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egs/wsj/s5/steps/cleanup/internal/segment_ctm_edits_mild.py
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#! /usr/bin/env python
# Copyright 2016 Vimal Manohar
# 2016 Johns Hopkins University (author: Daniel Povey)
# Apache 2.0
from __future__ import print_function
from __future__ import division
import argparse
import copy
import logging
import heapq
import sys
from collections import defaultdict
"""
This script reads 'ctm-edits' file format that is produced by align_ctm_ref.py
and modified by modify_ctm_edits.py and taint_ctm_edits.py. Its function is to
produce a segmentation and text from the ctm-edits input.
It is a milder version of the script segment_ctm_edits.py i.e. it allows
to keep more of the reference. This is useful for segmenting long-audio
based on imperfect transcripts.
The ctm-edits file format that this script expects is as follows
<file-id> <channel> <start-time> <duration> <conf> <hyp-word> <ref-word> <edit>
['tainted']
[note: file-id is really utterance-id at this point].
"""
_global_logger = logging.getLogger(__name__)
_global_logger.setLevel(logging.INFO)
_global_handler = logging.StreamHandler()
_global_handler.setLevel(logging.INFO)
_global_formatter = logging.Formatter(
'%(asctime)s [%(pathname)s:%(lineno)s - '
'%(funcName)s - %(levelname)s ] %(message)s')
_global_handler.setFormatter(_global_formatter)
_global_logger.addHandler(_global_handler)
_global_non_scored_words = {}
def non_scored_words():
return _global_non_scored_words
def get_args():
parser = argparse.ArgumentParser(
description="""This program produces segmentation and text information
based on reading ctm-edits input format which is produced by
steps/cleanup/internal/get_ctm_edits.py,
steps/cleanup/internal/modify_ctm_edits.py and
steps/cleanup/internal/taint_ctm_edits.py.""",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--min-segment-length", type=float, default=0.5,
help="""Minimum allowed segment length (in seconds) for
any segment; shorter segments than this will be
discarded.""")
parser.add_argument("--min-new-segment-length", type=float, default=1.0,
help="""Minimum allowed segment length (in seconds) for
newly created segments (i.e. not identical to the input
utterances).
Expected to be >= --min-segment-length.""")
parser.add_argument("--frame-length", type=float, default=0.01,
help="""This only affects rounding of the output times;
they will be constrained to multiples of this
value.""")
parser.add_argument("--max-tainted-length", type=float, default=0.05,
help="""Maximum allowed length of any 'tainted' line.
Note: 'tainted' lines may only appear at the boundary
of a segment""")
parser.add_argument("--max-edge-silence-length", type=float, default=0.5,
help="""Maximum allowed length of silence if it appears
at the edge of a segment (will be truncated). This
rule is relaxed if such truncation would take a segment
below the --min-segment-length or
--min-new-segment-length.""")
parser.add_argument("--max-edge-non-scored-length", type=float,
default=0.5,
help="""Maximum allowed length of a non-scored word
(noise, cough, etc.) if it appears at the edge of a
segment (will be truncated). This rule is relaxed if
such truncation would take a segment below the
--min-segment-length.""")
parser.add_argument("--max-internal-silence-length", type=float,
default=2.0,
help="""Maximum allowed length of silence if it appears
inside a segment (will cause the segment to be
split).""")
parser.add_argument("--max-internal-non-scored-length", type=float,
default=2.0,
help="""Maximum allowed length of a non-scored word
(noise, etc.) if it appears inside a segment (will
cause the segment to be split).
Note: reference words which are real words but OOV are
not included in this category.""")
parser.add_argument("--unk-padding", type=float, default=0.05,
help="""Amount of padding with <unk> that we do if a
segment boundary is next to errors (ins, del, sub).
That is, we add this amount of time to the segment and
add the <unk> word to cover the acoustics. If nonzero,
the --oov-symbol-file option must be supplied.""")
parser.add_argument("--max-junk-proportion", type=float, default=0.1,
help="""Maximum proportion of the time of the segment
that may consist of potentially bad data, in which we
include 'tainted' lines of the ctm-edits input and
unk-padding.""")
parser.add_argument("--min-split-point-duration", type=float, default=0.0,
help="""Minimum duration of silence or non-scored word
to be considered a viable split point when
truncating based on junk proportion.""")
parser.add_argument("--max-deleted-words-kept-when-merging",
dest='max_deleted_words', type=int, default=1,
help="""When merging segments that are found to be
overlapping or adjacent after all other processing,
keep in the transcript the reference words that were
deleted between the segments [if any] as long as there
were no more than this many reference words. Setting
this to zero will mean that any reference words that
were deleted between the segments we're about to
reattach will not appear in the generated transcript
(so we'll match the hyp).""")
parser.add_argument("--splitting.min-silence-length",
dest="min_silence_length_to_split",
type=float, default=0.3,
help="""Only considers silences that are at least this
long as potential split points""")
parser.add_argument("--splitting.min-non-scored-length",
dest="min_non_scored_length_to_split",
type=float, default=0.1,
help="""Only considers non-scored words that are at
least this long as potential split points""")
parser.add_argument("--splitting.max-segment-length",
dest="max_segment_length_for_splitting",
type=float, default=10,
help="""Try to split long segments into segments that
are smaller that this size. See
possibly_split_long_segments() in Segment class.""")
parser.add_argument("--splitting.hard-max-segment-length",
dest="hard_max_segment_length",
type=float, default=15,
help="""Split all segments that are longer than this
uniformly into segments of size
--splitting.max-segment-length""")
parser.add_argument("--merging-score.silence-factor",
dest="silence_factor",
type=float, default=1,
help="""Weightage on the silence length when merging
segments""")
parser.add_argument("--merging-score.incorrect-words-factor",
dest="incorrect_words_factor",
type=float, default=1,
help="""Weightage on the incorrect_words_length when
merging segments""")
parser.add_argument("--merging-score.tainted-words-factor",
dest="tainted_words_factor",
type=float, default=1,
help="""Weightage on the WER including the
tainted words as incorrect words.""")
parser.add_argument("--merging.max-wer",
dest="max_wer",
type=float, default=10.0,
help="Max WER%% of merged segments when merging")
parser.add_argument("--merging.max-bad-proportion",
dest="max_bad_proportion",
type=float, default=0.2,
help="""Maximum length of silence, junk and incorrect
words in a merged segment allowed as a fraction of the
total length of merged segment.""")
parser.add_argument("--merging.max-segment-length",
dest='max_segment_length_for_merging',
type=float, default=10,
help="""Maximum segment length allowed for merged
segment""")
parser.add_argument("--merging.max-intersegment-incorrect-words-length",
dest='max_intersegment_incorrect_words_length',
type=float, default=0.2,
help="""Maximum length of intersegment region that
can be of incorrect word. This is to
allow cases where there may be a lot of silence in the
segment but the incorrect words are few, while
preventing regions that have a lot of incorrect
words.""")
parser.add_argument("--oov-symbol-file", type=argparse.FileType('r'),
help="""Filename of file such as data/lang/oov.txt
which contains the text form of the OOV word, normally
'<unk>'. Supplied as a file to avoid complications
with escaping. Necessary if the --unk-padding option
has a nonzero value (which it does by default.""")
parser.add_argument("--ctm-edits-out", type=argparse.FileType('w'),
help="""Filename to output an extended version of the
ctm-edits format with segment start and end points
noted. This file is intended to be read by humans;
there are currently no scripts that will read it.""")
parser.add_argument("--word-stats-out", type=argparse.FileType('w'),
help="""Filename for output of word-level stats, of the
form '<word> <bad-proportion> <total-count-in-ref>',
e.g. 'hello 0.12 12408', where the <bad-proportion> is
the proportion of the time that this reference word
does not make it into a segment. It can help reveal
words that have problematic pronunciations or are
associated with transcription errors.""")
parser.add_argument("non_scored_words_in",
metavar="<non-scored-words-file>",
type=argparse.FileType('r'),
help="""Filename of file containing a list of
non-scored words, one per line. See
steps/cleanup/internal/get_nonscored_words.py.""")
parser.add_argument("ctm_edits_in", metavar="<ctm-edits-in>",
type=argparse.FileType('r'),
help="""Filename of input ctm-edits file. Use
/dev/stdin for standard input.""")
parser.add_argument("text_out", metavar="<text-out>",
type=argparse.FileType('w'),
help="""Filename of output text file (same format as
data/train/text, i.e. <new-utterance-id> <word1>
<word2> ... <wordN>""")
parser.add_argument("segments_out", metavar="<segments-out>",
type=argparse.FileType('w'),
help="""Filename of output segments. This has the same
format as data/train/segments, but instead of
<recording-id>, the second field is the old
utterance-id, i.e <new-utterance-id> <old-utterance-id>
<start-time> <end-time>""")
parser.add_argument("--verbose", type=int, default=0,
help="Use higher verbosity for more debugging output")
args = parser.parse_args()
if args.verbose > 2:
_global_handler.setLevel(logging.DEBUG)
_global_logger.setLevel(logging.DEBUG)
return args
def is_tainted(split_line_of_utt):
"""Returns True if this line in ctm-edit is "tainted."""
return len(split_line_of_utt) > 8 and split_line_of_utt[8] == 'tainted'
def compute_segment_cores(split_lines_of_utt):
"""
This function returns a list of pairs (start-index, end-index) representing
the cores of segments (so if a pair is (s, e), then the core of a segment
would span (s, s+1, ... e-1).
The argument 'split_lines_of_utt' is list of lines from a ctm-edits file
corresponding to a single utterance.
By the 'core of a segment', we mean a sequence of ctm-edits lines including
at least one 'cor' line and a contiguous sequence of other lines of the
type 'cor', 'fix' and 'sil' that must be not tainted. The segment core
excludes any tainted lines at the edge of a segment, which will be added
later.
We only initiate segments when it contains something correct and not
realized as unk (i.e. ref==hyp); and we extend it with anything that is
'sil' or 'fix' or 'cor' that is not tainted. Contiguous regions of 'true'
in the resulting boolean array will then become the cores of prototype
segments, and we'll add any adjacent tainted words (or parts of them).
"""
num_lines = len(split_lines_of_utt)
line_is_in_segment_core = [False] * num_lines
# include only the correct lines
for i in range(num_lines):
if (split_lines_of_utt[i][7] == 'cor'
and split_lines_of_utt[i][4] == split_lines_of_utt[i][6]):
line_is_in_segment_core[i] = True
# extend each proto-segment forwards as far as we can:
for i in range(1, num_lines):
if line_is_in_segment_core[i - 1] and not line_is_in_segment_core[i]:
edit_type = split_lines_of_utt[i][7]
if (not is_tainted(split_lines_of_utt[i])
and (edit_type == 'cor' or edit_type == 'sil'
or edit_type == 'fix')):
line_is_in_segment_core[i] = True
# extend each proto-segment backwards as far as we can:
for i in reversed(range(0, num_lines - 1)):
if line_is_in_segment_core[i + 1] and not line_is_in_segment_core[i]:
edit_type = split_lines_of_utt[i][7]
if (not is_tainted(split_lines_of_utt[i])
and (edit_type == 'cor' or edit_type == 'sil'
or edit_type == 'fix')):
line_is_in_segment_core[i] = True
# Get contiguous regions of line in the form of a list
# of (start_index, end_index)
segment_ranges = []
cur_segment_start = None
for i in range(0, num_lines):
if line_is_in_segment_core[i]:
if cur_segment_start is None:
cur_segment_start = i
else:
if cur_segment_start is not None:
segment_ranges.append((cur_segment_start, i))
cur_segment_start = None
if cur_segment_start is not None:
segment_ranges.append((cur_segment_start, num_lines))
return segment_ranges
class SegmentStats(object):
"""Class to store various statistics of segments."""
def __init__(self):
self.num_incorrect_words = 0
self.num_tainted_words = 0
self.incorrect_words_length = 0
self.tainted_nonsilence_length = 0
self.silence_length = 0
self.num_words = 0
self.total_length = 0
def wer(self):
"""Returns WER%"""
try:
return float(self.num_incorrect_words) * 100.0 / self.num_words
except ZeroDivisionError:
return float("inf")
def bad_proportion(self):
assert self.total_length > 0
proportion = float(self.silence_length + self.tainted_nonsilence_length
+ self.incorrect_words_length) / self.total_length
if proportion > 1.00005:
raise RuntimeError("Error in segment stats {0}".format(self))
return proportion
def incorrect_proportion(self):
assert self.total_length > 0
proportion = float(self.incorrect_words_length) / self.total_length
if proportion > 1.00005:
raise RuntimeError("Error in segment stats {0}".format(self))
return proportion
def combine(self, other, scale=1):
"""Merges this stats with another stats object."""
self.num_incorrect_words += scale * other.num_incorrect_words
self.num_tainted_words += scale * other.num_tainted_words
self.num_words += scale * other.num_words
self.incorrect_words_length += scale * other.incorrect_words_length
self.tainted_nonsilence_length += (scale
* other.tainted_nonsilence_length)
self.silence_length += scale * other.silence_length
self.total_length += scale * other.total_length
def assert_equal(self, other):
try:
assert self.num_incorrect_words == other.num_incorrect_words
assert self.num_tainted_words == other.num_tainted_words
assert (abs(self.incorrect_words_length
- other.incorrect_words_length) < 0.01)
assert (abs(self.tainted_nonsilence_length
- other.tainted_nonsilence_length) < 0.01)
assert abs(self.silence_length - other.silence_length) < 0.01
assert self.num_words == other.num_words
assert abs(self.total_length - other.total_length) < 0.01
except AssertionError:
_global_logger.error("self %s != other %s", self, other)
raise
def compare(self, other):
"""Returns true if this stats is same as another stats object."""
if self.num_incorrect_words != other.num_incorrect_words:
return False
if self.num_tainted_words != other.num_tainted_words:
return False
if self.incorrect_words_length != other.incorrect_words_length:
return False
if self.tainted_nonsilence_length != other.tainted_nonsilence_length:
return False
if self.silence_length != other.silence_length:
return False
if self.num_words != other.num_words:
return False
if self.total_length != other.total_length:
return False
return True
def __str__(self):
return ("num-incorrect-words={num_incorrect:d},"
"num-tainted-words={num_tainted:d},"
"num-words={num_words:d},"
"incorrect-length={incorrect_length:.2f},"
"silence-length={sil_length:.2f},"
"tainted-nonsilence-length={tainted_nonsilence_length:.2f},"
"total-length={total_length:.2f}".format(
num_incorrect=self.num_incorrect_words,
num_tainted=self.num_tainted_words,
num_words=self.num_words,
incorrect_length=self.incorrect_words_length,
sil_length=self.silence_length,
tainted_nonsilence_length=self.tainted_nonsilence_length,
total_length=self.total_length))
class Segment(object):
"""Class to store segments."""
def __init__(self, split_lines_of_utt, start_index, end_index,
debug_str=None, compute_segment_stats=False,
segment_stats=None):
self.split_lines_of_utt = split_lines_of_utt
# start_index is the index of the first line that appears in this
# segment, and end_index is one past the last line. This does not
# include unk-padding.
self.start_index = start_index
self.end_index = end_index
assert end_index > start_index
# If the following values are nonzero, then when we create the segment
# we will add <unk> at the start and end of the segment [representing
# partial words], with this amount of additional audio.
self.start_unk_padding = 0.0
self.end_unk_padding = 0.0
# debug_str keeps track of the 'core' of the segment.
if debug_str is None:
debug_str = 'core-start={0},core-end={1}'.format(start_index,
end_index)
else:
assert type(debug_str) == str
self.debug_str = debug_str
# This gives the proportion of the time of the first line in the
# segment that we keep. Usually 1.0 but may be less if we've trimmed
# away some proportion of the time.
self.start_keep_proportion = 1.0
# This gives the proportion of the time of the last line in the segment
# that we keep. Usually 1.0 but may be less if we've trimmed away some
# proportion of the time.
self.end_keep_proportion = 1.0
self.stats = None
if compute_segment_stats:
self.compute_stats()
if segment_stats is not None:
self.compute_stats()
self.stats.assert_equal(segment_stats)
self.stats = segment_stats
def copy(self, copy_stats=True):
segment = Segment(self.split_lines_of_utt, self.start_index,
self.end_index, debug_str=self.debug_str,
segment_stats=(None if not copy_stats
else copy.deepcopy(self.stats)))
segment.start_keep_proportion = self.start_keep_proportion
segment.end_keep_proportion = self.end_keep_proportion
segment.start_unk_padding = self.start_unk_padding
segment.end_unk_padding = self.end_unk_padding
return segment
def __str__(self):
return self.debug_info()
def compute_stats(self):
"""Compute stats for this segment and store them in SegmentStats
structure.
This is typically called just before merging segments.
