SKILL.md
$28
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}")