This repository provides comprehensive documentation and tutorials for deploying and using the So-VITS-SVC (Singing Voice Conversion) project locally. It targets users who want to perform AI-powered voice conversion, offering detailed guides for environment setup, data preparation, model training, and inference. The primary benefit is a structured approach to a complex process, making advanced voice conversion accessible.

How It Works

So-VITS-SVC utilizes a Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) architecture, specifically VITS, adapted for singing voice conversion. It employs speech encoders like HuBERT or Whisper-PPG to extract content and timbre information, a pitch predictor (F0) for vocal melody, and a diffusion model for enhanced audio quality. This combination allows for high-fidelity voice conversion by separating vocal content from speaker identity and style.

Quick Start & Requirements

• Install/Run: Clone the repository and follow the detailed environment setup. A Colab notebook is provided for cloud-based setup: sovits4_for_colab.ipynb.
• Prerequisites: NVIDIA GPU (>= 6GB VRAM recommended for training), Python 3.8.9, PyTorch (versions 11.7 or 11.8 recommended, >12.0 may be incompatible), FFmpeg. Virtual memory should be at least 30GB.
• Setup Time: Environment setup can take 30-60 minutes depending on download speeds and system configuration. Training requires significant time and GPU resources.

Highlighted Details

• Supports multiple speech encoders (HuBERT, Whisper-PPG, ContentVec) with varying trade-offs in articulation and VRAM usage.
• Offers various F0 predictors (RMVPE recommended for accuracy) and optional enhancements like shallow diffusion and clustering for timbre control.
• Detailed troubleshooting guide for common errors during installation, preprocessing, training, and inference.
• Includes instructions for both command-line and WebUI inference.

Maintenance & Community

• The project is based on the svc-develop-team/so-vits-svc repository.
• Links to Bilibili and GitHub for the author are provided.
• Community support is primarily through GitHub issues.

Licensing & Compatibility

• The underlying so-vits-svc project is typically under a permissive license (e.g., MIT), but users must comply with specific legal regulations mentioned in the README regarding voice and likeness.
• Commercial use requires careful attention to dataset authorization and usage terms of any synthesized audio.

Limitations & Caveats

• Training requires a significant amount of clean, high-quality vocal data (at least 30 minutes recommended).
• The documentation notes that Python versions higher than 3.8.9 might work but 3.8.9 is recommended for stability.
• GPU is mandatory for training; CPU inference is possible but slower.
• The README strongly advises against using unauthorized datasets for training due to legal implications.