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).
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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.
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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.
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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.
