Add test for language_weighted_sampler

tests/data_tests/test_samplers.py

@@ -0,0 +1,53 @@
+from torch.utils.data import RandomSampler
+from TTS.tts.datasets import load_tts_samples
+from TTS.config.shared_configs import BaseDatasetConfig
+from TTS.tts.utils.languages import get_language_weighted_sampler
+
+import functools
+
+dataset_config_en = BaseDatasetConfig(
+    name="ljspeech",
+    meta_file_train="metadata.csv",
+    meta_file_val="metadata.csv",
+    path="tests/data/ljspeech",
+    language="en",
+)
+
+dataset_config_pt = BaseDatasetConfig(
+    name="ljspeech",
+    meta_file_train="metadata.csv",
+    meta_file_val="metadata.csv",
+    path="tests/data/ljspeech",
+    language="pt-br",
+)
+
+# Adding the EN samples twice to create an 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 0.9 < lang_1/lang_2 < 1.1
+
+random_sampler = RandomSampler(train_samples)
+ids = functools.reduce(lambda a, b: a + b, [list(random_sampler) for i in range(100)])
+en, pt = 0, 0
+for id in ids:
+    if train_samples[id][3] == 'en':
+        en += 1
+    else:
+        pt += 1
+
+assert not is_balanced(en, pt), "Random sampler is supposed to be unbalanced"
+
+weighted_sampler = get_language_weighted_sampler(train_samples)
+ids = functools.reduce(lambda a, b: a + b, [list(weighted_sampler) for i in range(100)])
+en, pt = 0, 0
+for id in ids:
+    if train_samples[id][3] == 'en':
+        en += 1
+    else:
+        pt += 1
+
+assert is_balanced(en, pt), "Weighted sampler is supposed to be balanced"