174 lines
4.1 KiB
Markdown
174 lines
4.1 KiB
Markdown
# SmartMeeting
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SmartMeeting is a Docker Compose deployment for Speakr, FunASR Paraformer ASR,
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speaker embedding extraction, and the realtime WebSocket bridge.
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## Directory Layout
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```text
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/storage01/home/xll/202607/voice
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├── docker-compose.yml # Main Compose file
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├── config.env # Runtime configuration to edit
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├── asr/main.py # ASR adapter service code
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├── speakr/ # Speakr frontend/backend code
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├── voice-dected/ # Speaker embedding service code
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├── fastapi_wss/ # Realtime WebSocket bridge code
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└── deploy/data/ # Runtime data, ignored by git
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```
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## Start And Stop
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Run all services:
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```bash
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cd /storage01/home/xll/202607/voice
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docker compose up -d
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```
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Check status and logs:
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```bash
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docker compose ps
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docker compose logs -f
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```
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Stop services:
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```bash
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docker compose down
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```
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## Services And Ports
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| Service | Container | Port | Purpose |
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| --- | --- | --- | --- |
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| Speakr | `voice-speakr` | `8899` | Web UI and backend |
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| ASR | `asr-test` | `59805` | Paraformer HTTP and WebSocket ASR |
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| Speaker embedding | `voice-dected` | `18000` | `/extract_embedding` voiceprint service |
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| Realtime bridge | `fastapi-wss` | `10095` | Browser realtime ASR WebSocket bridge |
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| Redis | `voice-redis` | `16380` | Cache/session storage for realtime bridge |
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Main access URL:
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```text
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http://192.168.0.46:8899/tool/speakr/
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```
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For browser microphone recording, use a secure context. The quickest local
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workaround is an SSH tunnel:
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```bash
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ssh -L 8899:127.0.0.1:8899 xll@192.168.0.46
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```
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Then open:
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```text
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http://localhost:8899/tool/speakr/
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```
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## Configuration
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Most runtime settings are in:
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```text
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/storage01/home/xll/202607/voice/config.env
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```
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Edit this file first when changing service addresses, model endpoints, LLM
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settings, or speaker matching behavior. After editing:
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```bash
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cd /storage01/home/xll/202607/voice
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docker compose up -d
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```
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Important variables:
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```env
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ASR_BASE_URL=http://asr-test:59805
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TARGET_WS_URL=ws://asr-test:59805/ws/asr
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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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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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VOICEPRINT_STORE=/data/asr_voiceprints.json
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VOICEPRINT_MATCH_THRESHOLD=0.45
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```
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Notes:
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- Inside Docker Compose, use service names such as `asr-test`,
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`voice-dected`, and `redis`.
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- From outside Docker, use host ports such as `192.168.0.46:59805`,
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`192.168.0.46:18000`, and `192.168.0.46:10095`.
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- `VOICEPRINT_MATCH_THRESHOLD` controls how strict speaker matching is.
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Lower values match more easily; higher values reduce false matches.
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## Runtime Data
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Runtime data is stored under:
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```text
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/storage01/home/xll/202607/voice/deploy/data/
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```
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Important subdirectories:
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```text
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deploy/data/speakr/uploads/ # Uploaded/recorded audio
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deploy/data/speakr/instance/ # Speakr SQLite database
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deploy/data/asr/ # ASR voiceprint store
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deploy/data/redis/ # Redis persistence
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```
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These files are ignored by git.
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## Voiceprint Flow
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Speakr calls the ASR adapter for voiceprint platform compatibility:
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```text
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POST /voice_insert
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GET /get_voice_name
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DELETE /delete_voice
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```
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The ASR adapter stores embeddings in:
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```text
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deploy/data/asr/asr_voiceprints.json
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```
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During `/asr` transcription, the adapter converts uploaded audio to 16 kHz mono
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WAV, calls `voice-dected`, compares the embedding with stored voiceprints, and
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returns a matched speaker name when the similarity passes the threshold.
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## Useful Checks
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```bash
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curl http://127.0.0.1:59805/health
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curl http://127.0.0.1:59805/get_voice_name
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curl http://127.0.0.1:8899/tool/speakr/api/config
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```
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WebSocket check:
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```bash
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docker exec -it fastapi-wss python - <<'PY'
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import asyncio, json, websockets
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async def main():
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async with websockets.connect("ws://127.0.0.1:10095") as ws:
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await ws.send(json.dumps({"mode": "2pass", "is_speaking": True}))
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await ws.send(json.dumps({"type": "stop", "is_speaking": False}))
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print(await ws.recv())
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asyncio.run(main())
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PY
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```
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