You could try ours. They're very small and english only right now (training Spanish now). But they're a CPU optimized architecture and even the largest size will work on a single thread with realtime streaming.
You can run them client-side using wasm-simd. I setup a space for the small sized model. There's a big step up in accuracy going to the medium size though:
Yep exactly! Streaming models have to run faster, because they're more compute heavy. Some compute from every batch is wasted when the next set of frames come in. Streaming models also have higher WER than fully offline models.
Our models are more comparable to nemotron-3.5-asr-streaming in that regard and I think the medium/large sizes do perform quite favorably. For offline models, I don't think it's worthwhile for us to try and compete with parakeet v2/3. Those are already really good, and we're in the process of adding those to FUTO Voice.
These models are filling some gaps we see for the kinds of things we tend to work on. But broadly they can slot into a lot of places where there isn't accelerated compute (Raspberry pi 4, laptops, local client side transcription, etc).
Our "plus" models have speaker segmentation and speaker verification (embedding) that you can use for speech to text and diarization from one model. There's an example script in the model repo that shows how to do it.
You could try ours. They're very small and english only right now (training Spanish now). But they're a CPU optimized architecture and even the largest size will work on a single thread with realtime streaming.
https://huggingface.co/collections/futo-org/asr4all
You can run them client-side using wasm-simd. I setup a space for the small sized model. There's a big step up in accuracy going to the medium size though:
https://huggingface.co/spaces/futo-org/asr4all-client-side
That's fascinating. Are these for your keyboard?
You may have gone too fast honestly. Something like parakeet runs fast enough and seems to have much lower wer
Yep exactly! Streaming models have to run faster, because they're more compute heavy. Some compute from every batch is wasted when the next set of frames come in. Streaming models also have higher WER than fully offline models.
Our models are more comparable to nemotron-3.5-asr-streaming in that regard and I think the medium/large sizes do perform quite favorably. For offline models, I don't think it's worthwhile for us to try and compete with parakeet v2/3. Those are already really good, and we're in the process of adding those to FUTO Voice.
These models are filling some gaps we see for the kinds of things we tend to work on. But broadly they can slot into a lot of places where there isn't accelerated compute (Raspberry pi 4, laptops, local client side transcription, etc).
Parakeet. For my use it seems about equal to Whisper in WER, but is >10x faster.
2nding parakeet, had great results with a japanese-tuned variant, beat the pants off whisper-kotoba
I'm looking for a local model that can do diarization, have any suggestions for this?
I have https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize open in a tab to try out
A leader board on HF: https://huggingface.co/spaces/hf-audio/open_asr_leaderboard
Our "plus" models have speaker segmentation and speaker verification (embedding) that you can use for speech to text and diarization from one model. There's an example script in the model repo that shows how to do it.
https://huggingface.co/collections/futo-org/asr4all
If you're looking for a single stage "end-to-end" model just for diarization only, Nvidia released one recently: https://huggingface.co/nvidia/Nemotron-3-Diarization
I have been using different Parakeet models for STT over the past months and been pretty impressed with the local performance.