I'm really hoping they reuse Clippy for this because it'd be hilarious if Clippy ends up being the AI that conquers the world.

He's asking in the case of a predictor i.e. ResNet or other models that just categorizes

>That is the Million Dollar question (or really hundred million dollar question in terms of legal fees)

It's worth a lot more than that. The profit margins of AI focused companies are kind of on the line here.

messing with target sound extraction by adding just barebones masknet architechture on top of samplernn. I want to apply this architecture to extracting different layers in electronic misic. for example, pick out just the snare drum track from the full drum machine mix. It is easy to generate datasets using DawDreamer (generating random drum patterns using a sampler currently). considering adding conditioning by the output of a differentiable filter bank

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>and you'll probably get cool optical effects on the koala that the model has never seen before

How could we be absolutely certain the model has never seen said effects?

denoising diffusion probabilistic models:

Rdiffusion : Generate music from stable diffusion

Improve image segmentation : I remember someone doing image segmentation on these generative model, but not sure where.

I spend ~30-40 hours a week on ML-powered projects for work. Life is far too short to start doing ML projects in my free time too.

I am working on a customer lifetime value project to predict the worth of every customer

tested looks awesome but i have to purchase yearly plan which is 3500$ lol :D

That better be a classification joke because it’d be lame if you were just declining to share 😉

I'm sorry, but that's classified.

Thanks for the idea!

I'll check it out. Looks interesting, but not as good as TortoiseTTS, judging by the samples. Definitely worth looking at tho, thx

This one looks like what I'm looking for. Slow AF but I give it a book chapter and it gives me an audio narration. Seems pretty powerful if you have a lot of patience

Sure, I have a few links. All of them have an inference speed of 4-9 seconds.

https://huggingface.co/poom-sci/WangchanBERTa-finetuned-sentiment

https://huggingface.co/ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa

I call each checkpoint like this:

nlp = pipeline('sentiment-analysis',
            model=checkpoint, 
            tokenizer=checkpoint)

Thank you!

not likely, if found illegal, then you would have to "remove" the offending "images"

check out Triton for model deployment

And could be any retroactive penalty?

If you want ML TTS, there are a lot of open source models out there, problem is most of them are trained on the same data, so your going to get similar voice options for the most part. You can definitely train your own text to speech, and pretty easily as well, but I'm assuming you don't want to go that route. Maybe try starting with Coqui TTS, but for reading long documents it definitely has its fair share of issues.

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Thank you for your effort

Can you share the link to that Hugging Face model so I can see how I may help?

Exactly. I mean I can easily define L2-constraints for the weights of my network and then do constrained optimization, which would at least theoretically be equivalent to L2-regularization/weight decay. But this is not quite useful, I am wondering whether there are applications of constraints where it actually makes sense.

Do you have any links? That would be great!