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from pathlib import Path
import pytest
from torchaudio.datasets import dr_vctk
from torchaudio_unittest.common_utils import get_whitenoise, save_wav, TempDirMixin, TorchaudioTestCase
_SUBSETS = ["train", "test"]
_CONDITIONS = ["clean", "device-recorded"]
_SOURCES = ["DR-VCTK_Office1_ClosedWindow", "DR-VCTK_Office1_OpenedWindow"]
_SPEAKER_IDS = range(226, 230)
_CHANNEL_IDS = range(1, 6)
def get_mock_dataset(root_dir):
"""
root_dir: root directory of the mocked data
"""
mocked_samples = {}
dataset_dir = Path(root_dir) / "DR-VCTK" / "DR-VCTK"
dataset_dir.mkdir(parents=True, exist_ok=True)
config_dir = dataset_dir / "configurations"
config_dir.mkdir(parents=True, exist_ok=True)
sample_rate = 16000
seed = 0
for subset in _SUBSETS:
mocked_samples[subset] = []
for condition in _CONDITIONS:
audio_dir = dataset_dir / f"{condition}_{subset}set_wav_16k"
audio_dir.mkdir(parents=True, exist_ok=True)
config_filepath = config_dir / f"{subset}_ch_log.txt"
with open(config_filepath, "w") as f:
if subset == "train":
f.write("\n")
f.write("File Name\tMain Source\tChannel Idx\n")
for speaker_id in _SPEAKER_IDS:
utterance_id = 1
for source in _SOURCES:
for channel_id in _CHANNEL_IDS:
filename = f"p{speaker_id}_{utterance_id:03d}.wav"
f.write(f"{filename}\t{source}\t{channel_id}\n")
data = {}
for condition in _CONDITIONS:
data[condition] = get_whitenoise(
sample_rate=sample_rate, duration=0.01, n_channels=1, dtype="float32", seed=seed
)
audio_dir = dataset_dir / f"{condition}_{subset}set_wav_16k"
audio_file_path = audio_dir / filename
save_wav(audio_file_path, data[condition], sample_rate)
seed += 1
sample = (
data[_CONDITIONS[0]],
sample_rate,
data[_CONDITIONS[1]],
sample_rate,
"p" + str(speaker_id),
f"{utterance_id:03d}",
source,
channel_id,
)
mocked_samples[subset].append(sample)
utterance_id += 1
return mocked_samples
class TestDRVCTK(TempDirMixin, TorchaudioTestCase):
backend = "default"
root_dir = None
samples = {}
@classmethod
def setUpClass(cls):
cls.root_dir = cls.get_base_temp_dir()
cls.samples = get_mock_dataset(cls.root_dir)
def _test_dr_vctk(self, dataset, subset):
num_samples = 0
for i, (
waveform_clean,
sample_rate_clean,
waveform_dr,
sample_rate_dr,
speaker_id,
utterance_id,
source,
channel_id,
) in enumerate(dataset):
self.assertEqual(waveform_clean, self.samples[subset][i][0], atol=5e-5, rtol=1e-8)
assert sample_rate_clean == self.samples[subset][i][1]
self.assertEqual(waveform_dr, self.samples[subset][i][2], atol=5e-5, rtol=1e-8)
assert sample_rate_dr == self.samples[subset][i][3]
assert speaker_id == self.samples[subset][i][4]
assert utterance_id == self.samples[subset][i][5]
assert source == self.samples[subset][i][6]
assert channel_id == self.samples[subset][i][7]
num_samples += 1
assert num_samples == len(self.samples[subset])
def test_dr_vctk_train_str(self):
subset = "train"
dataset = dr_vctk.DR_VCTK(self.root_dir, subset=subset)
self._test_dr_vctk(dataset, subset)
def test_dr_vctk_test_str(self):
subset = "test"
dataset = dr_vctk.DR_VCTK(self.root_dir, subset=subset)
self._test_dr_vctk(dataset, subset)
def test_dr_vctk_train_path(self):
subset = "train"
dataset = dr_vctk.DR_VCTK(Path(self.root_dir), subset=subset)
self._test_dr_vctk(dataset, subset)
def test_dr_vctk_test_path(self):
subset = "test"
dataset = dr_vctk.DR_VCTK(Path(self.root_dir), subset=subset)
self._test_dr_vctk(dataset, subset)
def test_dr_vctk_invalid_subset(self):
subset = "invalid"
with pytest.raises(RuntimeError, match=f"The subset '{subset}' does not match any of the supported subsets"):
dr_vctk.DR_VCTK(self.root_dir, subset=subset)
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