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0.5 - Ice Melt

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@pzelasko pzelasko released this 27 Feb 04:18
v0.5
7d0b41d

New features:

Major overhaul of support for PyTorch Dataset API (#194 #197 #202)

Lhotse now implements a number of PyTorch datasets and samplers. The core features are:

  • familiar API (map-style datasets and cut samplers that work with standard DataLoader)
  • dynamic batch size, chosen based on constraints such as max_frames
  • bucketing or cut concatenation as strategies for avoiding too much padding
  • optional noise padding (using CutMix transform)
  • our samplers work with DDP training out-of-the-box (no need for DistributedSampler)
  • More details available at: https://lhotse.readthedocs.io/en/latest/datasets.html

Example code:

from torch.utils.data import DataLoader
from lhotse.dataset import SpeechRecognitionDataset, SingleCutSampler

cuts = CutSet(...)
dset = SpeechRecognitionDataset(cuts)
sampler = SingleCutSampler(cuts, max_frames=50000)
# Dataset performs batching by itself, so we have to indicate that
# to the DataLoader with batch_size=None
dloader = DataLoader(dset, sampler=sampler, batch_size=None, num_workers=1)
for batch in dloader:
    ...  # process data

Lazy (on-the-fly) resampling on Recording/RecordingSet (#185)

The resampling is performed at the moment of reading the audio samples from disk. It automatically adjusts the duration/num_samples in the data manifest.

recording = recording.resample(22050)
recording_set = recording_set.resample(8000)

New corpora:

  • AMI recipe extension to all microphone settings and official scenarios (#154 - kudos to @desh2608)

General improvements:

  • CutSet.subset() got first and last arguments (like Kaldi's subset_data_dir.sh) and a CLI mode (#188)
  • CutSet.from_manifest() creates deterministic Cut IDs by default (#186)
  • Padding cuts with arbitrary user specified values (now also works with custom feature extractors) (#187)
  • Improved code coverage measurements (now excludes test code and recipe code) (#191 #192)
  • Improved support for sampling rates other than 8k and 16k (#190 #195)
  • Documentation build fixes (#196)
  • Fixes in NSC recipe (#199)
  • Fixes in ASR dataset validation (#204)
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