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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 Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Microsoft Azure Yes 
OpenAI Yes 

Integrations

Microsoft Azure No 
OpenAI No 

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

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

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