fix(normalization): improve punctuation handling and spacing in text normalization

fix(subtitles): enhance ellipsis and paragraph break handling in subtitle processing
feat(gui): implement background update check for new versions
test(tests): add tests for ellipsis handling and paragraph breaks preservation

.github/FUNDING.yml

@@ -1,6 +1,6 @@
 # These are supported funding model platforms
 
-github: [jborza, jeremiahsb, mohangk]
+github: [jborza, jeremiahsb, mohangk, k0sm0naft]
 patreon: # Replace with a single Patreon username
 open_collective: # Replace with a single Open Collective username
 ko_fi: # Replace with a single Ko-fi username

.gitignore

@@ -39,3 +39,7 @@ dist/
 test_assets/
 dev_notes/
 .claude/
+.coverage
+
+# CodeGraph index (local, machine-specific)
+.codegraph/

AGENTS.md

@@ -0,0 +1,82 @@
+# AGENTS.md — Segmentation & Subtitle System Contract
+
+This document is the source of truth for how text is split for **voice
+processing** (TTS engine segmentation) and **subtitle processing**, across
+languages, TTS engines, and subtitle modes. It was written after a bug where
+sentence modes "processed all text as a whole" (one merged engine segment →
+one giant subtitle). **Do not change this behavior without updating this
+table.**
+
+## Voice processing — split pattern passed to the TTS engine
+
+`get_split_pattern(language, mode)` in `abogen/domain/split_pattern.py` is the
+default; the spaCy pre-TTS path overrides it. Both UIs must stay in sync:
+`spacy_pre_tts_segmentation` (`abogen/domain/conversion_pipeline.py`, WebUI)
+and the inline branch in `abogen/pyqt/conversion.py` (~line 860, PyQt).
+
+| Subtitle mode | English (en-US/en-GB) | Non-English, spaCy ON | Non-English, spaCy OFF | CJK (ja/zh) |
+|---|---|---|---|---|
+| Disabled | `\n` | spaCy pre-split, engine `\n` | `\n+` | `(?<=[.!?؟。！？।])\s*\|\n+` |
+| Line | `\n` | spaCy pre-split, engine `\n` | `\n` | `(?<=[.!?؟。！？।])\s*\|\n+` |
+| Sentence | `\n` | spaCy pre-split, engine `\n` | `(?<=[.!?؟。！？।])\s+\|\n+` | `(?<=[.!?؟。！？।])\s*\|\n+` |
+| Sentence + Comma | `\n` | spaCy pre-split, engine `\n` | `(?<=[.!?,؟。！？،،、।])\s+\|\n+` (commas kept) | `(?<=[.!?,؟。！？،،、।])\s*\|\n+` |
+| Sentence + Highlighting | `\n+` | `\n+` | `\n+` | `\n+` |
+| N words ("5 words") | `\n` (→ Disabled) | `\n+` | `\n+` | Disabled CJK pattern |
+
+Rules baked into this table:
+
+- **English voice splitting is ALWAYS newline-only** for Disabled, Line,
+  Sentence, and Sentence + Comma. English sentence/comma boundaries are
+  produced ONLY at subtitle time (spaCy post-TTS / regex fallback). Never add
+  punctuation to the English engine pattern.
+- **Non-English + spaCy ON**: spaCy pre-segments the text (pre-TTS); the
+  engine pattern is `\n` for Sentence AND Sentence + Comma — **never commas**.
+  spaCy is skipped when the toggle is off, mode is Disabled/Line, or input is
+  a subtitle file.
+- **Non-English + spaCy OFF** (toggle off, spaCy failure, subtitle input): the
+  default pattern is used — Sentence + Comma KEEPS its commas here. This is
+  the intentional fallback, not a bug.
+- CJK: punctuation-based patterns for Disabled/Line (historical); spacing is
+  `\s*` (no spaces needed between CJK chars).
+- Engine-level extra chunking (applies after the pattern): kokoro English
+  re-chunks at ~510 phonemes; kokoro non-English at ~400 chars; supertonic
+  caps each part at 300 chars.
+
+## Subtitle processing — post-TTS, from tokens
+
+| Mode | Behavior |
+|---|---|
+| Disabled | no subtitles |
+| Line | one entry per TTS segment (line) |
+| Sentence | sentence boundaries: English → spaCy; others → regex on `[.!?…]` |
+| Sentence + Comma | sentence + comma boundaries at subtitle time (both languages) — commas never affect voice |
+| Sentence + Highlighting | karaoke `{\kf…}` per word, grouped by sentence |
+| N words | groups of N words by whitespace counting |
+
+Token granularity (timing quality): kokoro English emits **per-word tokens**
+with timestamps; kokoro non-English and supertonic emit **no tokens** → each
+engine segment becomes one FakeToken, split by regex with proportional timing
+when it contains multiple sentences.
+
+## Hard invariants (breaking these reintroduces the original bug)
+
+1. `Pipeline.__call__` (`abogen/tts_plugin/utils.py`) must yield ONE `Segment`
+   per engine segment (with tokens) — never merge segments back into the
+   whole text. `SynthesizedAudio.segments` carries the per-segment data;
+   engines expose it in `plugins/kokoro/engine.py` and
+   `plugins/supertonic/engine.py`.
+2. `tts_segments` (`abogen/domain/conversion_pipeline.py`) restores trailing
+   whitespace on segment-boundary tokens ONLY for real per-word tokens, never
+   for FakeToken fallbacks.
+3. `_to_language_enum` must return `lang_code` as-is when it is already a
+   `Language` enum (`str(Language.ES)` is `"Language.ES"`, which silently
+   resolved to EN_US and disabled spaCy pre-TTS for every language in WebUI).
+4. English must never use spaCy for PRE-TTS segmentation — only for subtitles.
+
+## Guarded by tests
+
+- `tests/test_split_pattern.py` — English newline-only; non-English sentence
+  patterns; CJK behavior.
+- `tests/test_domain_conversion_pipeline.py` — `tts_segments` / spaCy
+  segmentation helpers.
+- Full suite: `python -m pytest tests/ -q` (expect 1566+ passing).

README.md

@@ -721,7 +721,7 @@ This project is available under the MIT License - see the [LICENSE](https://gith
 [Kokoro](https://github.com/hexgrad/kokoro) is licensed under [Apache-2.0](https://github.com/hexgrad/kokoro/blob/main/LICENSE) which allows commercial use, modification, distribution, and private use.
 
 ## `Star History`
-[![Star History Chart](https://api.star-history.com/svg?repos=denizsafak/abogen&type=Date)](https://www.star-history.com/#denizsafak/abogen&Date)
+[![Star History Chart](https://star-history.dera.page/svg?repos=denizsafak/abogen&type=Date)](https://star-history.dera.page/#denizsafak/abogen&Date)
 
 > [!NOTE]
 > Abogen supports subtitle generation for all languages. However, word-level subtitle modes (e.g., "1 word", "2 words", "3 words", etc.) are only available for English because [Kokoro provides timestamp tokens only for English text](https://github.com/hexgrad/kokoro/blob/6d87f4ae7abc2d14dbc4b3ef2e5f19852e861ac2/kokoro/pipeline.py#L383). For non-English languages, Abogen uses a duration-based fallback that supports sentence-level and comma-based subtitle modes ("Line", "Sentence", "Sentence + Comma"). If you need word-level subtitles for other languages, please request that feature in the [Kokoro project](https://github.com/hexgrad/kokoro).

abogen/application/__init__.py

@@ -0,0 +1,8 @@
+"""Application layer for conversion flow unification.
+
+This package contains the application-level orchestration logic
+that bridges UI adapters (PyQt, WebUI) with domain functions.
+
+The main entry point is ConversionService.run() which coordinates
+planning, execution, and finalization of a conversion job.
+"""

abogen/application/chapter_selection.py

@@ -0,0 +1,62 @@
+"""Chapter selection helpers for the application layer.
+
+Builds chapter payloads with smart defaults (preselection based on
+supplement score) and character counts. Used by both WebUI and PyQt.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Dict, List
+
+from abogen.domain.chapter_classification import (
+    ensure_at_least_one_chapter_enabled,
+    should_preselect_chapter,
+)
+from abogen.domain.text_utils import calculate_text_length
+
+
+def build_chapter_payload(
+    chapters: List[Any],
+    source_name: str = "",
+) -> List[Dict[str, Any]]:
+    """Build a chapter payload with preselection and character counts.
+
+    Args:
+        chapters: List of chapter-like objects with ``title`` and ``text`` attributes.
+        source_name: Fallback title for the placeholder chapter when *chapters* is empty.
+
+    Returns:
+        List of chapter dicts ready for ``PendingJob.chapters`` or ``ChapterChunkConfig``.
+    """
+    total = len(chapters)
+    payload: List[Dict[str, Any]] = []
+
+    for index, chapter in enumerate(chapters):
+        title = getattr(chapter, "title", "") or ""
+        text = getattr(chapter, "text", "") or ""
+        enabled = should_preselect_chapter(title, text, index, total)
+        payload.append(
+            {
+                "id": f"{index:04d}",
+                "index": index,
+                "title": title,
+                "text": text,
+                "characters": calculate_text_length(text),
+                "enabled": enabled,
+            }
+        )
+
+    if not payload:
+        payload.append(
+            {
+                "id": "0000",
+                "index": 0,
+                "title": source_name,
+                "text": "",
+                "characters": 0,
+                "enabled": True,
+            }
+        )
+
+    ensure_at_least_one_chapter_enabled(payload)
+    return payload

abogen/application/cleanup.py

@@ -0,0 +1,78 @@
+"""Application-layer cleanup — global resource disposal.
+
+Handles:
+- GPU/CUDA memory flush
+- TTS engine disposal (PluginManager)
+- UI-specific cleanup callbacks (registered by entry points)
+
+Called by shutdown.py at process exit and by run_conversion() per-conversion.
+"""
+
+from __future__ import annotations
+
+import gc
+import sys
+from typing import Callable
+
+_UI_CLEANUPS: list[Callable[[], None]] = []
+
+
+def flush_cuda() -> None:
+    """Run GC and release CUDA cache. Safe to call multiple times."""
+    gc.collect()
+    # Skip entirely if torch was never imported — importing it here just to
+    # check would add several seconds to shutdown with nothing to flush.
+    if "torch" not in sys.modules:
+        return
+    try:
+        torch = sys.modules["torch"]
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            torch.cuda.ipc_collect()
+    except Exception:
+        pass
+
+
+def dispose_engines() -> None:
+    """Dispose all cached TTS engines via PluginManager."""
+    try:
+        from abogen.tts_plugin.plugin_manager import get_plugin_manager
+        get_plugin_manager().dispose_all()
+    except Exception:
+        pass
+
+
+def _clear_global_voice_cache() -> None:
+    """Reset the global voice download cache state."""
+    try:
+        from abogen.voice_cache import clear_voice_cache
+        clear_voice_cache()
+    except Exception:
+        pass
+
+
+def register_ui_cleanup(fn: Callable[[], None]) -> None:
+    """Register a UI-specific cleanup callback (e.g. preview threads, temp files)."""
+    _UI_CLEANUPS.append(fn)
+
+
+def cleanup() -> None:
+    """Run all application-level cleanups. Idempotent."""
+    dispose_engines()
+    flush_cuda()
+    _clear_global_voice_cache()
+
+    for fn in _UI_CLEANUPS:
+        try:
+            fn()
+        except Exception:
+            pass
+    _UI_CLEANUPS.clear()
+
+
+__all__ = [
+    "flush_cuda",
+    "dispose_engines",
+    "register_ui_cleanup",
+    "cleanup",
+]

abogen/application/conversion_config.py

@@ -0,0 +1,95 @@
+"""Feature config objects for ConversionRequest.
+
+Each config object groups parameters for a specific feature.
+If the object is None, the feature is disabled.
+
+This keeps ConversionRequest clean: no boolean flags for feature toggles,
+no scattered parameters across unrelated fields.
+
+Domain config types (PronunciationConfig, SubtitleConfig) live in
+domain/config_types.py — domain defines the contract, app fills them.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+from abogen.domain.config_types import CoverConfig, PronunciationConfig, SubtitleConfig
+from abogen.domain.enums import OutputFormat, SaveMode
+
+
+@dataclass(frozen=True)
+class WordSubstitutionConfig:
+    """Word substitution settings.
+
+    When present on ConversionRequest, word substitution is applied
+    to the source text before chapter parsing.
+    """
+    substitutions_list: str = ""
+    case_sensitive: bool = False
+    replace_caps: bool = False
+    replace_numerals: bool = False
+    fix_punctuation: bool = False
+
+
+@dataclass(frozen=True)
+class SubtitleInputConfig:
+    """Subtitle file input settings.
+
+    When present on ConversionRequest, the source is treated as a
+    subtitle file (.srt/.ass/.vtt) or timestamp text, and the
+    subtitle-to-audio pipeline is used instead of normal text conversion.
+    """
+    is_timestamp_text: bool = False
+
+
+@dataclass(frozen=True)
+class Epub3ExportConfig:
+    """EPUB3 export settings.
+
+    When present on ConversionRequest, an EPUB3 package with
+    synchronized audio narration is generated after conversion.
+    """
+    book_id: str = ""
+
+
+@dataclass(frozen=True)
+class ChapterChunkConfig:
+    """Chapter and chunk configuration.
+
+    Groups chapter overrides, chunk data, and speaker settings
+    used by the planner to build segments.
+    """
+    chapter_overrides: List[Dict[str, Any]] = field(default_factory=list)
+    chunks: List[Dict[str, Any]] = field(default_factory=list)
+    chunk_level: str = "paragraph"
+    speaker_mode: str = "single"
+    speakers: Dict[str, Any] = field(default_factory=dict)
+
+    def __post_init__(self) -> None:
+        _VALID_CHUNK_LEVELS = ("paragraph", "sentence")
+        _VALID_SPEAKER_MODES = ("single", "multi")
+        if self.chunk_level not in _VALID_CHUNK_LEVELS:
+            raise ValueError(
+                f"chunk_level must be one of {_VALID_CHUNK_LEVELS}, got {self.chunk_level!r}"
+            )
+        if self.speaker_mode not in _VALID_SPEAKER_MODES:
+            raise ValueError(
+                f"speaker_mode must be one of {_VALID_SPEAKER_MODES}, got {self.speaker_mode!r}"
+            )
+
+
+@dataclass(frozen=True)
+class SaveConfig:
+    """Save/output settings.
+
+    Groups save mode, output folder, chapter splitting, and merge options.
+    """
+    mode: SaveMode = SaveMode.SAVE_NEXT_TO_INPUT
+    output_folder: Optional[Path] = None
+    save_chapters_separately: bool = False
+    merge_chapters_at_end: bool = True
+    separate_chapters_format: OutputFormat = OutputFormat.WAV
+    save_as_project: bool = False

abogen/application/conversion_executor.py

@@ -0,0 +1,660 @@
+"""Unified conversion executor.
+
+Takes a ConversionPlan and ports, executes the TTS conversion,
+and returns a ConversionResult. No UI imports allowed.
+
+This is Stage 6 of the conversion flow unification plan.
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+from contextlib import ExitStack
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple
+
+from abogen.application.conversion_models import (
+    ConversionPlan,
+)
+from abogen.application.conversion_ports import (
+    AudioSink,
+    ConversionEvents,
+    PipelineProvider,
+    SubtitleWriter,
+    VoiceResolver,
+)
+from abogen.application.conversion_result import ConversionResult
+from abogen.domain.audio_sink import open_audio_sink
+from abogen.domain.conversion_engine import (
+    SegmentStats,
+    SynthParams,
+    process_and_write_subtitles,
+    synthesize_text,
+)
+from abogen.domain.enums import OutputFormat, SubtitleMode
+from abogen.domain.normalization import TTSContext
+from abogen.domain.chapter_titles import (
+    apply_chapter_text_transforms,
+    headings_equivalent as _headings_equivalent,
+)
+from abogen.domain.output_paths import sanitize_filename_for_chapter
+from abogen.infrastructure.subtitle_writer import make_subtitle_writer
+
+
+# ─── MarkerCollector ───
+
+
+class MarkerCollector:
+    """Observes execution events and accumulates chapter/chunk markers.
+
+    Separates marker collection from synthesis logic.
+    """
+
+    def __init__(self) -> None:
+        self._chapter_markers: List[Dict[str, Any]] = []
+        self._chunk_markers: List[Dict[str, Any]] = []
+        self._current_chapter_voices: Set[Tuple[str, str]] = set()
+        self._current_chapter_index: int = 0
+        self._current_chapter_title: str = ""
+        self._current_chapter_start: float = 0.0
+
+    def on_chapter_start(
+        self, index: int, title: str, start_time: float
+    ) -> None:
+        """Record chapter start."""
+        self._current_chapter_index = index
+        self._current_chapter_title = title
+        self._current_chapter_start = start_time
+        self._current_chapter_voices.clear()
+
+    def on_segment(
+        self,
+        provider: str,
+        voice: Any,
+        voice_spec: str,
+        speaker_id: str = "narrator",
+    ) -> None:
+        """Record a voice used in this chapter (for multi-speaker tracking)."""
+        self._current_chapter_voices.add((provider, voice_spec))
+
+    def on_chunk(
+        self,
+        chunk_id: str,
+        chapter_index: int,
+        chunk_index: int,
+        start: float,
+        end: float,
+        speaker_id: str,
+        provider: str,
+        voice_spec: str,
+        level: str,
+        characters: int,
+    ) -> None:
+        """Record a chunk marker."""
+        self._chunk_markers.append({
+            "id": chunk_id,
+            "chapter_index": chapter_index,
+            "chunk_index": chunk_index,
+            "start": start,
+            "end": end,
+            "speaker_id": speaker_id,
+            "voice": {"provider": provider, "voice": voice_spec},
+            "level": level,
+            "characters": characters,
+        })
+
+    def on_chapter_end(self, end_time: float) -> None:
+        """Record chapter end and build chapter marker."""
+        voices = [
+            {"provider": p, "voice": v}
+            for p, v in sorted(self._current_chapter_voices)
+        ]
+        self._chapter_markers.append({
+            "chapter_index": self._current_chapter_index,
+            "index": self._current_chapter_index + 1,
+            "title": self._current_chapter_title,
+            "start": self._current_chapter_start,
+            "end": end_time,
+            "voices": voices,
+        })
+
+    def on_outro(
+        self,
+        start_time: float,
+        end_time: float,
+        provider: str,
+        voice_spec: str,
+    ) -> None:
+        """Record outro chapter marker."""
+        self._chapter_markers.append({
+            "chapter_index": len(self._chapter_markers),
+            "index": len(self._chapter_markers) + 1,
+            "title": "Outro",
+            "start": start_time,
+            "end": end_time,
+            "voices": [{"provider": provider, "voice": voice_spec}],
+        })
+
+    @property
+    def chapter_markers(self) -> List[Dict[str, Any]]:
+        return self._chapter_markers
+
+    @property
+    def chunk_markers(self) -> List[Dict[str, Any]]:
+        return self._chunk_markers
+
+
+def execute_conversion(
+    plan: ConversionPlan,
+    events: ConversionEvents,
+    pipeline_provider: PipelineProvider,
+    voice_resolver: VoiceResolver,
+    tts_context: TTSContext,
+    *,
+    check_cancelled: Optional[Callable[[], None]] = None,
+) -> ConversionResult:
+    """Execute a conversion plan and return the result.
+
+    Args:
+        plan: The conversion plan from build_conversion_plan()
+        events: UI-specific callbacks (log, progress, check_cancelled)
+        pipeline_provider: Provides TTS backends
+        voice_resolver: Resolves voice specs into loaded voices
+        tts_context: Normalization context for text processing
+        check_cancelled: Optional cancellation checker (overrides events.check_cancelled)
+
+    Returns:
+        ConversionResult with paths and markers
+
+    Raises:
+        ConversionCancelled: If conversion is cancelled
+    """
+    request = plan.request
+    result = ConversionResult(metadata=plan.metadata)
+    collector = MarkerCollector()
+
+    logging.info(
+        "[executor] Starting: chapters=%d intro=%s outro=%s merge=%s",
+        len(plan.chapters),
+        bool(plan.intro and plan.intro.enabled),
+        bool(plan.outro and plan.outro.enabled),
+        request.save.merge_chapters_at_end,
+    )
+
+    # Determine cancellation checker
+    if check_cancelled is None:
+        check_cancelled = lambda: events.check_cancelled()
+
+    # Stats for progress tracking
+    total_characters = sum(
+        len(ch.body_text) for ch in plan.chapters
+    )
+    if plan.intro and plan.intro.enabled:
+        total_characters += len(plan.intro.text)
+    if plan.outro and plan.outro.enabled:
+        total_characters += len(plan.outro.text)
+
+    stats = SegmentStats(
+        processed_chars=0,
+        current_time=0.0,
+        etr_start_time=time.time(),
+        total_characters=total_characters,
+    )
+
+    # Compute subtitle flag once (used in every synthesize_text call)
+    use_spacy = request.subtitle.mode not in (SubtitleMode.DISABLED, SubtitleMode.LINE)
+
+    # Output paths
+    output_layout = plan.output_layout
+    if not output_layout:
+        raise ValueError("ConversionPlan must have an output_layout")
+
+    # Determine if merged output is needed
+    merge_chapters = request.save.merge_chapters_at_end or not request.save.save_chapters_separately
+    if request.output_format == OutputFormat.M4B:
+        merge_chapters = True
+
+    # Resolve voices
+    base_voice_spec = request.voice or "M1"
+    logging.info("[executor] Resolving base voice: spec=%s", base_voice_spec)
+    base_provider, base_voice_choice, base_speed, base_steps = _resolve_voice(
+        voice_resolver, base_voice_spec, request,
+        log_callback=lambda msg: events.log(msg, level="warning"),
+    )
+    logging.info("[executor] Base voice resolved: provider=%s voice=%s speed=%.2f", base_provider, base_voice_choice, base_speed)
+
+    # Use ExitStack for resource management
+    with ExitStack() as stack:
+        # Open merged audio sink
+        audio_sink: Optional[AudioSink] = None
+        audio_path = None
+        if merge_chapters:
+            audio_path = output_layout.audio_dir / f"{_base_name(request)}{request.output_format.dot_ext}"
+            meta = plan.metadata if plan.metadata else None
+            audio_sink = stack.enter_context(
+                open_audio_sink(
+                    audio_path,
+                    request.output_format,
+                    metadata=meta,
+                    cancel_check=check_cancelled,
+                )
+            )
+            result.audio_path = audio_path
+
+        # Open subtitle writer if needed
+        subtitle_writer: Optional[SubtitleWriter] = None
+        if request.subtitle.mode != SubtitleMode.DISABLED and audio_sink:
+            subtitle_writer = make_subtitle_writer(
+                audio_path,
+                request.subtitle,
+            )
+            if subtitle_writer:
+                subtitle_writer.open()
+                stack.callback(subtitle_writer.close)
+                result.subtitle_paths.append(subtitle_writer.path)
+
+        effective_subtitle_mode = request.subtitle.mode if subtitle_writer else SubtitleMode.DISABLED
+
+        synth = SynthParams(
+            tts_context=tts_context,
+            stats=stats,
+            check_cancel=check_cancelled,
+            on_progress=lambda pct, etr: events.progress(pct, etr),
+            audio_sink=audio_sink,
+            subtitle_mode=effective_subtitle_mode,
+            max_subtitle_words=request.subtitle.max_words,
+            language=request.language,
+            use_spacy_segmentation=use_spacy,
+        )
+
+        # Chapter directory
+        chapter_dir = None
+        if request.save.save_chapters_separately and len(plan.chapters) > 1:
+            chapter_dir = output_layout.audio_dir / "chapters"
+            chapter_dir.mkdir(parents=True, exist_ok=True)
+
+        # Process intro
+        intro_emitted = False
+        if plan.intro and plan.intro.enabled and merge_chapters:
+            events.log(f"Title intro: {plan.intro.text[:80]}")
+            intro_provider, intro_voice, intro_speed, intro_steps = _resolve_voice(
+                voice_resolver, plan.intro.voice_spec, request,
+                log_callback=lambda msg: events.log(msg, level="warning"),
+            )
+            intro_backend = pipeline_provider.get(intro_provider, request.language, request.use_gpu)
+            synthesize_text(
+                text=plan.intro.text,
+                params=synth,
+                backend=intro_backend,
+                voice=intro_voice,
+                speed=intro_speed or request.speed,
+                total_steps=intro_steps,
+                chapter_sink=None,
+                preview_callback=lambda text: events.log(f"  {text[:80]}"),
+            )
+            intro_emitted = True
+            events.log("Intro synthesized.")
+
+        # Chapter loop
+        for chapter_idx, chapter in enumerate(plan.chapters, 1):
+            check_cancelled()
+
+            chapter_display = f"Chapter {chapter_idx}/{len(plan.chapters)}: {chapter.title}"
+            events.log(f"Processing {chapter_display}")
+            logging.info("[executor] Chapter %d/%d: %s", chapter_idx, len(plan.chapters), chapter.title)
+
+            # Resolve chapter voice
+            chapter_provider, chapter_voice, chapter_speed, chapter_steps = _resolve_voice(
+                voice_resolver, chapter.voice_spec, request,
+                log_callback=lambda msg: events.log(msg, level="warning"),
+            )
+            logging.info("[executor] Chapter %d voice: provider=%s voice=%s speed=%.2f", chapter_idx, chapter_provider, chapter_voice, chapter_speed)
+            chapter_backend = pipeline_provider.get(chapter_provider, request.language, request.use_gpu)
+
+            # Record chapter start for markers
+            collector.on_chapter_start(chapter_idx - 1, chapter.title, stats.current_time)
+
+            # Per-chapter sink
+            chapter_sink: Optional[AudioSink] = None
+            chapter_path = None
+            if chapter_dir:
+                chapter_filename = sanitize_filename_for_chapter(chapter.title, chapter_idx)
+                chapter_path = chapter_dir / f"{chapter_filename}.{request.save.separate_chapters_format}"
+                chapter_sink = stack.enter_context(
+                    open_audio_sink(
+                        chapter_path,
+                        request.save.separate_chapters_format,
+                        cancel_check=check_cancelled,
+                    )
+                )
+                result.chapter_paths.append(chapter_path)
+
+            # Per-chapter subtitle writer
+            chapter_subtitle_writer: Optional[SubtitleWriter] = None
+            if chapter_dir and request.subtitle.mode != SubtitleMode.DISABLED and chapter_sink:
+                from abogen.infrastructure.subtitle_writer import resolve_subtitle_format
+
+                chapter_filename = sanitize_filename_for_chapter(chapter.title, chapter_idx)
+                subtitle_ext, _ = resolve_subtitle_format(
+                    request.subtitle
+                )
+                chapter_subtitle_path = chapter_dir / f"{chapter_filename}.{subtitle_ext}"
+                chapter_subtitle_writer = make_subtitle_writer(
+                    chapter_subtitle_path,
+                    request.subtitle,
+                )
+                if chapter_subtitle_writer:
+                    chapter_subtitle_writer.open()
+                    result.subtitle_paths.append(chapter_subtitle_writer.path)
+
+            # Intro delay before first chapter
+            if not intro_emitted and plan.intro and plan.intro.enabled:
+                # Intro will be emitted with first chapter
+                intro_provider, intro_voice, intro_speed, intro_steps = _resolve_voice(
+                    voice_resolver, plan.intro.voice_spec, request,
+                    log_callback=lambda msg: events.log(msg, level="warning"),
+                )
+                intro_backend = pipeline_provider.get(intro_provider, request.language, request.use_gpu)
+                synthesize_text(
+                    text=plan.intro.text,
+                    params=synth,
+                    backend=intro_backend,
+                    voice=intro_voice,
+                    speed=intro_speed or request.speed,
+                    total_steps=intro_steps,
+                    chapter_sink=chapter_sink,
+                    preview_callback=lambda text: events.log(f"  Intro: {text[:80]}"),
+                )
+                intro_emitted = True
+                if request.chapter_intro_delay > 0:
+                    _append_silence(
+                        request.chapter_intro_delay,
+                        chapter_sink=chapter_sink,
+                        audio_sink=audio_sink,
+                        stats=stats,
+                    )
+
+            # Process heading
+            heading_text = ""
+            if chapter.title:
+                heading_text = _format_heading(chapter.title, chapter_idx, request)
+                if heading_text:
+                    synthesize_text(
+                        text=heading_text,
+                        params=synth,
+                        backend=chapter_backend,
+                        voice=chapter_voice,
+                        speed=chapter_speed or request.speed,
+                        chapter_sink=chapter_sink,
+                        preview_callback=lambda text: events.log(f"  Title: {text[:80]}"),
+                    )
+                    if request.chapter_intro_delay > 0:
+                        _append_silence(
+                            request.chapter_intro_delay,
+                            chapter_sink=chapter_sink,
+                            audio_sink=audio_sink,
+                            stats=stats,
+                        )
+
+            # Heading dedup: check if first line of body matches heading
+            pending_heading_strip = False
+            if heading_text and chapter.body_text:
+                first_line = next(
+                    (line.strip() for line in chapter.body_text.splitlines() if line.strip()),
+                    "",
+                )
+                if first_line and _headings_equivalent(first_line, heading_text):
+                    pending_heading_strip = True
+
+            # Process body segments
+            for seg_idx, segment in enumerate(chapter.segments):
+                check_cancelled()
+
+                # Apply heading dedup to first segment (consume-once)
+                seg_text = segment.text
+                if pending_heading_strip and seg_text.strip():
+                    seg_text, heading_removed, _ = apply_chapter_text_transforms(
+                        seg_text,
+                        heading_text=heading_text,
+                        raw_title=chapter.title,
+                        strip_heading=True,
+                        normalize_caps=False,
+                    )
+                    if heading_removed:
+                        pending_heading_strip = False
+                    if not seg_text.strip():
+                        continue
+
+                # Resolve segment voice (may differ from chapter voice)
+                if segment.voice_spec != chapter.voice_spec:
+                    seg_provider, seg_voice, seg_speed, seg_steps = _resolve_voice(
+                        voice_resolver, segment.voice_spec, request,
+                        log_callback=lambda msg: events.log(msg, level="warning"),
+                    )
+                    seg_backend = pipeline_provider.get(seg_provider, request.language, request.use_gpu)
+                else:
+                    seg_provider = chapter_provider
+                    seg_voice = chapter_voice
+                    seg_speed = chapter_speed
+                    seg_steps = chapter_steps
+                    seg_backend = chapter_backend
+
+                # Track voice for chapter marker
+                collector.on_segment(seg_provider, seg_voice, segment.voice_spec)
+
+                # spaCy pre-TTS segmentation
+                from abogen.domain.conversion_pipeline import spacy_pre_tts_segmentation
+
+                is_subtitle_input = bool(
+                    request.subtitle_input
+                )
+                spacy_segments, active_split = spacy_pre_tts_segmentation(
+                    seg_text,
+                    request.language,
+                    request.subtitle.mode,
+                    is_subtitle_input=is_subtitle_input,
+                    use_spacy_segmentation=use_spacy,
+                    log_callback=lambda msg: events.log(msg),
+                )
+
+                seg_start_time = stats.current_time
+                accumulated_tokens: List[Dict[str, Any]] = []
+                for spacy_seg in spacy_segments:
+                    if not spacy_seg.strip():
+                        continue
+                    _, seg_tokens = synthesize_text(
+                        text=spacy_seg,
+                        params=synth,
+                        backend=seg_backend,
+                        voice=seg_voice,
+                        speed=seg_speed or request.speed,
+                        total_steps=seg_steps,
+                        chapter_sink=chapter_sink,
+                        preview_callback=lambda text: events.log(f"  {text[:80]}"),
+                        split_pattern_override=active_split,
+                    )
+                    accumulated_tokens.extend(seg_tokens)
+
+                # Process subtitles
+                if audio_sink and accumulated_tokens:
+                    if subtitle_writer:
+                        process_and_write_subtitles(
+                            accumulated_tokens,
+                            subtitle_writer,
+                            subtitle=request.subtitle,
+                            language=request.language,
+                            use_spacy_segmentation=use_spacy,
+                            fallback_end_time=stats.current_time,
+                        )
+                    if chapter_subtitle_writer:
+                        process_and_write_subtitles(
+                            accumulated_tokens,
+                            chapter_subtitle_writer,
+                            subtitle=request.subtitle,
+                            language=request.language,
+                            use_spacy_segmentation=use_spacy,
+                            fallback_end_time=stats.current_time,
+                        )
+
+                # Record chunk marker
+                if segment.source in ("chunk", "voice_marker"):
+                    collector.on_chunk(
+                        chunk_id=segment.chunk_id or "",
+                        chapter_index=chapter_idx - 1,
+                        chunk_index=segment.chunk_index or seg_idx,
+                        start=seg_start_time,
+                        end=stats.current_time,
+                        speaker_id=segment.speaker_id or "narrator",
+                        provider=seg_provider,
+                        voice_spec=segment.voice_spec,
+                        level=segment.level or (request.chapter_chunk.chunk_level if request.chapter_chunk else "paragraph"),
+                        characters=len(segment.text),
+                    )
+
+            # Silence between chapters
+            if chapter_idx < len(plan.chapters) and request.silence_between_chapters > 0:
+                _append_silence(
+                    request.silence_between_chapters,
+                    chapter_sink=chapter_sink,
+                    audio_sink=audio_sink,
+                    stats=stats,
+                )
+
+            # Close chapter sink
+            if chapter_sink:
+                chapter_sink.close()
+
+            # Close chapter subtitle writer
+            if chapter_subtitle_writer:
+                chapter_subtitle_writer.close()
+
+            # Record chapter end for markers
+            collector.on_chapter_end(stats.current_time)
+            logging.info("[executor] Chapter %d/%d done: time=%.1fs", chapter_idx, len(plan.chapters), stats.current_time)
+
+        logging.info("[executor] All chapters done: total=%.1fs", stats.current_time)
+
+        # Process outro
+        if plan.outro and plan.outro.enabled and merge_chapters:
+            events.log(f"Closing outro: {plan.outro.text[:80]}")
+            outro_provider, outro_voice, outro_speed, outro_steps = _resolve_voice(
+                voice_resolver, plan.outro.voice_spec, request,
+                log_callback=lambda msg: events.log(msg, level="warning"),
+            )
+            outro_backend = pipeline_provider.get(outro_provider, request.language, request.use_gpu)
+
+            # Silence before outro
+            if request.silence_between_chapters > 0:
+                _append_silence(
+                    request.silence_between_chapters,
+                    chapter_sink=None,
+                    audio_sink=audio_sink,
+                    stats=stats,
+                )
+
+            outro_start = stats.current_time
+            synthesize_text(
+                text=plan.outro.text,
+                params=synth,
+                backend=outro_backend,
+                voice=outro_voice,
+                speed=outro_speed or request.speed,
+                total_steps=outro_steps,
+                chapter_sink=None,
+                preview_callback=lambda text: events.log(f"  {text[:80]}"),
+            )
+            # Record outro marker
+            collector.on_outro(outro_start, stats.current_time, outro_provider, plan.outro.voice_spec)
+            events.log("Outro synthesized.")
+
+    # Set result metadata
+    result.chapter_markers = collector.chapter_markers
+    result.chunk_markers = collector.chunk_markers
+    result.total_chapters = len(plan.chapters)
+    result.total_segments = sum(len(ch.segments) for ch in plan.chapters)
+    result.total_characters = total_characters
+
+    if output_layout.project_root:
+        result.project_root = output_layout.project_root
+
+    return result
+
+
+# ─── Helpers ────────────────────────────────────────────────────────
+
+
+def _resolve_voice(
+    resolver: VoiceResolver,
+    voice_spec: str,
+    request: Any,
+    *,
+    log_callback: Optional[Callable[[str], None]] = None,
+) -> Tuple[str, Any, Optional[float], Optional[int]]:
+    """Resolve a voice spec and return (provider, voice, speed, steps)."""
+    try:
+        resolved = resolver.resolve(voice_spec)
+        return (
+            resolved.provider,
+            resolved.voice,
+            resolved.speed,
+            resolved.supertonic_steps,
+        )
+    except Exception as exc:
+        # Fallback to base voice
+        base_spec = request.voice or "M1"
+        if log_callback:
+            log_callback(
+                f"Voice '{voice_spec}' failed to resolve: {exc}. "
+                f"Falling back to '{base_spec}'."
+            )
+        try:
+            resolved = resolver.resolve(base_spec)
+        except Exception as fallback_exc:
+            raise RuntimeError(
+                f"Both voice '{voice_spec}' and fallback '{base_spec}' failed to resolve. "
+                f"Primary error: {exc}; Fallback error: {fallback_exc}"
+            ) from fallback_exc
+        return (
+            resolved.provider,
+            resolved.voice,
+            resolved.speed,
+            resolved.supertonic_steps,
+        )
+
+
+def _base_name(request: Any) -> str:
+    """Get base name for output file."""
+    from abogen.domain.output_paths import sanitize_output_stem
+
+    if request.original_filename:
+        return sanitize_output_stem(request.original_filename)
+    return "output"
+
+
+def _format_heading(title: str, index: int, request: Any) -> str:
+    """Format chapter heading for TTS."""
+    from abogen.domain.chapter_titles import format_spoken_chapter_title
+
+    if request.auto_prefix_chapter_titles:
+        return format_spoken_chapter_title(title, index, apply_prefix=True)
+    return title
+
+
+def _append_silence(
+    duration: float,
+    *,
+    chapter_sink: Optional[AudioSink],
+    audio_sink: Optional[AudioSink],
+    stats: SegmentStats,
+) -> None:
+    """Append silence to sinks."""
+    from abogen.domain.audio_buffer import create_silence
+
+    silence = create_silence(duration)
+    if silence.size == 0:
+        return
+    if chapter_sink:
+        chapter_sink.write(silence)
+    if audio_sink:
+        audio_sink.write(silence)
+        stats.current_time += duration

