mirror of https://github.com/coqui-ai/TTS.git
78 lines
2.6 KiB
Python
78 lines
2.6 KiB
Python
from torch import nn
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import torch.nn.functional as F
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from TTS.vocoder.models.melgan_multiscale_discriminator import MelganMultiscaleDiscriminator
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class PeriodDiscriminator(nn.Module):
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def __init__(self, period):
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super(PeriodDiscriminator, self).__init__()
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layer = []
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self.period = period
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inp = 1
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for l in range(4):
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out = int(2**(5 + l + 1))
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layer += [
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nn.utils.weight_norm(
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nn.Conv2d(inp, out, kernel_size=(5, 1), stride=(3, 1))),
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nn.LeakyReLU(0.2)
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]
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inp = out
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self.layer = nn.Sequential(*layer)
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self.output = nn.Sequential(
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nn.utils.weight_norm(nn.Conv2d(out, 1024, kernel_size=(5, 1))),
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nn.LeakyReLU(0.2),
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nn.utils.weight_norm(nn.Conv2d(1024, 1, kernel_size=(3, 1))))
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def forward(self, x):
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batch_size = x.shape[0]
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pad = self.period - (x.shape[-1] % self.period)
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x = F.pad(x, (0, pad))
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y = x.view(batch_size, -1, self.period).contiguous()
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y = y.unsqueeze(1)
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out1 = self.layer(y)
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return self.output(out1)
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class HifiDiscriminator(nn.Module):
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def __init__(self,
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periods=[2, 3, 5, 7, 11],
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in_channels=1,
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out_channels=1,
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num_scales=3,
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kernel_sizes=(5, 3),
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base_channels=64,
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max_channels=1024,
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downsample_factors=(2, 2, 4, 4),
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pooling_kernel_size=4,
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pooling_stride=2,
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pooling_padding=1):
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super().__init__()
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self.discriminators = nn.ModuleList([
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PeriodDiscriminator(periods[0]),
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PeriodDiscriminator(periods[1]),
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PeriodDiscriminator(periods[2]),
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PeriodDiscriminator(periods[3]),
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PeriodDiscriminator(periods[4])
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])
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self.msd = MelganMultiscaleDiscriminator(
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in_channels=in_channels,
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out_channels=out_channels,
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num_scales=num_scales,
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kernel_sizes=kernel_sizes,
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base_channels=base_channels,
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max_channels=max_channels,
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downsample_factors=downsample_factors,
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pooling_kernel_size=pooling_kernel_size,
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pooling_stride=pooling_stride,
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pooling_padding=pooling_padding,
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groups_denominator=32,
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max_groups=16)
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def forward(self, x):
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scores, feats = self.msd(x)
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for key, disc in enumerate(self.discriminators):
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score = disc(x)
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scores.append(score)
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return scores, feats
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