Hello @ptrblck and thanks so much for answer!
I am not sure if the classification is a multi-label, because I have to recognize notes, but the target is represented with 0 and 1.  I have a Dataset with data of 88 notes. When I create a batch in my custom collate function, I join as many notes as batch size size. If I have a batch of 10, I collect 10 notes, so it have to recognize that 10 notes. In the next batch, it will have to recognize 10 notes more.
I print you the shape of input, target and outputs while debug in the train function in the first and second batch. I should say seq_len is different in each epoch.
First batch: 
Input.shape -> torch.Size([28471,252])
target.shape -> torch.Size([28471,88])
outputs.shape-> torch.Size([28471,88])

Second batch: 
Input.shape -> torch.Size([28146,252])
target.shape -> torch.Size([28146,88])
outputs.shape-> torch.Size([28146,88])

I am not sure if I am in the correct way. I am new in Pytorch. Thanks so much.