abogen/application/conversion_models.py

@@ -0,0 +1,97 @@
+"""Core models for conversion planning.
+
+These dataclasses represent the structured plan for a conversion job.
+They are UI-agnostic and describe WHAT to convert, not HOW to do it.
+
+The planning flow:
+    ConversionRequest -> ConversionPlan -> ConversionResult
+
+    ConversionPlan contains:
+    - ChapterPlan[]: chapters with their segments
+    - SegmentPlan[]: individual text segments with voice specs
+    - OutputLayout: where to write outputs
+    - IntroOutroSpec: optional intro/outro
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Dict, List, Optional
+
+if TYPE_CHECKING:
+    from abogen.application.conversion_request import ConversionRequest
+    from abogen.text_extractor import ExtractionResult
+
+
+@dataclass
+class SegmentPlan:
+    """A single text segment with its voice specification.
+
+    This is the unified model for:
+    - Regular chapter body text
+    - PyQt voice markers (<<VOICE:F1>>)
+    - WebUI chunks with per-chunk voice/speaker
+    - Intro/outro text
+    - Chapter headings
+    """
+
+    text: str
+    voice_spec: str
+    kind: str = "body"  # intro, heading, body, outro
+    speaker_id: str = "narrator"
+    chunk_id: Optional[str] = None
+    chunk_index: Optional[int] = None
+    level: Optional[str] = None  # chunk level (paragraph, sentence, etc.)
+    source: str = "chapter"  # chapter, voice_marker, chunk
+
+
+@dataclass
+class ChapterPlan:
+    """A chapter with its metadata and segments."""
+
+    index: int
+    title: str
+    original_title: str
+    body_text: str
+    segments: List[SegmentPlan]
+    voice_spec: str  # default voice for this chapter
+
+
+@dataclass
+class OutputLayout:
+    """Resolved output paths for a conversion job."""
+
+    parent_dir: Path
+    merged_path: Optional[Path] = None
+    chapter_dir: Optional[Path] = None
+    project_root: Optional[Path] = None
+    audio_dir: Optional[Path] = None
+    subtitle_dir: Optional[Path] = None
+    metadata_dir: Optional[Path] = None
+
+
+@dataclass
+class IntroOutroSpec:
+    """Intro/outro specification with resolved text and voice."""
+
+    enabled: bool = False
+    text: str = ""
+    voice_spec: str = ""
+    kind: str = "intro"  # intro or outro
+
+
+@dataclass
+class ConversionPlan:
+    """Complete plan for a conversion job.
+
+    This is the output of the planning phase and input to the executor.
+    """
+
+    request: ConversionRequest
+    metadata: Dict[str, Any]
+    chapters: List[ChapterPlan]
+    intro: Optional[IntroOutroSpec] = None
+    outro: Optional[IntroOutroSpec] = None
+    output_layout: Optional[OutputLayout] = None
+    extraction: Optional[ExtractionResult] = None

abogen/application/conversion_planner.py

@@ -0,0 +1,393 @@
+"""Unified conversion planner.
+
+Pure functions that take a ConversionRequest and produce a ConversionPlan.
+No side effects, no I/O — all complexity from both UIs in one place.
+
+This is Stage 2 of the conversion flow unification plan.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Dict, List, Optional, Tuple
+
+from abogen.application.conversion_models import (
+    ChapterPlan,
+    ConversionPlan,
+    IntroOutroSpec,
+    SegmentPlan,
+)
+from abogen.application.conversion_request import ConversionRequest
+from abogen.application.output_layout_service import resolve_output_layout
+from abogen.domain.chapter_overrides import apply_chapter_overrides
+from abogen.domain.file_type import auto_select_relevant_chapters
+from abogen.domain.intro_outro import resolve_intro, resolve_outro
+from abogen.domain.metadata_extraction import extract_metadata_for_file
+from abogen.domain.metadata_merge import merge_metadata
+from abogen.domain.voice_markers import split_text_by_voice_markers
+
+
+def build_conversion_plan(request: ConversionRequest) -> ConversionPlan:
+    """Build a complete conversion plan from a request.
+
+    This is the single entry point that both UIs will call.
+    It handles all the planning logic that was previously duplicated
+    in both PyQt and WebUI conversion runners.
+
+    Args:
+        request: Normalized conversion request
+
+    Returns:
+        ConversionPlan with all chapters, segments, and output layout
+
+    Raises:
+        ValueError: If request is invalid (no source, no chapters, etc.)
+    """
+    # 1. Extract and validate source
+    source_text = _extract_source_text(request)
+    if not source_text or not source_text.strip():
+        raise ValueError("No text content to convert")
+
+    # 2. Extract metadata
+    metadata, extraction = _extract_metadata(request)
+
+    # 3. Parse chapters
+    raw_chapters = _parse_chapters(source_text, request)
+
+    # 4. Apply chapter selection/overrides
+    selected_chapters = _apply_selection(raw_chapters, request)
+
+    # 5. Build segments for each chapter
+    chapters = _build_chapters(selected_chapters, request)
+
+    # 6. Build intro/outro
+    intro, outro = _build_intro_outro(metadata, request)
+
+    # 7. Resolve output layout
+    output_layout = resolve_output_layout(request)
+
+    logging.info(
+        "[planner] Plan built: chapters=%d intro=%s outro=%s",
+        len(chapters),
+        bool(intro and intro.enabled),
+        bool(outro and outro.enabled),
+    )
+
+    return ConversionPlan(
+        request=request,
+        metadata=metadata,
+        chapters=chapters,
+        intro=intro,
+        outro=outro,
+        output_layout=output_layout,
+        extraction=extraction,
+    )
+
+
+def _extract_source_text(request: ConversionRequest) -> Optional[str]:
+    """Extract text from request source."""
+    from abogen.subtitle_utils import clean_text
+
+    if request.direct_text:
+        text = clean_text(request.direct_text)
+    elif request.source_path and request.source_path.exists():
+        encoding = "utf-8"
+        try:
+            with open(request.source_path, "r", encoding=encoding, errors="replace") as f:
+                text = f.read()
+        except Exception:
+            return None
+        text = clean_text(text)
+    else:
+        return None
+
+    # Apply word substitutions if configured
+    if request.word_substitution:
+        from abogen.word_substitution import apply_word_substitutions
+
+        ws = request.word_substitution
+        text = apply_word_substitutions(
+            text,
+            ws.substitutions_list,
+            ws.case_sensitive,
+            ws.replace_caps,
+            ws.replace_numerals,
+            ws.fix_punctuation,
+        )
+
+    return text
+
+
+def _extract_metadata(
+    request: ConversionRequest,
+) -> Tuple[Dict[str, Any], Optional[Any]]:
+    """Extract metadata from source file.
+
+    Returns (metadata, extraction) tuple.
+    """
+    if request.direct_text:
+        return dict(request.metadata_tags), None
+
+    if request.source_path and request.source_path.exists():
+        try:
+            extraction = extract_metadata_for_file(
+                str(request.source_path), is_direct_text=False
+            )
+            metadata = dict(extraction.metadata) if extraction.metadata else {}
+        except Exception:
+            extraction = None
+            metadata = {}
+        metadata = merge_metadata(metadata, request.metadata_tags)
+        return metadata, extraction
+
+    return dict(request.metadata_tags), None
+
+
+def _parse_chapters(
+    source_text: str, request: ConversionRequest
+) -> List[Tuple[str, str, str]]:
+    """Parse source text into raw chapters.
+
+    Returns list of (title, body_text, default_voice) tuples.
+    """
+    from abogen.domain.text_chapters import parse_chapters_from_text
+
+    # Text is already cleaned in _extract_source_text, so clean=False here
+    chapters = parse_chapters_from_text(source_text, default_title="text", clean=False)
+
+    # Default voice from request
+    default_voice = request.voice or "M1"
+
+    return [(title, text, default_voice) for title, text in chapters]
+
+
+def _apply_selection(
+    raw_chapters: List[Tuple[str, str, str]], request: ConversionRequest
+) -> List[Tuple[str, str, str]]:
+    """Apply chapter selection and overrides."""
+    from abogen.text_extractor import ExtractedChapter
+
+    # Convert to ExtractedChapter objects for auto_select_relevant_chapters
+    extracted = [
+        ExtractedChapter(title=title, text=text)
+        for title, text, _ in raw_chapters
+    ]
+
+    # If user specified chapters, apply overrides
+    chapter_chunk = request.chapter_chunk
+    if chapter_chunk and chapter_chunk.chapter_overrides:
+        selected, _, diagnostics = apply_chapter_overrides(extracted, chapter_chunk.chapter_overrides)
+        if selected:
+            # Map back to (title, text, voice) tuples
+            result = []
+            for ch in selected:
+                # Find matching original chapter to get voice
+                voice = request.voice or "M1"
+                for orig_title, orig_text, orig_voice in raw_chapters:
+                    if orig_title == ch.title:
+                        voice = orig_voice
+                        break
+                result.append((ch.title, ch.text or "", voice))
+            return result
+        # If no chapters selected, fall through to auto-selection
+
+    # Auto-select relevant chapters
+    from abogen.domain.file_type import infer_file_type
+
+    file_type = infer_file_type(request.source_path) if request.source_path else "text"
+    result = auto_select_relevant_chapters(extracted, file_type)
+    filtered = result.kept
+
+    if filtered:
+        # Map back to (title, text, voice) tuples
+        result = []
+        for ch in filtered:
+            voice = request.voice or "M1"
+            for orig_title, orig_text, orig_voice in raw_chapters:
+                if orig_title == ch.title:
+                    voice = orig_voice
+                    break
+            result.append((ch.title, ch.text or "", voice))
+        return result
+
+    # Fall back to all chapters
+    return raw_chapters
+
+
+def _build_chapters(
+    selected_chapters: List[Tuple[str, str, str]], request: ConversionRequest
+) -> List[ChapterPlan]:
+    """Build ChapterPlan with SegmentPlan for each chapter."""
+    from abogen.domain.chapter_titles import normalize_chapter_opening_caps
+
+    chapters = []
+
+    for idx, (title, body_text, default_voice) in enumerate(selected_chapters, 1):
+        # Apply caps normalization to body text if enabled
+        if request.normalize_chapter_opening_caps and body_text:
+            body_text, _ = normalize_chapter_opening_caps(body_text)
+
+        # Build segments for this chapter (idx is 1-based, chunks use 0-based)
+        segments = _build_segments(body_text, default_voice, request, chapter_index=idx - 1)
+
+        chapter = ChapterPlan(
+            index=idx,
+            title=title,
+            original_title=title,
+            body_text=body_text,
+            segments=segments,
+            voice_spec=default_voice,
+        )
+        chapters.append(chapter)
+
+    return chapters
+
+
+def _build_segments(
+    body_text: str, default_voice: str, request: ConversionRequest,
+    chapter_index: int = 0,
+) -> List[SegmentPlan]:
+    """Build SegmentPlan list for a chapter's body text.
+
+    Handles voice markers (PyQt) and chunks (WebUI).
+    """
+    segments = []
+
+    # Check for chunks (WebUI style)
+    chapter_chunk = request.chapter_chunk
+    if chapter_chunk and chapter_chunk.chunks:
+        # Group chunks by chapter index
+        from abogen.domain.chunk_utils import group_chunks_by_chapter
+
+        chunk_groups = group_chunks_by_chapter(chapter_chunk.chunks)
+        chunks_for_chapter = chunk_groups.get(chapter_index, [])
+
+        for chunk_idx, chunk in enumerate(chunks_for_chapter):
+            chunk_text = chunk.get("normalized_text") or chunk.get("text", "")
+            if not chunk_text or not chunk_text.strip():
+                continue
+
+            chunk_voice = _resolve_chunk_voice(chunk, default_voice, request)
+            speaker_id = chunk.get("speaker_id", "narrator")
+
+            segments.append(
+                SegmentPlan(
+                    text=chunk_text.strip(),
+                    voice_spec=chunk_voice,
+                    kind="body",
+                    speaker_id=speaker_id,
+                    chunk_id=chunk.get("id"),
+                    chunk_index=chunk.get("chunk_index", chunk_idx),
+                    level=chunk.get("level", chapter_chunk.chunk_level),
+                    source="chunk",
+                )
+            )
+        return segments
+
+    # Check for voice markers (PyQt style)
+    # Detect markers even if validation fails (voice names may not be loaded yet)
+    from abogen.domain.voice_markers import _VOICE_MARKER_SEARCH_PATTERN
+
+    has_voice_markers = bool(_VOICE_MARKER_SEARCH_PATTERN.search(body_text))
+    voice_segments, last_voice, valid_count, invalid_count = split_text_by_voice_markers(
+        body_text, default_voice
+    )
+
+    if has_voice_markers or (len(voice_segments) > 1):
+        # Voice markers were used
+        for voice_name, segment_text in voice_segments:
+            if not segment_text or not segment_text.strip():
+                continue
+            segments.append(
+                SegmentPlan(
+                    text=segment_text.strip(),
+                    voice_spec=voice_name,
+                    kind="body",
+                    source="voice_marker",
+                )
+            )
+        return segments
+
+    # No voice markers — single segment for entire body
+    if body_text and body_text.strip():
+        segments.append(
+            SegmentPlan(
+                text=body_text.strip(),
+                voice_spec=default_voice,
+                kind="body",
+                source="chapter",
+            )
+        )
+
+    return segments
+
+
+def _resolve_chunk_voice(
+    chunk: Dict[str, Any], default_voice: str, request: ConversionRequest
+) -> str:
+    """Resolve voice for a chunk."""
+    # Check for speaker-based voice
+    speaker_id = chunk.get("speaker_id", "narrator")
+    speakers = request.chapter_chunk.speakers if request.chapter_chunk else {}
+    if speaker_id and speaker_id != "narrator" and speakers:
+        speaker_config = speakers.get(speaker_id, {})
+        if isinstance(speaker_config, dict):
+            voice = speaker_config.get("voice")
+            if voice:
+                return voice
+
+    # Check for direct voice field
+    voice = chunk.get("voice")
+    if voice:
+        return voice
+
+    return default_voice
+
+
+def _build_intro_outro(
+    metadata: Dict[str, Any], request: ConversionRequest
+) -> Tuple[Optional[IntroOutroSpec], Optional[IntroOutroSpec]]:
+    """Build intro and outro specs."""
+    intro_spec = None
+    outro_spec = None
+
+    # Intro
+    if request.read_title_intro:
+        resolved = resolve_intro(
+            metadata,
+            request.original_filename,
+            True,
+            request.voice or "M1",
+            request.voice or "M1",
+            [],
+        )
+        if resolved.enabled:
+            intro_spec = IntroOutroSpec(
+                enabled=True,
+                text=resolved.text,
+                voice_spec=resolved.voice_spec,
+                kind="intro",
+            )
+
+    # Outro
+    if request.read_closing_outro:
+        resolved = resolve_outro(
+            metadata,
+            request.original_filename,
+            True,
+            request.voice or "M1",
+            request.voice or "M1",
+            [],
+        )
+        if resolved.enabled:
+            outro_spec = IntroOutroSpec(
+                enabled=True,
+                text=resolved.text,
+                voice_spec=resolved.voice_spec,
+                kind="outro",
+            )
+
+    return intro_spec, outro_spec
+
+
+# Output layout resolution is now in application/output_layout_service.py

abogen/application/conversion_ports.py

@@ -0,0 +1,112 @@
+"""Ports / interfaces for the conversion service.
+
+These protocols define how the conversion service communicates with
+the outside world (UI, TTS backends, voice resolvers).
+
+The service ONLY depends on these interfaces, never on concrete
+implementations (PyQt signals, Flask Job, etc.).
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Protocol
+
+
+class ConversionCancelled(Exception):
+    """Raised when conversion is cancelled by user."""
+    pass
+
+
+class ConversionEvents(Protocol):
+    """UI-specific actions the conversion service delegates back to the caller.
+
+    Implementations:
+    - PyQt: emits signals (log_updated, progress_updated, etc.)
+    - WebUI: updates Job attributes (job.add_log, job.progress, etc.)
+    """
+
+    def log(self, message: str, level: str = "info") -> None:
+        """Log a message to the UI."""
+        ...
+
+    def progress(self, processed: int, total: int, etr: str) -> None:
+        """Update progress display."""
+        ...
+
+    def check_cancelled(self) -> None:
+        """Check if conversion was cancelled.
+
+        Should raise ConversionCancelled (or UI-specific exception)
+        if cancellation is requested. Normal return means "continue".
+        """
+        ...
+
+
+class PipelineProvider(Protocol):
+    """Provides access to TTS backends (Kokoro, SuperTonic, etc.).
+
+    Implementations:
+    - PyQt: wraps self.backend (single pipeline)
+    - WebUI: wraps PipelinePool (multi-provider)
+    """
+
+    def get(self, provider: str, language: str, use_gpu: bool) -> Any:
+        """Get a TTS backend instance."""
+        ...
+
+    def dispose_all(self) -> None:
+        """Dispose all backend resources."""
+        ...
+
+
+@dataclass
+class ResolvedVoice:
+    """A resolved voice ready for TTS synthesis."""
+
+    provider: str
+    resolved_spec: str
+    voice: Any  # loaded voice tensor or name
+    speed: float
+    supertonic_steps: int
+
+
+class VoiceResolver(Protocol):
+    """Resolves voice specs into loaded voice objects.
+
+    Implementations:
+    - PyQt: wraps load_voice_cached + VoiceCache
+    - WebUI: wraps resolve_voice_choice + PipelinePool + VoiceCache
+    """
+
+    def resolve(self, voice_spec: str) -> ResolvedVoice:
+        """Resolve a voice spec into a loaded voice."""
+        ...
+
+
+class SubtitleWriter(Protocol):
+    """Writes subtitle entries to a file."""
+
+    def open(self) -> None:
+        """Open the subtitle file for writing."""
+        ...
+
+    def write_entry(self, start: float, end: float, text: str) -> None:
+        """Write a single subtitle entry."""
+        ...
+
+    def close(self) -> None:
+        """Close the subtitle file."""
+        ...
+
+
+class AudioSink(Protocol):
+    """Writes audio data to a file."""
+
+    def write(self, audio: Any) -> None:
+        """Write audio samples to the sink."""
+        ...
+
+    def close(self) -> None:
+        """Close the audio file."""
+        ...

