GPT-SoVITS: Open-Source Voice Conversion Framework 134

GPT-SoVITS is an open-source Python RAG framework for voice cloning and text-to-speech conversion. With just 1 minute of voice data, you can train your own TTS model and perfectly clone your voice! GPT-SoVITS is fully compatible with Chinese and is currently one of the models with the best Chinese language support.

GitHub release page: https://github.com/RVC-Boss/GPT-SoVITS

Main features of GPT-SoVITS:

1. Zero-shot TTS: Input a 5-second voice sample to experience instant text-to-speech conversion.

2. Few-shot TTS: Only 1 minute of training data is needed to fine-tune the model, improving voice similarity and realism. The cloned voice will be much closer to the original, sounding more natural.

Cross-language support: Perform inference using languages different from the training dataset. Currently supports English, Japanese, and Chinese.

3. WebUI tools: Integrated tools include voice accompaniment separation, automatic training set segmentation, Chinese ASR, and text annotation, making it simple for beginners to create training datasets and GPT/SoVITS models.

4. Compatible with different operating systems: The project can be installed and run on various operating systems, including Windows.

5. Pre-trained models: The project provides some pre-trained models that you can download and use directly.

How to use GPT-SoVITS?

If you are a Windows user (tested with win>=10), you can install it directly via the pre-packaged archive. Just download the pre-packaged file, extract it, and double-click go-webui.bat to launch the GPT-SoVITS-WebUI.

GPT-SoVITS is a powerful voice cloning and text-to-speech conversion tool. It supports zero-shot and few-shot voice cloning, as well as cross-lingual text-to-speech conversion. It offers the advantages of requiring less data, delivering high quality, great flexibility, and ease of use, and can be applied to a variety of scenarios. If you are interested in GPT-SoVITS, you can check the project's open-source website for detailed steps. There are also corresponding tutorials available on Bilibili.