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Two settings control language, and both live with your model in Settings → Dictation. Naming your language instead of leaving it on Auto can improve accuracy noticeably, because the model stops spending effort guessing. It helps most on short recordings.
Both rows only appear when your current model supports them. If you cannot find Translate to English, the model you are on cannot translate. The whole group is hidden for models with nothing to configure.

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Which model for which languages

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Which models translate to English

Whisper Turbo is the one that catches people out. It is a Whisper model, so translation looks like it should be there, but the model does not support the translation task. Leave translation off and you get a transcript in the language you spoke. Turn it on and you get English regardless.
Non-Western names. Whisper models are much better at names that trip up English-only models, including Nepali, Indian, and other South Asian names. Add the tricky ones to Custom words so they are spelled your way every time.
Your choice is passed to the model only if that model claims to support the language. If it does not, SpeakoFlow falls back to autodetection rather than forcing a language the model cannot handle.So picking Japanese while an English-only model is active changes nothing.
Both are sent to the model as zh. The distinction exists in the language list for filtering, not as something the models treat separately.
Language and translation support are declared by the model file itself. For current-engine models, SpeakoFlow reads those capabilities out of the GGUF the first time the model actually loads and corrects its own catalog entry if it was wrong.
Whisper-family models receive them as a recognition prompt, which biases the model before it decides. Every other family gets a fuzzy correction pass over the finished transcript instead.Both work. The Whisper route tends to be more accurate because nothing has to be guessed after the fact.
Last modified on August 7, 2026