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)

abogen/webui/conversion_runner.py

@@ -13,7 +13,6 @@ from typing import Any, Callable, Dict, List, Mapping, Optional
 
 import numpy as np
 
-from abogen.tts_plugin.utils import is_plugin_registered
 from abogen.infrastructure.exporters import ExportService
 from abogen.epub3.exporter import build_epub3_package
 from abogen.kokoro_text_normalization import ApostropheConfig, normalize_for_pipeline, HAS_NUM2WORDS
@@ -31,10 +30,7 @@ from abogen.utils import (
     get_internal_cache_path,
     get_user_cache_path,
     get_user_output_path,
-    load_config,
 )
-from abogen.tts_plugin.utils import create_pipeline
-from abogen.voice_formulas import get_new_voice
 from abogen.voice_profiles import load_profiles, normalize_profile_entry
 from abogen.llm_client import LLMClientError
 from abogen.infrastructure.subtitle_writer import create_subtitle_writer
@@ -122,6 +118,8 @@ from abogen.domain.audio_buffer import (
 )
 from abogen.domain.audio_sink import AudioSink, open_audio_sink
 from abogen.domain.tokens import FakeToken
+from abogen.domain.pipeline_factory import PipelinePool
+from abogen.domain.voice_utils import resolve_voice_target as _resolve_voice_target
 
 
 from .service import Job, JobStatus
@@ -184,8 +182,7 @@ def run_conversion_job(job: Job) -> None:
     audio_output_path: Optional[Path] = None
     extraction: Optional[Any] = None
     pipeline: Any = None
-    pipelines: Dict[str, Any] = {}
+    pipeline_pool = PipelinePool()
-    kokoro_cache_ready = False
     normalized_profiles: Dict[str, Dict[str, Any]] = {}
     chunk_groups: Dict[int, List[Dict[str, Any]]] = {}
     active_chapter_configs: List[Dict[str, Any]] = []
@@ -202,75 +199,23 @@ def run_conversion_job(job: Job) -> None:
             if normalized:
                 normalized_profiles[str(name)] = normalized
 
-        def get_pipeline(provider: str) -> Any:
-            nonlocal kokoro_cache_ready
-            provider_norm = str(provider or "kokoro").strip().lower() or "kokoro"
-            if not is_plugin_registered(provider_norm):
-                provider_norm = "kokoro"
-
-            existing = pipelines.get(provider_norm)
-            if existing is not None:
-                return existing
-
-            if provider_norm == "supertonic":
-                pipelines[provider_norm] = create_pipeline(
-                    "supertonic",
-                )
-                return pipelines[provider_norm]
-
-            # Kokoro
-            cfg = load_config()
-            disable_gpu = not job.use_gpu or not cfg.get("use_gpu", True)
-            device = "cpu"
-            if not disable_gpu:
-                device = _select_device()
-            # Create KPipeline instance directly (uses new Plugin Architecture)
-            pipelines[provider_norm] = create_pipeline(
-                "kokoro",
-                lang_code=job.language,
-                device=device
-            )
-            if not kokoro_cache_ready:
-                _initialize_voice_cache(job)
-                kokoro_cache_ready = True
-            return pipelines[provider_norm]
-
-        def resolve_voice_target(raw_spec: str) -> tuple[str, str, Optional[float], Optional[int]]:
-            """Return (provider, voice_spec, speed_override, steps_override)."""
-            spec = str(raw_spec or "").strip()
-            speaker_name, _ = _split_speaker_reference(spec)
-            if speaker_name and speaker_name in normalized_profiles:
-                entry = normalized_profiles[speaker_name]
-                provider = str(entry.get("provider") or "kokoro").strip().lower() or "kokoro"
-                if provider == "supertonic":
-                    voice = str(entry.get("voice") or getattr(job, "voice", "M1") or "M1").strip() or "M1"
-                    steps = int(entry.get("total_steps") or getattr(job, "supertonic_total_steps", 5) or 5)
-                    speed = float(entry.get("speed") or getattr(job, "speed", 1.0) or 1.0)
-                    return "supertonic", _supertonic_voice_from_spec(voice, getattr(job, "voice", "M1")), speed, steps
-                formula = _formula_from_kokoro_entry(entry)
-                return "kokoro", formula or spec, None, None
-
-            fallback_provider = str(getattr(job, "tts_provider", "kokoro") or "kokoro").strip().lower() or "kokoro"
-            inferred = _infer_provider_from_spec(spec, fallback=fallback_provider)
-            if inferred == "supertonic":
-                return "supertonic", _supertonic_voice_from_spec(spec, getattr(job, "voice", "M1")), None, None
-            return "kokoro", spec, None, None
-
         def resolve_voice_choice(raw_spec: str) -> tuple[str, str, Any, Optional[float], Optional[int]]:
-            """Resolve a raw voice spec into (provider, resolved_spec, choice, speed, steps).
+            """Resolve a raw voice spec into (provider, resolved_spec, choice, speed, steps)."""
-
+            provider, resolved, speed, steps = _resolve_voice_target(
-            For Kokoro formulas, `choice` will be a resolved voice tensor (via `voice_formulas`).
+                raw_spec,
-            For SuperTonic, `choice` will be a valid SuperTonic voice id.
+                normalized_profiles,
-            """
+                job_voice=getattr(job, "voice", "M1"),
-
+                job_tts_provider=getattr(job, "tts_provider", "kokoro"),
-            provider, resolved, speed, steps = resolve_voice_target(raw_spec)
+                job_supertonic_total_steps=getattr(job, "supertonic_total_steps", 5),
+                job_speed=getattr(job, "speed", 1.0),
+            )
             cache_key = f"{provider}:{resolved}" if resolved else provider
             cached = voice_cache.get(cache_key)
             if cached is not None:
                 return provider, resolved, cached, speed, steps
 
