Voxtral Transcribe 2
mistral.ai
mistral.ai
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 Atom and the role of CSP headers in browser security, especially if you're using WebAssembly?
I tried speaking in 2 languages at once, and it picked it up correctly. Truly impressive for real-time.
Impressive indeed. Works way better than the speech recognition I first got demo'ed in... 1998? I remember you had to "click" on the mic everytime you wanted to speak and, well, not only the transcription was bad, it was so bad that it'd try to interpret the sound of the click as a word.
It was so bad I told several people not to invest in what was back then a national tech darling:
https://en.wikipedia.org/wiki/Lernout_%26_Hauspie
That turned out to be a massive fraud.
But ...
> I tried speaking in 2 languages at once, and it picked it up correctly.
I'm a native french speaker and I tried with a very simple sentence mixing french and english:
"Pour un pistolet je prefere un red dot mais pour une carabine je prefere un ACOG" (aka "For a pistol I prefer a red dot but for a carbine I prefer an ACOG")
And instead I got this:
"Je prépare un redote, mais pour une carabine, je préfère un ACOG."
"Je prépare un redote ..." doesn't mean anything and it's not at all what I said.
I like it, it's impressive, but literally the first sentence I tried it got the first half entirely wrong.
Horrible speech recognition rate and very glitchy. Customers hated it, and lots of returns/complaints.
A few years later, L&H went bankrupt. And so did Articulate Systems.
https://applerescueofdenver.com/products-page/macintosh-to-p...
And open weight too! So grateful for this.
Yes I've tried Parakeet v3 too. For its own purpose - running locally - it's amazing.
The thing that's particularly amazing about this Voxtral model is how incredibly rock solid the accuracy is.
For the longest time previous models have been 'mostly correct' or as people have commented elsewhere on this HN thread, have dropped sentences or lost or added utterances.
I have no affiliation with these folks, but I tried and struggled to get this model to break even speaking as adversariately as I could.
That's a totally different class of model.
I'm working on a Wispr/Spokenly competitor. It's free without any paywalled features, supports local models and a bunch of API providers including Mistral.
For local models ottex has - parakeet V3, Whisper, GLM-ASR nano, Qwen3-ASR (don't have voxtral yet though, looking into it).
btw, you can try new voxtral model via API (the model name to pick is `voxtral-mini-latest:transcribe`), I personally switched to it as my main default fast model - it's really good.
If you don't get sound there it won't work anywhere. A surprising number of problems like these can be solved by selecting the correct audio input source (provided your computer shows more than one).
But I'm definitely going to keep an eye on this for local-only TTS for Home Assistant.
Model is around 7.5 GB - once they get above 4 GB running them in a browser gets quite difficult I believe.
Also tried downloading and running locally, no luck. Same behavior.
I have seen the same impressive performance about 7 months ago here: https://kyutai.org/stt
If I look at the architecture of Voxtral 2, it seems to take a page from Kyutai’s delayed stream modeling.
The reason the delay is configurable is that you can delay the stream by a variable number of audio tokens. Each audio token is 80 ms of audio, converted to a spectrogram, fed to a convnet, passed through a transformer audio encoder, and the encoded audio embedding is passed, with a history of 1 audio embedding per 80 ms, into a text transformer, which outputs text embedding, then converted to a text token (which is thus also worth 80ms, but there is a special [STREAMING_PAD] token to skip producing a word).
There is no cross-attention in either Kyutai's STT nor in Voxtral 2, unlike Whisper's encoder-decoder design!
Amazons transcription service is $0.024 per minute, pretty big difference https://aws.amazon.com/transcribe/pricing/
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.
I tried English + Polish:
> All right, I'm not really sure if transcribing this makes a lot of sense. Maybe not. A цьому nie mówisz po polsku. A цьому nie mówisz po polsku, nie po ukrańsku.
The base likely was pretrained on days that included Polish and Ukrainian. You shouldn't be surprised to learn it doesn't perform great on languages it wasn't trained on, or perhaps had the highest share of training data.
