I have trained model with english data. Training is also converged and it is generating good wav samples from training data at checkpoint time. While evaluating trained model it takes too much time due to below reason and producing noisy output.
linear_outputs = tf.layers.dense(post_outputs, hp.num_freq) # [N, T_out, F]
shape of linear_outputs is [1,200000, 1025] (while evaluating) as compared to [1, <200, 1025] while training. which causes griffin_lim to take too much time for generating wav. Can someone please help why this is the case?
I have trained model with english data. Training is also converged and it is generating good wav samples from training data at checkpoint time. While evaluating trained model it takes too much time due to below reason and producing noisy output.
linear_outputs= tf.layers.dense(post_outputs, hp.num_freq) # [N, T_out, F] shape of linear_outputs is[1,200000, 1025](while evaluating) as compared to[1, <200, 1025]while training. which causes griffin_lim to take too much time for generating wav. Can someone please help why this is the case?