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AI4Good Project Group 2A

BirdCLEF 2022 training code for group 2A.

Setup

We recommend using a new anaconda environment with python 3.10. First clone the repo with git clone and then create the environment with

conda create -n birdclef python=3.10`
conda activate birdclef

Requirements

Our pipeline was tested under several Ubuntu environments. Using Windows is not recommended as there are problematic libraries that don't work well when using it.

Install the required modules with pip

pip install -r requirements.txt

Data Preparation

The quickest way would be to download our premade dataset directly from Kaggle. Put the dataset in a datasets folder in the project root directory.

Alternatively you can preprocess the data yourself. First download the original dataset from Kaggle and unpack it to a separated folder in data_processing. We chose the folder name birdclef-2022.

Data Processing

Then change the root and the out_folder variable in generate_specs.py to the train_audio path (found in the 2022 dataset) and your desired output folder name respectively. Provide also a root folder where there are background noise audio files for data augmentation or uncomment the noise processing.

Start the dataset generation by moving to the processing directory and executing the script with

cd data_processing
python3 generate_specs.py

Note that this takes a very long time. Afterwards move the generated folders inside the chosen spectrogram root folder (data and noise) to the Kaggle dataset folder (birdclef-2022 in our case).

Augmenting the Metadata

Since we changed the file structure, we must recalculate the metadata and provide group information for the k-fold splitting. Change the directory to model. Then change the root variable in the generate_augmented_df.py to your dataset root folder with the csv files and the generated data and noise folders.

cd model
python3 generate_augmented_df.py

The dataset is now ready for training.

Training

Run the training with the following command

python3 train.py --data_path ../datasets/birdclef-2022 --load_weights False

the --data_path argument is the relative path to the dataset. The --load_weights is for loading precalculated weights during pretraining on another dataset like the 2021 data (Dataset we used). For 3-fold models we have example weights that match the default config.

All the necessary configurations can be set in the config file. To use WANDB provide your api key in the config file and set up the correct project settings in the train script.

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BirdCLEF 2022 for AI4Good course

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