Xiaofei Wang
Software Engineer at VirgoCX
St. John's, Newfoundland and Labrador, Canada
Summary
👤
Senior🎓
Top SchoolXiaofei Wang is a software engineer with over 10 years of professional experience and 3+ years focused on Java/J2EE and full-stack web development, currently building systems at VirgoCX in St. John's, Newfoundland. He brings hands-on expertise in Spring Boot, MyBatis/Hibernate, Redis, and cloud/server deployment across AWS and Tencent Cloud, plus practical familiarity with tooling like Maven, Solr, Swagger, and FFmpeg. Xiaofei pairs backend depth (Oracle/MySQL, Nginx/Apache) with front-end skills in HTML5/CSS/JavaScript and has applied microservice and testing practices in production. Outside core work, he contributes to the espnet speech toolkit—adding language model support to the AMI ASR recipe—reflecting an interest in ML-driven engineering. A collaborative communicator and frequent freelance developer earlier in his career, he holds an M.C.A. from Memorial University and a China–Canada collaborative CS degree.
10 years of coding experience
3 years of employment as a software developer
Bachelor of Computer Science and Management, Computer Science and Technology(in Collaboration with Canada), GPA3.0, Bachelor of Computer Science and Management, Computer Science and Technology(in Collaboration with Canada), GPA3.0 at Tianjin University of TechnologyMaster of Computer Applications (M.C.A.), Computer Software Engineering, GPA3.0, Master of Computer Applications (M.C.A.), Computer Software Engineering, GPA3.0 at Memorial University of Newfoundland
English, Chinese
Github Skills (9)
pytorch10
voice-recognition10
speech-recognition10
deep-learning10
automatic-speech-recognition10
asr10
language-modeling10
machine-translation4
text-to-speech3
Programming languages (2)
Github contributions (5)
espnet/espnet
Nov 2018 - Aug 2019
End-to-End Speech Processing Toolkit
Role in this project:
ML Engineer
ML EngineerContributions:40 commits, 10 PRs, 8 comments in 8 months
Contributions summary:Xiaofei's contributions focus on enhancing the speech processing toolkit by incorporating language models into the AMI ASR recipe. This includes updating the recipe to include language model training and decoding, modifying the batch size for language model training, and adjusting language model weights. Additionally, the user makes modifications to use a smaller vocabulary size for the language model and updates the results.
speech-recognitionspeech-separationchainerspoken-language-understandingspeech-processing
Xiaofei-Wang/kaldi
Oct 2017 - Jun 2021
Contributions:57 pushes, 4 branches in 3 years 7 months
kaldilocation
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