deepgram-performance-tuning

# Deepgram Performance Tuning

## Overview
Optimize Deepgram integration performance through audio preprocessing, connection management, and configuration tuning.

## Prerequisites
- Working Deepgram integration
- Performance monitoring in place
- Audio processing capabilities
- Baseline metrics established

## Performance Factors

| Factor | Impact | Optimization |
|--------|--------|--------------|
| Audio Format | High | Use optimal encoding |
| Sample Rate | Medium | Match model requirements |
| File Size | High | Stream large files |
| Model Choice | High | Balance accuracy vs speed |
| Network Latency | Medium | Use closest region |
| Concurrency | Medium | Manage connections |

## Instructions

### Step 1: Optimize Audio Format
Preprocess audio for optimal transcription.

### Step 2: Configure Connection Pooling
Reuse connections for better throughput.

### Step 3: Tune API Parameters
Select appropriate model and features.

### Step 4: Implement Streaming
Use streaming for real-time and large files.

## Examples

### Audio Preprocessing
```typescript
// lib/audio-optimizer.ts
import ffmpeg from 'fluent-ffmpeg';
import { Readable } from 'stream';

interface OptimizedAudio {
  buffer: Buffer;
  mimetype: string;
  sampleRate: number;
  channels: number;
  duration: number;
}

export async function optimizeAudio(inputPath: string): Promise<OptimizedAudio> {
  return new Promise((resolve, reject) => {
    const chunks: Buffer[] = [];

    // Optimal settings for Deepgram
    ffmpeg(inputPath)
      .audioCodec('pcm_s16le')      // 16-bit PCM
      .audioChannels(1)              // Mono
      .audioFrequency(16000)         // 16kHz (optimal for speech)
      .format('wav')
      .on('error', reject)
      .on('end', () => {
        const buffer = Buffer.concat(chunks);
        resolve({
          buffer,
          mimetype: 'audio/wav',
          sampleRate: 16000,
          channels: 1,
          duration: buffer.length / (16000 * 2), // 16-bit = 2 bytes