From e4282a747c68c29dc216605ac8d634752553957e Mon Sep 17 00:00:00 2001 From: Bifang <915779419@qq.com> Date: Tue, 29 Sep 2026 09:57:24 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=A0=E9=99=A4=E5=A4=9A=E4=BD=99=E6=96=87?= =?UTF-8?q?=E6=A1=A3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .env.example | 2 +- .gitignore | 1 + README.md | 7 +- REALTIME_ASR_TROUBLESHOOTING.md | 310 -- app/api/v1/openai_compatible.py | 2 +- app/models/asr.py | 22 +- app/services/asr/model_plan.py | 19 +- app/services/asr/models.json | 4 +- app/services/asr/qwen3_engine.py | 16 +- app/services/qwen3_websocket_asr.py | 74 +- crg-mcp-plugin/README.md | 68 - crg-mcp-plugin/crg.cmd | 57 - crg-mcp-plugin/dist/crg-mcp-1.0.0.piplug | Bin 10403 -> 0 bytes crg-mcp-plugin/main.js | 18 - crg-mcp-plugin/manifest.json | 33 - crg-mcp-plugin/selfcheck.ps1 | 96 - docs/README_zh.md | 7 +- ...EALTIME_WEBSOCKET_PROCESSING_COMPARISON.md | 382 --- docs/TODO/ENTROPY_REDUCTION_PLAN.md | 66 - docs/TODO/X86_RUST_ALIGN_OPT_PLAN.md | 457 --- docs/deployment.md | 699 ---- docs/iluvatar_offline_deployment.md | 242 -- docs/metax_offline_deployment.md | 234 -- docs/mthreads_offline_deployment.md | 53 - docs/realtime_meeting_refactor_report.md | 412 --- docs/realtime_meeting_websocket.md | 417 --- docs/realtime_meeting_websocket_customer.md | 297 -- docs/国产化兼容适配汇报.md | 95 - environments/cpu/pyproject.toml | 54 - environments/cpu/uv.lock | 2832 ----------------- environments/iluvatar/pyproject.toml | 41 - environments/iluvatar/requirements.txt | 24 - environments/metax/pyproject.toml | 45 - environments/metax/requirements.txt | 23 - environments/mthreads/pyproject.toml | 41 - environments/mthreads/requirements.txt | 24 - 曦云系列_通用GPU_mx-smi使用手册_CN_V14.pdf | Bin 1549583 -> 0 bytes 37 files changed, 102 insertions(+), 7072 deletions(-) delete mode 100644 REALTIME_ASR_TROUBLESHOOTING.md delete mode 100644 crg-mcp-plugin/README.md delete mode 100644 crg-mcp-plugin/crg.cmd delete mode 100644 crg-mcp-plugin/dist/crg-mcp-1.0.0.piplug delete mode 100644 crg-mcp-plugin/main.js delete mode 100644 crg-mcp-plugin/manifest.json delete mode 100644 crg-mcp-plugin/selfcheck.ps1 delete mode 100644 docs/REALTIME_WEBSOCKET_PROCESSING_COMPARISON.md delete mode 100644 docs/TODO/ENTROPY_REDUCTION_PLAN.md delete mode 100644 docs/TODO/X86_RUST_ALIGN_OPT_PLAN.md delete mode 100644 docs/deployment.md delete mode 100644 docs/iluvatar_offline_deployment.md delete mode 100644 docs/metax_offline_deployment.md delete mode 100644 docs/mthreads_offline_deployment.md delete mode 100644 docs/realtime_meeting_refactor_report.md delete mode 100644 docs/realtime_meeting_websocket.md delete mode 100644 docs/realtime_meeting_websocket_customer.md delete mode 100644 docs/国产化兼容适配汇报.md delete mode 100644 environments/cpu/pyproject.toml delete mode 100644 environments/cpu/uv.lock delete mode 100644 environments/iluvatar/pyproject.toml delete mode 100644 environments/iluvatar/requirements.txt delete mode 100644 environments/metax/pyproject.toml delete mode 100644 environments/metax/requirements.txt delete mode 100644 environments/mthreads/pyproject.toml delete mode 100644 environments/mthreads/requirements.txt delete mode 100644 曦云系列_通用GPU_mx-smi使用手册_CN_V14.pdf diff --git a/.env.example b/.env.example index a83b43f..630617e 100644 --- a/.env.example +++ b/.env.example @@ -62,7 +62,7 @@ # 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= +QWEN3_ASR_MODEL=qwen3-asr-0.6b # ----------------------------------------------------------------------------- # 模型下载 / 缓存行为。 