We present SpeechMatrix, a large-scale multilingual corpus of speech-to-speech translations mined from real speech of European Parliament recordings. It contains speech alignments in 136 language pairs with a total of 418 thousand hours of speech. To evaluate the quality of this parallel speech, we train bilingual speech-to-speech translation models on mined data only and establish extensive baseline results on EuroParl-ST, VoxPopuli and FLEURS test sets. Enabled by the multilinguality of SpeechMatrix, we also explore multilingual speech-to-speech translation, a topic which was addressed by few other works. We also demonstrate that model pre-training and sparse scaling using Mixture-of-Experts bring large gains to translation performance. The mined data and models are freely available.
@article{arxiv.2211.04508,
title = {SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations},
author = {Paul-Ambroise Duquenne and Hongyu Gong and Ning Dong and Jingfei Du and Ann Lee and Vedanuj Goswani and Changhan Wang and Juan Pino and Benoît Sagot and Holger Schwenk},
journal= {arXiv preprint arXiv:2211.04508},
year = {2022}
}