abogen/application/conversion_request.py

@@ -0,0 +1,155 @@
+"""ConversionRequest — normalized input for a conversion job.
+
+This is NOT a WebUI Job and NOT a PyQt ConversionThread state.
+It describes the TASK, not the UI.
+
+UI adapters are responsible for converting their respective state
+into a ConversionRequest before calling ConversionService.run().
+"""
+
+from __future__ import annotations
+
+import dataclasses
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+from abogen.application.conversion_config import (
+    ChapterChunkConfig,
+    CoverConfig,
+    Epub3ExportConfig,
+    PronunciationConfig,
+    SaveConfig,
+    SubtitleConfig,
+    SubtitleInputConfig,
+    WordSubstitutionConfig,
+)
+from abogen.domain.enums import Language, OutputFormat
+
+
+class ConversionRequestError(ValueError):
+    """Raised when ConversionRequest has invalid field values."""
+
+
+# Numeric field constraints: attr -> (min, max)
+_NUMERIC_CONSTRAINTS: dict[str, tuple[float, float | None]] = {
+    "speed": (0.5, 3.0),
+    "supertonic_total_steps": (2, 15),
+    "silence_between_chapters": (0.0, None),
+    "chapter_intro_delay": (0.0, None),
+}
+
+
+@dataclass
+class ConversionRequest:
+    """Normalized request for a conversion job.
+
+    Only contains fields that describe the conversion task itself.
+    UI-only fields (display, logging, user prompts) stay in adapters.
+
+    Feature toggles use config objects (None = disabled):
+    - word_substitution, subtitle_input, chapter_chunk, epub3_export
+    - pronunciation (raw data, compiled by app layer)
+    - subtitle, save, cover (grouped parameters)
+
+    Validation runs on creation via __post_init__:
+    - None values → replaced with field default (from declaration)
+    - Numeric fields → clamped to valid range
+    """
+
+    # --- Source ---
+    source_path: Optional[Path] = None
+    direct_text: Optional[str] = None
+    original_filename: str = ""
+
+    # --- TTS Settings ---
+    language: Language = Language.EN_US
+    tts_provider: str = "kokoro"
+    voice: str = "M1"
+    voice_profile: Optional[str] = None
+    speed: float = 1.0
+    use_gpu: bool = True
+    supertonic_total_steps: int = 5
+
+    # --- Output Format ---
+    output_format: OutputFormat = OutputFormat.WAV
+
+    # --- Timing ---
+    silence_between_chapters: float = 2.0
+    chapter_intro_delay: float = 0.0
+
+    # --- Content Processing ---
+    replace_single_newlines: bool = False
+    read_title_intro: bool = False
+    read_closing_outro: bool = True
+    auto_prefix_chapter_titles: bool = True
+    normalize_chapter_opening_caps: bool = False
+
+    # --- Metadata ---
+    metadata_tags: Dict[str, Any] = field(default_factory=dict)
+
+    # --- Grouped configs ---
+    subtitle: SubtitleConfig = field(default_factory=SubtitleConfig)
+    save: SaveConfig = field(default_factory=SaveConfig)
+    cover: CoverConfig = field(default_factory=CoverConfig)
+    pronunciation: PronunciationConfig = field(default_factory=PronunciationConfig)
+
+    # --- Feature configs (None = disabled) ---
+    epub3_export: Optional[Epub3ExportConfig] = None
+    word_substitution: Optional[WordSubstitutionConfig] = None
+    subtitle_input: Optional[SubtitleInputConfig] = None
+    chapter_chunk: Optional[ChapterChunkConfig] = None
+
+    def __post_init__(self) -> None:
+        """Resolve None → default, then validate and clamp."""
+        _apply_none_defaults(self)
+        if not self.tts_provider:
+            self.tts_provider = "kokoro"
+        _coerce_enums(self)
+        _clamp_numerics(self)
+
+
+def _apply_none_defaults(obj: ConversionRequest) -> None:
+    """Replace None values with field defaults from dataclass declaration."""
+    for f in dataclasses.fields(obj):
+        if getattr(obj, f.name) is not None:
+            continue
+        if f.default is not dataclasses.MISSING:
+            setattr(obj, f.name, f.default)
+        elif f.default_factory is not dataclasses.MISSING:
+            setattr(obj, f.name, f.default_factory())
+
+
+# Enum fields that accept string coercion: attr -> (enum_class, fallback)
+_ENUM_COERCIONS: dict[str, tuple[type, Any]] = {
+    "language": (Language, Language.EN_US),
+    "output_format": (OutputFormat, OutputFormat.WAV),
+}
+
+
+def _coerce_enums(obj: ConversionRequest) -> None:
+    """Coerce string values to their expected enum types."""
+    for attr, (enum_cls, fallback) in _ENUM_COERCIONS.items():
+        val = getattr(obj, attr)
+        if isinstance(val, enum_cls):
+            continue
+        try:
+            setattr(obj, attr, enum_cls.from_str(str(val)))
+        except (ValueError, AttributeError):
+            setattr(obj, attr, fallback)
+
+
+def _clamp_numerics(obj: ConversionRequest) -> None:
+    """Clamp numeric fields to valid ranges."""
+    for attr, (min_v, max_v) in _NUMERIC_CONSTRAINTS.items():
+        val = getattr(obj, attr)
+        if val is None:
+            continue
+        if not isinstance(val, (int, float)):
+            raise ConversionRequestError(
+                f"{attr} must be a number, got {type(val).__name__}"
+            )
+        clamped = max(min_v, float(val))
+        if max_v is not None:
+            clamped = min(max_v, clamped)
+        setattr(obj, attr, clamped)

abogen/application/conversion_result.py

@@ -0,0 +1,50 @@
+"""ConversionResult — output of a successful conversion.
+
+Returned by ConversionService.run() after all synthesis and finalization.
+UI adapters consume this to update their respective state (Job, signals, etc.).
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+
+@dataclass
+class ConversionResult:
+    """Output of a successful conversion job."""
+
+    # --- Primary outputs ---
+    audio_path: Optional[Path] = None
+    subtitle_paths: List[Path] = field(default_factory=list)
+    chapter_paths: List[Path] = field(default_factory=list)
+
+    # --- Markers (for metadata/audiobookshelf) ---
+    chapter_markers: List[Dict[str, Any]] = field(default_factory=list)
+    chunk_markers: List[Dict[str, Any]] = field(default_factory=list)
+
+    # --- Metadata ---
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+    # --- Artifacts ---
+    artifacts: Dict[str, Path] = field(default_factory=dict)
+    project_root: Optional[Path] = None
+    epub_path: Optional[Path] = None
+
+    # --- Stats ---
+    total_chapters: int = 0
+    total_segments: int = 0
+    total_characters: int = 0
+
+    # --- Override usage tracking ---
+    usage_counter: Dict[str, int] = field(default_factory=dict)
+
+
+@dataclass
+class ConversionError:
+    """Error information when conversion fails."""
+
+    message: str
+    details: Optional[str] = None
+    is_cancelled: bool = False

abogen/application/conversion_service.py

@@ -0,0 +1,250 @@
+"""ConversionService — main orchestrator for the conversion flow.
+
+Ties together planner, executor, and finalizers into a single entry point.
+Both UIs (PyQt, WebUI) call ConversionService.run() to execute a conversion.
+
+Responsibilities:
+- Prepare TTSContext (normalization settings, pronunciation rules)
+- Build ConversionPlan via planner
+- Execute conversion via executor
+- Handle lifecycle (cleanup, error handling)
+- Return ConversionResult
+
+The service NEVER imports from PyQt or WebUI.
+"""
+
+from __future__ import annotations
+
+import logging
+from collections import defaultdict
+from typing import Any, Dict
+
+from abogen.application.conversion_executor import execute_conversion
+from abogen.application.conversion_models import ConversionPlan
+from abogen.application.conversion_planner import build_conversion_plan
+from abogen.application.conversion_ports import ConversionEvents
+from abogen.application.conversion_request import ConversionRequest
+from abogen.application.conversion_result import ConversionResult
+from abogen.domain.normalization import build_tts_context
+
+
+def run_conversion(
+    request: ConversionRequest,
+    events: ConversionEvents,
+) -> ConversionResult:
+    """Execute a conversion request and return the result.
+
+    This is the single entry point for both UIs. It orchestrates:
+    1. Voice infrastructure setup (pool, cache, resolver)
+    2. TTS context preparation
+    3. Conversion planning
+    4. Conversion execution
+    5. Resource cleanup
+
+    Args:
+        request: Normalized conversion request
+        events: UI-specific callbacks (log, progress, check_cancelled)
+
+    Returns:
+        ConversionResult with paths and markers
+
+    Raises:
+        ConversionCancelled: If conversion was cancelled
+        ValueError: If request is invalid
+        Exception: On TTS or I/O errors
+    """
+    from abogen.domain.pipeline_factory import PipelinePool
+    from abogen.domain.voice_loader import VoiceCache
+
+    pool = PipelinePool()
+    voice_cache = VoiceCache()
+
+    try:
+        # Stage 0: Create voice resolver
+        events.log("Preparing conversion pipeline")
+        logging.info(
+            "[app] run_conversion: provider=%s language=%s voice=%s speed=%.2f",
+            request.tts_provider, request.language, request.voice, request.speed,
+        )
+        resolver = _create_voice_resolver(request, pool, voice_cache)
+
+        # Stage 1: Prepare TTS context
+        usage_counter: Dict[str, int] = defaultdict(int)
+        tts_context = build_tts_context(
+            language=request.language,
+            subtitle=request.subtitle,
+            pronunciation=request.pronunciation,
+            usage_counter=usage_counter,
+            log_callback=lambda level, msg: events.log(msg, level=level),
+        )
+
+        # Stage 2: Build conversion plan
+        events.log("Building conversion plan")
+        plan = build_conversion_plan(request)
+
+        # Stage 3: Execute conversion
+        events.log("Starting conversion")
+        result = execute_conversion(
+            plan=plan,
+            events=events,
+            pipeline_provider=pool,
+            voice_resolver=resolver,
+            tts_context=tts_context,
+        )
+
+        # Propagate usage counter to result
+        result.usage_counter = dict(usage_counter)
+
+        # Stage 4: Finalize (m4b metadata embedding, EPUB3 generation)
+        _finalize(request, result, plan, events)
+
+        events.log("Conversion complete")
+        logging.info("[app] run_conversion completed successfully")
+        return result
+
+    except Exception as e:
+        events.log(f"Conversion failed: {e}", level="error")
+        logging.exception("[app] run_conversion failed: %s", e)
+        raise
+    finally:
+        pool.dispose_all()
+        voice_cache.clear()
+        from abogen.application.cleanup import flush_cuda
+        flush_cuda()
+
+
+def _create_voice_resolver(
+    request: ConversionRequest,
+    pool: Any,
+    cache: Any,
+) -> Any:
+    """Create AppVoiceResolver with loaded profiles.
+
+    Loads voice profiles from disk, normalizes them, and creates
+    an AppVoiceResolver that can resolve voice specs into loaded voices.
+    """
+    from abogen.application.voice_resolver import AppVoiceResolver
+    from abogen.voice_profiles import load_profiles, normalize_profile_entry
+
+    try:
+        profiles = load_profiles()
+    except Exception:
+        profiles = {}
+
+    normalized_profiles: Dict[str, Dict[str, Any]] = {}
+    for name, entry in (profiles or {}).items():
+        normalized = normalize_profile_entry(entry)
+        if normalized:
+            normalized_profiles[str(name)] = normalized
+
+    return AppVoiceResolver(request, normalized_profiles, pool, cache)
+
+
+def _finalize(
+    request: ConversionRequest,
+    result: ConversionResult,
+    plan: ConversionPlan,
+    events: ConversionEvents,
+) -> None:
+    """Post-conversion finalization (m4b metadata embedding, EPUB3 generation, etc.)."""
+    from abogen.domain.enums import OutputFormat
+
+    # m4b metadata embedding
+    if (
+        result.audio_path
+        and request.output_format == OutputFormat.M4B
+    ):
+
+        from abogen.infrastructure.exporters import ExportService
+
+        export_svc = ExportService()
+
+        try:
+            export_svc.embed_m4b_metadata(
+                audio_path=result.audio_path,
+                metadata=result.metadata or {},
+                chapters=result.chapter_markers or [],
+                cover=request.cover,
+                log_callback=lambda msg, level="info": events.log(msg, level=level),
+            )
+        except Exception as exc:
+            events.log(f"Failed to embed m4b metadata: {exc}", level="error")
+            raise RuntimeError(f"Failed to embed m4b metadata: {exc}") from exc
+
+    # EPUB3 generation
+    if request.epub3_export and plan.extraction:
+        audio_asset = result.audio_path
+        if not audio_asset and result.chapter_paths:
+            audio_asset = result.chapter_paths[0]
+
+        if audio_asset:
+            try:
+
+                from abogen.epub3.exporter import build_epub3_package
+
+                epub_root = result.project_root or plan.output_layout.parent_dir
+                from abogen.domain.output_paths import build_output_path
+
+                epub_output_path = build_output_path(epub_root, request.original_filename, "epub")
+                events.log("Generating EPUB 3 package...")
+                epub_path = build_epub3_package(
+                    output_path=epub_output_path,
+                    book_id=request.epub3_export.book_id,
+                    extraction=plan.extraction,
+                    metadata_tags=result.metadata or {},
+                    chapter_markers=result.chapter_markers or [],
+                    chunk_markers=result.chunk_markers or [],
+                    chunks=request.chapter_chunk.chunks if request.chapter_chunk else [],
+                    audio_path=audio_asset,
+                    speaker_mode=request.chapter_chunk.speaker_mode if request.chapter_chunk else "single",
+                    cover=request.cover,
+                )
+                result.epub_path = epub_path
+                result.artifacts["epub3"] = epub_path
+                events.log(f"EPUB 3 package created at {epub_path}")
+            except Exception as exc:
+                events.log(f"Failed to generate EPUB 3: {exc}", level="error")
+        else:
+            events.log("Skipped EPUB 3 generation: audio output unavailable.", level="warning")
+
+    # Build metadata payload and write metadata.json
+    if plan.output_layout and plan.output_layout.metadata_dir:
+        from abogen.domain.metadata_helpers import build_metadata_payload
+
+        metadata_payload = build_metadata_payload(
+            metadata=result.metadata,
+            chapter_markers=result.chapter_markers,
+            chunk_markers=result.chunk_markers,
+            chunk_level=request.chapter_chunk.chunk_level if request.chapter_chunk else None,
+            speaker_mode=request.chapter_chunk.speaker_mode if request.chapter_chunk else None,
+            speakers=request.chapter_chunk.speakers if request.chapter_chunk else None,
+            generate_epub3=bool(request.epub3_export),
+        )
+
+        metadata_dir = plan.output_layout.metadata_dir
+        metadata_dir.mkdir(parents=True, exist_ok=True)
+        metadata_file = metadata_dir / "metadata.json"
+
+        import json
+
+        metadata_file.write_text(json.dumps(metadata_payload, indent=2), encoding="utf-8")
+        result.artifacts["metadata"] = metadata_file
+        events.log(f"Metadata written to {metadata_file}")
+
+    # Record override usage
+    if result.usage_counter:
+        try:
+            from abogen.normalization_settings import record_override_usage
+
+            record_override_usage(result.usage_counter)
+        except Exception as exc:
+            events.log(f"Failed to record override usage: {exc}", level="debug")
+
+    # Post-conversion hooks (Audiobookshelf, etc.)
+    from abogen.application.integration_hooks import PostConversionHooks
+
+    hooks = PostConversionHooks()
+    hooks.run(request, result, events)
+
+
+

abogen/application/integration_hooks.py

@@ -0,0 +1,164 @@
+"""Post-conversion integration hooks.
+
+Called by ConversionService after finalization.
+Each integration is a method on PostConversionHooks — isolated, testable,
+and easy to extend with new hooks (Plex, Navidrome, etc.).
+
+The service NEVER imports from PyQt or WebUI.
+"""
+
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+from typing import Any, Mapping, Optional
+
+from abogen.application.conversion_ports import ConversionEvents
+from abogen.application.conversion_request import ConversionRequest
+from abogen.application.conversion_result import ConversionResult
+from abogen.domain.metadata_helpers import (
+    build_audiobookshelf_metadata as _build_abs_metadata,
+    load_audiobookshelf_chapters as _load_abs_chapters,
+)
+from abogen.domain.settings_core import (
+    build_audiobookshelf_config,
+    coerce_bool,
+    load_audiobookshelf_config,
+    stored_integration_config,
+)
+from abogen.integrations.audiobookshelf import (
+    AudiobookshelfClient,
+    AudiobookshelfUploadError,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class PostConversionHooks:
+    """Runs post-conversion integrations (Audiobookshelf, etc.).
+
+    Usage::
+
+        hooks = PostConversionHooks()
+        hooks.run(request, result, events)
+    """
+
+    def run(
+        self,
+        request: ConversionRequest,
+        result: ConversionResult,
+        events: ConversionEvents,
+    ) -> None:
+        """Run all registered post-conversion hooks."""
+        self._maybe_send_to_audiobookshelf(request, result, events)
+
+    # ------------------------------------------------------------------
+    # Audiobookshelf
+    # ------------------------------------------------------------------
+
+    def _maybe_send_to_audiobookshelf(
+        self,
+        request: ConversionRequest,
+        result: ConversionResult,
+        events: ConversionEvents,
+    ) -> None:
+        """Upload finished audiobook to Audiobookshelf if enabled."""
+        abs_settings = stored_integration_config("audiobookshelf")
+        if not abs_settings:
+            return
+
+        enabled = coerce_bool(abs_settings.get("enabled"), False)
+        auto_send = coerce_bool(abs_settings.get("auto_send"), False)
+        if not (enabled and auto_send):
+            return
+
+        config = build_audiobookshelf_config(abs_settings)
+        if config is None:
+            events.log(
+                "Audiobookshelf upload skipped: configure base URL, API token, "
+                "library ID, and folder ID first.",
+                level="warning",
+            )
+            return
+
+        audio_path = result.audio_path
+        if not audio_path or not audio_path.exists():
+            events.log(
+                "Audiobookshelf upload skipped: audio output not found.",
+                level="warning",
+            )
+            return
+
+        # Build metadata
+        filename = request.original_filename or "Audiobook"
+        lang = request.language.value if hasattr(request.language, "value") else str(request.language)
+        metadata = _build_abs_metadata(
+            result.metadata or {},
+            language=lang,
+            filename=Path(filename).stem,
+        )
+
+        # Load chapters from metadata artifact
+        chapters = None
+        if config.send_chapters:
+            metadata_artifact = result.artifacts.get("metadata")
+            if metadata_artifact:
+                metadata_path = (
+                    metadata_artifact
+                    if isinstance(metadata_artifact, Path)
+                    else Path(str(metadata_artifact))
+                )
+                chapters = _load_abs_chapters(metadata_path)
+
+        # Resolve cover
+        cover_path = None
+        if config.send_cover and request.cover and request.cover.path:
+            candidate = request.cover.path
+            if isinstance(candidate, Path) and candidate.exists():
+                cover_path = candidate
+
+        # Resolve subtitles
+        subtitles = None
+        if config.send_subtitles and result.subtitle_paths:
+            subtitles = [
+                p for p in result.subtitle_paths
+                if isinstance(p, Path) and p.exists()
+            ]
+
+        # Upload
+        client = AudiobookshelfClient(config)
+        display_title = metadata.get("title") or audio_path.stem
+
+        try:
+            existing_items = client.find_existing_items(
+                display_title, folder_id=config.folder_id,
+            )
+        except AudiobookshelfUploadError as exc:
+            events.log(f"Audiobookshelf lookup failed: {exc}", level="error")
+            return
+
+        if existing_items:
+            events.log(
+                f"Removing existing Audiobookshelf item(s) for '{display_title}'.",
+                level="info",
+            )
+            try:
+                client.delete_items(existing_items)
+            except Exception as exc:
+                events.log(
+                    f"Failed to remove existing item(s): {exc}", level="warning",
+                )
+
+        try:
+            client.upload_audiobook(
+                audio_path,
+                metadata=metadata,
+                cover_path=cover_path,
+                chapters=chapters,
+                subtitles=subtitles,
+            )
+            events.log("Audiobookshelf upload queued.", level="info")
+        except AudiobookshelfUploadError as exc:
+            events.log(f"Audiobookshelf upload failed: {exc}", level="error")
+        except Exception as exc:
+            events.log(f"Audiobookshelf integration error: {exc}", level="error")

abogen/application/output_layout_service.py

@@ -0,0 +1,149 @@
+"""Output layout resolution service.
+
+Determines where conversion outputs (audio, subtitles, metadata) should be written.
+Extracted from conversion_planner.py as a standalone service per plan Stage 5.
+
+Responsibilities:
+- Resolve base output directory from save_mode and source_path
+- Determine base filename from original_filename
+- Find unique output path to avoid overwrites
+- Resolve project layout (audio_dir, subtitle_dir, metadata_dir)
+- Force merged output for m4b format
+- Return OutputLayout dataclass
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from abogen.application.conversion_models import OutputLayout
+from abogen.application.conversion_request import ConversionRequest
+from abogen.domain.enums import OutputFormat, SaveMode, SubtitleFormat
+from abogen.domain.output_paths import (
+    resolve_project_layout,
+    resolve_unique_path,
+    sanitize_output_stem,
+)
+
+
+def resolve_output_layout(request: ConversionRequest) -> OutputLayout:
+    """Resolve output paths for a conversion request.
+
+    This is the single entry point for output path resolution,
+    used by both UIs and the conversion service.
+
+    Args:
+        request: Normalized conversion request
+
+    Returns:
+        OutputLayout with resolved paths
+    """
+    # Determine base output directory
+    if request.save.mode == SaveMode.CUSTOM_FOLDER and request.save.output_folder:
+        parent_dir = Path(request.save.output_folder)
+    elif request.source_path:
+        parent_dir = request.source_path.parent
+    else:
+        parent_dir = Path.cwd()
+
+    # Determine base name
+    if request.original_filename:
+        base_name = sanitize_output_stem(request.original_filename)
+    elif request.source_path:
+        base_name = sanitize_output_stem(request.source_path.stem)
+    else:
+        base_name = "output"
+
+    # Find unique output path
+    allowed_exts = {request.output_format, SubtitleFormat.SRT, SubtitleFormat.ASS, "vtt", "mp4", OutputFormat.M4B}
+    unique_base = resolve_unique_path(
+        parent_dir, base_name, "", allowed_extensions=allowed_exts
+    )
+
+    # Resolve project layout
+    project_root = None
+    audio_dir = parent_dir
+    subtitle_dir = None
+    metadata_dir = None
+
+    if request.save.save_as_project:
+        project_root, audio_dir, subtitle_dir, metadata_dir = resolve_project_layout(
+            original_filename=request.original_filename,
+            save_as_project=True,
+            base_dir=parent_dir,
+        )
+
+    return OutputLayout(
+        parent_dir=parent_dir,
+        project_root=project_root,
+        audio_dir=audio_dir,
+        subtitle_dir=subtitle_dir,
+        metadata_dir=metadata_dir,
+    )
+
+
+def resolve_merged_path(
+    layout: OutputLayout,
+    request: ConversionRequest,
+) -> Path:
+    """Resolve the merged output audio file path.
+
+    Args:
+        layout: Resolved output layout
+        request: Conversion request
+
+    Returns:
+        Path to the merged output file
+    """
+    base_name = sanitize_output_stem(
+        request.original_filename or "output"
+    )
+    return layout.audio_dir / f"{base_name}{request.output_format.dot_ext}"
+
+
+def resolve_chapter_path(
+    layout: OutputLayout,
+    request: ConversionRequest,
+    chapter_title: str,
+    chapter_index: int,
+) -> Path:
+    """Resolve the output path for a separate chapter file.
+
+    Args:
+        layout: Resolved output layout
+        request: Conversion request
+        chapter_title: Chapter title for filename
+        chapter_index: Chapter number (1-based)
+
+    Returns:
+        Path to the chapter output file
+    """
+    import re
+
+    slug = re.sub(r'[^\w\s-]', '', chapter_title.lower())
+    slug = re.sub(r'[\s_]+', '_', slug).strip('_')
+    if not slug:
+        slug = f"chapter_{chapter_index}"
+    filename = f"{chapter_index:02d}_{slug}.{request.save.separate_chapters_format}"
+    return layout.audio_dir / "chapters" / filename
+
+
+def should_merge_output(request: ConversionRequest) -> bool:
+    """Determine if merged output is required.
+
+    Rules:
+    - m4b format always forces merged output
+    - If save_chapters_separately is False, merged is required
+    - Otherwise, use merge_chapters_at_end setting
+
+    Args:
+        request: Conversion request
+
+    Returns:
+        True if merged output should be created
+    """
+    if request.output_format == OutputFormat.M4B:
+        return True
+    if not request.save.save_chapters_separately:
+        return True
+    return request.save.merge_chapters_at_end

abogen/application/voice_resolver.py

@@ -0,0 +1,81 @@
+"""AppVoiceResolver — voice resolution inside the application layer.
+
+Resolves voice specs into loaded voices using profiles, pipeline pool,
+and voice cache. Replaces UI-specific resolvers (WebUIVoiceResolver,
+PyQtVoiceResolver) with a single app-layer implementation.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Dict, Optional
+
+from abogen.application.conversion_ports import ResolvedVoice, VoiceResolver
+from abogen.application.conversion_request import ConversionRequest
+from abogen.domain.pipeline_factory import PipelinePool
+from abogen.domain.voice_loader import VoiceCache, resolve_voice
+from abogen.domain.voice_utils import resolve_voice_target
+
+
+class AppVoiceResolver:
+    """App-layer implementation of VoiceResolver protocol.
+
+    Uses ConversionRequest instead of Job. Loads profiles, creates
+    resolver internally — UIs don't need to manage this.
+    """
+
+    def __init__(
+        self,
+        request: ConversionRequest,
+        normalized_profiles: Dict[str, Dict[str, Any]],
+        pool: PipelinePool,
+        cache: VoiceCache,
+    ):
+        self._request = request
+        self._profiles = normalized_profiles
+        self._cache = cache
+        self._pool = pool
+
+    def resolve(self, voice_spec: str) -> ResolvedVoice:
+        """Resolve a voice spec into a loaded voice."""
+        provider, resolved, speed, steps = resolve_voice_target(
+            voice_spec,
+            self._profiles,
+            job_voice=self._request.voice,
+            job_tts_provider=self._request.tts_provider,
+            job_supertonic_total_steps=self._request.supertonic_total_steps,
+            job_speed=self._request.speed,
+        )
+
+        cache_key = f"{provider}:{resolved}" if resolved else provider
+        cached = self._cache.get(cache_key)
+        if cached is not None:
+            logging.info("[resolver] Cache hit: spec=%s -> provider=%s resolved=%s", voice_spec, provider, resolved)
+            return ResolvedVoice(
+                provider=provider,
+                resolved_spec=resolved,
+                voice=cached,
+                speed=speed,
+                supertonic_steps=steps or 0,
+            )
+
+        if provider == "kokoro":
+            kokoro_backend = self._pool.get(
+                "kokoro", self._request.language, self._request.use_gpu,
+            )
+            loaded = resolve_voice(
+                resolved, kokoro_backend, self._request.use_gpu, cache=self._cache,
+            )
+        else:
+            loaded = resolved
+
+        self._cache.set(cache_key, loaded)
+        logging.info("[resolver] Resolved: spec=%s -> provider=%s resolved=%s speed=%.2f steps=%s",
+                      voice_spec, provider, resolved, speed, steps)
+        return ResolvedVoice(
+            provider=provider,
+            resolved_spec=resolved,
+            voice=loaded,
+            speed=speed,
+            supertonic_steps=steps or 0,
+        )

abogen/book_parser.py

@@ -12,7 +12,8 @@ import fitz  # PyMuPDF
 import markdown
 
 from abogen.utils import detect_encoding
-from abogen.subtitle_utils import clean_text, calculate_text_length
+from abogen.subtitle_utils import clean_text
+from abogen.domain.text_utils import calculate_text_length
 
 # Pre-compile frequently used regex patterns
 _BRACKETED_NUMBERS_PATTERN = re.compile(r"\[\s*\d+\s*\]")

abogen/constants.py

@@ -1,4 +1,5 @@
 from abogen.utils import get_version
+from abogen.domain.enums import Language
 
 # Program Information
 PROGRAM_NAME = "abogen"
@@ -16,8 +17,22 @@ SUBTITLE_FORMATS = [
     ("ass_centered_narrow", "ASS (centered narrow)"),
 ]
 
-# Language description mapping
+# Language description mapping (Language enum → human-readable label).
 LANGUAGE_DESCRIPTIONS = {
+    Language.EN_US: "American English",
+    Language.EN_GB: "British English",
+    Language.ES: "Spanish",
+    Language.FR: "French",
+    Language.HI: "Hindi",
+    Language.IT: "Italian",
+    Language.JA: "Japanese",
+    Language.PT_BR: "Brazilian Portuguese",
+    Language.ZH: "Mandarin Chinese",
+}
+
+# Display-only mapping for kokoro codes → labels.
+# Used by voice catalog and PyQt (legacy) where kokoro codes are still present.
+KOKORO_CODE_LABELS = {
     "a": "American English",
     "b": "British English",
     "e": "Spanish",
@@ -55,25 +70,24 @@ SUPPORTED_INPUT_FORMATS = [
     "vtt",
 ]
 
-# Supported languages for subtitle generation
-# Currently, only 'a (American English)' and 'b (British English)' are supported for subtitle generation.
-# This is because tokens that contain timestamps are not generated for other languages in the Kokoro pipeline.
-# Please refer to: https://github.com/hexgrad/kokoro/blob/6d87f4ae7abc2d14dbc4b3ef2e5f19852e861ac2/kokoro/pipeline.py
-# 383 English processing (unchanged)
-# 384 if self.lang_code in 'ab':
-SUPPORTED_LANGUAGES_FOR_SUBTITLE_GENERATION = list(LANGUAGE_DESCRIPTIONS.keys())
+# Supported languages for subtitle generation.
+# All languages are supported: only English emits per-word timestamped tokens
+# in the Kokoro pipeline, but other languages fall back to segment-level fake
+# tokens (see abogen.domain.tokens.FakeToken), so subtitles are still
+# generated at segment granularity.
+SUPPORTED_LANGUAGES_FOR_SUBTITLE_GENERATION = list(Language)
 
 # Voice and sample text mapping
 SAMPLE_VOICE_TEXTS = {
-    "a": "This is a sample of the selected voice.",
-    "b": "This is a sample of the selected voice.",
-    "e": "Este es una muestra de la voz seleccionada.",
-    "f": "Ceci est un exemple de la voix sélectionnée.",
-    "h": "यह चयनित आवाज़ का एक नमूना है।",
-    "i": "Questo è un esempio della voce selezionata.",
-    "j": "これは選択した声のサンプルです。",
-    "p": "Este é um exemplo da voz selecionada.",
-    "z": "这是所选语音的示例。",
+    Language.EN_US: "This is a sample of the selected voice.",
+    Language.EN_GB: "This is a sample of the selected voice.",
+    Language.ES: "Este es una muestra de la voz seleccionada.",
+    Language.FR: "Ceci est un exemple de la voix sélectionnée.",
+    Language.HI: "यह चयनित आवाज़ का एक नमूना है।",
+    Language.IT: "Questo è un esempio della voce selezionata.",
+    Language.JA: "これは選択した声のサンプルです。",
+    Language.PT_BR: "Este é um exemplo da voz selecionada.",
+    Language.ZH: "这是所选语音的示例。",
 }
 
 COLORS = {

abogen/domain/audio_helpers.py

@@ -33,7 +33,7 @@ def build_ffmpeg_command(path: Path, fmt: str, metadata: Optional[Dict[str, str]
     if fmt == "mp3":
         base += ["-c:a", "libmp3lame", "-qscale:a", "2"]
     elif fmt == "opus":
-        base += ["-c:a", "libopus", "-b:a", "24000"]
+        base += ["-c:a", "libopus", "-b:a", "128000"]
     elif fmt == "m4b":
         base += ["-c:a", "aac", "-q:a", "2", "-movflags", "+faststart+use_metadata_tags"]
     else:

abogen/domain/audio_sink.py

@@ -54,7 +54,7 @@ def _ensure_ffmpeg() -> None:
 
 
 def _get_ffmpeg_cache_root() -> str:
-    from abogen.infrastructure.cache import get_internal_cache_path
+    from abogen.utils import get_internal_cache_path
 
     return get_internal_cache_path("ffmpeg")
 

abogen/domain/chunk_utils.py

@@ -7,8 +7,9 @@ text for TTS synthesis.
 from __future__ import annotations
 
 from collections import defaultdict
-from typing import Any, Dict, Iterable, Mapping, Optional
+from typing import Any, Dict, Iterable, Mapping
 
+from abogen.domain.enums import Language
 from abogen.pronunciation_store import increment_usage
 
 
@@ -44,7 +45,7 @@ def record_override_usage(
     if not usage_counter:
         return
 
-    language = getattr(job, "language", "") or "a"
+    language = getattr(job, "language", Language.EN_US) or Language.EN_US
     for normalized, amount in usage_counter.items():
         if amount <= 0:
             continue

abogen/domain/config_types.py

@@ -0,0 +1,52 @@
+"""Domain config types — shared contracts for domain functions.
+
+These dataclasses group parameters that domain functions receive.
+Domain defines them, app layer fills them.
+
+Why here (domain) and not application:
+- build_tts_context() is in domain → needs PronunciationConfig
+- make_subtitle_writer() is in infrastructure → needs SubtitleConfig
+- embed_m4b_metadata() is in infrastructure → needs CoverConfig
+- Domain should not depend on application layer (DIP)
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+from abogen.domain.enums import SubtitleFormat, SubtitleMode
+
+
+@dataclass(frozen=True)
+class PronunciationConfig:
+    """Pronunciation and normalization override settings.
+
+    Used by build_tts_context() to compile override rules.
+    """
+    pronunciation_overrides: List[Dict[str, Any]] = field(default_factory=list)
+    manual_overrides: List[Dict[str, Any]] = field(default_factory=list)
+    heteronym_overrides: List[Dict[str, Any]] = field(default_factory=list)
+    normalization_overrides: Optional[Dict[str, Any]] = None
+
+
+@dataclass(frozen=True)
+class SubtitleConfig:
+    """Subtitle output settings.
+
+    Used by make_subtitle_writer() and process_and_write_subtitles().
+    """
+    mode: SubtitleMode = SubtitleMode.DISABLED
+    format: SubtitleFormat = SubtitleFormat.SRT
+    max_words: int = 50
+
+
+@dataclass(frozen=True)
+class CoverConfig:
+    """Cover image settings.
+
+    Used by embed_m4b_metadata() and build_epub3_package().
+    """
+    path: Optional[Path] = None
+    mime: Optional[str] = None

abogen/domain/conversion_engine.py

@@ -19,10 +19,11 @@ from __future__ import annotations
 
 import time
 from dataclasses import dataclass, field
-from typing import Any, Callable, List, Optional, Protocol
+from typing import Any, Callable, Optional, Protocol
 
 from abogen.domain.audio_sink import AudioSink
 from abogen.domain.conversion_pipeline import tts_segments
+from abogen.domain.enums import Language, SubtitleMode
 from abogen.domain.normalization import TTSContext
 from abogen.domain.progress import calc_etr_str
 from abogen.domain.subtitle_generation import process_subtitle_tokens
@@ -55,44 +56,31 @@ class SegmentInfo:
 def run_tts_segment_loop(
     *,
     text: str,
+    params: SynthParams,
     backend: Any,
     voice: Any,
     speed: float,
     split_pattern: str,
-    stats: SegmentStats,
-    check_cancel: CancelChecker,
-    on_progress: Callable[[int, str], None],
+    total_steps: Optional[int] = None,
     chapter_sink: Optional[AudioSink] = None,
-    audio_sink: Optional[AudioSink] = None,
     preview_callback: Optional[Callable[[str], None]] = None,
     on_segment: Optional[Callable[[SegmentInfo], None]] = None,
-    subtitle_mode: str = "Disabled",
-    max_subtitle_words: int = 5,
-    lang_code: str = "a",
-    use_spacy_segmentation: bool = False,
 ) -> tuple[int, list]:
     """Run the core TTS segment iteration loop.
 
