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Ask HN: What's the current best local/open speech-to-speech setup?
I’m trying to do the “voice assistant” thing fully locally: mic → model → speaker, low latency, ideally streaming + interruptible (barge-in).
Qwen3 Omni looks perfect on paper (“real-time”, speech-to-speech, etc). But I’ve been poking around and I can’t find a single reproducible “here’s how I got the open weights doing real speech-to-speech locally” writeup. Lots of “speech in → text out” or “audio out after the model finishes”, but not a usable realtime voice loop. Feels like either (a) the tooling isn’t there yet, or (b) I’m missing the secret sauce.
What are people actually using in 2026 if they want open + local voice?
Is anyone doing true end-to-end speech models locally (streaming audio out), or is the SOTA still “streaming ASR + LLM + streaming TTS” glued together?
If you did get Qwen3 Omni speech-to-speech working: what stack (transformers / vLLM-omni / something else), what hardware, and is it actually realtime?
What’s the most “works today” combo on a single GPU?
Bonus: rough numbers people see for mic → first audio back
Would love pointers to repos, configs, or “this is the one that finally worked for me” war stories.
Home Assistant have a fully local voice assistant experience that's very pluggable and customisable. I believe it uses a fast whisper model for STT and piper for TTS.
You can run it on a raspberry pi (or ideally an N100+), and for the microphone/speaker part, you can make your own or buy their off the shelf voice hardware, which works really well.
https://www.home-assistant.io/voice-pe/
You should look into the new Nvidia model: https://research.nvidia.com/labs/adlr/personaplex/
It has dual channel input / output and a very permissible license
Oh man that space emergency example had me rolling
Ha --
and the "Customer Service - Banking" scenario claims that it demos "accent control" and the prompt gives the agent a definitely non-indian name, yet the agents sounds 100% Indian - I found that hilarious but also isn't it a bad example given they are claiming accent control as a feature?
Thanks for sharing this! I'm going to put this on my list to play around with. I'm not really an expert in this tech, I come from the audio background, but recently was playing around with streaming Speech-to-Text (using Whisper) / Text-to-Speech (using Kokoro at the time) on a local machine.
The most challenging part in my build was tuning the inference batch sizing here. I was able to get it working well for Speech-to-Text down to batch sizes of 200ms. I even implement a basic local agreement algorithm and it was still very fast (inferencing time, I think, was around 10-20ms?). You're basically limited by the minimum batch size, NOT inference time. Maybe that's a missing "secret sauce" suggested in the original post?
In the use case listed above, the TTS probably isn't a bottleneck as long as OP can generate tokens quickly.
All this being said a wrapped model like this that is able to handle hand-offs between these parts of the process sounds really useful and I'll definitely be interested in seeing how it performs.
Let me know if you guys play with this and find success.
oh - very interesting indeed! thanks
I'm using https://spokenly.app/ in local mode, which is free. Very happy with it. It supports a bunch of models, including whisper and parakeet. Right now I'm mostly using parakeet v3 on my desktop, but it tends to do a bit more errors, although it is very fast. I cycle betwen it and Distil-Whisper Large V3.5, which is a bit slower.
On iOS I'm also using the same app, with the Apple Speech model, which I found out to be better performing for me than the parakeet/whisper. One drawback for the apple model is that you need iOS/Mac 26+ - and I haven't bothered to update to Tahoe on my mac.
Both of the models work instantly for me (Mac M1, iphone 17 Pro).
Edit: Aaaand I just saw that you're looking for speech-to-speech. Oops, still sleeping.
Oh... Having a local-only voice assistant would be great. Maybe someone can share the practical side of this.
Do you have the GPU running all day at 200W to scan for wake words? Or is that running on the machine you are working on anyway?
Is this running from a headset microphone (while sitting at the desk?) or more like a USB speakerphone? Is there an Alexa jailbreak / alternative firmware as a frontend and run this on a GPU hidden away?
I built this recently. I used nvidia parakeet as STT, open wake word as the wake word detection, mistral ministral 14b as LLM and pocket tts for tts. Fits snugly in my 16 gb VRAM. Pocket is small and fast and has good enough voice cloning. I first used the chatterbox turbo model, which perform better and even supported some simple paralinguistic word like (chuckle) that made it more fun, but it was just a bit too big for my rig.
OP asked:
> Is anyone doing true end-to-end speech models locally (streaming audio out), or is the SOTA still “streaming ASR + LLM + streaming TTS” glued together?
Your setup is the latter, not the former.
It requires a bit of tinkering, but I think pipecat is the way to go. You can plug in pretty much any STT/LLM/TTS you want and go. It definitely supports local models but its up to you to get your hands on those models.
Not sure if there's any turnkey setups that are preconfigured for local install where you can just press play and go though.
Last I heard E2E speech to speech models are still pretty weak. I've had pretty bad results from gpt-realtime and that's a proprietary model, I'm assuming open source is a bit behind.
yes, I am currently playing with pipecat - both with ASR + LLM + TTS pipeline and also speech to text (ultravox) + TTS but haven't been successful with local speech to speech setups yet.
