audio:
  chunk_size: 485100 # samplerate * segment
  min_mean_abs: 0.000
  hop_length: 1024

training:
  batch_size: 8
  gradient_accumulation_steps: 1
  grad_clip: 0
  segment: 11
  shift: 1
  samplerate: 44100
  channels: 2
  normalize: true
  instruments: ['drums', 'bass', 'other', 'vocals']
  target_instrument: null
  num_epochs: 1000
  num_steps: 1000
  optimizer: adam
  lr: 9.0e-05
  patience: 2
  reduce_factor: 0.95
  q: 0.95
  coarse_loss_clip: true
  ema_momentum: 0.999
  other_fix: false # it's needed for checking on multisong dataset if other is actually instrumental
  use_amp: false # 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:
  num_overlap: 4
  batch_size: 8

model: hdemucs

hdemucs:  # see demucs/hdemucs.py for a detailed description
  channels: 48
  channels_time: null
  growth: 2
  nfft: 4096
  wiener_iters: 0
  end_iters: 0
  wiener_residual: False
  cac: True
  depth: 6
  rewrite: True
  hybrid: True
  hybrid_old: False
  multi_freqs: []
  multi_freqs_depth: 3
  freq_emb: 0.2
  emb_scale: 10
  emb_smooth: True
  kernel_size: 8
  stride: 4
  time_stride: 2
  context: 1
  context_enc: 0
  norm_starts: 4
  norm_groups: 4
  dconv_mode: 1
  dconv_depth: 2
  dconv_comp: 4
  dconv_attn: 4
  dconv_lstm: 4
  dconv_init: 0.001
  rescale: 0.1