| import torch.nn as nn | |
| import torch | |
| import torch.nn.functional as F | |
| class BandFilterGate(nn.Module): | |
| def __init__(self,emb_dim=48, n_freqs = 65): | |
| super().__init__() | |
| self.alpha = nn.Parameter(torch.empty(1,emb_dim,n_freqs,1).to(torch.float32)) | |
| self.beta = nn.Parameter(torch.empty(1,emb_dim,n_freqs,1).to(torch.float32)) | |
| nn.init.xavier_normal_(self.alpha) | |
| nn.init.xavier_normal_(self.beta) | |
| def forward(self,input,filters,bias): | |
| f = F.sigmoid(self.alpha*filters) | |
| b = F.tanh(self.beta*bias) | |
| return f*input + b | |