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What Makes Transformers and Diffusion Models Better for Modern Audio Generation?
Learn why Transformers and diffusion models pushed audio generation forward, combining long-term structure modeling with high-fidelity, natural-sounding output quality.
Question
Which of the following best explains why Transformers and Diffusion models have advanced audio generation since 2020?
A. Transformers reduce the need for large datasets; diffusion creates instant outputs.
B. Transformers model long-term structure, diffusion models generate detailed, natural outputs.
C. Transformers are for denoising; diffusion predicts tokens.
D. Both avoid high computational costs.
Answer
B. Transformers model long-term structure, diffusion models generate detailed, natural outputs.
Explanation
Transformers improved audio generation because they are strong at handling long-range dependencies and preserving coherent structure over time, which is essential in speech, music, and other sequential audio tasks. Diffusion models advanced the field by producing high-fidelity, natural-sounding audio, often with stronger detail and realism than simpler next-token approaches.
The other options are inaccurate. Transformers do not remove the need for large datasets, diffusion models do not produce instant outputs, and neither model family is mainly defined by low computational cost.