This model was contributed by patrickvonplaten. Hubert model was fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded Hubert is a speech model that accepts a float array corresponding to the raw waveform of the speech signal. Reduction on the more challenging dev-other and test-other evaluation subsets. Using a 1B parameter model, HuBERT shows up to 19% and 13% relative WER State-of-the-art wav2vec 2.0 performance on the Librispeech (960h) and Libri-light (60,000h) benchmarks with 10min, 1h,ġ0h, 100h, and 960h fine-tuning subsets. Teacher of 100 clusters, and using two iterations of clustering, the HuBERT model either matches or improves upon the HuBERT relies primarily on the consistency of the unsupervisedĬlustering step rather than the intrinsic quality of the assigned cluster labels. A key ingredient of ourĪpproach is applying the prediction loss over the masked regions only, which forces the model to learn a combinedĪcoustic and language model over the continuous inputs. Offline clustering step to provide aligned target labels for a BERT-like prediction loss. Propose the Hidden-Unit BERT (HuBERT) approach for self-supervised speech representation learning, which utilizes an Phase, and (3) sound units have variable lengths with no explicit segmentation. Multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are The abstract from the paper is the following: Hubert was proposed in HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Performance and Scalability: How To Fit a Bigger Model and Train It Faster.
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