Change the test_samplers to unittest format

tests/data_tests/test_samplers.py

@@ -1,5 +1,7 @@
 import functools
 
+import unittest
+
 import torch
 
 from TTS.config.shared_configs import BaseDatasetConfig
@@ -26,55 +28,57 @@
     language="pt-br",
 )
 
-# Adding the EN samples twice to create an unbalanced dataset
+# Adding the EN samples twice to create a language unbalanced dataset
 train_samples, eval_samples = load_tts_samples(
     [dataset_config_en, dataset_config_en, dataset_config_pt], eval_split=True
 )
 
-
-def is_balanced(lang_1, lang_2):
-    return 0.85 < lang_1 / lang_2 < 1.2
-
-
-random_sampler = torch.utils.data.RandomSampler(train_samples)
-ids = functools.reduce(lambda a, b: a + b, [list(random_sampler) for i in range(100)])
-en, pt = 0, 0
-for index in ids:
-    if train_samples[index][3] == "en":
-        en += 1
-    else:
-        pt += 1
-
-assert not is_balanced(en, pt), "Random sampler is supposed to be unbalanced"
-
-
-weighted_sampler = torch.utils.data.sampler.WeightedRandomSampler(get_language_balancer_weights(train_samples), len(train_samples))
-ids = functools.reduce(lambda a, b: a + b, [list(weighted_sampler) for i in range(100)])
-en, pt = 0, 0
-for index in ids:
-    if train_samples[index][3] == "en":
-        en += 1
-    else:
-        pt += 1
-
-assert is_balanced(en, pt), "Language Weighted sampler is supposed to be balanced"
-
-# test speaker weighted sampler
-
 # gerenate a speaker unbalanced dataset
 for i, sample in enumerate(train_samples):
     if i < 5:
         sample[2] = "ljspeech-0"
     else:
         sample[2] = "ljspeech-1"
 
-weighted_sampler = torch.utils.data.sampler.WeightedRandomSampler(get_speaker_balancer_weights(train_samples), len(train_samples))
-ids = functools.reduce(lambda a, b: a + b, [list(weighted_sampler) for i in range(100)])
-spk1, spk2 = 0, 0
-for index in ids:
-    if train_samples[index][2] == "ljspeech-0":
-        spk1 += 1
-    else:
-        spk2 += 1
 
-assert is_balanced(spk1, spk2), "Speaker Weighted sampler is supposed to be balanced"
+def is_balanced(lang_1, lang_2):
+    return 0.85 < lang_1 / lang_2 < 1.2
+
+
+class TestSamplers(unittest.TestCase):
+    def test_language_random_sampler(self):  # pylint: disable=no-self-use
+        random_sampler = torch.utils.data.RandomSampler(train_samples)
+        ids = functools.reduce(lambda a, b: a + b, [list(random_sampler) for i in range(100)])
+        en, pt = 0, 0
+        for index in ids:
+            if train_samples[index][3] == "en":
+                en += 1
+            else:
+                pt += 1
+
+        assert not is_balanced(en, pt), "Random sampler is supposed to be unbalanced"
+
+    def test_language_weighted_random_sampler(self):  # pylint: disable=no-self-use
+        weighted_sampler = torch.utils.data.sampler.WeightedRandomSampler(get_language_balancer_weights(train_samples), len(train_samples))
+        ids = functools.reduce(lambda a, b: a + b, [list(weighted_sampler) for i in range(100)])
+        en, pt = 0, 0
+        for index in ids:
+            if train_samples[index][3] == "en":
+                en += 1
+            else:
+                pt += 1
+
+        assert is_balanced(en, pt), "Language Weighted sampler is supposed to be balanced"
+
+    def test_speaker_weighted_random_sampler(self):  # pylint: disable=no-self-use
+
+        weighted_sampler = torch.utils.data.sampler.WeightedRandomSampler(get_speaker_balancer_weights(train_samples), len(train_samples))
+        ids = functools.reduce(lambda a, b: a + b, [list(weighted_sampler) for i in range(100)])
+        spk1, spk2 = 0, 0
+        for index in ids:
+            if train_samples[index][2] == "ljspeech-0":
+                spk1 += 1
+            else:
+                spk2 += 1
+
+        assert is_balanced(spk1, spk2), "Speaker Weighted sampler is supposed to be balanced"