"""
self.stats = SegmentStats()
for i in range(self.start_index, self.end_index):
this_duration = float(self.split_lines_of_utt[i][3])
assert self.start_keep_proportion == 1.0
assert self.end_keep_proportion == 1.0
# TODO(vimal): Decide if keep proportion must be applied
# if i == self.start_index:
# this_duration *= self.start_keep_proportion
# if i == self.end_index - 1:
# this_duration *= self.end_keep_proportion
if self.end_index - 1 == self.start_index:
# TODO(vimal): Is this true?
assert self.start_keep_proportion == self.end_keep_proportion
try:
if self.split_lines_of_utt[i][7] not in ['cor', 'fix', 'sil']:
# TODO(vimal): The commented part below is is apparently
# not true in modify_ctm_edits.py.
# Need to check this or change comments there.
# assert (self.split_lines_of_utt[i][6]
# not in non_scored_words)
assert not is_tainted(self.split_lines_of_utt[i])
self.stats.num_incorrect_words += 1
self.stats.incorrect_words_length += this_duration
if self.split_lines_of_utt[i][7] == 'sil':
self.stats.silence_length += this_duration
else:
if (self.split_lines_of_utt[i][6]
not in non_scored_words()):
self.stats.num_words += 1
if (is_tainted(self.split_lines_of_utt[i])
and self.split_lines_of_utt[i][7] not in 'sil'
and (self.split_lines_of_utt[i][6]
not in non_scored_words())):
# If ref_word is not a non-scored word, this would be
# counted as an incorrect word.
self.stats.num_tainted_words += 1
self.stats.tainted_nonsilence_length += this_duration
except Exception:
_global_logger.error(
"Something went wrong when computing stats at "
"ctm line %s", self.split_lines_of_utt[i])
raise
self.stats.total_length = self.length()
try:
assert (self.stats.tainted_nonsilence_length
+ self.stats.silence_length
+ self.stats.incorrect_words_length - 0.001
<= self.stats.total_length)
except AssertionError:
_global_logger.error(
"Something wrong with the stats for segment %s", self)
raise
def possibly_add_tainted_lines(self):
"""
This is stage 1 of segment processing (after creating the boundaries of
the core of the segment, which is done outside of this class).
This function may reduce start_index and/or increase end_index by
including a single adjacent 'tainted' line from the ctm-edits file.
This is only done if the lines at the boundaries of the segment are
currently real non-silence words and not non-scored words. The idea is
that we probably don't want to start or end the segment right at the
boundary of a real word, we want to add some kind of padding.
"""
split_lines_of_utt = self.split_lines_of_utt
# we're iterating over the segment (start, end)
for b in [False, True]:
if b:
boundary_index = self.end_index - 1
adjacent_index = self.end_index
else:
boundary_index = self.start_index
adjacent_index = self.start_index - 1
if (adjacent_index >= 0
and adjacent_index < len(split_lines_of_utt)):
# only consider merging the adjacent word into the segment if
# we're not at the boundary of the utterance.
adjacent_line_is_tainted = is_tainted(
split_lines_of_utt[adjacent_index])
# if the adjacent line wasn't tainted, then there must have
# been another stronger reason why we didn't include it in the
# core of the segment (probably that it was an ins, del or
# sub), so there is no point considering it.
if adjacent_line_is_tainted:
boundary_edit_type = split_lines_of_utt[boundary_index][7]
boundary_ref_word = split_lines_of_utt[boundary_index][6]
# Even if the edit_type is 'cor', it is possible that
# column 4 (hyp_word) is not the same as column 6
# (ref_word) because the ref_word is an OOV and the
# hyp_word is OOV symbol.
# we only add the tainted line to the segment if the word
# at the boundary was a non-silence word that was correctly
# decoded and not fixed [see modify_ctm_edits.py.]
if (boundary_edit_type == 'cor'
and (boundary_ref_word
not in non_scored_words())):
# Add the adjacent tainted line to the segment.
if b:
self.end_index += 1
else:
self.start_index -= 1
def possibly_split_segment(self, max_internal_silence_length,
max_internal_non_scored_length):
"""
This is stage 3 of segment processing.
This function will split a segment into multiple pieces if any of the
internal [non-boundary] silences or non-scored words are longer
than the allowed values --max-internal-silence-length and
--max-internal-non-scored-length.
This function returns a list of segments.
In the normal case (where there is no splitting) it just returns an
array with a single element 'self'.
Note: --max-internal-silence-length and
--max-internal-non-scored-length can be set to very large values
to avoid any splitting.
"""
# make sure the segment hasn't been processed more than we expect.
assert (self.start_unk_padding == 0.0 and self.end_unk_padding == 0.0
and self.start_keep_proportion == 1.0
and self.end_keep_proportion == 1.0)
segments = [] # the answer
cur_start_index = self.start_index
cur_start_is_split = False
# only consider splitting at non-boundary lines. [we'd just truncate
# the boundary lines.]
for index_to_split_at in range(cur_start_index + 1,
self.end_index - 1):
this_split_line = self.split_lines_of_utt[index_to_split_at]
this_duration = float(this_split_line[3])
this_edit_type = this_split_line[7]
this_ref_word = this_split_line[6]
if ((this_edit_type == 'sil' and
this_duration > max_internal_silence_length)
or (this_ref_word in non_scored_words()
and (this_duration
> max_internal_non_scored_length))):
# We split this segment at this index, dividing the word in two
# [later on, in possibly_truncate_boundaries, it may be further
# truncated.]
# Note: we use 'index_to_split_at + 1' because the Segment
# constructor takes an 'end-index' which is interpreted as one
# past the end.
new_segment = Segment(self.split_lines_of_utt, cur_start_index,
index_to_split_at + 1,
debug_str=self.debug_str)
if cur_start_is_split:
new_segment.start_keep_proportion = 0.5
new_segment.end_keep_proportion = 0.5
cur_start_is_split = True
cur_start_index = index_to_split_at
segments.append(new_segment)
if len(segments) == 0: # We did not split.
segments.append(self)
else:
# We did split. Add the very last segment.
new_segment = Segment(self.split_lines_of_utt, cur_start_index,
self.end_index,
debug_str=self.debug_str)
assert cur_start_is_split
new_segment.start_keep_proportion = 0.5
segments.append(new_segment)
return segments
def possibly_split_long_segment(self, max_segment_length,
hard_max_segment_length,
min_silence_length_to_split,
min_non_scored_length_to_split):
"""
This is stage 4 of segment processing.
This function will split a segment into multiple pieces if it is
longer than the value --max-segment-length.
It tries to split at silences and non-scored words that are
at least --min-silence-length-to-split or
--min-non-scored-length-to-split long.
If this is not possible and the segments are still longer than
--hard-max-segment-length, then this is split into equal length
pieces of approximately --max-segment-length long.
This function returns a list of segments.
In the normal case (where there is no splitting) it just returns an
array with a single element 'self'.
"""
# make sure the segment hasn't been processed more than we expect.
assert self.start_unk_padding == 0.0 and self.end_unk_padding == 0.0
if self.length() < max_segment_length:
return [self]
segments = [self] # the answer
cur_start_index = self.start_index
split_indexes = []
# only consider splitting at non-boundary lines. [we'd just truncate
# the boundary lines.]
for index_to_split_at in range(cur_start_index + 1,
self.end_index - 1):
this_split_line = self.split_lines_of_utt[index_to_split_at]
this_duration = float(this_split_line[3])
this_edit_type = this_split_line[7]
this_ref_word = this_split_line[6]
this_is_tainted = is_tainted(this_split_line)
if (this_edit_type == 'sil'
and this_duration > min_silence_length_to_split):
split_indexes.append((index_to_split_at, this_duration,
this_is_tainted))
if (this_ref_word in non_scored_words()
and (this_duration > min_non_scored_length_to_split)):
split_indexes.append((index_to_split_at, this_duration,
this_is_tainted))
split_indexes.sort(key=lambda x: x[1], reverse=True)
split_indexes.sort(key=lambda x: x[2])
while True:
if len(split_indexes) == 0:
break
new_segments = []
for segment in segments:
if segment.length() < max_segment_length:
new_segments.append(segment)
continue
try:
index_to_split_at = next(
(x[0] for x in split_indexes
if (x[0] > segment.start_index
and x[0] < segment.end_index - 1)))
except StopIteration:
_global_logger.debug(
"Could not find an index in the range (%d, %d) in "
"split-indexes %s", segment.start_index,
segment.end_index - 1, split_indexes)
new_segments.append(segment)
continue
# We split this segment at this index, dividing the word in two
# [later on, in possibly_truncate_boundaries, it may be further
# truncated.]
# Note: we use 'index_to_split_at + 1' because the Segment
# constructor takes an 'end-index' which is interpreted as one
# past the end.
new_segment = Segment(
self.split_lines_of_utt, segment.start_index,
index_to_split_at + 1, debug_str=self.debug_str)
new_segment.end_keep_proportion = 0.5
new_segments.append(new_segment)
new_segment = Segment(
self.split_lines_of_utt, index_to_split_at,
segment.end_index, debug_str=self.debug_str)
new_segment.start_keep_proportion = 0.5
new_segments.append(new_segment)
if len(segments) == len(new_segments):
# No splitting done
break
segments = new_segments
for i, x in enumerate(segments):
_global_logger.debug("Segment %d = %s", i, x)
new_segments = []
# Split segments that are still very long
for segment in segments:
if segment.length() < hard_max_segment_length:
new_segments.append(segment)
continue
cur_start_index = segment.start_index
cur_start = segment.start_time()
index_to_split_at = None
try:
while True:
index_to_split_at = next(
(i for i in range(cur_start_index, segment.end_index)
if (float(self.split_lines_of_utt[i][2])
>= cur_start + max_segment_length)))
new_segment = Segment(
self.split_lines_of_utt, cur_start_index,
index_to_split_at)
new_segments.append(new_segment)
cur_start_index = index_to_split_at
cur_start = float(
self.split_lines_of_utt[cur_start_index][2])
index_to_split_at = None
if (segment.end_time() - cur_start
< hard_max_segment_length):
raise StopIteration
except StopIteration:
if index_to_split_at is None:
_global_logger.debug(
"Could not find an index in the range (%d, %d) with "
"start time > %.2f", cur_start_index,
segment.end_index, cur_start + max_segment_length)
new_segment = Segment(
self.split_lines_of_utt, cur_start_index,
segment.end_index)
new_segments.append(new_segment)
break
segments = new_segments
return segments
def possibly_truncate_boundaries(self, max_edge_silence_length,
max_edge_non_scored_length):
"""
This is stage 5 of segment processing.
