Qustions-About the paper result about wave-u-net

[f90]

Hey!
For 1), the batch size is set to 16 in all our experiments and this ran fine with 8GB of GPU memory, so I am a bit surprised that you have so much memory issues? Is it the same with the singing voice separation models?

For 2), you are right that the compute_mean_metrics function can be used to compute the results. It is meant to be used as standalone function, so you should be able to do

import Evaluate
Evaluate.compute_mean_metrics(PATH_TO_JSONS)

from a Python console, where PATH_TO_JSONS is simply the path to the folder containing all the JSON files you want to evaluate, so that should be 50 (or 51 including test.json) files, one for each song. For details on the other parameters refer to the documentation of the compute_mean_metrics function.

draw_violin_sdr can be used with the same json path parameter to directly plot the distribution of SDR values so it builds on the other function.

[Wangbin1125]

@f90 According to your instruction, I have successfully solved my second problem.
I haven't trained the singing voice separation models yet, I'll try again for GPU memory problems and come back to you if there are any questions.
Thank you very much for your detailed reply again.

[f90]

No problem! Closing this for now due to inactivity.