Show HN: Nari Qwen3-TTS and Qwen3-ASR – High accuracy, low latency and cost
Comments
mowmiatlas
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
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
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?
ipsum2
If you're going to announce a TTS model, service, or whatever, you really need demos.
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.
meatmanek
> and Qwen3-ASR
Is the ASR inference engine open source as well?
nshm
Yes, and it is very good one. Leading position on private leaderboard on HF: https://huggingface.co/spaces/hf-audio/open_asr_leaderboard
verdverm
They have a number of demos and examples in their HF space
https://huggingface.co/Qwen/spaces
I saw a local-ai demo (something + gemma), where the person used ASR to get text and gemma to clean it up (like turning "question mark" into a literal "?", bullet points another one). The presenter also showed a gemma only option, that did both in one go, but had a higher WER on average, and even though the formatting statements were handled without a multi-stage pipeline, they preferred the multi-stage overall
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