Retrieval-based-Voice-Conversion (RVC) is an open-source real-time voice conversion project hosted on GitHub. According to available information, the project is still in the development stage (marked as "in preparation..." and "Currently under development..."), and is provided as the rvc library and API.
Purpose: Mainly used for real-time voice conversion, that is, changing the timbre or style of input speech while preserving the content.
Main capabilities: The information shows that it provides the models required for inference and training, and supports managing model paths through configuration files and environment variables. It supports Docker deployment, can build and run a server, and mounts weights, indices, and audio directories.
Usage: First install rvc via pip. Then create a project directory, in which the assets folder stores the models required for inference and training, and the result folder stores training results. During installation, the assets folder and .env file are generated (if the directory is empty or there is no assets folder). Users can edit the .env file to configure downloaded models or change settings. The method for downloading models is not fully provided in the information. Finally, specify the model location in the env file and start the server. It can be built and run via a script, or manually using Docker commands: build the image docker build -t "rvc" ., run the container docker run -it -p 8000:8000 -v "${PWD}/assets/weights:/weights:ro" -v "${PWD}/assets/indices:/indices:ro" -v "${PWD}/assets/audios:/audios:ro" "rvc", assuming the weights, indices, and input audio are stored under assets in the current directory.
Applicable scenarios: Suitable for developers and researchers who need voice conversion, and can be used for scenarios such as voice changing and speech synthesis.
Limitations: The project is still under development, and detailed information such as specific features, pricing model, supported platforms, and performance metrics has not yet been provided. A certain technical foundation is required before use, and users need to prepare models and configure the environment themselves.
