
Developed and deployed the KugelAudio TTS model within the Blaizzy/mlx-audio repository, focusing on high-quality multilingual speech synthesis using a hybrid autoregressive and diffusion architecture. Leveraged Python and advanced machine learning techniques to support 24 European languages, optimizing for efficient inference on Apple Silicon hardware. The work included comprehensive unit testing, robust configuration parsing, and detailed documentation to ensure reliability and ease of use in production environments. Emphasized code quality through improved scheduler inheritance and validation logic, while providing clear performance metrics and usage guidance. This approach enabled stable, scalable model deployment and enhanced audio processing capabilities for diverse language applications.
March 2026 monthly summary for Blaizzy/mlx-audio focused on delivering a cutting-edge KugelAudio TTS model and stabilizing multilingual inference on Apple Silicon. The work emphasizes business value through expanded language coverage, improved audio quality and efficiency, and strong test/documentation discipline that supports reliable production deployment.
March 2026 monthly summary for Blaizzy/mlx-audio focused on delivering a cutting-edge KugelAudio TTS model and stabilizing multilingual inference on Apple Silicon. The work emphasizes business value through expanded language coverage, improved audio quality and efficiency, and strong test/documentation discipline that supports reliable production deployment.

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