Kaldi Interoperability

Data import/export

We support importing Kaldi data directories that contain at least the wav.scp file, required to create the RecordingSet. Other files, such as segments, utt2spk, etc. are used to create the SupervisionSet. We also support converting feats.scp to FeatureSet, and reading features directly from Kaldi’s scp/ark files via kaldiio library (which is an optional Lhotse’s dependency).

We also allow to export a pair of RecordingSet and SupervisionSet to a Kaldi data directory.

We currently do not support the following (but may start doing so in the future):

  • Exporting Lhotse extracted features to Kaldi’s feats.scp

  • Export Lhotse’s multi-channel recording sets to Kaldi

Kaldi feature extractors

We support Kaldi-compatible log-mel filter energies (“fbank”) and MFCCs. We provide a PyTorch implementation that is GPU-compatible, allows batching, and backpropagation. To learn more about feature extraction in Lhotse, see Feature extraction.


Python methods related to Kaldi support:


Read a audio file, it supports pipeline style wave path and real waveform.


path (Union[Path, str]) – Path to an audio file or a Kaldi-style pipe.

Return type



float duration of the recording, in seconds.

lhotse.kaldi.load_kaldi_data_dir(path, sampling_rate, frame_shift=None)[source]

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.

Return type

Tuple[RecordingSet, Optional[SupervisionSet], Optional[FeatureSet]]

lhotse.kaldi.export_to_kaldi(recordings, supervisions, output_dir)[source]

Export a pair of RecordingSet and SupervisionSet to a Kaldi data directory. Currently, it only supports single-channel recordings that have a single AudioSource.

The RecordingSet and SupervisionSet must be compatible, i.e. it must be possible to create a CutSet out of them.

  • recordings (RecordingSet) – a RecordingSet manifest.

  • supervisions (SupervisionSet) – a SupervisionSet manifest.

  • output_dir (Union[Path, str]) – path where the Kaldi-style data directory will be created.

lhotse.kaldi.load_kaldi_text_mapping(path, must_exist=False)[source]

Load Kaldi files such as utt2spk, spk2gender, text, etc. as a dict.

Return type

Dict[str, Optional[str]]

lhotse.kaldi.save_kaldi_text_mapping(data, path)[source]

Save flat dicts to Kaldi files such as utt2spk, spk2gender, text, etc.

lhotse.kaldi.make_wavscp_string(source, sampling_rate)[source]
Return type



Converting Kaldi data directory called data/train, with 16kHz sampling rate recordings, to a directory with Lhotse manifests called train_manifests:

# Convert data/train to train_manifests/{recordings,supervisions}.json
lhotse kaldi import \
    data/train \
    16000 \

# Convert train_manifests/{recordings,supervisions}.json to data/train
lhotse kaldi export \
    train_manifests/recordings.json \
    train_manifests/supervisions.json \