End-to-end beat and downbeat tracking in the time domain.

Related tags

Deep Learningwavebeat
Overview

WaveBeat

End-to-end beat and downbeat tracking in the time domain.

| Paper | Code | Video | Slides |

Setup

First clone the repo.

git clone https://github.com/csteinmetz1/wavebeat.git
cd wavebeat

Setup a virtual environment and activate it. This requires that you use Python 3.8.

python3 -m venv env/
source env/bin/activate

Next install numpy, cython, and aiohttp first, manually.

pip install numpy cython aiohttp

Then install the wavebeat module.

python setup.py install

This will ensure that madmom installs properly, as it currently fails unless cython, numpy, and aiohttp are installed first.

Predicting beats

To begin you will first need to download the pre-trained model here. Place it in the checkpoints/ directory, rename to get the .ckpt file.

cd checkpoints
wget https://zenodo.org/record/5525120/files/wavebeat_epoch%3D98-step%3D24749.ckpt?download=1
mv wavebeat_epoch=98-step=24749.ckpt?download=1 wavebeat_epoch=98-step=24749.ckpt

Functional interface

If you would like to use the functional interface you can create a script and import wavebeat as follows.

from wavebeat.tracker import beatTracker

beat, downbeats = beatTracker('audio.wav')

Script interface

We provide a simple script interface to load an audio file and predict the beat and downbeat locations with a pre-trained model. Run the model by providing a path to an audio file.

python predict.py path_to_audio.wav

Evaluation

In order to run the training and evaluation code you will additionally need to install all of the development requirements.

pip install -r requirements.txt

To recreate our reported results you will first need to have access to the datasets. See the paper for details on where to find them.

Use the command below to run the evaluation on GPU.

python simple_test.py \
--logdir mdoels/wavebeatv1/ \
--ballroom_audio_dir /path/to/BallroomData \
--ballroom_annot_dir /path/to/BallroomAnnotations \
--beatles_audio_dir /path/to/The_Beatles \
--beatles_annot_dir /path/to/The_Beatles_Annotations/beat/The_Beatles \
--hainsworth_audio_dir /path/to/hainsworth/wavs \
--hainsworth_annot_dir /path/to/hainsworth/beat \
--rwc_popular_audio_dir /path/to/rwc_popular/audio \
--rwc_popular_annot_dir /path/to/rwc_popular/beat \
--gtzan_audio_dir /path/to/gtzan/ \
--gtzan_annot_dir /path/to/GTZAN-Rhythm/jams \
--smc_audio_dir /path/to/SMC_MIREX/SMC_MIREX_Audio \
--smc_annot_dir /path/to/SMC_MIREX/SMC_MIREX_Annotations_05_08_2014 \
--num_workers 8 \

Training

To train the model with the same hyperparameters as those used in the paper, assuming the datasets are available, run the following command.

python train.py \
--ballroom_audio_dir /path/to/BallroomData \
--ballroom_annot_dir /path/to/BallroomAnnotations \
--beatles_audio_dir /path/to/The_Beatles \
--beatles_annot_dir /path/to/The_Beatles_Annotations/beat/The_Beatles \
--hainsworth_audio_dir /path/to/hainsworth/wavs \
--hainsworth_annot_dir /path/to/hainsworth/beat \
--rwc_popular_audio_dir /path/to/rwc_popular/audio \
--rwc_popular_annot_dir /path/to/rwc_popular/beat \
--gpus 1 \
--preload \
--precision 16 \
--patience 10 \
--train_length 2097152 \
--eval_length 2097152 \
--model_type dstcn \
--act_type PReLU \
--norm_type BatchNorm \
--channel_width 32 \
--channel_growth 32 \
--augment \
--batch_size 16 \
--lr 1e-3 \
--gradient_clip_val 4.0 \
--audio_sample_rate 22050 \
--num_workers 24 \
--max_epochs 100 \

Cite

If you use this code in your work please consider citing us.

@inproceedings{steinmetz2021wavebeat,
    title={{WaveBeat}: End-to-end beat and downbeat tracking in the time domain},
    author={Steinmetz, Christian J. and Reiss, Joshua D.},
    booktitle={151st AES Convention},
    year={2021}}
Owner
Christian J. Steinmetz
Building tools for musicians and audio engineers (often with machine learning). PhD Student at Queen Mary University of London.
Christian J. Steinmetz
Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation

STCN Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang [a

Rex Cheng 456 Dec 12, 2022
10th place solution for Google Smartphone Decimeter Challenge at kaggle.

Under refactoring 10th place solution for Google Smartphone Decimeter Challenge at kaggle. Google Smartphone Decimeter Challenge Global Navigation Sat

12 Oct 25, 2022
Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation (ICCV2021)

Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation This is a pytorch project for the paper Dynamic Divide-and-Conquer Ad

DV Lab 29 Nov 21, 2022
Official Datasets and Implementation from our Paper "Video Class Agnostic Segmentation in Autonomous Driving".

