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

// 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
        });
      })
      .pipe()
      .on('data', (chunk: Buffer) => chunks.push(chunk));
  });
}

// For already loaded audio data
export async function optimizeAudioBuffer(
  audioBuffer: Buffer,
  inputFormat: string
): Promise<Buffer> {
  return new Promise((resolve, reject) => {
    const chunks: Buffer[] = [];
    const readable = new Readable();
    readable.push(audioBuffer);
    readable.push(null);

    ffmpeg(readable)
      .inputFormat(inputFormat)
      .audioCodec('pcm_s16le')
      .audioChannels(1)
      .audioFrequency(16000)
      .format('wav')
      .on('error', reject)
      .on('end', () => resolve(Buffer.concat(chunks)))
      .pipe()
      .on('data', (chunk: Buffer) => chunks.push(chunk));
  });
}

Connection Pooling

// lib/connection-pool.ts
import { createClient, DeepgramClient } from '@deepgram/sdk';

interface PoolConfig {
  minSize: number;
  maxSize: number;
  acquireTimeout: number;
  idleTimeout: number;
}

class DeepgramConnectionPool {
  private pool: DeepgramClient[] = [];
  private inUse: Set<DeepgramClient> = new Set();
  private waiting: Array<(client: DeepgramClient) => void> = [];
  private config: PoolConfig;
  private apiKey: string;

  constructor(apiKey: string, config: Partial<PoolConfig> = {}) {
    this.apiKey = apiKey;
    this.config = {
      minSize: config.minSize ?? 2,
      maxSize: config.maxSize ?? 10,
      acquireTimeout: config.acquireTimeout ?? 10000,
      idleTimeout: config.idleTimeout ?? 60000,
    };

    // Initialize minimum connections
    for (let i = 0; i < this.config.minSize; i++) {
      this.pool.push(createClient(this.apiKey));
    }
  }

  async acquire(): Promise<DeepgramClient> {
    // Try to get from pool
    if (this.pool.length > 0) {
      const client = this.pool.pop()!;
      this.inUse.add(client);
      return client;
    }

    // Create new if under max
    if (this.inUse.size < this.config.maxSize) {
      const client = createClient(this.apiKey);
      this.inUse.add(client);
      return client;
    }

    // Wait for available connection
    return new Promise((resolve, reject) => {
      const timeout = setTimeout(() => {
        const index = this.waiting.indexOf(resolve);
        if (index > -1) this.waiting.splice(index, 1);
        reject(new Error('Connection acquire timeout'));
      }, this.config.acquireTimeout);

      this.waiting.push((client) => {
        clearTimeout(timeout);
        resolve(client);
      });
    });
  }

  release(client: DeepgramClient): void {
    this.inUse.delete(client);

    if (this.waiting.length > 0) {
      const waiter = this.waiting.shift()!;
      this.inUse.add(client);
      waiter(client);
    } else {
      this.pool.push(client);
    }
  }

  async execute<T>(fn: (client: DeepgramClient) => Promise<T>): Promise<T> {
    const client = await this.acquire();
    try {
      return await fn(client);
    } finally {
      this.release(client);
    }
  }

  getStats() {
    return {
      poolSize: this.pool.length,
      inUse: this.inUse.size,
      waiting: this.waiting.length,
    };
  }
}

export const pool = new DeepgramConnectionPool(process.env.DEEPGRAM_API_KEY!);

Streaming for Large Files

// lib/streaming-transcription.ts
import { createClient } from '@deepgram/sdk';
import { createReadStream, statSync } from 'fs';

interface StreamingOptions {
  chunkSize: number;
  model: string;
}

export async function streamLargeFile(
  filePath: string,
  options: Partial<StreamingOptions> = {}
): Promise<string> {
  const { chunkSize = 1024 * 1024, model = 'nova-2' } = options;
  const client = createClient(process.env.DEEPGRAM_API_KEY!);

  const fileSize = statSync(filePath).size;
  const transcripts: string[] = [];

