pep8 check

tests/generic_utils_text.py

@@ -6,6 +6,7 @@ from TTS.layers.tacotron import Prenet, CBHG, Decoder, Encoder
 
 OUT_PATH = '/tmp/test.pth.tar'
 
+
 class ModelSavingTests(unittest.TestCase):
 
     def save_checkpoint_test(self):

tests/layers_tests.py

@@ -20,7 +20,7 @@ class PrenetTests(unittest.TestCase):
 class CBHGTests(unittest.TestCase):
 
     def test_in_out(self):
-        layer = CBHG(128, K= 6, projections=[128, 128], num_highways=2)
+        layer = CBHG(128, K=6, projections=[128, 128], num_highways=2)
         dummy_input = T.autograd.Variable(T.rand(4, 8, 128))
 
         print(layer)
@@ -38,11 +38,11 @@ class DecoderTests(unittest.TestCase):
         dummy_memory = T.autograd.Variable(T.rand(4, 2, 80))
 
         output, alignment = layer(dummy_input, dummy_memory)
-        
+
         assert output.shape[0] == 4
         assert output.shape[1] == 1, "size not {}".format(output.shape[1])
         assert output.shape[2] == 80 * 2, "size not {}".format(output.shape[2])
-        
+
 
 class EncoderTests(unittest.TestCase):
 
@@ -56,10 +56,10 @@ class EncoderTests(unittest.TestCase):
         assert output.shape[0] == 4
         assert output.shape[1] == 8
         assert output.shape[2] == 256  # 128 * 2 BiRNN
-        
+
 
 class L1LossMaskedTests(unittest.TestCase):
-    
+
     def test_in_out(self):
         layer = L1LossMasked()
         dummy_input = T.autograd.Variable(T.ones(4, 8, 128).float())
@@ -69,7 +69,7 @@ class L1LossMaskedTests(unittest.TestCase):
         assert output.shape[0] == 0
         assert len(output.shape) == 1
         assert output.data[0] == 0.0
-        
+
         dummy_input = T.autograd.Variable(T.ones(4, 8, 128).float())
         dummy_target = T.autograd.Variable(T.zeros(4, 8, 128).float())
         dummy_length = T.autograd.Variable((T.ones(4) * 8).long())
@@ -78,7 +78,8 @@ class L1LossMaskedTests(unittest.TestCase):
 
         dummy_input = T.autograd.Variable(T.ones(4, 8, 128).float())
         dummy_target = T.autograd.Variable(T.zeros(4, 8, 128).float())
-        dummy_length = T.autograd.Variable((T.arange(5,9)).long())
-        mask = ((_sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
+        dummy_length = T.autograd.Variable((T.arange(5, 9)).long())
+        mask = ((_sequence_mask(dummy_length).float() - 1.0)
+                * 100.0).unsqueeze(2)
         output = layer(dummy_input + mask, dummy_target, dummy_length)
         assert output.data[0] == 1.0, "1.0 vs {}".format(output.data[0])

tests/loader_tests.py

@@ -10,6 +10,7 @@ from TTS.datasets.LJSpeech import LJSpeechDataset
 file_path = os.path.dirname(os.path.realpath(__file__))
 c = load_config(os.path.join(file_path, 'test_config.json'))
 
+
 class TestDataset(unittest.TestCase):
 
     def __init__(self, *args, **kwargs):
@@ -30,7 +31,7 @@ class TestDataset(unittest.TestCase):
                                   c.ref_level_db,
                                   c.num_freq,
                                   c.power
-                                )
+                                  )
 
         dataloader = DataLoader(dataset, batch_size=2,
                                 shuffle=True, collate_fn=dataset.collate_fn,
@@ -46,7 +47,7 @@ class TestDataset(unittest.TestCase):
             mel_lengths = data[4]
             stop_target = data[5]
             item_idx = data[6]
-            
+
             neg_values = text_input[text_input < 0]
             check_count = len(neg_values)
             assert check_count == 0, \
@@ -70,7 +71,7 @@ class TestDataset(unittest.TestCase):
                                   c.ref_level_db,
                                   c.num_freq,
                                   c.power
-                                )
+                                  )
 
         # Test for batch size 1
         dataloader = DataLoader(dataset, batch_size=1,
@@ -98,8 +99,8 @@ class TestDataset(unittest.TestCase):
             assert stop_target.sum() == 1
             assert len(mel_lengths.shape) == 1
             assert mel_lengths[0] == mel_input[0].shape[0]
-            
-        # Test for batch size 2    
+
+        # Test for batch size 2
         dataloader = DataLoader(dataset, batch_size=2,
                                 shuffle=False, collate_fn=dataset.collate_fn,
                                 drop_last=False, num_workers=c.num_loader_workers)
@@ -115,11 +116,11 @@ class TestDataset(unittest.TestCase):
             stop_target = data[5]
             item_idx = data[6]
 
-            if mel_lengths[0] >  mel_lengths[1]:
+            if mel_lengths[0] > mel_lengths[1]:
                 idx = 0
             else:
                 idx = 1
-                
+
             # check the first item in the batch
             assert mel_input[idx, -1].sum() == 0
             assert mel_input[idx, -2].sum() != 0, mel_input
@@ -130,17 +131,13 @@ class TestDataset(unittest.TestCase):
             assert stop_target[idx].sum() == 1
             assert len(mel_lengths.shape) == 1
             assert mel_lengths[idx] == mel_input[idx].shape[0]
-            
+
             # check the second itme in the batch
             assert mel_input[1-idx, -1].sum() == 0
             assert linear_input[1-idx, -1].sum() == 0
             assert stop_target[1-idx, -1] == 1
             assert len(mel_lengths.shape) == 1
-            
+
             # check batch conditions
             assert (mel_input * stop_target.unsqueeze(2)).sum() == 0
             assert (linear_input * stop_target.unsqueeze(2)).sum() == 0
-
-
-
-