Video Class Agnostic Segmentation [Method Paper] [Benchmark Paper] [Project] [Demo] Official Datasets and Implementation from our Paper "Video Class A

Mennatullah Siam 26 Oct 24, 2022
Unofficial implementation of Google's FNet: Mixing Tokens with Fourier Transforms

FNet: Mixing Tokens with Fourier Transforms Pytorch implementation of Fnet : Mixing Tokens with Fourier Transforms. Citation: @misc{leethorp2021fnet,

Rishikesh (ऋषिकेश) 218 Jan 05, 2023
an implementation of Revisiting Adaptive Convolutions for Video Frame Interpolation using PyTorch

revisiting-sepconv This is a reference implementation of Revisiting Adaptive Convolutions for Video Frame Interpolation [1] using PyTorch. Given two f

Simon Niklaus 59 Dec 22, 2022
Vision Deep-Learning using Tensorflow, Keras.

Welcome! I am a computer vision deep learning developer working in Korea. This is my blog, and you can see everything I've studied here. https://www.n

kimminjun 6 Dec 14, 2022
Unified tracking framework with a single appearance model

Paper: Do different tracking tasks require different appearance model? [ArXiv] (comming soon) [Project Page] (comming soon) UniTrack is a simple and U

ZhongdaoWang 300 Dec 24, 2022
This is a Keras implementation of a CNN for estimating age, gender and mask from a camera.

face-detector-age-gender This is a Keras implementation of a CNN for estimating age, gender and mask from a camera. Before run face detector app, expr

Devdreamsolution 2 Dec 04, 2021
Mmdet benchmark with python

mmdet_benchmark 本项目是为了研究 mmdet 推断性能瓶颈,并且对其进行优化。 配置与环境 机器配置 CPU:Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz GPU:NVIDIA GeForce RTX 3080 10GB 内存:64G 硬盘:1T

杨培文 (Yang Peiwen) 24 May 21, 2022
Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.

pytorch Implementation of U-Net, R2U-Net, Attention U-Net, Attention R2U-Net U-Net: Convolutional Networks for Biomedical Image Segmentation https://a

leejunhyun 2k Jan 02, 2023
All the essential resources and template code needed to understand and practice data structures and algorithms in python with few small projects to demonstrate their practical application.

Data Structures and Algorithms Python INDEX 1. Resources - Books Data Structures - Reema Thareja competitiveCoding Big-O Cheat Sheet DAA Syllabus Inte

Shushrut Kumar 129 Dec 15, 2022
Bi-level feature alignment for versatile image translation and manipulation (Under submission of TPAMI)

Bi-level feature alignment for versatile image translation and manipulation (Under submission of TPAMI) Preparation Clone the Synchronized-BatchNorm-P

Fangneng Zhan 12 Aug 10, 2022
Official Matlab Implementation for "Tiny Obstacle Discovery by Occlusion-aware Multilayer Regression", TIP 2020

Tiny Obstacle Discovery by Occlusion-aware Multilayer Regression Official Matlab Implementation for "Tiny Obstacle Discovery by Occlusion-aware Multil

Xuefeng 5 Jan 15, 2022
A python tutorial on bayesian modeling techniques (PyMC3)

Bayesian Modelling in Python Welcome to "Bayesian Modelling in Python" - a tutorial for those interested in learning how to apply bayesian modelling t

Mark Regan 2.4k Jan 06, 2023
Evaluating deep transfer learning for whole-brain cognitive decoding

Evaluating deep transfer learning for whole-brain cognitive decoding This README file contains the following sections: Project description Repository

Armin Thomas 5 Oct 31, 2022
Official implementation of "CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding" (CVPR, 2022)

CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding (CVPR'22) Paper Link | Project Page Abstract : Manual an

Mohamed Afham 152 Dec 23, 2022
Occlusion robust 3D face reconstruction model in CFR-GAN (WACV 2022)

Occlusion Robust 3D face Reconstruction Yeong-Joon Ju, Gun-Hee Lee, Jung-Ho Hong, and Seong-Whan Lee Code for Occlusion Robust 3D Face Reconstruction

Yeongjoon 31 Dec 19, 2022
A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution (CVPR2022)

A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution (CVPR2022) https://arxiv.org/abs/2203.09388 Jianqi Ma, Zheto

MA Jianqi, shiki 104 Jan 05, 2023
Implementation for our ICCV 2021 paper: Dual-Camera Super-Resolution with Aligned Attention Modules

DCSR: Dual Camera Super-Resolution Implementation for our ICCV 2021 oral paper: Dual-Camera Super-Resolution with Aligned Attention Modules paper | pr

Tengfei Wang 110 Dec 20, 2022