Add AI-powered text segmentation for intelligent speaker assignment
- Added segment_text() method to OllamaService for AI-based text splitting - OllamaService now annotates each text segment with the appropriate speaker - Updated TTSService to use AI segmentation when available - Falls back to pattern-based segmentation when Ollama unavailable - AI intelligently assigns dialogue to speakers and narrative to Narrator - Added comprehensive test suite for AI segmentation - Verified AI correctly splits unmarked dialogue between multiple speakers This makes the tool much more powerful - users can now input plain narrative text without explicit role markers and the AI will intelligently assign voices to different characters and the narrator.
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backend/test_ai_segmentation.py
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149
backend/test_ai_segmentation.py
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#!/usr/bin/env python3
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"""
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Test script for AI-powered text segmentation.
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Tests the integration between OllamaService and TTSService for intelligent text splitting.
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"""
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import asyncio
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import sys
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from services.ollama_service import OllamaService
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from services.tts_service import TTSService
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async def test_ai_segmentation():
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"""Test AI segmentation with a sample narrative text."""
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print("Testing AI-Powered Text Segmentation\n" + "="*50)
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# Sample text without explicit role markers
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text = """The old wizard gazed into his crystal ball. "I see danger approaching," he whispered.
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A young knight heard the warning and drew his sword. "I will protect the kingdom," he declared boldly.
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The princess watched from her tower window. She knew the prophecy would come true."""
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print(f"\nInput text:\n{text}\n")
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# Initialize services
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ollama_service = OllamaService()
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tts_service = TTSService(ollama_service=ollama_service)
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# Check if Ollama is available
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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. Please start Ollama service.")
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return False
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print("✓ Ollama service is available\n")
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# Define role-voice mapping (simulating what user would assign)
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role_voice_map = {
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"Narrator": "af_bella",
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"The old wizard": "am_adam",
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"A young knight": "am_michael",
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"The princess": "af_sarah"
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}
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print(f"Role-voice mapping: {role_voice_map}\n")
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# Test AI segmentation
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try:
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print("Requesting AI segmentation...")
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segments = await tts_service.segment_text_with_ai(text, role_voice_map)
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if segments is None:
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print("❌ AI segmentation returned None")
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return False
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print(f"\n✓ AI segmentation successful! Generated {len(segments)} segments:\n")
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for i, (speaker, voice, content) in enumerate(segments, 1):
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print(f"Segment {i}:")
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print(f" Speaker: {speaker}")
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print(f" Voice: {voice}")
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print(f" Text: {content[:80]}{'...' if len(content) > 80 else ''}")
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print()
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# Verify segments are properly assigned
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speakers_used = set(seg[0] for seg in segments)
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print(f"Speakers used in segmentation: {speakers_used}")
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# Check if AI intelligently assigned different speakers
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if len(speakers_used) > 1:
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print("\n✓ SUCCESS: AI intelligently assigned multiple speakers!")
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return True
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else:
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print("\n⚠ WARNING: Only one speaker used. AI may not have detected dialogue.")
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return True # Still a success, just not ideal
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except Exception as e:
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print(f"\n❌ ERROR: {e}")
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import traceback
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traceback.print_exc()
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return False
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async def test_fallback_to_pattern():
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"""Test that pattern-based segmentation works as fallback."""
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print("\n" + "="*50)
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print("Testing Fallback to Pattern-Based Segmentation\n")
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# Text with explicit markers
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text = """Alice: Hello there!
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Bob: Hi Alice, how are you?
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[Narrator] They continued their conversation in the garden."""
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print(f"Input text:\n{text}\n")
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tts_service = TTSService()
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role_voice_map = {
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"Alice": "af_bella",
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"Bob": "am_adam",
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"Narrator": "af_sarah"
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}
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print(f"Role-voice mapping: {role_voice_map}\n")
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# Use pattern-based segmentation
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segments = tts_service.segment_text(text, role_voice_map)
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print(f"✓ Pattern segmentation generated {len(segments)} segments:\n")
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for i, (speaker, voice, content) in enumerate(segments, 1):
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print(f"Segment {i}: [{speaker}] {content}")
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# Verify all speakers were detected
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speakers_used = set(seg[0] for seg in segments)
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expected_speakers = {"Alice", "Bob", "Narrator"}
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if speakers_used == expected_speakers:
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print(f"\n✓ SUCCESS: All expected speakers detected: {speakers_used}")
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return True
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else:
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print(f"\n❌ FAIL: Expected {expected_speakers}, got {speakers_used}")
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return False
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async def main():
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"""Run all tests."""
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print("\n" + "="*70)
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print("AI-POWERED TEXT SEGMENTATION TEST SUITE")
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print("="*70 + "\n")
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# Test 1: AI segmentation
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test1_passed = await test_ai_segmentation()
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# Test 2: Pattern fallback
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test2_passed = await test_fallback_to_pattern()
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# Summary
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print("\n" + "="*70)
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print("TEST SUMMARY")
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print("="*70)
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print(f"AI Segmentation: {'✓ PASSED' if test1_passed else '❌ FAILED'}")
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print(f"Pattern Fallback: {'✓ PASSED' if test2_passed else '❌ FAILED'}")
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print("="*70 + "\n")
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return test1_passed and test2_passed
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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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