Skip to main content
deepgram-reference-architecture Implement Deepgram reference architecture for scalable transcription systems.
Use when designing transcription pipelines, building production architectures,
or planning Deepgram integration at scale.
Trigger: "deepgram architecture", "transcription pipeline", "deepgram system design",
"deepgram at scale", "enterprise deepgram", "deepgram queue".
Zur Installation springen Skills Marktplatz Entdecken und erkunden Sie KI-Skills, die von der Community erstellt wurden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Prompt-Details anzeigen Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill deepgram-reference-architectureDer Befehl bleibt in einer Zeile. Scrollen Sie horizontal, um ihn vor dem Kopieren vollständig zu prüfen.
Sie bevorzugen eine lokale Kopie? Laden Sie die Dateien herunter, die SkillsMP derzeit vorliegen.
ZIP herunterladen Herunterladen... Mehr aus diesem Repository
Verwandte Berufe SOC
Basierend auf der SOC-Berufsklassifikation
name deepgram-reference-architecture description Implement Deepgram reference architecture for scalable transcription systems.
Use when designing transcription pipelines, building production architectures,
or planning Deepgram integration at scale.
Trigger: "deepgram architecture", "transcription pipeline", "deepgram system design",
"deepgram at scale", "enterprise deepgram", "deepgram queue".
allowed-tools Read, Write, Edit, Bash(npm:*) version 1.13.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> tags ["saas","deepgram","architecture","scaling"] compatibility Designed for Claude Code, also compatible with Codex and OpenClaw
Deepgram Reference Architecture
Overview
Four reference architectures for Deepgram transcription at scale: synchronous REST for short files, async queue (BullMQ) for batch processing, WebSocket proxy for real-time streaming, and a hybrid router that auto-selects the best pattern based on audio duration.
Architecture Selection Guide
Pattern Best For Latency Throughput Complexity Sync REST Files <60s, low volume Low Low Simple Async Queue Batch, files >60s Medium High Medium WebSocket Proxy Live audio, real-time Real-time Medium Medium Hybrid Router Mixed workloads Varies High High Callback Files >5min, fire-and-forget N/A Very High Low
Instructions
Step 1: Synchronous REST Pattern
import express from 'express' ;
import { createClient } from '@deepgram/sdk' ;
const app = express ();
app.use (express.json ());
const deepgram = createClient (process.env .DEEPGRAM_API_KEY !);
app.post ('/api/transcribe' , async (req, res) => {
const { url, model = 'nova-3' , diarize = false } = req.body ;
try {
const { result, error } = await
listen
prerecorded
transcribeUrl
smart_format
true
utterances
if
return
status
502
json
error
message
json
transcript
results
channels
0
alternatives
0
transcript
confidence
results
channels
0
alternatives
0
confidence
duration
metadata
duration
request_id
metadata
request_id
utterances
results
utterances
undefined
catch
err
any
status
500
json
error
message
Step 2: Async Queue Pattern (BullMQ) import { Queue , Worker , Job } from 'bullmq' ;
import { createClient } from '@deepgram/sdk' ;
import Redis from 'ioredis' ;
const connection = new Redis (process.env .REDIS_URL ?? 'redis://localhost:6379' );
const transcriptionQueue = new Queue ('transcription' , { connection });
async function submitJob (audioUrl : string , options : Record <string , any > = {}const job = await transcriptionQueue.add ('transcribe' , {
audioUrl,
model : options.model ?? 'nova-3' ,
diarize : options.diarize ?? false ,
submittedAt : new Date ().toISOString (),
}, {
attempts : 3 ,
backoff : { type : 'exponential' , delay : 5000 },
removeOnComplete : { age : 86400 },
});
console .log (`Job submitted: ${job.id} ` );
return job.id ;
}
const deepgram = createClient (process.env .DEEPGRAM_API_KEY !);
const worker = new Worker ('transcription' , async (job : Job ) => {
const { audioUrl, model, diarize } = job.data ;
console .log (`Processing job ${job.id} : ${audioUrl} ` );
const { result, error } = await deepgram.listen .prerecorded .transcribeUrl (
{ url : audioUrl },
{ model, smart_format : true , diarize, utterances : diarize }
);
if (error) throw new Error (`Deepgram error: ${error.message} ` );
const output = {
transcript : result.results .channels [0 ].alternatives [0 ].transcript ,
confidence : result.results .channels [0 ].alternatives [0 ].confidence ,
duration : result.metadata .duration ,
request_id : result.metadata .request_id ,
};
console .log (`Job ${job.id} complete: ${output.duration} s audio` );
