Tracksy vs. OpenAI Jukebox

Tracksy represents a groundbreaking generative AI assistant designed for individuals to create their own unique music, irrespective of their background in music production. In a landscape where numerous artists face challenges such as lack of inspiration and insufficient resources, frequently resulting in unfinished projects, our leading product provides a game-changing answer. By utilizing cutting-edge Generative AI technology, Tracksy enables users to transform incomplete tracks into fully realized and instrumented compositions merely by uploading their work. This capability significantly enhances the creative process for musicians, producers, and creators alike, empowering them to delve deeper into the richness of musical exploration. Ultimately, Tracksy serves as a catalyst for innovation in music, bridging gaps and unlocking potential that many may not have believed possible.

We are thrilled to introduce Jukebox, an innovative neural network engineered to generate music across a wide variety of genres and styles, complete with basic vocalizations, all rendered as raw audio. In conjunction with the release of the model weights and accompanying code, we are providing a user-friendly tool that allows individuals to delve into the music samples produced by Jukebox. By entering specific parameters such as genre, artist, and lyrics, users can receive entirely original compositions created from scratch. Jukebox is adept at producing a diverse range of musical and vocal forms and can creatively interpret lyrics that were not included in its training dataset. The lyrics featured here have been collaboratively developed by OpenAI researchers and a language model. When given lyrics from its training set, Jukebox generates songs that significantly differ from the originals, demonstrating its impressive creative abilities. Users have the option to input a 12-second audio snippet for Jukebox to expand upon, resulting in an output that embodies a chosen artistic style. Our commitment to music innovation is driven by a desire to push the boundaries of generative models even further. By employing a quantization-based methodology known as VQ-VAE, Jukebox's autoencoder efficiently compresses audio into a discrete latent space, paving the way for groundbreaking sound generation. As we move forward with refining these technologies, we eagerly anticipate the myriad of creative avenues that await exploration. The future of music generation looks promising, and we are excited to be part of this transformative journey.