Audio Processing

Summary

This post demonstrates how to automatically transform a scientific paper (or any text/audio content) into a YouTube video using AI. We’ll leverage several powerful tools, including large language models (LLMs), Whisper for transcription, Stable Diffusion for image generation, and FFmpeg for video assembly. This process can streamline content creation and make research more accessible.

Overview

Our pipeline involves these steps:

Audio Generation (Optional): If starting from a text document, we’ll use a text-to-speech service (like NotebookLM, or others) to create an audio narration.
Transcription: We’ll use Whisper to transcribe the audio into text, including timestamps for each segment.
Database Storage: The transcribed text, timestamps, and metadata will be stored in an SQLite database for easy management.
Text Chunking: We’ll divide the transcript into logical chunks (e.g., by sentence or time duration).
Concept Summarization: An LLM will summarize the core concept of each chunk.
Image Prompt Generation: Another LLM will create a detailed image prompt based on the summary.
Image Generation: Stable Diffusion (or a similar tool) will generate images from the prompts.
Video Assembly: FFmpeg will combine the images and audio into a final video.

Prerequisites

Hugging Face CLI: Install it to download the Whisper model: pip install huggingface_hub
Whisper: Install the whisper-timestamped package, or your preferred Whisper implementation.
Ollama: You’ll need a running instance of Ollama to access the LLMs.
Stable Diffusion WebUI (or similar): For image generation.
FFmpeg: For video and audio processing. Ensure it’s in your system’s PATH.
Python Libraries: Install necessary Python packages: pip install pydub sqlite3 requests Pillow (and any others as needed).

1. Audio Generation (Optional)

If you’re starting with a text document, you’ll need to convert it to audio. Several cloud services and libraries can do this. For this example, we’ll assume you have an audio file (audio.wav).