import whisper from pytube import YouTube import gradio as gr import os import re import logging logging.basicConfig(level=logging.INFO) model = whisper.load_model("base") def get_text(url): #try: if url != '': output_text_transcribe = '' yt = YouTube(url) #video_length = yt.length --- doesn't work anymore - using byte file size of the audio file instead now #if video_length < 5400: video = yt.streams.filter(only_audio=True).first() out_file=video.download(output_path=".") file_stats = os.stat(out_file) logging.info(f'Size of audio file in Bytes: {file_stats.st_size}') if file_stats.st_size <= 30000000: base, ext = os.path.splitext(out_file) new_file = base+'.mp3' os.rename(out_file, new_file) a = new_file result = model.transcribe(a) return result['text'].strip() else: logging.error('Videos for transcription on this space are limited to about 1.5 hours. Sorry about this limit but some joker thought they could stop this tool from working by transcribing many extremely long videos. Please visit https://steve.digital to contact me about this space.') #finally: # raise gr.Error("Exception: There was a problem transcribing the audio.") def get_summary(article): first_sentences = ' '.join(re.split(r'(?<=[.:;])\s', article)[:5]) b = summarizer(first_sentences, min_length = 20, max_length = 120, do_sample = False) b = b[0]['summary_text'].replace(' .', '.').strip() return b with gr.Blocks() as demo: gr.Markdown("