     Args:
         text: Normalized text to synthesize.
+        params: Common synthesis parameters (stats, callbacks, sinks, etc.).
         backend: TTS pipeline instance (Kokoro or Supertonic).
         voice: Voice name/id for the backend.
         speed: Speech speed multiplier.
         split_pattern: Regex pattern used by the TTS engine for sentence splitting.
-        stats: Running character/timing stats (mutated in place).
-        check_cancel: Called each segment; if it returns True, iteration stops.
-        on_progress: Called with (percent, etr_str) after each segment.
-        chapter_sink: Optional audio sink for the current chapter.
-        audio_sink: Optional audio sink for the merged output.
+        total_steps: Inference quality steps (Supertonic only, ignored by Kokoro).
         preview_callback: Called with a short preview string per segment.
         on_segment: Called with a SegmentInfo for each segment *before*
             audio is written.  Useful for callers that need per-segment
             subtitle processing (e.g. PyQt dual-writer pattern).
             When provided, the default subtitle accumulation is skipped.
-        subtitle_mode: Subtitle mode string (e.g. "Disabled", "Sentence").
-        max_subtitle_words: Max words per subtitle entry.
-        lang_code: Language code for subtitle processing.
-        use_spacy_segmentation: Whether spaCy sentence boundaries are active.
 
     Returns:
         Tuple of (segment_count, accumulated_subtitle_tokens).
@@ -108,26 +96,27 @@ def run_tts_segment_loop(
         voice=voice,
         speed=speed,
         split_pattern=split_pattern,
-        current_time=stats.current_time,
+        current_time=params.stats.current_time,
+        total_steps=total_steps,
     ):
-        if check_cancel():
+        if params.check_cancel():
             break
 
         local_segments += 1
-        stats.processed_chars += len(seg.graphemes)
+        params.stats.processed_chars += len(seg.graphemes)
 
         # Progress
-        if stats.total_characters:
-            percent = min(int(stats.processed_chars / stats.total_characters * 100), 99)
+        if params.stats.total_characters:
+            percent = min(int(params.stats.processed_chars / params.stats.total_characters * 100), 99)
         else:
-            percent = 0 if stats.processed_chars == 0 else 99
+            percent = 0 if params.stats.processed_chars == 0 else 99
 
         etr_str = calc_etr_str(
-            time.time() - stats.etr_start_time,
-            stats.processed_chars,
-            stats.total_characters,
+            time.time() - params.stats.etr_start_time,
+            params.stats.processed_chars,
+            params.stats.total_characters,
         )
-        on_progress(percent, etr_str)
+        params.on_progress(percent, etr_str)
 
         # Preview / log
         if preview_callback:
@@ -140,23 +129,23 @@ def run_tts_segment_loop(
                 audio=seg.audio,
                 tokens=list(seg.tokens) if seg.tokens else [],
                 duration=seg.duration,
-                chunk_start=getattr(seg, "chunk_start", stats.current_time),
+                chunk_start=getattr(seg, "chunk_start", params.stats.current_time),
             )
             on_segment(info)
 
         # Write audio
         if chapter_sink:
             chapter_sink.write(seg.audio)
-        if audio_sink:
-            audio_sink.write(seg.audio)
+        if params.audio_sink:
+            params.audio_sink.write(seg.audio)
 
         # Accumulate subtitle tokens (default path; skipped if on_segment handles it)
-        if not on_segment and subtitle_mode != "Disabled" and seg.tokens:
+        if not on_segment and params.subtitle_mode != SubtitleMode.DISABLED and seg.tokens:
             accumulated_tokens.extend(seg.tokens)
 
         # Update timing
-        if audio_sink:
-            stats.current_time += seg.duration
+        if params.audio_sink:
+            params.stats.current_time += seg.duration
 
     return local_segments, accumulated_tokens
 
@@ -165,25 +154,35 @@ def process_and_write_subtitles(
     accumulated_tokens: list[dict],
     subtitle_writer: Any,
     *,
-    subtitle_mode: str,
-    max_subtitle_words: int,
-    lang_code: str,
+    subtitle: "SubtitleConfig | str",
+    max_subtitle_words: int | None = None,
+    language: Language,
     use_spacy_segmentation: bool,
     fallback_end_time: float,
 ) -> None:
     """Process accumulated subtitle tokens and write entries to a subtitle writer.
 
-    This is the standard subtitle post-processing step shared by both UIs.
+    Accepts a SubtitleConfig object or a subtitle mode string
+    for backward compatibility.
     """
+    from abogen.domain.config_types import SubtitleConfig
+
+    if isinstance(subtitle, SubtitleConfig):
+        mode_str = subtitle.mode.value
+        words = subtitle.max_words
+    else:
+        mode_str = subtitle
+        words = max_subtitle_words or 50
+
     if not accumulated_tokens or not subtitle_writer:
         return
     new_entries: list[tuple] = []
     process_subtitle_tokens(
         accumulated_tokens,
         new_entries,
-        max_subtitle_words,
-        subtitle_mode,
-        lang_code,
+        words,
+        mode_str,
+        language,
         use_spacy_segmentation=use_spacy_segmentation,
         fallback_end_time=fallback_end_time,
     )
@@ -191,24 +190,35 @@ def process_and_write_subtitles(
         subtitle_writer.write_entry(start=start, end=end, text=text)
 
 
+@dataclass(frozen=True)
+class SynthParams:
+    """Common parameters for synthesize_text calls.
+
+    Packed once by the executor to avoid repeating identical kwargs.
+    When adding new common params, change only this dataclass.
+    """
+    tts_context: TTSContext
+    stats: SegmentStats
+    check_cancel: CancelChecker
+    on_progress: Callable[[int, str], None]
+    audio_sink: Optional[AudioSink] = None
+    subtitle_mode: str = "Disabled"
+    max_subtitle_words: int = 50
+    language: Language = Language.EN_US
+    use_spacy_segmentation: bool = False
+
+
 def synthesize_text(
     *,
     text: str,
-    tts_context: TTSContext,
+    params: SynthParams,
     backend: Any,
     voice: Any,
     speed: float,
-    stats: SegmentStats,
-    check_cancel: CancelChecker,
-    on_progress: Callable[[int, str], None],
+    total_steps: Optional[int] = None,
     chapter_sink: Optional[AudioSink] = None,
-    audio_sink: Optional[AudioSink] = None,
     preview_callback: Optional[Callable[[str], None]] = None,
     on_segment: Optional[Callable[[SegmentInfo], None]] = None,
-    subtitle_mode: str = "Disabled",
-    max_subtitle_words: int = 5,
-    lang_code: str = "a",
-    use_spacy_segmentation: bool = False,
     split_pattern_override: Optional[str] = None,
 ) -> tuple[int, list]:
     """Normalize text and run TTS — the single entry point for both UIs.
@@ -216,22 +226,16 @@ def synthesize_text(
     Combines TTSContext.normalize() + run_tts_segment_loop() into one call.
     UI-specific concerns (provider resolution, progress display) stay in the UI.
     """
-    normalized = tts_context.normalize(text)
+    normalized = params.tts_context.normalize(text)
     return run_tts_segment_loop(
         text=normalized,
+        params=params,
         backend=backend,
         voice=voice,
         speed=speed,
-        split_pattern=split_pattern_override or tts_context.split_pattern,
-        stats=stats,
-        check_cancel=check_cancel,
-        on_progress=on_progress,
+        total_steps=total_steps,
+        split_pattern=split_pattern_override or params.tts_context.split_pattern,
         chapter_sink=chapter_sink,
-        audio_sink=audio_sink,
         preview_callback=preview_callback,
         on_segment=on_segment,
-        subtitle_mode=subtitle_mode,
-        max_subtitle_words=max_subtitle_words,
-        lang_code=lang_code,
-        use_spacy_segmentation=use_spacy_segmentation,
     )

abogen/domain/conversion_pipeline.py

@@ -8,7 +8,9 @@ from __future__ import annotations
 
 import logging
 from dataclasses import dataclass, field
-from typing import Any, Callable, Dict, Iterator, List, Optional
+
+from abogen.domain.enums import Language, SubtitleMode
+from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple
 
 import numpy as np
 
@@ -19,6 +21,109 @@ from abogen.domain.audio_buffer import SAMPLE_RATE
 
 logger = logging.getLogger(__name__)
 
+# Languages where spaCy is used for pre-TTS segmentation
+# English ("a", "b") is excluded — spaCy only used for post-TTS subtitles
+_SPACY_EXCLUDED_LANGS = {Language.EN_US, Language.EN_GB}
+
+# CJK languages — different spacing pattern
+_CJK_LANGS = {Language.ZH, Language.JA}
+
+
+def spacy_pre_tts_segmentation(
+    text: str,
+    lang_code: Any,
+    subtitle_mode: Any,
+    *,
+    is_subtitle_input: bool = False,
+    use_spacy_segmentation: bool = True,
+    log_callback: Optional[Callable[[str], None]] = None,
+) -> Tuple[List[str], str]:
+    """Segment text using spaCy before TTS, with split_pattern override.
+
+    For non-English languages, spaCy sentence segmentation produces better
+    sentence boundaries than regex. This function:
+    1. Checks if spaCy should be used (toggle on, not disabled mode, not subtitle input)
+    2. For non-English: runs spaCy segmentation, computes split_pattern override
+    3. For English: returns single segment with default pattern (spaCy only for subtitles)
+    4. If spaCy fails: falls back to default pattern
+
+    Args:
+        text: Text to segment.
+        lang_code: Language code (Language enum or string like "a", "de", "fr").
+        subtitle_mode: SubtitleMode enum or string.
+        is_subtitle_input: True if source is .srt/.ass/.vtt file.
+        use_spacy_segmentation: User toggle for spaCy segmentation.
+        log_callback: Optional logging function.
+
+    Returns:
+        Tuple of (text_segments, active_split_pattern).
+        text_segments is a list of sentences (always at least one element).
+        active_split_pattern is the regex to use for TTS backend splitting.
+    """
+    from abogen.domain.split_pattern import get_split_pattern
+
+    def _log(msg: str) -> None:
+        if log_callback:
+            log_callback(msg)
+
+    # Normalize language
+    lang_enum = _to_language_enum(lang_code)
+
+    # Default split pattern
+    default_split = get_split_pattern(lang_code, subtitle_mode)
+
+    # Check conditions
+    if not use_spacy_segmentation:
+        return [text], default_split
+
+    subtitle_mode_str = _to_subtitle_mode_str(subtitle_mode)
+    if subtitle_mode_str in ("Disabled", "Line"):
+        return [text], default_split
+
+    if is_subtitle_input:
+        return [text], default_split
+
+    # English: spaCy only for post-TTS subtitles, not pre-TTS
+    if lang_enum in _SPACY_EXCLUDED_LANGS:
+        return [text], default_split
+
+    # Non-English: run spaCy pre-TTS segmentation
+    from abogen.spacy_utils import segment_sentences
+
+    _log("Using spaCy for sentence segmentation (pre-TTS)...")
+    spacy_sentences = segment_sentences(text, lang_code, log_callback=log_callback)
+
+    if not spacy_sentences:
+        _log("spaCy: Fallback to default segmentation...")
+        return [text], default_split
+
+    _log(f"spaCy: Text segmented into {len(spacy_sentences)} sentences...")
+
+    # spaCy already split at sentence boundaries; the engine only needs to
+    # split on newlines. Commas are never used in the engine split pattern
+    # for non-English (Sentence + Comma splits at commas only at subtitle
+    # time, like English).
+    active_split = "\n"
+
+    return spacy_sentences, active_split
+
+
+def _to_language_enum(lang_code: Any) -> Language:
+    """Convert lang_code to Language enum (ISO code or Language enum)."""
+    if isinstance(lang_code, Language):
+        return lang_code
+    try:
+        return Language.from_str(str(lang_code))
+    except ValueError:
+        return Language.EN_US
+
+
+def _to_subtitle_mode_str(subtitle_mode: Any) -> str:
+    """Convert subtitle_mode to string."""
+    if isinstance(subtitle_mode, SubtitleMode):
+        return subtitle_mode.value
+    return str(subtitle_mode)
+
 
 @dataclass
 class SegmentResult:
@@ -38,6 +143,7 @@ def tts_segments(
     speed: float,
     split_pattern: str,
     current_time: float = 0.0,
+    total_steps: Optional[int] = None,
 ) -> Iterator[SegmentResult]:
     """Invoke TTS backend on (already normalized) text and yield SegmentResults.
 
@@ -51,18 +157,24 @@ def tts_segments(
         speed: TTS speed multiplier.
         split_pattern: Regex pattern for sentence splitting.
         current_time: Current position in the audio timeline (seconds).
+        total_steps: Inference quality steps (Supertonic only, ignored by Kokoro).
 
     Yields:
         SegmentResult for each non-empty TTS segment.
     """
-    segment_iter = backend(
-        text,
+    kwargs: dict[str, Any] = dict(
         voice=voice,
         speed=speed,
         split_pattern=split_pattern,
     )
+    if total_steps is not None:
+        kwargs["total_steps"] = total_steps
+
+    segment_iter = backend(text, **kwargs)
 
     chunk_start = current_time
+    prev_tokens: Optional[List[Dict[str, Any]]] = None
+    prev_was_fallback = True
 
     for segment in segment_iter:
         graphemes_raw = getattr(segment, "graphemes", "") or ""
@@ -75,8 +187,10 @@ def tts_segments(
         duration = len(audio) / SAMPLE_RATE
 
         tokens_list = getattr(segment, "tokens", [])
+        was_fallback = False
         if not tokens_list and graphemes:
             tokens_list = [FakeToken(graphemes, 0, duration)]
+            was_fallback = True
 
         tokens = [
             {
@@ -88,6 +202,18 @@ def tts_segments(
             for tok in tokens_list
         ]
 
+        # When the engine splits text on a punctuation pattern, the
+        # whitespace between segments is consumed by the split. Restore a
+        # trailing space on the boundary token of the previous segment so
+        # subtitle processing sees the original spacing (only for real
+        # per-word tokens; FakeToken fallbacks split via their own logic).
+        if (
+            not prev_was_fallback
+            and prev_tokens
+            and not prev_tokens[-1].get("whitespace")
+        ):
+            prev_tokens[-1]["whitespace"] = " "
+
         yield SegmentResult(
             graphemes=graphemes,
             audio=audio,
@@ -96,6 +222,8 @@ def tts_segments(
             tokens=tokens,
         )
 
+        prev_tokens = tokens
+        prev_was_fallback = was_fallback
         chunk_start += duration
 
 
@@ -107,6 +235,7 @@ def emit_text_segments(
     speed: float,
     split_pattern: str,
     current_time: float = 0.0,
+    total_steps: Optional[int] = None,
     # normalization
     heteronym_rules: Any = None,
     pronunciation_rules: Any = None,
@@ -157,6 +286,7 @@ def emit_text_segments(
         speed=speed,
         split_pattern=split_pattern,
         current_time=current_time,
+        total_steps=total_steps,
     )
 
 
@@ -174,7 +304,7 @@ def emit_text_to_sinks(
     # subtitle
     subtitle_writer: Any = None,
     subtitle_mode: str = "Disabled",
-    subtitle_lang: str = "a",
+    subtitle_lang: Language = Language.EN_US,
     max_subtitle_words: int = 50,
     use_spacy_segmentation: bool = True,
     # normalization
@@ -221,7 +351,7 @@ def emit_text_to_sinks(
 
     # Flush subtitle tokens
     if subtitle_writer and accumulated_tokens:
-        _use_spacy = subtitle_mode not in ("Disabled", "Line")
+        _use_spacy = subtitle_mode not in (SubtitleMode.DISABLED, SubtitleMode.LINE)
         new_entries: List[tuple] = []
         process_subtitle_tokens(
             accumulated_tokens,

abogen/domain/enums.py

@@ -0,0 +1,231 @@
+"""Domain enums — typed constants for values tied to business logic.
+
+Using Enum instead of bare strings ensures:
+- Invalid values are caught at construction time
+- IDE autocomplete and type checking work
+- Adding new values is explicit (must update Enum)
+"""
+
+from __future__ import annotations
+
+from enum import Enum
+from pathlib import Path
+
+
+class SubtitleMode(str, Enum):
+    """Subtitle generation mode."""
+    DISABLED = "Disabled"
+    LINE = "Line"
+    SENTENCE = "Sentence"
+    SENTENCE_COMMA = "Sentence + Comma"
+    SENTENCE_HIGHLIGHT = "Sentence + Highlighting"
+
+    @classmethod
+    def from_str(cls, value: str) -> SubtitleMode:
+        """Parse from user input: case-insensitive, strips whitespace."""
+        normalized = value.strip()
+        for member in cls:
+            if member.value.lower() == normalized.lower():
+                return member
+        raise ValueError(f"Invalid SubtitleMode: {value!r}. Valid: {[m.value for m in cls]}")
+
+
+class OutputFormat(str, Enum):
+    """Audio output format."""
+    WAV = "wav"
+    MP3 = "mp3"
+    FLAC = "flac"
+    OPUS = "opus"
+    M4B = "m4b"
+
+    @property
+    def dot_ext(self) -> str:
+        """File extension with dot: '.wav', '.mp3', etc."""
+        return f".{self.value}"
+
+    @property
+    def is_lossless(self) -> bool:
+        """True for lossless formats."""
+        return self in (self.WAV, self.FLAC)
+
+    @classmethod
+    def from_str(cls, value: str) -> OutputFormat:
+        """Parse from user input: strips dot prefix, case-insensitive."""
+        normalized = value.strip().lstrip(".").lower()
+        for member in cls:
+            if member.value == normalized:
+                return member
+        raise ValueError(f"Invalid OutputFormat: {value!r}. Valid: {[m.value for m in cls]}")
+
+
+class SaveMode(str, Enum):
+    """Where to save the output file."""
+    SAVE_NEXT_TO_INPUT = "save_next_to_input"
+    SAVE_TO_DESKTOP = "save_to_desktop"
+    CHOOSE_OUTPUT_FOLDER = "choose_output_folder"
+    DEFAULT_OUTPUT = "default_output"
+    CUSTOM_FOLDER = "custom_folder"
+
+
+class SubtitleFormat(str, Enum):
+    """Subtitle file format."""
+    SRT = "srt"
+    ASS = "ass"
+    VTT = "vtt"
+
+    @property
+    def dot_ext(self) -> str:
+        """File extension with dot: '.srt', '.ass'."""
+        return f".{self.value}"
+
+    @classmethod
+    def from_str(cls, value: str) -> SubtitleFormat:
+        """Parse from user input: strips dot prefix, case-insensitive."""
+        normalized = value.strip().lstrip(".").lower()
+        for member in cls:
+            if member.value == normalized:
+                return member
+        raise ValueError(f"Invalid SubtitleFormat: {value!r}. Valid: {[m.value for m in cls]}")
+
+
+class InputFormat(str, Enum):
+    """Input file format."""
+    EPUB = "epub"
+    PDF = "pdf"
+    TXT = "txt"
+    MD = "md"
+    SRT = "srt"
+    ASS = "ass"
+    VTT = "vtt"
+
+    @property
+    def is_book(self) -> bool:
+        """True for book/document formats (epub, pdf, txt, md)."""
+        return self in (self.EPUB, self.PDF, self.TXT, self.MD)
+
+    @property
+    def is_subtitle(self) -> bool:
+        """True for subtitle formats (srt, ass, vtt)."""
+        return self in (self.SRT, self.ASS, self.VTT)
+
+    @property
+    def dot_ext(self) -> str:
+        """File extension with dot: '.epub', '.srt', etc."""
+        return f".{self.value}"
+
+    @classmethod
+    def from_path(cls, path: Path) -> InputFormat:
+        """Detect format from file path extension."""
+        suffix = path.suffix.lower().lstrip(".")
+        if suffix == "markdown":
+            return cls.MD
+        try:
+            return cls(suffix)
+        except ValueError:
+            raise ValueError(f"Unsupported input format: {path.suffix!r}. Supported: {[m.value for m in cls]}")
+
+
+class Language(str, Enum):
+    """TTS language code (ISO 639-1 with region where needed).
+
+    Each engine maps these to its own internal language identifiers.
+    Engines report which languages they support via ``supported_languages()``.
+    """
+    EN_US = "en-US"
+    EN_GB = "en-GB"
+    ES = "es"
+    FR = "fr"
+    HI = "hi"
+    IT = "it"
+    JA = "ja"
+    PT_BR = "pt-BR"
+    ZH = "zh"
+    AR = "ar"
+    BG = "bg"
+    CS = "cs"
+    DA = "da"
+    DE = "de"
+    EL = "el"
+    ET = "et"
+    FI = "fi"
+    HR = "hr"
+    HU = "hu"
+    ID = "id"
+    KO = "ko"
+    LT = "lt"
+    LV = "lv"
+    NL = "nl"
+    PL = "pl"
+    RO = "ro"
+    RU = "ru"
+    SK = "sk"
+    SL = "sl"
+    SV = "sv"
+    TR = "tr"
+    UK = "uk"
+    VI = "vi"
+
+    @property
+    def display_name(self) -> str:
+        """Human-readable language name."""
+        _names = {
+            "en-US": "American English",
+            "en-GB": "British English",
+            "es": "Spanish",
+            "fr": "French",
+            "hi": "Hindi",
+            "it": "Italian",
+            "ja": "Japanese",
+            "pt-BR": "Brazilian Portuguese",
+            "zh": "Mandarin Chinese",
+            "ar": "Arabic",
+            "bg": "Bulgarian",
+            "cs": "Czech",
+            "da": "Danish",
+            "de": "German",
+            "el": "Greek",
+            "et": "Estonian",
+            "fi": "Finnish",
+            "hr": "Croatian",
+            "hu": "Hungarian",
+            "id": "Indonesian",
+            "ko": "Korean",
+            "lt": "Lithuanian",
+            "lv": "Latvian",
+            "nl": "Dutch",
+            "pl": "Polish",
+            "ro": "Romanian",
+            "ru": "Russian",
+            "sk": "Slovak",
+            "sl": "Slovenian",
+            "sv": "Swedish",
+            "tr": "Turkish",
+            "uk": "Ukrainian",
+            "vi": "Vietnamese",
+        }
+        return _names[self.value]
+
+    @property
+    def is_cjk(self) -> bool:
+        """True for CJK languages (Chinese, Japanese, Korean)."""
+        return self in (self.ZH, self.JA, self.KO)
+
+    @property
+    def supports_subtitle_tokens(self) -> bool:
+        """True if this language supports subtitle generation.
+
+        All languages are supported: languages without per-word timestamped
+        tokens fall back to segment-level fake tokens in the pipeline.
+        """
+        return True
+
+    @classmethod
+    def from_str(cls, value: str) -> Language:
+        """Parse from user input: ISO code, case-insensitive."""
+        if isinstance(value, Language):
+            return value
+        normalized = value.strip()
+        for member in cls:
+            if member.value.lower() == normalized.lower():
+                return member
+        raise ValueError(f"Invalid Language: {value!r}. Valid: {[m.value for m in cls]}")

abogen/domain/metadata_extraction.py

@@ -11,7 +11,6 @@ import logging
 import os
 import re
 import uuid
-from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple
 
 logger = logging.getLogger(__name__)

abogen/domain/metadata_helpers.py

@@ -21,6 +21,60 @@ _SERIES_NUMBER_KEYS = (
 )
 _SERIES_NUMBER_RE = re.compile(r"\d+(?:\.\d+)?")
 
+_SERIES_NAME_ALIASES = ("series", "series_name", "seriesname", "series_title", "seriestitle")
+_SERIES_INDEX_ALIASES = ("series_index", "series_sequence", "series_position", "book_number")
+_AUTHOR_ALIASES = ("author", "authors")
+_DESCRIPTION_ALIASES = ("description", "summary")
+_TAGS_ALIASES = ("tags", "keywords", "genre")
+
+
+def expand_metadata_aliases(tags: Mapping[str, Any]) -> Dict[str, Any]:
+    """Expand concept aliases so each concept has all canonical keys set.
+
+    One input concept fans out to multiple keys so that downstream consumers
+    can look up any variant and find the value.
+
+    Expanded concepts:
+        series     -> series, series_name, seriesname, series_title, seriestitle
+        series_index -> series_index, series_sequence, series_position, book_number
+        author     -> author, authors
+        description -> description, summary
+        tags       -> tags, keywords, genre
+    """
+    if not tags:
+        return {}
+
+    result: Dict[str, Any] = {}
+    for key, value in tags.items():
+        if value is None:
+            continue
+        text = str(value).strip() if not isinstance(value, (list, tuple, set)) else value
+        if not text:
+            continue
+        key_lower = str(key).strip().lower()
+        if not key_lower:
+            continue
+
+        if key_lower in _SERIES_NAME_ALIASES:
+            for alias in _SERIES_NAME_ALIASES:
+                result[alias] = text
+        elif key_lower in _SERIES_INDEX_ALIASES:
+            for alias in _SERIES_INDEX_ALIASES:
+                result[alias] = text
+        elif key_lower in _AUTHOR_ALIASES:
+            for alias in _AUTHOR_ALIASES:
+                result[alias] = text
+        elif key_lower in _DESCRIPTION_ALIASES:
+            for alias in _DESCRIPTION_ALIASES:
+                result[alias] = text
+        elif key_lower in _TAGS_ALIASES:
+            for alias in _TAGS_ALIASES:
+                result[alias] = text
+        else:
+            result[key_lower] = text
+
+    return result
+
 
 def normalize_metadata_map(values: Optional[Mapping[str, Any]]) -> Dict[str, str]:
     normalized: Dict[str, str] = {}
@@ -403,3 +457,40 @@ def load_audiobookshelf_chapters(
         if title and start is not None and end is not None:
             cleaned.append({"title": str(title), "start": start, "end": end})
     return cleaned or None
+
+
+def build_metadata_payload(
+    metadata: Optional[Dict[str, Any]] = None,
+    chapter_markers: Optional[List[Dict[str, Any]]] = None,
+    chunk_markers: Optional[List[Dict[str, Any]]] = None,
+    chunk_level: Optional[str] = None,
+    speaker_mode: Optional[str] = None,
+    speakers: Optional[Dict[str, Any]] = None,
+    generate_epub3: bool = False,
+) -> Dict[str, Any]:
+    """Build the canonical metadata payload dict for persistence and downstream use.
+
+    This is the single source of truth for metadata assembly. Both PyQt and WebUI
+    runners should call this instead of building the dict manually.
+
+    Args:
+        metadata: Normalized metadata tags dict.
+        chapter_markers: List of chapter marker dicts with title/start/end.
+        chunk_markers: List of chunk marker dicts.
+        chunk_level: Chunk granularity level (e.g. 'chapter', 'chunk').
+        speaker_mode: Speaker mode ('single', 'multi', etc.).
+        speakers: Speaker profile mapping.
+        generate_epub3: Whether EPUB3 generation is enabled.
+
+    Returns:
+        Complete metadata payload dict.
+    """
+    return {
+        "metadata": dict(metadata or {}),
+        "chapters": chapter_markers or [],
+        "chunks": chunk_markers or [],
+        "chunk_level": chunk_level,
+        "speaker_mode": speaker_mode,
+        "speakers": dict(speakers or {}),
+        "generate_epub3": generate_epub3,
+    }

abogen/domain/metadata_overrides.py

@@ -8,23 +8,22 @@ from __future__ import annotations
 
 from typing import Any, Dict, Mapping
 
+from abogen.domain.metadata_helpers import expand_metadata_aliases
+
 
 def normalize_opds_metadata(metadata_payload: Mapping[str, Any]) -> Dict[str, Any]:
     """Normalize OPDS/Calibre metadata into canonical override keys.
 
     Takes a metadata payload with various key aliases (e.g. 'series'/'series_name',
-    'tags'/'keywords', 'authors'/'creator') and returns a dict with canonical
-    keys set.
+    'tags'/'keywords', 'authors'/'creator') and returns a dict with all
+    concept aliases expanded.
 
     Args:
         metadata_payload: Raw metadata dict from OPDS/Calibre import.
 
     Returns:
-        Dict with canonical metadata keys (series, series_index, tags,
-        description, subtitle, publisher, authors).
+        Dict with all canonical metadata key aliases expanded.
     """
-    metadata_overrides: Dict[str, Any] = {}
-
     def _stringify(value: Any) -> str:
         if value is None:
             return ""
@@ -33,67 +32,25 @@ def normalize_opds_metadata(metadata_payload: Mapping[str, Any]) -> Dict[str, An
             return ", ".join(part for part in parts if part)
         return str(value).strip()
 
-    raw_series = metadata_payload.get("series") or metadata_payload.get("series_name")
-    series_name = str(raw_series or "").strip()
-    if series_name:
-        metadata_overrides["series"] = series_name
-        metadata_overrides.setdefault("series_name", series_name)
+    # Map OPDS-specific keys to common concept keys before expansion
+    normalized_input: Dict[str, Any] = {}
+    for key, value in metadata_payload.items():
+        if value is None:
+            continue
+        key_lower = str(key).strip().lower()
+        if not key_lower:
+            continue
+        text = _stringify(value)
+        if not text:
+            continue
 
-    series_index_value = (
-        metadata_payload.get("series_index")
-        or metadata_payload.get("series_position")
-        or metadata_payload.get("series_sequence")
-        or metadata_payload.get("book_number")
-    )
-    if series_index_value is not None:
-        series_index_text = str(series_index_value).strip()
-        if series_index_text:
-            metadata_overrides.setdefault("series_index", series_index_text)
-            metadata_overrides.setdefault("series_position", series_index_text)
-            metadata_overrides.setdefault("series_sequence", series_index_text)
-            metadata_overrides.setdefault("book_number", series_index_text)
+        # Map OPDS-specific author aliases
+        if key_lower in ("creator", "dc_creator"):
+            normalized_input["author"] = text
+        # Map OPDS-specific subtitle aliases
+        elif key_lower in ("sub_title", "calibre_subtitle"):
+            normalized_input["subtitle"] = text
+        else:
+            normalized_input[key_lower] = text
 
-    tags_value = metadata_payload.get("tags") or metadata_payload.get("keywords")
-    if tags_value:
-        tags_text = _stringify(tags_value)
-        if tags_text:
-            metadata_overrides.setdefault("tags", tags_text)
-            metadata_overrides.setdefault("keywords", tags_text)
-            metadata_overrides.setdefault("genre", tags_text)
-
-    description_value = metadata_payload.get("description") or metadata_payload.get("summary")
-    if description_value:
-        description_text = _stringify(description_value)
-        if description_text:
-            metadata_overrides.setdefault("description", description_text)
-            metadata_overrides.setdefault("summary", description_text)
-
-    subtitle_value = (
-        metadata_payload.get("subtitle")
-        or metadata_payload.get("sub_title")
-        or metadata_payload.get("calibre_subtitle")
-    )
-    if subtitle_value:
-        subtitle_text = _stringify(subtitle_value)
-        if subtitle_text:
-            metadata_overrides.setdefault("subtitle", subtitle_text)
-
-    publisher_value = metadata_payload.get("publisher")
-    if publisher_value:
-        publisher_text = _stringify(publisher_value)
-        if publisher_text:
-            metadata_overrides.setdefault("publisher", publisher_text)
-
-    authors_value = (
-        metadata_payload.get("authors")
-        or metadata_payload.get("author")
-        or metadata_payload.get("creator")
-        or metadata_payload.get("dc_creator")
-    )
-    if authors_value:
-        authors_text = _stringify(authors_value)
-        if authors_text:
-            metadata_overrides.setdefault("authors", authors_text)
-            metadata_overrides.setdefault("author", authors_text)
-
-    return metadata_overrides
+    return expand_metadata_aliases(normalized_input)

abogen/domain/normalization.py

@@ -13,8 +13,9 @@ resources so they can be created once and passed as a single object.
 from __future__ import annotations
 
 from dataclasses import dataclass, field
-from typing import Any, Dict, List, Mapping, Optional
+from typing import Any, Callable, Dict, List, Mapping, Optional
 
+from abogen.domain.enums import Language
 from abogen.kokoro_text_normalization import (
     ApostropheConfig,
     normalize_for_pipeline as _normalize_for_pipeline,
@@ -123,3 +124,122 @@ def prepare_text_for_tts(
     apostrophe_config = build_apostrophe_config(settings=runtime_settings, base=_BASE_APOSTROPHE_CONFIG)
 
     return _normalize_for_pipeline(result, config=apostrophe_config, settings=runtime_settings)
+
+
+def build_tts_context(
+    *,
+    language: Language,
+    subtitle: "SubtitleConfig | str" = "Disabled",
+    pronunciation: Optional["PronunciationConfig"] = None,
+    speakers: Optional[Dict[str, Any]] = None,
+    usage_counter: Optional[Dict[str, int]] = None,
+    log_callback: Optional[Callable[[str, str], None]] = None,
+) -> TTSContext:
+    """Build a TTSContext from raw data. Single entry point for both UIs.
+
+    Loads normalization settings, applies overrides, validates configuration,
+    merges pronunciation overrides, and compiles all rules.
+
+    Args:
+        language: Language enum value.
+        subtitle: SubtitleConfig object or subtitle mode string.
+        pronunciation: PronunciationConfig with override rules.
+        speakers: Speaker profile mapping.
+        usage_counter: Mutable dict for tracking override usage.
+        log_callback: Callable(level, message) for warnings.
+
+    Returns:
+        TTSContext ready for text normalization.
+    """
+    from abogen.domain.config_types import PronunciationConfig, SubtitleConfig
+    from abogen.domain.enums import SubtitleMode
+    from abogen.domain.pronunciation import (
+        compile_heteronym_sentence_rules,
+        compile_pronunciation_rules,
+        merge_pronunciation_overrides,
+    )
+    from abogen.domain.split_pattern import get_split_pattern
+
+    def _log(msg: str, level: str = "warning") -> None:
+        if log_callback:
+            log_callback(level, msg)
+
+    # Resolve subtitle mode
+    if isinstance(subtitle, SubtitleConfig):
+        resolved_subtitle = subtitle.mode
+    else:
+        try:
+            resolved_subtitle = SubtitleMode.from_str(subtitle) if not isinstance(subtitle, SubtitleMode) else subtitle
+        except ValueError:
+            resolved_subtitle = SubtitleMode.DISABLED
+
+    # Resolve pronunciation config
+    if pronunciation is None:
+        pronunciation = PronunciationConfig()
+
+    # Get runtime normalization settings
+    runtime_settings = get_runtime_settings()
+
+    # Apply per-job normalization overrides
+    if pronunciation.normalization_overrides:
+        runtime_settings = _apply_overrides(runtime_settings, pronunciation.normalization_overrides)
+
+    # Build apostrophe config
+    apostrophe_config = build_apostrophe_config(settings=runtime_settings)
+
+    # Validate LLM apostrophe mode
+    apostrophe_mode = str(runtime_settings.get("normalization_apostrophe_mode", "spacy")).lower()
+    if apostrophe_mode == "llm":
+        from abogen.normalization_settings import build_llm_configuration
+        llm_config = build_llm_configuration(runtime_settings)
+        if not llm_config.is_configured():
+            raise RuntimeError(
+                "LLM-based apostrophe normalization is selected, but the LLM configuration is incomplete."
+            )
+
+    # Check for num2words availability
+    if apostrophe_config.convert_numbers:
+        try:
+            import num2words  # noqa: F401
+        except ImportError:
+            _log(
+                "Number normalization is enabled but 'num2words' library is not available. "
+                "Numbers will NOT be converted to words."
+            )
+
+    # Compute split pattern
+    if not isinstance(language, Language):
+        raise TypeError(f"language must be Language enum, got {type(language).__name__}: {language!r}")
+    split_pattern = get_split_pattern(language, resolved_subtitle)
+
+    # Merge pronunciation overrides
+    source = {
+        "pronunciation_overrides": pronunciation.pronunciation_overrides,
+        "manual_overrides": pronunciation.manual_overrides,
+        "speakers": speakers or {},
+        "language": language,
+    }
+    merged_overrides = merge_pronunciation_overrides(source)
+
+    # Compile rules
+    pronunciation_rules = compile_pronunciation_rules(merged_overrides)
+    heteronym_rules = compile_heteronym_sentence_rules(pronunciation.heteronym_overrides)
+
+    if heteronym_rules:
+        _log(
+            f"Applying {len(heteronym_rules)} heteronym override(s) during conversion.",
+            level="debug",
+        )
+    if pronunciation_rules:
+        _log(
+            f"Applying {len(pronunciation_rules)} pronunciation override(s) during conversion.",
+            level="debug",
+        )
+
+    return TTSContext(
+        split_pattern=split_pattern,
+        pronunciation_rules=pronunciation_rules,
+        heteronym_rules=heteronym_rules,
+        normalization_overrides=pronunciation.normalization_overrides,
+        usage_counter=usage_counter if usage_counter is not None else {},
+    )

abogen/domain/output_paths.py

@@ -11,9 +11,8 @@ import platform
 import re
 from datetime import datetime
 from pathlib import Path
-from typing import Any, Callable, List, Optional, Tuple
+from typing import Callable, List, Optional, Tuple
 
-from abogen.subtitle_utils import sanitize_name_for_os
 from abogen.text_extractor import ExtractedChapter
 
 
@@ -21,6 +20,9 @@ _OUTPUT_SANITIZE_RE = re.compile(r"[^\w\-_.]+")
 
 # OS-specific illegal characters for filenames
 _WINDOWS_ILLEGAL_CHARS_RE = re.compile(r'[<>:"/\\|?*\x00-\x1f]')
+_MACOS_ILLEGAL_CHARS_RE = re.compile(r"[:]")
+_LINUX_ILLEGAL_CHARS_RE = re.compile(r"[/\x00]")
+_CONTROL_CHARS_RE = re.compile(r"[\x00-\x1f]")
 _UNIX_CONTROL_CHARS_RE = re.compile(r'[\x00-\x1f]')
 _RESERVED_NAMES = frozenset(
     {"CON", "PRN", "AUX", "NUL"}
@@ -29,6 +31,47 @@ _RESERVED_NAMES = frozenset(
 )
 
 
+def sanitize_name_for_os(name: str, is_folder: bool = True) -> str:
+    """Sanitize a filename or folder name based on the operating system.
+
+    Args:
+        name: The name to sanitize
+        is_folder: Whether this is a folder name (default: True)
+
+    Returns:
+        Sanitized name safe for the current OS
+    """
+    if not name:
+        return "audiobook"
+
+    system = platform.system()
+
+    if system == "Windows":
+        sanitized = _WINDOWS_ILLEGAL_CHARS_RE.sub("_", name)
+        sanitized = _CONTROL_CHARS_RE.sub("_", sanitized)
+        sanitized = sanitized.rstrip(". ")
+        if sanitized.upper() in _RESERVED_NAMES or sanitized.upper().split(".")[0] in _RESERVED_NAMES:
+            sanitized = f"_{sanitized}"
+    elif system == "Darwin":
+        sanitized = _MACOS_ILLEGAL_CHARS_RE.sub("_", name)
+        sanitized = _CONTROL_CHARS_RE.sub("_", sanitized)
+        if is_folder and sanitized.startswith("."):
+            sanitized = "_" + sanitized[1:]
+    else:
+        sanitized = _LINUX_ILLEGAL_CHARS_RE.sub("_", name)
+        sanitized = _UNIX_CONTROL_CHARS_RE.sub("_", sanitized)
+        if is_folder and sanitized.startswith("."):
+            sanitized = "_" + sanitized[1:]
+
+    if not sanitized or sanitized.strip() == "":
+        sanitized = "audiobook"
+
+    if len(sanitized) > 255:
+        sanitized = sanitized[:255].rstrip(". ")
+
+    return sanitized
+
+
 def slugify(title: str, index: int) -> str:
     sanitized = re.sub(r"[^\w\-]+", "_", title.lower()).strip("_")
     if not sanitized:
@@ -76,7 +119,7 @@ def sanitize_filename_for_chapter(title: str, index: int, max_len: int = 80) ->
     return f"{index:02d}_{sanitized}"
 
 
-def sanitize_output_stem(name: str) -> str:
+def sanitize_output_stem(name: str, index: int = 0) -> str:
     base = Path(name or "").stem
     sanitized = _OUTPUT_SANITIZE_RE.sub("_", base).strip("_")
     return sanitized or "output"
@@ -99,6 +142,9 @@ def apply_newline_policy(chapters: List[ExtractedChapter], replace_single_newlin
         chapter.text = newline_regex.sub(" ", chapter.text)
 
 
+from abogen.domain.enums import SaveMode
+
+
 def resolve_output_directory(
     *,
     save_mode: str,
@@ -108,13 +154,13 @@ def resolve_output_directory(
     user_output_path: Optional[Path],
     user_cache_outputs: Optional[Path],
 ) -> Path:
-    if save_mode == "Save to Desktop" and desktop_dir:
+    if save_mode in (SaveMode.SAVE_TO_DESKTOP, "Save to Desktop") and desktop_dir:
         return desktop_dir
-    if save_mode == "Save next to input file":
+    if save_mode in (SaveMode.SAVE_NEXT_TO_INPUT, "Save next to input file"):
         return stored_path.parent
-    if save_mode == "Choose output folder" and output_folder:
+    if save_mode in (SaveMode.CHOOSE_OUTPUT_FOLDER, "Choose output folder") and output_folder:
         return Path(output_folder)
-    if save_mode == "Use default save location" and user_output_path:
+    if save_mode in (SaveMode.DEFAULT_OUTPUT, "Use default save location") and user_output_path:
         return user_output_path
     return user_cache_outputs or Path(".")
 

abogen/domain/pipeline_factory.py

@@ -2,13 +2,18 @@
 
 Provides a unified interface for creating and managing TTS pipelines
 across all UI layers (WebUI, PyQt, CLI).
+
+Language handling: the engine owns the mapping between Language enum
+and its internal format. Callers pass Language enum; the engine
+converts internally. No engine-specific codes leak outside the engine.
 """
 
 from __future__ import annotations
 
-from typing import Any, Dict, Optional
+from typing import Any, Dict
 
 from abogen.domain.device import select_device
+from abogen.domain.enums import Language
 from abogen.domain.voice_resolution import initialize_voice_cache
 from abogen.tts_plugin.utils import create_pipeline, is_plugin_registered
 
@@ -25,22 +30,25 @@ def resolve_device(use_gpu: bool) -> str:
 
 def create_pipeline_for_job(
     provider: str,
-    language: str,
+    language: Language,
     use_gpu: bool,
 ) -> Any:
     """Create a TTS pipeline with proper device selection.
 
-    Handles provider validation, GPU decision, and plugin checks.
+    Args:
+        provider: TTS provider name ("kokoro" or "supertonic").
+        language: Language enum (app-layer type, not engine-specific).
+        use_gpu: Whether GPU acceleration is requested.
     """
     provider = str(provider or "kokoro").strip().lower() or "kokoro"
     if not is_plugin_registered(provider):
         provider = "kokoro"
 
     if provider == "supertonic":
-        return create_pipeline("supertonic")
+        return create_pipeline("supertonic", language=language)
 
     device = resolve_device(use_gpu)
-    return create_pipeline("kokoro", lang_code=language, device=device)
+    return create_pipeline("kokoro", language=language, device=device)
 
 
 def dispose_pipelines(pipelines: Dict[str, Any]) -> None:
@@ -59,7 +67,7 @@ class PipelinePool:
     Usage::
 
         pool = PipelinePool()
-        backend = pool.get("kokoro", "en", use_gpu=True)
+        backend = pool.get("kokoro", Language.EN_US, use_gpu=True)
         # ... use backend ...
         pool.dispose_all()
     """
@@ -71,18 +79,20 @@ class PipelinePool:
     def get(
         self,
         provider: str,
-        language: str,
+        language: Language,
         use_gpu: bool,
         *,
-        job: Any = None,
+        request: Any = None,
+        events: Any = None,
     ) -> Any:
         """Get or create a cached pipeline for the given provider.
 
         Args:
             provider: TTS provider name ("kokoro" or "supertonic").
-            language: Language code (for kokoro).
+            language: Language enum (app-layer type).
             use_gpu: Whether GPU acceleration is requested.
-            job: Optional job object for voice cache initialization.
+            request: ConversionRequest for voice cache initialization.
+            events: ConversionEvents for logging during cache init.
         """
         provider = str(provider or "kokoro").strip().lower() or "kokoro"
         if not is_plugin_registered(provider):
@@ -95,8 +105,8 @@ class PipelinePool:
         pipeline = create_pipeline_for_job(provider, language, use_gpu)
         self._pipelines[provider] = pipeline
 
-        if provider == "kokoro" and not self._voice_cache_initialized and job is not None:
-            initialize_voice_cache(job)
+        if provider == "kokoro" and not self._voice_cache_initialized and request is not None:
+            initialize_voice_cache(request, events=events)
             self._voice_cache_initialized = True
 
         return pipeline

abogen/domain/pronunciation.py

@@ -180,11 +180,20 @@ def merge_pronunciation_overrides(job: Any) -> List[Dict[str, Any]]:
     we must merge manual overrides so they always apply (before TTS).
 
     Precedence: manual overrides win over existing entries for the same normalized key.
+
+    Args:
+        job: Either a job-like object with attributes, or a dict with keys:
+            ``pronunciation_overrides``, ``manual_overrides``, ``speakers``, ``language``.
     """
 
     collected: Dict[str, Dict[str, Any]] = {}
 
-    existing = getattr(job, "pronunciation_overrides", None)
+    def _get(key: str, default: Any = None) -> Any:
+        if isinstance(job, Mapping):
+            return job.get(key, default)
+        return getattr(job, key, default)
+
+    existing = _get("pronunciation_overrides")
     if isinstance(existing, list):
         for entry in existing:
             if not isinstance(entry, Mapping):
@@ -204,10 +213,10 @@ def merge_pronunciation_overrides(job: Any) -> List[Dict[str, Any]]:
                 "notes": str(entry.get("notes") or "").strip() or None,
                 "context": str(entry.get("context") or "").strip() or None,
                 "source": str(entry.get("source") or "pronunciation"),
-                "language": getattr(job, "language", None),
+                "language": _get("language"),
             }
 
-    speakers = getattr(job, "speakers", None)
+    speakers = _get("speakers")
     if isinstance(speakers, dict):
         for payload in speakers.values():
             if not isinstance(payload, Mapping):
@@ -226,16 +235,16 @@ def merge_pronunciation_overrides(job: Any) -> List[Dict[str, Any]]:
                 "voice": str(
                     payload.get("resolved_voice")
                     or payload.get("voice")
-                    or getattr(job, "voice", "")
+                    or _get("voice", "")
                 ).strip()
                 or None,
                 "notes": None,
                 "context": None,
                 "source": "speaker",
-                "language": getattr(job, "language", None),
+                "language": _get("language"),
             }
 
-    manual = getattr(job, "manual_overrides", None)
+    manual = _get("manual_overrides")
     if isinstance(manual, list):
         for entry in manual:
             if not isinstance(entry, Mapping):
@@ -255,7 +264,7 @@ def merge_pronunciation_overrides(job: Any) -> List[Dict[str, Any]]:
                 "notes": str(entry.get("notes") or "").strip() or None,
                 "context": str(entry.get("context") or "").strip() or None,
                 "source": str(entry.get("source") or "manual"),
-                "language": getattr(job, "language", None),
+                "language": _get("language"),
             }
 
     return list(collected.values())

abogen/domain/settings_core.py

@@ -9,11 +9,11 @@ from __future__ import annotations
 
 import os
 import re
-from dataclasses import dataclass, field
-from typing import Any, Callable, Dict, Mapping, Optional, Sequence
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, Mapping, Optional
 
 from abogen.constants import (
-    LANGUAGE_DESCRIPTIONS,
+    KOKORO_CODE_LABELS,
     SUBTITLE_FORMATS,
     SUPPORTED_SOUND_FORMATS,
 )
@@ -135,10 +135,10 @@ def _norm_speaker_spec(value: Any, default: str) -> str:
 
 def _norm_language_list(value: Any, default: list) -> list:
     if isinstance(value, (list, tuple, set)):
-        return [code for code in value if isinstance(code, str) and code in LANGUAGE_DESCRIPTIONS]
+        return [code for code in value if isinstance(code, str) and code in KOKORO_CODE_LABELS]
     if isinstance(value, str):
         parts = [item.strip().lower() for item in value.split(",") if item.strip()]
-        return [code for code in parts if code in LANGUAGE_DESCRIPTIONS]
+        return [code for code in parts if code in KOKORO_CODE_LABELS]
     return default
 
 
@@ -578,3 +578,64 @@ def integration_defaults() -> Dict[str, Dict[str, Any]]:
             "timeout": 30.0,
         },
     }
+
+
+def stored_integration_config(name: str) -> Dict[str, Any]:
+    """Read raw integration config from config.json.
+
+    Reads ``config["integrations"][name]``.
+    """
+    from abogen.utils import load_config
+
+    cfg = load_config() or {}
+    integrations = cfg.get("integrations")
+    if isinstance(integrations, Mapping):
+        entry = integrations.get(name)
+        if isinstance(entry, Mapping):
+            return dict(entry)
+    return {}
+
+
+def load_audiobookshelf_config() -> Optional["AudiobookshelfConfig"]:
+    """Read Audiobookshelf settings from config.json and build typed config.
+
+    Returns ``None`` when the integration is not configured or required
+    fields are missing.
+    """
+    raw = stored_integration_config("audiobookshelf")
+    if not raw:
+        return None
+    return build_audiobookshelf_config(raw)
+
+
+def build_audiobookshelf_config(
+    settings: Mapping[str, Any],
+) -> Optional["AudiobookshelfConfig"]:
+    """Build :class:`AudiobookshelfConfig` from a settings dict.
+
+    Returns ``None`` when required fields (base_url, api_token, library_id)
+    are missing.
+    """
+    from abogen.integrations.audiobookshelf import AudiobookshelfConfig
+
+    base_url = str(settings.get("base_url") or "").strip()
+    api_token = str(settings.get("api_token") or "").strip()
+    library_id = str(settings.get("library_id") or "").strip()
+    if not (base_url and api_token and library_id):
+        return None
+    try:
+        timeout = float(settings.get("timeout", 3600.0))
+    except (TypeError, ValueError):
+        timeout = 3600.0
+    return AudiobookshelfConfig(
+        base_url=base_url,
+        api_token=api_token,
+        library_id=library_id,
+        collection_id=(str(settings.get("collection_id") or "").strip() or None),
+        folder_id=(str(settings.get("folder_id") or "").strip() or None),
+        verify_ssl=coerce_bool(settings.get("verify_ssl"), True),
+        send_cover=coerce_bool(settings.get("send_cover"), True),
+        send_chapters=coerce_bool(settings.get("send_chapters"), True),
+        send_subtitles=coerce_bool(settings.get("send_subtitles"), False),
+        timeout=timeout,
+    )

