diff --git a/yt_scribe.py b/yt_scribe.py
new file mode 100644
index 0000000..4ceb41b
--- /dev/null
+++ b/yt_scribe.py
@@ -0,0 +1,120 @@
+import os
+import whisper
+import torch
+from yt_dlp import YoutubeDL
+import warnings
+import argparse
+import json
+
+# Suppress PyTorch FutureWarning
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+# Function to download audio and get video metadata
+def download_audio(youtube_url, output_file="audio.mp3"):
+    """Download audio from a YouTube video and return metadata."""
+    ydl_opts = {
+        'format': 'bestaudio/best',
+        'extractaudio': True,
+        'audioformat': 'mp3',
+        'outtmpl': output_file,
+        'quiet': False
+    }
+    try:
+        with YoutubeDL(ydl_opts) as ydl:
+            info = ydl.extract_info(youtube_url, download=True)
+            metadata = {
+                "title": info.get('title', 'transcription').replace(" ", "_").replace("/", "_"),
+                "channel": info.get('channel', 'Unknown Channel'),
+                "publish_date": info.get('upload_date', 'Unknown Date'),
+            }
+        print(f"Audio downloaded successfully: {output_file}")
+        return output_file, metadata
+    except Exception as e:
+        print(f"Error downloading audio: {e}")
+        return None, None
+
+# Function to transcribe audio using Whisper with GPU support
+def transcribe_audio(audio_file, output_text_file, model_size="base", language=None):
+    """Transcribe audio using Whisper with GPU support."""
+    try:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        print(f"Using device: {device.upper()}")
+
+        model = whisper.load_model(model_size).to(device)
+        print(f"Transcribing audio using model: {model_size}...")
+
+        result = model.transcribe(audio_file, language=language)
+        detected_language = result['language'] if language is None else language
+        with open(output_text_file, "w", encoding="utf-8") as file:
+            file.write(result['text'])
+
+        print(f"Transcription saved to: {output_text_file}")
+        return result['text'], detected_language
+    except Exception as e:
+        print(f"Error transcribing audio: {e}")
+        return None, None
+
+# Main function
+def main():
+    parser = argparse.ArgumentParser(
+        description="YouTube Audio Transcription Script",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument("-u", "--urls", help="Comma-separated YouTube URLs or a file path", required=True)
+    parser.add_argument("-o", "--output_dir", help="Output directory", default=".")
+    parser.add_argument("-m", "--model_size", help="Whisper model size (tiny, base, small, medium, large)", default="base")
+    parser.add_argument("-l", "--language", help="Language code (e.g., 'en' for English, 'es' for Spanish)", default=None)
+    args = parser.parse_args()
+
+    if not args.urls:
+        print("Error: No URLs or file path provided. Use --help for usage information.")
+        return
+
+    if os.path.isfile(args.urls):
+        try:
+            with open(args.urls, "r", encoding="utf-8") as file:
+                youtube_urls = [line.strip() for line in file.readlines()]
+        except FileNotFoundError:
+            print("File not found. Exiting.")
+            return
+    else:
+        youtube_urls = [url.strip() for url in args.urls.split(",")]
+
+    # Process each YouTube URL
+    for youtube_url in youtube_urls:
+        print(f"\nProcessing: {youtube_url}")
+
+        # Step 1: Download audio
+        output_audio = "audio.mp3"
+        audio_file, metadata = download_audio(youtube_url, output_file=output_audio)
+        if not audio_file or not metadata:
+            print("Failed to download audio. Skipping.")
+            continue
+
+        # Set file paths
+        video_title = metadata['title']
+        output_text_file = os.path.join(args.output_dir, f"{video_title}_transcription.txt")
+        output_metadata_file = os.path.join(args.output_dir, f"{video_title}_metadata.json")
+
+        # Step 2: Transcribe audio
+        transcription, detected_language = transcribe_audio(audio_file, output_text_file, model_size=args.model_size, language=args.language)
+        if not transcription:
+            print("Failed to transcribe audio. Skipping.")
+            continue
+
+        # Save metadata
+        metadata['detected_language'] = detected_language
+        with open(output_metadata_file, "w", encoding="utf-8") as file:
+            json.dump(metadata, file, indent=4)
+        print(f"Metadata saved to: {output_metadata_file}")
+
+        # Step 3: Clean up
+        if os.path.exists(output_audio):
+            os.remove(output_audio)
+            print(f"Temporary audio file removed: {output_audio}")
+
+    print("\nProcess completed successfully.")
+
+if __name__ == "__main__":
+    main()
+