  Large diffusion models have been successful in text-to-audio (T2A) synthesis
tasks, but they often suffer from common issues such as semantic misalignment
and poor temporal consistency due to limited natural language understanding and
data scarcity. Additionally, 2D spatial structures widely used in T2A works
lead to unsatisfactory audio quality when generating variable-length audio
samples since they do not adequately prioritize temporal information. To
address these challenges, we propose Make-an-Audio 2, a latent diffusion-based
T2A method that builds on the success of Make-an-Audio. Our approach includes
several techniques to improve semantic alignment and temporal consistency:
Firstly, we use pre-trained large language models (LLMs) to parse the text into
structured <event & order> pairs for better temporal information capture. We
also introduce another structured-text encoder to aid in learning semantic
alignment during the diffusion denoising process. To improve the performance of
variable length generation and enhance the temporal information extraction, we
design a feed-forward Transformer-based diffusion denoiser. Finally, we use
LLMs to augment and transform a large amount of audio-label data into
audio-text datasets to alleviate the problem of scarcity of temporal data.
Extensive experiments show that our method outperforms baseline models in both
objective and subjective metrics, and achieves significant gains in temporal
information understanding, semantic consistency, and sound quality.
