SRT.py

import os
import re
from copy import copy, deepcopy
from csv import reader
from datetime import timedelta

import openai


class SRT_segment(object):
    def __init__(self, *args) -> None:
        if isinstance(args[0], dict):
            segment = args[0]
            self.start = segment['start']
            self.end = segment['end']
            self.start_ms = int((segment['start'] * 100) % 100 * 10)
            self.end_ms = int((segment['end'] * 100) % 100 * 10)

            if self.start_ms == self.end_ms and int(segment['start']) == int(segment['end']):  # avoid empty time stamp
                self.end_ms += 500

            self.start_time = timedelta(seconds=int(segment['start']), milliseconds=self.start_ms)
            self.end_time = timedelta(seconds=int(segment['end']), milliseconds=self.end_ms)
            if self.start_ms == 0:
                self.start_time_str = str(0) + str(self.start_time).split('.')[0] + ',000'
            else:
                self.start_time_str = str(0) + str(self.start_time).split('.')[0] + ',' + \
                                      str(self.start_time).split('.')[1][:3]
            if self.end_ms == 0:
                self.end_time_str = str(0) + str(self.end_time).split('.')[0] + ',000'
            else:
                self.end_time_str = str(0) + str(self.end_time).split('.')[0] + ',' + str(self.end_time).split('.')[1][
                                                                                      :3]
            self.source_text = segment['text'].lstrip()
            self.duration = f"{self.start_time_str} --> {self.end_time_str}"
            self.translation = ""

        elif isinstance(args[0], list):
            self.source_text = args[0][2]
            self.duration = args[0][1]
            self.start_time_str = self.duration.split(" --> ")[0]
            self.end_time_str = self.duration.split(" --> ")[1]

            # parse the time to float
            self.start_ms = int(self.start_time_str.split(',')[1]) / 10
            self.end_ms = int(self.end_time_str.split(',')[1]) / 10
            start_list = self.start_time_str.split(',')[0].split(':')
            self.start = int(start_list[0]) * 3600 + int(start_list[1]) * 60 + int(start_list[2]) + self.start_ms / 100
            end_list = self.end_time_str.split(',')[0].split(':')
            self.end = int(end_list[0]) * 3600 + int(end_list[1]) * 60 + int(end_list[2]) + self.end_ms / 100
            self.translation = ""

    def merge_seg(self, seg):
        """
        Merge the segment seg with the current segment in place.
        :param seg: Another segment that is strictly next to current one.
        :return: None
        """
        # assert seg.start_ms == self.end_ms, f"cannot merge discontinuous segments."
        self.source_text += f' {seg.source_text}'
        self.translation += f' {seg.translation}'
        self.end_time_str = seg.end_time_str
        self.end = seg.end
        self.end_ms = seg.end_ms
        self.duration = f"{self.start_time_str} --> {self.end_time_str}"
        pass

    def __add__(self, other):
        """
        Merge the segment seg with the current segment, and return the new constructed segment.
        No in-place modification.
        :param other: Another segment that is strictly next to added segment.
        :return: new segment of the two sub-segments
        """
        # assert other.start_ms == self.end_ms, f"cannot merge discontinuous segments."
        result = deepcopy(self)
        result.source_text += f' {other.source_text}'
        result.translation += f' {other.translation}'
        result.end_time_str = other.end_time_str
        result.end = other.end
        result.end_ms = other.end_ms
        result.duration = f"{self.start_time_str} --> {self.end_time_str}"
        return result

    def remove_trans_punc(self):
        """
        remove punctuations in translation text
        :return: None
        """
        punc_cn = "，。！？"
        translator = str.maketrans(punc_cn, ' ' * len(punc_cn))
        self.translation = self.translation.translate(translator)

    def __str__(self) -> str:
        return f'{self.duration}\n{self.source_text}\n\n'

    def get_trans_str(self) -> str:
        return f'{self.duration}\n{self.translation}\n\n'

    def get_bilingual_str(self) -> str:
        return f'{self.duration}\n{self.source_text}\n{self.translation}\n\n'


class SRT_script():
    def __init__(self, segments) -> None:
        self.segments = []
        for seg in segments:
            srt_seg = SRT_segment(seg)
            self.segments.append(srt_seg)

