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import os
from pathlib import Path
from torchaudio.datasets import speechcommands
from torchaudio_unittest.common_utils import get_whitenoise, normalize_wav, save_wav, TempDirMixin, TorchaudioTestCase
_LABELS = [
"bed",
"bird",
"cat",
"dog",
"down",
"eight",
"five",
"follow",
"forward",
"four",
"go",
"happy",
"house",
"learn",
"left",
"marvin",
"nine",
"no",
"off",
"on",
"one",
"right",
"seven",
"sheila",
"six",
"stop",
"three",
"tree",
"two",
"up",
"visual",
"wow",
"yes",
"zero",
]
def get_mock_dataset(dataset_dir):
"""
dataset_dir: directory to the mocked dataset
"""
mocked_samples = []
mocked_train_samples = []
mocked_valid_samples = []
mocked_test_samples = []
os.makedirs(dataset_dir, exist_ok=True)
sample_rate = 16000 # 16kHz sample rate
seed = 0
valid_file = os.path.join(dataset_dir, "validation_list.txt")
test_file = os.path.join(dataset_dir, "testing_list.txt")
with open(valid_file, "w") as valid, open(test_file, "w") as test:
for label in _LABELS:
path = os.path.join(dataset_dir, label)
os.makedirs(path, exist_ok=True)
for j in range(6):
# generate hash ID for speaker
speaker = "{:08x}".format(j)
for utterance in range(3):
filename = f"{speaker}{speechcommands.HASH_DIVIDER}{utterance}.wav"
file_path = os.path.join(path, filename)
seed += 1
data = get_whitenoise(
sample_rate=sample_rate,
duration=0.01,
n_channels=1,
dtype="int16",
seed=seed,
)
save_wav(file_path, data, sample_rate)
sample = (
normalize_wav(data),
sample_rate,
label,
speaker,
utterance,
)
mocked_samples.append(sample)
if j < 2:
mocked_train_samples.append(sample)
elif j < 4:
valid.write(f"{label}/{filename}\n")
mocked_valid_samples.append(sample)
elif j < 6:
test.write(f"{label}/{filename}\n")
mocked_test_samples.append(sample)
return mocked_samples, mocked_train_samples, mocked_valid_samples, mocked_test_samples
class TestSpeechCommands(TempDirMixin, TorchaudioTestCase):
root_dir = None
samples = []
train_samples = []
valid_samples = []
test_samples = []
@classmethod
def setUpClass(cls):
cls.root_dir = cls.get_base_temp_dir()
dataset_dir = os.path.join(cls.root_dir, speechcommands.FOLDER_IN_ARCHIVE, speechcommands.URL)
cls.samples, cls.train_samples, cls.valid_samples, cls.test_samples = get_mock_dataset(dataset_dir)
def _testSpeechCommands(self, dataset, data_samples):
num_samples = 0
for i, (data, sample_rate, label, speaker_id, utterance_number) in enumerate(dataset):
self.assertEqual(data, data_samples[i][0], atol=5e-5, rtol=1e-8)
assert sample_rate == data_samples[i][1]
assert label == data_samples[i][2]
assert speaker_id == data_samples[i][3]
assert utterance_number == data_samples[i][4]
num_samples += 1
assert num_samples == len(data_samples)
def testSpeechCommands_str(self):
dataset = speechcommands.SPEECHCOMMANDS(self.root_dir)
self._testSpeechCommands(dataset, self.samples)
def testSpeechCommands_path(self):
dataset = speechcommands.SPEECHCOMMANDS(Path(self.root_dir))
self._testSpeechCommands(dataset, self.samples)
def testSpeechCommandsSubsetTrain(self):
dataset = speechcommands.SPEECHCOMMANDS(self.root_dir, subset="training")
self._testSpeechCommands(dataset, self.train_samples)
def testSpeechCommandsSubsetValid(self):
dataset = speechcommands.SPEECHCOMMANDS(self.root_dir, subset="validation")
self._testSpeechCommands(dataset, self.valid_samples)
def testSpeechCommandsSubsetTest(self):
dataset = speechcommands.SPEECHCOMMANDS(self.root_dir, subset="testing")
self._testSpeechCommands(dataset, self.test_samples)
def testSpeechCommandsSum(self):
dataset_all = speechcommands.SPEECHCOMMANDS(self.root_dir)
dataset_train = speechcommands.SPEECHCOMMANDS(self.root_dir, subset="training")
dataset_valid = speechcommands.SPEECHCOMMANDS(self.root_dir, subset="validation")
dataset_test = speechcommands.SPEECHCOMMANDS(self.root_dir, subset="testing")
assert len(dataset_train) + len(dataset_valid) + len(dataset_test) == len(dataset_all)
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