如何将字幕文件转换为每个字幕只有一个句子？

Question

我正在尝试编写一种方法来转换字幕文件，以便每个字幕始终只有一个句子。

我的想法如下：

1. 对于每个字幕：

1.1->我得到字幕的持续时间

1.2->计算 characters_per_second

1.3->使用它来存储（里面dict_times_word_subtitle）说单词的时间i

2. 我从全文中提取句子

3. 对于每个句子：

3.1我在（内部dict_sentences_subtitle）存储用特定单词讲句子所花费的时间（从中我可以得到说出来的持续时间）

4. 我创建了一个新的srt文件（字幕文件），该文件与原始srt文件同时启动，然后可以从讲句子的持续时间中获取字幕时间。

现在，我已经编写了以下代码：

#---------------------------------------------------------
import pysrt
import re
from datetime import datetime, date, time, timedelta
#---------------------------------------------------------

def convert_subtitle_one_sentence(file_name):

    sub = pysrt.open(file_name)   

    ### ----------------------------------------------------------------------
    ### Store Each Word and the Average Time it Takes to Say it in a dictionary
    ### ----------------------------------------------------------------------

    dict_times_word_subtitle = {}
    running_variable = 0
    for i in range(len(sub)):

        subtitle_text = sub[i].text
        subtitle_duration = (datetime.combine(date.min, sub[i].duration.to_time()) - datetime.min).total_seconds()

        # Compute characters per second
        characters_per_second = len(subtitle_text)/subtitle_duration

        # Store Each Word and the Average Time (seconds) it Takes to Say in a Dictionary 

        for j,word in enumerate(subtitle_text.split()):
            if j == len(subtitle_text.split())-1:
                time = len(word)/characters_per_second
            else:
                time = len(word+" ")/characters_per_second

            dict_times_word_subtitle[str(running_variable)] = [word, time]
            running_variable += 1


    ### ----------------------------------------------------------------------
    ### Store Each Sentence and the Average Time to Say it in a Dictionary
    ### ----------------------------------------------------------------------  

    total_number_of_words = len(dict_times_word_subtitle.keys())

    # Get the entire text
    entire_text = ""
    for i in range(total_number_of_words):
        entire_text += dict_times_word_subtitle[str(i)][0] +" "


    # Initialize the dictionary 
    dict_times_sentences_subtitle = {}

    # Loop through all found sentences 
    last_number_of_words = 0
    for i,sentence in enumerate(re.findall(r'([A-Z][^\.!?]*[\.!?])', entire_text)):

        number_of_words = len(sentence.split())

        # Compute the time it takes to speak the sentence
        time_sentence = 0
        for j in range(last_number_of_words, last_number_of_words + number_of_words):
            time_sentence += dict_times_word_subtitle[str(j)][1] 

        # Store the sentence together with the time it takes to say the sentence
        dict_times_sentences_subtitle[str(i)] = [sentence, round(time_sentence,3)]

        ## Update last number_of_words
        last_number_of_words += number_of_words

    # Check if there is a non-sentence remaining at the end
    if j < total_number_of_words:
        remaining_string = ""
        remaining_string_time = 0
        for k in range(j+1, total_number_of_words):
            remaining_string += dict_times_word_subtitle[str(k)][0] + " "
            remaining_string_time += dict_times_word_subtitle[str(k)][1]

        dict_times_sentences_subtitle[str(i+1)] = [remaining_string, remaining_string_time]

    ### ----------------------------------------------------------------------
    ### Create a new Subtitle file with only 1 sentence at a time
    ### ----------------------------------------------------------------------  

    # Initalize new srt file
    new_srt = pysrt.SubRipFile()

    # Loop through all sentence
    # get initial start time (seconds)
    # /sf/ask/3137615141/
    start_time = (datetime.combine(date.min, sub[0].start.to_time()) - datetime.min).total_seconds()

    for i in range(len(dict_times_sentences_subtitle.keys())):


        sentence = dict_times_sentences_subtitle[str(i)][0]
        print(sentence)
        time_sentence = dict_times_sentences_subtitle[str(i)][1]
        print(time_sentence)
        item = pysrt.SubRipItem(
                        index=i,
                        start=pysrt.SubRipTime(seconds=start_time),
                        end=pysrt.SubRipTime(seconds=start_time+time_sentence),
                        text=sentence)

        new_srt.append(item)

