audio:
  chunk_size: 264600
  num_channels: 2
  sample_rate: 44100
  min_mean_abs: 0.000

model:
  sources: ['vocals', 'other']
  audio_channels: 2
  # dims: [4, 32, 64, 128] # small version
  dims: [4, 64, 128, 256]
  nfft: 4096
  hop_size: 1024
  win_size: 4096
  normalized: True
  band_configs: {
    'low': { 'SR': .175, 'stride': 1, 'kernel': 3 },
    'mid': { 'SR': .392, 'stride': 4, 'kernel': 4 },
    'high': { 'SR': .433, 'stride': 16, 'kernel': 16 }
  }
  conv_depths: [3, 2, 1]
  compress: 4
  conv_kernel: 3
  # Dual-path RNN
  num_dplayer: 6
  expand: 1
  # mamba
  use_mamba: False
  mamba_config: {
    'd_stat': 16,
    'd_conv': 4,
    'd_expand': 2
  }

training:
  batch_size: 4
  gradient_accumulation_steps: 2
  grad_clip: 0
  instruments:
    - vocals
    - other
  lr: 5.0e-04
  patience: 2
  reduce_factor: 0.95
  target_instrument: null
  num_epochs: 1000
  num_steps: 1000
  q: 0.95
  coarse_loss_clip: true
  ema_momentum: 0.999
  optimizer: adam
  other_fix: true # it's needed for checking on multisong dataset if other is actually instrumental
  use_amp: true # enable or disable usage of mixed precision (float16) - usually it must be true

augmentations:
  enable: true # enable or disable all augmentations (to fast disable if needed)
  loudness: true # randomly change loudness of each stem on the range (loudness_min; loudness_max)
  loudness_min: 0.5
  loudness_max: 1.5
  mixup: true # mix several stems of same type with some probability (only works for dataset types: 1, 2, 3)
  mixup_probs:
    !!python/tuple # 2 additional stems of the same type (1st with prob 0.2, 2nd with prob 0.02)
    - 0.2
    - 0.02
  mixup_loudness_min: 0.5
  mixup_loudness_max: 1.5

inference:
  batch_size: 8
  dim_t: 256
  num_overlap: 4