Add official FunASR realtime websocket service
This commit is contained in:
parent
d6bce1ef6a
commit
a85e8938d4
@ -43,6 +43,7 @@ docker compose down
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| --- | --- | --- | --- |
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| Speakr | `voice-speakr` | `8899` | 页面和后端 |
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| ASR | `asr-test` | `59805` | Paraformer HTTP / WebSocket 识别 |
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| 官方实时 ASR | `funasr-online` | `10096` | FunASR 官方实时 WebSocket |
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| 声纹提取 | `voice-dected` | `18000` | `/extract_embedding` 声纹向量服务 |
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| 实时桥接 | `fastapi-wss` | `10095` | 页面实时 ASR WebSocket 桥接 |
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| Redis | `voice-redis` | `16380` | 实时桥接的缓存/会话存储 |
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@ -85,7 +86,7 @@ docker compose up -d
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```env
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ASR_BASE_URL=http://asr-test:59805
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FUNASR_WEBSOCKET_IP=ws://fastapi-wss:10095/websocket_offline
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TARGET_WS_URL=ws://asr-test:59805/ws/asr
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TARGET_WS_URL=ws://funasr-online:10096
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VOICE_DETECTED_URL=http://voice-dected:8000
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SPEAKER_DET_URL=http://voice-dected:8000/extract_embedding
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REDIS_URL=redis://redis:6379/0
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@ -96,8 +97,6 @@ TEXT_MODEL_API_KEY=none
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VOICEPRINT_STORE=/data/asr_voiceprints.json
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VOICEPRINT_MATCH_THRESHOLD=0.45
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REALTIME_PARTIAL_MIN_SECONDS=1.5
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REALTIME_PARTIAL_INTERVAL_SECONDS=2.0
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```
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说明：
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86
config.env
86
config.env
@ -1,43 +1,43 @@
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# Shared runtime config for /storage01/home/xll/202607/voice/docker-compose.yml
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# Speakr / ASR
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USE_ASR_ENDPOINT=true
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ASR_BASE_URL=http://asr-test:59805
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ASR_DIARIZE=true
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ASR_MIN_SPEAKERS=1
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ASR_MAX_SPEAKERS=10
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# LLM used by Speakr summaries and chat
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TEXT_MODEL_BASE_URL=http://192.168.0.46:59800/v1
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TEXT_MODEL_NAME=46-qwen3.5-35B
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TEXT_MODEL_API_KEY=none
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# Realtime WebSocket bridge
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FUNASR_WEBSOCKET_IP=ws://fastapi-wss:10095/websocket_offline
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TARGET_WS_URL=ws://asr-test:59805/ws/asr
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REDIS_URL=redis://redis:6379/0
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LLM_API_KEY=none
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LLM_BASE_URL=http://192.168.0.46:59800/v1
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LLM_MODEL=46-qwen3.5-35B
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USE_LLM_ROLE_IDENTIFICATION=false
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LLM_ROLE_CANDIDATES=主持人,正方辩手,反方辩手,计时员,评委,嘉宾,未知
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LLM_ROLE_MIN_TEXT_LEN=8
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LLM_ROLE_CACHE_CONFIDENCE=0.82
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LLM_ROLE_HISTORY_MAX_CHARS=1200
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# ASR voiceprint adapter
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VOICE_DETECTED_URL=http://voice-dected:8000
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VOICEPRINT_STORE=/data/asr_voiceprints.json
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VOICEPRINT_MATCH_THRESHOLD=0.45
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REALTIME_PARTIAL_MIN_SECONDS=1.5
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REALTIME_PARTIAL_INTERVAL_SECONDS=2.0
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# fastapi_wss legacy/local ASR-with-speaker helper
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ASR_LOCAL_URL=http://asr-test:59805/asr
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SPEAKER_DET_URL=http://voice-dected:8000/extract_embedding
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# Runtime
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DEVICE=cuda
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CUDA_VISIBLE_DEVICES=0
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NVIDIA_VISIBLE_DEVICES=7
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PYTHONUNBUFFERED=1
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# Shared runtime config for /storage01/home/xll/202607/voice/docker-compose.yml
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# Speakr / ASR
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USE_ASR_ENDPOINT=true
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ASR_BASE_URL=http://asr-test:59805
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ASR_DIARIZE=true
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ASR_MIN_SPEAKERS=1
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ASR_MAX_SPEAKERS=10
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# LLM used by Speakr summaries and chat
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TEXT_MODEL_BASE_URL=http://192.168.0.46:59800/v1
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TEXT_MODEL_NAME=46-qwen3.5-35B
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TEXT_MODEL_API_KEY=none
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# Realtime WebSocket bridge
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FUNASR_WEBSOCKET_IP=ws://fastapi-wss:10095/websocket_offline
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TARGET_WS_URL=ws://funasr-online:10096
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REDIS_URL=redis://redis:6379/0
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LLM_API_KEY=none
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LLM_BASE_URL=http://192.168.0.46:59800/v1
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LLM_MODEL=46-qwen3.5-35B
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USE_LLM_ROLE_IDENTIFICATION=false
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LLM_ROLE_CANDIDATES=主持人,正方辩手,反方辩手,计时员,评委,嘉宾,未知
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LLM_ROLE_MIN_TEXT_LEN=8
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LLM_ROLE_CACHE_CONFIDENCE=0.82
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LLM_ROLE_HISTORY_MAX_CHARS=1200
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# ASR voiceprint adapter
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VOICE_DETECTED_URL=http://voice-dected:8000
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VOICEPRINT_STORE=/data/asr_voiceprints.json
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VOICEPRINT_MATCH_THRESHOLD=0.45
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REALTIME_PARTIAL_MIN_SECONDS=1.5
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REALTIME_PARTIAL_INTERVAL_SECONDS=2.0
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# fastapi_wss legacy/local ASR-with-speaker helper
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ASR_LOCAL_URL=http://asr-test:59805/asr
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SPEAKER_DET_URL=http://voice-dected:8000/extract_embedding
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# Runtime
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DEVICE=cuda
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CUDA_VISIBLE_DEVICES=0
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NVIDIA_VISIBLE_DEVICES=7
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PYTHONUNBUFFERED=1
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@ -1,128 +1,188 @@
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services:
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speakr:
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image: voice-speakr:runtime
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container_name: voice-speakr
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restart: unless-stopped
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working_dir: /app
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env_file:
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- ./config.env
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environment:
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PYTHONPATH: /app
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SQLALCHEMY_DATABASE_URI: sqlite:////data/instance/transcriptions.db
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UPLOAD_FOLDER: /data/uploads
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USE_ASR_ENDPOINT: "true"
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ASR_BASE_URL: http://asr-test:59805
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volumes:
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- ./speakr:/app
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- ./deploy/data/speakr/uploads:/data/uploads
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- ./deploy/data/speakr/instance:/data/instance
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ports:
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- "8899:8899"
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depends_on:
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- asr-test
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- voice-dected
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command:
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- sh
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- -c
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- >
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mkdir -p /data/uploads /data/instance &&
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python -c "from src.app import app; from src.clients import db; app.app_context().push(); db.create_all()" &&
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exec gunicorn --workers $${GUNICORN_WORKERS:-3} --threads $${GUNICORN_THREADS:-8}
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--bind 0.0.0.0:8899 --timeout $${GUNICORN_TIMEOUT:-600} src.app:app
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voice-dected:
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image: voice-dected:runtime
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container_name: voice-dected
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restart: unless-stopped
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working_dir: /app
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env_file:
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- ./config.env
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environment:
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PYTHONPATH: /app
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volumes:
