tf.nn.ctc_beam_search_decoder

Performs beam search decoding on the logits given in input.

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tf.compat.v1.nn.ctc_beam_search_decoder_v2

tf.nn.ctc_beam_search_decoder(
    inputs, sequence_length, beam_width=100, top_paths=1
)

Note: Although in general greedy search is a special case of beam-search with top_paths=1 and beam_width=1, ctc_beam_search_decoder differs from ctc_greedy_decoder in the treatment of blanks when computing the probability of a sequence:

• ctc_beam_search_decoder treats blanks as sequence termination
• ctc_greedy_decoder treats blanks as regular elements

Args
inputs	3-D float Tensor, size [max_time, batch_size, num_classes]. The logits.
sequence_length	1-D int32 vector containing sequence lengths, having size [batch_size].
beam_width	An int scalar >= 0 (beam search beam width).
top_paths	An int scalar >= 0, <= beam_width (controls output size).

Returns
A tuple (decoded, log_probabilities) where
decoded	A list of length top_paths, where decoded[j] is a SparseTensor containing the decoded outputs: decoded[j].indices: Indices matrix [total_decoded_outputs[j], 2]; The rows store: [batch, time]. decoded[j].values: Values vector, size [total_decoded_outputs[j]]. The vector stores the decoded classes for beam j. decoded[j].dense_shape: Shape vector, size (2). The shape values are: [batch_size, max_decoded_length[j]].
log_probability	A float matrix [batch_size, top_paths] containing sequence log-probabilities.