demucs VS open-unmix-pytorch

I've had the best results from Facebook's DEMUCs. It's not too difficult to install, and I like the sound quality of their mdx_extra model. This is the command line I use (this will use the 2 stem version -- vocals, and everything else)

facebookresearch/demucs: Stem seperation

For those interested, Facebook's Demucs page (https://github.com/facebookresearch/demucs) gives performance comparison for several models including open-unmix.

See also: https://www.stemroller.com This runs as a local app on Windows and Mac.

Demucs [1], one of the leading/SOTA systems, has an experimental 6-source model, `htdemucs_6s`, which adds piano and guitar:

>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.

I also believe Audioshake [2] (a company in the space) is doing guitar separation as well.

1: https://github.com/facebookresearch/demucs

I don't know which one /u/DarkMemoria used exactly but I use demucs. If you go to the Colab section you can run it by putting the audio files you want to separate into Google Drive.

Demucs v4 is the best open source currently, that's also what the snippet from the video sounds like uses

already exist, for example I use demucs to separate songs into 6 tracks, and then I mute what i need to be silenced in any daw.

I regularly use demucs (https://github.com/facebookresearch/demucs). It might be overwhelming when you are not used to work with the terminal, but it's as good as all the wrapper sites that ask for payment. Also, there are probably GUI projects that makes it even easier.

There's also the open source software https://github.com/facebookresearch/demucs which I assume is what many of the free websites are using behind the scenes. There's a demo site here: https://huggingface.co/spaces/akhaliq/demucs but I haven't tested it for time limits/upload limits etc.

I used demucs to try and separate the vocals from the instrumental with fairly decent results. When playing the instrumental you can still vaguely hear some remnants of the vocals and I worry when it's played over a real sound system at the wedding it will be very obvious.

In my first post, quite a lot of alternatives were discussed: https://news.ycombinator.com/item?id=36707877

The model I'm using is called Open-Unmix (https://github.com/sigsep/open-unmix-pytorch). In 2021, there was an update to Open-Unmix to include new weights, UMX-L, which made it perform better than it used to on the older weights (UMXHQ).

In the grand landscape of music demixing, I don't think UMX-L is near the top anymore.

_However_, the demixing performance of freemusicdemixer.com is very close to the full PyTorch performance of Open-Unmix UMX-L, despite the tricks I needed to get it working in the browser, such as splitting up the inference to operate on segments of the song, or making the LSTM operate on streaming segments rather than holding the entire track in the LSTM memory.

In my first release, I loaded and did inference on the entire track at once (like the PyTorch model), which frequently crashed or exceeded the 4GB WASM memory for medium or large-size tracks.

* Post-processing step (bigger impact)

I tried to tackle the post-processing step in my C++ code (which would win ~1 dB in quality across all targets) but it's too tricky for now [2]. Maybe some other day.

1: https://github.com/sevagh/free-music-demixer/blob/main/examp...

2: https://github.com/sigsep/open-unmix-pytorch/blob/master/ope...

Or open-unmix. I've found it performs better than spleeter in some of my tests.

There's maybe something useful for you here?

https://github.com/facebookresearch/demucs

https://github.com/sigsep/open-unmix-pytorch

https://github.com/bytedance/music_source_separation

https://github.com/deezer/spleeter

OpenUnmix ( is a neural network solution from Yuki Mitsufuji and Stefan Uhlich, music industry luminaries that work in Sony's core divisions.