Show HN: Nari Qwen3-TTS and Qwen3-ASR – High accuracy, low latency and cost
Hey HN, Toby from Nari Labs here. We've been working on making OSS speech models super-fast. Last year, we built Dia, the first OSS text-to-speech model capable of doing natural dialogue. Since then, so many more great speech models have been released to the public. But the market is still dominated by closed source models. We think that's an inference problem. Existing systems such as vLLM / SGLang are not well suited for multimodal inference. To prove this, we built an inference engine specialized for Qwen3-TTS and open-sourced it ( https://github.com/nari-labs/nari-qwen3-tts ). Running at sub-50 ms latency at 10 RPS, this showed open models can be run much faster and cheaper. Since then, we've been working hard to bring cheap, fast, and high quality serving to all. And we've even beat closed models at their game! Measured on the highly cited Coval (YC S24) voice AI benchmarks, our Qwen3-TTS endpoint not just is #2 in latency, but #1 in accuracy (WER) compared to 11Labs, Cartesia etc. while being the cheapest endpoint. Our Qwen3-ASR endpoint has the lowest latency and #2 accuracy, just 0.1% away from #1. It is the second cheapest model on the list. It took a lot of clever inference engineering to make these models quick, perform well while keeping costs low. Interestingly, Alibaba's official endpoints seem to perform worse in terms of accuracy and latency compared to ours. But nonetheless, much love to the Qwen team for OSS-ing these amazing speech models. We want to continue to push prices down to make speech technology a commodity - so that every app can have great TTS and STT without worrying about unit costs. We're also working on other parts of audio such as diarization - as well as video and world model inference. More to come!
Discussion Highlights (11 comments)
asaiacai
This is really cool work! I'm curious like what do you see as the biggest lever for speeding up TTS models or from a technical perspective that this was a promising direction in the first place to push on. If I were to guess, some distillation but I'm certain there are probably TTS model aware architectural changes that just make inference wayyyy faster?
meatmanek
> and Qwen3-ASR Is the ASR inference engine open source as well?
ipsum2
If you're going to announce a TTS model, service, or whatever, you really need demos.
rahimnathwani
For some reason it switched voices half way through a 33 second clip. For OP the clip name is nari-nina-01a0a12f-980a-765e-8029-fa56bd23210d.wav
mowmiatlas
Cool, I’ve released something to the same beat of the dr this weekend as well https://github.com/loudreader/loudkit I think real time natural tts should be possible everywhere soon
yoloakki
You definitely need independent evals by Datapoint AI or someone who can verify your claims about TTS quality
DylanMerigaud
Rooting for you on this one.
iharnoor
By next month the competition for TTS will be even more! Voice models are not winner take all market unlike LLM APIs Coming here as Developer Relations at AssemblyAI
konart
All TTS generations are too fast. It's almost I'm listening to a podcast on 1.25-1.5x speed.
apimade
https://apimade.com/audio-compare.html Added it to my blind TTS model comparison leaderboard. So far Darwin TTS is the open model leading the pack, ElevenLabs is at the lead.
karimf
This is awesome. Thanks for pushing the audio pareto frontier forward. Probably far fetched for now, but I think the next big evolution is building the pareto/much cheaper alternative to GPT-Live-1. The STT/TTS market is quite saturated, while today, there's almost no cheap/open source alternative to GPT-Live-1.