Add comprehensive TTS model and mirror documentation

docs/MODELS.md:
- Document 10+ abandoned TTS engines to integrate
- Piper TTS (integrated, fixed)
- Coqui XTTS v2 (integrated, company shut down)
- Silero TTS (HIGH PRIORITY - still active, fast)
- StyleTTS2 (HIGH PRIORITY - best quality)
- Fish Speech (active, good quality)
- Kokoro, Bark, Tortoise, MetaVoice (lower priority)
- Integration roadmap with time estimates
- Performance targets and storage requirements

docs/MIRRORS.md:
- Multi-tier mirror strategy for resilience
- Tier 1: Upstream (HuggingFace, PyPI, GitHub)
- Tier 2: Self-hosted MinIO on ai.foxhop.net
- Tier 3: Archive.org for public archival
- Tier 4: IPFS for decentralization
- Complete implementation with scripts and configs
- Fallback download logic
- Recovery scenarios
- Cost: $0-20/month

Raccoon mission: Ensure TTS keeps working when upstream dies.
Documentation-first approach before implementing features.

🦝 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Russell Ballestrini 2025-11-09 09:28:02 -05:00
parent e8183b4585
commit 3a9e27e556
2 changed files with 801 additions and 0 deletions

375
docs/MODELS.md Normal file
View file

@ -0,0 +1,375 @@
# TTS Models and Engines
**Raccoon Mission:** Rescue abandoned open-source TTS models and integrate them into UncloseAI Speech
## Currently Integrated
### 1. Piper TTS ✅
**Status:** Working with absolute paths
**Original Project:** rhasspy/piper (abandoned)
**Fork:** OHF-Voice/piper1-gpl v1.3.0
**Current Package:** PyPI `piper-tts>=1.2.0`
**Features:**
- Fast CPU-based neural TTS
- ~100+ high-quality voices
- Multilingual support
- ONNX runtime
- Low memory footprint (~100MB per voice)
**Voices Available:**
- English (US, GB, multiple accents)
- Spanish, French, German, Italian
- Russian, Polish, Ukrainian
- Chinese, Japanese, Korean
- Many more languages
**Model Source:**
- HuggingFace: `rhasspy/piper-voices`
- Direct download: `https://huggingface.co/rhasspy/piper-voices/resolve/v1.0.0/`
**Integration:**
- Used for `tts-1` model (fast, lower quality)
- Models stored in `/app/voices/en/en_US/libritts_r/medium/`
- Configuration via absolute paths in `voice_to_speaker.yaml`
**Example Config:**
```yaml
tts-1:
alloy:
model: /app/voices/en/en_US/libritts_r/medium/en_US-libritts_r-medium.onnx
speaker: 79
```
**Raccoon Notes:**
- Original rhasspy project abandoned
- OHF-Voice fork has no PyPI package
- Need to create our own PyPI package or vendor the code
- Mirror all voices to prevent HuggingFace dependency
---
### 2. Coqui XTTS v2 ✅
**Status:** Integrated as tts-1-hd
**Original Project:** coqui-ai/TTS (company shut down, archived)
**Current Package:** PyPI `coqui-tts[languages]`
**Features:**
- High-quality multilingual TTS
- Voice cloning from 6-second samples
- Emotional prosody control
- GPU accelerated (NVIDIA/ROCm)
- ~1.8GB model size
**Languages:**
- English, Spanish, French, German, Italian, Portuguese
- Polish, Turkish, Russian, Dutch, Czech
- Arabic, Chinese (Mandarin), Japanese, Hungarian, Korean, Hindi
**Model Source:**
- HuggingFace: `coqui/XTTS-v2`
- Auto-downloaded on first use
**Integration:**
- Used for `tts-1-hd` model (slow, high quality)
- Voice cloning with custom WAV samples
- Language auto-detection with `langdetect`
**Example Config:**
```yaml
tts-1-hd:
alloy:
model: xtts
speaker: /app/voices/alloy.wav
language: en
```
**Raccoon Notes:**
- Coqui company shut down in 2024
- Repository archived but code still works
- Community forks emerging
- Must mirror XTTS-v2 weights before they disappear
- Consider forking to uncloseai-xtts
---
## High Priority Integration Targets
### 3. Silero TTS 🎯
**Status:** NOT INTEGRATED - HIGH PRIORITY
**Project:** snakers4/silero-models (still active!)
**License:** Apache 2.0
**Why Integrate:**
- STILL ACTIVELY MAINTAINED
- Fast, small models (~50-100MB each)
- High quality for size
- Easy integration (PyTorch)
- Commercial-friendly license
**Features:**
- Multilingual: English, Russian, German, Spanish, French
- Multiple speakers per language
- Emotion control
- CPU friendly
- Real-time capable
**Models:**
- English: 4 speakers (en_v4)
- Russian: 8+ speakers (ru_v4)
- German: 1 speaker (de_v3)
- Spanish: 2 speakers (es_v1)
- French: 1 speaker (fr_v3)
**Model Source:**
- GitHub Releases: https://github.com/snakers4/silero-models/releases
- PyTorch Hub
- Direct ONNX models available
**Estimated Integration Effort:** 2-4 hours
- Add to requirements.txt: `silero` or direct PyTorch load
- Create `src/engines/silero.py`
- Download models to `/app/models/silero/`
- Add voice mappings to config
**Example Usage:**
```python
import torch
model, symbols, sample_rate, example_text, apply_tts = torch.hub.load(
repo_or_dir='snakers4/silero-models',
model='silero_tts',
language='en',
speaker='v4_en'
)
