text-to-speech · elevenlabs — Claude Code skill: what it does, install

---
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 90+ 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 90+ languages, multiple models for quality vs latency tradeoffs. Examples default to `eleven_v4`, the latest and highest-quality model.

> **Setup, before writing any code:** if `ELEVENLABS_API_KEY` isn't set for this project, run `npx -y @elevenlabs/cli@latest onboard init` from the app's folder first and follow what it prints: it signs the user in and stores a key for the project, so nobody copies one by hand. Otherwise see the [Installation Guide](references/installation.md). For JavaScript, use `@elevenlabs/*` packages only.

## Quick Start

### Python

```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_v4"
)

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

### JavaScript

```javascript
import { ElevenLabsClient } from "@elevenlabs/elevenlabs-js";
import { createWriteStream } from "fs";
import { Readable } from "stream";

const client = new ElevenLabsClient();
const audio = await client.textToSpeech.convert("JBFqnCBsd6RMkjVDRZzb", {
  text: "Hello, welcome to ElevenLabs!",
  modelId: "eleven_v4",
});
// convert() returns a web ReadableStream — bridge it to a Node stream to write to disk
Readable.fromWeb(audio).pipe(createWriteStream("output.mp3"));
```

### CLI

Use `say` to play text immediately with the default voice and `eleven_v3` model:

```bash
elevenlabs say "Hello!"
```

Pipe text into `say` when another command produces the input:

```bash
echo "The build finished successfully." | elevenlabs say
```

Use the API command when you need to set request parameters directly:

```bash
elevenlabs text-to-speech convert --voice-id JBFqnCBsd6RMkjVDRZzb \
  --text "Hello!" --model-id eleven_v4 --output output.mp3
```

The CLI reads `ELEVENLABS_API_KEY` from the environment automatically.

## Models

| Model ID | Languages | Latency | Best For |
|----------|-----------|---------|----------|
| `eleven_v4` | 90+ | Standard | **Default.** Highest quality, emotional range, audio tags, voice cloning accuracy |
| `eleven_v4_turbo` | 90+ | ~100ms | Real-time v4 quality for agents and interactive apps (via [Text to Dialogue WebSocket](references/streaming.md#eleven-v4-turbo-websocket)) |
| `eleven_flash_v2_5` | 32 | ~75ms | Lowest latency, lowest cost, `stream-input` WebSocket |
| `eleven_flash_v2` | English | ~75ms | English-only, lowest latency |
| `eleven_multilingual_v2` | 29 | Standard | Previous generation; most stable on very long-form generations |
| `eleven_v3` | 70+ | Standard | Previous generation expressive model |

`eleven_turbo_v2_5` and `eleven_turbo_v2` are superseded by the Flash models (same output, lower latency) — use `eleven_flash_v2_5` / `eleven_flash_v2` instead.

**Eleven v4 notes:** supports only `stability` and `similarity_boost` voice settings (no `style` or `speed`), does not support SSML, and is not available on the `stream-input` TTS WebSocket. Direct delivery with audio tags such as `[whispers]`, `[laughs]`, `[sarcastic]`.

## 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)

```python
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 (v2/v3 models; not supported on Eleven v4).
- **Speed**: Speech speed multiplier (v2/v3 models; not supported on Eleven v4).
- **Speaker boost**: Post-processing that enhances clarity and voice similarity.

```python
from elevenlabs import VoiceSettings

audio = client.text_to_speech.convert(
    text="Customize my voice settings.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_v4",
    voice_settings=VoiceSettings(
        stability=0.5,          # Lower = more expressive, higher = more consistent
        similarity_boost=0.75,  # Higher = closer to the reference voice
    )
)
```

## Language Selection

Use `language_code` with models that support language enforcement to guide pronunciation and text normalization. Unsupported language codes are ignored, and `language_code` is not supported on `eleven_multilingual_v2`.

```python
audio = client.text_to_speech.convert(
    text="Bonjour, comment allez-vous?",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_v4",
    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...")

```python
audio = client.text_to_speech.convert(
    text="Call 1-800-555-0123 on 01/15/2026",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_v4",
    apply_text_normalization="on"
)
```

## Audio Effects

Use `audio_effects` to process the generated audio with a filter preset, perceived distance, a reverb environment, a looped background noise bed, and stereo panning. Effects do not change the cost of a request.

| Field | Range | Description |
|-------|-------|-------------|
| `filter_preset_id` | Catalog ID | Filter that colors the voice, such as `phone` or `old_radio` |
| `distance` | 0-1 (default 0) | How far the speaker sounds from the microphone |
| `environment_id` | Catalog ID | Reverb environment, such as `small_room` or `hall` |
| `send_level` | 0-1 (default 1) | How much of the voice feeds the reverb; 0 turns reverb off |
| `background_noise_id` | Catalog ID | Background noise looped under the voice, such as `cafe` or `keyboard` |
| `pan` | -1 to 1 (default 0) | Stereo position of the voice, from hard left to hard right |

List valid filter preset, environment, and background noise IDs with the catalog endpoint (`GET /v1/audio-effects/catalog`):

```python
from elevenlabs import EffectsSpecInput

catalog = client.audio_effects.get_catalog()
for environment in catalog.environments:
    print(f"{environment.environment_id}: {environment.name}")

audio = client.text_to_speech.convert(
    text="Welcome to the concert hall.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_v4",
    audio_effects=EffectsSpecInput(
        environment_id="hall",
        distance=0.3,
        pan=-0.5,
    )
)
```

Reverb and non-zero `pan` produce stereo audio and are rejected for `ulaw_*`, `alaw_*`, and `pcm_*` output formats. Unknown effect IDs return a 404 error.

## 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:

```python
# First request
audio1 = client.text_to_speech.convert(
    text="This is the first part.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_v4",
    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",
    model_id="eleven_v4",
    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

Use the `stream` method to receive audio chunks as they're generated:

```python
audio_stream = client.text_to_speech.stream(
    text="This text will be streamed as audio.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_v4"
)

for chunk in audio_stream:
    play_audio(chunk)
```

For real-time apps where latency matters most, use `eleven_v4_turbo` (~100ms) over the Text to Dialogue WebSocket, or `eleven_flash_v2_5` (~75ms) over HTTP streaming or the `stream-input` WebSocket. See [references/streaming.md](references/streaming.md).

## Error Handling

```python
try:
    audio = client.text_to_speech.convert(
        text="Generate speech",
        voice_id="invalid-voice-id",
        model_id="eleven_v4"
    )
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`):

```python
response = client.text_to_speech.convert.with_raw_response(
    text="Hello!", voice_id="JBFqnCBsd6RMkjVDRZzb", model_id="eleven_v4"
)
audio = response.parse()
print(f"Characters used: {response.headers.get('x-character-count')}")
```

## References

- [Installation Guide](references/installation.md)
- [Streaming Audio](references/streaming.md)
- [Voice Settings](references/voice-settings.md)