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* add raw scaffold * implement feat extract layers * make style * remove + * correctly convert weights * make feat extractor work * make feature extraction proj work * run forward pass * finish forward pass * Succesful decoding example * remove unused files * more changes * add wav2vec tokenizer * add new structure * fix run forward * add other layer norm architecture * finish 2nd structure * add model tests * finish tests for tok and model * clean-up * make style * finish docstring for model and config * make style * correct docstring * correct tests * change checkpoints to fairseq * fix examples * finish wav2vec2 * make style * apply sylvains suggestions * apply lysandres suggestions * change print to log.info * re-add assert statement * add input_values as required input name * finish wav2vec2 tokenizer * Update tests/test_tokenization_wav2vec2.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * apply sylvains suggestions Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
66 lines
3.1 KiB
ReStructuredText
66 lines
3.1 KiB
ReStructuredText
..
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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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Wav2Vec2
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-----------------------------------------------------------------------------------------------------------------------
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The Wav2Vec2 model was proposed in `wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
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<https://arxiv.org/abs/2006.11477>`__ by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
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The abstract from the paper is the following:
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*We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on
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transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks
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the speech input in the latent space and solves a contrastive task defined over a quantization of the latent
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representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the
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clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state
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of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and
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pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech
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recognition with limited amounts of labeled data.*
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Tips:
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- Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
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- Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be decoded
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using :class:`~transformers.Wav2Vec2Tokenizer`.
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Wav2Vec2Config
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.Wav2Vec2Config
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:members:
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Wav2Vec2Tokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.Wav2Vec2Tokenizer
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:members: __call__, save_vocabulary
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Wav2Vec2Model
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.Wav2Vec2Model
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:members: forward
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Wav2Vec2ForMaskedLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.Wav2Vec2ForMaskedLM
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:members: forward
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