def test_cuda_reset_distribution(self):
samples = np.array([1.0, 0.5, 0.25, 0.125, 0.01], dtype=np.float32)
samples_batch = np.vstack([samples] * 10000)
sample_rate = 16000
augment = Gain(min_gain_in_db=-6, max_gain_in_db=6, p=0.5).cuda()
# Change the parameters after init
augment.min_gain_in_db = -18
augment.max_gain_in_db = 3
processed_samples = (augment(
samples=torch.from_numpy(samples_batch).cuda(),
sample_rate=sample_rate).cpu().numpy())
self.assertEqual(processed_samples.dtype, np.float32)
actual_gains_in_db = []
for i in range(processed_samples.shape[0]):
if not np.allclose(processed_samples[i], samples_batch[i]):
estimated_gain_factor = np.mean(processed_samples[i] /
samples_batch[i])
estimated_gain_factor_in_db = convert_amplitude_ratio_to_decibels(
torch.tensor(estimated_gain_factor)).item()
self.assertGreaterEqual(estimated_gain_factor_in_db, -18)
self.assertLessEqual(estimated_gain_factor_in_db, 3)
actual_gains_in_db.append(estimated_gain_factor_in_db)
mean_gain_in_db = np.mean(actual_gains_in_db)
self.assertGreater(mean_gain_in_db, (-18 + 3) / 2 - 1)
self.assertLess(mean_gain_in_db, (-18 + 3) / 2 + 1)
def test_invalid_distribution(self):
with self.assertRaises(ValueError):
Gain(min_gain_in_db=18, max_gain_in_db=-3, p=0.5)
augment = Gain(min_gain_in_db=-6, max_gain_in_db=-3, p=1.0)
# Change the parameters after init
augment.min_gain_in_db = 18
augment.max_gain_in_db = 3
with self.assertRaises(ValueError):
augment(
torch.tensor([[[1.0, 0.5, 0.25, 0.125]]], dtype=torch.float32),
16000)