Spaces:
Running
Running
File size: 7,800 Bytes
74a35d9 28d0c5f 74a35d9 28d0c5f 74a35d9 28d0c5f d46b8e0 74a35d9 28d0c5f 51b11b3 28d0c5f 0746ae5 28d0c5f d46b8e0 28d0c5f 0746ae5 28d0c5f 0746ae5 28d0c5f 0746ae5 28d0c5f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 |
import time
from string import punctuation
import numpy as np
from dtwalign import dtw_from_distance_matrix
from ortools.sat.python import cp_model
from . import WordMetrics, app_logger
offset_blank = 1
TIME_THRESHOLD_MAPPING = 5.0
def get_word_distance_matrix(words_estimated: list, words_real: list) -> np.array:
number_of_real_words = len(words_real)
number_of_estimated_words = len(words_estimated)
word_distance_matrix = np.zeros(
(number_of_estimated_words+offset_blank, number_of_real_words))
for idx_estimated in range(number_of_estimated_words):
for idx_real in range(number_of_real_words):
word_distance_matrix[idx_estimated, idx_real] = WordMetrics.edit_distance_python(
words_estimated[idx_estimated], words_real[idx_real])
if offset_blank == 1:
for idx_real in range(number_of_real_words):
word_distance_matrix[number_of_estimated_words,
idx_real] = len(words_real[idx_real])
return word_distance_matrix
def get_best_path_from_distance_matrix(word_distance_matrix):
modelCpp = cp_model.CpModel()
number_of_real_words = word_distance_matrix.shape[1]
number_of_estimated_words = word_distance_matrix.shape[0]-1
number_words = np.maximum(number_of_real_words, number_of_estimated_words)
estimated_words_order = [modelCpp.NewIntVar(0, int(
number_words - 1 + offset_blank), 'w%i' % i) for i in range(number_words+offset_blank)]
# They are in ascending order
for word_idx in range(number_words-1):
modelCpp.Add(
estimated_words_order[word_idx+1] >= estimated_words_order[word_idx])
total_phoneme_distance = 0
real_word_at_time = {}
for idx_estimated in range(number_of_estimated_words):
for idx_real in range(number_of_real_words):
real_word_at_time[idx_estimated, idx_real] = modelCpp.NewBoolVar(
'real_word_at_time'+str(idx_real)+'-'+str(idx_estimated))
modelCpp.Add(estimated_words_order[idx_estimated] == idx_real).OnlyEnforceIf(
real_word_at_time[idx_estimated, idx_real])
total_phoneme_distance += word_distance_matrix[idx_estimated,
idx_real]*real_word_at_time[idx_estimated, idx_real]
# If no word in time, difference is calculated from empty string
for idx_real in range(number_of_real_words):
word_has_a_match = modelCpp.NewBoolVar(
'word_has_a_match'+str(idx_real))
modelCpp.Add(sum([real_word_at_time[idx_estimated, idx_real] for idx_estimated in range(
number_of_estimated_words)]) == 1).OnlyEnforceIf(word_has_a_match)
total_phoneme_distance += word_distance_matrix[number_of_estimated_words,
idx_real]*word_has_a_match.Not()
# Loss should be minimized
modelCpp.Minimize(total_phoneme_distance)
solver = cp_model.CpSolver()
solver.parameters.max_time_in_seconds = TIME_THRESHOLD_MAPPING
status = solver.Solve(modelCpp)
mapped_indices = []
try:
for word_idx in range(number_words):
mapped_indices.append(
(solver.Value(estimated_words_order[word_idx])))
return np.array(mapped_indices, dtype=int)
except Exception as ex:
app_logger.error(f"ex:{ex}.")
return []
def get_resulting_string(mapped_indices: np.array, words_estimated: list, words_real: list) -> tuple[list, list]:
mapped_words = []
mapped_words_indices = []
WORD_NOT_FOUND_TOKEN = '-'
number_of_real_words = len(words_real)
for word_idx in range(number_of_real_words):
app_logger.debug(f"{word_idx} => {mapped_indices} == {word_idx}, {mapped_indices == word_idx} #")
position_of_real_word_indices = np.where(
mapped_indices == word_idx)[0].astype(int)
if len(position_of_real_word_indices) == 0:
mapped_words.append(WORD_NOT_FOUND_TOKEN)
mapped_words_indices.append(-1)
continue
if len(position_of_real_word_indices) == 1:
mapped_words.append(
words_estimated[position_of_real_word_indices[0]])
mapped_words_indices.append(position_of_real_word_indices[0])
continue
# Check which index gives the lowest error
if len(position_of_real_word_indices) > 1:
error = 99999
best_possible_combination = ''
best_possible_idx = -1
best_possible_combination, best_possible_idx = inner_get_resulting_string(
best_possible_combination, best_possible_idx, error, position_of_real_word_indices,
word_idx, words_estimated, words_real
)
mapped_words.append(best_possible_combination)
mapped_words_indices.append(best_possible_idx)
# continue
return mapped_words, mapped_words_indices
def inner_get_resulting_string(
best_possible_combination, best_possible_idx, error, position_of_real_word_indices, word_idx, words_estimated, words_real
):
for single_word_idx in position_of_real_word_indices:
idx_above_word = single_word_idx >= len(words_estimated)
if idx_above_word:
continue
error_word = WordMetrics.edit_distance_python(
words_estimated[single_word_idx], words_real[word_idx])
if error_word < error:
error = error_word * 1
best_possible_combination = words_estimated[single_word_idx]
best_possible_idx = single_word_idx
return best_possible_combination, best_possible_idx
def get_best_mapped_words(words_estimated: list, words_real: list) -> tuple[list, list]:
word_distance_matrix = get_word_distance_matrix(
words_estimated, words_real)
start = time.time()
mapped_indices = get_best_path_from_distance_matrix(word_distance_matrix)
duration_of_mapping = time.time()-start
# In case or-tools doesn't converge, go to a faster, low-quality solution
if len(mapped_indices) == 0 or duration_of_mapping > TIME_THRESHOLD_MAPPING+0.5:
mapped_indices = (dtw_from_distance_matrix(
word_distance_matrix)).path[:len(words_estimated), 1]
mapped_words, mapped_words_indices = get_resulting_string(
mapped_indices, words_estimated, words_real)
return mapped_words, mapped_words_indices
# Faster, but not optimal
def get_best_mapped_words_dtw(words_estimated: list, words_real: list) -> list:
from dtwalign import dtw_from_distance_matrix
word_distance_matrix = get_word_distance_matrix(
words_estimated, words_real)
mapped_indices = dtw_from_distance_matrix(
word_distance_matrix).path[:-1, 0]
mapped_words, mapped_words_indices = get_resulting_string(
mapped_indices, words_estimated, words_real)
return mapped_words, mapped_words_indices
def getWhichLettersWereTranscribedCorrectly(real_word, transcribed_word):
is_leter_correct = [None]*len(real_word)
for idx, letter in enumerate(real_word):
if letter == transcribed_word[idx] or letter in punctuation:
is_leter_correct[idx] = 1
else:
is_leter_correct[idx] = 0
return is_leter_correct
def parseLetterErrorsToHTML(word_real, is_leter_correct):
word_colored = ''
correct_color_start = '*'
correct_color_end = '*'
wrong_color_start = '-'
wrong_color_end = '-'
for idx, letter in enumerate(word_real):
if is_leter_correct[idx] == 1:
word_colored += correct_color_start + letter+correct_color_end
else:
word_colored += wrong_color_start + letter+wrong_color_end
return word_colored
|