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- ./voice-dected/src:/app
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ports:
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- "18000:8000"
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command:
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- uvicorn
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- app:app
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- --host
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- 0.0.0.0
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- --port
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- "8000"
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asr-test:
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image: paraformer-asr:latest
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container_name: asr-test
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restart: unless-stopped
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runtime: nvidia
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gpus:
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- driver: nvidia
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device_ids:
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- "7"
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capabilities:
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- gpu
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working_dir: /app
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env_file:
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- ./config.env
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environment:
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DEVICE: cuda
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CUDA_VISIBLE_DEVICES: "0"
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NVIDIA_VISIBLE_DEVICES: "7"
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VOICE_DETECTED_URL: http://voice-dected:8000
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VOICEPRINT_STORE: /data/asr_voiceprints.json
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volumes:
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- ./asr/main.py:/app/main.py:ro
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- /storage01/home/xll/audio:/app/audio:ro
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- /storage01/home/xll/weixiu-hj/seacomodel:/app/models/seacomodel:ro
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- /storage01/home/xll/weixiu-hj/vadmodel:/app/models/vadmodel:ro
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- /storage01/home/xll/weixiu-hj/ctpuncmodel:/app/models/ctpuncmodel:ro
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- /storage01/home/xll/weixiu-hj/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/hotword.txt:/app/hotword.txt:ro
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- ./deploy/data/asr:/data
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ports:
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- "59805:59805"
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depends_on:
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- voice-dected
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command:
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- uvicorn
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- main:app
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- --host
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- 0.0.0.0
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- --port
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- "59805"
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- --workers
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services:
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speakr:
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image: voice-speakr:runtime
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container_name: voice-speakr
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restart: unless-stopped
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working_dir: /app
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env_file:
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- ./config.env
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environment:
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PYTHONPATH: /app
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SQLALCHEMY_DATABASE_URI: sqlite:////data/instance/transcriptions.db
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UPLOAD_FOLDER: /data/uploads
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USE_ASR_ENDPOINT: "true"
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ASR_BASE_URL: http://asr-test:59805
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volumes:
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- ./speakr:/app
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- ./deploy/data/speakr/uploads:/data/uploads
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- ./deploy/data/speakr/instance:/data/instance
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ports:
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- "8899:8899"
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depends_on:
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- asr-test
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- voice-dected
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command:
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- sh
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- -c
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- >
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mkdir -p /data/uploads /data/instance &&
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python -c "from src.app import app; from src.clients import db; app.app_context().push(); db.create_all()" &&
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exec gunicorn --workers $${GUNICORN_WORKERS:-3} --threads $${GUNICORN_THREADS:-8}
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--bind 0.0.0.0:8899 --timeout $${GUNICORN_TIMEOUT:-600} src.app:app
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voice-dected:
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image: voice-dected:runtime
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container_name: voice-dected
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restart: unless-stopped
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working_dir: /app
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env_file:
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- ./config.env
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environment:
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PYTHONPATH: /app
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volumes:
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- ./voice-dected/src:/app
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ports:
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- "18000:8000"
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command:
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- uvicorn
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- app:app
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- --host
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- 0.0.0.0
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- --port
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- "8000"
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asr-test:
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image: paraformer-asr:latest
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container_name: asr-test
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restart: unless-stopped
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runtime: nvidia
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gpus:
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- driver: nvidia
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device_ids:
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- "7"
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capabilities:
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- gpu
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working_dir: /app
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env_file:
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- ./config.env
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environment:
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DEVICE: cuda
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CUDA_VISIBLE_DEVICES: "0"
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NVIDIA_VISIBLE_DEVICES: "7"
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VOICE_DETECTED_URL: http://voice-dected:8000
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VOICEPRINT_STORE: /data/asr_voiceprints.json
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volumes:
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- ./asr/main.py:/app/main.py:ro
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- /storage01/home/xll/audio:/app/audio:ro
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- /storage01/home/xll/weixiu-hj/seacomodel:/app/models/seacomodel:ro
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- /storage01/home/xll/weixiu-hj/vadmodel:/app/models/vadmodel:ro
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- /storage01/home/xll/weixiu-hj/ctpuncmodel:/app/models/ctpuncmodel:ro
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- /storage01/home/xll/weixiu-hj/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/hotword.txt:/app/hotword.txt:ro
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- ./deploy/data/asr:/data
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ports:
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- "59805:59805"
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depends_on:
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- voice-dected
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command:
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- uvicorn
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- main:app
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- --host
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- 0.0.0.0
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- --port
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- "59805"
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- --workers
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- "1"
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funasr-online:
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image: paraformer-asr:latest
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container_name: funasr-online
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restart: unless-stopped
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runtime: nvidia
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gpus:
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- driver: nvidia
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device_ids:
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- "7"
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capabilities:
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- gpu
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working_dir: /app/funasr_runtime
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env_file:
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- ./config.env
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environment:
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DEVICE: cuda
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CUDA_VISIBLE_DEVICES: "0"
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NVIDIA_VISIBLE_DEVICES: "7"
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MODELSCOPE_CACHE: /models
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TORCH_HOME: /models/torch
