Map model_type and doc pages names (#14944)

* Map model_type and doc pages names

* Add script

* Fix typo

* Quality

* Manual check for Auto

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

docs/source/model_doc/sew-d.mdx

@@ -0,0 +1,57 @@
+<!--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.
+-->
+
+# SEW-D
+
+## Overview
+
+SEW-D (Squeezed and Efficient Wav2Vec with Disentangled attention) was proposed in [Performance-Efficiency Trade-offs
+in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim,
+Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
+
+The abstract from the paper is the following:
+
+*This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition
+(ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance
+and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a
+pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a
+variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x
+inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference
+time, SEW reduces word error rate by 25-50% across different model sizes.*
+
+Tips:
+
+- SEW-D is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
+- SEWDForCTC is fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded
+  using [`Wav2Vec2CTCTokenizer`].
+
+This model was contributed by [anton-l](https://huggingface.co/anton-l).
+
+
+## SEWDConfig
+
+[[autodoc]] SEWDConfig
+
+## SEWDModel
+
+[[autodoc]] SEWDModel
+    - forward
+
+## SEWDForCTC
+
+[[autodoc]] SEWDForCTC
+    - forward
+
+## SEWDForSequenceClassification
+
+[[autodoc]] SEWDForSequenceClassification
+    - forward