# Silero TTS

**Project:** snakers4/silero-models
**Status:** ✅ INTEGRATED as tts-1-silero
**License:** Apache 2.0
**Maintenance:** ✨ ACTIVELY MAINTAINED

## Overview

Silero TTS is a collection of fast, small, and high-quality speech synthesis models maintained by Silero AI. Unlike many TTS projects that have been abandoned, Silero is **actively maintained** and continues to receive updates.

**Why Silero?**
- **Active Project** - Regular updates, responsive maintainers
- **Commercial-Friendly** - Apache 2.0 license
- **CPU Efficient** - Real-time synthesis without GPU
- **Small Models** - 50-100MB per language
- **High Quality** - Excellent quality for model size
- **Multilingual** - 5 languages with 148 total voices

## Integration Status

**Integrated:** November 2025
**Endpoint:** `tts-1-silero`
**API Compatibility:** OpenAI TTS API compatible

### Supported Languages

| Language | Speakers | Model Version | Voice IDs |
|----------|----------|---------------|-----------|
| English | 118 voices | v3_en | en_0 to en_117 + random |
| Russian | 6 voices | ru_v3 | ru_aidar, ru_baya, ru_kseniya, ru_xenia, ru_eugene, ru_random |
| German | 6 voices | v3_de | de_eva_k, de_karlsson, de_friedrich, de_hokuspokus, de_bernd_ungerer, de_random |
| Spanish | 4 voices | v3_es | es_0, es_1, es_2, es_random |
| French | 7 voices | v3_fr | fr_0 to fr_5 + fr_random |

**Total:** 148 voices across 5 languages

## Technical Specifications

**Architecture:** Neural TTS based on PyTorch
**Sample Rate:** 48kHz
**Model Size:** ~50-100MB per language
**Inference Speed:** Real-time on CPU (RTF ~0.1x)
**Memory Usage:** ~500MB RAM during inference

### Model Loading

Models are loaded via PyTorch Hub:

```python
import torch

model, example_text = torch.hub.load(
    repo_or_dir='snakers4/silero-models',
    model='silero_tts',
    language='en',
    speaker='v3_en',
    verbose=False
)

# Generate speech
audio = model.apply_tts(
    text="Hello from Silero TTS!",
    speaker='en_0',
    sample_rate=48000
)
```

## Integration Details

### Voice Configuration

Example configuration in `voice_to_speaker.yaml`:

```yaml
tts-1-silero:
  # OpenAI-compatible aliases
  alloy:
    language: en
    speaker: en_0
    silero_speaker: v3_en

  # English voices (118 total)
  en_0:
    language: en
    speaker: en_0
    silero_speaker: v3_en

  en_1:
    language: en
    speaker: en_1
    silero_speaker: v3_en

  # Russian voices
  ru_aidar:
    language: ru
    speaker: aidar
    silero_speaker: ru_v3

  # German voices
  de_eva_k:
    language: de
    speaker: eva_k
    silero_speaker: v3_de

  # Spanish voices
  es_0:
    language: es
    speaker: es_0
    silero_speaker: v3_es

  # French voices
  fr_0:
    language: fr
    speaker: fr_0
    silero_speaker: v3_fr
```

### Makefile Targets

```bash
# Download all Silero models (en, ru, de, es, fr)
make voices-silero

# Test Silero TTS endpoint
make test-silero
```

### API Usage

```bash
# English voice
curl -X POST http://localhost:8000/v1/audio/speech \
  -H "Content-Type: application/json" \
  -d '{
    "model": "tts-1-silero",
    "voice": "en_0",
    "input": "Hello from Silero TTS!"
  }' \
  -o output.mp3

# Russian voice
curl -X POST http://localhost:8000/v1/audio/speech \
  -H "Content-Type: application/json" \
  -d '{
    "model": "tts-1-silero",
    "voice": "ru_aidar",
    "input": "Привет от Silero TTS!"
  }' \
  -o output_ru.mp3

# German voice
curl -X POST http://localhost:8000/v1/audio/speech \
  -H "Content-Type: application/json" \
  -d '{
    "model": "tts-1-silero",
    "voice": "de_eva_k",
    "input": "Hallo von Silero TTS!"
  }' \
  -o output_de.mp3
```

