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OpenAI Whisper Large v3 (Video-to-Text) delivers high-accuracy multilingual transcription directly from video files, with automatic language detection and optional timestamped, subtitle-ready segments. Built for stable production use with a ready-to-use REST API, fast response, no cold starts, and predictable pricing.

speech-to-text
Input

Idle

wavespeed-ai/openai-whisper-with-video preview unavailable

$0.001per run·~1000 / $1

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Related Models

README

OpenAI Whisper (Large-v3) — Video-to-Text

OpenAI Whisper — Video-to-Text is a production-ready speech recognition endpoint powered by Whisper large-v3. It transcribes or translates speech directly from video files by extracting audio and returning clean, readable text, with optional word-level timestamps for subtitle and alignment workflows.

Built for stable production use with a ready-to-use REST API, no cold starts, and predictable pay-per-second pricing.

Key capabilities

  • Video input support (audio is extracted automatically)
  • Two tasks: transcribe and translate
  • Language selection: auto detection or manual language code
  • Optional word-level timestamps via enable_timestamps
  • Optional sync response via enable_sync_mode (API only)

Parameters

ParameterRequiredDescription
videoYesInput video (upload or public URL).
languageNoLanguage code or auto (default).
taskNotranscribe or translate.
enable_timestampsNoGenerate word-level timestamps (may increase processing time).
promptNoShort guidance text to steer transcription/translation style.
enable_sync_modeNoAPI only: wait for result and return it directly in the response.

How to use

  1. Upload video (or paste a public video URL).
  2. Set language:
  • Use auto for most cases.
  • Choose a specific language code if detection is unstable.
  1. Choose task:
  • transcribe for same-language transcription
  • translate for translated output
  1. (Optional) Enable enable_timestamps if you need subtitle timing/alignment.
  2. (Optional) Add a prompt to guide formatting or terminology (names, jargon, punctuation).
  3. Run and read the transcript output.

API note: enable_sync_mode is not shown as a normal UI option; it’s only available through the API.

Pricing

Modeenable_timestampsPrice per second
Standardfalse$0.001 / s
Timestampedtrue$0.002 / s

Examples

Video lengthStandardTimestamped
60s$0.06$0.12
600s (10 min)$0.60$1.20

Notes

  • If you use a URL, it must be publicly accessible; the UI showing a preview thumbnail is a good sanity check.
  • Timestamps are best for subtitles and editing, but may take longer to process.
  • For best accuracy, use clear speech and minimize background music/noise.

More Models to Try

  • OpenAI Whisper Turbo on WaveSpeedAI — Faster, cost-efficient speech-to-text for real-time or high-volume transcription pipelines while keeping strong multilingual recognition quality.

Duration limit

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

Note:This website uses AI models provided by third parties. Documentation prices are for reference and may be outdated. The Generate button shows an estimate; the final task charge prevails.

Openai Whisper With Video API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/openai-whisper-with-video 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 With Video below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "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-with-video" \
  -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-with-video";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
        "language": "auto",
        "task": "transcribe",
        "enable_timestamps": false
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "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-with-video", 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 With Video API — Frequently asked questions

What is the Openai Whisper With Video API?

Openai Whisper With Video is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. OpenAI Whisper Large v3 (Video-to-Text) delivers high-accuracy multilingual transcription directly from video files, with automatic language detection and optional timestamped, subtitle-ready segments. Built for stable production use with a ready-to-use REST API, fast response, no cold starts, and predictable pricing. You can call it programmatically or try it from the playground above.

How do I call the Openai Whisper With Video 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-with-video.

How much does Openai Whisper With Video cost per run?

Openai Whisper With Video 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 With Video accept?

Key inputs: `prompt`, `video`, `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-with-video.

How long does Openai Whisper With Video take to generate?

Median end-to-end generation time on WaveSpeedAI is around 12 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 With Video 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 With Video | AI Speech-to-Text API on WaveSpeedAI