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generator.py

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+import torch.nn as nn
+
+class SISUGenerator(nn.Module):
+    def __init__(self): # No noise_dim parameter
+        super(SISUGenerator, self).__init__()
+        self.model = nn.Sequential(
+            nn.Conv1d(2, 64, kernel_size=7, stride=1, padding=3), # Input 2 channels (low-quality audio)
+            nn.LeakyReLU(0.2),
+            nn.Conv1d(64, 64, kernel_size=7, stride=1, padding=3),
+            nn.LeakyReLU(0.2),
+            nn.Conv1d(64, 128, kernel_size=5, stride=2, padding=2),
+            nn.LeakyReLU(0.2),
+            nn.Conv1d(128, 128, kernel_size=5, stride=1, padding=2),
+            nn.LeakyReLU(0.2),
+            nn.ConvTranspose1d(128, 64, kernel_size=4, stride=2, padding=1),
+            nn.LeakyReLU(0.2),
+            nn.Conv1d(64, 64, kernel_size=3, stride=1, padding=1),
+            nn.LeakyReLU(0.2),
+            nn.Conv1d(64, 2, kernel_size=3, stride=1, padding=1), # Output 2 channels (high-quality audio)
+            nn.Tanh()
+        )
+
+    def forward(self, x):
+        return self.model(x)