init with embedding lyaers

layers/tacotron.py

@@ -323,6 +323,10 @@ class Decoder(nn.Module):
             [nn.GRUCell(256, 256) for _ in range(2)])
         # RNN_state -> |Linear| -> mel_spec
         self.proj_to_mel = nn.Linear(256, memory_dim * r)
+        # learn init values instead of zero init.
+        self.attention_rnn_init = nn.Embedding(1, 256)
+        self.memory_init = nn.Embedding(1, r * memory_dim)
+        self.decoder_rnn_inits = nn.Embedding(2, 256)
         self.stopnet = StopNet(256 + memory_dim * r)
         # self.init_layers()
 
@@ -354,12 +358,12 @@ class Decoder(nn.Module):
         B = inputs.size(0)
         T = inputs.size(1)
         # go frame as zeros matrix
-        initial_memory = inputs.data.new(B, self.memory_dim * self.r).zero_()
+        initial_memory = self.memory_init(inputs.data.new_zeros(B).long())
         # decoder states
-        attention_rnn_hidden = inputs.data.new(B, 256).zero_()
+        attention_rnn_hidden = self.attention_rnn_init(inputs.data.new_zeros(B).long())
         decoder_rnn_hiddens = [
-            inputs.data.new(B, 256).zero_()
-            for _ in range(len(self.decoder_rnns))
+            self.decoder_rnn_inits(inputs.data.new_tensor([idx]*B).long())
+            for idx in range(len(self.decoder_rnns))
         ]
         current_context_vec = inputs.data.new(B, self.in_features).zero_()
         # attention states