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Rewrite of the website-to-hyperframes skill that came out of 11
evaluation rounds. The honest read of those evals: prose-only
guidance had hit its ceiling — sub-agents kept reporting "0 errors,
looks good" without doing the work, producing slideshow-quality
videos with mismatched brand colors, missing logos, and beats that
didn't serve the storyboard. This restructure addresses the
failure modes that real videos showed, not theoretical ones.
**Step structure (replaces 7-step layout with concept-first 6-step)**
Old: capture → design → script → storyboard → vo → build → validate
New: capture → design → brief → storyboard → vo → build → validate
The brief step (Step 2) is new: a conversation-shaped step that
aligns message + audience + arc before any beat-writing happens.
Concept-first throughout — message → arc → beats that serve the arc
→ which assets and techniques bring each beat to life.
**Step 0 (capture)**
- "View the contact sheets — carefully, every cell, not a glance"
closes the failure mode where agents reported "viewed the contact
sheet" after one scroll and later wrote beats referencing assets
that didn't exist or missed the brand logo.
- Names the right artifacts to read in order (tokens.json →
design-styles.json → asset-descriptions.md → fonts-manifest.json),
with read-on-demand guidance for the rest.
**Step 1 (design)**
- DESIGN.md authoring guide. Restored component CSS sections
(Component Stylings, Spacing & Layout, Depth & Elevation) that
earlier batches over-collapsed.
**Step 2 (brief)**
- Strategy/messaging step. Clear instruction for "Surprise me" /
minimal direction: state the minimum context (where the video
runs, who it's for) and proceed bold.
**Step 3 (storyboard + script)**
- Concept gate at the top — answer "what makes this video distinct"
before writing beat 1.
- Brand-floor MUST rules (logo in opener + closer; signature visual
somewhere in the video).
- Captured assets (SVG logos, illustrations, hero art, gradients)
are first-class beat content alongside composed UIs — many of
them carry beats outright. The constraint is only that you start
from the message, not the asset inventory.
**Step 4 (vo)**
- TTS ranking: HeyGen first (auto word timestamps), ElevenLabs
second, Kokoro free. Audio timing reconciliation gate: if actual
audio duration ≠ storyboard planned ±15%, rescale beats or trim
script before Step 5.
**Step 5 (build) + beat-builder-guide.md**
- Sub-agent template now pastes brand values inline rather than
telling the sub-agent to re-read DESIGN.md. Targeted file reads
with specific sections + line ranges.
- "Patterns that ARE shots" affirmative list (captured logo
draw-on, hero illustration push-in, captured screenshot with
parallax layers, kinetic typography over captured asset).
- Webpage-mimicry patterns (full CSS browser chrome, parked-camera
composition, ±2px breathing motion) marked ⚠ rather than ❌ —
fine when the storyboard genuinely calls for them as the subject.
- Required cinematography per beat: shot type, camera move, depth
strategy, purpose.
**Step 6 (validate) — per-beat read protocol**
This replaces the previous "spawn verify-beats CLI" gate. A grep
of composition HTML can catch structural lies (missing hex codes,
wrong asset paths) but it can't catch boring beats, off-screen
logos, GSAP timelines that only cover the first 2 seconds, or
camera moves that don't match the storyboard. Those failures only
surface when somebody opens the file and reads it.
Per-beat verdict template names the brand hex codes used, captured
asset paths referenced, headline `font-size`, GSAP timeline
coverage, and storyboard alignment. Critic sub-agent scores a
"Captured asset utilization" dimension specifically so the eval
captures whether captured SVGs/illustrations carried beats or got
recreated as divs.
**Asset bundle**
- 20 Pixabay-licensed SFX files with `CREDITS.md` documenting
provenance. SFX assignment moved to Step 3 (creative decision)
so Step 5 implements rather than improvises.
- Capabilities reference + html-in-canvas-patterns updated:
Three.js 0.181.2 + ESM jsm imports, mulberry32 seeded PRNG for
deterministic shatter, 24-effect text-animation catalog
referenced (catalog itself lands in the hyperframes-skill PR).
