Adaptation of MVSep-MXD23 algorithm for Colab with few tweaks:

2.1 - fork of jarredou Colab

• Updated with voc_ft instead of Kim Vocal 2 or Kim Vocal 1, and UVR MDX Instr HQ3 instead of Kim Inst
• Divider tweak

2.0 - previously (jarredou)

• Fixed unprocessed high frequency bleed in vocals stem (vocals stem is now really fullband!).
• Added volume compensation (less bleed in overall).

v. 2.1 https://colab.research.google.com/github/deton24/MVSEP-MDX23-Colab_v2.1/blob/main/MVSep-MDX23-Colab.ipynb

v. 2.4 by jarredou https://colab.research.google.com/github/jarredou/MVSEP-MDX23-Colab_v2/blob/v2.4/MVSep-MDX23-Colab.ipynb

300000 chunks_size is necessary in MDX23 for 5:30 track to avoid CUDA memory issues. Default 500K for 3:46 was fine.

Wanted to keep it as private repo till jarredou do his own update, but Colab didn't work that way.

Original work =>

MVSEP-MDX23-music-separation-model

Model for Sound demixing challenge 2023: Music Demixing Track - MDX'23. Model perform separation of music into 4 stems "bass", "drums", "vocals", "other". Model won 3rd place in challenge (Leaderboard C).

Model based on Demucs4, MDX neural net architectures and some MDX weights from Ultimate Vocal Remover project (thanks Kimberley Jensen for great high quality vocal models). Brought to you by MVSep.com.

Usage

    python inference.py --input_audio mixture1.wav mixture2.wav --output_folder ./results/

With this command audios with names "mixture1.wav" and "mixture2.wav" will be processed and results will be stored in ./results/ folder in WAV format.

• Note 1: If you have not enough GPU memory you can use CPU (--cpu), but it will be slow. Additionally you can use single ONNX (--single_onnx), but it will decrease quality a little bit. Also reduce of chunk size can help (--chunk_size 200000).
• Note 2: In current revision code requires less GPU memory, but it process multiple files slower. If you want old fast method use argument --large_gpu. It will require > 11 GB of GPU memory, but will work faster.

Quality comparison

Quality comparison with best separation models performed on MultiSong Dataset.

Algorithm	SDR bass	SDR drums	SDR other	SDR vocals	SDR instrumental
MVSEP MDX23	12.5034	11.6870	6.5378	9.5138	15.8213
Demucs HT 4	12.1006	11.3037	5.7728	8.3555	13.9902
Demucs 3	10.6947	10.2744	5.3580	8.1335	14.4409
MDX B	---	----	---	8.5118	14.8192

• Note: SDR - signal to distortion ratio. Larger is better.

GUI

• Script for GUI (based on PyQt5): gui.py.
• You can download standalone program for Windows here: zip1, zip2. Unzip archives and to start program double click run.bat.
• Program will download all needed neural net models from internet at the first run.
• GUI supports Drag & Drop of multiple files.
• Progress bar available.

Citation

• arxiv paper

@misc{solovyev2023benchmarks,
      title={Benchmarks and leaderboards for sound demixing tasks}, 
      author={Roman Solovyev and Alexander Stempkovskiy and Tatiana Habruseva},
      year={2023},
      eprint={2305.07489},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}