Open Implementation and Study of BEST-RQ for Speech Processing
<|reference_start|>Open Implementation and Study of BEST-RQ for Speech Processing: 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.<|reference_end|>
@article{whetten2024open,
    title={Open Implementation and Study of BEST-RQ for Speech Processing},
    author={Ryan Whetten, Titouan Parcollet, Marco Dinarelli, Yannick Est`eve},
    journal={arXiv preprint arXiv:2405.04296},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04296},
    primaryClass={cs.CL cs.LG}
}