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import os
from pathlib import Path
from torchaudio.datasets import librispeech
from torchaudio_unittest.common_utils import get_whitenoise, normalize_wav, save_wav, TempDirMixin
# Used to generate a unique transcript for each dummy audio file
_NUMBERS = ["ZERO", "ONE", "TWO", "THREE", "FOUR", "FIVE", "SIX", "SEVEN", "EIGHT", "NINE"]
def get_mock_dataset(root_dir):
"""
root_dir: directory to the mocked dataset
"""
mocked_data = []
dataset_dir = os.path.join(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)
transcript = " ".join([_NUMBERS[x] for x in [speaker_id, chapter_id, utterance_id]])
trans_content.append(f"{speaker_id}-{chapter_id}-{utterance_id:04d} {transcript}")
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, transcript, speaker_id, chapter_id, utterance_id)
mocked_data.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))
return mocked_data
class LibriSpeechTestMixin(TempDirMixin):
root_dir = None
samples = []
@classmethod
def setUpClass(cls):
cls.root_dir = cls.get_base_temp_dir()
cls.samples = get_mock_dataset(cls.root_dir)
@classmethod
def tearDownClass(cls):
# In case of test failure
cls.librispeech_cls._ext_audio = ".flac"
def _test_librispeech(self, dataset):
num_samples = 0
for i, (data, sample_rate, transcript, 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 transcript == 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)
self.librispeech_cls._ext_audio = ".flac"
def test_librispeech_str(self):
self.librispeech_cls._ext_audio = ".wav"
dataset = self.librispeech_cls(self.root_dir)
self._test_librispeech(dataset)
def test_librispeech_path(self):
self.librispeech_cls._ext_audio = ".wav"
dataset = self.librispeech_cls(Path(self.root_dir))
self._test_librispeech(dataset)
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