README.md

metadata

title: Rvc Effects
sdk: gradio
emoji: 🔥
colorFrom: indigo
colorTo: purple

AICoverGen

An autonomous pipeline to create covers with any RVC v2 trained AI voice from YouTube videos or a local audio file. For developers who may want to add a singing functionality into their AI assistant/chatbot/vtuber, or for people who want to hear their favourite characters sing their favourite song.

AICoverGen Enhanced

AI-Powered Voice Cover Generation with Advanced Audio Enhancement

AICoverGen Enhanced is a powerful tool for creating AI voice covers with professional-grade audio enhancement features. This enhanced version includes advanced AI audio processing, EQ controls, dynamic range compression, and much more!

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✨ New Features

Advanced Audio Enhancement

• AI Noise Reduction – Remove background noise and artifacts
• Professional EQ – 5 EQ types: Balanced, Vocal Boost, Bass Boost, Treble Boost, Flat
• Dynamic Range Compression – Improve loudness and consistency
• Harmonic Enhancement – Add richness and warmth to vocals
• Stereo Widening – Enhance spatial imaging for stereo tracks
• Reverb Control – Add depth and professional polish
• Gain Control – Fine-tune volume (-20 to +20 dB)

Enhancement Types

• Full – Balanced enhancement with all features
• Light – Subtle improvements for natural sound
• Aggressive – Maximum enhancement for impact
• Custom – Use your specific settings

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🖥️ System Requirements

Minimum Requirements

• OS: Windows 10/11, Linux, or macOS
• Python: 3.9+ (3.10+ recommended)
• RAM: 8GB minimum, 16GB recommended
• Storage: 10GB free space
• GPU: NVIDIA GPU with CUDA support (recommended)

Recommended Setup

• OS: Windows 11 or Ubuntu 20.04+
• Python: 3.10 or 3.11
• RAM: 16GB or more
• GPU: NVIDIA RTX 3060 or better
• CUDA: 11.8 or 12.0+
• cuDNN: 8.6 or 9.0+

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⚙️ Installation Guide

Step 1: Clone the Repository

git clone https://github.com/SociallyIneptWeeb/AICoverGen.git
cd AICoverGen

Step 2: Create Virtual Environment

# Windows
python -m venv AICoverGen
AICoverGen\Scripts\activate

# Linux/macOS
python3 -m venv AICoverGen
source AICoverGen/bin/activate

Step 3: Install Dependencies

Option A: Automatic Installation (Recommended)

pip install -r requirements.txt

Option B: Manual Installation

# Core dependencies
pip install gradio==3.50.2 librosa==0.9.1 numpy==1.23.5 scipy==1.11.1 soundfile==0.12.1
pip install pedalboard==0.7.7 pydub==0.25.1 fairseq==0.12.2 faiss-cpu==1.7.3 pyworld==0.3.4
pip install praat-parselmouth>=0.4.2 ffmpeg-python>=0.2.0 tqdm==4.65.0 yt-dlp>=2025.9.23 sox==1.4.1

# AI Audio Enhancement dependencies
pip install noisereduce==3.0.3 scikit-learn==1.6.1

# PyTorch with CUDA support
pip install torch==2.0.1+cu118 --find-links https://download.pytorch.org/whl/torch_stable.html
pip install torchcrepe==0.0.20

# ONNX Runtime with CUDA support
pip install onnxruntime-gpu==1.18.0

Step 4: Download Models

python src/download_models.py

Step 5: Verify Installation

python src/audio_enhancer.py

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🚀 Usage

Quick Start

python src/webui.py

Then open your browser and go to: http://127.0.0.1:7860

1. Upload a song (YouTube URL or audio file)
2. Select a voice model from the dropdown
3. Configure audio enhancement:
   • Expand "AI Audio Enhancement" section
   • Choose enhancement type (Full/Light/Aggressive/Custom)
   • Adjust EQ type (Balanced/Vocal Boost/Bass Boost/Treble Boost/Flat)
   • Set noise reduction strength (0–100%)
   • Adjust gain (-20 to +20 dB)
   • Set compression ratio (1–10)
   • Add reverb amount (0–100%)
4. Click Generate and enjoy your enhanced AI cover!

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🛠️ Troubleshooting

CUDA Not Detected

# Check CUDA installation
nvidia-smi

# Verify PyTorch CUDA support
python -c "import torch; print(torch.cuda.is_available())"

# Check ONNX Runtime CUDA
python -c "import onnxruntime as ort; print('CUDA' in ort.get_available_providers())"

Audio Enhancement Errors

# Test audio enhancer
python src/audio_enhancer.py

# Check dependencies
pip list | grep -E "(noisereduce|scikit-learn|pedalboard)"

Memory Issues

• Reduce batch size in settings
• Use CPU-only mode for ONNX Runtime
• Close other applications to free RAM

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📂 Project Structure

AICoverGen_Enhanced/
├── src/
│   ├── webui.py             # Main web interface
│   ├── main.py              # Core pipeline with audio enhancement
│   ├── audio_enhancer.py    # AI audio enhancement module
│   ├── rvc.py               # RVC voice conversion
│   ├── mdx.py               # Audio separation
├── rvc_models/              # Voice models
├── mdxnet_models/           # Audio separation models
├── song_output/             # Generated covers
├── requirements.txt         # Dependencies
├── README_Enhanced.md       # This file

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🎚️ Audio Enhancement Features

AI Noise Reduction

• Uses ML to identify and remove background noise
• Preserves vocal clarity while eliminating artifacts
• Adjustable strength (0–100%)

Professional EQ

• Balanced: Gentle mid boost for clarity
• Vocal Boost: Emphasizes 800–3000 Hz range
• Bass Boost: Enhances 60–250 Hz
• Treble Boost: Brightens 4–16 kHz
• Flat: Minimal processing with high-pass filter

Dynamic Range Compression

• Improves loudness consistency
• Reduces dynamic range for streaming
• Configurable ratio (1–10)

Harmonic Enhancement

• Adds warmth and richness
• Uses soft saturation for natural harmonics

Stereo Widening

• Improves spatial imaging
• Enhances left-right separation
• Creates immersive experience

Reverb Control

• Adds subtle depth and space
• Professional room simulation
• Configurable wet/dry mix

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🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

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⚖️ License

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

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🙏 Acknowledgments

• Original AICoverGen by SociallyIneptWeeb
• RVC (Retrieval-based Voice Conversion) framework
• MDXNet for audio separation
• All the amazing open-source audio processing libraries