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Chinese voice typing on a PC, fully offline

In short: YazSes is free, open-source dictation software that types Chinese into any application on your computer, and it keeps working with the network cable pulled out. You hold a key, speak, release, and the characters appear where your cursor is. Nothing is uploaded by default, no account is needed, and there is no per-minute charge.

This page is deliberately specific about what works and what does not, because Chinese support in offline speech recognition is usually oversold. There is also a Simplified Chinese version of this page.

It is not on by default — you must switch models

YazSes ships with base.en, an English-only Whisper checkpoint. English-only checkpoints have no language tokens at all, so they cannot decode Mandarin: fed Chinese speech they produce fluent-looking English nonsense rather than an error, which is the worst possible failure mode. YazSes warns about this at start-up, but you still have to change the setting yourself.

In ~/.config/yazses/config.toml:

[stt]
model = "small"                  # multilingual (no .en suffix): base / small / medium / large-v3
language = "zh"                  # Mandarin
chinese_script = "simplified"    # or "traditional" for Taiwan and Hong Kong

Then:

yazses features enable chinese-script   # installs the `chinese` extra
yazses restart

Why chinese_script matters more than it sounds

Whisper decides per utterance whether to answer in Simplified or Traditional characters, and it is not consistent about it. Measured on 20 clean 16 kHz Mandarin utterances (the ASCEND test split, small model), 13 came back in Traditional characters — including ones where the recognition itself was completely correct. A mainland user dictating 简体中文 watches 繁體字 land in their editor.

That inconsistency costs far more accuracy than it appears to, because the recognition is usually right and only the script is wrong. Character error rate against Simplified references:

Model chinese_script = "" chinese_script = "simplified"
small 35.9% 16.9%
large-v3 12.3% 11.3%

Same audio, same model, one config key. The setting matters most for the small models, which is exactly what CPU users run — small improves by 19 points, large-v3 by only one, because the larger model already tends to answer in Simplified. If you are dictating on a laptop CPU rather than a GPU, this setting is doing most of the work.

The measurement and its caveats are documented in postprocess/han_script.py.

Conversion is a reversible character mapping, so it cannot repair a mishearing — it only stops a correct transcription from arriving in the wrong script.

How accurate is it really?

Those figures come from ASCEND, which is spontaneous conversation by Hong Kong speakers with Mandarin-English code-switching — a deliberately hard case, and only 20 utterances. Read speech into a decent microphone in a quiet room does better; a noisy room or a strong regional accent does worse. Model size is the biggest lever available to you: on the same clips, large-v3 reached 11.3% where small reached 16.9%.

Do not take anyone's benchmark as a promise about your voice. Test it on your own audio before you rely on it:

yazses transcribe my-recording.m4a

Larger models are more accurate and slower to decode on CPU. small is a reasonable starting point; move to medium or large-v3 if your machine can afford the latency.

Chinese dictation in YazSes is best described as usable but still rough. Field reports are genuinely wanted — open an issue with what worked and what did not.

Why offline matters here specifically

Most Chinese voice input is either a website or a mobile keyboard: you speak into their text box and copy the result out, and it stops working the moment the network does. YazSes runs as a background program on your own computer, so it types straight into Word, a browser, a code editor, or a terminal — on a train, on a bad connection, or on a machine that never touches the internet at all.

That property is the point for anyone whose audio must not leave the building: clinical notes, legal drafting, unpublished research, interview recordings covered by an ethics approval, or work on an air-gapped machine. See private and confidential dictation and the privacy statement.