feat: Add Piper TTS integration with pitch control support
- Created PiperTTSService with pitch shifting (-12 to +12 semitones) - Created UnifiedTTSService to manage multiple TTS engines - Added engine selection endpoints (GET/POST /api/tts/engines) - Added voices listing endpoint (GET /api/tts/voices) - Added pitch_adjustments parameter to TTS generation - Created install_piper.sh for easy Piper installation - Updated backend README with multi-engine documentation - Created comprehensive PIPER_TTS_GUIDE.md - Added TTS_ENGINE environment variable for engine selection - Updated requirements.txt with piper-tts and pydub This allows users to choose between Kokoro (high quality) and Piper (fast with pitch control) TTS engines at runtime.
This commit is contained in:
@@ -1,3 +1,4 @@
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PORT=8000
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LOG_LEVEL=info
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OLLAMA_BASE_URL=http://localhost:11434
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TTS_ENGINE=kokoro
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292
backend/PIPER_TTS_GUIDE.md
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292
backend/PIPER_TTS_GUIDE.md
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@@ -0,0 +1,292 @@
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# Piper TTS Integration Guide
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This guide covers the Piper TTS engine integration, including installation, configuration, and usage.
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## What is Piper TTS?
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Piper is a fast, local neural text-to-speech system that runs entirely on your machine. It offers:
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- **Fast synthesis**: Faster than Kokoro for most use cases
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- **Pitch control**: Adjust voice pitch per character (-12 to +12 semitones)
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- **Multiple voices**: Various English voices with different characteristics
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- **Low resource usage**: Smaller models and lower memory footprint
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- **Privacy**: All processing happens locally
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## Installation
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### Automated Installation (Recommended)
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```bash
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cd backend
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./install_piper.sh
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```
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This script will:
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1. Detect your OS and architecture
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2. Download the appropriate Piper binary
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3. Install it to `/usr/local/bin`
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4. Verify the installation
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### Manual Installation
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1. Visit [Piper Releases](https://github.com/rhasspy/piper/releases/tag/v1.2.0)
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2. Download the appropriate archive for your platform:
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- **Linux x86_64**: `piper_amd64.tar.gz`
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- **Linux ARM64**: `piper_arm64.tar.gz`
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- **macOS**: `piper_macos_x64.tar.gz`
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3. Extract the archive
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4. Copy the `piper` binary to a directory in your PATH
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### Verify Installation
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```bash
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piper --version
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```
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You should see output like: `1.2.0`
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## Available Voices
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Piper supports multiple English voices:
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| Voice ID | Language | Gender | Quality | Description |
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|----------|----------|--------|---------|-------------|
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| `en_US-lessac-medium` | US English | Male | Medium | Clear, neutral male voice |
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| `en_US-libritts-high` | US English | Neutral | High | High-quality neutral voice |
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| `en_US-amy-medium` | US English | Female | Medium | Clear female voice |
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| `en_GB-alan-medium` | UK English | Male | Medium | British male voice |
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| `en_GB-alba-medium` | UK English | Female | Medium | British female voice |
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Voice models are automatically downloaded on first use (~10-50MB each).
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## Configuration
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### Set Piper as Default Engine
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**Option 1: Environment Variable**
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```bash
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export TTS_ENGINE=piper
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```
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Add to your `.env` file:
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```bash
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TTS_ENGINE=piper
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```
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**Option 2: Runtime API Call**
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```bash
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curl -X POST http://localhost:8000/api/tts/engines \
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-H "Content-Type: application/json" \
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-d '{"engine": "piper"}'
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```
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### Voice Model Storage
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Voice models are stored in:
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```
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backend/piper_models/
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```
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Models are downloaded automatically on first use. You can pre-download models manually if needed.
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## Usage Examples
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### Basic Text-to-Speech
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```bash
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curl -X POST http://localhost:8000/api/tts \
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-H "Content-Type: application/json" \
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-d '{
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"text": "Hello, this is a test of Piper TTS.",
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"lang": "a",
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"roles": [
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{"id": "Narrator", "voice": "en_US-lessac-medium"}
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],
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"speed": 1.0
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}' \
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--output test.wav
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```
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### Multi-Voice with Pitch Control
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```bash
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curl -X POST http://localhost:8000/api/tts \
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-H "Content-Type: application/json" \
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-d '{
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"text": "Alice: Hello Bob!\nBob: Hi Alice, how are you?\nNarrator: They greeted each other warmly.",
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"lang": "a",
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"roles": [
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{"id": "Alice", "voice": "en_US-amy-medium"},
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{"id": "Bob", "voice": "en_US-lessac-medium"},
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{"id": "Narrator", "voice": "en_GB-alan-medium"}
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],
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"speed": 1.0,
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"pitch_adjustments": [
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{"id": "Alice", "pitch": 4},
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{"id": "Bob", "pitch": -3},
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{"id": "Narrator", "pitch": 0}
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]
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}' \
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--output audiobook.wav
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```
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### Pitch Adjustment Guidelines
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Pitch is measured in semitones (half-steps on a musical scale):
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- **+12 semitones**: One octave higher (very high-pitched)
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- **+6 semitones**: Noticeably higher (child-like)
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- **+3 semitones**: Slightly higher (feminine adjustment)
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- **0 semitones**: Original voice (no change)
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- **-3 semitones**: Slightly lower (masculine adjustment)
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- **-6 semitones**: Noticeably lower (deep voice)
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- **-12 semitones**: One octave lower (very deep)
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**Recommended ranges:**
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- Female characters: +2 to +5
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- Male characters: -2 to -5
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- Children: +5 to +8
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- Elderly/deep voices: -4 to -8
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## Python API Usage
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```python
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from services.unified_tts_service import UnifiedTTSService
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# Initialize with Piper engine
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tts = UnifiedTTSService(engine="piper")
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# Generate audiobook with pitch control
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audio_bytes = await tts.generate_audiobook(
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text="Alice: Hello!\nBob: Hi there!",
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lang="a",
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role_voice_map={
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"Alice": "en_US-amy-medium",
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"Bob": "en_US-lessac-medium"
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},
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speed=1.0,
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pitch_adjustments={
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"Alice": 3,
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"Bob": -2
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}
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)
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# Save to file
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with open("output.wav", "wb") as f:
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f.write(audio_bytes)
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```
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## Performance Comparison
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| Feature | Kokoro TTS | Piper TTS |
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|---------|------------|-----------|
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| Speed | Medium | Fast |
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| Quality | High | Medium-High |
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| Pitch Control | ❌ No | ✅ Yes |
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| Model Size | ~500MB | ~10-50MB per voice |
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| Memory Usage | Higher | Lower |
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| Voice Variety | 10+ voices | 5+ voices (expandable) |
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| Languages | 4 languages | English (more available) |
