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Home » News » Meta Unveils JASCO: Advanced Text-to-Music Model with Fine-Grained Control and High-Quality Output

Meta Unveils JASCO: Advanced Text-to-Music Model with Fine-Grained Control and High-Quality Output

Discover Meta's JASCO, an innovative text-to-music generation model offering fine-grained control and high-quality music samples. Learn how it integrates audio-based and symbolic criteria for superior music creation.

Kumud Sahni Pruthi by Kumud Sahni Pruthi
Saturday, 20 July 2024, 20:11 PM
in News
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JASCO, a temporally regulated text-to-music generation model with both audio-based and symbolic criteria, is presented by Meta. With fine-grained local controls, JASCO can produce high-quality music samples conditioned on global text descriptions. 

The flow-matching modelling paradigm and an innovative conditioning technique form the foundation of JASCO. This makes it possible to regulate the creation of music both generally (text description) and locally (chords, for example). 

Also Read: 10 Best Metaverse Virtual Worlds to Watch in 2024

To be more precise, meta uses temporal blurring and information bottleneck layers to extract pertinent data about particular settings. This makes it possible to include both audio-based and symbolic conditions in the same text-to-music paradigm. 

Meta-test different audio representations (e.g., segregated drum tracks, full-mix) and symbolic control signals (e.g., chords, melody). It also uses objective metrics and human research to assess JASCO, considering both generation quality and condition adherence. 

The findings indicate that, in terms of generation quality, JASCO is comparable to the assessed baselines, but it offers far superior and more flexible controls over the generated music. 

Also Read: Meta 3D Gen: New Algorithm Converts Text Descriptions into 3D Models

The following are the primary drawbacks of the suggested method: 

(i) The generated samples are shorter than the autoregressive alternative, at about 10 seconds, in line with earlier diffusion-based text-to-music models. While this can be extended with overlaps, it might restrict the model’s ability to capture the overall structure of the generated music.

(ii) despite producing the entire sequence at once, the generation time is slower than auto-regressive alternatives and does not support streaming.

Also Read: How to use Meta AI in WhatsApp, Instagram, Facebook, and Messenger to Get Rapid Answers

In Meta’s next effort, they plan to offer further editing choices, like the ability to add or replace specific instruments in a recording, along with additional controls like music dynamics and musical structure. 

Researchers at Meta think that this line of inquiry, especially the suggested methodology, has a lot of promise to empower artists, producers, and musicians who need more control over their creative process.

Read the complete paper presented by researchers at Meta here.

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Kumud Sahni Pruthi

Kumud Sahni Pruthi

A postgraduate in Science with an inclination towards education and technology. She always looks for ways to help people improve their lives by putting complex things into simple words through her writing.

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