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)