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| import os | |
| import openai | |
| import locale | |
| locale.getpreferredencoding = lambda: "UTF-8" | |
| import dl_translate as dlt | |
| from deep_translator import GoogleTranslator | |
| from languages import LANGUAGES | |
| OPENAI_API_KEY = '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' | |
| OPENAI_API_URL = 'https://api.openai.com/v1/chat/completions' | |
| openai.api_key = OPENAI_API_KEY | |
| class Translation: | |
| def __init__(self, transcript_dict, source_lang, target_lang, output_path): | |
| self.transcript_dict = transcript_dict | |
| self.output_path = os.path.join(os.getcwd(), output_path) | |
| # Languages | |
| self.source_lang = source_lang # Whisper Detected Language | |
| self.target_lang = target_lang | |
| # Transcript | |
| self.transcript = transcript_dict['text'].strip() | |
| self.subtitles = self.__get_subtitles() | |
| # Translation Model | |
| nllb_model = 'facebook/nllb-200-distilled-600M' | |
| # nllb_model = 'facebook/nllb-200-1.3B' | |
| # nllb_model = 'facebook/nllb-200-3.3B' | |
| # nllb_model = 'facebook/nllb-moe-54b' | |
| self.nllb = dlt.TranslationModel(nllb_model) | |
| def __get_subtitles(self): | |
| ''' | |
| Returns the subtitles from transcript dictionary | |
| ''' | |
| subtitles = [] | |
| for s in self.transcript_dict['segments']: | |
| segment = { | |
| 'start': s['start'], | |
| 'end': s['end'], | |
| 'text': s['text'].strip() | |
| } | |
| subtitles.append(segment) | |
| return subtitles | |
| def __correct_punctuation_gpt(self): | |
| ''' | |
| Corrects the Punctuation from GPT | |
| ''' | |
| system_prompt = """ | |
| You are a helpful NLP assistant. | |
| Your task is to identify language of the provided text, | |
| correct any spelling discrepancies in the transcribed text | |
| as well as add punctuation in the multilingual text if they are missing. | |
| Only add necessary punctuation such as periods, commas, and capitalization, | |
| and use only the context provided. | |
| You response should be as follows: | |
| Corrected Text: | |
| Here goes the corrected text with punctuation. | |
| """ | |
| user_prompt = f""" | |
| Here is the text: | |
| {self.transcript} | |
| """ | |
| response = openai.ChatCompletion.create( | |
| model="gpt-3.5-turbo", | |
| messages=[ | |
| {"role": "system", "content": system_prompt}, | |
| {"role": "user", "content": user_prompt}, | |
| ] | |
| ) | |
| text = response.choices[0].message.content.replace('Corrected Text:\n', '') | |
| return text | |
| def get_translated_transcript(self): | |
| ''' | |
| Translates the transcript into required language | |
| ''' | |
| # Correcting Punctuation using GPT | |
| transcript = self.__correct_punctuation_gpt() | |
| # Splitting Text into Sentences | |
| if self.source_lang in ['ar', 'ur']: | |
| splitter = '۔' | |
| else: | |
| splitter = '.' | |
| sentences = transcript.split(splitter) | |
| # Getting Translation using NLLB | |
| translated_transcript = '' | |
| for sentence in sentences: | |
| translated_sentence = self.nllb.translate(sentence, source=LANGUAGES[self.source_lang], target=LANGUAGES[self.target_lang]) | |
| translated_transcript += translated_sentence + splitter + ' ' | |
| # print('Text:', sentence) | |
| # print('Text:', translated_sentence) | |
| # print() | |
| translated_transcript = translated_transcript.strip() | |
| return translated_transcript | |
| def get_translated_subtitles(self): | |
| ''' | |
| Translates the subtitles into required language | |
| ''' | |
| # Creating copy of Transcript Dictionary | |
| subtitles = self.subtitles.copy() | |
| # Creating Instance for Google Translator | |
| gt = GoogleTranslator(source='auto', target=self.target_lang) | |
| for i, s in enumerate(subtitles): | |
| subtitles[i]['text'] = gt.translate(text=s['text']) | |
| return subtitles |