Seedream 5.0 Pro ya está aquí | Pruébalo en el Generador de Imágenes →

Kling Video to Audio | AI Video Dubbing

kwaivgi/

Kling Video-to-Audio auto-generates or extracts matching sound effects and audio tracks from video using KlingAI's audio generation model. Ready-to-use REST API, best performance, no coldstarts, affordable pricing.

video-dubbing
Entrada

Inactivo

$0.035por ejecución·~28 / $1

EjemplosVer todo

Modelos relacionados

README

Kuaivgi — Kling Video-to-Audio

Kling Video-to-Audio adds a complete soundtrack to a silent video using two short prompts: one for sound effects (SFX) and one for background music (BGM). It generates synchronized foley, ambience, and score cues that match on-screen action. Great for trailers, shorts, product shots, and mood pieces.

Highlights

  • Prompt-based SFX and BGM that follow scene energy and timing
  • Optional ASMR mode for hyper-detailed, close-mic textures
  • Works with cinematic, documentary, gameplay, and product footage
  • Fast iteration: tweak prompts, re-render, and compare

Parameters

  • video (required) URL or upload of the silent clip to be sonified.

  • sound_effect_prompt Describe on-screen events and textures to hear. Example: “Thunderstorm, heavy rain, distant thunder rolls, glass rattling, wind gusts, ocean waves slamming rocks.”

  • bgm_prompt Describe musical mood, instrumentation, and pacing. Example: “Brooding orchestral score, low strings, sparse piano hits, slow build with sub-bass swells.”

  • asmr_mode (checkbox) Enhances micro-details and proximity effect for immersive listening (ear-tingles, crisp foley).

How to Use

  1. Upload or paste the video URL.
  2. Write a concise sound_effect_prompt for foley/ambience.
  3. Add a bgm_prompt for the musical bed.
  4. Toggle asmr_mode if you want ultra-detailed textures.
  5. Click Run and download the generated audio track aligned to your clip.

Prompting Tips

  • Be concrete: call out specific events, materials, and distances

“Leather jacket rustle, footsteps on wet concrete, elevator ding, neon hum.”

  • For BGM, specify tempo/structure.
  • Keep SFX and BGM prompts stylistically consistent to avoid clashes.
  • If dialogue is needed, add it in post—this model focuses on SFX and score.

Output

  • An audio track designed to sync with the input video’s duration.
  • Format and delivery follow platform defaults (download URL in the response).

Pricing

  • Per-job pricing is $0.035

Notes

  • Start with clean, final-cut footage; large edits after sound design will desync cues.
  • Loudness is unmastered by design—normalize or master in your editor to your target LUFS.
  • Ensure you have rights to the video content you upload and follow platform policies for generated audio.
Nota:Este sitio web utiliza modelos de IA proporcionados por terceros.

Kling Video To Audio API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/kwaivgi/kling-video-to-audio 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 Kling Video To Audio below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "asmr_mode": false
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/kwaivgi/kling-video-to-audio" \
  -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/kwaivgi/kling-video-to-audio";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "asmr_mode": false
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "asmr_mode": 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/kwaivgi/kling-video-to-audio", 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)

Kling Video To Audio API — Frequently asked questions

What is the Kling Video To Audio API?

Kling Video To Audio is a Kuaishou model for AI inference, exposed as a REST API on WaveSpeedAI. Kling Video-to-Audio auto-generates or extracts matching sound effects and audio tracks from video using KlingAI's audio generation model. Ready-to-use REST API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Kling Video To Audio 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/kwaivgi/kwaivgi-kling-video-to-audio.

How much does Kling Video To Audio cost per run?

Kling Video To Audio starts at $0.035 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 Kling Video To Audio accept?

Key inputs: `video`, `asmr_mode`, `bgm_prompt`, `sound_effect_prompt`. 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/kwaivgi/kwaivgi-kling-video-to-audio.

How long does Kling Video To Audio take to generate?

Median end-to-end generation time on WaveSpeedAI is around 51 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 Kling Video To Audio outputs commercially?

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

Kling Video to Audio | AI Video Dubbing API on WaveSpeedAI