Original Reddit post

I built Arc-Transcriber, which does ASR-NL in 1-step. Every 100 ms, one model returns the transcript plus:

• Names linked to a list you give it (a roster, contacts, product names); new names are flagged “not on the list”
• Numbers with their values (scores, clocks, money, dates)
• Speakers and roles;
• Sound and channel events (telephone audio, clipping, poor audio)
• Whether the speech was addressed to an assistant; The 200+ labels are trained heads on the shared encoder states: queries per frame, span, utterance, speaker and event, emitted every 100 ms. The usual ASR, then NER, then entity-linking and then LLM chain is collapsed into the model’s own heads, so live “fact-checking” against your list happens inside the stream, not in a second pass. Currently it has two variants Arc mini (which uses moonshot pretrained encoder) and second Arc-v1 (which uses parakeet pretrained encoder). Weights: huggingface.co/WhissleAI/arc-v1.1 Code and CLI: github.com/WhissleAI/arc_transcriber We use it on-device in our Mac app. Voice search over 61k books primes Arc with matching titles while you’re still talking. Here is a video to give a glimpse on how it works. submitted by /u/Working_Hat5120

Originally posted by u/Working_Hat5120 on r/ArtificialInteligence