Easy to use Audio Tagging in PyTorch

Related tags

Deep Learningaudiopytorchaudio-classificationsound-event-detectionaudio-taggingcnn14
Overview

Audio Classification, Tagging & Sound Event Detection in PyTorch

Progress:

  • Fine-tune on audio classification
  • Fine-tune on audio tagging
  • Fine-tune on sound event detection
  • Add tagging metrics
  • Add Tutorial
  • Add Augmentation Notebook
  • Add more schedulers
  • Add FSDKaggle2019 dataset
  • Add MTT dataset
  • Add DESED

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:

  • MixUp
  • Background Noise
  • Gaussian Noise
  • Fade In/Out
  • Volume
  • CutMix

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:

sed_result


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}
}
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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)

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sithu3
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