Make lint

tests/tts_tests/test_glow_tts.py

@@ -1,8 +1,6 @@
 import copy
 import os
 import unittest
-from TTS.tts.utils.speakers import SpeakerManager
-from TTS.utils.logging.tensorboard_logger import TensorboardLogger
 
 import torch
 from torch import optim
@@ -11,7 +9,9 @@ from tests import get_tests_data_path, get_tests_input_path, get_tests_output_pa
 from TTS.tts.configs.glow_tts_config import GlowTTSConfig
 from TTS.tts.layers.losses import GlowTTSLoss
 from TTS.tts.models.glow_tts import GlowTTS
+from TTS.tts.utils.speakers import SpeakerManager
 from TTS.utils.audio import AudioProcessor
+from TTS.utils.logging.tensorboard_logger import TensorboardLogger
 
 # pylint: disable=unused-variable
 
@@ -31,7 +31,8 @@ def count_parameters(model):
 
 
 class TestGlowTTS(unittest.TestCase):
-    def _create_inputs(self):
+    @staticmethod
+    def _create_inputs():
         input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
         input_lengths = torch.randint(100, 129, (8,)).long().to(device)
         input_lengths[-1] = 128
@@ -40,7 +41,8 @@ class TestGlowTTS(unittest.TestCase):
         speaker_ids = torch.randint(0, 5, (8,)).long().to(device)
         return input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids
 
-    def _check_parameter_changes(self, model, model_ref):
+    @staticmethod
+    def _check_parameter_changes(model, model_ref):
         count = 0
         for param, param_ref in zip(model.parameters(), model_ref.parameters()):
             assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
@@ -166,7 +168,7 @@ class TestGlowTTS(unittest.TestCase):
 
     def _assert_inference_outputs(self, outputs, input_dummy, mel_spec):
         output_shape = outputs["model_outputs"].shape
-        self.assertEqual(outputs["model_outputs"].shape[::2] , mel_spec.shape[::2])
+        self.assertEqual(outputs["model_outputs"].shape[::2], mel_spec.shape[::2])
         self.assertEqual(outputs["logdet"], None)
         self.assertEqual(outputs["y_mean"].shape, output_shape)
         self.assertEqual(outputs["y_log_scale"].shape, output_shape)
@@ -185,7 +187,12 @@ class TestGlowTTS(unittest.TestCase):
     def test_inference_with_d_vector(self):
         input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids = self._create_inputs()
         d_vector = torch.rand(8, 256).to(device)
-        config = GlowTTSConfig(num_chars=32, use_d_vector_file=True, d_vector_dim=256, d_vector_file=os.path.join(get_tests_data_path(), "dummy_speakers.json"))
+        config = GlowTTSConfig(
+            num_chars=32,
+            use_d_vector_file=True,
+            d_vector_dim=256,
+            d_vector_file=os.path.join(get_tests_data_path(), "dummy_speakers.json"),
+        )
         model = GlowTTS.init_from_config(config, verbose=False).to(device)
         model.eval()
         outputs = model.inference(input_dummy, {"x_lengths": input_lengths, "d_vectors": d_vector})
@@ -268,7 +275,9 @@ class TestGlowTTS(unittest.TestCase):
         model = GlowTTS.init_from_config(config, verbose=False).to(device)
         model.run_data_dep_init = False
         model.train()
-        logger = TensorboardLogger(log_dir=os.path.join(get_tests_output_path(), "dummy_glow_tts_logs"), model_name = "glow_tts_test_train_log")
+        logger = TensorboardLogger(
+            log_dir=os.path.join(get_tests_output_path(), "dummy_glow_tts_logs"), model_name="glow_tts_test_train_log"
+        )
         criterion = model.get_criterion()
         outputs, _ = model.train_step(batch, criterion)
         model.train_log(batch, outputs, logger, None, 1)
@@ -316,14 +325,23 @@ class TestGlowTTS(unittest.TestCase):
         self.assertTrue(model.num_speakers == 2)
         self.assertTrue(hasattr(model, "emb_g"))
 
-        config = GlowTTSConfig(num_chars=32, num_speakers=2, use_speaker_embedding=True, speakers_file=os.path.join(get_tests_data_path(), "ljspeech", "speakers.json"))
+        config = GlowTTSConfig(
+            num_chars=32,
+            num_speakers=2,
+            use_speaker_embedding=True,
+            speakers_file=os.path.join(get_tests_data_path(), "ljspeech", "speakers.json"),
+        )
         model = GlowTTS.init_from_config(config, verbose=False).to(device)
         self.assertTrue(model.num_speakers == 10)
         self.assertTrue(hasattr(model, "emb_g"))
 
-        config = GlowTTSConfig(num_chars=32, use_d_vector_file=True, d_vector_dim=256, d_vector_file=os.path.join(get_tests_data_path(), "dummy_speakers.json"))
+        config = GlowTTSConfig(
+            num_chars=32,
+            use_d_vector_file=True,
+            d_vector_dim=256,
+            d_vector_file=os.path.join(get_tests_data_path(), "dummy_speakers.json"),
+        )
         model = GlowTTS.init_from_config(config, verbose=False).to(device)
         self.assertTrue(model.num_speakers == 1)
         self.assertTrue(not hasattr(model, "emb_g"))
         self.assertTrue(model.c_in_channels == config.d_vector_dim)
-