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volumes:
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- ./funasr_runtime:/app/funasr_runtime:ro
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- ./deploy/data/funasr-models:/models
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ports:
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- "10096:10096"
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command:
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- python
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- funasr_wss_server.py
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- --host
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- 0.0.0.0
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- --port
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- "10096"
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- --ngpu
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- "1"
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- --device
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- cuda
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- --ncpu
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- "4"
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- --asr_model_online
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- iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online
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- --vad_model
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- iic/speech_fsmn_vad_zh-cn-16k-common-pytorch
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- --punc_model
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- iic/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727
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- --worker_threads
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- "4"
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- --concurrent_asr_online
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- "1"
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- --concurrent_asr_offline
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- "1"
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- --concurrent_vad
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- "1"
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- --concurrent_punc
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- "1"
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- --certfile
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- ""
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- --keyfile
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- ""
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fastapi-wss:
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image: fastapi-wss:runtime
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container_name: fastapi-wss
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restart: unless-stopped
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working_dir: /app/src
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env_file:
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- ./config.env
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environment:
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TARGET_WS_URL: ws://asr-test:59805/ws/asr
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REDIS_URL: redis://redis:6379/0
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volumes:
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- ./fastapi_wss:/app
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ports:
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- "10095:10095"
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depends_on:
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- asr-test
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- redis
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command:
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- python
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- a2a_wss.py
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redis:
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image: redis:7-alpine
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container_name: voice-redis
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restart: unless-stopped
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volumes:
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- ./deploy/data/redis:/data
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ports:
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- "16380:6379"
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command:
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- redis-server
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- --appendonly
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- "yes"
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image: fastapi-wss:runtime
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container_name: fastapi-wss
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restart: unless-stopped
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working_dir: /app/src
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env_file:
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- ./config.env
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environment:
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TARGET_WS_URL: ws://funasr-online:10096
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REDIS_URL: redis://redis:6379/0
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volumes:
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- ./fastapi_wss:/app
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ports:
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- "10095:10095"
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depends_on:
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- funasr-online
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- redis
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command:
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- python
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- a2a_wss.py
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redis:
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image: redis:7-alpine
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container_name: voice-redis
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restart: unless-stopped
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volumes:
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- ./deploy/data/redis:/data
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ports:
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- "16380:6379"
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command:
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- redis-server
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- --appendonly
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- "yes"
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749
funasr_runtime/funasr_wss_server.py
Normal file
749
funasr_runtime/funasr_wss_server.py
Normal file
@ -0,0 +1,749 @@
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import asyncio
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import json
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import websockets
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import time
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import numpy as np
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import argparse
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import ssl
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import os
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import wave
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import functools
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from concurrent.futures import ThreadPoolExecutor
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from scipy.spatial.distance import cosine
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import torch # 保留不影响
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def to_python(obj):
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"""递归地把 numpy / torch 等类型转成纯 Python，可 JSON 序列化。"""
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try:
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import numpy as np # noqa
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import torch # noqa
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||||
except Exception:
|
||||
np = None
|
||||
torch = None
|
||||
|
||||
if np is not None and isinstance(obj, np.generic):
|
||||
return obj.item()
|
||||
if np is not None and isinstance(obj, np.ndarray):
|
||||
return obj.tolist()
|
||||
if torch is not None and isinstance(obj, torch.Tensor):
|
||||
return obj.cpu().tolist()
|
||||
|
||||
if isinstance(obj, dict):
|
||||
return {k: to_python(v) for k, v in obj.items()}
|
||||
if isinstance(obj, (list, tuple)):
|
||||
return [to_python(v) for v in obj]
|
||||
|
||||
return obj
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--host", type=str, default="0.0.0.0", required=False, help="host ip")
|
||||
parser.add_argument("--port", type=int, default=10095, required=False, help="grpc server port")
|
||||
|
||||
parser.add_argument(
|
||||
"--asr_model",
|
||||
type=str,
|
||||
default="iic/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404",
|
||||
help="model from modelscope",
|
||||
)
|
||||
parser.add_argument("--asr_model_revision", type=str, default="v2.0.4", help="")
|
||||
|
||||
parser.add_argument(
|
||||
"--asr_model_online",
|
||||
type=str,
|
||||
default="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online",
|
||||
help="model from modelscope",
|
||||
)
|
||||
parser.add_argument("--asr_model_online_revision", type=str, default="v2.0.4", help="")
|
||||
|
||||
parser.add_argument(
|
||||
"--vad_model",
|
||||
type=str,
|
||||
default="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
|
||||
help="model from modelscope",
|
||||
)
|
||||
parser.add_argument("--vad_model_revision", type=str, default="v2.0.4", help="")
|
||||
|
||||
parser.add_argument(
|
||||
"--punc_model",
|
||||
type=str,
|
||||
default="iic/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727",
|
||||
help="model from modelscope",
|
||||
)
|
||||
parser.add_argument("--punc_model_revision", type=str, default="v2.0.4", help="")
|
||||
|
||||
parser.add_argument("--ngpu", type=int, default=1, help="0 for cpu, 1 for gpu")
|
||||
parser.add_argument("--device", type=str, default="cuda", help="cuda, cpu")
|
||||
parser.add_argument("--ncpu", type=int, default=4, help="cpu cores")
|
||||
|
||||
parser.add_argument(
|
||||
"--certfile",
|
||||
type=str,
|
||||
default="../../ssl_key/server.crt",
|
||||
required=False,
|
||||
help="certfile for ssl",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--keyfile",
|
||||
type=str,
|
||||
default="../../ssl_key/server.key",
|
||||
required=False,
|
||||
help="keyfile for ssl",
|
||||
)
|
||||
|
||||
# ====== 保存 2pass 离线阶段送入 ASR 的音频片段（排查 VAD 切分）======
|
||||
parser.add_argument(
|
||||
"--save_offline_segments",
|
||||
action="store_true",
|
||||
help="Save each offline (2pass) audio segment sent to offline ASR as wav for debugging VAD split.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--save_offline_segments_dir",
|
||||
type=str,
|
||||
default="./offline_segments",
|
||||
help="Directory to save offline wav segments when --save_offline_segments is enabled.",
|
||||
)
|
||||
|
||||
# ====== 并发控制：核心新增 ======
|
||||
parser.add_argument(
|
||||
"--worker_threads",
|
||||
type=int,
|
||||
default=max(4, (os.cpu_count() or 4)),
|
||||
help="ThreadPoolExecutor max_workers. Used to offload blocking inference so event loop won't be blocked.",
|
||||
)
|
||||
parser.add_argument("--concurrent_vad", type=int, default=4, help="Max concurrent VAD generate() calls.")
