Now if only I could get it to play nice with my Chromecast… But I’m sure that’s on Google.
accessibility is honestly the first good use of ai. i hope they can find a way to make them better than youtube’s automatic captions though.
The app Be My Eyes pivoted from crowd sourced assistance to the blind, to using AI and it’s just fantastic. AI is truly helping lots of people in certain applications.
There are other good uses of AI. Medicine. Genetics. Research, even into humanities like history.
The problem always was the grifters who insist calling any program more complicated than adding two numbers AI in the first place, trying to shove random technologies into random products just to further their cancerous sales shell game.
The problem is mostly CEOs and salespeople thinking they are software engineers and scientists.
Spoiler: they won’t
Spoiler, they will! I use FUTO keyboard on android, it’s speech to text uses an ai model and it is amazing how great it works. The model it uses is absolutely tiny compared to what a PC could run so VLC’s implementation will likely be even better.
I know Jeff Geerling on Youtube uses OpenAIs Whisper to generate captions for his videos instead of relying on Youtube’s. Apparently they are much better than Youtube’s being nearly flawless. I would have a guess that Google wants to minimize the compute that they use when processing videos to save money.
I am still waiting for seek previews
Perhaps we could also get a built-in AI tool for automatic subtitle synchronization?
I’ve been waiting for
thisbreak-free playback for a long time. Just play Dark Side of the Moon without breaks in between tracks. Surely a single thread could look ahead and see the next track doesn’t need any different codecs launched, it’s technically identical to the current track, there’s no need to have a break. /rantI was part of that!
Thank you for your service
I know people are gonna freak out about the AI part in this.
But as a person with hearing difficulties this would be revolutionary. So much shit I usually just can’t watch because open subtitles doesn’t have any subtitles for it.
The most important part is that it’s a local
LLMmodel running on your machine. The problem with AI is less about LLMs themselves, and more about their control and application by unethical companies and governments in a world driven by profit and power. And it’s none of those things, it’s just some open source code running on your device. So that’s cool and good.Also the incessant ammounts of power/energy that they consume.
Running an llm llocally takes less power than playing a video game.
The training of the models themselves also takes a lot of power usage.
Source?
Any paper about any neural network.
Using a model to get one output is just a series of multiplications (not even that, we use vector multiplication but yeah), it’s less than or equal to rendering ONE frame in 4k games.
I don’t have a source for that, but the most that any locally-run program can cost in terms of power is basically the sum of a few things: maxed-out gpu usage, maxed-out cpu usage, maxed-out disk access. GPU is by far the most power-consuming of these things, and modern video games make essentially the most possible use of the GPU that they can get away with.
Running an LLM locally can at most max out usage of the GPU, putting it in the same ballpark as a video game. Typical usage of an LLM is to run it for a few seconds and then submit another query, so it’s not running 100% of the time during typical usage, unlike a video game (where it remains open and active the whole time, GPU usage dips only when you’re in a menu for instance.)
Data centers drain lots of power by running a very large number of machines at the same time.
From what I know, local LLMs take minutes to process a single prompt, not seconds, but I guess that depends on the use case.
But also games, dunno about maxing GPU in most games. I maxed mine for crypto mining, and that was power hungry. So I would put LLMs closer to crypto than games.
Not to mention games will entertain you way more for the same time.
Curious how resource intensive AI subtitle generation will be. Probably fine on some setups.
Trying to use madVR (tweaker’s video postprocessing) in the summer in my small office with an RTX 3090 was turning my office into a sauna. Next time I buy a video card it’ll be a lower tier deliberately to avoid the higher power draw lol.
Yeah, transcription is one of the only good uses for LLMs imo. Of course they can still produce nonsense, but bad subtitles are better none at all.
Indeed, YouTube had auto generated subtitles for a while now and they are far from perfect, yet I still find it useful.
This is great timing considering the recent Open Subtitles fiasco.
Huh?
Open Subtitles now only allows 5 downloads per 24 hours per IP. You have to pay for more.
Kind of annoying when searching for the exact sub file for the movie file you have.
