RVC-Boss/GPT-SoVITS

What it solves

GPT-SoVITS is designed to create highly realistic text-to-speech (TTS) and voice conversion models with minimal data. It solves the problem of needing massive datasets to clone a specific voice, allowing users to generate speech that maintains the timbre and emotional expression of a target speaker using only a few seconds or minutes of audio.

How it works

The project combines GPT-style autoregressive modeling with SoVITS for voice synthesis. It offers two primary modes of operation:

• Zero-shot TTS: Generates speech using a 5-second vocal sample as a reference without any training.
• Few-shot TTS: Fine-tunes a model using approximately 1 minute of training data to significantly increase voice similarity and realism.

It includes a comprehensive WebUI that handles the entire pipeline: separating vocals from background music (via UVR5), slicing audio into chunks, performing automatic speech recognition (ASR) for labeling, and finally training and inference.

Who it’s for

This tool is for content creators, developers, and AI enthusiasts who want to clone voices for applications like virtual assistants, game characters, or accessibility tools, especially those who need high-quality results with very limited source audio.

Highlights

• Cross-lingual Support: Can perform inference in English, Japanese, Korean, Cantonese, and Chinese, even if the training data was in a different language.
• Integrated Toolset: Built-in tools for voice accompaniment separation, automatic dataset segmentation, and multilingual ASR (Fun-ASR, SenseVoice).
• Multiple Model Versions: Offers various versions (v1 through v4 and v2Pro) optimized for different needs, such as higher audio fidelity (48k output in v4) or better stability and emotional expression (v3).
• High Inference Speed: Optimized for fast generation, with specific versions available for CPU-optimized inference.