EaseVoice Trainer
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Easevoice Trainer
Overview :
EaseVoice Trainer is a backend project designed to simplify and enhance the speech synthesis and conversion training process. This project is an improvement based on GPT-SoVITS, focusing on user experience and system maintainability. Its design philosophy differs from the original project, aiming to provide a more modular and customizable solution suitable for various scenarios, from small-scale experiments to large-scale production. This tool can help developers and researchers conduct speech synthesis and conversion research and development more efficiently.
Target Users :
This product is suitable for developers, researchers, and users interested in speech technology. EaseVoice Trainer provides a simple and user-friendly interface and functions, enabling users to quickly get started with speech synthesis and conversion projects. It is suitable for various fields, including education and research.
Total Visits: 485.5M
Top Region: US(19.34%)
Website Views : 38.1K
Use Cases
Educational institutions use this tool for teaching and research in speech synthesis courses.
Developers utilize EaseVoice Trainer to add speech interaction functionality to applications.
Researchers use this tool to optimize and evaluate speech models.
Features
User-friendly design: Simplified workflow and intuitive configuration, easy to deploy and manage.
Stability: Provides consistent and reliable performance during cloning and training.
Training Observability: Provides monitoring tools to clearly display the progress and performance indicators of cloning and model training.
Clear architecture: Front-end and back-end separation, improving modularity and maintainability.
RESTful API: Facilitates integration with other services and applications.
Scalability: Suitable for small-scale experiments and large-scale production.
Tensorboard Integration: For real-time monitoring and visualization of training progress.
How to Use
Ensure Python 3.9 or higher and uv are installed.
Download the pre-trained model and place it in the models directory.
Enter the project directory via the command line and execute 'uv run' to start the server.
If using Docker, build the Docker image first.
Run the Docker container and access http://localhost:8000 for operation.
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