Hierarchical Token Semantic Audio Transformer
Introduction
The Code Repository for "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection", in ICASSP 2022.
In this paper, we devise a model, HTS-AT, by combining a swin transformer with a token-semantic module and adapt it in to audio classification and sound event detection tasks. HTS-AT is an efficient and light-weight audio transformer with a hierarchical structure and has only 30 million parameters. It achieves new state-of-the-art (SOTA) results on AudioSet and ESC-50, and equals the SOTA on Speech Command V2. It also achieves better performance in event localization than the previous CNN-based models.
Classification Results on AudioSet, ESC-50, and Speech Command V2 (mAP)
Localization/Detection Results on DESED dataset (F1-Score)
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 variable "desed_folder" to your DESED address
change the classes_num to 527
./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
Open the jupyter notebook at esc-50/prep_esc50.ipynb and process it
Open the jupyter notebook at scv2/prep_scv2.ipynb and process it
python conver_desed.py
// will produce the npy data files
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"
dataset_type = "audioset"
balanced_data = True
loss_type = "clip_bce"
sample_rate = 32000
hop_size = 320
classes_num = 527
If you want to train/test your model on ESC-50, you need to set:
dataset_path = "your processed ESC-50 folder"
dataset_type = "esc-50"
loss_type = "clip_ce"
sample_rate = 32000
hop_size = 320
classes_num = 50
If you want to train/test your model on Speech Command V2, you need to set:
dataset_path = "your processed SCV2 folder"
dataset_type = "scv2"
loss_type = "clip_bce"
sample_rate = 16000
hop_size = 160
classes_num = 35
If you want to test your model on DESED, you need to set:
resume_checkpoint = "Your checkpoint on AudioSet"
heatmap_dir = "localization results output folder"
test_file = "output heatmap name"
fl_local = True
fl_dataset = "Your DESED npy file"
Train and Evaluation
Notice: Our model is run on DDP mode and requires at least two GPU cards. If you want to use a single GPU for training and evaluation, you need to mannually change sed_model.py and main.py
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
Ensemble Test: CUDA_VISIBLE_DEVICES=1,2,3,4 python main.py esm_test
// See config.py for settings of ensemble testing
Weight Average: python main.py weight_average
// See config.py for settings of weight averaging
Localization on DESED
CUDA_VISIBLE_DEVICES=1,2,3,4 python main.py test
// make sure that fl_local=True in config.py
python fl_evaluate.py
// organize and gather the localization results
fl_evaluate_f1.ipynb
// Follow the notebook to produce the results
Model Checkpoints:
We provide the model checkpoints on three datasets (and additionally DESED dataset) in this link. Feel free to download and test it.
Citing
@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}
}
Our work is based on Swin Transformer, which is a famous image classification transformer model.
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