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Stardust·减
2023-09-03 12:36:35 +08:00
committed by GitHub
parent 1835ed2442
commit 2ca8ee7853
1 changed files with 19 additions and 21 deletions
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40
models.py
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@@ -15,8 +15,6 @@ from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
from commons import init_weights, get_padding from commons import init_weights, get_padding
from text import symbols, num_tones, num_languages from text import symbols, num_tones, num_languages
class DurationDiscriminator(nn.Module): #vits2 class DurationDiscriminator(nn.Module): #vits2
# TODO : not using "spk conditioning" for now according to the paper.
# Can be a better discriminator if we use it.
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0): def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
super().__init__() super().__init__()
@@ -28,9 +26,9 @@ class DurationDiscriminator(nn.Module): #vits2
self.drop = nn.Dropout(p_dropout) self.drop = nn.Dropout(p_dropout)
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2) self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
#self.norm_1 = modules.LayerNorm(filter_channels) self.norm_1 = modules.LayerNorm(filter_channels)
self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2) self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
#self.norm_2 = modules.LayerNorm(filter_channels) self.norm_2 = modules.LayerNorm(filter_channels)
self.dur_proj = nn.Conv1d(1, filter_channels, 1) self.dur_proj = nn.Conv1d(1, filter_channels, 1)
self.pre_out_conv_1 = nn.Conv1d(2*filter_channels, filter_channels, kernel_size, padding=kernel_size//2) self.pre_out_conv_1 = nn.Conv1d(2*filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
@@ -38,8 +36,8 @@ class DurationDiscriminator(nn.Module): #vits2
self.pre_out_conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2) self.pre_out_conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
self.pre_out_norm_2 = modules.LayerNorm(filter_channels) self.pre_out_norm_2 = modules.LayerNorm(filter_channels)
# if gin_channels != 0: if gin_channels != 0:
# self.cond = nn.Conv1d(gin_channels, in_channels, 1) self.cond = nn.Conv1d(gin_channels, in_channels, 1)
self.output_layer = nn.Sequential( self.output_layer = nn.Sequential(
nn.Linear(filter_channels, 1), nn.Linear(filter_channels, 1),
@@ -50,13 +48,13 @@ class DurationDiscriminator(nn.Module): #vits2
dur = self.dur_proj(dur) dur = self.dur_proj(dur)
x = torch.cat([x, dur], dim=1) x = torch.cat([x, dur], dim=1)
x = self.pre_out_conv_1(x * x_mask) x = self.pre_out_conv_1(x * x_mask)
#x = torch.relu(x) x = torch.relu(x)
#x = self.pre_out_norm_1(x) x = self.pre_out_norm_1(x)
#x = self.drop(x) x = self.drop(x)
x = self.pre_out_conv_2(x * x_mask) x = self.pre_out_conv_2(x * x_mask)
#x = torch.relu(x) x = torch.relu(x)
#x = self.pre_out_norm_2(x) x = self.pre_out_norm_2(x)
#x = self.drop(x) x = self.drop(x)
x = x * x_mask x = x * x_mask
x = x.transpose(1, 2) x = x.transpose(1, 2)
output_prob = self.output_layer(x) output_prob = self.output_layer(x)
@@ -64,17 +62,17 @@ class DurationDiscriminator(nn.Module): #vits2
def forward(self, x, x_mask, dur_r, dur_hat, g=None): def forward(self, x, x_mask, dur_r, dur_hat, g=None):
x = torch.detach(x) x = torch.detach(x)
# if g is not None: if g is not None:
# g = torch.detach(g) g = torch.detach(g)
# x = x + self.cond(g) x = x + self.cond(g)
x = self.conv_1(x * x_mask) x = self.conv_1(x * x_mask)
#x = torch.relu(x) x = torch.relu(x)
#x = self.norm_1(x) x = self.norm_1(x)
#x = self.drop(x) x = self.drop(x)
x = self.conv_2(x * x_mask) x = self.conv_2(x * x_mask)
#x = torch.relu(x) x = torch.relu(x)
#x = self.norm_2(x) x = self.norm_2(x)
#x = self.drop(x) x = self.drop(x)
output_probs = [] output_probs = []
for dur in [dur_r, dur_hat]: for dur in [dur_r, dur_hat]: