| --- |
| license: cc-by-nc-sa-4.0 |
| pipeline_tag: audio-classification |
| tags: |
| - autrainer |
| - audio |
| - orthoptera-tagging |
| - HearTheSpecies |
| --- |
| |
| # InsectNet for the Biodiversity Exploratories |
| Model that tags audio files as belonging to one or more of 29 (t.b.d. below) prevalent Orthoptera species within the Biodiversity Exploratories. |
| We also have a Silence, Buzz, and Bird tag, but these predictions should be ignored and are only incorporated for the training. |
|
|
| # Installation |
|
|
| To use the model, you have to install autrainer, e.g. via pip: |
|
|
| ``` |
| pip install autrainer |
| ``` |
|
|
| This model has been trained and tested with autrainer version `0.6.0`. |
| For more information about autrainer, please refer to: https://autrainer.github.io/autrainer/index.html |
|
|
| # Usage |
|
|
| The model can be applied on all wav files present in a folder (`<data-root>`) and stored in another folder (`<output-root>`): |
|
|
| ``` |
| autrainer inference hf:AlexanderGbd/InsectNetLocal -r <data-root> <output-root> -w 4 -s 4 -sr 96000 |
| ``` |
| , where `-w` is the window size in seconds, `-s` is the step size in seconds and `-sr` is the sampling rate. |
| For other possible inference settings and all usable parameters, please have a look at the autrainer documentation. |
| However, the above settings are recommended. |
|
|
| ## Training |
|
|
| ### Pretraining |
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|
| TODO |
|
|
| ### 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. |
| We release the state that achieved the best performance on this validation set. |
| 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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|
