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Description
We are excited to unveil Jukebox, a cutting-edge neural network designed to create music, including basic vocalization, in diverse genres and artistic expressions as raw audio. Alongside the release of the model weights and code, we are offering a tool to help users explore the music samples generated by Jukebox. By inputting genre, artist, and lyrics, users can receive entirely new music pieces crafted from the ground up. Jukebox is capable of producing a vast array of musical and vocal styles, and it can also generalize to lyrics that were not part of the training dataset. The lyrics included here have been collaboratively crafted by researchers at OpenAI and a language model. When provided with lyrics from its training set, Jukebox generates songs that diverge significantly from the originals, showcasing its creative capabilities. Users can input a 12-second audio clip for Jukebox to build upon, with the final output reflecting a desired style. Our focus on music stems from a desire to advance the potential of generative models further. Utilizing a quantization-based approach called VQ-VAE, Jukebox’s autoencoder model effectively compresses audio into a discrete latent space, enabling innovative sound generation. As we continue to refine these technologies, we look forward to the creative possibilities that lie ahead.
Description
Recent advancements in text-based 3D object generation have yielded encouraging outcomes; however, leading methods generally need several GPU hours to create a single sample, which is a stark contrast to the latest generative image models capable of producing samples within seconds or minutes. In this study, we present a different approach to generating 3D objects that enables the creation of models in just 1-2 minutes using a single GPU. Our technique initiates by generating a synthetic view through a text-to-image diffusion model, followed by the development of a 3D point cloud using a second diffusion model that relies on the generated image for conditioning. Although our approach does not yet match the top-tier quality of existing methods, it offers a significantly faster sampling process, making it a valuable alternative for specific applications. Furthermore, we provide access to our pre-trained point cloud diffusion models, along with the evaluation code and additional models, available at this https URL. This contribution aims to facilitate further exploration and development in the realm of efficient 3D object generation.
API Access
Has API
API Access
Has API
Integrations
Microsoft Azure
OpenAI
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
OpenAI
Founded
2015
Country
United States
Website
openai.com/blog/jukebox/
Vendor Details
Company Name
OpenAI
Founded
2015
Country
United States
Website
openai.com/research/point-e