             if provider == "kokoro":
-                kokoro_backend = get_pipeline("kokoro")
+                kokoro_backend = pipeline_pool.get("kokoro", job.language, job.use_gpu, job=job)
                 choice = _resolve_voice(kokoro_backend, resolved, job.use_gpu)
             else:
                 choice = resolved
@@ -405,9 +350,13 @@ def run_conversion_job(job: Job) -> None:
 
         base_voice_spec = _job_voice_fallback(job)
         voice_cache: Dict[str, Any] = {}
-        base_provider, base_voice_resolved, _, _ = resolve_voice_target(base_voice_spec)
+        base_provider, base_voice_resolved, _, _ = _resolve_voice_target(
+            base_voice_spec, normalized_profiles,
+            job_voice=getattr(job, "voice", "M1"),
+            job_tts_provider=getattr(job, "tts_provider", "kokoro"),
+        )
         if base_provider == "kokoro" and base_voice_resolved and "*" not in base_voice_resolved:
-            kokoro_backend = get_pipeline("kokoro")
+            kokoro_backend = pipeline_pool.get("kokoro", job.language, job.use_gpu, job=job)
             voice_cache[f"kokoro:{base_voice_resolved}"] = _resolve_voice(kokoro_backend, base_voice_resolved, job.use_gpu)
         processed_chars = 0
         current_time = 0.0
@@ -473,7 +422,7 @@ def run_conversion_job(job: Job) -> None:
 
             provider = str(tts_provider or getattr(job, "tts_provider", "kokoro") or "kokoro").strip().lower() or "kokoro"
             if provider == "supertonic":
-                supertonic_pipeline = get_pipeline("supertonic")
+                supertonic_pipeline = pipeline_pool.get("supertonic", job.language, job.use_gpu, job=job)
                 voice_name = _supertonic_voice_from_spec(voice_choice, getattr(job, "voice", "M1"))
                 segment_iter = supertonic_pipeline(
                     normalized,
@@ -483,7 +432,7 @@ def run_conversion_job(job: Job) -> None:
                     total_steps=int(supertonic_steps_override if supertonic_steps_override is not None else getattr(job, "supertonic_total_steps", 5)),
                 )
             else:
-                kokoro_backend = get_pipeline("kokoro")
+                kokoro_backend = pipeline_pool.get("kokoro", job.language, job.use_gpu, job=job)
                 segment_iter = kokoro_backend(
                     normalized,
                     voice=voice_choice,
@@ -605,12 +554,18 @@ def run_conversion_job(job: Job) -> None:
             if not chapter_voice_spec:
                 chapter_voice_spec = base_voice_spec
 
