Voice models are not winner take all market unlike LLM APIs
Coming here as Developer Relations at AssemblyAI
11–20 of 37 posts
Voice models are not winner take all market unlike LLM APIs
Coming here as Developer Relations at AssemblyAI
> and Qwen3-ASR Is the ASR inference engine open source as well?
> and Qwen3-ASR Is the ASR inference engine open source as well?
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
> and Qwen3-ASR Is the ASR inference engine open source as well?
Yes, and it is very good one. Leading position on private leaderboard on HF: https://huggingface.co/spaces/hf-audio/open_asr_leaderboard
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
If you're going to announce a TTS model, service, or whatever, you really need demos.
All TTS generations are too fast. It's almost I'm listening to a podcast on 1.25-1.5x speed.
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?
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