MODEL_SUMMARY.md

AI Engine Model Summary

Simplified ASR-Only Configuration

This engine has been simplified to use ONLY the IndicWav2Vec Hindi model for Automatic Speech Recognition (ASR).

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Active Model

1. IndicWav2Vec Hindi (Primary & Only Model)

• Model ID: ai4bharat/indicwav2vec-hindi
• Type: Wav2Vec2ForCTC
• Purpose: Automatic Speech Recognition (ASR) for Hindi and Indian languages
• Status: ✅ Active - Loaded at startup
• Location: detect_stuttering.py lines 26, 148-156
• Authentication: Requires HF_TOKEN environment variable

Features:

• Speech-to-text transcription
• Confidence scoring from model predictions
• Text-based stutter analysis (simple repetition detection)

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Removed Models

The following models have been removed to simplify the engine:

1. ❌ MMS Language Identification (LID) - facebook/mms-lid-126

   • Previously used for language detection
   • No longer needed - IndicWav2Vec handles Hindi natively

2. ❌ Isolation Forest (sklearn)

   • Previously used for anomaly detection
   • Removed - using simple text-based analysis instead

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Removed Libraries

The following signal processing libraries are no longer used:

• ❌ parselmouth (Praat) - Voice quality analysis
• ❌ fastdtw - Repetition detection via DTW
• ❌ sklearn - Machine learning algorithms
• ❌ Complex acoustic feature extraction (MFCC, formants, etc.)

---

Current Pipeline

Audio Input
    ↓
IndicWav2Vec Hindi ASR
    ↓
Text Transcription
    ↓
Basic Text Analysis
    ↓
Results (transcript + simple stutter detection)

---

API Response Format

The simplified engine returns:

{
  "actual_transcript": "transcribed text",
  "target_transcript": "expected text (if provided)",
  "mismatched_chars": ["timestamps of low confidence regions"],
  "mismatch_percentage": 0.0,
  "ctc_loss_score": 0.0,
  "stutter_timestamps": [{"type": "repetition", "start": 0.0, "end": 0.5, ...}],
  "total_stutter_duration": 0.0,
  "stutter_frequency": 0.0,
  "severity": "none|mild|moderate|severe",
  "confidence_score": 0.8,
  "speaking_rate_sps": 0.0,
  "analysis_duration_seconds": 0.0,
  "model_version": "indicwav2vec-hindi-asr-v1"
}

---

Dependencies

Required:

• transformers 4.35.0 - For IndicWav2Vec model
• torch 2.0.1 - PyTorch backend
• librosa ≥0.10.0 - Audio loading (16kHz resampling)
• numpy - Array operations

Optional (for legacy methods, not used in ASR mode):

• parselmouth - Voice quality (not used)
• fastdtw - DTW algorithm (not used)
• sklearn - ML algorithms (not used)

---

Usage

from diagnosis.ai_engine.detect_stuttering import get_stutter_detector

detector = get_stutter_detector()
result = detector.analyze_audio(
    audio_path="path/to/audio.wav",
    proper_transcript="expected text",  # optional
    language="hindi"  # default: hindi
)

print(result['actual_transcript'])  # ASR transcription

---

Notes

• The engine focuses only on ASR transcription
• Stutter detection is simplified to text-based repetition analysis
• No complex acoustic feature extraction
• Faster and lighter than the previous multi-model approach
• Optimized for Hindi but can handle other Indian languages