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Kaldi Interoperability

Data import/export

We support importing Kaldi data directories that contain at least the wav.scp file, required to create the ~lhotse.audio.RecordingSet. Other files, such as segments, utt2spk, etc. are used to create the ~lhotse.supervision.SupervisionSet. We also support converting feats.scp to ~lhotse.features.base.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 ~lhotse.audio.RecordingSet and ~lhotse.supervision.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 features.

Python

Python methods related to Kaldi support:

lhotse.kaldi

CLI

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 \
    train_manifests

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