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## Troubleshooting
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### Piper Not Found
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**Error:** `piper: command not found`
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**Solution:**
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```bash
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# Check if piper is in PATH
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which piper
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# If not found, reinstall
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./install_piper.sh
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# Or add to PATH manually
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export PATH="/usr/local/bin:$PATH"
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```
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### Voice Model Download Fails
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**Error:** Model download timeout or failure
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**Solution:**
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1. Check internet connection
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2. Manually download models from [Piper releases](https://github.com/rhasspy/piper/releases)
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3. Place `.onnx` and `.onnx.json` files in `backend/piper_models/`
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### Audio Quality Issues
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**Problem:** Distorted or robotic audio
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**Solutions:**
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- Reduce pitch shift amount (stay within ±6 semitones)
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- Try different voice models
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- Adjust speed (try 0.9 or 1.1)
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- Check for text formatting issues
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### Synthesis Timeout
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**Error:** Piper synthesis times out
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**Solutions:**
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- Break text into smaller chunks
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- Increase timeout in `piper_tts_service.py`
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- Check system resources (CPU, memory)
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## Advanced Configuration
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### Custom Voice Models
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To add custom Piper voice models:
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1. Download `.onnx` and `.onnx.json` files
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2. Place in `backend/piper_models/`
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3. Update `AVAILABLE_VOICES` in `piper_tts_service.py`:
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```python
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AVAILABLE_VOICES = {
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"en_US-custom-voice": {"lang": "en", "gender": "female", "quality": "high"},
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# ... existing voices
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}
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```
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### Adjusting Audio Effects
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Modify `_apply_audio_effects()` in `piper_tts_service.py` to customize:
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- Pitch shifting algorithm
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- Speed adjustment method
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- Audio processing pipeline
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## Best Practices
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1. **Voice Selection**
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- Use distinct voices for different characters
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- Match voice gender to character when possible
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- Use British voices for variety
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2. **Pitch Adjustments**
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- Start with small adjustments (±2-3 semitones)
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- Test and iterate to find natural-sounding values
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- Avoid extreme values (±10+) unless intentional
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3. **Performance**
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- Pre-download voice models for faster first use
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- Cache generated audio when possible
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- Use appropriate chunk sizes for long texts
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4. **Quality**
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- Use high-quality voice models when available
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- Adjust speed slightly (0.95-1.05) for more natural pacing
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- Combine with AI segmentation for best results
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## Resources
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- [Piper GitHub Repository](https://github.com/rhasspy/piper)
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- [Piper Voice Samples](https://rhasspy.github.io/piper-samples/)
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- [Voice Model Downloads](https://github.com/rhasspy/piper/releases/tag/v1.2.0)
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- [Piper Documentation](https://github.com/rhasspy/piper/blob/master/README.md)
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## License
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Piper TTS is licensed under the MIT License.
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@@ -13,6 +13,7 @@ The backend provides REST API endpoints for:
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- Python 3.10 or higher
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- espeak-ng (system dependency for grapheme-to-phoneme conversion)
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- Piper TTS (optional, for alternative TTS engine with pitch control)
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- Docker (optional, for containerized deployment)
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## Local Development Setup
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@@ -62,7 +63,33 @@ pip install --upgrade pip
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pip install -r requirements.txt
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```
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### 3. Ollama Setup (Optional but Recommended)
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### 3. Piper TTS Setup (Optional)
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For an alternative TTS engine with pitch control support:
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**Installation:**
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```bash
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# Run the installation script
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./install_piper.sh
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```
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Or install manually:
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- Visit [Piper releases](https://github.com/rhasspy/piper/releases)
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- Download the appropriate binary for your platform
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- Extract and place in your PATH
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**Verify installation:**
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```bash
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piper --version
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```
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**Benefits of Piper TTS:**
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- ✨ Pitch shifting support (-12 to +12 semitones per role)
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- ✨ Fast, lightweight neural TTS
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- ✨ Multiple voice models available
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- ✨ Lower resource usage than Kokoro
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### 4. Ollama Setup (Optional but Recommended)
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For AI-powered speaker detection and text segmentation, install Ollama:
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@@ -98,7 +125,7 @@ curl http://localhost:11434/api/tags
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- ✨ Handles narrative text without explicit role markers
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- ✨ Falls back to pattern matching if unavailable
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### 4. Environment Configuration
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### 5. Environment Configuration
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```bash
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cp .env.example .env
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@@ -109,9 +136,10 @@ Edit `.env` if needed:
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PORT=8000
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LOG_LEVEL=info
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OLLAMA_BASE_URL=http://localhost:11434 # Optional: Ollama API URL
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TTS_ENGINE=kokoro # Options: kokoro, piper (default: kokoro)
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```
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### 5. Run the Server
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### 6. Run the Server
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```bash
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uvicorn main:app --reload --host 0.0.0.0 --port 8000
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@@ -179,6 +207,43 @@ The Dockerfile includes:
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- Storage: ~500MB for Kokoro models
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- CPU: Multi-core recommended
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## TTS Engine Selection
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The backend now supports multiple TTS engines that can be selected at runtime:
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### Available Engines
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|
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1. **Kokoro TTS** (default)
|
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- High-quality neural TTS
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- Multiple voices per language
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- No pitch control
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- ~500MB model size
|
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|
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2. **Piper TTS**
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- Fast, lightweight neural TTS
|
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- Pitch shifting support (-12 to +12 semitones)
|
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- Multiple voice models
|
||||
- Lower resource usage
|
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|
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### Switching Engines
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**Via Environment Variable:**
|
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```bash
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export TTS_ENGINE=piper # or kokoro
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uvicorn main:app --reload
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```
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**Via API:**
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```bash
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# Get available engines
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curl http://localhost:8000/api/tts/engines
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|
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# Set active engine
|
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curl -X POST http://localhost:8000/api/tts/engines \
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-H "Content-Type: application/json" \
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-d '{"engine": "piper"}'
|
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```
|
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|
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## API Endpoints
|
||||
|
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### GET /api/health
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@@ -243,6 +308,71 @@ curl -X POST http://localhost:8000/api/tts/roles \
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-d '{"text":"Alice: Hello!\nBob: Hi there!"}'
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```
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### GET /api/tts/engines
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Get list of available TTS engines and current selection.
