ChatTTS is a voice generation model optimized for conversational scenarios, particularly for dialogue tasks in large language model (LLM) assistants and applications like conversational audio and video introductions. Supporting both Chinese and English, it uses approximately 100,000 hours of training data. The project team plans to open-source a base model, enabling further community development. It offers an easy-to-use interface requiring only text input to generate corresponding voice files.
• multi-language support
• ease of use
• control and security
• open source plans
• dialog task compatibility
• large data training
Developers can integrate ChatTTS using the provided API and SDKs. The process involves initializing the model, loading pre-trained models, and using text-to-speech functions. Documentation and examples are available.
ChatTTS is trained on approximately 100,000 hours of Chinese and English data. This extensive dataset helps the model learn to produce high-quality, natural speech.
Yes, ChatTTS supports both Chinese and English.
ChatTTS is optimized for dialogue, supporting both Chinese and English, and trained on a vast dataset for high-quality speech. A base model will be open-sourced.
ChatTTS uses approximately 100,000 hours of Chinese and English data, including diverse spoken content for natural, high-quality speech generation.
Yes, the project team plans to release an open-source version trained on 40,000 hours of data.
ChatTTS uses a large and diverse dataset (100,000 hours of Chinese and English speech) and advanced machine learning techniques to ensure natural speech.
Yes, developers can fine-tune the model with their own datasets for specific applications or voices.
ChatTTS is compatible with web applications, mobile apps, desktop software, and embedded systems. SDKs and APIs support multiple programming languages.
Speech quality may vary based on text complexity and length. Real-time high-quality speech may require significant processing power.
Users can provide feedback through support channels (email, portal, forum), or contribute to the GitHub repository (if open-source).
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