YOLOX_AUDIO is an audio event detection model based on YOLOX

Related tags

Deep Learning YOLOX_AUDIO
Overview

Introduction

YOLOX_AUDIO is an audio event detection model based on YOLOX, an anchor-free version of YOLO. This repo is an implementated by PyTorch. Main goal of YOLOX_AUDIO is to detect and classify pre-defined audio events in multi-spectrogram domain using image object detection frameworks.

Updates!!

  • 【2021/11/15】 We released YOLOX_AUDIO to public

Quick Start

Installation

Step1. Install YOLOX_AUDIO.

git clone https://github.com/intflow/YOLOX_AUDIO.git
cd YOLOX_AUDIO
pip3 install -U pip && pip3 install -r requirements.txt
pip3 install -v -e .  # or  python3 setup.py develop

Step2. Install pycocotools.

pip3 install cython; pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
Data Preparation

Step1. Prepare audio wavform files for training. AUDIO_DATAPATH/wav

Step2. Write audio annotation files for training. AUDIO_DATAPATH/label.json

{
    "00000.wav": {
        "speaker": [
            "W",
            "M",
            "C",
            "W"
        ],
        "on_offset": [
            [
                1.34425,
                2.4083125
            ],
            [
                4.0082708333333334,
                4.5560625
            ],
            [
                6.2560416666666665,
                7.956104166666666
            ],
            [
                9.756083333333333,
                10.876624999999999
            ]
        ]
    },
    "00001.wav": {
        "speaker": [
            "W",
            "M",
            "C",
            "M",
            "W",
            "C"
        ],
        "on_offset": [
            [
                1.4325416666666666,
                2.7918958333333332
            ],
            [
                2.1762916666666667,
                4.109729166666667
            ],
            [
                7.109708333333334,
                8.530916666666666
            ],
            [
                8.514125,
                9.306104166666668
            ],
            [
                12.606083333333334,
                14.3345625
            ],
            [
                14.148958333333333,
                15.362958333333333
            ]
        ]
    },
    ...
}

Step3. Convert audio files into spectrogram images.

python tools/json_gen_audio2coco.py

Please change the dataset path and file names for your needs

root = '/data/AIGC_3rd_2021/GIST_tr2_veryhard5000_all_tr2'
os.system('rm -rf '+root+'/img/')
os.system('mkdir '+root+'/img/')
wav_folder_path = os.path.join(root, 'wav')
img_folder_path = os.path.join(root, 'img')
train_label_path = os.path.join(root, 'tr2_devel_5000.json')
train_label_merge_out = os.path.join(root, 'label_coco_bbox.json')
Training

Step1. Change Data loading path of exps/yolox_audio__tr2/yolox_x.py

        self.train_path = '/data/AIGC_3rd_2021/GIST_tr2_veryhard5000_all_tr2'
        self.val_path = '/data/AIGC_3rd_2021/tr2_set_01_tune'
        self.train_ann = "label_coco_bbox.json"
        self.val_ann = "label_coco_bbox.json"

Step2. Begin training:

python3 tools/train.py -expn yolox_audio__tr2 -n yolox_audio_x \
-f exps/yolox_audio__tr2/yolox_x.py -d 4 -b 32 --fp16 \
-c /data/pretrained/yolox_x.pth
  • -d: number of gpu devices
  • -b: total batch size, the recommended number for -b is num-gpu * 8
  • -f: path of experiement file
  • --fp16: mixed precision training
  • --cache: caching imgs into RAM to accelarate training, which need large system RAM.

We are encouraged to use pretrained YOLOX model for the training. https://github.com/Megvii-BaseDetection/YOLOX

Inference Run following demo_audio.py 
python3 tools/demo.py --demo image -expn yolox_audio__tr2 -n yolox_audio_x \
-f exps/yolox_audio__tr2/yolox_x.py \
-c YOLOX_outputs/yolox_audio__tr2/best_ckpt.pth \
--path /data/AIGC_3rd_2021/GIST_tr2_100/img/ \
--save_folder /data/yolox_out \
--conf 0.2 --nms 0.65 --tsize 256 --save_result --device gpu

From the demo_audio.py you can get on-offset VAD time and class of each audio chunk.

References

  • YOLOX baseline implemented by PyTorch: YOLOX
 @article{yolox2021,
  title={YOLOX: Exceeding YOLO Series in 2021},
  author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
  journal={arXiv preprint arXiv:2107.08430},
  year={2021}
}
  • Librosa for audio feature extraction: librosa
McFee, Brian, Colin Raffel, Dawen Liang, Daniel PW Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto. “librosa: Audio and music signal analysis in python.” In Proceedings of the 14th python in science conference, pp. 18-25. 2015.

Acknowledgement

This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-00014).