|
||||
parser.add_argument("--concurrent_asr_online", type=int, default=4, help="Max concurrent streaming ASR generate() calls.")
|
||||
parser.add_argument("--concurrent_asr_offline", type=int, default=2, help="Max concurrent offline ASR generate() calls.")
|
||||
parser.add_argument("--concurrent_punc", type=int, default=1, help="Max concurrent punctuation generate() calls.")
|
||||
parser.add_argument("--concurrent_sv", type=int, default=1, help="Max concurrent speaker verification generate() calls.")
|
||||
parser.add_argument(
|
||||
"--speaker_db_reload_sec",
|
||||
type=int,
|
||||
default=5,
|
||||
help="Reload speaker_db.json at most once every N seconds (avoid frequent disk IO).",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
websocket_users = set()
|
||||
SPEAKER_DB_PATH = os.path.join(os.path.dirname(__file__), "speaker_db.json")
|
||||
|
||||
|
||||
def _ensure_dir(p: str):
|
||||
try:
|
||||
os.makedirs(p, exist_ok=True)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
def _pcm_duration_ms(pcm_bytes: bytes, fs: int, ch: int = 1, sampwidth: int = 2) -> int:
|
||||
"""根据 fs/ch/sampwidth 计算 PCM 时长，避免写死 16k -> 32 bytes/ms。"""
|
||||
if not pcm_bytes:
|
||||
return 0
|
||||
bytes_per_ms = (fs * ch * sampwidth) / 1000.0
|
||||
if bytes_per_ms <= 0:
|
||||
return 0
|
||||
return int(len(pcm_bytes) / bytes_per_ms)
|
||||
|
||||
|
||||
def _safe_int(v, default):
|
||||
try:
|
||||
return int(v)
|
||||
except Exception:
|
||||
return default
|
||||
|
||||
|
||||
# ========= speaker db：加缓存，避免每段都读盘 =========
|
||||
_SPEAKER_DB_CACHE = {}
|
||||
_SPEAKER_DB_CACHE_TS = 0.0
|
||||
|
||||
|
||||
def _load_speaker_db_sync():
|
||||
if not os.path.exists(SPEAKER_DB_PATH):
|
||||
return {}
|
||||
try:
|
||||
with open(SPEAKER_DB_PATH, "r", encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
return data if isinstance(data, dict) else {}
|
||||
except Exception:
|
||||
return {}
|
||||
|
||||
|
||||
def get_speaker_db_cached(now_ts: float, reload_sec: int):
|
||||
global _SPEAKER_DB_CACHE, _SPEAKER_DB_CACHE_TS
|
||||
if (now_ts - _SPEAKER_DB_CACHE_TS) >= max(1, int(reload_sec)):
|
||||
_SPEAKER_DB_CACHE = _load_speaker_db_sync()
|
||||
_SPEAKER_DB_CACHE_TS = now_ts
|
||||
return _SPEAKER_DB_CACHE or {}
|
||||
|
||||
|
||||
def _save_wav_sync(out_path: str, audio_bytes: bytes, fs: int, ch: int, sampwidth: int):
|
||||
with wave.open(out_path, "wb") as wf:
|
||||
wf.setnchannels(ch)
|
||||
wf.setsampwidth(sampwidth)
|
||||
wf.setframerate(fs)
|
||||
wf.writeframes(audio_bytes)
|
||||
|
||||
|
||||
def save_offline_wav_segment_sync(websocket, audio_bytes: bytes, reason: str = "offline"):
|
||||
"""
|
||||
保存离线阶段送入 ASR 的音频片段，方便人工试听排查 VAD 切分是否正确。
|
||||
约定：audio_bytes 为 单声道 PCM16 little-endian（默认 16k）。
|
||||
（注意：这是同步函数，外层会放线程池执行）
|
||||
"""
|
||||
if not getattr(websocket, "save_offline_segments", False):
|
||||
return
|
||||
if "2pass" not in (getattr(websocket, "mode", "") or ""):
|
||||
return
|
||||
if not audio_bytes:
|
||||
return
|
||||
|
||||
fs = int(getattr(websocket, "audio_fs", 16000) or 16000)
|
||||
ch = 1
|
||||
sampwidth = 2 # int16
|
||||
|
||||
# int16 对齐
|
||||
if len(audio_bytes) % 2 == 1:
|
||||
audio_bytes = audio_bytes[:-1]
|
||||
if not audio_bytes:
|
||||
return
|
||||
|
||||
seg_idx = int(getattr(websocket, "offline_seg_idx", 0))
|
||||
websocket.offline_seg_idx = seg_idx + 1
|
||||
|
||||
duration_ms = _pcm_duration_ms(audio_bytes, fs=fs, ch=ch, sampwidth=sampwidth)
|
||||
|
||||
base_dir = getattr(websocket, "offline_save_dir", args.save_offline_segments_dir)
|
||||
_ensure_dir(base_dir)
|
||||
|
||||
