diff --git a/enhancer/models/waveunet.py b/enhancer/models/waveunet.py
new file mode 100644
index 0000000..2a9d083
--- /dev/null
+++ b/enhancer/models/waveunet.py
@@ -0,0 +1,64 @@
+from turtle import forward
+import torch.nn as nn
+
+
+class WavenetDecoder(nn.Module):
+
+    def __init__(
+        self,
+        in_channels:int,
+        out_channels:int,
+        kernel_size:int,
+        padding:int,
+        stride:int,
+        dilation:int=1,
+    ):
+        super(WavenetDecoder,self).__init__()
+        self.decoder = nn.Sequential(
+            nn.Conv1d(in_channels,out_channels,kernel_size,stride=stride,padding=padding),
+            nn.BatchNorm1d(out_channels),
+            nn.LeakyReLU(negative_slope=0.1)
+        )
+    
+    def forward(self,waveform):
+        
+        return self.decoder(waveform)
+
+class WavenetEncoder(nn.Module):
+
+    def __init__(
+        self,
+        in_channels:int,
+        out_channels:int,
+        kernel_size:int,
+        padding:int,
+        stride:int,
+        dilation:int=1,
+    ):
+        self.encoder = nn.Sequential(
+            nn.Conv1d(in_channels,out_channels,kernel_size,stride=stride,padding=padding),
+            nn.BatchNorm1d(out_channels),
+            nn.LeakyReLU(negative_slope=0.1)  
+        )
+        
+
+    def forward(
+        self,
+        waveform
+    ):
+        return self.encoder(waveform)
+
+
+
+
+class WaveUnet(nn.Module):
+
+    def __init__(
+        self
+    ):
+        pass
+
+    def forward(
+        self,waveform
+    ):
+        pass
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