XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale

Arun Babu; Changhan Wang; Andros Tjandra; Kushal Lakhotia; Qiantong Xu; Naman Goyal; Kritika Singh; Patrick von Platen; Yatharth Saraf; Juan Pino; Alexei Baevski; Alexis Conneau; Michael Auli

arXiv · 2111.09296

XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale

Arun Babu·Changhan Wang·Andros Tjandra·Kushal Lakhotia·Qiantong Xu·Naman Goyal·Kritika Singh·Patrick von Platen·Yatharth Saraf·Juan Pino·Alexei Baevski·Alexis Conneau·Michael Auli

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Abstract

This paper presents XLS-R, a large-scale model for cross-lingual speech representation learning based on wav2vec 2.0. We train models with up to 2B parameters on nearly half a million hours of publicly available speech audio in 128 languages, an order of magnitude more public data than the largest known prior work. Our evaluation covers a wide range of tasks, domains, data regimes and languages, both high and low-resource. On the CoVoST-2 speech translation benchmark, we improve the previous state of the art by an average of 7.4 BLEU over 21 translation directions into English. For speech recognition, XLS-R improves over the best known prior work on BABEL, MLS, CommonVoice as well as VoxPopuli, lowering error rates by 14-34% relative on average. XLS-R also sets a new state of the art on VoxLingua107 language identification. Moreover, we show that with sufficient model size, cross-lingual pretraining can outperform English-only pretraining when translating English speech into other languages, a setting which favors monolingual pretraining. We hope XLS-R can help to improve speech processing tasks for many more languages of the world.

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Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli. 2021-12-16. XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale. https://arxiv.org/abs/2111.09296

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