text-to-speech

name	text-to-speech
description	Convert text to speech using ElevenLabs voice AI. Use when generating audio from text, creating voiceovers, building voice apps, or synthesizing speech in 70+ languages.
license	MIT
compatibility	Requires internet access and an ElevenLabs API key (ELEVENLABS_API_KEY).
metadata	{"openclaw":{"requires":{"env":["ELEVENLABS_API_KEY"]},"primaryEnv":"ELEVENLABS_API_KEY"}}

ElevenLabs Text-to-Speech

Generate natural speech from text - supports 74+ languages, multiple models for quality vs latency tradeoffs.

Setup: See Installation Guide. For JavaScript, use @elevenlabs/* packages only.

Quick Start

Python

from elevenlabs.client import ElevenLabs

client = ElevenLabs()

audio = client.text_to_speech.convert(
    text="Hello, welcome to ElevenLabs!",
    voice_id="JBFqnCBsd6RMkjVDRZzb",  # George
    model_id="eleven_multilingual_v2"
)

with open("output.mp3", "wb") as f:
    for chunk in audio:
        f.write(chunk)

JavaScript

import { ElevenLabsClient } from "@elevenlabs/elevenlabs-js";
import { createWriteStream } from "fs";

const client = new ElevenLabsClient();
const audio = await client.textToSpeech.convert("JBFqnCBsd6RMkjVDRZzb", {
  text: "Hello, welcome to ElevenLabs!",
  modelId: "eleven_multilingual_v2",
});
audio.pipe(createWriteStream("output.mp3"));

cURL

curl -X POST "https://api.elevenlabs.io/v1/text-to-speech/JBFqnCBsd6RMkjVDRZzb" \
  -H "xi-api-key: $ELEVENLABS_API_KEY" -H "Content-Type: application/json" \
  -d '{"text": "Hello!", "model_id": "eleven_multilingual_v2"}' --output output.mp3

Models

Model ID	Languages	Latency	Best For
eleven_v3	74	Standard	Highest quality, emotional range
eleven_multilingual_v2	29	Standard	High quality, most use cases
eleven_flash_v2_5	32	~75ms	Ultra-low latency, real-time
eleven_flash_v2	English	~75ms	English-only, fastest
eleven_turbo_v2_5	32	Low	Balanced quality/speed

Voice IDs

Use pre-made voices or create custom voices in the dashboard.

Popular voices:

• JBFqnCBsd6RMkjVDRZzb - George (male, narrative)
• EXAVITQu4vr4xnSDxMaL - Sarah (female, soft)
• onwK4e9ZLuTAKqWW03F9 - Daniel (male, authoritative)
• XB0fDUnXU5powFXDhCwa - Charlotte (female, conversational)

voices = client.voices.get_all()
for voice in voices.voices:
    print(f"{voice.voice_id}: {voice.name}")

Voice Settings

Fine-tune how the voice sounds:

• Stability: How consistent the voice stays. Lower values = more emotional range and variation, but can sound unstable. Higher = steady, predictable delivery.
• Similarity boost: How closely to match the original voice sample. Higher values sound more like the original but may amplify audio artifacts.
• Style: Exaggerates the voice's unique style characteristics (only works with v2+ models).
• Speaker boost: Post-processing that enhances clarity and voice similarity.

from elevenlabs import VoiceSettings

audio = client.text_to_speech.convert(
    text="Customize my voice settings.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    voice_settings=VoiceSettings(
        stability=0.5,
        similarity_boost=0.75,
        style=0.5,
        use_speaker_boost=True
    )
)

Language Enforcement

Force specific language for pronunciation:

audio = client.text_to_speech.convert(
    text="Bonjour, comment allez-vous?",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_multilingual_v2",
    language_code="fr"  # ISO 639-1 code
)

Text Normalization

Controls how numbers, dates, and abbreviations are converted to spoken words. For example, "01/15/2026" becomes "January fifteenth, twenty twenty-six":

• "auto" (default): Model decides based on context
• "on": Always normalize (use when you want natural speech)
• "off": Speak literally (use when you want "zero one slash one five...")

audio = client.text_to_speech.convert(
    text="Call 1-800-555-0123 on 01/15/2026",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    apply_text_normalization="on"
)

Request Stitching

When generating long audio in multiple requests, the audio can have pops, unnatural pauses, or tone shifts at the boundaries. Request stitching solves this by letting each request know what comes before/after it:

# First request
audio1 = client.text_to_speech.convert(
    text="This is the first part.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    next_text="And this continues the story."
)

# Second request using previous context
audio2 = client.text_to_speech.convert(
    text="And this continues the story.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    previous_text="This is the first part."
)

Output Formats

Format	Description
mp3_44100_128	MP3 44.1kHz 128kbps (default) - compressed, good for web/apps
mp3_44100_192	MP3 44.1kHz 192kbps (Creator+) - higher quality compressed
pcm_16000	Raw uncompressed audio at 16kHz - use for real-time processing
pcm_22050	Raw uncompressed audio at 22.05kHz
pcm_24000	Raw uncompressed audio at 24kHz - good balance for streaming
pcm_44100	Raw uncompressed audio at 44.1kHz (Pro+) - CD quality
ulaw_8000	μ-law compressed 8kHz - standard for phone systems (Twilio, telephony)

Streaming

For real-time applications:

audio_stream = client.text_to_speech.convert(
    text="This text will be streamed as audio.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_flash_v2_5"  # Ultra-low latency
)

for chunk in audio_stream:
    play_audio(chunk)

See references/streaming.md for WebSocket streaming.

Error Handling

try:
    audio = client.text_to_speech.convert(
        text="Generate speech",
        voice_id="invalid-voice-id"
    )
except Exception as e:
    print(f"API error: {e}")

Common errors:

• 401: Invalid API key
• 422: Invalid parameters (check voice_id, model_id)
• 429: Rate limit exceeded

Tracking Costs

Monitor character usage via response headers (x-character-count, request-id):

response = client.text_to_speech.convert.with_raw_response(
    text="Hello!", voice_id="JBFqnCBsd6RMkjVDRZzb", model_id="eleven_multilingual_v2"
)
audio = response.parse()
print(f"Characters used: {response.headers.get('x-character-count')}")

References

• Installation Guide
• Streaming Audio
• Voice Settings