OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on
  E-Branchformer
Yifan Peng Jinchuan Tian William Chen Siddhant Arora Brian Yan Yui Sudo Muhammad Shakeel Kwanghee Choi Jiatong Shi Xuankai Chang Jee-weon Jung Shinji Watanabe
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.