epanns 0.1.5

Categorise sounds within an audio file

epanns

epanns is a tool for categorising sound within an audio file. It is uses the E-PANNs lightweight pre-trained model developed by Arshdeep Singh at the University of Surrey. Sounds are categorised using the Google AudioSet ontology.

Command-line usage

Use pipx to install it as a CLI tool

pipx install epanns

Running the following

epanns /path/to/audio.wav

will return the predicted categories and their probability as JSON

[
  [
    "Speech",
    0.7508
  ],
  [
    "Inside, small room",
    0.0186
  ],
  [
    "Computer keyboard",
    0.0145
  ]
]

To see the available options, run epanns --help

If you do not provide a checkpoint path, the model checkpoint will be downloaded on the first run and cached for future runs.

Library usage

Use pip to install

pip install epanns

Calling it as a library

from epanns.predict import predict
top_preds = predict('/path/to/audio.wav')
print(top_preds)

will return a list of tuples for the top predictions

[
  ('Speech', 0.7508),
  ('Inside, small room', 0.0186),
  ('Computer keyboard', 0.0145)
]

Acknowledgements

This software is based on the following research. Please cite these papers:

• Arshdeep Singh, Haohe Liu and Mark D PLumbley. "E-PANNS: Sound Recognition using Efficient Pre-Trained Audio Neural Networks", accepted in Internoise 2023.

• Singh, Arshdeep, and Mark D. Plumbley. "Efficient CNNs via Passive Filter Pruning." arXiv preprint arXiv:2304.02319 (2023).

• Qiuqiang Kong, Yin Cao, Turab Iqbal, Yuxuan Wang, Wenwu Wang, Mark D. Plumbley. "PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition." arXiv preprint arXiv:1912.10211 (2019).

The research was supported by Engineering and Physical Sciences Research Council (EPSRC) Grant EP/T019751/1 “AI for Sound (AI4S)”. Project link: https://ai4s.surrey.ac.uk/

Related links

• https://research.google.com/audioset/dataset
• https://github.com/qiuqiangkong/audioset_tagging_cnn
• https://github.com/qiuqiangkong/panns_inference
• https://github.com/yinkalario/Sound-Event-Detection-AudioSet

License

MIT