diff --git a/TTS/bin/train_vocoder_gan.py b/TTS/bin/train_vocoder_gan.py
index d36704fc..067a166f 100644
--- a/TTS/bin/train_vocoder_gan.py
+++ b/TTS/bin/train_vocoder_gan.py
@@ -14,9 +14,8 @@ from TTS.utils.arguments import parse_arguments, process_args
 from TTS.utils.audio import AudioProcessor
 from TTS.utils.generic_utils import (KeepAverage, count_parameters,
                                      remove_experiment_folder, set_init_dict)
-
-from TTS.utils.radam import RAdam
-
+from TTS.utils.io import copy_model_files, load_config
+from TTS.utils.tensorboard_logger import TensorboardLogger
 from TTS.utils.training import setup_torch_training_env
 from TTS.vocoder.datasets.gan_dataset import GANDataset
 from TTS.vocoder.datasets.preprocess import load_wav_data, load_wav_feat_data
@@ -161,8 +160,6 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
             torch.nn.utils.clip_grad_norm_(model_G.parameters(),
                                            c.gen_clip_grad)
         optimizer_G.step()
-        if scheduler_G is not None:
-            scheduler_G.step()
 
         loss_dict = dict()
         for key, value in loss_G_dict.items():
@@ -221,8 +218,6 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
                 torch.nn.utils.clip_grad_norm_(model_D.parameters(),
                                                c.disc_clip_grad)
             optimizer_D.step()
-            if scheduler_D is not None:
-                scheduler_D.step()
 
             for key, value in loss_D_dict.items():
                 if isinstance(value, (int, float)):
@@ -293,7 +288,12 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
                                           {'train/audio': sample_voice},
                                           c.audio["sample_rate"])
         end_time = time.time()
-        torch.cuda.empty_cache()
+
+    if scheduler_G is not None:
+        scheduler_G.step()
+
+    if scheduler_D is not None:
+        scheduler_D.step()
 
     # print epoch stats
     c_logger.print_train_epoch_end(global_step, epoch, epoch_time, keep_avg)