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Dictionary Workflow Improvements - Video to Vocabulary Automation

May 5, 2026 6 min read Dictionary Workflow

Proposed improvements for extracting vocabulary from videos and automating dictionary entry creation

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Dictionary Workflow Improvements

📊 Current State Analysis

✅ Strengths

  1. Well-structured data model: YAML format is clean and maintainable
  2. Dual rendering options: Accordion and table views work well
  3. Good separation of concerns: Data (YAML) separate from presentation (Hugo shortcodes)
  4. Urdu support: Font and RTL text handling is solid
  5. Documentation: Comprehensive guides exist

❌ Pain Points

  1. Manual vocabulary extraction: No automated way to extract words from videos
  2. Time-consuming data entry: Each word requires multiple fields to be filled manually
  3. No video metadata tracking: Source video links not tracked in data structure
  4. Missing translation support: No automated English-to-Urdu translation
  5. No difficulty assessment: Can’t automatically identify “difficult” words
  6. No batch processing: Can’t handle multiple videos at once

🎯 Proposed Improvements

1. Enhanced Data Structure

Update YAML schema to include video metadata:

 1# Video metadata (optional, at file level)
 2source_video:
 3  url: "https://youtube.com/watch?v=..."
 4  title: "Video Title"
 5  channel: "Channel Name"
 6  duration: "45:30"
 7  date_watched: "2026-05-05"
 8  transcript_source: "youtube-api" # or "manual", "whisper", etc.
 9
10# Individual vocabulary entries
11vocabulary:
12  - word: retrieval
13    part_of_speech: noun
14    urdu_meaning: بازیافت، واپس لانا
15    example_en: Efficient retrieval of information is crucial for RAG systems.
16    example_ur: RAG سسٹمز کے لیے معلومات کی موثر بازیافت بہت اہم ہے۔
17    additional_example_ur: ڈیٹا بیس سے دستاویزات کی بازیافت تیزی سے ہونی چاہیے۔
18    
19    # Enhanced metadata
20    timestamp: "12:34" # Where word appears in video
21    difficulty_level: 7  # 1-10 scale
22    frequency_in_source: 5  # How many times it appeared
23    context_tags:
24      - technical
25      - rag-specific
26    related_words:
27      - embedding
28      - vectorization

2. Automated Video-to-Vocabulary Workflow

Proposed multi-stage pipeline:

Video URL → Transcript Extraction → Text Processing → Word Analysis → 
Translation → Example Generation → YAML Creation → Hugo Integration

Stage 1: Transcript Extraction

  • Tools: YouTube API, Whisper (for non-YouTube videos)
  • Output: Timestamped transcript text

Stage 2: Text Processing & Word Extraction

  • Tools: NLTK, spaCy, or GPT-4 for NLP
  • Features:
    • Identify uncommon/difficult words (based on frequency lists)
    • Filter out common words (use CEFR levels or word frequency databases)
    • Extract context sentences
    • Part of speech tagging
    • Difficulty scoring

Stage 3: Translation & Example Generation

  • Tools:
    • GPT-4/Claude for Urdu translation
    • Context-aware example generation
  • Features:
    • Translate word meanings to Urdu
    • Translate example sentences to Urdu
    • Generate additional contextual examples
    • Validate Urdu script formatting

Stage 4: YAML File Generation

  • Automated creation of properly formatted YAML
  • Merge strategy for existing vocabulary files
  • Conflict resolution for duplicate words

Stage 5: Hugo Content Page Creation

  • Auto-generate index.md with proper frontmatter
  • Update shortcode references
  • Maintain consistent file structure

🛠️ Implementation Plan

Phase 1: Manual-Assisted Workflow (Quick Win)

Create a Python script that takes:

  • Video URL
  • Manual transcript (copy-paste)
  • Target difficulty level

Produces:

  • Extracted difficult words
  • Basic YAML structure
  • Suggested Urdu translations (via LLM)

User still reviews and edits before committing.

Phase 2: Semi-Automated Workflow

Improvements:

  • Automatic transcript extraction (YouTube API / youtube-transcript-api)
  • LLM-powered word selection and translation
  • Interactive review interface (CLI or web-based)
  • One-command generation

Phase 3: Fully Automated Workflow

Additional features:

  • Batch processing multiple videos
  • Learning from user corrections
  • Custom word frequency lists based on your level
  • Integration with spaced repetition systems
  • Anki deck generation from dictionary data

📝 Recommended Script Structure

New Scripts Directory

scripts/
  dictionary/
    extract_vocabulary.py        # Main orchestrator
    transcript_fetcher.py        # Get video transcripts
    word_analyzer.py             # Identify difficult words
    translator.py                # English to Urdu via LLM
    yaml_generator.py            # Create/update YAML files
    hugo_content_creator.py      # Generate Hugo content pages
    config.yaml                  # Configuration (API keys, thresholds)
    requirements.txt             # Python dependencies

Key Dependencies

 1# For transcript extraction
 2youtube-transcript-api
 3google-api-python-client
 4
 5# For NLP
 6nltk
 7spacy
 8wordfreq
 9
10# For LLM integration
11openai  # or anthropic for Claude
12langchain
13
14# For YAML
15pyyaml
16ruamel.yaml
17
18# For web scraping (if needed)
19beautifulsoup4
20requests

