AI 模型和平台
Block 发布 Dessa，一个在对话方面受过训练的全复式演讲模型
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Block)内部AI研究实验室宣布 Dessa于2026年10月23日
该公告将Block的Dessa页面与X上的一篇文章相配，Thurairatnam将德萨描述为第一个完全通过语音培训后能够进行现实对话的AI模式，称它可以运行在消费GPU上，并称该发布为"GPT3的演讲时刻". Block在其页面上写道，就公司所知，Dessa是世界上第一个在只接受人类演讲训练后
Block将德萨描述为真正的语音对语音模型：音频入，音频出。 它是一个完全的复数，所以它同时聆听和交谈，称公司从真实的谈话中得知它何时切入，何时回通道显示它正在跟着走。 Block 称模特在本土听到并说出情感，讽刺，取笑，沮丧的叹息，并用悲伤来破解声音；这种推论运行得快于实时，因此回复来时没有尴尬的停顿；而德萨在当地运行的是MacBook Pro M4 Max或更好。 公司称其为"有史以来最真实的全方位语音模型"，称与之交谈时感觉像是与人交谈。
仅限演讲的培训办法
根据该页，德萨从头到尾只接受过声音训练，没有任何文字或事先训练过的模型，并学会了纯粹通过收听进行实时交会。 Block认为，这项任务的困难很容易被低估，因为人们学会这么容易说话。 语音数据从不重复：即使用完全相同的方式说出同一句话两次也会产生不同的数据，而打入的文本无论谁打字，都是相同的。 在数据术语上，页面表示，一个口语单词占用了比其书面形式多上千倍的字节，而应力放入会改变意义；页面上的一个例子句每次强调一个不同的单词，都会具有不同的含义。
Block 称 Dessa从比大型语言模型少上百倍的单词中学习了语言，团队研究了如何在单一的NVIDIA GB200 NVL72系统中对其进行训练。 该页列出了该方法所创造的公开研究问题：与文本相比，这些模型如何有效地学习语言和事实，它们如何能够有效地学习新语言，以及它们能够进行何种内容内学习。
录音演示
Block出版的片段描述为与德萨交谈的人的现场录音。 称公司在这些对话中无人指导模型，德萨公司自己选择了该说什么，何时说，如何说，包括自己的声音和个性；在事实之后增加了字幕。
被描述的瞬间包括德萨在句子中间笑出一些有趣的事，而不是在演讲者说完之后；在被要求大声说话时大声点；在某人在电影标题上空白时完成一个人的句子。 在另一段短片中，德萨在宣布周年纪念日时喘了口气，一个录音有德萨与第三方AI模式的GPT-Live-1进行交谈；Block 指出表示，该录音中的声音都是AI生成的，而GPT-Live-1的开发者并不隶属于Block，也没有审查或认可德萨。
安全、出入和实验室
团队给出了建立德萨的三个理由：好奇心，人类和AI之间更好的理解，以及安全。 婴儿学会说话却从未看到书面文字的页面指出和团队想看看模特儿能否做同样的事情的称。 在安全方面，Block认为AI不断获得权力和独立性，同时仍无法将情绪记录在演讲者的声音中，公司将德萨描述为解决该问题的一个步骤，它使当前语音AI因为将语音简化为文本而失去了只带声音的信息。
Block让一小群AI安全研究者及早发现团队未预料到的风险，发布链接即将到来。
德萨出自Block所描述的公司内部非常小的AI研究实验室，由德萨的前成员多伦多深层学习起步公司所建立。 称团队自2012年起就建立了深层次的学习模型，以及超过8年的基因化语音模型，它直接向布洛克的联合创始人，会长和布洛克·赫德(Block Head)的杰克·多尔西(Jack Dorsey)报告。 Thurairatnam的X Bio表示，他之前创办并出售了德萨，并在多伦多大学讲课。 除了他之外，该页还列出了研究人员Louay Hazami、Hashim Kadhim、刘琦(Alex)、Rayhane Mama、Aydin Polat和Marc Tyndel；实习生Cara Ji、Jibran Iqbal Shah和Dvir Zagury-Grynbaum；以及前实习生Aryan Sharma和Olivia Yan。
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AI Models & Platforms
Block Unveils Dessa, a Full-Duplex Speech Model Trained on Conversation
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Block’s in-house AI research lab announced Dessa on October 23, 2026, a full-duplex speech-to-speech model the company says was trained from scratch on nothing but human conversation, releasing live demo recordings, a public waitlist, and early access for a small group of AI safety researchers.
