def test_sampling_rate_after_transform(transform):
expected_sampling_rate = transform.transforms[-1].output_sampling_rate
dataset = datasets.WhiteNoise(duration=0.5,
sampling_rate=48000,
transform=transform)
sampling_rate = datasets.sampling_rate_after_transform(dataset)
assert sampling_rate == expected_sampling_rate
assert dataset.sampling_rate == expected_sampling_rate
def test_whitenoise(duration, sampling_rate, mean, stdev):
dataset = datasets.WhiteNoise(duration=duration,
sampling_rate=sampling_rate,
mean=mean,
stdev=stdev)
noise, label = next(iter(dataset))
samples = int(np.ceil(duration * sampling_rate))
assert noise.shape == (1, samples)
assert label == 'white noise'
assert -1 <= np.max(np.abs(noise)) <= 1
assert len(dataset) == 1
noise, label = next(iter(dataset))
samples = int(np.ceil(duration * sampling_rate))
assert noise.shape == (1, samples)
assert label == 'white noise'
assert -1 <= np.max(np.abs(noise)) <= 1
assert len(dataset) == 1
# --- datasets/utils.py ---
crop = transforms.RandomCrop(8192)
resamp1 = transforms.Resample(48000, 44100)
resamp2 = transforms.Resample(44100, 16000)
t1 = transforms.Compose([crop, resamp1])
t2 = transforms.Compose([crop, resamp1, resamp2])
t3 = transforms.Compose([resamp1, crop, resamp2])
d0 = datasets.WhiteNoise(duration=0.5, sampling_rate=48000, transform=crop)
d1 = datasets.WhiteNoise(duration=0.5, sampling_rate=48000, transform=t1)
d2 = datasets.WhiteNoise(duration=0.5, sampling_rate=48000, transform=t2)
d3 = datasets.WhiteNoise(duration=0.5, sampling_rate=48000, transform=t3)
df_empty = pd.DataFrame()
df_a = pd.DataFrame(data=[0], columns=['a'])
df_ab = pd.DataFrame(data=[('0', 1)], columns=['a', 'b'])
@pytest.mark.parametrize('list_of_datasets', [
(d2, d3), pytest.param([d0, d1], marks=xfail(raises=ValueError))
])
def test_audioconcatdataset(list_of_datasets):
datasets.AudioConcatDataset(list_of_datasets)