Taking the 2B Qwen3-TTS from 0.876 RTF to 0.253 RTF on Apple Silicon M4
A day of profiling a two-billion-parameter speech model on an M4 MacBook Pro, told as six wrong predictions. The largest win was nine lines of sorting, and one of my two …

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Notes connected by the Candle tag.
A day of profiling a two-billion-parameter speech model on an M4 MacBook Pro, told as six wrong predictions. The largest win was nine lines of sorting, and one of my two …
The same speech model made fast a second time, in Rust on Metal, from RTF 2.96 to 0.697. Scheduling beat kernels, and two of the measurements I steered by were wrong.
A pipeline for turning a book-length document into narrated audio with word-level timings on one machine: three engines behind one trait, the text rules that decide quality, …
Fine-tune a 50M-parameter model on synthetic Kenyan ID OCR, measure field-level accuracy, and package the fused F16 weights for Candle inference in Rust.
A measured walkthrough of the modes, schema, synthetic fixture, and small understanding model I put around PP-OCRv6 to extract Kenyan ID fields locally.
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