def ctc_decode(y_pred, input_length, greedy=True, beam_width=100, top_paths=1):
"""Decodes the output of a softmax.
Can use either greedy search (also known as best path)
or a constrained dictionary search.
Arguments:
y_pred: tensor `(samples, time_steps, num_categories)`
containing the prediction, or output of the softmax.
input_length: tensor `(samples, )` containing the sequence length for
each batch item in `y_pred`.
greedy: perform much faster best-path search if `true`.
This does not use a dictionary.
beam_width: if `greedy` is `false`: a beam search decoder will be used
with a beam of this width.
top_paths: if `greedy` is `false`,
how many of the most probable paths will be returned.
Returns:
Tuple:
List: if `greedy` is `true`, returns a list of one element that
contains the decoded sequence.
If `false`, returns the `top_paths` most probable
decoded sequences.
Important: blank labels are returned as `-1`.
Tensor `(top_paths, )` that contains
the log probability of each decoded sequence.
"""
y_pred = math_ops.log(array_ops.transpose(y_pred, perm=[1, 0, 2]) + 1e-8)
input_length = math_ops.to_int32(input_length)
if greedy:
(decoded, log_prob) = ctc.ctc_greedy_decoder(
inputs=y_pred, sequence_length=input_length)
else:
(decoded, log_prob) = ctc.ctc_beam_search_decoder(
inputs=y_pred,
sequence_length=input_length,
beam_width=beam_width,
top_paths=top_paths)
decoded_dense = [
sparse_ops.sparse_to_dense(
st.indices, st.dense_shape, st.values, default_value=-1)
for st in decoded
]
return (decoded_dense, log_prob)
# HIGH ORDER FUNCTIONS
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