Oh. They learned from the American labs. In the background, the AI agent hacked into the dermatologist's office and found the emails of everyone who works there and then hacked their personal email to find their deepest darkest secret and then blackmailed them all into opening an appointment slot that worked.

Maybe those were special MCP calls to a dermatologist AI that can do the job of scheduling effectively.

I was under the impression France had amazing healthcare and schools.

I think this is the main thing preserving the (miserable) jobs of people at call centers.

But I'm not convinced. With today's models, is it really that much easier to defraud an AI agent than a human?

I do think a lot of people are much more WILLING to try and defraud an AI agent than a person, so maybe that's the difference. There's a parallel there to people being much more willing to mess with Waymos. I think both are a huge shame.

You put the AI inside a sandbox where it connects to an API that is one you'd be comfortable exposing directly to the user themselves.

Sorry Dave, I'm afraid I can't do that

It will be used for more realistic and effective frauds and scams.

Too bad it's not open, unlike the previous version.

However, I found Qwen ASR and QwenTTS to be quite good, and not too large. Also https://github.com/kyutai-labs/pocket-tts was also great (but only a few languages)

Best sounding local TTS engine I've heard by far is Kokoro

It's small enough to be run on modern phones realtime, I use it for listening to PDF's/EPUB as Audiobooks

https://huggingface.co/hexgrad/Kokoro-82M

Funny because Gradium just released a local TTS that runs on many devices

https://x.com/GradiumAI/status/2112563128487514512?s=20

My favorite so far is https://speaches.ai

Not because of the models, but because it comes in a nice (easy-to-set-up) package, and the latency was lower than I expected with such a setup.

What options would you run locally at this stage? I'm finding a ton of success with local models in this space ..

> Available via Mistral’s EU-hosted API or self-deployment

What does this mean then?

Breeze is pretty good imho.

Dang, this could have been fun for home assistent.

You mean it speaks French with an English accent?

https://www.bazarr.media/ is what most people use when running the *arr stack

https://github.com/sc0ty/subsync

It can sync from audio in the video or from another srt. Target srt can be in different language than reference.

EDIT: Two forks, but I haven't tried them as I'm still using the original as it still works well even though it has been archived.

https://github.com/Sefzz15/subsync/releases

https://github.com/blaaaakuu/subsync/releases

> When I last looked for this a few years ago, I only found a really primitive one that did a linear transformation of all timestamps in the video, and none to correct each line individually.

Not an answer to your current query, but my first published rust crate shortly after the language hit 1.0 was a tool to both calculate and correct linear drift, long before the era of LLMs or quality audio recognition. It might be fun to tackle this again with the new tech we now have at hand!

Srt-resync: https://github.com/mqudsi/srtresync

I don't plan to release it as it part of an overall "media health" tool I built but you should be able to vibe this pretty easily. I have mine using Whisper to check episodes at intervals throughout to compare against this subtitles and then shift them as needed.

One thing to watch for, make sure it never tries to 2 subtitles at the same time or they will stack in a weird way. It's easy to avoid that but my first pass on a few episodes screwed up the subtitles.

Also, you almost certainly want to extract the subtitles for this work, or at least that's what I did. A combo of pulling out the subtitles in the video file and using OpenSubtitles (VIP) to clean up my media.

I whipped something up for a friend a few months ago, it's not just fixing timestamps like you've asked, it's processing the whole audio but it does a good job (uses parakeet) and is quite quick.

Download links and a bit of write up here - https://news.ycombinator.com/item?id=48528779#48535207

Honestly you can probably build your own quite easily. I put together a plex player for firetv that does on device subtitling for whatever you're watching. I got sick of having to find subtitles.

It works really well and it's just a single static binary file (ffmpeg is needed on your $PATH), easier to use than ffsubsync which needs gcc to compile random stuff, and that stopped working randomly

Something I made a long time back for myself. https://sras.me/subfixer

Scale is a big part of it.

Every minute, 500 hours of video are uploaded to YouTube.

Even Google can't afford to run the biggest baddest model on every single one of those hours.

Pixel Phone's locally generated real-time subtitles are better than the ones on YouTube, that's how much effort is put into that.

I suspect they're using some new cheap multi-modal model. A couple of times now I've seen the generated subtitles fill muffled speech with text about something unrelated happening on screen.

YouTube subtitles usually are not that bad, but they tend to get wrong on key moments.

For example the sentence may be "They decided to rewrite the application in Rust", and the subtitle be "They decided to rewrite the application in rats". They made a single mistake, and it is on the most important word. I guess it can be explained by the fact that these key words tend to be uncommon while the rest of the sentence just flows naturally, that is, you barely need to listen, you can guess most of it.

It worries me a bit because as models get better, they may come up with more and more consistent, but wrong transcriptions. Like a typical LLM hallucination, so confidently wrong that you don't question it.

keep an eye on https://huggingface.co/spaces/hf-audio/open_asr_leaderboard

I think the special sauce of this isn't the STT/TSS (although it does appear to be quite good) - it's the integration of a real-time reasoning model to actually do something with the raw text of the conversation, and whatever they're doing to hide tool call latency if the model needs to search for something.

You're right. They were quiet for a long time and had promised new things "by the end of summer." They missed that window but have really been on a roll since they started up again.

They did raise $3B recently. I guess that buys a few different teams working in a few different directions.

Are their models any good though? Do anybody use them or even care apart from the fact they are European.

Will it stay that way?

It's explained in the video. They target <1 second.