diff --git a/examples/transformer_tts/synthesis.py b/examples/transformer_tts/synthesis.py
index dc80dc7..fb1bd2f 100644
--- a/examples/transformer_tts/synthesis.py
+++ b/examples/transformer_tts/synthesis.py
@@ -42,13 +42,13 @@ def synthesis(text_input, args):
     with dg.guard(place):
         with fluid.unique_name.guard():
             model = TransformerTTS(cfg)
-            model.set_dict(load_checkpoint(str(args.transformer_step), os.path.join(args.checkpoint_path, "nostop_token/transformer")))
+            model.set_dict(load_checkpoint(str(args.transformer_step), os.path.join(args.checkpoint_path, "transformer")))
             model.eval()
         
         with fluid.unique_name.guard():
-            model_postnet = Vocoder(cfg, args.batch_size)
-            model_postnet.set_dict(load_checkpoint(str(args.postnet_step), os.path.join(args.checkpoint_path, "postnet")))
-            model_postnet.eval()
+            model_vocoder = Vocoder(cfg, args.batch_size)
+            model_vocoder.set_dict(load_checkpoint(str(args.vocoder_step), os.path.join(args.checkpoint_path, "vocoder")))
+            model_vocoder.eval()
         # init input
         text = np.asarray(text_to_sequence(text_input))
         text = fluid.layers.unsqueeze(dg.to_variable(text),[0])
@@ -64,7 +64,7 @@ def synthesis(text_input, args):
             pos_mel = fluid.layers.unsqueeze(dg.to_variable(pos_mel),[0])
             mel_pred, postnet_pred, attn_probs, stop_preds, attn_enc, attn_dec = model(text, mel_input, pos_text, pos_mel)
             mel_input = fluid.layers.concat([mel_input, postnet_pred[:,-1:,:]], axis=1)
-        mag_pred = model_postnet(postnet_pred)
+        mag_pred = model_vocoder(postnet_pred)
 
         _ljspeech_processor = audio.AudioProcessor(
             sample_rate=cfg['audio']['sr'], 
diff --git a/examples/transformer_tts/train_vocoder.py b/examples/transformer_tts/train_vocoder.py
index cc32ca9..857fdf0 100644
--- a/examples/transformer_tts/train_vocoder.py
+++ b/examples/transformer_tts/train_vocoder.py
@@ -38,7 +38,7 @@ def main(args):
     
     if not os.path.exists(args.log_dir):
             os.mkdir(args.log_dir)
-    path = os.path.join(args.log_dir,'postnet')
+    path = os.path.join(args.log_dir,'vocoder')
 
     writer = SummaryWriter(path) if local_rank == 0 else None
 
@@ -51,7 +51,7 @@ def main(args):
 
 
         if args.checkpoint_path is not None:
-            model_dict, opti_dict = load_checkpoint(str(args.vocoder_step), os.path.join(args.checkpoint_path, "postnet"))
+            model_dict, opti_dict = load_checkpoint(str(args.vocoder_step), os.path.join(args.checkpoint_path, "vocoder"))
             model.set_dict(model_dict)
             optimizer.set_dict(opti_dict)
             global_step = args.vocoder_step
@@ -92,7 +92,7 @@ def main(args):
                     if global_step % args.save_step == 0:
                         if not os.path.exists(args.save_path):
                             os.mkdir(args.save_path)
-                        save_path = os.path.join(args.save_path,'postnet/%d' % global_step)
+                        save_path = os.path.join(args.save_path,'vocoder/%d' % global_step)
                         dg.save_dygraph(model.state_dict(), save_path)
                         dg.save_dygraph(optimizer.state_dict(), save_path)
 
@@ -100,7 +100,7 @@ def main(args):
             writer.close()
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description="Train postnet model")
+    parser = argparse.ArgumentParser(description="Train vocoder model")
     add_config_options_to_parser(parser)
     args = parser.parse_args()
     # Print the whole config setting.