Speech doesn’t come out as writing. We say “um”, repeat words, restart sentences, and say “two thousand dollars” where we’d type $2,000. ZenPolish is ZenVoice’s own model for the step between what you said and what you meant to write.

Today we’re introducing ZenPolish 1.7B v2.2. It removes fillers and false starts, fixes punctuation and capitalization, and writes numbers, dates, times and money the way you’d type them, while keeping your words. It runs entirely on your Mac, inside ZenVoice, with no network at inference time.

On 500 held-out dictations it makes a fifth of the word errors of the stock model it’s trained from, and adds 90% fewer words you never said. The biggest gains are in the hardest speech: fillers, restarts and thinking out loud.

We measure ZenPolish on 500 held-out dictations across seven kinds of speech: our original 234-row suite and 266 new rows built the same way. Every row was checked against the training data, and the numbers in it don’t overlap the training templates, so the model can’t have memorised the answers.

Fine-tuning is what does the work, not size: ZenPolish 1.7B v2.2 cuts its own stock base model’s word error rate from 33.7% to 6.7%, a fifth of it, and beats Qwen3-4B, a stock model more than twice its size, on every measure.

Here is what that looks like on real rows from the evaluation, next to the stock model it’s trained from. The difference shows most when speech is messy: the stock model keeps the “um” and “you know”; v2.2 finds the sentence underneath.

A typical dictation is a sentence or two. ZenPolish 1.7B v2.2 writes it in about 0.4 seconds on Apple Silicon, and needs 1.8 GB of memory while it runs.

ZenPolish 1.7B v2.2 is a LoRA fine-tune of Alibaba’s Qwen3-1.7B, trained at rank 32 with a cosine learning-rate schedule. It ships as a 6-bit MLX base with the adapter applied live when it loads. We don’t merge it into the weights: merging measurably hurt money formatting and filler removal.

The training set has about 90,000 examples from ten sources, including parliamentary transcripts, earnings calls, podcasts, conversational question answering and dialogue from books, alongside speech-disfluency data. For v2.2 we also made the labels consistent where earlier data disagreed with itself: fillers in the middle of a sentence, money, exclamations and casing.

ZenPolish 1.7B v2.2 arrives in ZenVoice 0.5.0. Choose it in Models → Dictation enhancement; it’s a 1.5 GB download, alongside Apple Intelligence and Qwen 3.5 2B.

The weights are open under Apache-2.0 on Hugging Face.