diff --git a/.gitignore b/.gitignore index 992df58..4e9f0d9 100644 --- a/.gitignore +++ b/.gitignore @@ -19,6 +19,7 @@ __pycache__/ *.py[cod] *$py.class *.so +crg-mcp-plugin # Model files (downloaded) *.pt diff --git a/README.md b/README.md index 0dbb4d7..1517440 100644 --- a/README.md +++ b/README.md @@ -574,11 +574,10 @@ Automatic long audio segmentation: | `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` +- **Default**: Use `qwen3-asr-0.6b` for both offline and realtime ASR. - **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 +- **Environment override**: Set `QWEN3_ASR_MODEL=qwen3-asr-1.7b` to use 1.7B instead of the shared 0.6B default. At startup the service checks the current runtime model plan and downloads missing models from ModelScope by default. @@ -594,7 +593,7 @@ Recommended public settings: | `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 | +| `QWEN3_ASR_MODEL` | `qwen3-asr-0.6b` | Select the shared offline/realtime model; set 1.7B to override | | `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 | diff --git a/REALTIME_ASR_TROUBLESHOOTING.md b/REALTIME_ASR_TROUBLESHOOTING.md deleted file mode 100644 index 7bb3391..0000000 --- a/REALTIME_ASR_TROUBLESHOOTING.md +++ /dev/null @@ -1,310 +0,0 @@ -# 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/api/v1/openai_compatible.py b/app/api/v1/openai_compatible.py index 6071b9c..7cfc200 100644 --- a/app/api/v1/openai_compatible.py +++ b/app/api/v1/openai_compatible.py @@ -346,7 +346,7 @@ def _get_openai_model_description() -> str: **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 +- 离线与实时 ASR 默认共用 Qwen3-ASR 0.6B；可通过 `QWEN3_ASR_MODEL` 切换到 1.7B """ diff --git a/app/models/asr.py b/app/models/asr.py index 3b938ed..4c696ea 100644 --- a/app/models/asr.py +++ b/app/models/asr.py @@ -200,7 +200,7 @@ class ASRHealthCheckResponse(HealthCheckResponse): "device": "cuda:0", "version": "1.0.0", "message": "ASR service is running normally", - "loaded_models": ["qwen3-asr-1.7b"], + "loaded_models": ["qwen3-asr-0.6b"], "memory_usage": { "gpu_memory_used": "2.1GB", "gpu_memory_total": "8.0GB", @@ -242,16 +242,16 @@ class ASRDeclaredEntryInfo(BaseModel): model_config = { "json_schema_extra": { "example": { - "id": "qwen3-asr-1.7b", + "id": "qwen3-asr-0.6b", "kind": "model", - "name": "Qwen3-ASR-1.7B", + "name": "Qwen3-ASR-0.6B", "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", + "path": "Qwen/Qwen3-ASR-0.6B", "exists": True, }, "realtime_model": None, @@ -270,9 +270,9 @@ class ASRRuntimeInfo(BaseModel): model_config = { "json_schema_extra": { "example": { - "loaded_model_ids": ["qwen3-asr-1.7b"], + "loaded_model_ids": ["qwen3-asr-0.6b"], "loaded_count": 1, - "default_offline_model_id": "qwen3-asr-1.7b", + "default_offline_model_id": "qwen3-asr-0.6b", } } } @@ -290,16 +290,16 @@ class ASRModelsResponse(BaseModel): "example": { "declared_entries": [ { - "id": "qwen3-asr-1.7b", + "id": "qwen3-asr-0.6b", "kind": "model", - "name": "Qwen3-ASR-1.7B", + "name": "Qwen3-ASR-0.6B", "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", + "path": "Qwen/Qwen3-ASR-0.6B", "exists": True, }, "realtime_model": None, @@ -307,9 +307,9 @@ class ASRModelsResponse(BaseModel): ], "declared_count": 2, "runtime": { - "loaded_model_ids": ["qwen3-asr-1.7b"], + "loaded_model_ids": ["qwen3-asr-0.6b"], "loaded_count": 1, - "default_offline_model_id": "qwen3-asr-1.7b", + "default_offline_model_id": "qwen3-asr-0.6b", }, } } diff --git a/app/services/asr/model_plan.py b/app/services/asr/model_plan.py index 2a13565..e05ec01 100644 --- a/app/services/asr/model_plan.py +++ b/app/services/asr/model_plan.py @@ -46,10 +46,10 @@ def get_qwen_model_override() -> Optional[str]: 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.""" +def select_qwen_model(all_model_ids: Optional[list[str]] = None) -> Optional[str]: + """选择离线与实时共用的 Qwen ASR 模型。""" from app.core.accelerator import get_accelerator_info - from app.core.device import detect_device, get_vram_gb + from app.core.device import detect_device from app.services.asr.qwenasr_rust import is_qwenasr_rust_available model_ids = all_model_ids or load_supported_model_ids() @@ -67,19 +67,16 @@ def detect_qwen_model_by_vram(all_model_ids: Optional[list[str]] = None) -> Opti 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 + # 离线与实时默认共用轻量模型；需要 1.7B 时通过 QWEN3_ASR_MODEL 显式指定。 + if "qwen3-asr-0.6b" in model_ids: + return "qwen3-asr-0.6b" + return "qwen3-asr-1.7b" if "qwen3-asr-1.7b" 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) + qwen_model = select_qwen_model(model_ids) if not qwen_model: override_model = get_qwen_model_override() if override_model: diff --git a/app/services/asr/models.json b/app/services/asr/models.json index 05e6cb3..1799d23 100644 --- a/app/services/asr/models.json +++ b/app/services/asr/models.json @@ -22,7 +22,7 @@ "th", "vi" ], - "default": true, + "default": false, "supports_realtime": true, "models": { "offline": "Qwen/Qwen3-ASR-1.7B" @@ -55,7 +55,7 @@ "th", "vi" ], - "default": false, + "default": true, "supports_realtime": true, "models": { "offline": "Qwen/Qwen3-ASR-0.6B" diff --git a/app/services/asr/qwen3_engine.py b/app/services/asr/qwen3_engine.py index 2ab2f3b..ab7e8a1 100644 --- a/app/services/asr/qwen3_engine.py +++ b/app/services/asr/qwen3_engine.py @@ -137,6 +137,7 @@ class Qwen3StreamingState: max_new_tokens: int = 32 language: Optional[str] = None chunk_count: int = 0 + # 引擎统一向 WebSocket 层提供当前整句文本快照，屏蔽 vLLM 快照与 Rust 增量的差异。 last_text: str = "" last_language: str = "" @@ -598,7 +599,12 @@ class Qwen3ASREngine(BaseASREngine): last_language=language or "", ) if self._backend == "vllm": - streaming_state = self.model.init_streaming_state(context=context, language=language, **kwargs) + # 流式 partial 与 final 共用离线转写相同的热词提示格式。 + streaming_state = self.model.init_streaming_state( + context=self._build_hotword_prompt_context(context), + language=language, + **kwargs, + ) return Qwen3StreamingState( internal_state=streaming_state, chunk_size_sec=float(kwargs.get("chunk_size_sec", 1.2)), @@ -633,7 +639,9 @@ class Qwen3ASREngine(BaseASREngine): language=state.language, ) state.chunk_count += 1 - state.last_text = text + # Rust 流式接口返回新增文本片段；引擎对上层统一提供整句快照。 + if text: + state.last_text += text state.last_language = state.language or "" return state @@ -659,7 +667,9 @@ class Qwen3ASREngine(BaseASREngine): max_new_tokens=state.max_new_tokens, language=state.language, ) - state.last_text = text + # 收尾接口同样只返回本次刷出的尾部增量，接到既有快照后再交给 WebSocket。 + if text: + state.last_text += text state.last_language = state.language or "" return state diff --git a/app/services/qwen3_websocket_asr.py b/app/services/qwen3_websocket_asr.py index d71f3ed..fcd0bf4 100644 --- a/app/services/qwen3_websocket_asr.py +++ b/app/services/qwen3_websocket_asr.py @@ -1168,8 +1168,8 @@ class Qwen3ASRService: 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 + # 默认使用 Qwen3 原生流式推理；显式传 false 时仍保留旧式 partial 兼容路径。 + return True async def _init_realtime_stream_state( self, @@ -1268,10 +1268,10 @@ class Qwen3ASRService: getattr(ctx.realtime_stream_state, "last_language", ""), ctx, ) - if stream_text.strip(): - return stream_text, stream_language + # 流状态有效但当前还没有解码文本时，等待下一个流式块，避免反复整段重转写。 + return stream_text, stream_language - # 流式文本为空时回退到当前音频窗口重转写。 + # 流式初始化失败或客户端显式关闭原生流式时，再回退到窗口转写。 partial_audio = np.asarray( ctx.stream_window_buffer if ctx.stream_window_buffer.size > 0 else ctx.segment_audio_buffer, dtype=np.float32, @@ -1628,17 +1628,42 @@ class Qwen3ASRService: ctx.segment_observed_language = language return observed + def _replace_segment_observed_text( + self, + ctx: ConnectionContext, + text: str, + language: str, + ) -> str: + """用 Qwen 流式接口当前返回的整句快照更新 partial，允许修订句尾。""" + 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 + + ctx.segment_observed_text = candidate + if language: + ctx.segment_observed_language = language + return candidate + def _update_best_partial( self, ctx: ConnectionContext, text: str, language: str, + *, + replace_snapshot: bool = False, ) -> None: candidate = self._sanitize_candidate_text(text) if not candidate: return if self._is_unstable_expansion(ctx.best_partial_text, candidate): return + if replace_snapshot: + # Qwen partial 是整句快照；修订句尾时应淘汰旧快照，避免旧文本被 final 选回。 + ctx.best_partial_text = candidate + ctx.best_partial_language = language or ctx.best_partial_language + return chosen = self._prefer_segment_text(ctx.best_partial_text, candidate) if chosen != ctx.best_partial_text: ctx.best_partial_text = chosen @@ -2356,18 +2381,27 @@ class Qwen3ASRService: 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) + has_native_stream_snapshot = ( + carry_audio.size == 0 + and self._should_use_native_partial_stream(ctx, engine) + and ctx.realtime_stream_state is not None + ) 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), + update_observed_text = ( + self._replace_segment_observed_text + if has_native_stream_snapshot + else self._update_segment_observed_text + ) + observed_text = update_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), + replace_snapshot=has_native_stream_snapshot, ) else: ctx.realtime_stream_state = None @@ -2629,7 +2663,7 @@ class Qwen3ASRService: ), "enable_native_partial_stream": payload.get( "enable_native_partial_stream", - False, + True, ), # 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), @@ -2736,6 +2770,10 @@ class Qwen3ASRService: continue if self._should_decode_turn_partial(ctx): + has_native_stream_snapshot = ( + self._should_use_native_partial_stream(ctx, engine) + and ctx.realtime_stream_state is not None + ) current, current_language = await self._decode_turn_partial_text( engine, ctx, @@ -2744,11 +2782,12 @@ class Qwen3ASRService: 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, + update_observed_text = ( + self._replace_segment_observed_text + if has_native_stream_snapshot + else self._update_segment_observed_text ) + observed = update_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 ( @@ -2762,7 +2801,12 @@ class Qwen3ASRService: 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) + self._update_best_partial( + ctx, + visible_observed, + current_language, + replace_snapshot=has_native_stream_snapshot, + ) sentence_payload = self._build_tencent_sentence( { diff --git a/crg-mcp-plugin/README.md b/crg-mcp-plugin/README.md deleted file mode 100644 index ff2626e..0000000 --- a/crg-mcp-plugin/README.md +++ /dev/null @@ -1,68 +0,0 @@ -# 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 deleted file mode 100644 index 4b695f5..0000000 --- a/crg-mcp-plugin/crg.cmd +++ /dev/null @@ -1,57 +0,0 @@ -@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 deleted file mode 100644 index 5a307a4016fcf082426a9222e3cb1258ec1aa10b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 10403 zcmb_iTTmQVdS-XCxpXd1Ta~Ijos4h*g=s*J9TzgHWdU7Q63hZ#mm^e6GtFMrt z_kf9{DlAzSB#@A;izQ@9*ph=~8)RE+ArRsrRe8u`p7Z3L?irr^o~L~OIo&hOFxZ=_ z#TGE<@}K|w|My=HiNinplV8+@U;pqQy`KH&b#?Ub&oG#>N99y5{Yy2KHJO>oNU3bf zVz0i^+Sw7UB0g#>R+b&@N-$l?8>y^n zvy5r8#G!b%>WsOjCDRtZZ7{3yg;~>anPVvvhQr*f=CBFP%`%1MG(*cNI#Z2t%{Gmk zYPhU1(K&dqnGH-juA1vNO}Ckon$V22W;e6Jo}&qTr*B6(dk-*6&l63Ry7s( zx@6X?j^mOOw&tqLHJMn*Z}~?Rw4`NNTFqz%80hLhFnqA@Xb&4Tabi{(SBJA^P9-(7 zSUB0y-99u5N#;l5mTkU`I}L49R7`5G?B=(pCBs6O!(5jwDbsLV#c&y zSGA#al@V>M2^1m6#kdKH-g5ktlg9rP1-x%Eg zT1Q(fI&`S7r?;!G`%v$Jj=YYnKkB(G*px_ZMBILo5+TMfeT5E5ukK!9GHocDC3%w$WB^W)o5yFj>(Z`DXB4K zRORI0R7^vk43Nq>`FlQ2gC!9yG&K3AlXkoxn`R3zrE4b}8 z3zOA%APw-%Mr?CTHCTR}xwZm}(E)V6y)l+iaN9I_kCbAVh6XKY{{~x0*_!1t;vy#Q zW!Y*5)}ycq(;kD$A!nEjM)5shl$cAr)lp$`NG9Hf1~@Ti$eMJsra^3Ryr}PJ@V+s= zJn_~`F@aob5JA4XWf(rqRo`(d6M@IuHV)UOq}*X!No)Cl&cEhWdop^O zqawPvT58x)6+4w3HsC^owdB#TJ(X3A(Qr(Mz=Q3u5jVn}lF4ATnjX&R=7boBw4)-3 zS~y9nzbDZr1ZAt1SuH1;jQ|0(BCDi`&f_@_+%0}zcxr?&+7keT2omU3BQ4U2#Fn(= zzyIsn^o^g^)zLpnOLB^4$ZtC>yLU@$H$##V&mk@OZt5CPp&3j!6&O8_rrZu|W)UO6 z$fzoV0MY0Ke#{I9)r4k~0SK=EYGH)Ofv{fgPgs)lX=@{SS9N5ZJ-~yHlWZqim6Rk* zK9WHmm1K<+^i9f6V0V^kBZbL{L;{*jJ6UB+ZKmvj=ves?s5A@TM2Wy8%ZK(rierky zk|Mbn2K8Yu!GFF^llzj$9!!$OD1x{{#NYx(RA#HA0Euc-u3?)whAc`A?8D1LBC}*bgaMIi*l9h_92{yS}xQC?d z2+1V;GJ-i>R3{^*Y%Y^`^WcbM0L)h@0S=Ba{3Fbho|81-J_d#9%9&lA)?;H7!f0YMk+gL0F!z(q5Dik0ltUTG_IKmE_+@xT9BT^;@VYYttJFLE>y?=c5)BB%ToH^seT?X0bt zMzu74jRnw-@gYOWsWjLHJhOhD6|)0jRxFkPCjpgMo7^h5icxqC?xO|$*snP9g=)B@ z5tq`crJIwG*S0vrGwC3?YDAE&PW`s$eoYiSM5siVL~aeJ8j$rkrLl0}vfzr0n648T zvlxun73N4jkVVD7Fm{ZOv0#qRmu1Ci&=5B1%807h6jyW=BBtSxK4~Sl&FE~(%)$CY zO;9Biokj2~^B5c|QX2~9MDGuhR)iwBj^vk|cgS1hm_te$F=p%Ha@r`CZl4SpsUi|c zkg-$2Z{p{1d^*LE2D8g)4k5__X(4gaf-G<#bJWDJ{5W5(8Ypp8S{f+K(t?_d7~9v; zDg%tPTu?{c)5f{&^&o+8BS)CVfUDSU-r}@jky~wP<%_*9zw%dgb@cBqU`zcyo!v)! 