Update wavenet.py (#1796)

* Update wavenet.py

Current version does not use "in_channels" argument. 
In glowTTS, we use normalizing flows and so "input dim" == "ouput dim" (channels and length). So, the existing code just uses hidden_channel sized tensor as input to first layer as well as outputs hidden_channel sized tensor. 
However, since it is a generic implementation, I believe it is better to update it for a more general use.

* "in_channels -> hidden_channels"

TTS/tts/layers/generic/wavenet.py

@@ -67,9 +67,14 @@ class WN(torch.nn.Module):
         for i in range(num_layers):
             dilation = dilation_rate**i
             padding = int((kernel_size * dilation - dilation) / 2)
-            in_layer = torch.nn.Conv1d(
-                hidden_channels, 2 * hidden_channels, kernel_size, dilation=dilation, padding=padding
-            )
+            if i == 0:
+                in_layer = torch.nn.Conv1d(
+                    in_channels, 2 * hidden_channels, kernel_size, dilation=dilation, padding=padding
+                )
+            else:
+                in_layer = torch.nn.Conv1d(
+                    hidden_channels, 2 * hidden_channels, kernel_size, dilation=dilation, padding=padding
+                )
             in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
             self.in_layers.append(in_layer)
 

TTS/tts/layers/glow_tts/glow.py

@@ -197,7 +197,7 @@ class CouplingBlock(nn.Module):
         end.bias.data.zero_()
         self.end = end
         # coupling layers
-        self.wn = WN(in_channels, hidden_channels, kernel_size, dilation_rate, num_layers, c_in_channels, dropout_p)
+        self.wn = WN(hidden_channels, hidden_channels, kernel_size, dilation_rate, num_layers, c_in_channels, dropout_p)
 
     def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs):  # pylint: disable=unused-argument
         """