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[Archived] Help with random window discriminator
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>>> alexdemartos
[October 26, 2020, 7:42pm]
,
I read you have achieved better results with RWD w.r.t. Multi-Scale
Melgan Discriminator. Also that FullBand MelGAN was working better for
you than MB-MelGAN. Is that right? Was it for a single-speaker corpus or
a multi-speaker one?
I've tried to port your implementation to kan-bayashi's PWG, but I am
having trouble with the downsample factors. My hop length is 300, and
this is what I've (unsucessfully) tried:
hop_length: 300
uncond_disc_donwsample_factors: [8, 4]
cond_disc_downsample_factors: [[5, 5, 3, 2, 2], [5, 5, 3, 2], [5, 5, 3], [5, 3, 2], [5, 2, 2]]
cond_disc_out_channels: [[128, 128, 256, 256], [128, 256, 256], [128, 256], [128, 256], [128, 256]]
window_sizes: [600, 1200, 2400, 6000, 9000]
I am getting the following error:
'parallel_wavegan/losses/stft_loss.py', line 52, in forward
return torch.norm(y_mag - x_mag, p='fro') / torch.norm(y_mag, p='fro')
RuntimeError: The size of tensor a (151) must match the size of tensor b
(38) at non-singleton dimension 1
Any advices? Thanks in advance
[This is an archived TTS discussion thread from discourse.mozilla.org/t/help-with-random-window-discriminator]
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