TwinMind Performance Tuning

Contents

• Overview
• Prerequisites
• Instructions
• Output
• Error Handling
• Examples
• Resources

Overview

Optimize TwinMind for better transcription accuracy (WER), faster processing, audio preprocessing, model selection per use case, streaming optimization, and transcript caching/deduplication.

Prerequisites

• TwinMind Pro/Enterprise account
• Understanding of audio processing concepts
• Access to quality metrics and logs

Instructions

Step 1: Understand Performance Metrics

Track key metrics: Word Error Rate (Ear-3: ~5.26%), Diarization Error Rate (~3.8%), confidence score, processing time, real-time factor (~0.3x), and first word latency (~300ms). Analyze metrics to generate recommendations.

Step 2: Audio Quality Optimization

Build preprocessing pipeline with ffmpeg: target 16kHz sample rate, mono channel, noise reduction (highpass 200Hz + lowpass 3kHz + FFT denoiser), and EBU R128 loudness normalization. Assess audio quality before transcription.

Step 3: Model Selection and Configuration

Create optimized configs per scenario: standard meeting (ear-3, auto language, diarization on), technical presentation (ear-3 + custom vocabulary), call center (diarization + profanity filter), medical (ear-3-custom), lecture (single speaker, no diarization), podcast (diarization on).

Step 4: Streaming Optimization

Configure streaming with 100ms chunks, 50ms overlap, 5s max buffer, interim results, and endpoint detection. Build OptimizedStreamingClient that accumulates chunks and processes when sufficient data is available.

Step 5: Caching and Deduplication

Implement TranscriptCache with SHA-256 audio hashing for deduplication, 24-hour TTL, and transcribeWithCache() that skips re-processing of identical audio.

See detailed implementation for complete audio preprocessing, model configs, streaming client, and caching code.

Output

• Performance metrics tracking
• Audio preprocessing pipeline
• Model configuration for use cases
• Streaming optimization
• Caching and deduplication

Error Handling

Examples

Basic usage: Apply twinmind performance tuning to a standard project setup with default configuration options.

Advanced scenario: Customize twinmind performance tuning for production environments with multiple constraints and team-specific requirements.

Performance Benchmarks

Resources

• TwinMind Ear-3 Model
• Audio Best Practices
• Streaming API

Next Steps

For cost optimization, see twinmind-cost-tuning.