зеркало из https://github.com/mozilla/DSAlign.git
Better key figure handling and additional score
This commit is contained in:
Tilman Kamp
Родитель
31da832843
Коммит
c6efe188a2
164
align/align.py
164
align/align.py
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@ -9,7 +9,7 @@ import numpy as np
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import wavTranscriber
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from collections import Counter
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from search import FuzzySearch
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from text import Alphabet, TextCleaner, levenshtein
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from text import Alphabet, TextCleaner, levenshtein, similarity
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def main(args):
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@ -86,24 +86,27 @@ def main(args):
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help='Writes clean aligned original transcripts to result file')
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output_group.add_argument('--output-aligned-raw', action="store_true",
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help='Writes raw aligned original transcripts to result file')
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output_group.add_argument('--output-wer', action="store_true",
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help='Writes word error rate (WER) to output')
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output_group.add_argument('--output-cer', action="store_true",
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help='Writes character error rate (CER) to output')
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output_group.add_argument('--output-min-length', type=int, required=False, default=1,
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help='Minimum phrase length (default: 1)')
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output_group.add_argument('--output-max-length', type=int, required=False,
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help='Maximum phrase length (default: no limit)')
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output_group.add_argument('--output-min-score', type=float, required=False, default=2.0,
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help='Minimum matching score (default: 2.0)')
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output_group.add_argument('--output-max-score', type=float, required=False,
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help='Maximum matching score (default: no limit)')
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for b in ['Min', 'Max']:
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for r in ['CER', 'WER']:
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output_group.add_argument('--output-' + b.lower() + '-' + r.lower(), type=float, required=False,
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help=b + 'imum ' + ('character' if r == 'CER' else 'word') +
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' error rate (' + r + ') the STT transcript of the audio ' +
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'has to have when compared with the original text')
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named_numbers = {
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'tlen': ('transcript length', int, None),
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'mlen': ('match length', int, None),
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'SWS': ('Smith-Waterman score', float, 'From 0.0 (not equal at all) to 100.0+ (pretty equal)'),
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'WNG': ('weighted N-gram intersection', float, 'From 0.0 (not equal at all) to 100.0 (totally equal)'),
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'CER': ('character error rate', float, 'From 0.0 (no different words) to 100.0+ (total miss)'),
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'WER': ('word error rate', float, 'From 0.0 (no wrong characters) to 100.0+ (total miss)')
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}
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for short in named_numbers.keys():
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long, atype, desc = named_numbers[short]
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desc = (' - value range: ' + desc) if desc else ''
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output_group.add_argument('--output-' + short.lower(), action="store_true",
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help='Writes {} ({}) to output'.format(long, short))
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for extreme in ['Min', 'Max']:
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output_group.add_argument('--output-' + extreme.lower() + '-' + short.lower(), type=atype, required=False,
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help='{}imum {} ({}) the STT transcript of the audio '
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'has to have when compared with the original text{}'
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.format(extreme, long, short, desc))
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args = parser.parse_args()
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@ -229,81 +232,84 @@ def main(args):
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result_fragments = []
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substitutions = Counter()
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statistics = Counter()
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def skip(message, index):
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logging.info('Fragment %d: %s' % (index, message))
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statistics[message] += 1
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end_fragments = (args.start + args.num_samples) if args.num_samples else len(fragments)
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fragments = fragments[args.start:end_fragments]
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def skip(index, reason):
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logging.info('Fragment {}: {}'.format(index, reason))
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statistics[reason] += 1
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def should_skip(number_key, index, show, get_value):
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kl = number_key.lower()
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should_output = getattr(args, 'output_' + kl)
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min_val, max_val = getattr(args, 'output_min_' + kl), getattr(args, 'output_max_' + kl)
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if should_output or min_val or max_val:
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val = get_value()
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if len(number_key) == 3:
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show.insert(0, '{}: {:.2f}'.format(number_key, val))
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if should_output:
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fragments[index][kl] = val
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reason_base = '{} ({})'.format(named_numbers[number_key][0], number_key)
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reason = None
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if min_val and val < min_val:
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reason = reason_base + ' too low'
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elif max_val and val > max_val:
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reason = reason_base + ' too high'
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if reason:
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skip(index, reason)
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return True
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return False
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for index, fragment in enumerate(fragments):
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time_start = fragment['time-start']
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time_length = fragment['time-length']
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fragment_transcript = fragment['transcript']
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if args.align_min_length and len(fragment_transcript) < args.align_min_length:
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skip('Transcript too short for alignment', index)
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continue
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if args.align_max_length and len(fragment_transcript) > args.align_max_length:
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skip('Transcript too long for alignment', index)
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continue
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match, match_distance, match_substitutions = search.find_best(fragment_transcript)
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if match is None:
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skip('No match for transcript', index)
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continue
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substitutions += match_substitutions
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fragment_matched = tc.clean_text[match.start:match.end]
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if args.output_min_length and len(fragment_matched) < args.output_min_length:
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skip('Match too short', index)
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continue
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if args.output_max_length and len(fragment_matched) > args.output_max_length:
