ChatTTS
An open source project for text-to-speech conversion.
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Introduce：
ChatTTS is an open source text-to-speech (TTS) model that allows users to convert text to speech. The model is primarily intended for academic research and educational purposes and is not intended for commercial or legal use. It uses deep learning technology to generate natural and smooth speech output, which is suitable for those who research and develop speech synthesis technology.
Stakeholders:
The ChatTTS model is suitable for use by speech technology researchers, developers, and educational institutions. Researchers can explore and improve speech synthesis technology through this model, developers can use it to quickly develop speech interaction applications, and educational institutions can use it to teach speech synthesis related courses.
Usage Scenario Examples:
- The researchers used the ChatTTS model to study speech synthesis technology.
- Developers use ChatTTS to create intelligent assistants or voice-interactive applications.
- Educational institutions use ChatTTS in their classrooms to teach the principles and applications of speech synthesis.
The features of the tool:
- Supports text-to-speech conversion, converting input text to natural speech.
- Use deep learning technology to provide high-quality speech synthesis effects.
- Suitable for academic research and education, not for commercial use.
- Provides code samples for researchers and developers to get started quickly.
- Supports custom model training to suit different speech synthesis needs.
- Provides detailed documentation and examples to help users understand and apply the model.
Steps for Use:
- Step 1: Visit ChatTTS ‘GitHub page to get basic information about the project.
- Step 2: Read the project’s README documentation for installation and usage guidelines.
- Step 3: Install the required dependency libraries and environment according to the guide.
- Step 4: Download and load the ChatTTS model.
- Step 5: Write the code, enter the text and call the model for speech synthesis.
- Step 6: Run the code, listen to the generated voice output, and debug as needed.
- Step 7: Based on the project documentation, explore the advanced features of the model, such as custom training.
Tool’s Tabs: Text to speech, deep learning
