[XLSR-Wav2Vec2] Add multi-lingual Wav2Vec2 models (#10648)

* add conversion script

* add wav2vec2 xslr models

* finish

* Update docs/source/model_doc/xlsr_wav2vec2.rst

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/model_doc/xlsr_wav2vec2.rst

@@ -0,0 +1,45 @@
+.. 
+    Copyright 2021 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
+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 :class:`~transformers.Wav2Vec2CTCTokenizer`.
+
+XLSR-Wav2Vec2's architecture is based on the Wav2Vec2 model, so one can refer to :doc:`Wav2Vec2's documentation page
+<wav2vec2>`.
+
+The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/fairseq/models/wav2vec>`__.