Agent skill · marketing growth · jeremylongshore
deepgram-performance-tuning
Optimize Deepgram API performance for faster transcription and lower
Why this skill is useful
Provides specific audio preprocessing scripts and model selection strategies for optimizing Deepgram API performance that the AI wouldn't reliably generate on its own.
What it needs
Requires ffmpeg installed locally. Requires deepgram account access. About 5k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Deepgram Performance Tuning Prerequisites A baseline for latency, throughput, quality, error/throttle rate, and a named service owner. Licensed non-sensitive fixtures, approved load window, and a rollback threshold. Examples Measure the development/staging transcription baseline with short fixtures, change one concurrency, streaming, or model parameter, and compare aggregate quality/latency/error results. Keep the change only within the signed threshold; revert on regression and never use customer recordings as performance fixtures. 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 Step 2: Model Selection Strategy Step 3: Streaming for Large Files Step 4: Parallel Batch Processing Step 5: Result Caching Step 6: Performance Benchmarking Output Audio preprocessing pipeline (16kHz mono, silence removal, noise reduction) Model selection strategy by priority (accuracy/speed/cost) Streaming transcription for large files (>60s) Parallel batch processing with configurable concurrency Redis-backed result caching with TTL Performance benchmarking script Error Handling Issue Cause Solution ------- ------- ---------- Slow transcription Unoptimized audio format Preprocess to 16kHz mono WAV 429 in batch Concurrency too high Reduce p-limit to 50% of plan limit ffmpeg not found Not install …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/deepgram-performance-tuning