Advanced RVC Inference

Advanced RVC Inference presents itself as a state-of-the-art web UI crafted to streamline rapid and effortless inference. This comprehensive toolset encompasses a model downloader, a voice splitter, and the added efficiency of batch inference.

Features

• Voice conversion with multiple pitch extraction methods
• Model training capabilities
• Text-to-speech integration
• Audio separation tools
• Web UI interface with Gradio

Table of Contents

• Installation
• Quick Start Guide
• Using the Web UI
• Command Line Usage
• Development Setup
• API Reference
• Troubleshooting
• Contributing
• License
• Support

Installation

# Clone the repository
git clone https://github.com/ArkanDash/Advanced-RVC-Inference.git
cd Advanced-RVC-Inference

# Install in development mode
pip install -e .

Quick Start Guide

1. Launch the web interface:

   python -m advanced_rvc_inference.app

2. Access the UI in your browser at the displayed URL (typically http://127.0.0.1:7860)

Using the Web UI

The web interface provides an intuitive way to use all features:

1. Voice Conversion: Upload your source audio and target model
2. Model Training: Upload datasets and configure training parameters
3. Batch Processing: Process multiple files simultaneously
4. Audio Analysis: Analyze audio characteristics and quality

Web UI Features

• Real-time Preview: Listen to results before saving
• Parameter Adjustment: Fine-tune pitch, tone, and other parameters
• Progress Monitoring: Track training and inference progress
• Model Management: Organize and manage your voice models

Development Setup

Prerequisites

• Python 3.10 or higher
• Git
• uv (optional but recommended)

Development Workflow

1. Clone the repository:

   git clone https://github.com/ArkanDash/Advanced-RVC-Inference.git
   cd Advanced-RVC-Inference

2. Install in development mode:

   pip install -e .
   # or with uv:
   uv pip install -e .

3. Run the application:

   python -m advanced_rvc_inference.app

Performance Optimization

• GPU Memory: Monitor GPU usage and adjust batch sizes accordingly
• CPU Usage: Use multiple CPU cores for preprocessing and feature extraction
• Disk Space: Ensure sufficient space for models and temporary files
• Network: Stable internet connection for model downloads

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run tests to ensure everything works
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

Code Standards

• Use 4 spaces for indentation (not tabs)
• Follow PEP 8 style guide
• Write docstrings for public functions
• Include type hints where appropriate
• Add tests for new functionality

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

If you encounter any issues, please open an issue on GitHub.

For questions and discussions, join our community:

• GitHub Discussions