whisper-api.com

OpenAPI-generated API documentation is reference documentation that is produced automatically from an OpenAPI description file rather than written by hand. You write (or generate) a machine-readable specification of your API — endpoints, parameters, request bodies, responses, schemas, and auth — and a documentation tool reads that file and renders a browsable, often interactive reference site. The spec becomes the single source of truth; the docs become a build artifact.

This differs from manually written docs in one fundamental way: with hand-written docs, the prose is the source of truth and the API is described separately. With spec-driven docs, the API description is the source, and every page, table, and code sample is derived from it.

How the workflow actually runs

A typical spec-driven documentation pipeline has five stages:

1. Author or generate the spec. You either write an OpenAPI document by hand (YAML or JSON), or generate it from code annotations, framework metadata, or a design-first editor. Design-first means the spec is written before implementation; code-first means it is extracted from existing code.
2. Validate and lint. The spec is checked against the OpenAPI schema and against style rules — consistent naming, required descriptions, no undocumented 4xx responses, no orphaned schemas.
3. Bundle and transform. Multi-file specs are combined, $ref pointers are resolved, and the document is optionally split into per-tag or per-version outputs.
4. Render. A documentation tool converts the spec into HTML: an endpoint list, a sidebar of operations, parameter tables, response schemas, and a "try it" console.
5. Publish and version. The rendered site is deployed, and each API version gets its own snapshot so consumers can read docs matching the version they call.

Steps 2 through 5 are usually automated in CI. If the spec fails validation, the docs build fails — which is the point.

Spec-driven vs. hand-written documentation

The practical conclusion most teams reach: generate the reference, write the guides. Reference material is repetitive and mechanical, which is exactly what generation is good at. Conceptual explanations, migration notes, and tutorials carry judgment that a spec cannot express.

What you get out of the box

Generated reference pages commonly include:

• An operation list grouped by tag or path, with HTTP method and path.
• Parameter tables showing name, location (path, query, header, cookie), type, required flag, and description.
• Request and response schemas rendered as expandable trees, including nested objects and arrays.
• Authentication details pulled from the securitySchemes section.
• Interactive request consoles that let a reader send a real call from the browser.
• Generated code samples in several languages, derived from the same schemas.
• Multiple output formats, such as a static site, a single HTML file, or a mock server.

Because all of these come from one document, changing a field name in the spec updates the parameter table, the schema tree, and every code sample at once.

Where spec-driven documentation breaks down

Generation is not free. The trade-offs are real:

Spec quality becomes documentation quality. A field with no description produces a table row with an empty cell. A vague summary produces a vague heading. Tools can enforce presence of descriptions via linting, but they cannot enforce that the description is useful.

Customization has limits. If you need a page that does not map to an OpenAPI concept — a conceptual overview, a pricing explanation, a comparison of two endpoints — you write it outside the generator and link to it.

Not everything is expressible. Webhooks, streaming responses, long-polling behavior, and complex multi-step flows are awkward or impossible to describe fully in OpenAPI. Those need prose.

The spec can go stale. If the spec is maintained separately from the implementation, it drifts just like hand-written docs. The mitigation is to generate the spec from code, or to test the implementation against the spec in CI.

Interactive consoles need care. A "try it" button that hits a production API with real credentials is a security and rate-limit problem. Point it at a sandbox, or disable it.

Deciding whether to adopt it

Adopt spec-driven reference documentation if most of these are true:

• Your API has more than a handful of endpoints, or changes frequently.
• You ship client SDKs or code samples in more than one language.
• Multiple teams consume the API and need a consistent, always-current reference.
• You already have, or are willing to maintain, an OpenAPI description.

Stay with hand-written docs, or a hybrid, if:

• Your API is small and stable, and the reference fits on one page.
• Your documentation is mostly conceptual and contains little endpoint-level detail.
• You cannot commit to keeping the spec in sync with the implementation.

A reasonable middle path: generate the reference from the spec, and hand-write the getting-started guide, authentication walkthrough, and error-handling page. Link the two directions so readers can move from concept to endpoint and back.

A minimal starting checklist

1. Produce one valid OpenAPI document for a single API version.
2. Add a linter with rules for descriptions, operation IDs, and error responses.
3. Wire the docs build into CI so a failing spec fails the build.
4. Render the reference and review it as a reader, not as the author.
5. Write the two or three conceptual pages the generator cannot produce.
6. Version the published docs alongside the API version.

The core idea is simple: describe the API once, in a format both machines and humans can read, and let the reference documentation fall out of that description. Everything else — tooling, hosting, interactivity — is a detail on top of that decision.

WhisperAPI is a hosted transcription service built on OpenAI's Whisper models, and it targets two distinct groups: developers who want to integrate speech-to-text into their own applications through a REST API, and non-technical users who just want to drag a file into a dashboard and get a transcript back. If you need to transcribe meetings, videos, podcasts, or interviews without running your own Whisper infrastructure, it fits. If you need on-premise processing or full control over the model weights, it does not.

The two audiences it splits its product around

The site's own structure makes the split explicit — it presents a "Robust API for Developers" section and a "No-Code Dashboard" section as parallel offerings.

For developers

The API is the primary product. According to the site, you get:

• A choice between different Whisper models to trade speed against accuracy
• Support for both direct file uploads and remote URLs
• Model parameter tuning for your specific use case
• Video and audio handled through the same endpoint

The documented request shape is a curl call to https://api.whisper-api.com/transcribe with a file, a language, an output format, a model size, and an X-API-Key header. That means integration is a single HTTP call — no SDK required, no queue to manage, no GPU to provision.

For non-developers

The dashboard covers the same job without code: drag-and-drop upload, real-time transcription progress, download in multiple formats, and a single place to manage past transcriptions. If your workflow is "I have a recording and I want text," this is the path.

What kinds of work it fits

The product description points at audio and video transcription generally, and the FAQ/use-case framing on the site centers on the common cases: meetings, videos, podcasts, and interviews. The concrete limits that matter for those jobs:

The 10GB ceiling and the absence of a minute cap are the two numbers that decide most "can I use this" questions. A two-hour interview or a full-length podcast episode is well inside both.

Who should look elsewhere

• Teams that need data to stay on their own infrastructure. Files auto-delete after 24 hours, but they still leave your environment. If your recordings can't be sent to a third party at all, a self-hosted Whisper setup is the alternative.
• Anyone who needs a specific Whisper variant or custom fine-tune. The API exposes model size selection, not arbitrary model hosting.
• High-volume pipelines where per-minute cost dominates. The site advertises pay-as-you-go pricing with no hidden fees, but the actual rate isn't in the material here — check the pricing page before committing at scale.

How to decide in one step

Ask whether you want to write code. If yes, the API is the fit and the curl example above is enough to test it. If no, the dashboard is the fit. Either way, the free starting point is 5 transcription credits with no credit card required, which is enough to run a real file through and judge accuracy on your own audio before paying anything.

One caveat worth stating plainly: the accuracy figure on the site (99.8%) is a vendor claim, not an independent benchmark. Test it against your own recordings — accents, crosstalk, and background noise are where transcription services diverge most.

A free hosted REST API like ReqRes gives you a real HTTP endpoint you can call immediately—no signup, no local server, no database setup. You get predictable JSON responses for users, resources, login, and registration, which makes it useful for front-end demos, integration tests, learning HTTP clients, and prototyping. What it is not is a production backend for your app: the data is shared, resets periodically, and you don't control the schema. If you need persistent, private data with auth and logs, that's where an account-based backend or a commercial licence comes in.

What "free REST API for testing and prototyping" actually means

The phrase sounds vague, so it helps to separate two things people often conflate:

• A mock/sample API — a public, hosted service with fixed or semi-fixed endpoints that return realistic-looking JSON. You don't own the data. It exists so you can point code at a URL and get a response.
• A real backend you configure — a service where you define collections, schemas, authentication, and logging, and where your data persists and belongs to you.

ReqRes's landing page describes both: a free REST API for testing and prototyping with real responses and no signup, plus an option to build your own backend with collections, auth, and logs at app.reqres.in. Those are different products with different trade-offs. The free public endpoints are the "point and go" part; the account-based backend is the "own your data" part.

