dmccreary/claude-skills/text-to-speech

Claude Code and agent skill by dmccreary/claude-skills

Trust tier 2
  • community
LLM review pending
Source check pending

Checking that the SKILL.md file is still in the repository.

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.

Install

npx vskill@latest install dmccreary/claude-skills/text-to-speech

SKILL.md

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
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
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
bash
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 IDLanguagesLatencyBest For
eleven_v370+StandardHighest quality, emotional range
eleven_multilingual_v229StandardHigh quality, long-form content
eleven_flash_v2_532~75msUltra-low latency, real-time
eleven_flash_v2English~75msEnglish-only, fastest
eleven_turbo_v2_532~250-300msBalanced quality/speed
eleven_turbo_v2English~250-300msEnglish-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)
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 (only works with v2+ models).
  • 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",
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:

python
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...")
python
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:

python
# 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

FormatDescription
mp3_44100_128MP3 44.1kHz 128kbps (default) - compressed, good for web/apps
mp3_44100_192MP3 44.1kHz 192kbps (Creator+) - higher quality compressed
mp3_44100_64MP3 44.1kHz 64kbps - lower quality, smaller files
mp3_22050_32MP3 22.05kHz 32kbps - smallest MP3 files
pcm_16000Raw PCM 16kHz - use for real-time processing
pcm_22050Raw PCM 22.05kHz
pcm_24000Raw PCM 24kHz - good balance for streaming
pcm_44100Raw PCM 44.1kHz (Pro+) - CD quality
pcm_48000Raw PCM 48kHz (Pro+) - highest quality
ulaw_8000μ-law 8kHz - standard for phone systems (Twilio, telephony)
alaw_8000A-law 8kHz - telephony (alternative to μ-law)
opus_48000_64Opus 48kHz 64kbps - efficient streaming codec
wav_44100WAV 44.1kHz - uncompressed with headers

Streaming

For real-time applications, use the stream method (returns 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_flash_v2_5" # Ultra-low latency
)
for chunk in audio_stream:
play_audio(chunk)

See references/streaming.md for WebSocket streaming.

Error Handling

python
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):

python
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

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
91
Repository forks
12
7-day trend score
23

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

Works with

Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.

  • Claude Code
  • Cursor
  • GitHub Copilot
  • Windsurf
  • Codex
  • Gemini CLI
  • Cline
  • Amp
  • Roo Code
  • Goose
  • Continue
  • Kilo Code
  • Junie
  • Trae
  • OpenHands
  • Qwen Code
  • AI Agent
  • Tabnine Chat
  • Sourcegraph Cody
  • Amazon Q
  • Firebase Genkit
  • Sweep
  • Devin
  • Phind
  • Mentat
  • Claude Desktop
  • Aider
  • GPT Pilot
  • Plandex
  • bolt.new
  • v0
  • Replit Agent
  • Anse
  • TypingMind
  • BetterChatGPT
  • LibreChat
  • Warp AI
  • Pieces
  • Fig AI
Embed badge
vskill VERIFIED badge
[![vskill VERIFIED](https://verified-skill.com/api/v1/skills/dmccreary/claude-skills/text-to-speech/badge)](https://verified-skill.com/skills/dmccreary/claude-skills/text-to-speech)

Something wrong with this skill or its page? Tell us.