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Text To Speech

ElevenLabsOfficialPublished by ElevenLabs

  • Skill
  • Tool integration
  • Automation and integrations
  • MIT
  • Medium risk

From the author

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.
— the description by ElevenLabs

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides an interface to the ElevenLabs text-to-speech API, enabling the conversion of text into spoken audio. It supports multiple models, voice selection, and various audio output formats. Developers can use the provided SDKs for Python and JavaScript or the CLI to generate audio files, stream audio in real-time, and apply voice settings or audio effects to customize the output.

What it does

  • Converts text input into synthesized speech using ElevenLabs models.
  • Supports streaming audio chunks for real-time applications.
  • Allows selection of specific voice IDs and model configurations.
  • Provides options for adjusting voice stability, similarity, and audio effects.
  • Enables text normalization control for dates, numbers, and abbreviations.
  • Supports request stitching for generating long-form audio across multiple calls.

When to use it

  • When building voice-enabled applications or interactive agents.
  • When you need to generate audio files from text programmatically.
  • When integrating text-to-speech features into Python or JavaScript projects.
  • When you require command-line access to speech synthesis services.

Advantages

  • Supports over 90 languages depending on the selected model.
  • Offers multiple output formats including MP3, PCM, and WAV.
  • Provides granular control over voice characteristics and audio environment settings.
  • Includes tools for monitoring API usage and character counts.
  • Supports both file-based generation and real-time streaming.

Usage examples

“Generate an audio file from the text 'Hello, welcome to ElevenLabs!' using the George voice.”
The agent uses the ElevenLabs client to convert the provided text into an MP3 file using the specified voice ID.
“Stream audio for the text 'This text will be streamed as audio.'”
The agent initiates a stream from the ElevenLabs API and processes the audio chunks as they are received.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/elevenlabs/text-to-speech/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip text-to-speech.zip -d .agents/skills

Documentation by ElevenLabs

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. 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_v4"
)

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";
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:

elevenlabs say "Hello!"

Pipe text into say when another command produces the input:

echo "The build finished successfully." | elevenlabs say

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

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 IDLanguagesLatencyBest For
eleven_v490+StandardDefault. Highest quality, emotional range, audio tags, voice cloning accuracy
eleven_v4_turbo90+~100msReal-time v4 quality for agents and interactive apps (via Text to Dialogue WebSocket)
eleven_flash_v2_532~75msLowest latency, lowest cost, stream-input WebSocket
eleven_flash_v2English~75msEnglish-only, lowest latency
eleven_multilingual_v229StandardPrevious generation; most stable on very long-form generations
eleven_v370+StandardPrevious 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)

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.

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.

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...")

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.

FieldRangeDescription
filter_preset_idCatalog IDFilter that colors the voice, such as phone or old_radio
distance0-1 (default 0)How far the speaker sounds from the microphone
environment_idCatalog IDReverb environment, such as small_room or hall
send_level0-1 (default 1)How much of the voice feeds the reverb; 0 turns reverb off
background_noise_idCatalog IDBackground 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):

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:

# 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

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

Use the stream method to receive 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_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.

Error Handling

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):

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

Files

references/streaming.md

Streaming Audio

Stream audio chunks as they're generated for lower latency.

Model Selection for Streaming

ModelLatencyTransportUse Case
eleven_v4StandardHTTP streaming, Text to Dialogue WebSocketHighest quality, 90+ languages
eleven_v4_turbo~100msText to Dialogue WebSocketReal-time v4 quality for agents and interactive apps
eleven_flash_v2_5~75msHTTP streaming, stream-input WebSocketLowest latency, 32 languages
eleven_flash_v2~75msHTTP streaming, stream-input WebSocketLowest latency, English only

eleven_turbo_v2_5 is superseded by eleven_flash_v2_5, which is functionally equivalent with lower latency.

Python Streaming

from elevenlabs import ElevenLabs

client = ElevenLabs()

audio_stream = client.text_to_speech.stream(
    text="This is a streaming example.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_v4"
)

with open("output.mp3", "wb") as f:
    for chunk in audio_stream:
        f.write(chunk)
Real-Time Playback
import subprocess

def play_stream(audio_stream):
    process = subprocess.Popen(
        ["ffplay", "-nodisp", "-autoexit", "-"],
        stdin=subprocess.PIPE
    )
    for chunk in audio_stream:
        process.stdin.write(chunk)
    process.stdin.close()
    process.wait()

audio_stream = client.text_to_speech.stream(
    text="Playing this audio in real-time.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_v4"
)
play_stream(audio_stream)

JavaScript Streaming

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

const client = new ElevenLabsClient();

const audioStream = await client.textToSpeech.convert("JBFqnCBsd6RMkjVDRZzb", {
  text: "Streaming audio in JavaScript.",
  modelId: "eleven_v4",
});

// Write to file (convert() returns a web ReadableStream — bridge to a Node stream first)
Readable.fromWeb(audioStream).pipe(createWriteStream("output.mp3"));

// Or process chunks
for await (const chunk of audioStream) {
  console.log(`Received ${chunk.length} bytes`);
}

Eleven v4 Turbo WebSocket

For real-time Eleven v4 quality (~100ms), stream text to eleven_v4_turbo over the Text to Dialogue WebSocket (/v1/text-to-dialogue/stream-input). This endpoint only accepts Eleven v3 and v4 models. eleven_v4_turbo allows exactly one registered voice per connection; eleven_v4 allows up to 10.

import asyncio
import base64
import json
import os
import websockets
from dotenv import load_dotenv

load_dotenv()

ELEVENLABS_API_KEY = os.getenv("ELEVENLABS_API_KEY")
VOICE_ID = "JBFqnCBsd6RMkjVDRZzb"
URI = (
    "wss://api.elevenlabs.io/v1/text-to-dialogue/stream-input"
    "?model_id=eleven_v4_turbo&output_format=mp3_44100_128"
)

async def stream_v4_turbo(text: str):
    async with websockets.connect(URI) as websocket:
        # First message registers voices and authenticates
        await websocket.send(json.dumps({
            "voices": [VOICE_ID],
            "xi_api_key": ELEVENLABS_API_KEY,
        }))
        await websocket.send(json.dumps({
            "inputs": [{"text": text, "voice_id": VOICE_ID, "new_turn": False}],
        }))
        # Flush remaining text and close after the final audio frame
        await websocket.send(json.dumps({"close_socket": True}))

        with open("output.mp3", "wb") as f:
            while True:
                msg = json.loads(await websocket.recv())
                if msg.get("error"):
                    raise RuntimeError(msg)
                if msg.get("audio"):
                    f.write(base64.b64decode(msg["audio"]))
                if msg.get("is_final"):
                    break

asyncio.run(stream_v4_turbo(
    "This is a longer line of dialogue so the server has enough text to start streaming audio. "
))
import "dotenv/config";
import * as fs from "node:fs";
import WebSocket from "ws";

const voiceId = "JBFqnCBsd6RMkjVDRZzb";
const uri =
  "wss://api.elevenlabs.io/v1/text-to-dialogue/stream-input?model_id=eleven_v4_turbo&output_format=mp3_44100_128";

const websocket = new WebSocket(uri);
const out = fs.createWriteStream("output.mp3");

websocket.on("open", () => {
  websocket.send(JSON.stringify({ voices: [voiceId], xi_api_key: process.env.ELEVENLABS_API_KEY }));
  websocket.send(JSON.stringify({
    inputs: [{
      text: "This is a longer line of dialogue so the server has enough text to start streaming audio. ",
      voice_id: voiceId,
      new_turn: false,
    }],
  }));
  websocket.send(JSON.stringify({ close_socket: true }));
});

websocket.on("message", (data) => {
  const msg = JSON.parse(data.toString());
  if (msg.error) return console.error(msg);
  if (msg.audio) out.write(Buffer.from(msg.audio, "base64"));
});

websocket.on("close", () => out.end());

Behavior notes:

  • The server buffers about 40 characters and 8 words before emitting audio. Send {"flush": true} to force generation of shorter text without closing.

