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--- |
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license: cc-by-nc-sa-4.0 |
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pipeline_tag: audio-classification |
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tags: |
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- autrainer |
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- audio |
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- orthoptera-tagging |
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- HearTheSpecies |
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--- |
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# InsectNet for the Biodiversity Exploratories |
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Model that tags audio files as belonging to one or more of 29 (t.b.d. below) prevalent Orthoptera species within the Biodiversity Exploratories. |
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We also have a Silence, Buzz, and Bird tag, but these predictions should be ignored and are only incorporated for the training. |
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# Installation |
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To use the model, you have to install autrainer, e.g. via pip: |
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``` |
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pip install autrainer |
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``` |
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This model has been trained and tested with autrainer version `0.6.0`. |
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For more information about autrainer, please refer to: https://autrainer.github.io/autrainer/index.html |
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# Usage |
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The model can be applied on all wav files present in a folder (`<data-root>`) and stored in another folder (`<output-root>`): |
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``` |
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autrainer inference hf:AlexanderGbd/InsectNetLocal -r <data-root> <output-root> -w 4 -s 4 -sr 96000 |
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``` |
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, where `-w` is the window size in seconds, `-s` is the step size in seconds and `-sr` is the sampling rate. |
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For other possible inference settings and all usable parameters, please have a look at the autrainer documentation. |
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However, the above settings are recommended. |
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## Training |
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### Pretraining |
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TODO |
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### Dataset |
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TODO |
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### Features |
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The audio recordings were resampled to 96kHz, as we wanted to avoid losing too much frequency information from the species. Log-Mel spectrograms were then extracted using torchlibrosa. |
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### Training process |
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The model has been trained for 30 epochs. At the end of each epoch, the model was evaluated on our validation set. |
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We release the state that achieved the best performance on this validation set. |
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All training hyperparameters can be found inside `conf/config.yaml` inside the model folder. |
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## Evaluation |
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The performance on the test set reached a (macro) f1-score of 0.70. |
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## Acknowledgments |
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TODO |
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Please acknowledge the work which produced the original model. We would appreciate an acknowledgment to autrainer. |
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