abogen/domain/speaker_metadata.py

@@ -0,0 +1,381 @@
+"""Speaker metadata functions for building and applying speaker rosters.
+
+This module contains the core logic for:
+- Building narrator and speaker rosters from analysis results
+- Matching speakers to configured presets
+- Applying speaker config presets to rosters
+- Preparing full speaker metadata for conversion
+
+Moved from webui/routes/utils/voice.py to be available across all UIs.
+"""
+
+from typing import Any, Dict, Iterable, List, Mapping, Optional, Tuple, cast
+
+from abogen.speaker_analysis import analyze_speakers
+from abogen.speaker_configs import slugify_label
+from abogen.domain.settings_core import load_settings
+
+
+def build_narrator_roster(
+    voice: str,
+    voice_profile: Optional[str],
+    existing: Optional[Mapping[str, Any]] = None,
+) -> Dict[str, Any]:
+    roster: Dict[str, Any] = {
+        "narrator": {
+            "id": "narrator",
+            "label": "Narrator",
+            "voice": voice,
+        }
+    }
+    if voice_profile:
+        roster["narrator"]["voice_profile"] = voice_profile
+    existing_entry: Optional[Mapping[str, Any]] = None
+    if existing is not None:
+        existing_entry = existing.get("narrator") if isinstance(existing, Mapping) else None
+    if isinstance(existing_entry, Mapping):
+        roster_entry = roster["narrator"]
+        for key in ("label", "voice", "voice_profile", "voice_formula", "pronunciation"):
+            value = existing_entry.get(key)
+            if value is not None and value != "":
+                roster_entry[key] = value
+    return roster
+
+
+def build_speaker_roster(
+    analysis: Dict[str, Any],
+    base_voice: str,
+    voice_profile: Optional[str],
+    existing: Optional[Mapping[str, Any]] = None,
+    order: Optional[Iterable[str]] = None,
+) -> Dict[str, Any]:
+    roster = build_narrator_roster(base_voice, voice_profile, existing)
+    existing_map: Dict[str, Any] = dict(existing) if isinstance(existing, Mapping) else {}
+    speakers = analysis.get("speakers", {}) if isinstance(analysis, dict) else {}
+    ordered_ids: Iterable[str]
+    if order is not None:
+        ordered_ids = [sid for sid in order if sid in speakers]
+    else:
+        ordered_ids = speakers.keys()
+
+    for speaker_id in ordered_ids:
+        payload = speakers.get(speaker_id, {})
+        if speaker_id == "narrator":
+            continue
+        if isinstance(payload, Mapping) and payload.get("suppressed"):
+            continue
+        previous = existing_map.get(speaker_id)
+        roster[speaker_id] = {
+            "id": speaker_id,
+            "label": payload.get("label") or speaker_id.replace("_", " ").title(),
+            "analysis_confidence": payload.get("confidence"),
+            "analysis_count": payload.get("count"),
+            "gender": payload.get("gender", "unknown"),
+        }
+        detected_gender = payload.get("detected_gender")
+        if detected_gender:
+            roster[speaker_id]["detected_gender"] = detected_gender
+        samples = payload.get("sample_quotes")
+        if isinstance(samples, list):
+            roster[speaker_id]["sample_quotes"] = samples
+        if isinstance(previous, Mapping):
+            for key in ("voice", "voice_profile", "voice_formula", "resolved_voice", "pronunciation"):
+                value = previous.get(key)
+                if value is not None and value != "":
+                    roster[speaker_id][key] = value
+            if "sample_quotes" not in roster[speaker_id]:
+                prev_samples = previous.get("sample_quotes")
+                if isinstance(prev_samples, list):
+                    roster[speaker_id]["sample_quotes"] = prev_samples
+            if "detected_gender" not in roster[speaker_id]:
+                prev_detected = previous.get("detected_gender")
+                if isinstance(prev_detected, str) and prev_detected:
+                    roster[speaker_id]["detected_gender"] = prev_detected
+    return roster
+
+
+def match_configured_speaker(
+    config_speakers: Mapping[str, Any],
+    roster_id: str,
+    roster_label: str,
+) -> Optional[Mapping[str, Any]]:
+    if not config_speakers:
+        return None
+    entry = config_speakers.get(roster_id)
+    if entry:
+        return cast(Mapping[str, Any], entry)
+    slug = slugify_label(roster_label)
+    if slug != roster_id and slug in config_speakers:
+        return cast(Mapping[str, Any], config_speakers[slug])
+    lower_label = roster_label.strip().lower()
+    for record in config_speakers.values():
+        if not isinstance(record, Mapping):
+            continue
+        if str(record.get("label", "")).strip().lower() == lower_label:
+            return record
+    return None
+
+
+def apply_speaker_config_to_roster(
+    roster: Mapping[str, Any],
+    config: Optional[Mapping[str, Any]],
+    *,
+    persist_changes: bool = False,
+    fallback_languages: Optional[Iterable[str]] = None,
+) -> Tuple[Dict[str, Any], List[str], Optional[Dict[str, Any]]]:
+    if not isinstance(roster, Mapping):
+        effective_languages = [code for code in (fallback_languages or []) if isinstance(code, str) and code]
+        return {}, effective_languages, None
+    updated_roster: Dict[str, Any] = {key: dict(value) for key, value in roster.items() if isinstance(value, Mapping)}
+    if not config:
+        effective_languages = [code for code in (fallback_languages or []) if isinstance(code, str) and code]
+        return updated_roster, effective_languages, None
+
+    speakers_map = config.get("speakers")
+    if not isinstance(speakers_map, Mapping):
+        effective_languages = [code for code in (fallback_languages or []) if isinstance(code, str) and code]
+        return updated_roster, effective_languages, None
+
+    config_languages = config.get("languages")
+    if isinstance(config_languages, list):
+        allowed_languages = [code for code in config_languages if isinstance(code, str) and code]
+    else:
+        allowed_languages = []
+    if not allowed_languages and fallback_languages:
+        allowed_languages = [code for code in fallback_languages if isinstance(code, str) and code]
+
+    default_voice = config.get("default_voice") if isinstance(config.get("default_voice"), str) else ""
+    used_voices = {entry.get("resolved_voice") or entry.get("voice") for entry in updated_roster.values()} - {None}
+    narrator_voice = ""
+    narrator_entry = updated_roster.get("narrator") if isinstance(updated_roster, Mapping) else None
+    if isinstance(narrator_entry, Mapping):
+        narrator_voice = str(
+            narrator_entry.get("resolved_voice")
+            or narrator_entry.get("default_voice")
+            or ""
+        ).strip()
+        if narrator_voice:
+            used_voices.add(narrator_voice)
+
+    config_changed = False
+    new_config_payload: Dict[str, Any] = {
+        "language": config.get("language", "a"),
+        "languages": allowed_languages,
+        "default_voice": default_voice,
+        "speakers": dict(speakers_map),
+        "version": config.get("version", 1),
+        "notes": config.get("notes", ""),
+    }
+
+    speakers_payload = new_config_payload["speakers"]
+
+    for speaker_id, roster_entry in updated_roster.items():
+        if speaker_id == "narrator":
+            continue
+        label = str(roster_entry.get("label") or speaker_id)
+        config_entry = match_configured_speaker(speakers_map, speaker_id, label)
+        if config_entry is None:
+            continue
+        voice_id = str(config_entry.get("voice") or "").strip()
+        voice_profile = str(config_entry.get("voice_profile") or "").strip()
+        voice_formula = str(config_entry.get("voice_formula") or "").strip()
+        resolved_voice = str(config_entry.get("resolved_voice") or "").strip()
+        languages = config_entry.get("languages") if isinstance(config_entry.get("languages"), list) else []
+        chosen_voice = resolved_voice or voice_formula or voice_id or roster_entry.get("voice")
+        usable_languages = languages or allowed_languages
+
+        if chosen_voice:
+            roster_entry["resolved_voice"] = chosen_voice
+            roster_entry["voice"] = chosen_voice if not voice_profile and not voice_formula else roster_entry.get("voice", chosen_voice)
+        if voice_profile:
+            roster_entry["voice_profile"] = voice_profile
+        if voice_formula:
+            roster_entry["voice_formula"] = voice_formula
+            roster_entry["resolved_voice"] = voice_formula
+        if not voice_formula and not voice_profile and resolved_voice:
+            roster_entry["resolved_voice"] = resolved_voice
+        roster_entry["config_languages"] = usable_languages or []
+
+        if chosen_voice:
+            used_voices.add(chosen_voice)
+
+        # persist updates back to config payload if required
+        if persist_changes:
+            slug = config_entry.get("id") or slugify_label(label)
+            speakers_payload[slug] = {
+                "id": slug,
+                "label": label,
+                "gender": config_entry.get("gender", "unknown"),
+                "voice": voice_id,
+                "voice_profile": voice_profile,
+                "voice_formula": voice_formula,
+                "resolved_voice": roster_entry.get("resolved_voice", resolved_voice or voice_id),
+                "languages": usable_languages,
+            }
+
+    new_config = new_config_payload if (persist_changes and config_changed) else None
+    return updated_roster, allowed_languages, new_config
+
+
+def prepare_speaker_metadata(
+    *,
+    chapters: List[Dict[str, Any]],
+    chunks: List[Dict[str, Any]],
+    analysis_chunks: Optional[List[Dict[str, Any]]] = None,
+    voice: str,
+    voice_profile: Optional[str],
+    threshold: int,
+    existing_roster: Optional[Mapping[str, Any]] = None,
+    run_analysis: bool = True,
+    speaker_config: Optional[Mapping[str, Any]] = None,
+    apply_config: bool = False,
+    persist_config: bool = False,
+    inject_recommended: Optional[Any] = None,
+) -> tuple[List[Dict[str, Any]], Dict[str, Any], Dict[str, Any], List[str], Optional[Dict[str, Any]]]:
+    chunk_list = [dict(chunk) for chunk in chunks]
+    analysis_source = [dict(chunk) for chunk in (analysis_chunks or chunks)]
+    threshold_value = max(1, int(threshold))
+    analysis_enabled = run_analysis
+    settings_state = load_settings()
+    global_random_languages = [
+        code
+        for code in settings_state.get("speaker_random_languages", [])
+        if isinstance(code, str) and code
+    ]
+
+    if not analysis_enabled:
+        for chunk in chunk_list:
+            chunk["speaker_id"] = "narrator"
+            chunk["speaker_label"] = "Narrator"
+        analysis_payload = {
+            "version": "1.0",
+            "narrator": "narrator",
+            "assignments": {str(chunk.get("id")): "narrator" for chunk in chunk_list},
+            "speakers": {
+                "narrator": {
+                    "id": "narrator",
+                    "label": "Narrator",
+                    "count": len(chunk_list),
+                    "confidence": "low",
+                    "sample_quotes": [],
+                    "suppressed": False,
+                }
+            },
+            "suppressed": [],
+            "stats": {
+                "total_chunks": len(chunk_list),
+                "explicit_chunks": 0,
+                "active_speakers": 0,
+                "unique_speakers": 1,
+                "suppressed": 0,
+            },
+        }
+        roster = build_narrator_roster(voice, voice_profile, existing_roster)
+        narrator_pron = roster["narrator"].get("pronunciation")
+        if narrator_pron:
+            analysis_payload["speakers"]["narrator"]["pronunciation"] = narrator_pron
+        return chunk_list, roster, analysis_payload, [], None
+
+    analysis_result = analyze_speakers(
+        chapters,
+        analysis_source,
+        threshold=threshold_value,
+        max_speakers=0,
+    )
+    analysis_payload = analysis_result.to_dict()
+    speakers_payload = analysis_payload.get("speakers", {})
+    ordered_ids = [
+        sid
+        for sid, meta in sorted(
+            (
+                (sid, meta)
+                for sid, meta in speakers_payload.items()
+                if sid != "narrator" and isinstance(meta, Mapping) and not meta.get("suppressed")
+            ),
+            key=lambda item: item[1].get("count", 0),
+            reverse=True,
+        )
+    ]
+    analysis_payload["ordered_speakers"] = ordered_ids
+    assignments = analysis_payload.get("assignments", {})
+    suppressed_ids = analysis_payload.get("suppressed", [])
+    suppressed_details: List[Dict[str, Any]] = []
+    speakers_payload = analysis_payload.get("speakers", {})
+    if isinstance(suppressed_ids, Iterable):
+        for suppressed_id in suppressed_ids:
+            speaker_meta = speakers_payload.get(suppressed_id) if isinstance(speakers_payload, dict) else None
+            if isinstance(speaker_meta, dict):
+                suppressed_details.append(
+                    {
+                        "id": suppressed_id,
+                        "label": speaker_meta.get("label")
+                        or str(suppressed_id).replace("_", " ").title(),
+                        "pronunciation": speaker_meta.get("pronunciation"),
+                    }
+                )
+            else:
+                suppressed_details.append(
+                    {
+                        "id": suppressed_id,
+                        "label": str(suppressed_id).replace("_", " ").title(),
+                        "pronunciation": None,
+                    }
+                )
+    analysis_payload["suppressed_details"] = suppressed_details
+    roster = build_speaker_roster(
+        analysis_payload,
+        voice,
+        voice_profile,
+        existing=existing_roster,
+        order=analysis_payload.get("ordered_speakers"),
+    )
+    applied_languages: List[str] = []
+    updated_config: Optional[Dict[str, Any]] = None
+    if apply_config and speaker_config:
+        roster, applied_languages, updated_config = apply_speaker_config_to_roster(
+            roster,
+            speaker_config,
+            persist_changes=persist_config,
+            fallback_languages=global_random_languages,
+        )
+        speakers_payload = analysis_payload.get("speakers")
+        if isinstance(speakers_payload, dict):
+            for roster_id, roster_payload in roster.items():
+                speaker_meta = speakers_payload.get(roster_id)
+                if isinstance(speaker_meta, dict):
+                    for key in ("voice", "voice_profile", "voice_formula", "resolved_voice"):
+                        value = roster_payload.get(key)
+                        if value:
+                            speaker_meta[key] = value
+    effective_languages: List[str] = []
+    if applied_languages:
+        effective_languages = applied_languages
+    elif isinstance(analysis_payload.get("config_languages"), list):
+        effective_languages = [
+            code for code in analysis_payload.get("config_languages", []) if isinstance(code, str) and code
+        ]
+    elif global_random_languages:
+        effective_languages = list(global_random_languages)
+
+    if effective_languages:
+        analysis_payload["config_languages"] = effective_languages
+    speakers_payload = analysis_payload.get("speakers")
+    if isinstance(speakers_payload, dict):
+        for roster_id, roster_payload in roster.items():
+            if roster_id in speakers_payload and isinstance(roster_payload, dict):
+                pronunciation_value = roster_payload.get("pronunciation")
+                if pronunciation_value:
+                    speakers_payload[roster_id]["pronunciation"] = pronunciation_value
+
+    fallback_languages = effective_languages or []
+    if callable(inject_recommended):
+        inject_recommended(roster, fallback_languages=fallback_languages)
+
+    for chunk in chunk_list:
+        chunk_id = str(chunk.get("id"))
+        speaker_id = assignments.get(chunk_id, "narrator")
+        chunk["speaker_id"] = speaker_id
+        speaker_meta = roster.get(speaker_id)
+        chunk["speaker_label"] = speaker_meta.get("label") if isinstance(speaker_meta, dict) else speaker_id
+
+    return chunk_list, roster, analysis_payload, applied_languages, updated_config

abogen/domain/split_pattern.py

@@ -1,40 +1,58 @@
 from __future__ import annotations
 
 """Unified split pattern logic extracted from 3 copies."""
-import re
+
+from abogen.domain.enums import Language, SubtitleMode
+
+# Canonical punctuation sets covering all supported scripts:
+# ASCII (. ! ?), ellipsis (…), Arabic ؟, CJK (。！？), Devanagari ।
+PUNCTUATION_SENTENCE = r".!?…؟。！？।"
+# Commas: ASCII , CJK fullwidth ，CJK ideographic 、
+PUNCTUATION_SENTENCE_COMMA = r".!?…,？。！？،，、।"
+PUNCTUATION_COMMAS = ",，、"
 
 
-PUNCTUATION_SENTENCE = r".!?。！？"
-PUNCTUATION_SENTENCE_COMMA = r".!?,。！？、，"
-
-
-def get_split_pattern(language: str, subtitle_mode: str) -> str:
+def get_split_pattern(language: Language, subtitle_mode: str) -> str:
     """Get the appropriate split pattern based on language and subtitle mode.
 
     Args:
-        language: Language code (a, b, e, f, etc.)
+        language: Language enum value, ISO code, or kokoro letter code.
         subtitle_mode: Subtitle mode ("Sentence", "Sentence + Comma", "Line", etc.)
 
     Returns:
         Split pattern string
     """
-    # For English, always use newline splitting only
-    if language in ("a", "b"):
-        return "\n"
+    try:
+        mode = SubtitleMode.from_str(subtitle_mode) if not isinstance(subtitle_mode, SubtitleMode) else subtitle_mode
+    except ValueError:
+        mode = SubtitleMode.DISABLED
+
+    # English: spaCy is NOT used for pre-TTS segmentation (it is only used
+    # for post-TTS subtitle boundaries), so sentence boundaries for English
+    # are applied at subtitle time, not in the TTS engine. Disabled, Line,
+    # Sentence, and Sentence + Comma all keep newline-only engine splitting.
+    if language in (Language.EN_US, Language.EN_GB):
+        if mode in (
+            SubtitleMode.DISABLED,
+            SubtitleMode.LINE,
+            SubtitleMode.SENTENCE,
+            SubtitleMode.SENTENCE_COMMA,
+        ):
+            return "\n"
 
     # Determine spacing pattern based on language
-    spacing = r"\s*" if language in ("z", "j") else r"\s+"
+    spacing = r"\s*" if language.is_cjk else r"\s+"
 
     # For CJK languages, when subtitle mode is Disabled or Line, prefer
     # punctuation-based splitting instead of plain newline splitting.
-    if subtitle_mode in ("Disabled", "Line") and language in ("z", "j"):
+    if mode in (SubtitleMode.DISABLED, SubtitleMode.LINE) and language.is_cjk:
         return rf"(?<=[{PUNCTUATION_SENTENCE}]){spacing}|\n+"
 
-    if subtitle_mode == "Line":
+    if mode == SubtitleMode.LINE:
         return "\n"
-    elif subtitle_mode == "Sentence":
+    elif mode == SubtitleMode.SENTENCE:
         return rf"(?<=[{PUNCTUATION_SENTENCE}]){spacing}|\n+"
-    elif subtitle_mode == "Sentence + Comma":
+    elif mode == SubtitleMode.SENTENCE_COMMA:
         return rf"(?<=[{PUNCTUATION_SENTENCE_COMMA}]){spacing}|\n+"
     else:
         return r"\n+"

abogen/domain/subtitle_generation.py

@@ -10,10 +10,36 @@ from __future__ import annotations
 import re
 from typing import List, Optional, Tuple
 
+from abogen.domain.enums import Language, SubtitleMode
+from abogen.domain.split_pattern import PUNCTUATION_SENTENCE, PUNCTUATION_SENTENCE_COMMA
 
-# Punctuation constants for sentence splitting
-PUNCTUATION_SENTENCE = ".!?\u061f\u3002\uff01\uff1f"  # .!? .?. ??
-PUNCTUATION_SENTENCE_COMMA = ".!?,\u3001\u061f\u3002\uff01\uff0c\uff1f"  # .!?, ,. ??
+_CLOSING_DELIMS = "\"\"\"\"'\"”’»›)]}」』"
+
+
+def _is_sentence_boundary(
+    token: dict,
+    current_sentence: List[dict],
+    separator: str,
+) -> bool:
+    """Check whether token ends a sentence, considering closing quotes and brackets."""
+    ws = token.get("whitespace", "") or ""
+    if not ws:
+        return False
+
+    # For Line mode, a newline in whitespace or text marks line boundary
+    if separator == r"\n":
+        return "\n" in ws or "\n" in str(token.get("text", ""))
+
+    text = str(token.get("text", ""))
+    if re.search(rf"{separator}[{re.escape(_CLOSING_DELIMS)}]*$", text):
+        return True
+
+    if len(current_sentence) >= 2 and text and all(c in _CLOSING_DELIMS for c in text):
+        prev_text = str(current_sentence[-2].get("text", ""))
+        if re.search(rf"{separator}$", prev_text):
+            return True
+
+    return False
 
 
 def process_subtitle_tokens(
@@ -21,7 +47,7 @@ def process_subtitle_tokens(
     subtitle_entries: List[Tuple[float, float, str]],
     max_subtitle_words: int,
     subtitle_mode: str,
-    lang_code: str,
+    language: Language,
     use_spacy_segmentation: bool = False,
     fallback_end_time: Optional[float] = None,
 ) -> None:
@@ -37,44 +63,62 @@ def process_subtitle_tokens(
         max_subtitle_words: Maximum number of words per subtitle entry.
         subtitle_mode: One of "Disabled", "Line", "Sentence", "Sentence + Comma",
             "Sentence + Highlighting", or a string like "5" for word-count mode.
-        lang_code: Language code for spaCy processing (e.g., "a" for English).
+        language: Language enum value for spaCy processing.
         use_spacy_segmentation: Whether to use spaCy for sentence boundary detection.
         fallback_end_time: Fallback end time for the last entry if none is available.
     """
     if not tokens_with_timestamps:
         return
 
+    if not isinstance(language, Language):
+        try:
+            language = Language.from_str(str(language))
+        except ValueError:
+            language = Language.EN_US
+
+    if isinstance(subtitle_mode, SubtitleMode):
+        subtitle_mode_str = subtitle_mode.value
+    else:
+        subtitle_mode_str = str(subtitle_mode)
+
     processed_tokens = tokens_with_timestamps
 
     # For English with spaCy enabled and sentence-based modes, use spaCy for sentence boundaries
     # spaCy is disabled when subtitle mode is "Disabled" or "Line"
     use_spacy_for_english = (
         use_spacy_segmentation
-        and subtitle_mode not in ["Disabled", "Line"]
-        and lang_code in ["a", "b"]
-        and subtitle_mode in ["Sentence", "Sentence + Comma"]
+        and subtitle_mode_str not in [SubtitleMode.DISABLED.value, SubtitleMode.LINE.value, "Disabled", "Line"]
+        and language in [Language.EN_US, Language.EN_GB]
+        and subtitle_mode_str in [SubtitleMode.SENTENCE.value, SubtitleMode.SENTENCE_COMMA.value, "Sentence", "Sentence + Comma"]
     )
 
-    if subtitle_mode == "Sentence + Highlighting":
+    if subtitle_mode_str in (SubtitleMode.SENTENCE_HIGHLIGHT.value, "Sentence + Highlighting"):
         _process_karaoke_highlighting(
             processed_tokens, subtitle_entries, max_subtitle_words, fallback_end_time
         )
-    elif subtitle_mode in ["Sentence", "Sentence + Comma", "Line"]:
-        if use_spacy_for_english and subtitle_mode != "Line":
+    elif subtitle_mode_str in [
+        SubtitleMode.SENTENCE.value,
+        SubtitleMode.SENTENCE_COMMA.value,
+        SubtitleMode.LINE.value,
+        "Sentence",
+        "Sentence + Comma",
+        "Line",
+    ]:
+        if use_spacy_for_english and subtitle_mode_str not in (SubtitleMode.LINE.value, "Line"):
             _process_spacy_sentences(
                 processed_tokens, subtitle_entries, max_subtitle_words,
-                subtitle_mode, lang_code, fallback_end_time
+                subtitle_mode_str, language, fallback_end_time
             )
         else:
             _process_regex_sentences(
                 processed_tokens, subtitle_entries, max_subtitle_words,
-                subtitle_mode, fallback_end_time
+                subtitle_mode_str, fallback_end_time
             )
     else:
         # Word count-based grouping (e.g., "5" for 5-word groups)
         _process_word_count(
             processed_tokens, subtitle_entries, max_subtitle_words,
-            subtitle_mode, fallback_end_time
+            subtitle_mode_str, fallback_end_time
         )
 
 
@@ -85,7 +129,7 @@ def _process_karaoke_highlighting(
     fallback_end_time: Optional[float],
 ) -> None:
     """Process tokens for Sentence + Highlighting mode (karaoke effect)."""
-    separator = rf"[{re.escape(PUNCTUATION_SENTENCE)}]"
+    separator = rf"[{PUNCTUATION_SENTENCE}]"
     current_sentence = []
     word_count = 0
 
@@ -93,10 +137,8 @@ def _process_karaoke_highlighting(
         current_sentence.append(token)
         word_count += 1
 
-        # Split sentences based on separator or word count
-        if (
-            re.search(separator, token["text"]) and token.get("whitespace") == " "
-        ) or word_count >= max_subtitle_words:
+        is_boundary = _is_sentence_boundary(token, current_sentence, separator)
+        if is_boundary or word_count >= max_subtitle_words:
             if current_sentence:
                 # Create karaoke subtitle entry for this sentence
                 start_time = current_sentence[0]["start"]
@@ -111,13 +153,18 @@ def _process_karaoke_highlighting(
                         if t.get("end") is not None and t.get("start") is not None
                         else 0.5
                     )
-                    duration_cs = int(duration * 100)
+                    try:
+                        duration_cs = int(duration * 100)
+                    except (ValueError, OverflowError, TypeError):
+                        duration_cs = 50
                     # Add karaoke effect
-                    karaoke_text += f"{{\\kf{duration_cs}}}{t['text']}{t.get('whitespace', '') or ''}"
+                    karaoke_text += f"{{\\kf{duration_cs}}}{t.get('text', '')}{t.get('whitespace', '') or ''}"
 
-                subtitle_entries.append(
-                    (start_time, end_time, karaoke_text.strip())
-                )
+                text_stripped = karaoke_text.strip()
+                if text_stripped:
+                    subtitle_entries.append(
+                        (start_time, end_time, text_stripped)
+                    )
                 current_sentence = []
                 word_count = 0
 
@@ -130,9 +177,14 @@ def _process_karaoke_highlighting(
         karaoke_text = ""
         for t in current_sentence:
             duration = t["end"] - t["start"] if t.get("end") and t.get("start") else 0.5
-            duration_cs = int(duration * 100)
-            karaoke_text += f"{{\\kf{duration_cs}}}{t['text']}{t.get('whitespace', '') or ''}"
-        subtitle_entries.append((start_time, end_time, karaoke_text.strip()))
+            try:
+                duration_cs = int(duration * 100)
+            except (ValueError, OverflowError, TypeError):
+                duration_cs = 50
+            karaoke_text += f"{{\\kf{duration_cs}}}{t.get('text', '')}{t.get('whitespace', '') or ''}"
+        text_stripped = karaoke_text.strip()
+        if text_stripped:
+            subtitle_entries.append((start_time, end_time, text_stripped))
 
     # Fallback for last entry
     _apply_fallback_end_time(subtitle_entries, fallback_end_time)
@@ -143,7 +195,7 @@ def _process_spacy_sentences(
     subtitle_entries: List[Tuple[float, float, str]],
     max_subtitle_words: int,
     subtitle_mode: str,
-    lang_code: str,
+    language: Language,
     fallback_end_time: Optional[float],
 ) -> None:
     """Process tokens using spaCy for sentence boundary detection."""
@@ -157,7 +209,7 @@ def _process_spacy_sentences(
         )
         return
 
-    nlp = get_spacy_model(lang_code)
+    nlp = get_spacy_model(language)
     if not nlp:
         _process_regex_sentences(
             tokens, subtitle_entries, max_subtitle_words,
@@ -168,7 +220,7 @@ def _process_spacy_sentences(
     # Build full text and track character positions to token indices
     full_text = ""
     for token in tokens:
-        text_part = token["text"] + (token.get("whitespace") or "")
+        text_part = str(token.get("text", "")) + (token.get("whitespace") or "")
         full_text += text_part
 
     # Get sentence boundaries from spaCy
@@ -176,7 +228,7 @@ def _process_spacy_sentences(
     sentence_boundaries = [sent.end_char for sent in doc.sents]
 
     # For "Sentence + Comma" mode, also split on commas
-    if subtitle_mode == "Sentence + Comma":
+    if subtitle_mode in (SubtitleMode.SENTENCE_COMMA.value, "Sentence + Comma"):
         comma_positions = [
             i + 1 for i, c in enumerate(full_text) if c == ","
         ]
@@ -184,6 +236,56 @@ def _process_spacy_sentences(
             set(sentence_boundaries + comma_positions)
         )
 
+    # spaCy does not treat ellipsis ("...", "..", "…") as a sentence
+    # boundary ("Lorem ipsum... Lorem..." stays one sentence), so ellipsis
+    # runs followed by whitespace/end would merge into a single subtitle
+    # entry. Add explicit boundaries after them. Single dots ("Mr.") stay
+    # spaCy's responsibility so abbreviations don't regress.
+    for m in re.finditer(r"\.{2,}(?=[\s\"'”’»›)\]}]|$)|…(?=[\s\"'”’»›)\]}]|$)", full_text):
+        sentence_boundaries.append(m.end())
+    # Double newlines are paragraph breaks: always split, even when spaCy
+    # sees no sentence boundary.
+    for m in re.finditer(r"\n{2,}", full_text):
+        sentence_boundaries.append(m.end())
+    sentence_boundaries = sorted(set(sentence_boundaries))
+
+    # Multi-sentence single FakeToken handling
+    if len(tokens) == 1 and len(sentence_boundaries) > 1:
+        single = tokens[0]
+        start_time = single.get("start", 0.0) or 0.0
+        end_time = single.get("end")
+        duration = (end_time - start_time) if (end_time is not None and end_time > start_time) else 0.0
+
+        prev_pos = 0
+        cur_start = start_time
+        total_chars = max(len(full_text), 1)
+
+        for i, b_pos in enumerate(sentence_boundaries):
+            piece = full_text[prev_pos:b_pos].strip()
+            if not piece:
+                prev_pos = b_pos
+                continue
+            if i == len(sentence_boundaries) - 1:
+                cur_end = end_time if end_time is not None else (cur_start + 1.0)
+            else:
+                cur_end = cur_start + duration * len(piece) / total_chars
+            subtitle_entries.append((cur_start, cur_end, piece))
+            cur_start = cur_end
+            prev_pos = b_pos
+
+        if prev_pos < len(full_text):
+            remainder = full_text[prev_pos:].strip()
+            if remainder:
+                remainder_end = end_time
+                if remainder_end is None:
+                    remainder_end = fallback_end_time
+                if remainder_end is None:
+                    remainder_end = cur_start
+                subtitle_entries.append((cur_start, remainder_end, remainder))
+
+        _apply_fallback_end_time(subtitle_entries, fallback_end_time)
+        return
+
     # Group tokens by sentence boundaries
     current_sentence = []
     word_count = 0
@@ -193,7 +295,7 @@ def _process_spacy_sentences(
     for token in tokens:
         current_sentence.append(token)
         word_count += 1
-        text_len = len(token["text"]) + len(token.get("whitespace") or "")
+        text_len = len(str(token.get("text", ""))) + len(token.get("whitespace") or "")
         current_char_pos += text_len
 
         # Check if we've hit a sentence boundary or max words
@@ -206,15 +308,19 @@ def _process_spacy_sentences(
                 start_time = current_sentence[0]["start"]
                 end_time = current_sentence[-1]["end"]
                 sentence_text = "".join(
-                    t["text"] + (t.get("whitespace") or "")
+                    str(t.get("text", "")) + (t.get("whitespace") or "")
                     for t in current_sentence
-                )
-                subtitle_entries.append(
-                    (start_time, end_time, sentence_text.strip())
-                )
+                ).strip()
+                if sentence_text:
+                    subtitle_entries.append(
+                        (start_time, end_time, sentence_text)
+                    )
                 current_sentence = []
                 word_count = 0
-            if at_boundary:
+            while (
+                boundary_idx < len(sentence_boundaries)
+                and current_char_pos >= sentence_boundaries[boundary_idx]
+            ):
                 boundary_idx += 1
 
     # Add remaining tokens
@@ -222,12 +328,13 @@ def _process_spacy_sentences(
         start_time = current_sentence[0]["start"]
         end_time = current_sentence[-1]["end"]
         sentence_text = "".join(
-            t["text"] + (t.get("whitespace") or "")
+            str(t.get("text", "")) + (t.get("whitespace") or "")
             for t in current_sentence
-        )
-        subtitle_entries.append(
-            (start_time, end_time, sentence_text.strip())
-        )
+        ).strip()
+        if sentence_text:
+            subtitle_entries.append(
+                (start_time, end_time, sentence_text)
+            )
 
     # Fallback for last entry
     _apply_fallback_end_time(subtitle_entries, fallback_end_time)
@@ -242,14 +349,12 @@ def _process_regex_sentences(
 ) -> None:
     """Process tokens using regex for sentence boundary detection."""
     # Define separator pattern based on mode
-    if subtitle_mode == "Line":
+    if subtitle_mode in (SubtitleMode.LINE.value, "Line"):
         separator = r"\n"
-    elif subtitle_mode == "Sentence":
-        # Use punctuation without comma
-        separator = rf"[{re.escape(PUNCTUATION_SENTENCE)}]"
+    elif subtitle_mode in (SubtitleMode.SENTENCE.value, "Sentence"):
+        separator = rf"[{PUNCTUATION_SENTENCE}]"
     else:  # Sentence + Comma
-        # Use punctuation with comma
-        separator = rf"[{re.escape(PUNCTUATION_SENTENCE_COMMA)}]"
+        separator = rf"[{PUNCTUATION_SENTENCE_COMMA}]"
 
     current_sentence = []
     word_count = 0
@@ -259,35 +364,63 @@ def _process_regex_sentences(
         word_count += 1
 
         # Split sentences based on separator or word count
-        if (
-            re.search(separator, token["text"]) and token.get("whitespace") == " "
-        ) or word_count >= max_subtitle_words:
+        is_boundary = _is_sentence_boundary(token, current_sentence, separator)
+        if is_boundary or word_count >= max_subtitle_words:
             if current_sentence:
                 # Create subtitle entry for this sentence
                 start_time = current_sentence[0]["start"]
                 end_time = current_sentence[-1]["end"]
 
-                # Simplified text joining logic
-                sentence_text = ""
-                for t in current_sentence:
-                    sentence_text += t["text"] + (t.get("whitespace") or "")
+                sentence_text = "".join(
+                    str(t.get("text", "")) + (t.get("whitespace") or "")
+                    for t in current_sentence
+                ).strip()
 
-                subtitle_entries.append(
-                    (start_time, end_time, sentence_text.strip())
-                )
+                if sentence_text:
+                    subtitle_entries.append(
+                        (start_time, end_time, sentence_text)
+                    )
                 current_sentence = []
                 word_count = 0
 
