tts
Skill1 starsChanged 28 days agoMITView on GitHub
Text-to-speech for voiceovers and spoken output. Renders narration for product, feature, and demo videos as per-scene audio files with exact durations, timing offsets, and captions, and speaks text aloud. Supports the built-in macOS/Linux voice, local Kokoro, OpenAI, ElevenLabs, Gemini, Cartesia, and Deepgram. Use when the user wants a voiceover, narration, or audio for a video or screencast, wants something read aloud or announced, wants an audio file (.wav/.mp3/.m4a), wants to compare or choose voices, or wants to set up TTS providers and API keys.
- Reads credentials
27 other projects use a version of this.
Adopted by
Most recently taken up by
- AxelGard/claude-une-tiradetheir own version, first seen Nov 8, 2026
- pietz/skillstheir own version, first seen Nov 8, 2026
- codewhale-hq/codewhale-plugin-marketplacetheir own version, first seen Nov 8, 2026
- Leostruka/devin-bundletheir own version, first seen Nov 1, 2026
- momori777/Artemistheir own version, first seen Oct 22, 2026
- huang-x-h/z-skillstheir own version, first seen Oct 20, 2026
Used with
Projects with it also use Video Generation, VLM and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
huang-x-hSkillVideo Generation
Implement AI-powered video generation capabilities using the z-ai-web-dev-sdk. Use this skill when the user needs to generate videos from text prompts or images, create video content programmatically, or build applications that produce video outputs. Supports asynchronous task management with status polling and result retrieval.
Also in 6 projects that use this
huang-x-hSkillVLM
Implement vision-based AI chat capabilities using the z-ai-web-dev-sdk. Use this skill when the user needs to analyze images, describe visual content, or create applications that combine image understanding with conversational AI. Supports image URLs and base64 encoded images for multimodal interactions.
Also in 6 projects that use this
dav-niu474Skillgift-evaluator
The PRIMARY tool for Spring Festival gift analysis and social interaction generation. Use this skill when users upload photos of gifts (alcohol, tea, supplements, etc.) to inquire about their value, authenticity, or how to respond socially. Integrates visual perception, market valuation, and HTML card generation.
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dav-niu474SkillPodcast Generate
Generate podcast episodes from user-provided content or by searching the web for specified topics. If user uploads a text file/article, creates a dual-host dialogue podcast (or single-host upon request). If no content is provided, searches the web for information about the user-specified topic and generates a podcast. Duration scales with content size (3-20 minutes, ~240 chars/min). Uses z-ai-web-dev-sdk for LLM script generation and TTS audio synthesis. Outputs both a podcast script (Markdown) and a complete audio file (WAV).
Also in 5 projects that use this
- +2 more from huang-x-h
- +6 more from dav-niu474
Other versions27 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 27
- jjyaoao/HelloAgents★ 3.2ksame name
- NoizAI/skills★ 526same name
- momori777/Artemis★ 437same name
- getsentry/junior★ 409same name
- marswaveai/skills★ 82same name
- crazynomad/skills★ 27same name
- sneakers-the-rat/muse-skills★ 24same name
- obra/muse-skills★ 20same name
- wpgaurav/WordPress-skills★ 19same name
- choupiyang/ZAI_SKILLS★ 13same name
17 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
The file
We split this skill into the rules it gives an agent
9 rules. 0 are common, 4 are distinctive, and 5 are very specific to this project.
0
Common
Most of the 21 edited versions we read keep these.
4
Distinctive
This author's own choices. Worth a look.
5
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 9
Run the launcher at ${CLAUDE_SKILL_DIR}/scripts/tts, which is scripts/tts in this skill's directory.
- Very specific: line 11
Run each tts command as its own Bash call.
- Distinctive: line 21
Put one segment per scene or shot, and reuse the video's scene ids so audio and visuals line up:
- Very specific: line 74
Placing audio: put each segment's file at its scene's start time, or use voiceover.wav as a single track with start/end as offsets.
- Distinctive: line 100
Summarize; don't recite: Use two to four spoken sentences unless asked to read the full text.
- Distinctive: line 101
Describe code in words: instead of reading it. Markdown and code blocks are stripped automatically.
- Very specific: line 106
Use the configured defaults (tts config show).
- Distinctive: line 108
Never send secrets, credentials, or private data to a cloud provider.
- Very specific: line 118
Kokoro not reachable: start it with docker run -d --name kokoro -p 8880:8880 ghcr.io/remsky/kokoro-fastapi-cpu:v0.9.0.
“Common” is measured: kept by at least 6 in 10 of the 21 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Text to speech
Run the launcher at ${CLAUDE_SKILL_DIR}/scripts/tts, which is scripts/tts in this skill's directory. Below it's written as tts. The first run downloads a ~7 MB binary and checks its SHA-256 before running it.
Run each tts command as its own Bash call. Don't chain it with ;, &&, or pipes, so it stays covered by the skill's pre-approved permission.
There are two jobs:
- Voiceovers for videos:
tts render. Produces files plus a timing manifest for the rest of the video pipeline. - Speaking aloud:
tts "text". Plays through the speakers and returns immediately.
Voiceovers for videos
1. Write a script
Put one segment per scene or shot, and reuse the video's scene ids so audio and visuals line up:
{
"provider": "elevenlabs",
"voice": "JBFqnCBsd6RMkjVDRZzb",
"instructions": "warm, confident product narrator",
"pause": 0.5,
"segments": [
{"id": "01-intro", "text": "Meet the new Insights dashboard."},
{"id": "02-filters", "text": "Filter any chart in one click: by team, by service, or by release."},
{"id": "03-alerts", "text": "Set an alert once, and we'll tell you the moment a metric drifts.", "pause": 1.0},
{"id": "04-outro", "text": "Insights is available today, on every plan."}
]
}- Top-level fields (
provider,voice,model,instructions,speed,pause) apply to every segment. Each segment can overridevoice,model,instructions,speed, andpause, which is the silence after it in the combined track. - Keep one provider and voice for the whole video so it sounds like one narrator.
