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import yt_dlp
import os
import gradio as gr
from transformers import pipeline
import whisper
def get_audio(url):
try:
# Configure yt-dlp options
ydl_opts = {
'format': 'bestaudio/best', # Choose best quality audio
'postprocessors': [{
'key': 'FFmpegExtractAudio',
'preferredcodec': 'mp3',
'preferredquality': '192',
}],
'outtmpl': 'audio_download.%(ext)s', # Output template
'quiet': True, # Less output
'no_warnings': True # No warnings
}
# Download the audio
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
ydl.download([url])
return 'audio_download.mp3' # Return the filename
except Exception as e:
raise gr.Error(f"Error downloading audio: {str(e)}")
# Load models
model = whisper.load_model("base")
summarizer = pipeline("summarization")
def get_text(url):
try:
audio_file = get_audio(url)
result = model.transcribe(audio_file)
# Cleanup
try:
os.remove(audio_file)
except:
pass
return result['text']
except Exception as e:
return f"Error: {str(e)}"
def get_summary(url):
try:
article = get_text(url)
summary = summarizer(article)
return summary[0]['summary_text']
except Exception as e:
return f"Error: {str(e)}"
# Create Gradio interface
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 and summary.</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) |