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

Issue	Cause	Solution
Slow transcription	Wrong audio format	Preprocess to 16kHz mono WAV
Connection exhaustion	No pooling	Use connection pool
High latency	Large files	Switch to streaming
Redundant API calls	No caching	Enable transcription cache

Examples

Performance Factors

Factor	Impact	Optimization
Audio Format	High	16-bit PCM, mono, 16kHz
File Size	High	Stream large files
Model Choice	High	Balance accuracy vs speed
Concurrency	Medium	Pool connections
Network Latency	Medium	Use closest region

Resources

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