def prepare_musan(
corpus_dir: Pathlike,
output_dir: Optional[Pathlike] = None,
parts: Sequence[str] = ("music", "speech", "noise"),
use_vocals: bool = True,
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
corpus_dir = Path(corpus_dir)
assert corpus_dir.is_dir(), f"No such directory: {corpus_dir}"
if not parts:
raise ValueError("No MUSAN parts specified for manifest preparation.")
if isinstance(parts, str):
parts = [parts]
manifests = {}
if "music" in parts:
manifests["music"] = prepare_music(corpus_dir, use_vocals=use_vocals)
validate_recordings_and_supervisions(**manifests["music"])
if "speech" in parts:
manifests["speech"] = {"recordings": scan_recordings(corpus_dir / "speech")}
validate(manifests["speech"]["recordings"])
if "noise" in parts:
manifests["noise"] = {"recordings": scan_recordings(corpus_dir / "noise")}
validate(manifests["noise"]["recordings"])
if output_dir is not None:
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
for part in manifests:
for key, manifest in manifests[part].items():
manifest.to_file(output_dir / f"musan_{key}_{part}.jsonl.gz")
return manifests
def _validate(self) -> None:
validate(self.cuts)
for cut in self.cuts:
for supervision in cut.supervisions:
assert (cut.start - 1e-5) <= supervision.start <= supervision.end <= (cut.end + 1e-5), \
f"Cutting in the middle of a supervision is currently not supported for the ASR task. " \
f"Cut ID violating the pre-condition: '{cut.id}'"
def prepare_musan(
corpus_dir: Pathlike,
output_dir: Optional[Pathlike] = None,
parts: Sequence[str] = ('music', 'speech', 'noise'),
use_vocals: bool = True,
) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
corpus_dir = Path(corpus_dir)
assert corpus_dir.is_dir(), f'No such directory: {corpus_dir}'
if not parts:
raise ValueError("No MUSAN parts specified for manifest preparation.")
if isinstance(parts, str):
parts = [parts]
manifests = {}
if 'music' in parts:
manifests['music'] = prepare_music(corpus_dir, use_vocals=use_vocals)
validate_recordings_and_supervisions(**manifests['music'])
if 'speech' in parts:
manifests['speech'] = {'recordings': scan_recordings(corpus_dir / 'speech')}
validate(manifests['speech']['recordings'])
if 'noise' in parts:
manifests['noise'] = {'recordings': scan_recordings(corpus_dir / 'noise')}
validate(manifests['noise']['recordings'])
if output_dir is not None:
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
for part in manifests:
for key, manifest in manifests[part].items():
manifest.to_json(output_dir / f'{key}_{part}.json')
return manifests
def __init__(
self,
cuts: CutSet,
cut_transforms: List[Callable[[CutSet], CutSet]] = None,
feature_input_strategy: InputStrategy = PrecomputedFeatures(),
feature_transforms: Union[Sequence[Callable], Callable] = None,
add_eos: bool = True,
add_bos: bool = True,
) -> None:
super().__init__()
validate(cuts)
for cut in cuts:
assert (len(cut.supervisions) == 1
), "Only the Cuts with single supervision are supported."
self.cuts = cuts
self.token_collater = TokenCollater(cuts,
add_eos=add_eos,
add_bos=add_bos)
self.cut_transforms = ifnone(cut_transforms, [])
self.feature_input_strategy = feature_input_strategy
if feature_transforms is None:
feature_transforms = []
elif not isinstance(feature_transforms, Sequence):
feature_transforms = [feature_transforms]
assert all(isinstance(transform, Callable) for transform in feature_transforms), \
"Feature transforms must be Callable"
self.feature_transforms = feature_transforms
def test_cut_set_batch_feature_extraction_resume(cut_set, overwrite):