        ## Update Start Time
        start_time += time_sentence

    new_srt.save(file_name)

问题：

没有错误消息，但是当我将其应用于真实的字幕文件然后观看视频时，字幕可以正确开始，但是随着视频的进行（错误进行），字幕与实际所说的越来越少。

示例：演讲者已结束演讲，但字幕不断出现。

简单示例进行测试

srt = """
1
00:00:13,100 --> 00:00:14,750
Dr. Martin Luther King, Jr.,

2
00:00:14,750 --> 00:00:18,636
in a 1968 speech where he reflects
upon the Civil Rights Movement,

3
00:00:18,636 --> 00:00:21,330
states, "In the end,

4
00:00:21,330 --> 00:00:24,413
we will remember not the words of our enemies

5
00:00:24,413 --> 00:00:27,280
but the silence of our friends."

6
00:00:27,280 --> 00:00:29,800
As a teacher, I've internalized this message.

"""

with open('test.srt', "w") as file:
    file.write(srt)


convert_subtitle_one_sentence("test.srt")

输出看起来像这样（是的，在句子识别标准（即Dr.）上还有一些工作要做）：

0
00:00:13,100 --> 00:00:13,336
Dr.

1
00:00:13,336 --> 00:00:14,750
Martin Luther King, Jr.

2
00:00:14,750 --> 00:00:23,514
Civil Rights Movement, states, "In the end, we will remember not the words of our enemies but the silence of our friends.

3
00:00:23,514 --> 00:00:26,175
As a teacher, I've internalized this message.

4
00:00:26,175 --> 00:00:29,859
our friends." As a teacher, I've internalized this message.

如您所见，原始的最后一个时间戳是，00:00:29,800而在输出文件中则是 00:00:29,859。在开始时这似乎并不多，但是随着视频时间的延长，差异会越来越大。

完整的示例视频可以在这里下载：https : //ufile.io/19nuvqb3

完整的字幕文件：https : //ufile.io/qracb7ai

！注意：字幕文件将被覆盖，因此您可能需要存储具有其他名称的副本以进行比较。

任何帮助解决该问题的方法都将受到欢迎！

确定如何解决的方法：

• 起始或结束原始字幕的单词的确切时间是已知的。这可用于交叉检查并相应地调整时间。

编辑：

这是一个用于创建词典的代码，该词典存储character，character_duration（字幕的平均数）以及开始或结束原始时间残差（如果该字符存在）。

sub = pysrt.open('video.srt')

running_variable = 0
dict_subtitle = {}

for i in range(len(sub)):

    # Extract Start Time Stamb
    timestamb_start = sub[i].start

    # Extract Text
    text =sub[i].text

    # Extract End Time Stamb
    timestamb_end = sub[i].end

    # Extract Characters per Second 
    characters_per_second = sub[i].characters_per_second

    # Fill Dictionary 
    for j,character in enumerate(" ".join(text.split())):
        character_duration = len(character)*characters_per_second
        dict_subtitle[str(running_variable)] = [character,character_duration,False, False]
        if j == 0: dict_subtitle[str(running_variable)] = [character, character_duration, timestamb_start, False]
        if j == len(text)-1 : dict_subtitle[str(running_variable)] = [character, character_duration, False, timestamb_end]
        running_variable += 1

更多视频可以尝试：

在这里您可以下载更多视频及其相应的字幕文件：https : //filebin.net/kwygjffdlfi62pjs

编辑3：

4
00:00:18,856 --> 00:00:25,904
Je rappelle la définition de ce qu'est un produit scalaire, <i>dot product</i> dans <i>Ⅎ</i>.

