Migrate doc files to Markdown. (#24376)

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Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

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Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

docs/source/en/model_doc/xlsr_wav2vec2.md

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