jukebox VS gpt-2

openai/jukebox: Music Generation

As many of you are probably aware CLIP research paper and open source code that's under MIT license underpins the entire generative image model industry. Let's not forget it's only a little over 2 years ago that this image was completely mindblowing, and now it's so basic. People took the research and released code it took off like wildfire. But Jukebox, released a year earlier, never did even though it's just as revolutionary.

I didn't even know about it until I heard Sam Altman casually mention it in an interview, I was expecting some basic tunes generator, but this is so amazing! I mean yeah the voices are not clear, it's muffled, but look at how far have image models progressed, if you applied the same amount of collaborative effort here, the results could be amazing! ElevenLabs showed how good and clear can AI-created voices sound. The only reason I can think of is that the Jukebox code is under view license only.

OpenAI had this figured out 3 years ago: https://openai.com/blog/jukebox/ . You could then even define your own text. Model is open source too.

This model is designed to output raw audio.

However, there are many models which do output midi. That's actually much simpler, and has been done already a few years ago.

I thought OpenAI did this. But then, I might misremember, because their Jukebox actually also seems to produce raw audio (https://openai.com/blog/jukebox/).

However, midi generation is so easy, you even find it in some tutorials: https://www.tensorflow.org/tutorials/audio/music_generation

There is https://openai.com/blog/jukebox/, which does generate music from prompt, but the prompt is not as detailed ("Hip Hop, in the style of Kanye West") and I don't think there is an easy way to use it online.

Did you arrive at this certainty through reading something other than what OpenAI has published? The document [0] that describes the training data for GPT-2 makes this assertion hilarious to me.

[0]: https://github.com/openai/gpt-2/blob/master/model_card.md#da...

2.) OpenAI's GPT-2: OpenAI released GPT-2 in 2019 and later made the code and pre-trained models available to the public. GPT-2 is not as advanced as GPT-3 or GPT-4, but it's still powerful and can be used for various natural language processing tasks. You can find the GPT-2 code and pre-trained models on GitHub: https://github.com/openai/gpt-2

The Product is not the only implementation / available source for this technology / service / "thing". Given the amount of other LLMs that are available as trained models, OpenAI does not control access to this type of technology, in fact they OpenSourced their Previous model (https://github.com/openai/gpt-2). T5 (https://huggingface.co/docs/transformers/model_doc/t5) and Bloom (https://huggingface.co/docs/transformers/model_doc/bloom) are well known Generators with very comparable complexity to GTP-3. The fact is that OpenAI has the most reliable implementation, but far from the only implementation.

You can try running GPT 2, I had some fun playing around with it, but it struggles with context. GPT 3 and now 3.5 blows it way out the water, but even if the model was available, you would need at least 350 GB of VRAM to run it. So unfortunately, for now it's out of reach.

The code for GPT-2 is here on github: https://github.com/openai/gpt-2

OpenAI attempted this with GPT-2’s incremental release strategy and was lambasted on moral and ethical grounds as well.

https://openai.com/blog/better-language-models/

So individuals working on a technology like this have no clear societal consensus to use as a guide when making the decision. Strong arguments can be made in both directions.