EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition
Keywords:Machine Learning Platform, Automatic Speech Recognition, Distributed Machine Learning
AbstractWe 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.
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
AAAI Demonstration Track