Koogu

A python package for developing and using Machine Learning (ML) solutions in Animal Bioacoustics.

The package offers tools for -

• preparing and processing audio for training ML models,
• training ML models and assessing their performance, and
• using trained ML models for automatic recognition.

How to use

Koogu offers tools for ML development from the simplest of bioacoustics applications to more complex scenarios. All stages of the workflow (preparation, training, inference, performance assessment) can be performed independently and are described below.

For those interested in a hands-on demo (on Google Colab) with real data, here is a video providing an instructional walk-through on using the package.

1. Data preparation

Imports needed:

from koogu import prepare

Describe what kind of processing is needed for your application.

The below example instructs the prepare module to break up audio data read from disk into 2 s clips with a 75% overlap between successive clips. Audio loaded from files will be resampled to the sampling frequency desired_fs Hz if they weren't already at that sampling frequency.

# Settings for handling raw audio
audio_settings = {
  'clip_length': 2.0,           # in seconds
  'clip_advance': 0.5,          # in seconds
  'desired_fs': 48000           # in Hz
}

# Path to the directory where processed/prepared audio will be written
prepared_audio_dir = '/mnt/projects/dolphins/prepared_clips'

Audio data can be organized in one of two ways and the appropriate function can be invoked.

• When annotations^† are available, place the audio files under a parent directory audio_root and place the annotations under a common directory annots_root, then build a Python list audio_annot_list containing pairs (as 2-element lists or tuples) that map an audio file to its corresponding annotation file. Audio files and annotation files may be organized into subdirectories under audio_root and annots_root, and the corresponding relative paths to the files can be specified in audio_annot_list.

  # Root directories under which audio & corresponding annotation files are available
  audio_root = '/mnt/projects/dolphins/training_data/audio'
  annots_root = '/mnt/project/dolphins/training_data/annotations'
  
  # Map audio files to corresponding annotation files
  audio_annot_list = [
    ['day1/rec_01.wav', 'day1/rec_01.selections.txt'],
    ['day1/rec_02.wav', 'day1/rec_02.selections.txt'],
    ['day2/rec_10_w_ship_noise.wav', 'day2/rec_10_w_ship_noise.selections.txt'],
    ['day3/clean_recording.wav', 'day3/clean_recording.selections.txt'],
  ]
  
  # Convert audio files into prepared data
  clip_counts = prepare.from_selection_table_map(
    audio_settings, audio_annot_list,
    audio_root, annots_root,
    output_root=prepared_audio_dir
  )
  

• When annotations are not available, place audio files corresponding to different classes in their respective subdirectories, then place all the class-specific directories under a parent directory audio_root. The subdirectories' names will be used as class labels.

  # Root directories under which audio & corresponding annotation files are available
  audio_root = '/mnt/projects/dolphins/training_data/audio'
  
  # List class-specific subdirectories to process
  class_dirs = ['bottlenose', 'spinner', 'dusky', 'long-beaked_common']
  
  # Convert audio files into prepared data
  clip_counts = prepare.from_top_level_dirs(
    audio_settings, class_dirs,
    audio_root,
    output_root=prepared_audio_dir
  )
  

^† Koogu currently supports annotations in Raven Lite / RavenPro selection table format, which is basically a simple tab-delimited text file providing (at a minimum) the start-time, end-time and label for each event/call. Values in the Tags column (must exist) will be used as class labels.

The two functions under prepare support a few customizations. Resampled and broken up waveforms and the respective class label info are stored under prepared_audio_dir in compressed numpy format. The return value clip_counts is a dictionary indicating the number of clips written for each class.

2. Training

Imports needed:

from koogu.model import Architectures
from koogu.data.feeder import SpectralDataFeeder
from koogu import train

• The first import provides a few varieties of customizable neural network architectures. Model complexity can be controlled with the customizations offered by the architecture-specific classes.

  User-defined architectures (including pre-trained models) can be implemented by extending koogu.model.BaseArchitecture.

• The feeder module makes available a few varieties of customizable Python classes, each offering different capabilities, for efficiently feeding "prepared" data into a training/evaluation pipeline. The above example imports a feeder that also transforms loaded waveforms into spectrograms.

  Additional customizations and inclusion of data augmentation operations are possible by overriding the classes' transform() method in an inherited class. Furthermore, user-defined feeders can be implemented by extending any of the available feeders or by extending koogu.data.feeder.BaseFeeder.

• The training process can be controlled, along with the hyperparameter and regularization settings, by setting the appropriate values in the dict that input to train().

A typical training/eval workflow is shown below.

# Settings describing the transformation of audio clips into
# time-frequency representations (spectrograms).
spec_settings = {
  'win_len': 0.008,                 # in seconds
  'win_overlap_prc': 0.50,          # as a fraction
  'bandwidth_clip': [2000, 45000],  # in Hz
  #'num_mels': 60                   # Uncomment to enable mel-scale conversion
}

# Set up a feeder that
#   i)   loads the prepared audio clips,
#   ii)  transforms the waveform clips into spectrogrms, and
#   iii) feeds them into the training pipeline.
data_feeder = SpectralDataFeeder(
  prepared_audio_dir,
  audio_settings['desired_fs'],
  data_settings,
  validation_split=0.15             # as a fraction
)


# Architecture choice and model customizations
model = Architectures.densenet(
  layers_per_block=[4, 8, 8, 4],
  growth_rate=12
)


# Settings that control the training process
training_settings = {
  'batch_size': 64,
  'epochs': 30,
  'learning_rate': 0.001,           # can set to a 'callable' for variable rate
  #'dropout_rate': 0.05,            # Uncomment to enable
  #'l2_weight_decay': 1e-4,         # Uncomment to enable
  #'optimizer': ['sgd', {}]         # choice & its settings; default is Adam
}

# Combine audio & spectrogram settings into one dict for convenience
data_settings = {
  'audio_settings': audio_settings,
  'spec_settings': spec_settings
}


# Path to the directory where trained model will be saved
model_dir = '/mnt/projects/dolphins/trained_models/DenseNet_1'

# Perform training
history = train(
  data_feeder,
  model_dir,
  data_settings,
  model,
  training_settings
)

3. Using a trained model on test data

Imports needed:

from koogu import recognize

During testing, it is useful to save raw per-clip detections which can be subsequently analyzed for assessing the model's recognition performance (Step 4).

# Path to a single audio file or to a directory (can contain subdirectories)
test_audio_root = '/mnt/projects/dolphins/test_data/audio'

# Output directory
raw_detections_root = '/mnt/projects/dolphins/test_audio_raw_detections'

recognize(
  model_dir,
  test_audio_root,
  raw_detections_dir=raw_detections_root,
  batch_size=64,    # Increasing this may improve speed on computers having higher resources
  recursive=True,   # Process subdirectories also
  show_progress=True
)

The recognize() function supports many customizations. See function documentation for more details.

4. Assessing performance

Imports needed:

from koogu import assessments

Similar to how training annotation data were presented in Step 1, performance assessments also requires annotations corresponding to the test audio files processed above.

# Root directory under which annotation files (corresponding to the test
# audio files used above) are available.
test_annots_root = '/mnt/project/dolphins/test_data/annotations'

# Map audio files to corresponding annotation files
test_audio_annot_list = [
  ['day7/clean_recording.wav', 'day7/clean_recording.selections.txt'],
  ['day7/rec_01.wav', 'day7/rec_01.selections.txt'],
  ['day8/rec_02.wav', 'day8/rec_02.selections.txt'],
  ['day9/rec_10_w_ship_noise.wav', 'day9/rec_10_w_ship_noise.selections.txt'],
  ['day9/rec_01.wav', 'day9/rec_01.selections.txt'],
]

# Initialize a metric object with the above info
metric = assessments.PrecisionRecall(
  test_audio_annot_list,
  raw_detections_root, test_annots_root)
# The metric supports several options (including setting explicit thresholds).
# Refer to class documentation for more details.

# Run the assessments and gather results
per_class_pr, overall_pr = metric.assess()

# Plot PR curves.
# (Note: the below example code requires the matplotlib package and assumes that
# pyplot was already imported from it as:
#   from matplotlib import pyplot as plt
# )
for class_name, pr in per_class_pr.items():
  print(class_name)
  plt.plot(pr['recall'], pr['precision'], 'rd-')
  plt.xlabel('Recall')
  plt.ylabel('Precision')
  plt.grid()
  plt.show()

# The thresholds at which the different precision-recall values were determined
# can be queried as-
print(metric.thresholds)

5. Using the trained model on new recordings

Imports needed:

from koogu import recognize

Once you are settled on a choice of detection threshold that yields a suitable precision-recall trade-off, you can apply the trained model on new recordings. Automatic recognition results are written out in Raven Lite / RavenPro selection table format after applying an algorithm to group together similar successive detections. The function supports many customizations. See function documentation for details.

# Path to a single audio file or to a directory (can contain subdirectories)
new_audio_root = '/mnt/projects/dolphins/new_audio/'

# Output directory
detections_output_dir = '/mnt/projects/dolphins/new_audio_detections'

recognize(
  model_dir,
  new_audio_root,
  output_dir=detections_output_dir,
  reject_class='Noise',   # suppress saving of detections of specific classes
  threshold=0.75,
  #combine_outputs=True,  # combine detections from sub-directory into single annotation files
  batch_size=64,
  recursive=True,   # Process subdirectories also
  show_progress=True
)