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audio:
  chunk_size: 384000
  dim_f: 1024
  dim_t: 801 # don't work (use in model)
  hop_length: 441 # don't work (use in model)
  n_fft: 2048
  num_channels: 1
  sample_rate: 44100
  min_mean_abs: 0.000

model:
  dim: 128
  depth: 16
  stereo: false
  num_stems: 1
  time_transformer_depth: 1
  freq_transformer_depth: 1
  linear_transformer_depth: 0
  freqs_per_bands: !!python/tuple
    - 2
    - 2
    - 2
    - 2
    - 2
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  dim_head: 16
  heads: 8
  attn_dropout: 0.0
  ff_dropout: 0.0
  flash_attn: true
  dim_freqs_in: 1025
  stft_n_fft: 2048
  stft_hop_length: 512
  stft_win_length: 2048
  stft_normalized: False
  mask_estimator_depth: 3
  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
  use_torch_checkpoint: True 
  skip_connection: False 


training:
  batch_size: 4
  gradient_accumulation_steps: 1
  grad_clip: 1000.0
  instruments: ['noreverb', 'reverb']
  lr: 5.0e-5
  patience: 5
  reduce_factor: 0.75
  target_instrument: noreverb
  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



inference:
  batch_size: 1
  dim_t: 871
  num_overlap: 2