Elevenlabs Multilingual v2

eleven-multilingual-v2

Face Analyzer by Each AI is an AI model that detects and analyzes gender, age, and race prediction.

Partner Model

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~0 sec

StatusActive

Version

0.0.1

Updated12 days ago

Live Demo

Average runtime: -

Input

Configure model parameters

Stability

Increasing stability will make the voice more consistent between re-generations, but it can also make it sounds a bit monotone. On longer text fragments we recommend lowering this value.

Enter your stability

Similarity

High enhancement boosts overall voice clarity and target speaker similarity. Very high values can cause artifacts, so adjusting this setting to find the optimal value is encouraged.

Enter your similarity

Text

The text that will get converted into speech.

Enter your text

Output

View generated results

Result

Preview, share or download your results with a single click.

Click to get incredible results!

Each execution costs $0.3 With $1 you can run this model about 3 times.

API Reference

View Full Documentation

Prerequisites

Create an API Key from the Eachlabs Console
Install the required dependencies for your chosen language (e.g., requests for Python)

API Integration Steps

1. Create a Prediction

Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.

import requests
import time

API_KEY = "YOUR_API_KEY"  # Replace with your API key
HEADERS = {
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
}

def create_prediction():
    response = requests.post(
        "https://api.eachlabs.ai/v1/prediction/",
        headers=HEADERS,
        json={
            "model": "eleven-multilingual-v2",
            "version": "0.0.1",
            "input": {
  "use_speaker_boost": false,
  "style": 0,
  "similarity_boost": 0.7,
  "stability": 0.5,
  "seed": 99999,
  "text": "In a quiet village where the sky brushes the fields in hues of gold, young Mia discovered a map leading to forgotten treasures. Little did she know, her cat Whiskers had a secret: he was the guardian of the map, tasked with guiding Mia to not only the treasure but also to her destiny.",
  "voice_id": "9BWtsMINqrJLrRacOk9x"
},
            "webhook_url": ""
        }
    )
    prediction = response.json()
    
    if prediction["status"] != "success":
        raise Exception(f"Prediction failed: {prediction}")
    
    return prediction["predictionID"]

2. Get Prediction Result

Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.

def get_prediction(prediction_id):
    while True:
        result = requests.get(
            f"https://api.eachlabs.ai/v1/prediction/{prediction_id}",
            headers=HEADERS
        ).json()
        
        if result["status"] == "success":
            return result
        elif result["status"] == "error":
            raise Exception(f"Prediction failed: {result}")
        
        time.sleep(1)  # Wait before polling again

3. Complete Example

Here's a complete example that puts it all together, including error handling and result processing. This shows how to create a prediction and wait for the result in a production environment.

try:
    # Create prediction
    prediction_id = create_prediction()
    print(f"Prediction created: {prediction_id}")
    
    # Get result
    result = get_prediction(prediction_id)
    print(f"Output URL: {result['output']}")
    print(f"Processing time: {result['metrics']['predict_time']}s")
except Exception as e:
    print(f"Error: {e}")

Additional Information

The API uses a two-step process: create prediction and poll for results
Response time: -
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

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