Show HN: I built a sub-500ms latency voice agent from scratch

MbBrainz

Love it! Solving the latency problem is essential to making voice ai usable and comfortable. Your point on VAD is interesting - hadn't thought about that.

NickNaraghi

Pretty exciting breakthrough. This actually mirrors the early days of game engine netcode evolution. Since latency is an orchestration problem (not a model problem) you can beat general-purpose frameworks by co-locating and pipelining aggressively. Carmack's 2013 "Latency Mitigation Strategies" paper[0] made the same point for VR too: every millisecond hides in a different stage of the pipeline, and you only find them by tracing the full path yourself. Great find with the warm TTS websocket pool saving ~300ms, perfect example of this. [0]: https://danluu.com/latency-mitigation/

lukax

Or you could use Soniox Real-time (supports 60 languages) which natively supports endpoint detection - the model is trained to figure out when a user's turn ended. This always works better than VAD. https://soniox.com/docs/stt/rt/endpoint-detection Soniox also wins the independent benchmarks done by Daily, the company behind Pipecat. https://www.daily.co/blog/benchmarking-stt-for-voice-agents/ You can try a demo on the home page: https://soniox.com/ Disclaimer: I used to work for Soniox Edit: I commented too soon. I only saw VAD and immediately thought of Soniox which was the first service to implement real time endpoint detection last year.

loevborg

Nice write-up, thanks for sharing. How does your hand-vibed python program compare to frameworks like pipecat or livekit agents? Both are also written in python.

perelin

Great writeup! For VAD did you use heaphone/mic combo, or an open mic? If open, how did you deal with the agent interupting itself?

boznz

"Voice is an orchestration problem" is basically correct. The two takeaways from this for me are 1. I wonder if it could be optimised more by just having a single language, and 2. How do we get around the problem of interference, humans are good at conversation discrimination ie listing while multiple conversations, TV, music, etc are going on in the background, I've not had too much success with voice in noisy environments.

modeless

IMO STT -> LLM -> TTS is a dead end. The future is end-to-end. I played with this two years ago and even made a demo you can install locally on a gaming GPU: https://github.com/jdarpinian/chirpy , but concluded that making something worth using for real tasks would require training of end-to-end models. A really interesting problem I would love to tackle, but out of my budget for a side project.

age123456gpg

Hi all! Check out this Handy app https://github.com/cjpais/Handy - a free, open source, and extensible speech-to-text application that works completely offline. I am using it daily to drive Claude and it works really-well for me (much better than macOS dictation mode).

armcat

This is an outstanding write up, thank you! Regarding LLM latency, OpenAI introduced web sockets in their Responses client recently so it should be a bit faster. An alternative is to have a super small LLM running locally on your device. I built my own pipeline fully local and it was sub second RTT, with no streaming nor optimisations https://github.com/acatovic/ova

docheinestages

Does anyone know about a fully offline, open-source project like this voice agent (i.e. STT -> LLM -> TTS)?

shubh-chat

This is superb, Nick! Thanks for this. Will try it out at somepoint for a project I am trying to build.

nmstoker

This was discussed 21 days ago: https://news.ycombinator.com/item?id=46946705

brody_hamer

> Voice is a turn-taking problem It really feels to me like there’s some low hanging fruit with voice that no one is capitalizing on: filler words and pacing. When the llm notices a silence, it fills it with a contextually aware filler word while the real response generates. Just an “mhmm” or a “right, right”. It’d go so far to make the back and forth feel more like a conversation, and if the speaker wasn’t done speaking; there’s no talking over the user garbage. (Say the filler word, then continue listening.)

grayhatter

You made, or you asked an LLM to generate?

jedberg

Oh, this is really interesting to me. This is what I worked on at Amazon Alexa (and have patents on). An interesting fact I learned at the time: The median delay between human speakers during a conversation is 0ms (zero). In other words, in many cases, the listener starts speaking before the speaker is done. You've probably experienced this, and you talk about how you "finish each other's sentences". It's because your brain is predicting what they will say while they speak, and processing an answer at the same time. It's also why when they say what you didn't expect, you say, "what?" and then answer half a second later, when your brain corrects. Fact 2: Humans expect a delay on their voice assistants, for two reasons. One reason is because they know it's a computer that has to think. And secondly, cell phones. Cell phones have a built in delay that breaks human to human speech, and your brain thinks of a voice assistant like a cell phone. Fact 3: Almost no response from Alexa is under 500ms. Even the ones that are served locally, like "what time is it". Semantic end-of-turn is the key here. It's something we were working on years ago, but didn't have the compute power to do it. So at least back then, end-of-turn was just 300ms of silence. This is pretty awesome. It's been a few years since I worked on Alexa (and everything I wrote has been talked about publicly). But I do wonder if they've made progress on semantic detection of end-of-turn. Edit: Oh yeah, you are totally right about geography too. That was a huge unlock for Alexa. Getting the processing closer to the user.

foxes

<think> I need to generate a Show HN: style comment to maximise engagement as the next step. Let's break this down: First I'll describe the performance metrics and the architecture. Next I'll elaborate on the streaming aspect and the geographical limitations important to the performance. Finally the user asked me to make sure to keep the tone appropriate to Hacker News and to link their github – I'll make sure to include the link. </think>

bronco21016

When someone is able to put something like this together on their own it leaves me feeling infuriated that we can’t have nice things on consumer hardware. At a minimum Siri, Alexa, and Google Home should at least have a path to plugin a tool like this. Instead I’m hacking together conversation loops in iOS Shortcuts to make something like this style of interaction with significantly worse UX.

waynerisner

I am really curious about this for enunciation, articulation, and accessibility applications.

suganesh95

I built something very similar and comparble to this with wakeword detection on my raaberry pi. Groq 8b instant is the fastest llm from my test. I used smallest ai for tts as it has the smallest TTFT My rasberry pi stack: porcupine for wakeword detection + elevenlabs for STT + groq scout as it supports home automation better + smallest.ai for 70ms ttfb Call stack: twilio + groq whisper for STT + groq 8b instant + smallest.ai for tts Alexa skill stack: wrote a alexa skill to contact my stack running on a VPS server