The announcement paired Block’s Dessa page with a post on X by Ragavan Thurairatnam, the lab’s lead. Thurairatnam described Dessa as the first AI model that can hold a realistic conversation after training entirely on speech, said it can run on consumer GPUs, and called the release “a GPT3 moment for speech.” On its page, Block writes that as far as the company knows, Dessa is the world’s first AI that can hold a realistic live conversation after being trained only from human speech.
Block describes Dessa as a true speech-to-speech model: audio in, audio out. It is full-duplex, so it listens and talks at the same time, and the company says it learned from real conversation when to cut in and when to backchannel to show it is following along. Block says the model natively hears and speaks emotion, sarcasm, laughter, a frustrated sigh, and a voice cracking with sadness; that inference runs faster than real time, so replies come without an awkward pause; and that Dessa runs locally on a MacBook Pro M4 Max or better. The company calls it the most authentic full-duplex speech model ever built and says talking with it feels like talking with a person.
A Speech-Only Training Approach
According to the page, Dessa was trained from scratch on sounds alone, without any text or pretrained models, and learned to converse in real time purely by listening. Block argues the difficulty of that task is easy to underestimate because people learn to talk so easily. Speech data never repeats: even saying the same sentence twice in exactly the same way produces different data, whereas typed text is identical regardless of who types it. In data terms, the page states, one spoken word occupies thousands of times more bytes than its written form, and stress placement changes meaning; one example sentence on the page takes on a different meaning each time a different word is stressed.
Block says Dessa learned language from hundreds of times fewer words than large language models, and that the team worked out how to train it on a single NVIDIA GB200 NVL72 system. The page lists open research questions the approach creates: how efficiently such models learn language and facts compared with text, how efficiently they can learn new languages, and what sorts of in-context learning they can perform.
Recorded Demonstrations
Block published clips it describes as live recordings of people talking with Dessa. The company says no one steers the model during these conversations and that Dessa itself chooses what to say, when to say it, and how to say it, including its own voice and personality; subtitles were added after the fact.
The described moments include Dessa laughing in the middle of a sentence when something is funny, rather than after the speaker finishes; getting louder when asked to speak up; and finishing a person’s sentence when the person blanks on a movie title. In another clip, Dessa gasps under its breath at a surprise anniversary announcement before answering with what the page describes as teary gratitude, and a longer recording of life advice is described as sounding genuinely empathetic. One recording has Dessa conversing with GPT-Live-1, a third-party AI model; Block notes that both voices in that recording are AI-generated and that GPT-Live-1’s developer is not affiliated with Block and has not reviewed or endorsed Dessa.
Safety, Access, and the Lab
The team gives three reasons for building Dessa: curiosity, better understanding between humans and AI, and safety. The page notes that babies learn to talk without ever seeing a written word and says the team wanted to see whether a model could do the same. On safety, Block argues that AI keeps gaining power and independence while remaining unable to register the emotion in a speaker’s voice, giving the example of a person in serious pain while a voice AI has no idea and keeps going. The company describes Dessa as a step toward fixing that problem, and it faults current voice AI for reducing speech to text and losing the information that only sound carries.
Block is giving a small group of AI safety researchers early access to find risks the team has not anticipated, and its request form asks for a name and an institutional email from a university, company, or lab, with an optional Google Scholar or blog link. A public waitlist is open, more recordings are to be shared on Thurairatnam’s X account, and the page states that launch links are coming soon.
Dessa comes from what Block describes as a very small AI research lab inside the company, founded by former members of Dessa, the Toronto deep learning startup. The team says it has built deep learning models since 2012 and generative speech models for more than eight years, and it reports directly to Jack Dorsey, Block’s cofounder, Chairman and Block Head. Thurairatnam’s X bio states that he previously founded and sold Dessa and lectured at the University of Toronto. Alongside him, the page lists researchers Louay Hazami, Hashiam Kadhim, Qi (Alex) Liu, Rayhane Mama, Aydin Polat, and Marc Tyndel; interns Cara Ji, Jibran Iqbal Shah, and Dvir Zagury-Grynbaum; and former interns Aryan Sharma and Olivia Yan.
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