EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition

Authors

  • Chengyu Wang Alibaba Group
  • Mengli Cheng Alibaba Group
  • Xu Hu ByteDance Inc.
  • Jun Huang Alibaba Group

Keywords:

Machine Learning Platform, Automatic Speech Recognition, Distributed Machine Learning

Abstract

We present EasyASR, a distributed machine learning platform for training and serving large-scale Automatic Speech Recognition (ASR) models, as well as collecting and processing audio data at scale. Our platform is built upon the Machine Learning Platform for AI of Alibaba Cloud. Its main functionality is to support efficient learning and inference for end-to-end ASR models on distributed GPU clusters. It allows users to learn ASR models with either pre-defined or user-customized network architectures via simple user interface. On EasyASR, we have produced state-of-the-art results over several public datasets for Mandarin speech recognition.

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Published

2021-05-18

How to Cite

Wang, C., Cheng, M., Hu, X., & Huang, J. (2021). EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 16111-16113. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/18028