| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d | |
| from modules.ddsp.vocoder import CombSub, Sins | |
| class DDSPgan(nn.Module): | |
| def __init__(self,config): | |
| super().__init__() | |
| if config['model_args']['type']=='CombSub': | |
| self.ddsp = CombSub( | |
| sampling_rate=config['audio_sample_rate'], | |
| block_size=config['hop_size'], | |
| win_length=config['win_size'], | |
| n_mag_harmonic=config['model_args']['n_mag_harmonic'], | |
| n_mag_noise=config['model_args']['n_mag_noise'], | |
| n_mels=config['audio_num_mel_bins']) | |
| elif config['model_args']['type']=='Sins': | |
| self.ddsp = Sins( | |
| sampling_rate=config['audio_sample_rate'], | |
| block_size=config['hop_size'], | |
| win_length=config['win_size'], | |
| n_harmonics=config['model_args']['n_harmonics'], | |
| n_mag_noise=config['model_args']['n_mag_noise'], | |
| n_mels=config['audio_num_mel_bins']) | |
| def forward(self,mel,f0,infer=False): | |
| signal, _, (s_h, s_n) = self.ddsp(mel.transpose(1,2), torch.unsqueeze(f0,dim=-1), infer=infer) | |
| return signal.unsqueeze(1) | |