twinmind-performance-tuning

name	twinmind-performance-tuning
description	Optimize TwinMind transcription accuracy and processing speed. Use when improving transcription quality, reducing latency, or tuning model parameters for specific use cases. Trigger with phrases like "twinmind performance", "improve transcription accuracy", "faster twinmind", "optimize twinmind", "transcription quality".
allowed-tools	Read, Write, Edit
version	1.0.0
license	MIT
author	Jeremy Longshore <[e-mail]>

TwinMind Performance Tuning

Overview

Optimize TwinMind for better transcription accuracy, faster processing, and improved user experience.

Prerequisites

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

Instructions

Step 1: Understand Performance Metrics

// src/twinmind/metrics/performance.ts
export interface TranscriptionMetrics {
  // Accuracy metrics
  wordErrorRate: number;         // WER - lower is better (Ear-3: ~5.26%)
  diarizationErrorRate: number;  // DER - speaker labeling accuracy (~3.8%)
  confidenceScore: number;       // Average confidence (0-1)

  // Timing metrics
  processingTime: number;        // Time to complete (ms)
  realtimeFactor: number;        // Processing time / audio duration
  firstWordLatency: number;      // Time to first result (streaming)

  // Quality metrics
  speakerCount: number;          // Detected speakers
  languageDetected: string;      // Detected language
  noiseLevel: string;            // low/medium/high
}

export async function getTranscriptionMetrics(transcriptId: string): Promise<TranscriptionMetrics> {
  const client = getTwinMindClient();
  const response = await client.get(`/transcripts/${transcriptId}/metrics`);
  return response.data;
}

// Log and analyze metrics
export function analyzePerformance(metrics: TranscriptionMetrics): string[] {
  const recommendations: string[] = [];

  if (metrics.wordErrorRate > 0.10) {
    recommendations.push('High WER - consider audio quality improvements');
  }

  if (metrics.diarizationErrorRate > 0.05) {
    recommendations.push('Speaker labeling issues - ensure clear audio separation');
  }

  if (metrics.realtimeFactor > 0.5) {
    recommendations.push('Slow processing - consider model optimization');
  }

  if (metrics.noiseLevel === 'high') {
    recommendations.push('High background noise - recommend noise reduction');
  }

  return recommendations;
}

Step 2: Audio Quality Optimization

// src/twinmind/preprocessing/audio.ts
import ffmpeg from 'fluent-ffmpeg';

interface AudioPreprocessOptions {
  targetSampleRate?: number;   // Default: 16000 Hz
  channels?: number;           // Default: 1 (mono)
  noiseReduction?: boolean;    // Enable noise reduction
  normalization?: boolean;     // Normalize volume
  format?: string;             // Output format (mp3, wav)
}

const defaultOptions: AudioPreprocessOptions = {
  targetSampleRate: 16000,
  channels: 1,
  noiseReduction: true,
  normalization: true,
  format: 'mp3',
};

export async function preprocessAudio(
  inputPath: string,
  outputPath: string,
  options: AudioPreprocessOptions = {}
): Promise<void> {
  const opts = { ...defaultOptions, ...options };

  return new Promise( {
     command = (inputPath)
      .(opts.!)
      .(opts.!);

    
     (opts.) {
      command = command.([
        ,     
        ,     
        ,      
      ]);
    }

    
     (opts.) {
      command = command.([
        , 
      ]);
    }

    command
      .(opts.!)
      .(,  ())
      .(, reject)
      .(outputPath);
  });
}


   (): <{
  :  |  |  | ;
  : [];
  : [];
}> {
   : [] = [];
   : [] = [];

  
   metadata =  (filePath);

  
   (metadata. < ) {
    issues.();
    recommendations.();
  }

  
   (metadata. && metadata. < ) {
    issues.();
    recommendations.();
  }

  
   (metadata. && metadata. > -) {
    issues.();
    recommendations.();
  }

  
   (metadata. && metadata. > -) {
    issues.();
    recommendations.();
  }

   quality = issues. ===  ?  :
                  issues. <=  ?  :
                  issues. <=  ?  : ;

   { quality, issues, recommendations };
}

Step 3: Model Selection and Configuration

// src/twinmind/models/config.ts
export interface ModelConfig {
  model: 'ear-3' | 'ear-2' | 'ear-3-custom';
  language?: string;           // 'auto' or ISO code (en, es, fr, etc.)
  diarization: boolean;        // Speaker labeling
  punctuation: boolean;        // Auto-punctuation
  profanityFilter: boolean;    // Filter explicit content
  vocabulary?: string[];       // Custom vocabulary boost
  speakerHints?: string[];     // Expected speaker names
}

