Jukebox: An Open-Source Neural Network Tool

Jukebox is an innovative open-source neural network tool that generates music and rudimentary singing as raw audio in various genres and artist styles. The code and model weights are released alongside an exploration tool for generated samples, allowing users to delve into the creative possibilities of AI-generated music.

How it Works

With Jukebox, users can provide input regarding genre, artist, and lyrics, and the tool outputs new music samples in response. This open-ended approach enables the generation of a wide range of music and singing styles that generalize well to unseen lyrics during training.

Conditional Generation

What's more impressive is Jukebox's ability to produce music that bears no resemblance to the songs upon which it trained when conditioned on lyrics seen during training. This highlights the tool's capacity for creative freedom and experimentation.

Autoencoder Compression

Jukebox tackles the challenging task of modeling raw audio sequences, which are extremely long, by using an autoencoder to compress them into a lower-dimensional space. This allows the tool to generate audio in that compressed space and then up-sample it back to the raw audio space, achieving a remarkable level of detail and expressiveness.

Generative Capabilities

Jukebox is an example of pushing the boundaries of generative models and offers more expressive capabilities compared to tools that generate music symbolically in the form of a piano roll. Its well-suited for users interested in experimenting with AI-generated music, making it an exciting development in the field of music technology.