    @classmethod
    def parse_from_srt_file(cls, path: str):
        with open(path, 'r', encoding="utf-8") as f:
            script_lines = [line.rstrip() for line in f.readlines()]

        segments = []
        for i in range(len(script_lines)):
            if i % 4 == 0:
                segments.append(list(script_lines[i:i + 4]))

        return cls(segments)

    def merge_segs(self, idx_list) -> SRT_segment:
        """
        Merge entire segment list to a single segment
        :param idx_list: List of index to merge
        :return: Merged list
        """
        if not idx_list:
            raise NotImplementedError('Empty idx_list')
        seg_result = deepcopy(self.segments[idx_list[0]])
        if len(idx_list) == 1:
            return seg_result

        for idx in range(1, len(idx_list)):
            seg_result += self.segments[idx_list[idx]]

        return seg_result

    def form_whole_sentence(self):
        """
        Concatenate or Strip sentences and reconstruct segments list. This is because of
        improper segmentation from openai-whisper.
        :return: None
        """
        merge_list = []  # a list of indices that should be merged e.g. [[0], [1, 2, 3, 4], [5, 6], [7]]
        sentence = []
        for i, seg in enumerate(self.segments):
            if seg.source_text[-1] in ['.', '!', '?'] and len(seg.source_text) > 10 and 'vs.' not in seg.source_text:
                sentence.append(i)
                merge_list.append(sentence)
                sentence = []
            else:
                sentence.append(i)

        segments = []
        for idx_list in merge_list:
            segments.append(self.merge_segs(idx_list))

        self.segments = segments

    def remove_trans_punctuation(self):
        """
        Post-process: remove all punc after translation and split
        :return: None
        """
        for i, seg in enumerate(self.segments):
            seg.remove_trans_punc()

    def set_translation(self, translate: str, id_range: tuple, model, video_name, video_link=None):
        start_seg_id = id_range[0]
        end_seg_id = id_range[1]

        src_text = ""
        for i, seg in enumerate(self.segments[start_seg_id - 1:end_seg_id]):
            src_text += seg.source_text
            src_text += '\n\n'

        def inner_func(target, input_str):
            response = openai.ChatCompletion.create(
                # model=model,
                model="gpt-3.5-turbo",
                messages=[
                    # {"role": "system", "content": "You are a helpful assistant that help calibrates English to Chinese subtitle translations in starcraft2."},
                    # {"role": "system", "content": "You are provided with a translated Chinese transcript; you must modify or split the Chinese sentence to match the meaning and the number of the English transcript exactly one by one. You must not merge ANY Chinese lines, you can only split them but the total Chinese lines MUST equals to number of English lines."},
                    # {"role": "system", "content": "There is no need for you to add any comments or notes, and do not modify the English transcript."},
                    # {"role": "user", "content": 'You are given the English transcript and line number, your task is to merge or split the Chinese to match the exact number of lines in English transcript, no more no less. For example, if there are more Chinese lines than English lines, merge some the Chinese lines to match the number of English lines. If Chinese lines is less than English lines, split some Chinese lines to match the english lines: "{}"'.format(input_str)}

                    {"role": "system",
                     "content": "你的任务是按照要求合并或拆分句子到指定行数，你需要尽可能保证句意，但必要时可以将一句话分为两行输出"},
                    {"role": "system", "content": "注意：你只需要输出处理过的中文句子，如果你要输出序号，请使用冒号隔开"},
                    {"role": "user", "content": '请将下面的句子拆分或组合为{}句:\n{}'.format(target, input_str)}
                    # {"role": "system", "content": "请将以下中文与其英文句子一一对应并输出："},
                    # {"role": "system", "content": "英文：{}".format(src_text)},
                    # {"role": "user", "content": "中文：{}\n\n".format(input_str)},
                ],
                temperature=0.15
            )
            # print(src_text)
            # print(input_str)
            # print(response['choices'][0]['message']['content'].strip())
            # exit()
            return response['choices'][0]['message']['content'].strip()