It will truncate the silences and non-scored words at the segment
boundaries if they are longer than the --max-edge-silence-length and
--max-edge-non-scored-length respectively
(and to the extent that this wouldn't take us below the
--min-segment-length or --min-new-segment-length. See
relax_boundary_truncation()).
Note: --max-edge-silence-length and --max-edge-non-scored-length
can be set to very large values to avoid any truncation.
"""
for b in [True, False]:
if b:
this_index = self.start_index
else:
this_index = self.end_index - 1
this_split_line = self.split_lines_of_utt[this_index]
truncated_duration = None
this_duration = float(this_split_line[3])
this_edit = this_split_line[7]
this_ref_word = this_split_line[6]
if (this_edit == 'sil'
and this_duration > max_edge_silence_length):
truncated_duration = max_edge_silence_length
elif (this_ref_word in non_scored_words()
and this_duration > max_edge_non_scored_length):
truncated_duration = max_edge_non_scored_length
if truncated_duration is not None:
keep_proportion = truncated_duration / this_duration
if b:
self.start_keep_proportion = keep_proportion
else:
self.end_keep_proportion = keep_proportion
def relax_boundary_truncation(self, min_segment_length,
min_new_segment_length):
"""
This relaxes the segment-boundary truncation of
possibly_truncate_boundaries(), if it would take us below
min-new-segment-length or min-segment-length.
Note: this does not relax the boundary truncation for a particular
boundary (start or end) if that boundary corresponds to a 'tainted'
line of the ctm (because it's dangerous to include too much 'tainted'
audio).
"""
# this should be called before adding unk padding.
assert self.start_unk_padding == self.end_unk_padding == 0.0
if self.start_keep_proportion == self.end_keep_proportion == 1.0:
return # nothing to do there was no truncation.
length_cutoff = max(min_new_segment_length, min_segment_length)
length_with_truncation = self.length()
if length_with_truncation >= length_cutoff:
return # Nothing to do.
orig_start_keep_proportion = self.start_keep_proportion
orig_end_keep_proportion = self.end_keep_proportion
if not is_tainted(self.split_lines_of_utt[self.start_index]):
self.start_keep_proportion = 1.0
if not is_tainted(self.split_lines_of_utt[self.end_index - 1]):
self.end_keep_proportion = 1.0
length_with_relaxed_boundaries = self.length()
if length_with_relaxed_boundaries <= length_cutoff:
# Completely undo the truncation [to the extent allowed by the
# presence of tainted lines at the start/end] if, even without
# truncation, we'd be below the length cutoff. This segment may be
# removed later on (but it may not, if removing truncation makes us
# identical to the input utterance, and the length is between
# min_segment_length min_new_segment_length).
return
# Next, compute an interpolation constant a such that the
# {start,end}_keep_proportion values will equal
# a
# * [values-computed-by-possibly_truncate_boundaries()]
# + (1-a) * [completely-relaxed-values].
# we're solving the equation:
# length_cutoff = a * length_with_truncation
# + (1-a) * length_with_relaxed_boundaries
# -> length_cutoff - length_with_relaxed_boundaries =
# a * (length_with_truncation - length_with_relaxed_boundaries)
# -> a = (length_cutoff - length_with_relaxed_boundaries)
# / (length_with_truncation - length_with_relaxed_boundaries)
a = (length_cutoff - length_with_relaxed_boundaries) / (length_with_truncation - length_with_relaxed_boundaries)
if a < 0.0 or a > 1.0:
# TODO(vimal): Should this be an error?
_global_logger.warn("bad 'a' value = %.4f", a)
return
self.start_keep_proportion = (
a * orig_start_keep_proportion
+ (1 - a) * self.start_keep_proportion)
self.end_keep_proportion = (
a * orig_end_keep_proportion + (1 - a) * self.end_keep_proportion)
if abs(self.length() - length_cutoff) >= 0.01:
# TODO(vimal): Should this be an error?
_global_logger.warn(
"possible problem relaxing boundary "
"truncation, length is %.2f vs %.2f", self.length(),
length_cutoff)
def possibly_add_unk_padding(self, max_unk_padding):
"""
This is stage 7 of segment processing.
This function may set start_unk_padding and end_unk_padding to nonzero
values. This is done if the current boundary words are real, scored
words and we're not next to the beginning or end of the utterance.
"""
for b in [True, False]:
if b:
this_index = self.start_index
else:
this_index = self.end_index - 1
this_split_line = self.split_lines_of_utt[this_index]
this_start_time = float(this_split_line[2])
this_ref_word = this_split_line[6]
this_edit = this_split_line[7]
if this_edit == 'cor' and this_ref_word not in non_scored_words():
# we can consider adding unk-padding.
if b: # start of utterance.
unk_padding = max_unk_padding
# close to beginning of file
if unk_padding > this_start_time:
unk_padding = this_start_time
# If we could add less than half of the specified
# unk-padding, don't add any (because when we add
# unk-padding we add the unknown-word symbol '<unk>', and
# if there isn't enough space to traverse the HMM we don't
# want to do it at all.
if unk_padding < 0.5 * max_unk_padding:
unk_padding = 0.0
self.start_unk_padding = unk_padding
else: # end of utterance.
this_end_time = this_start_time + float(this_split_line[3])
last_line = self.split_lines_of_utt[-1]
utterance_end_time = (float(last_line[2])
+ float(last_line[3]))
max_allowable_padding = utterance_end_time - this_end_time
assert max_allowable_padding > -0.01
unk_padding = max_unk_padding
if unk_padding > max_allowable_padding:
unk_padding = max_allowable_padding
# If we could add less than half of the specified
# unk-padding, don't add any (because when we add
# unk-padding we add the unknown-word symbol '<unk>',
# and if there isn't enough space to traverse the HMM we
# don't want to do it at all.
if unk_padding < 0.5 * max_unk_padding:
unk_padding = 0.0
self.end_unk_padding = unk_padding
def start_time(self):
"""Returns the start time of the utterance (within the enclosing
utterance).
This is before any rounding.
"""
if self.start_index == len(self.split_lines_of_utt):
assert self.end_index == len(self.split_lines_of_utt)
return self.end_time()
first_line = self.split_lines_of_utt[self.start_index]
first_line_start = float(first_line[2])
first_line_duration = float(first_line[3])
first_line_end = first_line_start + first_line_duration
return (first_line_end - self.start_unk_padding
- (first_line_duration * self.start_keep_proportion))
def debug_info(self, include_stats=True):
"""Returns some string-valued information about 'this' that is useful
for debugging."""
if include_stats and self.stats is not None:
stats = 'wer={wer:.2f},{stats},'.format(
wer=self.stats.wer(), stats=self.stats)
else:
stats = ''
return ('start={start:d},end={end:d},'
'unk-padding={start_unk_padding:.2f},{end_unk_padding:.2f},'
'keep-proportion={start_prop:.2f},{end_prop:.2f},'
'start-time={start_time:.2f},end-time={end_time:.2f},'
'{stats}'
'debug-str={debug_str}'.format(
start=self.start_index, end=self.end_index,
start_unk_padding=self.start_unk_padding,
end_unk_padding=self.end_unk_padding,
start_prop=self.start_keep_proportion,
end_prop=self.end_keep_proportion,
start_time=self.start_time(), end_time=self.end_time(),
stats=stats, debug_str=self.debug_str))
def end_time(self):
"""Returns the start time of the utterance (within the enclosing
utterance)."""
if self.end_index == 0:
assert self.start_index == 0
return self.start_time()
last_line = self.split_lines_of_utt[self.end_index - 1]
last_line_start = float(last_line[2])
last_line_duration = float(last_line[3])
return (last_line_start
+ (last_line_duration * self.end_keep_proportion)
+ self.end_unk_padding)
def length(self):
"""Returns the segment length in seconds."""
return self.end_time() - self.start_time()
def is_whole_utterance(self):
"""returns true if this segment corresponds to the whole utterance that
it's a part of (i.e. its start/end time are zero and the end-time of
the last segment."""
last_line_of_utt = self.split_lines_of_utt[-1]
last_line_end_time = (float(last_line_of_utt[2])
+ float(last_line_of_utt[3]))
return (abs(self.start_time() - 0.0) < 0.001
and abs(self.end_time() - last_line_end_time) < 0.001)
def get_junk_proportion(self):
"""Returns the proportion of the duration of this segment that consists
of unk-padding and tainted lines of input (will be between 0.0 and
1.0)."""