- Write for the ear: short sentences, numbers as spoken ("forty percent", "version two"), no markdown. If a product name is mispronounced, spell it phonetically in
text. - Choosing a provider and voice. If the script doesn't set a provider and
tts config showhas novoiceover_provider, ask the user which provider and voice to use. To let them compare, render short samples withtts -o sample-NAME.wav -p PROVIDER -v VOICE "One line of the script.". Offer to save their choice withtts config set voiceover_provider NAMEandtts config set NAME.voice VOICE. Kokoro andsystemare free and local; the rest are paid. ElevenLabs, OpenAI withinstructions, and Gemini sound the most natural.
2. Render
"${CLAUDE_SKILL_DIR}/scripts/tts" render script.json -o voiceover/This writes:
voiceover/<id>.wavfor each segment, with leading and trailing silence trimmed so durations are exact.voiceover/voiceover.wav, all segments joined with their pauses.voiceover/voiceover.srt, captions.voiceover/manifest.json, which is also printed to stdout:
{"provider": "elevenlabs", "sample_rate": 24000, "channels": 1, "duration": 17.08,
"combined": {"file": "/abs/voiceover/voiceover.wav", "duration": 17.08},
"captions": "/abs/voiceover/voiceover.srt",
"segments": [{"id": "01-intro", "file": "/abs/voiceover/01-intro.wav", "duration": 3.872,
"start": 0, "end": 3.872, "text": "...", "voice": "...", "cached": false}, ...]}Render options:
--format mp3|m4asets the combined track's format.--no-combinewrites only the per-segment files.-p/-v/-i/-sset defaults the script doesn't specify.--jobs Nsets parallelism.
render uses each provider's highest-fidelity model unless one is set: eleven_multilingual_v2 and gemini-3.8-flash-tts.
3. Use the timings
- Scene lengths: each scene should last at least its segment's
duration. Add a beat of breathing room, or extend the scene to match. - Placing audio: put each segment's file at its scene's start time, or use
voiceover.wavas a single track withstart/endas offsets. - Muxing with ffmpeg:
- The whole track:
ffmpeg -i video.mp4 -i voiceover/voiceover.wav -map 0:v -map 1:a -c:v copy -c:a aac -shortest out.mp4 - One segment at 4.47 s: add the input
-i voiceover/02-filters.wav, then-filter_complex "[1]adelay=4470:all=1[a]" -map 0:v -map "[a]".
- The whole track:
4. Iterate
Edit the script and run the same command again. Segments whose text, voice, model, style, and speed are unchanged are reused from the last render ("cached": true). Only the changed segments are re-rendered and billed. Use --force to re-render everything. If some segments fail, the others are kept, and re-running retries only the failures.
Speaking aloud
"${CLAUDE_SKILL_DIR}/scripts/tts" "The build passed and all 214 tests are green."
"${CLAUDE_SKILL_DIR}/scripts/tts" <<'EOF'
Longer text, or anything with quotes, goes on stdin.
EOFThis returns once audio is playing, and playback continues in the background. New speech interrupts current speech; -q queues it instead, and tts stop silences everything.
Other options:
-pprovider,-vvoice,-i "style",-sspeed.-wwaits for playback to finish.-o FILE [--json]writes one file instead of playing.--jsonprints its duration and path.
When speaking:
- Summarize; don't recite. Use two to four spoken sentences unless asked to read the full text.
- Describe code in words instead of reading it. Markdown and code blocks are stripped automatically.
- Keep notifications short, and use
-qfor a series of them.
Providers, cost, and privacy
- Use the configured defaults (
tts config show). Pass-ponly when the user or the script names a provider. systemandkokorostay on this machine and are free. The other providers send the text to that company and bill the user's account. Never change the default provider without asking.- Never send secrets, credentials, or private data to a cloud provider.
- Paid providers refuse more than 20,000 new characters per command. Shorten the text, or pass
--max-chars Nif the user wants it all.
Setup and troubleshooting
tts doctorshows the audio output, the default provider, and which providers are ready.tts providersandtts voices -p NAME [filter]list providers and voices.tts config set KEY VALUEsets defaults. Keys areprovider,voiceover_provider,max_chars, andNAME.voice|model|speed|instructions|base_url.- Missing API key: tell the user to set the env var (e.g.
ELEVENLABS_API_KEY) or runtts auth set elevenlabsin their own terminal. Never ask the user to paste a key into the chat, and never put one on a command line. - Untested cloud providers:
openai,gemini,cartesia, anddeepgramhaven't been tested against their live APIs yet. Onlysystem,kokoro, andelevenlabshave. If one of them fails with an error about the request itself (an unknown field, invalid parameter, or unexpected response), it's probably a bug in this skill, not something the user did. Show the user the exact error, suggest reporting it at github.com/captjt/skill-tts/issues, and offerkokoroorsystemas a fallback. Don't keep retrying with guessed parameters. Auth (401/403) and quota (429) errors are the user's to fix as usual. - Kokoro not reachable: start it with
docker run -d --name kokoro -p 8880:8880 ghcr.io/remsky/kokoro-fastapi-cpu:v0.9.0. - No audio device (SSH, containers): use
renderor-o; they don't need speakers.
Voices, models, and options for each provider are in references/providers.md.
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