guided attn #3

train.py

@@ -106,6 +106,8 @@ def train(model, criterion, data_loader, optimizer, epoch):
             
         # create attention mask
         # TODO: vectorize
+        N = text_input_var.shape[1]
+        T = mel_spec_var.shape[1]
         M = np.zeros([N, T])
         for t in range(T):
             for n in range(N):
@@ -126,7 +128,7 @@ def train(model, criterion, data_loader, optimizer, epoch):
             + 0.5 * criterion(linear_output[:, :, :n_priority_freq],
                               linear_spec_var[:, :, :n_priority_freq],
                               mel_lengths_var)
-        attention_loss = criterion(M, alignments, mel_lengths_var)
+        attention_loss = criterion(Mg, alignments, mel_lengths_var)
         loss = mel_loss + linear_loss + 0.2 * attention_loss
 
         # backpass and check the grad norm