# 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.*

This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).

# Usage 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 [`Wav2Vec2CTCTokenizer`].

## Resources

- [Audio classification task guide](../tasks/audio_classification)

- [Automatic speech recognition task guide](../tasks/asr)

## HubertConfig

[[autodoc]] HubertConfig

## HubertModel

[[autodoc]] HubertModel

    - forward

## HubertForCTC

[[autodoc]] HubertForCTC

    - forward

## HubertForSequenceClassification

[[autodoc]] HubertForSequenceClassification

    - forward

## TFHubertModel

[[autodoc]] TFHubertModel

    - call

## TFHubertForCTC

[[autodoc]] TFHubertForCTC

    - call