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Libri-Light: A Benchmark for ASR with Limited or No Supervision

You can track papers that use Libri-Light and their relative performance on Papers With Code: [test-clean] [test-other]

Description

This repository contains code and models associated with the Libri-Light dataset, which can be downloaded and prepared here. More information about dataset creation and baselines can be found in this arXiv Paper. Contained here is code for data preparation, pretrained models, and evaluation resources:

data_preparation/         # code to download the data; VAD and SNR code; json generation; stats; audio segmentation
eval/                     # ABX, PER, WER (evaluation metrics on LibriSpeech dev-clean, dev-other, test-clean, test-other)
baselines/                   # code, pretrained wav2letter models, baselines, and examples

To get started, first clone the repository:

git clone https://github.com/facebookresearch/libri-light

The environment is easiest to set up with Anaconda. Requirements can be installed by running:

conda env create -f environment.yml && conda activate libri-light

If you don't have conda you can get it here.

Goals and structure

Libri-Light offers 60+ k hours of unlabelled speech, a small training set for limited supervision (10h, 1h or 10 minutes of labelled speech), and a common set of metrics to evaluated three settings:

  1. the unsupervised/zero-resource setting. Here, models are trained only on unlabelleds speech and attempt to construct 'good' speech representations. They are evaluated with the ABX metric.
  2. the semi-supervised setting. Here, models are trained with the limited supervision dataset and exploit the unlabelled in various ways (as pretraining, to get pseudo-labels, etc). The models are evaluated using either PER or WER.
  3. the distant supervision setting. Here, models can use additional unaligned text to build a decoder. These models are evaluated using WER.

Documentation

Documentation for downloading Libri-Light or preparing the source files from scratch can be found in data_preparation.

The eval directory contains ABX, PER and WER evaluations on pretrained CPC models.

The baselines directory contains pretrained wav2letter baseline models and information about reproduction.

Citing

@INPROCEEDINGS{librilight,
  author={J. {Kahn} and M. {Rivière} and W. {Zheng} and E. {Kharitonov} and Q. {Xu} and P. E. {Mazaré} and J. {Karadayi} and V. {Liptchinsky} and R. {Collobert} and C. {Fuegen} and T. {Likhomanenko} and G. {Synnaeve} and A. {Joulin} and A. {Mohamed} and E. {Dupoux}},
  booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Libri-Light: A Benchmark for ASR with Limited or No Supervision}, 
  year={2020},
  pages={7669-7673},
  note = {\url{https://github.com/facebookresearch/libri-light}},
}

License

The Libri-light code is released under the MIT license. See LICENSE for additional details.

About

dataset for lightly supervised training using the librivox audio book recordings. https://librivox.org/.

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