# This test checks that we can keep writing to the same file
# and the previously written results are not lost.
# Since we don't have an easy way to interrupt the execution in a test,
# we just write another CutSet to the same file.
# The effect is the same.
extractor = Fbank()
cut_set = cut_set.resample(16000)
subsets = cut_set.split(num_splits=2)
processed = []
with NamedTemporaryFile() as feat_f, NamedTemporaryFile(
suffix=".jsonl.gz") as manifest_f:
for cuts in subsets:
processed.append(
cuts.compute_and_store_features_batch(
extractor=extractor,
storage_path=feat_f.name,
manifest_path=manifest_f.name,
num_workers=0,
overwrite=overwrite,
))
feat_f.flush()
manifest_f.flush()
merged = load_manifest(manifest_f.name)
if overwrite:
assert list(merged.ids) == list(subsets[-1].ids)
else:
assert list(merged.ids) == list(cut_set.ids)
validate(merged, read_data=True)
def validate(self):
validate(self.sources_set)
validate(self.mixtures_set)
# Make sure it's possible to iterate through the whole dataset and resolve the sources for each mixture
for cut in self.mixtures_set.mixed_cuts.values():
_, source_cuts = self._obtain_mixture(cut.id)
assert len(source_cuts) > 1
def __init__(
self,
cuts: CutSet,
):
super().__init__()
validate(cuts)
self.cuts = cuts
self.cut_ids = list(cuts.ids)
def test_cut_set_batch_feature_extraction(cut_set, extractor_type):
extractor = extractor_type()
cut_set = cut_set.resample(16000)
with NamedTemporaryFile() as tmpf:
cut_set_with_feats = cut_set.compute_and_store_features_batch(
extractor=extractor,
storage_path=tmpf.name,
num_workers=0,
)
validate(cut_set_with_feats, read_data=True)
def _validate(self) -> None:
validate(self.cuts)
tol = 1e-3 # 1ms
for cut in self.cuts:
for supervision in cut.supervisions:
assert supervision.start >= -tol, f"Supervisions starting before the cut are not supported for ASR" \
f" (sup id: {supervision.id}, cut id: {cut.id})"
assert supervision.duration <= cut.duration + tol, f"Supervisions ending after the cut " \
f"are not supported for ASR" \
f" (sup id: {supervision.id}, cut id: {cut.id})"
def test_extract_and_store_features_from_mixed_cut(cut, mix_eagerly):
mixed_cut = cut.append(cut)
extractor = Fbank(FbankConfig(sampling_rate=8000))
with TemporaryDirectory() as tmpdir, LilcomFilesWriter(tmpdir) as storage:
cut_with_feats = mixed_cut.compute_and_store_features(
extractor=extractor, storage=storage, mix_eagerly=mix_eagerly)
validate(cut_with_feats)
arr = cut_with_feats.load_features()
assert arr.shape[0] == 200
assert arr.shape[1] == extractor.feature_dim(mixed_cut.sampling_rate)
def validate_for_asr(cuts: CutSet) -> None:
validate(cuts)
tol = 2e-3 # 1ms
for cut in cuts:
for supervision in cut.supervisions:
assert supervision.start >= -tol, (
f"Supervisions starting before the cut are not supported for ASR"
f" (sup id: {supervision.id}, cut id: {cut.id})")
assert supervision.duration <= cut.duration + tol, (
f"Supervisions ending after the cut "
f"are not supported for ASR"
f" (sup id: {supervision.id}, cut id: {cut.id})")
def __init__(
self,
cuts: CutSet,
input_strategy: InputStrategy = PrecomputedFeatures(),
cut_transforms: Sequence[Callable[[CutSet], CutSet]] = None,
input_transforms: Sequence[Callable[[torch.Tensor], torch.Tensor]] = None
) -> None:
super().__init__()
validate(cuts)
self.cuts = cuts
self.input_strategy = input_strategy
self.cut_transforms = ifnone(cut_transforms, [])
self.input_transforms = ifnone(input_transforms, [])
def __getitem__(self, cuts: CutSet) -> Dict[str, torch.Tensor]:
validate(cuts)
cuts = cuts.sort_by_duration()
for tfnm in self.cut_transforms:
cuts = tfnm(cuts)
inputs, input_lens = self.input_strategy(cuts)
for tfnm in self.input_transforms:
inputs = tfnm(inputs)
return {
"inputs": inputs,
"input_lens": input_lens,
"is_voice": self.input_strategy.supervision_masks(cuts),
"cut": cuts,
}
def __init__(
self,
cuts: CutSet,
min_speaker_dim: Optional[int] = None,
global_speaker_ids: bool = False,
) -> None:
super().__init__()
validate(cuts)
self.cuts = cuts
self.speakers = {
spk: idx
for idx, spk in enumerate(self.cuts.speakers)
} if global_speaker_ids else None
self.min_speaker_dim = min_speaker_dim
def test_cut_set_batch_feature_extraction_manifest_path(
cut_set, suffix, exception_expectation):
extractor = Fbank()
cut_set = cut_set.resample(16000)
with NamedTemporaryFile() as feat_f, NamedTemporaryFile(
suffix=suffix) as manifest_f:
with exception_expectation:
cut_set_with_feats = cut_set.compute_and_store_features_batch(
extractor=extractor,
storage_path=feat_f.name,
manifest_path=manifest_f.name,
num_workers=0,
)
validate(cut_set_with_feats, read_data=True)
def __init__(self, cuts: CutSet, root_dir: Optional[Pathlike] = None):
super().__init__()
validate(cuts)
self.cuts = cuts
self.root_dir = Path(root_dir) if root_dir else None
self.cut_ids = list(self.cuts.ids)
# generate tokens from text
self.id_to_token = {}
self.token_set = set()
for cut in cuts:
assert len(
cut.supervisions
) == 1, 'Only the Cuts with single supervision are supported.'
