wavenet_softmax.yaml

data:
  batch_size: 16
  train_clip_seconds: 0.5
  sample_rate: 22050
  hop_length: 256
  win_length: 1024
  n_fft: 2048
  n_mels: 80
  valid_size: 16



model:
  upsampling_factors: [16, 16]
  n_loop: 10
  n_layer: 3
  filter_size: 2
  residual_channels: 128
  loss_type: "softmax"
  output_dim: 2048
  log_scale_min: -9

train:
  learning_rate: 0.001
  anneal_rate:  0.5
  anneal_interval: 200000
  gradient_max_norm: 100.0

  checkpoint_interval: 10000
  snap_interval: 10000
  eval_interval: 10000
  
  max_iterations: 2000000