def _finish_segment(self, reason: str) -> FunASRSegment:
+        """Flush the ASR cache, then release completed segment audio."""
+        text = await self.asr_turn.finish() if self.asr_turn is not None else ""
+        result = FunASRSegment(
+            text=text,
+            start_time_ms=self.segment_start_ms,
+            end_time_ms=self.segment_start_ms + self._segment_duration_ms(),
+            audio=bytes(self.segment_audio),
+            voiced_ms=self.segment_voiced_ms,
+            is_final=True,
+            sentence_id=self.segment_id,
+            reason=reason,
+        )
+        self.segment_id += 1
+        self.segment_audio.clear()
+        self.asr_turn = None
+        self.speech_started = False
+        self.segment_voiced_ms = 0.0
+        return result
+
+    async def feed(self, pcm_bytes: bytes) -> list[FunASRSegment]:
+        """Consume PCM16 and return FunASR partial/final events."""
+        if len(pcm_bytes) % 2:
+            raise ValueError("PCM16 音频必须包含完整的双字节采样")
+        self.vad_buffer.extend(pcm_bytes)
+        results: list[FunASRSegment] = []
+        while len(self.vad_buffer) >= self.vad_chunk_bytes:
+            chunk = bytes(self.vad_buffer[:self.vad_chunk_bytes])
+            del self.vad_buffer[:self.vad_chunk_bytes]
+            results.extend(await self._feed_vad_chunk(chunk))
+        return results
+
+    async def finish(self) -> list[FunASRSegment]:
+        """Flush buffered audio and discard all stream caches."""
+        results: list[FunASRSegment] = []
+        if self.vad_buffer:
+            chunk = bytes(self.vad_buffer)
+            self.vad_buffer.clear()
+            if not self.speech_started and chunk:
+                self._start_segment(self.total_samples / self.sample_rate * 1000)
+            self.total_samples += len(chunk) // 2
+            if self.speech_started:
+                self.segment_audio.extend(chunk)
+                self.segment_voiced_ms += len(chunk) / (self.sample_rate * 2) * 1000
+                if self.asr_turn is not None:
+                    for text in await self.asr_turn.append(chunk):
+                        results.append(self._partial(text))
+        if self.speech_started:
+            results.append(await self._finish_segment("eof"))
+        self.vad_cache = {}
+        self.pre_roll.clear()
+        return [event for event in results if event.text or event.is_final]

