Blaizzy/mlx-audio
Jun 2026 – Jun 2026
1 Month active
Languages Used
No languages
Technical Skills
Audio ProcessingCI/CDCode FormattingDeep LearningDigital Signal ProcessingFile System Management
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PROFILE
Hert4
Worked on the Blaizzy/mlx-audio repository to enhance multilingual zero-shot voice cloning by porting Confucius4-TTS to MLX, enabling torch-free inference on Apple Silicon. Leveraged Python and NumPy to implement 8-bit quantization for T2S and w2v-bert encoders, reducing memory usage and improving inference speed. Extended RoPE frequency tensors to support longer audio sequences, preventing runtime crashes. Developed a self-contained feature extraction pipeline using MLX, removing dependencies on external libraries like librosa. Addressed audio processing bugs by standardizing reference audio resampling and improved deployment portability by refactoring checkpoint resolution logic, resulting in a more robust and adaptable TTS stack.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
9Total
Bugs
2
Commits
9
Features
4
Lines of code
2,154
Activity Months1
Your Network
77 peopleWork History
June 2026
9 Commits • 4 Features
Jun 1, 2026June 2026 monthly summary for Blaizzy/mlx-audio: Delivered cross-MLX port and quality improvements for Confucius4-TTS, introduced 8-bit quantization for T2S and w2v-bert, extended RoPE for longer sequences, and established a self-contained feature extraction pipeline. Resolved critical audio and deployment bugs, including 16 kHz reference audio resampling and portable checkpoint resolution. These changes reduce memory footprint, speed up decoding, improve audio quality, and simplify deployment across Apple Silicon and diverse environments, strengthening the end-to-end TTS stack and enabling broader adoption.
9 Commits • 4 Features
Jun 1, 2026June 2026 monthly summary for Blaizzy/mlx-audio: Delivered cross-MLX port and quality improvements for Confucius4-TTS, introduced 8-bit quantization for T2S and w2v-bert, extended RoPE for longer sequences, and established a self-contained feature extraction pipeline. Resolved critical audio and deployment bugs, including 16 kHz reference audio resampling and portable checkpoint resolution. These changes reduce memory footprint, speed up decoding, improve audio quality, and simplify deployment across Apple Silicon and diverse environments, strengthening the end-to-end TTS stack and enabling broader adoption.
June 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability88.8%
Architecture95.6%
Performance91.2%
AI Usage84.4%
Skills & Technologies
Programming Languages
No languages yet
Technical Skills
Audio ProcessingCI/CDCode FormattingDeep LearningDigital Signal ProcessingFile System ManagementMLXMachine LearningModel OptimizationModel PortingModel QuantizationNumPyPythonQuantizationRefactoring
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