From 30ffd2caad7122ccbaecb767af6b7cb84648b0bd Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Stardust=C2=B7=E5=87=8F?= <2225664821@qq.com>
Date: Wed, 23 Aug 2023 16:52:17 +0800
Subject: [PATCH] Update models.py

---
 models.py | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/models.py b/models.py
index 00c9b62..58af3da 100644
--- a/models.py
+++ b/models.py
@@ -28,9 +28,9 @@ 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)
@@ -50,13 +50,13 @@ 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)
@@ -68,13 +68,13 @@ class DurationDiscriminator(nn.Module): #vits2
     #   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]: