
Over nine months, contributed to Blaizzy/mlx-audio by building and optimizing advanced audio processing and text-to-speech pipelines using Python, PyTorch, and MLX. Integrated models such as Wav2Vec2, MOSS-TTS, and ZONOS2, enabling real-time streaming, multilingual support, and voice cloning. Refactored core audio IO, removed external dependencies, and implemented custom signal processing utilities to improve maintainability and performance. Enhanced concurrency and memory management for scalable, multi-threaded deployments, while strengthening CI/CD workflows and documentation. Addressed bugs in token semantics, thread safety, and audio generation, delivering robust, production-ready features that support efficient, reliable, and extensible machine learning-driven audio applications.
July 2026 (2026-07) monthly summary for Blaizzy/mlx-audio: Delivered end-to-end MOSS-Music integration, memory-efficient Nemotron ASR chunked processing, MOSS Transcribe-Diarize optimization with caching, and audio input pipeline improvements with server-side resampling. Also executed maintenance to stabilize dependencies by pinning transformers and removing obsolete tests. These changes reduce memory usage, improve throughput, and increase reliability for production usage, enabling smoother model integration and scalable audio processing.
July 2026 (2026-07) monthly summary for Blaizzy/mlx-audio: Delivered end-to-end MOSS-Music integration, memory-efficient Nemotron ASR chunked processing, MOSS Transcribe-Diarize optimization with caching, and audio input pipeline improvements with server-side resampling. Also executed maintenance to stabilize dependencies by pinning transformers and removing obsolete tests. These changes reduce memory usage, improve throughput, and increase reliability for production usage, enabling smoother model integration and scalable audio processing.
June 2026 performance summary for Blaizzy/mlx-audio and ml-explore/mlx. Delivered major TTS capabilities, reliability improvements, and CI/CD enhancements that collectively expand end-user value, improve performance, and accelerate release cycles. Key features delivered: - Miso TTS support: end-user usage enabled with updated docs and model configurations (commit 352fdeecbe4b7ee08f761e1dbc888860e9e5967e). - Higgs Audio v3 TTS model: voice cloning and inline control tokens for emotional/prosodic adjustments (commit cfaeb122a1b4856726c5edbd6ed31b788e7c9810). - ZONOS2 TTS model: core support, optimization, streaming and docs; adds streaming and batching generation (commits 10b3c337f84da584c1c52d410c42717f670da6ed, afb03e843361dc9cff8938e3894b9436bc30e66a, 1043265955bcf22dcff45cddee038536ce53a9ce, cff92074c65638c13be7df7e70250e3f0c9bb216). - MOSS-TTS-Local-Transformer-v1.5: model support and streaming generation during inference (commits c033b4405d3825b01847b19ad5478597e8b296b9, 9f23edc2009d2a4170ca1c93548fc332a647dfd3). - MLX-Audio project infrastructure: CI/CD workflows, documentation, licensing, contribution guidelines (commit 17210815a4cf01d0a43fce467c352cf832880caa). Major bugs fixed: - EOS handling fix for interleaved audio and text generation in LFM2.5-Audio (commit 51c47a82f9f130f3a619f3779a6c2cfe5a5fcb28). - MLX models thread-safety improvements to address race conditions via eager evaluation of lazy arrays/tensors and additional tests (commits 992ff92a1e2ed6bd1a143764f1f4aa497463f5b0, 3b37f335dc78558a30a22a738d618112e6a55a12). - Thread-local compile cache cleanup on interpreter shutdown to prevent leaks (commit a8ae6d1d5c397439294e746285d77c4c038cf4c6). - Test suite optimization for STT integration tests by enabling conditional skip (commit e77ae57eccfd45433c6156997436b2dcc62806d4). Overall impact and accomplishments: - Expanded end-to-end TTS capabilities across multiple models with streaming, making it feasible to offer rich, real-time voice synthesis at scale. - Accelerated CI/CD pipelines and improved project governance, reducing onboarding friction and enabling faster releases. - Strengthened reliability and resource management through multi-threading safeguards and memory-management fixes, contributing to more robust production deployments. Technologies and skills demonstrated: - Large-scale model integration (Miso, Higgs Audio v3, ZONOS2, MOSS-TTS Local Transformer), streaming generation, and non-streaming workflows. - Advanced CI/CD, project configuration, docs governance, and licensing practices. - Multi-threaded programming discipline, thread-safety testing, and memory management optimizations. - Test optimization strategies to speed up CI while preserving coverage.
June 2026 performance summary for Blaizzy/mlx-audio and ml-explore/mlx. Delivered major TTS capabilities, reliability improvements, and CI/CD enhancements that collectively expand end-user value, improve performance, and accelerate release cycles. Key features delivered: - Miso TTS support: end-user usage enabled with updated docs and model configurations (commit 352fdeecbe4b7ee08f761e1dbc888860e9e5967e). - Higgs Audio v3 TTS model: voice cloning and inline control tokens for emotional/prosodic adjustments (commit cfaeb122a1b4856726c5edbd6ed31b788e7c9810). - ZONOS2 TTS model: core support, optimization, streaming and docs; adds streaming and batching generation (commits 10b3c337f84da584c1c52d410c42717f670da6ed, afb03e843361dc9cff8938e3894b9436bc30e66a, 1043265955bcf22dcff45cddee038536ce53a9ce, cff92074c65638c13be7df7e70250e3f0c9bb216). - MOSS-TTS-Local-Transformer-v1.5: model support and streaming generation during inference (commits c033b4405d3825b01847b19ad5478597e8b296b9, 9f23edc2009d2a4170ca1c93548fc332a647dfd3). - MLX-Audio project infrastructure: CI/CD workflows, documentation, licensing, contribution guidelines (commit 17210815a4cf01d0a43fce467c352cf832880caa). Major bugs fixed: - EOS handling fix for interleaved audio and text generation in LFM2.5-Audio (commit 51c47a82f9f130f3a619f3779a6c2cfe5a5fcb28). - MLX models thread-safety improvements to address race conditions via eager evaluation of lazy arrays/tensors and additional tests (commits 992ff92a1e2ed6bd1a143764f1f4aa497463f5b0, 3b37f335dc78558a30a22a738d618112e6a55a12). - Thread-local compile cache cleanup on interpreter shutdown to prevent leaks (commit a8ae6d1d5c397439294e746285d77c4c038cf4c6). - Test suite optimization for STT integration tests by enabling conditional skip (commit e77ae57eccfd45433c6156997436b2dcc62806d4). Overall impact and accomplishments: - Expanded end-to-end TTS capabilities across multiple models with streaming, making it feasible to offer rich, real-time voice synthesis at scale. - Accelerated CI/CD pipelines and improved project governance, reducing onboarding friction and enabling faster releases. - Strengthened reliability and resource management through multi-threading safeguards and memory-management fixes, contributing to more robust production deployments. Technologies and skills demonstrated: - Large-scale model integration (Miso, Higgs Audio v3, ZONOS2, MOSS-TTS Local Transformer), streaming generation, and non-streaming workflows. - Advanced CI/CD, project configuration, docs governance, and licensing practices. - Multi-threaded programming discipline, thread-safety testing, and memory management optimizations. - Test optimization strategies to speed up CI while preserving coverage.
May 2026 monthly summary for Blaizzy/mlx-audio: Delivered impactful real-time voice processing capabilities and expanded TTS features, while strengthening reliability through rigorous testing and robust multi-threading fixes. Key work includes real-time Silero VAD-based pipeline with barge-in, multi-input TTSD dialogue with MOSS-TTS 1.5 upgrade, the Dramabox TTS model with reference audio, and performance-focused improvements in sampling, style customization, and dynamic batching. The work reduced latency, broadened language/style coverage, and improved test stability, accelerating product readiness and deployment confidence.
May 2026 monthly summary for Blaizzy/mlx-audio: Delivered impactful real-time voice processing capabilities and expanded TTS features, while strengthening reliability through rigorous testing and robust multi-threading fixes. Key work includes real-time Silero VAD-based pipeline with barge-in, multi-input TTSD dialogue with MOSS-TTS 1.5 upgrade, the Dramabox TTS model with reference audio, and performance-focused improvements in sampling, style customization, and dynamic batching. The work reduced latency, broadened language/style coverage, and improved test stability, accelerating product readiness and deployment confidence.
April 2026 monthly wrap-up for Blaizzy/mlx-audio: Delivered broad architecture cleanup, performance improvements, and deployment simplifications that reduce maintenance burden and enable scalable real-time audio. Key work focused on unifying the audio processing stack, accelerating longform and streaming workloads, improving concurrency, and streamlining installation across models and components. Highlights include: (1) Unified internal audio IO and loudness processing that eliminates external dependencies (replacing librosa and pyloudnorm with internal utilities; replacing soundfile calls); (2) Inference broker to manage multiple concurrent requests with asynchronous adapters for speech and transcription, boosting throughput; (3) Mid-generation streaming for Higgs TTS to deliver overlap-add real-time audio during generation; (4) Dependency consolidation and modularization, removing pydub, reducing SciPy usage, removing explicit tiktoken dependency, removing onnxruntime, and moving misaki/Kitten-phonemizer to optional; (5) Standardized STS model loading workflow and expanded model support (including MOSS TTS multilingual and voice cloning) with a regression fix for Fish S2 Pro by exposing the model hidden state. These changes reduce install surface, increase stability, and enable faster, more personalizable audio experiences across products.
April 2026 monthly wrap-up for Blaizzy/mlx-audio: Delivered broad architecture cleanup, performance improvements, and deployment simplifications that reduce maintenance burden and enable scalable real-time audio. Key work focused on unifying the audio processing stack, accelerating longform and streaming workloads, improving concurrency, and streamlining installation across models and components. Highlights include: (1) Unified internal audio IO and loudness processing that eliminates external dependencies (replacing librosa and pyloudnorm with internal utilities; replacing soundfile calls); (2) Inference broker to manage multiple concurrent requests with asynchronous adapters for speech and transcription, boosting throughput; (3) Mid-generation streaming for Higgs TTS to deliver overlap-add real-time audio during generation; (4) Dependency consolidation and modularization, removing pydub, reducing SciPy usage, removing explicit tiktoken dependency, removing onnxruntime, and moving misaki/Kitten-phonemizer to optional; (5) Standardized STS model loading workflow and expanded model support (including MOSS TTS multilingual and voice cloning) with a regression fix for Fish S2 Pro by exposing the model hidden state. These changes reduce install surface, increase stability, and enable faster, more personalizable audio experiences across products.
March 2026 monthly summary for Blaizzy/mlx-audio: Delivered key STT and TTS improvements and a token semantics bug fix. STT Output Length Expansion extended default max tokens from 128 to 8192, enabling longer outputs and reducing post-processing. Added Fish Audio S2 Pro TTS model support for voice cloning and multi-speaker TTS. TTS Token Semantics Coherence Fix improved replacement of semantic tokens to handle repeated tokens more coherently. These changes improved downstream processing, expanded use cases, and strengthened model reliability, delivering measurable business value in end-user experience and deployment flexibility.
March 2026 monthly summary for Blaizzy/mlx-audio: Delivered key STT and TTS improvements and a token semantics bug fix. STT Output Length Expansion extended default max tokens from 128 to 8192, enabling longer outputs and reducing post-processing. Added Fish Audio S2 Pro TTS model support for voice cloning and multi-speaker TTS. TTS Token Semantics Coherence Fix improved replacement of semantic tokens to handle repeated tokens more coherently. These changes improved downstream processing, expanded use cases, and strengthened model reliability, delivering measurable business value in end-user experience and deployment flexibility.
February 2026 monthly summary for Blaizzy/mlx-audio: Implemented per-segment flow cache slicing to boost TTS throughput; fixed Pocket TTS voice matching parameter bug, restoring correct audio processing. Resulted in improved processing efficiency, reliability, and maintainability across the Pocket TTS workflow.
February 2026 monthly summary for Blaizzy/mlx-audio: Implemented per-segment flow cache slicing to boost TTS throughput; fixed Pocket TTS voice matching parameter bug, restoring correct audio processing. Resulted in improved processing efficiency, reliability, and maintainability across the Pocket TTS workflow.
January 2026: Delivered modular audio codec capabilities and advanced TTS processing for Blaizzy/mlx-audio. Key outcomes include standalone DACVAE codec integration with SAM Audio compatibility, release of Pocket TTS with transformer-based audio processing, and Mimi codec unification with cache and weight optimization. Expanded test coverage validated correctness and compatibility across codecs, enabling faster feature delivery and improved reliability. No critical bugs reported; the work focused on performance, interoperability, and business value.
January 2026: Delivered modular audio codec capabilities and advanced TTS processing for Blaizzy/mlx-audio. Key outcomes include standalone DACVAE codec integration with SAM Audio compatibility, release of Pocket TTS with transformer-based audio processing, and Mimi codec unification with cache and weight optimization. Expanded test coverage validated correctness and compatibility across codecs, enabling faster feature delivery and improved reliability. No critical bugs reported; the work focused on performance, interoperability, and business value.
Concise monthly summary for 2025-05 highlighting delivered features, critical fixes, impact, and technical proficiency on Blaizzy/mlx-audio.
Concise monthly summary for 2025-05 highlighting delivered features, critical fixes, impact, and technical proficiency on Blaizzy/mlx-audio.
Month: 2025-03 — Blaizzy/mlx-audio: Delivered a lean, higher-quality audio processing pipeline by removing external dependencies, introducing neural codecs, and enabling CLI playback. This month focused on reducing maintenance overhead, improving audio quality, and enabling faster iteration cycles. No major bugs reported this month.
Month: 2025-03 — Blaizzy/mlx-audio: Delivered a lean, higher-quality audio processing pipeline by removing external dependencies, introducing neural codecs, and enabling CLI playback. This month focused on reducing maintenance overhead, improving audio quality, and enabling faster iteration cycles. No major bugs reported this month.

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