Medix-AI/coqui-tts
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coqui-tts
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linter fixes #2

This commit is contained in:
Eren Gölge 2021-04-08 16:57:46 +02:00
parent 4d3e1e9d9a
commit cd69da4868
5 changed files with 4 additions and 6 deletions
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2
TTS/bin/train_vocoder_gan.py
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@ -479,7 +479,7 @@ def main(args): # pylint: disable=redefined-outer-name
optimizer_gen = getattr(torch.optim, c.optimizer) optimizer_gen = getattr(torch.optim, c.optimizer)
optimizer_gen = optimizer_gen(lr=c.lr_gen, **c.optimizer_params) optimizer_gen = optimizer_gen(lr=c.lr_gen, **c.optimizer_params)
optimizer_disc = getattr(torch.optim, c.optimizer) optimizer_disc = getattr(torch.optim, c.optimizer)
optimizer_disc= optimizer_disc(lr=c.lr_gen, **c.optimizer_params) optimizer_disc = optimizer_disc(lr=c.lr_gen, **c.optimizer_params)
# schedulers # schedulers
scheduler_gen = None scheduler_gen = None

2
TTS/vocoder/datasets/gan_dataset.py
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@ -120,7 +120,7 @@ class GANDataset(Dataset):
else: else:
audio = self.ap.load_wav(wavpath) audio = self.ap.load_wav(wavpath)
mel = np.load(feat_path) mel = np.load(feat_path)
audio, mel= self._pad_short_samples(audio, mel) audio, mel = self._pad_short_samples(audio, mel)
# correct the audio length wrt padding applied in stft # correct the audio length wrt padding applied in stft
audio = np.pad(audio, (0, self.hop_len), mode="edge") audio = np.pad(audio, (0, self.hop_len), mode="edge")

3
TTS/vocoder/models/melgan_discriminator.py
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@ -11,8 +11,7 @@ class MelganDiscriminator(nn.Module):
base_channels=16, base_channels=16,
max_channels=1024, max_channels=1024,
downsample_factors=(4, 4, 4, 4), downsample_factors=(4, 4, 4, 4),
groups_denominator=4, groups_denominator=4):
max_groups=256):
super(MelganDiscriminator, self).__init__() super(MelganDiscriminator, self).__init__()
self.layers = nn.ModuleList() self.layers = nn.ModuleList()

1
tests/test_vocoder_losses.py
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@ -89,4 +89,3 @@ def test_melgan_feature_loss():
loss_func = MelganFeatureLoss() loss_func = MelganFeatureLoss()
loss = loss_func(feats_fake, feats_real) loss = loss_func(feats_fake, feats_real)
assert loss.item() == 0 assert loss.item() == 0