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?
I meant the Nari inference engine for Qwen3-ASR. I'm aware that Qwen3-ASR is open source, but I don't see a repo under https://github.com/nari-labs for nari-qwen3-asr or similar.
the qwen3-asr inference repo is not OSSed as of now. we're planning to write a paper or tech report on it as it contains some general techniques for ASR inference.
kshmir 5 hours ago [-]
how do I follow you? I have a small 5090 doing inference all the time and I barely use tts but a lot of asr, mostly whisper, I ported your tech report for tts and implemented some improvements on my whisper inference based on your tech report as well!
would love to talk sometime!
verdverm 9 hours ago [-]
They have a number of demos and examples in their HF space
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
DylanMerigaud 10 hours ago [-]
Rooting for you on this one.
13 hours ago [-]
ipsum2 11 hours ago [-]
If you're going to announce a TTS model, service, or whatever, you really need demos.
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.
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.
https://huggingface.co/zeropointnine/Darwin-TTS-1.7B-Cross-Q...
For OP the clip name is nari-nina-01a0a12f-980a-765e-8029-fa56bd23210d.wav
Voice models are not winner take all market unlike LLM APIs
Coming here as Developer Relations at AssemblyAI
https://github.com/loudreader/loudkit
I think real time natural tts should be possible everywhere soon
Is the ASR inference engine open source as well?
The Huggingface link on https://narilabs.com/product/stt/ links to https://huggingface.co/Qwen/Qwen3-ASR-1.7B , not anything under https://huggingface.co/nari-labs
would love to talk sometime!
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