4 papers
TouchASP: Elastic Automatic Speech Perception that Everyone Can Touch
Xingchen Song, Chengdong Liang, Binbin Zhang +9
Large Automatic Speech Recognition (ASR) models demand a vast number of parameters, copious amounts of data, and significant computational resources during the training process. Ho…
TouchTTS: An Embarrassingly Simple TTS Framework that Everyone Can Touch
Xingchen Song, Mengtao Xing, Changwei Ma +9
It is well known that LLM-based systems are data-hungry. Recent LLM-based TTS works typically employ complex data processing pipelines to obtain high-quality training data. These s…
U2++ MoE: Scaling 4.7x parameters with minimal impact on RTF
Xingchen Song, Di Wu, Binbin Zhang +5
Scale has opened new frontiers in natural language processing, but at a high cost. In response, by learning to only activate a subset of parameters in training and inference, Mixtu…
The GUA-Speech System Description for CNVSRC Challenge 2023
Shengqiang Li, Chao Lei, Baozhong Ma +2
This study describes our system for Task 1 Single-speaker Visual Speech Recognition (VSR) fixed track in the Chinese Continuous Visual Speech Recognition Challenge (CNVSRC) 2023. S…