def test_fade(self, fade_shape):
transform = T.Fade(fade_shape=fade_shape)
waveform = get_whitenoise(sample_rate=8000,
duration=0.05,
n_channels=2)
self.assert_grad(transform, [waveform], nondet_tol=1e-10)
def test_dither_noise_shaping(self):
def func(tensor):
return F.dither(tensor, noise_shaping=True)
tensor = common_utils.get_whitenoise(n_channels=2)
self._assert_consistency(func, tensor)
def test_resample(self, orig_freq, new_freq):
transform = T.Resample(orig_freq=orig_freq, new_freq=new_freq)
waveform = get_whitenoise(sample_rate=8000,
duration=0.05,
n_channels=2)
self.assert_grad(transform, [waveform])
def test_Fade(self):
waveform = common_utils.get_whitenoise()
fade_in_len = 3000
fade_out_len = 3000
self._assert_consistency(T.Fade(fade_in_len, fade_out_len), waveform)
def test_dither_GPDF(self):
def func(tensor):
return F.dither(tensor, 'GPDF')
tensor = common_utils.get_whitenoise(n_channels=2)
self._assert_consistency(func, tensor, shape_only=True)
def get_mock_dataset(dataset_dir):
"""
dataset_dir: directory of the mocked dataset
"""
mocked_samples = {}
os.makedirs(dataset_dir, exist_ok=True)
sample_rate = 16000 # 16kHz
seed = 0
for release in ["release1", "release2", "release3"]:
data = get_whitenoise(sample_rate=sample_rate,
duration=10.00,
n_channels=1,
dtype="float32",
seed=seed)
if release in ["release1", "release2"]:
release_dir = os.path.join(
dataset_dir,
tedlium._RELEASE_CONFIGS[release]["folder_in_archive"],
tedlium._RELEASE_CONFIGS[release]["subset"],
)
else:
release_dir = os.path.join(
dataset_dir,
tedlium._RELEASE_CONFIGS[release]["folder_in_archive"],
tedlium._RELEASE_CONFIGS[release]["data_path"],
)
os.makedirs(release_dir, exist_ok=True)
os.makedirs(os.path.join(release_dir, "stm"),
exist_ok=True) # Subfolder for transcripts
os.makedirs(os.path.join(release_dir, "sph"),
exist_ok=True) # Subfolder for audio files
filename = f"{release}.sph"
path = os.path.join(os.path.join(release_dir, "sph"), filename)
save_wav(path, data, sample_rate)
trans_filename = f"{release}.stm"
trans_path = os.path.join(os.path.join(release_dir, "stm"),
trans_filename)
with open(trans_path, "w") as f:
f.write("".join(_UTTERANCES))
dict_filename = f"{release}.dic"
dict_path = os.path.join(release_dir, dict_filename)
with open(dict_path, "w") as f:
f.write("\n".join(_PHONEME))
# Create a samples list to compare with
mocked_samples[release] = []
for utterance in _UTTERANCES:
talk_id, _, speaker_id, start_time, end_time, identifier, transcript = utterance.split(
" ", 6)
start_time = int(float(start_time)) * sample_rate
end_time = int(float(end_time)) * sample_rate
sample = (
data[:, start_time:end_time],
sample_rate,
transcript,
talk_id,
speaker_id,
identifier,
)
mocked_samples[release].append(sample)
seed += 1
return mocked_samples
def test_mfcc(self, log_mels):
sample_rate = 8000
transform = T.MFCC(sample_rate=sample_rate, log_mels=log_mels)
waveform = get_whitenoise(sample_rate=sample_rate, duration=0.05, n_channels=2)
self.assert_grad(transform, [waveform])
def test_Vol(self):
waveform = common_utils.get_whitenoise()
self._assert_consistency(T.Vol(1.1), waveform)
def test_SpectralCentroid(self):
sample_rate = 44100
waveform = common_utils.get_whitenoise(sample_rate=sample_rate)
self._assert_consistency(T.SpectralCentroid(sample_rate=sample_rate),
waveform)
def test_resample_identity(self, resampling_method, sample_rate):
waveform = get_whitenoise(sample_rate=sample_rate, duration=1)
resampled = F.resample(waveform, sample_rate, sample_rate)
self.assertEqual(waveform, resampled)
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
def test_vol(self, gain, gain_type):
sample_rate = 8000
transform = T.Vol(gain=gain, gain_type=gain_type)
waveform = get_whitenoise(sample_rate=sample_rate, duration=0.05, n_channels=2)
self.assert_grad(transform, [waveform])
def test_amplitude_to_db(self):
sample_rate = 8000
transform = T.AmplitudeToDB()
waveform = get_whitenoise(sample_rate=sample_rate, duration=0.05, n_channels=2)
self.assert_grad(transform, [waveform])
def test_spectral_centroid(self):
sample_rate = 8000
transform = T.SpectralCentroid(sample_rate=sample_rate)
waveform = get_whitenoise(sample_rate=sample_rate, duration=0.05, n_channels=2)
self.assert_grad(transform, [waveform], nondet_tol=1e-10)
def test_lfilter_all_inputs(self):
torch.random.manual_seed(2434)
x = get_whitenoise(sample_rate=22050, duration=0.01, n_channels=2)
a = torch.tensor([0.7, 0.2, 0.6])
b = torch.tensor([0.4, 0.2, 0.9])
self.assert_grad(F.lfilter, (x, a, b))
def test_PitchShift(self):
sample_rate = 8000
n_steps = 4
waveform = common_utils.get_whitenoise(sample_rate=sample_rate)
self._assert_consistency(
T.PitchShift(sample_rate=sample_rate, n_steps=n_steps), waveform)
def test_biquad(self):
torch.random.manual_seed(2434)
x = get_whitenoise(sample_rate=22050, duration=0.01, n_channels=1)
a = torch.tensor([0.7, 0.2, 0.6])
b = torch.tensor([0.4, 0.2, 0.9])
self.assert_grad(F.biquad, (x, b[0], b[1], b[2], a[0], a[1], a[2]))
def test_MuLawEncoding(self):
tensor = common_utils.get_whitenoise()
self._assert_consistency(T.MuLawEncoding(), tensor)
def test_Resample(self):
sr1, sr2 = 16000, 8000
tensor = common_utils.get_whitenoise(sample_rate=sr1)
self._assert_consistency(T.Resample(float(sr1), float(sr2)), tensor)
def test_compute_kaldi_pitch(self):
sample_rate = 44100
waveform = common_utils.get_whitenoise(sample_rate=sample_rate)
self.assert_batch_consistencies(F.compute_kaldi_pitch,
waveform,
sample_rate=sample_rate)
