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"));

CLI

elevenlabs speech-to-speech convert \
  --voice-id JBFqnCBsd6RMkjVDRZzb \
  --audio source.mp3 \
  --model-id eleven_multilingual_sts_v2 \
  --output-format mp3_44100_128 \
  --output converted.mp3

Parameters

Models

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

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