  • Omit close_socket to keep the connection open between lines; send {"keep_alive": true} to reset the 20 second inactivity timeout.

  • Set new_turn: true when a speaker finishes a turn so prosody resets.

  • Add sync_alignment=true to the query string to receive alignment timing data.

  • Response fields are snake_case (is_final). Each open connection holds one dialogue session from a pool separate from standard concurrency.

WebSocket Streaming

For text-streaming input where you send text chunks as they arrive (e.g., from an LLM) using Flash or Multilingual v2 models. The stream-input WebSocket does not support eleven_v3 or eleven_v4 — use the Eleven v4 Turbo WebSocket for those.

Connection
wss://api.elevenlabs.io/v1/text-to-speech/{voiceId}/stream-input?model_id={modelId}

Note: This WebSocket does not support eleven_v3 or eleven_v4. Use eleven_flash_v2_5 here for lowest latency, or eleven_v4_turbo over the Text to Dialogue WebSocket.

Message Flow
  1. Initialize - Send voice settings and configuration

  2. Send text - Stream text chunks as they arrive

  3. Close - Send empty string to signal completion

  4. Receive - Process audio chunks as they're generated

Python WebSocket
import asyncio
import json
import base64
import os
import websockets
from dotenv import load_dotenv

load_dotenv()

ELEVENLABS_API_KEY = os.getenv("ELEVENLABS_API_KEY")

async def text_to_speech_ws_streaming(voice_id: str, model_id: str):
    uri = f"wss://api.elevenlabs.io/v1/text-to-speech/{voice_id}/stream-input?model_id={model_id}"

    async with websockets.connect(uri) as websocket:
        # Initialize connection
        await websocket.send(json.dumps({
            "text": " ",
            "voice_settings": {
                "stability": 0.5,
                "similarity_boost": 0.8
            },
            "generation_config": {
                "chunk_length_schedule": [120, 160, 250, 290]
            },
            "xi_api_key": ELEVENLABS_API_KEY
        }))

        # Send text chunks
        await websocket.send(json.dumps({"text": "Hello, "}))
        await websocket.send(json.dumps({"text": "this is streaming text "}))
        await websocket.send(json.dumps({"text": "from a WebSocket connection."}))

        # Close stream (empty text signals completion)
        await websocket.send(json.dumps({"text": ""}))

        # Receive and process audio chunks
        audio_chunks = []
        while True:
            message = await websocket.recv()
            data = json.loads(message)
            if data.get("audio"):
                audio_chunks.append(base64.b64decode(data["audio"]))
            elif data.get("isFinal"):
                break

        return b"".join(audio_chunks)

async def main():
    audio = await text_to_speech_ws_streaming(
        voice_id="JBFqnCBsd6RMkjVDRZzb",
        model_id="eleven_flash_v2_5"
    )
    with open("output.mp3", "wb") as f:
        f.write(audio)

if __name__ == "__main__":
    asyncio.run(main())
JavaScript WebSocket
import "dotenv/config";
import WebSocket from "ws";
import * as fs from "node:fs";

const ELEVENLABS_API_KEY = process.env.ELEVENLABS_API_KEY;

async function textToSpeechWsStreaming(voiceId, modelId) {
  const uri = `wss://api.elevenlabs.io/v1/text-to-speech/${voiceId}/stream-input?model_id=${modelId}`;

  return new Promise((resolve, reject) => {
    const websocket = new WebSocket(uri, {
      headers: { "xi-api-key": ELEVENLABS_API_KEY },
    });

    const audioChunks = [];

    websocket.on("open", () => {
      // Initialize connection
      websocket.send(
        JSON.stringify({
          text: " ",
          voice_settings: {
            stability: 0.5,
            similarity_boost: 0.8,
          },
          generation_config: {
            chunk_length_schedule: [120, 160, 250, 290],
          },
        })
      );

