Skip to main content Home Creators comeonoliver skillshub deepgram-sdk-patterns
deepgram-sdk-patterns Apply production-ready Deepgram SDK patterns for TypeScript and Python.
Use when implementing Deepgram integrations, refactoring SDK usage,
or establishing team coding standards for Deepgram.
Trigger: "deepgram SDK patterns", "deepgram best practices",
"deepgram code patterns", "idiomatic deepgram", "deepgram typescript".
Jump to install Skills Marketplace Discover and explore AI skills built by the community.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Show prompt details A direct command skips the review prompt. Inspect the source before running it.
npx skills add https://github.com/ComeOnOliver/skillshub --skill deepgram-sdk-patternsThe command stays on one line. Scroll horizontally to inspect it before copying.
Prefer a local copy? Download the files currently available to SkillsMP.
Download Zip Downloading... More from this repository 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.
Related occupations SOC
Based on SOC occupation classification
name deepgram-sdk-patterns description Apply production-ready Deepgram SDK patterns for TypeScript and Python.
Use when implementing Deepgram integrations, refactoring SDK usage,
or establishing team coding standards for Deepgram.
Trigger: "deepgram SDK patterns", "deepgram best practices",
"deepgram code patterns", "idiomatic deepgram", "deepgram typescript".
allowed-tools Read, Write, Edit version 1.0.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> compatible-with claude-code, codex, openclaw tags ["saas","deepgram","python","typescript","patterns"]
Deepgram SDK Patterns
Overview
Production patterns for @deepgram/sdk (TypeScript) and deepgram-sdk (Python). Covers singleton client, typed wrappers, text-to-speech with Aura, audio intelligence pipeline, error handling, and SDK v5 migration path.
Prerequisites
npm install @deepgram/sdk or pip install deepgram-sdkDEEPGRAM_API_KEY environment variable configured
Instructions
Step 1: Singleton Client (TypeScript)
import { createClient, DeepgramClient } from '@deepgram/sdk' ;
class DeepgramService {
private static instance : DeepgramService ;
private client : DeepgramClient ;
private constructor (const apiKey = process.env .DEEPGRAM_API_KEY ;
if (!apiKey) throw new Error ('DEEPGRAM_API_KEY is required' );
this .client = createClient (apiKey);
}
static getInstance (): DeepgramService {
if (!this .instance ) this .instance = new DeepgramService ();
return this .instance
getClient
DeepgramClient
return
this
client
export
const
DeepgramService
getInstance
getClient
Step 2: Text-to-Speech with Aura import { createClient } from '@deepgram/sdk' ;
import { writeFileSync } from 'fs' ;
const deepgram = createClient (process.env .DEEPGRAM_API_KEY !);
async function textToSpeech (text : string , outputPath : string const response = await deepgram.speak .request (
{ text },
{
model : 'aura-2-thalia-en' ,
encoding : 'linear16' ,
container : 'wav' ,
sample_rate : 24000 ,
}
);
const stream = await response.getStream ();
if (!stream) throw new Error ('No audio stream returned' );
const reader = stream.getReader ();
const chunks : Uint8Array [] = [];
while (true ) {
const { done, value } = await reader.read ();
if (done) break ;
chunks.push (value);
}
const buffer = Buffer .concat (chunks);
writeFileSync (outputPath, buffer);
console .log (`Audio saved: ${outputPath} (${buffer.length} bytes)` );
return buffer;
}
Step 3: Audio Intelligence Pipeline async function analyzeConversation (audioUrl : string const { result, error } = await deepgram.listen .prerecorded .transcribeUrl (
{ url : audioUrl },
{
model : 'nova-3' ,
smart_format : true ,
diarize : true ,
utterances : true ,
summarize : 'v2' ,
detect_topics : true ,
sentiment : true ,
intents : true ,
}
);
if (error) throw error;
return {
transcript : result.results .channels [0 ].alternatives [0 ].transcript ,
