def DummyManifest(type_: Type, *, begin_id: int, end_id: int) -> Manifest:
if type_ == RecordingSet:
return RecordingSet.from_recordings(
dummy_recording(idx) for idx in range(begin_id, end_id))
if type_ == SupervisionSet:
return SupervisionSet.from_segments(
dummy_supervision(idx) for idx in range(begin_id, end_id))
if type_ == FeatureSet:
# noinspection PyTypeChecker
return FeatureSet.from_features(
dummy_features(idx) for idx in range(begin_id, end_id))
def test_feature_set_prefix_path():
features = FeatureSet.from_features([
Features(type='fbank',
num_frames=1000,
num_features=40,
sampling_rate=16000,
storage_type='lilcom',
storage_path='feats/12345.llc',
start=0,
duration=10)
])
for feat in features.with_path_prefix('/data'):
assert feat.storage_path == '/data/feats/12345.llc'
def dummy_feature_set():
return FeatureSet.from_features([
Features(recording_id='rec1',
channels=0,
start=0,
duration=10,
type='fbank',
num_frames=1000,
num_features=23,
sampling_rate=16000,
storage_type='lilcom',
storage_path='dummy.llc')
])
def test_feature_set_prefix_path():
features = FeatureSet.from_features([
Features(
type="fbank",
num_frames=1000,
num_features=40,
frame_shift=0.01,
sampling_rate=16000,
storage_type="lilcom",
storage_path="feats/",
storage_key="12345.llc",
start=0,
duration=10,
)
])
for feat in features.with_path_prefix("/data"):
assert feat.storage_path == "/data/feats"
def dummy_feature_set():
return FeatureSet.from_features([
Features(
recording_id="rec1",
channels=0,
start=0,
duration=10,
type="fbank",
num_frames=1000,
num_features=23,
sampling_rate=16000,
storage_type="lilcom_files",
storage_path="feats",
storage_key="dummy.llc",
frame_shift=0.01,
)
])
def dummy_feature_set_lazy():
with NamedTemporaryFile(suffix=".jsonl.gz") as f:
feats = FeatureSet.from_features([
Features(
recording_id="rec1",
channels=0,
start=0,
duration=10,
type="fbank",
num_frames=1000,
num_features=23,
sampling_rate=16000,
storage_type="lilcom_files",
storage_path="feats",
storage_key="dummy.llc",
frame_shift=0.01,
)
])
feats.to_file(f.name)
f.flush()
yield FeatureSet.from_jsonl_lazy(f.name)
def load_kaldi_data_dir(
path: Pathlike,
sampling_rate: int,
frame_shift: Optional[Seconds] = None,
map_string_to_underscores: Optional[str] = None,
num_jobs: int = 1,
) -> Tuple[RecordingSet, Optional[SupervisionSet], Optional[FeatureSet]]:
"""
Load a Kaldi data directory and convert it to a Lhotse RecordingSet and
SupervisionSet manifests. For this to work, at least the wav.scp file must exist.
SupervisionSet is created only when a segments file exists.
All the other files (text, utt2spk, etc.) are optional, and some of them might
not be handled yet. In particular, feats.scp files are ignored.
:param map_string_to_underscores: optional string, when specified, we will replace
all instances of this string in SupervisonSegment IDs to underscores.
This is to help with handling underscores in Kaldi
(see :func:`.export_to_kaldi`). This is also done for speaker IDs.
"""
path = Path(path)
assert path.is_dir()
def fix_id(t: str) -> str:
if map_string_to_underscores is None:
return t
return t.replace(map_string_to_underscores, "_")
# must exist for RecordingSet
recordings = load_kaldi_text_mapping(path / "wav.scp", must_exist=True)
with ProcessPoolExecutor(num_jobs) as ex:
dur_vals = ex.map(get_duration, recordings.values())
durations = dict(zip(recordings.keys(), dur_vals))
recording_set = RecordingSet.from_recordings(
Recording(
id=recording_id,
sources=[
AudioSource(
type="command" if path_or_cmd.endswith("|") else "file",
channels=[0],
source=path_or_cmd[:-1] if path_or_cmd.
endswith("|") else path_or_cmd,
)
],
sampling_rate=sampling_rate,
num_samples=compute_num_samples(durations[recording_id],
sampling_rate),
duration=durations[recording_id],
) for recording_id, path_or_cmd in recordings.items())
supervision_set = None
segments = path / "segments"
if segments.is_file():
with segments.open() as f:
supervision_segments = [
sup_string.strip().split() for sup_string in f
]
texts = load_kaldi_text_mapping(path / "text")
speakers = load_kaldi_text_mapping(path / "utt2spk")
genders = load_kaldi_text_mapping(path / "spk2gender")
languages = load_kaldi_text_mapping(path / "utt2lang")
supervision_set = SupervisionSet.from_segments(
SupervisionSegment(
id=fix_id(segment_id),
recording_id=recording_id,
start=float(start),
duration=add_durations(
float(end), -float(start), sampling_rate=sampling_rate),
channel=0,
text=texts[segment_id],
language=languages[segment_id],
speaker=fix_id(speakers[segment_id]),
gender=genders[speakers[segment_id]],
) for segment_id, recording_id, start, end in supervision_segments)
feature_set = None
feats_scp = path / "feats.scp"
if feats_scp.exists() and is_module_available("kaldi_native_io"):
if frame_shift is not None:
import kaldi_native_io
from lhotse.features.io import KaldiReader
feature_set = FeatureSet.from_features(
Features(
type="kaldi_native_io",
num_frames=mat_shape.num_rows,
num_features=mat_shape.num_cols,
frame_shift=frame_shift,
sampling_rate=sampling_rate,
start=0,
duration=mat_shape.num_rows * frame_shift,
storage_type=KaldiReader.name,
storage_path=str(feats_scp),
storage_key=utt_id,
recording_id=supervision_set[fix_id(utt_id)].
recording_id if supervision_set is not None else utt_id,
channels=0,
) for utt_id, mat_shape in kaldi_native_io.
SequentialMatrixShapeReader(f"scp:{feats_scp}"))
else:
warnings.warn("Failed to import Kaldi 'feats.scp' to Lhotse: "
"frame_shift must be not None. "
"Feature import omitted.")
return recording_set, supervision_set, feature_set
