From ca4b39cdcdddbdbc1be9d17a5ea1cb8de62c72ac Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Stardust=C2=B7=E5=87=8F?= <star_dust_chen@foxmail.com>
Date: Sun, 3 Sep 2023 14:23:04 +0800
Subject: [PATCH] fix dur

---
 models.py | 42 +++++++++++++++++++++---------------------
 1 file changed, 21 insertions(+), 21 deletions(-)

diff --git a/models.py b/models.py
index a8e1e9f..5248dcc 100644
--- a/models.py
+++ b/models.py
@@ -26,18 +26,18 @@ class DurationDiscriminator(nn.Module): #vits2
 
     self.drop = nn.Dropout(p_dropout)
     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.norm_2 = modules.LayerNorm(filter_channels)
+    #self.norm_2 = modules.LayerNorm(filter_channels)
     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_norm_1 = modules.LayerNorm(filter_channels)
+    #self.pre_out_norm_1 = modules.LayerNorm(filter_channels)
     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:
-       self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+    #if gin_channels != 0:
+    #   self.cond = nn.Conv1d(gin_channels, in_channels, 1)
 
     self.output_layer = nn.Sequential(
         nn.Linear(filter_channels, 1), 
@@ -48,13 +48,13 @@ class DurationDiscriminator(nn.Module): #vits2
     dur = self.dur_proj(dur)
     x = torch.cat([x, dur], dim=1)
     x = self.pre_out_conv_1(x * x_mask)
-    x = torch.relu(x)
-    x = self.pre_out_norm_1(x)
-    x = self.drop(x)
+    #x = torch.relu(x)
+    #x = self.pre_out_norm_1(x)
+    #x = self.drop(x)
     x = self.pre_out_conv_2(x * x_mask)
-    x = torch.relu(x)
-    x = self.pre_out_norm_2(x)
-    x = self.drop(x)
+    #x = torch.relu(x)
+    #x = self.pre_out_norm_2(x)
+    #x = self.drop(x)
     x = x * x_mask
     x = x.transpose(1, 2)
     output_prob = self.output_layer(x)
@@ -62,17 +62,17 @@ class DurationDiscriminator(nn.Module): #vits2
 
   def forward(self, x, x_mask, dur_r, dur_hat, g=None):
     x = torch.detach(x)
-    if g is not None:
-       g = torch.detach(g)
-       x = x + self.cond(g)
+    #if g is not None:
+    #   g = torch.detach(g)
+    #   x = x + self.cond(g)
     x = self.conv_1(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_1(x)
-    x = self.drop(x)
+    #x = torch.relu(x)
+    #x = self.norm_1(x)
+    #x = self.drop(x)
     x = self.conv_2(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_2(x)
-    x = self.drop(x)
+    #x = torch.relu(x)
+    #x = self.norm_2(x)
+    #x = self.drop(x)
 
     output_probs = []
     for dur in [dur_r, dur_hat]: