I just want to translate a movie language audio from one to another, whats the easiest way to do this at home?
Meta AI announces Massive Multilingual Speech code, models for 1000+ languages
121–130 of 232 posts
Re: Meta AI announces Massive Multilingual Speech code, models for 1000+ languages
#122Earlier quoted context omitted.
Wait why is renting a GPU in the cloud not a solution? You can even try multiple options and see which ones are capable enough for your use case. Look into some barebones cloud GPU services, for example Lambda Labs which is significantly cheaper than AWS/GCP but offers basically nothing besides the machine with a GPU. You could even try something like Vast in which people rent out their personal GPU machines for chea…
My annoyance was managing state. I’d have to spend hours installing tools, downloading data, updating code, then when I want to go to bed I have to package it up and store as much as I can on s3 before shutting off the $$ server.
aws s3 sync + image snapshot
Re: Meta AI announces Massive Multilingual Speech code, models for 1000+ languages
#123Earlier quoted context omitted.
Yep... you spend hours messing around with docker containers and debugging all the weird build errors. I am less familiar with storing data in a db (for ml hosting concerns), but I'd imagine it would add overhead (as opposed to accessing files on disk). You also have to deal with hosting a db and configuring the schema.
You "spend hours messing around" with everything you don't know or understand at first. One could say the same about writing the software itself. At its core Dockerfiles are just shell scripts with worse syntax, so it's not really that much more to learn. Once you get it done once, you don't have to screw around with it anymore, and you have it on any box you want in seconds. In either case you have to spend hours sc…
I applaud your experience, but honestly I agree with parent: knowledge acquisition for a side project may not be the best use of their time, especially if it significantly impedes actually launching/finishing a first iteration.
It's a similar situation for most apps/services/startup ideas: you don't necessarily need a planet scale solution in the beginning. Containers are great and solve lots of problems, but they are not a panacea and come with their own drawbacks. Anecdotally, I personally wanted to make a small local 3 node Kubernetes cluster at one time on my beefy hypervisor. By the time I learned the ins and outs of Kubernetes networking, I lost momentum. It also didn't end up giving me what I wanted out of it. Educational, sure, but in the end not useful to me.
Re: Meta AI announces Massive Multilingual Speech code, models for 1000+ languages
#124I would like to use stuff like this as a side-project. Buy a Nvidia Geforce GPU and stick it into my 24/7 server and play around with it in my free time, to see what can be done. The issue with all these AI models is that there's no information on which GPU is enough for which task. I'm absolutely clueless if a single RTX 4000 SFF with its 20GB VRAM and only 70W of max power usage will be a waste of money, or really…
You can finetune whisper, stable diffusion, and LLM up to about 15B parameters with 24GB VRAM. Which leads you to what hardware to get. Best bang for the $ right now is definitely a used 3090 at ~$700. If you want more than 24GB vram just rent the hardware as it will be cheaper. If you're not willing to drop $700 don't buy anything just rent. I have had decent luck with vast.ai
There is the world of used Nvidia Teslas, like the M40. Very cheap, but some assembly required.
Re: Meta AI announces Massive Multilingual Speech code, models for 1000+ languages
#125According to [1] on the accompanying blog post, this brings the Whisper 44.3 WER down to 18.7, although it’s unclear to me how much better this is at primarily English speech recognition. I’d love to see a full comparison of accuracy improvements as well as a proper writeup of how much more power it takes to run this in production or on mobile vs something like whisper.
[1]: https://scontent-sjc3-1.xx.fbcdn.net/v/t39.8562-6/346801894_...
Re: Meta AI announces Massive Multilingual Speech code, models for 1000+ languages
#126Earlier quoted context omitted.
Yep... you spend hours messing around with docker containers and debugging all the weird build errors. I am less familiar with storing data in a db (for ml hosting concerns), but I'd imagine it would add overhead (as opposed to accessing files on disk). You also have to deal with hosting a db and configuring the schema.
You "spend hours messing around" with everything you don't know or understand at first. One could say the same about writing the software itself. At its core Dockerfiles are just shell scripts with worse syntax, so it's not really that much more to learn. Once you get it done once, you don't have to screw around with it anymore, and you have it on any box you want in seconds. In either case you have to spend hours sc…
I'm having trouble imagining what data I would store in a database as opposed to a filesystem if my goal is to experiment with large models like Stable Diffusion.
Re: Meta AI announces Massive Multilingual Speech code, models for 1000+ languages
#127Earlier quoted context omitted.
It is called SignWriting for all you multiple naysayers. https://en.m.wikipedia.org/wiki/SignWriting
I stand corrected, thanks. There's a lot of info on the internet that says a written form of ASL doesn't exist, which is what I found when I Googled it. Looking into it, it seems very much at the experimental stage in terms of digital representation -- while Unicode symbols exist, they require being placed in 2D boxes (using a drawing tool like SVG). It seems like it's only in the past few years that there have been…
SignWriting has been documented at National Deafness section in California State University of Northridge South Library since 1968 and at Gallaudet University, Washington, D.C. since 1950s.
Re: Meta AI announces Massive Multilingual Speech code, models for 1000+ languages
#128Earlier quoted context omitted.
It is called SignWriting for all you multiple naysayers. https://en.m.wikipedia.org/wiki/SignWriting
Most hearing impaired people have never heard or don't care to use SignWriting. You are right about it's existence, for what it's worth.
right, a minor point, hearing-impaired is an ablist slang.
Instead, go by hard-of-hearing, people with hearing loss or simply, Deaf.
Also "deaf" is barely ok when use alone for the above generalized replacement however "deaf" often poorly is refers to most severe of hearing-loss as determined by differing standards of hearing loss, but capitalized "D"eaf is a direct reference to those of actively using sign language and engage within deaf culture, whether it would be American Sign Language, British Sign Language, or some 40-odd variants and different nationalities.
Re: Meta AI announces Massive Multilingual Speech code, models for 1000+ languages
#129I would like to use stuff like this as a side-project. Buy a Nvidia Geforce GPU and stick it into my 24/7 server and play around with it in my free time, to see what can be done. The issue with all these AI models is that there's no information on which GPU is enough for which task. I'm absolutely clueless if a single RTX 4000 SFF with its 20GB VRAM and only 70W of max power usage will be a waste of money, or really…
I think you just need to educate yourself a bit about the space.
These models are very small (the large version is only 1B parameters) so should run on a 4GB gaming GPU.
Re: Meta AI announces Massive Multilingual Speech code, models for 1000+ languages
#130I would like to use stuff like this as a side-project. Buy a Nvidia Geforce GPU and stick it into my 24/7 server and play around with it in my free time, to see what can be done. The issue with all these AI models is that there's no information on which GPU is enough for which task. I'm absolutely clueless if a single RTX 4000 SFF with its 20GB VRAM and only 70W of max power usage will be a waste of money, or really…
Here's a sheet I came across today...
https://www.reddit.com/r/LocalLLaMA/comments/11o6o3f/how_to_...
Granted, it's talking about quantized models, which use less memory. But you can see the 30B models taking 36 GB at 8-bit, and at least 20 GB at 4-bit.
The page even lists the recommended cards.
But as others have pointed out, you may get more bang "renting" as in purchasing cloud instances able to run these workloads. Buying a system costs about as much as buying instance time for one year. Theoretically, if you only run sporadic workloads when you're playing around it would cost less. If you're training... that's a different story.