diff --git a/tests/test_tacotron2_model.py b/tests/test_tacotron2_model.py
index 5dfd7759..43003227 100644
--- a/tests/test_tacotron2_model.py
+++ b/tests/test_tacotron2_model.py
@@ -2,7 +2,6 @@ import os
 import copy
 import torch
 import unittest
-import numpy as np
 
 from torch import optim
 from torch import nn
@@ -21,7 +20,8 @@ c = load_config(os.path.join(file_path, 'test_config.json'))
 
 
 class TacotronTrainTest(unittest.TestCase):
-    def test_train_step(self):
+    @staticmethod
+    def test_train_step():
         input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
         input_lengths = torch.randint(100, 128, (8, )).long().to(device)
         input_lengths = torch.sort(input_lengths, descending=True)[0]
@@ -71,59 +71,3 @@ class TacotronTrainTest(unittest.TestCase):
             ), "param {} with shape {} not updated!! \n{}\n{}".format(
                 count, param.shape, param, param_ref)
             count += 1
-
-
-class TacotronGSTTrainTest(unittest.TestCase):
-    def test_train_step(self):
-        input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
-        input_lengths = torch.randint(100, 128, (8, )).long().to(device)
-        input_lengths = torch.sort(input_lengths, descending=True)[0]
-        mel_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
-        mel_postnet_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
-        mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
-        mel_lengths[0] = 30
-        stop_targets = torch.zeros(8, 30, 1).float().to(device)
-        speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
-
-        for idx in mel_lengths:
-            stop_targets[:, int(idx.item()):, 0] = 1.0
-
-        stop_targets = stop_targets.view(input_dummy.shape[0],
-                                         stop_targets.size(1) // c.r, -1)
-        stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
-
-        criterion = MSELossMasked(seq_len_norm=False).to(device)
-        criterion_st = nn.BCEWithLogitsLoss().to(device)
-        model = Tacotron2(num_chars=24,
-                          gst=True,
-                          r=c.r,
-                          num_speakers=5).to(device)
-        model.train()
-        model_ref = copy.deepcopy(model)
-        count = 0
-        for param, param_ref in zip(model.parameters(),
-                                    model_ref.parameters()):
-            assert (param - param_ref).sum() == 0, param
-            count += 1
-        optimizer = optim.Adam(model.parameters(), lr=c.lr)
-        for i in range(5):
-            mel_out, mel_postnet_out, align, stop_tokens = model.forward(
-                input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids)
-            assert torch.sigmoid(stop_tokens).data.max() <= 1.0
-            assert torch.sigmoid(stop_tokens).data.min() >= 0.0
-            optimizer.zero_grad()
-            loss = criterion(mel_out, mel_spec, mel_lengths)
-            stop_loss = criterion_st(stop_tokens, stop_targets)
-            loss = loss + criterion(mel_postnet_out, mel_postnet_spec, mel_lengths) + stop_loss
-            loss.backward()
-            optimizer.step()
-        # check parameter changes
-        count = 0
-        for param, param_ref in zip(model.parameters(),
-                                    model_ref.parameters()):
-            # ignore pre-higway layer since it works conditional
-            # if count not in [145, 59]:
-            assert (param != param_ref).any(
-            ), "param {} with shape {} not updated!! \n{}\n{}".format(
-                count, param.shape, param, param_ref)
-            count += 1
diff --git a/utils/generic_utils.py b/utils/generic_utils.py
index 8b4b1f12..3bb99e08 100644
--- a/utils/generic_utils.py
+++ b/utils/generic_utils.py
@@ -359,8 +359,8 @@ def check_config(c):
 
     # GST
     _check_argument('use_gst', c, restricted=True, val_type=bool)
-    _check_argument('gst_style_input', c, restricted=True, val_type=str)
     _check_argument('gst', c, restricted=True, val_type=dict)
+    _check_argument('gst_style_input', c['gst'], restricted=True, val_type=str)
     _check_argument('gst_embedding_dim', c['gst'], restricted=True, val_type=int, min_val=1)
     _check_argument('gst_num_heads', c['gst'], restricted=True, val_type=int, min_val=1)
     _check_argument('gst_style_tokens', c['gst'], restricted=True, val_type=int, min_val=1)