deepgram-performance-tuning

# 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 ```bash # Optimal format for Deepgram: 16kHz, 16-bit, mono, WAV ffmpeg -i input.mp3 \ -ar 16000 \ # 16kHz sample rate (ideal for speech) -ac 1 \ # Mono channel -acodec pcm_s16le \ # 16-bit signed LE PCM -f wav \ output.wav # Remove silence (saves API cost + processing time) ffmpeg -i input.wav \ -af "silenceremove=stop_periods=-1:stop_duration=0.5:stop_threshold=-30dB" \ -ar 16000 -ac 1 -acodec pcm_s16le \ trimmed.wav # Noise reduction + normalization ffmpeg -i input.wav \ -af "highpass=f=200,lowpass=f=3000,loudnorm=I=-16:TP=-1.5:LRA=11" \ -ar 16000 -ac 1 -acodec pcm_s16l...