Audio Script Writer

Overview

Content transformation tool that converts written medical and scientific materials into professionally structured audio scripts suitable for podcasts, educational videos, audiobooks, and voiceover narration.

Key Capabilities:

• Format Conversion: Research papers → podcast scripts
• Spoken Word Optimization: Sentence restructuring for listening
• Pronunciation Guides: Medical terminology phonetic spelling
• Timing Estimation: Duration calculations for production planning
• Multi-Format Output: Podcast, video, lecture, audiobook templates
• Voice Direction: Tone, pace, and emphasis cues for narrators

When to Use

✅ Use this skill when:

• Creating medical education podcasts from journal articles
• Converting conference presentations to video scripts
• Developing audiobook versions of medical textbooks
• Scripting patient education audio materials
• Producing research summary videos for social media
• Adapting written case reports for audio case studies
• Creating voiceover scripts for e-learning modules

❌ Do NOT use when:

• Live presentation without script → Use improvisation
• Highly visual content (surgery videos) → Use visual-focused tools
• Interactive audio (Q&A format) → Use dialogue scripting tools
• Music or sound design planning → Use audio production software
• Voice recording itself → This creates scripts, not audio

Integration:

• Upstream: abstract-summarizer (content condensation), lay-summary-gen (patient-friendly language)
• Downstream: medical-translation (multi-language scripts), voice-cloning-tool (AI narration)

Core Capabilities

1. Spoken Word Transformation

Convert written text to conversational audio style:

from scripts.audio_writer import AudioScriptWriter

writer = AudioScriptWriter()

# Transform written content
script = writer.convert_to_audio(
    source_text=research_paper,
    format="podcast",  # podcast, video, lecture, audiobook
    target_audience="medical_students",
    duration_minutes=15
)

print(script.spoken_text)
# Converts: "The pathophysiology of diabetes mellitus involves..."
# To: "So what exactly happens in diabetes? Well, it all starts when..."

Transformation Rules:

2. Pronunciation Guide Generation

Create phonetic spelling for medical terms:

# Generate pronunciation guide
pronunciation = writer.create_pronunciation_guide(
    text=script,
    include_phonetic=True,
    include_syllables=True
)

# Output:
# "Hyperlipidemia: hi-per-lip-i-DEE-mee-uh"
# "Metformin: met-FOR-min"
# "Atherosclerosis: ath-er-oh-skleh-ROH-sis"

Guide Elements:

• Phonetic Spelling: IPA or simplified phonetics
• Syllable Breaks: hy-per-ten-sion
• Emphasis Marking: Primary stress (CAPS), secondary stress
• Alternative Pronunciations: Regional variations (UK vs US)
• Sound-Alikes: "rhymes with..." for difficult terms

3. Timing and Pacing

Calculate speaking duration and mark pacing cues:

# Analyze timing
timing = writer.calculate_timing(
    script=script,
    speaking_rate="conversational",  # slow, conversational, fast
    include_pauses=True
)

print(f"Estimated duration: {timing.duration_minutes} minutes")
print(f"Word count: {timing.word_count}")
print(f"Pace: {timing.words_per_minute} WPM")

Speaking Rates:

Pacing Cues:

[BREATHE] - Brief pause for narrator
[PAUSE 2s] - Two-second pause for emphasis
[SLOW DOWN] - Reduce pace for key point
[SPEED UP] - Increase energy/excitement
[BEAT] - Dramatic pause

4. Multi-Format Templates

Generate scripts for different audio formats:

# Podcast episode
podcast = writer.create_podcast_script(
    content=article,
    episode_format="interview",  # solo, interview, panel
    include_intro_music=True,
    ad_breaks=[5, 12]  # minutes
)

# Educational video
video = writer.create_video_script(
    content=lecture_slides,
    visual_cues=True,  # Mark where visuals change
    b_roll_notes=True  # Suggest supplemental footage
)

Format Types:

Common Patterns

Pattern 1: Research Paper to Podcast

Scenario: Convert published study to 15-minute podcast episode.

# Convert paper to podcast script
python scripts/main.py \
  --input paper.pdf \
  --format podcast \
  --duration 15 \
  --style conversational \
  --include-intro-outro \
  --output podcast_script.txt

# Generate pronunciation guide
python scripts/main.py \
  --input podcast_script.txt \
  --generate-pronunciation \
  --output pronunciation_guide.txt

Structure:

[INTRO MUSIC 5s]

HOST: Welcome to Medical Research Today. I'm your host...

