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| import unittest | |
| import torch | |
| from training.preprocess.audio_processor import AudioProcessor | |
| class TestAudioProcessor(unittest.TestCase): | |
| def setUp(self): | |
| self.audio_processor = AudioProcessor() | |
| self.y = torch.randn(1, 22050) # 1 second of audio at 22050Hz | |
| self.n_fft = 2048 | |
| self.hop_length = 512 | |
| self.win_length = 2048 | |
| self.num_mels = 128 | |
| self.sample_rate = 22050 | |
| self.fmin = 0 | |
| self.fmax = 8000 | |
| def test_name_mel_basis(self): | |
| spec = torch.randn(1, 1025, 100) | |
| name = self.audio_processor.name_mel_basis(spec, self.n_fft, self.fmax) | |
| self.assertEqual(name, f"{self.n_fft}_{self.fmax}_{spec.dtype}_{spec.device}") | |
| def test_amp_to_db_and_db_to_amp(self): | |
| magnitudes = torch.abs(torch.randn(1, 100)) | |
| db = self.audio_processor.amp_to_db(magnitudes) | |
| amp = self.audio_processor.db_to_amp(db) | |
| self.assertTrue(torch.allclose(magnitudes, amp, atol=1e-4)) | |
| def test_wav_to_spec(self): | |
| spec = self.audio_processor.wav_to_spec(self.y, self.n_fft, self.hop_length, self.win_length) | |
| self.assertEqual(spec.shape, (1, self.n_fft // 2 + 1, self.num_mels // 3 + 1)) | |
| def test_wav_to_energy(self): | |
| energy = self.audio_processor.wav_to_energy(self.y, self.n_fft, self.hop_length, self.win_length) | |
| self.assertEqual(energy.shape, (1, self.num_mels // 3 + 1)) | |
| def test_spec_to_mel(self): | |
| spec = torch.randn(1, 1025, 100) | |
| mel = self.audio_processor.spec_to_mel( | |
| spec, self.n_fft, self.num_mels, self.sample_rate, self.fmin, self.fmax, | |
| ) | |
| self.assertEqual(mel.shape, (1, self.num_mels, 100)) | |
| def test_wav_to_mel(self): | |
| mel = self.audio_processor.wav_to_mel(self.y, self.n_fft, self.num_mels, self.sample_rate, self.hop_length, self.win_length, self.fmin, self.fmax) | |
| self.assertEqual(mel.shape, (1, self.num_mels, self.num_mels // 3 + 1)) | |
| if __name__ == "__main__": | |
| unittest.main() | |