
text-to-speech
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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/connorads/dotfiles --skill text-to-speech- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads text-to-speech automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
ElevenLabs Text-to-Speech
Generate natural speech from text - supports 70+ languages, multiple models for quality vs latency tradeoffs.
Setup: See Installation Guide. For JavaScript, use
@elevenlabs/*packages only.
Quick Start
Python
from elevenlabs 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 | 70+ | Standard | Highest quality, emotional range |
eleven_multilingual_v2 | 29 | Standard | High quality, long-form content |
eleven_flash_v2_5 | 32 | ~75ms | Ultra-low latency, real-time |
eleven_flash_v2 | English | ~75ms | English-only, fastest |
eleven_turbo_v2_5 | 32 | ~250-300ms | Balanced quality/speed |
eleven_turbo_v2 | English | ~250-300ms | English-only, balanced |
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,
speed=1.0, # 0.25 to 4.0 (default 1.0)
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 |
mp3_44100_64 | MP3 44.1kHz 64kbps - lower quality, smaller files |
mp3_22050_32 | MP3 22.05kHz 32kbps - smallest MP3 files |
pcm_16000 | Raw PCM 16kHz - use for real-time processing |
pcm_22050 | Raw PCM 22.05kHz |
pcm_24000 | Raw PCM 24kHz - good balance for streaming |
pcm_44100 | Raw PCM 44.1kHz (Pro+) - CD quality |
pcm_48000 | Raw PCM 48kHz (Pro+) - highest quality |
ulaw_8000 | μ-law 8kHz - standard for phone systems (Twilio, telephony) |
alaw_8000 | A-law 8kHz - telephony (alternative to μ-law) |
opus_48000_64 | Opus 48kHz 64kbps - efficient streaming codec |
wav_44100 | WAV 44.1kHz - uncompressed with headers |
Streaming
For real-time applications, use the stream method (returns audio chunks as they're generated):
audio_stream = client.text_to_speech.stream(
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
FAQ
What does the text-to-speech skill do?
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.
How do I install text-to-speech?
Run npx skills add https://github.com/connorads/dotfiles --skill text-to-speech in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/text-to-speech/ (personal) or .claude/skills/text-to-speech/ (project).
Which AI agents can use text-to-speech?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is text-to-speech safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains text-to-speech?
It is published by connorads in connorads/dotfiles. It has 61 recorded installs.
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