For the TTS part: https://github.com/supertone-inc/supertonic
Looking for an iOS app to test this as I’m generally curious about the capabilities of on devices TTS (yet to find an app, but there are loads for text gen)
It can’t be too far off considering Siri and TTS has been on devices for ages
What exactly do you want the pipeline to do that cares about the input being "speech", or indeed that's different from just sending mic -> speaker directly? (I can imagine a few different things, but I want to figure out if your use case sounds like mine, or what suggestions are appropriate for what tasks.)
I did a MLX "streaming ASR + LLM + streaming TTS" pipeline in early 2024. I haven't worked on it since then so it's dated. There are now better versions of all the models I used.
I was able to conversational latency with the ability to interrupt the pipeline on a Mac, using a variety of tricks. It's MLX, so only relevant if you have a Mac.
https://github.com/andrewgph/local_voice
For MLX speech to speech, I've seen:
The mlx-audio package has some MLX implementations of speech to speech models: https://github.com/Blaizzy/mlx-audio/tree/main
kyutai Moshi, maybe old now but has a MLX implementation of their speech to speech model: https://github.com/kyutai-labs/moshi
Tangential: What hardware are you using for the interface on these? Is there a good array microphone that performs on par with echos/ghomes/homepods?
speech to speech is not nearly as good as livekit IMO ("old school" sequence of transcribe, LLM, synthesize). depends on what you're doing of course, but this is just because the LLMs are just way smarter than the speech to speech models which are pretty much the worst (again IMO) at anything beyond basic banter. and livekit is just a framework so you can hook it up with any models in the stack. im not an expert on the local parts but i would assume this pretty easy to glue together.
I have used https://github.com/SaynaAI/sayna . What I like the most is that you can switch between the providers easily and see what works for you the best. It also supports local models.
I haven't tried them myself but the Kyutai has a couple projects that could fit.
https://kyutai.org
Anyone using any reasonably good small speech to text os models?
I’m using whisper with superwhisper on my mac. I’ve assigned a key on my keyboard, when I press the key it starts listening and when I release it, the text gets copied to the current cursor location. It works pretty well.
For my inputs, whisper distil-large-v3.5 is the best. I tried Parakeet 0.6 v3 last night but it has higher error rates than I'd like (but it is fast...)
Nice I'll try it, as of now for my personal stt workflow I use eleven labs api which is pretty generous but curious to play around with other options
I assume that will be better than whisper - I haven't benchmarked it against cloud models, the project I'm working on cannot send data out to cloud models
oh I've been looking into whisper and vosk in the last few days. I'll probably go with whisper (with whisper.cpp) but has anyone compared it to vosk models?
It was a little annoying getting old qt5 tools installed but I really enjoyed using dsnote / Speech Note. Huge model selection for my amd gpu. Good tool. I haven't done enough specific studying yet to give you suggestions for which model to go with. WhisperFlow is very popular.
Kyutai some very interesting work always. Their delayed streams work is bleeding edge & sounds very promising especially for low latency. Not sure why I have not yet tried it tbh. https://github.com/kyutai-labs/delayed-streams-modeling
There's also a really nice elegant simple app Handy. Only supports Whisper and Parakeet V3 but nice app & those are amazing models. https://github.com/cjpais/Handy
I have a great local assistant that works end-to-end with voice. It's built on local, web-first technologies, it fits small LLMs in memory and manages inference and TTS/STT without stuttering. I've been shaping it up over a couple years and constantly switching out new models.
If you want something simple that runs in browser, look at vosk-browser[0] and vits-web[1].
I'd also recommend checking out KittenTTS[2], I use it and it's great for the size/performance. However, you'd need to implement a custom JavaScript harness for the model since it's a python project. If you need help with that, shoot me an email and I can share some code.
There are other great approaches too if you don't mind python, personally I chose the web as a platform in order to make my agent fully portable and remote once I release it.
And of course, NVIDIA's new model just came out last week[3] but I haven't gotten to test it out just yet, and also there was the recent Sparrow-1[4] announcement which shows people are finally putting money into the problems plaguing voice agents that are rigged up from several models and glue infrastructure, vs a single end-to-end model or at least a conversational turn-taking model to keep things on rails.
[0] https://www.npmjs.com/package/vosk-browser
[1] https://github.com/diffusionstudio/vits-web
[2] https://github.com/KittenML/KittenTTS
[3] https://research.nvidia.com/labs/adlr/personaplex/
[4] https://www.tavus.io/post/sparrow-1-human-level-conversation...
https://handy.computer got good marks from a very nontechnical user in my life this week!
Local, FOSS
To save a click, it's just a fancy front end for Whisper plus a weaker CPU-only model. It has a demo video that seems impressive, but the speech is careful to sound casual while having no meaningful flaws that would cause it to mess up. If you want to make a speech to speech tool, which is what this post asks about, it would make more sense to go straight to Whisper.
I use it, sponsor it, and did a small pr. One of its goals is to be the most “forkable” starting point if i recall. But yes its just voice input. It’s meaningfully better than the mac dictation for me.
you can use gpu too. i have to admit the app is very easy to use and super convenient. kudos to creator
Yes, and with GPU, it's Whisper, which has been mentioned elsewhere in this article's comments. I mean that handy.computer provides the other option as a fallback for those who can't or don't want to use the GPU.
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