diff --git a/model/modules.py b/model/modules.py
index 6423a2d..5849ed9 100644
--- a/model/modules.py
+++ b/model/modules.py
@@ -1,6 +1,5 @@
 import torch
-import torch.nn.functional as Func
-from torch.nn import Module
+from torch.nn import Module, GRU
 from torch.nn import Linear, Conv1d, MaxPool1d, Dropout, BatchNorm1d, ReLU, Sigmoid
 
 
@@ -129,8 +128,6 @@ class Conv1dBanks(Module):
     def __init__(self, k, in_dims, out_dims, activation):
         super(Conv1dBanks, self).__init__()
 
-        self.conv1_norm_outputs = []
-
         self.k = k
         self.in_dims = in_dims
         self.out_dims = out_dims
@@ -138,22 +135,20 @@ class Conv1dBanks(Module):
 
     def forward(self, inputs):
         # inputs : (N, in_dims , L_in)
+        conv1_norm_outputs = []
         for k_size in range(1, 1 + self.k):
             conv1d_norm = Conv1dNorm(self.in_dims, self.out_dims, k_size, self.activation)
             # conv1d_norm_outputs : (N, out_dims, L_out)
             # L_in == L_out
             conv1d_norm_outputs = conv1d_norm(inputs)
-            self.conv1_norm_outputs.append(conv1d_norm_outputs)
+            conv1_norm_outputs.append(conv1d_norm_outputs)
 
         # conv1d_banks : (N, k*out_dims, L_out)
-        conv1d_banks = torch.cat(self.conv1_norm_outputs, 1)
+        conv1d_banks = torch.cat(conv1_norm_outputs, 1)
 
         return conv1d_banks
 
 
-# TODO : Add GRU Model
-
-
 class CBHG(Module):
     def __init__(self, k, num_highway_blocks, in_dims, out_dims, activation):
         super(CBHG, self).__init__()
@@ -172,34 +167,45 @@ class CBHG(Module):
 
         self.highway = HighwayNet(in_dims=out_dims)
 
+        self.gru = GRU(out_dims, out_dims, batch_first=True, bidirectional=True)
+
     def forward(self, inputs):
         # inputs : (N, in_dims, L_in)
         # conv1d_banks_outputs : (N, k*out_dims, L_in)
         conv1d_banks_outputs = self.conv1d_banks(inputs)
-        print("conv1d_banks_outputs : " + str(conv1d_banks_outputs.shape))
 
         # Cut out the rest
         max_pool1d_outputs = self.max_pool1d(conv1d_banks_outputs)
         max_pool1d_outputs = max_pool1d_outputs[:, :, :-1]
         # max_pool1d_outputs : (N, k*out_dims, L_in)
-        print("max_pool1d_outputs : " + str(max_pool1d_outputs.shape))
 
         assert conv1d_banks_outputs.shape == max_pool1d_outputs.shape
 
         # projection1_outputs : (N, out_dims, L_in)
         projection1_outputs = self.projection1(max_pool1d_outputs)
-        print("projection1_outputs : " + str(projection1_outputs.shape))
         # projection2_outputs : (N, out_dims, L_in)
         projection2_outputs = self.projection2(projection1_outputs)
-        print("projection2_outputs : " + str(projection2_outputs.shape))
 
         # residual_connections : (N, out_dims, L_in)
         residual_connections = projection2_outputs + inputs
         highway_data = residual_connections.transpose(2, 1)
+        # highway_data : (N, L_in, out_dims)
 
+        # highway_data : (N, L_in, out_dims)
         for i in range(self.num_highway_blocks):
             highway_data = self.highway(highway_data)
 
-        # TODO : Add GRU Model
+        # gru_output : (N, L_in, out_dims*2)
+        gru_output, _ = self.gru(highway_data)
 
-        return highway_data
+        debug = False
+        if debug:
+            print("conv1d_banks_outputs : " + str(conv1d_banks_outputs.shape))
+            print("max_pool1d_outputs : " + str(max_pool1d_outputs.shape))
+            print("projection1_outputs : " + str(projection1_outputs.shape))
+            print("projection2_outputs : " + str(projection2_outputs.shape))
+            print("highway_data_1 : " + str(highway_data.shape))
+            print("highway_data_2 : " + str(highway_data.shape))
+            print("gru_output : " + str(gru_output.shape))
+
+        return gru_output