ChatTTS-Text-to-speech
ChatTTS is an open source text-to-speech (TTS) model that allows users to convert text to speech. This model is primarily intended for academic research and educational purposes and is not suitable for commercial or legal use. It uses deep learning technology to generate natural and fluent speech output, and is suitable for people who research and develop speech synthesis technology.
Target group:
“The ChatTTS model is suitable for speech technology researchers, developers, and educational institutions. Researchers can use this model to explore and improve speech synthesis technology, developers can use it to quickly develop speech interaction applications, and educational institutions can use it to teach speech synthesis-related courses.”
Example usage scenarios:
Researchers use the ChatTTS model to study speech synthesis technology.
Developers use ChatTTS to develop intelligent assistants or voice interaction applications.
Educational institutions use ChatTTS in the classroom to teach the principles and applications of speech synthesis.
feature of product:
Supports text-to-speech conversion, converting input text into natural speech.
Use deep learning technology to provide high-quality speech synthesis effects.
For academic research and education, not for commercial use.
Provide code examples to help researchers and developers get started quickly.
Supports custom model training to meet different speech synthesis needs.
Provide detailed documentation and examples to help users understand and apply the model.
Usage Tutorial:
Step 1: Visit the GitHub page of ChatTTS to learn basic project information.
Step 2: Read the project’s README document to get installation and usage instructions.
Step 3: Install the required dependent libraries and environment according to the guide.
Step 4: Download and load the ChatTTS model.
Step 5: Write code, input text and call the model for speech synthesis.
Step 6: Run the code, listen to the generated speech output, and debug as needed.
Step 7: Explore the advanced features of the model, such as custom training, according to the project documentation.
