reformatting and styling

tests/test_speaker_encoder.py

@@ -2,9 +2,9 @@ import os
 import unittest
 
 import torch as T
-from tests import get_tests_input_path
 
-from TTS.speaker_encoder.losses import GE2ELoss, AngleProtoLoss
+from tests import get_tests_input_path
+from TTS.speaker_encoder.losses import AngleProtoLoss, GE2ELoss
 from TTS.speaker_encoder.model import SpeakerEncoder
 from TTS.utils.io import load_config
 
@@ -17,9 +17,7 @@ class SpeakerEncoderTests(unittest.TestCase):
     def test_in_out(self):
         dummy_input = T.rand(4, 20, 80)  # B x T x D
         dummy_hidden = [T.rand(2, 4, 128), T.rand(2, 4, 128)]
-        model = SpeakerEncoder(
-            input_dim=80, proj_dim=256, lstm_dim=768, num_lstm_layers=3
-        )
+        model = SpeakerEncoder(input_dim=80, proj_dim=256, lstm_dim=768, num_lstm_layers=3)
         # computing d vectors
         output = model.forward(dummy_input)
         assert output.shape[0] == 4
@@ -36,9 +34,7 @@ class SpeakerEncoderTests(unittest.TestCase):
         output_norm = T.nn.functional.normalize(output, dim=1, p=2)
         assert_diff = (output_norm - output).sum().item()
         assert output.type() == "torch.FloatTensor"
-        assert (
-            abs(assert_diff) < 1e-4
-        ), f" [!] output_norm has wrong values - {assert_diff}"
+        assert abs(assert_diff) < 1e-4, f" [!] output_norm has wrong values - {assert_diff}"
         # compute d for a given batch
         dummy_input = T.rand(1, 240, 80)  # B x T x D
         output = model.compute_embedding(dummy_input, num_frames=160, overlap=0.5)
@@ -74,6 +70,7 @@ class GE2ELossTests(unittest.TestCase):
         output = loss.forward(dummy_input)
         assert output.item() < 0.005
 
+
 class AngleProtoLossTests(unittest.TestCase):
     # pylint: disable=R0201
     def test_in_out(self):
@@ -103,6 +100,7 @@ class AngleProtoLossTests(unittest.TestCase):
         output = loss.forward(dummy_input)
         assert output.item() < 0.005
 
+
 # class LoaderTest(unittest.TestCase):
 #     def test_output(self):
 #         items = libri_tts("/home/erogol/Data/Libri-TTS/train-clean-360/")