import whisper from pytube import YouTube from transformers import pipeline import gradio as gr model = whisper.load_model("base") summarizer = pipeline("summarization") def get_audio(url): yt = YouTube(url) video = yt.streams.filter(only_audio=True).first() out_file=video.download(output_path=".") base, ext = os.path.splitext(out_file) new_file = base+'.mp3' os.rename(out_file, new_file) a = new_file return a def get_text(url): result = model.transcribe(get_audio(url)) return result['text'] def get_summary(url): article = get_text(url) b = summarizer(article) b = b[0]['summary_text'] return b with gr.Blocks() as demo: gr.Markdown("

Youtube video transcription with OpenAI's Whisper

") gr.Markdown("
Enter the link of any youtube video to get the transcription of the video and a summary of the video in the form of text.
") with gr.Tab('Get the transcription of any Youtube video'): with gr.Row(): input_text_1 = gr.Textbox(placeholder='Enter the Youtube video URL', label='URL') output_text_1 = gr.Textbox(placeholder='Transcription of the video', label='Transcription') result_button_1 = gr.Button('Get Transcription') with gr.Tab('Summary of Youtube video'): with gr.Row(): input_text = gr.Textbox(placeholder='Enter the Youtube video URL', label='URL') output_text = gr.Textbox(placeholder='Summary text of the Youtube Video', label='Summary') result_button = gr.Button('Get Summary') result_button.click(get_summary, inputs = input_text, outputs = output_text) result_button_1.click(get_text, inputs = input_text_1, outputs = output_text_1) demo.launch()