ChatTTS

ChatTTS

ChatTTS is an open-source text-to-speech model built for conversational scenarios, generating natural Chinese and English speech for LLM assistants and dialogue applications.

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text-to-speechAI API
Last Updated Nov 10, 2026
ChatTTS screenshot

About

ChatTTS is a voice generation model built specifically for conversational text-to-speech. Rather than targeting narration or long-form reading, it is designed to render dialogue naturally, which makes it a fit for the spoken responses of large language model assistants and for conversational audio and video introductions.

The model supports both Chinese and English and was trained on roughly 100,000 hours of speech data in those two languages, which the project credits for the naturalness of its output. The code is distributed through GitHub under the 2noise/ChatTTS repository, and the project team has stated plans to release an open-source base model so academic researchers and developers can study and extend the technology. The official site also hosts a free online demo where visitors can hear the model generate speech from sample text without installing anything.

Key Features

Bilingual support: ChatTTS generates speech in both Chinese and English, so a single model can serve multilingual applications without switching tools.
Dialogue-focused synthesis: The model is optimized for conversational text rather than formal narration, producing speech suited to back-and-forth exchanges and assistant replies.
Large-scale training data: Training covered approximately 100,000 hours of Chinese and English audio, which the team identifies as the basis of its voice quality.
Simple input model: Users supply only text; the model returns a corresponding audio file, keeping the workflow straightforward.
Open-source plans: The team intends to release a trained base model for the research and developer community.
Control and safety work: Ongoing efforts include improving controllability, adding watermarks to generated audio, and integrating the model with LLMs.
Programmatic access: Developers clone the repository, install the package with pip, and call a small Python API to generate speech.

Pricing & Fees

  • Pricing: Not publicly listed on the pages we reviewed; check the official site for current plans.
  • Free online demo: The official site offers a browser demo with example texts, though it can display a "service is busy" message under load.
  • Self-hosted code: The full pipeline is available on GitHub, so developers can run it locally with PyTorch.
  • Audio output: Example scripts play generated audio at a 24,000 Hz sample rate.

Who It's For

ChatTTS is aimed primarily at developers and researchers building conversational applications, especially those adding spoken output to large language model assistants. It also suits teams producing dialogue-heavy audio content, such as video introductions, educational and training narration, and any service that needs text converted into natural Chinese or English speech. Because the code lives on GitHub and the team plans an open base model, academics studying speech synthesis can inspect and build on it directly rather than treating it as a closed black-box service.

FAQ

What is ChatTTS?ChatTTS is a text-to-speech model designed for conversational scenarios, trained on approximately 100,000 hours of Chinese and English speech data.

Is ChatTTS free to use?The code is publicly available on GitHub and the official site offers a free online demo; commercial pricing is not publicly listed on the pages reviewed, so check the official site for current terms.

Which languages does ChatTTS support?It supports Chinese and English, generating speech in both languages from the same model.

How do developers use ChatTTS?They clone the GitHub repository, install the package with pip alongside PyTorch, initialize the model, and call the infer method on input text to produce audio.

What sets ChatTTS apart from other text-to-speech models?It is specifically optimized for dialogue tasks rather than general narration, and the team is adding watermarks and controllability features with LLM integration in mind.