| 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() |
