k2-fsa/sherpa-onnx
Dec 2025 – Dec 2025
1 Month active
Languages Used
C++Python
Technical Skills
API designC++ developmentC++ programmingMachine learningPython developmentalgorithm optimization
Mahmoud ghareeb
PROFILE
Mahmoud Ghareeb
Mahmoud Ghareeb developed token-level confidence scoring for offline transducer-based ASR models in the k2-fsa/sherpa-onnx repository, focusing on delivering per-token log probabilities to enhance evaluation and downstream decision-making. He updated the C++ core logic, extended the C-API, and integrated Python bindings to ensure robust data flow of confidence signals throughout the system. Mahmoud also addressed a bug in the modified beam search decoder, clarifying variable names and improving the accuracy of token log probability storage. His work demonstrated depth in C++ development, API design, and algorithm optimization, resulting in a more reliable and extensible offline ASR deployment pipeline.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
62
Activity Months1
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Work History
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary focused on delivering token-level confidence signals for offline transducer-based ASR models and ensuring robust decoding data flow across the project k2-fsa/sherpa-onnx.
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary focused on delivering token-level confidence signals for offline transducer-based ASR models and ensuring robust decoding data flow across the project k2-fsa/sherpa-onnx.
December 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
API designC++ developmentC++ programmingMachine learningPython developmentalgorithm optimizationmachine learning
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