diff --git a/codes/models/audio/mel2vec.py b/codes/models/audio/mel2vec.py
index 7e1996a0..d2210ad1 100644
--- a/codes/models/audio/mel2vec.py
+++ b/codes/models/audio/mel2vec.py
@@ -389,6 +389,24 @@ class Mel2Vec(nn.Module):
                                               ResBlock(dims=1, channels=inner_dim, dropout=dropout),
                                               )
             self.dim_reduction_mult = 4
+        elif feature_producer_type == 'deep_residual':
+            self.input_blocks = nn.Sequential(nn.Conv1d(mel_input_channels, inner_dim//2, kernel_size=5, padding=2, stride=2),
+                                              nn.GroupNorm(num_groups=8, num_channels=inner_dim//2, affine=True),
+                                              nn.GELU(),
+                                              ResBlock(dims=1, channels=inner_dim//2, dropout=dropout),
+                                              ResBlock(dims=1, channels=inner_dim//2, dropout=dropout),
+                                              ResBlock(dims=1, channels=inner_dim//2, dropout=dropout),
+                                              ResBlock(dims=1, channels=inner_dim//2, dropout=dropout),
+                                              ResBlock(dims=1, channels=inner_dim//2, dropout=dropout),
+                                              nn.Conv1d(inner_dim//2, inner_dim, kernel_size=3, padding=1, stride=2),
+                                              nn.GELU(),
+                                              ResBlock(dims=1, channels=inner_dim, dropout=dropout),
+                                              ResBlock(dims=1, channels=inner_dim, dropout=dropout),
+                                              ResBlock(dims=1, channels=inner_dim, dropout=dropout),
+                                              ResBlock(dims=1, channels=inner_dim, dropout=dropout),
+                                              ResBlock(dims=1, channels=inner_dim, dropout=dropout),
+                                              )
+            self.dim_reduction_mult = 4
         elif feature_producer_type == 'voice_8x':
             self.input_blocks = nn.Sequential(nn.Conv1d(mel_input_channels, inner_dim//4, kernel_size=5, padding=2, stride=2),
                                               nn.GroupNorm(num_groups=8, num_channels=inner_dim//4, affine=True),