Spaces:
Running
Running
File size: 2,067 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 |
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()
|