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skip('Match too long', index)
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continue
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score = match_distance/max(len(fragment_matched), len(fragment_transcript))
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sample_numbers = ['Score %.2f' % score]
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if args.output_min_score and score < args.output_min_score:
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skip('Matching score too low', index)
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continue
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if args.output_max_score and score > args.output_max_score:
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skip('Matching score too high', index)
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continue
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original_start = tc.get_original_offset(match.start)
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original_end = tc.get_original_offset(match.end)
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result_fragment = {
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'time-start': time_start,
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'time-length': time_length,
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'text-start': original_start,
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'text-length': original_end-original_start,
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'score': score
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'time-length': time_length
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}
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if args.output_cer or args.output_min_cer or args.output_max_cer:
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cer = levenshtein(fragment_transcript, fragment_matched)/len(fragment_matched)
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sample_numbers.insert(0, 'CER: %.2f' % cer * 100)
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if args.output_cer:
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result_fragment['cer'] = cer
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if args.output_min_cer and score < args.output_min_cer:
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skip('Character error rate (CER) too low', index)
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continue
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if args.output_max_cer and score > args.output_max_cer:
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skip('Character error rate (CER) too high', index)
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continue
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if args.output_wer or args.output_min_wer or args.output_max_wer:
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wer = levenshtein(fragment_transcript.split(), fragment_matched.split())/len(fragment_matched.split())
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sample_numbers.insert(0, 'WER: %.2f' % wer * 100)
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if args.output_wer:
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result_fragment['wer'] = wer
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if args.output_min_wer and score < args.output_min_wer:
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skip('Word error rate (WER) too low', index)
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continue
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if args.output_max_wer and score > args.output_max_wer:
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skip('Word error rate (WER) too high', index)
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sample_numbers = []
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if should_skip('tlen', index, sample_numbers, lambda: len(fragment_transcript)):
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continue
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if args.output_stt:
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result_fragment['stt'] = fragment_transcript
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if args.output_aligned:
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result_fragment['aligned'] = fragment_matched
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match, match_distance, match_substitutions = search.find_best(fragment_transcript)
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if match is None:
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skip(index, 'No match for transcript')
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continue
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original_start = tc.get_original_offset(match.start)
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original_end = tc.get_original_offset(match.end)
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result_fragment['text-start'] = original_start
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result_fragment['text-length'] = original_end - original_start
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if args.output_aligned_raw:
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result_fragment['aligned-raw'] = original_transcript[original_start:original_end]
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substitutions += match_substitutions
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fragment_matched = tc.clean_text[match.start:match.end]
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if should_skip('mlen', index, sample_numbers, lambda: len(fragment_matched)):
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continue
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if args.output_aligned:
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result_fragment['aligned'] = fragment_matched
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if should_skip('SWS', index, sample_numbers,
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lambda: match_distance / max(len(fragment_matched), len(fragment_transcript))):
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continue
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if should_skip('WNG', index, sample_numbers,
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lambda: 100 * similarity(fragment_matched, fragment_transcript)):
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continue
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if should_skip('CER', index, sample_numbers,
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lambda: 100 * levenshtein(fragment_transcript, fragment_matched) / len(fragment_matched)):
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continue
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if should_skip('WER', index, sample_numbers,
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lambda: 100 * levenshtein(fragment_transcript.split(),
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fragment_matched.split())/len(fragment_matched.split())):
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continue
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result_fragments.append(result_fragment)
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logging.debug('Fragment %d aligned with %s' % (index, ' '.join(sample_numbers)))
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logging.debug('- T: ' + args.text_context * ' ' + '"%s"' % fragment_transcript)
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@ -12,7 +12,7 @@ class FuzzySearch(object):
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match_score=100,
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mismatch_score=-100,
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gap_score=-100,
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letter_similarities=None):
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char_similarities=None):
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self.text = text
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self.max_candidates = max_candidates
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self.candidate_threshold = candidate_threshold
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@ -21,7 +21,7 @@ class FuzzySearch(object):
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self.match_score = match_score
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self.mismatch_score = mismatch_score
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self.gap_score = gap_score
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self.letter_similarities = letter_similarities
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self.char_similarities = char_similarities
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self.ngrams = {}
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for i, ngram in enumerate(ngrams(' ' + text + ' ', 3)):
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if ngram in self.ngrams:
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@ -38,8 +38,8 @@ class FuzzySearch(object):
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def char_similarity(self, a, b):
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key = FuzzySearch.char_pair(a, b)
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if self.letter_similarities and key in self.letter_similarities:
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return self.letter_similarities[key]
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if self.char_similarities and key in self.char_similarities:
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return self.char_similarities[key]
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return self.match_score if a == b else self.mismatch_score
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def sw_align(self, a, start, end):
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