Thank you for your answer.
I think maybe I did use the word2vec weight to convert my words before feeding to the LSTM. So I believe that my input already been embedding? So I don’t need the embedding layer anymore. I’m not so sure if that is correct.
Here is my code to convert data before input to the model :

for sentence in txt_sequence:
temp = []
for word in sentence :
temp.append(w2v_model.wv[word]) ## this will extract the vector of the word from weight of w2v
x.append(temp)

After that, I pad my x with padding-zero provide from Keras. So I did remove the embedding layers and everything work. But I’m not sure is it the most efficient way.