You might also like...
a Pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in 2021"

A pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in 2021" 1. Notes This is a pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in

null 91 Dec 26, 2022
load .txt to train YOLOX, same as Yolo others

YOLOX train your data you need generate data.txt like follow format (per line- one image). prepare one data.txt like this: img_path1 x1,y1,x2,y2,clas

LiMingf 18 Aug 18, 2022
A set of tools for converting a darknet dataset to COCO format working with YOLOX

darknet格式数据→COCO darknet训练数据目录结构（详情参见dataset/darknet）： darknet ├── class.names ├── gen_config.data ├── gen_train.txt ├── gen_valid.txt └── images

RapidAI-NG 148 Jan 3, 2023
FairMOT for Multi-Class MOT using YOLOX as Detector

FairMOT-X Project Overview FairMOT-X is a multi-class multi object tracker, which has been tailored for training on the BDD100K MOT Dataset. It makes

Jonathan Tan 33 Dec 28, 2022
YOLOX Win10 Project
YOLOX Win10 Project

Introduction 这是一个用于Windows训练YOLOX的项目，相比于官方项目，做了一些适配和修改： 1、解决了Windows下import yolox失败，No such file or directory: 'xxx.xml'等路径问题 2、CUDA out of memory等显存不

null 5 Jun 8, 2022
YOLOX-RMPOLY
YOLOX-RMPOLY

本算法为适应robomaster比赛，而改动自矩形识别的yolox算法。 基于旷视科技YOLOX，实现对不规则四边形的目标检测 TODO 修改onnx推理模型 更改/添加标注： 1.yolox/models/yolox_polyhead.py: 1.1继承yolox/models/yolo_

null 3 Feb 25, 2022
The backbone CSPDarkNet of YOLOX.

YOLOX-Backbone The backbone CSPDarkNet of YOLOX. In this project, you can enjoy: CSPDarkNet-S CSPDarkNet-M CSPDarkNet-L CSPDarkNet-X CSPDarkNet-Tiny C

Jianhua Yang 9 Aug 22, 2022
BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation
BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation

BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation This is a demo implementation of BYOL for Audio (BYOL-A), a self-sup

NTT Communication Science Laboratories 160 Jan 4, 2023
Cancer-and-Tumor-Detection-Using-Inception-model - In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks, specifically here the Inception model by google.
Cancer-and-Tumor-Detection-Using-Inception-model - In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks, specifically here the Inception model by google.

Cancer-and-Tumor-Detection-Using-Inception-model In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks

Deepak Nandwani 1 Jan 1, 2022
Owner
intflow Inc.
Official Code Repositories of intflow.ai
intflow Inc.
GitHub
Yolox-bytetrack-sample - Python sample of MOT (Multiple Object Tracking) using YOLOX and ByteTrack

yolox-bytetrack-sample YOLOXとByteTrackを用いたMOT(Multiple Object Tracking)のPythonサン

KazuhitoTakahashi 12 Nov 9, 2022
Generic Event Boundary Detection: A Benchmark for Event Segmentation

Generic Event Boundary Detection: A Benchmark for Event Segmentation We release our data annotation & baseline codes for detecting generic event bound

null 47 Nov 22, 2022
YOLOX + ROS(1, 2) object detection package

YOLOX + ROS(1, 2) object detection package

Ar-Ray 158 Dec 21, 2022
Scikit-event-correlation - Event Correlation and Forecasting over High Dimensional Streaming Sensor Data algorithms

scikit-event-correlation Event Correlation and Changing Detection Algorithm Theo

Intellia ICT 5 Oct 30, 2022
Event-forecasting - Event Forecasting Algorithms With Python

event-forecasting Event Forecasting Algorithms Theory Correlating events in comp

Intellia ICT 4 Feb 15, 2022
Event sourced bank - A wide-and-shallow example using the Python event sourcing library

Event Sourced Bank A "wide but shallow" example of using the Python event sourci

null 3 Mar 9, 2022
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with ONNX, TensorRT, ncnn, and OpenVINO supported.

Introduction YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and ind

null 7.7k Jan 3, 2023
using yolox+deepsort for object-tracker

YOLOX_deepsort_tracker yolox+deepsort实现目标跟踪 最新的yolox尝尝鲜~~（yolox正处在频繁更新阶段，因此直接链接yolox仓库作为子模块） Install Clone the repository recursively: git clone --rec

null 245 Dec 26, 2022
MegEngine implementation of YOLOX

Introduction YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and ind

旷视天元 MegEngine 77 Nov 22, 2022
yolox_backbone is a deep-learning library and is a collection of YOLOX Backbone models.

YOLOX-Backbone yolox-backbone is a deep-learning library and is a collection of YOLOX backbone models. Install pip install yolox-backbone Load a Pret

Yonghye Kwon 21 Dec 28, 2022