-            chapter_provider, chapter_voice_resolved, chapter_speed, chapter_steps = resolve_voice_target(chapter_voice_spec)
+            chapter_provider, chapter_voice_resolved, chapter_speed, chapter_steps = _resolve_voice_target(
+                chapter_voice_spec, normalized_profiles,
+                job_voice=getattr(job, "voice", "M1"),
+                job_tts_provider=getattr(job, "tts_provider", "kokoro"),
+                job_supertonic_total_steps=getattr(job, "supertonic_total_steps", 5),
+                job_speed=getattr(job, "speed", 1.0),
+            )
             chapter_cache_key = f"{chapter_provider}:{chapter_voice_resolved}" if chapter_voice_resolved else chapter_provider
             if chapter_provider == "kokoro":
                 voice_choice = voice_cache.get(chapter_cache_key)
                 if voice_choice is None:
-                    kokoro_backend = get_pipeline("kokoro")
+                    kokoro_backend = pipeline_pool.get("kokoro", job.language, job.use_gpu, job=job)
                     voice_choice = _resolve_voice(kokoro_backend, chapter_voice_resolved, job.use_gpu)
                     voice_cache[chapter_cache_key] = voice_choice
             else:
@@ -743,12 +698,18 @@ def run_conversion_job(job: Job) -> None:
                         chunk_steps_use = chapter_steps
                         chunk_voice_choice = voice_choice
                     else:
-                        chunk_provider, chunk_voice_resolved, chunk_speed_use, chunk_steps_use = resolve_voice_target(chunk_voice_spec)
+                        chunk_provider, chunk_voice_resolved, chunk_speed_use, chunk_steps_use = _resolve_voice_target(
+                            chunk_voice_spec, normalized_profiles,
+                            job_voice=getattr(job, "voice", "M1"),
+                            job_tts_provider=getattr(job, "tts_provider", "kokoro"),
+                            job_supertonic_total_steps=getattr(job, "supertonic_total_steps", 5),
+                            job_speed=getattr(job, "speed", 1.0),
+                        )
                         chunk_cache_key = f"{chunk_provider}:{chunk_voice_resolved}" if chunk_voice_resolved else chunk_provider
                         if chunk_provider == "kokoro":
                             chunk_voice_choice = voice_cache.get(chunk_cache_key)
                             if chunk_voice_choice is None:
-                                kokoro_backend = get_pipeline("kokoro")
+                                kokoro_backend = pipeline_pool.get("kokoro", job.language, job.use_gpu, job=job)
                                 chunk_voice_choice = _resolve_voice(
                                     kokoro_backend,
                                     chunk_voice_resolved,
@@ -1054,12 +1015,7 @@ def run_conversion_job(job: Job) -> None:
 
         # Explicitly release the pipeline and force garbage collection to prevent
         # memory accumulation in the worker process, which can lead to host lockups.
-        for p in pipelines.values():
+        pipeline_pool.dispose_all()
-            try:
-                p.dispose()
-            except Exception:
-                pass
-        pipelines.clear()
         pipeline = None
         gc.collect()
         try:
@@ -1100,21 +1056,6 @@ def run_conversion_job(job: Job) -> None:
                 ) from exc
 
 
-def _load_pipeline(job: Job):
-    cfg = load_config()
-    disable_gpu = not job.use_gpu or not cfg.get("use_gpu", True)
-    provider = str(getattr(job, "tts_provider", "kokoro") or "kokoro").strip().lower()
-    if provider == "supertonic":
-        return create_pipeline(
-            "supertonic",
-        )
-
-    device = "cpu"
-    if not disable_gpu:
-        device = _select_device()
-    return create_pipeline("kokoro", lang_code=job.language, device=device)
-
-
 def _prepare_output_dir(job: Job) -> Path:
     from platformdirs import user_desktop_dir  # type: ignore[import-not-found]
 

