1) We have added the DrumSep model. This model produces a detailed separation of the drum track into 4 types: 'kick', 'snare', 'cymbals', 'toms'. The DrumSep model from this github repository is used. The model has two operating modes. The first (default) in the begining applies the Demucs4 HT model to the track, which extracts only the drum part. Next, the DrumSep model is applied. If your track consists only of drums, then it makes sense to use the second mode, where the DrumSep model is applied directly to the loaded audio. Demos available here.

2) A similar LarsNet model was also added, which divides the track into 5 types: 'kick', 'snare', 'cymbals', 'toms', 'hihat'. The model used is from this github repository and trained on the StemGMD dataset. The model has two operating modes. The first (default) applies the Demucs4 HT model to the track, which extracts only the drum part from the track. Next, the LarsNet model is used. If your track consists only of drums, then it makes sense to use the second mode. Unfortunately, subjectively, the quality of separation is inferior in quality to the DrumSep model. Demos available here.