Workflow: ChatGPT scripts -> ElevenLabs TTS for weekly ads

Recommendation (short):
Build a simple automated pipeline: calendar or content spreadsheet -> ChatGPT API to draft multi-length ad scripts -> human-light QA + automated checks -> ElevenLabs TTS via API to render voice variants -> store audio + sidecar metadata -> push to channel schedulers/ad servers via Zapier/Make or a CI job. Start small (Zapier + Airtable) and graduate to code (Node/Python + Airflow/GitHub Actions) as volume/complexity grows.

Decision criteria (pick based on these):
- Budget & engineering: no-code (Zapier/Make + Airtable) if low budget/limited dev time; custom API integration if you need scale, security, or advanced control.
- Output quality & control: ElevenLabs for premium, expressive TTS; choose custom voice capturing if brand voice matters.
- Team size & QA tolerance: small solo teams can rely on a short human QA step; agencies/brands should have multi-step QA and compliance review.

Practical checklist (automatable steps + checks):
1) Trigger: new weekly campaign row in Airtable / Google Calendar event with metadata (campaign, ad type, channels, target LUFS).
2) Script generation (ChatGPT): call ChatGPT API with a template prompt (see below) to get 15s/30s/60s variants + short taglines + CTA.
3) Automated script checks: profanity filter, brand-term presence, length estimate (words → seconds), and mandatory legal lines present.
4) Human QA: edit for brand tone, legal/reg compliance sign-off, and confirm key product facts.
5) TTS prep: create SSML or plain text with pronunciation overrides; include pause markers and emphasis where needed.
6) Render (ElevenLabs API): render named voices and 2-3 style variants; produce WAV/MP3 at target sample rate.
7) Audio QA: headphone listen, loudness target (choose based on channel: streaming/podcast ~ -14 LUFS; broadcast ~ -23 LUFS), no clipping, no artifacts. Use automated LUFS and silence detection scripts.
8) Metadata & sidecar: generate JSON with fields: campaign, date, length, voice, variant, transcript, tags, platform-specific notes, contact for approvals.
9) Store & name files (see naming convention). Push to distribution: social scheduler, ad server, programmatic DSP, or FTP to stations.
10) Schedule + monitoring: schedule publish; set alerts for failures and weekly spot-checks.

File naming convention (practical):
YYYYMMDD_Campaign_ShotType_Length_Voice_Version.mp3
Example: 20260701_SummerSale_Skippable_30s_FemaleV2_R1.mp3
Keep a matching sidecar: same-name.json (transcript + metadata).

Prompt template for ChatGPT (concise):
"Write 15s, 30s, and 60s ad scripts for [CampaignName]. Tone: [friendly/urgent/luxury]. Include CTA: [CTA]. Required line: [legal]. Highlight [1-2 features]. Provide alt taglines (3). Return script durations, suggested pauses, and a 1-line summary for voice direction."

TTS tips (ElevenLabs specifics):
- Use named voice + 'style' or SSML for emphasis and pauses.
- Provide phonetic spelling for tricky brand/product names.
- Render short preview first, then full render once pronunciation is confirmed.

QA acceptance criteria (minimal):
- Script approved by brand manager.
- LUFS within channel target, no clipping.
- Pronunciation approved (human or phoneme override).
- Metadata complete and file correctly named.

Best-for / Avoid-if
- Best for: teams wanting fast creative-to-audio turns with natural voices (elevenlabs + ChatGPT). Great when you need many variants and fast iteration.
- Avoid if: you must use union-affiliated performers or need live human emotion nuances for premium broadcast; then use voice actors.

Final note: start with a simple Airtable + Zapier flow and two QA checkpoints (script + audio). Move to custom API orchestration when you need multi-channel scheduling, stricter security, or higher throughput.

Tools referenced: ChatGPT (scripting) and ElevenLabs (TTS).

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