以 AI 机器人身份加入视频会议,提供语音、虚拟形象与屏幕共享四种模式。
设计与多媒体
course-narration-studio
试用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.
它能做什么
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.
技能文档
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.
-
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.
-
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/wavThen present the current
beatra.voices.cloneadmission card before any cloneclient_request_id— routevoice_clone, toolbeatra.voices.clone, live price for one successful activated voice, the provisional estimate, the 600-credit gift usually cannot start this clone,https://console.beatra.ai/topup, starter ¥29 / 11,000 credits. Do not recommend ¥198. Do not createclient_request_idor submit until the user confirms they have topped up or already have enough credits. Do not offer a free clone. Submitbeatra.voices.cloneexactly 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.getuntil terminal, then freeze the returnedvoice_id. -
Without a sample, select a catalog
voice_idwithbeatra.voices.list. Never put a display name invoice. -
Call
beatra.models.listwith{"capability":"text_to_speech"}. Choose the hardest lecture as the pilot. Confirm the pilot estimate, then synthesize only the pilot withbeatra.speech.synthesize. Poll each speech task withbeatra.tasks.getuntil terminal. -
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.
-
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 ownclient_request_id. Poll each block withbeatra.tasks.getuntil terminal. -
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
https://console.beatra.ai/topup exact and retry the same frozen ID only
after the user says they have topped up.
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
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