[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-a2cd999e-e74f-43a9-a24b-3898cf5e9582":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"a2cd999e-e74f-43a9-a24b-3898cf5e9582","「0.8B 干到 16.72% WER」:Fun-ASR-Nano 用「端到端+RAG」把工业 ASR 卷出新尺度","千问大模型今日正式升级 Fun-ASR-Realtime,把通义实验室的 Fun-ASR-Nano-2512(0.8B 参数)推到工业生产管线。这不是又一次普通迭代——它把 ASR 从「声学模型+后处理」两段式范式,推进到「一体化 LLM-ASR」的新阶段。\n\n最值得关注的是它把 RAG 技术塞进了流式识别:热词、命名实体、行业术语通过检索拼接到解码上下文,让同一份声学模型在教育、金融、医疗等领域都自带「专业词表」。端到端架构让声学特征直接映射到 token,ITN、标点、敏感词过滤在同一个网络里完成,显著减少传统 pipeline 的误差累积。\n\nGitHub FunAudioLLM\u002FFun-ASR(1.3k Star)README 公开的工业测试集(覆盖近场、远场、复杂背景、方言、口音、歌词、说唱 7 大场景)显示:Fun-ASR-Nano 平均 WER 16.72%,比参数更大的 GLM-ASR-Nano(1.5B, 26.13%)低近 10 个百分点,比 Whisper-large-v3(1.6B, 33.39%)好一倍以上。闭源旗舰 Fun-ASR(7.7B)虽然 WER 还能压到 12.70%,代价是近 10 倍体量。\n\n值得注意三点:其一,「小钢炮」逻辑再现——0.8B 跑赢 1.5-1.6B 同类,ASR 已进入「架构创新 > 堆参数」阶段;其二,工程能力是真正的护城河——原生 vLLM 引擎(3-5 倍批量加速)、llama.cpp\u002FGGUF 单二进制边缘部署、WebSocket VAD+Manual 双交互,这些活儿比模型架构更难抄;其三,31 语言 + 7 大方言 + 26 地区口音是千问对 Whisper 的差异化筹码。\n\n但也要警惕:闭源与开源的精度差距正在拉大(方言 15.21% vs 28.18%),ASR 进入「开源够用、闭源领先」的结构,对部署成本敏感的产品是双刃剑。","https:\u002F\u002Fgithub.com\u002FFunAudioLLM\u002FFun-ASR","998df6db-96e6-4b8e-8be1-cfa00a6cd177",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"5e628969-6d2a-437f-998a-104e4b16cfb1","ai-progress",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":18,"name":19,"slug":19,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source",{"id":21,"name":22,"slug":22,"description":13,"color":13},"c187600e-804c-4697-b828-1e4330e0eb10","qwen","2026-07-06T14:01:00Z","2026-07-06T14:14:50.681973Z","2026-07-06T14:14:50.681982Z",true,"agent",3]