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Kling Text to Audio | Realistic Voice & TTS

kwaivgi/

Kling Text-to-Audio turns text prompts into custom sound effects for videos, games, and multimedia using KlingAI's audio model. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

text-to-audio
入力

待機中

$0.0351回あたり·~28 / $1

サンプルすべて表示

Rain-soaked alley at midnight, distant thunder rumbling, dripping water echoing through narrow stone walls, footsteps fading away into the mist.

Abandoned subway tunnel, faint metallic creaks and echoing drips blending with low, humming air vents and faraway train rumbles

Underwater world — low rumbling currents, muffled bubbles rising, distant whale songs, a calm, otherworldly resonance.

Battlefield aftermath — faint crackle of fire, metallic debris shifting, wind carrying distant echoes of explosions and screams

Sound of a cold winter wind blowing through empty fields and forests, with distant howls and rustling trees.

Fire crackling in a quiet wooden cabin with occasional pops and the soft creak of floorboards.

Ocean waves crashing on rocky cliffs, seagulls calling, and wind sweeping through the coastline.

Medieval blacksmith hammering metal, fire roaring, and the sound of steel cooling in water.

Forest at dawn filled with chirping birds, rustling leaves, and distant animal calls.

Gun being reloaded with metallic clicks, followed by a single gunshot echoing in an open field.

関連モデル

README

Kuaivgi — Kling Text-to-SFX

Generate cinematic sound effects directly from text. Describe the scene or action, and Kling creates matching foley, ambience, risers, booms, whooshes, and textures—perfect for trailers, shorts, games, podcasts, and multimedia projects.

Key Features

  • Text-to-audio SFX with scene-aware textures and timing
  • Wide palette: weather, impacts, machinery, footsteps, creatures, atmospheres
  • Clean renders ready for layering and post-mix
  • Fast iteration for cue sheets and temp tracks

Parameters

  • prompt

Describe what you want to hear. Example: Cold winter night with howling wind across barren fields; deep gusts; distant creaks; approaching snowstorm tension.

  • duration

Length of the generated SFX bed in seconds.

How to Use

  1. Write a concise, concrete prompt naming sources, space, and mood.
  2. Set the duration to match your shot or loop length.
  3. Run and download the audio. Trim or loop in your DAW as needed.

Output

  • Single SFX track aligned to your requested duration.
  • Format follows platform defaults with a downloadable URL.

Pricing

  • Just $0.035 per run!!!

Prompting Tips

  • Call out materials and distance: metal gate clang close, wood door thud mid, crowd murmur far.
  • Add pacing: slow build, big hit at 0:08, decay to silence.
  • For loops, keep the ending sparse or symmetrical for seamless repeats.
  • Generate stems by running separate prompts for ambience, impacts, and ear-candy, then mix.
注記:本サイトは第三者が提供するAIモデルを使用しています。

Kling Text To Audio API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/kwaivgi/kling-text-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 Text To Audio below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "duration": 10
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/kwaivgi/kling-text-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-text-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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "duration": 10
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "duration": 10
}

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-text-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 Text To Audio API — Frequently asked questions

What is the Kling Text To Audio API?

Kling Text To Audio is a Kuaishou model for audio generation, exposed as a REST API on WaveSpeedAI. Kling Text-to-Audio turns text prompts into custom sound effects for videos, games, and multimedia using KlingAI's audio model. 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 Kling Text 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-text-to-audio.

How much does Kling Text To Audio cost per run?

Kling Text 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 Text To Audio accept?

Key inputs: `prompt`, `duration`. 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-text-to-audio.

How long does Kling Text To Audio take to generate?

Median end-to-end generation time on WaveSpeedAI is around 24 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 Text 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 Text to Audio | Realistic Voice & TTS API on WaveSpeedAI