5
00:00:24,855 --> 00:00:30,431
Donc je prends deux vecteurs dans <i>Ⅎ</i> et je définis cette opération-là, linéaire, <i>u 

。

Answer 1

我已根据要求重新编码以依赖该pysrt包，以及re.
这个想法是基于 start_times 构建一个字典。

如果开始时间存在，则会将数据添加到该时间的条目中，但同时更新 end_time，因此结束时间会随文本提前。

如果不存在开始时间，则它只是一个新的字典条目。

只有当我们知道一个句子已经完成时，开始时间才会提前。

所以本质上，我们开始构建一个具有固定开始时间的句子。通过添加更多文本并更新结束时间，继续构建句子，直到句子完成。在这里，我们使用当前记录提前开始时间，我们知道这是一个新句子。

具有多个句子的副标题条目将被分解，并pysrt character_per_second在分解之前使用整个副标题条目的条目计算开始和结束时间。

最后，一个新的字幕文件从字典中的条目写入磁盘。

显然，由于只有一个文件可供使用，我很可能会错过一些字幕布局驼峰，但至少它为您提供了一个工作起点。

代码自始至终都带有注释，因此大多数事情都应该很清楚，例如如何以及为什么。

编辑：我改进了对现有词典开始时间的检查，并更改了用于确定句子是否结束的方法，即在分割后将句号放回到文本中。
您提到的第二个视频确实有稍微偏离的字幕，首先请注意，根本没有毫秒值。

以下代码在第二个视频上效果不错，在第一个视频上效果很好。

编辑 2：添加连续句号和 html <> 标签删除

编辑 3：事实证明，pysrt从每秒字符数的计算中删除了 html 标签。我现在也这样做了，这意味着<html>格式可以保留在字幕中。

编辑4：这个版本处理数学和化学公式中的句号，加上ip数字等。基本上句号并不意味着句号的地方。它还允许以 ? 结尾的句子。和 ！

import pysrt
import re

abbreviations = ['Dr.','Mr.','Mrs.','Ms.','etc.','Jr.','e.g.'] # You get the idea!
abbrev_replace = ['Dr','Mr','Mrs','Ms','etc','Jr','eg']
subs = pysrt.open('new.srt')
subs_dict = {}          # Dictionary to accumulate new sub-titles (start_time:[end_time,sentence])
start_sentence = True   # Toggle this at the start and end of sentences

# regex to remove html tags from the character count
tags = re.compile(r'<.*?>')

# regex to split on ".", "?" or "!" ONLY if it is preceded by something else
# which is not a digit and is not a space. (Not perfect but close enough)
# Note: ? and ! can be an issue in some languages (e.g. french) where both ? and !
# are traditionally preceded by a space ! rather than!
end_of_sentence = re.compile(r'([^\s\0-9][\.\?\!])')

# End of sentence characters
eos_chars = set([".","?","!"])

for sub in subs:
    if start_sentence:
        start_time = sub.start
        start_sentence = False
    text = sub.text

    #Remove multiple full-stops e.g. "and ....."
    text = re.sub('\.+', '.', text)

    # Optional
    for idx, abr in enumerate(abbreviations):
        if abr in text:
            text = text.replace(abr,abbrev_replace[idx])
    # A test could also be made for initials in names i.e. John E. Rotten - showing my age there ;)

    multi = re.split(end_of_sentence,text.strip())
    cps = sub.characters_per_second

    # Test for a sub-title with multiple sentences
    if len(multi) > 1:
        # regex end_of_sentence breaks sentence start and sentence end into 2 parts
        # we need to put them back together again.
        # hence the odd range because the joined end part is then deleted
        for cnt in range(divmod(len(multi),2)[0]): # e.g. len=3 give 0 | 5 gives 0,1  | 7 gives 0,1,2
            multi[cnt] = multi[cnt] + multi[cnt+1]
            del multi[cnt+1]

        for part in multi:
            if len(part): # Avoid blank parts
                pass
            else:
                continue
            # Convert start time to seconds
            h,m,s,milli = re.split(':|,',str(start_time))
            s_time = (3600*int(h))+(60*int(m))+int(s)+(int(milli)/1000)

