Zhifu Gao is a senior algorithm engineer with nine years of experience specializing in speech recognition and ML engineering, currently working at Alibaba DAMO Academy in Hefei. He has contributed to prominent open-source projects like ModelScope and FunASR, driving ASR pipeline integration, platform adaptation (including macOS), and updates to keep toolkits aligned with state-of-the-art models. His work spans practical inference engineering—batch padding, long-input support, and parameter-passing bug fixes—bridging research-quality models to production-ready pipelines. Trained with a master's from the University of Science and Technology of China, he combines deep technical rigor with hands-on system integration experience, often tackling the less visible but critical engineering details that make speech models robust in real-world deployments.
9 years of coding experience
Anhui University
Master's degree, Master's degree at University of Science and Technology of China
A Fundamental End-to-End Speech Recognition Toolkit and Open Source SOTA Pretrained Models, Supporting Speech Recognition, Voice Activity Detection, Text Post-processing etc.
Role in this project:
ML Engineer
Contributions:3 releases, 330 reviews, 361 commits in 3 months
Contributions summary:Zhifu's commits primarily involve modifications and additions to the `funasr` project, which is a speech recognition toolkit. The changes involve adapting the paraformer model, including batch padding, and long input support. The commits also show work on integrating the model with Modelscope and include modifications to pre-existing inference scripts and configuration files.
ModelScope: bring the notion of Model-as-a-Service to life.
Role in this project:
ML Engineer
Contributions:5 reviews, 12 PRs, 11 pushes in 1 year 3 months
Contributions summary:Zhifu primarily contributed to the integration and maintenance of Automatic Speech Recognition (ASR) pipelines within the ModelScope framework, specifically focusing on the integration of funasr. The contributions include supporting funasr for different platforms like macOS, modifying pipeline parameters, and removing legacy easyasr dependency, also fixing bugs related to model parameter passing. Additionally, the user updated funasr to version 1.0, indicating efforts to keep the project up-to-date with the latest advancements in speech recognition technology.
nlppytorchmulti-modalpythonscience
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