Update README.md

README.md

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-license: cc-by-nc-sa-4.0
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+---
+license: cc-by-nc-sa-4.0
+pipeline_tag: audio-classification
+tags:
+- autrainer
+- audio
+- ecoacoustic-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.
+
+# Installation
+
+To use the model, you have to install autrainer, e.g. via pip:
+
+```
+pip install autrainer
+```
+
+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/InsectNet-BE -r <data-root> <output-root>`
+
+For possible inference settings (e.g. using sliding window) and all usable parameters, please have a look at the autrainer documentation.
+
+## 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.