config_vocals_segm_models.yaml

audio:
 chunk_size: 261632
 dim_f: 4096
 dim_t: 512
 hop_length: 512
 n_fft: 8192
 num_channels: 2
 sample_rate: 44100
 min_mean_abs: 0.001

model:
 encoder_name: tu-maxvit_large_tf_512 # look here for possibilities: https://github.com/qubvel/segmentation_models.pytorch#encoders-
 decoder_type: unet # unet, fpn
 act: gelu
 num_channels: 128
 num_subbands: 8

training:
 batch_size: 8
 gradient_accumulation_steps: 1
 grad_clip: 0
 instruments:
 - vocals
 - other
 lr: 5.0e-05
 patience: 2
 reduce_factor: 0.95
 target_instrument: null
 num_epochs: 1000
 num_steps: 2000
 augmentation: false # enable augmentations by audiomentations and pedalboard
 augmentation_type: simple1
 use_mp3_compress: false # Deprecated
 augmentation_mix: true # Mix several stems of the same type with some probability
 augmentation_loudness: true # randomly change loudness of each stem
 augmentation_loudness_type: 1 # Type 1 or 2
 augmentation_loudness_min: 0.5
 augmentation_loudness_max: 1.5
 q: 0.95
 coarse_loss_clip: true
 ema_momentum: 0.999
 optimizer: adamw
 other_fix: true # it's needed for checking on multisong dataset if other is actually instrumental

inference:
 batch_size: 1
 dim_t: 512
 num_overlap: 4