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import whisper
from pytube import YouTube
from transformers import pipeline
import gradio as gr
import os
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) |