assemblyai-performance-tuning

# AssemblyAI Performance Tuning ## Overview Optimize AssemblyAI transcription performance through model selection, parallel processing, caching, and webhook-based architectures. ## Prerequisites - `assemblyai` package installed - Understanding of async patterns - Redis or in-memory cache available (optional) ## Latency Benchmarks (Actual) ### Async Transcription | Audio Duration | Approx. Processing Time | Notes | |----------------|------------------------|-------| | 30 seconds | ~10-15 seconds | Includes queue time | | 5 minutes | ~30-60 seconds | Scales sub-linearly | | 1 hour | ~3-5 minutes | Depends on queue load | | 10 hours | ~15-30 minutes | Max async duration | ### Streaming | Metric | Value | |--------|-------| | First partial transcript | ~300ms (P50) | | Final transcript latency | ~500ms (P50) | | End-of-turn detection | Automatic with endpointing | ### Model Speed vs. Accuracy | Model | Speed | Accuracy | Price/hr | |-------|-------|----------|----------| | `nano` | Fastest | Good | $0.12 | | `best` (Universal-3) | Standard | Highest | $0.37 | | `nova-3` (streaming) | Real-time | High | $0.47 | | `nova-3-pro` (streaming) | Real-time | Highest | $0.47 | ## Instructions ### Step 1: Choose the Right Model ```typescript import { AssemblyAI } from 'assemblyai'; const client = new AssemblyAI({ apiKey: process.env.ASSEMBLYAI_API_KEY!, }); // For highest accuracy (default) const accurate = await client.transcripts.transcribe({ audio: audioUrl, speech_mode...