        lines = translate.split('\n\n')
        if len(lines) < (end_seg_id - start_seg_id + 1):
            count = 0
            solved = True
            while count < 5 and len(lines) != (end_seg_id - start_seg_id + 1):
                count += 1
                print("Solving Unmatched Lines|iteration {}".format(count))
                # input_str = "\n"
                # initialize GPT input
                # for i, seg in enumerate(self.segments[start_seg_id-1:end_seg_id]):
                #    input_str += 'Sentence %d: ' %(i+1)+ seg.source_text + '\n'
                #    #Append to prompt string
                #    #Adds sentence index let GPT keep track of sentence breaks
                # input_str += translate
                # append translate to prompt
                flag = True
                while flag:
                    flag = False
                    # print("translate:")
                    # print(translate)
                    try:
                        # print("target")
                        # print(end_seg_id - start_seg_id + 1)
                        translate = inner_func(end_seg_id - start_seg_id + 1, translate)
                    except Exception as e:
                        print("An error has occurred during solving unmatched lines:", e)
                        print("Retrying...")
                        flag = True
                lines = translate.split('\n')
                # print("result")
                # print(len(lines))

            if len(lines) < (end_seg_id - start_seg_id + 1):
                solved = False
                print("Failed Solving unmatched lines, Manually parse needed")

            if not os.path.exists("./logs"):
                os.mkdir("./logs")
            if video_link:
                log_file = "./logs/log_link.csv"
                log_exist = os.path.exists(log_file)
                with open(log_file, "a") as log:
                    if not log_exist:
                        log.write("range_of_text,iterations_solving,solved,file_length,video_link" + "\n")
                    log.write(str(id_range) + ',' + str(count) + ',' + str(solved) + ',' + str(
                        len(self.segments)) + ',' + video_link + "\n")
            else:
                log_file = "./logs/log_name.csv"
                log_exist = os.path.exists(log_file)
                with open(log_file, "a") as log:
                    if not log_exist:
                        log.write("range_of_text,iterations_solving,solved,file_length,video_name" + "\n")
                    log.write(str(id_range) + ',' + str(count) + ',' + str(solved) + ',' + str(
                        len(self.segments)) + ',' + video_name + "\n")

            print(lines)
            # print(id_range)
            # for i, seg in enumerate(self.segments[start_seg_id-1:end_seg_id]):
            #    print(seg.source_text)
            # print(translate)

        for i, seg in enumerate(self.segments[start_seg_id - 1:end_seg_id]):
            # naive way to due with merge translation problem
            # TODO: need a smarter solution

            if i < len(lines):
                if "Note:" in lines[i]:  # to avoid note
                    lines.remove(lines[i])
                    max_num -= 1
                    if i == len(lines) - 1:
                        break
                try:
                    seg.translation = lines[i].split("：" or ":" or ".")[1]
                except:
                    seg.translation = lines[i]

    def split_seg(self, seg, text_threshold, time_threshold):
        # evenly split seg to 2 parts and add new seg into self.segments

        # ignore the initial comma to solve the recursion problem
        if len(seg.source_text) > 2:
            if seg.source_text[:2] == ', ':
                seg.source_text = seg.source_text[2:]
        if seg.translation[0] == '，':
            seg.translation = seg.translation[1:]

        source_text = seg.source_text
        translation = seg.translation

        # split the text based on commas
        src_commas = [m.start() for m in re.finditer(',', source_text)]
        trans_commas = [m.start() for m in re.finditer('，', translation)]
        if len(src_commas) != 0:
            src_split_idx = src_commas[len(src_commas) // 2] if len(src_commas) % 2 == 1 else src_commas[
                len(src_commas) // 2 - 1]
        else:
            src_space = [m.start() for m in re.finditer(' ', source_text)]
            if len(src_space) > 0:
                src_split_idx = src_space[len(src_space) // 2] if len(src_space) % 2 == 1 else src_space[
                    len(src_space) // 2 - 1]
            else:
                src_split_idx = 0

        if len(trans_commas) != 0:
            trans_split_idx = trans_commas[len(trans_commas) // 2] if len(trans_commas) % 2 == 1 else trans_commas[
                len(trans_commas) // 2 - 1]
        else:
            trans_split_idx = len(translation) // 2

