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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.

Colab UI

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

# 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:

Credits

Repo Owner
Vietnamese-RVC PhamHuynhAnh16
Applio IAHispano

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  • Python 96.6%
  • Jupyter Notebook 3.0%
  • Batchfile 0.4%