Wav2Vec2 meets phonemes (#14353)
* up * add tokenizer * improve more * finish tokenizer * finish * adapt speech recognition script * adapt convert * more fixes * more fixes * update phonemizer wav2vec2 * better naming * fix more tests * more fixes swedish * correct tests * finish * improve script * remove file * up * lets get those 100 model architectures until the end of the month * make fix-copies * correct more * correct script * more fixes * more fixes * add to docs * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * replace assert * fix copies * fix docs * new try docs * boom boom * update * add phonemizer to audio tests * make fix-copies * up * upload models * some changes * Update tests/test_tokenization_wav2vec2_phoneme.py Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com> * more fixes * remove @ Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
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@@ -284,6 +284,8 @@
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title: VisualBERT
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- local: model_doc/wav2vec2
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title: Wav2Vec2
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- local: model_doc/wav2vec2_phoneme
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title: Wav2Vec2Phoneme
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- local: model_doc/wavlm
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title: WavLM
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- local: model_doc/xlm
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@@ -296,6 +298,8 @@
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title: XLNet
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- local: model_doc/xlsr_wav2vec2
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title: XLSR-Wav2Vec2
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- local: model_doc/xls_r
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title: XLS-R
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title: Models
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- sections:
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- local: internal/modeling_utils
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@@ -172,11 +172,13 @@ conversion utilities for the following models.
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1. **[VisualBERT](model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
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1. **[WavLM](model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
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1. **[Wav2Vec2](model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
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1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/master/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
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1. **[XLM](model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
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1. **[XLM-ProphetNet](model_doc/xlmprophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
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1. **[XLM-RoBERTa](model_doc/xlmroberta)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
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1. **[XLNet](model_doc/xlnet)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
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1. **[XLSR-Wav2Vec2](model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
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1. **[XLS-R](https://huggingface.co/docs/master/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
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### Supported frameworks
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56
docs/source/model_doc/wav2vec2_phoneme.mdx
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docs/source/model_doc/wav2vec2_phoneme.mdx
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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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-->
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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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47
docs/source/model_doc/xls_r.rst
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47
docs/source/model_doc/xls_r.rst
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..
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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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XLS-R
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-----------------------------------------------------------------------------------------------------------------------
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The XLS-R model was proposed in `XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale
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<https://arxiv.org/abs/2111.09296>`__ by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman
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Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
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The abstract from the paper is the following:
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*This paper presents XLS-R, a large-scale model for cross-lingual speech representation learning based on wav2vec 2.0.
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We train models with up to 2B parameters on nearly half a million hours of publicly available speech audio in 128
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languages, an order of magnitude more public data than the largest known prior work. Our evaluation covers a wide range
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of tasks, domains, data regimes and languages, both high and low-resource. On the CoVoST-2 speech translation
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benchmark, we improve the previous state of the art by an average of 7.4 BLEU over 21 translation directions into
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English. For speech recognition, XLS-R improves over the best known prior work on BABEL, MLS, CommonVoice as well as
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VoxPopuli, lowering error rates by 14-34% relative on average. XLS-R also sets a new state of the art on VoxLingua107
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language identification. Moreover, we show that with sufficient model size, cross-lingual pretraining can outperform
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English-only pretraining when translating English speech into other languages, a setting which favors monolingual
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pretraining. We hope XLS-R can help to improve speech processing tasks for many more languages of the world.*
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Tips:
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- XLS-R is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
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- XLS-R model was trained using connectionist temporal classification (CTC) so the model output has to be decoded using
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:class:`~transformers.Wav2Vec2CTCTokenizer`.
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Relevant checkpoints can be found under https://huggingface.co/models?other=xls_r.
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XLS-R's architecture is based on the Wav2Vec2 model, so one can refer to :doc:`Wav2Vec2's documentation page
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<wav2vec2>`.
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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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