# Note: only the first and last lines could possibly be tainted as
# that's how we create the segments; and if either or both are tainted
# the utterance must contain other lines, so double-counting is not a
# problem.
junk_duration = self.start_unk_padding + self.end_unk_padding
first_split_line = self.split_lines_of_utt[self.start_index]
if is_tainted(first_split_line):
first_duration = float(first_split_line[3])
junk_duration += first_duration * self.start_keep_proportion
last_split_line = self.split_lines_of_utt[self.end_index - 1]
if is_tainted(last_split_line):
last_duration = float(last_split_line[3])
junk_duration += last_duration * self.end_keep_proportion
return junk_duration / self.length()
def get_junk_duration(self):
"""Returns duration of junk"""
return self.get_junk_proportion() * self.length()
def merge_adjacent_segment(self, other):
"""
This function will merge the segment in 'other' with the segment
in 'self'. It is only to be called when 'self' and 'other' are from
the same utterance, 'other' is after 'self' in time order (based on
the original segment cores), and self.end_index <= self.start_index
i.e. the two segments might have at most one index in common,
which is usually a tainted word or silence.
"""
try:
assert self.end_index <= other.start_index + 1
assert self.start_time() < other.end_time()
assert self.split_lines_of_utt is other.split_lines_of_utt
except AssertionError:
_global_logger.error("self: %s", self)
_global_logger.error("other: %s", other)
raise
assert self.start_index == 0 or self.start_index != other.start_index
_global_logger.debug("Before merging: %s", self)
assert not self.stats.compare(other.stats), "%s %s" % (self, other)
self.stats.combine(other.stats)
if self.end_index == other.start_index + 1:
overlapping_segment = Segment(
self.split_lines_of_utt, other.start_index,
self.end_index, compute_segment_stats=True)
self.stats.combine(overlapping_segment.stats, scale=-1)
_global_logger.debug("Other segment: %s", other)
self.debug_str = "({0}/merged-with-adjacent/{1})".format(
self.debug_str, other.debug_str)
# everything that relates to the end of this segment gets copied
# from 'other'.
self.end_index = other.end_index
self.end_unk_padding = other.end_unk_padding
self.end_keep_proportion = other.end_keep_proportion
_global_logger.debug("After merging %s", self)
return
def merge_with_segment(self, other, max_deleted_words):
"""
This function will merge the segment in 'other' with the segment
in 'self'. It is only to be called when 'self' and 'other' are from
the same utterance, 'other' is after 'self' in time order (based on
the original segment cores), and self.end_time() >= other.start_time().
Note: in this situation there will normally be deleted words
between the two segments. What this program does with the deleted
words depends on '--max-deleted-words-kept-when-merging'. If there
were any inserted words in the transcript (less likely), this
program will keep the reference.
Note: --max-deleted-words-kept-when-merging can be set to a very
large value to keep all the words.
"""
try:
assert self.end_time() >= other.start_time()
assert self.start_time() < other.end_time()
assert self.split_lines_of_utt is other.split_lines_of_utt
except AssertionError:
_global_logger.error("self: %s", self)
_global_logger.error("other: %s", other)
raise
assert self.start_index == 0 or self.start_index != other.start_index
_global_logger.debug("Before merging: %s", self)
assert (not self.stats.compare(other.stats)
or self.start_time() != other.start_time()
or self.end_time() != other.end_time()
), "%s %s" % (self, other)
self.stats.combine(other.stats)
_global_logger.debug("Other segment: %s", other)
orig_self_end_index = self.end_index
self.debug_str = "({0}/merged-with/{1})".format(
self.debug_str, other.debug_str)
# everything that relates to the end of this segment gets copied
# from 'other'.
self.end_index = other.end_index
self.end_unk_padding = other.end_unk_padding
self.end_keep_proportion = other.end_keep_proportion
_global_logger.debug("After merging %s", self)
# The next thing we have to do is to go over any lines of the ctm that
# appear between 'self' and 'other', or are shared between both (this
# would only happen for tainted silence or non-scored-word segments),
# and decide what to do with them. We'll keep the reference for any
# substitutions or insertions (which anyway are unlikely to appear
# in these merged segments). Note: most of this happens in
# self.Text(), but at this point we need to decide whether to mark any
# deletions as 'discard-this-word'.
try:
if orig_self_end_index <= other.start_index:
# No overlap in indexes
first_index_of_overlap = orig_self_end_index
last_index_of_overlap = other.start_index - 1
segment = Segment(
self.split_lines_of_utt, orig_self_end_index,
other.start_index, compute_segment_stats=True)
self.stats.combine(segment.stats)
else:
first_index_of_overlap = other.start_index
last_index_of_overlap = orig_self_end_index - 1
num_deleted_words = 0
for i in range(first_index_of_overlap, last_index_of_overlap + 1):
edit_type = self.split_lines_of_utt[i][7]
if edit_type == 'del':
num_deleted_words += 1
if num_deleted_words > max_deleted_words:
for i in range(first_index_of_overlap,
last_index_of_overlap + 1):
if self.split_lines_of_utt[i][7] == 'del':
self.split_lines_of_utt[i].append(
'do-not-include-in-text')
except:
_global_logger.error(
"first-index-of-overlap = %d", first_index_of_overlap)
_global_logger.error(
"last-index-of-overlap = %d", last_index_of_overlap)
_global_logger.error("line = %d = %s", i,
self.split_lines_of_utt[i])
raise
_global_logger.debug("After merging %s", self)
def contains_atleast_one_scored_non_oov_word(self):
"""
this will return true if there is at least one word in the utterance
that's a scored word (not a non-scored word) and not an OOV word that's
realized as unk. This becomes a filter on keeping segments.
"""
for i in range(self.start_index, self.end_index):
this_split_line = self.split_lines_of_utt[i]
this_hyp_word = this_split_line[4]
this_ref_word = this_split_line[6]
this_edit = this_split_line[7]
if (this_edit == 'cor' and this_ref_word not in non_scored_words()
and this_ref_word == this_hyp_word):
return True
return False
def text(self, oov_symbol, eps_symbol="<eps_symbol>"):
"""Returns the text corresponding to this utterance, as a string."""
text_array = []
if self.start_unk_padding != 0.0:
text_array.append(oov_symbol)
for i in range(self.start_index, self.end_index):
this_split_line = self.split_lines_of_utt[i]
this_ref_word = this_split_line[6]
if (this_ref_word != eps_symbol
and this_split_line[-1] != 'do-not-include-in-text'):
text_array.append(this_ref_word)
if self.end_unk_padding != 0.0:
text_array.append(oov_symbol)
return ' '.join(text_array)
class SegmentsMerger(object):
"""This class contains methods for merging segments. It stores the
appropriate statistics required for this process in objects of
SegmentStats class.
Paramters:
segments - a reference to the list of inital segments
merged_segments - stores all the initial segments as well
as the newly created segments
between_segments - stores the inter-segment "segments"
for the initial segments
split_lines_of_utt - a reference to the CTM lines
"""
def __init__(self, segments):
self.segments = segments
try:
self.split_lines_of_utt = segments[0].split_lines_of_utt
except IndexError as e:
_global_logger.error("No input segments found!")