- Visual vocabulary rewritten: replaces user-word lookup tables
with brand-first derivation across 6 axes; user words land as
modifiers, not replacements.
225 lines
12 KiB
Markdown
225 lines
12 KiB
Markdown
# Step 4: VO, Timing + Captions
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## If Step 2 said "no narration"
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Skip the TTS sections below. The storyboard already has beat durations planned based on pacing and rhythm — those become `data-start` and `data-duration` values directly in Step 5.
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**Background music:** Ask the user before moving to Step 5:
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> "Do you have a music track for this video? If not, I can suggest where to find one:
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>
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> - **Artlist.io** or **Musicbed** — licensed music for commercial use
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> - **Uppbeat.io** or **Pixabay Music** — free tracks with attribution
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> - **Freesound.org** — free samples and loops
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>
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> Or share a reference track ('something like this') and I can find something similar."
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If the user provides a track: note the file path and BPM in the storyboard for Step 5 to wire into `index.html`. If they skip music entirely, the video uses SFX only — confirm that's intentional.
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Move to Step 5.
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---
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## Generate a test clip before full narration — calibrate timing first
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Generate a 2-sentence test clip NOW using the script's opening lines. Measure the actual duration. Kokoro compresses scripts by ~40% (35s planned → 19s actual) and HeyGen runs faster than expected. If you discover the audio is 40% shorter than expected, you'll need to revise the storyboard beat timings before investing time in full narration generation.
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**Do this before committing to beat count and durations:**
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```bash
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# Quick Kokoro test (2 sentences):
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npx hyperframes tts "First sentence. Second sentence." --voice af_nova --output /tmp/test-tts.wav
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# Measure: seconds ÷ words × total script words = estimated full audio length
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```
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If the estimate puts your video at ±15% of the planned duration, proceed. If it's more than 15% off, recalibrate the script length first.
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## Background music
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**Always ask about background music** — even when narration is present:
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> "Do you want background music under the narration? (Artlist.io, Musicbed for licensed; Uppbeat/Pixabay for free; or share a reference track). Even a subtle ambient underscore makes pauses between sentences feel intentional rather than empty."
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If they want music, note the track in the storyboard for Step 5 to wire into `index.html`.
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## TTS Provider
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Ask the user which voice provider they'd like:
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> **Which voice provider would you like to use for narration?**
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>
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> 1. **HeyGen TTS** — Good quality voices, and it returns word-level timestamps automatically (saves a separate transcription step). Requires HeyGen API key.
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> 2. **ElevenLabs** — Large voice library, very natural output. Requires ElevenLabs API key. Does not return word timestamps — you'll transcribe separately.
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> 3. **Kokoro** (Free) — Runs locally, no API key needed. Decent quality but more robotic than the others. Good for drafts or budget runs.
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If the user picks ElevenLabs or HeyGen and doesn't have a key set up yet, help them:
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- **ElevenLabs:** "Add `ELEVENLABS_API_KEY=your-key` to a `.env` file in the project root, or just paste it here and I'll set it up."
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- **HeyGen:** "Add `HEYGEN_API_KEY=your-key` to a `.env` file, or paste it here."
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Don't judge or critique if the user pastes a key directly in chat — just use it and move on.
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## Audition voices
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After the provider is selected, audition at least 2 voices with the first sentence of SCRIPT.md.
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**ElevenLabs:**
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- If the ElevenLabs MCP is available: use `mcp__elevenlabs__search_voices` to browse, `mcp__elevenlabs__text_to_speech` to generate.
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- If no MCP: call the REST API directly:
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```bash
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# List voices
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curl -s "https://api.elevenlabs.io/v1/voices" \
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-H "xi-api-key: $ELEVENLABS_API_KEY" | jq '.voices[:5] | .[].name'
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# Generate speech (replace VOICE_ID with chosen voice)
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curl -s -X POST "https://api.elevenlabs.io/v1/text-to-speech/VOICE_ID" \
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-H "xi-api-key: $ELEVENLABS_API_KEY" \
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-H "Content-Type: application/json" \
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-d '{"text":"First sentence of your script","model_id":"eleven_multilingual_v2"}' \
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--output narration.mp3
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```
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- Does not return word timestamps — transcribe separately after generating.
