SoundLight is a framework for automatic generation of lighting projects for lighting controllers. It analyzes a song and creates project files compatible with various lighting control software, streamlining the process of synchronizing light shows to music.

• About
• Repository Structure
• Prerequisites for Development
• Installation
• Usage
• Contributing
• License
• Contact

SoundLight automates the process of creating synchronized lighting projects from audio tracks. It performs audio analysis, feature extraction, and exports the results to formats compatible with lighting controllers such as Daslight and Sweetlight. The framework is modular, allowing for custom analysis, generation, and export algorithms.

The goal of the repository is to provide a flexible, extensible, and easy-to-use tool for DJs, lighting designers, and event organizers who want to automate lightshow creation.

SoundLight/
│
├── notebooks/                # Jupyter notebooks for UI, tests, analysis, etc.
│   ├── UI.ipynb
│   ├── Analysis.ipynb
│   ├── dev_analysis.ipynb
│   ├── Generation.ipynb
│   ├── Exporter.ipynb
│   ├── Installation.ipynb
│   └── tests/
│       ├── notes.ipynb
│       └── tempo3.ipynb
│
├── src/                      # Main source code
│   ├── core/
│   │   ├── analysis/
│   │   │   └── simpleAlgorithm/
│   │   ├── export/
│   │   │   └── daslight5/
│   │   │       ├── features/
│   │   │       └── sections/
│   │   ├── generation/
│   │   │   └── simpleGenerator/
│   │   ├── model/
│   │   └── ...
│   ├── lib/
│   └── ...
│
├── test/                     # Unit and integration tests
│   ├── core/
│   │   ├── analysis/
│   │   ├── export/
│   │   ├── generation/
│   │   ├── model/
│   │   └── ...
│   ├── conftest.py
│   └── ...
│
├── resources/                # Audio and patch resources
│   ├── *.json
│   └── *.wav
│
├── output/                   # Output files (e.g., .dvc)
│
├── doc/                      # Documentation in Markdown
│   ├── Analysis.md
│   ├── Generation.md
│   └── SoundLight.md
│
├── requirements_1.txt        # Python requirements (part 1)
├── requirements_2.txt        # Python requirements (part 2)
├── dependencies.py           # Dependency checker script
├── run.py                    # Main entry point
├── soundlight.toml           # Project configuration
├── pytest.ini                # Pytest configuration
└── ...

Each directory contains code or resources relevant to its purpose. The src/core directory is the main entry point for the framework logic.

Before running or developing SoundLight, ensure you have:

• Python 3.10: The framework is developed and tested with Python 3.10.
  Install with:

  sudo apt-get install python3.10 python3.10-dev

• Build tools:

  sudo apt-get install build-essential

• ffmpeg: Required for audio conversion and processing.
  Install with:

  sudo apt install ffmpeg

• pip: For installing Python dependencies.
• Other dependencies: Listed in requirements_1.txt and requirements_2.txt (includes tinytag, librosa, pytest, madmom, allin1, etc.).
• NATTEN: Special installation required (see below).
• MADMOM: Install from GitHub (see below).
• All-In-One: Install via pip (see below).

1. Install ffmpeg:

   sudo apt install ffmpeg

2. Install Python 3.10 and development headers:

   sudo apt-get install python3.10 python3.10-dev

3. Install build tools:

   sudo apt-get install build-essential

4. Upgrade setuptools and wheel:

   pip install --upgrade setuptools wheel

5. Install Python dependencies:

   pip install -r requirements_1.txt
   pip install -r requirements_2.txt

6. Install NATTEN:
   • GPU (replace cu124 with your CUDA version):

     pip install natten==0.17.1+torch240cu124 -f https://shi-labs.com/natten/wheels

   • CPU:

     pip install natten==0.17.1+torch240cpu -f https://shi-labs.com/natten/wheels

   See Shi-Labs.com for more info.
7. Install MADMOM with admin privileges:

   sudo pip install --upgrade --no-deps --force-reinstall --quiet 'git+https://github.com/CPJKU/madmom.git'

8. Install All-In-One:

   sudo pip install allin1

If you wish to run SoundLight on Windows, you must first install WSL (Windows Subsystem for Linux) and then follow the Linux installation steps inside your WSL environment.

1. Install WSL:

   wsl --install

2. Open your WSL terminal and follow the Linux instructions above.

SoundLight can be used as a library in your own Python scripts or directly via script.

• Directly via script (see run.py for an example):

  python run.py

The typical workflow is:

1. Initialize a SoundLight instance with a config file.
2. Add and select a song.
3. Set the patch and algorithms.
4. Set the export path.
5. Run the process to analyze, generate, and export the project.

Contributions are welcome! To contribute:

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

Please ensure your code is tested and follows the existing style.

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

For questions, suggestions, or feedback, please contact:

• Author: Álvaro González Erigoyen.
• Email: alvaroglezeri[at]pm.com

Feel free to open issues in the repository if you encounter any problems.

This repository was inspired by the need for automated lightshow generation. Special thanks to all contributors and supporters, and to the authors of open-source libraries used in this project.