MovieTranscriptor

Creating an agentic AI to extract audio dialogues from movies and store them in a structured dataset 

1. Objective Definition
   Extract individual dialogue clips from movie files and store them along with their corresponding text (transcription) in a structured dataset.

2. Key Requirements

   • Audio Processing: Split the movie into dialogue-based segments.

   • Speech-to-Text Conversion: Transcribe each dialogue to text.

   • Data Structuring: Store extracted audio and text in a table format like the one shown.

   • Automation with an Agentic AI: Build an AI system that uses large language models (LLMs) for automation.

Tools and Libraries

• FFmpeg: For splitting and extracting audio from movies.

• Speech Recognition Models:

  • OpenAI Whisper (Highly accurate for multilingual transcription)

  • Google Speech-to-Text or Hugging Face ASR models

• Python Libraries:

  • ffmpeg-python (for audio splitting)

  • openai (for Whisper API)

  • pandas (for structured data storage)

  • numpy (for processing)

  • torch (if using Whisper locally)

Agentic AI for Automation

To make the process more autonomous:

• Monitor Folder: Automatically detect new movie files.

• Process in Parallel: Split audio and transcribe simultaneously.

• LLM Integration: Use an LLM to verify transcription quality or classify speaker emotions.

Future Improvements

• Speaker Identification: Assign unique speaker IDs using a speaker diarization model.

• Emotion Detection: Add emotion tags for each dialogue.

• Multilingual Support: Whisper handles multiple languages if required.