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Separate Anything You Describe

This repository contains the official implementation of "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 out the separated audio examples on the Demo Page!

Setup

Clone the repository and setup the conda environment:

git clone https://github.com/Audio-AGI/AudioSep.git && \
cd AudioSep && \ 
conda env create -f environment.yml && \
conda activate AudioSep

Download model weights at checkpoint/.

If you're using this checkpoint for the DCASE 2024 Task 9 challenge participation, please note that this checkpoint was trained using audio at 32k Hz, with a window size of 2048 points and a hop size of 320 points in the STFT operation, which is different with the challenge baseline system provided (16k Hz, window size 1024, hop size 160).

Inference

from pipeline import build_audiosep, inference
import torch

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model = build_audiosep(
      config_yaml='config/audiosep_base.yaml', 
      checkpoint_path='checkpoint/audiosep_base_4M_steps.ckpt', 
      device=device)

audio_file = 'path_to_audio_file'
text = 'textual_description'
output_file='separated_audio.wav'

# AudioSep processes the audio at 32 kHz sampling rate  
inference(model, audio_file, text, output_file, device)

To load directly from Hugging Face, you can do the following:

from models.audiosep import AudioSep
from utils import get_ss_model
import torch

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

ss_model = get_ss_model('config/audiosep_base.yaml')

model = AudioSep.from_pretrained("nielsr/audiosep-demo", ss_model=ss_model)

audio_file = 'path_to_audio_file'
text = 'textual_description'
output_file='separated_audio.wav'

# AudioSep processes the audio at 32 kHz sampling rate  
inference(model, audio_file, text, output_file, device)

Use chunk-based inference to save memory:

inference(model, audio_file, text, output_file, device, use_chunk=True)

Training

To utilize your audio-text paired dataset:

1. Format your dataset to match our JSON structure. Refer to the provided template at datafiles/template.json.

2. Update the config/audiosep_base.yaml file by listing your formatted JSON data files under datafiles. For example:

data:
    datafiles:
        - 'datafiles/your_datafile_1.json'
        - 'datafiles/your_datafile_2.json'
        ...

Train AudioSep from scratch:

python train.py --workspace workspace/AudioSep --config_yaml config/audiosep_base.yaml --resume_checkpoint_path checkpoint/ ''

Finetune AudioSep from pretrained checkpoint:

python train.py --workspace workspace/AudioSep --config_yaml config/audiosep_base.yaml --resume_checkpoint_path path_to_checkpoint

Benchmark Evaluation

Download the evaluation data under the evaluation/data folder. The data should be organized as follows:

evaluation:
    data:
        - audioset/
        - audiocaps/
        - vggsound/
        - music/
        - clotho/
        - esc50/

Run benchmark inference script, the results will be saved at eval_logs/

python benchmark.py --checkpoint_path audiosep_base_4M_steps.ckpt

"""
Evaluation Results:

VGGSound Avg SDRi: 9.144, SISDR: 9.043
MUSIC Avg SDRi: 10.508, SISDR: 9.425
ESC-50 Avg SDRi: 10.040, SISDR: 8.810
AudioSet Avg SDRi: 7.739, SISDR: 6.903
AudioCaps Avg SDRi: 8.220, SISDR: 7.189
Clotho Avg SDRi: 6.850, SISDR: 5.242
"""

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},
}

Contributors :