---
name: voice-changer
description: Transform the voice in an audio recording into a different target voice while preserving emotion, timing, and delivery using the ElevenLabs Voice Changer (speech-to-speech) API. Use when converting one voice to another, changing the speaker/narrator of an existing recording, dubbing a voice-over in a different voice, creating character voices from a scratch performance, anonymizing a speaker, or any "voice conversion / voice transfer / speech-to-speech" task. Make sure to use this skill whenever the user mentions voice changing, voice conversion, speech-to-speech, swapping a voice in audio, re-voicing a clip, or applying a different voice to an existing recording — even if they don't explicitly say "voice changer".
license: MIT
compatibility: Requires internet access and an ElevenLabs API key (ELEVENLABS_API_KEY).
metadata: {"openclaw": {"requires": {"env": ["ELEVENLABS_API_KEY"]}, "primaryEnv": "ELEVENLABS_API_KEY"}}
---

# 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](references/installation.md). 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

```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

```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

```bash
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)

```python
voices = client.voices.get_all()
for voice in voices.voices:
    print(f"{voice.voice_id}: {voice.name}")
```

## Converting from a URL

```python
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:

```python
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:

```python
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:

```python
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.

```python
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

```python
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](references/installation.md)


---
Source attribution: mashharuki
Original project: https://github.com/mashharuki/AWS-SummitHackathon-2026/blob/5f2b67fd174e65b5eca533128877f7d97699007d/.claude/skills/voice-changer/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 SABOROU Team

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: mashharuki
Original project: https://github.com/mashharuki/AWS-SummitHackathon-2026/blob/5f2b67fd174e65b5eca533128877f7d97699007d/.claude/skills/voice-changer/SKILL.md
Attribution basis: Source repository record

Fields of work: Content & Media, Marketing
Roles: Content Marketer, Influencer Marketer, Video Producer
Specialties: Specialty not specified
Tools: ElevenLabs
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 333d152ec6198de272f6819474b4bf6871e750be1546286fa8cd7add12a95ec9