deepgram-performance-tuning
Installation
SKILL.md
Deepgram Performance Tuning
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
| Factor | Impact | Default | Optimized |
|---|---|---|---|
| Audio format | High | Any format | 16kHz mono WAV |
| Model | High | nova-3 | base (speed) or nova-3 (accuracy) |
| File size | High | Full file sync | Stream >60s, callback >5min |
| Concurrency | Medium | Sequential | 50 parallel (p-limit) |
| Caching | Medium | None | Redis hash by audio+options |
| Features | Medium | All enabled | Disable unused (diarize, utterances) |
Instructions
Step 1: Audio Preprocessing with ffmpeg
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