audio = apply_tts(text=text, speaker='en_0', sample_rate=sample_rate)
```
**Raccoon Priority:** ⭐⭐⭐⭐⭐ (Active project, great quality/size ratio)
---
### 4. StyleTTS2 🎯
**Status:** NOT INTEGRATED - HIGH PRIORITY
**Project:** yl4579/StyleTTS2 (research, somewhat active)
**License:** MIT
**Why Integrate:**
- State-of-the-art quality
- Best prosody and naturalness
- Voice cloning capability
- Style/emotion control
- Research-grade results
**Features:**
- Human-level prosody
- Zero-shot voice cloning
- Style transfer
- Emotion and speaking style control
- LibriTTS trained models
**Challenges:**
- Complex dependencies
- Requires phonemizer
- Slower than other engines
- GPU recommended
**Model Source:**
- HuggingFace: `yl4579/StyleTTS2-LibriTTS`
- GitHub releases
**Estimated Integration Effort:** 6-8 hours
- Complex dependency chain
- Need phonemizer setup
- Create custom engine wrapper
- May need model quantization for production
**Raccoon Priority:** ⭐⭐⭐⭐ (Best quality, but complex)
---
### 5. Fish Speech 🎯
**Status:** NOT INTEGRATED - MEDIUM PRIORITY
**Project:** fishaudio/fish-speech (active)
**License:** Apache 2.0
**Why Integrate:**
- Fast and efficient
- Good multilingual support
- Active development
- Clean API
**Features:**
- Fast inference
- Multilingual (EN, ZH, JA)
- Voice cloning
- Streaming support
- Modern architecture
**Model Source:**
- HuggingFace: `fishaudio/fish-speech-1`
- GitHub releases
**Estimated Integration Effort:** 4-6 hours
**Raccoon Priority:** ⭐⭐⭐ (Active, good quality, but newer/less proven)
---
## Medium Priority Targets
### 6. Kokoro TTS
**Status:** NOT INTEGRATED
**Project:** hexgrad/kokoro (new, active)
**License:** Apache 2.0
**Features:**
- Fast, small, quality
- Multiple voices
- Good English support
- Emerging project
**Raccoon Priority:** ⭐⭐⭐ (Promising but new)
---
### 7. Bark (Suno AI)
**Status:** NOT INTEGRATED
**Project:** suno-ai/bark (archived, company pivoted to music)
**License:** MIT
**Why Consider:**
- Can generate music and sound effects
- Non-verbal sounds (laughs, sighs)
- Multiple languages
- Background audio
**Why Low Priority:**
- Very slow generation
- Large models (~10GB)
- Company abandoned it
- Quality inconsistent
**Raccoon Priority:** ⭐⭐ (Unique features, but slow and abandoned)
---
## Low Priority / Archived
### 8. Tortoise TTS
**Status:** NOT INTEGRATED
**Project:** neonbjb/tortoise-tts (low activity)
**License:** Apache 2.0
**Features:**
- Very high quality
- Voice cloning
**Why Low Priority:**
- Extremely slow (minutes per sentence)
- Not practical for API use
- Better alternatives exist now
**Raccoon Priority:** ⭐ (Too slow for production)
---
### 9. MetaVoice
**Status:** NOT INTEGRATED
**Project:** metavoiceio/metavoice-src (partially abandoned)
**License:** Apache 2.0
**Features:**
- Long-form TTS
- Emotional control
- Voice cloning
**Why Low Priority:**
- Unclear maintenance status
- Complex setup
- Alternatives are better
**Raccoon Priority:** ⭐ (Uncertain future)
---
### 10. Mozilla TTS
**Status:** NOT INTEGRATED
**Project:** mozilla/TTS (archived, became Coqui)
**License:** MPL 2.0
**Why Skip:**
- Fully superseded by Coqui
- No unique capabilities
- Outdated architecture
**Raccoon Priority:** ⛔ (Skip - use Coqui instead)
---
## Integration Roadmap
### Phase 1: Quick Wins (Next 1-2 weeks)
1. ✅ Fix Piper absolute paths
2. ✅ Audit repository
3. [ ] Integrate Silero TTS (2-4 hours)
4. [ ] Set up model mirror on ai.foxhop.net
5. [ ] Test Silero with existing API
### Phase 2: High Quality (2-4 weeks)
1. [ ] Integrate StyleTTS2
2. [ ] Create engine abstraction layer
3. [ ] Refactor speech.py to use engines
4. [ ] Add Fish Speech support
### Phase 3: Resilience (1-2 months)
1. [ ] Implement binary mirror system
2. [ ] Create fallback download logic
3. [ ] Archive critical models to Archive.org
4. [ ] Document all model sources
### Phase 4: Advanced Features (2+ months)
1. [ ] Voice cloning API endpoint
2. [ ] Emotion/style control
3. [ ] Streaming TTS
4. [ ] Multi-speaker conversations
## Model Storage Requirements
Current:
- Piper voices: ~2GB (all languages)
- XTTS v2: ~1.8GB
With all planned engines:
- Silero models: ~500MB (all languages)
- StyleTTS2: ~2GB (base model)
- Fish Speech: ~1.5GB
- **Total: ~8GB** for complete coverage
Mirror storage needed: ~20GB (with redundancy and archives)
## Performance Targets
| Engine | Speed (RTF) | Quality | Use Case |
|--------|-------------|---------|----------|
| Piper | 0.05x | Good | Fast API responses |
| Silero | 0.1x | Good | Balanced speed/quality |
| XTTS | 0.3x | Excellent | Voice cloning |
| StyleTTS2 | 0.5x | Best | Premium quality |
| Fish Speech | 0.15x | Very Good | Multilingual |
RTF = Real-time factor (lower is faster, 1.0 = real-time)
---
**Last Updated:** 2025-11-09
**Raccoon Status:** 🦝 Actively hunting for TTS models in the dumpsters of abandoned repos