realtime_websocket/funasr_server.py

@@ -0,0 +1,642 @@
+"""Browser-facing WebSocket adapter for the migrated FunASR engine.
+
+The frontend contract remains the existing start/binary/stop protocol. All
+speech activity detection and streaming ASR state now come from FunASR; this
+module only translates engine events into the existing sentence snapshots.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import logging
+import os
+import time
+import webbrowser
+from dataclasses import dataclass, replace
+from pathlib import Path
+from typing import Any
+from uuid import uuid4
+
+from aiohttp import WSMsgType, web
+from dotenv import load_dotenv
+
+try:
+    from .auxiliary_service import AuxiliaryModelService, AuxiliaryServiceConfig
+    from .funasr_engine import FunASRModelService, FunASRSegment, FunASRServiceConfig
+    from .speaker_assembler import SegmentAssembler
+except ImportError:
+    from auxiliary_service import AuxiliaryModelService, AuxiliaryServiceConfig
+    from funasr_engine import FunASRModelService, FunASRSegment, FunASRServiceConfig
+    from speaker_assembler import SegmentAssembler
+
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+load_dotenv(PROJECT_ROOT / ".env")
+
+WEB_HOST = os.getenv("WEB_HOST", "0.0.0.0")
+WEB_PORT = int(os.getenv("WEB_PORT", "8082"))
+WEB_DISPLAY_HOST = os.getenv("WEB_DISPLAY_HOST", "127.0.0.1")
+LOCAL_ENGINE_URL = "local://funasr"
+PARTIAL_BYTES_PER_SECOND = 16000 * 2
+MIN_SPEAKER_VOICE_MS = 800
+LOGGER = logging.getLogger(__name__)
+
+
+class EndOfStream:
+    """Queue marker that cannot be confused with an audio frame."""
+
+
+EOF = EndOfStream()
+MODEL_SERVICE_KEY = web.AppKey("model_service", FunASRModelService)
+AUXILIARY_SERVICE_KEY = web.AppKey("auxiliary_service", AuxiliaryModelService)
+
+
+@dataclass(frozen=True)
+class SpeakerJob:
+    """Completed FunASR turn waiting for optional CAM++ speaker matching."""
+
+    sentence_id: int
+    audio: bytes
+    start_time_ms: float
+    end_time_ms: float
+    voiced_ms: float
+
+
+@dataclass
+class SessionMetrics:
+    """Small runtime snapshot shown in the existing frontend."""
+
+    started_at: float
+    audio_bytes: int = 0
+    input_chunks: int = 0
+    partial_count: int = 0
+    partial_revisions: int = 0
+    first_partial_ms: float | None = None
+    final_ms: float | None = None
+
+    def snapshot(self) -> dict[str, Any]:
+        """Return JSON-safe metrics relative to session start."""
+        return {
+            "audio_bytes": self.audio_bytes,
+            "input_chunks": self.input_chunks,
+            "partial_count": self.partial_count,
+            "partial_revisions": self.partial_revisions,
+            "first_partial_ms": self.first_partial_ms,
+            "final_ms": self.final_ms,
+            "elapsed_ms": round((time.perf_counter() - self.started_at) * 1000, 1),
+        }
+
+
+class IncrementalWavDecoder:
+    """Strip a streamed RIFF header before forwarding PCM16 to FunASR."""
+
+    def __init__(self) -> None:
+        self.buffer = bytearray()
+        self.payload_started = False
+        self.riff_read = False
+        self.format_valid = False
+        self.data_remaining: int | None = None
+
+    def feed(self, chunk: bytes) -> bytes:
+        """Parse complete RIFF chunks without buffering the whole recording."""
+        if self.payload_started:
+            if self.data_remaining is None:
+                return chunk
+            payload = chunk[: self.data_remaining]
+            self.data_remaining -= len(payload)
+            return payload
+
+        self.buffer.extend(chunk)
+        if not self.riff_read:
+            if len(self.buffer) < 12:
+                return b""
+            if self.buffer[:4] != b"RIFF" or self.buffer[8:12] != b"WAVE":
+                raise ValueError("文件不是有效的 RIFF/WAV 音频")
+            del self.buffer[:12]
+            self.riff_read = True
+
+        while len(self.buffer) >= 8:
+            kind = bytes(self.buffer[:4])
+            size = int.from_bytes(self.buffer[4:8], "little")
+            if size > 1024 * 1024:
+                raise ValueError("WAV 元数据头过大，请转换为标准 PCM WAV")
+            chunk_size = 8 + size + (size % 2)
+            if len(self.buffer) < chunk_size:
+                return b""
+            body = self.buffer[8 : 8 + size]
+            if kind == b"fmt ":
+                if size < 16:
+                    raise ValueError("WAV fmt 区块不完整")
+                fields = (
+                    int.from_bytes(body[0:2], "little"),
+                    int.from_bytes(body[2:4], "little"),
+                    int.from_bytes(body[4:8], "little"),
+                    int.from_bytes(body[14:16], "little"),
+                )
+                if fields != (1, 1, 16000, 16):
+                    raise ValueError("WAV 必须为 16kHz、单声道、PCM16")
+                self.format_valid = True
+            if kind == b"data":
+                if not self.format_valid or size % 2:
+                    raise ValueError("WAV 必须为 16kHz、单声道、PCM16")
+                self.payload_started = True
+                self.data_remaining = size
+                del self.buffer[:8]
+                payload = bytes(self.buffer[:size])
+                del self.buffer[: min(size, len(self.buffer))]
+                self.data_remaining -= len(payload)
+                return payload
+            del self.buffer[:chunk_size]
+        return b""
+
+    def finish(self) -> None:
+        """Reject a truncated or header-only WAV before final inference."""
+        if not self.payload_started:
+            raise ValueError("WAV 文件不完整，未收到 data 音频区块")
+        if self.data_remaining not in (None, 0):
+            raise ValueError("WAV 文件不完整，未收到全部音频数据")
+
+
+class RealtimeSession:
+    """Translate FunASR events into the existing sentence/display protocol."""
+
+    def __init__(
+        self,
+        ws: web.WebSocketResponse,
+        model_service: FunASRModelService,
+        auxiliary_service: AuxiliaryModelService | None,
+        start: dict[str, Any],
+    ) -> None:
+        self.ws = ws
+        self.model_service = model_service
+        self.auxiliary_service = auxiliary_service
+        self.start = start
+        self.engine = model_service.create_session()
+        self.session_id = uuid4().hex
+        self.send_lock = asyncio.Lock()
+        self.audio_queue: asyncio.Queue[bytes | EndOfStream] = asyncio.Queue(maxsize=128)
+        self.speaker_queue: asyncio.Queue[SpeakerJob | EndOfStream] = asyncio.Queue(maxsize=64)
+        self.assembler = SegmentAssembler()
+        self.metrics = SessionMetrics(time.perf_counter())
+        self.source = str(start.get("source") or "mic")
+        self.file_name = str(start.get("file_name") or "audio.pcm")
+        self.wav_decoder = (
+            IncrementalWavDecoder()
+            if self.source == "file" and Path(self.file_name).suffix.lower() == ".wav"
+            else None
+        )
+        self.merge_adjacent = self._parse_flag(start.get("display_merge"), True)
+        # Speaker labels are required for every browser session.
+        self.speaker_enabled = True
+        self.speaker_warning_sent = False
+        self.input_stopped = False
+
+    @staticmethod
+    def _parse_flag(value: Any, default: bool) -> bool:
+        """Accept booleans, 0/1 and string flags from old frontend clients."""
+        if value is None:
+            return default
+        if isinstance(value, str):
+            return value.strip().lower() not in {"", "0", "false", "no", "off"}
+        return bool(value)
+
+    async def emit(self, payload: dict[str, Any]) -> None:
+        """Send ordered JSON while the browser connection is still alive."""
+        async with self.send_lock:
+            if not self.ws.closed:
+                await self.ws.send_json(payload)
+
+    async def emit_state(self, sentence: dict[str, Any] | None = None) -> None:
+        """Send both the legacy sentence event and the full display snapshot."""
+        state = {
+            "type": "display_state",
+            "raw_segments": self.assembler.raw_snapshot(),
+            "display_blocks": self.assembler.display_blocks(self.merge_adjacent),
+            "metrics": self.metrics.snapshot(),
+        }
+        if sentence is not None:
+            await self.emit(
+                {
+                    "type": "sentences",
+                    "sentences": [sentence],
+                    "metrics": self.metrics.snapshot(),
+                }
+            )
+        await self.emit(state)
+
+    async def warn_speaker(self, message: str) -> None:
+        """Expose speaker-service failures without interrupting ASR."""
+        if self.speaker_warning_sent:
+            return
+        await self.emit(
+            {
+                "type": "speaker_warning",
+                "session_id": self.session_id,
+                "speaker_service_url": getattr(
+                    getattr(self.auxiliary_service, "config", None), "base_url", None
+                ),
+                "message": message,
+            }
+        )
+        self.speaker_warning_sent = True
+
+    async def _emit_engine_segment(self, segment: FunASRSegment) -> None:
+        """Map one FunASR partial/final event to a stable sentence ID."""
+        if not segment.text:
+            if segment.is_final and self.assembler.segments.pop(segment.sentence_id, None) is not None:
+                await self.emit_state()
+            return
+
+        sentence_type = 1 if segment.is_final else 0
+        sentence = self.assembler.apply_sentence(
+            {
+                "sentence_id": segment.sentence_id,
+                "sentence": segment.text,
+                "sentence_type": sentence_type,
+                "start_time": segment.start_time_ms,
+                "end_time": segment.end_time_ms,
+                "speaker_id": -1,
+                "speaker_name": "",
+                "speaker_evidence": "pending",
+                "speaker_confidence": 0.0,
+                "speaker_strategy": "funasr_pending",
+                "commit_reason": segment.reason,
+                "speaker_status": (
+                    "queued" if segment.is_final else "waiting_final"
+                )
+                if self.speaker_enabled
+                else "disabled",
+                "speaker_reason": (
+                    "等待 CAM++ 声纹处理"
+                    if segment.is_final
+                    else "FunASR 流式结果，等待片段结束"
+                )
+                if self.speaker_enabled
+                else "说话人分离已关闭",
+            }
+        )
+        if sentence_type == 0:
+            self.metrics.partial_count += 1
+            if sentence["revision_count"] > 0:
+                self.metrics.partial_revisions += 1
+            if self.metrics.first_partial_ms is None:
+                self.metrics.first_partial_ms = round(
+                    (time.perf_counter() - self.metrics.started_at) * 1000, 1
+                )
+        else:
+            self.metrics.final_ms = round(
+                (time.perf_counter() - self.metrics.started_at) * 1000, 1
+            )
+        await self.emit_state(sentence)
+
+        if segment.is_final and self.speaker_enabled:
+            await self.speaker_queue.put(
+                SpeakerJob(
+                    sentence_id=segment.sentence_id,
+                    audio=segment.audio,
+                    start_time_ms=segment.start_time_ms,
+                    end_time_ms=segment.end_time_ms,
+                    voiced_ms=segment.voiced_ms,
+                )
+            )
+
+    async def _resolve_speaker(self, job: SpeakerJob) -> None:
+        """Keep the existing optional CAM++ display integration."""
+        if not self.speaker_enabled:
+            return
+
+        async def update_status(status: str, reason: str) -> None:
+            updated = self.assembler.apply_speaker_update(
+                {
+                    "sentence_id": job.sentence_id,
+                    "speaker_id": -1,
+                    "speaker_evidence": "pending",
+                    "speaker_confidence": 0.0,
+                    "speaker_status": status,
+                    "speaker_reason": reason,
+                }
+            )
+            await self.emit_state(updated)
+
+        if job.voiced_ms < MIN_SPEAKER_VOICE_MS:
+            await update_status(
+                "insufficient_audio",
+                f"有效语音不足 {MIN_SPEAKER_VOICE_MS}ms，不继承上一位说话人",
+            )
+            return
+        if self.auxiliary_service is None:
+            await update_status("service_unavailable", "未配置说话人辅助模型服务")
+            await self.warn_speaker("未配置辅助模型服务，无法执行说话人分离")
+            return
+
+        await update_status("processing", "正在提取 CAM++ 声纹并匹配说话人")
+        try:
+            speaker = await self.auxiliary_service.resolve_speaker(
+                job.audio,
+                self.session_id,
+                job.start_time_ms,
+                job.end_time_ms,
+            )
+        except Exception as exc:
+            LOGGER.exception("speaker resolve failed: session=%s sentence=%s", self.session_id, job.sentence_id)
+            await update_status("service_error", str(exc))
+            await self.warn_speaker(str(exc))
+            return
+
+        if not speaker:
+            await update_status("no_embedding", "辅助服务未返回可用声纹结果")
+            return
+        update = dict(speaker)
+        update["sentence_id"] = job.sentence_id
+        update["speaker_name"] = str(update.get("speaker_name") or "")
+        updated = self.assembler.apply_speaker_update(update)
+        if updated is not None:
+            await self.emit_state(updated)
+
+    async def process_speakers(self) -> None:
+        """Process completed turns in order so speaker clusters stay stable."""
+        while True:
+            item = await self.speaker_queue.get()
+            if isinstance(item, EndOfStream):
+                return
+            await self._resolve_speaker(item)
+
+    async def _feed(self, chunk: bytes) -> None:
+        """Forward raw PCM to FunASR and publish all returned events."""
+        if not chunk:
+            return
+        self.metrics.audio_bytes += len(chunk)
+        for segment in await self.engine.feed(chunk):
+            await self._emit_engine_segment(segment)
+
+    async def process_audio(self) -> None:
+        """Consume audio until EOF, with no local RMS/VLLM segmentation path."""
+        while True:
+            item = await self.audio_queue.get()
+            if isinstance(item, EndOfStream):
+                break
+            self.metrics.input_chunks += 1
+            chunk = self.wav_decoder.feed(item) if self.wav_decoder is not None else item
+            await self._feed(chunk)
+
+        if self.wav_decoder is not None:
+            self.wav_decoder.finish()
+        for segment in await self.engine.finish():
+            await self._emit_engine_segment(segment)
+        await self.emit({"type": "metrics", "metrics": self.metrics.snapshot()})
+
+    async def close(self) -> None:
+        """Drop per-session FunASR caches and temporary state."""
+        self.engine = None  # type: ignore[assignment]
+
+
+async def index_handler(_: web.Request) -> web.FileResponse:
+    """Serve the browser page without stale-cache surprises."""
+    return web.FileResponse(
+        Path(__file__).parent / "static" / "index.html",
+        headers={"Cache-Control": "no-store"},
+    )
+
+
+async def config_handler(request: web.Request) -> web.Response:
+    """Expose local FunASR settings using the old response field names."""
+    model_service: FunASRModelService = request.app[MODEL_SERVICE_KEY]
+    auxiliary = request.app.get(AUXILIARY_SERVICE_KEY)
+    response = web.json_response(
+        {
+            "model_service_url": LOCAL_ENGINE_URL,
+            "model": model_service.config.model,
+            "engine": "funasr",
+            "speaker_service_url": getattr(
+                getattr(auxiliary, "config", None), "base_url", None
+            ),
+        }
+    )
+    # The standalone frontend reads this API from its own origin.
+    response.headers["Access-Control-Allow-Origin"] = os.getenv(
+        "FRONTEND_ORIGIN", "http://127.0.0.1:8080"
+    )
+    return response
+
+
+async def websocket_handler(request: web.Request) -> web.WebSocketResponse:
+    """Keep the old browser protocol while using FunASR internally."""
+    ws = web.WebSocketResponse(max_msg_size=64 * 1024 * 1024)
+    await ws.prepare(request)
+    model_service: FunASRModelService = request.app[MODEL_SERVICE_KEY]
+    auxiliary_service = request.app.get(AUXILIARY_SERVICE_KEY)
+    processing: asyncio.Task[None] | None = None
+    speaker_processing: asyncio.Task[None] | None = None
+    session: RealtimeSession | None = None
+    input_finished = False
+
+    try:
+        first = await ws.receive()
+        if first.type != WSMsgType.TEXT:
+            await ws.send_json({"type": "error", "message": "first message must be JSON start"})
+            return ws
+        try:
+            start = json.loads(first.data)
+        except json.JSONDecodeError:
+            await ws.send_json({"type": "error", "message": "invalid start JSON"})
+            return ws
+        if not isinstance(start, dict) or start.get("type") != "start":
+            await ws.send_json({"type": "error", "message": "first message must have type=start"})
+            return ws
+
+        source = str(start.get("source") or "mic")
+        suffix = Path(str(start.get("file_name") or "")).suffix.lower()
+        if source == "file" and suffix not in {".pcm", ".wav"}:
+            await ws.send_json(
+                {
+                    "type": "error",
+                    "message": "实时流式测试的文件模式只支持 PCM 或 WAV，请改用麦克风、PCM 或 WAV",
+                }
+            )
+            return ws
+
+        session = RealtimeSession(ws, model_service, auxiliary_service, start)
+        speaker_health: dict[str, Any] | None = None
+        speaker_health_error: str | None = None
+        if session.speaker_enabled and auxiliary_service is not None:
+            try:
+                speaker_health = await asyncio.wait_for(auxiliary_service.health(), timeout=5)
+                if speaker_health.get("speaker_embedding_ready", speaker_health.get("ready")) is False:
+                    speaker_health_error = "辅助模型服务未就绪，请检查 /health 返回的 models 状态"
+            except Exception as exc:
+                speaker_health_error = f"说话人辅助服务不可用：{exc}"
+
+        await session.emit(
+            {
+                "type": "start",
+                "model_service_url": LOCAL_ENGINE_URL,
+                "model": model_service.config.model,
+                "engine": "funasr",
+                "session_id": session.session_id,
+                "enable_native_partial_stream": True,
+                "native_partial_supported": True,
+                "partial_mode": "funasr_streaming_cache",
+                "speaker_diarization_enabled": session.speaker_enabled,
+                "speaker_service_url": getattr(
+                    getattr(auxiliary_service, "config", None), "base_url", None
+                ),
+                "speaker_service_health": speaker_health,
+                "speaker_gap_enabled": False,
+                "sentence_strategy": start.get("sentence_strategy", 0),
+                "display_state_supported": True,
+            }
+        )
+        if speaker_health_error:
+            await session.warn_speaker(speaker_health_error)
+
+        processing = asyncio.create_task(session.process_audio())
+        if session.speaker_enabled:
+            speaker_processing = asyncio.create_task(session.process_speakers())
+
+        async def receive_or_raise() -> Any:
+            """Wake up immediately when the engine worker fails."""
+            receive_task = asyncio.create_task(ws.receive())
+            workers = [task for task in (processing, speaker_processing) if task is not None]
+            done, _ = await asyncio.wait(
+                [receive_task, *workers],
+                return_when=asyncio.FIRST_COMPLETED,
+            )
+            if receive_task in done:
+                return await receive_task
+            receive_task.cancel()
+            await asyncio.gather(receive_task, return_exceptions=True)
+            for worker in workers:
+                if worker in done:
+                    await worker
+            raise RuntimeError("FunASR 实时处理任务意外结束")
+
+        while not ws.closed:
+            message = await receive_or_raise()
+            if message.type == WSMsgType.BINARY:
+                await session.audio_queue.put(bytes(message.data))
+                continue
+            if message.type == WSMsgType.TEXT:
+                try:
+                    control = json.loads(message.data)
+                except json.JSONDecodeError:
+                    continue
+                if not isinstance(control, dict):
+                    continue
+                if control.get("type") in {"eof", "stop"}:
+                    input_finished = True
+                    session.input_stopped = control.get("type") == "stop"
+                    await session.emit({"type": "draining", "message": "FunASR 正在完成最终识别"})
+                    await session.audio_queue.put(EOF)
+                    break
+                if control.get("type") == "abort":
+                    return ws
+            if message.type in {WSMsgType.ERROR, WSMsgType.CLOSE, WSMsgType.CLOSED}:
+                return ws
+
+        if input_finished:
+            await processing
+            if speaker_processing is not None:
+                await session.speaker_queue.put(EOF)
+                await speaker_processing
+            await session.emit_state()
+            await session.emit(
+                {
+                    "type": "end",
+                    "metrics": session.metrics.snapshot(),
+                    "sentences": session.assembler.raw_snapshot(),
+                    "display_blocks": session.assembler.display_blocks(session.merge_adjacent),
+                }
+            )
+    except asyncio.CancelledError:
+        raise
+    except Exception as exc:
+        LOGGER.exception("FunASR WebSocket session failed")
+        if not ws.closed:
+            await ws.send_json({"type": "error", "message": str(exc)})
+    finally:
+        for task in (processing, speaker_processing):
+            if task is not None and not task.done():
+                task.cancel()
+        await asyncio.gather(
+            *(task for task in (processing, speaker_processing) if task is not None),
+            return_exceptions=True,
+        )
+        if session is not None:
+            reset = getattr(session.auxiliary_service, "reset_speaker_session", None)
+            await session.close()
+            if reset is not None:
+                try:
+                    await asyncio.wait_for(reset(session.session_id), timeout=5)
+                except Exception:
+                    LOGGER.warning("speaker session cleanup failed: %s", session.session_id, exc_info=True)
+        if not ws.closed:
+            await ws.close()
+    return ws
+
+
+async def start_app(
+    model: str | None = None,
+    device: str | None = None,
+) -> web.Application:
+    """Create the FunASR-backed frontend application."""
+    config = FunASRServiceConfig.from_env()
+    if model:
+        config = replace(config, model=model)
+    if device:
+        config = replace(config, device=device)
+
+    app = web.Application()
+    app[MODEL_SERVICE_KEY] = FunASRModelService(config)
+    app[AUXILIARY_SERVICE_KEY] = AuxiliaryModelService(
+        AuxiliaryServiceConfig(
+            base_url=os.getenv("AUXILIARY_SERVICE_URL", "http://127.0.0.1:8010")
+        )
+    )
+
+    async def lifecycle(application: web.Application):
+        await application[MODEL_SERVICE_KEY].start()
+        await application[AUXILIARY_SERVICE_KEY].start()
+        try:
+            health = await application[AUXILIARY_SERVICE_KEY].health()
+            if not health.get("speaker_embedding_ready"):
+                raise RuntimeError("CAM++ speaker service is not ready")
+        except Exception:
+            await application[AUXILIARY_SERVICE_KEY].close()
+            await application[MODEL_SERVICE_KEY].close()
+            raise
+        yield
+        await application[AUXILIARY_SERVICE_KEY].close()
+        await application[MODEL_SERVICE_KEY].close()
+
+    app.cleanup_ctx.append(lifecycle)
+    app.router.add_get("/", index_handler)
+    app.router.add_get("/api/config", config_handler)
+    app.router.add_static("/static/", Path(__file__).parent / "static")
+    app.router.add_get("/ws", websocket_handler)
+    app.router.add_static("/", Path(__file__).parent / "static", show_index=False)
+    return app
+
+
+def main() -> None:
+    """Start the FunASR-backed browser demo."""
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--model", default=os.getenv("FUNASR_ASR_MODEL"))
+    parser.add_argument("--device", default=os.getenv("FUNASR_DEVICE"))
+    parser.add_argument("--no-browser", action="store_true")
+    args = parser.parse_args()
+    logging.basicConfig(level=logging.INFO)
+    if not args.no_browser:
+        webbrowser.open(f"http://{WEB_DISPLAY_HOST}:{WEB_PORT}/")
+    print(f"FunASR demo: http://{WEB_DISPLAY_HOST}:{WEB_PORT}/", flush=True)
+    print(f"FunASR model: {args.model or FunASRServiceConfig.from_env().model}", flush=True)
+    web.run_app(
+        start_app(model=args.model, device=args.device),
+        host=WEB_HOST,
+        port=WEB_PORT,
+    )
+
+
+if __name__ == "__main__":
+    main()