characters = list(cut.supervisions[0].text)
self.token_set.update(set(characters))
self.id_to_token[cut.id] = characters
self.token_set = sorted(list(self.tokens))
def __init__(
self,
cuts: CutSet,
uem: Optional[SupervisionSet] = None,
min_speaker_dim: Optional[int] = None,
global_speaker_ids: bool = False,
) -> None:
super().__init__()
validate(cuts)
if not uem:
self.cuts = cuts
else:
# We use the `overlap` method in intervaltree to get overlapping regions
# between the supervision segments and the UEM segments
recordings = RecordingSet(
{c.recording.id: c.recording
for c in cuts if c.has_recording})
uem_intervals = CutSet.from_manifests(
recordings=recordings,
supervisions=uem,
).index_supervisions()
supervisions = []
for cut_id, tree in cuts.index_supervisions().items():
if cut_id not in uem_intervals:
supervisions += [it.data for it in tree]
continue
supervisions += {
it.data.trim(it.end, start=it.begin)
for uem_it in uem_intervals[cut_id]
for it in tree.overlap(begin=uem_it.begin, end=uem_it.end)
}
self.cuts = CutSet.from_manifests(
recordings=recordings,
supervisions=SupervisionSet.from_segments(supervisions),
)
self.speakers = ({
spk: idx
for idx, spk in enumerate(self.cuts.speakers)
} if global_speaker_ids else None)
self.min_speaker_dim = min_speaker_dim
def test_validate_cut_with_temporal_array(caplog):
# Note: "caplog" is a special variable in pytest that captures logs.
caplog.set_level(logging.WARNING)
with NamedTemporaryFile(suffix=".h5") as f, NumpyHdf5Writer(
f.name) as writer:
cut = MonoCut(
id="cut1",
start=0,
duration=4.9,
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)
validate(cut)
assert ("MonoCut cut1: possibly mismatched duration between cut (4.9s) "
"and temporal array in custom field 'alignment' (num_frames=131 "
"* frame_shift=0.4 == duration=52.400000000000006)" in caplog.text)
def validate(self):
super().validate()
validate(self.nonsources_set)
def validate_(manifest: Pathlike, read_data: bool):
"""Validate a Lhotse manifest file."""
from lhotse import load_manifest, validate
data = load_manifest(manifest)
validate(data, read_data=read_data)
def validate_(manifest: Pathlike, read_data: bool):
"""Validate a Lhotse manifest file."""
data = load_manifest(manifest)
validate(data, read_data=read_data)
def _validate(self):
validate(self.cuts)
assert all(cut.has_features for cut in self.cuts)
def validate_for_tts(cuts: CutSet) -> None:
validate(cuts)
for cut in cuts:
assert (len(cut.supervisions) == 1
), "Only the Cuts with single supervision are supported."
def _validate(self):
validate(self.cuts)
assert all(cut.has_recording for cut in self.cuts)
def test_validate_feature_set_runs():
features = DummyManifest(FeatureSet, begin_id=0, end_id=100)
validate(features)
def _validate(self, cuts: CutSet) -> None:
validate(cuts)
assert all(cut.has_features for cut in cuts)
def _validate(self, cuts: CutSet) -> None:
validate(cuts)
assert all(cut.has_recording for cut in cuts)
def _validate(self) -> None:
validate(self.cuts)