File size: 1,805 Bytes
3978e51 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
name: "MultiMaskMultiSourceBandSplitRNN"
audio:
chunk_size: 264600
num_channels: 2
sample_rate: 44100
min_mean_abs: 0.001
model:
in_channel: 1
stems: ['vocals', 'other']
band_specs: "musical"
n_bands: 64
fs: 44100
require_no_overlap: false
require_no_gap: true
normalize_channel_independently: false
treat_channel_as_feature: true
n_sqm_modules: 8
emb_dim: 128
rnn_dim: 256
bidirectional: true
rnn_type: "GRU"
mlp_dim: 512
hidden_activation: "Tanh"
hidden_activation_kwargs: null
complex_mask: true
n_fft: 2048
win_length: 2048
hop_length: 512
window_fn: "hann_window"
wkwargs: null
power: null
center: true
normalized: true
pad_mode: "constant"
onesided: true
training:
batch_size: 4
gradient_accumulation_steps: 4
grad_clip: 0
instruments:
- vocals
- other
lr: 9.0e-05
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: true # 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: 1
dim_t: 256
num_overlap: 4 |