diff --git a/tests/test_layers.py b/tests/test_layers.py
index 6e3c4b1..7d02b67 100644
--- a/tests/test_layers.py
+++ b/tests/test_layers.py
@@ -119,7 +119,7 @@ class EncoderTests(unittest.TestCase):
 class L1LossMaskedTests(unittest.TestCase):
     def test_in_out(self):
         # test input == target
-        layer = L1LossMasked()
+        layer = L1LossMasked(seq_len_norm=False)
         dummy_input = T.ones(4, 8, 128).float()
         dummy_target = T.ones(4, 8, 128).float()
         dummy_length = (T.ones(4) * 8).long()
diff --git a/tests/test_tacotron2_model.py b/tests/test_tacotron2_model.py
index a26f1dd..aa2869e 100644
--- a/tests/test_tacotron2_model.py
+++ b/tests/test_tacotron2_model.py
@@ -38,7 +38,7 @@ class TacotronTrainTest(unittest.TestCase):
                                          stop_targets.size(1) // c.r, -1)
         stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
 
-        criterion = MSELossMasked().to(device)
+        criterion = MSELossMasked(seq_len_norm=False).to(device)
         criterion_st = nn.BCEWithLogitsLoss().to(device)
         model = Tacotron2(num_chars=24, r=c.r, num_speakers=5).to(device)
         model.train()
diff --git a/tests/test_tacotron_model.py b/tests/test_tacotron_model.py
index 7e5e8da..ac6712b 100644
--- a/tests/test_tacotron_model.py
+++ b/tests/test_tacotron_model.py
@@ -44,7 +44,7 @@ class TacotronTrainTest(unittest.TestCase):
         stop_targets = (stop_targets.sum(2) >
                         0.0).unsqueeze(2).float().squeeze()
 
-        criterion = L1LossMasked().to(device)
+        criterion = L1LossMasked(seq_len_norm=False).to(device)
         criterion_st = nn.BCEWithLogitsLoss().to(device)
         model = Tacotron(
             num_chars=32,
@@ -106,7 +106,7 @@ class TacotronGSTTrainTest(unittest.TestCase):
         stop_targets = (stop_targets.sum(2) >
                         0.0).unsqueeze(2).float().squeeze()
 
-        criterion = L1LossMasked().to(device)
+        criterion = L1LossMasked(seq_len_norm=False).to(device)
         criterion_st = nn.BCEWithLogitsLoss().to(device)
         model = Tacotron(
             num_chars=32,