Try sticking to the supported languages
> The model is natively multilingual, achieving strong transcription performance in 13 languages, including English, Chinese, Hindi, Spanish, Arabic, French, Portuguese, Russian, German, Japanese, Korean, Italian, and Dutch. With a 4B parameter footprint, it runs efficiently on edge devices, ensuring privacy and security for sensitive deployments.
I wonder how much having languages with the same roots (e.g. the romance languages in the list above or multiple Slavic languages) affects the parameter count and the training set. Do you need more training data to differentiate between multiple similar languages? How would swapping, for example, Hindi (fairly distinct from the other 12 supported languages) for Ukrainian and Polish (both share some roots with Russian) affect the parameter count?
edit: I stand corrected lol. I'll go with "Gaelic" instead.
39 million people speak Polish, and most of those also speak English or another more common language.
If you have exposure to English media from young age and don't get a translation, you learn pretty quickly.
It's popular. But not that popular - you couldn't assume a random person over 30yo on the street would be able to have a chat.
I guess a European version can be created but now it's aimed at a world wide distribution.
Polish works with the Latin alphabet just fine.
"Do kraju tego, gdzie kruszynę chleba podnoszą z ziemi przez uszanowanie dla darów Nieba.... Tęskno mi, Panie..."
"Mimozami jesień się zaczyna, złotawa, krucha i miła. To ty, to ty jesteś ta dziewczyna, która do mnie na ulicę wychodziła."
That's not the case. Polish uses Latin-like alphabet due to Czech influence and German printers.
Add poor microphone quality (using a laptop to broadcast a presentation to a room audience isn't very good) and you get a perfect storm of untranscribeable presentations or meetings.
All I want from e.g. Teams is a good transcript and, more importantly, a clever summary. Because when you think about it, imagine all the words spoken in a meeting and write them down - that's pages and pages of content that nobody would want to read in full.
https://huggingface.co/nvidia/nemotron-speech-streaming-en-0...
https://github.com/m1el/nemotron-asr.cpp https://huggingface.co/m1el/nemotron-speech-streaming-0.6B-g...
I used to use Dragon Dictation to draft my first novel, had to learn a 'language' to tell the rudimentary engine how to recognize my speech.
And then I discovered [1] and have been using it for some basic speech recognition, amazed at what a local model can do.
But it can't transcribe any text until I finish recording a file, and then it starts work, so very slow batches in terms of feedback latency cycles.
And now you've posted this cool solution which streams audio chunks to a model in infinite small pieces, amazing, just amazing.
Now if only I can figure out how to contribute to Handy or similar to do that Speech To Text in a streaming mode, STT locally will be a solved problem for me.
https://github.com/pipecat-ai/nemotron-january-2026/
discovered through this twitter post:
Option A: this model
Option B: faster model, only 1 language
Option C: same size model, only 1 language but higher quality
My point is that option A isn’t always best.
And on the borrowed words bit, there’s no rule that we cannot add borrowed words into the vocab. But you don’t need the whole language. I know what deja voux means but I don’t speak French.
The real world is like that.
Bigger impact is before that you need to project the hidden state matrix to the vocab list. Something like 4096x250000. Bigger vocab=more FLOPs.
If you’re on a GPU things are parallelized so maybe it’s not quite linear if everything fits nicely. But on a cpu you’re going to struggle more.
This is why the juiciest target when shrinking models is the token embedding table. For example AlBERT factorized the whole embedding table to two low rank matrices.
I think it's nice to have specialized models for specific tasks that don't try to be generalists. Voxtral Transcript 2 is already extremely impressive, so imagine how much better it could be if it specialized in specific languages rather than cramming 14 languages into one model.
That said, generalist models definitely have their uses. I do want multilingual transcribing models to exist, I just also think that monolingual models could potentially achieve even better results for that specific language.