        # split the time duration based on text length
        time_split_ratio = trans_split_idx / (len(seg.translation) - 1)

        src_seg1 = source_text[:src_split_idx]
        src_seg2 = source_text[src_split_idx:]
        trans_seg1 = translation[:trans_split_idx]
        trans_seg2 = translation[trans_split_idx:]

        start_seg1 = seg.start
        end_seg1 = start_seg2 = seg.start + (seg.end - seg.start) * time_split_ratio
        end_seg2 = seg.end

        seg1_dict = {}
        seg1_dict['text'] = src_seg1
        seg1_dict['start'] = start_seg1
        seg1_dict['end'] = end_seg1
        seg1 = SRT_segment(seg1_dict)
        seg1.translation = trans_seg1

        seg2_dict = {}
        seg2_dict['text'] = src_seg2
        seg2_dict['start'] = start_seg2
        seg2_dict['end'] = end_seg2
        seg2 = SRT_segment(seg2_dict)
        seg2.translation = trans_seg2

        result_list = []
        if len(seg1.translation) > text_threshold and (seg1.end - seg1.start) > time_threshold:
            result_list += self.split_seg(seg1, text_threshold, time_threshold)
        else:
            result_list.append(seg1)

        if len(seg2.translation) > text_threshold and (seg2.end - seg2.start) > time_threshold:
            result_list += self.split_seg(seg2, text_threshold, time_threshold)
        else:
            result_list.append(seg2)

        return result_list

    def check_len_and_split(self, text_threshold=30, time_threshold=1.0):
        # DEPRECATED
        # if sentence length >= threshold and sentence duration > time_threshold, split this segments to two
        segments = []
        for seg in self.segments:
            if len(seg.translation) > text_threshold and (seg.end - seg.start) > time_threshold:
                seg_list = self.split_seg(seg, text_threshold, time_threshold)
                segments += seg_list
            else:
                segments.append(seg)

        self.segments = segments

        pass

    def check_len_and_split_range(self, range, text_threshold=30, time_threshold=1.0):
        # if sentence length >= text_threshold, split this segments to two
        start_seg_id = range[0]
        end_seg_id = range[1]
        extra_len = 0
        segments = []
        for i, seg in enumerate(self.segments[start_seg_id - 1:end_seg_id]):
            if len(seg.translation) > text_threshold and (seg.end - seg.start) > time_threshold:
                seg_list = self.split_seg(seg, text_threshold, time_threshold)
                segments += seg_list
                extra_len += len(seg_list) - 1
            else:
                segments.append(seg)

        self.segments[start_seg_id - 1:end_seg_id] = segments
        return extra_len

    def correct_with_force_term(self):
        ## force term correction

        # load term dictionary
        with open("./finetune_data/dict_enzh.csv", 'r', encoding='utf-8') as f:
            term_enzh_dict = {rows[0]: rows[1] for rows in reader(f)}

        # change term
        for seg in self.segments:
            ready_words = seg.source_text.split(" ")
            for i in range(len(ready_words)):
                word = ready_words[i]
                [real_word, pos] = self.get_real_word(word)
                if real_word in term_enzh_dict:
                    new_word = word.replace(word[:pos], term_enzh_dict.get(real_word))
                else:
                    new_word = word
                ready_words[i] = new_word
            seg.source_text = " ".join(ready_words)
        pass

    def spell_check_term(self):
        ## known bug: I've will be replaced because i've is not in the dict

        import enchant
        dict = enchant.Dict('en_US')
        term_spellDict = enchant.PyPWL('./finetune_data/dict_freq.txt')

        for seg in self.segments:
            ready_words = seg.source_text.split(" ")
            for i in range(len(ready_words)):
                word = ready_words[i]
                [real_word, pos] = self.get_real_word(word)
                if not dict.check(word[:pos]):
                    suggest = term_spellDict.suggest(real_word)
                    if suggest and enchant.utils.levenshtein(word, suggest[0]) < (len(word)+len(suggest[0]))/4:  # relax spell check
                        