summary : result.results .summary ?.short ,
topics : result.results .topics ?.segments ?.map ((s : any ({
text : s.text ,
topics : s.topics .map ((t : any t.topic ),
})),
sentiments : result.results .sentiments ?.segments ?.map ((s : any ({
text : s.text ,
sentiment : s.sentiment ,
confidence : s.sentiment_score ,
})),
intents : result.results .intents ?.segments ?.map ((s : any ({
text : s.text ,
intent : s.intents [0 ]?.intent ,
confidence : s.intents [0 ]?.confidence_score ,
})),
};
}
Step 4: Python Production Patterns from deepgram import DeepgramClient, PrerecordedOptions, LiveOptions, SpeakOptions
import os
class DeepgramService :
_instance = None
def __new__ (cls ):
if cls._instance is None :
cls._instance = super ().__new__(cls)
cls._instance.client = DeepgramClient(os.environ["DEEPGRAM_API_KEY" ])
return cls._instance
def transcribe_url (self, url: str , **kwargs ):
options = PrerecordedOptions(
model=kwargs.get("model" , "nova-3" ),
smart_format=True ,
diarize=kwargs.get("diarize" , False ),
summarize=kwargs.get("summarize" , False ),
)
source = {"url" : url}
return self .client.listen.rest.v("1" ).transcribe_url(source, options)
def transcribe_file (self, path: str , **kwargs ):
with open (path, "rb" ) as f:
source = {"buffer" : f.read(), "mimetype" : self ._mimetype(path)}
options = PrerecordedOptions(
model=kwargs.get("model" , "nova-3" ),
smart_format=True ,
diarize=kwargs.get("diarize" , False ),
)
return self .client.listen.rest.v("1" ).transcribe_file(source, options)
def text_to_speech (self, text: str , output_path: str ):
options = SpeakOptions(model="aura-2-thalia-en" , encoding="linear16" )
response = self .client.speak.rest.v("1" ).save(output_path, {"text" : text}, options)
return response
@staticmethod
def _mimetype (path: str ) -> str :
ext = path.rsplit("." , 1 )[-1 ].lower()
return {"wav" : "audio/wav" , "mp3" : "audio/mpeg" , "flac" : "audio/flac" ,
"ogg" : "audio/ogg" , "m4a" : "audio/mp4" }.get(ext, "audio/wav" )
Step 5: Typed Response Helpers
interface TranscriptWord {
word : string ;
start : number ;
end : number ;
confidence : number ;
speaker ?: number ;
punctuated_word ?: string ;
}
interface TranscriptResult {
transcript : string ;
confidence : number ;
words : TranscriptWord [];
duration : number ;
requestId : string ;
}
function parseResult (result : any TranscriptResult {
const alt = result.results .channels [0 ].alternatives [0 ];
return {
transcript : alt.transcript ,
confidence : alt.confidence ,
words : alt.words ?? [],
duration : result.metadata .duration ,
requestId : result.metadata .request_id ,
};
}
Step 6: SDK v5 Migration Notes
import { createClient } from '@deepgram/sdk' ;
const dg = createClient (apiKey);
await dg.listen .prerecorded .transcribeUrl (source, options);
await dg.listen .live (options);
await dg.speak .request ({ text }, options);
import { DeepgramClient } from '@deepgram/sdk' ;
const dg = new DeepgramClient ({ apiKey });
await dg.listen .v1 .media .transcribeUrl (source, options);
await dg.listen .v1 .connect (options);
await dg.speak .v1 .audio .generate ({ text }, options);
Output
Singleton client pattern with environment validation
Text-to-speech (Aura-2) with stream-to-file
Audio intelligence pipeline (summary, topics, sentiment, intents)
Python production service class
Typed response helpers
v5 migration reference
Error Handling Error Cause Solution 401 UnauthorizedInvalid API key Check DEEPGRAM_API_KEY value 400 Unsupported formatBad audio codec Convert to WAV/MP3/FLAC speak.request is not a functionSDK version mismatch Check import, v5 uses speak.v1.audio.generate Empty TTS response Empty text input Validate text is non-empty before calling summarize returns nullFeature not enabled Pass summarize: 'v2' (string, not boolean)
Resources
Next Steps Proceed to deepgram-data-handling for transcript storage and processing patterns.
