 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 o