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今天11月5日周四5 条
11月4日周三
  1. OpenRouter48

    更出色的转写,同样小巧的模型。 来自 @moondreamai 的 Parakeet Ultra 现已上线 OpenRouter 🎙️

    引用Models on OpenRouter@ModelsOnOR

    Parakeet Ultra from @moondreamai is live on OpenRouter Moondream's post-trained version of NVIDIA Parakeet TDT 0.6B v3: same architecture and speed, lower word error rate on every benchmark group. 25 European languages, with segment and word timestamps. $0.06 per hour of audio, served by @PrimeIntellect. http://openrouter.ai/moondream/parakeet-ultra

  2. OpenRouter42

    你的应用刚刚找到了自己的声音。实际上有超过 2,900 种声音 🎙️ HeyGen Voice Turbo 在 OpenRouter 上免费至 11 月 30 日。$0,无需额度,39 种语言。 https://openrouter.ai/heygen/heygen-voice-1-turbo:free

    引用Models on OpenRouter@ModelsOnOR

    HeyGen Voice Turbo from @HeyGen is free on OpenRouter until November 30! The lower-latency version of HeyGen's text-to-speech model, the one that topped the Artificial Analysis TTS leaderboard. $0, no credits needed. Over 2,900 voice options, 39 languages, up to 5,000 characters per request, streamed as 44.1 kHz audio. See more: http://openrouter.ai/heygen/heygen-voice-1-turbo:free

11月3日周二
  1. Rohan Paul53

    Hume 推出 Audio Decision API,可直接从音频本身回答自定义问题(如有几个说话人、是否有人在笑、来电者是否沮丧),无需转写步骤,并为每个答案返回概率。该 API 将叹气、笑声、犹豫、重叠语音和背景声音纳入模型输入,支持实时或处理数小时录音,基于 10M+ 条人类判断训练。传统方案靠读取文本转写判断通话会丢失语气,该 API 直接听语音本身,能识别讽刺语气。

    引用Hume AI@hume_ai

    Introducing the Hume Audio Decision API. Ask anything: How many speakers? Is anyone laughing? Is the caller frustrated? What used to take 10+ models is now one call. Trained on 10M+ human judgments, so it answers the way human listeners would. Realtime, or across hours of recordings. Request access below.

11月2日周一
  1. Ant Ling48

    很高兴看到 Ming-3.0-tts-0.5B 在 RTX 3090 上本地运行!在 @ItsmeAjayKV 的测试中,仅用他本人声音的 5 秒样本就足以完成克隆——即使录音中有风扇噪音。🫶 感谢分享,AJ!我们很期待看到你接下来构建的作品。

    引用AJ@ItsmeAjayKV

    Tested voice cloning with Ming-3.0-tts-0.5B by @AntLingAGI and it is even more insane. For a model of this size, voice cloning happens really well, i tested my own voice, half drunk, in a room full of noise (fan noise mostly) and it instantly cloned it. It needed just a 5-second sample of my voice, and it speaks faster than real time: about 4 seconds of speech in 2–3 seconds. All running locally on my RTX 3090. Ming takes about 3.3 GB of VRAM, and the whole model is under 3 GB on disk. Great time for Local AI.

10月31日周六
  1. IT之家·人工智能44

    香港“港话通”上线 AI 音乐功能,接入 YuE2 音乐大模型可免费生成粤语歌

    香港生成式人工智能研发中心(HKGAI)研发的本地 AI 智能助理“港话通”(HKChat)于 10 月 30 日更新,推出接入 YuE2 音乐大模型的 AI 音乐生成功能。用户哼唱或上传图片并输入构思,即可生成含专业编曲和人声献唱的原创歌曲,该功能以粤语流行曲为特色,现阶段向所有用户免费开放。

  2. Rohan Paul50

    VoiceArena 推出 Turing TTS 评测,让听众听同一句话的人类语音和模型语音并挑出真人,挑错比例即 Fool Rate。最佳模型 Gemini 3.8 Flash TTS 的 Fool Rate 仅约 10%,而偏好榜前四名模型统计上并列,说明整个领域距难以与真人区分还有距离。

    引用Shobhit Banga@shobhitbanga

    Drop 2/15 Are we actually measuring the things we ultimately want our voice AI models to achieve? The best AI voices are becoming hard to tell apart. From each other. Not from humans. 🚀 Introducing Turing TTS: an evaluation that measures how often human listeners mistake AI-generated speech for real human speech. We tested leading TTS models against real human speech. Even the best model fooled listeners only ~10% of the time. With Turing TTS, we hope to contribute to a broader discussion within the research community about how we measure progress in voice AI. If one of our goals is to develop speech that is indistinguishable from human speech, how well do our current evaluation methods capture progress toward that goal? We don’t claim that Turing TTS is the right way forward, or that human indistinguishability should be the goal of every TTS system. Rather, we hope these findings encourage the community to collectively examine whether blind pairwise human evaluations of naturalness are sufficient, and what else we might need to measure. Results are now live on @voicearena_ai.

