cjwbw / audiosep

Separate Anything You Describe

  • Public
  • 3.4K runs
  • GitHub
  • Paper
  • License
Run with an API
Playground API Examples README Versions

Run time and cost

This model costs approximately $0.089 to run on Replicate, or 11 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 92 seconds. The predict time for this model varies significantly based on the inputs.

Readme

Separate Anything You Describe

We introduce AudioSep, a foundation model for open-domain sound separation with natural language queries. AudioSep demonstrates strong separation performance and impressive zero-shot generalization ability on numerous tasks such as audio event separation, musical instrument separation, and speech enhancement. Check the separated audio examples in the Demo Page!

Cite this work

If you found this tool useful, please consider citing

@article{liu2023separate,
  title={Separate Anything You Describe},
  author={Liu, Xubo and Kong, Qiuqiang and Zhao, Yan and Liu, Haohe and Yuan, Yi and Liu, Yuzhuo and Xia, Rui and Wang, Yuxuan and Plumbley, Mark D and Wang, Wenwu},
  journal={arXiv preprint arXiv:2308.05037},
  year={2023}
}

@inproceedings{liu22w_interspeech,
  title={Separate What You Describe: Language-Queried Audio Source Separation},
  author={Liu, Xubo and Liu, Haohe and Kong, Qiuqiang and Mei, Xinhao and Zhao, Jinzheng and Huang, Qiushi and Plumbley, Mark D and Wang, Wenwu},
  year=2022,
  booktitle={Proc. Interspeech},
  pages={1801--1805},
}