tests/test_domain_pipeline_factory.py

@@ -0,0 +1,175 @@
+from __future__ import annotations
+
+from unittest.mock import MagicMock, patch
+
+from abogen.domain.pipeline_factory import (
+    PipelinePool,
+    create_pipeline_for_job,
+    dispose_pipelines,
+    resolve_device,
+)
+
+
+class TestResolveDevice:
+    @patch("abogen.utils.load_config", return_value={"use_gpu": True})
+    @patch("abogen.domain.pipeline_factory.select_device", return_value="cuda:0")
+    def test_gpu_enabled(self, _sel, _cfg):
+        assert resolve_device(use_gpu=True) == "cuda:0"
+
+    @patch("abogen.utils.load_config", return_value={"use_gpu": True})
+    def test_gpu_disabled_by_job(self, _cfg):
+        assert resolve_device(use_gpu=False) == "cpu"
+
+    @patch("abogen.utils.load_config", return_value={"use_gpu": False})
+    @patch("abogen.domain.pipeline_factory.select_device", return_value="cuda:0")
+    def test_gpu_disabled_by_config(self, _sel, _cfg):
+        assert resolve_device(use_gpu=True) == "cpu"
+
+
+class TestCreatePipelineForJob:
+    @patch("abogen.domain.pipeline_factory.create_pipeline")
+    @patch("abogen.domain.pipeline_factory.is_plugin_registered", return_value=True)
+    def test_supertonic_provider(self, _reg, mock_create):
+        mock_create.return_value = MagicMock()
+        result = create_pipeline_for_job("supertonic", "en", use_gpu=True)
+        mock_create.assert_called_once_with("supertonic")
+        assert result is mock_create.return_value
+
+    @patch("abogen.domain.pipeline_factory.create_pipeline")
+    @patch("abogen.domain.pipeline_factory.is_plugin_registered", return_value=True)
+    @patch("abogen.domain.pipeline_factory.resolve_device", return_value="cpu")
+    def test_kokoro_provider(self, _dev, _reg, mock_create):
+        mock_create.return_value = MagicMock()
+        result = create_pipeline_for_job("kokoro", "en", use_gpu=False)
+        mock_create.assert_called_once_with("kokoro", lang_code="en", device="cpu")
+        assert result is mock_create.return_value
+
+    @patch("abogen.domain.pipeline_factory.create_pipeline")
+    @patch("abogen.domain.pipeline_factory.is_plugin_registered", return_value=False)
+    @patch("abogen.domain.pipeline_factory.resolve_device", return_value="cpu")
+    def test_unknown_provider_falls_back_to_kokoro(self, _dev, _reg, mock_create):
+        mock_create.return_value = MagicMock()
+        result = create_pipeline_for_job("unknown_provider", "en", use_gpu=False)
+        mock_create.assert_called_once_with("kokoro", lang_code="en", device="cpu")
+
+    @patch("abogen.domain.pipeline_factory.create_pipeline")
+    @patch("abogen.domain.pipeline_factory.is_plugin_registered", return_value=True)
+    @patch("abogen.domain.pipeline_factory.resolve_device", return_value="cpu")
+    def test_empty_provider_defaults_to_kokoro(self, _dev, _reg, mock_create):
+        mock_create.return_value = MagicMock()
+        result = create_pipeline_for_job("", "en", use_gpu=False)
+        mock_create.assert_called_once_with("kokoro", lang_code="en", device="cpu")
+
+    @patch("abogen.domain.pipeline_factory.create_pipeline")
+    @patch("abogen.domain.pipeline_factory.is_plugin_registered", return_value=True)
+    @patch("abogen.domain.pipeline_factory.resolve_device", return_value="cpu")
+    def test_none_provider_defaults_to_kokoro(self, _dev, _reg, mock_create):
+        mock_create.return_value = MagicMock()
+        result = create_pipeline_for_job(None, "en", use_gpu=False)
+        mock_create.assert_called_once_with("kokoro", lang_code="en", device="cpu")
+
+
+class TestDisposePipelines:
+    def test_disposes_all_and_clears(self):
+        p1 = MagicMock()
+        p2 = MagicMock()
+        pipelines = {"kokoro": p1, "supertonic": p2}
+        dispose_pipelines(pipelines)
+        p1.dispose.assert_called_once()
+        p2.dispose.assert_called_once()
+        assert pipelines == {}
+
+    def test_handles_dispose_error(self):
+        p1 = MagicMock()
+        p1.dispose.side_effect = RuntimeError("boom")
+        pipelines = {"kokoro": p1}
+        dispose_pipelines(pipelines)
+        assert pipelines == {}
+
+    def test_empty_dict(self):
+        pipelines = {}
+        dispose_pipelines(pipelines)
+        assert pipelines == {}
+
+
+class TestPipelinePool:
+    @patch("abogen.domain.pipeline_factory.create_pipeline_for_job")
+    @patch("abogen.domain.pipeline_factory.initialize_voice_cache")
+    def test_get_creates_and_caches(self, _cache, mock_create):
+        mock_pipeline = MagicMock()
+        mock_create.return_value = mock_pipeline
+        pool = PipelinePool()
+
+        result = pool.get("kokoro", "en", use_gpu=True)
+        assert result is mock_pipeline
+        mock_create.assert_called_once()
+
+        result2 = pool.get("kokoro", "en", use_gpu=True)
+        assert result2 is mock_pipeline
+        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_get_initializes_voice_cache_once(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.get("kokoro", "en", use_gpu=True, job=job)
+        assert mock_cache.call_count == 1
+
+    @patch("abogen.domain.pipeline_factory.initialize_voice_cache")
+    @patch("abogen.domain.pipeline_factory.create_pipeline_for_job")
+    def test_get_no_job_skips_voice_cache(self, mock_create, mock_cache):
+        mock_create.return_value = MagicMock()
+        pool = PipelinePool()
+        pool.get("kokoro", "en", use_gpu=True)
+        mock_cache.assert_not_called()
+
+    @patch("abogen.domain.pipeline_factory.create_pipeline_for_job")
+    def test_get_separately_per_provider(self, 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
+        assert mock_create.call_count == 2
+
+    @patch("abogen.domain.pipeline_factory.create_pipeline_for_job")
+    @patch("abogen.domain.pipeline_factory.initialize_voice_cache")
+    def test_dispose_all(self, mock_cache, mock_create):
+        p1 = MagicMock(name="kokoro")
+        p2 = MagicMock(name="supertonic")
+        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 == {}
+        assert pool._voice_cache_initialized is False
+
+    @patch("abogen.domain.pipeline_factory.create_pipeline_for_job")
+    def test_dispose_empty_pool(self, mock_create):
+        pool = PipelinePool()
+        pool.dispose_all()
+        mock_create.assert_not_called()
+
+    @patch("abogen.domain.pipeline_factory.create_pipeline_for_job")
+    @patch("abogen.domain.pipeline_factory.initialize_voice_cache")
+    @patch("abogen.domain.pipeline_factory.is_plugin_registered", return_value=False)
+    def test_unknown_provider_falls_back(self, _reg, _cache, mock_create):
+        mock_create.return_value = MagicMock()
+        pool = PipelinePool()
+        pool.get("bogus_provider", "en", use_gpu=True)
+        mock_create.assert_called_once_with("kokoro", "en", True)