realtime_websocket/requirements.txt

@@ -1,2 +1,6 @@
 aiohttp==3.11.11
 python-dotenv>=1.0
+funasr==1.4.16
+modelscope[framework]==1.34.0
+soundfile==0.13.1
+librosa==0.11.0

realtime_websocket/static/app.js

@@ -3,8 +3,6 @@ const elEngineModel = document.getElementById('engineModel');
 const elModelServiceUrl = document.getElementById('modelServiceUrl');
 const elSpeakerStatus = document.getElementById('speakerStatus');
 const elDisplayMerge = document.getElementById('displayMerge');
-const elSpeakerDiarization = document.getElementById('speakerDiarization');
-const elDiarizationLabel = document.getElementById('diarizationLabel');
 const elSentenceStrategy = document.getElementById('sentenceStrategy');
 const elBtnStart = document.getElementById('btnStart');
 const elBtnStop = document.getElementById('btnStop');
@@ -450,7 +448,7 @@ async function startRecognition() {
     sending = true;
 
     const currentSession = ++sessionId;
-    const useSpeaker = elSpeakerDiarization.checked;
+    const useSpeaker = true; // Speaker labels are mandatory for this deployment.
     let receivedTerminal = false;
 
     // 构造 WebSocket 首条 start 消息。
@@ -475,8 +473,10 @@ async function startRecognition() {
         speed_factor: speedFactor
     };
 
-    const protocol = location.protocol === 'https:' ? 'wss:' : 'ws:';
-    ws = new WebSocket(`${protocol}//${location.host}/ws`);
+    const backendUrl = (window.ASR_BACKEND_URL || location.origin).replace(/\/$/, '');
+    const websocketUrl = new URL(backendUrl + '/ws');
+    websocketUrl.protocol = backendUrl.startsWith('https:') ? 'wss:' : 'ws:';
+    ws = new WebSocket(websocketUrl);
     ws.binaryType = 'arraybuffer';
 
     ws.onopen = () => {
@@ -725,7 +725,9 @@ function stopMicCapture() {
 }
 
 // 展示实际部署端点及模型，避免沿用旧 SDK 的无效引擎配置。
-fetch('/api/config').then(response => response.json()).then(config => {
+// The static frontend reads API and WS addresses from runtime-config.js.
+const backendUrl = (window.ASR_BACKEND_URL || location.origin).replace(/\/$/, '');
+fetch(backendUrl + '/api/config').then(response => response.json()).then(config => {
     if (!ws) {
         elEngineModel.value = config.model;
         elModelServiceUrl.value = config.model_service_url;

realtime_websocket/static/index.html

@@ -20,11 +20,11 @@
                 <div class="form-stack">
                     <div class="form-group">
                         <label for="engineModel">引擎模型</label>
-                        <input type="text" id="engineModel" value="Qwen/Qwen3-ASR-0.6B">
+                        <input type="text" id="engineModel" value="paraformer-zh-streaming">
                     </div>
                     <div class="form-group">
-                        <label for="modelServiceUrl">vLLM 服务地址</label>
-                        <input type="text" id="modelServiceUrl" value="http://127.0.0.1:9950/v1">
+                        <label for="modelServiceUrl">FunASR 本地引擎</label>
+                        <input type="text" id="modelServiceUrl" value="local://funasr" readonly>
                     </div>
                     <div class="form-group">
                         <label for="sentenceStrategy">分句策略</label>
@@ -39,9 +39,9 @@
                         <div class="form-group">
                             <label>话者分离</label>
                             <label class="toggle">
-                                <input type="checkbox" id="speakerDiarization" checked>
+                                <input type="checkbox" id="speakerDiarization" checked disabled>
                                 <span class="toggle-slider"></span>
-                                <span class="toggle-label" id="diarizationLabel">开启</span>
+                                <span class="toggle-label" id="diarizationLabel">必需</span>
                             </label>
                         </div>
                     </div>
@@ -114,6 +114,7 @@
     </div>
 
     <!-- 版本号变更用于刷新浏览器缓存，确保加载“未知说话人”标签逻辑。 -->
-    <script src="app.js?v=213"></script>
+    <script src="runtime-config.js"></script>
+    <script src="app.js?v=214"></script>
 </body>
 </html>

realtime_websocket/tests/test_funasr_engine.py

@@ -0,0 +1,75 @@
+"""Unit tests for the migrated FunASR streaming lifecycle."""
+
+from __future__ import annotations
+
+import asyncio
+import unittest
+from unittest.mock import patch
+
+try:
+    from realtime_websocket.funasr_engine import (
+        FunASRRealtimeSession,
+        FunASRServiceConfig,
+        _result_text,
+        _vad_events,
+    )
+except ModuleNotFoundError:
+    from funasr_engine import (
+        FunASRRealtimeSession,
+        FunASRServiceConfig,
+        _result_text,
+        _vad_events,
+    )
+
+
+class FakeService:
+    def __init__(self) -> None:
+        self.config = FunASRServiceConfig(
+            device="cpu",
+            vad_device="cpu",
+            vad_chunk_ms=1000,
+            chunk_size=(0, 2, 1),
+        )
+        self.vad_calls = 0
+        self.asr_calls: list[dict[str, object]] = []
+
+    async def generate_vad(self, audio, status, chunk_ms):
+        self.vad_calls += 1
+        return [(0, -1)] if self.vad_calls == 1 else [(-1, 2000)]
+
+    async def generate_asr(self, audio, status):
+        self.asr_calls.append(status)
+        return "第一块" if len(self.asr_calls) == 1 else "第二块"
+
+
+class FunASREngineTests(unittest.TestCase):
+    def test_result_normalization(self) -> None:
+        self.assertEqual(_result_text([{"text": "你好"}]), "你好")
+        self.assertEqual(_result_text({"value": "片段"}), "片段")
+        self.assertEqual(
+            _vad_events([{"value": [[0, -1], [-1, 800]]}]),
+            [(0.0, -1.0), (-1.0, 800.0)],
+        )
+
+    def test_stream_has_independent_cache_and_final_flush(self) -> None:
+        async def exercise() -> None:
+            service = FakeService()
+            with patch.object(FunASRRealtimeSession, "_to_float32", staticmethod(lambda value: value)):
+                first = FunASRRealtimeSession(service)
+                second = FunASRRealtimeSession(service)
+                first_events = await first.feed(b"\\x01\\x00" * 16000)
+                second_events = await second.feed(b"\\x01\\x00" * 16000)
+                first_events += await first.feed(b"\\x01\\x00" * 16000)
+                first_events += await first.finish()
+                self.assertTrue(any(not event.is_final for event in first_events))
+                self.assertTrue(any(event.is_final for event in first_events))
+                self.assertEqual(first.segment_id, 1)
+                self.assertEqual(second.segment_id, 0)
+            self.assertTrue(any(status["is_final"] is True for status in service.asr_calls))
+            self.assertGreaterEqual(len(service.asr_calls), 2)
+
+        asyncio.run(exercise())
+
+
+if __name__ == "__main__":
+    unittest.main()

