We introduce VoxPopuli, a large-scale multilingual corpus providing 100K hours of unlabelled speech data in 23 languages. It is the largest open data to date for unsupervised representation learning as well as semi-supervised learning. VoxPopuli also contains 1.8K hours of transcribed speeches in 16 languages and their aligned oral interpretations into 5 other languages totaling 5.1K hours. We provide speech recognition baselines and validate the versatility of VoxPopuli unlabelled data in semi-supervised learning under challenging out-of-domain settings. We will release the corpus at https://github.com/facebookresearch/voxpopuli under an open license.
@article{arxiv.2101.00390,
title = {VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation},
author = {Changhan Wang and Morgane Rivière and Ann Lee and Anne Wu and Chaitanya Talnikar and Daniel Haziza and Mary Williamson and Juan Pino and Emmanuel Dupoux},
journal= {arXiv preprint arXiv:2101.00390},
year = {2021}
}