|
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**Response:**
|
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```json
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{
|
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"engines": [
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{"name": "kokoro", "active": true},
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{"name": "piper", "active": false}
|
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],
|
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"current": "kokoro"
|
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}
|
||||
```
|
||||
|
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**Example:**
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||||
```bash
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curl http://localhost:8000/api/tts/engines
|
||||
```
|
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|
||||
### POST /api/tts/engines
|
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Set the active TTS engine.
|
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|
||||
**Request Body:**
|
||||
```json
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||||
{
|
||||
"engine": "piper"
|
||||
}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
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||||
{
|
||||
"status": "ok",
|
||||
"engine": "piper"
|
||||
}
|
||||
```
|
||||
|
||||
**Example:**
|
||||
```bash
|
||||
curl -X POST http://localhost:8000/api/tts/engines \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"engine": "piper"}'
|
||||
```
|
||||
|
||||
### GET /api/tts/voices
|
||||
|
||||
Get available voices for all TTS engines.
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"voices": {
|
||||
"kokoro": ["af_bella", "af_sarah", "am_adam", "am_michael", ...],
|
||||
"piper": ["en_US-lessac-medium", "en_US-amy-medium", ...]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Example:**
|
||||
```bash
|
||||
curl http://localhost:8000/api/tts/voices
|
||||
```
|
||||
|
||||
### POST /api/tts
|
||||
|
||||
Generate multi-voice audiobook with AI-powered text segmentation.
|
||||
@@ -271,6 +401,7 @@ When Ollama is available, the system uses AI to intelligently split text and ass
|
||||
- `lang` (required): Language code (a=American English, b=British English, f=French, j=Japanese)
|
||||
- `roles` (required): Array of role-to-voice mappings
|
||||
- `speed` (optional): Playback speed (0.5-1.5, default: 1.0)
|
||||
- `pitch_adjustments` (optional): Array of role-to-pitch mappings (Piper only, -12 to +12 semitones)
|
||||
|
||||
**Response:**
|
||||
- Content-Type: `audio/wav`
|
||||
@@ -285,7 +416,7 @@ When Ollama is available, the system uses AI to intelligently split text and ass
|
||||
- Content-Disposition: `attachment; filename="audiobook.wav"`
|
||||
- Body: Audio file bytes
|
||||
|
||||
**Example:**
|
||||
**Example (Kokoro):**
|
||||
```bash
|
||||
curl -X POST http://localhost:8000/api/tts \
|
||||
-H "Content-Type: application/json" \
|
||||
@@ -301,6 +432,26 @@ curl -X POST http://localhost:8000/api/tts \
|
||||
--output audiobook.wav
|
||||
```
|
||||
|
||||
**Example (Piper with pitch control):**
|
||||
```bash
|
||||
curl -X POST http://localhost:8000/api/tts \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"text": "Alice: Hello!\nBob: Hi!",
|
||||
"lang": "a",
|
||||
"roles": [
|
||||
{"id": "Alice", "voice": "en_US-amy-medium"},
|
||||
{"id": "Bob", "voice": "en_US-lessac-medium"}
|
||||
],
|
||||
"speed": 1.0,
|
||||
"pitch_adjustments": [
|
||||
{"id": "Alice", "pitch": 3},
|
||||
{"id": "Bob", "pitch": -2}
|
||||
]
|
||||
}' \
|
||||
--output audiobook.wav
|
||||
```
|
||||
|
||||
## Kokoro TTS Configuration
|
||||
|
||||
### Supported Languages
|
||||
|
||||
67
backend/install_piper.sh
Executable file
67
backend/install_piper.sh
Executable file
@@ -0,0 +1,67 @@
|
||||
#!/bin/bash
|
||||
# Script to install Piper TTS
|
||||
|
||||
echo "Installing Piper TTS..."
