Deepgram Performance Tuning

Prerequisites

• A baseline for latency, throughput, quality, error/throttle rate, and a named service owner.
• Licensed non-sensitive fixtures, approved load window, and a rollback threshold.

Examples

Measure the development/staging transcription baseline with short fixtures, change one concurrency, streaming, or model parameter, and compare aggregate quality/latency/error results. Keep the change only within the signed threshold; revert on regression and never use customer recordings as performance fixtures.

Overview

Optimize Deepgram transcription performance through audio preprocessing with ffmpeg, model selection for speed vs accuracy, streaming for large files, parallel processing, result caching, and connection reuse. Targets: <2s latency for short files, 100+ files/minute batch throughput.

Performance Levers

Instructions

Step 1: Audio Preprocessing with ffmpeg

# Optimal format for Deepgram: 16kHz, 16-bit, mono, WAV
ffmpeg -i input.mp3 \
  -ar 16000 \          # 16kHz sample rate (ideal for speech)
  -ac 1 \              # Mono channel
  -acodec pcm_s16le \  # 16-bit signed LE PCM
  -f wav \
  output.wav

# Remove silence (saves API cost + processing time)
ffmpeg -i input.wav \
  -af "silenceremove=stop_periods=-1:stop_duration=0.5:stop_threshold=-30dB" \
  -ar 16000 -ac 1 -acodec pcm_s16le \
  trimmed.wav

# Noise reduction + normalization
ffmpeg -i input.wav \
  -af "highpass=f=200,lowpass=f=3000,loudnorm=I=-16:TP=-1.5:LRA=11" \
  -ar 16000 -ac 1 -acodec pcm_s16le \
  clean.wav

import { execSync } from 'child_process';
import { statSync } from 'fs';

function preprocessAudio(inputPath: string, outputPath: string): {
  originalSize: number;
  optimizedSize: number;
  savings: string;
} {
  const originalSize = statSync(inputPath).size;

  execSync(`ffmpeg -y -i "${inputPath}" \
    -af "silenceremove=stop_periods=-1:stop_duration=0.5:stop_threshold=-30dB,\
    highpass=f=200,lowpass=f=3000" \
    -ar 16000 -ac 1 -acodec pcm_s16le \
    "${outputPath}" 2>/dev/null`);

  const optimizedSize = statSync(outputPath).size;
  const savings = ((1 - optimizedSize / originalSize) * 100).toFixed(1);

  console.log(`Preprocessed: ${inputPath}`);
  console.log(`  Original: ${(originalSize / 1024).toFixed(0)}KB`);
  console.log(`  Optimized: ${(optimizedSize / 1024).toFixed(0)}KB (${savings}% smaller)`);

  return { originalSize, optimizedSize, savings };
}

Step 2: Model Selection Strategy

import { createClient } from '@deepgram/sdk';

type Priority = 'accuracy' | 'speed' | 'cost';

function selectModel(priority: Priority, audioDuration: number): string {
  // Nova-3: Best accuracy, fast, $0.0043/min (STT)
  // Nova-2: Proven stable, fast, $0.0043/min
  // Base:   Fastest, lower accuracy, $0.0048/min
  // Whisper: Multilingual (100+ langs), slower, $0.0048/min

  switch (priority) {
    case 'accuracy':
      return 'nova-3';
    case 'speed':
      return audioDuration > 300 ? 'base' : 'nova-2';  // Base for long files
    case 'cost':
      return 'nova-2';  // Same price as Nova-3, slightly faster
    default:
      return 'nova-3';
  }
}

// Feature cost: disable what you don't need
function optimizedOptions(priority: Priority) {
  return {
    model: selectModel(priority, 0),
    smart_format: true,      // Free — always enable
    punctuate: true,         // Free — always enable
    // These add processing time:
    diarize: priority === 'accuracy',   // Adds latency
    utterances: priority === 'accuracy',
    paragraphs: priority === 'accuracy',
    summarize: false,        // Only when needed
    detect_topics: false,    // Only when needed
    sentiment: false,        // Only when needed
  };
}

