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import whisper
from pytube import YouTube
from transformers import pipeline
import gradio as gr
import os
import re
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(article):
first_sentences = ' '.join(re.split(r'(?<=[.:;])\s', article)[:5])
b = summarizer(first_sentences, min_length=20, max_length=30, do_sample=False)
b = b[0]['summary_text']
return b
with gr.Blocks() as demo:
gr.Markdown("<h1><center>Free YouTube URL Video to Text using OpenAI's Whisper Model</center></h1>")
gr.Markdown("<center>Enter the link of any YouTube video to generate a text transcript of the video and then create a summary of the video transcript.</center>")
input_text_url = gr.Textbox(placeholder='Youtube video URL', label='URL')
result_button_transcribe = gr.Button('1. Transcribe')
output_text_transcribe = gr.Textbox(placeholder='Transcript of the YouTube video.', label='Transcript')
result_button_summary = gr.Button('2. Create Summary')
output_text_summary = gr.Textbox(placeholder='Summary of the YouTube video transcript.', label='Summary')
result_button_transcribe.click(get_text, inputs=input_text_url, outputs=output_text_transcribe)
result_button_summary.click(get_summary, inputs=output_text_transcribe, outputs=output_text_summary)
demo.launch(debug = True) |