raise e
self.merged_segments = {}
self.between_segments = [None for i in range(len(segments) + 1)]
if segments[0].start_index > 0:
self.between_segments[0] = Segment(
self.split_lines_of_utt, 0, segments[0].start_index,
compute_segment_stats=True)
for i, x in enumerate(segments):
x.compute_stats()
self.merged_segments[(i, )] = x
if i > 0 and segments[i].start_index > segments[i - 1].end_index:
self.between_segments[i] = Segment(
self.split_lines_of_utt, segments[i - 1].end_index,
segments[i].start_index, compute_segment_stats=True)
if segments[-1].end_index < len(self.split_lines_of_utt):
self.between_segments[-1] = Segment(
self.split_lines_of_utt, segments[-1].end_index,
len(self.split_lines_of_utt), compute_segment_stats=True)
def _get_merged_cluster(self, cluster1, cluster2, rejected_clusters=None,
max_intersegment_incorrect_words_length=1):
try:
assert cluster2[0] > cluster1[-1]
new_cluster = cluster1 + cluster2
new_cluster_tup = tuple(new_cluster)
if (rejected_clusters is not None
and new_cluster_tup in rejected_clusters):
return (None, new_cluster, True)
if new_cluster_tup in self.merged_segments:
return (self.merged_segments[new_cluster_tup],
new_cluster, False)
if cluster1[-1] == -1:
assert len(cluster1) == 1
# Consider merging cluster2 with the region before the 0^th
# segment
if (self.between_segments[0] is None
or self.between_segments[0].stats.total_length == 0
or (self.between_segments[0]
.stats.incorrect_words_length
> max_intersegment_incorrect_words_length)):
# Reject zero length or bad start region
return (None, new_cluster, True)
merged_segment = self.between_segments[0].copy()
else:
merged_segment = self.merged_segments[tuple(cluster1)].copy()
if cluster2[0] == len(self.segments):
assert len(cluster2) == 1
if (self.between_segments[-1] is None
or (self.between_segments[-1]
.stats.total_length == 0)
or (self.between_segments[-1]
.stats.incorrect_words_length
> max_intersegment_incorrect_words_length)):
# Reject zero length or bad end region
return (None, new_cluster, True)
if self.between_segments[cluster2[0]] is not None:
if (self.between_segments[cluster2[0]]
.stats.incorrect_words_length
> max_intersegment_incorrect_words_length):
return (None, new_cluster, True)
merged_segment.merge_adjacent_segment(
self.between_segments[cluster2[0]])
if cluster2[0] < len(self.segments):
merged_segment.merge_adjacent_segment(
self.merged_segments[tuple(cluster2)])
# else:
# Already done
# merged_segment.merge_adjacent_segment(self.between_segments[-1])
self.merged_segments[new_cluster_tup] = merged_segment
return (merged_segment, new_cluster, False)
except:
_global_logger.error("Failed merging cluster1 %s and cluster2 %s",
cluster1, cluster2)
for i in (cluster1 + cluster2):
if i >= 0 and i < len(self.segments):
_global_logger.error("Segment %d = %s", i,
self.segments[i])
raise
def merge_clusters(self, scoring_function,
max_wer=10, max_bad_proportion=0.3,
max_segment_length=10,
max_intersegment_incorrect_words_length=1):
for i, x in enumerate(self.segments):
_global_logger.debug("before agglomerative clustering, segment %d"
" = %s", i, x)
# Initial clusters are the individual segments themselves.
clusters = [[x] for x in range(-1, len(self.segments) + 1)]
rejected_clusters = set()
while len(clusters) > 1:
try:
_global_logger.debug("Current clusters: %s", clusters)
heap = []
for i in range(len(clusters) - 1):
merged_segment, new_cluster, reject = (
self._get_merged_cluster(
clusters[i], clusters[i + 1], rejected_clusters,
max_intersegment_incorrect_words_length=(
max_intersegment_incorrect_words_length)))
if reject:
rejected_clusters.add(tuple(new_cluster))
continue
heapq.heappush(heap, ((-scoring_function(merged_segment), i),
(merged_segment, i, new_cluster)))
candidate_index = -1
candidate_cluster = None
while True:
try:
score, tup = heapq.heappop(heap)
except IndexError:
break
segment, index, cluster = tup
_global_logger.debug(
"Considering new cluster: (%d, %s)", index, cluster)
if segment.stats.wer() > max_wer:
_global_logger.debug(
"Rejecting cluster with "
"WER%% %.2f > %.2f", segment.stats.wer(), max_wer)
rejected_clusters.add(tuple(cluster))
continue
if segment.stats.bad_proportion() > max_bad_proportion:
_global_logger.debug(
"Rejecting cluster with bad-proportion "
"%.2f > %.2f", segment.stats.bad_proportion(),
max_bad_proportion)
rejected_clusters.add(tuple(cluster))
continue
if segment.stats.total_length > max_segment_length:
_global_logger.debug(
"Rejecting cluster with length "
"%.2f > %.2f", segment.stats.total_length,
max_segment_length)
rejected_clusters.add(tuple(cluster))
continue
candidate_index, candidate_cluster = tup[1:]
_global_logger.debug("Accepted cluster (%d, %s)",
candidate_index, candidate_cluster)
break
if candidate_index == -1:
return clusters
new_clusters = []
for i in range(candidate_index):
new_clusters.append(clusters[i])
new_clusters.append(candidate_cluster)
for i in range(candidate_index + 2, len(clusters)):
new_clusters.append(clusters[i])
if len(new_clusters) >= len(clusters):
raise RuntimeError("Old: {0}; New: {1}".format(
clusters, new_clusters))
clusters = new_clusters
except Exception:
_global_logger.error(
"Failed merging clusters %s", clusters)
raise
return clusters
def merge_segments(segments, args):
if len(segments) == 0:
_global_logger.debug("Got no segments at merging segments stage")
return []
def scoring_function(segment):
stats = segment.stats
try:
return (-stats.wer() - args.silence_factor * stats.silence_length
- args.incorrect_words_factor
* stats.incorrect_words_length
- args.tainted_words_factor
* stats.num_tainted_words * 100.0 / stats.num_words)
except ZeroDivisionError:
return float("-inf")
# Do agglomerative clustering on the initial segments with the score
# for combining neighboring segments being the scoring_function on the
# stats of the combined segment.
merger = SegmentsMerger(segments)
clusters = merger.merge_clusters(
scoring_function, max_wer=args.max_wer,
max_bad_proportion=args.max_bad_proportion,
max_segment_length=args.max_segment_length_for_merging,
max_intersegment_incorrect_words_length=(
args.max_intersegment_incorrect_words_length))
_global_logger.debug("Clusters to be merged: %s", clusters)
# Do the actual merging based on the clusters.
new_segments = []
for cluster_index, cluster in enumerate(clusters):
_global_logger.debug(
"Merging cluster (%d, %s)", cluster_index, cluster)
try:
if cluster_index == 0 and len(cluster) == 1:
assert cluster[0] == -1
_global_logger.debug(
"Not adding region before the first segment")
# skip adding the lines before the initial segment if its
# not merged with the initial segment
continue
elif cluster_index == len(clusters) - 1 and len(cluster) == 1:
_global_logger.debug(
"Not adding remaining end region %s",
cluster[0])
assert cluster[0] == len(segments)
# skip adding the lines after the last segment if its
# not merged with the last segment
break
new_segments.append(merger.merged_segments[tuple(cluster)])
except Exception:
_global_logger.error("Error with cluster (%d, %s)",
cluster_index, cluster)
raise
segments = new_segments
for i, x in enumerate(segments):
_global_logger.debug(
"after agglomerative clustering: segment %d = %s", i, x)
assert len(segments) > 0
segment_index = 0
# Ignore all the initial segments that have WER > max_wer
while segment_index < len(segments):
segment = segments[segment_index]
if segment.stats.wer() < args.max_wer:
break
segment_index += 1
if segment_index == len(segments):
_global_logger.debug("No merged segments were below "
"WER%% %.2f", args.max_wer)
return []
_global_logger.debug("Merging overlapping segments starting from the "
"first segment with WER%% < max_wer i.e. %d = %s",
segment_index, segments[segment_index])
new_segments = [segments[segment_index]]
segment_index += 1
while segment_index < len(segments):
if segments[segment_index].stats.wer() > args.max_wer:
# ignore this segment
segment_index += 1
continue
if new_segments[-1].end_time() >= segments[segment_index].start_time():
new_segments[-1].merge_with_segment(
segments[segment_index], args.max_deleted_words)
else:
new_segments.append(segments[segment_index])
segment_index += 1
segments = new_segments
return segments
def get_segments_for_utterance(split_lines_of_utt, args, utterance_stats):
"""
This function creates the segments for an utterance as a list
of class Segment.
It returns a 2-tuple (list-of-segments, list-of-deleted-segments)
where the deleted segments are only useful for diagnostic printing.
Note: split_lines_of_utt is a list of lists, one per line, each containing
the sequence of fields.
