EzAudio: Enhancing Text-to-Audio Generation with Efficient Diffusion Transformer
<|reference_start|>EzAudio: Enhancing Text-to-Audio Generation with Efficient Diffusion Transformer: Latent diffusion models have shown promising results in text-to-audio (T2A) generation tasks, yet previous models have encountered difficulties in generation quality, computational cost, diffusion sampling, and data preparation. In this paper, we introduce EzAudio, a transformer-based T2A diffusion model, to handle these challenges. Our approach includes several key innovations: (1) We build the T2A model on the latent space of a 1D waveform Variational Autoencoder (VAE), avoiding the complexities of handling 2D spectrogram representations and using an additional neural vocoder. (2) We design an optimized diffusion transformer architecture specifically tailored for audio latent representations and diffusion modeling, which enhances convergence speed, training stability, and memory usage, making the training process easier and more efficient. (3) To tackle data scarcity, we adopt a data-efficient training strategy that leverages unlabeled data for learning acoustic dependencies, audio caption data annotated by audio-language models for text-to-audio alignment learning, and human-labeled data for fine-tuning. (4) We introduce a classifier-free guidance (CFG) rescaling method that simplifies EzAudio by achieving strong prompt alignment while preserving great audio quality when using larger CFG scores, eliminating the need to struggle with finding the optimal CFG score to balance this trade-off. EzAudio surpasses existing open-source models in both objective metrics and subjective evaluations, delivering realistic listening experiences while maintaining a streamlined model structure, low training costs, and an easy-to-follow training pipeline. Code, data, and pre-trained models are released at: https://haidog-yaqub.github.io/EzAudio-Page/.<|reference_end|>
@article{hai2024ezaudio:,
    title={EzAudio: Enhancing Text-to-Audio Generation with Efficient Diffusion
  Transformer},
    author={Jiarui Hai, Yong Xu, Hao Zhang, Chenxing Li, Helin Wang, Mounya
  Elhilali, Dong Yu},
    journal={arXiv preprint arXiv:2409.10819},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10819},
    primaryClass={eess.AS cs.SD}
}