use BN for prenet

config_cluster.json

@@ -63,4 +63,4 @@
     "use_phonemes": true,           // use phonemes instead of raw characters. It is suggested for better pronounciation.
     "phoneme_language": "en-us",     // depending on your target language, pick one from  https://github.com/bootphon/phonemizer#languages
     "text_cleaner": "phoneme_cleaners"
-}
+}

layers/tacotron2.py

@@ -25,20 +25,43 @@ class Linear(nn.Module):
         return self.linear_layer(x)
 
 
+class LinearBN(nn.Module):
+    def __init__(self,
+                 in_features,
+                 out_features,
+                 bias=True,
+                 init_gain='linear'):
+        super(LinearBN, self).__init__()
+        self.linear_layer = torch.nn.Linear(
+            in_features, out_features, bias=bias)
+        self.bn = nn.BatchNorm1d(out_features)
+        self._init_w(init_gain)
+
+    def _init_w(self, init_gain):
+        torch.nn.init.xavier_uniform_(
+            self.linear_layer.weight,
+            gain=torch.nn.init.calculate_gain(init_gain))
+
+    def forward(self, x):
+        out = self.linear_layer(x)
+        out = out.transpose(0, 1).transpose(1, 2)
+        out = self.bn(out)
+        out = out.transpose(1, 2).transpose(0, 1)
+        return out
+
+
 class Prenet(nn.Module):
     def __init__(self, in_features, out_features=[256, 256]):
         super(Prenet, self).__init__()
         in_features = [in_features] + out_features[:-1]
         self.layers = nn.ModuleList([
-            Linear(in_size, out_size, bias=False)
+            LinearBN(in_size, out_size, bias=False)
             for (in_size, out_size) in zip(in_features, out_features)
         ])
 
     def forward(self, x):
         for linear in self.layers:
-            # Prenet uses dropout also at inference time. Otherwise,
-            # it degrades the inference time attention.
-            x = F.dropout(F.relu(linear(x)), p=0.5, training=True)
+            x = F.relu(linear(x))
         return x