Koogu

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

Koogu (ಕೂಗು; /ko͞ogu/) is a word in the Kannada language, meaning "call" (used as a noun) or "to call" (used as a verb).

The package offers tools for -

• preparing audio (pre-process and transform) to form inputs to ML models,
• training ML models,
• assessing their performance, and
• using trained ML models for automating analyses of large datasets.

Installation

Koogu can be installed, via PyPI, as follows

pip install koogu

Dependencies

TensorFlow

koogu uses TensorFlow as backend ML framework. Please ensure that either a CPU or a GPU version of TensorFlow is installed prior to installing koogu.

SoundFile, audioread, resampy

koogu uses these packages for handling audio files. These dependencies will be automatically installed when installing koogu.

Others

Besides TensorFlow, all other dependencies will be automatically installed.

How to use

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

If you are 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.

For a more thorough coverage, please refer to Koogu's online documentation.

1. Data preparation

Imports needed:

from koogu.data import preprocess
from koogu.data import feeder

Describe what kind of processing is needed for your application.

The below example instructs the preprocess 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 further organized into subdirectories under audio_root and annots_root, and the corresponding relative paths to the files can be specified in audio_annot_list (audio_annot_list may also be set as the path to a csv file that contains the mapping pairs).

  # 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 = preprocess.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 (default), Audacity and SonicVisualizer formats. In the Raven format, by default, values in the Tags column (if it exists) will be used as class labels. You could also force to look for class labels in a different column by specifying the name of the corresponding column.

The two functions under preprocess 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.

The feeder module makes available customizable Python classes that offer the ability to efficiently feed "prepared" data into a training/evaluation pipeline.

In common bioacoustic applications, audio clips must be transformed into spectrograms before they can be fed to a model. For this purpose, we can use the SpectralDataFeeder which transforms loaded waveforms into spectrograms on-the-fly.

# 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 pre-processed audio clips,
#   ii)  transforms the waveform clips into spectrograms, and
#   iii) feeds them into the training pipeline.
data_feeder = feeder.SpectralDataFeeder(
  prepared_audio_dir,
  audio_settings['desired_fs'],
  spec_settings,
  validation_split=0.15             # as a fraction
)

Additional customizations of the transformation operation are possible by overriding the feeder class' transform() method in an inherited class. Performing on-the-fly transformations offers us the ability to apply randomized data augmentations in both time- and spectrotemporal domains independently. Data augmentations can be included by overriding the feeder class' pre_transform() and post_transform() methods. Furthermore, user-defined feeders can be implemented by extending any of the available feeders or by extending koogu.data.feeder.BaseFeeder.

2. Training

Imports needed:

from koogu.model import architectures
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 training process can be controlled, along with hyperparameter and regularization settings, by assigning appropriate values to the various fields in the dict that is input to train().

A typical training/eval workflow is shown below.

# 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,                      # connect to the 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 desired 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
)