| import torch.nn.functional as F | |
| import torch | |
| import numpy as np | |
| def mae(input, target): | |
| return torch.mean(torch.abs(input - target)) | |
| def logmae_wav(model, output_dict, target): | |
| loss = torch.log10(torch.clamp(mae(output_dict['wav'], target), 1e-8, np.inf)) | |
| return loss | |
| def get_loss_func(loss_type): | |
| if loss_type == 'logmae_wav': | |
| return logmae_wav | |
| elif loss_type == 'mae': | |
| return mae | |
| else: | |
| raise Exception('Incorrect loss_type!') | |