The official code repo of "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection"

Last update: Jan 01, 2023

Overview

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

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

ESC-50

Open the jupyter notebook at esc-50/prep_esc50.ipynb and process it

Speech Command V2

Open the jupyter notebook at scv2/prep_scv2.ipynb and process it

DESED Dataset

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.

The official code repo of "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection"

Related tags

Overview

Hierarchical Token Semantic Audio Transformer

Introduction

Classification Results on AudioSet, ESC-50, and Speech Command V2 (mAP)

Localization/Detection Results on DESED dataset (F1-Score)

Getting Started

Install Requirments

Download and Processing Datasets

Set the Configuration File: config.py

Train and Evaluation

Localization on DESED

Model Checkpoints:

Citing

Owner

Knut(Ke) Chen

automatic color-grading

clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation

The repo contains the code of the ACL2020 paper `Dice Loss for Data-imbalanced NLP Tasks`

Bayesian inference for Permuton-induced Chinese Restaurant Process (NeurIPS2021).

MDMM - Learning multi-domain multi-modality I2I translation

4D Human Body Capture from Egocentric Video via 3D Scene Grounding

Python wrappers to the C++ library SymEngine, a fast C++ symbolic manipulation library.

Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes

iBOT: Image BERT Pre-Training with Online Tokenizer

PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds

code for `Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation`

Awesome-google-colab - Google Colaboratory Notebooks and Repositories

FairyTailor: Multimodal Generative Framework for Storytelling

Learning Facial Representations from the Cycle-consistency of Face (ICCV 2021)

A Real-World Benchmark for Reinforcement Learning based Recommender System

Augmentation for Single-Image-Super-Resolution

PyTorch implementation of our ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer.

Analysis of rationale selection in neural rationale models

Weakly Supervised Learning of Rigid 3D Scene Flow

CVPR2021 Workshop - HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization.