app.py

import gradio as gr
import torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration
import requests
from bs4 import BeautifulSoup
import tempfile
import os
import soundfile as sf
from spellchecker import SpellChecker

# Check if CUDA is available and set the device
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")

# Load the Whisper model and processor
model_name = "openai/whisper-small"
processor = WhisperProcessor.from_pretrained(model_name)
model = WhisperForConditionalGeneration.from_pretrained(model_name).to(device)

spell = SpellChecker()

def download_audio_from_url(url):
    try:
        if "share" in url:
            print("Processing shareable link...")
            response = requests.get(url)
            soup = BeautifulSoup(response.content, 'html.parser')
            video_tag = soup.find('video')
            if video_tag and 'src' in video_tag.attrs:
                video_url = video_tag['src']
                print(f"Extracted video URL: {video_url}")
            else:
                raise ValueError("Direct video URL not found in the shareable link.")
        else:
            video_url = url
        
        print(f"Downloading video from URL: {video_url}")
        response = requests.get(video_url)
        audio_bytes = response.content
        print(f"Successfully downloaded {len(audio_bytes)} bytes of data")
        return audio_bytes
    except Exception as e:
        print(f"Error in download_audio_from_url: {str(e)}")
        raise

def correct_spelling(text):
    words = text.split()
    corrected_words = [spell.correction(word) or word for word in words]
    return ' '.join(corrected_words)

def format_transcript(transcript):
    sentences = transcript.split('.')
    formatted_transcript = []
    current_speaker = None
    for sentence in sentences:
        if ':' in sentence:
            speaker, content = sentence.split(':', 1)
            if speaker != current_speaker:
                formatted_transcript.append(f"\n\n{speaker.strip()}:{content.strip()}.")
                current_speaker = speaker
            else:
                formatted_transcript.append(f"{content.strip()}.")
        else:
            formatted_transcript.append(sentence.strip() + '.')
    return ' '.join(formatted_transcript)

def transcribe_audio(audio_file):
    try:
        # Load and preprocess the audio
        audio_input, sample_rate = sf.read(audio_file)
        input_features = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_features.to(device)

        # Generate token ids
        predicted_ids = model.generate(input_features)

        # Decode token ids to text
        transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)

        return transcription[0]
    except Exception as e:
        print(f"Error in transcribe_audio: {str(e)}")
        raise

def transcribe_video(url):
    try:
        print(f"Attempting to download audio from URL: {url}")
        audio_bytes = download_audio_from_url(url)
        print(f"Successfully downloaded {len(audio_bytes)} bytes of audio data")
        
        # Save audio bytes to a temporary file
        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio:
            temp_audio.write(audio_bytes)
            temp_audio_path = temp_audio.name

        print("Starting audio transcription...")
        transcript = transcribe_audio(temp_audio_path)
        print("Transcription completed successfully")
        
        # Clean up the temporary file
        os.unlink(temp_audio_path)

        # Apply spelling correction and formatting
        transcript = correct_spelling(transcript)
        transcript = format_transcript(transcript)

        return transcript
    except Exception as e:
        error_message = f"An error occurred: {str(e)}"
        print(error_message)
        return error_message

def download_transcript(transcript):
    with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.txt') as temp_file:
        temp_file.write(transcript)
        temp_file_path = temp_file.name
    return temp_file_path

# Create the Gradio interface
with gr.Blocks(title="Video Transcription") as demo:
    gr.Markdown("# Video Transcription")
    video_url = gr.Textbox(label="Video URL")
    transcribe_button = gr.Button("Transcribe")
    transcript_output = gr.Textbox(label="Transcript", lines=20)
    download_button = gr.Button("Download Transcript")
    download_link = gr.File(label="Download Transcript")
    
    transcribe_button.click(fn=transcribe_video, inputs=video_url, outputs=transcript_output)
    download_button.click(fn=download_transcript, inputs=transcript_output, outputs=download_link)

demo.launch()