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111 lines
4.0 KiB
111 lines
4.0 KiB
"""Wrapper for various CTC decoders in SWIG."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import swig_ctc_decoders
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#import multiprocessing
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from pathos.multiprocessing import Pool
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def ctc_best_path_decoder(probs_seq, vocabulary):
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"""Wrapper for ctc best path decoder in swig.
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:param probs_seq: 2-D list of probability distributions over each time
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step, with each element being a list of normalized
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probabilities over vocabulary and blank.
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:type probs_seq: 2-D list
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:param vocabulary: Vocabulary list.
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:type vocabulary: list
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:return: Decoding result string.
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:rtype: basestring
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"""
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return swig_ctc_decoders.ctc_best_path_decoder(probs_seq.tolist(),
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vocabulary)
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def ctc_beam_search_decoder(
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probs_seq,
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beam_size,
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vocabulary,
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blank_id,
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cutoff_prob=1.0,
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ext_scoring_func=None, ):
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"""Wrapper for CTC Beam Search Decoder.
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:param probs_seq: 2-D list of probability distributions over each time
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step, with each element being a list of normalized
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probabilities over vocabulary and blank.
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:type probs_seq: 2-D list
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:param beam_size: Width for beam search.
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:type beam_size: int
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:param vocabulary: Vocabulary list.
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:type vocabulary: list
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:param blank_id: ID of blank.
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:type blank_id: int
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:param cutoff_prob: Cutoff probability in pruning,
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default 1.0, no pruning.
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:type cutoff_prob: float
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:param ext_scoring_func: External scoring function for
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partially decoded sentence, e.g. word count
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or language model.
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:type external_scoring_func: callable
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:return: List of tuples of log probability and sentence as decoding
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results, in descending order of the probability.
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:rtype: list
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"""
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return swig_ctc_decoders.ctc_beam_search_decoder(
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probs_seq.tolist(), beam_size, vocabulary, blank_id, cutoff_prob,
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ext_scoring_func)
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def ctc_beam_search_decoder_batch(probs_split,
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beam_size,
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vocabulary,
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blank_id,
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num_processes,
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cutoff_prob=1.0,
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ext_scoring_func=None):
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"""Wrapper for CTC beam search decoder in batch
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"""
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# TODO: to resolve PicklingError
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if not num_processes > 0:
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raise ValueError("Number of processes must be positive!")
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pool = Pool(processes=num_processes)
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results = []
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args_list = []
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for i, probs_list in enumerate(probs_split):
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args = (probs_list, beam_size, vocabulary, blank_id, cutoff_prob,
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ext_scoring_func)
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args_list.append(args)
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results.append(pool.apply_async(ctc_beam_search_decoder, args))
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pool.close()
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pool.join()
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beam_search_results = [result.get() for result in results]
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"""
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len_args = len(probs_split)
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beam_search_results = pool.map(ctc_beam_search_decoder,
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probs_split,
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[beam_size for i in xrange(len_args)],
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[vocabulary for i in xrange(len_args)],
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[blank_id for i in xrange(len_args)],
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[cutoff_prob for i in xrange(len_args)],
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[ext_scoring_func for i in xrange(len_args)]
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)
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"""
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'''
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processes = [mp.Process(target=ctc_beam_search_decoder,
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args=(probs_list, beam_size, vocabulary, blank_id, cutoff_prob,
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ext_scoring_func) for probs_list in probs_split]
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for p in processes:
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p.start()
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for p in processes:
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p.join()
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beam_search_results = []
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'''
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return beam_search_results
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