# Fast Timing-Conditioned Latent Audio Diffusion

**Authors:** Zach Evans, CJ Carr, Josiah Taylor, Scott H. Hawley, Jordi Pons  
**arXiv:** [2402.04825](https://arxiv.org/abs/2402.04825)  
**Date:** 2024-02-07  
**Summary page:** https://www.summarizepaper.com/en/paper/2402.04825v1/

## Key points

- The research focuses on generating high-quality audio content efficiently and effectively from text prompts.
- The proposed approach, Stable Audio, leverages latent diffusion within a generative model framework to generate long-form, variable-length stereo music and sounds at 44.1kHz.
- Stable Audio demonstrates impressive capabilities by rendering stereo signals of up to 95 seconds at 44.1kHz in just 8 seconds on an A100 GPU.
- Unlike existing models, Stable Audio excels in generating music with structure and delivering stereo sounds.
- The authors provide access to their code repository, metrics, and a demo for further exploration of their work.

## Summary

, , , , 

The research addresses the challenge of generating high-quality audio content efficiently and effectively from text prompts. This task is known to be computationally demanding, making it a difficult feat to achieve. Previous works in this field have not effectively tackled the natural variation in duration that music and sound effects exhibit, hindering their ability to produce accurate results. However, the focus of this study is on developing an efficient method for generating long-form, variable-length stereo music and sounds at 44.1kHz using text prompts within a generative model framework. The proposed approach, known as Stable Audio, leverages latent diffusion with its latent space defined by a fully-convolutional variational autoencoder. This latent space is conditioned on both text prompts and timing embeddings, providing precise control over the content and length of the generated audio. Remarkably, Stable Audio demonstrates impressive capabilities by rendering stereo signals of up to 95 seconds at 44.1kHz in just 8 seconds on an A100 GPU. Despite its computational efficiency and rapid inference speed, Stable Audio excels in two public text-to-music and -audio benchmarks. Unlike existing state-of-the-art models, Stable Audio stands out for its ability to generate music with structure and deliver stereo sounds. The authors provide access to their code repository (https://github.com/Stability-AI/stable-audio-tools), metrics (https://github.com/Stability-AI/stable-audio-metrics), and a demo (https://stability-ai.github.io/stable-audio-demo) for further exploration of their innovative work. Through their research findings and technological advancements, Evans et al. 's study significantly contributes to advancing the state-of-the-art in  from . Their  for generating  at 44.1kHz not only showcases impressive computational efficiency but also excels in producing high-quality and structured music. The authors' provision of access to their code, metrics, and demo further encourages exploration and advancement in this field.