layers.py: rewrite

layers.py

@@ -10,7 +10,7 @@ class LinearNorm(torch.nn.Module):
         super(LinearNorm, self).__init__()
         self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
 
-        torch.nn.init.xavier_uniform(
+        torch.nn.init.xavier_uniform_(
             self.linear_layer.weight,
             gain=torch.nn.init.calculate_gain(w_init_gain))
 
@@ -31,7 +31,7 @@ class ConvNorm(torch.nn.Module):
                                     padding=padding, dilation=dilation,
                                     bias=bias)
 
-        torch.nn.init.xavier_uniform(
+        torch.nn.init.xavier_uniform_(
             self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
 
     def forward(self, signal):
@@ -42,7 +42,7 @@ class ConvNorm(torch.nn.Module):
 class TacotronSTFT(torch.nn.Module):
     def __init__(self, filter_length=1024, hop_length=256, win_length=1024,
                  n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0,
-                 mel_fmax=None):
+                 mel_fmax=8000.0):
         super(TacotronSTFT, self).__init__()
         self.n_mel_channels = n_mel_channels
         self.sampling_rate = sampling_rate