Superwhisper’s S1‑mini is a 0.6 billion‑parameter text normalizer that sits after any speech‑to‑text engine and turns raw transcripts into clean written text. It removes filler words, resolves self‑corrections, adds punctuation and capitalization, and writes out spoken numbers, dates, currency and email addresses. The model is released with open weights on Hugging Face under Apache 2.0 plus a naming clause, while the larger S1‑Voice and S1‑Language models remain cloud‑only services.
A key practical point is that S1‑mini is not a transcriber or a chat model; it expects a three‑axis control line placed above the transcript. The axes are Styling (casual, semi‑casual, semi‑formal, formal), Structure (prose or lists) and Context (general or email). Every combination was trained, so sending values outside those sets or altering the system prompt can degrade output.
Deployment is straightforward because the quantized Q4_K_M GGUF file is only 462 MB and runs on a laptop CPU. Solo developers can bundle it in a desktop app, and enterprises can run it inside a VPC to keep transcripts on‑premises. However, two settings break most integrations if overlooked: the model must be called with enable_thinking set to false, and decoding must be greedy (temperature 0). The underlying Qwen3 chat template defaults to thinking on, so omitting the flag usually yields unusable output. Passing temperature 0 explicitly overrides the inherited 0.6 temperature stored in the GGUF metadata.
For best results, feed the model roughly 1 000 tokens of raw ASR output, keep the control line unchanged, and treat an empty string as a valid result when the input contains only fillers. Reported token accuracy on a held‑out set exceeds 94 % and email address reconstruction is correct in over 90 % of cases.
Adopting S1‑mini lets teams replace post‑processing scripts with a single, controllable model that works offline, scales from personal notebooks to secure enterprise pipelines, and delivers consistently clean text for dictation apps, meeting notes, live captioning and voice‑driven CRM entry.
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