Openai Whisper | AI Speech-to-Text API

OpenAI Whisper (Large-v3)

OpenAI Whisper (Large-v3) is a production-ready speech recognition model that transcribes or translates audio into clean, readable text. With support for dozens of languages, optional word-level timestamps, and flexible task modes — it's ideal for subtitling, transcription, and multilingual workflows.

Why It Stands Out

Transcribe or translate: Choose between same-language transcription or translation to English.
Multilingual support: Transcribe audio in dozens of languages with automatic language detection.
Word-level timestamps: Generate precise timing data for subtitle alignment and editing workflows.
Prompt-guided output: Steer formatting, terminology, or punctuation with custom prompts.
Prompt Enhancer: Built-in AI-powered prompt optimization for better transcription guidance.
Flexible input: Supports direct audio upload or public URL.

Parameters

Parameter	Required	Description
audio	Yes	Upload or link to an audio file (MP3 / WAV / M4A, etc.).
language	No	Language code for transcription; use "auto" for automatic detection.
task	No	Choose "transcribe" for same-language or "translate" for English output.
enable_timestamps	No	Generate word-level timestamps (may increase processing time).
prompt	No	Short guidance text to steer transcription style or terminology.
enable_sync_mode	No	Wait for result before returning response (API only).

How to Use

Upload your audio — drag and drop a file or paste a public URL.
Select language — choose a specific language or use "auto" for detection.
Choose task — select "transcribe" for same-language output or "translate" for English.
Enable timestamps (optional) — turn on for word-level timing data.
Add a prompt (optional) — guide formatting, punctuation, or specific terminology.
Click Run and wait for transcription to complete.
Copy or download the transcribed text.

Best Use Cases

Subtitle Generation — Create accurate, timed transcripts for video subtitling.
Meeting Transcription — Convert recorded meetings, interviews, and calls into searchable text.
Translation Workflows — Translate foreign-language audio directly to English text.
Content Repurposing — Turn podcasts, webinars, and lectures into written content.
Accessibility — Generate transcripts to make audio content accessible.

Pricing

Mode	Price
Standard (no timestamps)	$0.001 / s
With timestamps enabled	$0.002 / s

Total cost = duration of audio (in seconds) × price per second

Examples

60s audio (standard) → 60 × $0.001 = $0.06
60s audio (with timestamps) → 60 × $0.002 = $0.12
10 min (600s) audio (standard) → 600 × $0.001 = $0.60
10 min (600s) audio (with timestamps) → 600 × $0.002 = $1.20

Pro Tips for Best Quality

Use clear audio with minimal background noise for optimal accuracy.
Specify the language manually if auto-detection is inconsistent.
Enable timestamps only when needed for subtitles or alignment — it doubles the cost.
Add a prompt to guide transcription — include names, jargon, or formatting preferences.
Use "translate" task for non-English audio when you need English output.

Notes

Ensure uploaded audio URLs are publicly accessible.
Timestamps are best for subtitles and editing, but may take longer to process.
Processing time varies based on audio duration and current queue load.

Openai Whisper API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/openai-whisper with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Openai Whisper below.

HTTP example

# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/openai-whisper" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d '{
    "audio": "https://example.com/your-audio.mp3",
    "language": "auto",
    "task": "transcribe",
    "enable_timestamps": false,
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "enable_sync_mode": false
}'

# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY"

# When status is "completed", read the output from data.outputs[0].

Node.js example

// npm install wavespeed
const WaveSpeed = require('wavespeed');

const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env

const result = await client.run("wavespeed-ai/openai-whisper", {
        "audio": "https://example.com/your-audio.mp3",
        "language": "auto",
        "task": "transcribe",
        "enable_timestamps": false,
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "enable_sync_mode": false
});

console.log(result.outputs[0]); // → URL of the generated output

Python example

# pip install wavespeed
import wavespeed

output = wavespeed.run(
    "wavespeed-ai/openai-whisper",
    {
    "audio": "https://example.com/your-audio.mp3",
    "language": "auto",
    "task": "transcribe",
    "enable_timestamps": false,
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "enable_sync_mode": false
}
)

print(output["outputs"][0])  # → URL of the generated output

Openai Whisper API — Frequently asked questions

What is the Openai Whisper API?

Openai Whisper is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Whisper Large v3 speech-to-text: instant, accurate multilingual transcripts with automatic language detection and punctuation. Upload audio to get transcripts. Ready-to-use REST API, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Openai Whisper API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID; poll the prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/openai-whisper.

How much does Openai Whisper cost per run?

Openai Whisper starts at $0.001 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Openai Whisper accept?

Key inputs: `prompt`, `audio`, `enable_sync_mode`, `enable_timestamps`, `language`, `task`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/openai-whisper.

How long does Openai Whisper take to generate?

Average end-to-end generation time on WaveSpeedAI is around 13 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.

Can I use Openai Whisper outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

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