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Turbo V2

elevenlabs /

ElevenLabs Turbo V2 is a Text-To-Speech model available via WaveSpeedAI, billed at $0.05 per 1000 characters for API requests. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

text-to-audio
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$0.05실행당·~20 / $1

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관련 모델

README

ElevenLabs — Turbo V2 Text-to-Speech

Turbo V2 turns written text into natural-sounding speech with clear pronunciation, smooth pacing, and expressive tone—ideal for voiceovers, narration, tutorials, podcasts, and digital content. It supports a rich library of multi-lingual voices and fast turnaround for production workflows. See the list here.

Key Features

  • Fast, expressive synthesis with humanlike prosody
  • Multi-language support and robust English number/date reading
  • Fine control via similarity and stability sliders
  • Speaker Boost for crisper English numerals, times, and measurements
  • Works with built-in voices and your custom voice IDs

Pricing

  • $0.05 per 1,000 characters
  • If the input length is less than 1000 characters, it will be counted as 1000 characters to pay.

How to Use

  1. Enter your script in the text field.
  2. Set voice_id to a built-in or custom voice (for example: Gigi, Callum, Alice). See the full catalog in the voice list.
  3. Optional controls • similarity: 0–1 (higher = closer to the base voice timbre) • stability: 0–1 (higher = more consistent delivery) • use_speaker_boost: improves English number and unit reading
  4. Run to synthesize and preview your audio.

Notes

  • For the best rhythm, use clear punctuation and split very long text into smaller chunks.
  • voice_id must be valid; if you see a voice-ID error, pick one from the official list above.
참고:이 웹사이트는 제3자가 제공하는 AI 모델을 사용합니다.

Turbo v2 API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/elevenlabs/turbo-v2 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 Turbo v2 below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "text": "A clear example input",
    "voice_id": "Alice",
    "similarity": 1,
    "stability": 0.5,
    "use_speaker_boost": true
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/elevenlabs/turbo-v2" \
  -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/elevenlabs/turbo-v2";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "text": "A clear example input",
        "voice_id": "Alice",
        "similarity": 1,
        "stability": 0.5,
        "use_speaker_boost": true
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "text": "A clear example input",
    "voice_id": "Alice",
    "similarity": 1,
    "stability": 0.5,
    "use_speaker_boost": True
}

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/elevenlabs/turbo-v2", 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)

Turbo v2 API — Frequently asked questions

What is the Turbo v2 API?

Turbo v2 is a ElevenLabs model for audio generation, exposed as a REST API on WaveSpeedAI. ElevenLabs Turbo V2 is a Text-To-Speech model available via WaveSpeedAI, billed at $0.05 per 1000 characters for API requests. 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 Turbo v2 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/elevenlabs/elevenlabs-turbo-v2.

How much does Turbo v2 cost per run?

Turbo v2 starts at $0.050 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 Turbo v2 accept?

Key inputs: `similarity`, `stability`, `text`, `use_speaker_boost`, `voice_id`. 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/elevenlabs/elevenlabs-turbo-v2.

How long does Turbo v2 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 Turbo v2 outputs commercially?

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

Turbo V2 | Realistic Voice & TTS API | WaveSpeedAI