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name: "course-narration-studio" description: "Turn lesson materials into polished course narration audio. This course narration studio organizes lessons into speakable scripts, records the teacher-voice narration for each section, and delivers slide-ready audio for courses, training, and knowledge-product audio."

Course Narration Studio

Turn finished lecture text into an ordered narration pack. A course is a sequence of lessons with a delivery date, not one long audiobook file. Finish one speakable pilot lecture, then expand the remaining pack.

Scope and adjacent routes

Use this Skill when a course, training program, or internal enablement deck needs one consistent teacher voice across many lectures.

Keep one lecture workspace separate from the remaining pack. Strip slide titles, stage labels, and picture notes out of the spoken text before any paid read. After the user accepts the pilot, expand the rest of the course the same way.

Route a literary book or long manuscript to ai-audiobook-narration. Route a cloned-voice series with recurring episodes to voice-clone-series-studio. Route a talking-head lecture video to course-video-studio. Route a single short voiceover to short-form-voiceover-audio or voiceover-narration-studio.

Inputs and defaults

The hard inputs are:

• final readable text for at least the pilot lecture;
• lesson order;
• the target language;
• a pronunciation table whenever the lecture contains recurring names, terms, or acronyms.

If those terms exist and the table is empty, stop and collect the readings. Do not guess them. A narrator sample is optional. Without a sample, choose a catalog voice with beatra.voices.list. Reuse already-known tone, speed, and format.

Defaults: model: "auto" only when every voice-compatible live card supports the language; format: "mp3"; speed: 1.0. Keep each submitted input at or below 50,000 characters. Split on lesson, section, or sentence boundaries, never mid-sentence. Write short spoken sentences. Explain a new term in plain language the first time it appears, using the table's reading. Do not send picture notes, page headings, or bracket labels into input. Keep educational pace in the sentences themselves; do not invent a words-per-minute control.

Planning, casting, and the pronunciation table are free. Clone, if used, and each synthesis block are paid.

Golden path

Submit every Beatra call through the bundled scripts/mcp_client.py. Do not configure a host Beatra Connector. Do not use REST/OpenAPI as a fallback.

1. Build the lecture ledger: lesson titles, order, one teaching point per section, and the pronunciation table. Turn the pilot lecture into speakable text only. Confirm the table before it is frozen.

2. If the user brought a narrator sample, confirm consent first: the user must state this is their voice or that the speaker authorized this cloning use. Access to a file is not consent. A suitable sample is currently about 10 to 300 seconds, no larger than 20 MiB, and clean single-speaker speech. Only after consent, upload a local file through the bundled client:

   python3 scripts/mcp_client.py upload ./narrator-sample.wav --mime-type audio/wav
   

   Then present the current beatra.voices.clone admission card before any clone client_request_id — route voice_clone, tool beatra.voices.clone, live price for one successful activated voice, the provisional estimate, the 600-credit gift usually cannot start this clone, and what happens if the balance is short. Do not create client_request_id or submit until the user confirms they have topped up or already have enough credits. Do not offer a free clone. Submit beatra.voices.clone exactly once:

   {
     "sample": {"type": "artifact", "artifact_id": "artifact_..."},
     "display_name": "Course Teacher",
     "consent_attested": true,
     "model": "auto",
     "client_request_id": "opaque-course-clone"
   }
   

   Poll with beatra.tasks.get until terminal, then freeze the returned voice_id.

3. Without a sample, select a catalog voice_id with beatra.voices.list. Never put a display name in voice.

4. Call beatra.models.list with {"capability":"text_to_speech"}. Choose the hardest lecture as the pilot. Confirm the pilot estimate, then synthesize only the pilot with beatra.speech.synthesize. Poll each speech task with beatra.tasks.get until terminal.

5. Deliver the pilot audio. After the user listens, revise only unclear or misread blocks: edit the spoken text, then resubmit those blocks with new request IDs. Do not regenerate the whole lecture. Poll each revised block to terminal.

6. After the user accepts the revised pilot, confirm the remaining-pack estimate and synthesize the rest with beatra.speech.synthesize, one block per call, each with its own client_request_id. Poll each block with beatra.tasks.get until terminal.

7. Deliver labeled lesson audio in teaching order with actual duration, usage, and billing.net_charged_credits. Review terms, numbers, and voice consistency.

Decisions that require confirmation

Confirm the pronunciation table, the voice, the pilot, and the remaining pack estimate. A changed lesson text is new paid work for the changed blocks only.

Recovery

Recover a lost create response only with the identical frozen payload and ID. Recover a lost task ID through beatra.tasks.list and beatra.tasks.get. Cancel only at the user's request. On insufficient_balance for clone or TTS, keep the top-up URL inside the balance error exact and retry the same frozen ID only after the user says they have topped up.

Account balance

When the user asks how many credits remain or whether a live estimate fits, call beatra.wallet.get. When they ask what was charged, call beatra.wallet.ledger. Both are read-only. Do not invent an account-balance or top-up tool. Do not make wallet.get a required step before every paid submit.

When a model card comes back carrying a top_up block, relay its tiers as the card lists them and in that order. Do not rank them, do not talk one down, and do not pick one for the user. Which tier suits them is their call, made on the wallet page with the whole list in front of them. Never quote a tier from memory.

References by task

• Read Course narration workflow for ledgers, pilots, payloads, and review.
• Read Installation and authentication only when authorization needs attention.
• Read Installation registration for first-use registration.
• Read Tasks and results and Billing, errors, and recovery for task and billing facts.
• Read Bundled MCP Client diagnostics when the bundled client cannot connect.
• Read Automatic updates and safety for update controls.
• Read Uninstall and disconnect only when the user asks to remove the package.

Runtime and safe automatic updates

Use or invoke the bundled scripts/mcp_client.py for every Beatra operation. Before ordinary commands it silently checks for a newer release at most once every 24 hours per installation. Silent checks are enabled by default, and a newer release installs without separate confirmation.

The updater accepts only the fixed official discovery address and immutable Beatra CDN path embedded for this package, channel, and locale. It verifies the discovery data, archive, manifest, and every file's size and checksum before replacement. It replaces only package-owned files and rejects redirects, downgrades, wrong package/channel/locale/version data, unexpected URLs, unsafe archives, and files outside the owned destination.

Update checks, downloads, verification, replacement, rollback, and recovery fail open: the current installation remains usable and the user's original command continues. An update failure never authorizes retrying a paid generation. The automatic-update choice persists across later commands for this installation:

python3 scripts/mcp_client.py update --auto off
python3 scripts/mcp_client.py update --auto on
python3 scripts/mcp_client.py update --check