ChatTTS

ChatTTS - Text-to-Speech for Conversational Scenarios Introduction

ChatTTS stands as a pioneering voice generation model tailored specifically for conversational scenarios. It's optimized for dialog tasks typically assigned to large language model (LLM) assistants and is also suitable for applications such as conversational audio and video introductions. Supporting both Chinese and English, ChatTTS is trained on an extensive dataset, ensuring high quality and naturalness in speech synthesis.

ChatTTS Features

• Multi-language Support: ChatTTS caters to a global audience by supporting both English and Chinese, facilitating seamless communication across language barriers.
• Large Data Training: With approximately 10 million hours of Chinese and English data used for training, ChatTTS offers high-quality, natural-sounding voice synthesis.
• Dialog Task Compatibility: Designed for dialog tasks, ChatTTS enhances the interaction experience in various applications and services with its fluid conversational capabilities.
• Open Source Plans: The project team intends to open source a trained base model, fostering innovation and development within the academic and developer communities.
• Control and Security: ChatTTS emphasizes model controllability, watermark integration, and safe LLM integration, ensuring reliability and security.
• Ease of Use: Requiring only text information as input, ChatTTS simplifies voice synthesis needs, making it accessible and convenient for users.

How to Use ChatTTS

Getting started with ChatTTS involves a few simple steps:

1. Download from GitHub: Clone the repository from GitHub with git clone https://github.com/2noise/ChatTTS.
2. Install Dependencies: Ensure you have the necessary packages, such as torch and ChatTTS, installed via pip.
3. Import Required Libraries: Use libraries like torch, ChatTTS, and Audio from IPython.display for your script.
4. Initialize ChatTTS: Create an instance of the ChatTTS class and load the pre-trained models.
5. Prepare Your Text: Define the text you wish to convert to speech.
6. Generate Speech: Utilize the infer method to generate speech from the text, enabling the decoder for better quality.
7. Play the Audio: Use the Audio class to play the generated audio, setting the sample rate to 24,000 Hz.

Frequently Asked Questions

How can developers integrate ChatTTS into their applications?

Developers can integrate ChatTTS by utilizing the provided API and SDKs, following documentation and examples for a smooth integration process.

What applications is ChatTTS suitable for?

ChatTTS is versatile, suitable for conversational tasks in LLM assistants, dialogue speech generation, video introductions, educational content, and any service requiring text-to-speech functionality.

What makes ChatTTS unique?

ChatTTS is uniquely optimized for conversational scenarios, supports multiple languages, and is trained on a vast dataset, ensuring natural speech synthesis. Its open-source plans further set it apart, promoting community-driven development.

Is ChatTTS open-source?

The project team plans to release an open-source version of ChatTTS trained on 40,000 hours of data, enabling further exploration and innovation in text-to-speech technology.

Can ChatTTS be customized for specific applications or voices?

Yes, developers can fine-tune ChatTTS with their datasets for specific use cases or to develop unique voice profiles, offering flexibility and adaptability across different applications.

ChatTTS Usage Scenarios

• Conversational AI Assistants: Enhance the user experience with natural-sounding responses in chatbots and virtual assistants.
• Educational Content: Generate speech for educational materials, making learning more accessible and engaging.
• Video Content Creation: Use ChatTTS for creating conversational audio tracks for video introductions or tutorials.
• Multilingual Services: Leverage ChatTTS's multi-language support to offer services across different languages, breaking down communication barriers.