

JASCO
Overview :
JASCO is a text-to-music generation model that combines symbolic and audio-based conditioning. It can generate high-quality music samples based on global text descriptions and fine-grained local controls. Built upon the stream matching modeling paradigm and a novel conditioning method, JASCO allows music generation to be controlled simultaneously by both local (e.g., chord) and global (text description) cues. By utilizing information bottleneck layers and temporal blurring, it extracts information relevant to specific controls, enabling the combination of symbolic and audio-based conditioning within the same text-to-music model.
Target Users :
JASCO is suitable for music creators, music theorists, and anyone interested in music generation technology. It can help users generate music that conforms to specific styles and emotions through text descriptions, providing new tools and inspiration sources for music composition.
Use Cases
Music creators use JASCO to generate music in specific styles based on text descriptions.
Music theorists utilize JASCO to explore the impact of different text descriptions on music generation.
Educators use JASCO as a teaching tool to help students understand the relationship between music and text.
Features
Supports global text descriptions and fine-grained local controls.
Based on the stream matching modeling paradigm and novel conditioning methods.
Applies information bottleneck layers and temporal blurring techniques.
Can combine symbolic and audio-based conditioning.
Evaluates generation quality and conditioning adherence through objective metrics and human studies.
Compares favorably to baseline models in terms of generation quality while offering more flexible control.
How to Use
Visit the official website of JASCO.
Familiarize yourself with the basic principles and functionalities of JASCO.
Choose or input the desired text description for the music you want to generate.
Select local control conditions as needed, such as chords or melodies.
Adjust other generation parameters, such as tempo or style.
Initiate the music generation process and wait for the results.
Evaluate the generated music samples and make adjustments based on feedback.
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