Update README.md

README.md

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-license: cc-by-4.0
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+---
+license: cc-by-4.0
+tags:
+- autrainer
+---
+
+# Baseline model for audio classification of orthopera and hemiptera
+
+The baseline model which was used in the ECOSoundSet-paper, in order to tag audio files, with one or more of 86 species belonging to the orthoptera or hemiptera insect orders.
+
+## Installation
+
+To use the model, you have to install autrainer, e.g. via pip:
+
+`pip install autrainer`
+
+## 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:autrainer/edansa-2019-cnn10-32k-t <data-root> <output-root>`
+
+## Training
+
+### Pretraining
+
+The model has been originally trained on AudioSet by Kong et. al.
+
+### Dataset
+
+The model has been further trained (finetuned) on audio segments of the ECOSoundSet dataset, which is soon to be submitted,
+and will referenced here as soon as it is publicly available. 
+
+
+### 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. The train, dev, and test set can be accessed in the `splits`-folder.
+
+
+## Evaluation
+
+The performance on the test set reached a 0.56 (macro) f1-score.
+
+
+## Acknowledgments
+
+Please acknowledge the work which produced the original model and the ECOSoundSet dataset. We would also appreciate an acknowledgment to autrainer.
+