def cut_set():
cut = MonoCut(
id="cut-1",
start=0.0,
duration=10.0,
channel=0,
features=Features(
type="fbank",
num_frames=100,
num_features=40,
frame_shift=0.01,
sampling_rate=16000,
start=0.0,
duration=10.0,
storage_type="lilcom",
storage_path="irrelevant",
storage_key="irrelevant",
),
recording=Recording(
id="rec-1",
sampling_rate=16000,
num_samples=160000,
duration=10.0,
sources=[
AudioSource(type="file", channels=[0], source="irrelevant")
],
),
supervisions=[
SupervisionSegment(id="sup-1",
recording_id="irrelevant",
start=0.5,
duration=6.0),
SupervisionSegment(id="sup-2",
recording_id="irrelevant",
start=7.0,
duration=2.0),
],
)
return CutSet.from_cuts([
cut,
fastcopy(cut, id="cut-nosup", supervisions=[]),
fastcopy(cut, id="cut-norec", recording=None),
fastcopy(cut, id="cut-nofeat", features=None),
cut.pad(duration=30.0, direction="left"),
cut.pad(duration=30.0, direction="right"),
cut.pad(duration=30.0, direction="both"),
cut.mix(cut, offset_other_by=5.0, snr=8),
])
def test_cut_load_custom_recording_pad_left():
sampling_rate = 16000
duration = 52.4
audio = np.random.randn(1, compute_num_samples(
duration, sampling_rate)).astype(np.float32)
audio /= np.abs(audio).max() # normalize to [-1, 1]
with NamedTemporaryFile(suffix=".wav") as f:
torchaudio.save(f.name, torch.from_numpy(audio), sampling_rate)
f.flush()
os.fsync(f)
recording = Recording.from_file(f.name)
# Note: MonoCut doesn't normally have an "alignment" attribute,
# and a "load_alignment()" method.
# We are dynamically extending it.
cut = MonoCut(
id="x",
start=0,
duration=duration,
channel=0,
recording=dummy_recording(0, duration=duration),
)
cut.my_favorite_song = recording
cut_pad = cut.pad(duration=60.0, direction="left")
restored_audio = cut_pad.load_my_favorite_song()
assert restored_audio.shape == (1, 960000) # 16000 * 60
np.testing.assert_almost_equal(0, restored_audio[:, :-audio.shape[1]])
np.testing.assert_almost_equal(audio, restored_audio[:,
-audio.shape[1]:])
def cut_set():
cut = MonoCut(id='cut-1',
start=0.0,
duration=10.0,
channel=0,
features=Features(
type='fbank',
num_frames=100,
num_features=40,
frame_shift=0.01,
sampling_rate=16000,
start=0.0,
duration=10.0,
storage_type='lilcom',
storage_path='irrelevant',
storage_key='irrelevant',
),
recording=Recording(id='rec-1',
sampling_rate=16000,
num_samples=160000,
duration=10.0,
sources=[
AudioSource(type='file',
channels=[0],
source='irrelevant')
]),
supervisions=[
SupervisionSegment(id='sup-1',
recording_id='irrelevant',
start=0.5,
duration=6.0),
SupervisionSegment(id='sup-2',
recording_id='irrelevant',
start=7.0,
duration=2.0)
])
return CutSet.from_cuts([
cut,
fastcopy(cut, id='cut-nosup', supervisions=[]),
fastcopy(cut, id='cut-norec', recording=None),
fastcopy(cut, id='cut-nofeat', features=None),
cut.pad(duration=30.0, direction='left'),
cut.pad(duration=30.0, direction='right'),
cut.pad(duration=30.0, direction='both'),
cut.mix(cut, offset_other_by=5.0, snr=8)
])
def test_cut_load_array_pad():
"""Check that loading a custom Array works after padding."""
ivector = np.arange(20).astype(np.float32)
with NamedTemporaryFile(suffix=".h5") as f, LilcomHdf5Writer(f.name) as writer:
cut = MonoCut(
id="x", start=0, duration=5, channel=0, recording=dummy_recording(1)
)
cut.ivector = writer.store_array(key="utt1", value=ivector)
cut = cut.pad(duration=7.6)
restored_ivector = cut.load_ivector()
np.testing.assert_equal(ivector, restored_ivector)
def test_cut_load_temporal_array_pad(pad_value):
"""Check the array loaded via TemporalArray is padded along with the cut."""
with NamedTemporaryFile(suffix=".h5") as f, NumpyHdf5Writer(f.name) as writer:
cut = MonoCut(
id="x",
start=0,
duration=52.4, # 131 frames x 0.4s frame shift == 52.4s
channel=0,
recording=dummy_recording(1),
)
alignment = np.random.randint(500, size=131)
cut.alignment = writer.store_array(
key="utt1", value=alignment, frame_shift=0.4, temporal_dim=0
)
cut_pad = cut.pad(duration=60.0, pad_value_dict={"alignment": pad_value})
alignment_pad = cut_pad.load_alignment()
assert alignment_pad.shape == (150,) # 60.0 / 0.4 == 150
np.testing.assert_equal(alignment_pad[:131], alignment)
np.testing.assert_equal(alignment_pad[131:], pad_value)
