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config_musdb18_scnet.yaml
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config_musdb18_scnet.yaml
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audio:
chunk_size: 485100 # 44100 * 11
num_channels: 2
sample_rate: 44100
min_mean_abs: 0.000
model:
sources:
- drums
- bass
- other
- vocals
audio_channels: 2
dims:
- 4
- 32
- 64
- 128
nfft: 4096
hop_size: 1024
win_size: 4096
normalized: True
band_SR:
- 0.175
- 0.392
- 0.433
band_stride:
- 1
- 4
- 16
band_kernel:
- 3
- 4
- 16
conv_depths:
- 3
- 2
- 1
compress: 4
conv_kernel: 3
num_dplayer: 6
expand: 1
training:
batch_size: 10
gradient_accumulation_steps: 1
grad_clip: 0
instruments:
- drums
- bass
- other
- vocals
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: 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
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
normalize: true