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**HeyGen TTS:**
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- If the HeyGen MCP is available: use the TTS tool directly.
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- If no MCP: use the v3 API (current; v1/v2 deprecated, supported until Oct 2026):
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```bash
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# List voices — response shape: { "data": [...], "has_more": bool }
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# data is a direct list (NOT data.voices — that was v2)
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curl -s "https://api.heygen.com/v3/voices?engine=starfish&type=public&limit=20" \
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-H "x-api-key: $HEYGEN_API_KEY" | python3 -c \
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"import json,sys; v=json.load(sys.stdin)['data']; [print(x['voice_id'], x['name'], x['language']) for x in v[:10]]"
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# Generate audio — response: { "data": { "audio_url": ..., "word_timestamps": [...] } }
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curl -s -X POST "https://api.heygen.com/v3/voices/speech" \
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-H "x-api-key: $HEYGEN_API_KEY" \
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-H "Content-Type: application/json" \
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-d '{"text":"Your script here","voice_id":"VOICE_ID","speed":1.0}' \
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import json,sys
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r=json.load(sys.stdin)
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d=r['data']
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print(d['audio_url'])
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open('transcript_raw.json','w').write(json.dumps(d.get('word_timestamps',[]),indent=2))
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"
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# Then download the audio
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curl -sL "AUDIO_URL_FROM_ABOVE" --output narration.mp3
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```
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- Returns word-level timestamps directly in the response — no separate transcription step needed.
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**Kokoro (free, local):**
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```bash
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npx hyperframes tts SCRIPT.md --voice af_nova --output narration.wav
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```
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No API key, no MCP needed. Runs locally. Use `--list` to see all 54 available voices.
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Pick the voice that sounds most natural and conversational. Listen for pacing — does it breathe between sentences? Does it sound like a person or a robot?
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## Script length check
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Before generating, verify the script makes sense for the video. Word count depends entirely on the creative direction. The storyboard's pacing and style determine how much narration the video needs.
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The key check: are there stretches where NOTHING is happening — no narration AND no compelling visual movement? Those are dead spots that lose the viewer. Every second needs either spoken words or strong visual energy carrying it.
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## Generate full narration
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Generate the full script as `narration.wav` (or `.mp3`) in the project directory.
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**If any command hangs for more than 60 seconds — don't just wait.** The user is sitting there watching you do nothing. Escalation order:
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1. **Try again** — kill the process, run the same command again (transient failures are common)
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2. **Try different flags** — smaller model (`--model tiny.en`), different voice, shorter test sentence first
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3. **Try a different tool for the same task** — if `hyperframes transcribe` hangs, try `whisper-cli` directly, or Groq API (`npx hyperframes transcribe --provider groq`), or OpenAI API
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4. **Switch provider entirely** — if ElevenLabs is down, try HeyGen or Kokoro. If Kokoro hangs, try ElevenLabs.
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Never sit idle for 10 minutes hoping a stuck process will finish.
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**Kokoro pronunciation issues:** Kokoro mispronounces product names and tech terms. Always apply substitutions before generating. Known problems and fixes:
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- `API` → `A P I` (spell it out)
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- `UI` → `U I`, `SaaS` → `sass`, `DevOps` → `dev ops`
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- Product names with unusual spelling: test the first sentence first and listen. Common failure: "Vercel" → "versatile", "WorkOS" → "work O S", "One API" → "Wanna PI"
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- If a name sounds wrong: write it phonetically in `narration.txt` (e.g., `Vercel` → `Ver-sell`, `Supabase` → `Soopa-base`)
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- Always generate a short test clip with the first 2 sentences before generating the full audio
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- **No SSML tags** — Kokoro reads them as literal text. `<break time="1s"/>` is spoken as "break time equals one slash." Use blank lines or `...` for pauses in `narration.txt`
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For ElevenLabs and HeyGen TTS, substitutions are usually unnecessary — they handle product names correctly.
