diff --git a/dnn/torch/osce/models/silk_feature_net_pl.py b/dnn/torch/osce/models/silk_feature_net_pl.py
index c766d0ab..5efa7e70 100644
--- a/dnn/torch/osce/models/silk_feature_net_pl.py
+++ b/dnn/torch/osce/models/silk_feature_net_pl.py
@@ -66,18 +66,17 @@ class SilkFeatureNetPL(nn.Module):
         self.conv1 = norm(nn.Conv1d(feature_dim, self.hidden_feature_dim, 1))
         self.conv2 = norm(nn.Conv1d(4 * self.hidden_feature_dim, num_channels, 2))
         self.tconv = norm(nn.ConvTranspose1d(num_channels, num_channels, 4, 4))
-        gru_input_dim = num_channels + self.repeat_upsamp_dim if self.repeat_upsamp else num_channels
-        self.gru   = norm(norm(nn.GRU(gru_input_dim, num_channels, batch_first=True), name='weight_hh_l0'), name='weight_ih_l0')
+        self.gru   = norm(norm(nn.GRU(num_channels, num_channels, batch_first=True), name='weight_hh_l0'), name='weight_ih_l0')
 
         if softquant:
             self.conv2 = soft_quant(self.conv2)
-            if not self.repeat_upsamp: self.tconv = soft_quant(self.tconv)
+            self.tconv = soft_quant(self.tconv)
             self.gru = soft_quant(self.gru, names=['weight_hh_l0', 'weight_ih_l0'])
 
 
         if sparsify:
             mark_for_sparsification(self.conv2, (sparsification_density[0], [8, 4]))
-            if not self.repeat_upsamp: mark_for_sparsification(self.tconv, (sparsification_density[1], [8, 4]))
+            mark_for_sparsification(self.tconv, (sparsification_density[1], [8, 4]))
             mark_for_sparsification(
                 self.gru,
                 {