You can use table below to choose BS Roformer batch_size parameter for training based on your GPUs. Batch size values provided for single GPU. If you have several GPUs you need to multiply value on number of GPUs.
| chunk_size | dim | depth | batch_size (A6000 48GB) | batch_size (3090/4090 24GB) | batch_size (16GB) |
|---|---|---|---|---|---|
| 131584 | 128 | 6 | 10 | 5 | 3 |
| 131584 | 256 | 6 | 8 | 4 | 2 |
| 131584 | 384 | 6 | 7 | 3 | 2 |
| 131584 | 512 | 6 | 6 | 3 | 2 |
| 131584 | 256 | 8 | 6 | 3 | 2 |
| 131584 | 256 | 12 | 4 | 2 | 1 |
| 263168 | 128 | 6 | 4 | 2 | 1 |
| 263168 | 256 | 6 | 3 | 1 | 1 |
| 352800 | 128 | 6 | 2 | 1 | - |
| 352800 | 256 | 6 | 2 | 1 | - |
| 352800 | 384 | 12 | 1 | - | - |
| 352800 | 512 | 12 | - | - | - |
Parameters obtained with initial config:
audio:
chunk_size: 131584
dim_f: 1024
dim_t: 515
hop_length: 512
n_fft: 2048
num_channels: 2
sample_rate: 44100
min_mean_abs: 0.000
model:
dim: 384
depth: 12
stereo: true
num_stems: 1
time_transformer_depth: 1
freq_transformer_depth: 1
linear_transformer_depth: 0
freqs_per_bands: !!python/tuple
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 12
- 12
- 12
- 12
- 12
- 12
- 12
- 12
- 24
- 24
- 24
- 24
- 24
- 24
- 24
- 24
- 48
- 48
- 48
- 48
- 48
- 48
- 48
- 48
- 128
- 129
dim_head: 64
heads: 8
attn_dropout: 0.1
ff_dropout: 0.1
flash_attn: false
dim_freqs_in: 1025
stft_n_fft: 2048
stft_hop_length: 512
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: 1
gradient_accumulation_steps: 1
grad_clip: 0
instruments:
- vocals
- other
lr: 3.0e-05
patience: 2
reduce_factor: 0.95
target_instrument: vocals
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