Dubbing logo

Dubbing

Organization
sarvamai
dubbing

Write correct Sarvam Dubbing API code — async create → upload → start → poll → export pipeline for localizing video/audio into 12 Indian languages with speaker voice cloning. Use this skill when building a dub-a-video/audio feature in Python or JS/TS. For translating written documents, use translate/document instead. For live dubbing in chat via MCP, use sarvam-mcp instead.

Overview

Publishersarvamai
Repositoryskills
Skill namedubbing
Stars
72
Forks
24
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by sarvamai on GitHub. Read the source before you install it.

Installation

Install the Dubbing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/sarvamai/skills/tree/main/dubbing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/sarvamai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/dubbing .claude/skills/dubbing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dubbing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Dubbing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Dubbing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Dubbing — Sarvam AI

Live in-chat dubbing → sarvam-mcp (sarvam_tools_dub). This skill = SDK code.

Translating a PDF/Word/Excel file's text? → translate/document — a different API.

[!IMPORTANT] Auth: api-subscription-key header — NOT Authorization: Bearer. Base URL: https://api.sarvam.ai. client.dubbing.upload() requires sarvamai>=0.1.31a1.

What this API does

Localizes a video or audio file into up to 12 Indian languages in one job, preserving each speaker's voice via voice cloning, and exports dubbed video, isolated audio, and SRT subtitles. Async, like translate/document, but exports fire automatically once the pipeline finishes — no manual per-language trigger call needed (unlike document translation).

Every integration follows five steps: create → upload → start → poll status → poll exports.

Quick Start (Python)

python
import os, time
from pathlib import Path
from sarvamai import SarvamAI

client = SarvamAI(api_subscription_key=os.environ["SARVAM_API_KEY"])
media = Path("sample.mp4")

# 1. Create job (does not accept the file itself)
created = client.dubbing.create(
    source_language_code="en-IN",
    target_language_codes=["hi-IN", "ta-IN"],
    export_options=["video", "srt"],
    voice_cloning=True,
    num_speakers=1,
    job_name=media.name,
)
job_id = created.data.job_id

# 2. Upload — the SDK handles the signed PUT + x-ms-blob-type header for you
client.dubbing.upload(created.data.upload_url, media)

# 3. Start the pipeline — wrap in try/except (see Gotchas: start() currently
#    throws a pydantic ValidationError even on success in some SDK versions)
try:
    client.dubbing.start(job_id=job_id)
except Exception:
    pass  # job starts server-side regardless — confirm via get_live_status below

# 4. Poll until the dub itself is done (exports still render after this)
TERMINAL = {"completed", "partial_failure", "failed", "deleted"}
while True:
    job = client.dubbing.get_live_status(job_id=job_id).data
    if job.status in TERMINAL:
        break
    time.sleep(15)

if job.status == "failed":
    raise RuntimeError(job.error_message or "job failed")

# 5. Poll export-status for the actual download links (limit defaults to 5 — set it high)
exports = client.dubbing.get_export_status(job_id=job_id, limit=100).data.exports
downloads = {(e.target_language, e.export_type): e.download_url for e in exports if e.status == "completed"}
print(downloads)

Quick Start (JavaScript/TypeScript)

typescript
import fs from "fs";
import { SarvamAIClient } from "sarvamai";

const client = new SarvamAIClient({ apiSubscriptionKey: process.env.SARVAM_API_KEY });

// 1. Create job — JS wants the RAW WIRE field names src_lang/target_langs
//    here, NOT source_language_code/target_language_codes (see Gotchas —
//    this differs from what Sarvam's own docs sample currently shows)
const created = await client.dubbing.create({
  src_lang: "en-IN",
  target_langs: ["hi-IN", "ta-IN"],
  export_options: ["video", "srt"],
  voice_cloning: true,
  num_speakers: 1,
  job_name: "sample.mp4",
});
const jobId = created.data.job_id;

// 2. Upload to the signed URL (no documented JS upload helper — PUT it yourself)
await fetch(created.data.upload_url, {
  method: "PUT",
  headers: { "Content-Type": "video/mp4", "x-ms-blob-type": "BlockBlob" },
  body: fs.readFileSync("sample.mp4"),
});

// 3. Start — job_id is positional
await client.dubbing.start(jobId);

// 4. Poll live status — also positional
let job;
do {
  job = (await client.dubbing.getLiveStatus(jobId)).data;
  await new Promise((r) => setTimeout(r, 15000));
} while (!["completed", "partial_failure", "failed", "deleted"].includes(job.status));

