Blaizzy/mlx-audio
Dec 2025 – Dec 2025
1 Month active
Languages Used
Python
Technical Skills
Audio ProcessingMachine LearningMachine Learning EngineeringPythonPython package developmentSignal Processing
joshwhiton
PROFILE
Joshwhiton
Contributed to the Blaizzy/mlx-audio repository by developing preemphasis filtering for Parakeet STT and mlx-audio pipelines, aligning preprocessing with NVIDIA NeMo to improve consonant recognition and resolve training-inference mismatches. The implementation introduced a configurable preemphasis coefficient and high-pass filter before STFT, ensuring backward compatibility and allowing users to disable the feature as needed. Addressed a critical dependency issue by updating pyproject.toml to include mlx-lm, preventing ModuleNotFoundError for end users. Additionally, streamlined Kokoro module initialization by removing redundant logger reconfiguration. Work demonstrated strong skills in Python, audio processing, machine learning engineering, and backend development for speech recognition systems.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
2
Lines of code
31
Activity Months1
Your Network
70 peopleWork History
December 2025
5 Commits • 2 Features
Dec 1, 2025In 2025-12, Blaizzy/mlx-audio delivered a high-impact feature to align preprocessing with NVIDIA NeMo: preemphasis filtering for Parakeet STT and mlx-audio, introducing a preemphasis coefficient and high-pass filter before STFT to boost high-frequency consonants and resolve train/inference mismatches, while remaining backward-compatible with older configs and enabling disable via preemph=0.0. This work, captured in commits 70a6d18da97a7199f1f5faa85641d5c6483b801f, 168baea2f25c600bc0a8f1c72eac2318ff6465b0, and e6ea9acdd430c4a9ed988e01aeb510d2a8791482, aligns weight conventions with NeMo and reduces production gaps (#286). We also fixed a critical dependency issue by declaring mlx-lm in pyproject.toml to prevent ModuleNotFoundError (#344), and cleaned Kokoro module initialization by removing redundant logger reconfiguration to streamline startup (#348). Overall, these changes improve model accuracy, reliability, and deployment ergonomics, delivering measurable business value through better speech recognition accuracy and smoother releases.
5 Commits • 2 Features
Dec 1, 2025In 2025-12, Blaizzy/mlx-audio delivered a high-impact feature to align preprocessing with NVIDIA NeMo: preemphasis filtering for Parakeet STT and mlx-audio, introducing a preemphasis coefficient and high-pass filter before STFT to boost high-frequency consonants and resolve train/inference mismatches, while remaining backward-compatible with older configs and enabling disable via preemph=0.0. This work, captured in commits 70a6d18da97a7199f1f5faa85641d5c6483b801f, 168baea2f25c600bc0a8f1c72eac2318ff6465b0, and e6ea9acdd430c4a9ed988e01aeb510d2a8791482, aligns weight conventions with NeMo and reduces production gaps (#286). We also fixed a critical dependency issue by declaring mlx-lm in pyproject.toml to prevent ModuleNotFoundError (#344), and cleaned Kokoro module initialization by removing redundant logger reconfiguration to streamline startup (#348). Overall, these changes improve model accuracy, reliability, and deployment ergonomics, delivering measurable business value through better speech recognition accuracy and smoother releases.
December 2025
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance88.0%
AI Usage44.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Audio ProcessingMachine LearningMachine Learning EngineeringPythonPython package developmentSignal ProcessingSpeech Recognitionbackend developmentdependency managementlogging
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