TextToSpeechSkills
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For video creators

Narrate YouTube videos from scripts, outlines, and agent drafts

Create consistent narration for explainers, tutorials, faceless channels, product videos, and Shorts. Keep a stable channel voice with templates, then use tags to make hooks, transitions, and calls to action sound intentional.

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Who is this for?

TextToSpeechSkills helps creators turn YouTube scripts, outlines, and LLM drafts into consistent narration. A channel can save its narrator as a voice template, then use expression tags to control hooks, transitions, emphasis, and calls to action. Creators can generate narration from the UI, batch longer scripts as async jobs, or connect an LLM app through MCP so the script-to-audio workflow happens inside the writing process. This is useful for tutorials, explainers, faceless channels, product videos, and short-form content.

Easy LLM setup

LLM-ready even for non-technical teams

Paste your script into an LLM, ask it to add approved expression tags, then use the MCP tool to generate narration without leaving your writing flow.

Read setup guide
01Create a scoped key
02Install MCP
03Choose a voice template
04Generate audio from chat

Consistent channel voice

Save your narrator once and reuse the same voice template across intros, explainers, product demos, and series episodes.

Script markup that stays readable

Use simple tags for pacing and emphasis while keeping the script clean enough for editors and collaborators.

Batch-friendly audio jobs

Generate voiceovers from longer scripts with polling, webhooks, and clear credit previews before production.

When this helps

YouTube creators, video editors, faceless channels, and product marketers usually need a repeatable path for writing, review, generation, billing, and reuse. The most important jobs here are consistent channel voice, script markup that stays readable, batch-friendly audio jobs. Those are the moments where voice becomes part of real work instead of a one-off export.

How the workflow works

Start with readable text, add expression tags when tone matters, choose an approved voice template, and create a speech job through the UI, API, or MCP. The same pattern works for YouTube narration text-to-speech, TTS for video narration, AI voiceover API, which makes it easier for humans and LLM apps to share one process without exposing internal routing or credentials.

Before you roll it out

Decide which templates are approved, which expression tags are allowed, who can create workspace keys, and which usage limits are acceptable. Those choices keep automated voice generation useful without letting it sprawl from the first paid Test plan through Pro, Scale, and Business usage.

Common questions

What teams usually ask before starting

These are the practical details that matter before a team adds speech generation to a real workflow.

Who should use Text-to-Speech for YouTube Narration?

YouTube creators, video editors, faceless channels, and product marketers should use this page when they want generated speech that is easy to review, consistent across prompts, and simple to connect to LLM tools. The core workflow combines expression tags, voice templates, credit previews, and job-based generation.

Can a non-technical user connect this to an LLM app?

Paste your script into an LLM, ask it to add approved expression tags, then use the MCP tool to generate narration without leaving your writing flow. The setup guide keeps the first path short while still giving developers a clean API when the workflow moves into a product backend.

How does pricing stay predictable?

Every paid plan uses credits. Teams can add credit packs when needed, and workspaces on Pro and higher add central billing for $2 per user per month.

Related reading

Keep exploring TextToSpeechSkills

Use these guides to move from a first audio test to a repeatable workflow for your team.

LLM Text-to-Speech Setup GuideA simple guide for connecting TextToSpeechSkills to LLM apps with MCP, scoped keys, voice templates, and expression tags.Text-to-Speech MCP for LLM AppsConnect LLM apps to expressive text-to-speech with MCP tools for tags, voice templates, credit previews, jobs, and audio URLs.Text-to-Speech Skills for LLM WorkflowsInstall text-to-speech skills that teach LLM apps how to use expression tags, saved voices, MCP tools, and speech jobs safely.TTS MCP ServerInstall an MCP server that lets LLM agents validate expression markup, check credit use, use voice templates, and create speech jobs.How to Add Text-to-Speech to an LLM AppA practical launch guide for adding text-to-speech to LLM apps with expression tags, voice templates, MCP setup, jobs, and team billing.Expression Tags for Better AI Voice OutputLearn how expression tags make text-to-speech scripts easier to review, automate, and reuse across LLM apps, creators, games, and support flows.

API playground

Plain JSON in, speech job out

{
  "text": "[quiet] hello. [loud and angry] how are you?",
  "voice_template": "vt_calm_narrator_v1",
  "generation_mode": "instant",
  "format": "mp3"
}
202 queued for polling200 audio ready

MCP install

Agent tools included at launch

Claude Desktoppnpm --package texttospeechskills dlx tts-skills-mcp
Codexpnpm --package texttospeechskills dlx tts-skills-mcp
Cursorpnpm --package texttospeechskills dlx tts-skills-mcp
Skills helperpnpm --package texttospeechskills dlx tts-skills tags