override compute_gst in tacotron2 model

models/tacotron2.py

@@ -47,17 +47,22 @@ def __init__(self,
         decoder_in_features = 512+speaker_embedding_dim+gst_embedding_dim
         encoder_in_features = 512 if num_speakers > 1 else 512
         proj_speaker_dim = 80 if num_speakers > 1 else 0
-        # base layers
+
+        # embedding layer
         self.embedding = nn.Embedding(num_chars, 512, padding_idx=0)
+
+        # speaker embedding layer
         if num_speakers > 1:
             self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
             self.speaker_embedding.weight.data.normal_(0, 0.3)
+
         self.encoder = Encoder(encoder_in_features)
         self.decoder = Decoder(decoder_in_features, self.decoder_output_dim, r, attn_type, attn_win,
                                attn_norm, prenet_type, prenet_dropout,
                                forward_attn, trans_agent, forward_attn_mask,
                                location_attn, attn_K, separate_stopnet, proj_speaker_dim)
         self.postnet = Postnet(self.postnet_output_dim)
+
         # global style token layers
         if self.gst:
             self.gst_layer = GST(num_mel=80,
@@ -81,6 +86,24 @@ def shape_outputs(mel_outputs, mel_outputs_postnet, alignments):
         mel_outputs_postnet = mel_outputs_postnet.transpose(1, 2)
         return mel_outputs, mel_outputs_postnet, alignments
 
+    def compute_gst(self, inputs, style_input):
+        """ Compute global style token """
+        device = inputs.device
+        if isinstance(style_input, dict):
+            query = torch.zeros(1, 1, self.gst_embedding_dim//2).to(device)
+            _GST = torch.tanh(self.gst_layer.style_token_layer.style_tokens)
+            gst_outputs = torch.zeros(1, 1, self.gst_embedding_dim).to(device)
+            for k_token, v_amplifier in style_input.items():
+                key = _GST[int(k_token)].unsqueeze(0).expand(1, -1, -1)
+                gst_outputs_att = self.gst_layer.style_token_layer.attention(query, key)
+                gst_outputs = gst_outputs + gst_outputs_att * v_amplifier
+        elif style_input is None:
+            gst_outputs = torch.zeros(1, 1, self.gst_embedding_dim).to(device)
+        else:
+            gst_outputs = self.gst_layer(style_input) # pylint: disable=not-callable
+        embedded_gst = gst_outputs.repeat(1, inputs.size(1), 1)
+        return inputs, embedded_gst
+
     def forward(self, text, text_lengths, mel_specs=None, mel_lengths=None, speaker_ids=None):
         # compute mask for padding
         # B x T_in_max (boolean)

models/tacotron_abstract.py

@@ -164,22 +164,12 @@ def compute_speaker_embedding(self, speaker_ids):
             self.speaker_embeddings_projected = self.speaker_project_mel(
                 self.speaker_embeddings).squeeze(1)
 
-    def compute_gst(self, inputs, style_input):
-        device = inputs.device
-        if isinstance(style_input, dict):
-            query = torch.zeros(1, 1, self.gst_embedding_dim//2).to(device)
-            _GST = torch.tanh(self.gst_layer.style_token_layer.style_tokens)
-            gst_outputs = torch.zeros(1, 1, self.gst_embedding_dim).to(device)
-            for k_token, v_amplifier in style_input.items():
-                key = _GST[int(k_token)].unsqueeze(0).expand(1, -1, -1)
-                gst_outputs_att = self.gst_layer.style_token_layer.attention(query, key)
-                gst_outputs = gst_outputs + gst_outputs_att * v_amplifier
-        elif style_input is None:
-            gst_outputs = torch.zeros(1, 1, self.gst_embedding_dim).to(device)
-        else:
-            gst_outputs = self.gst_layer(style_input) # pylint: disable=not-callable     
-        embedded_gst = gst_outputs.repeat(1, inputs.size(1), 1)
-        return inputs, embedded_gst
+    def compute_gst(self, inputs, mel_specs):
+        """ Compute global style token """
+        # pylint: disable=not-callable
+        gst_outputs = self.gst_layer(mel_specs)
+        inputs = self._add_speaker_embedding(inputs, gst_outputs)
+        return inputs
 
     @staticmethod
     def _add_speaker_embedding(outputs, speaker_embeddings):