Add unit tests

TTS/tts/utils/data.py

@@ -62,8 +62,8 @@ def get_length_balancer_weights(items: list, num_buckets=10):
     # create the $num_buckets buckets classes based in the dataset max and min length
     max_length = int(max(audio_lengths))
     min_length = int(min(audio_lengths))
-    step = int((max_length - min_length) / num_buckets)
-    buckets_classes = [i + step for i in range(min_length, max_length, step)]
+    step = int((max_length - min_length) / num_buckets) + 1
+    buckets_classes = [i + step for i in range(min_length, (max_length - step) + num_buckets + 1, step)]
     # add each sample in their respective length bucket
     buckets_names = np.array(
         [buckets_classes[bisect.bisect_left(buckets_classes, item["audio_length"])] for item in items]

tests/data_tests/test_samplers.py

@@ -1,4 +1,5 @@
 import functools
+import random
 import unittest
 
 import torch
@@ -6,6 +7,7 @@ import torch
 from TTS.config.shared_configs import BaseDatasetConfig
 from TTS.encoder.utils.samplers import PerfectBatchSampler
 from TTS.tts.datasets import load_tts_samples
+from TTS.tts.utils.data import get_length_balancer_weights
 from TTS.tts.utils.languages import get_language_balancer_weights
 from TTS.tts.utils.speakers import get_speaker_balancer_weights
 
@@ -136,3 +138,28 @@ class TestSamplers(unittest.TestCase):
                 else:
                     spk2 += 1
             assert spk1 == spk2, "PerfectBatchSampler is supposed to be perfectly balanced"
+
+    def test_length_weighted_random_sampler(self):  # pylint: disable=no-self-use
+        for _ in range(1000):
+            # gerenate a lenght unbalanced dataset with random max/min audio lenght
+            min_audio = random.randrange(1, 22050)
+            max_audio = random.randrange(44100, 220500)
+            for idx, item in enumerate(train_samples):
+                # increase the diversity of durations
+                random_increase = random.randrange(100, 1000)
+                if idx < 5:
+                    item["audio_length"] = min_audio + random_increase
+                else:
+                    item["audio_length"] = max_audio + random_increase
+
+            weighted_sampler = torch.utils.data.sampler.WeightedRandomSampler(
+                get_length_balancer_weights(train_samples, num_buckets=2), len(train_samples)
+            )
+            ids = functools.reduce(lambda a, b: a + b, [list(weighted_sampler) for i in range(100)])
+            len1, len2 = 0, 0
+            for index in ids:
+                if train_samples[index]["audio_length"] < max_audio:
+                    len1 += 1
+                else:
+                    len2 += 1
+            assert is_balanced(len1, len2), "Length Weighted sampler is supposed to be balanced"