MusicSepGUI: A User-Friendly Interface for Music Source Separation

MusicSepGUI is a graphical user interface (GUI) designed to simplify the process of music source separation using Music-Source-Separation-Training by ZFTurbo. It builds upon the Colab notebook by jarredou, found here.

Key Features:

• Multi-Model Processing:
  • Sequential Mode: Process multiple models in sequence, where the output of one model becomes the input for the next.
  • Independent Mode: Run multiple models independently on the same input audio.
• Ensemble Mode: Combine the outputs of multiple models using various averaging techniques (powered by ensemble.py - see details here).
• Model Management: Download models directly from the GUI with no external downloading needed, constantly updated!
• Advanced Options: Fine-tune parameters like chunk size, overlap, and export format.

Prerequisites:

1. Python 3.9 or higher: Ensure you have Python 3.9 or a later version installed on your system.
2. Music-Source-Separation-Training: This repository needs to be installed prior, follow their readme to get started.

Installation:

1. Download AutoGUI.py
2. Place AutoGUI.py in your main Music-Source-Separation-Training folder.
3. Download requirements.txt, open a terminal in the folder where you've placed it, and run pip install -r requirements.txt.

Usage:

1. Launch the GUI:
2. Configure Input/Output:
   • Select your input audio folder.
   • Choose an output directory.
   • Optionally, enable "Organize Output Per Model" to create subfolders for each model's output.
3. Select a Model:
   • Choose a "Model Type" (e.g., VOCALS, DRUMS, BASS).
   • Select a specific model from the list.
   • Click "Update Models" to refresh the list from the GitHub repository.
4. Multi-Model Processing (Optional):
   • Click "Multi-Model" to open the Multi-Model window.
   • Add models to the "Model Order" list.
   • Choose between "Sequential" and "Independent" processing modes.
5. Ensemble Mode (Optional):
   • Click "Ensemble" to open the Ensemble window.
   • Select the "other" stem output files from different models.
   • Adjust weights for each input.
   • Choose an ensemble type (see Ensemble Documentation).
6. Advanced Options (Optional):
   • Enable/disable Test Time Augmentation (TTA).
   • Adjust the "Overlap" and "Chunk Size" parameters.
7. Separate:
   • Click the "Separate" button to start the separation process.

Troubleshooting:

• "Could not find inference.py": Make sure AutoGUI.py is placed in your main Music-Source-Separation-Training folder, where inference.py is located.
• "Could not find ensemble.py": If using Ensemble mode, ensure that ensemble.py is also present in the same directory.

Contributing:

Contributions to MusicSepGUI are welcome! If you have suggestions, bug reports, or want to contribute code, please open an issue or submit a pull request on GitHub.

Contact:

For questions or support, please open an issue on this GitHub repository.