|
||||
|
||||
# Detect OS
|
||||
OS="$(uname -s)"
|
||||
ARCH="$(uname -m)"
|
||||
|
||||
case "${OS}" in
|
||||
Linux*)
|
||||
if [ "${ARCH}" = "x86_64" ]; then
|
||||
PIPER_URL="https://github.com/rhasspy/piper/releases/download/v1.2.0/piper_amd64.tar.gz"
|
||||
elif [ "${ARCH}" = "aarch64" ]; then
|
||||
PIPER_URL="https://github.com/rhasspy/piper/releases/download/v1.2.0/piper_arm64.tar.gz"
|
||||
else
|
||||
echo "Unsupported architecture: ${ARCH}"
|
||||
exit 1
|
||||
fi
|
||||
;;
|
||||
Darwin*)
|
||||
if [ "${ARCH}" = "x86_64" ] || [ "${ARCH}" = "arm64" ]; then
|
||||
PIPER_URL="https://github.com/rhasspy/piper/releases/download/v1.2.0/piper_macos_x64.tar.gz"
|
||||
else
|
||||
echo "Unsupported architecture: ${ARCH}"
|
||||
exit 1
|
||||
fi
|
||||
;;
|
||||
*)
|
||||
echo "Unsupported OS: ${OS}"
|
||||
exit 1
|
||||
;;
|
||||
esac
|
||||
|
||||
# Create temp directory
|
||||
TEMP_DIR=$(mktemp -d)
|
||||
cd "${TEMP_DIR}"
|
||||
|
||||
# Download Piper
|
||||
echo "Downloading Piper from ${PIPER_URL}..."
|
||||
curl -L -o piper.tar.gz "${PIPER_URL}"
|
||||
|
||||
# Extract
|
||||
echo "Extracting..."
|
||||
tar -xzf piper.tar.gz
|
||||
|
||||
# Install to /usr/local/bin (requires sudo)
|
||||
echo "Installing to /usr/local/bin (may require password)..."
|
||||
sudo cp piper/piper /usr/local/bin/
|
||||
sudo chmod +x /usr/local/bin/piper
|
||||
|
||||
# Cleanup
|
||||
cd -
|
||||
rm -rf "${TEMP_DIR}"
|
||||
|
||||
# Verify installation
|
||||
if command -v piper &> /dev/null; then
|
||||
echo "✓ Piper TTS installed successfully!"
|
||||
piper --version
|
||||
else
|
||||
echo "✗ Installation failed"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "To download voice models, they will be automatically downloaded on first use."
|
||||
echo "Or you can manually download from: https://github.com/rhasspy/piper/releases/tag/v1.2.0"
|
||||
113
backend/main.py
113
backend/main.py
@@ -11,7 +11,7 @@ from pydantic import BaseModel, Field
|
||||
|
||||
# Import services
|
||||
from services.role_detector import RoleDetector
|
||||
from services.tts_service import TTSService
|
||||
from services.unified_tts_service import UnifiedTTSService
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(
|
||||
@@ -54,6 +54,11 @@ class RoleVoiceMapping(BaseModel):
|
||||
id: str = Field(..., description="Role identifier")
|
||||
voice: str = Field(..., description="Kokoro voice ID")
|
||||
|
||||
class RolePitchMapping(BaseModel):
|
||||
"""Model for role-to-pitch mapping."""
|
||||
id: str = Field(..., description="Role identifier")
|
||||
pitch: int = Field(0, description="Pitch shift in semitones (-12 to +12)")
|
||||
|
||||
class TTSRequest(BaseModel):
|
||||
"""Request model for TTS generation endpoint."""
|
||||
segments: Optional[List[ScriptSegment]] = Field(None, description="Optional edited script segments")
|
||||
@@ -61,6 +66,7 @@ class TTSRequest(BaseModel):
|
||||
lang: str = Field(..., description="Language code (a, b, f, j)")
|
||||
roles: List[RoleVoiceMapping] = Field(..., description="Role to voice mappings")
|
||||
speed: Optional[float] = Field(1.0, description="Playback speed (0.5-1.5)")
|
||||
pitch_adjustments: Optional[List[RolePitchMapping]] = Field(None, description="Optional pitch adjustments per role (Piper only)")
|
||||
|
||||
class VoicePreviewRequest(BaseModel):
|
||||
"""Request model for voice preview endpoint."""
|
||||
@@ -75,6 +81,24 @@ class ErrorResponse(BaseModel):
|
||||
message: str = Field(..., description="Human-readable error message")
|
||||
details: Optional[dict] = Field(None, description="Additional error details")
|
||||
|
||||
class EngineInfo(BaseModel):
|
||||
"""Model for TTS engine information."""
|
||||
name: str = Field(..., description="Engine identifier")
|
||||
active: bool = Field(..., description="Whether this engine is currently active")
|
||||
|
||||
class EnginesResponse(BaseModel):
|
||||
"""Response model for available engines."""
|
||||
engines: List[EngineInfo] = Field(..., description="List of available TTS engines")
|
||||
current: str = Field(..., description="Currently active engine")
|
||||
|
||||
class SetEngineRequest(BaseModel):
|
||||
"""Request model for setting TTS engine."""
|
||||
engine: str = Field(..., description="Engine to activate (kokoro or piper)")
|
||||
|
||||
class VoicesResponse(BaseModel):
|
||||
"""Response model for available voices."""
|
||||
voices: dict = Field(..., description="Dict mapping engine names to voice lists")
|
||||
|
||||
# Initialize FastAPI app
|
||||
app = FastAPI(
|
||||
title="Text to Audiobook API",
|
||||
@@ -120,7 +144,7 @@ async def general_exception_handler(request: Request, exc: Exception):
|
||||
|
||||
# Initialize services
|
||||
role_detector = RoleDetector()
|
||||
tts_service = TTSService()
|
||||
tts_service = UnifiedTTSService()
|
||||
|
||||
# API Endpoints
|
||||
@app.get("/api/health")
|
||||
@@ -129,6 +153,80 @@ async def health_check():
|
||||
logger.info("Health check requested")
|
||||
return {"status": "ok"}
|
||||
|
||||
@app.get("/api/tts/engines", response_model=EnginesResponse)
|
||||
async def get_engines():
|
||||
"""
|
||||
Get list of available TTS engines and current selection.
