OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer

Yifan Peng, Jinchuan Tian, William Chen, Arora, Brian Yan, Yui Sudo, Muhammad Shakeel, Kwanghee Choi, Jiatong Shi, Xuankai Chang, Jee-weon Jung, Shinji Watanabe

OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer: 14 upvotes on Hugging Face Daily Papers, #9 of 14 papers on 2024-01-31. Day-by-day upvote history.

Recent studies have advocated for fully open foundation models to promote transparency and open science. As an initial step, the Open Whisper-style Speech Model (OWSM) reproduced OpenAI's Whisper using publicly available data and open-source toolkits. With the aim of reproducing Whisper, the previous OWSM v1 through v3 models were still based on Transformer, which might lead to inferior performance compared to other state-of-the-art speech encoders. In this work, we aim to improve the performance and efficiency of OWSM without extra training data. We present E-Branchformer based OWSM v3.1 models at two scales, i.e., 100M and 1B. The 1B model is the largest E-Branchformer based speech model that has been made publicly available. It outperforms the previous OWSM v3 in a vast majority of evaluation benchmarks, while demonstrating up to 25% faster inference speed. We publicly release the data preparation scripts, pre-trained models and training logs.

Paper page on Hugging Face · arXiv

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