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| import os | |
| import unittest | |
| from training.datasets.libritts_r import LIBRITTS_R, load_libritts_item | |
| class TestLibriTTS(unittest.TestCase): | |
| def setUp(self): | |
| # Set up any necessary values for the tests | |
| self.fileid = "1061_146197_000015_000000" | |
| self.path = "datasets_cache/LIBRITTS/LibriTTS/train-clean-360" | |
| self.ext_audio = ".wav" | |
| self.ext_original_txt = ".original.txt" | |
| self.ext_normalized_txt = ".normalized.txt" | |
| def test_load_libritts_item(self): | |
| # Test the load_libritts_item function | |
| waveform, sample_rate, original_text, normalized_text, speaker_id, chapter_id, utterance_id = load_libritts_item( | |
| self.fileid, | |
| self.path, | |
| self.ext_audio, | |
| self.ext_original_txt, | |
| self.ext_normalized_txt, | |
| ) | |
| base_path = os.path.join( | |
| self.path, | |
| f"{speaker_id}", | |
| f"{chapter_id}", | |
| ) | |
| # Check that the files were created | |
| self.assertTrue( | |
| os.path.exists( | |
| os.path.join( | |
| base_path, | |
| self.fileid + self.ext_original_txt, | |
| ), | |
| ), | |
| ) | |
| self.assertTrue( | |
| os.path.exists( | |
| os.path.join( | |
| base_path, | |
| self.fileid + self.ext_normalized_txt, | |
| ), | |
| ), | |
| ) | |
| def test_selected_speaker_ids(self): | |
| # Initialize the dataset with selected speaker IDs | |
| dataset = LIBRITTS_R(root="datasets_cache/LIBRITTS", url="train-clean-100", selected_speaker_ids=[19, 26]) | |
| # Iterate over the dataset and check the speaker IDs | |
| for _, _, _, _, speaker_id, _, _ in dataset: | |
| # Assert that the speaker ID is in the list of selected speaker IDs | |
| self.assertIn(speaker_id, [19, 26]) | |
| def test_max_audio_length(self): | |
| # Initialize the dataset with a maximum audio length | |
| dataset = LIBRITTS_R( | |
| root="datasets_cache/LIBRITTS", | |
| url="train-clean-100", | |
| max_audio_length=3.0, | |
| selected_speaker_ids=[19, 26], | |
| ) | |
| # Iterate over the dataset and check the audio lengths | |
| for waveform, sample_rate, _, _, speaker_id, _, _ in dataset: | |
| # Get the duration of the waveform in seconds | |
| duration = waveform.shape[1] / sample_rate | |
| # Assert that the speaker ID is in the list of selected speaker IDs | |
| self.assertIn(speaker_id, [19, 26]) | |
| # Assert that the duration is less than or equal to the maximum length | |
| self.assertLessEqual(duration, 3.0) | |
| def test_min_audio_length(self): | |
| # Initialize the dataset with a minimum audio length | |
| dataset = LIBRITTS_R( | |
| root="datasets_cache/LIBRITTS", | |
| url="train-clean-100", | |
| min_audio_length=30.0, | |
| ) | |
| # Iterate over the dataset and check the audio lengths | |
| for waveform, sample_rate, _, _, _, _, _ in dataset: | |
| # Get the duration of the waveform in seconds | |
| duration = waveform.shape[1] / sample_rate | |
| # Assert that the duration is greater than or equal to the minimum length | |
| self.assertGreaterEqual(duration, 30.0) | |
| # Add any other assertions you want to make about the return values | |
| def tearDown(self): | |
| speaker_id, chapter_id, _, _ = self.fileid.split("_") | |
| normalized_text_filename = self.fileid + self.ext_normalized_txt | |
| normalized_text_path = os.path.join(self.path, speaker_id, chapter_id, normalized_text_filename) | |
| original_text_filename = self.fileid + self.ext_original_txt | |
| original_text_path = os.path.join(self.path, speaker_id, chapter_id, original_text_filename) | |
| # Clean up any created files after tests are done | |
| if os.path.exists(normalized_text_path): | |
| os.remove(normalized_text_path) | |
| if os.path.exists(original_text_path): | |
| os.remove(original_text_path) | |
| if __name__ == "__main__": | |
| unittest.main() | |