Show HN: Multimodal perception system for real-time conversation
raven.tavuslabs.org41 points by mert_gerdan 8 hours ago
41 points by mert_gerdan 8 hours ago
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...
This is super interesting. But I have to wonder how much it costs on the back end - it sounds like it’s essentially just running a boatload of specialized agents, constantly, throughout the whole interaction (and with super-token-rich input for each). Neat for a demo, but what would it cost to run this for a 30 minute job interview? Or a 7 hour deposition? Another concern I’d have is bias. If I am prone to speaking loudly, is it going to say I’m shrill? If my camera is not aligned well, is it going to say I’m not making eye contact? So the conversational agent runs on a provisioned chunk of compute already, but that chunk isn't utilized to 100% of its provisioned capacity. For this perception system we're taking advantage of the spare compute left on what's provisioned for a top-level agent, so turning this on costs nothing "extra" Bias is a concern for sure, though it adapts to your speech pattern and behaviors in the duration of a single conversation, so ack'ing you not making eye contact because say your camera is on a different monitor, it'll make the mistake once and not refer to that again. Old Macs in the background. Electronic soundtrack reminscent of Chariots of Fire, which played during the Mac intro. Hmm.. My first thought is that great, now not only will e.g., HR/screening/hiring hand-off the reading/discerning tasks to an ML model, they'll now outsource the things that require any sort of emotional understanding (compassion, stress, anxiety, social awkwardness, etc) to a model too. One part of me has a tendency to think "good, take some subjectivity away from a human with poor social skills", but another part of me is repulsed by the concept because we see how otherwise capable humans will defer to "expertise" of an LLM due to a notion of perceived "expertise" in the machine, or laziness (see recent kerfuffles in the legal field over hallucinated citations, etc.) Objective classification in CV is one thing, but subjective identification (psychology, pseudoscientific forensic sociology, etc) via a multi-modal model triggers a sort of danger warning in me as initial reaction. Neat work, though, from a technical standpoint. Appreciate the feedback truly. It's an interesting concept to explore, deferring human "expertise" to technology has been happening throughout the years (most definitely accelerated in recent times), for which we have found ways to adapt / abstract over the work being deferred, but the growing pains are probably the most acute when such deferment happens rapidly, as in the case of AI. Don't want this to turn into a Matt Damon in Elysium type of situation for sure with that scene with the parole officer hahah (which would stem from a poor integration of such subjective signals into existing workflows, more so than the availability of those signals) For emotional intelligence, I personally see this as a prerequisite for any voice / language model that's interacting with humans, just like how an autonomous car has to be able to identify a pothole, so does a voice / video agent navigating a pothole in a conversation. HR: 1187 at Hunterwasser. Candidate: That's the hotel. HR: What? Candidate: Where I live. HR: Nice place? Candidate: Yeah, sure. I guess. Is that part of the test? HR: No. Just warming you up, that's all. Wonder how it works? High level, rolling buffer that uses the spare compute we're allocated for a conversation to achieve <80ms p50 results, using signals labeled from raw convo data to align a small language model to produce these natural language descriptions
arctic-true - 3 hours ago
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