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transformers/docs/source/en/model_doc/sew.md

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+*This model was released on 2021-09-14 and added to Hugging Face Transformers on 2021-10-15.*
+
+# SEW
+
+<div class="flex flex-wrap space-x-1">
+<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+## Overview
+
+SEW (Squeezed and Efficient Wav2Vec) was proposed in [Performance-Efficiency Trade-offs in Unsupervised Pre-training
+for Speech Recognition](https://huggingface.co/papers/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.*
+
+This model was contributed by [anton-l](https://huggingface.co/anton-l).
+
+## Usage tips
+
+- SEW is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
+- SEWForCTC is fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded using
+  [`Wav2Vec2CTCTokenizer`].
+
+> [!NOTE]
+> The `head_mask` argument is ignored when using all attention implementation other than "eager". If you have a `head_mask` and want it to have effect, load the model with `XXXModel.from_pretrained(model_id, attn_implementation="eager")`
+
+## Resources
+
+- [Audio classification task guide](../tasks/audio_classification)
+- [Automatic speech recognition task guide](../tasks/asr)
+
+## SEWConfig
+
+[[autodoc]] SEWConfig
+
+## SEWModel
+
+[[autodoc]] SEWModel
+    - forward
+
+## SEWForCTC
+
+[[autodoc]] SEWForCTC
+    - forward
+
+## SEWForSequenceClassification
+
+[[autodoc]] SEWForSequenceClassification
+    - forward