具有潛在擴散的長格式音樂生成

Long-form music generation with latent diffusion

April 16, 2024

作者: Zach Evans, Julian D. Parker, CJ Carr, Zack Zukowski, Josiah Taylor, Jordi Pons

cs.AI

摘要

最近，基於音訊的音樂生成模型取得了巨大進展，但迄今為止尚未成功生成具有連貫音樂結構的完整音樂曲目。我們展示通過在長時間上下文上訓練生成模型，可以生成長達4分45秒的長格式音樂。我們的模型由在高度降採樣的連續潛在表示（潛在速率為21.5Hz）上運行的擴散-變壓器組成。根據音質和提示對齊的指標，它獲得了最先進的生成結果，主觀測試顯示它生成具有連貫結構的完整音樂。

English

Audio-based generative models for music have seen great strides recently, but so far have not managed to produce full-length music tracks with coherent musical structure. We show that by training a generative model on long temporal contexts it is possible to produce long-form music of up to 4m45s. Our model consists of a diffusion-transformer operating on a highly downsampled continuous latent representation (latent rate of 21.5Hz). It obtains state-of-the-art generations according to metrics on audio quality and prompt alignment, and subjective tests reveal that it produces full-length music with coherent structure.