From e8622f9afc4eba139bf796c210f5c01081000472 Mon Sep 17 00:00:00 2001 From: taylorchu Date: Mon, 7 Aug 2023 14:48:56 -0700 Subject: [PATCH] word timing tweaks (#1559) * word timing tweaks * comment on eot * clearer comments --- whisper/timing.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/whisper/timing.py b/whisper/timing.py index 207d877d..56e84d43 100644 --- a/whisper/timing.py +++ b/whisper/timing.py @@ -214,6 +214,13 @@ def find_alignment( text_indices, time_indices = dtw(-matrix) words, word_tokens = tokenizer.split_to_word_tokens(text_tokens + [tokenizer.eot]) + if len(word_tokens) <= 1: + # return on eot only + # >>> np.pad([], (1, 0)) + # array([0.]) + # This results in crashes when we lookup jump_times with float, like + # IndexError: arrays used as indices must be of integer (or boolean) type + return [] word_boundaries = np.pad(np.cumsum([len(t) for t in word_tokens[:-1]]), (1, 0)) jumps = np.pad(np.diff(text_indices), (1, 0), constant_values=1).astype(bool) @@ -297,8 +304,6 @@ def add_word_timestamps( # hack: truncate long words at sentence boundaries. # a better segmentation algorithm based on VAD should be able to replace this. if len(word_durations) > 0: - median_duration = np.median(word_durations) - max_duration = median_duration * 2 sentence_end_marks = ".。!！?？" # ensure words at sentence boundaries are not longer than twice the median word duration. for i in range(1, len(alignment)):