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* Reorganize doc for multilingual support * Fix style * Style * Toc trees * Adapt templates
44 lines
2.7 KiB
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44 lines
2.7 KiB
Plaintext
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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# XLS-R
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## Overview
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The XLS-R model was proposed in [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman
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Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
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The abstract from the paper is the following:
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*This paper presents XLS-R, a large-scale model for cross-lingual speech representation learning based on wav2vec 2.0.
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We train models with up to 2B parameters on nearly half a million hours of publicly available speech audio in 128
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languages, an order of magnitude more public data than the largest known prior work. Our evaluation covers a wide range
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of tasks, domains, data regimes and languages, both high and low-resource. On the CoVoST-2 speech translation
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benchmark, we improve the previous state of the art by an average of 7.4 BLEU over 21 translation directions into
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English. For speech recognition, XLS-R improves over the best known prior work on BABEL, MLS, CommonVoice as well as
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VoxPopuli, lowering error rates by 14-34% relative on average. XLS-R also sets a new state of the art on VoxLingua107
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language identification. Moreover, we show that with sufficient model size, cross-lingual pretraining can outperform
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English-only pretraining when translating English speech into other languages, a setting which favors monolingual
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pretraining. We hope XLS-R can help to improve speech processing tasks for many more languages of the world.*
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Tips:
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- XLS-R is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
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- XLS-R model was trained using connectionist temporal classification (CTC) so the model output has to be decoded using
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[`Wav2Vec2CTCTokenizer`].
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Relevant checkpoints can be found under https://huggingface.co/models?other=xls_r.
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XLS-R's architecture is based on the Wav2Vec2 model, so one can refer to [Wav2Vec2's documentation page](wav2vec2).
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The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/fairseq/models/wav2vec).
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