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

Chengyu Wang, Mengli Cheng, Xu Hu, Jun Huang

AAAI 2021 pp. 16111-16113

doi:10.1609/AAAI.V35I18.18028 /aaai/2021/wang2021aaai-easyasr/

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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Cite

Text

Wang et al. "EasyASR: A Distributed Machine Learning Platform for End-to-End Automatic Speech Recognition." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I18.18028

Markdown

[Wang et al. "EasyASR: A Distributed Machine Learning Platform for End-to-End Automatic Speech Recognition." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/wang2021aaai-easyasr/) doi:10.1609/AAAI.V35I18.18028

BibTeX

@inproceedings{wang2021aaai-easyasr,
  title     = {{EasyASR: A Distributed Machine Learning Platform for End-to-End Automatic Speech Recognition}},
  author    = {Wang, Chengyu and Cheng, Mengli and Hu, Xu and Huang, Jun},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {16111-16113},
  doi       = {10.1609/AAAI.V35I18.18028},
  url       = {https://mlanthology.org/aaai/2021/wang2021aaai-easyasr/}
}