Diffusion models have shown promising results in cross-modal generation
tasks, including text-to-image and text-to-audio generation. However,
generating music, as a special type of audio, presents unique challenges due to
limited availability of music data and sensitive issues related to copyright
and plagiarism. In this paper, to tackle these challenges, we first construct a
state-of-the-art text-to-music model, MusicLDM, that adapts Stable Diffusion
and AudioLDM architectures to the music domain. We achieve this by retraining
the contrastive language-audio pretraining model (CLAP) and the Hifi-GAN
vocoder, as components of MusicLDM, on a collection of music data samples.
Then, to address the limitations of training data and to avoid plagiarism, we
leverage a beat tracking model and propose two different mixup strategies for
data augmentation: beat-synchronous audio mixup and beat-synchronous latent
mixup, which recombine training audio directly or via a latent embeddings
space, respectively. Such mixup strategies encourage the model to interpolate
between musical training samples and generate new music within the convex hull
of the training data, making the generated music more diverse while still
staying faithful to the corresponding style. In addition to popular evaluation
metrics, we design several new evaluation metrics based on CLAP score to
demonstrate that our proposed MusicLDM and beat-synchronous mixup strategies
improve both the quality and novelty of generated music, as well as the
correspondence between input text and generated music.

通过构建一种文本到音乐模型，并利用分布式扩散模型和音频广义线性模型来进行训练以生成新音乐，解决了音乐生成过程中的数据不足、版权和抄袭等问题。同时通过节拍跟踪和两种不同的数据增强策略，即拍子同步音频混合和拍子同步潜在混合，实现对训练数据的重组，从而生成多样化且保持风格一致的音乐。最终，通过基于对比语音 - 音频预训练模型的评估指标，进一步证明了所提出的音乐生成模型和拍子同步混合策略能够提高生成音乐的质量、创新性和与输入文本的对应关系。