Spaces:
Running
Running
File size: 3,875 Bytes
9d61c9b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 |
import os
import unittest
import torch
from torch.utils.data import DataLoader
from training.datasets import LibriTTSDatasetAcoustic
class TestLibriTTSDatasetAcoustic(unittest.TestCase):
def setUp(self):
self.batch_size = 2
self.lang = "en"
self.download = False
self.dataset = LibriTTSDatasetAcoustic(
root="datasets_cache/LIBRITTS",
lang=self.lang,
download=self.download,
)
# def test_len(self):
# self.assertEqual(len(self.dataset), 33236)
# def test_getitem(self):
# sample = self.dataset[0]
# self.assertEqual(sample["id"], "1034_121119_000001_000001")
# self.assertEqual(sample["speaker"], 1034)
# self.assertEqual(sample["text"].shape, torch.Size([6]))
# self.assertEqual(sample["mel"].shape, torch.Size([100, 58]))
# self.assertEqual(sample["pitch"].shape, torch.Size([58]))
# self.assertEqual(sample["raw_text"], "The Law.")
# self.assertEqual(sample["normalized_text"], "The Law.")
# self.assertFalse(sample["pitch_is_normalized"])
# self.assertEqual(sample["lang"], 3)
# self.assertEqual(sample["attn_prior"].shape, torch.Size([6, 58]))
# self.assertEqual(sample["wav"].shape, torch.Size([1, 14994]))
# self.assertEqual(sample["energy"].shape, torch.Size([1, 957]))
# def test_cache_item(self):
# dataset = LibriTTSDatasetAcoustic(
# cache=True,
# )
# idxs = [0, 1, 1000, 1002, 2010]
# for idx in idxs:
# # Get a sample from the dataset
# sample = dataset[idx]
# cache_subdir_path = os.path.join(dataset.cache_dir, dataset.cache_subdir(idx))
# cache_file = os.path.join(cache_subdir_path, f"{idx}.pt")
# # Check if the data is in the cache
# self.assertTrue(os.path.exists(cache_file))
# # Load the data from the cache file
# cached_sample = torch.load(cache_file)
# # Check if the cached data is the same as the original data
# for key in sample:
# if torch.is_tensor(sample[key]):
# self.assertTrue(torch.all(sample[key] == cached_sample[key]))
# else:
# self.assertEqual(sample[key], cached_sample[key])
# def test_collate_fn(self):
# data = [
# self.dataset[0],
# self.dataset[2],
# ]
# # Call the collate_fn method
# result = self.dataset.collate_fn(data)
# # Check the output
# self.assertEqual(len(result), 13)
# # Check that all the batches are the same size
# for batch in result:
# self.assertEqual(len(batch), 2)
def test_normalize_pitch(self):
pitches = [
torch.tensor([100.0, 200.0, 300.0]),
torch.tensor([150.0, 250.0, 350.0]),
]
result = self.dataset.normalize_pitch(pitches)
expected_output = (100.0, 350.0, 225.0, 93.54143524169922)
self.assertEqual(result, expected_output)
# def test_dataloader(self):
# # Create a DataLoader from the dataset
# dataloader = DataLoader(
# self.dataset,
# batch_size=self.batch_size,
# shuffle=False,
# collate_fn=self.dataset.collate_fn,
# )
# iter_dataloader = iter(dataloader)
# # Iterate over the DataLoader and check the output
# for _, items in enumerate([next(iter_dataloader), next(iter_dataloader)]):
# # items = batch[0]
# # Check the batch size
# self.assertEqual(len(items), 13)
# for it in items:
# self.assertEqual(len(it), self.batch_size)
if __name__ == "__main__":
unittest.main()
|