diff --git a/datasets/preprocess.py b/datasets/preprocess.py
index 0aabbdf6..89d464a0 100644
--- a/datasets/preprocess.py
+++ b/datasets/preprocess.py
@@ -55,6 +55,6 @@ def nancy(root_path, meta_file):
             id = line.split()[1]
             text = line[line.find('"')+1:line.rfind('"')-1]
             wav_file = root_path + 'wavn/' + id + '.wav'
-            items.append(text, wav_file)
+            items.append([text, wav_file])
     random.shuffle(items)    
     return items
\ No newline at end of file
diff --git a/train.py b/train.py
index 3613d1dc..6d4e7975 100644
--- a/train.py
+++ b/train.py
@@ -51,6 +51,7 @@ def train(model, criterion, criterion_st, data_loader, optimizer, optimizer_st,
         mel_input = data[3]
         mel_lengths = data[4]
         stop_targets = data[5]
+        avg_text_length = torch.mean(text_lengths.float())
 
         # set stop targets view, we predict a single stop token per r frames prediction
         stop_targets = stop_targets.view(text_input.shape[0],
@@ -68,13 +69,13 @@ def train(model, criterion, criterion_st, data_loader, optimizer, optimizer_st,
 
         # dispatch data to GPU
         if use_cuda:
-            text_input = text_input.cuda()
-            text_lengths = text_lengths.cuda()
-            mel_input = mel_input.cuda()
-            mel_lengths = mel_lengths.cuda()
-            linear_input = linear_input.cuda()
-            stop_targets = stop_targets.cuda()
-
+            text_input = text_input.cuda(non_blocking=True)
+            text_lengths = text_lengths.cuda(non_blocking=True)
+            mel_input = mel_input.cuda(non_blocking=True)
+            mel_lengths = mel_lengths.cuda(non_blocking=True)
+            linear_input = linear_input.cuda(non_blocking=True)
+            stop_targets = stop_targets.cuda(non_blocking=True)
+    
         # compute mask for padding
         mask = sequence_mask(text_lengths)
 
@@ -129,10 +130,10 @@ def train(model, criterion, criterion_st, data_loader, optimizer, optimizer_st,
             print(
                 "   | > Step:{}/{}  GlobalStep:{}  TotalLoss:{:.5f}  LinearLoss:{:.5f}  "
                 "MelLoss:{:.5f}  StopLoss:{:.5f}  GradNorm:{:.5f}  "
-                "GradNormST:{:.5f}  StepTime:{:.2f} LR:{:.6f}".format(
+                "GradNormST:{:.5f}  AvgTextLen:{:.1f}  StepTime:{:.2f} LR:{:.6f}".format(
                     num_iter, batch_n_iter, current_step, loss.item(),
                     linear_loss.item(), mel_loss.item(), stop_loss.item(),
-                    grad_norm, grad_norm_st, step_time, current_lr),
+                    grad_norm, grad_norm_st, avg_text_length, step_time, current_lr),
                 flush=True)
 
         avg_linear_loss += linear_loss.item()