Arc 发布约 70 MB 流式语音模型，单步完成转写与实体链接
Arc: a ~70 MB streaming speech model that transcribes links names and metadata to your own list as they're spoken, with no second model [p]
作者发布 Arc-Transcriber，一个约 70 MB 的流式语音模型，每 100 ms 输出转写结果，同时给出与自定义名单匹配的人名、数字数值、说话人角色、音频事件以及是否在对助手说话等标注，共 200 多个训练好的决策头。
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.
submitted by /u/Working_Hat5120
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来源：r/MachineLearning · reddit.com