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| | | import logging |
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| | | import argparse |
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| | | import numpy as np |
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| | | import edit_distance |
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| | | # import edit_distance |
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| | | from itertools import zip_longest |
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| | |
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| | | return res |
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| | | |
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| | | class AverageShiftCalculator(): |
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| | | def __init__(self): |
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| | | logging.warning("Calculating average shift.") |
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| | | def __call__(self, file1, file2): |
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| | | uttid_list1, ts_dict1 = self.read_timestamps(file1) |
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| | | uttid_list2, ts_dict2 = self.read_timestamps(file2) |
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| | | uttid_intersection = self._intersection(uttid_list1, uttid_list2) |
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| | | res = self.as_cal(uttid_intersection, ts_dict1, ts_dict2) |
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| | | logging.warning("Average shift of {} and {}: {}.".format(file1, file2, str(res)[:8])) |
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| | | logging.warning("Following timestamp pair differs most: {}, detail:{}".format(self.max_shift, self.max_shift_uttid)) |
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| | | |
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| | | def _intersection(self, list1, list2): |
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| | | set1 = set(list1) |
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| | | set2 = set(list2) |
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| | | if set1 == set2: |
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| | | logging.warning("Uttid same checked.") |
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| | | return set1 |
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| | | itsc = list(set1 & set2) |
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| | | logging.warning("Uttid differs: file1 {}, file2 {}, lines same {}.".format(len(list1), len(list2), len(itsc))) |
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| | | return itsc |
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| | | |
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| | | def read_timestamps(self, file): |
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| | | # read timestamps file in standard format |
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| | | uttid_list = [] |
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| | | ts_dict = {} |
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| | | with codecs.open(file, 'r') as fin: |
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| | | for line in fin.readlines(): |
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| | | text = '' |
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| | | ts_list = [] |
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| | | line = line.rstrip() |
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| | | uttid = line.split()[0] |
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| | | uttid_list.append(uttid) |
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| | | body = " ".join(line.split()[1:]) |
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| | | for pd in body.split(';'): |
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| | | if not len(pd): continue |
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| | | # pdb.set_trace() |
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| | | char, start, end = pd.lstrip(" ").split(' ') |
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| | | text += char + ',' |
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| | | ts_list.append((float(start), float(end))) |
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| | | # ts_lists.append(ts_list) |
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| | | ts_dict[uttid] = (text[:-1], ts_list) |
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| | | logging.warning("File {} read done.".format(file)) |
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| | | return uttid_list, ts_dict |
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| | | |
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| | | def _shift(self, filtered_timestamp_list1, filtered_timestamp_list2): |
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| | | shift_time = 0 |
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| | | for fts1, fts2 in zip(filtered_timestamp_list1, filtered_timestamp_list2): |
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| | | shift_time += abs(fts1[0] - fts2[0]) + abs(fts1[1] - fts2[1]) |
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| | | num_tokens = len(filtered_timestamp_list1) |
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| | | return shift_time, num_tokens |
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| | | |
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| | | def as_cal(self, uttid_list, ts_dict1, ts_dict2): |
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| | | # calculate average shift between timestamp1 and timestamp2 |
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| | | # when characters differ, use edit distance alignment |
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| | | # and calculate the error between the same characters |
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| | | self._accumlated_shift = 0 |
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| | | self._accumlated_tokens = 0 |
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| | | self.max_shift = 0 |
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| | | self.max_shift_uttid = None |
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| | | for uttid in uttid_list: |
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| | | (t1, ts1) = ts_dict1[uttid] |
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| | | (t2, ts2) = ts_dict2[uttid] |
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| | | _align, _align2, _align3 = [], [], [] |
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| | | fts1, fts2 = [], [] |
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| | | _t1, _t2 = [], [] |
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| | | sm = edit_distance.SequenceMatcher(t1.split(','), t2.split(',')) |
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| | | s = sm.get_opcodes() |
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| | | for j in range(len(s)): |
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| | | if s[j][0] == "replace" or s[j][0] == "insert": |
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| | | _align.append(0) |
