app.py

import gradio as gr
from moviepy.editor import VideoFileClip
from transformers import pipeline
import os

# Initialize the Whisper model
whisper_model = pipeline("automatic-speech-recognition", model="openai/whisper-large")

def convert_video_to_wav(video_path):
    # Extract audio from video using moviepy and save as WAV
    video_clip = VideoFileClip(video_path)
    audio = video_clip.audio
    wav_file = "temp_audio.wav"
    audio.write_audiofile(wav_file, codec='pcm_s16le')  # Write as WAV format
    return wav_file

def convert_audio_to_srt(wav_file):
    # Transcribe the audio using the Whisper model
    transcription = whisper_model(wav_file)
    
    # Save the transcription to an SRT file with simple formatting
    srt_file = "transcription.srt"
    with open(srt_file, "w", encoding="utf-8") as f:
        for i, segment in enumerate(transcription['text'].split('.')):
            f.write(f"{i+1}\n")  # Subtitle index
            f.write(f"00:00:{i*2:02d},000 --> 00:00:{i*2+2:02d},000\n")  # Timestamp (basic)
            f.write(f"{segment.strip()}\n\n")  # Transcription text
    
    # Clean up temp audio file
    os.remove(wav_file)
    
    return srt_file

def process_video(video):
    # Save the uploaded video file to a temporary location
    video_path = video.name
    
    # Process the video to extract audio and convert to srt
    wav_file = convert_video_to_wav(video_path)  # Convert video to WAV
    srt_file = convert_audio_to_srt(wav_file)  # Convert WAV to SRT
    
    return srt_file  # Return the path of the generated SRT file

# Gradio Interface
interface = gr.Interface(
    fn=process_video, 
    inputs=gr.File(label="Upload video file", file_types=['mp4', 'avi', 'mkv']),  # Video file input
    outputs=gr.File(label="Download SRT File"),  # Output the SRT file for download
    title="Video to SRT Subtitle Generator",
    description="Upload a video file (e.g., .mp4), and the app will generate a subtitle file (SRT format) using Whisper model."
)

interface.launch()