                        with open("dislog.log","a") as log:
                            if not os.path.exists("dislog.log"):
                                log.write("word \t suggest \t levenshtein \n")
                            log.write(word + "\t" + suggest[0] + "\t" + str(enchant.utils.levenshtein(word, suggest[0]))+'\n')                            
                        #print(word + ":" + suggest[0] + ":---:levenshtein:" + str(enchant.utils.levenshtein(word, suggest[0])))                                                
                        new_word = word.replace(word[:pos],suggest[0])
                    else:
                        new_word = word
                else: 
                    new_word = word
                    else:
                        new_word = word
                else: 
                    new_word = word
                ready_words[i] = new_word
            seg.source_text = " ".join(ready_words)
        pass

    def spell_correction(self, word: str, arg: int):
        try:
            arg in [0, 1]
        except ValueError:
            print('only 0 or 1 for argument')

        def uncover(word: str):
            if word[-2:] == ".\n":
                real_word = word[:-2].lower()
                n = -2
            elif word[-1:] in [".", "\n", ",", "!", "?"]:
                real_word = word[:-1].lower()
                n = -1
            else:
                real_word = word.lower()
                n = 0
            return real_word, len(word) + n

        real_word = uncover(word)[0]
        pos = uncover(word)[1]
        new_word = word
        if arg == 0:  # term translate mode
            with open("finetune_data/dict_enzh.csv", 'r', encoding='utf-8') as f:
                term_enzh_dict = {rows[0]: rows[1] for rows in reader(f)}
            if real_word in term_enzh_dict:
                new_word = word.replace(word[:pos], term_enzh_dict.get(real_word))
        elif arg == 1:  # term spell check mode
            import enchant
            dict = enchant.Dict('en_US')
            term_spellDict = enchant.PyPWL('./finetune_data/dict_freq.txt')
            if not dict.check(real_word):
                if term_spellDict.suggest(real_word):  # relax spell check
                    new_word = word.replace(word[:pos], term_spellDict.suggest(real_word)[0])
        return new_word

    def get_real_word(self, word: str):
        if word[-2:] == ".\n":
            real_word = word[:-2].lower()
            n = -2
        elif word[-1:] in [".", "\n", ",", "!", "?"]:
            real_word = word[:-1].lower()
            n = -1
        else:
            real_word = word.lower()
            n = 0
        return real_word, len(word) + n

    ## WRITE AND READ FUNCTIONS ##

    def get_source_only(self):
        # return a string with pure source text
        result = ""
        for i, seg in enumerate(self.segments):
            result+=f'{seg.source_text}\n\n\n'#f'SENTENCE {i+1}: {seg.source_text}\n\n\n'
        
        return result

    def reform_src_str(self):
        result = ""
        for i, seg in enumerate(self.segments):
            result += f'{i + 1}\n'
            result += str(seg)
        return result

    def reform_trans_str(self):
        result = ""
        for i, seg in enumerate(self.segments):
            result += f'{i + 1}\n'
            result += seg.get_trans_str()
        return result

    def form_bilingual_str(self):
        result = ""
        for i, seg in enumerate(self.segments):
            result += f'{i + 1}\n'
            result += seg.get_bilingual_str()
        return result

    def write_srt_file_src(self, path: str):
        # write srt file to path
        with open(path, "w", encoding='utf-8') as f:
            f.write(self.reform_src_str())
        pass

    def write_srt_file_translate(self, path: str):
        with open(path, "w", encoding='utf-8') as f:
            f.write(self.reform_trans_str())
        pass

    def write_srt_file_bilingual(self, path: str):
        with open(path, "w", encoding='utf-8') as f:
            f.write(self.form_bilingual_str())
        pass

    def realtime_write_srt(self, path, range, length, idx):
        # DEPRECATED
        start_seg_id = range[0]
        end_seg_id = range[1]
        with open(path, "a", encoding='utf-8') as f:
            # for i, seg in enumerate(self.segments[start_seg_id-1:end_seg_id+length]):
            #     f.write(f'{i+idx}\n')
            #     f.write(seg.get_trans_str())
            for i, seg in enumerate(self.segments):
                if i < range[0] - 1: continue
                if i >= range[1] + length: break
                f.write(f'{i + idx}\n')
                f.write(seg.get_trans_str())
        pass

    def realtime_bilingual_write_srt(self, path, range, length, idx):
        # DEPRECATED
        start_seg_id = range[0]
        end_seg_id = range[1]
        with open(path, "a", encoding='utf-8') as f:
            for i, seg in enumerate(self.segments):
                if i < range[0] - 1: continue
                if i >= range[1] + length: break
                f.write(f'{i + idx}\n')
                f.write(seg.get_bilingual_str())
        pass