Easy to use Audio Tagging in PyTorch

Last update: Dec 22, 2022

Overview

Audio Classification, Tagging & Sound Event Detection in PyTorch

Progress:

Model Zoo

AudioSet Pretrained Models

Model	Task	mAP ^(%)	Sample Rate ^(kHz)	Window Length	Num Mels	Fmax	Weights
CNN14	Tagging	43.1	32	1024	64	14k	download
CNN14_16k	Tagging	43.8	16	512	64	8k	download

CNN14_DecisionLevelMax	SED	38.5	32	1024	64	14k	download

Note: These models will be used as a pretrained model in the fine-tuning tasks below. Check out audioset-tagging-cnn, if you want to train on AudioSet dataset.

Fine-tuned Classification Models

Model	Dataset	Accuracy ^(%)	Sample Rate ^(kHz)	Weights
CNN14	ESC50 (Fold-5)	95.75	32	download
CNN14	FSDKaggle2018 (test)	93.56	32	download
CNN14	SpeechCommandsv1 (val/test)	96.60/96.77	32	download

Fine-tuned Tagging Models

Model	Dataset	mAP(%)	AUC	d-prime	Sample Rate ^(kHz)	Config	Weights
CNN14	FSDKaggle2019	-	-	-	32	-	-

Fine-tuned SED Models

Model	Dataset	F1	Sample Rate ^(kHz)	Config	Weights
CNN14_DecisionLevelMax	DESED	-	32	-	-

Supported Datasets

Dataset	Task	Classes	Train	Val	Test	Audio Length	Audio Spec	Size
ESC-50	Classification	50	2,000	5 folds	-	5s	44.1kHz, mono	600MB
UrbanSound8k	Classification	10	8,732	10 folds	-	<=4s	Vary	5.6GB
FSDKaggle2018	Classification	41	9,473	-	1,600	300ms~30s	44.1kHz, mono	4.6GB
SpeechCommandsv1	Classification	30	51,088	6,798	6,835	<=1s	16kHz, mono	1.4GB
SpeechCommandsv2	Classification	35	84,843	9,981	11,005	<=1s	16kHz, mono	2.3GB

FSDKaggle2019*	Tagging	80	4,970+19,815	-	4,481	300ms~30s	44.1kHz, mono	24GB
MTT*	Tagging	50	19,000	-	-	-	-	3GB

DESED*	SED	10	-	-	-	10	-	-

Notes: * datasets are not available yet. Classification dataset are treated as multi-class/single-label classification and tagging and sed datasets are treated as multi-label classification.

Dataset Structure (click to expand)

Download the dataset and prepare it into the following structure.

datasets
|__ ESC50
    |__ audio

|__ Urbansound8k
    |__ audio

|__ FSDKaggle2018
    |__ audio_train
    |__ audio_test
    |__ FSDKaggle2018.meta
        |__ train_post_competition.csv
        |__ test_post_competition_scoring_clips.csv

|__ SpeechCommandsv1/v2
    |__ bed
    |__ bird
    |__ ...
    |__ testing_list.txt
    |__ validation_list.txt

Augmentations (click to expand)

Currently, the following augmentations are supported. More will be added in the future. You can test the effects of augmentations with this notebook

WaveForm Augmentations:

Spectrogram Augmentations:

Time Masking
Frequency Masking
Filter Augmentation

Usage

Requirements (click to expand)

python >= 3.6
pytorch >= 1.8.1
torchaudio >= 0.8.1

Other requirements can be installed with pip install -r requirements.txt.

Configuration (click to expand)

Create a configuration file in configs. Sample configuration for ESC50 dataset can be found here.
Copy the contents of this and then edit the fields you think if it is needed.
This configuration file is needed for all of training, evaluation and prediction scripts.

Training (click to expand)

To train with a single GPU:

$ python tools/train.py --cfg configs/CONFIG_FILE_NAME.yaml

To train with multiple gpus, set DDP field in config file to true and run as follows:

$ python -m torch.distributed.launch --nproc_per_node=2 --use_env tools/train.py --cfg configs/CONFIG_FILE_NAME.yaml

Evaluation (click to expand)

Make sure to set MODEL_PATH of the configuration file to your trained model directory.

$ python tools/val.py --cfg configs/CONFIG_FILE.yaml

Audio Classification/Tagging Inference

Set MODEL_PATH of the configuration file to your model's trained weights.
Change the dataset name in DATASET >> NAME as your trained model's dataset.
Set the testing audio file path in TEST >> FILE.
Run the following command.

$ python tools/infer.py --cfg configs/CONFIG_FILE.yaml

## for example
$ python tools/infer.py --cfg configs/audioset.yaml

You will get an output similar to this:

Class                     Confidence
----------------------  ------------
Speech                     0.897762
Telephone bell ringing     0.752206
Telephone                  0.219329
Inside, small room         0.20761
Music                      0.0770325

Sound Event Detection Inference

Set MODEL_PATH of the configuration file to your model's trained weights.
Change the dataset name in DATASET >> NAME as your trained model's dataset.
Set the testing audio file path in TEST >> FILE.
Run the following command.

$ python tools/sed_infer.py --cfg configs/CONFIG_FILE.yaml

## for example
$ python tools/sed_infer.py --cfg configs/audioset_sed.yaml

You will get an output similar to this:

Class                     Start    End
----------------------  -------  -----
Speech                      2.2    7
Telephone bell ringing      0      2.5

The following plot will also be shown, if you set PLOT to true:

References (click to expand)

Citations (click to expand)

@misc{kong2020panns,
      title={PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition}, 
      author={Qiuqiang Kong and Yin Cao and Turab Iqbal and Yuxuan Wang and Wenwu Wang and Mark D. Plumbley},
      year={2020},
      eprint={1912.10211},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}

@misc{gong2021ast,
      title={AST: Audio Spectrogram Transformer}, 
      author={Yuan Gong and Yu-An Chung and James Glass},
      year={2021},
      eprint={2104.01778},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}

@misc{nam2021heavily,
      title={Heavily Augmented Sound Event Detection utilizing Weak Predictions}, 
      author={Hyeonuk Nam and Byeong-Yun Ko and Gyeong-Tae Lee and Seong-Hu Kim and Won-Ho Jung and Sang-Min Choi and Yong-Hwa Park},
      year={2021},
      eprint={2107.03649},
      archivePrefix={arXiv},
      primaryClass={eess.AS}
}

You might also like...

TorchMetrics is a collection of 25+ PyTorch metrics implementations and an easy-to-use API to create custom metrics.

Machine learning metrics for distributed, scalable PyTorch applications.

1.2k Jan 6, 2023

TorchFlare is a simple, beginner-friendly, and easy-to-use PyTorch Framework train your models effortlessly.

TorchFlare TorchFlare is a simple, beginner-friendly and an easy-to-use PyTorch Framework train your models without much effort. It provides an almost

85 Dec 26, 2022

A more easy-to-use implementation of KPConv based on PyTorch.

A more easy-to-use implementation of KPConv This repo contains a more easy-to-use implementation of KPConv based on PyTorch. Introduction KPConv is a

36 Dec 29, 2022

Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

Deep-Learning-based-Spectrum-Sensing Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectru

10 Dec 14, 2022

Fast image augmentation library and easy to use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about library: https://www.mdpi.com/2078-2489/11/2/125

Albumentations Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to inc

11.4k Jan 9, 2023

Fast, flexible and easy to use probabilistic modelling in Python.

Please consider citing the JMLR-MLOSS Manuscript if you've used pomegranate in your academic work! pomegranate is a package for building probabilistic

3k Dec 29, 2022

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

3k Jan 3, 2023

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

2.8k Feb 12, 2021

A fast and easy to use, moddable, Python based Minecraft server!

PyMine PyMine - The fastest, easiest to use, Python-based Minecraft Server! Features Note: This list is not always up to date, and doesn't contain all

144 Dec 30, 2022

Releases(v0.2.0)

v0.2.0(Aug 17, 2021)
This release includes the following:

Fine-tuned on ESC50, FSDKaggle2018, SpeechCommandsv1

Add waveform augmentations

Add spectrogram augmentations

Add augmentation testing notebook

Add tagging metrics

Source code(tar.gz)
Source code(zip)
v0.1.0(Aug 13, 2021)
Add the following datasets:

ESC50

UrbanSound8k

FSDKaggle2018

SpeechCommandsv1/v2

Release fine-tuned model on ESC50.
Source code(tar.gz)
Source code(zip)

Owner

sithu3

AI Developer

GitHub Repository

The codebase for our paper "Generative Occupancy Fields for 3D Surface-Aware Image Synthesis" (NeurIPS 2021)

Generative Occupancy Fields for 3D Surface-Aware Image Synthesis (NeurIPS 2021) Project Page | Paper Xudong Xu, Xingang Pan, Dahua Lin and Bo Dai GOF

97 Nov 10, 2022

official implementation for the paper "Simplifying Graph Convolutional Networks"

Simplifying Graph Convolutional Networks Updates As pointed out by #23, there was a subtle bug in our preprocessing code for the reddit dataset. After

727 Jan 01, 2023

Phylogeny Partners

Phylogeny-Partners Two states models Instalation You may need to install the cython, networkx, numpy, scipy package: pip install cython, networkx, num

1 Sep 19, 2022

Code, environments, and scripts for the paper: "How Private Is Your RL Policy? An Inverse RL Based Analysis Framework"

Privacy-Aware Inverse RL (PRIL) Analysis Framework Code, environments, and scripts for the paper: "How Private Is Your RL Policy? An Inverse RL Based

1 Dec 06, 2021

Voice assistant - Voice assistant with python

🌐 Python Voice Assistant 🌵 - User's greeting 🌵 - Writing tasks to todo-list ?

