EzAudio: Enhancing Text-to-Audio Generation with Efficient Diffusion
  Transformer
Authors: name: Jiarui Hai, name: Yong Xu, name: Hao Zhang, name: Chenxing Li, name: Helin Wang, name: Mounya Elhilali, name: Dong Yu

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/.