Deepgram Performance Tuning

Deepgram Performance Tuning

Contents

• Overview
• Prerequisites
• Instructions
• Output
• Error Handling
• Examples
• Resources

Overview

Optimize Deepgram integration performance through audio preprocessing (16kHz mono PCM), connection pooling, model selection, streaming for large files, parallel processing, and result caching.

Prerequisites

• Working Deepgram integration
• Performance monitoring in place
• Audio processing capabilities (ffmpeg)
• Baseline metrics established

Instructions

Step 1: Optimize Audio Format

Preprocess audio to 16-bit PCM, mono channel, 16kHz sample rate WAV format using ffmpeg. This is optimal for Deepgram's speech models.

Step 2: Configure Connection Pooling

Create a pool of Deepgram clients (min 2, max 10) with acquire timeout and idle timeout. Use execute() pattern to auto-acquire and release connections.

Step 3: Select Optimal Model

Choose Nova-2 for best accuracy/speed balance. Use Base model for cost-sensitive batch jobs. Match model to priority: accuracy, speed, or cost.

Step 4: Implement Streaming for Large Files

Use live transcription WebSocket for files over 60 seconds. Stream file data in chunks (1MB) and collect final transcripts.

Step 5: Enable Parallel Processing

Use p-limit to process multiple audio files concurrently (default 5). Track per-file timing and total throughput.

Step 6: Cache Transcription Results

Hash audio URL + options as cache key. Store in Redis with configurable TTL. Return cached results for repeated requests.

See detailed implementation for advanced patterns.

Output

• Audio preprocessing pipeline
• Connection pool with auto-management
• Model selection engine
• Streaming transcription for large files
• Parallel processing with concurrency control
• Redis-backed result caching

Error Handling

Examples

Performance Factors

Resources

• Deepgram Performance Guide
• Audio Format Best Practices
• FFmpeg Documentation