Thank you for your reply.
Maybe I’m doing something wrong while iterating or in the model parameters. because using the model below, I get an error about the size. The input should be 7 frames, each of size (1, 13) (or (1, 14)  considering the label.
and the output channels is 1024 as an example.
model = nn.Sequential(torch.nn.Conv1d(13, 1024, kernel_size=7, stride=1, padding=0, dilation=1, groups=1, bias=True),
torch.nn.BatchNorm1d(1024),
torch.nn.ReLU(inplace=True),
torch.nn.Conv1d(1024, 1024, kernel_size=1, stride=1, padding=0, dilation=1, groups=1, bias=True),
torch.nn.BatchNorm1d(1024),
torch.nn.ReLU(inplace=True),
torch.nn.Conv1d(1024, 50, kernel_size=1, stride=1, padding=0, dilation=1, groups=1, bias=True))

classifier = nn.Linear(1024, 50)

This is the training script:
class IterMeter(object):
    """keeps track of total iterations"""
    def __init__(self):
        self.val = 0

    def step(self):
        self.val += 1

    def get(self):
        return self.val


def train(model, device, train_loader, criterion, optimizer, scheduler, epoch, iter_meter, experiment):
    model.train()
    data_len = len(train_loader.dataset)
    with experiment.train():
        for batch_idx, _data in enumerate(train_loader):

            optimizer.zero_grad()

            output = model(x_train) 

            loss = criterion(output, y_train)
            loss.backward()

            experiment.log_metric('loss', loss.item(), step=iter_meter.get())
            experiment.log_metric('learning_rate', scheduler.get_lr(), step=iter_meter.get())

            optimizer.step()
            scheduler.step()
            iter_meter.step()
            if batch_idx % 100 == 0 or batch_idx == data_len:
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                    epoch, batch_idx * len(x_train), data_len,
                    100. * batch_idx / len(train_loader), loss.item()))

iter_meter = IterMeter()
for epoch in range(1, epochs + 1):
    train(model, device, train_loader, criterion, optimizer, scheduler, epoch, iter_meter, experiment)

I’m getting an error
RuntimeError: Expected 3-dimensional input for 3-dimensional weight [1024, 13, 7], but got 2-dimensional input of size [3082092, 13] instead