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import os
from torchaudio.datasets import voxceleb1
from torchaudio_unittest.common_utils import get_whitenoise, save_wav, TempDirMixin, TorchaudioTestCase
_NUM_SPEAKERS = 3
_NUM_YOUTUBE = 5
def _save_sample(dataset_dir: str, sample_rate: int, speaker_id: int, youtube_id: int, utterance_id: int, seed: int):
"""Create and save audio samples to corresponding files
Args:
dataset_dir (str): The directory of the dataset.
sample_rate (int): Sample rate of waveform.
speaker_id (int): The index of speaker sub directory.
youtube_id (int): The index of youtube sub directory.
utterance_id (int): The utterance index.
seed (int): The seed to generate the waveform.
Returns:
Tuple[torch.Tensor, int, int, str, str]
The waveform Tensor, sample rate, speaker label, file_name, and the file path.
"""
# add random string before youtube_id
youtube_id = "Zxhsj" + str(youtube_id)
path = os.path.join(dataset_dir, "id10" + str(speaker_id), youtube_id)
os.makedirs(path, exist_ok=True)
filename = str(utterance_id) + ".wav"
file_path = os.path.join(path, filename)
waveform = get_whitenoise(
sample_rate=sample_rate,
duration=0.01,
n_channels=1,
seed=seed,
)
save_wav(file_path, waveform, sample_rate)
file_name = "-".join(["id10" + str(speaker_id), youtube_id, str(utterance_id)])
file_path = "/".join(["id10" + str(speaker_id), youtube_id, str(utterance_id) + ".wav"])
return waveform, sample_rate, speaker_id, file_name, file_path
def get_mock_iden_dataset(root_dir: str, meta_file: str):
"""Get the mocked dataset for VoxCeleb1Identification dataset.
Args:
root_dir (str): Directory to the mocked dataset
meta_file (str): The file name which stores the file list.
Returns:
Tuple[List, List, List]:
The mocked samples for train, dev, and test subsets.
"""
os.makedirs(root_dir, exist_ok=True)
wav_dir = os.path.join(root_dir, "wav")
os.makedirs(wav_dir, exist_ok=True)
mocked_train_samples, mocked_dev_samples, mocked_test_samples = [], [], []
sample_rate = 16000
seed = 0
idx = 1
with open(os.path.join(root_dir, meta_file), "w") as f:
for speaker_id in range(_NUM_SPEAKERS):
for youtube_id in range(_NUM_YOUTUBE):
waveform, sample_rate, speaker_id, file_name, file_path = _save_sample(
wav_dir, sample_rate, speaker_id, youtube_id, idx, seed
)
sample = (waveform, sample_rate, speaker_id, file_name)
if idx % 1 == 0:
mocked_train_samples.append(sample)
f.write(f"1 {file_path}\n")
elif idx % 2 == 0:
mocked_dev_samples.append(sample)
f.write(f"2 {file_path}\n")
else:
mocked_test_samples.append(sample)
f.write(f"3 {file_path}\n")
idx += 1
return (
mocked_train_samples,
mocked_dev_samples,
mocked_test_samples,
)
def get_mock_veri_dataset(root_dir: str, meta_file: str):
"""Get the mocked dataset for VoxCeleb1Verification dataset.
Args:
root_dir (str): Directory to the mocked dataset
meta_file (str): The file name which stores the file list.
Returns:
List[Sample]:
The mocked samples.
"""
os.makedirs(root_dir, exist_ok=True)
wav_dir = os.path.join(root_dir, "wav")
os.makedirs(wav_dir, exist_ok=True)
mocked_samples = []
sample_rate = 16000
seed = 0
idx = 1
with open(os.path.join(root_dir, meta_file), "w") as f:
for speaker_id1 in range(_NUM_SPEAKERS):
for speaker_id2 in range(_NUM_SPEAKERS):
for youtube_id in range(_NUM_YOUTUBE):
waveform_spk1, sample_rate, _, file_name_spk1, file_path_spk1 = _save_sample(
wav_dir, sample_rate, speaker_id1, youtube_id, idx, seed
)
waveform_spk2, sample_rate, _, file_name_spk2, file_path_spk2 = _save_sample(
wav_dir, sample_rate, speaker_id1, youtube_id, idx + 1, seed
)
if speaker_id1 == speaker_id2:
label = 1
else:
label = 0
sample = (waveform_spk1, waveform_spk2, sample_rate, label, file_name_spk1, file_name_spk2)
mocked_samples.append(sample)
f.write(f"{label} {file_path_spk1} {file_path_spk2}\n")
idx += 2
return mocked_samples
class TestVoxCeleb1Identification(TempDirMixin, TorchaudioTestCase):
root_dir = None
meta_file = "iden_list.txt"
train_samples = {}
dev_samples = {}
test_samples = {}
@classmethod
def setUpClass(cls):
cls.root_dir = cls.get_base_temp_dir()
(cls.train_samples, cls.dev_samples, cls.test_samples) = get_mock_iden_dataset(cls.root_dir, cls.meta_file)
def _testVoxCeleb1Identification(self, dataset, data_samples):
num_samples = 0
for i, (waveform, sample_rate, speaker_id, file_id) in enumerate(dataset):
self.assertEqual(waveform, data_samples[i][0])
assert sample_rate == data_samples[i][1]
assert speaker_id == data_samples[i][2]
assert file_id == data_samples[i][3]
num_samples += 1
assert num_samples == len(data_samples)
def testVoxCeleb1SubsetTrain(self):
dataset = voxceleb1.VoxCeleb1Identification(self.root_dir, subset="train", meta_url=self.meta_file)
self._testVoxCeleb1Identification(dataset, self.train_samples)
def testVoxCeleb1SubsetDev(self):
dataset = voxceleb1.VoxCeleb1Identification(self.root_dir, subset="dev", meta_url=self.meta_file)
self._testVoxCeleb1Identification(dataset, self.dev_samples)
def testVoxCeleb1SubsetTest(self):
dataset = voxceleb1.VoxCeleb1Identification(self.root_dir, subset="test", meta_url=self.meta_file)
self._testVoxCeleb1Identification(dataset, self.test_samples)
class TestVoxCeleb1Verification(TempDirMixin, TorchaudioTestCase):
root_dir = None
meta_file = "veri_test.txt"
train_samples = {}
dev_samples = {}
test_samples = {}
@classmethod
def setUpClass(cls):
cls.root_dir = cls.get_base_temp_dir()
(cls.samples) = get_mock_veri_dataset(cls.root_dir, cls.meta_file)
def testVoxCeleb1Verification(self):
dataset = voxceleb1.VoxCeleb1Verification(self.root_dir, meta_url=self.meta_file)
num_samples = 0
for i, (waveform_spk1, waveform_spk2, sample_rate, label, file_id_spk1, file_id_spk2) in enumerate(dataset):
self.assertEqual(waveform_spk1, self.samples[i][0])
self.assertEqual(waveform_spk2, self.samples[i][1])
assert sample_rate == self.samples[i][2]
assert label == self.samples[i][3]
assert file_id_spk1 == self.samples[i][4]
assert file_id_spk2 == self.samples[i][5]
num_samples += 1
assert num_samples == len(self.samples)
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