Deepgram Core Workflow A: Pre-recorded Transcription
Overview
Production pre-recorded transcription service using Deepgram's REST API. Covers transcribeUrl and transcribeFile, speaker diarization, audio intelligence (summarization, topic detection, sentiment, intent), batch processing with concurrency control, and callback-based async transcription for large files.
Prerequisites
@deepgram/sdk installed, DEEPGRAM_API_KEY configured- Audio files: WAV, MP3, FLAC, OGG, M4A, or WebM
- For batch:
p-limit package (npm install p-limit)
Instructions
Step 1: Transcription Service Class
import { createClient, DeepgramClient } from '@deepgram/sdk';
import { readFileSync } from 'fs';
interface TranscribeOptions {
model?: 'nova-3' | 'nova-2' | 'nova-2-meeting' | 'nova-2-phonecall' | 'base';
language?: string;
diarize?: boolean;
utterances?: boolean;
paragraphs?: boolean;
smart_format?: boolean;
summarize?: boolean;
detect_topics?: boolean;
sentiment?: boolean;
intents?: boolean;
keywords?: string[];
callback?: string;
}
class DeepgramTranscriber {
private client: DeepgramClient;
constructor(apiKey: string) {
this.client = createClient(apiKey);
}
async transcribeUrl(url: string, opts: TranscribeOptions = {}) {
const { result, error } = await this.client.listen.prerecorded.transcribeUrl(
{ url },
{
model: opts.model ?? 'nova-3',
language: opts.language ?? 'en',
smart_format: opts.smart_format ?? true,
diarize: opts.diarize ?? false,
utterances: opts.utterances ?? false,
paragraphs: opts.paragraphs ?? false,
summarize: opts.summarize ? 'v2' : undefined,
detect_topics: opts.detect_topics ?? false,
sentiment: opts.sentiment ?? false,
intents: opts.intents ?? false,
keywords: opts.keywords,
callback: opts.callback,
}
);
if (error) throw new Error(`Transcription failed: ${error.message}`);
return result;
}
async transcribeFile(filePath: string, opts: TranscribeOptions = {}) {
const audio = readFileSync(filePath);
const mimetype = this.detectMimetype(filePath);
const { result, error } = await this.client.listen.prerecorded.transcribeFile(
audio,
{
model: opts.model ?? 'nova-3',
smart_format: opts.smart_format ?? true,
mimetype,
diarize: opts.diarize ?? false,
utterances: opts.utterances ?? false,
summarize: opts.summarize ? 'v2' : undefined,
detect_topics: opts.detect_topics ?? false,
sentiment: opts.sentiment ?? false,
}
);
if (error) throw new Error(`File transcription failed: ${error.message}`);
return result;
}
private detectMimetype(path: string): string {
const ext = path.split('.').pop()?.toLowerCase();
const map: Record<string, string> = {
wav: 'audio/wav', mp3: 'audio/mpeg', flac: 'audio/flac',
ogg: 'audio/ogg', m4a: 'audio/mp4', webm: 'audio/webm',
};
return map[ext ?? ''] ?? 'audio/wav';
}
}
Step 2: Extract Structured Results
function formatResult(result: any) {
const channel = result.results.channels[0];
const alt = channel.alternatives[0];
return {
transcript: alt.transcript,
confidence: alt.confidence,
words: alt.words?.map((w: any) => ({
word: w.word,
start: w.start,
end: w.end,
confidence: w.confidence,
speaker: w.speaker,
punctuated_word: w.punctuated_word,
})),
utterances: result.results.utterances?.map((u: any) => ({
speaker: u.speaker,
text: u.transcript,
start: u.start,
end: u.,
: u.,
})),
: result..?.,
: result..?.,
: result..?.,
: result..?.,
: {
: result..,
: result..,
: result..,
: result..,
},
};
}
Step 3: Batch Processing
import pLimit from 'p-limit';
async function batchTranscribe(
files: string[],
opts: TranscribeOptions = {},
concurrency = 5
) {
const transcriber = new DeepgramTranscriber(process.env.DEEPGRAM_API_KEY!);
const limit = pLimit(concurrency);
const results = await Promise.allSettled(
files.map(file =>
limit(async () => {
const result = await transcriber.transcribeFile(file, opts);
console.log(`Done: ${file} (${result.metadata.duration}s)`);
return { file, result: formatResult(result) };
})
)
);
const succeeded = results.filter(r => r.status === 'fulfilled');
const failed = results.filter(r => r.status === );
.();
results;
}
Step 4: Async Callback Transcription (Large Files)
async function submitAsync(audioUrl: string, callbackUrl: string) {
const transcriber = new DeepgramTranscriber(process.env.DEEPGRAM_API_KEY!);
const result = await transcriber.transcribeUrl(audioUrl, {
model: 'nova-3',
diarize: true,
callback: callbackUrl,
});
console.log('Submitted. Request ID:', result.metadata.request_id);
}
Step 5: Keyword Boosting
const result = await transcriber.transcribeUrl(audioUrl, {
model: 'nova-3',
keywords: [
'Kubernetes:1.5',
'PostgreSQL:1.5',
'microservices:1.3',
],
});
Output
DeepgramTranscriber class with URL and file transcription- Structured result extraction with word-level timing, speakers, and intelligence
- Batch processing with configurable concurrency via
p-limit
- Async callback pattern for large files
- Keyword boosting for domain vocabulary
Error Handling
| Error | Cause | Solution |
|---|
400 Bad Request | Invalid audio format | Verify file header bytes (WAV: RIFF, MP3: 0xFFF3/0xFFFB) |
413 Payload Too Large | File exceeds limit | Use callback URL for async processing |
| Empty transcript | No speech in audio | Check audio volume, try alternatives: 3 for confidence |
408 Timeout | Long file, sync mode | Switch to callback-based async |
| Low confidence | Background noise | Preprocess: ffmpeg -i input.wav -af "highpass=f=200,lowpass=f=3000" clean.wav |
Resources
Next Steps
Proceed to deepgram-core-workflow-b for real-time streaming transcription.
