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things I hate: "Click me to try now!" banners that lead to a warning screen that says "Oh, only paying members, whoops!" So, you don't mean 'try this out', you mean 'buy this product'. Let's not act like it's a free sampler. I can't comment on the model : i'm not giving them money.
You can try it on HF: https://huggingface.co/spaces/mistralai/Voxtral-Mini-Realtim...
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This demo is really impressive: https://huggingface.co/spaces/mistralai/Voxtral-Mini-Realtim... Don't be confused if it says "no microphone", the moment you click the record button it will request browser permission and then start working. I spoke fast and dropped in some jargon and it got it all right - I said this and it transcribed it exactly right, WebAssembly spelling included: > Can you tell me about RSS and At…
I tried speaking in 2 languages at once, and it picked it up correctly. Truly impressive for real-time.
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> At approximately 4% word error rate on FLEURS and $0.003/min Amazons transcription service is $0.024 per minute, pretty big difference https://aws.amazon.com/transcribe/pricing/
Is it 0.003 per minute of audio uploaded, or "compute minute"? For example fal.ai has a Whisper API endpoint priced at "$0.00125 per compute second" which (at 10-25x realtime) is EXTREMELY cheaper than all the competitors.
Native diarization, this looks exciting. edit: or not, no diarization in real-time. https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-26... ~9GB model.
The diarization is on Voxtral Mini Transcribe V2, not Voxtral Mini 4B.
Italian represents, I believe, the most phonetically advanced human language. It has the right compromise among information density, understandability, and ability to speech much faster to compensate the redundancy. It's like if it had error correction built-in. Note that it's not just that it has the lower error rate, but is also underrepresented in most datasets.
Earlier quoted context omitted.
WER is slightly misleading, but Whisper Large v3 WER is classically around 10%, I think, and 12% with Turbo. The thing that makes it particularly misleading is that models that do transcription to lowercase and then use inverse text normalization to restore structure and grammar end up making a very different class of mistakes than Whisper, which goes directly to final form text including punctuation and quotes and t…
On the topic of things being misleading, GPT-4o transcriber is a very _different_ transcriber to Whisper. I would say not better or worse, despite characterizations such. So it is a little difficult to compare on just the numbers. There's a reason that quite a lot of good transcribers still use V2, not V3.