VideoToNotes

Generate structured study notes from video transcripts and slide presentations using AI.

Overview

Two pipelines are available depending on your source material:

Video Notes (New-VideoNotes.ps1)

Transforms YouTube videos or local transcripts into comprehensive markdown notes:

1. Transcribe - Download YouTube video and transcribe audio via Azure Speech (optional)
2. Normalize - Convert varied index formats to consistent JSON (LLM)
3. Chunk - Split transcript into ~20KB pieces (Python)
4. Extract - Generate notes per section from relevant chunks (LLM)
5. Assemble - Build final markdown document (deterministic)

Presentation Notes (New-PresentationNotes.ps1)

Transforms a PDF slide deck and video recording into annotated notes:

1. Extract - Render PDF pages as images and extract text per slide (Python)
2. Transcribe - Extract audio from the video and transcribe via Azure Speech
3. Align - Map transcript segments to slides using LLM
4. Annotate - Generate detailed notes per slide using LLM
5. Assemble - Build final markdown with embedded slide images

Quick Start

Video Notes from YouTube

# Using GitHub Models (default)
.\New-VideoNotes.ps1-YouTubeUrl "https://www.youtube.com/watch?v=VIDEO_ID"# Using Azure OpenAI
.\New-VideoNotes.ps1-YouTubeUrl "https://www.youtube.com/watch?v=VIDEO_ID"`-Provider Azure `-AzureEndpoint "https://<name>.cognitiveservices.azure.com/"`-AzureDeployment "gpt-4.1-mini"

Video Notes from Existing Files

.\New-VideoNotes.ps1-Index "staging\video_title\contents.md"`-Transcript "staging\video_title\transcript.srt"

Presentation Notes

# From a local video file
.\New-PresentationNotes.ps1-PdfFile "slides.pdf"-VideoFile "recording.mp4"# From a stream URL
.\New-PresentationNotes.ps1-PdfFile "slides.pdf"`-StreamUrl "https://example.com/manifest.mpd"# From a pre-existing transcript
.\New-PresentationNotes.ps1-PdfFile "slides.pdf"-Transcript "transcript.srt"# Using Azure OpenAI
.\New-PresentationNotes.ps1-PdfFile "slides.pdf"-VideoFile "recording.mp4"`-Provider Azure `-AzureEndpoint "https://<name>.cognitiveservices.azure.com/"`-AzureDeployment "gpt-4.1-mini"

Project Structure

VideoToNotes/
├── New-VideoNotes.ps1 # Entry point for video notes pipeline
├── New-PresentationNotes.ps1 # Entry point for presentation notes pipeline
├── requirements.txt # Python dependencies
├── terraform/ # Azure infrastructure (Terraform)
│ ├── main.tf # Resource definitions
│ ├── variables.tf # Input variables
│ ├── outputs.tf # Output values (endpoints, CLI commands)
│ ├── providers.tf # Provider configuration (azurerm, random)
│ └── terraform.tfvars # Variable values (non-sensitive)
├── staging/
│ └── <video_title>/ # Video-specific working folders
│ ├── contents.json # Extracted chapters (JSON)
│ ├── contents.md # Extracted chapters (Markdown)
│ ├── transcript.srt # Transcribed audio (SRT format)
│ └── debug/ # Intermediate files
├── input/ # Manual input files
├── output/ # Generated notes
└── src/
├── prompts/ # LLM prompt templates
│ ├── normalize.md
│ ├── extract.md
│ ├── merge.md
│ ├── section_extract.md
│ ├── targeted_extract.md
│ ├── slide_align.md
│ └── slide_annotate.md
├── powershell/
│ ├── Split-Transcript.ps1
│ ├── Get-YouTubeContents.ps1
│ ├── Invoke-VideoTranscription.ps1
│ └── Invoke-YouTubeTranscription.ps1
└── python/
├── notes_generator/ # Video notes pipeline
│ ├── main.py
│ ├── llm_client.py # GitHub Models + Azure OpenAI clients
│ ├── generate_contents.py # Fallback TOC generator
│ ├── models.py
│ ├── prompt_loader.py
│ └── stages/
│ ├── normalize.py
│ ├── chunk.py
│ ├── extract_by_section.py
│ ├── merge.py
│ ├── validate.py
│ └── assemble.py
└── presentation_notes/ # Presentation notes pipeline
├── main.py
├── pdf_processor.py
├── models.py
├── prompt_loader.py
└── stages/
├── align.py
├── annotate.py
└── assemble.py
└── tests/

Prerequisites

• Python 3.10+
• PowerShell 7+ (pwsh)
• VS Code with recommended extensions

For YouTube Transcription

Tool	Purpose	Installation
yt-dlp	YouTube audio download	winget install yt-dlp
ffmpeg	Audio conversion	winget install ffmpeg
Azure Speech CLI	Transcription	dotnet tool install -g Microsoft.CognitiveServices.Speech.CLI

For Azure OpenAI Provider

• Azure CLI (az) installed and logged in (az login)
• An Azure AI Services resource (see Terraform Deployment below)

Installation

The PowerShell wrapper handles environment setup automatically on first run.

