def test_collate_custom_numbers():
cuts = CutSet.from_json("test/fixtures/ljspeech/cuts.json")
expected_snrs = []
for cut in cuts:
expected_snrs.append(random.random() * 20)
cut.snr = expected_snrs[-1]
snrs = collate_custom_field(cuts, "snr")
assert isinstance(snrs, torch.Tensor)
assert snrs.dtype == torch.float32
assert snrs.shape == (len(cuts), )
for idx, snr in enumerate(expected_snrs):
assert isclose(snrs[idx], snr, abs_tol=1e-5)
def test_collate_custom_temporal_array_ints(pad_value):
CODEBOOK_SIZE = 512
FRAME_SHIFT = 0.04
cuts = CutSet.from_json("test/fixtures/ljspeech/cuts.json")
max_num_frames = max(
seconds_to_frames(cut.duration, FRAME_SHIFT) for cut in cuts)
with NamedTemporaryFile(suffix=".h5") as f, NumpyHdf5Writer(
f.name) as writer:
expected_codebook_indices = []
for cut in cuts:
expected_codebook_indices.append(
np.random.randint(CODEBOOK_SIZE,
size=(seconds_to_frames(
cut.duration,
FRAME_SHIFT), )).astype(np.int16))
cut.codebook_indices = writer.store_array(
cut.id,
expected_codebook_indices[-1],
frame_shift=FRAME_SHIFT,
temporal_dim=0,
)
codebook_indices, codebook_indices_lens = collate_custom_field(
cuts, "codebook_indices", pad_value=pad_value)
assert isinstance(codebook_indices_lens, torch.Tensor)
assert codebook_indices_lens.dtype == torch.int32
assert codebook_indices_lens.shape == (len(cuts), )
assert codebook_indices_lens.tolist() == [
seconds_to_frames(c.duration, FRAME_SHIFT) for c in cuts
]
assert isinstance(codebook_indices, torch.Tensor)
assert codebook_indices.dtype == torch.int16
assert codebook_indices.shape == (len(cuts), max_num_frames)
for idx, cbidxs in enumerate(expected_codebook_indices):
exp_len = cbidxs.shape[0]
# PyTorch < 1.9.0 doesn't have an assert_equal function.
np.testing.assert_equal(codebook_indices[idx, :exp_len].numpy(),
cbidxs)
expected_pad_value = 0 if pad_value is None else pad_value
np.testing.assert_equal(codebook_indices[idx, exp_len:].numpy(),
expected_pad_value)
def test_collate_custom_array():
EMBEDDING_SIZE = 300
cuts = CutSet.from_json("test/fixtures/ljspeech/cuts.json")
with NamedTemporaryFile(suffix=".h5") as f, NumpyHdf5Writer(
f.name) as writer:
expected_xvectors = []
for cut in cuts:
expected_xvectors.append(
np.random.randn(EMBEDDING_SIZE).astype(np.float32))
cut.xvector = writer.store_array(cut.id, expected_xvectors[-1])
xvectors = collate_custom_field(cuts, "xvector")
assert isinstance(xvectors, torch.Tensor)
assert xvectors.dtype == torch.float32
assert xvectors.shape == (len(cuts), EMBEDDING_SIZE)
for idx, xvec in enumerate(expected_xvectors):
torch.testing.assert_allclose(xvectors[idx], xvec)
def test_collate_custom_temporal_array_floats(pad_value):
VOCAB_SIZE = 500
cuts = CutSet.from_json("test/fixtures/ljspeech/cuts.json")
max_num_frames = max(cut.num_frames for cut in cuts)
with NamedTemporaryFile(suffix=".h5") as f, NumpyHdf5Writer(
f.name) as writer:
expected_posteriors = []
for cut in cuts:
expected_posteriors.append(
np.random.randn(cut.num_frames, VOCAB_SIZE).astype(np.float32))
cut.posterior = writer.store_array(
cut.id,
expected_posteriors[-1],
frame_shift=cut.frame_shift,
temporal_dim=0,
)
posteriors, posterior_lens = collate_custom_field(cuts,
"posterior",
pad_value=pad_value)
assert isinstance(posterior_lens, torch.Tensor)
assert posterior_lens.dtype == torch.int32
assert posterior_lens.shape == (len(cuts), )
assert posterior_lens.tolist() == [c.num_frames for c in cuts]
assert isinstance(posteriors, torch.Tensor)
assert posteriors.dtype == torch.float32
assert posteriors.shape == (len(cuts), max_num_frames, VOCAB_SIZE)
for idx, post in enumerate(expected_posteriors):
exp_len = post.shape[0]
torch.testing.assert_allclose(posteriors[idx, :exp_len], post)
expected_pad_value = 0 if pad_value is None else pad_value
torch.testing.assert_allclose(
posteriors[idx, exp_len:],
expected_pad_value *
torch.ones_like(posteriors[idx, exp_len:]),
)
def test_padding_issue_478():
"""
https://github.com/lhotse-speech/lhotse/issues/478
"""
with NamedTemporaryFile(suffix=".h5") as f, NumpyHdf5Writer(
f.name) as writer:
# Prepare data for cut 1.
cut1 = MonoCut("c1",
start=0,
duration=4.9,
channel=0,
recording=dummy_recording(1))
ali1 = np.random.randint(500, size=(121, ))
cut1.label_alignment = writer.store_array("c1",
ali1,
frame_shift=0.04,
temporal_dim=0)
# Prepare data for cut 2.
cut2 = MonoCut("c2",
start=0,
duration=4.895,
channel=0,
recording=dummy_recording(2))
ali2 = np.random.randint(500, size=(121, ))
cut2.label_alignment = writer.store_array("c2",
ali2,
frame_shift=0.04,
temporal_dim=0)
# Test collation behavior on this cutset.
cuts = CutSet.from_cuts([cut1, cut2])
label_alignments, label_alignment_lens = collate_custom_field(
cuts, "label_alignment")
np.testing.assert_equal(label_alignments[0].numpy(), ali1)
np.testing.assert_equal(label_alignments[1].numpy(), ali2)
def test_collate_custom_attribute_missing():
cuts = CutSet.from_json("test/fixtures/ljspeech/cuts.json")
with pytest.raises(AttributeError):
collate_custom_field(cuts, "nonexistent_attribute")
def test_collate_custom_temporal_array_ints(pad_direction):
CODEBOOK_SIZE = 512
FRAME_SHIFT = 0.04
EXPECTED_PAD_VALUE = 0
cuts = CutSet.from_json("test/fixtures/ljspeech/cuts.json")
max_num_frames = max(
seconds_to_frames(cut.duration, FRAME_SHIFT) for cut in cuts)
with NamedTemporaryFile(suffix=".h5") as f, NumpyHdf5Writer(
f.name) as writer:
expected_codebook_indices = []
for cut in cuts:
expected_codebook_indices.append(
np.random.randint(CODEBOOK_SIZE,
size=(seconds_to_frames(
cut.duration,
FRAME_SHIFT), )).astype(np.int16))
cut.codebook_indices = writer.store_array(
cut.id,
expected_codebook_indices[-1],
frame_shift=FRAME_SHIFT,
temporal_dim=0,
)
codebook_indices, codebook_indices_lens = collate_custom_field(
cuts, "codebook_indices", pad_direction=pad_direction)
assert isinstance(codebook_indices_lens, torch.Tensor)
assert codebook_indices_lens.dtype == torch.int32
assert codebook_indices_lens.shape == (len(cuts), )
assert codebook_indices_lens.tolist() == [
seconds_to_frames(c.duration, FRAME_SHIFT) for c in cuts
]
assert isinstance(codebook_indices, torch.Tensor)
assert (codebook_indices.dtype == torch.int64
) # the dtype got promoted by default
assert codebook_indices.shape == (len(cuts), max_num_frames)
for idx, cbidxs in enumerate(expected_codebook_indices):
exp_len = cbidxs.shape[0]
# PyTorch < 1.9.0 doesn't have an assert_equal function.
if pad_direction == "right":
np.testing.assert_equal(
codebook_indices[idx, :exp_len].numpy(), cbidxs)
np.testing.assert_equal(
codebook_indices[idx, exp_len:].numpy(),
EXPECTED_PAD_VALUE)
if pad_direction == "left":
np.testing.assert_equal(
codebook_indices[idx, -exp_len:].numpy(), cbidxs)
np.testing.assert_equal(
codebook_indices[idx, :-exp_len].numpy(),
EXPECTED_PAD_VALUE)
if pad_direction == "both":
half = (max_num_frames - exp_len) // 2
np.testing.assert_equal(codebook_indices[idx, :half].numpy(),
EXPECTED_PAD_VALUE)
np.testing.assert_equal(
codebook_indices[idx, half:half + exp_len].numpy(), cbidxs)
if half > 0:
# indexing like [idx, -0:] would return the whole array rather
# than an empty slice.
np.testing.assert_equal(
codebook_indices[idx, -half:].numpy(),
EXPECTED_PAD_VALUE)
