|
|
import gradio as gr |
|
|
from pytube import YouTube |
|
|
import whisper |
|
|
from transformers import pipeline |
|
|
from bark import generate_audio, preload_models |
|
|
from moviepy import VideoFileClip |
|
|
import os |
|
|
import tempfile |
|
|
|
|
|
|
|
|
asr_model = whisper.load_model("base") |
|
|
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-hi") |
|
|
preload_models() |
|
|
|
|
|
def process_video(link): |
|
|
try: |
|
|
|
|
|
yt = YouTube(link) |
|
|
audio_stream = yt.streams.filter(only_audio=True).first() |
|
|
temp_dir = tempfile.mkdtemp() |
|
|
audio_path = os.path.join(temp_dir, "audio.mp4") |
|
|
audio_stream.download(filename=audio_path) |
|
|
|
|
|
|
|
|
result = asr_model.transcribe(audio_path) |
|
|
english_text = result["text"] |
|
|
|
|
|
|
|
|
hindi_text = translator(english_text)[0]['translation_text'] |
|
|
|
|
|
|
|
|
hindi_audio = generate_audio(hindi_text) |
|
|
hindi_audio_path = os.path.join(temp_dir, "hindi.wav") |
|
|
with open(hindi_audio_path, "wb") as f: |
|
|
f.write(hindi_audio) |
|
|
|
|
|
|
|
|
audio_clip = mp.AudioFileClip(hindi_audio_path) |
|
|
video_clip = mp.ColorClip(size=(1280, 720), color=(0, 0, 0), duration=audio_clip.duration) |
|
|
final_video = video_clip.set_audio(audio_clip) |
|
|
output_path = os.path.join(temp_dir, "final_output.mp4") |
|
|
final_video.write_videofile(output_path, fps=24) |
|
|
|
|
|
return hindi_text, output_path |
|
|
|
|
|
except Exception as e: |
|
|
return f"Error: {str(e)}", None |
|
|
|
|
|
gr.Interface( |
|
|
fn=process_video, |
|
|
inputs=gr.Textbox(label="YouTube Video Link"), |
|
|
outputs=[ |
|
|
gr.Textbox(label="Hindi Translation"), |
|
|
gr.Video(label="Dubbed Hindi Video") |
|
|
], |
|
|
title="🎙️ YouTube Hindi Dubber AI", |
|
|
description="Paste a YouTube video link. This app transcribes it, translates it to Hindi, and dubs it using Bark AI." |
|
|
).launch() |
