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75 lines
3.1 KiB
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75 lines
3.1 KiB
Markdown
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# SEW
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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## Overview
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SEW (Squeezed and Efficient Wav2Vec) was proposed in [Performance-Efficiency Trade-offs in Unsupervised Pre-training
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for Speech Recognition](https://huggingface.co/papers/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q.
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Weinberger, Yoav Artzi.
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The abstract from the paper is the following:
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*This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition
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(ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance
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and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a
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pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a
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variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x
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inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference
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time, SEW reduces word error rate by 25-50% across different model sizes.*
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This model was contributed by [anton-l](https://huggingface.co/anton-l).
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## Usage tips
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- SEW is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
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- SEWForCTC is fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded using
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[`Wav2Vec2CTCTokenizer`].
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> [!NOTE]
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> 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")`
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## Resources
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- [Audio classification task guide](../tasks/audio_classification)
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- [Automatic speech recognition task guide](../tasks/asr)
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## SEWConfig
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[[autodoc]] SEWConfig
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## SEWModel
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[[autodoc]] SEWModel
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- forward
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## SEWForCTC
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[[autodoc]] SEWForCTC
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- forward
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## SEWForSequenceClassification
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[[autodoc]] SEWForSequenceClassification
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- forward
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