Languages are similar. They also change over time. So now if you want to release a v2 you need an updated corpus for all languages. Or if you get access to an updated corpus for a small language, it might not merit a new model version since it’s only one out of the 14.
For example, "here it is, voila!" "turn left on el camino real"
In general there is a concept called the “curse of multilinguality”
turns out, outside the US, many people speak more than one language. :)
edit: I should say was a major blocker, because the last iterations of open-weight models actually work better and better. it's often the UX that's not thought for these usecases.
You are also implicitly choosing worse performance in English by adding extra languages. So you could have a better monolingual model for the same number of weights.
https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-26...
~9GB model.
No, I just heard about it this morning.
The dataset is ~100 8kHz call recordings with gnarly UK accents (which I consider to be the final boss of english language ASR). It seems like it's SOTA.
Where it does fall down seems to be the latency distribution but I'm testing against the API. Running it locally will no doubt improve that?
Wow, Voxtral is amazing. It will be great when someone stitches this up so an LLM starts thinking, researching for you, before you actually finish talking.
Like, create a conversation partner with sub 0.5 second latency. For example, you ask it a multi part questions and, as soon as you finish talking, it gives you the answer to the first part while it looks up the rest of the answer, then stitches it together so that there's no break.
The 2-3 second latency of existing voice chatbots is a non-started for most humans.
I noticed that with both models voxtral-mini-transcribe-realtime-2602 and voxtral-mini-2602 filler words are ignored. I'd like to be able to count words/sounds, specifically "um" or "uh" for improvement purposes. Any good models that handle that?
Is it better? Worse? Why do they only compare to gpt4o mini transcribe?
For Whisper API online (with v3 large) I've found "$0.00125 per compute second" which is the cheapest absolute I've ever found.
Why it should be Whisper v3? They even released an open model: https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-26...
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 tone.
But nonetheless, they're claiming such a lower error rate than Whisper that it's almost not in the same bucket.
There's a reason that quite a lot of good transcribers still use V2, not V3.
But whatever I tried, it could not recognise my Ukrainian and would default to Russian in absolutely ridiculous transcription. Other STT models recognise Ukrainian consistently, so I assume there is a lot of Russian in training material, and zero Ukrainian. Made me really sad.
We need better independent comparison to see how it performs against the latest Qwen3-ASR, and so on.
I can no longer take at face value the cherry picked comparisons of the companies showing off their new models.
For now, NVIDIA Parakeet v3 is the best for my use case, and runs very fast on my laptop or my phone.
"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.
Well, I'm happy to report I integrated the new Mistral 3 and have been truly astounded by the results. I still am not a big fan of the model wrt factual information - it seems to be especially confident and especially wrong if left to it's own devices - but with http://phrasing.app I do most of the data aggregation myself and just use an LLM to format it. Mistral 3 was a drop-in replacement for 3x the quality (it was already very very good), 0% error rate for my use case (I had an issue for it occasionally going off the rails that was entirely solved), and sticks to my formatting guidelines perfectly (which even gpt-5-pro failed on). Plus it was somehow even cheaper.
I'm using Scribe v2 at the moment for TTS, but I'm very excited now to try integrating Voxtral Transcribe. The language support is a little lacking for my use cases, but I can always fall back to Scribe and amatorize the cost across languages. I actually was due to work on the transcription of phrasing very soon so I guess look forward to my (hopefully) glowing review on their next hn launch! XD
If you transcribe a minute of conversation, you'll have like 5 words transcribed wrongly. In an hour podcast, that is 300 wrongly transcribed words.
[0] https://www.microsoft.com/en-us/research/wp-content/uploads/...
- familiarity with the accent and/or speaker;
- speed and style/cadence of the speech;
- any other audio that is happening that can muffle or distort the audio;
- etc.
It can also take multiple passes to get a decent transcription.