-    # Add any remaining tokens as a sentence
+    # Add any remaining tokens as a sentence (split multi-sentence FakeToken)
     if current_sentence:
         start_time = current_sentence[0]["start"]
         end_time = current_sentence[-1]["end"]
 
-        # Simplified text joining logic
-        sentence_text = ""
-        for t in current_sentence:
-            sentence_text += t["text"] + (t.get("whitespace") or "")
-        subtitle_entries.append((start_time, end_time, sentence_text.strip()))
+        sentence_text = "".join(
+            str(t.get("text", "")) + (t.get("whitespace") or "")
+            for t in current_sentence
+        ).strip()
+
+        if len(current_sentence) == 1:
+            split_pat = (
+                r"\n+"
+                if separator == r"\n"
+                else rf"(?<={separator})\s+|(?<={separator}[{re.escape(_CLOSING_DELIMS)}])\s+"
+            )
+            parts = [p.strip() for p in re.split(split_pat, sentence_text) if p.strip()]
+            if len(parts) > 1:
+                d = (end_time - start_time) if (end_time is not None and start_time is not None and end_time > start_time) else 0.0
+                total_len = max(len(sentence_text), 1)
+                cur_s = start_time if start_time is not None else 0.0
+                for i, p in enumerate(parts):
+                    if i == len(parts) - 1 and end_time is not None:
+                        e = end_time
+                    else:
+                        e = cur_s + d * len(p) / total_len
+                    subtitle_entries.append((cur_s, e, p))
+                    cur_s = e
+                current_sentence = []
+
+        if current_sentence and sentence_text:
+            safe_start = start_time if start_time is not None else 0.0
+            safe_end = end_time
+            if safe_end is None:
+                safe_end = fallback_end_time
+            if safe_end is None:
+                safe_end = safe_start
+            subtitle_entries.append((safe_start, safe_end, sentence_text))
 
     # Fallback for last entry
     _apply_fallback_end_time(subtitle_entries, fallback_end_time)
@@ -320,27 +453,29 @@ def _process_word_count(
             # Split after counting N spaces
             if space_count >= word_count:
                 text = "".join(
-                    t["text"] + (t.get("whitespace") or "")
+                    str(t.get("text", "")) + (t.get("whitespace") or "")
                     for t in current_group
-                )
-                subtitle_entries.append(
-                    (
-                        current_group[0]["start"],
-                        current_group[-1]["end"],
-                        text.strip(),
+                ).strip()
+                if text:
+                    subtitle_entries.append(
+                        (
+                            current_group[0]["start"],
+                            current_group[-1]["end"],
+                            text,
+                        )
                     )
-                )
                 current_group = []
                 space_count = 0
 
     # Add any remaining tokens
     if current_group:
         text = "".join(
-            t["text"] + (t.get("whitespace") or "") for t in current_group
-        )
-        subtitle_entries.append(
-            (current_group[0]["start"], current_group[-1]["end"], text.strip())
-        )
+            str(t.get("text", "")) + (t.get("whitespace") or "") for t in current_group
+        ).strip()
+        if text:
+            subtitle_entries.append(
+                (current_group[0]["start"], current_group[-1]["end"], text)
+            )
 
     # Fallback for last entry
     _apply_fallback_end_time(subtitle_entries, fallback_end_time)

abogen/domain/subtitle_processor.py

@@ -14,7 +14,6 @@ from typing import Any, Callable, List, Optional, Tuple
 import numpy as np
 
 from abogen.domain.audio_buffer import (
-    create_silence,
     fit_audio_to_duration,
     ffmpeg_time_stretch,
     mix_audio,

abogen/domain/text_utils.py

@@ -0,0 +1,22 @@
+"""Text utility functions for the domain layer."""
+
+from __future__ import annotations
+
+import re
+
+# Pre-compiled patterns for calculate_text_length
+_METADATA_TAG_PATTERN = re.compile(r"<<METADATA_[^:]+:[^>]*>>")
+_CHAPTER_MARKER_PATTERN = re.compile(r"<<CHAPTER_MARKER:[^>]*>>")
+_VOICE_MARKER_PATTERN = re.compile(r"<<VOICE:[^>]*>>")
+
+
+def calculate_text_length(text: str) -> int:
+    """Calculate character count, ignoring internal markers and newlines.
+
+    Strips chapter markers, voice markers, and metadata tags before counting.
+    """
+    text = _CHAPTER_MARKER_PATTERN.sub("", text)
+    text = _VOICE_MARKER_PATTERN.sub("", text)
+    text = _METADATA_TAG_PATTERN.sub("", text)
+    text = text.replace("\n", "").strip()
+    return len(text)

abogen/domain/title_builder.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from pathlib import Path
-from typing import Any, Dict, List, Mapping, Optional
+from typing import Any, List, Mapping, Optional
 
 from .metadata_helpers import (
     ensure_sentence,

abogen/domain/voice_catalog.py

@@ -0,0 +1,112 @@
+"""Voice catalog — shared voice metadata for all UIs.
+
+Builds a unified catalog of available voices with metadata (language,
+gender, display name). Used by both WebUI and PyQt for voice selection UIs.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Dict, Iterable, List, Mapping, Optional
+
+from abogen.constants import LANGUAGE_DESCRIPTIONS
+from abogen.tts_plugin.utils import get_voices
+
+
+def build_voice_catalog() -> List[Dict[str, str]]:
+    """Build voice catalog with metadata for all available voices.
+
+    Returns a list of dicts, each containing:
+        - id: voice ID (e.g. "af_heart")
+        - language: language code (e.g. "a", "e")
+        - language_label: human-readable language name
+        - gender: "Female", "Male", or "Unknown"
+        - gender_code: "f", "m", or ""
+        - display_name: human-readable voice name
+    """
+    from plugins.kokoro.engine import language_for_voice_id
+
+    catalog: List[Dict[str, str]] = []
+    gender_map = {"f": "Female", "m": "Male"}
+    for voice_id in get_voices("kokoro"):
+        prefix, _, rest = voice_id.partition("_")
+        gender_code = prefix[1] if len(prefix) > 1 else ""
+        lang = language_for_voice_id(voice_id)
+        catalog.append(
+            {
+                "id": voice_id,
+                "language": lang.value,
+                "language_label": LANGUAGE_DESCRIPTIONS.get(lang, lang.value.upper()),
+                "gender": gender_map.get(gender_code, "Unknown"),
+                "gender_code": gender_code,
+                "display_name": rest.replace("_", " ").title() if rest else voice_id,
+            }
+        )
+    return catalog
+
+
+def filter_voice_catalog(
+    catalog: Iterable[Mapping[str, Any]],
+    *,
+    gender: str,
+    allowed_languages: Optional[Iterable[str]] = None,
+) -> List[str]:
+    """Filter voice catalog by gender and language.
+
+    Returns voice IDs that match the criteria. Falls back to broader
+    matches if no exact matches are found.
+
+    Args:
+        catalog: Voice catalog entries (from build_voice_catalog).
+        gender: Gender filter ("male", "female", or "unknown").
+        allowed_languages: Optional list of allowed language codes.
+
+    Returns:
+        List of matching voice IDs.
+    """
+    allowed_set = {code.lower() for code in (allowed_languages or []) if isinstance(code, str) and code}
+    gender_normalized = (gender or "unknown").lower()
+    gender_code = ""
+    if gender_normalized == "male":
+        gender_code = "m"
+    elif gender_normalized == "female":
+        gender_code = "f"
+
+    matches: List[str] = []
+    seen: set[str] = set()
+
+    def _consider(entry: Mapping[str, Any]) -> None:
+        voice_id = entry.get("id")
+        if not isinstance(voice_id, str) or not voice_id:
+            return
+        if voice_id in seen:
+            return
+        seen.add(voice_id)
+        matches.append(voice_id)
+
+    primary: List[Mapping[str, Any]] = []
+    fallback: List[Mapping[str, Any]] = []
+    for entry in catalog:
+        if not isinstance(entry, Mapping):
+            continue
+        voice_lang = str(entry.get("language", "")).lower()
+        voice_gender_code = str(entry.get("gender_code", "")).lower()
+        if allowed_set and voice_lang not in allowed_set:
+            continue
+        if gender_code and voice_gender_code != gender_code:
+            fallback.append(entry)
+            continue
+        primary.append(entry)
+
+    for entry in primary:
+        _consider(entry)
+
+    if not matches:
+        for entry in fallback:
+            _consider(entry)
+
+    if not matches:
+        for entry in catalog:
+            if isinstance(entry, Mapping):
+                _consider(entry)
+
+    return matches

abogen/domain/voice_loader.py

@@ -6,7 +6,7 @@ PyQt and WebUI interfaces.
 
 from __future__ import annotations
 
-from typing import Any, Dict, Optional, Tuple
+from typing import Any, Dict, Optional
 
 from abogen.voice_formulas import get_new_voice
 
@@ -32,7 +32,11 @@ class VoiceCache:
     def clear(self) -> None:
         """Clear all cached voices."""
         self._cache.clear()
-    
+
+    def keys(self):
+        """Return cached voice specs."""
+        return self._cache.keys()
+
     def __contains__(self, voice_spec: str) -> bool:
         return self.contains(voice_spec)
 

abogen/domain/voice_markers.py

@@ -0,0 +1,117 @@
+"""Voice marker parsing and text splitting.
+
+Handles <<VOICE:name>> markers in text, splitting text into voice-specific
+segments. This is domain logic about text segmentation by voice, not subtitle
+processing.
+"""
+
+from __future__ import annotations
+
+import re
+from typing import List, Tuple
+
+_VOICE_MARKER_PATTERN = re.compile(r"<<VOICE:[^>]*>>")
+_VOICE_MARKER_SEARCH_PATTERN = re.compile(r"<<VOICE:(.*?)>>")
+
+
+def validate_voice_name(voice_name: str) -> Tuple[bool, str | None]:
+    """Validate voice name against available voices (case-insensitive).
+
+    Handles both single voices and formulas like 'af_heart*0.5 + am_echo*0.5'.
+
+    Returns:
+        Tuple of (is_valid, invalid_voice_name):
+            - is_valid: True if all voices in the name/formula are valid
+            - invalid_voice_name: The first invalid voice found, or None if all valid
+    """
+    from abogen.tts_plugin.utils import get_voices
+
+    voice_lookup_lower = {v.lower() for v in get_voices("kokoro")}
+    voice_name = voice_name.strip()
+
+    if "*" in voice_name:
+        voices = voice_name.split("+")
+        for term in voices:
+            if "*" in term:
+                base_voice = term.split("*")[0].strip()
+                if base_voice.lower() not in voice_lookup_lower:
+                    return False, base_voice
+        return True, None
+    else:
+        if voice_name.lower() not in voice_lookup_lower:
+            return False, voice_name
+        return True, None
+
+
+def split_text_by_voice_markers(
+    text: str, default_voice: str
+) -> Tuple[List[Tuple[str, str]], str, int, int]:
+    """Split text by voice markers, returning list of (voice, text) tuples.
+
+    Returns the last voice used so it can persist across chapters.
+    Voice names are normalized to lowercase to match canonical voice names.
+
+    Args:
+        text: Text potentially containing <<VOICE:name>> markers
+        default_voice: Voice to use if no markers found or before first marker
+
+    Returns:
+        Tuple of (segments_list, last_voice_used, valid_count, invalid_count):
+            - segments_list: List of (voice_name, segment_text) tuples
+            - last_voice_used: The voice that should continue into next chapter
+            - valid_count: Number of valid voice markers processed
+            - invalid_count: Number of invalid voice markers skipped
+    """
+    from abogen.tts_plugin.utils import get_voices
+
+    voice_splits = list(_VOICE_MARKER_SEARCH_PATTERN.finditer(text))
+
+    if not voice_splits:
+        return [(default_voice, text)], default_voice, 0, 0
+
+    segments: List[Tuple[str, str]] = []
+    current_voice = default_voice
+    valid_markers = 0
+    invalid_markers = 0
+
+    first_start = voice_splits[0].start()
+    if first_start > 0:
+        intro_text = text[:first_start].strip()
+        if intro_text:
+            segments.append((current_voice, intro_text))
+
+    for idx, match in enumerate(voice_splits):
+        voice_name = match.group(1).strip()
+        start = match.end()
+        end = voice_splits[idx + 1].start() if idx + 1 < len(voice_splits) else len(text)
+        segment_text = text[start:end].strip()
+
+        is_valid, invalid_voice = validate_voice_name(voice_name)
+        if is_valid:
+            if "*" in voice_name:
+                normalized_parts = []
+                for part in voice_name.split("+"):
+                    part = part.strip()
+                    if "*" in part:
+                        voice_part, weight = part.split("*", 1)
+                        voice_part_lower = voice_part.strip().lower()
+                        canonical_voice = next(
+                            (v for v in get_voices("kokoro") if v.lower() == voice_part_lower),
+                            voice_part.strip()
+                        )
+                        normalized_parts.append(f"{canonical_voice}*{weight.strip()}")
+                current_voice = " + ".join(normalized_parts)
+            else:
+                voice_name_lower = voice_name.lower()
+                current_voice = next(
+                    (v for v in get_voices("kokoro") if v.lower() == voice_name_lower),
+                    voice_name
+                )
+            valid_markers += 1
+        else:
+            invalid_markers += 1
+
+        if segment_text:
+            segments.append((current_voice, segment_text))
+
+    return segments, current_voice, valid_markers, invalid_markers

abogen/domain/voice_resolution.py

@@ -2,14 +2,17 @@
 
 Functions for resolving voice specifications, collecting required voice IDs,
 and determining the voice to use for chapters and chunks.
+
+All functions accept ConversionRequest (the app-layer contract) instead of
+UI-specific objects. This keeps the domain layer UI-agnostic.
 """
 
 from __future__ import annotations
 
-from typing import Any, Dict, Optional, Set
+from typing import Any, Dict, Mapping, Optional, Set, Tuple
 
 from abogen.tts_plugin.utils import get_voices, get_default_voice
-from abogen.voice_formulas import extract_voice_ids
+from abogen.voice_formulas import extract_voice_ids, pairs_to_formula
 from abogen.voice_cache import ensure_voice_assets
 
 
@@ -29,12 +32,28 @@ def spec_to_voice_ids(spec: Any) -> Set[str]:
     return set()
 
 
-def job_voice_fallback(job: Any) -> str:
-    base = str(getattr(job, "voice", "") or "").strip()
+def _get_chapter_overrides(request: Any) -> list:
+    """Extract chapter overrides from ConversionRequest."""
+    cc = getattr(request, "chapter_chunk", None)
+    if cc is not None:
+        return getattr(cc, "chapter_overrides", []) or []
+    return []
+
+
+def _get_chunks(request: Any) -> list:
+    """Extract chunks from ConversionRequest."""
+    cc = getattr(request, "chapter_chunk", None)
+    if cc is not None:
+        return getattr(cc, "chunks", []) or []
+    return []
+
+
+def job_voice_fallback(request: Any) -> str:
+    base = str(getattr(request, "voice", "") or "").strip()
     if base and base != "__custom_mix":
         return base
 
-    speakers = getattr(job, "speakers", None)
+    speakers = getattr(request, "speakers", None)
     if isinstance(speakers, dict):
         narrator = speakers.get("narrator")
         if isinstance(narrator, dict):
@@ -52,7 +71,7 @@ def job_voice_fallback(job: Any) -> str:
                 if candidate and candidate != "__custom_mix":
                     return candidate
 
-    for chapter in getattr(job, "chapters", []) or []:
+    for chapter in _get_chapter_overrides(request):
         if not isinstance(chapter, dict):
             continue
         for key in ("resolved_voice", "voice_formula", "voice"):
@@ -63,24 +82,24 @@ def job_voice_fallback(job: Any) -> str:
     return ""
 
 
-def collect_required_voice_ids(job: Any) -> Set[str]:
+def collect_required_voice_ids(request: Any) -> Set[str]:
     voices: Set[str] = set()
-    voices.update(spec_to_voice_ids(job.voice))
-    voices.update(spec_to_voice_ids(job_voice_fallback(job)))
+    voices.update(spec_to_voice_ids(request.voice))
+    voices.update(spec_to_voice_ids(job_voice_fallback(request)))
 
-    for chapter in getattr(job, "chapters", []) or []:
+    for chapter in _get_chapter_overrides(request):
         if not isinstance(chapter, dict):
             continue
         for key in ("resolved_voice", "voice_formula", "voice"):
             voices.update(spec_to_voice_ids(chapter.get(key)))
 
-    for chunk in getattr(job, "chunks", []) or []:
+    for chunk in _get_chunks(request):
         if not isinstance(chunk, dict):
             continue
         for key in ("resolved_voice", "voice_formula", "voice"):
             voices.update(spec_to_voice_ids(chunk.get(key)))
 
-    speakers = getattr(job, "speakers", {})
+    speakers = getattr(request, "speakers", {})
     if isinstance(speakers, dict):
         for payload in speakers.values() or []:
             if not isinstance(payload, dict):
@@ -92,30 +111,38 @@ def collect_required_voice_ids(job: Any) -> Set[str]:
     return voices
 
 
-def initialize_voice_cache(job: Any) -> None:
+def initialize_voice_cache(request: Any, events: Any = None) -> None:
+    """Initialize voice cache by downloading required voice assets.
+
+    Args:
+        request: ConversionRequest with voice/chapter/chunk/speaker info.
+        events: ConversionEvents for logging (optional, for backward compat).
+    """
+    log = (lambda msg, level="info": events.log(msg, level=level)) if events else (lambda msg, level="info": None)
+
     try:
-        targets = collect_required_voice_ids(job)
+        targets = collect_required_voice_ids(request)
         downloaded, errors = ensure_voice_assets(
             targets,
-            on_progress=lambda message: job.add_log(message, level="debug"),
+            on_progress=lambda message: log(message, level="debug"),
         )
     except RuntimeError as exc:
-        job.add_log(f"Voice cache unavailable: {exc}", level="warning")
+        log(f"Voice cache unavailable: {exc}", level="warning")
         return
 
     if downloaded:
-        job.add_log(
+        log(
             f"Cached {len(downloaded)} voice asset{'s' if len(downloaded) != 1 else ''} locally.",
             level="info",
         )
 
     for voice_id, error in errors.items():
-        job.add_log(f"Failed to cache voice '{voice_id}': {error}", level="warning")
+        log(f"Failed to cache voice '{voice_id}': {error}", level="warning")
 
 
-def chapter_voice_spec(job: Any, override: Optional[Dict[str, Any]]) -> str:
+def chapter_voice_spec(request: Any, override: Optional[Dict[str, Any]]) -> str:
     if not override:
-        return job_voice_fallback(job)
+        return job_voice_fallback(request)
 
     resolved = str(override.get("resolved_voice", "")).strip()
     if resolved:
@@ -129,17 +156,17 @@ def chapter_voice_spec(job: Any, override: Optional[Dict[str, Any]]) -> str:
     if voice:
         return voice
 
-    return job_voice_fallback(job)
+    return job_voice_fallback(request)
 
 
-def chunk_voice_spec(job: Any, chunk: Dict[str, Any], fallback: str) -> str:
+def chunk_voice_spec(request: Any, chunk: Dict[str, Any], fallback: str) -> str:
     for key in ("resolved_voice", "voice_formula", "voice"):
         value = chunk.get(key)
         if value:
             return str(value)
 
     speaker_id = chunk.get("speaker_id")
-    speakers = getattr(job, "speakers", None)
+    speakers = getattr(request, "speakers", None)
     if isinstance(speakers, dict) and speaker_id in speakers:
         speaker_entry = speakers.get(speaker_id) or {}
         if isinstance(speaker_entry, dict):
@@ -163,7 +190,7 @@ def chunk_voice_spec(job: Any, chunk: Dict[str, Any], fallback: str) -> str:
 
     if fallback:
         return fallback
-    return job_voice_fallback(job)
+    return job_voice_fallback(request)
 
 
 def resolve_fallback_voice_spec(
@@ -188,3 +215,141 @@ def resolve_fallback_voice_spec(
     if not spec:
         spec = get_default_voice(provider)
     return spec
+
+
+# ---------------------------------------------------------------------------
+# Voice choice resolution (shared by all UIs)
+# ---------------------------------------------------------------------------
+
+
+def formula_from_profile(entry: Dict[str, Any]) -> Optional[str]:
+    """Convert a voice profile entry to a voice formula string.
+
+    Handles both Kokoro (voices list) and SuperTonic (single voice) profiles.
+    Returns None if the entry has no usable voice data.
+    """
+    if not isinstance(entry, dict):
+        return None
+    voices = entry.get("voices") or []
+    if not voices:
+        return None
+    return pairs_to_formula(voices)
+
+
+def resolve_profile_voice(
+    profile_name: Optional[str],
+    *,
+    profiles: Optional[Mapping[str, Any]] = None,
+) -> Tuple[str, Optional[str]]:
+    """Resolve a profile name to (formula, language).
+
+    Args:
+        profile_name: Name of the profile to resolve.
+        profiles: Pre-loaded profiles dict. If None, loads from disk.
+
+    Returns:
+        (formula_string, language_code) or ("", None) if not found.
+    """
+    if not profile_name:
+        return "", None
+    source = profiles if isinstance(profiles, Mapping) else None
+    if source is None:
+        from abogen.voice_profiles import load_profiles
+        source = load_profiles()
+    entry = source.get(profile_name) if isinstance(source, Mapping) else None
+    if not isinstance(entry, Mapping):
+        return "", None
+    formula = formula_from_profile(dict(entry)) or ""
+    language = entry.get("language") if isinstance(entry.get("language"), str) else None
+    if isinstance(language, str):
+        language = language.strip().lower() or None
+    return formula, language
+
+
+def resolve_voice_setting(
+    value: Any,
+    *,
+    profiles: Optional[Mapping[str, Any]] = None,
+) -> Tuple[str, Optional[str], Optional[str]]:
+    """Resolve a raw voice setting value into (spec, profile_name, language).
+
+    Parses 'profile:name' or 'speaker:name' prefixes and resolves
+    the profile to a formula string.
+
+    Args:
+        value: Raw voice value from user input (e.g. "af_heart", "profile:MyMix").
+        profiles: Pre-loaded profiles dict. If None, loads from disk.
+
+    Returns:
+        (resolved_spec, profile_name, language) — profile_name and language
+        are None when the input is a plain voice spec.
+    """
+    from abogen.domain.settings_core import split_profile_spec
+
+    base_spec, profile_name = split_profile_spec(value)
+    if profile_name:
+        formula, language = resolve_profile_voice(profile_name, profiles=profiles)
+        return formula or "", profile_name, language
+    return base_spec, None, None
+
+
+def resolve_voice_choice(
+    language: str,
+    base_voice: str,
+    profile_name: str,
+    custom_formula: str,
+    profiles: Dict[str, Any],
+) -> Tuple[str, str, Optional[str]]:
+    """Resolve a user's voice selection into (resolved_voice, resolved_language, selected_profile).
+
+    Handles three input modes:
+    1. Profile selection → resolves to formula (Kokoro) or speaker reference (SuperTonic)
+    2. Custom formula → used directly
+    3. Plain voice spec → passed through
+
+    Args:
+        language: Current language code (e.g. "a", "e").
+        base_voice: Base voice spec (voice ID or formula).
+        profile_name: Selected profile name (empty string if none).
+        custom_formula: Custom formula string (empty string if none).
+        profiles: Dict of all available profiles.
+
+    Returns:
+        (resolved_voice, resolved_language, selected_profile)
+    """
+    from abogen.voice_profiles import normalize_profile_entry
+
+    resolved_voice = base_voice
+    resolved_language = language
+    selected_profile = None
+
+    if profile_name:
+        entry_raw = profiles.get(profile_name)
+        entry = normalize_profile_entry(entry_raw)
+        provider = str((entry or {}).get("provider") or "").strip().lower()
+
+        # Provider-aware behavior:
+        # - Kokoro profiles typically represent mixes (formula strings).
+        # - SuperTonic profiles represent a discrete voice id + settings.
+        #   In that case, we return a speaker reference so downstream can
+        #   resolve provider per-speaker and allow mixed-provider casting.
+        if provider == "supertonic":
+            resolved_voice = f"speaker:{profile_name}"
+            selected_profile = profile_name
+            profile_language = (entry or {}).get("language")
+            if profile_language:
+                resolved_language = str(profile_language)
+        else:
+            formula = formula_from_profile(entry or {}) if entry else None
+            if formula:
+                resolved_voice = formula
+                selected_profile = profile_name
+                profile_language = (entry or {}).get("language")
+                if profile_language:
+                    resolved_language = profile_language
+
+    if custom_formula:
+        resolved_voice = custom_formula
+        selected_profile = None
+
+    return resolved_voice, resolved_language, selected_profile

abogen/domain/voice_utils.py

@@ -1,8 +1,7 @@
 from __future__ import annotations
 
-from typing import Any, Dict, Mapping, Optional, Tuple, Set
+from typing import Any, Dict, Mapping, Optional, Tuple
 
-from abogen.voice_formulas import extract_voice_ids, get_new_voice
 from abogen.tts_plugin.utils import get_voices
 
 

abogen/entity_analysis.py

@@ -9,15 +9,26 @@ from collections import Counter
 from dataclasses import dataclass, field
 from typing import Any, Dict, Iterable, List, Mapping, Optional, Tuple
 
-try:  # pragma: no cover - fallback when spaCy not available during tests
-    import spacy  # type: ignore[import-not-found]
-except Exception:  # pragma: no cover - spaCy optional during runtime bootstrap
-    spacy = None
-
 _Language = Any  # type: ignore[misc,assignment]
 Doc = Any  # type: ignore[misc,assignment]
 Span = Any  # type: ignore[misc,assignment]
 
+_SPACY: Any = None
+_SPACY_LOADED = False
+
+
+def _get_spacy() -> Any:
+    """Import spaCy lazily (it pulls in torch/thinc, ~2s at startup)."""
+    global _SPACY, _SPACY_LOADED
+    if not _SPACY_LOADED:
+        _SPACY_LOADED = True
+        try:  # pragma: no cover - fallback when spaCy not available during tests
+            import spacy  # type: ignore[import-not-found]
+        except Exception:  # pragma: no cover - spaCy optional during runtime bootstrap
+            spacy = None
+        _SPACY = spacy
+    return _SPACY
+
 
 _TITLE_PREFIXES = (
     "mr",
@@ -167,6 +178,7 @@ def _resolve_model_name(language: str) -> str:
 
 
 def _load_model(language: str) -> Any:
+    spacy = _get_spacy()
     if spacy is None:
         raise EntityModelError(
             "spaCy is not available. Install spaCy to enable entity extraction."

abogen/epub3/exporter.py

@@ -12,6 +12,7 @@ from typing import Any, Dict, Iterable, List, Optional, Pattern, Sequence, Tuple
 import zipfile
 
 from abogen.text_extractor import ExtractedChapter, ExtractionResult
+from abogen.domain.metadata_helpers import normalize_metadata_map
 
 
 @dataclass(slots=True)
@@ -22,7 +23,7 @@ class ChunkOverlay:
     start: Optional[float]
     end: Optional[float]
     speaker_id: str
-    voice: Optional[str]
+    voice: Optional[Dict[str, str]]
     level: Optional[str] = None
     group_id: Optional[str] = None
 
@@ -59,7 +60,7 @@ class EPUB3PackageBuilder:
         self.output_path = output_path
         self.book_id = book_id or str(uuid.uuid4())
         self.extraction = extraction
-        self.metadata_tags = _normalize_metadata(metadata_tags)
+        self.metadata_tags = normalize_metadata_map(metadata_tags)
         self.chapter_markers = list(chapter_markers or [])
         self.chunk_markers = list(chunk_markers or [])
         self.chunks = list(chunks or [])
@@ -273,7 +274,7 @@ class EPUB3PackageBuilder:
                     start=_safe_float(marker.get("start")),
                     end=_safe_float(marker.get("end")),
                     speaker_id=speaker_id,
-                    voice=str(voice) if voice else None,
+                    voice=voice if isinstance(voice, dict) else None,
                     level=str(level) if level else None,
                     group_id=normalized_group_id,
                 )
@@ -516,9 +517,14 @@ def build_epub3_package(
     chunks: Iterable[Dict[str, Any]],
     audio_path: Path,
     speaker_mode: str = "single",
+    cover: "CoverConfig | None" = None,
     cover_image_path: Optional[Path] = None,
     cover_image_mime: Optional[str] = None,
 ) -> Path:
+    from abogen.domain.config_types import CoverConfig
+    if isinstance(cover, CoverConfig):
+        cover_image_path = cover.path
+        cover_image_mime = cover.mime
     builder = EPUB3PackageBuilder(
         output_path=output_path,
         book_id=book_id,
@@ -545,15 +551,6 @@ class ChunkLookup:
     by_chapter: Dict[int, List[Dict[str, Any]]]
 
