# XLSR-Wav2Vec2

## Overview

The XLSR-Wav2Vec2 model was proposed in [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael

Auli.

The abstract from the paper is the following:

*This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw

waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over

masked latent speech representations and jointly learns a quantization of the latents shared across languages. The

resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly

outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction

of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to

a comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong

individual models. Analysis shows that the latent discrete speech representations are shared across languages with

increased sharing for related languages. We hope to catalyze research in low-resource speech understanding by releasing

XLSR-53, a large model pretrained in 53 languages.*

The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/fairseq/models/wav2vec).

## Usage tips

- XLSR-Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.

- XLSR-Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be

  decoded using [`Wav2Vec2CTCTokenizer`].

XLSR-Wav2Vec2's architecture is based on the Wav2Vec2 model, so one can refer to [Wav2Vec2's documentation page](wav2vec2).