wav_name = (getattr(websocket, "wav_name", "microphone") or "microphone").replace("/", "_")
|
||||
ts = int(time.time() * 1000)
|
||||
fname = f"{wav_name}_{ts}_seg{seg_idx:04d}_{reason}_{duration_ms}ms.wav"
|
||||
out_path = os.path.join(base_dir, fname)
|
||||
|
||||
try:
|
||||
_save_wav_sync(out_path, audio_bytes, fs=fs, ch=ch, sampwidth=sampwidth)
|
||||
print(f"[SAVE_OFFLINE_SEG] {out_path} ({duration_ms} ms, {len(audio_bytes)} bytes)")
|
||||
except Exception as e:
|
||||
print(f"[SAVE_OFFLINE_SEG] failed: {e}")
|
||||
|
||||
|
||||
print("model loading")
|
||||
from funasr import AutoModel # noqa
|
||||
|
||||
# ====== 离线 ASR ======
|
||||
model_asr = AutoModel(
|
||||
model="paraformer-zh",
|
||||
model_revision="v2.0.4",
|
||||
ngpu=args.ngpu,
|
||||
ncpu=args.ncpu,
|
||||
device=args.device,
|
||||
disable_pbar=True,
|
||||
disable_log=True,
|
||||
)
|
||||
|
||||
# streaming asr
|
||||
model_asr_streaming = AutoModel(
|
||||
model=args.asr_model_online,
|
||||
model_revision=args.asr_model_online_revision,
|
||||
ngpu=args.ngpu,
|
||||
ncpu=args.ncpu,
|
||||
device=args.device,
|
||||
disable_pbar=True,
|
||||
disable_log=True,
|
||||
)
|
||||
|
||||
# vad
|
||||
model_vad = AutoModel(
|
||||
model=args.vad_model,
|
||||
model_revision=args.vad_model_revision,
|
||||
ngpu=args.ngpu,
|
||||
ncpu=args.ncpu,
|
||||
device=args.device,
|
||||
disable_pbar=True,
|
||||
disable_log=True,
|
||||
)
|
||||
|
||||
# punc
|
||||
if args.punc_model != "":
|
||||
model_punc = AutoModel(
|
||||
model=args.punc_model,
|
||||
model_revision=args.punc_model_revision,
|
||||
ngpu=args.ngpu,
|
||||
ncpu=args.ncpu,
|
||||
device=args.device,
|
||||
disable_pbar=True,
|
||||
disable_log=True,
|
||||
)
|
||||
else:
|
||||
model_punc = None
|
||||
|
||||
# sv
|
||||
model_sv = AutoModel(
|
||||
model="iic/speech_campplus_sv_zh-cn_16k-common",
|
||||
ngpu=args.ngpu,
|
||||
device=args.device,
|
||||
disable_pbar=True,
|
||||
disable_log=True,
|
||||
)
|
||||
|
||||
print("model loaded! (now supports multi-client with non-blocking inference)")
|
||||
|
||||
|
||||
# ====== 线程池 + 并发阈值（核心）======
|
||||
EXECUTOR = ThreadPoolExecutor(max_workers=int(args.worker_threads))
|
||||
|
||||
SEM_VAD = asyncio.Semaphore(max(1, int(args.concurrent_vad)))
|
||||
SEM_ASR_ONLINE = asyncio.Semaphore(max(1, int(args.concurrent_asr_online)))
|
||||
SEM_ASR_OFFLINE = asyncio.Semaphore(max(1, int(args.concurrent_asr_offline)))
|
||||
SEM_PUNC = asyncio.Semaphore(max(1, int(args.concurrent_punc)))
|
||||
SEM_SV = asyncio.Semaphore(max(1, int(args.concurrent_sv)))
|
||||
SEM_WAV = asyncio.Semaphore(max(1, 4)) # 保存 wav 一般不需要太大
|
||||
|
||||
|
||||
async def run_blocking(fn, *a, sem: asyncio.Semaphore | None = None, **kw):
|
||||
"""
|
||||
把阻塞函数丢线程池执行，避免卡 event loop。
|
||||
sem 用于限流（避免 GPU / 模型被打爆）。
|
||||
"""
|
||||
loop = asyncio.get_running_loop()
|
||||
call = functools.partial(fn, *a, **kw)
|
||||
if sem is None:
|
||||
return await loop.run_in_executor(EXECUTOR, call)
|
||||
async with sem:
|
||||
return await loop.run_in_executor(EXECUTOR, call)
|
||||
|
||||
|
||||
def _generate_sync(model, audio_or_text, status_dict):
|
||||
# 注意：status_dict 里包含 cache，会被 generate 更新
|
||||
return model.generate(input=audio_or_text, **status_dict)
|
||||
|
||||
|
||||
async def ws_reset(websocket):
|
||||
print("ws reset now, total num is ", len(websocket_users))
|
||||
|
||||
websocket.status_dict_asr_online["cache"] = {}
|
||||
websocket.status_dict_asr_online["is_final"] = True
|
||||
websocket.status_dict_vad["cache"] = {}
|
||||
websocket.status_dict_vad["is_final"] = True
|
||||
websocket.status_dict_punc["cache"] = {}
|
||||
|
||||
await websocket.close()
|
||||