Manual setup:

python -m venv .venv
.venv\Scripts\Activate.ps1
pip install -r requirements.txt

Or use VS Code tasks: Ctrl+Shift+P → "Tasks: Run Task" → "Setup: Install Dependencies"

Terraform Deployment

The terraform/ directory provisions all required Azure resources:

Resource	Purpose
Resource Group	project-videonotes-tf
Azure AI Services	Speech transcription + OpenAI chat completions
GPT-4.1-mini deployment	LLM for notes extraction pipeline
Storage Account	Audio uploads for batch transcription

Deploy

cd terraform
terraform init
terraform plan
terraform apply

Configure spx After Deployment

After terraform apply, run the commands shown in the spx_config_commands output:

terraform output spx_config_commands
# Run the two spx config commands it prints

Get Azure Provider Arguments

$endpoint= terraform output -raw azure_openai_endpoint
$deployment= terraform output -raw model_deployment_name

Then pass them to either script with -Provider Azure -AzureEndpoint $endpoint -AzureDeployment $deployment.

Usage

New-VideoNotes.ps1 Parameters

Parameter	Required	Default	Description
-YouTubeUrl	Yes*	—	YouTube video URL
-Index	Yes*	—	Path to index/TOC file with timestamps
-Transcript	Yes*	—	Path to SRT transcript file
-Output	No	Auto-generated	Output path for notes
-ExtractModel	No	gpt-4.1-mini	Model for extraction stage
-MergeModel	No	gpt-4.1-mini	Model for merge stage
-Language	No	en-US	Language code for transcription
-KeepIntermediateFiles	No	false	Keep audio files after transcription
-Provider	No	GitHub	LLM provider: GitHub or Azure
-AzureEndpoint	No†	—	Azure OpenAI endpoint URL
-AzureDeployment	No†	—	Azure OpenAI deployment name

*Either -YouTubeUrl OR both -Index and -Transcript are required. †Required when -Provider Azure is specified.

New-PresentationNotes.ps1 Parameters

Parameter	Required	Default	Description
-PdfFile	Yes	—	Path to PDF slide deck
-VideoFile	Yes*	—	Path to local video file
-StreamUrl	Yes*	—	URL to a stream manifest (.mpd, .m3u8)
-Transcript	Yes*	—	Path to pre-existing SRT transcript
-Output	No	Auto-generated	Output path for notes
-Model	No	gpt-4.1-mini	Model for alignment and annotation
-Language	No	en-US	Language code for transcription
-Dpi	No	200	DPI for PDF rendering
-KeepIntermediateFiles	No	false	Keep audio files after transcription
-Provider	No	GitHub	LLM provider: GitHub or Azure
-AzureEndpoint	No†	—	Azure OpenAI endpoint URL
-AzureDeployment	No†	—	Azure OpenAI deployment name

*One of -VideoFile, -StreamUrl, or -Transcript is required. †Required when -Provider Azure is specified.

Input File Formats

Index File (Video Notes)

Various formats are supported — the normalize stage uses an LLM to convert any format to structured JSON.

FreeCodeCamp style:

☁️ Introduction
🎤 (00:00:00) Introduction to AI-900
🎤 (00:08:18) Exam Guide Breakdown

Simple style:

00:00 - Introduction
00:15 - AI models and their knowledge
01:31 - RAG to the rescue

Transcript File

SRT format (produced by Azure Speech CLI or provided externally):

100:00:00,000 --> 00:00:04,500
Hey, this is the introduction to the course.
200:00:04,500 --> 00:00:09,000
We'll cover the fundamentals today.

AI Models and Providers

Both scripts support two LLM providers:

Provider	Auth	When to Use
GitHub (default)	gh CLI token	Local development, no Azure subscription needed
Azure	az CLI token (managed identity-compatible)	Production, cost control, private deployments

Stage	Default Model	Purpose
Normalize	gpt-4.1-mini	Index → structured JSON
Extract	gpt-4.1-mini	Per-section notes generation
Align	gpt-4.1-mini	Slide-to-transcript mapping
Annotate	gpt-4.1-mini	Per-slide notes generation

Pipeline Stages

Video Notes

Stage	Input	Output	Technology
Normalize	Raw index file	Structured JSON	LLM
Chunk	SRT transcript	ZIP of ~20KB chunks	Python
Extract	Index + chunks	Notes per section	LLM
Assemble	Extracted sections	Final markdown	Python

Presentation Notes

Stage	Input	Output	Technology
Extract	PDF file	Slide images + text	Python (pdf2image)
Transcribe	Video/stream	SRT transcript	Azure Speech CLI
Align	Slides + transcript	Slide-transcript mapping	LLM
Annotate	Slide mapping	Notes per slide	LLM
Assemble	Annotated slides	Final markdown	Python

Output Format

### Section Title**Timestamp**: 00:12:51 – 00:13:58
**Key Concepts**- Concept 1
**Definitions**-**Term 1**: Definition text
**Key Facts**- Fact 1
**Examples**- Example 1
**Key Takeaways 🎯**- Tip 1

Debugging

VS Code debug configurations are provided:

Configuration	Description
PowerShell: Transcript Chunker	Debug the chunker with a sample file
PowerShell: Interactive Session	REPL for testing
Python: Current File	Debug any open Python file
Python: Notes Generator (Full Pipeline)	Debug the complete video notes pipeline
Python: Extract/Merge/Assemble Stage Only	Debug individual stages

Press F5 to launch the selected configuration.

Author

Greg Tate