// 5. Poll export status for download links
const exports = (await client.dubbing.getExportStatus(jobId, { limit: 100 })).data.exports;
console.log(exports.filter((e) => e.status === "completed"));

Gotchas

GotchaDetail
Exports are automaticUnlike translate/document's per-language trigger_export, dubbing auto-produces every format in export_options once the pipeline finishes (as long as editor_flow stays false, the default). There is no manual export-trigger call.
editor_flow: true doubles the cost₹80/min vs ₹40/min on Starter, and it suppresses auto-export entirely (exports must be triggered manually in Creator Studio instead) — leave it false for API integrations.
Python has a documented upload helperclient.dubbing.upload(upload_url, media) (sarvamai>=0.1.31a1) handles the signed PUT including the x-ms-blob-type: BlockBlob header, and accepts a path or an open binary file. No JS equivalent exists — PUT the file yourself with fetch and the same header, same as translate/document.
Python start() can throw even when the job starts fineVerified live against sarvamai==0.1.34: client.dubbing.start(job_id=...) raised pydantic.ValidationError: data.job_id Field required — the server's actual response body is {"data": {"project_id": ..., "status": "queued", ...}}, no job_id key, which the Python SDK's response model doesn't expect. The job does start server-side regardless (confirmed via get_live_status and a raw REST call) — wrap the start() call in try/except and move on to polling get_live_status(), which parses fine. This is Python-specific: client.dubbing.start(jobId) in JS (sarvamai@1.1.10) returned the same project_id-shaped body without error, since the JS SDK doesn't validate response shape as strictly. Re-check if a newer sarvamai (Python) has fixed this before assuming it's still broken.
JS create() needs src_lang/target_langs, not source_language_code/target_language_codesVerified live against sarvamai@1.1.10: passing source_language_code/target_language_codes — the exact field names shown in Sarvam's own published TS "SDK Code" sample for this endpoint — returns 400 InvalidInputError: src_lang: Field required, target_langs: Field required. The JS SDK does not translate those field names to wire format for this call; only src_lang/target_langs work. This is a bug in Sarvam's own docs example, not a hypothetical — don't trust that sample as-is. (Python's source_language_code/target_language_codes do work correctly — this is JS-only.)
job_id is positional in JSclient.dubbing.start(jobId), .getLiveStatus(jobId), .getExportStatus(jobId, { limit }) — not { job_id }. Python keeps the kwarg form (job_id=...).
limit defaults to 5 on export-statusA 2-language × 3-format job has 6 export entries; the default limit=5 silently truncates the list. Pass limit comfortably above languages × formats (max 100).
export vs exports in live-statusA single-target-language job populates export (object) and leaves exports null; 2+ languages does the reverse. Handle both keys — don't assume one is always populated. Use export-status (not live-status) as the source of truth for downloads either way.
partial_failure is terminal and has real outputTreat it alongside completed when deciding to stop polling — some languages may have succeeded even if others failed. failed is the only terminal status with nothing to collect.
completed job ≠ downloadable files yetExports render after the dub finishes. Poll export-status and only download entries whose own status is completed.
Odia is or-IN hereSame as translate/document, NOT od-IN (the STT/text-translation code) — dubbing and document translation share the or-IN convention.
Media validated at start, not at uploadStorage accepts whatever bytes you send; an unsupported/corrupt file only surfaces as a failed job (check error_message) after start, not as an upload-time error.
Signed URLs expireupload_url and download_url are short-lived (~24h for downloads). Re-poll export-status for a fresh download link rather than caching the URL.
File limitsMax size/duration depend on plan: 2GB/1hr (Starter), 3GB/1hr (Pro), 4GB/4hr (Business).

Full Docs

Fetch voice options, register/tone control, and the full job-lifecycle reference from:

Frequently asked questions

What does the Dubbing AI skill do?

Write correct Sarvam Dubbing API code — async create → upload → start → poll → export pipeline for localizing video/audio into 12 Indian languages with speaker voice cloning. Use this skill when building a dub-a-video/audio feature in Python or JS/TS. For translating written documents, use translate/document instead. For live dubbing in chat via MCP, use sarvam-mcp instead.

Why use Dubbing on TypingMind?

Because you install it once and use it with any model. Dubbing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Dubbing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sarvamai/skills/tree/main/dubbing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Dubbing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Dubbing?

As many as you like. As long as a model supports skills, you can use Dubbing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Dubbing AI skill free?

Yes. It is published on GitHub by sarvamai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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