realtime_websocket/tests/test_funasr_server.py

@@ -0,0 +1,112 @@
+"""WebSocket contract tests for the FunASR browser adapter."""
+
+from __future__ import annotations
+
+import asyncio
+from types import SimpleNamespace
+import unittest
+
+from aiohttp import web
+from aiohttp.test_utils import AioHTTPTestCase
+
+from realtime_websocket.funasr_engine import FunASRSegment
+from realtime_websocket.funasr_server import (
+    AUXILIARY_SERVICE_KEY,
+    MODEL_SERVICE_KEY,
+    config_handler,
+    websocket_handler,
+)
+
+
+class FakeFunASRSession:
+    def __init__(self) -> None:
+        self.sent = False
+
+    async def feed(self, audio: bytes):
+        if self.sent:
+            return []
+        self.sent = True
+        return [
+            FunASRSegment(
+                text="实时片段",
+                start_time_ms=0,
+                end_time_ms=len(audio) / 32,
+                audio=b"",
+                voiced_ms=len(audio) / 32,
+                is_final=False,
+                sentence_id=0,
+            )
+        ]
+
+    async def finish(self):
+        return [
+            FunASRSegment(
+                text="最终片段",
+                start_time_ms=0,
+                end_time_ms=1000,
+                audio=b"\x01\x00" * 8000,
+                voiced_ms=1000,
+                is_final=True,
+                sentence_id=0,
+                reason="eof",
+            )
+        ]
+
+
+class FakeFunASRService:
+    config = SimpleNamespace(model="fake-funasr")
+
+    def create_session(self):
+        return FakeFunASRSession()
+
+
+class FakeAuxiliaryService:
+    config = SimpleNamespace(base_url="http://fake-speaker")
+
+    async def health(self):
+        return {"ready": True, "speaker_embedding_ready": True}
+
+    async def reset_speaker_session(self, session_id):
+        return None
+
+
+class FunASRWebSocketTests(AioHTTPTestCase):
+    def get_app(self):
+        app = web.Application()
+        app[MODEL_SERVICE_KEY] = FakeFunASRService()
+        app[AUXILIARY_SERVICE_KEY] = FakeAuxiliaryService()
+        app.router.add_get("/api/config", config_handler)
+        app.router.add_get("/ws", websocket_handler)
+        return app
+
+    async def test_frontend_contract_uses_funasr_streaming_mode(self):
+        ws = await self.client.ws_connect("/ws")
+        await ws.send_json({"type": "start", "speaker_diarization": 0})
+        start = await ws.receive_json()
+        self.assertEqual(start["type"], "start")
+        self.assertEqual(start["engine"], "funasr")
+        self.assertEqual(start["partial_mode"], "funasr_streaming_cache")
+
+        await ws.send_bytes(b"\x01\x00" * 16000)
+        await ws.send_json({"type": "eof"})
+
+        messages = []
+        async with asyncio.timeout(5):
+            while True:
+                message = await ws.receive_json()
+                messages.append(message)
+                if message["type"] == "end":
+                    break
+
+        sentence_events = [
+            item for item in messages
+            if item["type"] == "sentences" and item["sentences"]
+        ]
+        self.assertTrue(any(item["sentences"][0]["sentence_type"] == 0 for item in sentence_events))
+        self.assertEqual(sentence_events[-1]["sentences"][0]["sentence"], "最终片段")
+        self.assertEqual(messages[-1]["type"], "end")
+        await ws.close()
+
+
+if __name__ == "__main__":
+    unittest.main()

realtime_websocket/tests/test_speaker_assembler.py

@@ -4,7 +4,10 @@ from __future__ import annotations
 
 import unittest
 
-from speaker_assembler import SegmentAssembler
+try:
+    from realtime_websocket.speaker_assembler import SegmentAssembler
+except ModuleNotFoundError:
+    from speaker_assembler import SegmentAssembler
 
 
 class SegmentAssemblerTests(unittest.TestCase):

requirements-auxiliary.txt

@@ -1,6 +1,6 @@
 aiohttp==3.11.11
 python-dotenv>=1.0
-funasr==1.3.1
+funasr==1.4.16
 modelscope[framework]==1.34.0
 soundfile==0.13.1
 librosa==0.11.0

requirements-funasr.txt

@@ -0,0 +1,8 @@
+# Install a torch/torchaudio build matching the host CUDA or CPU environment first.
+aiohttp==3.11.11
+python-dotenv>=1.0
+funasr==1.4.16
+modelscope[framework]==1.34.0
+soundfile==0.13.1
+librosa==0.11.0
+numpy>=1.24

scripts/auxiliary_server.py

@@ -32,10 +32,9 @@ MODELS_DIR = Path(os.getenv("MODEL_DIR", str(PROJECT_ROOT / "models"))).resolve(
 AUXILIARY_DEVICE = os.getenv("AUXILIARY_DEVICE", "cuda:0")
 ONLINE_SPEAKER_MATCH_THRESHOLD = 0.68
 MIN_ONLINE_SPEAKER_AUDIO_MS = 800
-# 实时 WebSocket 必须使用 VAD + CAM++ 声纹模型进行在线聚类。完整的
-# speech_campplus_speaker-diarization_common 是整段离线 diarization 接口，
-# 与实时每个 turn 的 CAM++ embedding 不是同一加载路径；它仍可按需加载。
-DEFAULT_PRELOAD_KINDS = {"vad", "speaker_verification"}
+# Realtime VAD is loaded by the WebSocket process. This service loads CAM++.
+# Its standalone VAD HTTP endpoint remains available on demand.
+DEFAULT_PRELOAD_KINDS = {"speaker_verification"}
 
