git clone https://github.com/Useforclaude/quantum-sync-v5.git
cd quantum-sync-v5/quantum-sync-v11-production

# Configure to use this mirror
# Edit tts_engines/xtts.py, change model_name to:
# model_name = "useclaude/quantum-sync-xtts-v2"

python main_v11.py input/file.srt \
  --voice MyVoice \
  --voice-sample /path/to/voice.wav \
  --tts-engine xtts-v2 \
  --tts-language en

Direct Usage with TTS Library

python

from TTS.api import TTS

# Use this mirror
tts = TTS(model_name="useclaude/quantum-sync-xtts-v2")

# Generate speech
tts.tts_to_file(
    text="Hello, this is a test.",
    speaker_wav="reference_voice.wav",
    language="en",
    file_path="output.wav"
)

Voice Cloning Example

python

from TTS.api import TTS

# Initialize
tts = TTS(model_name="useclaude/quantum-sync-xtts-v2")

# Clone voice from reference audio (6-30 seconds)
tts.tts_to_file(
    text="The quick brown fox jumps over the lazy dog.",
    speaker_wav="my_voice_sample.wav",  # Your voice reference
    language="en",
    file_path="output_cloned.wav"
)

📊 Performance

From Quantum Sync Production Tests (2025-10-13):

Metric	Value
Synthesis Speed	~3.7 segments/minute
Processing Time	17 min for 277 segments (23 min audio)
Duration Accuracy	~87% audio, ~13% silence gaps
Timeline Drift	-1.7% (excellent)
Voice Quality	8/10
Cloning Accuracy	Excellent
VRAM Usage	6-8 GB

Comparison:

XTTS-v2: 15-17 min, 8/10 quality, FREE, 87% audio
F5-TTS: 20-25 min, 7/10 quality, FREE, 55% audio
AWS Polly: 5 min, 9/10 quality, ~$0.06, no cloning

🎛️ Advanced Parameters

python

# Speed control (0.5 - 2.0)
tts.tts_to_file(
    text="Hello world",
    speaker_wav="voice.wav",
    language="en",
    speed=0.8,  # Slower speech
    file_path="output.wav"
)

# Temperature control (0.1 - 1.0)
tts.tts_to_file(
    text="Hello world",
    speaker_wav="voice.wav",
    language="en",
    temperature=0.75,  # More expressive
    file_path="output.wav"
)

📦 Model Files

quantum-sync-xtts-v2/
├── model.pth              (1.87 GB - Neural network weights)
├── config.json            (Model configuration)
├── vocab.json             (Vocabulary for tokenization)
├── speakers_xtts.pth      (Speaker embeddings)
├── dvae.pth               (DVAE component)
├── mel_stats.pth          (Mel-spectrogram statistics)
├── LICENSE                (MPL 2.0)
└── README.md              (This file)

📜 License

Mozilla Public License 2.0 (MPL 2.0)

This model is licensed under the Mozilla Public License 2.0. You can:

✅ Use commercially (no restrictions)
✅ Modify the model
✅ Distribute the model
✅ Use in proprietary software

Requirements:

Include license and copyright notice
State changes if you modify the model
Disclose source for modifications

Full License: LICENSE

🙏 Attribution

Original Work:

Project: Coqui TTS
Model: XTTS-v2
Authors: Coqui TTS Team
License: Mozilla Public License 2.0

This Mirror:

Purpose: Backup for Quantum Sync project
Maintained by: [Your Name/Organization]
Original Source: https://huggingface.co/coqui/XTTS-v2

All credit goes to the original Coqui TTS team. This is simply a mirror for backup and convenience.

📚 Documentation

Quantum Sync Documentation:

Original Documentation:

Coqui TTS GitHub
XTTS-v2 Paper (if available)

🔗 Links

This Mirror: https://huggingface.co/useclaude/quantum-sync-xtts-v2
Original Model: https://huggingface.co/coqui/XTTS-v2
Quantum Sync Project: https://github.com/Useforclaude/quantum-sync-v5
TTS Library: https://github.com/coqui-ai/TTS

⚠️ Disclaimer

This is an unofficial mirror maintained for backup purposes. For the latest version and official support, please refer to the original model and Coqui TTS repository.

📊 Model Card

Model Description

XTTS-v2 is a state-of-the-art zero-shot multi-lingual text-to-speech model that can clone voices from short audio samples (6-30 seconds).

Key Features:

Zero-shot voice cloning
Multi-lingual support (13 languages)
High-quality natural speech
No fine-tuning required
Commercial use allowed

Intended Use

Primary Use Cases:

Voice cloning for content creation
Multi-lingual speech synthesis
Accessibility applications
Audiobook narration
Video dubbing

Out-of-Scope Use:

Impersonation without consent
Generating misleading content
Illegal activities

Training Data

XTTS-v2 was trained on diverse multi-lingual speech data. For details, see the original model card.

Performance

See Performance section above for detailed benchmarks from Quantum Sync project.

Ethical Considerations

Voice Cloning Ethics:

Always obtain consent before cloning someone's voice
Clearly label AI-generated content
Do not use for impersonation or fraud
Follow local regulations on synthetic media

Limitations

May not perfectly preserve all voice characteristics
Quality varies with reference audio quality
Requires GPU for reasonable speed
~6-8 GB VRAM recommended
Some languages may have better quality than others

🛠️ Technical Specifications

Model Type: Autoregressive Transformer-based TTS

Framework: PyTorch

Input: Text + Reference Audio (6-30 sec WAV)

Output: 24kHz WAV audio

Inference Time: ~3-5 seconds per segment (GPU)

Hardware Requirements:

GPU: NVIDIA with CUDA support
VRAM: 6-8 GB recommended
RAM: 16 GB
Disk: ~2 GB for model

Software Requirements:

Python 3.9+
PyTorch 2.0+
TTS library
CUDA 11.8+ (for GPU)

📞 Support

For this mirror:

Issues: Quantum Sync GitHub Issues

For original model:

Issues: Coqui TTS GitHub Issues

Last Updated: 2025-10-13

Mirror Version: 1.0

Model Version: XTTS-v2 (Latest as of upload date)