Especially when half those subtitle files appear to be AI generated anyway, or have weird Asian gambling ads shoved in.
Glad MKV seems to be the standard now, and include subs from the original sources.
Use https://opensubtitles.com/, rather than https://opensubtitles.org/.
I don’t mind the idea, but I would be curious where the training data comes from. You can’t just train them off of the user’s (unsubtitled) videos, because you need subtitles to know if the output is right or wrong. I checked their twitter post, but it didn’t seem to help.
subtitles aren’t a unique dataset it’s just audio to text
They may have to give it some special training to be able to understand audio mixed by the Chris Nolan school of wtf are they saying.
No, if you have a center track you can just use that. Volume isn’t a problem for a computer listening to it since they don’t use the physical speakers.
I hope they’re using Open Subtitles, or one of the many academic Speech To Text datasets that exist.
Et tu, Brute?
VLC automatic subtitles generation and translation based on local and open source AI models running on your machine working offline, and supporting numerous languages!
Oh, so it’s basically like YouTube’s auto-generatedd subtitles. Never mind.
Hopefully better than YouTube’s, those are often pretty bad, especially for non-English videos.
Youtube’s removal of community captions was the first time I really started to hate youtube’s management, they removed an accessibility feature for no good reason, making my experience with it significantly worse. I still haven’t found a replacement for it (at least, one that actually works)
and if you are forced to use the auto-generated ones remember no [__] swearing either! as we all know disabled people are small children who need to be coddled!
Same here. It kick-started my hatred of YouTube, and they continued to make poor decision after poor decision.
They are terrible.
They’re awful for English videos too, IMO. Anyone with any kind of accent(read literally anyone except those with similar accents to the team that developed the auto-caption) it makes egregious errors, it’s exceptionally bad with Australian, New Zealand, English, Irish, Scottish, Southern US, and North Eastern US. I’m my experience “using” it i find it nigh unusable.
Try it with videos featuring Kevin Bridges, Frankie Boyle, or Johnny Vegas
I’ve been working on something similar-ish on and off.
There are three (good) solutions involving open-source models that I came across:
- KenLM/STT
- DeepSpeech
- Vosk
Vosk has the best models. But they are large. You can’t use the gigaspeech model for example (which is useful even with non-US english) to live-generate subs on many devices, because of the memory requirements. So my guess would be, whatever VLC will provide will probably suck to an extent, because it will have to be fast/lightweight enough.
What also sets vosk-api apart is that you can ask it to provide multiple alternatives (10 is usually used).
One core idea in my tool is to combine all alternatives into one text. So suppose the model predicts text to be either “… still he …” or “… silly …”. My tool can give you “… (still he|silly) …” instead of 50/50 chancing it.
I love that approach you’re taking! So many times, even in shows with official subs, they’re wrong because of homonyms and I’d really appreciate a hedged transcript.
@neme @TheImpressiveX [Music]
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That would depend on the LLM and the data used to train it.
IIRC you can’t use LLMs for this.
I didn’t read the article, but I would have assumed that the AI was using predictive text to guess at the next word. Speech recognition is already pretty good, but it often misses contextual cues that an LLM would be good at spotting. Like, “The famous French impressionist painter mayonnaise…”
Probably something like https://github.com/openai/whisper which isn’t an LLM, but is a different type of model dedicated to speech recognition
That makes sense.
In my experiments, local Whisper models I can run locally are comparable to YouTube’s — which is to say, not production-quality but certainly better then nothing.
I’ve also had some success cleaning up the output with a modest LLM. I suspect the VLC folks could do a good job with this, though I’m put off by the mention of cloud services. Depends on how they implement it.
Since VLC runs on just about everything, I’d imagine that the cloud service will be best for the many devices that just don’t have the horsepower to run an LLM locally.
True. I guess they will require you to enter your own OpenAI/Anthropic/whatever API token, because there’s no way they can afford to do that centrally. Hopefully you can point it to whatever server you like (such as a selfhosted ollama or similar).
