66 lines
2.5 KiB
Markdown
66 lines
2.5 KiB
Markdown
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# SEW-D
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## Overview
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SEW-D (Squeezed and Efficient Wav2Vec with Disentangled attention) was proposed in [Performance-Efficiency Trade-offs
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in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim,
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Jing Pan, Kyu Han, Kilian Q. 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-D is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
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- SEWDForCTC is fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded
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using [`Wav2Vec2CTCTokenizer`].
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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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## SEWDConfig
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[[autodoc]] SEWDConfig
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## SEWDModel
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[[autodoc]] SEWDModel
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- forward
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## SEWDForCTC
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[[autodoc]] SEWDForCTC
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- forward
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## SEWDForSequenceClassification
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[[autodoc]] SEWDForSequenceClassification
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- forward
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