return output;
}, {
connection,
concurrency : 10 ,
limiter : {
max : 50 ,
duration : 60000 ,
},
});
worker.on ('completed' , (job ) => console .log (`Completed: ${job.id} ` ));
worker.on ('failed' , (job, err ) => console .error (`Failed: ${job?.id} ` , err.message ));
Step 3: WebSocket Proxy for Real-Time import { WebSocketServer , WebSocket } from 'ws' ;
import { createClient, LiveTranscriptionEvents } from '@deepgram/sdk' ;
const wss = new WebSocketServer ({ port : 8080 });
wss.on ('connection' , (clientWs : WebSocket {
console .log ('Client connected' );
const deepgram = createClient (process.env .DEEPGRAM_API_KEY !);
const dgConnection = deepgram.listen .live ({
model : 'nova-3' ,
smart_format : true ,
interim_results : true ,
utterance_end_ms : 1000 ,
encoding : 'linear16' ,
sample_rate : 16000 ,
channels : 1 ,
});
dgConnection.on (LiveTranscriptionEvents .Transcript , (data ) => {
const transcript = data.channel .alternatives [0 ]?.transcript ;
if (transcript && clientWs.readyState === WebSocket .OPEN ) {
clientWs.send (JSON .stringify ({
type : 'transcript' ,
text : transcript,
is_final : data.is_final ,
speech_final : data.speech_final ,
}));
}
});
dgConnection.on (LiveTranscriptionEvents .UtteranceEnd , () => {
if (clientWs.readyState === WebSocket .OPEN ) {
clientWs.send (JSON .stringify ({ type : 'utterance_end' }));
}
});
clientWs.on ('message' , (data : Buffer {
if (dgConnection.getReadyState () === 1 ) {
dgConnection.send (data);
}
});
clientWs.on ('close' , () => {
dgConnection.finish ();
console .log ('Client disconnected' );
});
dgConnection.on (LiveTranscriptionEvents .Error , (err ) => {
console .error ('Deepgram error:' , err.message );
clientWs.close ();
});
});
console .log ('WebSocket proxy on ws://localhost:8080' );
Step 4: Hybrid Router import { createClient } from '@deepgram/sdk' ;
class TranscriptionRouter {
private client : ReturnType <typeof createClient>;
private queue : typeof transcriptionQueue;
constructor (apiKey : string , queue : any this .client = createClient (apiKey);
this .queue = queue;
}
async route (audioUrl : string , options : {
mode?: 'sync' | 'async' | 'callback' | 'auto' ;
estimatedDuration?: number ; // seconds
callbackUrl?: string ;
model?: string ;
diarize?: boolean ;
} = {}const mode = options.mode ?? 'auto' ;
const duration = options.estimatedDuration ?? 0 ;
const selectedMode = mode === 'auto'
? duration > 300 ? 'callback'
: duration > 60 ? 'async'
: 'sync'
: mode;
console .log (`Routing: ${selectedMode} (est. ${duration} s)` );
switch (selectedMode) {
case 'sync' :
return this .syncTranscribe (audioUrl, options);
case 'async' :
return this .asyncTranscribe (audioUrl, options);
case 'callback' :
return this .callbackTranscribe (audioUrl, options);
}
}
private async syncTranscribe (url : string , opts : any const { result, error } = await this .client .listen .prerecorded .transcribeUrl (
{ url },
{ model : opts.model ?? 'nova-3' , smart_format : true , diarize : opts.diarize }
);
if (error) throw error;
return { mode : 'sync' , result };
}
private async asyncTranscribe (url : string , opts : any const jobId = await submitJob (url, opts);
return { mode : 'async' , jobId };
}
private async callbackTranscribe (url : string , opts : any const { result } = await this .client .listen .prerecorded .transcribeUrl (
{ url },
{ model : opts.model ?? 'nova-3' , smart_format : true , callback : opts.callbackUrl }
);
return { mode : 'callback' , requestId : result.metadata .request_id };
}
}
Step 5: Architecture Diagram ┌──────────────┐
│ Client │
└──────┬───────┘
│
┌──────▼───────┐
│ API Gateway │
│ /transcribe │
└──────┬───────┘
│
┌──────▼───────┐
│ Hybrid Router │
└──┬───┬───┬───┘
│ │ │
┌───────────┘ │ └───────────┐
▼ ▼ ▼
┌──────────┐ ┌──────────┐ ┌──────────┐
│ Sync │ │ Queue │ │ Callback │
│ (<60s) │ │ (BullMQ) │ │ (>5min) │
└────┬─────┘ └────┬─────┘ └────┬─────┘
│ │ │
└──────────┬───┘──────────────┘
│
┌───────▼──────┐
│ Deepgram │
│ API │
└───────┬──────┘
│
┌───────▼──────┐
│ Results │
│ Store │
└──────────────┘
Output
Sync REST endpoint for short files
BullMQ queue with workers for batch processing
WebSocket proxy for real-time streaming
Hybrid router with auto-mode selection
Architecture diagram
Error Handling Issue Cause Solution Sync timeout on large file Wrong pattern selected Use async queue or callback Queue backlog growing Workers overloaded Scale workers, increase concurrency WebSocket disconnects Network instability Auto-reconnect with backoff Callback not received Endpoint unreachable Check HTTPS, verify callback URL
Resources 