What you can do with the no-signup public endpoints

1. Front-end demos without a backend

If you're building a UI and need data to render, you can fetch from a public endpoint instead of hardcoding arrays. This keeps your demo code closer to real fetch logic:

async function loadUsers(page = 1) {
  const res = await fetch(`https://reqres.in/api/users?page=${page}`);
  if (!res.ok) throw new Error(`HTTP ${res.status}`);
  const { data, total, page: current } = await res.json();
  return { users: data, total, page: current };
}

You get pagination fields, a data array, and support metadata—enough to build list views, loading states, and empty states.

2. Integration and contract tests

You can assert that your HTTP layer handles status codes, headers, and JSON shapes correctly. Typical checks:

• GET /api/users/2 returns 200 with a data object.
• GET /api/users/23 returns 404 (a non-existent user).
• POST /api/login with valid credentials returns a token; with missing fields returns 400.

This is useful for testing your client wrapper, retry logic, error handling, and serialization—without spinning up your own server.

3. Learning HTTP clients and tooling

If you're new to fetch, Axios, curl, Postman, or HTTPie, a hosted API is a low-friction target. You can practice:

• Sending query parameters (?page=2, ?delay=3).
• Setting headers and reading response headers.
• Handling POST, PUT, PATCH, DELETE.
• Observing status codes for success and failure.

4. Deliberate failure and latency testing

Endpoints that return 404 on purpose, or that accept a delay parameter, let you test how your app behaves when things go wrong or slow down. That's hard to do reliably against a happy-path local mock.

What the public endpoints are not good for

The key limitation: you don't own the data, and other people are hitting the same endpoints. Treat responses as illustrative, not authoritative.

When you'd move to an account-based backend

Consider app.reqres.in (collections, auth, logs) when any of these are true:

• You need your own collections and fields, not the fixed demo schema.
• You need data to persist between sessions and belong only to you.
• You need real authentication flows you can rely on in a demo or internal tool.
• You need request logs to debug what your client actually sent.
• You're working with a team and need shared, stable endpoints.

The trade-off is setup and, eventually, cost. The public endpoints require none; the backend requires an account and configuration.

Where pricing and licensing become relevant

The site signals a commercial licence and an upgrade path (with Stripe as the payment platform), but specific prices, plan tiers, and limits aren't stated here—so don't assume numbers. What you can reason about:

• Prototyping and learning → free public endpoints are usually enough.
• Internal tools, demos for clients, or anything you don't want reset → an account-based backend is the natural next step.
• Production or commercial use → check the licence terms and any paid plan, because "free for testing" and "free for commercial production" are not the same thing.

Before committing, read the current terms on the site rather than relying on secondhand summaries, since pricing and licence scope change.

A quick decision checklist

1. Do you need data that persists and is private? If yes → account-based backend.
2. Do you need a custom schema? If yes → account-based backend.
3. Are you only testing HTTP behavior, UI rendering, or learning a client? If yes → free public endpoints.
4. Will this touch real users or revenue? If yes → review the licence and any paid plan first.
5. Do you need logs and team access? If yes → account-based backend.

If you answer "no" to 1, 2, 4, and 5, the free hosted API is likely all you need. If you answer "yes" to any of them, plan for the account-based path.

WhisperAPI's stated privacy policy is short and specific: uploaded files auto-delete after 24 hours. That single retention rule is the main privacy guarantee the site makes, and it's the detail to check against your own requirements before you send sensitive audio or video. If your content can't tolerate being stored on a third-party server for up to a day, this policy won't be enough on its own — you'd need to confirm the details with the provider directly.

What the retention policy actually says

The site lists "Privacy First" as a feature with one supporting line: files auto-delete after 24 hours. There is no separate privacy page, data-processing agreement, or encryption detail referenced in the available page content, so the 24-hour auto-deletion is the only retention commitment you can rely on from the public materials.

What that means in practice:

• Uploaded files are not kept permanently. The deletion is automatic, not something you have to request.
• The window is up to 24 hours. A file may be removed sooner, but the stated maximum is one day.
• Transcripts are a separate question. The policy describes file deletion; it doesn't state how long generated transcripts persist in your account or dashboard. Treat transcript retention as unconfirmed unless the provider says otherwise.