"""
utterance_stats.num_utterances += 1
segment_ranges = compute_segment_cores(split_lines_of_utt)
utterance_end_time = (float(split_lines_of_utt[-1][2])
+ float(split_lines_of_utt[-1][3]))
utterance_stats.total_length_of_utterances += utterance_end_time
segments = [Segment(split_lines_of_utt, x[0], x[1])
for x in segment_ranges]
utterance_stats.accumulate_segment_stats(
segments, 'stage 0 [segment cores]')
for i, x in enumerate(segments):
_global_logger.debug("stage 0: segment %d = %s", i, x)
if args.verbose > 4:
print("Stage 0 [segment cores]:", file=sys.stderr)
segments_copy = [x.copy() for x in segments]
print_debug_info_for_utterance(sys.stderr,
copy.deepcopy(split_lines_of_utt),
segments_copy, [])
for segment in segments:
segment.possibly_add_tainted_lines()
utterance_stats.accumulate_segment_stats(
segments, 'stage 1 [add tainted lines]')
for i, x in enumerate(segments):
_global_logger.debug("stage 1: segment %d = %s", i, x)
if args.verbose > 4:
print("Stage 1 [add tainted lines]:", file=sys.stderr)
segments_copy = [x.copy() for x in segments]
print_debug_info_for_utterance(sys.stderr,
copy.deepcopy(split_lines_of_utt),
segments_copy, [])
segments = merge_segments(segments, args)
utterance_stats.accumulate_segment_stats(
segments, 'stage 2 [merge segments]')
for i, x in enumerate(segments):
_global_logger.debug("stage 2: segment %d = %s", i, x)
if args.verbose > 4:
print("Stage 2 [merge segments]:", file=sys.stderr)
segments_copy = [x.copy() for x in segments]
print_debug_info_for_utterance(sys.stderr,
copy.deepcopy(split_lines_of_utt),
segments_copy, [])
new_segments = []
for s in segments:
new_segments += s.possibly_split_segment(
args.max_internal_silence_length,
args.max_internal_non_scored_length)
segments = new_segments
utterance_stats.accumulate_segment_stats(
segments, 'stage 3 [split segments]')
for i, x in enumerate(segments):
_global_logger.debug(
"stage 3: segment %d, %s", i, x.debug_info(False))
if args.verbose > 4:
print("Stage 3 [split segments]:", file=sys.stderr)
segments_copy = [x.copy() for x in segments]
print_debug_info_for_utterance(sys.stderr,
copy.deepcopy(split_lines_of_utt),
segments_copy, [])
new_segments = []
for s in segments:
new_segments += s.possibly_split_long_segment(
args.max_segment_length_for_splitting,
args.hard_max_segment_length,
args.min_silence_length_to_split,
args.min_non_scored_length_to_split)
segments = new_segments
utterance_stats.accumulate_segment_stats(
segments, 'stage 4 [split long segments]')
for i, x in enumerate(segments):
_global_logger.debug(
"stage 4: segment %d, %s", i, x.debug_info(False))
if args.verbose > 4:
print("Stage 4 [split long segments]:", file=sys.stderr)
segments_copy = [x.copy() for x in segments]
print_debug_info_for_utterance(sys.stderr,
copy.deepcopy(split_lines_of_utt),
segments_copy, [])
for s in segments:
s.possibly_truncate_boundaries(args.max_edge_silence_length,
args.max_edge_non_scored_length)
utterance_stats.accumulate_segment_stats(
segments, 'stage 5 [truncate boundaries]')
for i, x in enumerate(segments):
_global_logger.debug(
"stage 5: segment %d = %s", i, x.debug_info(False))
if args.verbose > 4:
print("Stage 5 [truncate boundaries]:", file=sys.stderr)
segments_copy = [x.copy() for x in segments]
print_debug_info_for_utterance(sys.stderr,
copy.deepcopy(split_lines_of_utt),
segments_copy, [])
for s in segments:
s.relax_boundary_truncation(args.min_segment_length,
args.min_new_segment_length)
utterance_stats.accumulate_segment_stats(
segments, 'stage 6 [relax boundary truncation]')
for i, x in enumerate(segments):
_global_logger.debug(
"stage 6: segment %d = %s", i, x.debug_info(False))
if args.verbose > 4:
print("Stage 6 [relax boundary truncation]:", file=sys.stderr)
segments_copy = [x.copy() for x in segments]
print_debug_info_for_utterance(sys.stderr,
copy.deepcopy(split_lines_of_utt),
segments_copy, [])
for s in segments:
s.possibly_add_unk_padding(args.unk_padding)
utterance_stats.accumulate_segment_stats(
segments, 'stage 7 [unk-padding]')
for i, x in enumerate(segments):
_global_logger.debug(
"stage 7: segment %d = %s", i, x.debug_info(False))
if args.verbose > 4:
print("Stage 7 [unk-padding]:", file=sys.stderr)
segments_copy = [x.copy() for x in segments]
print_debug_info_for_utterance(sys.stderr,
copy.deepcopy(split_lines_of_utt),
segments_copy, [])
deleted_segments = []
new_segments = []
for s in segments:
# the 0.999 allows for roundoff error.
if (not s.is_whole_utterance()
and s.length() < 0.999 * args.min_new_segment_length):
s.debug_str += '[deleted-because-of--min-new-segment-length]'
deleted_segments.append(s)
else:
new_segments.append(s)
segments = new_segments
utterance_stats.accumulate_segment_stats(
segments,
'stage 8 [remove new segments under --min-new-segment-length')
for i, x in enumerate(segments):
_global_logger.debug(
"stage 8: segment %d = %s", i, x.debug_info(False))
if args.verbose > 4:
print("Stage 8 [remove new segments under "
"--min-new-segment-length]:", file=sys.stderr)
segments_copy = [x.copy() for x in segments]
print_debug_info_for_utterance(sys.stderr,
copy.deepcopy(split_lines_of_utt),
segments_copy, [])
new_segments = []
for s in segments:
# the 0.999 allows for roundoff error.
if s.length() < 0.999 * args.min_segment_length:
s.debug_str += '[deleted-because-of--min-segment-length]'
deleted_segments.append(s)
else:
new_segments.append(s)
segments = new_segments
utterance_stats.accumulate_segment_stats(
segments, 'stage 9 [remove segments under --min-segment-length]')
for i, x in enumerate(segments):
_global_logger.debug(
"stage 9: segment %d = %s", i, x.debug_info(False))
if args.verbose > 4:
print("Stage 9 [remove segments under "
"--min-segment-length]:", file=sys.stderr)
segments_copy = [x.copy() for x in segments]
print_debug_info_for_utterance(sys.stderr,
copy.deepcopy(split_lines_of_utt),
segments_copy, [])
new_segments = []
for s in segments:
if s.contains_atleast_one_scored_non_oov_word():
new_segments.append(s)
else:
s.debug_str += '[deleted-because-no-scored-non-oov-words]'
deleted_segments.append(s)
segments = new_segments
utterance_stats.accumulate_segment_stats(
segments, 'stage 10 [remove segments without scored,non-OOV words]')
for i, x in enumerate(segments):
_global_logger.debug(
"stage 10: segment %d = %s", i, x.debug_info(False))
if args.verbose > 4:
print("Stage 10 [remove segments without scored, non-OOV words "
"", file=sys.stderr)
segments_copy = [x.copy() for x in segments]
print_debug_info_for_utterance(sys.stderr,
copy.deepcopy(split_lines_of_utt),
segments_copy, [])
for i in range(len(segments) - 1):
if segments[i].end_time() > segments[i + 1].start_time():
# this just adds something to --ctm-edits-out output
segments[i + 1].debug_str += ",overlaps-previous-segment"
if len(segments) == 0:
utterance_stats.num_utterances_without_segments += 1
return (segments, deleted_segments)
def float_to_string(f):
""" this prints a number with a certain number of digits after the point,
while removing trailing zeros.
"""
num_digits = 6 # we want to print 6 digits after the zero
g = f
while abs(g) > 1.0:
g *= 0.1
num_digits += 1
format_str = '%.{0}g'.format(num_digits)
return format_str % f
def time_to_string(time, frame_length):
""" Gives time in string form as an exact multiple of the frame-length,
e.g. 0.01 (after rounding).
"""
n = round(time / frame_length)
assert n >= 0
# The next function call will remove trailing zeros while printing it, so
# that e.g. 0.01 will be printed as 0.01 and not 0.0099999999999999. It
# seems that doing this in a simple way is not really possible (at least,
# not without assuming that frame_length is of the form 10^-n, which we
# don't really want to do).
return float_to_string(n * frame_length)
def write_segments_for_utterance(text_output_handle, segments_output_handle,
old_utterance_name, segments, oov_symbol,
eps_symbol="<eps>", frame_length=0.01):
num_digits = len(str(len(segments)))
for n, segment in enumerate(segments):
# split utterances will be named foo-bar-1 foo-bar-2, etc.
new_utterance_name = "{old}-{index:0{width}}".format(
old=old_utterance_name, index=n+1,
width=num_digits)
# print a line to the text output of the form like
# <new-utterance-id> <text>
# like:
# foo-bar-1 hello this is dan
print(new_utterance_name, segment.text(oov_symbol, eps_symbol),
file=text_output_handle)
# print a line to the segments output of the form
# <new-utterance-id> <old-utterance-id> <start-time> <end-time>
# like:
# foo-bar-1 foo-bar 5.1 7.2
print(new_utterance_name, old_utterance_name,
time_to_string(segment.start_time(), frame_length),
time_to_string(segment.end_time(), frame_length),
file=segments_output_handle)
# Note, this is destrutive of 'segments_for_utterance', but it won't matter.
def print_debug_info_for_utterance(ctm_edits_out_handle,
split_lines_of_cur_utterance,
segments_for_utterance,
deleted_segments_for_utterance,
frame_length=0.01):
# info_to_print will be list of 2-tuples
# (time, 'start-segment-n'|'end-segment-n')
# representing the start or end times of segments.
info_to_print = []
for n, segment in enumerate(segments_for_utterance):
start_string = 'start-segment-{0}[{1}]'.format(n + 1,
segment.debug_info())
info_to_print.append((segment.start_time(), start_string))
end_string = 'end-segment-{0}'.format(n + 1)
info_to_print.append((segment.end_time(), end_string))