            # test for existing data
            try:
                existing_data = subs_dict[str(start_time)]
                end_time = str(existing_data[0])
                h,m,s,milli = re.split(':|,',str(existing_data[0]))
                e_time = (3600*int(h))+(60*int(m))+int(s)+(int(milli)/1000)
            except:
                existing_data = []
                e_time = s_time

            # End time is the start time or existing end time + the time taken to say the current words
            # based on the calculated number of characters per second
            # use regex "tags" to remove any html tags from the character count.

            e_time = e_time + len(tags.sub('',part)) / cps

            # Convert start to a timestamp
            s,milli = divmod(s_time,1)
            m,s = divmod(int(s),60)
            h,m = divmod(m,60)
            start_time = "{:02d}:{:02d}:{:02d},{:03d}".format(h,m,s,round(milli*1000))

            # Convert end to a timestamp
            s,milli = divmod(e_time,1)
            m,s = divmod(int(s),60)
            h,m = divmod(m,60)
            end_time = "{:02d}:{:02d}:{:02d},{:03d}".format(h,m,s,round(milli*1000))

            # if text already exists add the current text to the existing text
            # if not use the current text to write/rewrite the dictionary entry
            if existing_data:
                new_text = existing_data[1] + " " + part
            else:
                new_text = part
            subs_dict[str(start_time)] = [end_time,new_text]

            # if sentence ends re-set the current start time to the end time just calculated
            if any(x in eos_chars for x in part):
                start_sentence = True
                start_time = end_time
                print ("Split",start_time,"-->",end_time,)
                print (new_text)
                print('\n')
            else:
                start_sentence = False

    else:   # This is Not a multi-part sub-title

        end_time = str(sub.end)

        # Check for an existing dictionary entry for this start time
        try:
            existing_data = subs_dict[str(start_time)]
        except:
            existing_data = []

        # if it already exists add the current text to the existing text
        # if not use the current text
        if existing_data:
            new_text = existing_data[1] + " " + text
        else:
            new_text = text
        # Create or Update the dictionary entry for this start time
        # with the updated text and the current end time
        subs_dict[str(start_time)] = [end_time,new_text]

        if any(x in eos_chars for x in text):
            start_sentence = True
            print ("Single",start_time,"-->",end_time,)
            print (new_text)
            print('\n')
        else:
            start_sentence = False

# Generate the new sub-title file from the dictionary
idx=0
outfile = open('video_new.srt','w')
for key, text in subs_dict.items():
    idx+=1
    outfile.write(str(idx)+"\n")
    outfile.write(key+" --> "+text[0]+"\n")
    outfile.write(text[1]+"\n\n")
outfile.close()

通过上述文件代码后的输出video.srt如下：

1
00:00:13,100 --> 00:00:27,280
Dr Martin Luther King, Jr, in a 1968 speech where he reflects
upon the Civil Rights Movement, states, "In the end, we will remember not the words of our enemies but the silence of our friends."

2
00:00:27,280 --> 00:00:29,800
As a teacher, I've internalized this message.

3
00:00:29,800 --> 00:00:39,701
Every day, all around us, we see the consequences of silence manifest themselves in the form of discrimination, violence, genocide and war.

4
00:00:39,701 --> 00:00:46,178
In the classroom, I challenge my students to explore the silences in their own lives through poetry.

5
00:00:46,178 --> 00:00:54,740
We work together to fill those spaces, to recognize them, to name them, to understand that they don't
have to be sources of shame.

6
00:00:54,740 --> 00:01:14,408
In an effort to create a culture within my classroom where students feel safe sharing the intimacies of their own silences, I have four core principles posted on the board that sits in the front of my class, which every student signs
at the beginning of the year: read critically, write consciously, speak clearly, tell your truth.

7
00:01:14,408 --> 00:01:18,871
And I find myself thinking a lot about that last point, tell your truth.

8
00:01:18,871 --> 00:01:28,848
And I realized that if I was going to ask my students to speak up, I was going to have to tell my truth and be honest with them about the times where I failed to do so.

