57 lines
3.2 KiB
Markdown
57 lines
3.2 KiB
Markdown
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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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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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-->
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*This model was released on 2020-06-24 and added to Hugging Face Transformers on 2023-06-20.*
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# XLSR-Wav2Vec2
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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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</div>
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## Overview
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The XLSR-Wav2Vec2 model was proposed in [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://huggingface.co/papers/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael
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Auli.
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The abstract from the paper is the following:
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*This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw
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waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over
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masked latent speech representations and jointly learns a quantization of the latents shared across languages. The
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resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly
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outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction
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of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to
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a comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong
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individual models. Analysis shows that the latent discrete speech representations are shared across languages with
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increased sharing for related languages. We hope to catalyze research in low-resource speech understanding by releasing
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XLSR-53, a large model pretrained in 53 languages.*
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The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/fairseq/models/wav2vec).
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Note: Meta (FAIR) released a new version of [Wav2Vec2-BERT 2.0](https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-bert) - it's pretrained on 4.5M hours of audio. We especially recommend using it for fine-tuning tasks, e.g. as per [this guide](https://huggingface.co/blog/fine-tune-w2v2-bert).
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## Usage tips
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- XLSR-Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
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- XLSR-Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be
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decoded using [`Wav2Vec2CTCTokenizer`].
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<Tip>
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XLSR-Wav2Vec2's architecture is based on the Wav2Vec2 model, so one can refer to [Wav2Vec2's documentation page](wav2vec2).
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</Tip>
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