diff --git a/synthesizer/models/tacotron.py b/synthesizer/models/tacotron.py
index 87a481a..c1d7db3 100644
--- a/synthesizer/models/tacotron.py
+++ b/synthesizer/models/tacotron.py
@@ -358,27 +358,6 @@ class Tacotron(nn.Module):
     @r.setter
     def r(self, value):
         self.decoder.r = self.decoder.r.new_tensor(value, requires_grad=False)
-
-    def compute_gst(self, inputs, style_input, speaker_embedding=None):
-        """ Compute global style token """
-        device = inputs.device
-        if isinstance(style_input, dict):
-            query = torch.zeros(1, 1, self.gst_embedding_dim//2).to(device)
-            if speaker_embedding is not None:
-                query = torch.cat([query, speaker_embedding.reshape(1, 1, -1)], dim=-1)
-
-            _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, speaker_embedding) # pylint: disable=not-callable
-        inputs = self._concat_speaker_embedding(inputs, gst_outputs)
-        return inputs
     
     @staticmethod
     def _concat_speaker_embedding(outputs, speaker_embeddings):
@@ -486,7 +465,7 @@ class Tacotron(nn.Module):
                 speaker_embedding_style = (gst_embed[style_idx] * scale).astype(np.float32)
                 speaker_embedding_style = torch.from_numpy(np.tile(speaker_embedding_style, (x.shape[0], 1))).to(device)
             else:
-                speaker_embedding_style = torch.zeros(2, 1, self.speaker_embedding_size).to(device)
+                speaker_embedding_style = torch.zeros(speaker_embedding.size()[0], 1, self.speaker_embedding_size).to(device)
             style_embed = self.gst(speaker_embedding_style, speaker_embedding)
             encoder_seq = self._concat_speaker_embedding(encoder_seq, style_embed)
             # style_embed = style_embed.expand_as(encoder_seq)