ElevenLabs Voice Changer

Transform the voice in an audio recording into a different target voice. Voice Changer (previously called Speech-to-Speech — the API endpoint and SDK methods still use the speech_to_speech / speechToSpeech name) keeps the original performance — emotion, pacing, intonation, breaths, whispers, laughs, cries — and only swaps who is speaking.

Setup: See Installation Guide. For JavaScript, use @elevenlabs/* packages only.

Key Facts

• Maximum input length: 5 minutes per request — split longer recordings into chunks and stitch the outputs.
• Maximum file size: 50 MB per request — compress to MP3 if your source is larger.
• Pricing: 1,000 characters per minute of audio processed (duration-based, not text-based).
• Recommended model: eleven_multilingual_sts_v2 — often outperforms eleven_english_sts_v2 even for English-only content.

Quick Start

Python

from elevenlabs import ElevenLabs

client = ElevenLabs()

with open("source.mp3", "rb") as audio_file:
    audio_stream = client.speech_to_speech.convert(
        voice_id="JBFqnCBsd6RMkjVDRZzb",  # George
        audio=audio_file,
        model_id="eleven_multilingual_sts_v2",
        output_format="mp3_44100_128",
    )

with open("converted.mp3", "wb") as f:
    for chunk in audio_stream:
        f.write(chunk)

JavaScript

import { ElevenLabsClient } from "@elevenlabs/elevenlabs-js";
import { createReadStream, createWriteStream } from "fs";

const client = new ElevenLabsClient();

const audioStream = await client.speechToSpeech.convert("JBFqnCBsd6RMkjVDRZzb", {
  audio: createReadStream("source.mp3"),
  modelId: "eleven_multilingual_sts_v2",
  outputFormat: "mp3_44100_128",
});

audioStream.pipe(createWriteStream("converted.mp3"));

cURL

curl -X POST "https://api.elevenlabs.io/v1/speech-to-speech/JBFqnCBsd6RMkjVDRZzb?output_format=mp3_44100_128" \
  -H "xi-api-key: $ELEVENLABS_API_KEY" \
  -F "audio=@source.mp3" \
  -F "model_id=eleven_multilingual_sts_v2" \
  --output converted.mp3

Parameters

Parameter	Type	Default	Description
voice_id	string (required)	—	Target voice to speak in. Use a pre-made voice ID, a cloned voice, or a voice from the library
audio	file (required)	—	Source audio whose performance (emotion, timing, delivery) will be preserved
model_id	string	eleven_english_sts_v2	eleven_multilingual_sts_v2 for 29 languages, eleven_english_sts_v2 for English-only
output_format	string	mp3_44100_128	See output formats table below
voice_settings	JSON string	—	Override stored voice settings for this request only
seed	integer	—	Best-effort deterministic sampling (0 – 4294967295)
remove_background_noise	boolean	false	Run the isolation model on the input before conversion
file_format	string	other	other for any encoded audio, or pcm_s16le_16 for 16-bit PCM mono @ 16kHz little-endian (lower latency)
optimize_streaming_latency	int (query)	—	0–4. Trade quality for latency. 4 is fastest but disables the text normalizer
enable_logging	boolean (query)	true	Set to false for zero-retention mode (enterprise only — disables history/stitching)

Models

Model ID	Languages	Best For
eleven_multilingual_sts_v2	29	Recommended for everything — often outperforms the English model even on English audio
eleven_english_sts_v2	English	API default — English-only fallback

Only models whose can_do_voice_conversion property is true can be used here. Voice Changer does not currently have a low-latency "flash/turbo" tier — if you need one, keep pcm_s16le_16 input, an opus_* / low-bitrate mp3_* output, and raise optimize_streaming_latency.

Languages (eleven_multilingual_sts_v2)

English (US, UK, AU, CA), Japanese, Chinese, German, Hindi, French (FR, CA), Korean, Portuguese (BR, PT), Italian, Spanish (ES, MX), Indonesian, Dutch, Turkish, Filipino, Polish, Swedish, Bulgarian, Romanian, Arabic (SA, AE), Czech, Greek, Finnish, Croatian, Malay, Slovak, Danish, Tamil, Ukrainian, Russian.

Target Voices

Use any voice ID from pre-made voices, your cloned voices, or the voice library.

