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!
- AudioSep training & fine-tuning code release.
- AudioSep base model checkpoint release.
- Evaluation benchmark release.
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 AudioSepDownload model weights at checkpoint/.
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)To utilize your audio-text paired dataset:
-
Format your dataset to match our JSON structure. Refer to the provided template at
datafiles/template.json. -
Update the
config/audiosep_base.yamlfile by listing your formatted JSON data files underdatafiles. 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_checkpointDownload 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
"""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},
}
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