Multimodal perception system for real-time conversation
I work on real-time voice/video AI at Tavus and for the past few years, I’ve mostly focused on how machines respond in a conversation. One thing that’s always bothered me is that almost all conversational systems still reduce everything to transcripts, and throw away a ton of signals that need to be used downstream. Some existing emotion understanding models try to analyze and classify into small sets of arbitrary boxes, but they either aren’t fast / rich enough to do this with conviction in real-time. So I built a multimodal perception system which gives us a way to encode visual…
In plain words
This multimodal perception system processes real-time voice and video during conversations, capturing visual and audio signals that traditional text-based systems discard. Rather than forcing emotions into predefined categories, it encodes these signals and translates them into natural language descriptions via a fine-tuned language model. Built for developers working with conversational AI, it enables agents to genuinely perceive both sight and sound, with results accessible through an OpenAI-compatible tool interface for live interactions.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
I work on real-time voice/video AI at Tavus and for the past few years, I’ve mostly focused on how machines respond in a conversation. One thing that’s always bothered me is that almost all conversational systems still reduce everything to transcripts, and throw away a ton of signals that need to be used downstream. Some existing emotion understanding models try to analyze and classify into small sets of arbitrary boxes, but they either aren’t fast / rich enough to do this with conviction in real-time. So I built a multimodal perception system which gives us a way to encode visual and audio conversational signals and have them translated into natural language by aligning a small LLM on these signals, such that the agent can "see" and "hear" you, and that you can interface with it via an OpenAI compatible tool schema in a live conversation. It outputs short natural language descriptions of what’s going on in the interaction - things like uncertainty building, sarcasm, disengagement, or even shift in attention of a single turn in a convo. Some quick specs: - Runs in real-time per conversation - Processing at ~15fps video + overlapping audio alongside the conversation - Handles nuanced emotions, whispers vs shouts - Trained on synthetic + internal convo data Happy to answer questions or go deeper on architecture/tradeoffs More details here: https://www.tavus.io/post/raven-1-bringing-emotional-intelli...
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
