AI Multilingual Dubbing

AI Multilingual Dubbing

Create organized narration audio for every target market from scripts the user provides or approves. Reuse the message, destination, languages, voice direction, segment boundaries, and timing targets already visible in the conversation. Move by the shortest route to a representative pilot, then produce the rest of the approved audio scope.

Start from the spoken scripts

Build a segment × locale production matrix. Each cell records the locale and BCP-47 tag, approved text, protected terms, pronunciation notes, voice, delivery direction, format, timing target, and status. Use natural segments such as a scene, lesson, chapter, ad, or publishable message—never a mid-sentence split.

If the source exists only as video or audio, use an actually available host capability to obtain a transcript or ask the user for one. Keep that transcript as draft text until the user approves what will be spoken. The speech operation accepts text, not source media. When localization is requested, help draft the target-language wording in the conversation, preserve product names, claims, numbers, units, URLs, and calls to action, and obtain content approval. Do not claim professional or native-language review unless the user supplies that fact.

Use matrix design and locale readiness and quality for the complete preparation and review gates.

Choose a voice and a useful pilot

Reuse an accepted voice when its returned card supports the target language. Otherwise call beatra.voices.list, filter by language and expressed preferences, and recommend two or three suitable candidates from their live cards. Use the available preview URLs for an unpaid audition; do not synthesize paid audio only to discover a voice.

Default to model: "auto", speed 1.0, mp3 for direct delivery, no emotion, and unchanged pitch, volume, and sample rate. Use wav or flac for downstream editing when that destination is known. Call beatra.models.list with capability: "text_to_speech" only when the user asks about current models or cost, selects a model, or language compatibility needs live confirmation. Every speech request uses the approved cell text as input, the selected returned voice ID as voice, and the cell's target BCP-47 tag as language.

For a multi-cell release, choose one representative high-risk segment per locale as the pilot. Prefer names, numbers, technical terms, offers, or calls to action. For timing-sensitive work, record the target duration, compare it with the returned pilot duration, and adjust the smallest useful script or speed decision after approval. Treat timing as a design constraint, not a promise of exact synchronization.

Confirm the paid scope once

Planning, copy drafting, voice discovery, and previews do not use the paid speech operation. Before any beatra.speech.synthesize call, present the complete paid scope in one readable matrix: every included pilot or production cell, its locale, approved text, voice, format, delivery direction, and timing target. State that each listed cell is one paid render. Clear approval of the complete matrix authorizes every listed cell once; do not ask for a second confirmation. A changed or newly added cell is new paid work and needs a revised scope and approval.

Create one stable opaque client_request_id for each approved locale/segment render. Submit every approved cell once, retain its task_id, and poll that same task with beatra.tasks.get. Honor deadline_at when it is returned by the task; when none is present, stop active polling after 30 minutes, report the current task state, and resume later instead of waiting indefinitely or creating a replacement. The bundled client handles its own cached, best-effort, non-billable installation registration; do not add a manual registration step to this creative workflow.

Use the bundled MCP Client

Use only this Skill's bundled scripts/mcp_client.py for every remote MCP operation. Pass the MCP tool name as the CLI argument and send one JSON object on stdin. Do not configure or call a host Beatra Connector, and do not use REST/OpenAPI as a fallback. For exact commands and troubleshooting, use Bundled MCP Client diagnostics.

Recover the task and deliver the audio

A queued or running task is work in progress, not a reason to submit again. If a response is uncertain but every generation argument is identical, retry with the same client_request_id. Any change to text, voice, language, model, speed, format, pitch, volume, sample rate, or emotion creates a new request identity and new paid work. If the task ID is lost, use beatra.tasks.list with the text_to_speech capability, call beatra.tasks.get for every plausible candidate, and compare its complete task.input, resolved model, and timing to the saved matrix cell before creating anything. A list item alone is not enough to identify the original request. Cancel only when the user asks; if beatra.tasks.cancel conflicts, continue polling the same task.

On success, deliver the actual task.output.audio.url, artifact ID, MIME type, size, sample rate when returned, and task.output.audio.duration_seconds, grouped by locale and segment. Include the actual task identity and billing result. Review pronunciation, register, pauses, voice fit, pace, tone, and timing only when the host can actually play the audio; otherwise say listening review is incomplete and deliver the factual result. Revise only the affected cell after a new paid confirmation. Keep accepted cells untouched and do not claim translation, captions, lip sync, voice preservation, audio/video editing, or exact timing.

Use recovery and delivery for the detailed task and acceptance path.

References by task

• Matrix, segmentation, casting, defaults, and pilots: matrix design
• Locale-copy readiness and listening review: locale readiness and quality
• Exact retry, cancellation, terminal results, and delivery: recovery and delivery
• First installation or expired authorization: installation and authentication
• Bundled MCP Client commands and diagnostics: Bundled MCP Client diagnostics
• Installation registration behavior: installation registration
• Task lookup and result fields: tasks and results
• Balance, uncertain submissions, billing, and errors: billing, errors, and recovery
• Disconnecting the installation: uninstall and disconnect
• Official sources, integrity checks, recovery, and controls: automatic updates and safety

Runtime and safe automatic updates

Before ordinary Beatra commands, the bundled client may silently check this installed package channel for a newer version, at most once every 24 hours. When a higher version is available, it installs the update automatically without separate confirmation. It downloads only from the fixed official Beatra discovery and immutable CDN paths for the embedded identity, and replaces only files owned by this package after verifying the archive, manifest, and every packaged file. It rejects redirects, downgrades, unsafe archives, unexpected destinations, and a different package, channel, or locale. If any update or recovery step fails, the current installation remains usable and the user's original command continues.

The setting persists 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

Use the first command to disable silent checks, the second to restore them, and the third to inspect the official available version without changing files. See automatic updates and safety for the complete behavior.