Recent years have witnessed significant advancements in self-supervised
learning (SSL) methods for speech-processing tasks. Various speech-based SSL
models have been developed and present promising performance on a range of
downstream tasks including speech recognition. However, existing speech-based
SSL models face a common dilemma in terms of computational cost, which might
hinder their potential application and in-depth academic research. To address
this issue, we first analyze the computational cost of different modules during
HuBERT pre-training and then introduce a stack of efficiency optimizations,
which is named Fast-HuBERT in this paper. The proposed Fast-HuBERT can be
trained in 1.1 days with 8 V100 GPUs on the Librispeech 960h benchmark, without
performance degradation, resulting in a 5.2x speedup, compared to the original
implementation. Moreover, we explore two well-studied techniques in the
Fast-HuBERT and demonstrate consistent improvements as reported in previous
work.

近年来，自我监督学习方法在语音处理任务中取得了显著进展。本文提出了一种名为 Fast-HuBERT 的高效优化方法，通过分析 HuBERT 预训练的计算成本并引入一系列效率优化，实现了与原始实现相比，无性能降低、在 Librispeech 960h 基准上训练时间为 1.1 天、速度提升 5.2 倍的效果。此外，我们在 Fast-HuBERT 中探索了两种技术，并展示了与之前工作相一致的改进效果。