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FlacJacket

FlacJacket is a web application that analyzes long audio files (like DJ mixes and live concert recordings) from platforms such as SoundCloud or YouTube. It identifies individual tracks within these recordings using audio fingerprinting technology and allows users to download them in high-quality formats.

Features

  • URL input support for SoundCloud and YouTube
  • High-quality audio downloading
  • Advanced audio fingerprinting and track detection
  • Track metadata display
  • High-quality track downloads
  • Real-time analysis status updates
  • Task monitoring dashboard

Tech Stack

Backend

  • Flask (Python web framework)
  • SQLAlchemy (Database ORM)
  • Celery (Async task processing)
  • yt-dlp (YouTube downloading)
  • scdl (SoundCloud downloading)
  • librosa (Audio processing)
  • PostgreSQL (Database)
  • Redis (Message broker)
  • Flower (Celery monitoring)
  • Structlog (Structured logging)

Frontend

  • Next.js 14
  • TypeScript
  • Tailwind CSS
  • Material-UI components
  • React Hooks for state management

Setup

Prerequisites

  • Docker and Docker Compose
  • Git

Quick Start with Docker

  1. Clone the repository:
git clone https://github.com/yourusername/flacjacket.git
cd flacjacket
  1. Start the application:
docker compose up -d

The services will be available at:

Manual Development Setup

Backend Setup

  1. Create a Python virtual environment:
cd backend
python -m venv venv
source venv/bin/activate  # On Windows: .\venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt
  1. Set up environment variables:
cp .env.example .env
# Edit .env with your configuration
  1. Initialize the database:
flask db upgrade
  1. Start the Flask development server:
flask run
  1. Start Celery worker:
celery -A app.celery worker --loglevel=info
  1. Start Flower monitoring (optional):
celery -A app.celery flower

Frontend Setup

  1. Install dependencies:
cd frontend
npm install
  1. Start the development server:
npm run dev

API Endpoints

Analysis

  • POST /api/analysis - Start a new analysis 
    {
  "url": "https://soundcloud.com/example/track"
}
  • GET /api/analysis/:id - Get analysis status and results
  • GET /api/analyses - List all analyses
  • DELETE /api/analysis/:id - Delete an analysis

Health Check

  • GET /api/health - Check API health status

Architecture

The application is composed of several Docker containers:

  • frontend: Next.js web application
  • backend: Flask API server
  • celery_worker: Async task processor for audio analysis
  • postgres: PostgreSQL database
  • redis: Message broker for Celery
  • flower: Celery task monitoring dashboard

Monitoring and Logging

The application includes comprehensive monitoring and logging:

  1. Task Monitor Dashboard

    • Access at http://localhost:5555
    • Real-time task status and progress
    • Historical task data and statistics
    • Worker status and resource usage

  2. Structured Logging

    • Detailed task execution logs
    • Error tracking and debugging information
    • Performance metrics
    • Audit trail for all operations

Contributing

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add some amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

License

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