|
||||
|
||||
async def clear_websocket():
|
||||
for websocket in list(websocket_users):
|
||||
await ws_reset(websocket)
|
||||
websocket_users.clear()
|
||||
|
||||
|
||||
async def ws_serve(websocket, path=None):
|
||||
# websockets 新版本不会传 path，这里做兼容
|
||||
if path is None:
|
||||
path = getattr(websocket, "path", None)
|
||||
frames = []
|
||||
frames_asr = []
|
||||
frames_asr_online = []
|
||||
global websocket_users
|
||||
websocket_users.add(websocket)
|
||||
|
||||
websocket.status_dict_asr = {} # hotword 等
|
||||
websocket.status_dict_asr_online = {"cache": {}, "is_final": False}
|
||||
websocket.status_dict_vad = {"cache": {}, "is_final": False}
|
||||
websocket.status_dict_punc = {"cache": {}}
|
||||
|
||||
websocket.chunk_interval = 10
|
||||
websocket.vad_pre_idx = 0
|
||||
speech_start = False
|
||||
speech_end_i = -1
|
||||
|
||||
websocket.wav_name = "microphone"
|
||||
websocket.mode = "2pass"
|
||||
websocket.is_speaking = True # ✅ 默认初始化，避免 AttributeError
|
||||
|
||||
# 保存离线片段
|
||||
websocket.audio_fs = 16000
|
||||
websocket.offline_seg_idx = 0
|
||||
websocket.save_offline_segments = bool(args.save_offline_segments)
|
||||
websocket.offline_save_dir = args.save_offline_segments_dir
|
||||
if websocket.save_offline_segments:
|
||||
_ensure_dir(websocket.offline_save_dir)
|
||||
print(f"[SAVE_OFFLINE_SEG] enabled, dir={websocket.offline_save_dir}")
|
||||
|
||||
print("new user connected", flush=True)
|
||||
|
||||
try:
|
||||
async for message in websocket:
|
||||
# ========== 1) 先处理“文本配置消息” ==========
|
||||
if isinstance(message, str):
|
||||
try:
|
||||
messagejson = json.loads(message)
|
||||
except Exception as e:
|
||||
print("bad json message:", e, message[:200])
|
||||
continue
|
||||
|
||||
print("=============messagejson============", messagejson)
|
||||
|
||||
if "is_speaking" in messagejson:
|
||||
websocket.is_speaking = bool(messagejson["is_speaking"])
|
||||
websocket.status_dict_asr_online["is_final"] = (not websocket.is_speaking)
|
||||
|
||||
if "chunk_interval" in messagejson:
|
||||
websocket.chunk_interval = _safe_int(
|
||||
messagejson["chunk_interval"], websocket.chunk_interval
|
||||
)
|
||||
|
||||
if "wav_name" in messagejson:
|
||||
websocket.wav_name = messagejson.get("wav_name") or websocket.wav_name
|
||||
|
||||
if "chunk_size" in messagejson:
|
||||
chunk_size = messagejson["chunk_size"]
|
||||
if isinstance(chunk_size, str):
|
||||
chunk_size = [x.strip() for x in chunk_size.split(",") if x.strip()]
|
||||
websocket.status_dict_asr_online["chunk_size"] = [int(x) for x in chunk_size]
|
||||
|
||||
if "encoder_chunk_look_back" in messagejson:
|
||||
websocket.status_dict_asr_online["encoder_chunk_look_back"] = messagejson[
|
||||
"encoder_chunk_look_back"
|
||||
]
|
||||
|
||||
if "decoder_chunk_look_back" in messagejson:
|
||||
websocket.status_dict_asr_online["decoder_chunk_look_back"] = messagejson[
|
||||
"decoder_chunk_look_back"
|
||||
]
|
||||
|
||||
if "hotwords" in messagejson:
|
||||
hotword_data = messagejson["hotwords"]
|
||||
websocket.status_dict_asr["hotword"] = hotword_data
|
||||
websocket.status_dict_asr_online["hotword"] = hotword_data
|
||||
print(f"热词已更新: {hotword_data}")
|
||||
|
||||
if "mode" in messagejson:
|
||||
websocket.mode = messagejson["mode"] or websocket.mode
|
||||
|
||||
if "audio_fs" in messagejson:
|
||||
websocket.audio_fs = _safe_int(messagejson["audio_fs"], 16000)
|
||||
|
||||
continue
|
||||
|
||||
# ========== 2) 处理“二进制音频消息” ==========
|
||||
if "chunk_size" not in websocket.status_dict_asr_online:
|
||||
print("[WARN] chunk_size not set yet, skip audio frame (send config first).")
