riffusion-app VS bumblebee

git clone https://github.com/hmartiro/riffusion-appThis is a separate, required project necessary to do anything with the riffusion-inference server

Other author here! This got a posted a little earlier than we intended so we didn't have our GPUs scaled up yet. Please hang on and try throughout the day!

Meanwhile, please read our about page http://riffusion.com/about

It’s all open source and the code lives at https://github.com/hmartiro/riffusion-app

This has been our hobby project for the past few months. Seeing the incredible results of stable diffusion, we were curious if we could fine tune the model to output spectrograms and then convert to audio clips. The answer to that was a resounding yes, and we became addicted to generating music from text prompts. There are existing works for generating audio or MIDI from text, but none as simple or general as fine tuning the image-based model. Taking it a step further, we made an interactive experience for generating looping audio from text prompts in real time. To do this we built a web app where you type in prompts like a jukebox, and audio clips are generated on the fly. To make the audio loop and transition smoothly, we implemented a pipeline that does img2img conditioning combined with latent space interpolation.

Despite some limitations, you will probably find Bumblebee (https://github.com/elixir-nx/bumblebee) interesting.

"Bumblebee provides pre-trained Neural Network models on top of Axon. It includes integration with HuggingFace Models, allowing anyone to download and perform Machine Learning tasks with few lines of code"

That's not bad at all!

There is also Nx and Bumblebee in Elixir land - it really changes the how one approaches running models in production. The fact that one can put together a service (or local process) running any model published to hugging face in a couple of lines of code is amazing.

[0] https://github.com/elixir-nx/bumblebee/blob/main/examples/ph...

[1] https://gist.github.com/toranb/8be408eaa97d5a5b795aec7d7fbee...

Theses notes describes how this can be done with the Elixir language and Nx, Axon (Nx-powered Neural Network library) and Bumblebee which provides pre-trained Neural Network models.

Apart from running code inside a "markdown" file, livebook can do much more. You have Smart cells to show charts, run sql queries against a db, run Neural Network tasks such as Image-To-Text generation using Bumblebee[1], etc. It is collaborative as well.

[1] https://github.com/elixir-nx/bumblebee

Elixir is an increasingly interesting platform for ML (see Nx, Axon, and more recently BumbleBee https://github.com/elixir-nx/bumblebee). I'm pretty happy to see this book released in beta.

José from the Livebook team. I don't think I can make a pitch because I have limited Python/R experience to use as reference.

My suggestion is for you to give it a try for a day or two and see what you think. I am pretty sure you will find weak spots and I would be very happy to hear any feedback you may have. You can find my email on my GitHub profile (same username).

In general we have grown a lot since the Numerical Elixir effort started two years ago. Here are the main building blocks:

* Nx (https://github.com/elixir-nx/nx/tree/main/nx#readme): equivalent to Numpy, deeply inspired by JAX. Runs on both CPU and GPU via Google XLA (also used by JAX/Tensorflow) and supports tensor serving out of the box

* Axon (https://github.com/elixir-nx/axon): Nx-powered neural networks

* Bumblebee (https://github.com/elixir-nx/bumblebee): Equivalent to HuggingFace Transformers. We have implemented several models and that's what powers the Machine Learning integration in Livebook (see the announcement for more info: https://news.livebook.dev/announcing-bumblebee-gpt2-stable-d...)

* Explorer (https://github.com/elixir-nx/explorer): Series and DataFrames, as per this thread.

* Scholar (https://github.com/elixir-nx/scholar): Nx-based traditional Machine Learning. This one is the most recent effort of them all. We are treading the same path as scikit-learn but quite early on. However, because we are built on Nx, everything is derivable, GPU-ready, distributable, etc.

Regarding visualization, we have "smart cells" for VegaLite and MapLibre, similar to how we did "Data Transformations" in the video above. They help you get started with your visualizations and you can jump deep into the code if necessary.

I hope this helps!

The current pipeline expects PCM audio and, if data is coming from a microphone in the browser, you can do the initial processing and conversion in the browser (see the JS in this single file Phoenix app speech to text example [0]).

On the other hand, if you expect a variety of formats (mp3, wav, etc), then shelling out or embedding ffmpeg is probably the quickest path to achieve something. The Membrane Framework[1] is an option here too which includes streaming. I believe Lars is going to do a cool demo with Membrane at ElixirConf EU next week.

[0]: https://github.com/elixir-nx/bumblebee/blob/main/examples/ph...

[1]: https://membrane.stream/

Outside of that, Elixir can be used for data pipelines, audio-video processing, and it is making inroads on Machine Learning with projects like Livebook, Nx, and Bumblebee.