|
||||
|
||||
Returns:
|
||||
EnginesResponse with list of engines and current active engine
|
||||
"""
|
||||
logger.info("Engines list requested")
|
||||
|
||||
try:
|
||||
available = tts_service.get_available_engines()
|
||||
current = tts_service.engine_name
|
||||
|
||||
engines = [
|
||||
EngineInfo(name=engine, active=(engine == current))
|
||||
for engine in available
|
||||
]
|
||||
|
||||
return EnginesResponse(engines=engines, current=current)
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting engines: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
@app.post("/api/tts/engines")
|
||||
async def set_engine(request: SetEngineRequest):
|
||||
"""
|
||||
Set the active TTS engine.
|
||||
|
||||
Args:
|
||||
request: SetEngineRequest with engine name
|
||||
|
||||
Returns:
|
||||
Success message with new engine
|
||||
|
||||
Raises:
|
||||
400: If engine name is invalid
|
||||
"""
|
||||
logger.info(f"Engine change requested: {request.engine}")
|
||||
|
||||
try:
|
||||
tts_service.set_engine(request.engine)
|
||||
return {"status": "ok", "engine": request.engine}
|
||||
except ValueError as e:
|
||||
logger.error(f"Invalid engine: {str(e)}")
|
||||
return JSONResponse(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
content={
|
||||
"error": "ValidationError",
|
||||
"message": str(e),
|
||||
"details": {"field": "engine"}
|
||||
}
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"Error setting engine: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
@app.get("/api/tts/voices", response_model=VoicesResponse)
|
||||
async def get_voices():
|
||||
"""
|
||||
Get available voices for all TTS engines.
|
||||
|
||||
Returns:
|
||||
VoicesResponse with dict mapping engine names to voice lists
|
||||
"""
|
||||
logger.info("Voices list requested")
|
||||
|
||||
try:
|
||||
voices = tts_service.get_available_voices()
|
||||
return VoicesResponse(voices=voices)
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting voices: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
@app.post("/api/tts/roles", response_model=RolesResponse)
|
||||
async def detect_roles(request: RoleDetectionRequest):
|
||||
"""
|
||||
@@ -420,6 +518,11 @@ async def generate_audiobook(request: TTSRequest):
|
||||
# Convert roles list to dictionary for TTS service
|
||||
role_voice_map = {role.id: role.voice for role in request.roles}
|
||||
|
||||
# Convert pitch adjustments to dictionary if provided
|
||||
pitch_map = None
|
||||
if request.pitch_adjustments:
|
||||
pitch_map = {p.id: p.pitch for p in request.pitch_adjustments}
|
||||
|
||||
try:
|
||||
# Generate audiobook using segments if provided, otherwise use text
|
||||
if request.segments:
|
||||
@@ -428,7 +531,8 @@ async def generate_audiobook(request: TTSRequest):
|
||||
segments=[(seg.speaker, seg.text) for seg in request.segments],
|
||||
lang=request.lang,
|
||||
role_voice_map=role_voice_map,
|
||||
speed=request.speed
|
||||
speed=request.speed,
|
||||
pitch_adjustments=pitch_map
|
||||
)
|
||||
else:
|
||||
logger.info("Generating audiobook from text with automatic segmentation")
|
||||
@@ -436,7 +540,8 @@ async def generate_audiobook(request: TTSRequest):
|
||||
text=request.text,
|
||||
lang=request.lang,
|
||||
role_voice_map=role_voice_map,
|
||||
speed=request.speed
|
||||
speed=request.speed,
|
||||
pitch_adjustments=pitch_map
|
||||
)
|
||||
|
||||
# Create streaming response with proper headers
|
||||
|
||||
@@ -6,3 +6,5 @@ python-multipart==0.0.6
|
||||
pydantic==2.5.3
|
||||
numpy==1.26.3
|
||||
httpx==0.26.0
|
||||
piper-tts==1.2.0
|
||||
pydub==0.25.1
|
||||
|
||||
219
backend/services/piper_tts_service.py
Normal file
219
backend/services/piper_tts_service.py
Normal file
@@ -0,0 +1,219 @@
|
||||
"""
|
||||
Piper TTS service for generating multi-voice audiobooks.
|
||||
"""
|
||||
import logging
|
||||
import io
|
||||
import os
|
||||
import subprocess
|
||||
from typing import Dict, List, Tuple, Optional
|
||||
import numpy as np
|
||||
import soundfile as sf
|
||||
from pydub import AudioSegment
|
||||
from pydub.effects import speedup
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class PiperTTSService:
|
||||
"""
|
||||
Service for text-to-speech synthesis using Piper TTS.
|
||||
|
||||
Piper is a fast, local neural TTS system that supports multiple voices
|
||||
and allows pitch modification through audio processing.
|
||||
"""
|
||||
|
||||
# Default voice for unmapped roles
|
||||
DEFAULT_VOICE = "en_US-lessac-medium"
|
||||
|
||||
# Sample rate for Piper TTS (typically 22050)
|
||||
SAMPLE_RATE = 22050
|
||||
|
||||
# Available Piper voices with their characteristics
|
||||
AVAILABLE_VOICES = {
|
||||
# English voices
|
||||
"en_US-lessac-medium": {"lang": "en", "gender": "male", "quality": "medium"},
|
||||
"en_US-libritts-high": {"lang": "en", "gender": "neutral", "quality": "high"},
|
||||
"en_US-amy-medium": {"lang": "en", "gender": "female", "quality": "medium"},
|
||||
"en_GB-alan-medium": {"lang": "en", "gender": "male", "quality": "medium"},
|
||||
"en_GB-alba-medium": {"lang": "en", "gender": "female", "quality": "medium"},
|
||||
}
|
||||
|
||||
def __init__(self):
|
||||
"""Initializes Piper TTS service."""
|
||||
self.models_dir = os.path.join(os.path.dirname(__file__), "..", "piper_models")
|
||||
os.makedirs(self.models_dir, exist_ok=True)
|
||||
logger.info("PiperTTSService initialized")
|
||||
|
||||
def _check_piper_installed(self) -> bool:
|
||||
"""Check if Piper is installed and accessible."""