[BREATHE]

HOST: Today we're diving into a fascinating study about...

[PAUSE]

HOST: So what did the researchers find? Well...

[BREATHE]

HOST: Dr. Smith, one of the study authors, explains...

[SOUND BITE: Interview clip]

...

[OUTRO MUSIC]

Pattern 2: Medical Lecture Recording

Scenario: Convert lecture notes to video script for online course.

# Create lecture script
lecture = writer.create_lecture_script(
    notes=lecture_content,
    duration=45,  # minutes
    break_intervals=[15, 30],  # minutes for student breaks
    interaction_points=True  # "Pause and think..." prompts
)

# Add visual cues
script = writer.add_visual_cues(
    script=lecture,
    slide_transitions=True,
    animation_notes=True
)

Lecture Elements:

• Learning objectives at start
• Periodic comprehension checks
• Break reminders
• Transition phrases between topics
• Summary and key takeaways

Pattern 3: Patient Education Audio

Scenario: Create audio guide for diabetes management.

# Patient-friendly script
patient_script = writer.create_patient_script(
    medical_content=diabetes_guide,
    reading_level=6,  # 6th grade
    empathetic_tone=True,
    key_points_highlighted=True
)

# Slow, clear pacing
patient_script.adjust_pacing(
    wpm=130,
    pause_after_sentences=1.5  # seconds
)

Patient Script Features:

• Simple language (avoid medical jargon)
• Empathetic tone
• Clear action steps
• Reassuring statements
• Repetition of key points

Pattern 4: Conference Presentation to Video

Scenario: Adapt live presentation to YouTube video format.

# Convert presentation script
python scripts/main.py \
  --input presentation_transcript.txt \
  --format video \
  --platform youtube \
  --include-hooks true \
  --engagement-cues true \
  --output youtube_script.txt

YouTube Optimization:

• Hook in first 30 seconds
• Engagement questions for comments
• Call to action (subscribe, like)
• Timestamp markers for chapters
• B-roll suggestions for visual interest

Complete Workflow Example

From research paper to published podcast:

# Step 1: Extract and summarize content
python scripts/main.py \
  --input paper.pdf \
  --extract-key-points \
  --output key_points.txt

# Step 2: Convert to audio script
python scripts/main.py \
  --input key_points.txt \
  --format podcast \
  --duration 20 \
  --output raw_script.txt

# Step 3: Add production elements
python scripts/main.py \
  --input raw_script.txt \
  --add-music-cues \
  --add-sound-effects \
  --add-pacing-marks \
  --output production_script.txt

# Step 4: Generate pronunciation guide
python scripts/main.py \
  --input production_script.txt \
  --generate-pronunciation \
  --output pronunciations.txt

# Step 5: Create timing breakdown
python scripts/main.py \
  --input production_script.txt \
  --calculate-timing \
  --output timing_breakdown.txt

Python API:

from scripts.audio_writer import AudioScriptWriter
from scripts.pronunciation import PronunciationGuide
from scripts.timing import TimingCalculator

# Initialize
writer = AudioScriptWriter()
pronouncer = PronunciationGuide()
timing = TimingCalculator()

# Read source material
with open("research_article.txt", "r") as f:
    content = f.read()

# Step 1: Convert to spoken format
script = writer.convert_to_audio(
    text=content,
    format="podcast",
    target_duration=15,  # minutes
    audience="general_medical"
)

# Step 2: Add production elements
script_with_cues = writer.add_production_cues(
    script=script,
    music_stings=True,
    transition_effects=True
)

# Step 3: Generate pronunciation guide
medical_terms = pronouncer.extract_terms(script_with_cues)
pronunciation_guide = pronouncer.create_guide(medical_terms)

# Step 4: Calculate timing
timing_analysis = timing.calculate(
    script=script_with_cues,
    speaking_rate=150  # WPM
)

# Export complete production package
writer.export_production_package(
    script=script_with_cues,
    pronunciation=pronunciation_guide,
    timing=timing_analysis,
    output_dir="podcast_production/"
)

Quality Checklist

Content Quality:

• Written content accurate and current
• Sources cited (even if not spoken)
• Medical facts verified by expert
• Appropriate for target audience level
• No confidential patient information

Audio Optimization:

• Sentences 15-20 words maximum
• Abbreviations expanded on first use
• Complex terms have pronunciation guides
• Active voice preferred over passive
• Transitions smooth and conversational

Production Quality:

• Timing realistic for content density
• Pacing cues appropriate for subject
• Music/sound cues marked clearly
• Pronunciation guide comprehensive
• Script formatted for easy reading

Before Recording:

• CRITICAL: Script read aloud for flow
• Difficult pronunciations practiced
• Timing tested with stopwatch
• Technical terms confirmed with subject expert
• Copyright cleared for any quoted material

Common Pitfalls

Content Issues:

• ❌ Too dense → Information overload for listeners

  • ✅ Break complex topics into multiple episodes

• ❌ Visual dependencies → "As shown in Figure 3..."

  • ✅ Describe visuals or omit visual-dependent content

• ❌ Citation overload → Every sentence has reference

  • ✅ Save citations for show notes, not narration

Audio Issues:

• ❌ Written-style language → "Furthermore, the aforementioned..."

  • ✅ Conversational: "Plus, this thing we talked about..."

• ❌ No pauses → Relentless information delivery

  • ✅ Build in breathing room; let points sink in

• ❌ Ignoring pronunciation → Mispronounced medical terms

  • ✅ Research and practice all technical terms

Production Issues:

• ❌ Underestimating time → 10 minutes of script takes 12+ to record

  • ✅ Add 20% buffer for retakes and natural pacing

• ❌ Complex sentence structures → Tongue twisters for narrator

  • ✅ Short sentences; avoid nested clauses

References

Available in references/ directory:

• audio_writing_best_practices.md - Broadcast writing guidelines
• medical_pronunciation_guide.md - Common terms phonetics
• podcast_production_standards.md - Industry format standards
• accessibility_guidelines.md - Inclusive audio content
• platform_requirements.md - YouTube, Spotify, Apple specs
• voice_care_tips.md - Narrator health and performance

Scripts

Located in scripts/ directory:

• main.py - CLI interface for script conversion
• audio_writer.py - Core text-to-audio transformation
• pronunciation.py - Medical terminology phonetics
• timing.py - Duration calculation and pacing
• format_templates.py - Podcast, video, lecture templates
• voice_direction.py - Narrator cues and direction
• accessibility.py - Alternative format generation

Limitations

• Voice Performance: Script is text only; actual delivery varies by narrator
• Accent Variations: Pronunciation guides may not match all dialects
• Cultural Context: Humor and references may not translate across cultures
• Copyright: Cannot use copyrighted material without permission
• Technical Accuracy: Does not verify medical content (input-dependent)
• Live Elements: Cannot script unscripted interviews or Q&A

Parameters

Usage

Basic Usage

# Convert from file
python scripts/main.py --input article.txt --duration 5 --output script.json

# Direct text input
python scripts/main.py --text "Medical research findings..." --duration 3

# From stdin
cat article.txt | python scripts/main.py --duration 5 --style conversational

# With specific style and pace
python scripts/main.py --input paper.txt --style educational --pace slow

Risk Assessment

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access
• Output does not expose sensitive information
• Prompt injection protections in place
• Input validation for file paths
• Output directory restricted to workspace
• Script execution in sandboxed environment

Prerequisites

# Python 3.7+
# No additional packages required (uses standard library)

Evaluation Criteria

Success Metrics

• Successfully converts text to audio-optimized script
• Expands abbreviations and converts numbers to words
• Calculates estimated duration based on word count
• Applies style-specific formatting
• Provides pronunciation notes for medical terms

Test Cases

1. Basic Conversion: Convert text file → Returns audio script with metadata
2. Abbreviation Handling: Text with "e.g., i.e., etc." → All expanded in output
3. Number Conversion: Input with "1 in 4" → Output with "one in four"

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Add support for custom abbreviation dictionaries
  • Integrate with text-to-speech engines
  • Add multilingual support

🎙️ Pro Tip: The best audio scripts sound natural when spoken. Always read your script aloud before finalizing—if you stumble over a sentence, your narrator will too. Revise for the ear, not the eye.