🔧 Proposed CLI Interface

 1# Basic usage
 2python scripts/dictionary/extract_vocabulary.py \
 3  --video-url "https://youtube.com/watch?v=..." \
 4  --topic "rag-course" \
 5  --difficulty-threshold 6 \
 6  --output data/dictionary/rag-course/
 7
 8# Advanced usage with options
 9python scripts/dictionary/extract_vocabulary.py \
10  --video-url "https://youtube.com/watch?v=..." \
11  --topic "rag-course" \
12  --difficulty-threshold 6 \
13  --max-words 50 \
14  --include-phrases \
15  --skip-technical-terms \
16  --review-mode interactive \
17  --create-hugo-page
18
19# Batch processing
20python scripts/dictionary/extract_vocabulary.py \
21  --playlist "https://youtube.com/playlist?list=..." \
22  --topic "full-course" \
23  --batch-mode

💡 LLM Prompt Strategy

For Word Extraction & Difficulty Assessment

You are an English vocabulary tutor for an advanced learner whose first language is Urdu.

Given this transcript from a video, identify 20-30 words that would be:
1. Challenging but learnable for an advanced English student
2. Important for understanding the topic
3. Not commonly used in everyday conversation
4. Worth adding to a vocabulary list

For each word provide:
- The word
- Part of speech
- Difficulty rating (1-10, where 10 is most difficult)
- The sentence from the transcript where it appears
- Why this word is important/useful

Transcript:
[TRANSCRIPT TEXT]

Topic: [TOPIC NAME]

For Urdu Translation

You are a professional English-to-Urdu translator specializing in educational vocabulary.

Translate the following English word and its example sentence to Urdu:

Word: [WORD]
Part of Speech: [POS]
English meaning: [DEFINITION]
Example sentence: [SENTENCE]

Provide:
1. Urdu meaning (with alternate translations if applicable)
2. Urdu translation of the example sentence (natural, contextual)
3. An additional Urdu example sentence showing different usage

Use proper Urdu script and grammar. Be contextual and natural.

📋 Updated Workflow Steps

Old Workflow (Current)

  1. 🎥 Watch video manually
  2. ✍️ Note difficult words by hand
  3. 📖 Look up meanings manually
  4. 🔍 Find/create example sentences
  5. 🌏 Translate to Urdu manually
  6. ⌨️ Type everything into YAML file
  7. 📝 Create Hugo content page
  8. ✅ Test and deploy

Time per video: 2-4 hours

New Workflow (Proposed)

  1. 🎥 Copy video URL
  2. 💻 Run extraction script
  3. ✅ Review AI-generated vocabulary (5-10 minutes)
  4. ✏️ Edit/approve entries
  5. 🚀 Auto-generate YAML + Hugo page
  6. ✅ Test and deploy

Time per video: 15-30 minutes

🎨 Additional Enhancements

1. Interactive Review Interface

  • Web-based dashboard for reviewing extracted vocabulary
  • Accept/reject/edit interface
  • Save preferences for future runs

2. Integration with Anki System

  • Since you already have Anki scripts, create bridge
  • Auto-generate Anki cards from dictionary entries
  • Sync vocabulary learning across platforms

3. Progress Tracking

  • Dashboard showing vocabulary growth over time
  • Words learned per source
  • Difficulty distribution
  • Review patterns

4. Smart Word Selection

  • Learn from your past selections
  • Prioritize words from your field of interest
  • Avoid words you already know (from past entries)
  • Focus on high-value vocabulary

5. Collaborative Features

  • Share vocabulary lists with others
  • Import from others’ collections
  • Community-driven translations
  • Correction suggestions

🚀 Quick Start Implementation

Minimal Viable Product (MVP)

Goal: Get basic automation working today

Scope:

  1. Script to fetch YouTube transcript
  2. LLM call to extract 20 words with meanings
  3. LLM call to translate to Urdu
  4. Generate YAML file
  5. Manual review and edit

Time to implement: 2-3 hours

Immediate value: Reduce manual work by 70%

📖 Recommended Next Steps

  1. Review this proposal and decide which features are most valuable
  2. Set up Python environment with required dependencies
  3. Create MVP script for basic video-to-YAML conversion
  4. Test on one video and iterate
  5. Add features incrementally based on usage
  6. Document the workflow for future reference

🔗 Related Resources

📞 Questions to Consider

  1. Which video platforms do you primarily use? (YouTube, Coursera, Udemy, etc.)
  2. What’s your target difficulty level for vocabulary? (Intermediate, Advanced, Native-level)
  3. How many videos per week do you typically want to process?
  4. Do you prefer CLI or web interface for review?
  5. Should this integrate with your existing Anki workflow?
  6. What LLM service do you have access to? (OpenAI, Anthropic, local models)

Status: Proposal - Ready for Implementation Created: 2026-05-05 Next Action: Discuss priorities and create MVP

Dictionary Workflow Improvements - Implementation Summary