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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 deleted file mode 100644 index f429523..0000000 --- a/crg-mcp-plugin/manifest.json +++ /dev/null @@ -1,33 +0,0 @@ -{ - "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 deleted file mode 100644 index 5270319..0000000 --- a/crg-mcp-plugin/selfcheck.ps1 +++ /dev/null @@ -1,96 +0,0 @@ -<# - 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/docs/README_zh.md b/docs/README_zh.md index c0d29ff..d831140 100644 --- a/docs/README_zh.md +++ b/docs/README_zh.md @@ -526,11 +526,10 @@ curl -X POST "http://localhost:8000/stream/v1/asr?enable_speaker_diarization=tru | `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` +- **默认选择**: 离线和实时 ASR 统一使用 `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 +- **环境变量覆盖**: 设置 `QWEN3_ASR_MODEL=qwen3-asr-1.7b` 可切换到 1.7B；默认使用 0.6B 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。 @@ -546,7 +545,7 @@ curl -X POST "http://localhost:8000/stream/v1/asr?enable_speaker_diarization=tru | `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` | +| `QWEN3_ASR_MODEL` | `qwen3-asr-0.6b` | 离线与实时共用模型；设为 1.7B 可覆盖默认值 | | `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 | diff --git a/docs/REALTIME_WEBSOCKET_PROCESSING_COMPARISON.md b/docs/REALTIME_WEBSOCKET_PROCESSING_COMPARISON.md deleted file mode 100644 index 8ea96ee..0000000 --- a/docs/REALTIME_WEBSOCKET_PROCESSING_COMPARISON.md +++ /dev/null @@ -1,382 +0,0 @@ -# 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 deleted file mode 100644 index ec95b91..0000000 --- a/docs/TODO/ENTROPY_REDUCTION_PLAN.md +++ /dev/null @@ -1,66 +0,0 @@ -# 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 deleted file mode 100644 index 845d37e..0000000 --- a/docs/TODO/X86_RUST_ALIGN_OPT_PLAN.md +++ /dev/null @@ -1,457 +0,0 @@ -# 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 deleted file mode 100644 index fddb2eb..0000000 --- a/docs/deployment.md +++ /dev/null @@ -1,699 +0,0 @@ -# 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 deleted file mode 100644 index 4f9b4d6..0000000 --- a/docs/iluvatar_offline_deployment.md +++ /dev/null @@ -1,242 +0,0 @@ -# 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 deleted file mode 100644 index 66b4b0a..0000000 --- a/docs/metax_offline_deployment.md +++ /dev/null @@ -1,234 +0,0 @@ -# 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 deleted file mode 100644 index 538d819..0000000 --- a/docs/mthreads_offline_deployment.md +++ /dev/null @@ -1,53 +0,0 @@ -# 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 deleted file mode 100644 index 63b83a1..0000000 --- a/docs/realtime_meeting_refactor_report.md +++ /dev/null @@ -1,412 +0,0 @@ -# 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 deleted file mode 100644 index b44b666..0000000 --- a/docs/realtime_meeting_websocket.md +++ /dev/null @@ -1,417 +0,0 @@ -# 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 deleted file mode 100644 index 5063cbc..0000000 --- a/docs/realtime_meeting_websocket_customer.md +++ /dev/null @@ -1,297 +0,0 @@ -# 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 deleted file mode 100644 index 4572240..0000000 --- a/docs/国产化兼容适配汇报.md +++ /dev/null @@ -1,95 +0,0 @@ -# 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 deleted file mode 100644 index f624140..0000000 --- a/environments/cpu/pyproject.toml +++ /dev/null @@ -1,54 +0,0 @@ -[project] -name = "qwen3-asr-cpu-env" -version = "1.0.0" -description = "CPU runtime environment for 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