|
|
| import gradio as gr |
|
|
| def dub_video(video_url): |
| |
| |
| |
| return "Processed video path will be returned here (replace with actual function call)" |
|
|
| demo = gr.Interface( |
| fn=dub_video, |
| inputs=gr.Textbox(label="Enter video URL"), |
| outputs=gr.Video(label="Hindi Dubbed Video"), |
| title="Video Dubbing AI (Hindi)", |
| description="Enter a video URL to get it dubbed in Hindi." |
| ) |
|
|
| demo.launch() |
|
|
| from pytube import YouTube |
| from moviepy.editor import VideoFileClip |
| from transformers import WhisperProcessor, WhisperForConditionalGeneration |
| import librosa |
|
|
| |
| video_url = "https://www.youtube.com/watch?v=YOUR_VIDEO_ID" |
| yt = YouTube(video_url) |
| stream = yt.streams.filter(only_audio=True).first() |
| stream.download(filename="video_audio.mp4") |
|
|
| |
| video = VideoFileClip("video_audio.mp4") |
| audio = video.audio |
| audio.write_audiofile("output_audio.wav") |
|
|
| |
| processor = WhisperProcessor.from_pretrained("openai/whisper-small") |
| model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") |
| audio, sr = librosa.load("output_audio.wav", sr=16000) |
| input_features = processor(audio, sampling_rate=sr, return_tensors="pt").input_features |
| predicted_ids = model.generate(input_features) |
| transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] |
| print(transcription) |
|
|
| def translate_long_text(text): |
| chunks = [text[i:i+400] for i in range(0, len(text), 400)] |
| translated_chunks = [] |
| |
| for chunk in chunks: |
| translated = translator(chunk, max_length=512)[0]['translation_text'] |
| translated_chunks.append(translated) |
| |
| return " ".join(translated_chunks) |
|
|
| long_english_text = "Your long English text here..." |
| hindi_translation = translate_long_text(long_english_text) |
|
|
|
|
