I've always believed that intelligence should come from simple, general building blocks that learn their own abstractions directly from raw data; in other words, the bitter lesson. I think we're finally seeing a real step towards AI that can learn from all data. Language modeling on raw bytes is a microcosm of this ideal, and to me it's a central problem in AI: if we can't build LLMs that learn end-to-end from bytes, how will we handle the rest of the world's data that we understand even less? Last year we introduced hierarchical networks (H-Nets) as a first step. Our new version finds up to 3-5x efficiency gains in controlled scaling law experiments, and at larger scale matches an open-source model trained on 11T tokens with ~10x less data and compute. The posts below walk through what we found and why we think it works.

the team continues cooking 👩‍🍳 this is now an unprecedented gap on both the super competitive Provider Voices leaderboard as well as the newer Controlled Voices leaderboard (a stronger benchmark that can't be benchmaxxed, requiring truly better algorithms) Cartesia's TTS model is not only the best, it continues improving at a faster rate than the competition - Sonic 3.5 was released only 3 months ago. ig Sonic now means both the fastest model and the fastest team 💀

Within the span of a week, we launched streaming TTS (text-to-speech) and STT (speech-to-text) models that topped the leaderboards. I'm incredibly proud of the research team for their relentless pursuit of improvement, which have unlocked new state-of-the-art audio models on the Pareto frontier of speed and quality. As a research problem, speech requires fusing both text and audio and is the gateway to general multimodal models. We built Sonic-3.5 and Ink-2 from the ground up, developing multiple innovations along the way in a direction that will scale to general real-time intelligence. I've personally been deeply involved in building these models and more; it's been a blast working with the incredibly talented research team here @cartesia, and I can't wait to show the world what's coming next :)