## Wrapper Implementation

The Silero wrapper in `speech.py`:

```python
class silero_wrapper():
    """Wrapper for Silero TTS models

    Silero torch.hub.load returns: (model, example_text)
    The model has a method apply_tts(text, speaker, sample_rate)
    """
    def __init__(self, language='en', speaker='v3_en', device='cpu'):
        self.language = language
        self.speaker = speaker
        self.device = device

        logger.info(f"Loading Silero model for {language} with speaker {speaker} on {device}")

        import torch
        try:
            # torch.hub.load returns (model, example_text)
            self.model, example_text = torch.hub.load(
                repo_or_dir='snakers4/silero-models',
                model='silero_tts',
                language=language,
                speaker=speaker,
                verbose=False
            )

            self.model.to(device)  # Move to device (in-place for Silero)
            self.sample_rate = 48000  # Silero uses 48kHz
            logger.info(f"Successfully loaded Silero {language}/{speaker}, example: {example_text}")
        except Exception as e:
            logger.error(f"Failed to load Silero model: {e}")
            raise

    def tts(self, text, speaker_id='en_0'):
        """Generate speech from text"""
        import torch

        with torch.no_grad():
            # Use model's apply_tts method
            audio = self.model.apply_tts(
                text=text,
                speaker=speaker_id,
                sample_rate=self.sample_rate
            )
            # audio is a tensor, convert to numpy float32
            return audio.cpu().numpy().tobytes()
```

## Performance Characteristics

**Speed:** Real-time on CPU
**Quality:** Good - excellent for model size
**Latency:** Low (~100-200ms for short phrases)
**Memory:** Efficient - models stay loaded in RAM

### Benchmarks (Approximate)

| Text Length | Generation Time (CPU) | RTF |
|-------------|----------------------|-----|
| 10 words | ~0.5s | 0.15x |
| 50 words | ~2.0s | 0.10x |
| 100 words | ~4.0s | 0.08x |

RTF = Real-time factor (lower is faster)

## Voice Quality

Silero voices are optimized for:
- **Clarity** - Clean, intelligible speech
- **Naturalness** - Good prosody for synthesized speech
- **Consistency** - Stable quality across different texts
- **Speed** - Fast enough for real-time applications

Not optimized for:
- Emotional expression (limited)
- Voice cloning (not supported)
- Singing or non-speech audio

## Known Issues

### Russian and Spanish Voice Formats

**Issue:** Some Russian and Spanish voices return error responses
**Affected:** `ru_*` and `es_*` voices
**Status:** Under investigation
**Workaround:** Use English, German, or French voices

**Tracking:** See GitHub issue or `docs/MODELS.md` for updates

## Raccoon Mission Notes

**Rescue Status:** ⭐⭐⭐⭐⭐ **EXCELLENT**

**Why Silero is a Perfect Raccoon Rescue:**
1. **Active Maintenance** - Regular updates, no abandonment risk
2. **Open License** - Apache 2.0, commercial-friendly
3. **High Quality/Size Ratio** - Best bang for buck
4. **Multi-language** - 5 languages with more planned
5. **CPU Friendly** - No GPU required
6. **Easy Integration** - PyTorch Hub makes it simple

**Integration Success:**
- ✅ All 5 languages configured
- ✅ 148 voices mapped
- ✅ OpenAI API compatibility
- ✅ Makefile automation
- ⚠️ Russian/Spanish voices need debugging

## Future Enhancements

**Planned:**
1. Fix Russian and Spanish voice issues
2. Add emotion control (Silero supports this)
3. Implement voice caching for faster switching
4. Add streaming support
5. Create voice sample gallery

**Possible:**
- Additional languages (Ukrainian, Uzbek, Tatar available)
- Fine-tuning for specific use cases
- Model quantization for even smaller sizes

## Resources

**Official Links:**
- GitHub: https://github.com/snakers4/silero-models
- Documentation: https://github.com/snakers4/silero-models/wiki
- PyTorch Hub: https://pytorch.org/hub/snakers4_silero-models_tts/
- Models: https://models.silero.ai/

**Community:**
- Actively maintained by Silero AI team
- Responsive to issues and pull requests
- Growing user base

**Papers:**
- No formal academic paper (production-focused)
- Extensive documentation and examples

## License

Apache License 2.0 - Commercial use permitted

```
Copyright (c) 2020-2025 Silero AI

Licensed under the Apache License, Version 2.0
```

---

**Integration Date:** November 2025
**Raccoon Rating:** 🦝🦝🦝🦝🦝 (5/5 - Perfect rescue!)
**Maintenance:** ✅ Active
**Recommendation:** **Highly Recommended** - Best quality/performance/license combo