API Integration

Video Caption API for High-Volume, QA-Safe Subtitle Automation

Automate caption generation with explicit job states, language controls, and production-safe retries for teams shipping large short-form volumes.

Problem

At scale, subtitle operations often fail at QA and consistency: manual timing checks, language mismatch, and repeated revisions become a hidden production bottleneck.

Result with Ssemble

Ssemble enables deterministic caption processing with clear status/error contracts so engineering teams can automate routine QA and escalate only true edge cases.

Implementation essentials

Base URL: https://aiclipping.ssemble.com/api/v1

Integration architecture (ingest → process → poll/webhook → publish)

1) Ingest

Store source URL/file URL plus caption config fields (language, captionLanguage, templateId, preferredLength).

2) Process

Call POST /shorts/create with caption parameters and persist requestId + correlation id.

3) Poll / Webhook

Check GET /shorts/:id/status (or list endpoint) on controlled cadence until terminal state.

4) Publish

Retrieve completed assets, run automated subtitle QA checks, and hand off to publishing queue.

Endpoint examples

POST /shorts/create— Create captioned short with language/template options
GET /shorts/:id/status— Track processing stage and failureReason
GET /shorts— Batch-monitor jobs without one-call-per-job overhead

Auth guidance

Use server-side X-API-Key only. Keep separate keys for staging and production to reduce rotation risk and environment coupling.

Rate limits and retries

Avoid tight polling loops. Use batch status checks, cache stable results, and on 429 follow reset/retryAfter before backoff retries.

Request example

POST /shorts/create
{
  "url": "https://www.youtube.com/watch?v=abc123",
  "start": 0,
  "end": 540,
  "language": "en",
  "captionLanguage": "en",
  "templateId": "660b8531aedb346bbe26eb43",
  "preferredLength": "under60sec",
  "layout": "auto"
}

Response example

200 OK
{
  "data": {
    "requestId": "665a1b2c3d4e5f6a7b8c9d0e",
    "status": "processing",
    "creditsUsed": 1,
    "estimatedCompletionTime": "2026-03-10T06:05:00.000Z"
  }
}

Error scenario example

400 Bad Request
{
  "error": {
    "code": "invalid_request",
    "message": "Invalid captionLanguage code",
    "details": { "captionLanguage": "xx" }
  }
}

Versioning

Pin endpoint versions and validate request schemas in CI to catch integration drift before production release.

Support

For failed jobs, keep request payload hash + requestId + error.details so teams can separate bad inputs from pipeline incidents.

Quickstart

Create SSEMBLE_API_KEY in dashboard and store in backend runtime.
Send minimal POST /shorts/create payload with source + timing + language.
Poll GET /shorts/:id/status every 10 seconds until completed/failed/cancelled.
Fetch output from GET /shorts/:id and run subtitle QA assertions.
Add bounded retries for transient 429/5xx and route exhausted jobs to DLQ.

Start with caption endpoint docs

Start integration

1) Auth setup

Secure key handling and auth errors

2) Create endpoint

Request body fields and validation

3) Error codes

Retryable and terminal error taxonomy

4) Examples

cURL/JavaScript/Python integration snippets

3-step workflow

Capture source media and caption settings (language/template/preferred length) in your queue model.
Submit create requests, monitor status transitions, and route terminal states automatically.
Publish completed outputs after automated subtitle readability and policy checks.

Why teams choose Ssemble

Designed for teams where subtitle consistency impacts brand quality and viewer retention.
Supports state-driven QA routing instead of manual inbox triage.
Scales across multiple brands by externalizing style/language policy in your integration layer.

Next step

Open caption API quickstart See pricing Developer docs

FAQ

What usually causes caption pipeline failures?

Common causes are invalid timing windows, unavailable source URLs, and invalid language/template values. Validate these before queueing.

Should we poll each job individually at scale?

Only for low volume. At higher volume, aggregate status monitoring reduces API load and improves reliability.

How do we make retries safe?

Retry only transient conditions (429/5xx/timeouts) and use request fingerprints to prevent duplicate processing.

Are completion times fixed?

No. Completion can vary by source length, queue conditions, and options. Treat ETA as advisory.

Can we enforce per-client subtitle styles?

Yes. Keep style/template mappings in your config layer and inject per tenant at request creation.

What production alerts matter most?

Monitor failure rate by error code, queue age, and stuck-processing counts to detect incidents early.