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import os
from torchaudio.datasets import librispeech
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
get_whitenoise,
save_wav,
normalize_wav,
)
# Used to generate a unique utterance for each dummy audio file
NUMBERS = [
'ZERO',
'ONE',
'TWO',
'THREE',
'FOUR',
'FIVE',
'SIX',
'SEVEN',
'EIGHT',
'NINE'
]
class TestLibriSpeech(TempDirMixin, TorchaudioTestCase):
backend = 'default'
root_dir = None
samples = []
@classmethod
def setUpClass(cls):
cls.root_dir = cls.get_base_temp_dir()
dataset_dir = os.path.join(
cls.root_dir, librispeech.FOLDER_IN_ARCHIVE, librispeech.URL
)
os.makedirs(dataset_dir, exist_ok=True)
sample_rate = 16000 # 16kHz
seed = 0
for speaker_id in range(5):
speaker_path = os.path.join(dataset_dir, str(speaker_id))
os.makedirs(speaker_path, exist_ok=True)
for chapter_id in range(3):
chapter_path = os.path.join(speaker_path, str(chapter_id))
os.makedirs(chapter_path, exist_ok=True)
trans_content = []
for utterance_id in range(10):
filename = f'{speaker_id}-{chapter_id}-{utterance_id:04d}.wav'
path = os.path.join(chapter_path, filename)
utterance = ' '.join(
[NUMBERS[x] for x in [speaker_id, chapter_id, utterance_id]]
)
trans_content.append(
f'{speaker_id}-{chapter_id}-{utterance_id:04d} {utterance}'
)
data = get_whitenoise(
sample_rate=sample_rate,
duration=0.01,
n_channels=1,
dtype='float32',
seed=seed
)
save_wav(path, data, sample_rate)
sample = (
normalize_wav(data),
sample_rate,
utterance,
speaker_id,
chapter_id,
utterance_id
)
cls.samples.append(sample)
seed += 1
trans_filename = f'{speaker_id}-{chapter_id}.trans.txt'
trans_path = os.path.join(chapter_path, trans_filename)
with open(trans_path, 'w') as f:
f.write('\n'.join(trans_content))
@classmethod
def tearDownClass(cls):
# In case of test failure
librispeech.LIBRISPEECH._ext_audio = '.flac'
def test_librispeech(self):
librispeech.LIBRISPEECH._ext_audio = '.wav'
dataset = librispeech.LIBRISPEECH(self.root_dir)
print(dataset._path)
num_samples = 0
for i, (
data, sample_rate, utterance, speaker_id, chapter_id, utterance_id
) in enumerate(dataset):
self.assertEqual(data, self.samples[i][0], atol=5e-5, rtol=1e-8)
assert sample_rate == self.samples[i][1]
assert utterance == self.samples[i][2]
assert speaker_id == self.samples[i][3]
assert chapter_id == self.samples[i][4]
assert utterance_id == self.samples[i][5]
num_samples += 1
assert num_samples == len(self.samples)
librispeech.LIBRISPEECH._ext_audio = '.flac'
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