def test_serialize_parameters(self):
transform = AddBackgroundNoise(sounds_path=os.path.join(
DEMO_DIR, "background_noises"),
p=1.0)
samples = np.random.normal(0, 1, size=1024).astype(np.float32)
transform.randomize_parameters(samples, sample_rate=44100)
json.dumps(transform.serialize_parameters())
def test_freeze_and_unfreeze_parameters(self):
samples = np.zeros((20,), dtype=np.float32)
sample_rate = 16000
augmenter = Compose(
[
AddBackgroundNoise(
sounds_path=os.path.join(DEMO_DIR, "background_noises"),
min_snr_in_db=15,
max_snr_in_db=35,
p=1.0,
),
ClippingDistortion(p=0.5),
]
)
perturbed_samples1 = augmenter(samples=samples, sample_rate=sample_rate)
augmenter.freeze_parameters()
for transform in augmenter.transforms:
self.assertTrue(transform.are_parameters_frozen)
perturbed_samples2 = augmenter(samples=samples, sample_rate=sample_rate)
assert_array_equal(perturbed_samples1, perturbed_samples2)
augmenter.unfreeze_parameters()
for transform in augmenter.transforms:
self.assertFalse(transform.are_parameters_frozen)
def test_picklability(self):
transform = AddBackgroundNoise(
sounds_path=os.path.join(DEMO_DIR, "background_noises"), p=1.0
)
pickled = pickle.dumps(transform)
unpickled = pickle.loads(pickled)
self.assertEqual(transform.sound_file_paths, unpickled.sound_file_paths)
def test_add_background_noise_when_noise_sound_is_too_short(self):
samples = np.sin(np.linspace(0, 440 * 2 * np.pi,
224000)).astype(np.float32)
sample_rate = 16000
augmenter = Compose([
AddBackgroundNoise(
sounds_path=os.path.join(DEMO_DIR, "background_noises"),
min_snr_in_db=15,
max_snr_in_db=35,
p=1.0,
)
])
samples_out = augmenter(samples=samples, sample_rate=sample_rate)
self.assertEqual(samples_out.dtype, np.float32)
def test_try_add_almost_silent_file(self):
samples = np.sin(np.linspace(0, 440 * 2 * np.pi,
30000)).astype(np.float32)
sample_rate = 48000
augmenter = Compose([
AddBackgroundNoise(
sounds_path=os.path.join(DEMO_DIR, "almost_silent"),
min_snr_in_db=15,
max_snr_in_db=35,
p=1.0,
)
])
samples_out = augmenter(samples=samples, sample_rate=sample_rate)
assert not np.allclose(samples, samples_out)
self.assertEqual(samples_out.dtype, np.float32)
def test_try_add_digital_silence(self):
samples = np.sin(np.linspace(0, 440 * 2 * np.pi,
40000)).astype(np.float32)
sample_rate = 48000
augmenter = Compose([
AddBackgroundNoise(
sounds_path=os.path.join(DEMO_DIR, "digital_silence"),
min_snr_in_db=15,
max_snr_in_db=35,
p=1.0,
)
])
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
samples_out = augmenter(samples=samples, sample_rate=sample_rate)
assert "is too silent to be added as noise" in str(w[-1].message)
assert np.allclose(samples, samples_out)
self.assertEqual(samples_out.dtype, np.float32)
def test_randomize_parameters_and_apply(self):
samples = 1.0 / np.arange(1, 21, dtype=np.float32)
sample_rate = 44100
augmenter = Compose([
AddBackgroundNoise(
sounds_path=os.path.join(DEMO_DIR, "background_noises"),
min_snr_in_db=15,
max_snr_in_db=35,
p=1.0,
),
ClippingDistortion(p=0.5),
FrequencyMask(min_frequency_band=0.3,
max_frequency_band=0.5,
p=0.5),
TimeMask(min_band_part=0.2, max_band_part=0.5, p=0.5),
Shift(min_fraction=0.5, max_fraction=0.5, p=0.5),
])
augmenter.freeze_parameters()
augmenter.randomize_parameters(samples=samples,
sample_rate=sample_rate)
parameters = [
transform.parameters for transform in augmenter.transforms
]
perturbed_samples1 = augmenter(samples=samples,
sample_rate=sample_rate)
perturbed_samples2 = augmenter(samples=samples,
sample_rate=sample_rate)
assert_array_equal(perturbed_samples1, perturbed_samples2)
augmenter.unfreeze_parameters()
for transform_parameters, transform in zip(parameters,
augmenter.transforms):
self.assertTrue(transform_parameters == transform.parameters)
self.assertFalse(transform.are_parameters_frozen)