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Openai Whisper

wavespeed-ai /

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.

speech-to-text
Entrada

Ocioso

{
  "text": "The world needs more of your unique talents Don't hold back Let's bring your vision to life and share it with the world"
}

$0.001por execução·~1000 / $1

ExemplosVer todos

Modelos relacionados

README

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

ParameterRequiredDescription
audioYesUpload or link to an audio file (MP3 / WAV / M4A, etc.).
languageNoLanguage code for transcription; use "auto" for automatic detection.
taskNoChoose "transcribe" for same-language or "translate" for English output.
enable_timestampsNoGenerate word-level timestamps (may increase processing time).
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 — choose a specific language or use "auto" for detection.
  3. Choose task — select "transcribe" for same-language output or "translate" for English.
  4. Enable timestamps (optional) — turn on for word-level timing data.
  5. Add a prompt (optional) — guide formatting, punctuation, or specific terminology.
  6. Click Run and wait for transcription to complete.
  7. 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

ModePrice
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.

Duration limit

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

Nota:Este site utiliza modelos de IA fornecidos por terceiros.

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. 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 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",
    "task": "transcribe",
    "enable_timestamps": false
}
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" \
  -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";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "audio": "https://interactive-examples.mdn.mozilla.net/media/cc0-audio/t-rex-roar.mp3",
        "language": "auto",
        "task": "transcribe",
        "enable_timestamps": false
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "task": "transcribe",
    "enable_timestamps": False
}

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", 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 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 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.

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?

Median end-to-end generation time on WaveSpeedAI is around 16 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 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 | AI Speech-to-Text API | WaveSpeedAI