| ### Batch sizes for BSRoformer | |
| 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 | |
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| - 12 | |
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| - 24 | |
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| - 48 | |
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| - 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 | |
| ``` | |