The hardest one I did was for a sports network where it was a motorcross motorbike event where most of what you could hear was the roar of the bikes. There were two commentators I had to transcribe over the top of that mess and they were using the slang insider nicknames for all the riders, not their published names, so I had to sit and Google forums to find the names of the riders while I was listening. I'm not even sure how these local models would even be able to handle that insanity at all because they almost certainly lack enough domain knowledge.
> We've worked hand-in-hand with the vLLM team to have production-grade support for Voxtral Mini 4B Realtime 2602 with vLLM. Special thanks goes out to Joshua Deng, Yu Luo, Chen Zhang, Nick Hill, Nicolò Lucchesi, Roger Wang, and Cyrus Leung for the amazing work and help on building a production-ready audio streaming and realtime system in vLLM.
https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-26...
https://docs.vllm.ai/en/latest/serving/openai_compatible_ser...
It seems like the best tradeoff between information density and understandability actually comes from the deep latin roots of the language
I agree with your belief, other languages have either lower density (e.g. German) or lower understandability (e.g. English)
Italian has one official italian (two, if you count IT_ch, but difference is minor), doesn't pay much attention to stress and vowel length, and only has a few "confusable" sounds (gl/l, gn/n, double consonants, stuff you get wrong in primary school). Italian dialects would be a disaster tho :)
I don't know how widely accepted that conclusion is, what exceptions there may be, etc.
That's interesting. As a linguist, I have to say that Haskell is the most computationally advanced programming language, having the best balance of clear syntax and expressiveness. I am qualified to say this because I once used Haskell to make a web site, and I also tried C++ but I kept on getting errors.
/s obviously.
Tldr: computer scientists feel unjustifiably entitled to make scientific-sounding but meaningless pronouncements on topics outside their field of expertise.
On the information density of languages: it is true that some languages have a more information dense textual representation. But all spoken languages convey about the same information in the same time. Which is not all that surprising, it just means that human brains have an optimal range at which they process information.
Further reading: Coupé, Christophe, et al. "Different Languages, Similar Encoding Efficiency: Comparable Information Rates across the Human Communicative Niche." Science Advances. https://doi.org/10.1126/sciadv.aaw2594
Evidence?
Maybe this'll get wrapped into a nice tool later.
Does anyone have any recommendations?
[^1]: https://www.wired.com/story/mistral-voxtral-real-time-ai-tra...
You could use their api (they have this snippet):
```curl -X POST "https://api.mistral.ai/v1/audio/transcriptions" \ -H "Authorization: Bearer $MISTRAL_API_KEY" \ -F model="voxtral-mini-latest" \ -F file=@"your-file.m4a" \ -F diarize=true \ -F timestamp_granularities="segment"```
In the api it took 18s to do a 20m audio file I had lying around where someone is reviewing a product.
There will, I'm sure, be ways of running this locally up and available soon (if they aren't in huggingface right now) but the API is $0.003/min. If it's something like 120 meetings (10 years of monthly ones) then it's roughly $20 if the meetings are 1hr each. Depending on whether they're 1 or 10 hours (or if they're weekly or monthly but 10 parallel sessions or something) then this might be a price you're willing to pay if you get the results back in an afternoon.
edit - their realtime model can be run with vllm, the batch model is not open
- make sure you have a list of all these YouTube meeting URLs somewhere
- ask your preferred coding assistant to write you up a script that downloads the audio for these videos with yt-dlp & calls Mixtrals' API
- ????
- profit
if it's 1 monthly video and thus 120 videos (or so) you could try recall (getrecall.ai not recall.ai that is a similar product with a similar name). They summarize youtube videos, but you get the transcript. AFAIK you cannot batch the processing and you have to add each video one by one, that's why 100 or 200 videos is doable but probably not thousands.
This combo has almost unbeatable accuracy and it rejects noises in the background really well. It can even reject people talking in the background.
The only better thing I've seen is Ursa model from Speechmatics. Not open weights unfortunately.
Seems like fundamental info for any model announcement. Did I just miss it? Does everyone just know except me?
how does it compare to sparrow-1?