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| | | if s[j][0] == "replace" or s[j][0] == "delete": |
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| | | _align3.append(0) |
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| | | elif s[j][0] == "equal": |
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| | | _align.append(1) |
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| | | _align3.append(1) |
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| | | else: |
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| | | continue |
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| | | # use s to index t2 |
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| | | for a, ts , t in zip(_align, ts2, t2.split(',')): |
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| | | if a: |
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| | | fts2.append(ts) |
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| | | _t2.append(t) |
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| | | sm2 = edit_distance.SequenceMatcher(t2.split(','), t1.split(',')) |
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| | | s = sm2.get_opcodes() |
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| | | for j in range(len(s)): |
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| | | if s[j][0] == "replace" or s[j][0] == "insert": |
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| | | _align2.append(0) |
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| | | elif s[j][0] == "equal": |
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| | | _align2.append(1) |
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| | | else: |
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| | | continue |
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| | | # use s2 tp index t1 |
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| | | for a, ts, t in zip(_align3, ts1, t1.split(',')): |
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| | | if a: |
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| | | fts1.append(ts) |
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| | | _t1.append(t) |
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| | | if len(fts1) == len(fts2): |
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| | | shift_time, num_tokens = self._shift(fts1, fts2) |
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| | | self._accumlated_shift += shift_time |
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| | | self._accumlated_tokens += num_tokens |
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| | | if shift_time/num_tokens > self.max_shift: |
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| | | self.max_shift = shift_time/num_tokens |
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| | | self.max_shift_uttid = uttid |
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| | | else: |
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| | | logging.warning("length mismatch") |
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| | | return self._accumlated_shift / self._accumlated_tokens |
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| | | # class AverageShiftCalculator(): |
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| | | # def __init__(self): |
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| | | # logging.warning("Calculating average shift.") |
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| | | # def __call__(self, file1, file2): |
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| | | # uttid_list1, ts_dict1 = self.read_timestamps(file1) |
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| | | # uttid_list2, ts_dict2 = self.read_timestamps(file2) |
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| | | # uttid_intersection = self._intersection(uttid_list1, uttid_list2) |
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| | | # res = self.as_cal(uttid_intersection, ts_dict1, ts_dict2) |
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| | | # logging.warning("Average shift of {} and {}: {}.".format(file1, file2, str(res)[:8])) |
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| | | # logging.warning("Following timestamp pair differs most: {}, detail:{}".format(self.max_shift, self.max_shift_uttid)) |
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| | | # |
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| | | # def _intersection(self, list1, list2): |
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| | | # set1 = set(list1) |
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| | | # set2 = set(list2) |
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| | | # if set1 == set2: |
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| | | # logging.warning("Uttid same checked.") |
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| | | # return set1 |
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| | | # itsc = list(set1 & set2) |
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| | | # logging.warning("Uttid differs: file1 {}, file2 {}, lines same {}.".format(len(list1), len(list2), len(itsc))) |
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| | | # return itsc |
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| | | # |
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| | | # def read_timestamps(self, file): |
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| | | # # read timestamps file in standard format |
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| | | # uttid_list = [] |
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| | | # ts_dict = {} |
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| | | # with codecs.open(file, 'r') as fin: |
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| | | # for line in fin.readlines(): |
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| | | # text = '' |
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| | | # ts_list = [] |
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| | | # line = line.rstrip() |
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| | | # uttid = line.split()[0] |
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| | | # uttid_list.append(uttid) |
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| | | # body = " ".join(line.split()[1:]) |
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| | | # for pd in body.split(';'): |
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| | | # if not len(pd): continue |
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| | | # # pdb.set_trace() |
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| | | # char, start, end = pd.lstrip(" ").split(' ') |
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| | | # text += char + ',' |
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| | | # ts_list.append((float(start), float(end))) |
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| | | # # ts_lists.append(ts_list) |
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| | | # ts_dict[uttid] = (text[:-1], ts_list) |
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| | | # logging.warning("File {} read done.".format(file)) |
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| | | # return uttid_list, ts_dict |
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| | | # |
