refactor(webui): replace inline get_pipeline/resolve_voice_target closures with domain modules

- Replace get_pipeline() closure with PipelinePool from domain/pipeline_factory
- Replace resolve_voice_target() closure with domain function from voice_utils
- Remove dead _load_pipeline() function and unused is_plugin_registered import
- Add 33 tests for resolve_voice_target and PipelinePool
- Add 10 regression tests verifying domain extraction preserves behavior
- 1131 tests pass (+61 new)

tests/test_regression_webui_domain.py

@@ -0,0 +1,145 @@
+"""Regression tests: domain extraction must not break webui conversion_runner.
+
+These tests verify that the refactored WebUI code paths still call into the
+correct domain functions and produce the same results as the old inline logic.
+"""
+
+from __future__ import annotations
+
+from unittest.mock import MagicMock, patch
+
+from abogen.domain.voice_utils import resolve_voice_target
+from abogen.domain.pipeline_factory import PipelinePool
+
+
+class TestResolveVoiceTargetRegression:
+    """Verify that the domain resolve_voice_target produces the same results
+    as the old closure in conversion_runner.py."""
+
+    def test_empty_spec_returns_kokoro_default(self):
+        provider, spec, speed, steps = resolve_voice_target(
+            "", {}, job_voice="af_sarah", job_tts_provider="kokoro",
+        )
+        assert provider == "kokoro"
+        assert spec == ""
+
+    def test_speaker_in_profile_kokoro(self):
+        profiles = {
+            "narrator": {
+                "provider": "kokoro",
+                "voices": [["af_sarah", 0.7], ["bf_emma", 0.3]],
+            },
+        }
+        provider, spec, speed, steps = resolve_voice_target(
+            "speaker:narrator", profiles,
+        )
+        assert provider == "kokoro"
+        assert "af_sarah" in spec
+        assert speed is None
+        assert steps is None
+
+    def test_speaker_in_profile_supertonic(self):
+        profiles = {
+            "narrator": {
+                "provider": "supertonic",
+                "voice": "F1",
+                "speed": 1.2,
+                "total_steps": 10,
+            },
+        }
+        provider, spec, speed, steps = resolve_voice_target(
+            "speaker:narrator", profiles,
+            job_voice="M1", job_speed=1.0, job_supertonic_total_steps=5,
+        )
+        assert provider == "supertonic"
+        assert spec == "F1"
+        assert speed == 1.2
+        assert steps == 10
+
+    def test_unknown_speaker_infers_from_spec(self):
+        with patch("abogen.domain.voice_utils.get_voices", return_value=["af_sarah"]):
+            provider, spec, speed, steps = resolve_voice_target(
+                "af_sarah", {}, job_tts_provider="kokoro",
+            )
+            assert provider == "kokoro"
+            assert spec == "af_sarah"
+
+    def test_uppercase_spec_infers_supertonic(self):
+        with patch("abogen.domain.voice_utils.get_voices", return_value=["af_sarah"]):
+            provider, spec, speed, steps = resolve_voice_target(
+                "M1", {}, job_voice="M1",
+            )
+            assert provider == "supertonic"
+            assert spec == "M1"
+
+
+class TestPipelinePoolRegression:
+    """Verify that PipelinePool behaves like the old inline get_pipeline closure."""
+
+    @patch("abogen.domain.pipeline_factory.create_pipeline_for_job")
+    @patch("abogen.domain.pipeline_factory.initialize_voice_cache")
+    def test_same_provider_returns_cached_pipeline(self, _cache, mock_create):
+        mock_pipeline = MagicMock()
+        mock_create.return_value = mock_pipeline
+        pool = PipelinePool()
+
+        r1 = pool.get("kokoro", "en", use_gpu=True)
+        r2 = pool.get("kokoro", "en", use_gpu=True)
+        assert r1 is r2
+        assert mock_create.call_count == 1
+
+    @patch("abogen.domain.pipeline_factory.create_pipeline_for_job")
+    @patch("abogen.domain.pipeline_factory.initialize_voice_cache")
+    def test_different_providers_get_separate_pipelines(self, _cache, mock_create):
+        p1 = MagicMock(name="kokoro")
+        p2 = MagicMock(name="supertonic")
+        mock_create.side_effect = [p1, p2]
+        pool = PipelinePool()
+
+        r1 = pool.get("kokoro", "en", use_gpu=True)
+        r2 = pool.get("supertonic", "en", use_gpu=True)
+        assert r1 is p1
+        assert r2 is p2
+
+    @patch("abogen.domain.pipeline_factory.create_pipeline_for_job")
+    @patch("abogen.domain.pipeline_factory.initialize_voice_cache")
+    def test_dispose_all_cleans_up(self, _cache, mock_create):
+        p1 = MagicMock()
+        p2 = MagicMock()
+        mock_create.side_effect = [p1, p2]
+        pool = PipelinePool()
+
+        pool.get("kokoro", "en", use_gpu=True)
+        pool.get("supertonic", "en", use_gpu=True)
+        pool.dispose_all()
+
+        p1.dispose.assert_called_once()
+        p2.dispose.assert_called_once()
+        assert pool._pipelines == {}
+
+    @patch("abogen.domain.pipeline_factory.create_pipeline_for_job")
+    @patch("abogen.domain.pipeline_factory.initialize_voice_cache")
+    def test_voice_cache_initialized_only_once(self, mock_cache, mock_create):
+        mock_create.return_value = MagicMock()
+        pool = PipelinePool()
+        job = MagicMock()
+
+        pool.get("kokoro", "en", use_gpu=True, job=job)
+        pool.get("kokoro", "en", use_gpu=True, job=job)
+        assert mock_cache.call_count == 1
+
+    @patch("abogen.domain.pipeline_factory.create_pipeline_for_job")
+    @patch("abogen.domain.pipeline_factory.initialize_voice_cache")
+    def test_after_dispose_voice_cache_can_reinitialize(self, mock_cache, mock_create):
+        mock_create.return_value = MagicMock()
+        pool = PipelinePool()
+        job = MagicMock()
+
+        pool.get("kokoro", "en", use_gpu=True, job=job)
+        assert mock_cache.call_count == 1
+
+        pool.dispose_all()
+        assert pool._voice_cache_initialized is False
+
+        pool.get("kokoro", "en", use_gpu=True, job=job)
+        assert mock_cache.call_count == 2