Thank you for your time @Nikronic.
These are things I originally thought were wrong, as I was not used to the Dataloader format, but my data is being loaded properly. The inputs and labels correctly correspond, and are shuffled.
Regarding the weight initialization, you are correct they need to be changed, but this does not change the performance. I did not share that model to simplify the original post. Please find it below:
class CNN(nn.Module):   
    def __init__(self):
        super(CNN, self).__init__()

        # define the dropout layer
        self.dropout  = nn.Dropout(p = 0.5)
        
        # add a zero to the left and right hand sides of the input
        self.zero_pad = nn.ZeroPad2d((1,1,0,0))
        
        # convolutional layers w/ weight initialization
        self.conv1 = nn.Conv2d(1, 256, kernel_size=(1,3), stride=1, padding = (0,1), bias = True)
        torch.nn.init.xavier_uniform_(self.conv1.weight)
        self.conv2 = nn.Conv2d(256, 80, kernel_size=(2,3), stride=1, padding = (0,1), bias = True)
        torch.nn.init.xavier_uniform_(self.conv2.weight)
        
        # dense layers w/ weight initialization
        self.dense1 = nn.Linear(80*130, 256, bias =True)
        torch.nn.init.kaiming_normal_(self.dense1.weight, nonlinearity='relu')
        self.dense2 = nn.Linear(256,11, bias = True)
        torch.nn.init.kaiming_normal_(self.dense2.weight, nonlinearity='sigmoid')
        
    # Defining the forward pass    
    def forward(self, x):
        x = self.zero_pad(x)
        x = F.relu(self.conv1(x))
        x = self.dropout(x)
        x = F.relu(self.conv2(x))
        x = self.dropout(x)
        x = x.view(x.size(0), -1)
        x = F.relu(self.dense1(x))
        x = self.dense2(x)
        return x
    
model = CNN()

I am under the inclination the error is in the forward of my model or the training script itself.
Thanks again for your time