10 Dec 26, 2022

Phy-Q: A Benchmark for Physical Reasoning

Phy-Q: A Benchmark for Physical Reasoning Cheng Xue*, Vimukthini Pinto*, Chathura Gamage* Ekaterina Nikonova, Peng Zhang, Jochen Renz School of Comput

29 Dec 19, 2022

A DCGAN to generate anime faces using custom mined dataset

Anime-Face-GAN-Keras A DCGAN to generate anime faces using custom dataset in Keras. Dataset The dataset is created by crawling anime database websites

190 Jan 03, 2023

Official implementation for the paper: "Multi-label Classification with Partial Annotations using Class-aware Selective Loss"

Multi-label Classification with Partial Annotations using Class-aware Selective Loss Paper | Pretrained models Official PyTorch Implementation Emanuel

99 Dec 27, 2022

ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation

ST++ This is the official PyTorch implementation of our paper: ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation. Lihe Ya

147 Jan 03, 2023

A large-scale database for graph representation learning

29 Nov 25, 2022

Implementation of a Transformer that Ponders, using the scheme from the PonderNet paper

Ponder(ing) Transformer Implementation of a Transformer that learns to adapt the number of computational steps it takes depending on the difficulty of

65 Oct 04, 2022

Official repository for GCR rerank, a GCN-based reranking method for both image and video re-ID

53 Nov 22, 2022

EPSANet：An Efficient Pyramid Split Attention Block on Convolutional Neural Network

EPSANet：An Efficient Pyramid Split Attention Block on Convolutional Neural Network This repo contains the official Pytorch implementaion code and conf

175 Jan 07, 2023

Official implementation of Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models at NeurIPS 2021

Representer Point Selection via Local Jacobian Expansion for Classifier Explanation of Deep Neural Networks and Ensemble Models This repository is the

2 Dec 01, 2021

Easy to use Audio Tagging in PyTorch

Related tags

Overview

Audio Classification, Tagging & Sound Event Detection in PyTorch

Model Zoo

Supported Datasets

Usage

You might also like...

TorchMetrics is a collection of 25+ PyTorch metrics implementations and an easy-to-use API to create custom metrics.

TorchFlare is a simple, beginner-friendly, and easy-to-use PyTorch Framework train your models effortlessly.

A more easy-to-use implementation of KPConv based on PyTorch.

Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

Fast image augmentation library and easy to use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about library: https://www.mdpi.com/2078-2489/11/2/125

Fast, flexible and easy to use probabilistic modelling in Python.

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

A fast and easy to use, moddable, Python based Minecraft server!

Releases(v0.2.0)

v0.2.0(Aug 17, 2021)

v0.1.0(Aug 13, 2021)

Owner

sithu3

The codebase for our paper "Generative Occupancy Fields for 3D Surface-Aware Image Synthesis" (NeurIPS 2021)

official implementation for the paper "Simplifying Graph Convolutional Networks"

Phylogeny Partners

Code, environments, and scripts for the paper: "How Private Is Your RL Policy? An Inverse RL Based Analysis Framework"

Voice assistant - Voice assistant with python

Phy-Q: A Benchmark for Physical Reasoning

A DCGAN to generate anime faces using custom mined dataset

Official implementation for the paper: "Multi-label Classification with Partial Annotations using Class-aware Selective Loss"

ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation

A large-scale database for graph representation learning

Implementation of a Transformer that Ponders, using the scheme from the PonderNet paper

Official repository for GCR rerank, a GCN-based reranking method for both image and video re-ID

EPSANet：An Efficient Pyramid Split Attention Block on Convolutional Neural Network

Official implementation of Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models at NeurIPS 2021

A2LP for short, ECCV2020 spotlight, Investigating SSL principles for UDA problems

git《USD-Seg:Learning Universal Shape Dictionary for Realtime Instance Segmentation》(2020) GitHub: [fig2]

MQBench: Towards Reproducible and Deployable Model Quantization Benchmark

InvTorch: memory-efficient models with invertible functions

JDet is Object Detection Framework based on Jittor.

Subnet Replacement Attack: Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networks