Developer presents Arc-Transcriber streaming speech model
WhissleAI developer Working_Hat5120 presented Arc-Transcriber on r/MachineLearning, describing it as an approximately 70 MB streaming model that combines speech recognition and named-entity linking in one pass. The developer says the model returns a transcript every 100 milliseconds and can link spoken names to a user-supplied list, extract values, identify speakers and audio events, and apply more than 200 trained decision heads. Two variants are described: Arc mini with a Moonshot-pretrained encoder and Arc-v1 with a Parakeet-pretrained encoder.
The available weights and code make this a notable release, but its model size, latency, and capabilities remain claims from the developer rather than independently verified results.
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. - Inbuilt tags for dialog act, speaker segmentation and more. Total 200+ trained decision heads.
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.
#Arc-Transcriber#Arc mini#Arc-v1#WhissleAI#Moonshot#Parakeet