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* save intermediate * add wav2vec2 conformer * add more code * more * first test passes * make all checkpoints work * update * up * more clean ups * save clean-up * save clean-up * save more * remove bogus * finalize design conformer * remove vision * finish all tests * more changes * finish code * add doc tests * add slow tests * fix autoconfig test * up * correct docstring * up * update * fix * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com> * Update docs/source/en/model_doc/wav2vec2-conformer.mdx * upload * save copied from * correct configs * fix model outputs * add to docs * fix imports * finish * finish code * correct copied from * correct again * correct make fix * improve make fix copies * save * correct fix copy from * correct init structure * correct * fix import * apply suggestions Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
70 lines
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70 lines
2.8 KiB
Plaintext
<!--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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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
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# UniSpeech
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## Overview
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The UniSpeech model was proposed in [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael
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Zeng, Xuedong Huang .
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The abstract from the paper is the following:
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*In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both
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unlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastive
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self-supervised learning are conducted in a multi-task learning manner. The resultant representations can capture
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information more correlated with phonetic structures and improve the generalization across languages and domains. We
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evaluate the effectiveness of UniSpeech for cross-lingual representation learning on public CommonVoice corpus. The
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results show that UniSpeech outperforms self-supervised pretraining and supervised transfer learning for speech
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recognition by a maximum of 13.4% and 17.8% relative phone error rate reductions respectively (averaged over all
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testing languages). The transferability of UniSpeech is also demonstrated on a domain-shift speech recognition task,
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i.e., a relative word error rate reduction of 6% against the previous approach.*
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Tips:
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- UniSpeech is a speech model that accepts a float array corresponding to the raw waveform of the speech signal. Please
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use [`Wav2Vec2Processor`] for the feature extraction.
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- UniSpeech model can be fine-tuned using connectionist temporal classification (CTC) so the model output has to be
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decoded using [`Wav2Vec2CTCTokenizer`].
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This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The Authors' code can be
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found [here](https://github.com/microsoft/UniSpeech/tree/main/UniSpeech).
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## UniSpeechConfig
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[[autodoc]] UniSpeechConfig
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## UniSpeech specific outputs
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[[autodoc]] models.unispeech.modeling_unispeech.UniSpeechForPreTrainingOutput
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## UniSpeechModel
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[[autodoc]] UniSpeechModel
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- forward
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## UniSpeechForCTC
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[[autodoc]] UniSpeechForCTC
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- forward
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## UniSpeechForSequenceClassification
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[[autodoc]] UniSpeechForSequenceClassification
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- forward
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## UniSpeechForPreTraining
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[[autodoc]] UniSpeechForPreTraining
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- forward
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