 
 def _coerce_finite_float(value: object) -> float | None:
@@ -102,8 +101,7 @@ class AuxiliaryRuntime:
         raw = os.getenv("AUXILIARY_PRELOAD_KINDS", "")
         if not raw.strip():
             return set(DEFAULT_PRELOAD_KINDS)
-        # 无论环境变量如何设置，VAD 和 CAM++ speaker_verification 都是核心
-        # 依赖；额外类型只会增加预加载项，不能绕过核心模型校验。
+        # CAM++ is required; optional model kinds can be added for other endpoints.
         return DEFAULT_PRELOAD_KINDS | {item.strip() for item in raw.split(",") if item.strip()}
 
     def _load_asset(self, model_id: str, config: dict[str, Any], path: Path) -> Any | None:
@@ -136,9 +134,12 @@ class AuxiliaryRuntime:
         preload_kinds = self._preload_kinds()
         loaded_kinds: set[str] = set()
         for model_id, config in self.assets.items():
-            path = model_directory(model_id, self.manifest, MODELS_DIR)
-            record: dict[str, Any] = {"path": str(path), "asset_ready": _asset_ready(path, config)}
             kind = str(config.get("kind") or "")
+            path = model_directory(model_id, self.manifest, MODELS_DIR)
+            if kind == "speaker_verification" and os.getenv("CAM_MODEL_PATH"):
+                # The backend launcher passes the selected local CAM++ directory.
+                path = Path(os.environ["CAM_MODEL_PATH"]).resolve()
+            record: dict[str, Any] = {"path": str(path), "asset_ready": _asset_ready(path, config)}
             if kind not in preload_kinds:
                 record["state"] = "optional_not_preloaded" if record["asset_ready"] else "optional_missing"
                 record["preload"] = False
@@ -166,7 +167,7 @@ class AuxiliaryRuntime:
                 else:
                     record["state"] = "load_error"
                     record["error"] = "model loader returned no model"
-                    if kind in {"vad", "speaker_verification"}:
+                    if kind == "speaker_verification":
                         failures.append(model_id)
             except Exception as exc:
                 record["state"] = "load_error"
@@ -182,15 +183,11 @@ class AuxiliaryRuntime:
                 + (f", error={record['error']}" if record.get("error") else ""),
                 flush=True,
             )
-        # VAD 和 CAM++ speaker_verification 都是核心依赖，缺失/加载异常时
-        # 立即失败并列出路径与底层错误，避免页面一直显示“未确认”。
+        # The WebSocket service owns the required VAD; this process requires CAM++.
         required_failures = [
             model_id for model_id in failures
-            if self.assets[model_id].get("kind") == "vad"
-            or (
-                self.assets[model_id].get("kind") == "speaker_verification"
+            if self.assets[model_id].get("kind") == "speaker_verification"
             and self._speaker_embedding_model_id() is None
-            )
         ]
         if required_failures:
             details = "; ".join(
@@ -200,8 +197,7 @@ class AuxiliaryRuntime:
             )
             raise RuntimeError(
                 "Auxiliary core model is missing or failed to load: " + details
-                + ". Run `python scripts/download_models.py --auxiliary-only` "
-                "or set MODEL_DIR to the directory containing the downloaded assets."
+                + ". Set MODEL_DIR or CAM_MODEL_PATH to the local CAM++ asset."
             )
 
     def _find_model(self, kind: str) -> Any:
@@ -243,7 +239,11 @@ class AuxiliaryRuntime:
 
     async def vad(self, audio_path: str) -> Any:
         """使用临时音频文件执行一次串行化的 VAD 推理。"""
+        # Realtime VAD lives in the WebSocket process; this HTTP endpoint loads on demand.
+        try:
             model = self._find_model("vad")
+        except RuntimeError:
+            model = self._load_optional_kind("vad")
         async with self.inference_lock:
             return await asyncio.to_thread(model.generate, input=audio_path, cache={})
 
@@ -433,9 +433,8 @@ async def health_handler(request: web.Request) -> web.Response:
          if config.get("kind") == "vad" and model_id in runtime.models),
         None,
     )
-    # ready 表示实时链路的两个核心模型都可用；完整 diarization 是否
-    # 预加载不影响这里的结果。
-    ready = vad_model_id is not None and speaker_model_id is not None
+    # Readiness here reports the CAM++ model used by the realtime backend.
+    ready = speaker_model_id is not None
     return web.json_response(
         {
             "ready": ready,

