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audio:
chunk_size: 352800
dim_f: 1024
dim_t: 256
hop_length: 441
n_fft: 2048
num_channels: 2
sample_rate: 44100
min_mean_abs: 000
model:
dim: 384
depth: 6
stereo: true
num_stems: 1
time_transformer_depth: 1
freq_transformer_depth: 1
num_bands: 60
dim_head: 64
heads: 8
attn_dropout: 0
ff_dropout: 0
flash_attn: True
dim_freqs_in: 1025
sample_rate: 44100 # needed for mel filter bank from librosa
stft_n_fft: 2048
stft_hop_length: 441
stft_win_length: 2048
stft_normalized: False
mask_estimator_depth: 2
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
training:
batch_size: 4
gradient_accumulation_steps: 1
grad_clip: 0
instruments:
- karaoke
- other
lr: 1.0e-05
patience: 2
reduce_factor: 0.95
target_instrument: karaoke
num_epochs: 1000
num_steps: 2000
augmentation: false # enable augmentations by audiomentations and pedalboard
augmentation_type: null
use_mp3_compress: false # Deprecated
augmentation_mix: false # Mix several stems of the same type with some probability
augmentation_loudness: false # randomly change loudness of each stem
augmentation_loudness_type: 1 # Type 1 or 2
augmentation_loudness_min: 0
augmentation_loudness_max: 0
q: 0.95
coarse_loss_clip: false
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
inference:
batch_size: 1
dim_t: 256
num_overlap: 4 |