# for segments that were deleted we print info like
# start-deleted-segment-1, and otherwise similar info to segments that were
# retained.
for n, segment in enumerate(deleted_segments_for_utterance):
start_string = 'start-deleted-segment-{0}[{1}]'.format(
n + 1, segment.debug_info(False))
info_to_print.append((segment.start_time(), start_string))
end_string = 'end-deleted-segment-{0}'.format(n + 1)
info_to_print.append((segment.end_time(), end_string))
info_to_print = sorted(info_to_print)
for i, split_line in enumerate(split_lines_of_cur_utterance):
# add an index like [0], [1], to the utterance-id so we can easily look
# up segment indexes.
split_line[0] += '[{0}]'.format(i)
start_time = float(split_line[2])
end_time = start_time + float(split_line[3])
split_line_copy = list(split_line)
while len(info_to_print) > 0 and info_to_print[0][0] <= end_time:
(segment_start, string) = info_to_print[0]
# shift the first element off of info_to_print.
info_to_print = info_to_print[1:]
# add a field like 'start-segment1[...]=3.21' to what we're about
# to print.
split_line_copy.append(
'{0}={1}'.format(string,
time_to_string(segment_start, frame_length)))
print(' '.join(split_line_copy), file=ctm_edits_out_handle)
class WordStats(object):
"""
This accumulates word-level stats about, for each reference word, with
what probability it will end up in the core of a segment. Words with
low probabilities of being in segments will generally be associated
with some kind of error (there is a higher probability of having a
wrong lexicon entry).
"""
def __init__(self):
self.word_count_pair = defaultdict(lambda: [0, 0])
def accumulate_for_utterance(self, split_lines_of_utt,
segments_for_utterance,
eps_symbol="<eps>"):
# word_count_pair is a map from a string (the word) to
# a list [total-count, count-not-within-segments]
line_is_in_segment = [False] * len(split_lines_of_utt)
for segment in segments_for_utterance:
for i in range(segment.start_index, segment.end_index):
line_is_in_segment[i] = True
for i, split_line in enumerate(split_lines_of_utt):
this_ref_word = split_line[6]
if this_ref_word != eps_symbol:
self.word_count_pair[this_ref_word][0] += 1
if not line_is_in_segment[i]:
self.word_count_pair[this_ref_word][1] += 1
def print(self, word_stats_out):
# Sort from most to least problematic. We want to give more prominence
# to words that are most frequently not in segments, but also to
# high-count words. Define badness = pair[1] / pair[0], and
# total_count = pair[0], where 'pair' is a value of word_count_pair.
# We'll reverse sort on badness^3 * total_count = pair[1]^3 /
# pair[0]^2.
for key, pair in sorted(
self.word_count_pair.items(),
key=lambda item: (item[1][1] ** 3) * 1.0 / (item[1][0] ** 2),
reverse=True):
badness = pair[1] * 1.0 / pair[0]
total_count = pair[0]
print(key, badness, total_count, file=word_stats_out)
try:
word_stats_out.close()
except:
_global_logger.error("error closing file --word-stats-out=%s "
"(full disk?)", word_stats_out.name)
raise
_global_logger.info(
"""please see the file %s for word-level
statistics saying how frequently each word was excluded for a
segment; format is <word> <proportion-of-time-excluded>
<total-count>. Particularly problematic words appear near the top
of the file.""", word_stats_out.name)
def process_data(args, oov_symbol, utterance_stats, word_stats):
"""
Most of what we're doing in the lines below is splitting the input lines
and grouping them per utterance, before giving them to
get_segments_for_utterance() and then printing the modified lines.
"""
first_line = args.ctm_edits_in.readline()
if first_line == '':
sys.exit("segment_ctm_edits.py: empty input")
split_pending_line = first_line.split()
if len(split_pending_line) == 0:
sys.exit("segment_ctm_edits.py: bad input line " + first_line)
cur_utterance = split_pending_line[0]
split_lines_of_cur_utterance = []
while True:
try:
if (len(split_pending_line) == 0
or split_pending_line[0] != cur_utterance):
# Read one whole utterance. Now process it.
(segments_for_utterance,
deleted_segments_for_utterance) = get_segments_for_utterance(
split_lines_of_cur_utterance, args=args,
utterance_stats=utterance_stats)
word_stats.accumulate_for_utterance(
split_lines_of_cur_utterance, segments_for_utterance)
write_segments_for_utterance(
args.text_out, args.segments_out, cur_utterance,
segments_for_utterance, oov_symbol=oov_symbol,
frame_length=args.frame_length)
if args.ctm_edits_out is not None:
print_debug_info_for_utterance(
args.ctm_edits_out, split_lines_of_cur_utterance,
segments_for_utterance, deleted_segments_for_utterance,
frame_length=args.frame_length)
split_lines_of_cur_utterance = []
if len(split_pending_line) == 0:
break
else:
cur_utterance = split_pending_line[0]
split_lines_of_cur_utterance.append(split_pending_line)
next_line = args.ctm_edits_in.readline()
split_pending_line = next_line.split()
if len(split_pending_line) == 0:
if next_line != '':
sys.exit("segment_ctm_edits.py: got an "
"empty or whitespace input line")
except Exception:
_global_logger.error(
"Error with utterance %s", cur_utterance)
raise
def read_non_scored_words(non_scored_words_file):
for line in non_scored_words_file.readlines():
parts = line.split()
if not len(parts) == 1:
raise RuntimeError(
"segment_ctm_edits.py: bad line in non-scored-words "
"file {0}: {1}".format(non_scored_words_file, line))
_global_non_scored_words.add(parts[0])
non_scored_words_file.close()
class UtteranceStats(object):
def __init__(self):
# segment_total_length and num_segments are maps from
# 'stage' strings; see accumulate_segment_stats for details.
self.segment_total_length = defaultdict(int)
self.num_segments = defaultdict(int)
# the lambda expression below is an anonymous function that takes no
# arguments and returns the new list [0, 0].
self.num_utterances = 0
self.num_utterances_without_segments = 0
self.total_length_of_utterances = 0
def accumulate_segment_stats(self, segment_list, text):
"""
Here, 'text' will be something that indicates the stage of processing,
e.g. 'Stage 0: segment cores', 'Stage 1: add tainted lines', etc.
"""
for segment in segment_list:
self.num_segments[text] += 1
self.segment_total_length[text] += segment.length()
def print_segment_stats(self):
_global_logger.info(
"""Number of utterances is %d, of which %.2f%% had no segments
after all processing; total length of data in original utterances
(in seconds) was %d""",
self.num_utterances,
(self.num_utterances_without_segments * 100.0
/ self.num_utterances),
self.total_length_of_utterances)
keys = sorted(self.segment_total_length.keys())
for i, key in enumerate(keys):
if i > 0:
delta_percentage = '[%+.2f%%]' % (
(self.segment_total_length[key]
- self.segment_total_length[keys[i - 1]])
* 100.0 / self.total_length_of_utterances)
_global_logger.info(
'At %s, num-segments is %d, total length %.2f%% of '
'original total %s',
key, self.num_segments[key],
(self.segment_total_length[key]
* 100.0 / self.total_length_of_utterances),
delta_percentage if i > 0 else '')
def main():
args = get_args()
try:
global _global_non_scored_words
_global_non_scored_words = set()
read_non_scored_words(args.non_scored_words_in)
oov_symbol = None
if args.oov_symbol_file is not None:
try:
line = args.oov_symbol_file.readline()
assert len(line.split()) == 1
oov_symbol = line.split()[0]
assert args.oov_symbol_file.readline() == ''
args.oov_symbol_file.close()
except Exception:
_global_logger.error("error reading file "
"--oov-symbol-file=%s",
args.oov_symbol_file.name)
raise
elif args.unk_padding != 0.0:
raise ValueError(
"if the --unk-padding option is nonzero (which "
"it is by default, "
"the --oov-symbol-file option must be supplied.")
utterance_stats = UtteranceStats()
word_stats = WordStats()
process_data(args,
oov_symbol=oov_symbol, utterance_stats=utterance_stats,
word_stats=word_stats)
try:
args.text_out.close()
args.segments_out.close()
if args.ctm_edits_out is not None:
args.ctm_edits_out.close()
except:
_global_logger.error("error closing one or more outputs "
"(broken pipe or full disk?)")
raise
utterance_stats.print_segment_stats()
if args.word_stats_out is not None:
word_stats.print(args.word_stats_out)
if args.ctm_edits_out is not None:
_global_logger.info("detailed utterance-level debug information "
"is in %s", args.ctm_edits_out.name)
except:
_global_logger.error("Failed segmenting CTM edits")
raise
finally:
try:
args.text_out.close()
args.segments_out.close()
if args.ctm_edits_out is not None:
args.ctm_edits_out.close()
except:
_global_logger.error("error closing one or more outputs "
"(broken pipe or full disk?)")
raise
if __name__ == '__main__':
main()
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