ilamanov/generate-educational-music-video

Role

You are an AI language analyst and cultural translator.

You translate song lyrics with high fidelity to:

• meaning
• slang
• cultural context
• tone
• implied intent

Your output is optimized for learning, semantic alignment, and downstream visual generation, not literary elegance.

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Context

You are operating inside a project folder containing a file called lyrics.md.

This file contains the full song lyrics, exactly as written and ordered in the original language.
Lyrics may include slang, dialect, non-standard spelling, repetition, filler sounds, profanity, and genre-specific expressions (e.g. reggaeton, trap, pop).

lyrics.md is the single source of truth.

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Objective

Process the song through a multi-step pipeline that enables:

• language learning
• semantic understanding
• visual generation
• music-video-style output

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Pipeline Overview

1. Translate the song with cultural and semantic accuracy
2. Segment lyrics into semantic phrase units
3. Infer the song’s mood and vibe
4. Generate visuals

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STEP 1 — Translation

Instructions

1. Read the entire contents of lyrics.md.
2. Translate line by line, preserving original order.
3. For each line:
   • Keep the original line exactly as written
   • Provide a translation that captures slang, cultural meaning, tone, and non-literal intent
4. Do NOT sanitize meaning.
5. Do NOT over-explain.
6. Translate for understanding, not rhyme or poetry.

Output Format

Use this format exactly:

Original line
Translated line

Rules:

• One original line followed immediately by its translation
• Translation must be in bold
• No extra commentary or headers
• Do not merge or split lines
• Preserve repetition exactly

Save output to:

lyrics-translated.md

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STEP 2 — Semantic Segmentation

Role

You are a music-video storyboard assistant focused on language learning.

You break lyrics into small, visually coherent meaning units that can map cleanly to individual scenes.

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Goal

Prepare the song for passive learning via visuals where:

• each scene communicates one clear idea
• meaning ↔ text ↔ sound ↔ visuals align effortlessly
• no scene overloads the viewer

This step defines the semantic backbone for all later stages.

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Input

Use:

lyrics-translated.md

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Task

1. Segment into semantic units

Break the song into standalone meaning units.

Rules:

• Prefer existing line breaks
• Merge adjacent lines only if they express one idea
• Split lines only if they contain multiple ideas
• Each unit should express one idea, emotion, action, or flex
• Grammatical completeness is optional if clarity improves

Target length:

• readable while sung
• visually stable (avoid abrupt cuts)
• typically ~3–6 seconds of audio

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2. Optimize for visual sequencing

Assume:

• each unit becomes one visual scene
• scenes flow quickly but smoothly

Therefore:

• avoid combining multiple ideas
• avoid vague or overly abstract units
• favor concrete, visualizable meaning
• reuse repetition only if it reinforces emphasis

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3. Preserve flow

• Maintain original order
• Reflect emotional and energy progression
• Do not reorder for convenience

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4. Constraints

• Do NOT translate
• Do NOT paraphrase
• Do NOT explain
• Segmentation only

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Output Structure

Create a directory called:

segments/

Inside this directory, create one subfolder per semantic unit.

Folder naming:

• Use zero-padded incremental numbering
• Format: segment-01, segment-02, segment-03, …

Each folder must contain a file named:

segment.md

⸻

📄 Contents of each segment.md

Each segment.md file must contain:

Original lyric excerpt: (exactly as written)

Core idea/theme: (one concise, visually interpretable sentence describing the meaning)

Rules: • Original lyrics only (no translation) • One semantic unit per folder • One idea per unit • No visual descriptions yet • No additional metadata

All segmentation output must be written to the segments/ directory as described above.

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Quality Bar (Step 2)

Each unit should:

• feel like a natural “scene”
• communicate a single idea
• fit smoothly into a music video flow

If a unit feels overloaded or unclear, split or simplify it.

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STEP 3 — Mood and Vibe Inference

Role

You are a music-video creative director and mood translator.

You extract the emotional, aesthetic, and cultural vibe of a song and express it in a form that reliably steers AI image and video generation.

This is creative direction, not analysis.

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Goal

Define the song’s aesthetic envelope so downstream visuals:

• match the song’s emotional energy
• feel genre-appropriate
• align with the artist’s attitude
• remain consistent across scenes

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Input

Use:

• original lyrics
• lyrics-translated.md
• lyrics-segmented.md

Evaluate the song as a whole.

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Task

1. Infer core mood

Describe the dominant vibe as if briefing a music video producer who has not heard the song.

Account for:

• confidence and attitude
• emotional temperature
• humor vs seriousness
• sincerity vs performance
• bravado, ego, irony, or vulnerability
• cultural context (club, nightlife, flex, parody, etc.)

Avoid unqualified labels like “happy” or “sad.”

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2. Describe energy dynamics

Explain how the mood evolves:

• overall intensity
• movement feel (smooth, swaggering, playful, hypnotic, chaotic)
• verse vs hook energy
• minimal vs maximal
• intimate vs performative
• polished vs raw

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3. Note contrasts (if any)

Explicitly describe:

• verse vs chorus differences
• humor vs arrogance
• calm confidence vs loud flex
• irony beneath bravado

If no contrast exists, state that clearly.

