Adding harmonized teacher-forcing

layers/tacotron.py

@@ -286,7 +286,15 @@ class Decoder(nn.Module):
         memory_input = initial_memory
         while True:
             if t > 0:
-                memory_input = outputs[-1] if greedy else memory[t - 1]
+                if greedy:
+                    memory_input = outputs[-1]
+                else:
+                    # combine prev. model output and prev. real target
+                    memory_input = torch.div(outputs[-1] + memory[t-1], 2.0)
+                    # add a random noise
+                    noise = torch.autograd.Variable(
+                        memory_input.data.new(memory_input.size()).normal_(0.0, 1.0))
+                    memory_input = memory_input + noise
 
             # Prenet
             processed_memory = self.prenet(memory_input)
@@ -338,4 +346,4 @@ class Decoder(nn.Module):
 
 
 def is_end_of_frames(output, eps=0.2): #0.2
-    return (output.data <= eps).all()
+    return (output.data <= eps).all()

models/tacotron.py

@@ -34,7 +34,7 @@ class Tacotron(nn.Module):
 
         # (B, T', mel_dim*r)
         mel_outputs, alignments = self.decoder(
-            encoder_outputs, mel_specs, input_lengths=input_lengths)
+            encoder_outputs, mel_specs)
 
         # Post net processing below