Pre-training speech models on large volumes of data has achieved remarkable
success. OpenAI Whisper is a multilingual multitask model trained on 680k hours
of supervised speech data. It generalizes well to various speech recognition
and translation benchmarks even in a zero-shot setup. However, the full
pipeline for developing such models (from data collection to training) is not
publicly accessible, which makes it difficult for researchers to further
improve its performance and address training-related issues such as efficiency,
robustness, fairness, and bias. This work presents an Open Whisper-style Speech
Model (OWSM), which reproduces Whisper-style training using an open-source
toolkit and publicly available data. OWSM even supports more translation
directions and can be more efficient to train. We will publicly release all
scripts used for data preparation, training, inference, and scoring as well as
pre-trained models and training logs to promote open science.

使用开源工具包与公开可用的数据，本文提出了一种 Open Whisper-style Speech Model (OWSM) 来训练语音模型，以解决以前无法公开访问的全流程模型开发所面临的性能提升、效率、鲁棒性、公正性和偏见等问题，并公开发布所有脚本、预训练模型和训练日志，以促进开放科学。