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tf.nn.ctc_beam_search_decoder_v2

Performs beam search decoding on the logits given in input.

Aliases:

  • tf.compat.v1.nn.ctc_beam_search_decoder_v2
  • tf.compat.v2.nn.ctc_beam_search_decoder
  • tf.nn.ctc_beam_search_decoder_v2
tf.nn.ctc_beam_search_decoder_v2(
    inputs,
    sequence_length,
    beam_width=100,
    top_paths=1
)

Defined in python/ops/ctc_ops.py.

Note The ctc_greedy_decoder is a special case of the ctc_beam_search_decoder with top_paths=1 and beam_width=1 (but that decoder is faster for this special case).

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.