audio:
  chunk_size: 131584
  dim_f: 1024
  dim_t: 256
  hop_length: 512
  n_fft: 2048
  num_channels: 2
  sample_rate: 44100
  min_mean_abs: 0.001

model:
  dim: 192
  depth: 8
  stereo: true
  num_stems: 1
  time_transformer_depth: 1
  freq_transformer_depth: 1
  linear_transformer_depth: 0
  num_bands: 60
  dim_head: 64
  heads: 8
  attn_dropout: 0.1
  ff_dropout: 0.1
  flash_attn: True
  dim_freqs_in: 1025
  sample_rate: 44100 # needed for mel filter bank from librosa
  stft_n_fft: 2048
  stft_hop_length: 512
  stft_win_length: 2048
  stft_normalized: False
  mask_estimator_depth: 2
  multi_stft_resolution_loss_weight: 1.0
  multi_stft_resolutions_window_sizes: !!python/tuple
    - 4096
    - 2048
    - 1024
    - 512
    - 256
  multi_stft_hop_size: 147
  multi_stft_normalized: False
  mlp_expansion_factor: 4 # Probably too big (requires a lot of memory for weights)
  use_torch_checkpoint: False  # it allows to greatly reduce GPU memory consumption during training (not fully tested)
  skip_connection: False # Enable skip connection between transformer blocks - can solve problem with gradients and probably faster training

training:
  batch_size: 7
  gradient_accumulation_steps: 1
  grad_clip: 0
  instruments:
    - vocals
    - bass
    - drums
    - other
  lr: 5.0e-05
  patience: 2
  reduce_factor: 0.95
  target_instrument: vocals
  num_epochs: 1000
  num_steps: 1000
  q: 0.95
  coarse_loss_clip: true
  ema_momentum: 0.999
  optimizer: adam
  other_fix: false # 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

inference:
  batch_size: 1
  dim_t: 256
  num_overlap: 4