This repository contains the code and models of "Separate What You Describe: Language-Queried Audio Source Separation" [INTERSPEECH 2022].

Check the examples and presentation video in the Demo Page!

Clone the repository and setup the conda environment:

git clone https://github.com/liuxubo717/LASS.git && \
cd LASS && \ 
conda env create -f environment.yml && \
conda activate LASS 

For running the inference of the pre-trained LASS-Net model, please download our pre-trained checkpoint and put it under ckpt/. We prepare ten audio mixtures (in examples/) with text queries (as illustrated in the Demo Page) for a toy inference running.

Run inference with AudioCaps text queries:

python inference.py -q AudioCaps

Or run inference with our collected human annotations:

python inference.py -q Human

The separated audio clips will be automatically saved in output/.

Due to the copyright of AudioSet we cannot release the raw data. The training and evaluation indexes in this work are available at dataset/. For faciliating the reproduction and comparsion, we release our code of creating audio mixtures at utils/create_mixtures.py. Here is an example of usage:

from utils.create_mixtures import add_noise_and_scale
wav1 = torch.randn(1, 32000)
wav2 = torch.randn(1, 32000)
target, noise, snr, scale = add_noise_and_scale(wav1, wav2)

• Provide conda-pack envs
• Inference code and model release
• Dataset release
• Training code release

@inproceedings{liu2022separate,
  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},
  booktitle = {INTERSPEEH},
  year = {2022}
}