diff --git a/synthesizer/models/tacotron.py b/synthesizer/models/tacotron.py
index 0904c8d..0ed665f 100644
--- a/synthesizer/models/tacotron.py
+++ b/synthesizer/models/tacotron.py
@@ -4,6 +4,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from synthesizer.models.global_style_token import GlobalStyleToken
+from synthesizer.gst_hyperparameters import GSTHyperparameters as gst_hp
 
 
 class HighwayNetwork(nn.Module):
@@ -254,12 +255,12 @@ class Decoder(nn.Module):
         self.prenet = PreNet(n_mels, fc1_dims=prenet_dims[0], fc2_dims=prenet_dims[1],
                              dropout=dropout)
         self.attn_net = LSA(decoder_dims)
-        self.attn_rnn = nn.GRUCell(encoder_dims + prenet_dims[1] + speaker_embedding_size, decoder_dims)
-        self.rnn_input = nn.Linear(encoder_dims + decoder_dims + speaker_embedding_size, lstm_dims)
+        self.attn_rnn = nn.GRUCell(encoder_dims + prenet_dims[1] + speaker_embedding_size + gst_hp.E, decoder_dims)
+        self.rnn_input = nn.Linear(encoder_dims  + decoder_dims + speaker_embedding_size + gst_hp.E, lstm_dims)
         self.res_rnn1 = nn.LSTMCell(lstm_dims, lstm_dims)
         self.res_rnn2 = nn.LSTMCell(lstm_dims, lstm_dims)
         self.mel_proj = nn.Linear(lstm_dims, n_mels * self.max_r, bias=False)
-        self.stop_proj = nn.Linear(encoder_dims + speaker_embedding_size + lstm_dims, 1)
+        self.stop_proj = nn.Linear(encoder_dims + speaker_embedding_size + lstm_dims + gst_hp.E, 1)
 
     def zoneout(self, prev, current, p=0.1):
         device = next(self.parameters()).device  # Use same device as parameters
@@ -336,7 +337,7 @@ class Tacotron(nn.Module):
         self.speaker_embedding_size = speaker_embedding_size
         self.encoder = Encoder(embed_dims, num_chars, encoder_dims,
                                encoder_K, num_highways, dropout)
-        self.encoder_proj = nn.Linear(encoder_dims + speaker_embedding_size, decoder_dims, bias=False)
+        self.encoder_proj = nn.Linear(encoder_dims + speaker_embedding_size + gst_hp.E, decoder_dims, bias=False)
         self.gst = GlobalStyleToken()
         self.decoder = Decoder(n_mels, encoder_dims, decoder_dims, lstm_dims,
                                dropout, speaker_embedding_size)
@@ -379,7 +380,7 @@ class Tacotron(nn.Module):
         go_frame = torch.zeros(batch_size, self.n_mels, device=device)
 
         # Need an initial context vector
-        context_vec = torch.zeros(batch_size, self.encoder_dims + self.speaker_embedding_size, device=device)
+        context_vec = torch.zeros(batch_size, self.encoder_dims + self.speaker_embedding_size + gst_hp.E, device=device)
 
         # SV2TTS: Run the encoder with the speaker embedding
         # The projection avoids unnecessary matmuls in the decoder loop
@@ -388,7 +389,7 @@ class Tacotron(nn.Module):
         if self.gst is not None:
             style_embed = self.gst(speaker_embedding) 
             style_embed = style_embed.expand_as(encoder_seq)
-            encoder_seq = encoder_seq + style_embed
+            encoder_seq = torch.cat((encoder_seq, style_embed), 2)
         encoder_seq_proj = self.encoder_proj(encoder_seq)
 
         # Need a couple of lists for outputs
@@ -440,23 +441,24 @@ class Tacotron(nn.Module):
         go_frame = torch.zeros(batch_size, self.n_mels, device=device)
 
         # Need an initial context vector
-        context_vec = torch.zeros(batch_size, self.encoder_dims + self.speaker_embedding_size, device=device)
+        context_vec = torch.zeros(batch_size, self.encoder_dims + self.speaker_embedding_size + gst_hp.E, device=device)
 
         # SV2TTS: Run the encoder with the speaker embedding
         # The projection avoids unnecessary matmuls in the decoder loop
         encoder_seq = self.encoder(x, speaker_embedding)
 
         # put after encoder 
-        if self.gst is not None and style_idx >= 0 and style_idx < 10:
-            gst_embed = self.gst.stl.embed.cpu().data.numpy()  #[0, number_token]
-            gst_embed = np.tile(gst_embed, (1, 8))
-            scale = np.zeros(512)
-            scale[:] = 0.3
-            speaker_embedding = (gst_embed[style_idx] * scale).astype(np.float32)
-            speaker_embedding = torch.from_numpy(np.tile(speaker_embedding, (x.shape[0], 1))).to(device)
+        if self.gst is not None:
+            if style_idx >= 0 and style_idx < 10:
+                gst_embed = self.gst.stl.embed.cpu().data.numpy()  #[0, number_token]
+                gst_embed = np.tile(gst_embed, (1, 8))
+                scale = np.zeros(512)
+                scale[:] = 0.3
+                speaker_embedding = (gst_embed[style_idx] * scale).astype(np.float32)
+                speaker_embedding = torch.from_numpy(np.tile(speaker_embedding, (x.shape[0], 1))).to(device)
             style_embed = self.gst(speaker_embedding)
             style_embed = style_embed.expand_as(encoder_seq)
-            encoder_seq = encoder_seq + style_embed
+            encoder_seq = torch.cat((encoder_seq, style_embed), 2)
         encoder_seq_proj = self.encoder_proj(encoder_seq)
 
         # Need a couple of lists for outputs