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/
126 lines
2.6 KiB
Markdown
126 lines
2.6 KiB
Markdown
# TTS (Text-to-Speech) Service
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Text-to-speech service using Piper for low-latency speech synthesis.
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## Features
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- HTTP endpoint for text-to-speech synthesis
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- Low-latency processing (< 500ms)
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- Multiple voice support
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- WAV audio output
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- Streaming support (for long text)
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## Installation
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### Install Piper
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```bash
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# Download Piper binary
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# See: https://github.com/rhasspy/piper
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# Download voices
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# See: https://huggingface.co/rhasspy/piper-voices
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# Place piper binary in tts/piper/
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# Place voices in tts/piper/voices/
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```
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### Install Python Dependencies
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```bash
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pip install -r requirements.txt
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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 server
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python3 -m tts.server
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# Or use uvicorn directly
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uvicorn tts.server:app --host 0.0.0.0 --port 8003
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```
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### Python API
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```python
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from tts.service import TTSService
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service = TTSService(
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voice="en_US-lessac-medium",
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sample_rate=22050
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)
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# Synthesize text
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audio_data = service.synthesize("Hello, this is a test.")
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with open("output.wav", "wb") as f:
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f.write(audio_data)
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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 /synthesize` - Synthesize speech from text
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- Body: `{"text": "Hello", "voice": "en_US-lessac-medium", "format": "wav"}`
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- `GET /synthesize?text=Hello&voice=en_US-lessac-medium&format=wav` - Synthesize (GET)
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- `GET /voices` - Get available voices
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## Configuration
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- **Voice**: en_US-lessac-medium (default)
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- **Sample Rate**: 22050 Hz
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- **Format**: WAV (default), RAW
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- **Latency**: < 500ms for short text
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## Integration
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The TTS service is called by:
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1. LLM response handler
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2. Conversation manager
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3. Direct HTTP requests
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Output is:
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1. Played through speakers
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2. Streamed to clients
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3. Saved to file (optional)
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## Testing
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```bash
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# Test health
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curl http://localhost:8003/health
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# Test synthesis
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curl -X POST http://localhost:8003/synthesize \
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-H "Content-Type: application/json" \
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-d '{"text": "Hello, this is a test.", "format": "wav"}' \
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--output output.wav
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# Test GET endpoint
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curl "http://localhost:8003/synthesize?text=Hello" --output output.wav
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```
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## Notes
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- Requires Piper binary and voice files
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- First run may be slower (model loading)
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- Supports multiple languages (with appropriate voices)
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- Low resource usage (CPU-only, no GPU required)
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## Voice Selection
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For the "family agent" persona:
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- **Recommended**: `en_US-lessac-medium` (warm, friendly, clear)
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- **Alternative**: Other English voices from Piper voice collection
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## Future Enhancements
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- Streaming synthesis for long text
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- Voice cloning
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- Emotion/prosody control
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- Multiple language support
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