tests/test_domain_voice_utils.py

@@ -0,0 +1,195 @@
+from __future__ import annotations
+
+from unittest.mock import patch
+
+from abogen.domain.voice_utils import (
+    coerce_truthy,
+    formula_from_kokoro_entry,
+    infer_provider_from_spec,
+    resolve_voice_target,
+    split_speaker_reference,
+    supertonic_voice_from_spec,
+)
+
+
+class TestSplitSpeakerReference:
+    def test_speaker_prefix(self):
+        assert split_speaker_reference("speaker:af_sarah") == ("af_sarah", "speaker:af_sarah")
+
+    def test_profile_prefix(self):
+        assert split_speaker_reference("profile:custom") == ("custom", "profile:custom")
+
+    def test_no_prefix(self):
+        assert split_speaker_reference("af_sarah") == (None, "af_sarah")
+
+    def test_empty(self):
+        assert split_speaker_reference("") == (None, "")
+
+    def test_none(self):
+        assert split_speaker_reference(None) == (None, "")
+
+    def test_unknown_prefix(self):
+        assert split_speaker_reference("unknown:name") == (None, "unknown:name")
+
+    def test_empty_name_after_colon(self):
+        assert split_speaker_reference("speaker:") == (None, "speaker:")
+
+
+class TestSupertonicVoiceFromSpec:
+    def test_uppercase_passthrough(self):
+        assert supertonic_voice_from_spec("M1", "M1") == "M1"
+
+    def test_lowercase_converted(self):
+        assert supertonic_voice_from_spec("m1", "M1") == "M1"
+
+    def test_empty_spec_uses_fallback(self):
+        assert supertonic_voice_from_spec("", "F1") == "F1"
+
+    def test_formula_spec_uses_fallback(self):
+        assert supertonic_voice_from_spec("af_sarah*0.5+bf_emma*0.5", "M1") == "M1"
+
+    def test_empty_both_gives_default(self):
+        assert supertonic_voice_from_spec("", "") == "M1"
+
+
+class TestFormulaFromKokoroEntry:
+    def test_single_voice(self):
+        entry = {"voices": [["af_sarah", 1.0]]}
+        result = formula_from_kokoro_entry(entry)
+        assert "af_sarah" in result
+        assert "1.000000" in result
+
+    def test_weighted_mix(self):
+        entry = {"voices": [["af_sarah", 0.6], ["bf_emma", 0.4]]}
+        result = formula_from_kokoro_entry(entry)
+        assert "af_sarah" in result
+        assert "bf_emma" in result
+        assert "+" in result
+
+    def test_empty_voices(self):
+        assert formula_from_kokoro_entry({"voices": []}) == ""
+
+    def test_missing_voices_key(self):
+        assert formula_from_kokoro_entry({}) == ""
+
+    def test_invalid_entries_filtered(self):
+        entry = {"voices": [["af_sarah", "bad"], ["bf_emma", 0.5]]}
+        result = formula_from_kokoro_entry(entry)
+        assert "bf_emma" in result
+        assert "af_sarah" not in result
+
+
+class TestCoerceTruthy:
+    def test_bool_passthrough(self):
+        assert coerce_truthy(True) is True
+        assert coerce_truthy(False) is False
+
+    def test_string_true(self):
+        assert coerce_truthy("yes") is True
+        assert coerce_truthy("1") is True
+
+    def test_string_false(self):
+        assert coerce_truthy("false") is False
+        assert coerce_truthy("0") is False
+        assert coerce_truthy("") is False
+
+    def test_none_default(self):
+        assert coerce_truthy(None) is True
+        assert coerce_truthy(None, False) is False
+
+    def test_int(self):
+        assert coerce_truthy(1) is True
+        assert coerce_truthy(0) is False
+
+
+class TestInferProviderFromSpec:
+    @patch("abogen.domain.voice_utils.get_voices", return_value=["af_sarah", "bf_emma"])
+    def test_known_kokoro_voice(self, _mock):
+        assert infer_provider_from_spec("af_sarah") == "kokoro"
+
+    @patch("abogen.domain.voice_utils.get_voices", return_value=["af_sarah"])
+    def test_uppercase_supertonic(self, _mock):
+        assert infer_provider_from_spec("M1") == "supertonic"
+
+    @patch("abogen.domain.voice_utils.get_voices", return_value=["af_sarah"])
+    def test_formula_kokoro(self, _mock):
+        assert infer_provider_from_spec("af_sarah*0.5+bf_emma*0.5") == "kokoro"
+
+    @patch("abogen.domain.voice_utils.get_voices", return_value=["af_sarah"])
+    def test_empty_fallback(self, _mock):
+        assert infer_provider_from_spec("", "kokoro") == "kokoro"
+
+    @patch("abogen.domain.voice_utils.get_voices", return_value=["af_sarah"])
+    def test_unknown_falls_back(self, _mock):
+        assert infer_provider_from_spec("unknown_xyz", "supertonic") == "supertonic"
+
+
+class TestResolveVoiceTarget:
+    def test_empty_spec_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_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_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
+
+    @patch("abogen.domain.voice_utils.get_voices", return_value=["af_sarah"])
+    def test_direct_supertonic_spec(self, _mock):
+        provider, spec, speed, steps = resolve_voice_target(
+            "M1", {},
+            job_voice="M1",
+        )
+        assert provider == "supertonic"
+        assert spec == "M1"
+
+    @patch("abogen.domain.voice_utils.get_voices", return_value=["af_sarah"])
+    def test_direct_kokoro_spec(self, _mock):
+        provider, spec, speed, steps = resolve_voice_target(
+            "af_sarah", {},
+            job_tts_provider="kokoro",
+        )
+        assert provider == "kokoro"
+        assert spec == "af_sarah"
+
+    def test_profile_missing_provider_defaults_kokoro(self):
+        profiles = {
+            "narrator": {
+                "voices": [["af_sarah", 1.0]],
+            },
+        }
+        provider, spec, speed, steps = resolve_voice_target(
+            "speaker:narrator", profiles,
+        )
+        assert provider == "kokoro"

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