Spaces:
Sleeping
Sleeping
| from __gin__ import dynamic_registration | |
| from gamadhani import src | |
| from gamadhani.src import dataset | |
| from gamadhani.src import model_diffusion | |
| from gamadhani.src import task_functions | |
| from gamadhani.utils import utils | |
| import torch | |
| # Macros: | |
| # ============================================================================== | |
| LR = 0.0001 | |
| SEQ_LEN = 1200 | |
| TRANSPOSE_VALUE = 400 | |
| # Parameters for torch.optim.AdamW: | |
| # ============================================================================== | |
| torch.optim.AdamW.betas = (0.9, 0.99) | |
| torch.optim.AdamW.lr = %LR | |
| # Parameters for utils.build_warmed_exponential_lr_scheduler: | |
| # ============================================================================== | |
| utils.build_warmed_exponential_lr_scheduler.cycle_length = 200000 | |
| utils.build_warmed_exponential_lr_scheduler.eta_max = %LR | |
| utils.build_warmed_exponential_lr_scheduler.eta_min = 0.1 | |
| utils.build_warmed_exponential_lr_scheduler.peak_iteration = 10000 | |
| utils.build_warmed_exponential_lr_scheduler.start_factor = 0.01 | |
| # Parameters for model_diffusion.UNetBase.configure_optimizers: | |
| # ============================================================================== | |
| model_diffusion.UNetBase.configure_optimizers.optimizer_cls = @torch.optim.AdamW | |
| model_diffusion.UNetBase.configure_optimizers.scheduler_cls = \ | |
| @utils.build_warmed_exponential_lr_scheduler | |
| # Parameters for dataset.Task: | |
| # ============================================================================== | |
| src.dataset.Task.kwargs = { | |
| "decoder_key" : 'pitch', | |
| "max_clip" : 600, | |
| "min_clip" : 200, | |
| "min_norm_pitch" : -4915, | |
| "pitch_downsample" : 10, | |
| "seq_len" : %SEQ_LEN, | |
| "time_downsample" : 2} | |
| # Parameters for train/dataset.pitch_read_w_downsample: | |
| # ============================================================================== | |
| # train/dataset.Task.kwargs = {"transpose_pitch": %TRANSPOSE_VALUE} | |
| # Parameters for train/dataset.Task: | |
| # ============================================================================== | |
| src.dataset.Task.read_fn = @src.task_functions.pitch_read_downsample_diff | |
| src.dataset.Task.invert_fn = @src.task_functions.invert_pitch_read_downsample_diff | |
| # Parameters for model_diffusion.UNet: | |
| # ============================================================================== | |
| model_diffusion.UNet.dropout = 0.3 | |
| model_diffusion.UNet.features = [512, 640, 1024] | |
| model_diffusion.UNet.inp_dim = 1 | |
| model_diffusion.UNet.kernel_size = 5 | |
| model_diffusion.UNet.nonlinearity = 'mish' | |
| model_diffusion.UNet.norm = True | |
| model_diffusion.UNet.num_attns = 4 | |
| model_diffusion.UNet.num_convs = 4 | |
| model_diffusion.UNet.num_heads = 8 | |
| model_diffusion.UNet.project_dim = 256 | |
| model_diffusion.UNet.seq_len = %SEQ_LEN | |
| model_diffusion.UNet.strides = [4, 2, 2] | |
| model_diffusion.UNet.time_dim = 128 | |