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import gradio as gr
import os
import subprocess
from transformers import pipeline
from langdetect import detect
def download_audio(youtube_url):
output_file = "audio.mp3"
# Remove old file if exists
if os.path.exists(output_file):
os.remove(output_file)
cmd = [
"yt-dlp", "-x", "--audio-format", "mp3", "-o", output_file, youtube_url
]
subprocess.run(cmd, check=True)
return output_file
def process_youtube(youtube_url):
try:
audio_path = download_audio(youtube_url)
except Exception as e:
return "Error downloading audio: " + str(e), "", "", ""
try:
asr = pipeline("automatic-speech-recognition", model="openai/whisper-large")
result = asr(audio_path)
transcript = result["text"]
except Exception as e:
return "Error in transcription: " + str(e), "", "", ""
try:
detected_lang = detect(transcript)
except Exception:
detected_lang = "unknown"
lang_map = {'en': 'English', 'hi': 'Hindi', 'ta': 'Tamil'}
lang_text = lang_map.get(detected_lang, detected_lang)
transcript_en = transcript
if detected_lang != "en":
try:
asr_translate = pipeline(
"automatic-speech-recognition",
model="openai/whisper-large",
task="translate"
)
result_translate = asr_translate(audio_path)
transcript_en = result_translate["text"]
except Exception as e:
transcript_en = f"Error translating: {e}"
try:
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
summary = summarizer(transcript_en, max_length=100, min_length=30, do_sample=False)
summary_text = summary[0]["summary_text"]
except Exception as e:
summary_text = f"Error summarizing: {e}"
if os.path.exists(audio_path):
os.remove(audio_path)
return lang_text, transcript, transcript_en, summary_text
with gr.Blocks() as demo:
gr.Markdown("## YouTube Transcript, Translation & Summary (Powered by Whisper + Hugging Face)")
url_input = gr.Textbox(label="YouTube URL")
btn = gr.Button("Process")
lang_out = gr.Textbox(label="Detected Language")
transcript_out = gr.Textbox(label="Original Transcript")
transcript_en_out = gr.Textbox(label="English Transcript (if translated)")
summary_out = gr.Textbox(label="Summary")
btn.click(
process_youtube,
inputs=[url_input],
outputs=[lang_out, transcript_out, transcript_en_out, summary_out]
)
demo.launch()
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