diff --git a/train.py b/train.py
index 54ec06ef..2c2fd3c6 100644
--- a/train.py
+++ b/train.py
@@ -119,10 +119,12 @@ def train(model, criterion, data_loader, optimizer, epoch):
 
         # loss computation
         mel_loss = criterion(mel_output, mel_spec, mel_lengths)
-        linear_loss = 0.5 * criterion(linear_output, linear_spec, mel_lengths) \
-            + 0.5 * criterion(linear_output[:, :, :n_priority_freq],
-                              linear_spec[:, :, :n_priority_freq],
-                              mel_lengths)
+        linear_loss = criterion(linear_output, linear_spec, mel_lengths)
+        if c.priority_freq:
+            linear_loss =  0.5 * linear_loss +
+                + 0.5 * criterion(linear_output[:, :, :n_priority_freq],
+                                  linear_spec[:, :, :n_priority_freq],
+                                  mel_lengths)
         loss = mel_loss + linear_loss
         if c.mk > 0.0:
             attention_loss = criterion(alignments, M, mel_lengths)
@@ -244,10 +246,12 @@ def evaluate(model, criterion, data_loader, current_step):
 
             # loss computation
             mel_loss = criterion(mel_output, mel_spec, mel_lengths)
-            linear_loss = 0.5 * criterion(linear_output, linear_spec, mel_lengths) \
-                + 0.5 * criterion(linear_output[:, :, :n_priority_freq],
-                                  linear_spec[:, :, :n_priority_freq],
-                                  mel_lengths)
+            linear_loss = criterion(linear_output, linear_spec, mel_lengths)
+            if c.priority_freq:
+                linear_loss =  0.5 * linear_loss +
+                    + 0.5 * criterion(linear_output[:, :, :n_priority_freq],
+                                      linear_spec[:, :, :n_priority_freq],
+                                      mel_lengths)
             loss = mel_loss + linear_loss
 
             step_time = time.time() - start_time