Update README.md

README.md

@@ -1,3 +1,68 @@
----
-license: cc-by-nc-sa-4.0
----
+---
+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.