mirror of https://github.com/coqui-ai/TTS.git
1) Combine MSD with Multi-Period disc
2) Add remove weight norm layer on Generator
This commit is contained in:
rishikksh20
Eren Gölge
parent
4493feb95c
commit
7b7c5d635f
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@ -24,6 +24,14 @@ class ResStack(nn.Module):
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return x1 + x2
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def remove_weight_norm(self):
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# nn.utils.remove_weight_norm(self.resstack[2])
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# nn.utils.remove_weight_norm(self.resstack[4])
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for idx, layer in enumerate(self.resstack):
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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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nn.utils.remove_weight_norm(self.shortcut)
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class MRF(nn.Module):
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@ -38,3 +46,8 @@ class MRF(nn.Module):
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x2 = self.resblock2(x)
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x3 = self.resblock3(x)
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return x1 + x2 + x3
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def remove_weight_norm(self):
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self.resblock1.remove_weight_norm()
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self.resblock2.remove_weight_norm()
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self.resblock3.remove_weight_norm()
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@ -18,7 +18,7 @@ class Generator(nn.Module):
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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.ConvTranspose1d(inp, out, k, k // 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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@ -37,3 +37,24 @@ class Generator(nn.Module):
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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()
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@ -1,27 +1,37 @@
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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(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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)
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self.discriminator = nn.ModuleList([
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nn.Sequential(
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nn.utils.weight_norm(nn.Conv2d(1, 64, kernel_size=(5, 1), stride=(3, 1))),
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nn.LeakyReLU(0.2, inplace=True),
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),
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nn.Sequential(
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nn.utils.weight_norm(nn.Conv2d(64, 128, kernel_size=(5, 1), stride=(3, 1))),
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nn.LeakyReLU(0.2, inplace=True),
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),
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nn.Sequential(
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nn.utils.weight_norm(nn.Conv2d(128, 256, kernel_size=(5, 1), stride=(3, 1))),
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nn.LeakyReLU(0.2, inplace=True),
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),
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nn.Sequential(
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nn.utils.weight_norm(nn.Conv2d(256, 512, kernel_size=(5, 1), stride=(3, 1))),
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nn.LeakyReLU(0.2, inplace=True),
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),
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nn.Sequential(
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nn.utils.weight_norm(nn.Conv2d(512, 1024, kernel_size=(5, 1))),
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nn.LeakyReLU(0.2, inplace=True),
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),
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nn.utils.weight_norm(nn.Conv2d(1024, 1, kernel_size=(3, 1))),
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])
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def forward(self, x):
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batch_size = x.shape[0]
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@ -29,23 +39,29 @@ class PeriodDiscriminator(nn.Module):
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x = F.pad(x, (0, pad), "reflect")
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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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features = list()
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for module in self.discriminator:
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y = module(y)
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features.append(y)
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return features[-1], features[:-1]
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class MPD(nn.Module):
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def __init__(self, periods=[2, 3, 5, 7, 11], segment_length=16000):
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super(MPD, self).__init__()
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self.mpd1 = PeriodDiscriminator(periods[0])
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self.mpd2 = PeriodDiscriminator(periods[1])
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self.mpd3 = PeriodDiscriminator(periods[2])
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self.mpd4 = PeriodDiscriminator(periods[3])
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self.mpd5 = PeriodDiscriminator(periods[4])
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class HiFiDiscriminator(nn.Module):
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def __init__(self, periods=[2, 3, 5, 7, 11]):
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super(HiFiDiscriminator, self).__init__()
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self.discriminators = nn.ModuleList([ 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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def forward(self, x):
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out1 = self.mpd1(x)
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out2 = self.mpd2(x)
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out3 = self.mpd3(x)
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out4 = self.mpd4(x)
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out5 = self.mpd5(x)
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return out1, out2, out3, out4, out5
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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, feat = disc(x)
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scores.append(score)
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feats.append(feat)
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return scores, feats
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