EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition
This provides a scalable solution for ASR development, particularly for Mandarin speech recognition, but is incremental as it builds upon existing cloud infrastructure.
The paper tackles the challenge of training and serving large-scale end-to-end Automatic Speech Recognition (ASR) models by introducing EasyASR, a distributed machine learning platform built on Alibaba Cloud, which achieves state-of-the-art results on multiple public Mandarin speech recognition datasets.
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.