论文标题
EasyAsr：用于端到端自动语音识别的分布式机器学习平台
EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition
论文作者
论文摘要
我们提出了EasyAsr，这是一个用于培训和提供大规模自动语音识别（ASR）模型的分布式机器学习平台，以及按大规模收集和处理音频数据。我们的平台建立在阿里巴巴云AI的机器学习平台上。它的主要功能是支持分布式GPU群集上端到端ASR模型的有效学习和推断。它允许用户通过简单的用户界面学习使用预定义或用户定义的网络体系结构的ASR模型。在EasyAsr上，我们在几个公共数据集上产生了最先进的结果，以识别普通话。
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.
