开发者发布 Arc-Transcriber 流式语音模型
WhissleAI 开发者 Working_Hat5120 在 r/MachineLearning 展示了 Arc-Transcriber，称其是一款约 70 MB、可在一次处理中结合语音识别与命名实体链接的流式模型。开发者表示，该模型每 100 毫秒返回转写结果，并能匹配用户提供的名称列表、提取数值、识别说话人和音频事件，还包含超过 200 个训练决策头。其 Arc mini 使用 Moonshot 预训练编码器，Arc-v1 使用 Parakeet 预训练编码器。
模型权重和代码的提供使该项目值得关注，但其模型大小、延迟和能力目前主要来自开发者自述，尚未得到独立验证。
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