Apple MLX port of MediaTek-Research/Breeze-ASR-26, the Taiwanese Hokkien (Taigi) ASR model from MediaTek Research's MR Breeze 3 series, 4-bit palette quantized (group size 64) for compact on-device inference.
Runs on Apple Silicon Macs (M1/M2/M3/M4) via the mlx-whisper package — same API as mlx-community/whisper-* checkpoints.
In our 2026-04-30 head-to-head against the fp16 variant on a real Taigi sample (a Mandarin-speaking creator using the Taigi word 漂泊pio-pôa), the 4-bit variant transcribed 漂泊 correctly while fp16 produced 瀟灑 instead. Both runs identical otherwise (same audio, same mlx-whisper version, language=zh).
This is counterintuitive — quantization usually degrades quality. One possible explanation: 4-bit palette quantization (mapping weights to 16 representative values per group) may re-calibrate outlier weights in a way that better generalizes to underrepresented Taigi tokens. Reproducible on M1 Max; worth verifying on your own samples.
For Taigi-heavy use cases, try this 4-bit variant first. For pure Mandarin or read-speech benchmarks, the fp16 variant remains the safer default.
Limitations (inherited from base model)
Outputs Mandarin Chinese characters, not Taigi orthography (台語正字 / 台羅)
Trained on ~10,000 hours of synthetic Taigi speech — distribution gap with real spontaneous speech
English brand/proper nouns are aggressively transliterated: in our Mandarin test, Hello became 哈囉, Austin became Alstin, Netflix became Nathalie 的時事. ASR-25 (MediaTek-Research/Breeze-ASR-25) handles these correctly. Do not use this model for content with frequent English code-switching.
All segments come back as one ~30-second block regardless of audio content (model training behaviour, not framework setting). Post-process if you need finer subtitle granularity.
Quantization Details
parameter
value
method
palette (lookup-table)
bits
4
group size
64
Performed via mlx.nn.quantize after weight conversion from HuggingFace transformers safetensors → MLX Whisper format.
Conversion
Built with a custom wrapper around mlx-examples/whisper/convert.py that adds sharded-safetensors loader support (the source repo ships weights as 5 GB + 1 GB shards, which the upstream converter doesn't handle).
