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Squash long words at window and sentence boundaries (#226)

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Port commit 255887f219
This commit is contained in:
Guillaume Klein
2023-07-03 10:20:20 +02:00
committed by GitHub
parent fee52c9229
commit 19c294f978
1 changed files with 30 additions and 10 deletions
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34
faster_whisper/transcribe.py
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@@ -732,8 +732,18 @@ class WhisperModel:
word_index += 1 word_index += 1
if len(words) > 0: if len(words) > 0:
# adjust the segment-level timestamps based on the word-level timestamps
segment["start"] = words[0]["start"] segment["start"] = words[0]["start"]
# hack: prefer the segment-level end timestamp if the last word is too long.
# a better segmentation algorithm based on VAD should be able to replace this.
if (
segment["end"] > words[-1]["start"]
and segment["end"] + 0.5 < words[-1]["end"]
):
# adjust the word-level timestamps based on the segment-level timestamps
words[-1]["end"] = segment["end"]
else:
# adjust the segment-level timestamps based on the word-level timestamps
segment["end"] = words[-1]["end"] segment["end"] = words[-1]["end"]
segment["words"] = words segment["words"] = words
@@ -779,20 +789,30 @@ class WhisperModel:
for i, j in zip(word_boundaries[:-1], word_boundaries[1:]) for i, j in zip(word_boundaries[:-1], word_boundaries[1:])
] ]
# hack: ensure the first and second word is not longer than twice the median word duration. # hack: truncate long words at the start of a window and the start of a sentence.
# a better segmentation algorithm based on VAD should be able to replace this. # a better segmentation algorithm based on VAD should be able to replace this.
word_durations = end_times - start_times word_durations = end_times - start_times
word_durations = word_durations[word_durations.nonzero()] word_durations = word_durations[word_durations.nonzero()]
if len(word_durations) > 0: if len(word_durations) > 0:
median_duration = np.median(word_durations) median_duration = np.median(word_durations)
max_duration = median_duration * 2 max_duration = median_duration * 2
if len(word_durations) >= 2 and word_durations[1] > max_duration: sentence_end_marks = ".。!！?？"
boundary = max(end_times[2] / 2, end_times[2] - max_duration) # ensure words at sentence boundaries are not longer than twice the median
end_times[0] = start_times[1] = boundary # word duration.
for i in range(1, len(start_times)):
if end_times[i] - start_times[i] > max_duration:
if words[i] in sentence_end_marks:
end_times[i] = start_times[i] + max_duration
elif words[i - 1] in sentence_end_marks:
start_times[i] = end_times[i] - max_duration
# ensure the first and second word is not longer than twice the median word duration.
if len(start_times) > 0 and end_times[0] - start_times[0] > max_duration:
if ( if (
len(word_durations) >= 1 len(start_times) > 1
and end_times[0] - start_times[0] > max_duration and end_times[1] - start_times[1] > max_duration
): ):
boundary = max(end_times[1] / 2, end_times[1] - max_duration)
end_times[0] = start_times[1] = boundary
start_times[0] = max(0, end_times[0] - max_duration) start_times[0] = max(0, end_times[0] - max_duration)
return [ return [