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