Clean-Tracks - Audio Censorship System

An intelligent audio processing tool that automatically detects and censors explicit content in audio files, featuring both a user-friendly web interface and powerful command-line tools.

🎯 Overview

Clean-Tracks uses advanced speech recognition (OpenAI Whisper) to detect and remove explicit words from audio files while preserving audio quality. Perfect for content creators, educators, and parents who need to make audio content appropriate for all audiences.

✨ Features

• 🎵 Multi-Format Support: Process MP3, WAV, FLAC, M4A, OGG, and more
• 🤖 AI-Powered Detection: Uses Whisper for accurate speech recognition
• 🎨 Web Interface: Intuitive drag-and-drop interface with real-time progress
• ⚡ Command Line: Powerful CLI for batch processing and automation
• 📝 Customizable Word Lists: Manage your own explicit word lists with severity levels
• 🔊 Multiple Censorship Styles: Choose between silence, beep, or white noise
• 📊 Visual Feedback: See waveforms with detected words highlighted
• 🚀 Batch Processing: Process multiple files simultaneously
• 📱 Mobile Responsive: Works on desktop, tablet, and mobile devices

🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/yourusername/clean-tracks.git
cd clean-tracks

# Create virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run setup
python setup.py install

Web Interface

# Start the web server
python -m clean_tracks.web

# Open browser to http://localhost:5000

Command Line

# Process a single file
clean-tracks process audio.mp3 --output clean_audio.mp3

# Batch process files
clean-tracks batch *.mp3 --output-dir cleaned/

# Manage word lists
clean-tracks words add "example" --severity high
clean-tracks words list
clean-tracks words export my_words.csv

📋 Requirements

• Python 3.11+
• FFmpeg (for audio processing)
• 4GB RAM minimum (8GB recommended for large files)
• GPU optional but recommended for faster processing

🛠️ Technology Stack

• Backend: Python, Flask, OpenAI Whisper
• Audio Processing: PyDub, Librosa, FFmpeg
• Frontend: HTML5, Bootstrap 5, JavaScript
• Real-time: WebSocket (Socket.io)
• Database: SQLite
• Testing: Pytest, Playwright

📖 Documentation

• User Guide
• API Documentation
• Development Guide
• Word List Management

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

📄 License

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

🙏 Acknowledgments

• Built on top of OpenAI's Whisper for speech recognition
• Uses components adapted from the Personal AI Assistant project
• Inspired by the need for family-friendly content creation tools

📧 Support

For issues, questions, or suggestions, please open an issue on GitHub or contact the maintainers.

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Note: This project is in active development. Features and API may change.