|
||||
continue
|
||||
|
||||
try:
|
||||
websocket.status_dict_vad["chunk_size"] = int(
|
||||
websocket.status_dict_asr_online["chunk_size"][1] * 60 / websocket.chunk_interval
|
||||
)
|
||||
except Exception as e:
|
||||
print("[WARN] set vad chunk_size failed:", e)
|
||||
continue
|
||||
|
||||
pcm = message
|
||||
frames.append(pcm)
|
||||
|
||||
duration_ms = _pcm_duration_ms(pcm, fs=websocket.audio_fs, ch=1, sampwidth=2)
|
||||
websocket.vad_pre_idx += duration_ms
|
||||
|
||||
# online asr
|
||||
frames_asr_online.append(pcm)
|
||||
websocket.status_dict_asr_online["is_final"] = (speech_end_i != -1)
|
||||
|
||||
if (len(frames_asr_online) % websocket.chunk_interval == 0) or websocket.status_dict_asr_online["is_final"]:
|
||||
if websocket.mode in ("2pass", "online"):
|
||||
audio_in = b"".join(frames_asr_online)
|
||||
try:
|
||||
await async_asr_online(websocket, audio_in)
|
||||
except Exception:
|
||||
print(f"error in asr streaming, {websocket.status_dict_asr_online}")
|
||||
frames_asr_online = []
|
||||
|
||||
if speech_start:
|
||||
frames_asr.append(pcm)
|
||||
|
||||
# vad online
|
||||
try:
|
||||
speech_start_i, speech_end_i = await async_vad(websocket, pcm)
|
||||
except Exception as e:
|
||||
print("error in vad:", e)
|
||||
speech_start_i, speech_end_i = -1, -1
|
||||
|
||||
if speech_start_i != -1:
|
||||
speech_start = True
|
||||
if duration_ms > 0:
|
||||
beg_bias = (websocket.vad_pre_idx - speech_start_i) // duration_ms
|
||||
else:
|
||||
beg_bias = 0
|
||||
frames_pre = frames[-beg_bias:] if beg_bias > 0 else []
|
||||
frames_asr = []
|
||||
frames_asr.extend(frames_pre)
|
||||
|
||||
# ========== 3) 2pass：离线阶段触发点 ==========
|
||||
if (speech_end_i != -1) or (not websocket.is_speaking):
|
||||
if websocket.mode in ("2pass", "offline"):
|
||||
audio_in = b"".join(frames_asr)
|
||||
reason = "vad_end" if speech_end_i != -1 else "not_speaking"
|
||||
|
||||
# 保存 wav：放线程池，避免磁盘 IO 卡 loop
|
||||
if websocket.save_offline_segments and audio_in:
|
||||
try:
|
||||
await run_blocking(
|
||||
save_offline_wav_segment_sync,
|
||||
websocket,
|
||||
audio_in,
|
||||
reason,
|
||||
sem=SEM_WAV,
|
||||
)
|
||||
except Exception as e:
|
||||
print("[SAVE_OFFLINE_SEG] async failed:", e)
|
||||
|
||||
try:
|
||||
await async_asr(websocket, audio_in)
|
||||
except Exception as e:
|
||||
print("error in asr offline:", e)
|
||||
|
||||
frames_asr = []
|
||||
speech_start = False
|
||||
frames_asr_online = []
|
||||
websocket.status_dict_asr_online["cache"] = {}
|
||||
|
||||
if not websocket.is_speaking:
|
||||
websocket.vad_pre_idx = 0
|
||||
frames = []
|
||||
websocket.status_dict_vad["cache"] = {}
|
||||
speech_end_i = -1
|
||||
else:
|
||||
frames = frames[-20:]
|
||||
|
||||
except websockets.ConnectionClosed:
|
||||
print("ConnectionClosed...", websocket_users, flush=True)
|
||||
await ws_reset(websocket)
|
||||
if websocket in websocket_users:
|
||||
websocket_users.remove(websocket)
|
||||
except websockets.InvalidState:
|
||||
print("InvalidState...")
|
||||
except Exception as e:
|
||||
print("Exception:", e)
|
||||
try:
|
||||
await ws_reset(websocket)
|
||||
except Exception:
|
||||
pass
|
||||
if websocket in websocket_users:
|
||||
websocket_users.remove(websocket)
|
||||
|
||||
|
||||
# ===================== 推理：全部改为“线程池 + 限流” =====================
|
||||
|
||||
async def async_vad(websocket, audio_in: bytes):
|
||||
# model_vad.generate 是阻塞的，必须 offload
|
||||
out = await run_blocking(_generate_sync, model_vad, audio_in, websocket.status_dict_vad, sem=SEM_VAD)
|
||||
segments_result = out[0].get("value", [])
|
||||
|
||||
speech_start = -1
|
||||
speech_end = -1
|
||||
|
||||
if len(segments_result) == 0 or len(segments_result) > 1:
|
||||
return speech_start, speech_end
|
||||
if segments_result[0][0] != -1:
|
||||
speech_start = segments_result[0][0]
|
||||
if segments_result[0][1] != -1:
|
||||
speech_end = segments_result[0][1]
|
||||
return speech_start, speech_end
|
||||
|
||||
|
||||
def _sv_and_match_sync(audio_in: bytes, reload_sec: int):
|
||||
"""
|
||||
同步执行：SV embedding + speaker_db 匹配
|
||||
返回 (spk_name, best_score)
|
||||
"""
|
||||
spk_name = "unknown"
|
||||
best_score = 0.0
|
||||
|
||||
sv_out = model_sv.generate(input=audio_in, embedding=True)[0]
|
||||
embedding = sv_out["spk_embedding"][0].cpu().numpy()
|
||||
|
||||
now_ts = time.time()
|
||||
local_speaker_db = get_speaker_db_cached(now_ts, reload_sec=reload_sec)
|
||||
if local_speaker_db:
|
||||
for name, ref_embedding in local_speaker_db.items():
|
||||
if ref_embedding is None:
|
||||
continue
|
||||
arr = np.array(ref_embedding, dtype=np.float32)
|
||||
similarity = 1.0 - cosine(embedding, arr)
|
||||
print("sv similarity with {}: {}".format(name, similarity))
|
||||
if similarity > best_score and similarity > 0.2:
|
||||
best_score = similarity
|
||||
spk_name = name
|
||||
|
||||
return spk_name, float(best_score)
|
||||
|
||||
|
||||
async def async_asr(websocket, audio_in: bytes):
|
||||
