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App.py
import gradio as gr
from pytube import YouTube
import whisper
from transformers import pipeline
from bark import generate_audio, preload_models
import os
import moviepy.editor as mp
# Load models
asr_model = whisper.load_model("base")
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-hi")
# Preload Bark models
preload_models()
def process_video(link):
try:
yt = YouTube(link)
stream = yt.streams.filter(only_audio=True).first()
file_path = stream.download(filename="audio.mp4")
# Step 2: Transcribe English
result = asr_model.transcribe(file_path)
english_text = result["text"]
# Step 3: Translate to Hindi
hindi_text = translator(english_text)[0]['translation_text']
# Step 4: Generate Hindi Audio using Bark
hindi_audio = generate_audio(hindi_text)
with open("hindi.wav", "wb") as f:
f.write(hindi_audio)
# Step 5: Merge audio with video (optional)
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