  // Use live transcription for streaming
  const connection = client.listen.live({
    model,
    smart_format: true,
    punctuate: true,
  });

  return new Promise((resolve, reject) => {
    connection.on('open', () => {
      const stream = createReadStream(filePath, { highWaterMark: chunkSize });

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

      stream.on('end', () => {
        connection.finish();
      });

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

    connection.on('transcript', (data) => {
      if (data.is_final) {
        transcripts.push(data.channel.alternatives[0].transcript);
      }
    });

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

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

Model Selection for Speed

// lib/model-selector.ts
interface ModelConfig {
  name: string;
  accuracy: 'high' | 'medium' | 'low';
  speed: 'fast' | 'medium' | 'slow';
  costPerMinute: number;
}

const models: Record<string, ModelConfig> = {
  'nova-2': {
    name: 'Nova-2',
    accuracy: 'high',
    speed: 'fast',
    costPerMinute: 0.0043,
  },
  'nova': {
    name: 'Nova',
    accuracy: 'high',
    speed: 'fast',
    costPerMinute: 0.0043,
  },
  'enhanced': {
    name: 'Enhanced',
    accuracy: 'medium',
    speed: 'fast',
    costPerMinute: 0.0145,
  },
  'base': {
    name: 'Base',
    accuracy: 'low',
    speed: 'fast',
    costPerMinute: 0.0048,
  },
};

export function selectModel(requirements: {
  prioritize: 'accuracy' | 'speed' | 'cost';
  minAccuracy?: 'high' | 'medium' | 'low';
}): string {
  const { prioritize, minAccuracy = 'low' } = requirements;

  const accuracyOrder = ['high', 'medium', 'low'];
  const minAccuracyIndex = accuracyOrder.indexOf(minAccuracy);

  const eligible = Object.entries(models).filter(([_, config]) =>
    accuracyOrder.indexOf(config.accuracy) <= minAccuracyIndex
  );

  if (prioritize === 'accuracy') {
    return eligible.reduce((best, [name, config]) =>
      accuracyOrder.indexOf(config.accuracy) < accuracyOrder.indexOf(models[best].accuracy)
        ? name : best
    , eligible[0][0]);
  }

  if (prioritize === 'cost') {
    return eligible.reduce((best, [name, config]) =>
      config.costPerMinute < models[best].costPerMinute ? name : best
    , eligible[0][0]);
  }

  // Default: balance speed and accuracy
  return 'nova-2';
}

Parallel Processing

// lib/parallel-transcription.ts
import { pool } from './connection-pool';
import pLimit from 'p-limit';

interface TranscriptionResult {
  file: string;
  transcript: string;
  duration: number;
}

export async function transcribeMultiple(
  audioUrls: string[],
  concurrency = 5
): Promise<TranscriptionResult[]> {
  const limit = pLimit(concurrency);
  const startTime = Date.now();

  const results = await Promise.all(
    audioUrls.map((url, index) =>
      limit(async () => {
        const itemStart = Date.now();

        const result = await pool.execute(async (client) => {
          const { result, error } = await client.listen.prerecorded.transcribeUrl(
            { url },
            { model: 'nova-2', smart_format: true }
          );

          if (error) throw error;
          return result;
        });

        return {
          file: url,
          transcript: result.results.channels[0].alternatives[0].transcript,
          duration: Date.now() - itemStart,
        };
      })
    )
  );

  console.log(`Processed ${audioUrls.length} files in ${Date.now() - startTime}ms`);
  console.log(`Average per file: ${(Date.now() - startTime) / audioUrls.length}ms`);

  return results;
}

Caching Results

// lib/transcription-cache.ts
import { createHash } from 'crypto';
import { redis } from './redis';

interface CacheOptions {
  ttl: number; // seconds
}

export class TranscriptionCache {
  private ttl: number;

  constructor(options: Partial<CacheOptions> = {}) {
    this.ttl = options.ttl ?? 3600; // 1 hour default
  }

  private getCacheKey(audioUrl: string, options: Record<string, unknown>): string {
    const hash = createHash('sha256')
      .update(JSON.stringify({ audioUrl, options }))
      .digest('hex');
    retur

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