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Hubert (#11889)

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Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* apply suggestions lysandre & suraj

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
This commit is contained in:
Patrick von Platen
2021-06-16 12:14:12 +01:00
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Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Hubert
-----------------------------------------------------------------------------------------------------------------------
Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Hubert was proposed in `HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
<https://arxiv.org/abs/2106.07447>`__ by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan
Salakhutdinov, Abdelrahman Mohamed.
The abstract from the paper is the following:
*Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are
multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training
phase, and (3) sound units have variable lengths with no explicit segmentation. To deal with these three problems, we
propose the Hidden-Unit BERT (HuBERT) approach for self-supervised speech representation learning, which utilizes an
offline clustering step to provide aligned target labels for a BERT-like prediction loss. A key ingredient of our
approach is applying the prediction loss over the masked regions only, which forces the model to learn a combined
acoustic and language model over the continuous inputs. HuBERT relies primarily on the consistency of the unsupervised
clustering step rather than the intrinsic quality of the assigned cluster labels. Starting with a simple k-means
teacher of 100 clusters, and using two iterations of clustering, the HuBERT model either matches or improves upon the
state-of-the-art wav2vec 2.0 performance on the Librispeech (960h) and Libri-light (60,000h) benchmarks with 10min, 1h,
10h, 100h, and 960h fine-tuning subsets. Using a 1B parameter model, HuBERT shows up to 19% and 13% relative WER
reduction on the more challenging dev-other and test-other evaluation subsets.*
Tips:
- Hubert is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
- Hubert model was fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded
using :class:`~transformers.Wav2Vec2CTCTokenizer`.
This model was contributed by `patrickvonplaten <https://huggingface.co/patrickvonplaten>`__.
HubertConfig
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.HubertConfig
:members:
HubertModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.HubertModel
:members: forward
HubertForCTC
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.HubertForCTC
:members: forward