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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>
68 lines
2.3 KiB
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68 lines
2.3 KiB
Plaintext
<!--Copyright 2022 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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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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specific language governing permissions and limitations under the License.
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# Wav2Vec2-Conformer
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## Overview
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The Wav2Vec2-Conformer weights were released by the Meta AI team within the [Fairseq library](https://github.com/pytorch/fairseq/blob/main/examples/wav2vec/README.md#pre-trained-models).
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Tips:
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- Wav2Vec2-Conformer follows the same architecture as Wav2Vec2, but replaces the *Attention*-block with a *Conformer*-block
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as introduced in [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100).
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- Wav2Vec2-Conformer uses the same tokenizer and feature extractor as Wav2Vec2.
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- Wav2Vec2-Conformer can use either no relative position embeddings, Transformer-XL-like position embeddings, or
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rotary position embeddings by setting the correct `config.position_embeddings_type`.
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This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).
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The original code can be found [here](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec).
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## Wav2Vec2ConformerConfig
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[[autodoc]] Wav2Vec2ConformerConfig
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## Wav2Vec2Conformer specific outputs
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[[autodoc]] models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput
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## Wav2Vec2ConformerModel
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[[autodoc]] Wav2Vec2ConformerModel
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- forward
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## Wav2Vec2ConformerForCTC
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[[autodoc]] Wav2Vec2ConformerForCTC
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- forward
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## Wav2Vec2ConformerForSequenceClassification
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[[autodoc]] Wav2Vec2ConformerForSequenceClassification
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- forward
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## Wav2Vec2ConformerForAudioFrameClassification
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[[autodoc]] Wav2Vec2ConformerForAudioFrameClassification
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- forward
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## Wav2Vec2ConformerForXVector
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[[autodoc]] Wav2Vec2ConformerForXVector
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- forward
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## Wav2Vec2ConformerForPreTraining
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[[autodoc]] Wav2Vec2ConformerForPreTraining
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- forward
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