ChatTTS: Revolutionizing Text-to-Speech for Conversations

ChatTTS is a remarkable voice generation model specifically designed for conversational scenarios. It is tailored for the dialogue tasks of large language model (LLM) assistants and various applications such as conversational audio and video introductions.

Key Features of ChatTTS:

• Multi-language Support: It supports both English and Chinese, serving a wide range of users and breaking language barriers.
• Large Data Training: Trained with approximately 100,000 hours of Chinese and English data, ensuring high-quality and natural-sounding voice synthesis.
• Dialog Task Compatibility: Well-suited for handling dialog tasks assigned to large language models, providing a more natural and fluid interaction experience.
• Open Source Plans: The project team intends to open source a trained base model, facilitating further research and development in the community.
• Control and Security: Committed to enhancing the controllability of the model, adding watermarks, and integrating it with LLMs to ensure safety and reliability.
• Ease of Use: Requires only text input to generate corresponding voice files, offering a convenient experience for users with voice synthesis needs.

How to Use ChatTTS: The process of using ChatTTS is straightforward. Users can follow these simple steps:

1. Download the code from GitHub: git clone https://github.com/2noise/ChatTTS.
2. Install the necessary dependencies, including torch and ChatTTS, using pip.
3. Import the required libraries for the script.
4. Initialize the ChatTTS class and load the pre-trained models.
5. Define the text to be converted to speech.
6. Generate speech using the infer method and enable the decoder.
7. Play the generated audio using the Audio class from IPython.display.

Frequently Asked Questions:

• Developers can integrate ChatTTS into their applications using the provided API and SDKs. Detailed documentation and examples are available.
• ChatTTS can be used for various applications, including conversational tasks for large language model assistants, generating dialogue speech, video introductions, educational and training content speech synthesis, and any application or service requiring text-to-speech functionality.
• ChatTTS is trained on approximately 100,000 hours of Chinese and English data. The project team also plans to open-source a base model trained on 40,000 hours of data.
• ChatTTS is unique as it is optimized for dialogue scenarios, supports multiple languages, and plans to release an open-source version.
• A large and diverse dataset of approximately 100,000 hours of Chinese and English speech is used to train ChatTTS.
• Yes, an open-source version of ChatTTS trained on 40,000 hours of data is planned to be available for developers and researchers.
• ChatTTS ensures the naturalness of synthesized speech through extensive training on a large and diverse dataset and the use of advanced machine learning techniques.
• ChatTTS can be customized for specific applications or voices by fine-tuning the model using users' own datasets.
• ChatTTS is designed to be compatible with various platforms and environments, including web applications, mobile apps, desktop software, and embedded systems. The provided SDKs and APIs support multiple programming languages.
• While ChatTTS is powerful, the quality of synthesized speech may vary depending on the input text, and the model's performance can be influenced by computational resources. However, continuous updates are being made to address these limitations.
• Users can provide feedback or report issues through a support system, which may include email support, a dedicated support portal, or a community forum. They can also contribute to the project's GitHub repository if it is open-source.