lidge-jun/speech

Speech Generation Skill

Generate spoken audio for the current project (narration, product demo voiceover, IVR prompts, accessibility reads). Defaults to gpt-4o-mini-tts-2025-12-15 and built-in voices, and prefers the bundled CLI for deterministic, reproducible runs.

Workflow

1. Decide intent: single clip or batch (multiple lines/prompts → batch).
2. Collect inputs: exact text (verbatim), voice, delivery style, format, constraints.
3. If batch: write a temporary JSONL under tmp/ (one job per line), run once, then delete the JSONL.
4. Augment instructions into a short labeled spec without rewriting the input text.
5. Run the bundled CLI (scripts/text_to_speech.py) with sensible defaults (see references/cli.md).
6. For important clips, validate: intelligibility, pacing, pronunciation, and adherence to constraints.
7. Iterate with a single targeted change (voice, speed, or instructions), then re-check.
8. Save/return final outputs and note the final text + instructions + flags used.

Temp and output conventions

• Use tmp/speech/ for intermediate files (for example JSONL batches); delete when done.
• Write final artifacts under output/speech/ when working in this repo.
• Use --out or --out-dir to control output paths; keep filenames stable and descriptive.

Dependencies (install if missing)

Prefer uv for dependency management.

Python packages:

uv pip install openai

If uv is unavailable:

python3 -m pip install openai

Environment

• OPENAI_API_KEY required for live API calls.
• If missing, direct user to https://platform.openai.com/api-keys — have them set it as env var locally (never paste in chat).

Defaults & rules

• Use gpt-4o-mini-tts-2025-12-15 unless the user requests another model.
• Default voice: cedar. If the user wants a brighter tone, prefer marin.
• Built-in voices only. Custom voices are out of scope for this skill.
• instructions are supported for GPT-4o mini TTS models, but not for tts-1 or tts-1-hd.
• Input length must be <= 4096 characters per request. Split longer text into chunks.
• Enforce 50 requests/minute. The CLI caps --rpm at 50.
• Require OPENAI_API_KEY before any live API call.
• Provide a clear disclosure to end users that the voice is AI-generated.
• Use the OpenAI Python SDK (openai package) for all API calls; do not use raw HTTP.
• Prefer the bundled CLI (scripts/text_to_speech.py) over writing new one-off scripts.
• Never modify scripts/text_to_speech.py. If something is missing, ask the user before doing anything else.

Instruction augmentation

Reformat user direction into a short labeled spec. Make implicit details explicit; keep invented additions to zero.

Template (include relevant lines only):

Voice Affect: <overall character and texture of the voice>
Tone: <attitude, formality, warmth>
Pacing: <slow, steady, brisk>
Emotion: <key emotions to convey>
Pronunciation: <words to enunciate or emphasize>
Pauses: <where to add intentional pauses>
Emphasis: <key words or phrases to stress>
Delivery: <cadence or rhythm notes>

• Keep it short — only details the user implied. Preserve input text verbatim.
• Ask only if a missing detail would block success.

Examples

Single example (narration)

Input text: "Welcome to the demo. Today we'll show how it works."
Instructions:
Voice Affect: Warm and composed.
Tone: Friendly and confident.
Pacing: Steady and moderate.
Emphasis: Stress "demo" and "show".

Batch example (IVR prompts)

{"input":"Thank you for calling. Please hold.","voice":"cedar","response_format":"mp3","out":"hold.mp3"}
{"input":"For sales, press 1. For support, press 2.","voice":"marin","instructions":"Tone: Clear and neutral. Pacing: Slow.","response_format":"wav"}

Instruction tips

• Structure: affect → tone → pacing → emotion → pronunciation/pauses → emphasis.
• 4–8 short lines; avoid conflicting guidance.
• Add pronunciation hints for names/acronyms. Repeat invariants across iterations to reduce drift.
• More: references/prompting.md. Copy/paste specs: references/sample-prompts.md.

Guidance by use case

Use these modules when the request is for a specific delivery style. They provide targeted defaults and templates.

• Narration / explainer: references/narration.md
• Product demo / voiceover: references/voiceover.md
• IVR / phone prompts: references/ivr.md
• Accessibility reads: references/accessibility.md

CLI + environment notes

• CLI commands + examples: references/cli.md
• API parameter quick reference: references/audio-api.md
• Instruction patterns + examples: references/voice-directions.md
• If network approvals / sandbox settings are getting in the way: references/codex-network.md