Fre-GAN: Adversarial Frequency-consistent Audio Synthesis
python train.py --config config.json
@misc{kim2021fregan,
title={Fre-GAN: Adversarial Frequency-consistent Audio Synthesis},
author={Ji-Hoon Kim and Sang-Hoon Lee and Ji-Hyun Lee and Seong-Whan Lee},
year={2021},
eprint={2106.02297},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
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https://arxiv.org/pdf/2106.02297.pdf
In section 2.3 "After each level of DWT, all the frequency sub-bands are channel-wise concatenated and passed to convolutional layers"
https://github.com/rishikksh20/Fre-GAN-pytorch/blob/91d0e4678199003f6b32d53c7501e6123fb70fc5/discriminator.py#L242-L246
You are concatenating on the length dim resulting in an odd looking tensor where the first half is audio features and the 2nd half is DWT features, and local waveform/DWT information can't mix properly.
Is there any reason for this? I feel very confused looking at this, but you've done it twice so I assume there's some reason for this.
https://github.com/rishikksh20/Fre-GAN-pytorch/blob/91d0e4678199003f6b32d53c7501e6123fb70fc5/generator.py#L170 I think this line should be remove_weight_norm(l), l.remove_weight_norm() will result in AttributeError: 'Sequential' object has no attribute 'remove_weight_norm'. https://github.com/rishikksh20/Fre-GAN-pytorch/blob/91d0e4678199003f6b32d53c7501e6123fb70fc5/generator.py#L172 there should be remove_weight_norm(l[1]). the previous form will result in AttributeError: 'Sequential' object has no attribute 'remove_weight_norm'
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if i >= self.cond_level:
mel = self.cond_up[i - self.cond_level](mel)
x += mel
if i > self.cond_level:
if output is None:
output = self.res_output[i - self.cond_level - 1](x)
else:
output = self.res_output[i - self.cond_level - 1](output)
if i > self.cond_level:
if output is None:
output = self.res_output[i - self.cond_level - 1](x)
else:
output = self.res_output[i - self.cond_level - 1](output)
if i >= self.cond_level:
mel = self.cond_up[i - self.cond_level](mel)
x += mel
Hi! How this work compares with UnivNet for which one you already implemented code: https://github.com/rishikksh20/UnivNet-pytorch This paper is a little bit newer but afaik they're more concerned about generalizability of model for unseen speakers whlie this work focuses on overall quality (especially in high frequences) can you maybe elaborate?
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