|
||||
try:
|
||||
result = subprocess.run(
|
||||
["piper", "--version"],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=5
|
||||
)
|
||||
return result.returncode == 0
|
||||
except (subprocess.SubprocessError, FileNotFoundError):
|
||||
return False
|
||||
|
||||
def _download_voice_model(self, voice: str) -> bool:
|
||||
"""
|
||||
Download a Piper voice model if not already present.
|
||||
|
||||
Args:
|
||||
voice: Voice ID to download
|
||||
|
||||
Returns:
|
||||
True if model is available, False otherwise
|
||||
"""
|
||||
model_path = os.path.join(self.models_dir, f"{voice}.onnx")
|
||||
config_path = os.path.join(self.models_dir, f"{voice}.onnx.json")
|
||||
|
||||
# Check if model already exists
|
||||
if os.path.exists(model_path) and os.path.exists(config_path):
|
||||
return True
|
||||
|
||||
logger.info(f"Downloading Piper voice model: {voice}")
|
||||
|
||||
# Download using piper's built-in download
|
||||
try:
|
||||
subprocess.run(
|
||||
["piper", "--model", voice, "--download-dir", self.models_dir],
|
||||
check=True,
|
||||
timeout=60
|
||||
)
|
||||
return os.path.exists(model_path)
|
||||
except subprocess.SubprocessError as e:
|
||||
logger.error(f"Failed to download voice model {voice}: {e}")
|
||||
return False
|
||||
|
||||
def synthesize_with_pitch(
|
||||
self,
|
||||
text: str,
|
||||
voice: str,
|
||||
speed: float = 1.0,
|
||||
pitch_shift: int = 0
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Synthesizes text with Piper and applies pitch shifting.
|
||||
|
||||
Args:
|
||||
text: Text to synthesize
|
||||
voice: Piper voice ID
|
||||
speed: Playback speed (0.5-2.0)
|
||||
pitch_shift: Semitones to shift pitch (-12 to +12)
|
||||
|
||||
Returns:
|
||||
Audio samples as numpy array
|
||||
"""
|
||||
# Ensure voice model is available
|
||||
if not self._download_voice_model(voice):
|
||||
logger.warning(f"Voice {voice} not available, using default")
|
||||
voice = self.DEFAULT_VOICE
|
||||
self._download_voice_model(voice)
|
||||
|
||||
model_path = os.path.join(self.models_dir, f"{voice}.onnx")
|
||||
|
||||
# Generate audio with Piper
|
||||
try:
|
||||
# Run piper command
|
||||
process = subprocess.Popen(
|
||||
["piper", "--model", model_path, "--output-raw"],
|
||||
stdin=subprocess.PIPE,
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.PIPE
|
||||
)
|
||||
|
||||
# Send text and get audio
|
||||
stdout, stderr = process.communicate(input=text.encode('utf-8'), timeout=30)
|
||||
|
||||
if process.returncode != 0:
|
||||
logger.error(f"Piper synthesis failed: {stderr.decode()}")
|
||||
return np.array([], dtype=np.float32)
|
||||
|
||||
# Convert raw audio to numpy array
|
||||
audio_data = np.frombuffer(stdout, dtype=np.int16)
|
||||
audio_float = audio_data.astype(np.float32) / 32768.0
|
||||
|
||||
# Apply pitch shift and speed change using pydub
|
||||
if pitch_shift != 0 or speed != 1.0:
|
||||
audio_float = self._apply_audio_effects(
|
||||
audio_float,
|
||||
self.SAMPLE_RATE,
|
||||
speed,
|
||||
pitch_shift
|
||||
)
|
||||
|
||||
return audio_float
|
||||
|
||||
except subprocess.TimeoutExpired:
|
||||
logger.error("Piper synthesis timed out")
|
||||
return np.array([], dtype=np.float32)
|
||||
except Exception as e:
|
||||
logger.error(f"Error in Piper synthesis: {e}")
|
||||
return np.array([], dtype=np.float32)
|
||||
|
||||
def _apply_audio_effects(
|
||||
self,
|
||||
audio: np.ndarray,
|
||||
sample_rate: int,
|
||||
speed: float,
|
||||
pitch_shift: int
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Apply speed and pitch effects to audio using pydub.
|
||||
|
||||
Args:
|
||||
audio: Audio samples
|
||||
sample_rate: Sample rate
|
||||
speed: Speed multiplier
|
||||
pitch_shift: Semitones to shift
|
||||
|
||||
Returns:
|
||||
Processed audio samples
|
||||
"""
|
||||
try:
|
||||
# Convert numpy to pydub AudioSegment
|
||||
audio_int16 = (audio * 32768.0).astype(np.int16)
|
||||
audio_bytes = audio_int16.tobytes()
|
||||
|
||||
audio_segment = AudioSegment(
|
||||
audio_bytes,
|
||||
frame_rate=sample_rate,
|
||||
sample_width=2,
|
||||
channels=1
|
||||
)
|
||||
|
||||
# Apply pitch shift
|
||||
if pitch_shift != 0:
|
||||
# Pitch shift by changing frame rate then resampling
|
||||
new_sample_rate = int(sample_rate * (2.0 ** (pitch_shift / 12.0)))
|
||||
audio_segment = audio_segment._spawn(
|
||||
audio_segment.raw_data,
|
||||
overrides={'frame_rate': new_sample_rate}
|
||||
)
|
||||
audio_segment = audio_segment.set_frame_rate(sample_rate)
|
||||
|
||||
# Apply speed change
|
||||
if speed != 1.0:
|
||||
audio_segment = speedup(audio_segment, playback_speed=speed)
|
||||
|
||||
# Convert back to numpy
|
||||
samples = np.array(audio_segment.get_array_of_samples())
|
||||
audio_float = samples.astype(np.float32) / 32768.0
|
||||
|
||||
return audio_float
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error applying audio effects: {e}")
|
||||
return audio
|
||||
|
||||
def get_available_voices(self) -> Dict[str, Dict[str, str]]:
|
||||
"""
|
||||
Returns dictionary of available Piper voices.
