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Openai Whisper Turbo

wavespeed-ai /

Accurate speech-to-text with OpenAI Whisper Large v3 Turbo: multilingual transcripts with auto language detection and punctuation. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

speech-to-text
Ввод

Ожидание

{
  "text": "Welcome to WaveSpeed AI."
}

$0.0007за запуск·~1428 / $1

ПримерыСмотреть всё

Похожие модели

README

OpenAI Whisper Turbo

OpenAI Whisper Turbo is a fast, accurate speech-to-text transcription model powered by OpenAI's Whisper architecture. It converts audio into clean, readable text with support for multiple languages — ideal for transcription, subtitling, and voice-driven workflows.

Why It Stands Out

  • High-speed transcription: Optimized for fast processing without sacrificing accuracy.
  • Multilingual support: Transcribe audio in dozens of languages with automatic language detection.
  • Prompt-guided transcription: Steer output 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

ParameterRequiredDescription
audioYesUpload or link to an audio file (MP3 / WAV / M4A, etc.).
languageNoLanguage code for transcription; leave empty for auto-detection.
promptNoShort guidance text to steer transcription style or terminology.
enable_sync_modeNoWait for result before returning response (API only).

How to Use

  1. Upload your audio — drag and drop a file or paste a public URL.
  2. Select language (optional) — choose a specific language or let the model auto-detect.
  3. Add a prompt (optional) — guide formatting, punctuation, or specific terminology.
  4. Click Run and wait for transcription to complete.
  5. Copy or download the transcribed text.

Best Use Cases

  • Meeting Transcription — Convert recorded meetings, interviews, and calls into searchable text.
  • Subtitle Generation — Create accurate transcripts for video subtitling workflows.
  • Content Repurposing — Turn podcasts, webinars, and lectures into written content.
  • Voice Notes — Quickly transcribe voice memos and audio notes.
  • Accessibility — Generate transcripts to make audio content accessible.

Pricing

MetricPrice
Per second of audio$0.0007 / s

Total cost = duration of audio (in seconds) × $0.0007

Examples

  • 60s audio → 60 × $0.0007 = $0.042
  • 5 min (300s) audio → 300 × $0.0007 = $0.21
  • 30 min (1800s) audio → 1800 × $0.0007 = $1.26

Pro Tips for Best Quality

  • Use clear audio with minimal background noise for optimal accuracy.
  • Specify the language if auto-detection is inconsistent.
  • Add a prompt to guide transcription — include names, jargon, or formatting preferences.
  • For long recordings, consider splitting into smaller segments for faster processing.

Notes

  • Ensure uploaded audio URLs are publicly accessible.
  • Processing time varies based on audio duration and current queue load.

Duration limit

The maximum supported audio duration is 10 minutes. Longer audio is automatically trimmed to 10 minutes before processing.

Примечание:Этот сайт использует модели ИИ, предоставляемые третьими лицами.

Openai Whisper Turbo API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/openai-whisper-turbo with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Openai Whisper Turbo below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "audio": "https://interactive-examples.mdn.mozilla.net/media/cc0-audio/t-rex-roar.mp3",
    "language": "auto"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/openai-whisper-turbo" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d "$REQUEST_BODY")

TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
  RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
    -H "Authorization: Bearer $WAVESPEED_API_KEY")
  RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
  STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
  case "$STATUS" in
    completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/wavespeed-ai/openai-whisper-turbo";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');

async function requestJson(url, options = {}) {
  const response = await fetch(url, options);
  if (!response.ok) throw new Error(await response.text());
  return response.json();
}

// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "audio": "https://interactive-examples.mdn.mozilla.net/media/cc0-audio/t-rex-roar.mp3",
        "language": "auto"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;

// 2. Poll until the prediction finishes.
while (true) {
  const resultBody = await requestJson(resultUrl, {
    headers: { "Authorization": `Bearer ${apiKey}` },
  });
  const result = resultBody.data ?? resultBody;
  if (result.status === "completed") {
    console.log(result.outputs);
    break;
  }
  if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
  if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
  await new Promise(resolve => setTimeout(resolve, 2000));
}
Python example
import json
import os
import time
from urllib.request import Request, urlopen

api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
    "audio": "https://interactive-examples.mdn.mozilla.net/media/cc0-audio/t-rex-roar.mp3",
    "language": "auto"
}

def request_json(url, data=None):
    request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
    with urlopen(request) as response:
        return json.load(response)

# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/wavespeed-ai/openai-whisper-turbo", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
    raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"

# 2. Poll until the prediction finishes.
while True:
    result_body = request_json(result_url)
    result = result_body.get("data", result_body)
    status = result.get("status")
    if status == "completed":
        print(result.get("outputs", []))
        break
    if status in {"failed", "cancelled", "timeout"}:
        raise RuntimeError(result)
    if status not in {"created", "processing"}:
        raise RuntimeError(f"Unexpected status: {status}")
    time.sleep(2)

Openai Whisper Turbo API — Frequently asked questions

What is the Openai Whisper Turbo API?

Openai Whisper Turbo is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Accurate speech-to-text with OpenAI Whisper Large v3 Turbo: multilingual transcripts with auto language detection and punctuation. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Openai Whisper Turbo 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 result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/openai-whisper-turbo.

How much does Openai Whisper Turbo cost per run?

Openai Whisper Turbo 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 Turbo accept?

Key inputs: `prompt`, `audio`, `enable_sync_mode`, `language`. 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-turbo.

How long does Openai Whisper Turbo take to generate?

Median end-to-end generation time on WaveSpeedAI is around 3 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Openai Whisper Turbo 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.

Openai Whisper Turbo | AI Speech-to-Text API | WaveSpeedAI