I tried this too:
python -m demucs.separate -n demucs -o output --models models -d cpu tracks walk.wav
Maybe I'm not doing it right
Hi open-unmix users. Just a quick note here:
Since the release of open-unmix in September 2019, the source separation landscape has evolved regarding pre-trained models.
Open-unmix was designed as a baseline to make it easier to do source separation research. Now, spleeter has been made available and got much popularity since its release. We believe it is a good implementation for users to apply source separation in a signal processing pipeline.
For this reason, we decided to not release the tensorflow implementation of open-unmix publicly, but rather to make it available for commercial applications.
We continue to improve and update the PyTorch release and we believe that in terms of academic research, this follows the current trend in the community to vastly favor this framework.
@faroit I don't understand why joint models may have better performance! Only one model with 1/4 of parameters of 4 models in total, should not perform better. My understanding is the trade-off is other way around. I mean a single model is more efficient (smaller in size) but has worse performance.
For example, you need 10 models for separating mixtures of 10 sources, which is not efficient, the overall performance is better than a single model (with the same size as one of those 10 models).
--bandwidthto something like
22050. Hope that helps