so-vits-svc-fork

SoftVC VITS Singing Voice Conversion Fork is a software tool designed for singing voice conversion. It allows users to transform vocal recordings from one singer to another, offering real-time conversion capabilities and a user-friendly interface for both novice and experienced users.

Key features:
- Real-time Voice Conversion: Enables live transformation of vocal input.
- QuickVC Integration: Incorporates QuickVC technology for enhanced voice conversion quality.
- User Interface Options: Provides both a graphical user interface and a command-line interface for flexible usage.
- Improved Pitch Estimation: Utilizes CREPE for more accurate pitch detection and analysis.
- Accelerated Training: Offers training speeds approximately twice as fast as the original repository.
- Simplified Installation: Supports installation via pip or pipx with automatic model and dependency downloads.
- Code Quality: Implements code formatting using black, isort, and autoflake for improved readability.
- Audio Preprocessing Tools: Includes utilities for resampling and speech diarization of audio files.
- Versatile Model Handling: Supports both training new models and inference with existing ones.
- Cloud Compatibility: Can be utilized on cloud platforms like Google Colab and Paperspace for users without high-performance GPUs.
- Local Processing: Operates on local systems with GPUs having at least 4 GB of VRAM.
- Model Exportation: Supports exporting models to ONNX format (functionality currently limited).
- Comprehensive Documentation: Provides detailed guides and help commands for user assistance.

How it works:
1. Install the package using pip or pipx, which automatically sets up required models and dependencies.
2. Preprocess audio files using commands such as 'pre-resample', 'pre-config', and 'pre-hubert'.
3. Train models using the 'train' command.
4. Perform voice conversion using the 'infer' command or the graphical interface.

Integrations:
PyTorch, Hugging Face, CIVITAI, Pyannote.audio

Use of AI:
The software employs generative AI techniques for real-time voice conversion. It uses CREPE for pitch estimation and leverages PyTorch for model training, incorporating features like QuickVC.

AI foundation model:
The project is built on PyTorch, a widely-used deep learning framework.

Target users:
- Developers working on singing voice conversion projects
- Researchers studying voice conversion and analysis
- Musicians experimenting with vocal transformations

How to access:
The software is available as an open-source project on GitHub. It can be installed and used via command-line interface on local systems or compatible cloud platforms.