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67 lines
3.1 KiB
ReStructuredText
67 lines
3.1 KiB
ReStructuredText
..
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Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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SEW-D
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-----------------------------------------------------------------------------------------------------------------------
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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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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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 :class:`~transformers.Wav2Vec2CTCTokenizer`.
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This model was contributed by `anton-l <https://huggingface.co/anton-l>`__.
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SEWDConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.SEWDConfig
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:members:
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SEWDModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.SEWDModel
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:members: forward
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SEWDForCTC
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.SEWDForCTC
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:members: forward
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SEWDForSequenceClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.SEWDForSequenceClassification
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:members: forward
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