Step 3: Streaming for Large Files

import { createClient, LiveTranscriptionEvents } from '@deepgram/sdk';
import { createReadStream } from 'fs';

async function streamLargeFile(filePath: string): Promise<string> {
  const deepgram = createClient(process.env.DEEPGRAM_API_KEY!);
  const transcripts: string[] = [];

  return new Promise((resolve, reject) => {
    const connection = deepgram.listen.live({
      model: 'nova-3',
      smart_format: true,
      encoding: 'linear16',
      sample_rate: 16000,
      channels: 1,
    });

    connection.on(LiveTranscriptionEvents.Open, () => {
      // Stream file in 32KB chunks
      const stream = createReadStream(filePath, { highWaterMark: 32 * 1024 });

      stream.on('data', (chunk: Buffer) => {
        connection.send(chunk);
      });

      stream.on('end', () => {
        // Signal end of audio
        connection.finish();
      });

      stream.on('error', reject);
    });

    connection.on(LiveTranscriptionEvents.Transcript, (data) => {
      if (data.is_final) {
        const text = data.channel.alternatives[0]?.transcript;
        if (text) transcripts.push(text);
      }
    });

    connection.on(LiveTranscriptionEvents.Close, () => {
      resolve(transcripts.join(' '));
    });

    connection.on(LiveTranscriptionEvents.Error, reject);
  });
}

Step 4: Parallel Batch Processing

import pLimit from 'p-limit';
import { createClient } from '@deepgram/sdk';

async function batchTranscribe(
  files: string[],
  concurrency = 50,   // Stay under your plan's concurrency limit
  model = 'nova-3'
) {
  const client = createClient(process.env.DEEPGRAM_API_KEY!);
  const limit = pLimit(concurrency);
  const startTime = Date.now();

  const results = await Promise.allSettled(
    files.map((file, i) =>
      limit(async () => {
        const fileStart = Date.now();
        const { result, error } = await client.listen.prerecorded.transcribeFile(
          require('fs').readFileSync(file),
          { model, smart_format: true, mimetype: 'audio/wav' }
        );
        if (error) throw error;

        const elapsed = Date.now() - fileStart;
        console.log(`[${i + 1}/${files.length}] ${file} — ${elapsed}ms (${result.metadata.duration}s audio)`);
        return { file, result, elapsed };
      })
    )
  );

  const totalTime = Date.now() - startTime;
  const succeeded = results.filter(r => r.status === 'fulfilled').length;
  console.log(`\nBatch: ${succeeded}/${files.length} in ${totalTime}ms`);
  console.log(`Throughput: ${(files.length / (totalTime / 60000)).toFixed(1)} files/min`);

  return results;
}

Step 5: Result Caching

import { createHash } from 'crypto';
import Redis from 'ioredis';

const redis = new Redis(process.env.REDIS_URL ?? 'redis://localhost:6379');

function cacheKey(audioUrl: string, options: Record<string, any>): string {
  const hash = createHash('sha256')
    .update(audioUrl + JSON.stringify(options))
    .digest('hex');
  return `dg:cache:${hash}`;
}

async function cachedTranscribe(
  client: ReturnType<typeof createClient>,
  url: string,
  options: Record<string, any>,
  ttlSeconds = 3600  // 1 hour default
) {
  const key = cacheKey(url, options);

  // Check cache
  const cached = await redis.get(key);
  if (cached) {
    console.log('Cache hit:', url.substring(0, 60));
    return JSON.parse(cached);
  }

  // Transcribe and cache
  const { result, error } = await client.listen.prerecorded.transcribeUrl(
    { url }, options
  );
  if (error) throw error;

  await redis.setex(key, ttlSeconds, JSON.stringify(result));
  console.log('Cached result:', url.substring(0, 60));
  return result;
}

Step 6: Performance Benchmarking

async function benchmark(audioUrl: string) {
  const client = createClient(process.env.DEEPGRAM_API_KEY!);
  const models = ['nova-3', 'nova-2', 'base'] as const;

  console.log('Performance Benchmark');
  console.log('='.repeat(60));

  for (const model of models) {
    const times: number[] = [];
    for (let i = 0; i < 3; i++) {
      const start = Date.now();
      const { result, error } = await client.listen.prerecorded.transcribeUrl(
        { url: audioUrl }, { model, smart_format: true }
      );
      times.push(Date.now() - start);
      if (error) { console.error(`${model} error:`, error.message); break; }
    }
    const avg = times.reduce((a, b) => a + b, 0) / times.length;
    console.log(`${model}: avg ${avg.toFixed(0)}ms (${times.map(t => `${t}ms`).join(', ')})`);
  }
}

Output

• Audio preprocessing pipeline (16kHz mono, silence removal, noise reduction)
• Model selection strategy by priority (accuracy/speed/cost)
• Streaming transcription for large files (>60s)
• Parallel batch processing with configurable concurrency
• Redis-backed result caching with TTL
• Performance benchmarking script

Error Handling

Resources

• Audio Best Practices
• Model Options
• Concurrency Limits