| audio: | |
| chunk_size: 261632 | |
| dim_f: 4096 | |
| dim_t: 512 | |
| hop_length: 512 | |
| n_fft: 8192 | |
| num_channels: 2 | |
| sample_rate: 44100 | |
| min_mean_abs: 0.001 | |
| model: | |
| encoder_name: tu-maxvit_large_tf_512 # look here for possibilities: https://github.com/qubvel/segmentation_models.pytorch#encoders- | |
| decoder_type: unet # unet, fpn | |
| act: gelu | |
| num_channels: 128 | |
| num_subbands: 8 | |
| loss_multistft: | |
| fft_sizes: | |
| - 1024 | |
| - 2048 | |
| - 4096 | |
| hop_sizes: | |
| - 512 | |
| - 1024 | |
| - 2048 | |
| win_lengths: | |
| - 1024 | |
| - 2048 | |
| - 4096 | |
| window: "hann_window" | |
| scale: "mel" | |
| n_bins: 128 | |
| sample_rate: 44100 | |
| perceptual_weighting: true | |
| w_sc: 1.0 | |
| w_log_mag: 1.0 | |
| w_lin_mag: 0.0 | |
| w_phs: 0.0 | |
| mag_distance: "L1" | |
| training: | |
| batch_size: 8 | |
| gradient_accumulation_steps: 1 | |
| grad_clip: 0 | |
| instruments: | |
| - vocals | |
| - other | |
| lr: 5.0e-05 | |
| patience: 2 | |
| reduce_factor: 0.95 | |
| target_instrument: null | |
| num_epochs: 1000 | |
| num_steps: 2000 | |
| q: 0.95 | |
| coarse_loss_clip: true | |
| ema_momentum: 0.999 | |
| optimizer: adamw | |
| 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: 512 | |
| num_overlap: 4 |