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audio:
chunk_size: 485100
dim_f: 1024
dim_t: 1101
hop_length: 882
n_fft: 2048
num_channels: 2
sample_rate: 44100
min_mean_abs: 0.000
model:
dim: 384
depth: 8
stereo: true
num_stems: 4
linear_transformer_depth: 0
time_transformer_depth: 1
freq_transformer_depth: 1
num_bands: 60
dim_head: 64
heads: 8
attn_dropout: 0.0
ff_dropout: 0.0
flash_attn: true
dim_freqs_in: 2049
sample_rate: 44100 # needed for mel filter bank from librosa
stft_n_fft: 4096
stft_hop_length: 882
stft_win_length: 4096
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
mlp_expansion_factor: 4
use_torch_checkpoint: False # it allows to greatly reduce GPU memory consumption during training (not fully tested)
skip_connection: True # Enable skip connection between transformer blocks - can solve problem with gradients and probably faster training
training:
batch_size: 1
gradient_accumulation_steps: 1
grad_clip: 0
instruments: ['drums', 'bass', 'other', 'vocals']
lr: 1.0
patience: 3
reduce_factor: 0.95
target_instrument: null
num_epochs: 1000
num_steps: 1000
q: 0.95
coarse_loss_clip: false
ema_momentum: 0.999
optimizer: prodigy
read_metadata_procs: 8 # Number of processes to use during metadata reading for dataset. Can speed up metadata generation
normalize: false
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
inference:
batch_size: 4
dim_t: 1101
num_overlap: 4
normalize: false |