ASR = automatic speech recognition. I think this is the same as STT, speech to text?
It seems to want an NVidia GPU. I didn't look closely to find out if it can run usably without one. For me, usable might mean 10% of realtime, i.e. an overnight run on my old x86 box for 1 hour of transcription. Of course faster is always nice. YMMV.
> Transcribe is built for recorded audio, not live streaming.
Bit unfortunate! Nemotron Streaming in Handy has basically solved dictation for me. It recognizes German mixed with English terms and brand names really well.
There are apps on the Google Play Store where you can run on-device models or connect to OpenRouter where they are run for you. Im using Qwen 3 ASR locally on my android, it's the second most accurate open model after the one in this post.
I played around with Nemotron 3 recently. It's OK, but makes an error every few sentences, at least on the data I worked with (standard vocab). I'm sure we'll get there, but I was pleasantly surprised by how I can recognize words better than it can.
testing it against parakeet right now for ASR, it's slower, heavier, and has a higher error rate. but i'm doing a pass with the embedded translation to see if that's any good, it might be worth it.
e: also i'm testing it with a Q4 quant that might not be full accuracy
tried running it against a full anime episode, both vocal isolated and raw. it got confused by english words in the theme song and failed spectacularly three times
I have a few thousand audio interviews as MP3, around an hour each and with specialized vocabulary. Typically there are no more than 2 to 3 speakers. Audio quality ranges from mediocre to fair to good.
I was thinking of creating a local app that calls OpenAI’s VTT model. In my most recent evaluation, the OpenAI model was best for my use case. But is that indeed the case?
ASR = automatic speech recognition. I think this is the same as STT, speech to text?
It seems to want an NVidia GPU. I didn't look closely to find out if it can run usably without one. For me, usable might mean 10% of realtime, i.e. an overnight run on my old x86 box for 1 hour of transcription. Of course faster is always nice. YMMV.
> Transcribe is built for recorded audio, not live streaming.
Bit unfortunate! Nemotron Streaming in Handy has basically solved dictation for me. It recognizes German mixed with English terms and brand names really well.
Can you share some info how you got that working?
https://handy.computer/
Select nemotron in the model selection
Oh, since it's called "handy" I expected it would work on mobile phones, as per the name suggests. That seems it only has desktop operating systems.
Also, there's no mention of Nemotron in that handy. Is that the same Handy GGP was talking about?
There are apps on the Google Play Store where you can run on-device models or connect to OpenRouter where they are run for you. Im using Qwen 3 ASR locally on my android, it's the second most accurate open model after the one in this post.
I played around with Nemotron 3 recently. It's OK, but makes an error every few sentences, at least on the data I worked with (standard vocab). I'm sure we'll get there, but I was pleasantly surprised by how I can recognize words better than it can.
Agreed. I find Parakeet or Qwen ASR better.
I’m still sad Voxtral 3 wasn’t made open source by Mistral.
From the model card, this supports near-field only speaker transcription, experimental but still something not available in any other OSS Speech LLM.
How is it for Japanese
testing it against parakeet right now for ASR, it's slower, heavier, and has a higher error rate. but i'm doing a pass with the embedded translation to see if that's any good, it might be worth it.
e: also i'm testing it with a Q4 quant that might not be full accuracy
ugh, translation is worse than passing a parakeet transcript to a cloud llm. disappointing model.
tried running it against a full anime episode, both vocal isolated and raw. it got confused by english words in the theme song and failed spectacularly three times
You can use fast transcription on CPU these days, for free and with diarization. Check yapsnap https://github.com/kouhxp/yapsnap
how is this related with the main topic? also it seems your project (by your bio), it would be nice to disclose it as this is self-promotion.
I have a few thousand audio interviews as MP3, around an hour each and with specialized vocabulary. Typically there are no more than 2 to 3 speakers. Audio quality ranges from mediocre to fair to good.
I was thinking of creating a local app that calls OpenAI’s VTT model. In my most recent evaluation, the OpenAI model was best for my use case. But is that indeed the case?
We may never know.