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| | | # def _shift(self, filtered_timestamp_list1, filtered_timestamp_list2): |
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| | | # shift_time = 0 |
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| | | # for fts1, fts2 in zip(filtered_timestamp_list1, filtered_timestamp_list2): |
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| | | # shift_time += abs(fts1[0] - fts2[0]) + abs(fts1[1] - fts2[1]) |
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| | | # num_tokens = len(filtered_timestamp_list1) |
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| | | # return shift_time, num_tokens |
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| | | # |
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| | | # # def as_cal(self, uttid_list, ts_dict1, ts_dict2): |
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| | | # # # calculate average shift between timestamp1 and timestamp2 |
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| | | # # # when characters differ, use edit distance alignment |
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| | | # # # and calculate the error between the same characters |
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| | | # # self._accumlated_shift = 0 |
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| | | # # self._accumlated_tokens = 0 |
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| | | # # self.max_shift = 0 |
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| | | # # self.max_shift_uttid = None |
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| | | # # for uttid in uttid_list: |
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| | | # # (t1, ts1) = ts_dict1[uttid] |
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| | | # # (t2, ts2) = ts_dict2[uttid] |
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| | | # # _align, _align2, _align3 = [], [], [] |
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| | | # # fts1, fts2 = [], [] |
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| | | # # _t1, _t2 = [], [] |
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| | | # # sm = edit_distance.SequenceMatcher(t1.split(','), t2.split(',')) |
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| | | # # s = sm.get_opcodes() |
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| | | # # for j in range(len(s)): |
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| | | # # if s[j][0] == "replace" or s[j][0] == "insert": |
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| | | # # _align.append(0) |
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| | | # # if s[j][0] == "replace" or s[j][0] == "delete": |
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| | | # # _align3.append(0) |
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| | | # # elif s[j][0] == "equal": |
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| | | # # _align.append(1) |
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| | | # # _align3.append(1) |
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| | | # # else: |
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| | | # # continue |
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| | | # # # use s to index t2 |
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| | | # # for a, ts , t in zip(_align, ts2, t2.split(',')): |
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| | | # # if a: |
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| | | # # fts2.append(ts) |
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| | | # # _t2.append(t) |
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| | | # # sm2 = edit_distance.SequenceMatcher(t2.split(','), t1.split(',')) |
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| | | # # s = sm2.get_opcodes() |
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| | | # # for j in range(len(s)): |
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| | | # # if s[j][0] == "replace" or s[j][0] == "insert": |
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| | | # # _align2.append(0) |
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| | | # # elif s[j][0] == "equal": |
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| | | # # _align2.append(1) |
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| | | # # else: |
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| | | # # continue |
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| | | # # # use s2 tp index t1 |
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| | | # # for a, ts, t in zip(_align3, ts1, t1.split(',')): |
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| | | # # if a: |
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| | | # # fts1.append(ts) |
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| | | # # _t1.append(t) |
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| | | # # if len(fts1) == len(fts2): |
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| | | # # shift_time, num_tokens = self._shift(fts1, fts2) |
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| | | # # self._accumlated_shift += shift_time |
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| | | # # self._accumlated_tokens += num_tokens |
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| | | # # if shift_time/num_tokens > self.max_shift: |
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| | | # # self.max_shift = shift_time/num_tokens |
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| | | # # self.max_shift_uttid = uttid |
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| | | # # else: |
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| | | # # logging.warning("length mismatch") |
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| | | # # return self._accumlated_shift / self._accumlated_tokens |
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| | | |
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| | | |
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| | | def convert_external_alphas(alphas_file, text_file, output_file): |
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| | | |
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| | | def main(args): |
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| | | if args.mode == 'cal_aas': |
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| | | asc = AverageShiftCalculator() |
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| | | asc(args.input, args.input2) |
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| | | elif args.mode == 'read_ext_alphas': |
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| | | # if args.mode == 'cal_aas': |
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| | | # asc = AverageShiftCalculator() |
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| | | # asc(args.input, args.input2) |
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| | | if args.mode == 'read_ext_alphas': |
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| | | convert_external_alphas(args.input, args.input2, args.output) |
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| | | else: |
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| | | logging.error("Mode {} not in SUPPORTED_MODES: {}.".format(args.mode, SUPPORTED_MODES)) |
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