10月30日周五
  1. Rohan Paul51

    Cartesia 于 10 月 30 日发布面向企业的端到端语音智能体平台 Managed Agents,在 Artificial Analysis Speech Agent Arena 和 ServiceNow EVA-Bench 均排名第一。

    引用Cartesia@cartesia

    Introducing Cartesia Managed Agents: our end-to-end voice agent platform for enterprise. #1 on @ArtificialAnlys Speech Agent Arena and @ServiceNow EVA-Bench, built by co-designing our voice models and harness as one system. Check it out → https://cartesia.ai/blog/managed-agents

  2. Alex Heath53

    Granola 宣布新增语音输入功能,不限字数且免费,可在任意应用中代替用户打字,与其既有的会议笔记功能互补。引用转发的记者 Alex Heath 表示自己整天在 Mac 上使用该语音输入,称不再需要另装听写应用。

    引用Granola@meetgranola

    Granola now does voice typing. Unlimited words, for free. Granola already writes your meeting notes. Now it can do the typing for you, in any app.

  3. Armin Ronacher AI 博客60

    Qwen3-TTS 语音嵌入实践:克隆声音混合、指令控制与性别向量运算

    作者分享用 Qwen3-TTS 为游戏配音制作自定义声音的实践。1.7B-Base 模型可从 28 秒录音提取 2048 维说话人嵌入,通过加权平均混合出独特声音,再把克隆嵌入传入 CustomVoice 模型即可对克隆声音下指令(如悲伤、低语),比预期效果更好;作者还演示了用 14 对男女声音求平均得出性别轴移动音色的方法,并测试了嵌入做说话人识别的效果。

  4. TechCrunch:AI49

    Granola 推出听写功能,与 Wispr 正面竞争

    以会议记录工具起家的 Granola 上线听写功能,用户在 Mac 上按 Fn 键即可说话,工具会自动整理并格式化语音,最终文本可粘贴到所有应用,支持写笔记、口述提示词和回复邮件消息,且不限字数、免费使用。该功能将很快登陆 iOS 和 Windows。此举发生在 Wispr 推出会议记录工具数月之后,Granola 与 Wispr 估值分别为 15 亿美元和 20 亿美元。

  5. Rohan Paul45

    无脚本通话很难大规模收集。 如今的语音 AI 大多是从 LLM 编写的虚假对话、再由 TTS 朗读出来中学习,所以它从未听过人们互相抢话; 真的很好奇他们是如何让对话持续 10 到 20 分钟的。 他们发布了 Joy,10 万小时的无脚本双人对话,覆盖 10 种语言 https://x.com/shobhitbanga/status/2115862857351061694/video/1

    引用Shobhit Banga@shobhitbanga

    Drop 1/15: Introducing Joy, a dual-channel conversational dataset. Real, unscripted conversations between two people, each on their own track. Built to teach voice models how people actually talk: when to listen, when to jump in, when to say “hmm”. 100,000 hours. 50,000 speakers. 10 languages.

  6. Rohan Paul56

    10 人初创公司 Phonic 发布语音到语音模型 Phonic-1,官方称其为唯一能在来电者说到一半时就执行查询和预订的语音模型。

    引用Moin Nadeem@moinnadeem

    Introducing Phonic-1, the most advanced speech-to-speech model for agentic tasks — from @phonic_co, the speech-to-speech model company. We’ve had a breakout year and raised a $27.5M Series A led by @firstround with participation from @Lux_Capital, @felicis, and Definition.

10月29日周四
  1. MarkTechPost49

    Block 发布 Dessa:仅用语音训练的全双工语音模型

    Block 发布研究预览版 Dessa,一款仅用人类对话音频训练的全双工语音到语音模型,不使用文本和预训练模型。它用 1 台 NVIDIA GB200 NVL72(72 块 GPU)训练,可在 MacBook Pro M4 Max 及以上本地运行且快于实时。目前仅有演示,未公布 benchmark 分数、参数量、权重、论文或 API,需申请 waitlist。

  2. Gemini Notebook50

    NotebookLM 宣布 Live Chat 移动端现已面向全部用户开放,此前已向 Ultra 用户完成 100% 推送。用户可与笔记本进行实时语音对话,支持约 100 种语言,由其最新音频模型驱动,可就资料提问并获得近乎完全免提的分步指引。

    引用Gemini Notebook@Gemini_Notebook

    Live Chat is now 100% rolled out to all Ultra users on mobile 📱✨ Have real-time voice conversations with your notebooks across ~100 languages. Powered by our latest audio models, ask questions about your sources and get step-by-step guidance (almost) completely hands-free.

  3. Aravind Srinivas50

    Perplexity CEO Aravind Srinivas 转发团队消息,宣布 Perplexity 网页版上线新的语音模式。该模式支持自然对话、全语音操作应用,并可同时跨多个会话工作,由 GPT-Live-1 驱动。

    引用Houssein Djirdeh@hdjirdeh

    We worked with @therealbradysm, @annuskabarta, and the rest of the multimodal team to bring this incredible new voice mode to Perplexity web. - Natural back-and-forth conversation - Navigate the app entirely by voice - Work across multiple sessions at once Runs on GPT-Live-1. Lots more improvements to come!