From the article:

Demucs is a state-of-the-art music source separation model, currently capable of separating drums, bass, and vocals from the rest of the accompaniment. Demucs is based on a U-Net convolutional architecture inspired by Wave-U-Net. The v4 version features Hybrid Transformer Demucs, a hybrid spectrogram/waveform separation model using Transformers. It is based on Hybrid Demucs (also provided in this repo), with the innermost layers replaced by a cross-domain Transformer Encoder. This Transformer uses self-attention within each domain, and cross-attention across domains. The model achieves a SDR of 9.00 dB on the MUSDB HQ test set. Moreover, when using sparse attention kernels to extend its receptive field and per source fine-tuning, we achieve state-of-the-art 9.20 dB of SDR.

Samples are available on our sample page. Checkout our paper for more information. It has been trained on the MUSDB HQ dataset + an extra training dataset of 800 songs. This model separates drums, bass and vocals and other stems for any song.

As Hybrid Transformer Demucs is brand new, it is not activated by default, you can activate it in the usual commands described hereafter with -n htdemucs_ft. The single, non fine-tuned model is provided as -n htdemucs, and the retrained baseline as -n hdemucs_mmi. The Sparse Hybrid Transformer model decribed in our paper is not provided as its requires custom CUDA code that is not ready for release yet. We are also releasing an experimental 6 sources model, that adds a guitar and piano source. Quick testing seems to show okay quality for guitar, but a lot of bleeding and artifacts for the piano source.