XTTS-v1
| Property | Value |
|---|---|
| License | Coqui Public Model License |
| Author | Coqui |
| Community Stats | 366 likes, 2860 downloads |
| Tags | Text-to-Speech, Coqui |
What is XTTS-v1?
XTTS-v1 is an advanced text-to-speech model built on Tortoise that revolutionizes voice cloning technology. It enables cross-language voice cloning and multilingual speech generation using just a 6-second audio sample, making it highly efficient and practical for various applications. The model operates at a 24kHz sampling rate, ensuring high-quality audio output.
Implementation Details
The model is implemented through the Coqui TTS framework and supports both inference and fine-tuning capabilities. It can be utilized through the TTS API, command-line interface, or directly through Python code. The model includes sophisticated features for voice synthesis and clone transfer, making it accessible for both developers and researchers.
- Supports 14 languages including English, Spanish, French, German, Italian, Portuguese, Polish, Turkish, Russian, Dutch, Czech, Arabic, Chinese, and Japanese
- Integrates with Coqui Studio and Coqui API platforms
- Implements voice cloning with minimal audio input requirements
- Features emotion and style transfer capabilities
Core Capabilities
- Cross-language voice cloning with preservation of speaker identity
- Multi-lingual speech generation with natural-sounding output
- High-quality 24kHz audio synthesis
- Flexible integration options through multiple interfaces
Frequently Asked Questions
Q: What makes this model unique?
XTTS-v1's ability to clone voices across languages with just a 6-second audio sample sets it apart from traditional TTS models. Its multi-lingual capabilities and emotion transfer features make it particularly versatile for various applications.
Q: What are the recommended use cases?
The model is ideal for applications requiring voice cloning across languages, such as localization of content, personal AI assistants, educational tools, and content creation platforms. It's particularly useful when working with multiple languages or when voice preservation across languages is crucial.
