Spaces:
Sleeping
Sleeping
| import os | |
| import re | |
| from copy import copy, deepcopy | |
| from csv import reader | |
| from datetime import timedelta | |
| import logging | |
| 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 | |
| """ | |
| 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) | |
| 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 | |
| """ | |
| logging.info("Forming whole sentences...") | |
| 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: | |
| if len(idx_list) > 1: | |
| logging.info("merging segments: %s", idx_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() | |
| logging.info("Removed punctuation in translation.") | |
| 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): | |
| # if sentence length >= threshold and sentence duration > time_threshold, split this segments to two | |
| logging.info("performing check_len_and_split") | |
| segments = [] | |
| for i, seg in enumerate(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) | |
| logging.info("splitting segment {} in to {} parts".format(i+1, len(seg_list))) | |
| segments += seg_list | |
| else: | |
| segments.append(seg) | |
| self.segments = segments | |
| logging.info("check_len_and_split finished") | |
| pass | |
| def check_len_and_split_range(self, range, text_threshold=30, time_threshold=1.0): | |
| # DEPRECATED | |
| # 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 | |
| logging.info("performing 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)} | |
| keywords = list(term_enzh_dict.keys()) | |
| keywords.sort(key=lambda x: len(x), reverse=True) | |
| for word in keywords: | |
| for i, seg in enumerate(self.segments): | |
| if word in seg.source_text.lower(): | |
| seg.source_text = re.sub(fr"({word}es|{word}s?)\b", "{}".format(term_enzh_dict.get(word)), seg.source_text, flags=re.IGNORECASE) | |
| logging.info("replace term: " + word + " --> " + term_enzh_dict.get(word) + " in time stamp {}".format(i+1)) | |
| logging.info("source text becomes: " + seg.source_text) | |
| comp_dict = [] | |
| def fetchfunc(self,word,threshold): | |
| import enchant | |
| result = word; | |
| threshold = threshold*len(word) | |
| if len(self.comp_dict)==0: | |
| with open("./finetune_data/dict_enzh.csv", 'r', encoding='utf-8') as f: | |
| self.comp_dict = {rows[0]: rows[1] for rows in reader(f)} | |
| temp = "" | |
| for matched in self.comp_dict: | |
| if enchant.utils.levenshtein(word, matched)<enchant.utils.levenshtein(word, temp): | |
| temp = matched | |
| if enchant.utils.levenshtein(word, temp) < threshold: | |
| result = temp | |
| return result | |
| def spell_check_term(self): | |
| ## known bug: I've will be replaced because i've is not in the dict | |
| logging.info("performing spell check") | |
| 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]) and not term_spellDict.check(real_word): | |
| new_word = word.replace(word[:pos],self.fetchfunc(word[:pos],0.5)) | |
| logging.info(real_word + "\t" + self.fetchfunc(word[:pos],0.5) + "\t" + str(enchant.utils.levenshtein(real_word, self.fetchfunc(word[:pos],0.5)))+'\n') | |
| #suggest = term_spellDict.suggest(real_word) | |
| #if suggest and enchant.utils.levenshtein(real_word, suggest[0]) < (len(real_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") | |
| # logging.info(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 | |
| 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): | |
| logging.info("writing to " + path) | |
| with open(path, "w", encoding='utf-8') as f: | |
| f.write(self.reform_trans_str()) | |
| pass | |
| def write_srt_file_bilingual(self, path: str): | |
| logging.info("writing to " + path) | |
| 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 | |