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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_percentage: (float) Proportion of the data containing singing voice (instead of vocal streams having silence)
  • in_memory_percentage: (float) Percentage of the dataset to load into memory, useful when dataset requires more memory than available
  • path: (string) Path to dataset
  • 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: (in the case of multi-instrument)
    • out_1: First term in the three term loss (vocals)
      • 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 the three term loss (drums)
      • l1: (float) Percentage weight given to L1 loss
      • l2: (float) Percentage weight given to L2 loss
      • weight: (float) Percentage weight given to second term
    • out_3: Third term in the three term loss (bass)
      • l1: (float) Percentage weight given to L1 loss
      • l2: (float) Percentage weight given to L2 loss
      • weight: (float) Percentage weight given to third term
  • loss: (in the case of singing-voice)
    • out_1: First term in the two term loss (singing voice)
      • 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 the two term loss (dissimilarity singing voice)
      • 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_steps_test: (int) Total number of steps (batches of samples) to yield from validation generator before stopping at the end of every epoch.
  • num_steps_train: (int) Total number of steps (batches of samples) to yield from training generator before declaring one epoch finished and starting the next epoch.
  • path: (string) Path to the folder containing all files pertaining to the training session
  • verbosity: (int) Keras verbosity level