Make-An-Audio 2

Make-An-Audio 2 is a text-to-audio generation technology based on the diffusion model, jointly developed by researchers from Zhejiang University, ByteDance, and the Chinese University of Hong Kong. The technology improves the quality of generated audio by using pre-trained large language models (LLMs) to parse text, optimizing semantic alignment and temporal consistency. It also designs a feedforward Transformer-based diffusion denoiser to improve the performance of variable-length audio generation and enhance the extraction of temporal information. Furthermore, the problem of temporal data scarcity is solved by using LLMs to convert large amounts of audio label data into audio text datasets.

The target audience of this technology is researchers and developers in the field of audio synthesis, as well as application scenarios that require high-quality text-to-audio conversion, such as automatic dubbing, audiobook production, etc. Through its advanced technology, Make-An-Audio 2 is able to generate high-quality audio that is semantically aligned and time-consistent with the text content to meet the needs of these users.