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config.json - Configuring a training session

The parameters present in a config.json file allow one to configure a training session. Each of these parameters is described below:

Dataset

How the data is used for training

  • extract_voice: (bool) Only train on audio where voice is present
  • in_memory_percentage: (float) Percentage of the dataset to load into memory, useful when dataset requires more memory than available
  • noise_only_percent: (float) Percentage of training samples that should feature only noise at input and silence at output
  • num_condition_classes: (int) Number of condition classes
  • path: (string) Path to dataset
  • regain: (float) RMS energy to which all samples should be regained to
  • sample_rate: (int) Sample rate to which all samples should be resampled to
  • type: (string) Identifier of which dataset is being used for training

Model

What the model will be

  • condition_encoding: (string) Which numerical representation to encode integer condition values to, either binary or one-hot
  • dilations: (int) Maximum dilation factor as an exponent of 2, e.g. dilations = 9 results in a maximum dilation of 2^9 = 512
  • filters:
    • lengths:
      • res: (int) Lengths of convolution kernels in residual blocks
      • final: ([int, int]) Lengths of convolution kernels in final layers, individually definable
      • skip: (int) Lengths of convolution kernels in skip connections
    • depths:
      • res: (int) Number of filters in residual-block convolution layers
      • skip: (int) Number of filters in skip connections
      • final: ([int, int]) Number of filters in final layers, individually definable
  • num_stacks: (int) Number of stacks, as defined in the paper
  • target_field_length: (int) Length of the output
  • target_padding: (int) Number of samples used for padding the target_field per side

Training

How training will be carried out

  • batch_size: (int) Number of samples per batch
  • early_stopping_patience: (int) Number of epochs to wait without improvement in accuracy before stopping training
  • loss:
    • out_1: First term in the two term loss
      • l1: (float) Percentage weight given to L1 loss
      • l2: (float) Percentage weight given to L2 loss
      • weight: (float) Percentage weight given to first term
  • out_2: Second term in two term loss
    • l1: (float) Percentage weight given to L1 loss
    • l2: (float) Percentage weight given to L2 loss
    • weight: (float) Percentage weight given to second term
  • num_epochs: (int) Maximum number of epochs to train for
  • num_train_samples: (int) Number of training samples presented to the model in one epoch,
  • num_test_samples: (int) Number of test samples presented to the model in one epoch,
  • path: (string) Path to the folder containing all files pertaining to the training session
  • verbosity: (int) Keras verbosity level