| from youtube_transcript_api import YouTubeTranscriptApi | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| def Summarizer(link, model): | |
| video_id = link.split("=")[1] | |
| try: | |
| transcript = YouTubeTranscriptApi.get_transcript(video_id) | |
| FinalTranscript = ' '.join([i['text'] for i in transcript]) | |
| if model == "Pegasus": | |
| checkpoint = "google/pegasus-large" | |
| elif model == "mT5": | |
| checkpoint = "csebuetnlp/mT5_multilingual_XLSum" | |
| elif model == "BART": | |
| checkpoint = "sshleifer/distilbart-cnn-12-6" | |
| tokenizer = AutoTokenizer.from_pretrained(checkpoint) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) | |
| inputs = tokenizer(FinalTranscript, | |
| max_length=1024, | |
| truncation=True, | |
| return_tensors="pt") | |
| summary_ids = model.generate(inputs["input_ids"]) | |
| summary = tokenizer.batch_decode(summary_ids, | |
| skip_special_tokens=True, | |
| clean_up_tokenization_spaces=False) | |
| return summary[0] | |
| except Exception as e: | |
| return "TranscriptsDisabled: Transcript is not available \nTry another video" | |