The issue is telling you the dimension of x before calling x = self.fc1(x)
which  is (64,8). That means your FC needs to be
  self.fc1 = nn.Linear(8, arbitrary_number)

Consider that your tensor is Nx64x8, the fully connected will apply the same kernel to all the 64 channels. You may want to reshape everything:
class CNNnet(nn.Module):
    def __init__(self):
        super(CNNnet, self).__init__()
        self.conv1 = nn.Conv1d(in_channels=12, out_channels=32, kernel_size=3, stride=1, padding=1, bias=True)  #
        self.relu = nn.ReLU()
        self.pool = nn.MaxPool1d(kernel_size=2)
        self.conv2 = nn.Conv1d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)  # 36795
        self.relu = nn.ReLU()
        self.pool = nn.MaxPool1d(kernel_size=2)

        self.fc1 = nn.Linear(64*8,256)
        self.dropout = nn.Dropout(p=0.5)
        self.fc2 = nn.Linear(256, 128)
        self.dropout = nn.Dropout(p=0.5)
        self.fc3 = nn.Linear(128, 8)

    def forward(self, x):
        x = x.view(-1, 12, 32)
        x = self.conv1(x)
        x = self.relu(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = self.pool(x)
        x=x.view(-1,8*64)
        x = self.fc1(x)
        x = self.dropout(x)
        x = self.fc2(x)
        x = self.dropout(x)
        x = self.fc3(x)
        return x