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assemblyai-performance-tuning Optimize AssemblyAI API performance with caching, parallel processing, and model selection.
Use when experiencing slow transcriptions, implementing caching strategies,
or optimizing throughput for batch transcription workloads.
Trigger with phrases like "assemblyai performance", "optimize assemblyai",
"assemblyai latency", "assemblyai caching", "assemblyai slow", "assemblyai batch".
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下载 Zip 下载中... 同仓库更多 Skills Implement user sign-up and sign-in flows with Clerk.
Use when building authentication UI, customizing sign-in experience,
or implementing OAuth social login.
Trigger with phrases like "clerk sign-in", "clerk sign-up",
"clerk login flow", "clerk OAuth", "clerk social login".
Implement session management and middleware with Clerk.
Use when managing user sessions, configuring route protection,
or implementing token refresh and custom JWT templates.
Trigger with phrases like "clerk session", "clerk middleware",
"clerk route protection", "clerk token", "clerk JWT".
Configure enterprise SSO, role-based access control, and organization management.
Use when implementing SSO integration, configuring role-based permissions,
or setting up organization-level controls.
Trigger with phrases like "clerk SSO", "clerk RBAC",
"clerk enterprise", "clerk roles", "clerk permissions", "clerk organizations".
jeremylongshore
jeremylongshore/claude-code-plugins-plus-skills
打开 GitHub 仓库 name assemblyai-performance-tuning description Optimize AssemblyAI API performance with caching, parallel processing, and model selection.
Use when experiencing slow transcriptions, implementing caching strategies,
or optimizing throughput for batch transcription workloads.
Trigger with phrases like "assemblyai performance", "optimize assemblyai",
"assemblyai latency", "assemblyai caching", "assemblyai slow", "assemblyai batch".
allowed-tools Read, Write, Edit version 1.5.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> tags ["saas","ai","speech-to-text","assemblyai","transcription","performance"] compatibility Designed for Claude Code
AssemblyAI Performance Tuning
Overview
Optimize AssemblyAI transcription performance through model selection, parallel processing, caching, and webhook-based architectures.
Prerequisites
assemblyai package installedUnderstanding of async patterns
Redis or in-memory cache available (optional)
Latency Benchmarks (Actual)
Async Transcription
Audio Duration Approx. Processing Time Notes 30 seconds ~10-15 seconds Includes queue time 5 minutes ~30-60 seconds Scales sub-linearly 1 hour ~3-5 minutes Depends on queue load 10 hours ~15-30 minutes Max async duration
Streaming
Metric Value First partial transcript ~300ms (P50) Final transcript latency ~500ms (P50) End-of-turn detection Automatic with endpointing
Model Speed vs. Accuracy
Model Speed Accuracy Price/hr nanoFastest Good $0.12 best (Universal-3)Standard Highest $0.37 nova-3 (streaming)Real-time High $0.47 nova-3-pro (streaming)Real-time Highest $0.47
Instructions
Step 1: Choose the Right Model
import { AssemblyAI } from 'assemblyai' ;
const client = new AssemblyAI ({
apiKey
env
ASSEMBLYAI_API_KEY
const
await
transcripts
transcribe
audio
speech_model
'best'
const
await
transcripts
transcribe
audio
speech_model
'nano'
Step 2: Parallel Batch Processing import PQueue from 'p-queue' ;
const queue = new PQueue ({ concurrency : 10 });
async function batchTranscribe (audioUrls : string []const results = await Promise .all (
audioUrls.map (url =>add (() =>
client.transcripts .transcribe ({ audio : url, speech_model : 'nano' })
)
)
);
return results.filter (t =>status === 'completed' );
}
const urls = Array .from ({ length : 100 }, (_, i ) => `https://storage.example.com/audio-${i} .mp3` );
const transcripts = await batchTranscribe (urls);
console .log (`Completed: ${transcripts.length} /${urls.length} ` );
Step 3: Use Webhooks Instead of Polling
const slow = await client.transcripts .transcribe ({ audio : audioUrl });
const fast = await client.transcripts .submit ({
audio : audioUrl,
webhook_url : 'https://your-app.com/webhooks/assemblyai' ,
});
Step 4: Cache Transcript Results import { LRUCache } from 'lru-cache' ;
import type { Transcript } from 'assemblyai' ;
const transcriptCache = new LRUCache <string , Transcript >({
max : 500 ,
ttl : 60 * 60 * 1000 ,
});
async function getCachedTranscript (transcriptId : string Promise <Transcript > {
const cached = transcriptCache.get (transcriptId);
if (cached) return cached;
const transcript = await client.transcripts .get (transcriptId);
if (transcript.status === 'completed' ) {
transcriptCache.set (transcriptId, transcript);
}
return transcript;
}
Step 5: Redis Cache for Distributed Systems import Redis from 'ioredis' ;
const redis = new Redis (process.env .REDIS_URL !);
async function getCachedTranscriptRedis (transcriptId : string Promise <Transcript > {
const cached = await redis.get (`transcript:${transcriptId} ` );
if (cached) return JSON .parse (cached);
const transcript = await client.transcripts .get (transcriptId);
if (transcript.status === 'completed' ) {
await redis.setex (
`transcript:${transcriptId} ` ,
3600 ,
JSON .stringify (transcript)
);
}
return transcript;
}
Step 6: Minimize Feature Overhead
const minimal = await client.transcripts .transcribe ({
audio : audioUrl,
speech_model : 'nano' ,
punctuate : true ,
format_text : true ,
});
const full = await client.transcripts .transcribe ({
audio : audioUrl,
speech_model : 'best' ,
speaker_labels : true ,
sentiment_analysis : true ,
entity_detection : true ,
auto_highlights : true ,
content_safety : true ,
iab_categories : true ,
summarization : true ,
summary_type : 'bullets' ,
});
Step 7: Performance Monitoring async function timedTranscribe (audioUrl : string , options : Record <string , any > = {}const start = Date .now ();
const transcript = await client.transcripts .transcribe ({
audio : audioUrl,
...options,
});
const durationMs = Date .now () - start;
const stats = {
transcriptId : transcript.id ,
status : transcript.status ,
audioDuration : transcript.audio_duration ,
processingTimeMs : durationMs,
ratio : transcript.audio_duration
? (durationMs / 1000 / transcript.audio_duration ).toFixed (2 )
: 'N/A' ,
wordCount : transcript.words ?.length ?? 0 ,
model : options.speech_model ?? 'best' ,
};
console .log ('Transcription stats:' , stats);
return { transcript, stats };
}
Output
Optimal model selection based on speed/accuracy/cost trade-offs
Parallel batch processing with concurrency control
Webhook-based architecture (eliminates polling overhead)
In-memory and Redis caching for transcript retrieval
Performance monitoring with processing time ratios
Error Handling Issue Cause Solution Slow transcription Large file + best model Use nano model or split audio Queue backlog Too many concurrent submissions Limit concurrency with p-queue Cache stale data Transcript re-processed Set appropriate TTL, invalidate on webhook Polling overhead Using transcribe() for many files Switch to submit() + webhooks
Resources
Next Steps For cost optimization, see assemblyai-cost-tuning.
