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Remove evaluation set details from the org card
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README.md
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desktop. Local speech to text, spoken answers, translation, and dictation cleanup, on
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Windows, macOS and Linux. Transcription runs on your own machine.
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This organisation publishes the models
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models measured score worse than leaving the transcript alone.
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Built by [Abhishek Barali](https://huggingface.co/AbhishekBarali) 路
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[speakoflow.com](https://speakoflow.com) 路
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desktop. Local speech to text, spoken answers, translation, and dictation cleanup, on
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Windows, macOS and Linux. Transcription runs on your own machine.
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This organisation publishes the models behind it. Everything here is Apache-2.0 and runs
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locally.
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**speakoflow-mini-0.8b-GGUF** is a 0.8B dictation cleanup model that runs on a desktop CPU.
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It applies the correction the speaker actually made and returns everything else byte for
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byte. On a held-out evaluation it scores 70.7% overall, against 65.0% for a hosted frontier
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model and 50.0% for doing nothing at all. Five quantisations are published and each one's
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cost is measured rather than estimated.
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That last number is the interesting one. Grammatical error correction, ASR error correction
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and spoken-to-written conversion all score whether a required edit was *made*. None scores
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whether an unrequired edit was *withheld*, and that is the failure that ruins a dictation
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product. Four of the seven systems measured do worse than leaving the transcript alone.
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Built by [Abhishek Barali](https://huggingface.co/AbhishekBarali) 路
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[speakoflow.com](https://speakoflow.com) 路
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