Text To Speech

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.

Project defaults — load .env FIRST

Before any TTS call in this repo, load .env and use the project defaults defined there. Pull voice_id, model_id, and all voice_settings from environment variables — do not hardcode them, even in throwaway scripts.

Full snippets (Python / JS / cURL) and the speed-selection rule live in references/voice-settings.md. The Quick Start below shows hardcoded values for illustration only — every real call must read from env.

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

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

Word/character timestamps — default for any sync use case

If downstream code needs to know when each word is spoken (subtitles, captions, marker highlights, animation triggers, scene transitions tied to narration), use convert_with_timestamps — never generate audio first and run Whisper on it. ElevenLabs returns character-level alignment alongside the audio in a single call, so timestamps come from the same model that produced the audio (sample-accurate, no transcription drift, no extra dependency).

Python — audio + word-level transcript

import base64, json, os, wave
from dotenv import load_dotenv
from elevenlabs import ElevenLabs, VoiceSettings

load_dotenv()
client = ElevenLabs(api_key=os.environ["ELEVENLABS_API_KEY"])

resp = client.text_to_speech.convert_with_timestamps(
    voice_id=os.environ["ELEVENLABS_VOICE_ID"],
    text="Claude just got fifteen new connectors. AllTrails. Spotify.",
    model_id=os.environ["ELEVENLABS_MODEL_ID"],
    output_format="pcm_44100",
    voice_settings=VoiceSettings(
        stability=float(os.environ["ELEVENLABS_STABILITY"]),
        similarity_boost=float(os.environ["ELEVENLABS_SIMILARITY_BOOST"]),
        style=float(os.environ["ELEVENLABS_STYLE"]),
        speed=float(os.environ["ELEVENLABS_SPEED"]),
        use_speaker_boost=True,
    ),
)

# 1. Audio: base64-decode and wrap raw PCM in a WAV header.
pcm = base64.b64decode(resp.audio_base_64)
with wave.open("narration.wav", "wb") as f:
    f.setnchannels(1); f.setsampwidth(2); f.setframerate(44100)
    f.writeframes(pcm)

# 2. Word-level transcript: collapse character alignment into whitespace-delimited tokens.
align = resp.normalized_alignment or resp.alignment  # normalized strips punctuation oddities
words, current = [], None
for ch, t0, t1 in zip(align.characters, align.character_start_times_seconds, align.character_end_times_seconds):
    if ch.isspace():
        if current: words.append(current); current = None
    else:
        if current is None: current = {"word": ch, "start": t0, "end": t1}
        else: current["word"] += ch; current["end"] = t1
if current: words.append(current)

with open("transcript.json", "w", encoding="utf-8") as f:
    json.dump(words, f, ensure_ascii=False, indent=2)

Response shape

AudioWithTimestampsResponse has:

• audio_base_64 — the audio (base64-encoded; decode before writing to disk)
• alignment — character-level: characters[], character_start_times_seconds[], character_end_times_seconds[]
• normalized_alignment — same shape, but for the normalized text (numbers expanded, abbreviations spelled out, etc.). Prefer this when grouping into words — it matches what the model actually spoke.

When to skip timestamps

Plain convert (no timestamps) is fine when the audio is the only output and nothing downstream needs sync — e.g. one-off voiceovers, podcasts where word-by-word timing doesn't matter. For anything visual that has to land on a syllable, use convert_with_timestamps.

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