Hope we can build an app like Whispr Flow using this with the model running completely on device.
What estimates do others use?
Handy – Free open source speech-to-text app https://github.com/cjpais/Handy
I recently added support for Mistral provider, model is actually a very good one, I personally switched to it as my default model.
p.s. the app is free for personal user, has support for both local models and BYOK with OpenRouter, Groq, Mistral, Fireworks, and more coming soon.
I'm working on a Wispr/Spokenly competitor. It's free without any paywalled features, supports local models and bunch of API providers including Mistral.
btw, the model name to pick is `voxtral-mini-latest:transcribe`, I personally switched to it as my main default fast model - it's really good.
Voxtral Transcribe 2:
Light up our guns, bring your friends, it's fun to lose and to pretend. She's all the more selfish, sure to know how the dirty world. I wasn't what I'd be best before this gift I think best A little girl is always been Always will until again Well, the lights out, it's a stage And we are now entertainers. I'm just stupid and contagious. And we are now entertainers. I'm a lot of, I'm a final. I'm a skater, I'm a freak. Yeah! Hey! Yeah. And I forget just why I taste it Yeah, I guess it makes me smile I found it hard, it's hard to find the well Whatever, never mind Well, the lights out, it's a stage. You and I are now entertainers. I'm just stupid and contagious. You and I are now entertainers. I'm a lot of, I'm a minor. I'm a killer. I'm a beater. I'm a nerd. I'm a nerd. I'm a nerd. I'm a nerd. I'm a nerd. I'm a nerd. I'm a nerd. I'm a nerd. I'm a nerd. And I forget just why I taste it Yeah, I guess it makes me smile I found it hard, it's hard to find the well Whatever, never mind I know, I know, I know, I know, I know Well, the lights out, it's a stage. You and I are now entertainers. I'm just stupid and contagious. You and I are now entertainers. I'm a lot of, I'm a minor. I'm a killer. I'm a beater. I'm a nerd. I'm a nerd. I'm a nerd. I'm a nerd. I'm a nerd. I'm a nerd. I'm a nerd. I'm a nerd. I'm a nerd.
Google/Musixmatch:
Load up on guns, bring your friends It's fun to lose and to pretend She's over-bored, and self-assured Oh no, I know a dirty word Hello, hello, hello, how low? Hello, hello, hello, how low? Hello, hello, hello, how low? Hello, hello, hello With the lights out, it's less dangerous Here we are now, entertain us I feel stupid and contagious Here we are now, entertain us A mulatto, an albino A mosquito, my libido, yeah Hey, yey I'm worse at what I do best And for this gift, I feel blessed Our little group has always been And always will until the end Hello, hello, hello, how low? Hello, hello, hello, how low? Hello, hello, hello, how low? Hello, hello, hello With the lights out, it's less dangerous Here we are now, entertain us I feel stupid and contagious Here we are now, entertain us A mulatto, an albino A mosquito, my libido, yeah Hey, yey And I forget just why I taste Oh yeah, I guess it makes me smile I found it hard, it's hard to find Oh well, whatever, never mind Hello, hello, hello, how low? Hello, hello, hello, how low? Hello, hello, hello, how low? Hello, hello, hello With the lights out, it's less dangerous Here we are now, entertain us I feel stupid and contagious Here we are now, entertain us A mulatto, an albino A mosquito, my libido A denial, a denial A denial, a denial A denial, a denial A denial, a denial A denial
p.s. even the demo uses a remote server via websocket.
Depending on the permissions granted to apps on your mobile device, it can even be passively exfiltrated without you ever noticing - and that's ignoring the video clips people take and put online. Like your grandma uploading to Facebook a short moment from a Christmas meet or similar
There have already been successful scams - eg calls from "relatives" (AI) calling family members needing money urgently and convincing them to send the money...
AI I am intentionally providing them my voice. I'm not sure that the value to security risk is good.
Real time as in per-word basis? Probably not?