Add comprehensive end-to-end test for AI-powered audiobook generation

- Tests complete workflow from unmarked text to segmented audio
- Demonstrates AI role detection on narrative text
- Shows AI text segmentation with intelligent speaker assignment
- Validates dialogue detection and speaker distribution
- Provides quality analysis of segmentation results

backend/test_end_to_end_ai.py

@@ -0,0 +1,168 @@
+#!/usr/bin/env python3
+"""
+End-to-end test demonstrating AI-powered audiobook generation.
+Shows the complete workflow from unmarked text to multi-voice audio.
+"""
+import asyncio
+import sys
+from services.role_detector import RoleDetector
+from services.tts_service import TTSService
+from services.ollama_service import OllamaService
+
+
+async def test_complete_workflow():
+    """Test the complete AI-powered audiobook generation workflow."""
+    print("\n" + "="*70)
+    print("END-TO-END AI-POWERED AUDIOBOOK GENERATION TEST")
+    print("="*70 + "\n")
+    
+    # Sample story without any role markers
+    story = """The old wizard Merlin gazed into his crystal ball with concern. 
+"Dark times are coming," he whispered to himself.
+
+A young knight named Arthur entered the chamber. "Master Merlin, I heard your summons," he said with determination.
+
+Merlin turned to face him. "Arthur, the kingdom needs you now more than ever."
+
+Arthur drew his sword. "I am ready to defend our people."
+
+From the shadows, the sorceress Morgana watched silently. She knew the prophecy would unfold exactly as foretold."""
+    
+    print("STORY TEXT (unmarked narrative):")
+    print("-" * 70)
+    print(story)
+    print("-" * 70 + "\n")
+    
+    # Initialize services
+    ollama_service = OllamaService()
+    role_detector = RoleDetector(ollama_service=ollama_service)
+    tts_service = TTSService(ollama_service=ollama_service)
+    
+    # Check Ollama availability
+    is_available = await ollama_service.is_available()
+    if not is_available:
+        print("❌ Ollama is not available. This test requires Ollama.")
+        print("   Please start Ollama with: ollama serve")
+        return False
+    
+    print("✓ Ollama service is available\n")
+    
+    # Step 1: Detect roles using AI
+    print("STEP 1: AI Role Detection")
+    print("-" * 70)
+    try:
+        roles = await role_detector.detect_roles(story)
+        print(f"✓ Detected {len(roles)} roles:\n")
+        for role in roles:
+            print(f"  - {role['label']}")
+        print()
+    except Exception as e:
+        print(f"❌ Role detection failed: {e}")
+        return False
+    
+    # Step 2: User assigns voices (simulated)
+    print("STEP 2: Voice Assignment (simulated user input)")
+    print("-" * 70)
+    role_voice_map = {}
+    voice_options = ["af_bella", "am_adam", "am_michael", "af_sarah", "bf_emma"]
+    
+    for i, role in enumerate(roles):
+        voice = voice_options[i % len(voice_options)]
+        role_voice_map[role['id']] = voice
+        print(f"  {role['label']} → {voice}")
+    print()
+    
+    # Step 3: AI text segmentation
+    print("STEP 3: AI Text Segmentation")
+    print("-" * 70)
+    try:
+        segments = await tts_service.segment_text_with_ai(story, role_voice_map)
+        
+        if segments is None:
+            print("❌ AI segmentation failed")
+            return False
+        
+        print(f"✓ AI segmented text into {len(segments)} segments:\n")
+        
+        for i, (speaker, voice, text) in enumerate(segments, 1):
+            # Truncate long text for display
+            display_text = text[:60] + "..." if len(text) > 60 else text
+            print(f"  {i}. [{speaker}] ({voice})")
+            print(f"     \"{display_text}\"")
+        print()
+        
+        # Verify intelligent segmentation
+        speakers_used = set(seg[0] for seg in segments)
+        print(f"Speakers used: {speakers_used}")
+        
+        # Check if dialogue was properly assigned
+        has_multiple_speakers = len(speakers_used) > 1
+        has_narrator = "Narrator" in speakers_used or any("Merlin" in s or "Arthur" in s or "Morgana" in s for s in speakers_used)
+        
+        if has_multiple_speakers and has_narrator:
+            print("✓ AI successfully assigned dialogue to characters and narrative to appropriate speakers")
+        else:
+            print("⚠ Segmentation may not be optimal")
+        print()
+        
+    except Exception as e:
+        print(f"❌ Segmentation failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+    
+    # Step 4: Verify segmentation quality
+    print("STEP 4: Segmentation Quality Analysis")
+    print("-" * 70)
+    
+    # Count segments per speaker
+    speaker_counts = {}
+    for speaker, _, _ in segments:
+        speaker_counts[speaker] = speaker_counts.get(speaker, 0) + 1
+    
+    print("Segments per speaker:")
+    for speaker, count in sorted(speaker_counts.items(), key=lambda x: x[1], reverse=True):
+        print(f"  {speaker}: {count} segments")
+    print()
+    
+    # Check for dialogue detection
+    dialogue_keywords = ['"', "'", "said", "whispered", "replied"]
+    dialogue_segments = 0
+    for _, _, text in segments:
+        if any(kw in text.lower() for kw in dialogue_keywords):
+            dialogue_segments += 1
+    
+    print(f"Dialogue segments detected: {dialogue_segments}/{len(segments)}")
+    print()
+    
+    # Summary
+    print("="*70)
+    print("TEST SUMMARY")
+    print("="*70)
+    print(f"✓ Role Detection: {len(roles)} roles detected")
+    print(f"✓ Voice Assignment: {len(role_voice_map)} voices assigned")
+    print(f"✓ Text Segmentation: {len(segments)} segments created")
+    print(f"✓ Speaker Distribution: {len(speakers_used)} unique speakers used")
+    print(f"✓ Dialogue Detection: {dialogue_segments} dialogue segments")
+    print()
+    print("🎉 AI-POWERED AUDIOBOOK GENERATION: SUCCESS!")
+    print("="*70 + "\n")
+    
+    return True
+
+
+async def main():
+    """Run the end-to-end test."""
+    try:
+        success = await test_complete_workflow()
+        return success
+    except Exception as e:
+        print(f"\n❌ Test failed with exception: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+if __name__ == '__main__':
+    success = asyncio.run(main())
+    sys.exit(0 if success else 1)