The preprocessing results in less than 20000 mixtures in training set of wsj0-mix2. about two_step_mask_learning HOT 9 CLOSED

Yes actually for the data augmentation experiments (where I am onling mixing the audio sources) I needed all the files to have at least 4 secs of audio so I chopped them. In the WSJ case this is not needed. I will try to push a newer and cleaner version of this code after I am done with some deadlines that I have.

Glad that you liked the work and I would be happy to answer other questions, if you have any! :)

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Section 3.2.1. refers to the WSJ case only. With no online mixing so you can use all mixtures after zero-padding.

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Sorry for this confusion but I wanted to match the experiments from other works but also create the 4sec online mixing procedure described in 3.2.2 in the paper.

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@etzinis Thank you so much for your kind reply! I am still running the speech separation experiment code. Looking forward to your new release : )

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Because as I said before, I will not be able to put out the new release until I am finished with some urgent things. I would suggest you to just create the WSJ with the matlab script provided here: http://wham.whisper.ai/README.html and then use my script with wav_timelength=4s https://github.com/etzinis/two_step_mask_learning/blob/master/two_step_mask_learning/utils/preprocess_wsj0mix.py

I will leave this issue open in order to fix it on the new release.

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So I have rechecked what you said and it seems that indeed when using the 'max' folder from the wsj2mix dataset you have the following files created:
Training: 19855
Testing: 2988
Validation: 4980

and this is caused because as you said in lines 139-140 I have discarded the files with a duration lower than 4secs. However, this amount is like neglecting 0.7% on the training dataset and 0.4% on the testing and validation which I consider is negligible compared to the total size of the dataset. Moreover zeros do not contribute to any SI-SDR loss so either-way I am just making my configuration a tiny bit harder than the initial setup. If you want to just use the remaining 0.4% as well you can just zero pad in 139-140 lines.

I close this issue for now.

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I have also added the code of padding now in the corresponding lines so the output distribution of samples will be:
Training: 20000
Testing: 3000
Validation: 5000

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Thanks for noticing that @flyjiang92 🍺 😃

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@etzinis Thank you so much for your response and the code updating! 👍

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