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@ -8,8 +8,8 @@ import numpy as np
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import multiprocessing
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def ctc_best_path_decode(probs_seq, vocabulary):
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"""Best path decoding, also called argmax decoding or greedy decoding.
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def ctc_best_path_decoder(probs_seq, vocabulary):
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"""Best path decoder, also called argmax decoder or greedy decoder.
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Path consisting of the most probable tokens are further post-processed to
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remove consecutive repetitions and all blanks.
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@ -40,73 +40,84 @@ def ctc_best_path_decode(probs_seq, vocabulary):
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def ctc_beam_search_decoder(probs_seq,
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beam_size,
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vocabulary,
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blank_id=0,
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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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nproc=False):
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'''Beam search decoder for CTC-trained network, using beam search with width
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beam_size to find many paths to one label, return beam_size labels in
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the descending order of probabilities. The implementation is based on Prefix
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Beam Search(https://arxiv.org/abs/1408.2873), and the unclear part is
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redesigned.
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:param probs_seq: 2-D list with length num_time_steps, each element
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is a list of normalized probabilities over vocabulary
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and blank for one time step.
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"""Beam search decoder for CTC-trained network. It utilizes beam search
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to approximately select top best decoding labels and returning results
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in the descending order. The implementation is based on Prefix
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Beam Search (https://arxiv.org/abs/1408.2873), and the unclear part is
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redesigned. Two important modifications: 1) in the iterative computation
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of probabilities, the assignment operation is changed to accumulation for
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one prefix may comes from different paths; 2) the if condition "if l^+ not
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in A_prev then" after probabilities' computation is deprecated for it is
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hard to understand and seems unnecessary.
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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, default 0.
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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 defined scoring function for
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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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and language model.
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:type external_scoring_function: function
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or language model.
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:type external_scoring_func: callable
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:param nproc: Whether the decoder used in multiprocesses.
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:type nproc: bool
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:return: Decoding log probabilities and result sentences in descending order.
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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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"""
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# dimension check
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for prob_list in probs_seq:
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if not len(prob_list) == len(vocabulary) + 1:
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raise ValueError("probs dimension mismatched with vocabulary")
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num_time_steps = len(probs_seq)
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raise ValueError("The shape of prob_seq does not match with the "
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"shape of the vocabulary.")
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# blank_id check
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probs_dim = len(probs_seq[0])
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if not blank_id < probs_dim:
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if not blank_id < len(probs_seq[0]):
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raise ValueError("blank_id shouldn't be greater than probs dimension")
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# If the decoder called in the multiprocesses, then use the global scorer
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# instantiated in ctc_beam_search_decoder_nproc().
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# instantiated in ctc_beam_search_decoder_batch().
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if nproc is True:
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global ext_nproc_scorer
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ext_scoring_func = ext_nproc_scorer
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## initialize
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# the set containing selected prefixes
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prefix_set_prev = {'\t': 1.0}
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probs_b_prev, probs_nb_prev = {'\t': 1.0}, {'\t': 0.0}
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# prefix_set_prev: the set containing selected prefixes
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# probs_b_prev: prefixes' probability ending with blank in previous step
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# probs_nb_prev: prefixes' probability ending with non-blank in previous step
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prefix_set_prev, probs_b_prev, probs_nb_prev = {
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'\t': 1.0
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}, {
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'\t': 1.0
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}, {
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'\t': 0.0
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}
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## extend prefix in loop
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for time_step in xrange(num_time_steps):
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# the set containing candidate prefixes
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prefix_set_next = {}
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probs_b_cur, probs_nb_cur = {}, {}
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prob = probs_seq[time_step]
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prob_idx = [[i, prob[i]] for i in xrange(len(prob))]
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for time_step in xrange(len(probs_seq)):
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# prefix_set_next: the set containing candidate prefixes
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# probs_b_cur: prefixes' probability ending with blank in current step
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# probs_nb_cur: prefixes' probability ending with non-blank in current step
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prefix_set_next, probs_b_cur, probs_nb_cur = {}, {}, {}
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prob_idx = list(enumerate(probs_seq[time_step]))
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cutoff_len = len(prob_idx)
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#If pruning is enabled
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if (cutoff_prob < 1.0):
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if cutoff_prob < 1.0:
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prob_idx = sorted(prob_idx, key=lambda asd: asd[1], reverse=True)
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cutoff_len = 0
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cum_prob = 0.0
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cutoff_len, cum_prob = 0, 0.0
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for i in xrange(len(prob_idx)):
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cum_prob += prob_idx[i][1]
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cutoff_len += 1
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@ -162,54 +173,53 @@ def ctc_beam_search_decoder(probs_seq,
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prefix_set_prev = dict(prefix_set_prev)
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beam_result = []
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for (seq, prob) in prefix_set_prev.items():
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for seq, prob in prefix_set_prev.items():
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if prob > 0.0 and len(seq) > 1:
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result = seq[1:]
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# score last word by external scorer
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if (ext_scoring_func is not None) and (result[-1] != ' '):
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prob = prob * ext_scoring_func(result)
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log_prob = np.log(prob)
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beam_result.append([log_prob, result])
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beam_result.append((log_prob, result))
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## output top beam_size decoding results
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beam_result = sorted(beam_result, key=lambda asd: asd[0], reverse=True)
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return beam_result
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def ctc_beam_search_decoder_nproc(probs_split,
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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=0,
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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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num_processes=None):
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'''Beam search decoder using multiple processes.
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ext_scoring_func=None):
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"""CTC beam search decoder using multiple processes.
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:param probs_seq: 3-D list with length batch_size, each element
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is a 2-D list of probabilities can be used by
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ctc_beam_search_decoder.
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:param probs_seq: 3-D list with each element as an instance of 2-D list
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of probabilities used by ctc_beam_search_decoder().
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:type probs_seq: 3-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, default 0.
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:param blank_id: ID of blank.
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:type blank_id: int
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:param num_processes: Number of parallel processes.
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:type num_processes: int
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:param cutoff_prob: Cutoff probability in pruning,
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default 0, no pruning.
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default 1.0, no pruning.
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:param num_processes: Number of parallel processes.
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:type num_processes: int
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:type cutoff_prob: float
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:param ext_scoring_func: External defined scoring function for
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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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and language model.
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:type external_scoring_function: function
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:param num_processes: Number of processes, default None, equal to the
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number of CPUs.
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:type num_processes: int
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:return: Decoding log probabilities and result sentences in descending order.
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or language model.
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:type external_scoring_function: 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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if num_processes is None:
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num_processes = multiprocessing.cpu_count()
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"""
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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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@ -227,7 +237,5 @@ def ctc_beam_search_decoder_nproc(probs_split,
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pool.close()
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pool.join()
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beam_search_results = []
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for result in results:
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beam_search_results.append(result.get())
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beam_search_results = [result.get() for result in results]
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return beam_search_results
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Yibing Liu
8 years ago