| | --- |
| | 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 |
| |
|
| | TODO |
| |
|
| | ### Dataset |
| |
|
| | TODO |
| |
|
| |
|
| | ### Features |
| |
|
| | 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. |
| |
|
| | ### Training process |
| |
|
| | 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. |
| |
|
| |
|
| | ## Evaluation |
| |
|
| | The performance on the test set reached a (macro) f1-score of 0.70. |
| |
|
| |
|
| | ## Acknowledgments |
| |
|
| | TODO |
| |
|
| | Please acknowledge the work which produced the original model. We would appreciate an acknowledgment to autrainer. |
| |
|
