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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
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