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docs/source/en/model_doc/wav2vec2.mdx

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+<!--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.
+-->
+
+# Wav2Vec2
+
+## Overview
+
+The Wav2Vec2 model was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
+
+The abstract from the paper is the following:
+
+*We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on
+transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks
+the speech input in the latent space and solves a contrastive task defined over a quantization of the latent
+representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the
+clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state
+of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and
+pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech
+recognition with limited amounts of labeled data.*
+
+Tips:
+
+- Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
+- Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be decoded
+  using [`Wav2Vec2CTCTokenizer`].
+
+This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).
+
+
+## Wav2Vec2Config
+
+[[autodoc]] Wav2Vec2Config
+
+## Wav2Vec2CTCTokenizer
+
+[[autodoc]] Wav2Vec2CTCTokenizer
+    - __call__
+    - save_vocabulary
+    - decode
+    - batch_decode
+
+## Wav2Vec2FeatureExtractor
+
+[[autodoc]] Wav2Vec2FeatureExtractor
+    - __call__
+
+## Wav2Vec2Processor
+
+[[autodoc]] Wav2Vec2Processor
+    - __call__
+    - pad
+    - from_pretrained
+    - save_pretrained
+    - batch_decode
+    - decode
+    - as_target_processor
+
+## Wav2Vec2ProcessorWithLM
+
+[[autodoc]] Wav2Vec2ProcessorWithLM
+    - __call__
+    - pad
+    - from_pretrained
+    - save_pretrained
+    - batch_decode
+    - decode
+    - as_target_processor
+
+## Wav2Vec2 specific outputs
+
+[[autodoc]] models.wav2vec2_with_lm.processing_wav2vec2_with_lm.Wav2Vec2DecoderWithLMOutput
+
+[[autodoc]] models.wav2vec2.modeling_wav2vec2.Wav2Vec2BaseModelOutput
+
+[[autodoc]] models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput
+
+[[autodoc]] models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput
+
+[[autodoc]] models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2ForPreTrainingOutput
+
+## Wav2Vec2Model
+
+[[autodoc]] Wav2Vec2Model
+    - forward
+
+## Wav2Vec2ForCTC
+
+[[autodoc]] Wav2Vec2ForCTC
+    - forward
+
+## Wav2Vec2ForSequenceClassification
+
+[[autodoc]] Wav2Vec2ForSequenceClassification
+    - forward
+
+## Wav2Vec2ForAudioFrameClassification
+
+[[autodoc]] Wav2Vec2ForAudioFrameClassification
+    - forward
+
+## Wav2Vec2ForXVector
+
+[[autodoc]] Wav2Vec2ForXVector
+    - forward
+
+## Wav2Vec2ForPreTraining
+
+[[autodoc]] Wav2Vec2ForPreTraining
+    - forward
+
+## TFWav2Vec2Model
+
+[[autodoc]] TFWav2Vec2Model
+    - call
+
+## TFWav2Vec2ForCTC
+
+[[autodoc]] TFWav2Vec2ForCTC
+    - call
+
+## FlaxWav2Vec2Model
+
+[[autodoc]] FlaxWav2Vec2Model
+    - __call__
+
+## FlaxWav2Vec2ForCTC
+
+[[autodoc]] FlaxWav2Vec2ForCTC
+    - __call__
+
+## FlaxWav2Vec2ForPreTraining
+
+[[autodoc]] FlaxWav2Vec2ForPreTraining
+    - __call__