Code for detecting and classifying bat echolocation calls in high frequency audio recordings.
We recommend using an isolated Python environment to avoid dependency issues. Choose one of the following options:
- Install the Anaconda Python 3.10 distribution for your operating system from here. Create a new environment and activate it:
conda create -y --name batdetect2 python==3.10
conda activate batdetect2- If you already have Python installed (version >= 3.8,< 3.11) and prefer using virtual environments then:
python -m venv .venv
source .venv/bin/activateYou can use pip to install batdetect2:
pip install batdetect2Alternatively, download this code from the repository (by clicking on the green button on top right) and unzip it. Once unzipped, run this from extracted folder.
pip install .Make sure you have the environment activated before installing batdetect2.
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You can try a demo of the model (for UK species) on huggingface.
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Alternatively, click here to run the model using Google Colab. You can also run this notebook locally.
After following the above steps to install the code you can run the model on your own data.
You can run the model by opening the command line and typing:
batdetect2 detect AUDIO_DIR ANN_DIR DETECTION_THRESHOLDe.g.
batdetect2 detect example_data/audio/ example_data/anns/ 0.3AUDIO_DIR is the path on your computer to the audio wav files of interest.
ANN_DIR is the path on your computer where the model predictions will be saved. The model will output both .csv and .json results for each audio file.
DETECTION_THRESHOLD is a number between 0 and 1 specifying the cut-off threshold applied to the calls. A smaller number will result in more calls detected, but with the chance of introducing more mistakes.
There are also optional arguments, e.g. you can request that the model outputs features (i.e. estimated call parameters) such as duration, max_frequency, etc. by setting the flag --spec_features. These will be saved as *_spec_features.csv files:
batdetect2 detect example_data/audio/ example_data/anns/ 0.3 --spec_features
You can also specify which model to use by setting the --model_path argument. If not specified, it will default to using a model trained on UK data e.g.
batdetect2 detect example_data/audio/ example_data/anns/ 0.3 --model_path models/Net2DFast_UK_same.pth.tar
If you prefer to process your data within a Python script then you can use the batdetect2 Python API.
from batdetect2 import api
AUDIO_FILE = "example_data/audio/20170701_213954-MYOMYS-LR_0_0.5.wav"
# Process a whole file
results = api.process_file(AUDIO_FILE)
# Or, load audio and compute spectrograms
audio = api.load_audio(AUDIO_FILE)
spec = api.generate_spectrogram(audio)
# And process the audio or the spectrogram with the model
detections, features, spec = api.process_audio(audio)
detections, features = api.process_spectrogram(spec)
# Do something else ...You can integrate the detections or the extracted features to your custom analysis pipeline.
Take a look at the steps outlined in finetuning readme here for a description of how to train your own model.
The raw audio data and annotations used to train the models in the paper will be added soon. The audio interface used to annotate audio data for training and evaluation is available here.
The models developed and shared as part of this repository should be used with caution. While they have been evaluated on held out audio data, great care should be taken when using the model outputs for any form of biodiversity assessment. Your data may differ, and as a result it is very strongly recommended that you validate the model first using data with known species to ensure that the outputs can be trusted.
For more information please consult our FAQ.
If you find our work useful in your research please consider citing our paper which you can find here:
@article{batdetect2_2022,
title = {Towards a General Approach for Bat Echolocation Detection and Classification},
author = {Mac Aodha, Oisin and Mart\'{i}nez Balvanera, Santiago and Damstra, Elise and Cooke, Martyn and Eichinski, Philip and Browning, Ella and Barataudm, Michel and Boughey, Katherine and Coles, Roger and Giacomini, Giada and MacSwiney G., M. Cristina and K. Obrist, Martin and Parsons, Stuart and Sattler, Thomas and Jones, Kate E.},
journal = {bioRxiv},
year = {2022}
}
Thanks to all the contributors who spent time collecting and annotating audio data.
- Release the code and pretrained model
- Release the datasets and annotations used the experiments in the paper
- Add the scripts used to generate the tables and figures from the paper
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