Zero Shot Audio Source Separation

Introduction

The Code Repository for "Zero-shot Audio Source Separation through Query-based Learning from Weakly-labeled Data", in AAAI 2022.

In this paper, we propose a three-component pipline that allows you to train a audio source separator to separate any source from the track. All you need is a mixture audio to separate, and a given source sample as a query. Then the model will separate your specified source from the track. Our model lies in a zero-shot setting because we never use the seapration dataset but a general audio dataset AudioSet. However, we achieve a very competible separation performance (SDR) in MUSDB18 Dataset compared with those supervised models. Our model has a generalization ability to unseen sources out of the training set. Indeed, we do not even require the separation dataset for training but solely AudioSet.

The demos and introduction are presented in our short instroduction video and full presentation video.

More demos will be presented in my personal website (now under construction)

Chckout this interactive demo at Replicate Thanks @ariel415el for creating this!

Model Arch

Main Separation Performance on MUSDB18 Dataset

We achieve a very competible separation performance (SDR) in MUSDB18 Dataset with neither seeing the MUSDB18 training data nor speficying source targets, compared with those supervised models.

Additionally, our model can easily separate many other sources, such as violin, harmonica, guitar, etc. (demos shown in the above video link)

MUSDB results

Getting Started

Install Requirments

pip install -r requirements.txt

Download and Processing Datasets

config.py

change the varible "dataset_path" to your audioset address
change the classes_num to 527

AudioSet


./create_index.sh # 
// remember to change the pathes in the script
// more information about this script is in https://github.com/qiuqiangkong/audioset_tagging_cnn

python main.py save_idc // count the number of samples in each class and save the npy files


* [MUSDB18](https://sigsep.github.io/datasets/musdb.html) - You can directly use [our processed musdb audio files](https://drive.google.com/drive/folders/1VwRnCxp3t2bXUS_MbXiFiggwkkJQEmha?usp=sharing) in 32000Hz sample rate. Or you set the "musdb_path" in the download path, and:

python main.py musdb_process // Notice that the training set is a highlight version, while the testing set is the full version


### Set the Configuration File: config.py

The script *config.py* contains all configurations you need to assign to run your code. 

Please read the introduction comments in the file and change your settings.

For the most important part:

If you want to train/test your model on AudioSet, you need to set:

dataset_path = "your processed audioset folder" balanced_data = True sample_rate = 32000 hop_size = 320 classes_num = 527


### Train and Evaluation

#### Train the sound event detection system ST-SED/HTS-AT
We further integrated this system ST-SED into an independent repository, and evaluteed it on more datasets, improved it a lot and achieved better performance. 

You can follow [this repo](https://github.com/RetroCirce/HTS-Audio-Transformer) to train and evalute the sound event detection system ST-SED (or a more relevant name HTS-AT), the configuation file for training the model for this separation task should be [htsat_config.py](htsat_config.py).

For this separation task, if you want to save time, you can also download [the checkpoint](https://drive.google.com/drive/folders/1RouwHsGsMs8n3l_jF8XifWtbPzur_YQS?usp=sharing) directly.

#### Train, Evaluate and Inference the Seapration Model

All scripts is run by main.py:

Train: CUDA_VISIBLE_DEVICES=1,2,3,4 python main.py train

Test: CUDA_VISIBLE_DEVICES=1,2,3,4 python main.py test

We recommend using at least 4 GPU cards with above 20GB memories per card. In our training phrase, we use 8 Nvidia V-100 (32GB) GPUs. 

We provide a quick **inference** interface by:

CUDA_VISIBLE_DEVICES=1 python main.py inference

Where you can separate any given source from the track. You need to set the value of "inference_file" and "inference_query" in *config.py*. Just check the comment and get it started. And for the inference, we recommend to use only one card (because it is already enough).

#### Model Checkpoints:

We provide the model checkpoints in this [link](https://drive.google.com/drive/folders/1RouwHsGsMs8n3l_jF8XifWtbPzur_YQS?usp=sharing). Feel free to download and test it.

## Citing

@inproceedings{zsasp-ke2022, author = {Ke Chen and Xingjian Du and Bilei Zhu and Zejun Ma and Taylor Berg-Kirkpatrick and Shlomo Dubnov}, title = {Zero-shot Audio Source Separation via Query-based Learning from Weakly-labeled Data}, booktitle = {{AAAI} 2022} }

@inproceedings{htsat-ke2022, author = {Ke Chen and Xingjian Du and Bilei Zhu and Zejun Ma and Taylor Berg-Kirkpatrick and Shlomo Dubnov}, title = {HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection}, booktitle = {{ICASSP} 2022} }

RetroCirce / Zero_Shot_Audio_Source_Separation

readme