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| from datetime import timedelta | |
| import os | |
| import whisper | |
| from csv import reader | |
| import re | |
| import openai | |
| class SRT_segment(object): | |
| def __init__(self, *args) -> None: | |
| if isinstance(args[0], dict): | |
| segment = args[0] | |
| start_ms = int((segment['start']*100)%100*10) | |
| end_ms = int((segment['end']*100)%100*10) | |
| start_time = str(timedelta(seconds=int(segment['start']), milliseconds=start_ms)) | |
| end_time = str(timedelta(seconds=int(segment['end']), milliseconds=end_ms)) | |
| if start_ms == 0: | |
| self.start_time_str = str(0)+start_time.split('.')[0]+',000' | |
| else: | |
| self.start_time_str = str(0)+start_time.split('.')[0]+','+start_time.split('.')[1][:3] | |
| if end_ms == 0: | |
| self.end_time_str = str(0)+end_time.split('.')[0]+',000' | |
| else: | |
| self.end_time_str = str(0)+end_time.split('.')[0]+','+end_time.split('.')[1][:3] | |
| self.source_text = segment['text'] | |
| 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] | |
| self.translation = "" | |
| def merge_seg(self, seg): | |
| self.source_text += seg.source_text | |
| self.translation += seg.translation | |
| self.end_time_str = seg.end_time_str | |
| self.duration = f"{self.start_time_str} --> {self.end_time_str}" | |
| pass | |
| 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 = f.read().splitlines() | |
| 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: | |
| final_seg = self.segments[idx_list[0]] | |
| if len(idx_list) == 1: | |
| return final_seg | |
| for idx in range(1, len(idx_list)): | |
| final_seg.merge_seg(self.segments[idx_list[idx]]) | |
| return final_seg | |
| def form_whole_sentence(self): | |
| 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] == '.': | |
| 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 # need memory release? | |
| def set_translation(self, translate:str, id_range:tuple): | |
| start_seg_id = id_range[0] | |
| end_seg_id = id_range[1] | |
| lines = translate.split('\n\n') | |
| if len(lines) != (end_seg_id - start_seg_id + 1): | |
| 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 | |
| response = openai.ChatCompletion.create( | |
| 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 need to reformat the Chinese sentence to match the meaning and sentence number as the English transcript"}, | |
| {"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": 'Reformat the Chinese with the English transcript given: "{}"'.format(input_str)} | |
| ], | |
| temperature=0.15 | |
| ) | |
| translate = response['choices'][0]['text'].strip() | |
| #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]) | |
| if i == len(lines) - 1: | |
| break | |
| try: | |
| seg.translation = lines[i].split("：" or ": ")[1] | |
| except: | |
| seg.translation = lines[i] | |
| pass | |
| def split_seg(self, seg_id): | |
| # TODO: evenly split seg to 2 parts and add new seg into self.segments | |
| pass | |
| def check_len_and_split(self, threshold): | |
| # TODO: if sentence length >= threshold, split this segments to two | |
| pass | |
| def get_source_only(self): | |
| # return a string with pure source text | |
| result = "" | |
| for i, seg in enumerate(self.segments): | |
| result+=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 correct_with_force_term(self): | |
| ## force term correction | |
| # TODO: shortcut translation i.e. VA, ob | |
| # TODO: variety of translation | |
| # load term dictionary | |
| with open("finetune_data/dict.csv",'r', encoding='utf-8') as f: | |
| csv_reader = reader(f) | |
| term_dict = {rows[0]:rows[1] for rows in csv_reader} | |
| # change term | |
| for seg in self.segments: | |
| ready_words = re.sub('\n', '\n ', seg.source_text).split(" ") | |
| for i in range(len(ready_words)): | |
| word = ready_words[i] | |
| if word[-2:] == ".\n" : | |
| if word[:-2].lower() in term_dict : | |
| new_word = word.replace(word[:-2], term_dict.get(word[:-2].lower())) + ' ' | |
| ready_words[i] = new_word | |
| else: | |
| ready_words[i] = word + ' ' | |
| elif word.lower() in term_dict : | |
| new_word = word.replace(word,term_dict.get(word.lower())) + ' ' | |
| ready_words[i] = new_word | |
| else : | |
| ready_words[i]= word + ' ' | |
| seg.source_text = re.sub('\n ', '\n', "".join(ready_words)) | |
| print(self) | |
| pass | |