Fix the bug of DecoderPrenetWrapper

We should put DecoderPrenetWrapper outside AttentionWrapper. Otherwise, the AttentionWrapper concatenates the input and previous attention vector before calling the internal cell, so dropout in the DecoderPrenetWrapper will drop 50% information of previous attention vector.

models/tacotron.py

@@ -49,10 +49,13 @@ def initialize(self, inputs, input_lengths, mel_targets=None, linear_targets=Non
 
       # Attention
       attention_cell = AttentionWrapper(
-        DecoderPrenetWrapper(GRUCell(hp.attention_depth), is_training, hp.prenet_depths),
+        GRUCell(hp.attention_depth),
         BahdanauAttention(hp.attention_depth, encoder_outputs),
         alignment_history=True,
         output_attention=False)                                                  # [N, T_in, attention_depth=256]
+
+      # Apply prenet before concatenation in AttentionWrapper.
+      attention_cell = DecoderPrenetWrapper(attention_cell, is_training, hp.prenet_depths)
 
       # Concatenate attention context vector and RNN cell output into a 2*attention_depth=512D vector.
       concat_cell = ConcatOutputAndAttentionWrapper(attention_cell)              # [N, T_in, 2*attention_depth=512]