// Optimized configs for different scenarios
export const modelConfigs: Record<string, ModelConfig> = {
  // Standard meeting transcription
  meeting: {
    model: 'ear-3',
    language: 'auto',
    diarization: true,
    punctuation: true,
    profanityFilter: false,
  },

  // Technical presentation with jargon
  technical: {
    model: 'ear-3',
    language: ,
    : ,
    : ,
    : ,
    : [
      , , , , ,
      , , , 
    ],
  },

  
  : {
    : ,
    : ,
    : ,  
    : ,
    : ,
  },

  
  : {
    : ,  
    : ,
    : ,
    : ,
    : ,
    : [
      
      , , ,
      , , ,
    ],
  },

  
  : {
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    : ,  
    : ,
    : ,
  },

  
  : {
    : ,
    : ,
    : ,
    : ,
    : ,
  },
};

  ():  {
   modelConfigs[useCase] || modelConfigs.;
}

Step 4: Streaming Optimization

// src/twinmind/streaming/optimized.ts
export interface StreamingConfig {
  chunkDurationMs: number;     // Audio chunk size
  overlapMs: number;           // Overlap for continuity
  maxBufferMs: number;         // Maximum buffer before processing
  interimResults: boolean;     // Return partial results
  endpointDetection: boolean;  // Auto-detect speech endpoints
}

const defaultStreamingConfig: StreamingConfig = {
  chunkDurationMs: 100,        // 100ms chunks for low latency
  overlapMs: 50,               // 50ms overlap
  maxBufferMs: 5000,           // 5 second max buffer
  interimResults: true,
  endpointDetection: true,
};

export class OptimizedStreamingClient {
  private config: StreamingConfig;
  private buffer: Float32Array[] = [];
  private lastInterimResult = '';

  constructor(config: <> = {}) {
    . = { ...defaultStreamingConfig, ...config };
  }

   (: ): <{
    ?: ;
    ?: ;
    : ;
  }> {
    ..(audioChunk);

    
     totalMs = .. * (..);

     (totalMs >= .. * ) {
      
       result =  .(.());

       (result.) {
        . = [];  
         {
          : result.,
          : result.,
        };
      }  {
        
        .();
         {
          : result.,
          : result.,
        };
      }
    }

     { :  };
  }

   ():  {
     totalLength = ..( sum + arr., );
     result =  (totalLength);
     offset = ;
     ( arr  .) {
      result.(arr, offset);
      offset += arr.;
    }
     result;
  }

   ():  {
    
     (.. > ) {
      . = ..(-);
    }
  }

    (: ): <{
    : ;
    : ;
    : ;
  }> {
    
     client = ();
     response =  client.(, {
      : .(audio.).(),
      : ..,
    });
     response.;
  }
}

Step 5: Caching and Deduplication

// src/twinmind/optimization/cache.ts
import crypto from 'crypto';

interface CachedTranscript {
  hash: string;
  transcriptId: string;
  createdAt: Date;
  expiresAt: Date;
}

class TranscriptCache {
  private cache = new Map<string, CachedTranscript>();
  private ttlMs = 24 * 60 * 60 * 1000; // 24 hours

  // Generate hash of audio content for deduplication
  async hashAudio(audioUrl: string): Promise<string> {
    const response = await fetch(audioUrl);
    const buffer = await response.arrayBuffer();

    return crypto
      .createHash('sha256')
      .update(Buffer.from(buffer))
      .digest('hex');
  }

  get(hash: string):  |  {
     cached = ..(hash);

     (!cached)  ;

     ( () > cached.) {
      ..(hash);
       ;
    }

     cached.;
  }

  (: , : ):  {
    ..(hash, {
      hash,
      transcriptId,
      :  (),
      :  (.() + .),
    });
  }

  
   (: ): <> {
     hash =  .(audioUrl);

    
     cachedId = .(hash);
     (cachedId) {
      .();
       cachedId;
    }

    
    .();
     client = ();
     result =  client.(audioUrl);

    
    .(hash, result.);

     result.;
  }
}

  transcriptCache =  ();

Output

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

Performance Benchmarks

Metric	Target	Ear-3 Actual
Word Error Rate	< 10%	~5.26%
Diarization Error Rate	< 5%	~3.8%
Real-time Factor	< 0.5x	~0.3x
First Word Latency	< 500ms	~300ms
Languages Supported	100+	140+

Error Handling

Issue	Cause	Solution
High WER	Poor audio quality	Apply preprocessing
Slow processing	Large file	Use streaming API
Wrong language	Auto-detect failed	Specify language explicitly
Missing speakers	Low audio separation	Improve microphone setup

Resources

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

Next Steps

For cost optimization, see twinmind-cost-tuning.