Block的AI研究实验室有发布 Dessa，一个只接受过人类对话训练的全复式演讲模型。 Block 宣布于2026年10月23日作为研究预览。 德萨(德語：Dessa)是来自布洛克的语音对语音模型，同时收听并对话。 Block 称它没有使用文字，也没有预先训练过的模型。 这与今天大多数语音agents后面的文本第一堆不同。
TL; DR 数据
- 运行在：一个MacBook Pro M4 Max或者更好，本地，比实时快。
- 表现：目前为止，只有民主。 现场录音显示回声道，中场取笑和自然中断。
- 最佳：在1个NVIDIA GB200 NVL72架(72 GPU)上被训练。 莫希使用了1,016 H100 GPU.
- 最糟糕的是：0 公布的基准测试分数。 没有重量，纸张，API或价格。
布洛克的德萨是什么？
德萨是一个真正的语音对语音模型：音频输入，音频输出。 根据Block的项目页面，它知道的一切来自人类的演讲。
它充满了分歧，所以它一边听一边说话。 封锁称它从真正的谈话中学到了什么时候切入。 它还学会了何时以“是”或“得到”等短提示回流。
Block的演示片段显示Dessa笑了一半，与OpenAI的GPT-Live-1交谈。 屏蔽称没有人驾驶任何剪辑。
唯言之训如何能行？
大多数语音系统链3部分：语音识别，一种文本模型，再进行语音合成。 Block团队认为，链子会丢掉情感，讽刺，取笑和叹息。 德萨直接听到和发出这些信号。
Block尚未发布该架构。 以下是它披露的情况：
- 培训数据：只有人的对话音频。 没有文本和预训模型。
- 计算：1NVIDIA GB200 NVL72，连接了72个布莱克威尔GPU和36个GraceCPU.
- 数据效率：Block 称 Dessa从比LLMs需要少上百倍的词学语言。
- 推论：比实时更快，在MacBook Pro M4 Max上或者更好。
实验室也解释了为什么这很难。 讲话从不重复一个口语词比同个字在文本中要多上千倍。
京泰的"莫希"，最出名的开放全方位模型，走另一条路线。 莫希从文本语言模型主干开始，在音频符号之前预测出时间相通的文本符号。 其型号卡列出了700万小时的预训音频和1,016个H100GPU.
德萨与莫希，PersonaPlex和GPT-Live-1的对比如何？.
关键外卖
- 德萨是全方位的语音对语音的，只接受人类对话的音频培训。
- Block在1GB200 NVL72架上训练；Moshi使用了1,016 H100s.
- 它在当地运行在MacBook Pro M4 Max，比实时快。
- Block发布0基准测试，没有参数数和重量。
- 访问是一个等待名单。
看看德萨计划页面与莫希纸，PersonaPlex模型卡和GPT-Live-1多克相提并论。 并加入我们的通讯。 等等，你在电报里吗？现在你也可以加入我们的电报。
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米哈尔·萨特(Michal Sutter)是一名数据科学专业人员，拥有帕多瓦大学的数据科学硕士。 在统计分析、机器学习和数据工程的坚实基础上，Michal出色地将复杂的数据集转化为可操作的见解。
本文由 AI宝库自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
Block’s AI research lab has released Dessa, a full-duplex speech model trained only on human conversation. Block announced it on October 23, 2026 as a research preview. Dessa is a speech-to-speech model from Block that listens and talks at the same time. Block says it used no text and no pretrained models. That is a different design from the text-first stacks behind most voice agents today.
TL;DR
- Runs on: A MacBook Pro M4 Max or better, locally, faster than real time.
- Performance: Demo-only so far. Live recordings show backchannels, mid-sentence laughs and natural interruptions.
- Best: Trained on 1 NVIDIA GB200 NVL72 rack (72 GPUs). Moshi used 1,016 H100 GPUs.
- Worst: 0 published benchmark scores. No weights, paper, API or price.
What is Block’s Dessa?
Dessa is a true speech-to-speech model: audio in, audio out. According to Block’s project page, everything it knows came from human speech.
It is full-duplex, so it listens while it talks. Block says it learned from real conversation when to cut in. It also learned when to backchannel with short cues like “yeah” or “got it”.
Block’s demo clips show Dessa laughing mid-sentence and getting louder when asked to speak up. One clip shows it talking with OpenAI’s GPT-Live-1. Block says nobody steered any clip.
How does speech-only training work?
Most voice systems chain 3 parts: speech recognition, a text model, then speech synthesis. Block team argues that chain throws away emotion, sarcasm, laughter and sighs. Dessa hears and produces those signals directly.
Block has not published the architecture. Here is what it has disclosed:
- Training data: human conversation audio only. No text and no pretrained models.
- Compute: 1 NVIDIA GB200 NVL72, which links 72 Blackwell GPUs and 36 Grace CPUs.
- Data efficiency: Block says Dessa learned language from hundreds of times fewer words than LLMs need.
- Inference: faster than real time, on a MacBook Pro M4 Max or better.
The lab also explains why this is hard. Speech never repeats exactly. A spoken word takes thousands of times more bytes than the same word in text.
Kyutai’s Moshi, the best-known open full-duplex model, takes another route. Moshi starts from a text language model backbone and predicts time-aligned text tokens before audio tokens. Its model card lists 7 million hours of pretraining audio and 1,016 H100 GPUs.
How does Dessa compare with Moshi, PersonaPlex and GPT-Live-1?
Key Takeaways
- Dessa is full-duplex and speech-to-speech, trained only on human conversation audio.
- Block trained it on 1 GB200 NVL72 rack; Moshi used 1,016 H100s.
- It runs locally on a MacBook Pro M4 Max, faster than real time.
- Block published 0 benchmarks, no parameter count and no weights.
- Access is a waitlist.
Check out the Dessa project page. Compare it with the Moshi paper, the PersonaPlex model card and the GPT-Live-1 docs. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.
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