def test_AudioToMelSpectrogramPreprocessor1(self):
# Test 1 that should test the pure stft implementation as much as possible
instance1 = modules.AudioToMelSpectrogramPreprocessor(dither=0,
stft_conv=False,
mag_power=1.0,
normalize=False,
preemph=0.0,
log=False,
pad_to=0)
instance2 = modules.AudioToMelSpectrogramPreprocessor(dither=0,
stft_conv=True,
mag_power=1.0,
normalize=False,
preemph=0.0,
log=False,
pad_to=0)
# Ensure that the two functions behave similarily
for _ in range(10):
input_signal = torch.randn(size=(4, 512))
length = torch.randint(low=161, high=500, size=[4])
res1, length1 = instance1(input_signal=input_signal, length=length)
res2, length2 = instance2(input_signal=input_signal, length=length)
for len1, len2 in zip(length1, length2):
assert len1 == len2
assert res1.shape == res2.shape
diff = torch.mean(torch.abs(res1 - res2))
assert diff <= 1e-3
diff = torch.max(torch.abs(res1 - res2))
assert diff <= 1e-2
def test_AudioToMelSpectrogramPreprocessor_batch(self):
# Test 1 that should test the pure stft implementation as much as possible
instance1 = modules.AudioToMelSpectrogramPreprocessor(
normalize="per_feature", dither=0, pad_to=0)
# Ensure that the two functions behave similarily
for _ in range(10):
input_signal = torch.randn(size=(4, 512))
length = torch.randint(low=161, high=500, size=[4])
with torch.no_grad():
# batch size 1
res_instance, length_instance = [], []
for i in range(input_signal.size(0)):
res_ins, length_ins = instance1(
input_signal=input_signal[i:i + 1],
length=length[i:i + 1])
res_instance.append(res_ins)
length_instance.append(length_ins)
res_instance = torch.cat(res_instance, 0)
length_instance = torch.cat(length_instance, 0)
# batch size 4
res_batch, length_batch = instance1(input_signal=input_signal,
length=length)
assert res_instance.shape == res_batch.shape
assert length_instance.shape == length_batch.shape
diff = torch.mean(torch.abs(res_instance - res_batch))
assert diff <= 1e-3
diff = torch.max(torch.abs(res_instance - res_batch))
assert diff <= 1e-3
def test_SpectrogramAugmentationr_numba_kernel(self, caplog):
numba_utils.skip_numba_cuda_test_if_unsupported(__NUMBA_MINIMUM_VERSION__)
logging._logger.propagate = True
original_verbosity = logging.get_verbosity()
logging.set_verbosity(logging.DEBUG)
caplog.set_level(logging.DEBUG)
# Make sure constructor works
instance1 = modules.SpectrogramAugmentation(
freq_masks=10, time_masks=3, rect_masks=3, use_numba_spec_augment=True
)
assert isinstance(instance1, modules.SpectrogramAugmentation)
# Make sure forward doesn't throw with expected input
instance0 = modules.AudioToMelSpectrogramPreprocessor(dither=0)
input_signal = torch.randn(size=(8, 512))
length = torch.randint(low=161, high=500, size=[8])
res0 = instance0(input_signal=input_signal, length=length)
res = instance1(input_spec=res0[0], length=length)
assert res.shape == res0[0].shape
# check tha numba kernel debug message indicates that it is available for use
assert """Numba SpecAugment kernel is available""" in caplog.text
logging._logger.propagate = False
logging.set_verbosity(original_verbosity)
def test_AudioToMelSpectrogramPreprocessor2(self):
# Test 2 that should test the stft implementation as used in ASR models
instance1 = modules.AudioToMelSpectrogramPreprocessor(dither=0, stft_conv=False)
instance2 = modules.AudioToMelSpectrogramPreprocessor(dither=0, stft_conv=True)
# Ensure that the two functions behave similarily
for _ in range(5):
input_signal = torch.randn(size=(4, 512))
length = torch.randint(low=161, high=500, size=[4])
res1, length1 = instance1(input_signal=input_signal, length=length)
res2, length2 = instance2(input_signal=input_signal, length=length)
for len1, len2 in zip(length1, length2):
assert len1 == len2
assert res1.shape == res2.shape
diff = torch.mean(torch.abs(res1 - res2))
assert diff <= 3e-3
diff = torch.max(torch.abs(res1 - res2))
assert diff <= 3
def test_MaskedPatchAugmentation(self):
# Make sure constructor works
audio_length = 128
instance1 = modules.MaskedPatchAugmentation(patch_size=16, mask_patches=0.5, freq_masks=2, freq_width=10)
assert isinstance(instance1, modules.MaskedPatchAugmentation)
# Make sure forward doesn't throw with expected input
instance0 = modules.AudioToMelSpectrogramPreprocessor(dither=0)
input_signal = torch.randn(size=(4, 512))
length = torch.randint(low=161, high=500, size=[4])
res0 = instance0(input_signal=input_signal, length=length)
res = instance1(input_spec=res0[0], length=length)
assert res.shape == res0[0].shape
def test_SpectrogramAugmentationr(self):
# Make sure constructor works
instance1 = modules.SpectrogramAugmentation(freq_masks=10,
time_masks=3,
rect_masks=3)
assert isinstance(instance1, modules.SpectrogramAugmentation)
# Make sure forward doesn't throw with expected input
instance0 = modules.AudioToMelSpectrogramPreprocessor(dither=0)
input_signal = torch.randn(size=(4, 512))
length = torch.randint(low=161, high=500, size=[4])
res0 = instance0(input_signal=input_signal, length=length)
res = instance1(input_spec=res0[0])
assert res.shape == res0[0].shape
def test_CropOrPadSpectrogramAugmentation(self):
# Make sure constructor works
audio_length = 128
instance1 = modules.CropOrPadSpectrogramAugmentation(audio_length=audio_length)
assert isinstance(instance1, modules.CropOrPadSpectrogramAugmentation)
# Make sure forward doesn't throw with expected input
instance0 = modules.AudioToMelSpectrogramPreprocessor(dither=0)
input_signal = torch.randn(size=(4, 512))
length = torch.randint(low=161, high=500, size=[4])
res0 = instance0(input_signal=input_signal, length=length)
res, new_length = instance1(input_signal=res0[0], length=length)
assert res.shape == torch.Size([4, 64, audio_length])
assert all(new_length == torch.tensor([128] * 4))