MusicMixer is a tool I designed to augment music datasets -- specifically, I was working on song2tab. It provides a simple web interface to apply a variety of audio effects to songs, creating an enriched dataset that can be used to improve the performance of the song2tab or any audio-based/music-based deep learning model.
- Audio File Conversion: Convert audio files into different formats for compatibility with various machine learning tools and libraries.
- Audio Effect Application: Apply multiple audio effects, such as reverb, delay, distortion, and more, with randomization to create a diverse dataset.
- Batch Processing: Upload and process multiple audio files or zip archives simultaneously.
- Web Interface: Hosted at musicmixer.pro, providing an easy-to-use interface for audio processing.
- Downloadable Results: Download individual processed files or a zip archive of all processed files.
MusicMixer is developed using Python and Flask. To install and run the project locally, you will need Poetry for dependency management.
- Python 3.7 or higher
- Poetry
First, clone the repository from GitHub:
git clone https://github.com/c0nD/MusicMixer
cd song2tabNext, use Poetry to install the dependencies (from the root dir):
poetry installRunning the app.py file and going to the specified local-host URL will allow you to use the web interface.
This project is licensed under the MIT License. Please check the link for further information.