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Repo to explain FastAI modeling methodology

Model data

The base data used here is hosted on Current test set for evaluation is hosted on Orca Sound website . The model was trained with the following dataset -

And testing in evaluation section of FastAI start script is done on this data -

Data Directory structure in this folder(not uploaded on Git) -

fastai/data
    - train
        - train_data_09222019
            - wav
            - train.tsv
        - Round2_OS_07_05
            - wav
            - train.tsv
        - Round3_OS_09_27_2017
            - wav
            - train.tsv
        - mldata
            - all
                - models
                - negative
                - positive

    - test
        - all
        - OrcasoundLab07052019_Test
            - test2Sec
            - wav
            - test.tsv
        - OrcasoundLab09272017_Test
            - wav
            - test.csv

End-To-End development Process

Step 1 - OPTIONAL: Creating a more ML-ready dataset inline with other popular sound dataset - 1_DataCreation.ipynb notebook

  • NOTE: For Quick Start, you may see *.wav files in train/mldata/all/[negative|positive] and in test/all/[negative|positive]; in this case, there's no need to run this script (i.e. no need to generate new samples)
  • Extracting small audio segments for positive and negative label and store them in positive and negative folder for training (Filtering any call with less than 1 second duration)
  • Also create 2 sec audio sample from testing data for formal ML evaluation

Step2: Transfer leaning a ResNet18 model with on the fly spectogram creation with frequency masking for data augmentation - 2_FastAI_StarterScript.ipynb

  • Step 1: Create a Dataloader using FastAI library (Defining parameters to create a spectogram)
  • Step 2: Create a DataBunch by splitting training data in 80:20% for validation using training phase, also defining augmentation technique (Frequency transformation)
  • Step 3: Training a model using ResNet18 (pre-trained on ImageNet Data)
  • Step 4: Running evaluation on Test set
  • Step 5: Running scoring for official evaluation

Step3: Inference Testing3_InferenceTesting.ipynb

Notebook showing how to use the inference.py to load the model and do predictions by defining a clip path. The inference.py returns a dictionary -

  • local_predictions: A list of predictions (1/0) for each second of wav file (list of integers)
  • local_confidences: A list of probability of the local prediction being a whale call or not (list of float)
  • global_predictions: A global prediction(1/0) for the clip containing a valid whale call (integer)
  • global_confidences: Probability of the prediction being a whale call or not(float)

All the files generated or used during training are here:. NOTE: You must hold "Contributor" role assignment in the OrcaSound Azure tenant to access.

  • Models/stg2-rn18.pkl - Trained ResNet18 model
  • mldata.7z is data used for training the model
  • All.zip is testing data for early evaluations
  • test2Sec.zip is data used for scoring for official evaluations

Dependencies -