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If transcription is close but not right, there are four levers worth pulling, in roughly the order they pay off.

1. Name your language

Auto-detect is the default, and it’s the weakest setting for accuracy because the model spends part of its effort deciding what language you’re in. If you nearly always dictate in one language, set it explicitly under SettingsPreferences under App, in the Language section. This is the single cheapest improvement for most people.

2. Teach it your words

Names, jargon, product names and acronyms are what transcription gets wrong most often, and no amount of model tuning fixes a word the model has never seen. The custom dictionary exists for this — add the terms you use and they’ll be recognised. See custom dictionary.
If you’ve named your voice agent, its name is added to the dictionary automatically so it’s recognised reliably when you address it.

3. Check your microphone

The built-in microphone on a laptop is usually the weakest link, especially in a room with any background noise. A headset — even a cheap one — is a bigger improvement than changing models. Pick which microphone OpenWhispr uses under SettingsPreferences under App. If it isn’t picking up at all, see my microphone isn’t working. Speaking at a normal pace helps more than speaking slowly and clearly. These models are trained on natural speech, and over-enunciating is further from what they expect, not closer.

4. Choose a better engine

Transcription runs on one of several engines, and they aren’t equally accurate. Set yours under SettingsSpeech-to-Text under AI Models, on the tab for the mode you’re changing — Dictation, Note Recording, or Audio Upload. If you’re running a local model and accuracy is poor, a larger model is usually the answer — see local models.

What cleanup does and doesn’t fix

Dictation cleanup tidies grammar and punctuation, but it can’t recover a word that was misheard — it only sees the transcript, not your audio. Persistent wrong words are a transcription problem, not a cleanup one.