refactor: both UIs use shared tts_segments() from domain/conversion_pipeline.py

- domain/conversion_pipeline.py: add tts_segments() for pre-normalized text;
  emit_text_segments() now delegates to tts_segments() internally
- pyqt/conversion.py: inner TTS loop replaced with tts_segments() iterator;
  removed FakeToken import (handled by domain)
- webui/conversion_runner.py: emit_text() inner loop replaced with
  tts_segments() iterator; removed FakeToken import
- Both UIs now share the same TTS emission logic — normalization + backend
  invocation + segment iteration + token extraction
- +3 tests for tts_segments (no-normalization, chunk_start, basic)
- 1188 tests pass

tests/test_domain_conversion_pipeline.py

@@ -1,4 +1,4 @@
-"""Tests for domain/conversion_pipeline.py — emit_text_segments."""
+"""Tests for domain/conversion_pipeline.py — tts_segments, emit_text_segments."""
 
 from dataclasses import dataclass, field
 from typing import Any, List, Optional
@@ -7,7 +7,7 @@ from unittest.mock import MagicMock
 import numpy as np
 import pytest
 
-from abogen.domain.conversion_pipeline import emit_text_segments, SegmentResult
+from abogen.domain.conversion_pipeline import tts_segments, emit_text_segments, SegmentResult
 
 
 @dataclass
@@ -135,3 +135,49 @@ class TestEmitTextSegments:
         assert seg.graphemes == "Test"
         assert seg.duration == 2.0
         assert len(seg.audio) == 48000
+
+
+class TestTtsSegments:
+    def test_yields_segments_from_normalized_text(self):
+        audio = np.ones(24000, dtype="float32")
+        segments = [FakeSegment("Hello", audio)]
+        results = list(tts_segments(
+            "Already normalized text",
+            backend=make_backend(segments),
+            voice="A",
+            speed=1.0,
+            split_pattern=r"\s+",
+        ))
+        assert len(results) == 1
+        assert results[0].graphemes == "Hello"
+
+    def test_no_normalization_performed(self):
+        """tts_segments should NOT normalize — it passes text directly to backend."""
+        received_texts = []
+
+        def capture_backend(text, voice=None, speed=1.0, split_pattern=None):
+            received_texts.append(text)
+            yield FakeSegment("ok", np.ones(24000, dtype="float32"))
+
+        list(tts_segments(
+            "Raw unnormalized text",
+            backend=capture_backend,
+            voice="A",
+            speed=1.0,
+            split_pattern=r"\s+",
+        ))
+        assert received_texts[0] == "Raw unnormalized text"
+
+    def test_chunk_start_increments(self):
+        audio = np.ones(24000, dtype="float32")
+        segments = [FakeSegment("A", audio), FakeSegment("B", audio)]
+        results = list(tts_segments(
+            "test",
+            backend=make_backend(segments),
+            voice="A",
+            speed=1.0,
+            split_pattern=r"\s+",
+            current_time=10.0,
+        ))
+        assert results[0].chunk_start == 10.0
+        assert results[1].chunk_start == 11.0