Hubert Extra Large Finetuned Facebook's Hubert The extra large model fine tuned on 960h of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. The model is a fine tuned version of hubert xlarge ll60k. Paper Authors: Wei Ning Hsu, Benjamin Bolte, Yao Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed Abstract 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…
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