Why the 24-hour window matters for sensitive content

A short, automatic retention period is the practical difference between a service you can use for confidential material and one you can't. For example, if you're transcribing an internal team meeting, a customer call, or a research interview, the exposure is bounded: the file exists on the provider's systems for at most a day, then it's gone without any action from you.

That said, "auto-deletes after 24 hours" is a retention statement, not a full security statement. The available materials don't describe encryption at rest or in transit, access controls, or where files are stored. If your use case requires those guarantees, ask the provider before uploading.

How to decide whether it fits your use case

Practical steps before you upload

1. Check what you're sending. If the file contains anything you wouldn't want stored for a day on someone else's server, resolve that first.
2. Confirm the retention terms in writing if the 24-hour rule is load-bearing for your decision — the site states it as a feature, but a direct confirmation is stronger.
3. Ask about transcripts, not just files. The stated policy covers uploaded files; clarify how long the resulting transcripts are kept.
4. Use the free credits to test with non-sensitive material first, so you can see how the dashboard handles your files before committing real content.

The bottom line: WhisperAPI's public privacy position rests entirely on automatic 24-hour file deletion. That's a clear, useful commitment for everyday transcription, but it isn't a substitute for a full data-handling agreement if your content is regulated or highly confidential.

WhisperAPI accepts both audio and video files—either uploaded directly or passed as remote URLs—transcribes them in 98+ languages, and lets you download the result as JSON, TEXT, VTT, DOCX, or PDF. You can also pick between different Whisper model sizes to trade speed against accuracy. The service is built on OpenAI Whisper and offers both a REST API and a no-code dashboard, so the same capabilities are available whether you integrate programmatically or work through the browser.

Input: what you can send

WhisperAPI handles two input methods:

• Direct file uploads — send a local audio or video file in the request.
• Remote URLs — point the API at a file hosted elsewhere instead of uploading it.

Both audio and video files are processed through the same API, so you don't need separate endpoints or pipelines for each media type. The site states file uploads are supported up to 10GB, with no minute limits on transcription duration.

Languages

WhisperAPI supports 98+ languages, described as covering all major languages with high accuracy. Because it runs on OpenAI Whisper, language coverage follows that engine's multilingual capability. You can also pass a language parameter in the API request (for example, language=en) to specify the expected language rather than relying on detection.

Output formats

Transcripts can be generated, previewed, and downloaded in multiple formats. The formats shown on the site are:

In the API, the output format is set with the format parameter (for example, format=srt in the documented example). The dashboard offers the same download options through its interface.

Choosing a model: speed vs accuracy

WhisperAPI lets you select between different Whisper models, which is the main lever for balancing turnaround time against transcription quality. The site's example uses model_size=large-v2, and its sample transcript was generated with the Whisper Large-v2 model. Larger models generally favor accuracy; smaller ones favor speed. The site advertises industry-leading 99.8% accuracy across audio types and results "in minutes, not hours."

Using the API

The documented request pattern is a curl call with form fields for the file, language, format, and model size, plus an API key header:

curl \
  -F "[email protected]" \
  -F "language=en" \
  -F "format=srt" \
  -F "model_size=large-v2" \
  -H "X-API-Key: YOUR_API_KEY" \
  https://api.whisper-api.com/transcribe

• Input: the file (or a remote URL), plus optional language, format, and model_size fields.
• Action: the API sends the media to the selected Whisper model for transcription.
• Expected result: a transcript returned in your chosen format, downloadable or consumable programmatically.

The site also notes you can fine-tune model parameters for specific use cases, and that files auto-delete after 24 hours.

No-code alternative

If you don't want to write code, the dashboard provides a drag-and-drop interface with real-time transcription progress, multi-format downloads, and a single place to manage all transcriptions. The output formats and language support are the same as the API's.

Getting started

The site advertises a free start with 5 transcription credits and states no credit card is required to begin. Beyond that, pricing is pay-as-you-go, with Paddle listed as the payment platform. For current rates and credit details, check the pricing page directly, since specific prices aren't stated in the material summarized here.