It’s not just computing power - you don’t always want your device burning massive amounts of battery.
Yeah I’ve used local whisper and LLMs to automatically summarize Youtube-videos and podcasts to text with good results.
Cool, thanks for sharing!
I see you prompt it to “Make sure to only use knowledge found in the following audio transcription”. Have you found that sufficient to eliminate hallucination and going off track?
Yes I have been impressed with the quality of summaries keeping to the content. I have seen, rare, attribution errors though, where who said what got mixed up in unfortunate ways.
All hail the peak humanity levels of VLC devs.
FOSS FTW
And yet they still can’t seek backwards
Iirc this is because of how they’ve optimized the file reading process; it genuinely might be more work to add efficient frame-by-frame backwards seeking than this AI subtitle feature.
That said, jfc please just add backwards seeking. It is so painful to use VLC for reviewing footage. I don’t care how “inefficient” it is, my computer can handle any operation on a 100mb file.
If you have time to read the issue thread about it, it’s infuriating. There are multiple viable suggestions that are dismissed because they don’t work in certain edge cases where it would be impossible for any method at all to work, and which they could simply fail gracefully for.
That kind of attitude in development drives me absolutely insane. See also: support for DHCPv6 in Android. There’s a thread that has been raging for I think over a decade now
I now know more about Android IPv6 than ever before
Same for simply allowing to pause on click… Luckily extension exists but it’s sad that you need one.
You can easily write a video reader using openCV that would be able to read backward using cache
I know AI has some PR issues at the moment but I can’t see how this could possibly be interpreted as a net negative here.
In most cases, people will go for (manually) written subtitles rather than autogenerated ones, so the use case here would most often be in cases where there isn’t a better, human-created subbing available.
I just can’t see AI / autogenerated subtitles of any kind taking jobs from humans because they will always be worse/less accurate in some way.
Autogenerated subtitles are pretty awesome for subtitle editors I’d imagine.
even if they get the words wrong, but the timestamps right, it’d still save a lot of time
We started doing subtitling near the end of my time as an editor and I had to create the initial English ones (god forbid we give the translation company another couple hundred bucks to do it) and yeah…the timestamps are the hardest part.
I can type at 120 wpm but that’s not very helpful when you can only write a sentence at a time
and yeah…the timestamps are the hardest part.
So, if you can tell us, how did the process work?
Do you run the video and type the subtitles in some program at the same time, and it keeps score of the time at which you typed, which you manually adjust for best timing of the subtitle appearance afterwards? Or did you manually note down timestamps from the start?
We were an Adobe house so I did it inside of premiere. I can’t remember if it was built in or a plugin but there was two ways depending on if the shoot was scripted or ad-libbed. If it was scripted, I’d import a txt file into premiere and break it apart as needed with markers on the timeline. It was tedious but by far better than the alternative - manually typing it at each marker.
I initially tried making the markers all first but I kept running into issues with the timing. Subtitles have both a beginning and an end timestamp and I often wouldn’t leave enough room to be able to actually read it.
This was over a decade ago, I’ll bet it’s gotten easier. I know Premiere has a transcription feature that’s pretty good
That’s interesting thank you.
I only did it once for a school project involving translation of a film scene (also over a decade ago) but we just manually wrote an SRT file, that was miserable 😄
Oh man that sounds awful… If I had to write it manually, I think I’d use Excel
Is there a cross section of people who do live subtitles and people that have experience being a stenographer? Asking as I would imagine that using a stenographic keyboard would allow them to keep up with what’s being said.
Probably! Though live captions aren’t quite the same as they’re not timestamped
I can’t see how this could possibly be interpreted as a net negative here
Not judging this as bad or good, but for sure if it’s offline generated it will bloat the size of the program.
Yeah this is exactly what we should want from AI. Filling in an immediate need, but also recognizing it won’t be as good as a pro translation.
I believe it’s limited in scope to speech recognition at this stage but hey ho
Solving problems related to accessibility is a worthy goal.
It’s nice to see a good application of ai. I hope my low end stuff will be able to run it.