      // Send text chunks
      websocket.send(JSON.stringify({ text: "Hello, " }));
      websocket.send(JSON.stringify({ text: "this is streaming text " }));
      websocket.send(JSON.stringify({ text: "from a WebSocket connection." }));

      // Close stream
      websocket.send(JSON.stringify({ text: "" }));
    });

    websocket.on("message", (event) => {
      const data = JSON.parse(event.toString());
      if (data.audio) {
        audioChunks.push(Buffer.from(data.audio, "base64"));
      } else if (data.isFinal) {
        websocket.close();
        resolve(Buffer.concat(audioChunks));
      }
    });

    websocket.on("error", reject);
  });
}

const audio = await textToSpeechWsStreaming(
  "JBFqnCBsd6RMkjVDRZzb",
  "eleven_flash_v2_5"
);
fs.writeFileSync("output.mp3", audio);
Input Messages

Initialization (first message):

{
  "text": " ",
  "voice_settings": {
    "stability": 0.5,
    "similarity_boost": 0.8,
    "use_speaker_boost": false
  },
  "generation_config": {
    "chunk_length_schedule": [120, 160, 250, 290]
  },
  "xi_api_key": "your_api_key"
}

Text chunks:

{ "text": "Your text content here" }

Force flush (generate audio immediately):

{ "text": "End of sentence.", "flush": true }

Close connection:

{ "text": "" }
Output Messages

Audio chunk:

{
  "audio": "base64_encoded_audio_data"
}

Stream complete:

{
  "isFinal": true
}
Key Parameters
ParameterDescription
chunk_length_scheduleArray of character counts that trigger audio generation. The model waits until it has this many characters before generating audio, which improves quality but adds latency. Lower values = faster response, higher values = better prosody. Example: [120, 160, 250, 290] means generate after 120 chars, then after 160 more, etc.
flushSet true to force immediate audio generation without waiting for the character threshold. Use at the end of sentences or when you need audio NOW.
voice_settingsAdjustable per-message: stability, similarity_boost, use_speaker_boost
Important Notes
  • Inactivity timeout: Connection closes after 20 seconds without activity. Send a space " " to keep alive.

  • TTFB (Time to First Byte): How long until audio starts playing. Affected by chunk_length_schedule - the model waits for enough text before generating.

  • Model limitation: The stream-input WebSocket does not support eleven_v3 or eleven_v4; use the Text to Dialogue WebSocket for those.

  • Best practice: Use flush: true at conversation turn endings to ensure the buffered text gets spoken.

  • Alignment data: Word-level timestamps available via alignment field for lip-sync or captions.

Best Practices

  1. Pick a real-time model:

    • eleven_v4_turbo for the most expressive real-time speech (~100ms, Text to Dialogue WebSocket)

    • eleven_flash_v2_5 for the lowest latency and cost, multilingual (~75ms)

    • eleven_flash_v2 for English-only (~75ms)

  2. Buffer audio before playback to prevent choppy output

  3. Handle disconnections gracefully in WebSocket streams

  4. Choose output format based on use case:

    • pcm_24000 - lowest latency processing

    • mp3_44100_128 - direct playback

    • ulaw_8000 - telephony/Twilio integration

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an API key, Needs Python, Needs Node.js

The author says: Requires internet access and an ElevenLabs API key (ELEVENLABS_API_KEY).

Security

What our scanner noticed· Medium risk

  • Instructions that use the network
  • Downloads piped into a shell

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Attribution and licence

Published by
ElevenLabs
Publisher
ElevenLabs (Official)
Source
elevenlabs/skills · text-to-speech · commit 9763c39
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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