Whitebalance Setup

These should be the minimum steps required to get AudioSep up and running locally

pyenv virtualenv 3.10.9 audiosep
pip install torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1
pip install -r requirements.txt

Download model weights to checkpoint/ dir.

Use inference.ipynb to run inference locally.

---

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 the separated audio examples in the Demo Page!

---

TODO

• AudioSep training & finetuning code release.
• AudioSep base model checkpoint release.
• Evaluation benchmark release.

---

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/.

---

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:

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},
}