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}")

Converting from a URL

import requests
from io import BytesIO
from elevenlabs import ElevenLabs

client = ElevenLabs()

audio_url = "https://storage.googleapis.com/eleven-public-cdn/audio/marketing/nicole.mp3"
response = requests.get(audio_url)
audio_data = BytesIO(response.content)

audio_stream = client.speech_to_speech.convert(
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    audio=audio_data,
    model_id="eleven_multilingual_sts_v2",
    output_format="mp3_44100_128",
)

with open("converted.mp3", "wb") as f:
    for chunk in audio_stream:
        f.write(chunk)

Voice Settings Override

Fine-tune the target voice for a single request without changing its stored defaults:

from elevenlabs import VoiceSettings

audio_stream = client.speech_to_speech.convert(
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    audio=audio_file,
    model_id="eleven_multilingual_sts_v2",
    voice_settings=VoiceSettings(
        stability=0.5,
        similarity_boost=0.75,
        style=0.0,
        use_speaker_boost=True,
    ),
)

• Stability: lower = more emotional range (follows the source more freely), higher = steadier delivery.
• Similarity boost: higher = closer to the target voice's timbre, may amplify source artifacts.
• Style: exaggerates the target voice's unique characteristics (v2+ models).
• Speaker boost: post-processing to sharpen clarity of the target voice.

Cleaning Up Noisy Source Audio

If the input recording is noisy, either pre-process with the voice-isolator skill or pass remove_background_noise=True to do it in a single call:

audio_stream = client.speech_to_speech.convert(
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    audio=audio_file,
    model_id="eleven_multilingual_sts_v2",
    remove_background_noise=True,
)

Cleaner input almost always produces better conversion — the model is trying to match phonemes and prosody, and background noise gets in the way.

Low-Latency PCM Input

If you already have raw 16-bit PCM mono @ 16kHz, passing file_format="pcm_s16le_16" skips decoding and reduces latency:

audio_stream = client.speech_to_speech.convert(
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    audio=pcm_bytes,
    model_id="eleven_multilingual_sts_v2",
    file_format="pcm_s16le_16",
)

Pair this with optimize_streaming_latency (0–4) as a query param for further latency reductions at some quality cost.

Output Formats

Format	Description
mp3_44100_128	MP3 44.1kHz 128kbps (default) — good for web/apps
mp3_44100_192	MP3 44.1kHz 192kbps (Creator+) — higher quality
mp3_44100_64	MP3 44.1kHz 64kbps — smaller files
mp3_22050_32	MP3 22.05kHz 32kbps — smallest MP3
pcm_16000	Raw PCM 16kHz — real-time pipelines
pcm_24000	Raw PCM 24kHz — good streaming balance
pcm_44100	Raw PCM 44.1kHz (Pro+) — CD quality
pcm_48000	Raw PCM 48kHz (Pro+) — highest quality
ulaw_8000	μ-law 8kHz — Twilio / telephony
alaw_8000	A-law 8kHz — telephony
opus_48000_64	Opus 48kHz 64kbps — efficient streaming

Deterministic Output

Pass a seed to make repeated conversions of the same input return (best-effort) identical audio — useful for testing and A/B comparisons.

audio_stream = client.speech_to_speech.convert(
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    audio=audio_file,
    model_id="eleven_multilingual_sts_v2",
    seed=12345,
)

Input Audio Best Practices

The conversion quality is bounded by the input recording — the model can only swap the timbre, not rescue a bad source. A few practical rules:

• Be expressive. Whisper, shout, laugh, cry — the model preserves all of it. Flat input gives you flat output.
• Watch microphone gain. Too quiet and the model under-detects phonemes; too loud and clipping bleeds into the conversion. Aim for healthy peaks, no clipping.
• Accent and cadence transfer from the source, not the target. If you read in an American accent and target the British "George" voice, you get George's timbre with an American accent. To dub into a different accent or language, record someone speaking in that target accent/language and convert into a cloned/library voice.
• Clean up noise first. Either pass remove_background_noise=True or run the source through the voice-isolator skill before conversion. Noise hurts more here than in TTS.
• Split long recordings. Anything over 5 minutes must be chunked. Cut at natural pauses, convert each piece, and concatenate the resulting audio.

Common Workflows

• Re-voice a narration — keep the performance of a scratch recording, swap in a different narrator voice.
• Localize / dub — convert a voice-over into the same speaker's cloned voice in another language (using eleven_multilingual_sts_v2).
• Create character voices — act out a line yourself, convert into a distinctive character voice for games or animation.
• Anonymize a speaker — replace a recognizable voice with a neutral pre-made voice while preserving what was said and how.
• Pair with voice-isolator — isolate the source voice first (or set remove_background_noise=True) for noisy field recordings before conversion.
• Pair with voice cloning — clone a target voice from a short sample, then use its voice_id here as the conversion target.

Error Handling

try:
    audio_stream = client.speech_to_speech.convert(
        voice_id="JBFqnCBsd6RMkjVDRZzb",
        audio=audio_file,
        model_id="eleven_multilingual_sts_v2",
    )
except Exception as e:
    print(f"Voice changer failed: {e}")

Common errors:

• 401: Invalid API key
• 422: Invalid parameters (check voice_id, model_id, or file_format vs the supplied audio)
• 429: Rate limit exceeded

References

• Installation Guide