VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation
TL
TL;DR
VoxPopuli is introduced, a large-scale multilingual corpus providing 400K hours of unlabeled speech data in 23 languages and it is the largest open data to date for unsupervised representation learning as well as semi-supervised learning.
Abstract
We introduce VoxPopuli, a large-scale multilingual corpus providing 400K hours of unlabeled 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 15 languages and their aligned oral interpretations into 15 target languages totaling 17.3K hours. We provide speech recognition (ASR) baselines and validate the versatility of VoxPopuli unlabeled data in semi-supervised ASR and speech-to-text translation under challenging out-of-domain settings. The corpus is available at https://github.com/facebookresearch/voxpopuli.
References50 items
1
MuST-C: A multilingual corpus for end-to-end speech translation
2
Towards Unsupervised Learning of Speech Features in the Wild
3
Joint Masked CPC And CTC Training For ASR
4
MLS: A Large-Scale Multilingual Dataset for Speech Research
5