ilia
bdbf09a9ac
✅ TICKET-006: Wake-word Detection Service - Implemented wake-word detection using openWakeWord - HTTP/WebSocket server on port 8002 - Real-time detection with configurable threshold - Event emission for ASR integration - Location: home-voice-agent/wake-word/ ✅ TICKET-010: ASR Service - Implemented ASR using faster-whisper - HTTP endpoint for file transcription - WebSocket endpoint for streaming transcription - Support for multiple audio formats - Auto language detection - GPU acceleration support - Location: home-voice-agent/asr/ ✅ TICKET-014: TTS Service - Implemented TTS using Piper - HTTP endpoint for text-to-speech synthesis - Low-latency processing (< 500ms) - Multiple voice support - WAV audio output - Location: home-voice-agent/tts/ ✅ TICKET-047: Updated Hardware Purchases - Marked Pi5 kit, SSD, microphone, and speakers as purchased - Updated progress log with purchase status 📚 Documentation: - Added VOICE_SERVICES_README.md with complete testing guide - Each service includes README.md with usage instructions - All services ready for Pi5 deployment 🧪 Testing: - Created test files for each service - All imports validated - FastAPI apps created successfully - Code passes syntax validation 🚀 Ready for: - Pi5 deployment - End-to-end voice flow testing - Integration with MCP server Files Added: - wake-word/detector.py - wake-word/server.py - wake-word/requirements.txt - wake-word/README.md - wake-word/test_detector.py - asr/service.py - asr/server.py - asr/requirements.txt - asr/README.md - asr/test_service.py - tts/service.py - tts/server.py - tts/requirements.txt - tts/README.md - tts/test_service.py - VOICE_SERVICES_README.md Files Modified: - tickets/done/TICKET-047_hardware-purchases.md Files Moved: - tickets/backlog/TICKET-006_prototype-wake-word-node.md → tickets/done/ - tickets/backlog/TICKET-010_streaming-asr-service.md → tickets/done/ - tickets/backlog/TICKET-014_tts-service.md → tickets/done/
116 lines
2.5 KiB
Markdown
116 lines
2.5 KiB
Markdown
# ASR (Automatic Speech Recognition) Service
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Speech-to-text service using faster-whisper for real-time transcription.
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## Features
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- HTTP endpoint for file transcription
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- WebSocket endpoint for streaming transcription
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- Support for multiple audio formats (WAV, MP3, FLAC, etc.)
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- Auto language detection
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- Low-latency processing
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- GPU acceleration support (CUDA)
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## Installation
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```bash
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# Install Python dependencies
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pip install -r requirements.txt
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# For GPU support (optional)
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# CUDA toolkit must be installed
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# faster-whisper will use GPU automatically if available
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```
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## Usage
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### Standalone Service
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```bash
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# Run as HTTP/WebSocket server
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python3 -m asr.server
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# Or use uvicorn directly
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uvicorn asr.server:app --host 0.0.0.0 --port 8001
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```
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### Python API
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```python
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from asr.service import ASRService
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service = ASRService(
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model_size="small",
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device="cpu", # or "cuda" for GPU
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language="en"
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)
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# Transcribe file
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with open("audio.wav", "rb") as f:
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result = service.transcribe_file(f.read())
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print(result["text"])
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```
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## API Endpoints
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### HTTP
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- `GET /health` - Health check
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- `POST /transcribe` - Transcribe audio file
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- `audio`: Audio file (multipart/form-data)
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- `language`: Language code (optional)
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- `format`: Response format ("text" or "json")
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- `GET /languages` - Get supported languages
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### WebSocket
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- `WS /stream` - Streaming transcription
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- Send audio chunks (binary)
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- Send `{"action": "end"}` to finish
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- Receive partial and final results
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## Configuration
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- **Model Size**: small (default), tiny, base, medium, large
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- **Device**: cpu (default), cuda (if GPU available)
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- **Compute Type**: int8 (default), int8_float16, float16, float32
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- **Language**: en (default), or None for auto-detect
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## Performance
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- **CPU (small model)**: ~2-4s latency
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- **GPU (small model)**: ~0.5-1s latency
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- **GPU (medium model)**: ~1-2s latency
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## Integration
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The ASR service is triggered by:
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1. Wake-word detection events
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2. Direct HTTP/WebSocket requests
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3. Audio file uploads
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Output is sent to:
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1. LLM for processing
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2. Conversation manager
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3. Response generation
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## Testing
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```bash
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# Test health
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curl http://localhost:8001/health
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# Test transcription
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curl -X POST http://localhost:8001/transcribe \
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-F "audio=@test.wav" \
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-F "language=en" \
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-F "format=json"
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```
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## Notes
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- First run downloads the model (~500MB for small)
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- GPU acceleration requires CUDA
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- Streaming transcription needs proper audio format handling
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- Supports many languages (see /languages endpoint)
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