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**Also save the exact spoken text** — with pronunciation substitutions applied (e.g., `API` → `A P I`, `$2T` → `two trillion` and etc.) — as `narration.txt` in the same directory. This is the string passed to TTS, distinct from `SCRIPT.md` which is the human-readable creative doc. Having `narration.txt` makes it trivial to regenerate the audio later with a different voice without re-deriving the substitutions. Name it exactly `narration.txt`.
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## Transcribe for word-level timestamps
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**If you used HeyGen v3 TTS:** word timestamps were returned in the generate call. Normalize the format before saving — HeyGen v3 uses `word` but the pipeline expects `text`:
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```python
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import json
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raw = json.load(open('transcript_raw.json'))
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normalized = [{"text": w["word"], "start": w["start"], "end": w["end"]} for w in raw]
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json.dump(normalized, open('transcript.json', 'w'), indent=2)
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```
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No separate transcription step needed.
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**If you used ElevenLabs or Kokoro:**
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```bash
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npx hyperframes transcribe narration.wav
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```
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Produces `transcript.json` with `[{ text, start, end }]` for every word. These timestamps are the source of truth for all beat durations.
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## Map timestamps to beats
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Go through STORYBOARD.md beat by beat. For each beat:
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1. Find the first word of that beat's VO cue in `transcript.json`
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2. Find the last word of that beat's VO cue
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3. Set `beat.start = firstWord.start`, `beat.end = lastWord.end`
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4. Add 0.3-0.5s padding at the end for visual breathing room
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Update STORYBOARD.md with real durations. Replace estimated times (e.g., "0:00-0:05") with actual timestamps as precise as possible (e.g., "0.00-3.21s").
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Beat boundaries land on word onsets — hard cuts to the VO.
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## Timing reconciliation — required before Step 5
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After mapping all beats, compare real total audio duration against the storyboard's planned duration:
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```
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real_total = last_word.end + cta_hold (typically 2–3s)
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planned_total = sum of all beat planned durations
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delta = |real_total - planned_total|
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```
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**If delta > 15% of planned total — do not proceed to Step 5 without resolving it.** Common causes and fixes:
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- **Audio shorter than planned (most common with Kokoro):** Kokoro generates compressed speech with minimal pauses. Proportionally scale all non-CTA beat durations down to match the real audio. Example: planned 30s, audio 19s — multiply each beat duration by 19/30 (excluding the CTA hold). Update STORYBOARD.md.
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- **Audio much longer than planned (>30% over):** The script was too long for the intended duration. Trim the script (remove one beat's VO), regenerate audio, re-transcribe.
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- **CTA beat timing:** The CTA beat should hold for 2–3 seconds after the last spoken word — not extend to fill empty time. `cta_start = last_word.end + 0.3s`, `cta_duration = 2.5s`. Hard cap. Dead silence after the CTA hold loses the viewer.
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**Always tell the user** if you adjusted durations significantly from the storyboard plan. They approved a specific beat structure — if it changed, they need to know.
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## Captions
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After the narration is generated and transcribed, ask the user:
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> **Would you like captions on the video?**
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>
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> - **Yes** — per-word captions synced to the narration. Great for social media (most viewers watch on mute) and accessibility.
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> - **No** — narration audio only, no text overlay.
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If yes, captions are built as a separate composition (`compositions/captions.html`) in Step 5. The `transcript.json` drives the timing — each word appears/highlights as it's spoken. Read [the captions reference](../../hyperframes/references/captions.md) for styling options (scale-pop, typewriter, fade+slide, etc.) and positioning rules.
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## Save timing data for Step 5
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Record the final beat timings (start, duration) so Step 5 (Build) can use them when building `index.html`. The storyboard now has real timestamps — these become `data-start` and `data-duration` values on each scene slot when the root composition is assembled in Step 5.
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