Open-source turn detection model for voice AI
Hey HN, it’s Russ - cofounder of LiveKit. An open source stack for building realtime AI applications. We’re sharing our first homegrown AI model for turn detection. Here’s a live demo: https://cerebras.vercel.app/ Voice AI has come a long way in the last year. We now have end-to-end systems that can generate a response to user input in 300-500ms — human level speeds! As latency reduces, a common problem that surfaces is the LLM responds too quickly. Any time there’s a short pause in a user’s speech, it ends up interrupting them. This is largely due to how voice AI applications…
Open-source turn detection model for voice AI launched on December 23, 2024 with 8 votes and more than 75% of that year's launches.
What it does
In the maker’s words, at launch
Hey HN, it’s Russ - cofounder of LiveKit. An open source stack for building realtime AI applications. We’re sharing our first homegrown AI model for turn detection. Here’s a live demo: https://cerebras.vercel.app/ Voice AI has come a long way in the last year. We now have end-to-end systems that can…
Does a similar job
all alternatives →
Open-source, native audio turn detection model2025 · github.com · ▲126Our goal with this project is to build a completely open source, state of the art turn detection model that can be used in any voice AI application. I've been experimenting with LLM voice conversations since GPT-4 was first released. (There's a previous front page Show HN about Pipecat, the open source voice AI orchestration framework I work on. [1]) It's been almost two years, and for most of that time, I've been expecting that someone would "solve" turn detection. We all built initial, pretty good 80/20 versions of turn detection on top of VAD (voice activity detection) models. And…
I built a sub-500ms latency voice agent from scratchMar 2026 · ntik.me · ▲570I built a voice agent from scratch that averages ~400ms end-to-end latency (phone stop → first syllable). That’s with full STT → LLM → TTS in the loop, clean barge-ins, and no precomputed responses. What moved the needle: Voice is a turn-taking problem, not a transcription problem. VAD alone fails; you need semantic end-of-turn detection. The system reduces to one loop: speaking vs listening. The two transitions - cancel instantly on barge-in, respond instantly on end-of-turn - define the experience. STT → LLM → TTS must stream. Sequential pipelines are dead on arrival for natural…
Real-time AI Voice Chat at ~500ms Latency2025 · github.com · ▲524Have a natural, spoken conversation with AI! Contribute to KoljaB/RealtimeVoiceChat development by creating an account on GitHub.
Conversational AI 2.0 From ElevenLabs2025 · elevenlabs.io · ▲652Luke from ElevenLabs here. We just released Scribe Realtime v2, our new real-time Speech to Text model. We rebuilt it from the ground up around a new architecture that uses predictive transcription – it anticipates the next likely word before it’s spoken. That makes it both fast (around 150 ms latency) and highly accurate. Scribe v2 Realtime outperforms every low latency transcription model across 30 commonly used EU and Asian languages – 93.5% accuracy, compared to Gemini 2.5 Flash (91.4%), GPT-4o MiniTranscribe (90.7%), and Deepgram Nova 3 (85%). It supports 90+ languages including…
Conversational Replicas by Tavus2024 · tavus.io · ▲694Hey there, I’m Brian. I've been shipping conversational models over here at Tavus for the past two years. I want to tell you about our new audio-understanding model: Sparrow-2! It’s a new category of model and a unique new approach to conversational audio. Earlier this year we launched Sparrow-1, (at the time) our SoTA turn taking model. Since our Sparrow-1 launch, I’ve spent a lot of time listening to humans talking and trying to really understand how people know when to talk, when to listen, and when to wait. I’ve also been hunting down failure modes of the current SoTA models. And while…
An open source framework for voice assistants2024 · github.com · ▲346I've been obsessed for the past ~year with the possibilities of talking to LLMs. I built a bunch of one-off prototypes, shared code on X, started a Meetup group in SF, and co-hosted a big hackathon. It turns out that there are a few low-level problems that everybody building conversational/real-time AI needs to solve on the way to building/shipping something that works well: low-latency media transport, echo cancellation, voice activity detection, phrase endpointing, pipelining data between models/services, handling voice interruptions, swapping out different…
More ai this month
the category →



Paintings by AI models: programs run through a simulation of oil paint.
AI · 20d ago · stillwet.art
PixVerse R2▲381A real-time world model you can explore and change
AI · 29d ago · world.pixverse.video

Source: Show HN post ↗ · launch text quoted in part