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# this is the time dimension of CTC (batch x time x mfcc)
#input_length = np.array([get_xsize(mfcc) for mfcc in X_data])
input_length = np.array(x_val)
# print("3. input_length shape:", input_length.shape)
# print("3. input_length =", input_length)
assert(input_length.shape == (self.batch_size,))
# 4. label_length (required for CTC loss)
# this is the length of the number of label of a sequence
#label_length = np.array([len(l) for l in labels])
label_length = np.array(y_val)
# print("4. label_length shape:", label_length.shape)
# print("4. label_length =", label_length)
assert(label_length.shape == (self.batch_size,))
hi, I want to make a ctc demo, I do not know the "label_length.shape" and "input_length.shape", how to calculate them ? and what means them ? thanks you.
The text was updated successfully, but these errors were encountered:
@moses1994 The shape is a member of numpy.array, which is a tuple representing the dimension of the array. shape of (2, 3) means a 2-dimentional matrix of 2x3. In this code, the label_length is an 1-dimensional array, and each element is the length of the transcript in the batch. So it's shape is (batch,). You don't need to calculate the shape of an array.
3. input_length (required for CTC loss)
hi, I want to make a ctc demo, I do not know the "label_length.shape" and "input_length.shape", how to calculate them ? and what means them ? thanks you.
The text was updated successfully, but these errors were encountered: