Orcasound Baseline Detector 🎱 🐋

The implementation here focuses on binary detection of orca calls (that are in the audible range, hence fun to listen to and annotate :) ) We change the audio-preprocessing front-end to better match this task & fine-tune the fully-connected layers and classification head of the AudioSet model, specifically a PyTorch port of the model/weights. The model is generating local predictions on a fixed window size of ~2.45s. Sampling and aggregation strategies for more global detection at minute/hourly/day-wise time scale would be a welcome contribution (helpful for a real-time detection pipeline, or processing 2-3 months of historical data from different hydrophone nodes).

1. The model was bootstrapped with scraped open data from WHOI Marine Mammal Database (see src.scraper and notebooks/DataPreparation for details)
2. Labelled data in live conditions from Orcasound hydrophones has subsequently been added using the Pod.Cast tool by prioritizing labelling in an active-learning-like fashion after the initial bootstrap. (DataArchives details on all datasets)
3. The mel spectrogram generation is changed to better suit this task (for details on choice of filterbank see notebooks/DataPreparation. Implementation is in data_ml/src.params and data_ml/src.dataloader)
4. Given limited domain data, and need for robustness to different acoustic conditions (hydrophone nodes, SNR, noise/disturbances) in live conditions, the baseline uses transfer learning.
5. Data augmentation in the style of SpecAug is also implemented, that acts as a helpful form of regularization

Directory structure:

- data_ml 						(current directory)
    train.py
    test.py
	- src						(module library)
	- notebooks					(for evaluation, data preparation)
    - tools
- models
- runs
- live_inference				(deploy trained model)

See documentation at DataArchives for details on how to access and read datasets in a standard form.

Examples

Download datasets

This is a convenience script to download & uncompress latest combined training (Round1,2,3 etc.) & test datasets.

python data_ml/tools/download_datasets.py <LOCATION> (--only_train/--only_test)

Training

Pardon the brevity here, this is just a rough starting point, that will evolve significantly! Some of the code is still pretty rough, however src.model and src.dataloader are useful places to start.

Training converges quite fast (~5 minutes on a GPU). Train/validation tensorboard logs & model checkpoints are saved to a directory in runRootPath.

python train.py -dataPath ../train_data -runRootPath ../runs/test --preTrainedModelPath ../models/pytorch_vggish.pth -model AudioSet_fc_all -lr 0.0005

Notebook for validation and testing

See notebook Evaluation.ipynb (might be pretty rough, but should give a general idea)

Reproduce testing results

1. Download the test data by using the script
2. Download a trained model from model path to a location on your machine <model-download-location>
3. Regenerate test results

python tools/prepare_test_and_model_data.py --test_path <test-data-download-dir> --model_path <model-download-dir>

python data_ml/test.py --test_path <test-data-download-location> --model_path <model-download-location>

Setup instructions

1. [Windows] Get pyenv-win to manage python versions:

   1. git clone https://github.com/pyenv-win/pyenv-win.git %USERPROFILE%/.pyenv
   2. Add the following to your shell PATH %USERPROFILE%\.pyenv\pyenv-win\bin, %USERPROFILE%\.pyenv\pyenv-win\shims

2. [Mac] Get pyenv to manage python versions:

   1. Use homebrew and run brew update && brew install pyenv
   2. Follow from step 3 onwards here. This essentially adds the pyenv init command to your shell on startup
   3. FYI this is a commands reference

3. [Common] Install and maintain the right Python version (3.6.8)

   1. Run pyenv --version to check installation
   2. Run pyenv rehash from your home directory, install python 3.6.8 with pyenv install -l 3.6.8 (use 3.6.8-amd64 on Windows if relevant) and run pyenv rehash again
   3. Cd to /PodCast and set a local python version pyenv local 3.6.8 (or 3.6.8-amd64). This saves a .python-version file that tells pyenv what to use in this dir
   4. Type python --version and check you're using the right one

(feel free to skip 1, 2, 3 if you prefer to use your own Python setup and are familiar with many of this)

4. Create a virtual environment to isolate and install package dependencies
   1. In your working directory, run python -m venv podcast-venv. This creates a directory podcast-venv with relevant files/scripts.
   2. On Mac, activate this environment with source podcast-venv/bin/activate and when you're done, deactivate On Windows, activate with .\podcast-venv\Scripts\activate.bat and .\podcast-venv\Scripts\deactivate.bat when done
   3. In an active environment, cd to /data_ml and run python -m pip install --upgrade pip && pip install -r requirements.txt