Self-Supervised Learning (SSL) has proven to be useful in various speech tasks. However, these methods are generally very demanding in terms of data, memory, and computational resources. BERT-based Speech pre-Training with Random-projection Quantizer (BEST-RQ), is an SSL method that has shown great performance on Automatic Speech Recognition (ASR) while being simpler than other SSL methods, such as wav2vec 2.0. Despite BEST-RQ's great performance, details are lacking in the original paper, such as the amount of GPU/TPU hours used in pre-training, and there is no official easy-to-use open-source implementation. Furthermore, BEST-RQ has not been evaluated on other downstream tasks aside from ASR and speech translation. In this work, we describe a re-implementation of a Random-projection quantizer and perform a preliminary study with a comparison to wav2vec 2.0 on four downstream tasks. We discuss the details and differences of our implementation. We show that a random projection quantizer can achieve similar downstream performance as wav2vec 2.0 while decreasing training time by over a factor of two.
@article{arxiv.2405.04296,
title = {Open Implementation and Study of BEST-RQ for Speech Processing},
author = {Ryan Whetten and Titouan Parcollet and Marco Dinarelli and Yannick Estève},
journal= {arXiv preprint arXiv:2405.04296},
year = {2024}
}
Comments
Accepted in IEEE ICASSP 2024 workshop on Self-supervision in Audio, Speech and Beyond (SASB 2024)