GPT-SoVITS Project Detailed Introduction

Project Overview

GPT-SoVITS is a revolutionary text-to-speech (TTS) and voice cloning project developed and maintained by the RVC-Boss team. The core feature of this project is its ability to train high-quality TTS models using extremely limited voice data (as little as 1 minute), achieving true few-shot voice cloning technology.

The project is based on the GPT and SoVITS technology architecture, combining the powerful expressive capabilities of large language models with high-quality speech synthesis technology, providing users with a complete voice cloning solution.

Core Features and Characteristics

1. Zero-Shot and Few-Shot TTS

• Zero-Shot TTS: Realize instant text-to-speech conversion with only 5 seconds of voice samples.
• Few-Shot TTS: Fine-tune the model using 1 minute of training data to significantly improve voice similarity and realism.
• Fast Training: Significantly reduces training time and data requirements compared to traditional TTS models.

2. Cross-Lingual Support

• Supports multi-lingual inference for Chinese, English, Japanese, Korean, and Cantonese.
• Able to perform inference between different languages, even if the training data is different from the target language.
• Optimized text front-end processing to improve the synthesis quality of each language.

3. Integrated WebUI Tool

• Vocal-Instrumental Separation: Uses UVR5 technology to separate vocals and background music in audio.
• Automatic Training Set Segmentation: Intelligently segments long audio into short segments suitable for training.
• Chinese ASR: Integrated Chinese Automatic Speech Recognition function.
• Text Annotation: Assists users in creating high-quality training datasets.
• One-Click Operation: Simplifies complex model training processes, suitable for beginners.

4. Multi-Version Support

The project provides multiple versions to suit different needs:

V1 Version

• Complete basic functions
• Suitable for beginners to get started

V2 Version

• Supports Korean and Cantonese
• Optimized text front-end processing
• Pre-trained model expanded from 2k hours to 5k hours
• Improved synthesis quality of low-quality reference audio

V3 Version

• Higher timbre similarity
• More stable GPT model, reducing repetition and omissions
• Supports richer emotional expression
• Native output of 24k audio

V4 Version

• Fixed the metallic sound artifact issue in V3 version
• Native output of 48k audio to prevent audio blurring
• Considered a direct replacement for V3

V2Pro Version

• Hardware cost and speed comparable to V2
• Performance surpasses V4 version
• Suitable for application scenarios with high performance requirements

5. Multi-Platform Support

• Windows: Provides an integrated installation package that can be launched by double-clicking.
• Linux: Supports conda environment installation.
• macOS: Supports Apple Silicon chips.
• Docker: Provides complete Docker image support.
• Cloud Deployment: Supports AutoDL cloud Docker experience.

6. Rich Model Ecosystem

• Pre-trained models cover various languages and scenarios.
• Supports model mixing and custom training.
• Provides audio super-resolution models.
• Continuously updated model library.

Technical Architecture

Core Components

1. GPT Module: Responsible for text understanding and speech feature generation.
2. SoVITS Module: Responsible for high-quality speech synthesis.
3. WebUI Interface: Provides a user-friendly operating interface.
4. Data Processing Tools: Includes audio processing, ASR, segmentation, and other functions.

Supported Audio Formats

• Input: Supports various common audio formats.
• Output: 24k/48k high-quality audio.
• Processing: Supports real-time processing and batch processing.

Application Scenarios

1. Content Creation

• Audiobook production
• Video dubbing
• Podcast programs
• Educational content

2. Commercial Applications

• Customer service voice system
• Advertising voice-over
• Brand voice customization
• Multi-language localization

3. Entertainment Applications

• Game character voice-over
• Virtual streamer
• Voice assistant
• Creative audio production

4. Research and Development

• Speech synthesis research
• Multi-language processing
• Acoustic model optimization
• AI voice technology verification

Project Advantages

1. Technical Advantages

• High Data Efficiency: Requires a minimum of only 1 minute of training data.
• Excellent Quality: Synthesis effect close to human voice.
• Fast Speed: Fast training and inference.
• Strong Stability: Reduces repetition and omission phenomena.

2. Ease of Use Advantages

• User-Friendly Interface: Integrated WebUI operation is simple.
• Complete Documentation: Provides detailed user guides.
• Community Support: Active open-source community.
• Continuous Updates: Regularly releases new features and improvements.

3. Open Source Advantages

• MIT License: Open source and free to use.
• Transparent Code: Can be freely modified and customized.
• Community Contributions: Accepts community contributions and feedback.
• Technology Sharing: Promotes technical exchange and development.

System Requirements

Hardware Requirements

• GPU: NVIDIA graphics card supporting CUDA 12.4/12.8 (recommended).
• CPU: Supports CPU operation (lower performance).
• Memory: Recommended 16GB or more RAM.
• Storage: At least 10GB of available space.

Software Environment

• Python: 3.9-3.11 version.
• PyTorch: 2.5.1 or higher version.
• CUDA: 12.4 or 12.8 version.
• FFmpeg: Audio processing dependency.

Installation and Usage

Quick Installation (Windows)

1. Download the integrated installation package.
2. Unzip and double-click go-webui.bat.
3. Wait for the startup to complete before using.

Development Environment Installation

# Create conda environment
conda create -n GPTSoVits python=3.10
conda activate GPTSoVits

# Install dependencies
bash install.sh --device <CU126|CU128|ROCM|CPU> --source <HF|HF-Mirror|ModelScope>

Docker Deployment

# Use Docker Compose
docker compose run --service-ports GPT-SoVITS-CU128

Summary

The GPT-SoVITS project represents a significant breakthrough in voice cloning technology. It democratizes high-quality speech synthesis technology, allowing ordinary users to easily create personalized voice models. The open-source nature of the project promotes rapid technological development and widespread application, bringing new possibilities to the field of voice AI.