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* Reorganize doc for multilingual support * Fix style * Style * Toc trees * Adapt templates
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57 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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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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# Wav2Vec2Phoneme
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## Overview
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The Wav2Vec2Phoneme model was proposed in [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition (Xu et al.,
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2021](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
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The abstract from the paper is the following:
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*Recent progress in self-training, self-supervised pretraining and unsupervised learning enabled well performing speech
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recognition systems without any labeled data. However, in many cases there is labeled data available for related
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languages which is not utilized by these methods. This paper extends previous work on zero-shot cross-lingual transfer
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learning by fine-tuning a multilingually pretrained wav2vec 2.0 model to transcribe unseen languages. This is done by
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mapping phonemes of the training languages to the target language using articulatory features. Experiments show that
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this simple method significantly outperforms prior work which introduced task-specific architectures and used only part
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of a monolingually pretrained model.*
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Tips:
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- Wav2Vec2Phoneme uses the exact same architecture as Wav2Vec2
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- Wav2Vec2Phoneme is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
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- Wav2Vec2Phoneme model was trained using connectionist temporal classification (CTC) so the model output has to be
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decoded using [`Wav2Vec2PhonemeCTCTokenizer`].
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- Wav2Vec2Phoneme can be fine-tuned on multiple language at once and decode unseen languages in a single forward pass
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to a sequence of phonemes
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- By default the model outputs a sequence of phonemes. In order to transform the phonemes to a sequence of words one
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should make use of a dictionary and language model.
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Relevant checkpoints can be found under https://huggingface.co/models?other=phoneme-recognition.
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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/master/fairseq/models/wav2vec).
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Wav2Vec2Phoneme's architecture is based on the Wav2Vec2 model, so one can refer to [`Wav2Vec2`]'s documentation page except for the tokenizer.
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## Wav2Vec2PhonemeCTCTokenizer
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[[autodoc]] Wav2Vec2PhonemeCTCTokenizer
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- __call__
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- batch_decode
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- decode
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- phonemize
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