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  • Jan 16 16:36
    loxosceles edited #29
  • Jan 16 16:26
    faroit labeled #29
  • Jan 16 16:26
    faroit labeled #29
  • Jan 16 16:22
    loxosceles opened #29
  • Jan 12 22:18
    romanbsd commented #28
  • Jan 04 13:57
    romanbsd closed #28
  • Jan 04 13:57
    romanbsd commented #28
  • Jan 03 22:58
    faroit commented #28
  • Jan 02 14:00
    romanbsd opened #28
  • Dec 28 2019 08:24
    aliutkus commented #14
  • Dec 28 2019 08:23
    aliutkus commented #14
  • Dec 27 2019 16:45
    tommy-fox commented #14
  • Dec 20 2019 09:50

    faroit on master

    update test for better naming o… (compare)

  • Dec 06 2019 08:34

    faroit on master

    modify output file names (compare)

  • Dec 06 2019 08:31

    faroit on jointmodel

    fix joint test outputs (compare)

  • Dec 05 2019 15:26

    faroit on jointmodel

    not needed implement inference (compare)

  • Dec 04 2019 16:49

    faroit on jointmodel

    fix shape (compare)

  • Dec 04 2019 16:40

    faroit on jointmodel

    redudant code (compare)

  • Dec 03 2019 22:40

    faroit on jointmodel

    start joint model Merge branch 'refactor_datasets… ideas and 5 more (compare)

  • Nov 27 2019 19:21
    Vichoko commented #26
Fabian-Robert Stöter
@faroit
demucs is incredible on drums and bass
jhm0799
@jhm0799
Holy cow. I'm listening to examples right now. These are impressive results
WavUNet is the worst I think, at least out of all the ones it lists. The drums are very "crunchy", if that makes sense.. I appreciate the effort in any regard
jhm0799
@jhm0799
Huh
I can't get demucs to work
It is talking about CUDA when in fact I installed the CPU version (the YML file, via Anaconda Navigator)
I wonder if either --dl or -n is causing it to use the GPU
My command is:
python -m demucs.separate --dl -n demucs walk.wav output
-n might mean "NVIDIA"
I'll try it without that one
Nope
Removing those 2 parameters still gives an error about CUDA
RuntimeError: CUDA out of memory. Tried to allocate 158.00 MiB (GPU 0; 2.00 GiB total capacity; 1.27 GiB already allocated; 145.36 MiB free; 21.81 MiB cached)
jhm0799
@jhm0799
Tried this:
python -m demucs.separate -d cpu -n demucs c:\walk.wav -o c:\output
"TypeError: argument of type 'WindowsPath' is not iterable
jhm0799
@jhm0799
I give up. Can't figure this out

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

jhm0799
@jhm0799
I changed python3 to python since it says python3 isn't a valid command
Fabian-Robert Stöter
@faroit
please report this to the demucs repo. I think they would love some feedback
I was able to run the cpu version without any problems
jhm0799
@jhm0799
Will do. Was it on Windows?
Fabian-Robert Stöter
@faroit
OS X
jhm0799
@jhm0799

This error appears just before the WindowsPath one:

needquote = (" " in arg) or ("\t" in arg) or not arg

I think I'm mis-typing the command or something

Ah alright

Fabian-Robert Stöter
@faroit

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.

Russell Izadi
@russellizadi_twitter
Hi, I just started exploring the unmix code and I have a question:
Since a model is needed for each target, how do you manage to deal with a relatively large number of sources? For example, consider the separation of a mixture of two mnist images. Do you estimate 10 separate models?!
I think open-unmix is mainly developed for music separation but it could be perfect to add more features so more separation tasks could be performed using this clean and organized repo.
Fabian-Robert Stöter
@faroit
@russellizadi_twitter yes, we believe that training separate models is a good tradeoff between efficiency and performance. Joint models may be a bit better in performance but many data sets are unbalanced when it comes to the number of sources. Therefore it makes sense to train separate models.
Aadi Bajpai
@aadibajpai
In a signal processing pipeline, would you recommend demucs or spleeter if I only really need to work with vocals?
Currently using umx, of course.
Fabian-Robert Stöter
@faroit
I depends on what you need
Well, they all have their pros. We designed open unmix to serve as a baseline for researchers not to beat SOTA. Spleeter serves end users that want to use separation in their pipeline. Demucs
represents a new line of research for end-to-end source separation.
so if you don't have your own training data set and you are okay with the quality of <11 kHz, you go with spleeter
Aadi Bajpai
@aadibajpai
We're basically trying to play with lyrics-to-audio alignment and vocal isolation is the first step there so any errors just propagate further. UMX has been better than whatever we had tried before but I feel we should try spleeter once in the pipeline. I was a bit confused about that quality loss thing initially.
Russell Izadi
@russellizadi_twitter

@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).

And a question, what are the best practices for defining/adding new models in a project like this? Do you just change the OpenUnmix model or define a new model and change the train/test/... files accordingly?
Fabian-Robert Stöter
@faroit
@russellizadi_twitter by single model I mean a joint model that has e.g. 4 times the number of parameters and the 4 outputs (heads) are joint at the end to be able to train with multiple sources jointly
@russellizadi_twitter concerning improving the performance, I would suggest to start on the model side. The training code is very simple by design and can easily be adopted. I would suggest to stick with out data loaders since we put a lot of work into these to make them flexible and efficient. Also see our documentation here: https://github.com/sigsep/open-unmix-pytorch/blob/master/docs/extensions.md
PeturBryde
@PeturBryde
Hello! I'm wondering whether the umx and umxhq models are both trained with the default parameters of train.py, and in particular whether they both use --bandwidth 16000? What would be the benefits of training with a higher bandwidth setting?
Fabian-Robert Stöter
@faroit
@PeturBryde there a some differences to the defaults. You'll find the exact training configuration here: https://zenodo.org/record/3370486 and https://zenodo.org/record/3370489 in the training-json-logs.zip file
@PeturBryde concerning the bandwidth argument: this does only affect the internal parameters of the model. UMXHQ can predict spectra with >16 kHz even though most layers only use up to 16khz as the model does bandwidth extension internally. We didn't observe better quality when increasing --bandwidth to something like 22050. Hope that helps
PeturBryde
@PeturBryde
@faroit Thank you, that's very helpful!
jhm0799
@jhm0799
I actually used denucs for something lol
Nothing commercial of course, but still something nonetheless
Spleeter failed to give good results so i tried demucs. It worked a lot better
jhm0799
@jhm0799
A song is out there which has no instrumental and I wanted one. None of the tools I've used have been able to remove the vocals as good as I wanted it, so i decided to resequence the song (or attempt to). This failed as its a very complex song, so I only re-sequenced some of it (bass, a few synths) and I layered the drums and "other" stem (extracted with denucs). That makes it sound great
Denucs fails to grab reverb it seems
jhm0799
@jhm0799
The bass synth really throws off the drum extractor on Spleeter lol
jhm0799
@jhm0799
Anyways, I was in for a surprise when I ran Demucs on a track which a friend of mine made. It extracted a bass part which didn't exist in the original track. Meaning: It extracted a different sequence of notes... It must've guessed a new melody or something. I asked my friend about it and he said "It most definitely messed up". haha
It sounds like a proper bassline though