9
00:01:28,848 --> 00:01:44,479
So I tell them that growing up, as a kid in a Catholic family in New Orleans, during Lent I was always taught that the most meaningful thing one could do was to give something up, sacrifice something you typically indulge in to prove to God you understand his sanctity.

10
00:01:44,479 --> 00:01:50,183
I've given up soda, McDonald's, French fries, French kisses, and everything in between.

11
00:01:50,183 --> 00:01:54,071
But one year, I gave up speaking.

12
00:01:54,071 --> 00:02:03,286
I figured the most valuable thing I could sacrifice was my own voice, but it was like I hadn't realized that I had given that up a long time ago.

13
00:02:03,286 --> 00:02:23,167
I spent so much of my life telling people the things they wanted to hear instead of the things they needed to, told myself I wasn't meant to be anyone's conscience because I still had to figure out being my own, so sometimes I just wouldn't say anything, appeasing ignorance with my silence, unaware that validation doesn't need words to endorse its existence.

14
00:02:23,167 --> 00:02:29,000
When Christian was beat up for being gay, I put my hands in my pocket and walked with my head
down as if I didn't even notice.

15
00:02:29,000 --> 00:02:39,502
I couldn't use my locker for weeks
because the bolt on the lock reminded me of the one I had put on my lips when the homeless man on the corner looked at me with eyes up merely searching for an affirmation that he was worth seeing.

16
00:02:39,502 --> 00:02:43,170
I was more concerned with
touching the screen on my Apple than actually feeding him one.

17
00:02:43,170 --> 00:02:46,049
When the woman at the fundraising gala said "I'm so proud of you.

18
00:02:46,049 --> 00:02:53,699
It must be so hard teaching
those poor, unintelligent kids," I bit my lip, because apparently
we needed her money more than my students needed their dignity.

19
00:02:53,699 --> 00:03:02,878
We spend so much time listening to the things people are saying that we rarely pay attention to the things they don't.

20
00:03:02,878 --> 00:03:06,139
Silence is the residue of fear.

21
00:03:06,139 --> 00:03:09,615
It is feeling your flaws gut-wrench guillotine your tongue.

22
00:03:09,615 --> 00:03:13,429
It is the air retreating from your chest because it doesn't feel safe in your lungs.

23
00:03:13,429 --> 00:03:15,186
Silence is Rwandan genocide.

24
00:03:15,186 --> 00:03:16,423
 Silence is Katrina.

25
00:03:16,553 --> 00:03:19,661
It is what you hear when there
aren't enough body bags left.

26
00:03:19,661 --> 00:03:22,062
It is the sound after the noose is already tied.

27
00:03:22,062 --> 00:03:22,870
It is charring.

28
00:03:22,870 --> 00:03:23,620
 It is chains.

29
00:03:23,620 --> 00:03:24,543
 It is privilege.

30
00:03:24,543 --> 00:03:25,178
 It is pain.

31
00:03:25,409 --> 00:03:28,897
There is no time to pick your battles when your battles have already picked you.

32
00:03:28,897 --> 00:03:31,960
I will not let silence wrap itself around my indecision.

33
00:03:31,960 --> 00:03:36,287
I will tell Christian that he is a lion, a sanctuary of bravery and brilliance.

34
00:03:36,287 --> 00:03:42,340
I will ask that homeless man what his name is and how his day was, because sometimes all people want to be is human.

35
00:03:42,340 --> 00:03:51,665
I will tell that woman that my students can talk about transcendentalism like their last name was Thoreau, and just because you watched
one episode of "The Wire" doesn't mean you know anything about my kids.

36
00:03:51,665 --> 00:04:03,825
So this year, instead of giving something up, I will live every day as if there were a microphone tucked under my tongue, a stage on the underside of my inhibition.

37
00:04:03,825 --> 00:04:10,207
Because who has to have a soapbox when all you've ever needed is your voice?

38
00:04:10,207 --> 00:04:12,712
Thank you.

39
00:04:12,712 --> 00:00:00,000
(Applause)