| 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("<h1><center>Youtube video transcription with OpenAI's Whisper</center></h1>") | |
| gr.Markdown("<center>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.</center>") | |
| 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(debug=True) |