|
||||
|
||||
Returns:
|
||||
Dict mapping voice IDs to their characteristics
|
||||
"""
|
||||
return self.AVAILABLE_VOICES.copy()
|
||||
283
backend/services/unified_tts_service.py
Normal file
283
backend/services/unified_tts_service.py
Normal file
@@ -0,0 +1,283 @@
|
||||
"""
|
||||
Unified TTS service that supports multiple TTS engines (Kokoro, Piper).
|
||||
"""
|
||||
import logging
|
||||
import os
|
||||
from typing import Dict, List, Tuple, Optional
|
||||
from enum import Enum
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class TTSEngine(str, Enum):
|
||||
"""Available TTS engines."""
|
||||
KOKORO = "kokoro"
|
||||
PIPER = "piper"
|
||||
|
||||
|
||||
class UnifiedTTSService:
|
||||
"""
|
||||
Unified service that delegates to specific TTS engines.
|
||||
Allows runtime selection between Kokoro and Piper TTS.
|
||||
"""
|
||||
|
||||
def __init__(self, engine: Optional[str] = None, ollama_service=None):
|
||||
"""
|
||||
Initialize unified TTS service.
|
||||
|
||||
Args:
|
||||
engine: TTS engine to use ('kokoro' or 'piper').
|
||||
Defaults to TTS_ENGINE env var or 'kokoro'
|
||||
ollama_service: Optional OllamaService for AI segmentation
|
||||
"""
|
||||
self.engine_name = engine or os.getenv("TTS_ENGINE", "kokoro")
|
||||
self.ollama_service = ollama_service
|
||||
|
||||
# Lazy load engines
|
||||
self._kokoro_service = None
|
||||
self._piper_service = None
|
||||
|
||||
logger.info(f"UnifiedTTSService initialized with engine: {self.engine_name}")
|
||||
|
||||
@property
|
||||
def kokoro_service(self):
|
||||
"""Lazy load Kokoro service."""
|
||||
if self._kokoro_service is None:
|
||||
from .tts_service import TTSService
|
||||
self._kokoro_service = TTSService(self.ollama_service)
|
||||
return self._kokoro_service
|
||||
|
||||
@property
|
||||
def piper_service(self):
|
||||
"""Lazy load Piper service."""
|
||||
if self._piper_service is None:
|
||||
from .piper_tts_service import PiperTTSService
|
||||
self._piper_service = PiperTTSService()
|
||||
return self._piper_service
|
||||
|
||||
def set_engine(self, engine: str):
|
||||
"""
|
||||
Change the active TTS engine.
|
||||
|
||||
Args:
|
||||
engine: Engine name ('kokoro' or 'piper')
|
||||
"""
|
||||
if engine not in [e.value for e in TTSEngine]:
|
||||
raise ValueError(f"Unknown engine: {engine}. Use 'kokoro' or 'piper'")
|
||||
|
||||
self.engine_name = engine
|
||||
logger.info(f"Switched TTS engine to: {engine}")
|
||||
|
||||
def get_available_engines(self) -> List[str]:
|
||||
"""
|
||||
Get list of available TTS engines.
|
||||
|
||||
Returns:
|
||||
List of engine names
|
||||
"""
|
||||
return [e.value for e in TTSEngine]
|
||||
|
||||
def get_available_voices(self) -> Dict[str, List[str]]:
|
||||
"""
|
||||
Get available voices for all engines.
|
||||
|
||||
Returns:
|
||||
Dict mapping engine names to lists of voice IDs
|
||||
"""
|
||||
voices = {}
|
||||
|
||||
# Kokoro voices (from documentation)
|
||||
voices["kokoro"] = [
|
||||
"af_bella", "af_nicole", "af_sarah", "af_sky",
|
||||
"am_adam", "am_michael", "bf_emma", "bf_isabella",
|
||||
"bm_george", "bm_lewis"
|
||||
]
|
||||
|
||||
# Piper voices
|
||||
try:
|
||||
piper_voices = self.piper_service.get_available_voices()
|
||||
voices["piper"] = list(piper_voices.keys())
|
||||
except Exception as e:
|
||||
logger.warning(f"Could not load Piper voices: {e}")
|
||||
voices["piper"] = []
|
||||
|
||||
return voices
|
||||
|
||||
async def generate_script_segments(
|
||||
self,
|
||||
text: str,
|
||||
roles: List[str]
|
||||
) -> List[Tuple[str, str]]:
|
||||
"""
|
||||
Generate script segments with speaker assignments.
|
||||
Uses AI when available, falls back to pattern matching.
|
||||
|
||||
Args:
|
||||
text: Input text to segment
|
||||
roles: List of detected role IDs/names
|
||||
|
||||
Returns:
|
||||
List of tuples: (speaker_name, text_content)
|
||||
"""
|
||||
# This is engine-agnostic, use Kokoro's implementation
|
||||
return await self.kokoro_service.generate_script_segments(text, roles)
|
||||
|
||||
async def generate_audiobook(
|
||||
self,
|
||||
text: str,
|
||||
lang: str,
|
||||
role_voice_map: Dict[str, str],
|
||||
speed: float = 1.0,
|
||||
pitch_adjustments: Optional[Dict[str, int]] = None
|
||||
) -> bytes:
|
||||
"""
|
||||
Generate audiobook using the selected TTS engine.
