dataset for lightly supervised training using the librivox audio book recordings. https://librivox.org/.
You can track papers that use Libri-Light and their relative performance on Papers With Code: [test-clean] [test-other]
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
condayou can get it here.
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:
Documentation for downloading Libri-Light or preparing the source files from scratch can be found in
data_preparation.
evaldirectory contains ABX, PER and WER evaluations on pretrained CPC models.
baselinesdirectory contains pretrained wav2letter baseline models and information about reproduction.
@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}}, }
The Libri-light code is released under the MIT license. See LICENSE for additional details.