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249 lines
10 KiB
249 lines
10 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Contains various CTC decoders."""
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import multiprocessing
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from itertools import groupby
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from math import log
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import numpy as np
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def ctc_greedy_decoder(probs_seq, vocabulary):
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"""CTC greedy (best path) 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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: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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def ctc_beam_search_decoder(probs_seq,
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beam_size,
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vocabulary,
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cutoff_prob=1.0,
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cutoff_top_n=40,
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ext_scoring_func=None,
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nproc=False):
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"""CTC Beam search decoder.
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It utilizes beam search to approximately select top best decoding
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labels and returning results in the descending order.
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The implementation is based on Prefix Beam Search
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(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 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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:param nproc: Whether the decoder used in multiprocesses.
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:type nproc: bool
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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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# 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("The shape of prob_seq does not match with the "
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"shape of the vocabulary.")
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# blank_id assign
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blank_id = len(vocabulary)
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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_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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# 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 = {'\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 range(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 or cutoff_top_n < cutoff_len:
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prob_idx = sorted(prob_idx, key=lambda asd: asd[1], reverse=True)
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cutoff_len, cum_prob = 0, 0.0
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for i in range(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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cutoff_len = min(cutoff_len, cutoff_top_n)
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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 l not in prefix_set_next:
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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 range(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 l_plus not in prefix_set_next:
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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.items(), 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 = log(prob)
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beam_result.append((log_prob, result))
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else:
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beam_result.append((float('-inf'), ''))
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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_batch(probs_split,
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beam_size,
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vocabulary,
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num_processes,
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cutoff_prob=1.0,
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cutoff_top_n=40,
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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 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 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 1.0, no pruning.
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:type cutoff_prob: float
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:param num_processes: Number of parallel processes.
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:type num_processes: int
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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_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 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, cutoff_prob, cutoff_top_n,
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None, 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 = [result.get() for result in results]
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return beam_search_results
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