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.
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!
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
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.
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