refactor: create domain/conversion_pipeline.py with shared TTS emission loop

- domain/conversion_pipeline.py: emit_text_segments() — generator yielding
  SegmentResult for each TTS segment; emit_text_to_sinks() — convenience
  wrapper handling audio writing + token accumulation + subtitle flushing
- Both WebUI and PyQt can call these instead of reimplementing the TTS loop
- Caller provides backend, voice, speed, split_pattern; domain handles
  normalization, TTS invocation, token extraction
- +7 tests (segment yielding, empty audio skip, chunk_start, tokens, fallback)
- 1185 tests pass

tests/test_domain_conversion_pipeline.py

@@ -0,0 +1,137 @@
+"""Tests for domain/conversion_pipeline.py — emit_text_segments."""
+
+from dataclasses import dataclass, field
+from typing import Any, List, Optional
+from unittest.mock import MagicMock
+
+import numpy as np
+import pytest
+
+from abogen.domain.conversion_pipeline import emit_text_segments, SegmentResult
+
+
+@dataclass
+class FakeSegment:
+    graphemes: str
+    audio: Any
+    tokens: list = field(default_factory=list)
+
+
+@dataclass
+class FakeTokenObj:
+    text: str
+    start_ts: float
+    end_ts: float
+    whitespace: str = ""
+
+
+def make_backend(segments):
+    """Create a mock TTS backend that yields FakeSegments."""
+    def backend(text, voice=None, speed=1.0, split_pattern=None):
+        for seg in segments:
+            yield seg
+    return backend
+
+
+class TestEmitTextSegments:
+    def test_yields_segments(self):
+        audio = np.ones(24000, dtype="float32")
+        segments = [FakeSegment("Hello", audio)]
+        results = list(emit_text_segments(
+            "Hello world",
+            backend=make_backend(segments),
+            voice="A",
+            speed=1.0,
+            split_pattern=r"\s+",
+        ))
+        assert len(results) == 1
+        assert results[0].graphemes == "Hello"
+        assert results[0].duration == 1.0
+
+    def test_skips_empty_audio(self):
+        segments = [
+            FakeSegment("Hello", np.ones(24000, dtype="float32")),
+            FakeSegment("", np.array([], dtype="float32")),
+            FakeSegment("World", np.ones(12000, dtype="float32")),
+        ]
+        results = list(emit_text_segments(
+            "test",
+            backend=make_backend(segments),
+            voice="A",
+            speed=1.0,
+            split_pattern=r"\s+",
+        ))
+        assert len(results) == 2
+        assert results[0].graphemes == "Hello"
+        assert results[1].graphemes == "World"
+
+    def test_chunk_start_increments(self):
+        audio = np.ones(24000, dtype="float32")
+        segments = [FakeSegment("A", audio), FakeSegment("B", audio)]
+        results = list(emit_text_segments(
+            "test",
+            backend=make_backend(segments),
+            voice="A",
+            speed=1.0,
+            split_pattern=r"\s+",
+            current_time=5.0,
+        ))
+        assert results[0].chunk_start == 5.0
+        assert results[1].chunk_start == 6.0
+
+    def test_tokens_extracted(self):
+        token = FakeTokenObj("Hello", 0.0, 0.5, " ")
+        audio = np.ones(24000, dtype="float32")
+        segments = [FakeSegment("Hello", audio, [token])]
+        results = list(emit_text_segments(
+            "test",
+            backend=make_backend(segments),
+            voice="A",
+            speed=1.0,
+            split_pattern=r"\s+",
+            current_time=2.0,
+        ))
+        assert len(results[0].tokens) == 1
+        assert results[0].tokens[0]["start"] == 2.0
+        assert results[0].tokens[0]["end"] == 2.5
+        assert results[0].tokens[0]["text"] == "Hello"
+
+    def test_fake_token_fallback(self):
+        """When no tokens provided, creates a single FakeToken for the segment."""
+        audio = np.ones(24000, dtype="float32")
+        segments = [FakeSegment("Hello", audio)]  # No tokens
+        results = list(emit_text_segments(
+            "test",
+            backend=make_backend(segments),
+            voice="A",
+            speed=1.0,
+            split_pattern=r"\s+",
+        ))
+        assert len(results[0].tokens) == 1
+        assert results[0].tokens[0]["text"] == "Hello"
+
+    def test_empty_text(self):
+        results = list(emit_text_segments(
+            "",
+            backend=make_backend([]),
+            voice="A",
+            speed=1.0,
+            split_pattern=r"\s+",
+        ))
+        assert len(results) == 0
+
+    def test_segment_result_fields(self):
+        audio = np.ones(48000, dtype="float32")
+        segments = [FakeSegment("Test", audio)]
+        results = list(emit_text_segments(
+            "test",
+            backend=make_backend(segments),
+            voice="A",
+            speed=1.0,
+            split_pattern=r"\s+",
+        ))
+        seg = results[0]
+        assert isinstance(seg, SegmentResult)
+        assert seg.graphemes == "Test"
+        assert seg.duration == 2.0
+        assert len(seg.audio) == 48000