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import os
from torchaudio.datasets import yesno
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
get_whitenoise,
save_wav,
normalize_wav,
)
class TestYesNo(TempDirMixin, TorchaudioTestCase):
backend = 'default'
root_dir = None
data = []
labels = [
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1],
[0, 1, 0, 1, 0, 1, 1, 0],
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1],
]
@classmethod
def setUpClass(cls):
cls.root_dir = cls.get_base_temp_dir()
base_dir = os.path.join(cls.root_dir, 'waves_yesno')
os.makedirs(base_dir, exist_ok=True)
for i, label in enumerate(cls.labels):
filename = f'{"_".join(str(l) for l in label)}.wav'
path = os.path.join(base_dir, filename)
data = get_whitenoise(sample_rate=8000, duration=6, n_channels=1, dtype='int16', seed=i)
save_wav(path, data, 8000)
cls.data.append(normalize_wav(data))
def test_yesno(self):
dataset = yesno.YESNO(self.root_dir)
n_ite = 0
for i, (waveform, sample_rate, label) in enumerate(dataset):
expected_label = self.labels[i]
expected_data = self.data[i]
self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8)
assert sample_rate == 8000
assert label == expected_label
n_ite += 1
assert n_ite == len(self.data)
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