Deep-learning text-to-speech tool for generating voices of various characters (opens in new tab)

This is not true. A _lot_ of AI applications use algorithms such as logistic regression or random forests and don’t need GPUs - partly, of course, because GPUs are so expensive and these approaches are good enough (or more than good enough) for many applications.

There are few key reasons why most realtime inference is done on the cloud:

- Scale. Deep learning models especially tend to have poor latency, especially as they grow in size. As a result, you need to scale up replicas to meet demand at a way lower level of traffic than you do for a normal web app. At one point, AI Dungeon needed over 700 servers to support just thousands of concurrent players.

- Cost. Related to the above, GPUs are really expensive to buy. A g4dn.xlarge instance (the most popular AWS EC2 instance for GPU inference) is $0.526/hour on demand. To hit $3,000 per month in spend, you'd need to be running ~8 of them 24/7. Prices vary with purchasing GPUs, but you could expect 8 NVIDIA T4's to run around $20,000 at minimum, plus the cost of other components and maintainence. To be clear, that's very conservative--it's unlikely you'll get consistent traffic. What's more likely is you'll have some periods of very little traffic where you need one or two GPUs, and other high load periods where you'll need 10+.

3. Less universal of an issue, but the cloud gives you much better access to chips at lower switching costs. If NVIDIA releases a new GPU that's even better for inference, switching to it (once its available on your cloud) will be a tweak in your YAML. If you ever switch to ASICs like AWS's Inferentia or GCP's TPUs, which in many cases give way better performance and economics than GPUs, you'll also naturally have to be on their cloud.

However, there is a lot that can be done to lower the cost of inference even in the cloud. I listed some things in a comment higher up, but basically, there are some assumptions you can make with inference that allow you to optimize pretty hard on instance price and autoscaling behavior.

I have no reason to disbelieve it.

I'm not primarily interested about the dollars, I'm interested in allowing communities to do creative things. I think people are looking at this tech like it's only going to be used for deepfakes, and they're underestimating the extent it's going to be used to create voice-acted game mods, animations, anonymization tools, and other creative/helpful projects.

If you're really worried about this stuff though, you can take some comfort in the fact that by far the worst examples on the site are of real-world voices. This is currently technology that as far as I can see is far more suited for generating new voices or voicing cartoon characters with well-defined patterns/inflections than it is for imitating the president.

If a voice could be copyrighted, or if this was a trademark issue or something, I strongly suspect that this site would not fall under fair use regardless of whether or not it was commercial. But again, IANAL, so I don't feel confident making any kind of strong claim about that either.

I do plan to compile and publish my findings in the future, but nothing is set in stone yet. I know that the model can be improved even further, and I'd prefer to be as comprehensive as possible.

AI and ML users are massively benefiting from open source but too often refuse to release their data. It's like we're back in the middle ages and alchemy is back in style.