diff --git a/enhancer/models/waveunet.py b/enhancer/models/waveunet.py
index 2a9d083..bb20c3f 100644
--- a/enhancer/models/waveunet.py
+++ b/enhancer/models/waveunet.py
@@ -1,4 +1,3 @@
-from turtle import forward
 import torch.nn as nn
 
 
@@ -8,14 +7,14 @@ class WavenetDecoder(nn.Module):
         self,
         in_channels:int,
         out_channels:int,
-        kernel_size:int,
-        padding:int,
-        stride:int,
+        kernel_size:int=5,
+        padding:int=2,
+        stride:int=1,
         dilation:int=1,
     ):
         super(WavenetDecoder,self).__init__()
         self.decoder = nn.Sequential(
-            nn.Conv1d(in_channels,out_channels,kernel_size,stride=stride,padding=padding),
+            nn.Conv1d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,dilation=dilation),
             nn.BatchNorm1d(out_channels),
             nn.LeakyReLU(negative_slope=0.1)
         )
@@ -30,13 +29,14 @@ class WavenetEncoder(nn.Module):
         self,
         in_channels:int,
         out_channels:int,
-        kernel_size:int,
-        padding:int,
-        stride:int,
+        kernel_size:int=15,
+        padding:int=7,
+        stride:int=1,
         dilation:int=1,
     ):
+        super(WavenetEncoder,self).__init__()
         self.encoder = nn.Sequential(
-            nn.Conv1d(in_channels,out_channels,kernel_size,stride=stride,padding=padding),
+            nn.Conv1d(in_channels,out_channels,kernel_size,stride=stride,padding=padding,dilation=dilation),
             nn.BatchNorm1d(out_channels),
             nn.LeakyReLU(negative_slope=0.1)  
         )
@@ -54,11 +54,50 @@ class WavenetEncoder(nn.Module):
 class WaveUnet(nn.Module):
 
     def __init__(
-        self
+        self,
+        inp_channels:int=1,
+        num_layers:int=12,
+        initial_output_channels:int=24
     ):
-        pass
+        super(WaveUnet,self).__init__()
+
+        self.encoders = nn.ModuleList()
+        self.decoders = nn.ModuleList()
+        out_channels = initial_output_channels
+        for layer in range(num_layers):
+
+            encoder = WavenetEncoder(inp_channels,out_channels)
+            self.encoders.append(encoder)
+
+            inp_channels = out_channels
+            out_channels += initial_output_channels
+            if layer == num_layers -1 :
+                decoder = WavenetDecoder(num_layers * initial_output_channels + inp_channels,inp_channels)
+            else:
+                decoder = WavenetDecoder(inp_channels+out_channels,inp_channels)
+
+            self.decoders.insert(0,decoder)
+
+        bottleneck_dim = num_layers * initial_output_channels
+        self.bottleneck = nn.Sequential(
+            nn.Conv1d(bottleneck_dim,bottleneck_dim, 15, stride=1,
+                      padding=7),
+            nn.BatchNorm1d(bottleneck_dim),
+            nn.LeakyReLU(negative_slope=0.1, inplace=True)
+        )
+        
+        
 
     def forward(
         self,waveform
     ):
-        pass
\ No newline at end of file
+        
+        for encoder in self.encoders:
+            out = encoder(waveform)
+
+        out = self.bottleneck(out)
+
+        for decoder in self.decoders:
+            out = decoder(out)
+
+        return decoder
\ No newline at end of file