diff --git a/datasets/LJSpeech.py b/datasets/LJSpeech.py
index 7638e708..0c90ab75 100644
--- a/datasets/LJSpeech.py
+++ b/datasets/LJSpeech.py
@@ -18,7 +18,7 @@ class LJSpeechDataset(Dataset):
                  frame_length_ms, preemphasis, ref_level_db, num_freq, power,
                  min_seq_len=0):
 
-        with open(csv_file, "r") as f:
+        with open(csv_file, "r", encoding="utf8") as f:
             self.frames = [line.split('|') for line in f]
         self.root_dir = root_dir
         self.outputs_per_step = outputs_per_step
diff --git a/train.py b/train.py
index 837b5f24..a227a1ac 100644
--- a/train.py
+++ b/train.py
@@ -48,12 +48,6 @@ OUT_PATH = create_experiment_folder(OUT_PATH, c.model_name, args.debug)
 CHECKPOINT_PATH = os.path.join(OUT_PATH, 'checkpoints')
 shutil.copyfile(args.config_path, os.path.join(OUT_PATH, 'config.json'))
 
-parser.add_argument('--finetine_path', type=str)
-# save config to tmp place to be loaded by subsequent modules.
-file_name = str(os.getpid())
-tmp_path = os.path.join("/tmp/", file_name+'_tts')
-pickle.dump(c, open(tmp_path, "wb"))
-
 # setup tensorboard
 LOG_DIR = OUT_PATH
 tb = SummaryWriter(LOG_DIR)
@@ -150,9 +144,9 @@ def train(model, criterion, criterion_st, data_loader, optimizer, optimizer_st,
         #                                    ('grad_norm_st', grad_norm_st.item())])
 
         if current_step % c.print_step == 0:
-            print(" | | > Step:{}  GlobalStep:{}  TotalLoss:{:.5f}  LinearLoss:{:.5f}  MelLoss:\
-                  {:.5f}  StopLoss:{:.5f}  GradNorm:{:.5f}  \
-                  GradNormST:{:.5f}  StepTime:{:.2f}".format(num_iter, current_step,
+            print(" | | > Step:{}  GlobalStep:{}  TotalLoss:{:.5f}  LinearLoss:{:.5f}"\
+                  "MelLoss:{:.5f}  StopLoss:{:.5f}  GradNorm:{:.5f}"\
+                  "GradNormST:{:.5f}  StepTime:{:.2f}".format(num_iter, current_step,
                                              loss.item(),
                                              linear_loss.item(),
                                              mel_loss.item(),
@@ -215,9 +209,9 @@ def train(model, criterion, criterion_st, data_loader, optimizer, optimizer_st,
     avg_total_loss = avg_mel_loss + avg_linear_loss + avg_stop_loss
 
     # print epoch stats
-    print(" | | > EPOCH END -- GlobalStep:{}  AvgTotalLoss:{:.5f}  \
-          AvgLinearLoss:{:.5f}  AvgMelLoss:{:.5f}  \
-          AvgStopLoss:{:.5f}  EpochTime:{:.2f}".format(current_step,
+    print(" | | > EPOCH END -- GlobalStep:{}  AvgTotalLoss:{:.5f}"\
+          "AvgLinearLoss:{:.5f}  AvgMelLoss:{:.5f}"\
+          "AvgStopLoss:{:.5f}  EpochTime:{:.2f}".format(current_step,
                                                        avg_total_loss,
                                                        avg_linear_loss,
                                                        avg_mel_loss,
@@ -290,8 +284,8 @@ def evaluate(model, criterion, criterion_st, data_loader, current_step):
             #                                    ('mel_loss', mel_loss.item()),
             #                                    ('stop_loss', stop_loss.item())])
             if current_step % c.print_step == 0:
-                print(" | | > TotalLoss: {:.5f}   LinearLoss: {:.5f}   MelLoss: \
-                      {:.5f}   StopLoss: {:.5f}  ".format(loss.item(),
+                print(" | | > TotalLoss: {:.5f}   LinearLoss: {:.5f}   MelLoss:{:.5f}"\
+                      "StopLoss: {:.5f}  ".format(loss.item(),
                                                           linear_loss.item(),
                                                           mel_loss.item(),
                                                           stop_loss.item()))