scripts/run_backend.py

@@ -0,0 +1,162 @@
+#!/usr/bin/env python3
+"""Start the local CAM++ model service and FunASR WebSocket backend together."""
+
+from __future__ import annotations
+
+import json
+import os
+import subprocess
+import sys
+import time
+from pathlib import Path
+from urllib.error import URLError
+from urllib.request import urlopen
+
+from dotenv import load_dotenv
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from scripts.model_manifest import auxiliary_models, load_manifest, model_directory
+
+load_dotenv(PROJECT_ROOT / ".env")
+
+# FunASR's published short names resolve to these ModelScope local directories.
+LOCAL_MODEL_NAMES = {
+    "paraformer-zh-streaming": (
+        "iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online",
+    ),
+    "fsmn-vad": (
+        "iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
+        "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
+    ),
+}
+
+
+def local_model(requested: str, models_dir: Path) -> Path:
+    """Resolve a model ID or path beneath MODEL_DIR without a network fallback."""
+    name = requested.strip()
+    direct = Path(name)
+    candidates = [direct] if direct.is_absolute() else [
+        PROJECT_ROOT / direct, models_dir / direct,
+        *(models_dir / alias for alias in LOCAL_MODEL_NAMES.get(name, ())),
+    ]
+    for candidate in candidates:
+        if candidate.is_dir() and (candidate / "configuration.json").is_file():
+            return candidate.resolve()
+    checked = ", ".join(str(path) for path in candidates)
+    raise FileNotFoundError(f"Local model '{name}' was not found; checked: {checked}")
+
+
+def local_cam_model(models_dir: Path) -> Path:
+    """Require one complete CAM++ speaker verification asset from the manifest."""
+    manifest = load_manifest()
+    override = os.getenv("CAM_MODEL_PATH", "").strip()
+    if override:
+        path = Path(override)
+        if not path.is_absolute():
+            path = models_dir / path
+        path = path.resolve()
+        configs = [
+            config for config in auxiliary_models(manifest).values()
+            if config.get("kind") == "speaker_verification"
+        ]
+        if path.is_dir() and configs and all(
+            (path / relative).is_file() for relative in configs[0].get("required_files", [])
+        ):
+            return path
+        raise FileNotFoundError(f"CAM++ model is missing or incomplete: {path}")
+    checked = []
+    for model_id, config in auxiliary_models(manifest).items():
+        if config.get("kind") != "speaker_verification":
+            continue
+        path = model_directory(model_id, manifest, models_dir)
+        required = [path / relative for relative in config.get("required_files", [])]
+        if path.is_dir() and all(item.is_file() for item in required):
+            return path.resolve()
+        checked.append(str(path))
+    raise FileNotFoundError("CAM++ speaker model is missing; checked: " + ", ".join(checked))
+
+
+def wait_for_health(url: str, process: subprocess.Popen, key: str, seconds: int = 300) -> None:
+    """Wait until a child has loaded its models, failing when it exits early."""
+    deadline = time.monotonic() + seconds
+    while time.monotonic() < deadline:
+        code = process.poll()
+        if code is not None:
+            raise RuntimeError(f"Service exited before it became ready ({url}, exit={code})")
+        try:
+            with urlopen(url, timeout=2) as response:
+                data = json.load(response)
+            if data.get(key):
+                return
+        except (OSError, ValueError, URLError):
+            pass
+        time.sleep(0.5)
+    raise TimeoutError(f"Service did not become ready within {seconds}s: {url}")
+
+
+def stop_child(process: subprocess.Popen | None) -> None:
+    """Stop a supervised model or WebSocket process on shutdown."""
+    if process is None or process.poll() is not None:
+        return
+    process.terminate()
+    try:
+        process.wait(timeout=10)
+    except subprocess.TimeoutExpired:
+        process.kill()
+        process.wait()
+
+
+def main() -> None:
+    """Require ASR, VAD, and CAM++ before exposing the backend WebSocket."""
+    models_dir = Path(os.getenv("MODEL_DIR", "models"))
+    if not models_dir.is_absolute():
+        models_dir = PROJECT_ROOT / models_dir
+    models_dir = models_dir.resolve()
+    asr = local_model(os.getenv("FUNASR_ASR_MODEL", "paraformer-zh-streaming"), models_dir)
+    vad = local_model(os.getenv("FUNASR_VAD_MODEL", "fsmn-vad"), models_dir)
+    cam = local_cam_model(models_dir)
+    print(f"Local models: ASR={asr}; VAD={vad}; CAM++={cam}", flush=True)
+
+    env = os.environ.copy()
+    env.update({
+        "MODEL_DIR": str(models_dir),
+        "FUNASR_ASR_MODEL": str(asr),
+        "FUNASR_VAD_MODEL": str(vad),
+        "CAM_MODEL_PATH": str(cam),
+        "AUXILIARY_SERVICE_URL": "http://127.0.0.1:8010",
+    })
+    auxiliary = None
+    websocket = None
+    try:
+        auxiliary = subprocess.Popen(
+            [sys.executable, "-m", "scripts.auxiliary_server"],
+            cwd=PROJECT_ROOT, env=env,
+        )
+        wait_for_health("http://127.0.0.1:8010/health", auxiliary, "speaker_embedding_ready")
+        websocket = subprocess.Popen(
+            [sys.executable, "-m", "scripts.run_funasr_demo", "--no-browser"],
+            cwd=PROJECT_ROOT, env=env,
+        )
+        web_port = int(env.get("WEB_PORT", "8082"))
+        wait_for_health(
+            f"http://127.0.0.1:{web_port}/api/config", websocket, "engine"
+        )
+        print(f"Backend ready: ws://127.0.0.1:{web_port}/ws", flush=True)
+        while True:
+            for label, process in (("CAM++", auxiliary), ("WebSocket", websocket)):
+                code = process.poll()
+                if code is not None:
+                    raise RuntimeError(f"{label} service exited (exit={code})")
+            time.sleep(0.5)
+    except KeyboardInterrupt:
+        pass
+    finally:
+        stop_child(websocket)
+        stop_child(auxiliary)
+
+
+if __name__ == "__main__":
+    main()

scripts/run_frontend.py

@@ -0,0 +1,57 @@
+#!/usr/bin/env python3
+"""Serve the browser UI independently of the FunASR backend."""
+
+from __future__ import annotations
+
+import json
+import os
+from http.server import SimpleHTTPRequestHandler, ThreadingHTTPServer
+from pathlib import Path
+from urllib.parse import urlsplit
+
+from dotenv import load_dotenv
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+STATIC_ROOT = PROJECT_ROOT / "realtime_websocket" / "static"
+load_dotenv(PROJECT_ROOT / ".env")
+
+
+class FrontendHandler(SimpleHTTPRequestHandler):
+    """Serve static assets and a browser-visible backend URL at runtime."""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, directory=str(STATIC_ROOT), **kwargs)
+
+    def do_GET(self) -> None:
+        if self.path.split("?", 1)[0] != "/runtime-config.js":
+            return super().do_GET()
+        backend_url = os.getenv("BACKEND_PUBLIC_URL", "http://127.0.0.1:8082").rstrip("/")
+        parsed = urlsplit(backend_url)
+        if parsed.scheme not in {"http", "https"} or not parsed.netloc or parsed.path:
+            self.send_error(500, "BACKEND_PUBLIC_URL must be an HTTP origin")
+            return
+        # JSON escaping also yields a valid JavaScript string literal.
+        body = ("window.ASR_BACKEND_URL = " + json.dumps(backend_url) + ";\n").encode("utf-8")
+        self.send_response(200)
+        self.send_header("Content-Type", "application/javascript; charset=utf-8")
+        self.send_header("Cache-Control", "no-store")
+        self.send_header("Content-Length", str(len(body)))
+        self.end_headers()
+        self.wfile.write(body)
+
+
+def main() -> None:
+    host = os.getenv("FRONTEND_HOST", "127.0.0.1")
+    port = int(os.getenv("FRONTEND_PORT", "8080"))
+    server = ThreadingHTTPServer((host, port), FrontendHandler)
+    print(f"Frontend: http://{host}:{port}/", flush=True)
+    try:
+        server.serve_forever()
+    except KeyboardInterrupt:
+        pass
+    finally:
+        server.server_close()
+
+
+if __name__ == "__main__":
+    main()

scripts/run_funasr_demo.py

@@ -0,0 +1,17 @@
+#!/usr/bin/env python3
+"""Start the FunASR-backed browser demo."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from realtime_websocket.funasr_server import main
+
+
+if __name__ == "__main__":
+    main()