def test_feature_set_serialization(format, compressed): feature_set = FeatureSet(features=[ Features(recording_id='irrelevant', channels=0, start=0.0, duration=20.0, type='fbank', num_frames=2000, num_features=20, sampling_rate=16000, storage_type='lilcom', storage_path='/irrelevant/path.llc') ]) with NamedTemporaryFile(suffix='.gz' if compressed else '') as f: if format == 'json': feature_set.to_json(f.name) feature_set_deserialized = FeatureSet.from_json(f.name) if format == 'yaml': feature_set.to_yaml(f.name) feature_set_deserialized = FeatureSet.from_yaml(f.name) assert feature_set_deserialized == feature_set
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 overlapping_supervisions_cut(): return MonoCut( id="cut-1", start=0.0, duration=0.5, channel=0, features=Features( recording_id="recording-1", channels=0, start=0, duration=0.5, type="fbank", num_frames=50, num_features=80, frame_shift=0.01, sampling_rate=16000, storage_type="lilcom", storage_path="test/fixtures/dummy_feats/storage/", storage_key="e66b6386-aee5-4a5a-8369-fdde1d2b97c7.llc", ), supervisions=[ SupervisionSegment(id="s1", recording_id="recording-1", start=0.0, duration=0.2), SupervisionSegment(id="s2", recording_id="recording-1", start=0.1, duration=0.2), SupervisionSegment(id="s3", recording_id="recording-1", start=0.2, duration=0.2), SupervisionSegment(id="s4", recording_id="recording-1", start=0.3, duration=0.2), ], )
def overlapping_supervisions_cut(): return Cut( id='cut-1', start=0.0, duration=0.5, channel=0, features=Features( recording_id='recording-1', channels=0, start=0, duration=0.5, type='fbank', num_frames=50, num_features=80, sampling_rate=16000, storage_type='lilcom', storage_path= 'test/fixtures/dummy_feats/storage/e66b6386-aee5-4a5a-8369-fdde1d2b97c7.llc' ), supervisions=[ SupervisionSegment(id='s1', recording_id='recording-1', start=0.0, duration=0.2), SupervisionSegment(id='s2', recording_id='recording-1', start=0.1, duration=0.2), SupervisionSegment(id='s3', recording_id='recording-1', start=0.2, duration=0.2), SupervisionSegment(id='s4', recording_id='recording-1', start=0.3, duration=0.2) ])
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
def dummy_features(): return Features( recording_id='irrelevant', channels=0, start=0.0, duration=10.0, type='fbank', num_frames=1000, num_features=80, sampling_rate=16000, storage_type='irrelevant', storage_path='irrelevant', storage_key='irrelevant', frame_shift=0.01 )