Skip to main content Accueil Créateurs comeonoliver skillshub elevenlabs-performance-tuning
elevenlabs-performance-tuning Optimize ElevenLabs TTS latency with model selection, streaming, caching, and audio format tuning.
Use when experiencing slow TTS responses, implementing real-time voice features,
or optimizing audio generation throughput.
Trigger: "elevenlabs performance", "optimize elevenlabs", "elevenlabs latency",
"elevenlabs slow", "fast TTS", "reduce elevenlabs latency", "TTS streaming".
Aller à l'installation Skills Marketplace Découvrez et explorez les compétences IA créées par la communauté.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Afficher les détails du prompt Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
npx skills add https://github.com/ComeOnOliver/skillshub --skill elevenlabs-performance-tuningLa commande reste sur une seule ligne. Faites défiler horizontalement pour la vérifier avant de la copier.
Vous préférez une copie locale ? Téléchargez les fichiers actuellement disponibles dans SkillsMP.
Télécharger Zip Téléchargement... Plus depuis ce dépôt Review product and feature risk before an AI coding agent starts implementation.
Use Xquik for X data and confirmation-gated X actions: tweet search, user lookup, follower export, media download, monitors, webhooks, MCP, and SDK workflows.
Canton Network open-source ecosystem guide covering DAML SDK, Canton runtime, and Splice applications. Use when working with Canton Network, DAML smart contracts, or building decentralized applications.
Métiers associés SOC
Basé sur la classification professionnelle SOC
name elevenlabs-performance-tuning description Optimize ElevenLabs TTS latency with model selection, streaming, caching, and audio format tuning.
Use when experiencing slow TTS responses, implementing real-time voice features,
or optimizing audio generation throughput.
Trigger: "elevenlabs performance", "optimize elevenlabs", "elevenlabs latency",
"elevenlabs slow", "fast TTS", "reduce elevenlabs latency", "TTS streaming".
allowed-tools Read, Write, Edit version 1.0.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> tags ["saas","voice","ai","elevenlabs","performance","optimization"] compatible-with claude-code
ElevenLabs Performance Tuning
Overview
Optimize ElevenLabs TTS latency and throughput through model selection, streaming strategies, audio format tuning, and caching. Latency ranges from ~75ms (Flash) to ~500ms (v3) depending on configuration.
Prerequisites
ElevenLabs SDK installed
Understanding of your latency requirements
Audio playback infrastructure (browser, mobile, server-side)
Instructions
Step 1: Model Selection for Latency
The single biggest performance lever is model choice:
Model Avg Latency Quality Languages Use Case eleven_flash_v2_5~75ms Good 32 Real-time chat, IVR, gaming eleven_turbo_v2_5~150ms Good 32 Balanced speed/quality eleven_multilingual_v2~300ms High 29 Narration, content creation eleven_v3~500ms Highest 70+ Maximum expressiveness
function selectModel (useCase : "realtime" | "balanced" | "quality" | "max_quality" string {
const models = {
realtime : "eleven_flash_v2_5" ,
balanced : "eleven_turbo_v2_5" ,
quality : "eleven_multilingual_v2" ,
max_quality : "eleven_v3" ,
};
return models[useCase];
}
Step 2: Output Format Optimization
Smaller formats = faster transfer:
Format Size/Second Quality Best For mp3_44100_128~16 KB/s High Downloads, archival mp3_22050_32~4 KB/s Medium Streaming, mobile pcm_16000~32 KB/s Raw Server-side processing pcm_44100~88 KB/s Raw High-quality processing ulaw_8000~8 KB/s Phone Telephony/IVR
const streamingConfig = {
output_format : "mp3_22050_32" ,
model_id : "eleven_flash_v2_5" ,
};
const downloadConfig = {
output_format : "mp3_44100_128" ,
model_id : "eleven_multilingual_v2" ,
};
Step 3: HTTP Streaming for Time-to-First-Byte Use the streaming endpoint to start playback before full generation completes:
import { ElevenLabsClient } from "@elevenlabs/elevenlabs-js" ;
const client = new ElevenLabsClient ();
async function streamToResponse (
text : string ,
voiceId : string ,
res : Response | import ("express" ).Response
) {
const startTime = performance.now ();
const stream = await client.textToSpeech .stream (voiceId, {
text,
model_id : "eleven_flash_v2_5" ,
output_format : "mp3_22050_32" ,
voice_settings : {
stability : 0.5 ,
similarity_boost : 0.75 ,
style : 0.0 ,
},
});
let firstChunk = true ;
for await (const chunk of stream) {
if (firstChunk) {
const ttfb = performance.now () - startTime;
console .log (`Time to first byte: ${ttfb.toFixed(0 )} ms` );
firstChunk = false ;
}
(res as any ).write (chunk);
}
(res as any ).end ();
}
Step 4: WebSocket Streaming for Lowest Latency For interactive applications where text arrives in chunks (e.g., from an LLM):
import WebSocket from "ws" ;
interface WSStreamConfig {
voiceId : string ;
modelId ?: string ;
chunkLengthSchedule ?: number [];
}
async function createTTSStream (config : WSStreamConfig const model = config.modelId || "eleven_flash_v2_5" ;
const url = `wss://api.elevenlabs.io/v1/text-to-speech/${config.voiceId} /stream-input?model_id=${model} ` ;
const ws = new WebSocket (url);
const audioChunks : Buffer [] = [];
let totalLatency = 0 ;
let firstAudioTime = 0 ;
await new Promise <void >((resolve, reject ) => {
ws.on ("open" , resolve);
ws.on ("error" , reject);
});
ws.send (JSON .stringify ({
text : " " ,
xi_api_key : process.env .ELEVENLABS_API_KEY ,
voice_settings : { stability : 0.5 , similarity_boost : 0.75 },
chunk_length_schedule : config.chunkLengthSchedule || [50 , 120 , 200 ],
}));
return {
sendText (text : string send (JSON .stringify ({ text }));
},
finish (): Promise <Buffer > {
return new Promise ((resolve ) => {
const sendTime = Date .now ();
ws.on ("message" , (data : Buffer {
const msg = JSON .parse (data.toString ());
if (msg.audio ) {
if (!firstAudioTime) {
firstAudioTime = Date .now ();
totalLatency = firstAudioTime - sendTime;
}
audioChunks.push (Buffer .from (msg.audio , "base64" ));
}
if (msg.isFinal ) {
console .log (`WebSocket TTFB: ${totalLatency} ms` );
ws.close ();
resolve (Buffer .concat (audioChunks));
}
});
ws.send (JSON .stringify ({ text : "" }));
});
},
};
}
const stream = await createTTSStream ({
voiceId : "21m00Tcm4TlvDq8ikWAM" ,
chunkLengthSchedule : [50 , 100 , 150 ],
});
stream.sendText ("Hello, " );
stream.sendText ("how are " );
stream.sendText ("you today?" );
const audio = await stream.finish ();
Step 5: Audio Caching Cache generated audio for repeated content (greetings, prompts, errors):
import { LRUCache } from "lru-cache" ;
import crypto from "crypto" ;
const audioCache = new LRUCache <string , Buffer >({
max : 500 ,
maxSize : 100 * 1024 * 1024 ,
sizeCalculation : (value ) => value.length ,
ttl : 24 * 60 * 60 * 1000 ,
});
function cacheKey (text : string , voiceId : string , modelId : string string {
return crypto.createHash ("sha256" )
.update (`${voiceId} :${modelId} :${text} ` )
.digest ("hex" );
}
async function cachedTTS (
text : string ,
voiceId : string ,
modelId = "eleven_multilingual_v2"
): Promise <Buffer > {
const key = cacheKey (text, voiceId, modelId);
const cached = audioCache.get (key);
if (cached) {
console .log ("[Cache HIT]" , key.substring (0 , 8 ));
return cached;
}
const stream = await client.textToSpeech .convert (voiceId, {
text,
model_id : modelId,
});
const chunks : Buffer [] = [];
for await (const chunk of stream as any ) {
chunks.push (Buffer .from (chunk));
}
const audio = Buffer .concat (chunks);
audioCache.set (key, audio);
console .log ("[Cache MISS]" , key.substring (0 , 8 ), `${audio.length} bytes` );
return audio;
}
Step 6: Parallel Generation Generate multiple audio segments concurrently:
import PQueue from "p-queue" ;
const queue = new PQueue ({ concurrency : 5 });
async function generateChapters (
chapters : { title: string ; text: string }[],
voiceId : string
): Promise <Buffer []> {
const results = await Promise .all (
chapters.map (chapter =>add (async () => {
const start = performance.now ();
const audio = await cachedTTS (chapter.text , voiceId);
const duration = performance.now () - start;
console .log (`${chapter.title} : ${duration.toFixed(0 )} ms` );
return audio;
})
)
);
return results as Buffer [];
}
Performance Optimization Checklist Optimization Latency Impact Implementation Flash model -60% vs v2, -85% vs v3 Change model_id Streaming endpoint -50% time-to-first-byte Use .stream() instead of .convert() WebSocket streaming Best for LLM integration See Step 4 Smaller output format -30% transfer time mp3_22050_32 vs mp3_44100_128Audio caching -99% for repeated content LRU cache with SHA-256 keys style: 0-10-20% latency Remove style exaggeration Concurrency queue Maximize throughput p-queue matching plan limit
Error Handling Issue Cause Solution High TTFB Wrong model Switch to eleven_flash_v2_5 Choppy streaming Network buffering Use pcm_16000 for direct playback Cache miss storm TTL expired for popular content Use stale-while-revalidate pattern WebSocket drops Network instability Reconnect with buffered text Memory pressure Audio cache too large Set maxSize limit on LRU cache
Resources
Next Steps For cost optimization, see elevenlabs-cost-tuning.