|
||||
|
||||
Args:
|
||||
text: Input text
|
||||
lang: Language code
|
||||
role_voice_map: Role to voice ID mapping
|
||||
speed: Playback speed
|
||||
pitch_adjustments: Optional dict of role to pitch shift (semitones)
|
||||
|
||||
Returns:
|
||||
Audio data as bytes (WAV format)
|
||||
"""
|
||||
if self.engine_name == TTSEngine.KOKORO:
|
||||
# Kokoro doesn't support pitch adjustment
|
||||
return await self.kokoro_service.generate_audiobook(
|
||||
text, lang, role_voice_map, speed
|
||||
)
|
||||
|
||||
elif self.engine_name == TTSEngine.PIPER:
|
||||
# Use Piper with pitch support
|
||||
return await self._generate_with_piper(
|
||||
text, lang, role_voice_map, speed, pitch_adjustments or {}
|
||||
)
|
||||
|
||||
else:
|
||||
raise ValueError(f"Unknown engine: {self.engine_name}")
|
||||
|
||||
async def generate_audiobook_from_segments(
|
||||
self,
|
||||
segments: List[Tuple[str, str]],
|
||||
lang: str,
|
||||
role_voice_map: Dict[str, str],
|
||||
speed: float = 1.0,
|
||||
pitch_adjustments: Optional[Dict[str, int]] = None
|
||||
) -> bytes:
|
||||
"""
|
||||
Generate audiobook from pre-segmented script.
|
||||
|
||||
Args:
|
||||
segments: List of (speaker_name, text_content) tuples
|
||||
lang: Language code
|
||||
role_voice_map: Role to voice ID mapping
|
||||
speed: Playback speed
|
||||
pitch_adjustments: Optional dict of role to pitch shift (semitones)
|
||||
|
||||
Returns:
|
||||
Audio data as bytes (WAV format)
|
||||
"""
|
||||
if self.engine_name == TTSEngine.KOKORO:
|
||||
return await self.kokoro_service.generate_audiobook_from_segments(
|
||||
segments, lang, role_voice_map, speed
|
||||
)
|
||||
|
||||
elif self.engine_name == TTSEngine.PIPER:
|
||||
return await self._generate_from_segments_with_piper(
|
||||
segments, lang, role_voice_map, speed, pitch_adjustments or {}
|
||||
)
|
||||
|
||||
else:
|
||||
raise ValueError(f"Unknown engine: {self.engine_name}")
|
||||
|
||||
async def _generate_with_piper(
|
||||
self,
|
||||
text: str,
|
||||
lang: str,
|
||||
role_voice_map: Dict[str, str],
|
||||
speed: float,
|
||||
pitch_adjustments: Dict[str, int]
|
||||
) -> bytes:
|
||||
"""Generate audiobook using Piper TTS with pitch control."""
|
||||
import io
|
||||
import numpy as np
|
||||
import soundfile as sf
|
||||
|
||||
# Segment text using Kokoro's logic
|
||||
segments = await self.kokoro_service.segment_text_with_ai(text, role_voice_map)
|
||||
|
||||
if segments is None:
|
||||
segments = self.kokoro_service.segment_text(text, role_voice_map)
|
||||
|
||||
if not segments:
|
||||
raise ValueError("No segments to synthesize")
|
||||
|
||||
# Synthesize each segment with Piper
|
||||
audio_segments = []
|
||||
for role_id, voice, content in segments:
|
||||
pitch_shift = pitch_adjustments.get(role_id, 0)
|
||||
logger.debug(
|
||||
f"Synthesizing with Piper: role='{role_id}', voice='{voice}', "
|
||||
f"pitch={pitch_shift}"
|
||||
)
|
||||
|
||||
audio = self.piper_service.synthesize_with_pitch(
|
||||
content, voice, speed, pitch_shift
|
||||
)
|
||||
audio_segments.append(audio)
|
||||
|
||||
# Concatenate
|
||||
final_audio = np.concatenate([s for s in audio_segments if len(s) > 0])
|
||||
|
||||
# Convert to WAV bytes
|
||||
buffer = io.BytesIO()
|
||||
sf.write(buffer, final_audio, self.piper_service.SAMPLE_RATE, format='WAV')
|
||||
buffer.seek(0)
|
||||
|
||||
logger.info(f"Generated Piper audiobook: {len(buffer.getvalue())} bytes")
|
||||
return buffer.read()
|
||||
|
||||
async def _generate_from_segments_with_piper(
|
||||
self,
|
||||
segments: List[Tuple[str, str]],
|
||||
lang: str,
|
||||
role_voice_map: Dict[str, str],
|
||||
speed: float,
|
||||
pitch_adjustments: Dict[str, int]
|
||||
) -> bytes:
|
||||
"""Generate audiobook from segments using Piper TTS."""
|
||||
import io
|
||||
import numpy as np
|
||||
import soundfile as sf
|
||||
|
||||
if not segments:
|
||||
raise ValueError("No segments provided")
|
||||
|
||||
# Synthesize each segment
|
||||
audio_segments = []
|
||||
for speaker, text in segments:
|
||||
voice = role_voice_map.get(speaker, self.piper_service.DEFAULT_VOICE)
|
||||
pitch_shift = pitch_adjustments.get(speaker, 0)
|
||||
|
||||
logger.debug(
|
||||
f"Synthesizing segment: speaker='{speaker}', voice='{voice}', "
|
||||
f"pitch={pitch_shift}"
|
||||
)
|
||||
|
||||
audio = self.piper_service.synthesize_with_pitch(
|
||||
text, voice, speed, pitch_shift
|
||||
)
|
||||
audio_segments.append(audio)
|
||||
|
||||
# Concatenate
|
||||
final_audio = np.concatenate([s for s in audio_segments if len(s) > 0])
|
||||
|
||||
# Convert to WAV bytes
|
||||
buffer = io.BytesIO()
|
||||
sf.write(buffer, final_audio, self.piper_service.SAMPLE_RATE, format='WAV')
|
||||
buffer.seek(0)
|
||||
|
||||
logger.info(f"Generated Piper audiobook from segments: {len(buffer.getvalue())} bytes")
|
||||
return buffer.read()
|
||||
Reference in New Issue
Block a user