In the free-music-demixer repo, there is a `file_demixer` utility [1] which you can compile using the CMakeLists file - it will use the same quantized weights a

In the free-music-demixer repo, there is a `file_demixer` utility [1] which you can compile using the CMakeLists file - it will use the same quantized weights and produce the same "lower quality" output as the website. Clone the git repo with submodules because it compiles the vendored libnyquist library to load audio files. Of course, you can always run the upstream umxl model easily: ``` pip install openunmix # pulls in pytorch ``` Installing openunmix installs the `umx` cli: ``` $ umx --help usage: umx [-h] [--model MODEL] [--targets TARGETS [TARGETS ...]] [--outdir OUTDIR] [--ext EXT] [--start START] [--duration DURATION] [--no-cuda] [--audio-backend AUDIO_BACKEND] [--niter NITER] [--wiener-win-len WIENER_WIN_LEN] [--residual RESIDUAL] [--aggregate AGGREGATE] [--filterbank FILTERBANK] [--verbose] input [input ...] UMX Inference positional arguments: input List of paths to wav/flac files. ... ``` It will download the weights automatically for you and demix at a higher quality than my site, for two reasons: * Unquantized weights (small impact) * 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/examples/file_demixer/file_demixer.cpp https://github.com/sevagh/free-music-demixer/blob/main/examp... 2: https://github.com/sigsep/open-unmix-pytorch/blob/master/openunmix/filtering.py#L338 https://github.com/sigsep/open-unmix-pytorch/blob/master/ope...