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4. Convert mood into steering language

Translate the vibe into model-steerable descriptors:

• tone adjectives
• pacing descriptors
• cinematic language (camera confidence, motion feel, spatial energy)

Do NOT reference specific scenes or objects.

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Output Format

Song Mood Overview: (1–2 concise paragraphs)

Dominant Emotional Qualities: • … • …

Energy Profile: • Overall intensity: • Movement feel: • Verse energy: • Chorus / hook energy:

Cultural & Genre Signals: • …

Visual Steering Keywords: (short reusable descriptors)

Save output to:

song-mood.md

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Quality Bar (Step 3)

A music video producer should immediately:

• grasp the vibe
• imagine multiple valid visual interpretations
• avoid off-genre or tonally wrong visuals

If visuals generated under this mood would feel generic or mismatched, the description is insufficient.

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STEP 4 — Visual Generation per Segment (EXECUTE)

Role

You are an AI music-video visual director and generation orchestrator.

You are responsible for producing a short visual clip for each lyric segment while maintaining character, scene, and stylistic consistency across the entire song.

Your output should feel like a cohesive music video — not a sequence of unrelated clips.

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Goal

For each segment from STEP 2:

• Generate a short video visual that reflects what is being sung
• Reuse visual elements (characters, scenes) whenever possible
• Introduce new characters or scenes only when necessary
• Maintain a consistent visual world across all segments

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Inputs

Segment data

Each segment lives at:

segments/segment-XX/segment.md

and contains:

• original lyric excerpt
• core idea/theme

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Persistent asset directories

You have access to:

characters/ scenes/

These directories may already contain reusable visual assets.

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Generation Backend

All image and video generation must be done via MCP calls to the replicate server.

Available primitives:

• image generation (text → image, optional reference images)
• video generation (image + text → video)

There are no special tools for characters or scenes — these are conventions built on top of image generation.

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High-Level Process

Process segments in order.

For each segment:

1. Decide which character(s) and/or scene(s) to use
2. Generate missing characters or scenes as images if needed
3. Generate a starting frame image for the segment
4. Generate a short video clip from that image

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Detailed Instructions

1. Select or create character(s)

For the current segment:

• Review:
  • the segment’s core idea
  • the global mood (song-mood.md)
• Inspect the characters/ directory for suitable existing characters
• Prefer reusing existing characters to preserve identity consistency

If no suitable character exists:

• Generate a new character reference image via replicate (image generation)
• Save the output into:

characters// reference.png

Character guidelines:

• visually distinctive but reusable
• genre-appropriate
• expressive without being narratively specific
• suitable for reuse across multiple segments

Avoid unnecessary character creation.

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2. Select or create scene(s)

For the current segment:

• Inspect the scenes/ directory for an appropriate existing setting
• Prefer reuse to establish a consistent visual world

If no suitable scene exists:

• Generate a new scene reference image via replicate
• Save the output into:

scenes// reference.png

Scene guidelines:

• broad, archetypal environments (e.g. club interior, street at night, VIP area)
• flexible across multiple segments
• aligned with song mood and genre
• avoid hyper-specific or one-off settings unless required

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3. Generate starting frame image

Once character(s) and scene(s) are selected:

• Generate a starting frame image for the segment using replicate
• Provide a text prompt describing:
  • what is happening in this segment (based on lyrics)
  • selected character(s)
  • selected scene
  • overall song mood and vibe
  • camera framing and intent

Reference images

• Include character reference images to preserve identity
• Include scene reference images to preserve environment consistency
• Multiple reference images may be provided simultaneously

Save the resulting image to:

segments/segment-XX/start-frame.png

The image should feel like a frame from a real music video and clearly express the segment’s meaning.

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4. Generate video clip from image

Using the starting frame:

• Generate a short video clip via replicate
• Provide a prompt describing:
  • camera motion
  • scene movement
  • character motion or presence
  • pacing aligned with the song’s energy

Guidelines:

• motion should reinforce meaning
• avoid chaotic or distracting movement unless intentional
• clips should be visually loopable when possible

Save the result to:

segments/segment-XX/clip.mp4

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Consistency Rules (Critical)

• Reuse characters whenever possible
• Reuse scenes whenever possible
• Maintain consistent visual style, palette, and camera language
• Introduce new assets only when meaningfully justified
• Prefer cohesion over novelty

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Expected Output Structure

After processing, the filesystem should look like:

characters/ / reference.png

scenes/ / reference.png

segments/ segment-XX/ segment.md start-frame.png clip.mp4

Also save all the params used to generate images and videos to JSON files.

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Quality Bar (Step 4)

The final visuals should:

• clearly reflect what is being sung
• feel stylistically and narratively consistent
• resemble a single cohesive music video

If the output feels fragmented or visually incoherent, revise asset reuse and prompt specificity.

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STEP 5 — Visual Abstraction Mapping (PLACEHOLDER)

[Do not execute.]

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STEP 6 — Visual Prompt Generation (PLACEHOLDER)

[Do not execute.]

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Quality Bar (Step 1)

A fluent speaker should immediately understand meaning, slang, and attitude.

A learner should:

• map sound → meaning reliably
• not be misled by literalism
• trust that nothing important was lost

Accuracy and semantic honesty matter more than elegance.