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
Asterisk is a framework to generate high-quality training datasets at scale

Asterisk is a framework to generate high-quality training datasets at scale

Mona Nashaat 44 Apr 25, 2022
[CVPR 2022 Oral] Crafting Better Contrastive Views for Siamese Representation Learning

Crafting Better Contrastive Views for Siamese Representation Learning (CVPR 2022 Oral) 2022-03-29: The paper was selected as a CVPR 2022 Oral paper! 2

249 Dec 28, 2022
Implementations of LSTM: A Search Space Odyssey variants and their training results on the PTB dataset.

An LSTM Odyssey Code for training variants of "LSTM: A Search Space Odyssey" on Fomoro. Check out the blog post. Training Install TensorFlow. Clone th

Fomoro AI 95 Apr 13, 2022
git《Tangent Space Backpropogation for 3D Transformation Groups》(CVPR 2021) GitHub:1]

LieTorch: Tangent Space Backpropagation Introduction The LieTorch library generalizes PyTorch to 3D transformation groups. Just as torch.Tensor is a m

Princeton Vision & Learning Lab 482 Jan 06, 2023
Orchestrating Distributed Materials Acceleration Platform Tutorial

Orchestrating Distributed Materials Acceleration Platform Tutorial This tutorial for orchestrating distributed materials acceleration platform was pre

BIG-MAP 1 Jan 25, 2022
A Marvelous ChatBot implement using PyTorch.

PyTorch Marvelous ChatBot [Update] it's 2019 now, previously model can not catch up state-of-art now. So we just move towards the future a transformer

JinTian 223 Oct 18, 2022
Implementation of Rotary Embeddings, from the Roformer paper, in Pytorch

Rotary Embeddings - Pytorch A standalone library for adding rotary embeddings to transformers in Pytorch, following its success as relative positional

Phil Wang 110 Dec 30, 2022
On Out-of-distribution Detection with Energy-based Models

On Out-of-distribution Detection with Energy-based Models This repository contains the code for the experiments conducted in the paper On Out-of-distr

Sven 19 Aug 07, 2022
Plotting points that lie on the intersection of the given curves using gradient descent.

Plotting intersection of curves using gradient descent Webapp Link --- What's the app about Why this app Plotting functions and their intersection. A

Divakar Verma 2 Jan 09, 2022
Learning recognition/segmentation models without end-to-end training. 40%-60% less GPU memory footprint. Same training time. Better performance.

InfoPro-Pytorch The Information Propagation algorithm for training deep networks with local supervision. (ICLR 2021) Revisiting Locally Supervised Lea

78 Dec 27, 2022
Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning, CVPR 2021

Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning By Zhenda Xie*, Yutong Lin*, Zheng Zhang, Yue Ca

Zhenda Xie 293 Dec 20, 2022
Code for paper Novel View Synthesis via Depth-guided Skip Connections

Novel View Synthesis via Depth-guided Skip Connections Code for paper Novel View Synthesis via Depth-guided Skip Connections @InProceedings{Hou_2021_W

8 Mar 14, 2022
Official implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

DiscoGAN Official PyTorch implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. Prerequisites Python 2.7

SK T-Brain 754 Dec 29, 2022
Implementation for On Provable Benefits of Depth in Training Graph Convolutional Networks

Implementation for On Provable Benefits of Depth in Training Graph Convolutional Networks Setup This implementation is based on PyTorch = 1.0.0. Smal

Weilin Cong 8 Oct 28, 2022
Lepard: Learning Partial point cloud matching in Rigid and Deformable scenes

Lepard: Learning Partial point cloud matching in Rigid and Deformable scenes [Paper] Method overview 4DMatch Benchmark 4DMatch is a benchmark for matc

103 Jan 06, 2023
Streamlit tool to explore coco datasets

What is this This tool given a COCO annotations file and COCO predictions file will let you explore your dataset, visualize results and calculate impo

Jakub Cieslik 75 Dec 16, 2022
Self-training with Weak Supervision (NAACL 2021)

This repo holds the code for our weak supervision framework, ASTRA, described in our NAACL 2021 paper: "Self-Training with Weak Supervision"

Microsoft 148 Nov 20, 2022
Python implementation of MULTIseq barcode alignment using fuzzy string matching and GMM barcode assignment

Python implementation of MULTIseq barcode alignment using fuzzy string matching and GMM barcode assignment.

MT Schmitz 2 Feb 11, 2022
Save-restricted-v-3 - Save restricted content Bot For telegram

Save restricted content Bot Contact: Telegram A stable telegram bot to get restr

DEVANSH 11 Dec 21, 2022
OBBDetection: an oriented object detection toolbox modified from MMdetection

OBBDetection note: If you have questions or good suggestions, feel free to propose issues and contact me. introduction OBBDetection is an oriented obj

MIXIAOXIN_HO 3 Nov 11, 2022