mode = "2pass-offline" if "2pass" in (websocket.mode or "") else websocket.mode
|
||||
|
||||
if len(audio_in) <= 0:
|
||||
message = {
|
||||
"mode": mode,
|
||||
"text": "",
|
||||
"wav_name": websocket.wav_name,
|
||||
"is_final": True,
|
||||
}
|
||||
await websocket.send(json.dumps(message, ensure_ascii=False))
|
||||
return
|
||||
|
||||
# 1) ASR（阻塞，线程池执行）
|
||||
rec_result_list = await run_blocking(
|
||||
_generate_sync,
|
||||
model_asr,
|
||||
audio_in,
|
||||
websocket.status_dict_asr,
|
||||
sem=SEM_ASR_OFFLINE,
|
||||
)
|
||||
rec_result = rec_result_list[0]
|
||||
|
||||
print("offline_asr, raw:", rec_result)
|
||||
print("offline_asr, keys:", rec_result.keys())
|
||||
|
||||
text = rec_result.get("text", "")
|
||||
timestamp = rec_result.get("timestamp", None)
|
||||
sentence_info = rec_result.get("sentence_info", None)
|
||||
|
||||
# 2) 声纹识别（阻塞，线程池执行）
|
||||
spk_name = "unknown"
|
||||
best_score = 0.0
|
||||
try:
|
||||
spk_name, best_score = await run_blocking(
|
||||
_sv_and_match_sync,
|
||||
audio_in,
|
||||
int(args.speaker_db_reload_sec),
|
||||
sem=SEM_SV,
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"声纹识别失败: {e}")
|
||||
|
||||
# 3) 标点（阻塞，线程池执行）
|
||||
punc_array = None
|
||||
if model_punc is not None and len(text) > 0:
|
||||
try:
|
||||
# punc 只对文本处理
|
||||
punc_out = await run_blocking(
|
||||
_generate_sync,
|
||||
model_punc,
|
||||
text,
|
||||
websocket.status_dict_punc,
|
||||
sem=SEM_PUNC,
|
||||
)
|
||||
punc_result = punc_out[0]
|
||||
print("offline, after punc", punc_result)
|
||||
|
||||
if "text" in punc_result and punc_result["text"]:
|
||||
text = punc_result["text"]
|
||||
if "punc_array" in punc_result:
|
||||
punc_array = punc_result["punc_array"]
|
||||
except Exception as e:
|
||||
print("punc failed:", e)
|
||||
|
||||
# 4) 构造最终 message
|
||||
if len(text) > 0:
|
||||
print("======offline final text:", text)
|
||||
message = {
|
||||
"mode": mode,
|
||||
"spk_name": spk_name,
|
||||
"spk_score": float(best_score),
|
||||
"text": text,
|
||||
"wav_name": websocket.wav_name,
|
||||
"is_final": True,
|
||||
}
|
||||
if timestamp is not None:
|
||||
message["timestamp"] = to_python(timestamp)
|
||||
if sentence_info is not None:
|
||||
message["sentence_info"] = to_python(sentence_info)
|
||||
if punc_array is not None:
|
||||
message["punc_array"] = to_python(punc_array)
|
||||
|
||||
try:
|
||||
await websocket.send(json.dumps(message, ensure_ascii=False))
|
||||
except Exception as e:
|
||||
print("send json failed:", e)
|
||||
print("message types:", {k: type(v) for k, v in message.items()})
|
||||
else:
|
||||
message = {
|
||||
"mode": mode,
|
||||
"spk_name": spk_name,
|
||||
"spk_score": float(best_score),
|
||||
"text": "",
|
||||
"wav_name": websocket.wav_name,
|
||||
"is_final": True,
|
||||
}
|
||||
await websocket.send(json.dumps(message, ensure_ascii=False))
|
||||
|
||||
|
||||
async def async_asr_online(websocket, audio_in: bytes):
|
||||
if len(audio_in) <= 0:
|
||||
return
|
||||
|
||||
# streaming generate 也是阻塞：线程池执行
|
||||
rec_out = await run_blocking(
|
||||
_generate_sync,
|
||||
model_asr_streaming,
|
||||
audio_in,
|
||||
websocket.status_dict_asr_online,
|
||||
sem=SEM_ASR_ONLINE,
|
||||
)
|
||||
rec_result = rec_out[0]
|
||||
print("online, ", rec_result)
|
||||
|
||||
# 2pass：online 只要 partial，不发 final（final 交给 offline）
|
||||
if websocket.mode == "2pass" and websocket.status_dict_asr_online.get("is_final", False):
|
||||
return
|
||||
|
||||
if rec_result.get("text"):
|
||||
mode = "2pass-online" if "2pass" in (websocket.mode or "") else websocket.mode
|
||||
message = {
|
||||
"mode": mode,
|
||||
"text": rec_result["text"],
|
||||
"wav_name": websocket.wav_name,
|
||||
"is_final": bool(
|
||||
websocket.status_dict_asr_online.get("is_final", False) or (not websocket.is_speaking)
|
||||
),
|
||||
}
|
||||
await websocket.send(json.dumps(message, ensure_ascii=False))
|
||||
|
||||
|
||||
# ===================== 启动服务 =====================
|
||||
|
||||
async def main():
|
||||
if len(args.certfile) > 0:
|
||||
ssl_context = ssl.SSLContext(ssl.PROTOCOL_TLS_SERVER)
|
||||
ssl_context.load_cert_chain(args.certfile, keyfile=args.keyfile)
|
||||
server = await websockets.serve(
|
||||
ws_serve,
|
||||
args.host,
|
||||
args.port,
|
||||
subprotocols=["binary"],
|
||||
ping_interval=None,
|
||||
ssl=ssl_context,
|
||||
)
|
||||
else:
|
||||
server = await websockets.serve(
|
||||
ws_serve,
|
||||
args.host,
|
||||
args.port,
|
||||
subprotocols=["binary"],
|
||||
ping_interval=None,
|
||||
)
|
||||
|
||||
print(f"WS server started at ws(s)://{args.host}:{args.port}")
|
||||
await server.wait_closed()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
asyncio.run(main())
|
||||
finally:
|
||||
try:
|
||||
EXECUTOR.shutdown(wait=False, cancel_futures=True)
|
||||
except Exception:
|
||||
pass
|
||||
1
funasr_runtime/requirements_server.txt
Normal file
1
funasr_runtime/requirements_server.txt
Normal file
@ -0,0 +1 @@
|
||||
websockets
|
||||
Loading…
x
Reference in New Issue
Block a user