Delete test_all_samples.py

test_all_samples.py

@@ -1,139 +0,0 @@
-"""
-Test all samples with the enhanced Kopparapu scoring
-"""
-import os
-import sys
-from pipeline import AuthenticityDetectionPipeline
-
-def test_all_samples():
-    print("=" * 70)
-    print("TESTING ALL SAMPLES WITH ENHANCED KOPPARAPU SCORING")
-    print("=" * 70)
-    
-    # Initialize pipeline
-    pipeline = AuthenticityDetectionPipeline(whisper_model_size="base")
-    
-    # Sample directories
-    samples_dir = "/Users/ranamhamoud/Downloads/apis/samples_wav"
-    
-    # Collect results
-    results = {
-        'read': [],
-        'spontaneous': []
-    }
-    
-    # Test read samples
-    print("\n" + "=" * 70)
-    print("READ SPEECH SAMPLES")
-    print("=" * 70)
-    
-    for i in range(1, 11):
-        filepath = os.path.join(samples_dir, f"read{i}.wav")
-        if os.path.exists(filepath):
-            print(f"\n--- Testing read{i}.wav ---")
-            try:
-                result = pipeline.analyze_audio(filepath)
-                score = result['final_assessment']['composite_authenticity_score']
-                verdict = result['final_assessment']['verdict']
-                kop_score = result['speech_recognition']['kopparapu_score']
-                kop_class = result['speech_recognition']['kopparapu_classification']
-                
-                results['read'].append({
-                    'name': f'R{i}',
-                    'composite': score,
-                    'kopparapu': kop_score,
-                    'verdict': verdict
-                })
-                
-                print(f"  Composite Score: {score:.3f}")
-                print(f"  Kopparapu Score: {kop_score:.3f} ({kop_class})")
-                print(f"  Verdict: {verdict}")
-            except Exception as e:
-                print(f"  ERROR: {e}")
-    
-    # Test spontaneous samples
-    print("\n" + "=" * 70)
-    print("SPONTANEOUS SPEECH SAMPLES")
-    print("=" * 70)
-    
-    for i in range(1, 11):
-        filepath = os.path.join(samples_dir, f"spontaneous{i}.wav")
-        if os.path.exists(filepath):
-            print(f"\n--- Testing spontaneous{i}.wav ---")
-            try:
-                result = pipeline.analyze_audio(filepath)
-                score = result['final_assessment']['composite_authenticity_score']
-                verdict = result['final_assessment']['verdict']
-                kop_score = result['speech_recognition']['kopparapu_score']
-                kop_class = result['speech_recognition']['kopparapu_classification']
-                
-                results['spontaneous'].append({
-                    'name': f'S{i}',
-                    'composite': score,
-                    'kopparapu': kop_score,
-                    'verdict': verdict
-                })
-                
-                print(f"  Composite Score: {score:.3f}")
-                print(f"  Kopparapu Score: {kop_score:.3f} ({kop_class})")
-                print(f"  Verdict: {verdict}")
-            except Exception as e:
-                print(f"  ERROR: {e}")
-    
-    # Summary
-    print("\n" + "=" * 70)
-    print("SUMMARY")
-    print("=" * 70)
-    
-    print("\n### READ SAMPLES (should be detected as inauthentic, score < 0.3)")
-    print("-" * 50)
-    print(f"{'Sample':<8} {'Composite':<12} {'Kopparapu':<12} {'Verdict':<20}")
-    print("-" * 50)
-    
-    read_correct = 0
-    for r in results['read']:
-        correct = "✓" if r['composite'] < 0.3 else "✗"
-        if r['composite'] < 0.3:
-            read_correct += 1
-        print(f"{r['name']:<8} {r['composite']:<12.3f} {r['kopparapu']:<12.3f} {r['verdict']:<20} {correct}")
-    
-    print("\n### SPONTANEOUS SAMPLES (should be detected as authentic, score >= 0.3)")
-    print("-" * 50)
-    print(f"{'Sample':<8} {'Composite':<12} {'Kopparapu':<12} {'Verdict':<20}")
-    print("-" * 50)
-    
-    spont_correct = 0
-    for r in results['spontaneous']:
-        correct = "✓" if r['composite'] >= 0.3 else "✗"
-        if r['composite'] >= 0.3:
-            spont_correct += 1
-        print(f"{r['name']:<8} {r['composite']:<12.3f} {r['kopparapu']:<12.3f} {r['verdict']:<20} {correct}")
-    
-    # Accuracy metrics
-    print("\n" + "=" * 70)
-    print("ACCURACY METRICS")
-    print("=" * 70)
-    
-    total_read = len(results['read'])
-    total_spont = len(results['spontaneous'])
-    
-    read_acc = (read_correct / total_read * 100) if total_read > 0 else 0
-    spont_acc = (spont_correct / total_spont * 100) if total_spont > 0 else 0
-    overall_acc = ((read_correct + spont_correct) / (total_read + total_spont) * 100) if (total_read + total_spont) > 0 else 0
-    
-    print(f"\nRead Detection:        {read_correct}/{total_read} = {read_acc:.1f}%")
-    print(f"Spontaneous Detection: {spont_correct}/{total_spont} = {spont_acc:.1f}%")
-    print(f"Overall Accuracy:      {read_correct + spont_correct}/{total_read + total_spont} = {overall_acc:.1f}%")
-    
-    # Kopparapu-specific accuracy
-    print("\n### Kopparapu Score Analysis")
-    read_kop_correct = sum(1 for r in results['read'] if r['kopparapu'] >= 0.5)
-    spont_kop_correct = sum(1 for r in results['spontaneous'] if r['kopparapu'] < 0.5)
-    
-    print(f"Read (Kopparapu >= 0.5):        {read_kop_correct}/{total_read}")
-    print(f"Spontaneous (Kopparapu < 0.5): {spont_kop_correct}/{total_spont}")
-
-
-if __name__ == "__main__":
-    test_all_samples()
-