mirror of https://github.com/coqui-ai/TTS.git
60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
from torch import nn
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from TTS.vocoder.layers.hifigan import MRF
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class Generator(nn.Module):
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def __init__(self, input_channel=80, hu=512, ku=[16, 16, 4, 4], kr=[3, 7, 11], Dr=[1, 3, 5]):
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super(Generator, self).__init__()
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self.input = nn.Sequential(
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nn.ReflectionPad1d(3),
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nn.utils.weight_norm(nn.Conv1d(input_channel, hu, kernel_size=7))
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)
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generator = []
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for k in ku:
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inp = hu
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out = int(inp / 2)
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generator += [
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nn.LeakyReLU(0.2),
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nn.utils.weight_norm(nn.ConvTranspose1d(inp, out, k, k//2)),
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MRF(kr, out, Dr)
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]
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hu = out
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self.generator = nn.Sequential(*generator)
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self.output = nn.Sequential(
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nn.LeakyReLU(0.2),
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nn.ReflectionPad1d(3),
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nn.utils.weight_norm(nn.Conv1d(hu, 1, kernel_size=7, stride=1)),
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nn.Tanh()
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)
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def forward(self, x):
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x1 = self.input(x)
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x2 = self.generator(x1)
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out = self.output(x2)
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return out
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def remove_weight_norm(self):
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for idx, layer in enumerate(self.input):
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if len(layer.state_dict()) != 0:
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try:
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nn.utils.remove_weight_norm(layer)
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except:
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layer.remove_weight_norm()
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for idx, layer in enumerate(self.output):
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if len(layer.state_dict()) != 0:
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try:
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nn.utils.remove_weight_norm(layer)
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except:
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layer.remove_weight_norm()
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for idx, layer in enumerate(self.generator):
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if len(layer.state_dict()) != 0:
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try:
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nn.utils.remove_weight_norm(layer)
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except:
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layer.remove_weight_norm() |