Clean 1-hot version

nnet/xvector.py

@@ -109,10 +109,6 @@ class Xtractor(torch.nn.Module):
         result = self.activation(seg_emb_5)
         return result
 
-    def LossFN(self, x, label):
-        loss = - torch.trace(torch.mm(torch.log10(x), torch.t(label)))
-        return loss
-
     def init_weights(self):
         """
         """
@@ -155,30 +151,6 @@ class Xtractor(torch.nn.Module):
 
         return seg_emb_1, seg_emb_2, seg_emb_3, seg_emb_4, seg_emb_5, seg_emb_6
 
-class XtractorHot(Xtractor):
-    def __init__(self, spk_number, dropout):
-        super(Xtractor, self).__init__()
-        self.frame_conv0 = torch.nn.Conv1d(20, 512, 5, dilation=1)
-        self.frame_conv1 = torch.nn.Conv1d(512, 512, 3, dilation=2)
-        self.frame_conv2 = torch.nn.Conv1d(512, 512, 3, dilation=3)
-        self.frame_conv3 = torch.nn.Conv1d(512, 512, 1)
-        self.frame_conv4 = torch.nn.Conv1d(512, 3 * 512, 1)
-        self.seg_lin0 = torch.nn.Linear(3 * 512 * 2, 512)
-        self.dropout_lin0 = torch.nn.Dropout(p=dropout)
-        self.seg_lin1 = torch.nn.Linear(512, 512)
-        self.dropout_lin1 = torch.nn.Dropout(p=dropout)
-        self.seg_lin2 = torch.nn.Linear(512, spk_number)
-        #
-        self.norm0 = torch.nn.BatchNorm1d(512)
-        self.norm1 = torch.nn.BatchNorm1d(512)
-        self.norm2 = torch.nn.BatchNorm1d(512)
-        self.norm3 = torch.nn.BatchNorm1d(512)
-        self.norm4 = torch.nn.BatchNorm1d(3 * 512)
-        self.norm6 = torch.nn.BatchNorm1d(512)
-        self.norm7 = torch.nn.BatchNorm1d(512)
-        #
-        self.activation = torch.nn.LeakyReLU(0.2)
-
     def forward(self, x):
         frame_emb_0 = self.norm0(self.activation(self.frame_conv0(x)))
         frame_emb_1 = self.norm1(self.activation(self.frame_conv1(frame_emb_0)))
@@ -557,8 +529,6 @@ def extract_parallel(args, fs_params):
     for p in processes:
         p.join()
 
-    print("Process parallel fini")
-
     return x_server_1, x_server_2, x_server_3, x_server_4, x_server_5, x_server_6