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Turbo V2.5

elevenlabs /

ElevenLabs Turbo V2.5 is a text-to-speech model available via WaveSpeedAI, billed at $0.05 per 1000 characters for TTS requests. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

text-to-audio
इनपुट

निष्क्रिय

$0.05प्रति रन·~20 / $1

उदाहरणसभी देखें

संबंधित मॉडल

README

ElevenLabs — Turbo V2.5 Text-to-Speech

Turbo V2.5 converts written text into natural, expressive speech with clear pronunciation, smooth pacing, and lively 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
  • Multilingual support plus strong English numeral/date reading
  • Fine control via similarity and stability sliders
  • Speaker Boost for crisper English numbers, times, and measurements

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. Choose a voice_id (for example: Gigi, Callum, Alice). Refer to the full catalog in the voice list above.
  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. Click Run to synthesize and preview your audio.

Notes

  • For steady rhythm, use clear punctuation and split very long text into shorter segments.
  • voice_id must be valid; if you see a voice-ID error, select one from the official list.
  • Speaker Boost is especially helpful for finance, time, and measurement scripts.
नोट:यह वेबसाइट तृतीय पक्षों द्वारा प्रदान किए गए AI मॉडलों का उपयोग करती है।

Turbo v2.5 API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/elevenlabs/turbo-v2.5 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 v2.5 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-v2.5" \
  -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-v2.5";
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-v2.5", 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 v2.5 API — Frequently asked questions

What is the Turbo v2.5 API?

Turbo v2.5 is a ElevenLabs model for audio generation, exposed as a REST API on WaveSpeedAI. ElevenLabs Turbo V2.5 is a text-to-speech model available via WaveSpeedAI, billed at $0.05 per 1000 characters for TTS 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 v2.5 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-v2.5.

How much does Turbo v2.5 cost per run?

Turbo v2.5 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 v2.5 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-v2.5.

How long does Turbo v2.5 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 v2.5 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 V2.5 | Realistic Voice & TTS API | WaveSpeedAI