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289 lines
11 KiB
289 lines
11 KiB
"""Contains various CTC decoders."""
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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 os
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from itertools import groupby
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import numpy as np
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import kenlm
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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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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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:param probs_seq: 2-D list of probabilities over the vocabulary for each
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character. Each element is a list of float probabilities
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for one character.
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:type probs_seq: 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: baseline
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"""
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# dimension verification
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for probs in probs_seq:
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if not len(probs) == len(vocabulary) + 1:
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raise ValueError("probs_seq dimension mismatchedd with vocabulary")
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# argmax to get the best index for each time step
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max_index_list = list(np.array(probs_seq).argmax(axis=1))
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# remove consecutive duplicate indexes
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index_list = [index_group[0] for index_group in groupby(max_index_list)]
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# remove blank indexes
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blank_index = len(vocabulary)
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index_list = [index for index in index_list if index != blank_index]
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# convert index list to string
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return ''.join([vocabulary[index] for index in index_list])
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class Scorer(object):
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"""External defined scorer to evaluate a sentence in beam search
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decoding, consisting of language model and word count.
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:param alpha: Parameter associated with language model.
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:type alpha: float
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:param beta: Parameter associated with word count.
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:type beta: float
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:model_path: Path to load language model.
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:type model_path: basestring
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"""
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def __init__(self, alpha, beta, model_path):
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self._alpha = alpha
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self._beta = beta
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if not os.path.isfile(model_path):
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raise IOError("Invaid language model path: %s" % model_path)
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self._language_model = kenlm.LanguageModel(model_path)
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# n-gram language model scoring
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def language_model_score(self, sentence):
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#log prob of last word
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log_cond_prob = list(
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self._language_model.full_scores(sentence, eos=False))[-1][0]
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return np.power(10, log_cond_prob)
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# word insertion term
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def word_count(self, sentence):
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words = sentence.strip().split(' ')
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return len(words)
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# execute evaluation
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def __call__(self, sentence, log=False):
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"""Evaluation function, gathering all the scores.
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:param sentence: The input sentence for evalutation
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:type sentence: basestring
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:param log: Whether return the score in log representation.
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:type log: bool
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:return: Evaluation score, in the decimal or log.
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:rtype: float
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"""
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lm = self.language_model_score(sentence)
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word_cnt = self.word_count(sentence)
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if log == False:
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score = np.power(lm, self._alpha) \
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* np.power(word_cnt, self._beta)
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else:
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score = self._alpha * np.log(lm) \
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+ self._beta * np.log(word_cnt)
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return score
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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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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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: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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: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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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 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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:rtype: list
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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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# 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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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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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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## 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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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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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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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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if cum_prob >= cutoff_prob:
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break
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prob_idx = prob_idx[0:cutoff_len]
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for l in prefix_set_prev:
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if not prefix_set_next.has_key(l):
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probs_b_cur[l], probs_nb_cur[l] = 0.0, 0.0
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# extend prefix by travering prob_idx
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for index in xrange(cutoff_len):
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c, prob_c = prob_idx[index][0], prob_idx[index][1]
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if c == blank_id:
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probs_b_cur[l] += prob_c * (
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probs_b_prev[l] + probs_nb_prev[l])
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else:
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last_char = l[-1]
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new_char = vocabulary[c]
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l_plus = l + new_char
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if not prefix_set_next.has_key(l_plus):
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probs_b_cur[l_plus], probs_nb_cur[l_plus] = 0.0, 0.0
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if new_char == last_char:
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probs_nb_cur[l_plus] += prob_c * probs_b_prev[l]
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probs_nb_cur[l] += prob_c * probs_nb_prev[l]
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elif new_char == ' ':
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if (ext_scoring_func is None) or (len(l) == 1):
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score = 1.0
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else:
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prefix = l[1:]
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score = ext_scoring_func(prefix)
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probs_nb_cur[l_plus] += score * prob_c * (
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probs_b_prev[l] + probs_nb_prev[l])
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else:
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probs_nb_cur[l_plus] += prob_c * (
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probs_b_prev[l] + probs_nb_prev[l])
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# add l_plus into prefix_set_next
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prefix_set_next[l_plus] = probs_nb_cur[
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l_plus] + probs_b_cur[l_plus]
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# add l into prefix_set_next
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prefix_set_next[l] = probs_b_cur[l] + probs_nb_cur[l]
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# update probs
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probs_b_prev, probs_nb_prev = probs_b_cur, probs_nb_cur
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## store top beam_size prefixes
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prefix_set_prev = sorted(
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prefix_set_next.iteritems(), key=lambda asd: asd[1], reverse=True)
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if beam_size < len(prefix_set_prev):
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prefix_set_prev = prefix_set_prev[:beam_size]
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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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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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## 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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beam_size,
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vocabulary,
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blank_id=0,
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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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: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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: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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:type blank_id: int
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:param cutoff_prob: Cutoff probability in pruning,
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default 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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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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: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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if not num_processes > 0:
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raise ValueError("Number of processes must be positive!")
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# use global variable to pass the externnal scorer to beam search decoder
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global ext_nproc_scorer
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ext_nproc_scorer = ext_scoring_func
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nproc = True
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pool = multiprocessing.Pool(processes=num_processes)
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results = []
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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, None,
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nproc)
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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 = []
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for result in results:
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beam_search_results.append(result.get())
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return beam_search_results
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