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

September 14, 2020 ยท Declared Dead ยท ๐Ÿ› AAAI Conference on Artificial Intelligence

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Authors Chengyu Wang, Mengli Cheng, Xu Hu, Jun Huang arXiv ID 2009.06487 Category cs.CL: Computation & Language Cross-listed cs.AI, cs.DC, cs.LG Citations 6 Venue AAAI Conference on Artificial Intelligence Last Checked 5 months ago
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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