 
-def _normalize_metadata(metadata: Optional[Dict[str, Any]]) -> Dict[str, str]:
-    normalized: Dict[str, str] = {}
-    for key, value in (metadata or {}).items():
-        if value is None:
-            continue
-        normalized[str(key).lower()] = str(value)
-    return normalized
-
-
 def _combine_metadata(*sources: Dict[str, Any]) -> Dict[str, str]:
     combined: Dict[str, str] = {}
     for source in sources:
@@ -696,7 +693,12 @@ def _group_chunks_for_render(chunks: Sequence[ChunkOverlay]) -> List[Tuple[Optio
 def _render_chunk_inline(chunk: ChunkOverlay) -> str:
     escaped_id = html.escape(chunk.id)
     speaker_attr = f" data-speaker=\"{html.escape(chunk.speaker_id)}\"" if chunk.speaker_id else ""
-    voice_attr = f" data-voice=\"{html.escape(chunk.voice)}\"" if chunk.voice else ""
+    voice_str = None
+    if chunk.voice and isinstance(chunk.voice, dict):
+        name = chunk.voice.get("voice", "")
+        provider = chunk.voice.get("provider", "")
+        voice_str = f"{name}@{provider}" if name and provider else name or None
+    voice_attr = f" data-voice=\"{html.escape(voice_str)}\"" if voice_str else ""
     level_attr = f" data-level=\"{html.escape(chunk.level)}\"" if chunk.level else ""
     raw_text = chunk.text or ""
     escaped_text = html.escape(raw_text)

abogen/heteronym_overrides.py

@@ -5,10 +5,21 @@ import re
 from dataclasses import dataclass
 from typing import Any, Dict, Iterable, List, Mapping, Optional, Sequence, Tuple
 
-try:  # pragma: no cover - optional dependency
-    import spacy  # type: ignore
-except Exception:  # pragma: no cover - spaCy may be unavailable in minimal environments
-    spacy = None
+_SPACY: Any = None
+_SPACY_LOADED = False
+
+
+def _get_spacy() -> Any:
+    """Import spaCy lazily (it pulls in torch/thinc, ~2s at startup)."""
+    global _SPACY, _SPACY_LOADED
+    if not _SPACY_LOADED:
+        _SPACY_LOADED = True
+        try:  # pragma: no cover - optional dependency
+            import spacy  # type: ignore
+        except Exception:  # pragma: no cover - spaCy may be unavailable in minimal environments
+            spacy = None
+        _SPACY = spacy
+    return _SPACY
 
 
 @dataclass(frozen=True)
@@ -184,6 +195,7 @@ def _build_replacement_sentence(
 
 
 def _load_spacy(language: str) -> Any:
+    spacy = _get_spacy()
     if spacy is None:
         return None
 
@@ -221,7 +233,7 @@ def extract_heteronym_overrides(
     if not lang.startswith("en"):
         return []
 
-    if spacy is None:
+    if _get_spacy() is None:
         return []
 
     nlp = _load_spacy(lang)

abogen/hf_tracker.py

@@ -19,7 +19,7 @@ def tracked_hf_hub_download(*args, **kwargs):
     try:
         local_kwargs = dict(kwargs)
         local_kwargs["local_files_only"] = True
-        hf_hub_download(*args, **local_kwargs)
+        return hf_hub_download(*args, **local_kwargs)
     except Exception:
         repo_id = kwargs.get("repo_id", "<unknown repo>")
         filename = kwargs.get("filename", "<unknown file>")

abogen/infrastructure/exporters.py

@@ -10,22 +10,14 @@ from typing import Any, Dict, List, Optional, Mapping, Sequence
 import static_ffmpeg
 
 from abogen.domain.metadata_helpers import (
-    normalize_metadata_casefold,
     split_people_field,
     split_simple_list,
     first_nonempty,
     extract_year,
     normalize_series_sequence,
-    build_audiobookshelf_metadata as _build_abs_metadata,
-    load_audiobookshelf_chapters as _load_abs_chapters,
     _SERIES_SEQUENCE_TAG_KEYS,
 )
 from abogen.epub3.exporter import build_epub3_package
-from abogen.integrations.audiobookshelf import (
-    AudiobookshelfClient,
-    AudiobookshelfConfig,
-    AudiobookshelfUploadError,
-)
 from abogen.utils import create_process
 
 logger = logging.getLogger(__name__)
@@ -84,9 +76,14 @@ class ExportService:
             title = chapter.get("title")
             if title:
                 lines.append(f"title={self._escape_ffmetadata_value(title)}")
-            voice = chapter.get("voice")
-            if voice:
-                lines.append(f"voice={self._escape_ffmetadata_value(voice)}")
+            voices = chapter.get("voices")
+            if voices and isinstance(voices, list):
+                voice_str = ", ".join(
+                    f"{v.get('voice', '')}@{v.get('provider', '')}"
+                    for v in voices if v.get("voice")
+                )
+                if voice_str:
+                    lines.append(f"voice={self._escape_ffmetadata_value(voice_str)}")
         
         return "\n".join(lines) + "\n"
     
@@ -127,11 +124,16 @@ class ExportService:
         audio_path: Path,
         metadata: Dict[str, Any],
         chapters: List[Dict[str, Any]],
+        cover: "CoverConfig | None" = None,
         cover_path: Optional[Path] = None,
         cover_mime: Optional[str] = None,
         log_callback: Optional[callable] = None,
     ) -> None:
         """Embed metadata and chapters into M4B file using FFmpeg + Mutagen."""
+        from abogen.domain.config_types import CoverConfig
+        if isinstance(cover, CoverConfig):
+            cover_path = cover.path
+            cover_mime = cover.mime
         ffmetadata_path = self.write_ffmetadata_file(audio_path, metadata, chapters)
         
         metadata_args = self._metadata_to_ffmpeg_args(metadata)
@@ -310,132 +312,7 @@ class ExportService:
             cover_image_mime=cover_mime,
         )
     
-    # ----------------------------------------------------------------------
-    # Audiobookshelf Integration
-    # ----------------------------------------------------------------------
-    
-    def build_audiobookshelf_metadata(self, job: Any) -> Dict[str, Any]:
-        """Build Audiobookshelf metadata from job."""
-        filename = Path(getattr(job, "original_filename", "") or "").stem or "Audiobook"
-        return _build_abs_metadata(
-            getattr(job, "metadata_tags", {}),
-            language=getattr(job, "language", "") or "",
-            filename=filename,
-        )
 
-    def load_audiobookshelf_chapters(self, job: Any) -> Optional[List[Dict[str, Any]]]:
-        """Load chapters from job artifacts for Audiobookshelf."""
-        metadata_ref = job.result.artifacts.get("metadata") if getattr(job, "result", None) else None
-        if not metadata_ref:
-            return None
-        metadata_path = metadata_ref if isinstance(metadata_ref, Path) else Path(str(metadata_ref))
-        return _load_abs_chapters(metadata_path)
-    
-    def upload_audiobookshelf(
-        self,
-        job: Any,
-        audio_path: Path,
-        subtitle_paths: List[Path],
-        chapters: List[Dict[str, Any]],
-        metadata: Dict[str, Any],
-        cover_path: Optional[Path] = None,
-        config: Optional[AudiobookshelfConfig] = None,
-        log_callback: Optional[callable] = None,
-    ) -> None:
-        """Upload to Audiobookshelf."""
-        if config is None:
-            cfg = getattr(job, "_abs_config", None)
-            if cfg is None:
-                from abogen.utils import load_config
-                global_cfg = load_config() or {}
-                abs_cfg = global_cfg.get("audiobookshelf")
-                if isinstance(abs_cfg, Mapping):
-                    config = AudiobookshelfConfig(
-                        base_url=str(abs_cfg.get("base_url") or "").strip(),
-                        api_token=str(abs_cfg.get("api_token") or "").strip(),
-                        library_id=str(abs_cfg.get("library_id") or "").strip(),
-                        collection_id=(str(abs_cfg.get("collection_id") or "").strip() or None),
-                        folder_id=str(abs_cfg.get("folder_id") or "").strip(),
-                        verify_ssl=self._coerce_bool(abs_cfg.get("verify_ssl"), True),
-                        send_cover=self._coerce_bool(abs_cfg.get("send_cover"), True),
-                        send_chapters=self._coerce_bool(abs_cfg.get("send_chapters"), True),
-                        send_subtitles=self._coerce_bool(abs_cfg.get("send_subtitles"), False),
-                        timeout=float(abs_cfg.get("timeout", 3600.0)),
-                    )
-                else:
-                    if log_callback:
-                        log_callback("Audiobookshelf upload skipped: not configured", "warning")
-                    return
-
-        if not config.base_url or not config.api_token or not config.library_id:
-            if log_callback:
-                log_callback("Audiobookshelf upload skipped: configure base URL, API token, and library ID first", "warning")
-            return
-        if not config.folder_id:
-            if log_callback:
-                log_callback("Audiobookshelf upload skipped: enter folder name or ID in settings", "warning")
-            return
-
-        if not audio_path.exists():
-            if log_callback:
-                log_callback("Audiobookshelf upload skipped: audio output not found", "warning")
-            return
-        
-        existing_subtitles = [p for p in subtitle_paths if p.exists()] if config.send_subtitles else None
-        chapters_to_send = chapters if config.send_chapters else None
-        
-        client = AudiobookshelfClient(config)
-        
-        display_title = metadata.get("title") or audio_path.stem
-        try:
-            existing_items = client.find_existing_items(display_title, folder_id=config.folder_id)
-        except AudiobookshelfUploadError as exc:
-            if log_callback:
-                log_callback(f"Audiobookshelf lookup failed: {exc}", "error")
-            return
-        
-        if existing_items:
-            if log_callback:
-                log_callback(f"Removing existing Audiobookshelf item(s) for '{display_title}' before upload.", "info")
-            try:
-                client.delete_items(existing_items)
-            except Exception as exc:
-                if log_callback:
-                    log_callback(f"Failed to remove existing item(s): {exc}", "warning")
-        
-        cover_to_send = cover_path
-        if config.send_cover and cover_to_send:
-            if isinstance(cover_to_send, str):
-                cover_to_send = Path(cover_to_send)
-            if not cover_to_send.exists():
-                cover_to_send = None
-        
-        client.upload_audiobook(
-            audio_path,
-            metadata=metadata,
-            cover_path=cover_to_send,
-            chapters=chapters_to_send,
-            subtitles=existing_subtitles,
-        )
-        
-        if log_callback:
-            log_callback("Audiobookshelf upload queued.", "info")
-    
-    # ----------------------------------------------------------------------
-    # Helpers
-    # ----------------------------------------------------------------------
-    
-    @staticmethod
-    def _coerce_bool(value: Any, default: bool = True) -> bool:
-        if isinstance(value, bool):
-            return value
-        if isinstance(value, str):
-            lowered = value.strip().lower()
-            if lowered in {"true", "1", "yes", "on"}:
-                return True
-            if lowered in {"false", "0", "no", "off"}:
-                return False
-            return default
         if value is None:
             return default
         return bool(value)

abogen/infrastructure/subtitle_writer.py

@@ -6,23 +6,10 @@ from enum import Enum
 from pathlib import Path
 from typing import List, Optional, TextIO
 
+from abogen.domain.enums import SubtitleFormat, SubtitleMode
 from abogen.subtitle_utils import clean_subtitle_text
 
 
-class SubtitleFormat(Enum):
-    SRT = "srt"
-    ASS = "ass"
-    VTT = "vtt"
-
-
-class SubtitleMode(Enum):
-    DISABLED = "Disabled"
-    LINE = "Line"
-    SENTENCE = "Sentence"
-    SENTENCE_COMMA = "Sentence + Comma"
-    SENTENCE_HIGHLIGHT = "Sentence + Highlighting"
-
-
 class SubtitleAlignment(Enum):
     LEFT = "left"
     CENTER = "center"
@@ -233,8 +220,10 @@ class AssWriter(SubtitleWriter):
         
         style = "Default"
         if self.config.mode == SubtitleMode.SENTENCE_HIGHLIGHT:
-            # Add karaoke tags for highlighting
-            text = self._add_karaoke_tags(text)
+            # Entries from process_subtitle_tokens already carry per-word
+            # {\kf...} timing; only synthesize simplified tags when absent.
+            if "{\\k" not in text:
+                text = self._add_karaoke_tags(text)
             style = "Highlight"
         
         alignment_tag = r"{\an5}" if self._is_centered else ""
@@ -261,6 +250,19 @@ class AssWriter(SubtitleWriter):
         return f"{hours}:{minutes:02d}:{secs:05.2f}"
 
 
+def _coerce_mode(mode: str) -> SubtitleMode:
+    """Parse a subtitle mode, tolerating word-count strings like "5 words".
+
+    Word-count modes are grouped upstream (subtitle_generation) and the writer
+    only branches on SubtitleMode.SENTENCE_HIGHLIGHT, so any non-highlight
+    fallback is behaviorally equivalent for the writers.
+    """
+    try:
+        return SubtitleMode(mode)
+    except ValueError:
+        return SubtitleMode.SENTENCE
+
+
 def create_subtitle_writer(
     path: Path,
     format: str,
@@ -270,7 +272,7 @@ def create_subtitle_writer(
 ) -> SubtitleWriter:
     """Factory function to create subtitle writer."""
     fmt = SubtitleFormat(format.lower())
-    mode = SubtitleMode(mode)
+    mode = _coerce_mode(mode)
     align = SubtitleAlignment(alignment.lower())
 
     config = SubtitleConfig(
@@ -291,24 +293,28 @@ def create_subtitle_writer(
 
 
 def resolve_subtitle_format(
-    subtitle_format: str | None,
-    subtitle_mode: str,
+    subtitle: "SubtitleConfig | str | None",
+    subtitle_mode: str | None = None,
 ) -> tuple[str, str]:
-    """Resolve a subtitle_format setting string to (file_extension, alignment).
+    """Resolve a subtitle config to (file_extension, alignment).
 
-    Handles the PyQt convention where format strings encode alignment
-    (e.g. ``"ass_centered_narrow"`` → extension ``"ass"``, alignment
-    ``"center_narrow"``).
-
-    Also enforces that ``"Sentence + Highlighting"`` mode requires ASS.
+    Accepts a SubtitleConfig object or individual format/mode strings
+    for backward compatibility.
 
     Returns:
         Tuple of (file_extension, alignment) suitable for
         :func:`create_subtitle_writer`.
     """
-    fmt = (subtitle_format or "srt").lower()
+    from abogen.domain.config_types import SubtitleConfig
 
-    if subtitle_mode == "Sentence + Highlighting" and fmt == "srt":
+    if isinstance(subtitle, SubtitleConfig):
+        fmt = subtitle.format.value.lower()
+        mode_str = subtitle.mode.value
+    else:
+        fmt = (subtitle or "srt").lower()
+        mode_str = subtitle_mode or "Disabled"
+
+    if mode_str == "Sentence + Highlighting" and fmt == "srt":
         fmt = "ass"
 
     if "ass" in fmt:
@@ -330,26 +336,39 @@ def resolve_subtitle_format(
 
 def make_subtitle_writer(
     audio_path: Path,
-    subtitle_format: str | None,
-    subtitle_mode: str,
-    max_words: int = 50,
+    subtitle: "SubtitleConfig | str | None",
+    subtitle_mode: str | None = None,
+    max_words: int | None = None,
 ) -> SubtitleWriter | None:
     """Convenience: resolve format and create a writer, or return None if disabled.
 
-    Returns ``None`` when ``subtitle_mode`` is ``"Disabled"`` or the
+    Accepts a SubtitleConfig object or individual format/mode strings
+    for backward compatibility.
+
+    Returns ``None`` when subtitle mode is ``"Disabled"`` or the
     format is unsupported.
     """
-    if subtitle_mode == "Disabled":
-        return None
+    from abogen.domain.config_types import SubtitleConfig
 
-    extension, alignment = resolve_subtitle_format(subtitle_format, subtitle_mode)
+    if isinstance(subtitle, SubtitleConfig):
+        mode_str = subtitle.mode.value
+        if mode_str == "Disabled":
+            return None
+        words = subtitle.max_words
+    else:
+        mode_str = subtitle_mode or subtitle or "Disabled"
+        if mode_str == "Disabled":
+            return None
+        words = max_words or 50
+
+    extension, alignment = resolve_subtitle_format(subtitle, subtitle_mode)
     try:
         return create_subtitle_writer(
             audio_path.with_suffix(f".{extension}"),
             extension,
-            subtitle_mode,
+            mode_str,
             alignment=alignment,
-            max_words=max_words,
+            max_words=words,
         )
     except (ValueError, KeyError):
         return None

abogen/kokoro_text_normalization.py

@@ -672,6 +672,15 @@ def tokenize_with_spans(text: str) -> List[Tuple[str, int, int]]:
     ]
 
 
+_OPENING_PUNCTUATION_CHARS = "«‹“‘([{¡¿「『"
+_CLOSING_PUNCTUATION_CHARS = "»›”’)]}」』"
+_STANDARD_PUNCTUATION_CHARS = ",.;:!?%"
+
+_OPENING_PUNCT_CLASS = re.escape(_OPENING_PUNCTUATION_CHARS)
+_CLOSING_PUNCT_CLASS = re.escape(_CLOSING_PUNCTUATION_CHARS)
+_STANDARD_PUNCT_CLASS = re.escape(_STANDARD_PUNCTUATION_CHARS)
+
+
 def _cleanup_spacing(text: str) -> str:
     if not text:
         return text
@@ -679,22 +688,39 @@ def _cleanup_spacing(text: str) -> str:
     for marker in ("\ufeff", "\u200b", "\u200c", "\u200d", "\u2060"):
         text = text.replace(marker, "")
 
-    # Collapse spaces before closing punctuation.
-    text = re.sub(r"\s+([,.;:!?%])", r"\1", text)
-    text = re.sub(r"\s+([’\"”»›)\]\}])", r"\1", text)
+    # Collapse spaces before standard punctuation and unambiguous closing quotes/brackets.
+    text = re.sub(rf"\s+([{_STANDARD_PUNCT_CLASS}])", r"\1", text)
+    text = re.sub(rf"\s+([{_CLOSING_PUNCT_CLASS}])", r"\1", text)
 
-    # Remove spaces directly after opening punctuation/quotes.
-    text = re.sub(r"([«‹“‘\"'(\[\{])\s+", r"\1", text)
+    # Remove spaces directly after unambiguous opening punctuation/quotes.
+    text = re.sub(rf"([{_OPENING_PUNCT_CLASS}])\s+", r"\1", text)
+
+    # Handle ambiguous straight quotes (\", ')
+    # 1. Remove spaces directly after opening straight quotes:
+    #    e.g. ' \" word' -> ' \"word', '^\" word' -> '\"word', '(\" word' -> '(\"word'
+    text = re.sub(rf"(^|[\s{_OPENING_PUNCT_CLASS}])([\"\'])\s+", r"\1\2", text)
+    # 2. Collapse spaces directly before closing straight quotes:
+    #    e.g. 'word \" ' -> 'word\" ', 'word \".' -> 'word\".'
+    text = re.sub(rf"\s+([\"\'])([\s{_STANDARD_PUNCT_CLASS}{_CLOSING_PUNCT_CLASS}]|$)", r"\1\2", text)
 
     # Ensure spaces exist after sentence punctuation when followed by a word/quote.
-    text = re.sub(r"([,.;:!?%])(?![\s”'\"’»›)])", r"\1 ", text)
-    text = re.sub(r"([”\"’])(?![\s.,;:!?\"”’»›)])", r"\1 ", text)
+    # Runs of punctuation ("...", "?!?", "!!") must stay together: no space
+    # inside the run, only after it ("a...b" -> "a... b").
+    text = re.sub(rf"([{_STANDARD_PUNCT_CLASS}])(?![\s{_STANDARD_PUNCT_CLASS}{_CLOSING_PUNCT_CLASS}\"\'”’»›)])", r"\1 ", text)
+    # Ensure space after unambiguous closing quote when followed by a word (e.g. '”Next' -> '” Next')
+    text = re.sub(rf"([{_CLOSING_PUNCT_CLASS}])(?![\s{_STANDARD_PUNCT_CLASS}{_CLOSING_PUNCT_CLASS}\"\'”’»›)])", r"\1 ", text)
+    # Straight double quote closing (preceded by non-whitespace) followed directly by a word/number/opening
+    text = re.sub(rf"(\S\")([A-Za-z0-9{_OPENING_PUNCT_CLASS}])", r"\1 \2", text)
+    # Straight single quote closing (preceded by punctuation, not internal word apostrophe) followed by a word
+    text = re.sub(rf"([{_STANDARD_PUNCT_CLASS}{_CLOSING_PUNCT_CLASS}]\')([A-Za-z0-9{_OPENING_PUNCT_CLASS}])", r"\1 \2", text)
 
     # Tighten hyphen/em dash spacing between word characters.
     text = re.sub(r"(?<=\w)\s*([-–—])\s*(?=\w)", r"\1", text)
 
-    # Normalize multiple spaces.
-    text = re.sub(r"\s{2,}", " ", text)
+    # Normalize multiple spaces, preserving paragraph breaks (double
+    # newlines must survive so the TTS engine can split on them).
+    text = re.sub(r"[^\S\n]{2,}", " ", text)
+    text = re.sub(r"\n{3,}", "\n\n", text)
     return text.strip()
 
 
@@ -1622,8 +1648,18 @@ def normalize_apostrophes(
         results.append((tok, category, norm))
         normalized_tokens.append(norm)
 
-    filtered = [token for token in normalized_tokens if token]
-    normalized_text = _cleanup_spacing(" ".join(filtered))
+    out_pieces: List[str] = []
+    last_end = 0
+    for (tok, start, end), norm in zip(token_entries, normalized_tokens):
+        if start > last_end:
+            out_pieces.append(text[last_end:start])
+        out_pieces.append(norm)
+        last_end = end
+    if last_end < len(text):
+        out_pieces.append(text[last_end:])
+
+    reconstructed = "".join(out_pieces)
+    normalized_text = _cleanup_spacing(reconstructed)
     return normalized_text, results
 
 
@@ -1824,7 +1860,10 @@ def _normalize_grouped_numbers(text: str, cfg: ApostropheConfig) -> str:
         for digit in trimmed_fraction:
             if not digit.isdigit():
                 return token
-            digit_words.append(_DIGIT_WORDS[int(digit)])
+            try:
+                digit_words.append(_DIGIT_WORDS[int(digit)])
+            except (ValueError, IndexError):
+                return token
 
         spoken = f"{integer_words} point {' '.join(digit_words)}"
         return f"minus {spoken}" if is_negative else spoken
@@ -1846,18 +1885,27 @@ def _normalize_grouped_numbers(text: str, cfg: ApostropheConfig) -> str:
             # Magnitude case: $2.5 million -> two point five million dollars
             if "." in amount_str:
                 integer_part, fraction_part = amount_str.split(".", 1)
-                integer_val = int(integer_part)
+                try:
+                    integer_val = int(integer_part)
+                except ValueError:
+                    return match.group(0)
                 integer_words = _int_to_words(integer_val, language)
 
                 # Spell out fraction digits
                 digit_words = []
                 for digit in fraction_part:
                     if digit.isdigit():
-                        digit_words.append(_DIGIT_WORDS[int(digit)])
+                        try:
+                            digit_words.append(_DIGIT_WORDS[int(digit)])
+                        except (ValueError, IndexError):
+                            return match.group(0)
 
                 amount_spoken = f"{integer_words} point {' '.join(digit_words)}"
             else:
-                amount_spoken = _int_to_words(int(amount), language)
+                try:
+                    amount_spoken = _int_to_words(int(amount), language)
+                except (ValueError, OverflowError):
+                    return match.group(0)
 
             currency_names = {
                 "$": "dollars",

abogen/pyqt/book_handler.py

@@ -36,10 +36,8 @@ from abogen.domain.metadata_extraction import (
     format_metadata_tags,
 )
 
-from abogen.subtitle_utils import (
-    clean_text,
-    calculate_text_length,
-)
+from abogen.subtitle_utils import clean_text
+from abogen.domain.text_utils import calculate_text_length
 
 import os
 import logging
@@ -47,9 +45,9 @@ import urllib.parse
 import textwrap
 
 # Setup logging
-logging.basicConfig(
-    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
-)
+from abogen.utils import setup_console_logging
+
+setup_console_logging()
 
 _HTML_TAG_PATTERN = re.compile(r"<[^>]+>")
 _LEADING_DASH_PATTERN = re.compile(r"^\s*[-–—]\s*")

abogen/pyqt/conversion.py

@@ -1,7 +1,8 @@
 import os
-import re
 import time
+import logging
 import hashlib  # For generating unique cache filenames
+from pathlib import Path
 from platformdirs import user_desktop_dir
 from PyQt6.QtCore import QThread, pyqtSignal, Qt, QTimer
 from PyQt6.QtWidgets import QCheckBox, QVBoxLayout, QDialog, QLabel, QDialogButtonBox
@@ -9,7 +10,6 @@ from contextlib import ExitStack, contextmanager
 import numpy as np
 import soundfile as sf
 from abogen.utils import (
-    create_process,
     get_user_cache_path,
     detect_encoding,
 )
@@ -23,46 +23,35 @@ from abogen.constants import (
 )
 from abogen.infrastructure.subtitle_writer import make_subtitle_writer, resolve_subtitle_format
 from abogen.domain.split_pattern import get_split_pattern
+from abogen.domain.enums import Language
 from abogen.domain.subtitle_processor import (
     parse_subtitle_file,
     process_subtitle_entries,
 )
 from abogen.domain.output_paths import (
     resolve_output_directory,
-    build_output_path,
-    sanitize_output_stem,
     sanitize_filename_for_chapter,
     resolve_unique_path,
 )
 from abogen.domain.audio_helpers import build_ffmpeg_command, to_float32
-from abogen.domain.audio_sink import AudioSink, open_audio_sink
-from abogen.domain.conversion_engine import synthesize_text, SegmentStats, SegmentInfo
+from abogen.domain.audio_sink import open_audio_sink
+from abogen.domain.conversion_engine import run_tts_segment_loop, synthesize_text, SynthParams, SegmentStats, SegmentInfo
 from abogen.domain.intro_outro import resolve_intro, resolve_outro
 from abogen.domain.audio_buffer import (
     create_silence,
-    mix_audio,
-    normalize_audio,
-    SAMPLE_RATE,
 )
 from abogen.domain.subtitle_generation import process_subtitle_tokens
 from abogen.domain.voice_loader import VoiceCache, load_voice_cached, resolve_voice
-from abogen.domain.progress import calc_etr_str
-from abogen.domain.normalization import TTSContext
-from abogen.domain.pronunciation import (
-    compile_pronunciation_rules,
-    compile_heteronym_sentence_rules,
-    merge_pronunciation_overrides,
-)
 from abogen.domain.metadata_extraction import (
-    extract_metadata_and_build_args,
-    extract_metadata_for_file,
+    extract_metadata_from_text,
 )
 from abogen.domain.text_chapters import parse_chapters_from_text
 from abogen.infrastructure.exporters import ExportService
 import abogen.hf_tracker as hf_tracker
 import static_ffmpeg
 import threading  # for efficient waiting
-import subprocess
+
+logger = logging.getLogger(__name__)
 
 
 
@@ -241,11 +230,6 @@ class ConversionThread(QThread):
     log_updated = pyqtSignal(object)  # Updated signal for log updates
     chapters_detected = pyqtSignal(int)  # Signal for chapter detection
 
-    # Punctuation constants for unified handling across languages
-    PUNCTUATION_SENTENCE = ".!?।。！？"
-    PUNCTUATION_SENTENCE_COMMA = ".!?,।。！？、，"
-    PUNCTUATION_COMMAS = ",，、"
-
     def __init__(
         self,
         file_name,
@@ -366,7 +350,7 @@ class ConversionThread(QThread):
         return samples_processed
 
     def run(self):
-        print(
+        logger.info(
             f"\nVoice: {self.voice}\nLanguage: {self.lang_code}\nSpeed: {self.speed}\nGPU: {self.use_gpu}\nFile: {self.file_name}\nSubtitle mode: {self.subtitle_mode}\nOutput format: {self.output_format}\nSave option: {self.save_option}\n"
         )
         try:
@@ -524,6 +508,9 @@ class ConversionThread(QThread):
                 ) as file:
                     text = file.read()
 
+            # Extract metadata BEFORE clean_text strips the tags
+            self._extracted_metadata = extract_metadata_from_text(text)
+
             # Clean up text using utility function
             text = clean_text(text)
 
@@ -549,18 +536,18 @@ class ConversionThread(QThread):
                 )
 
             # --- Compile normalization rules (heteronym + pronunciation) ---
-            from abogen.domain.normalization import TTSContext
-            pronunciation_overrides = merge_pronunciation_overrides(
-                getattr(self, "pronunciation_overrides", None),
-                getattr(self, "manual_overrides", None),
-            )
-            self._tts_context = TTSContext(
-                split_pattern=self.split_pattern,
-                pronunciation_rules=compile_pronunciation_rules(pronunciation_overrides),
-                heteronym_rules=compile_heteronym_sentence_rules(
-                    getattr(self, "heteronym_overrides", None)
+            from abogen.domain.config_types import PronunciationConfig
+            from abogen.domain.normalization import build_tts_context
+            self._tts_context = build_tts_context(
+                language=self.lang_code,
+                subtitle=self.subtitle_mode,
+                pronunciation=PronunciationConfig(
+                    pronunciation_overrides=getattr(self, "pronunciation_overrides", None) or [],
+                    manual_overrides=getattr(self, "manual_overrides", None) or [],
+                    heteronym_overrides=getattr(self, "heteronym_overrides", None) or [],
+                    normalization_overrides=getattr(self, "normalization_overrides", None),
                 ),
-                normalization_overrides=getattr(self, "normalization_overrides", None),
+                log_callback=lambda level, msg: self.log_updated.emit((msg, "grey" if level == "info" else "orange")),
             )
 
             # --- Chapter splitting logic ---
@@ -759,7 +746,7 @@ class ConversionThread(QThread):
             intro_emitted = False
             if merge_chapters_at_end:
                 intro_spec = resolve_intro(
-                    extract_metadata_for_file(self.file_name, self.is_direct_text),
+                    self._extracted_metadata,
                     os.path.basename(self.file_name) if self.file_name else "",
                     getattr(self, "read_title_intro", False),
                     self.voice, self.voice, list(self.voice_cache._cache.keys()),
@@ -773,17 +760,20 @@ class ConversionThread(QThread):
                         etr_start_time=self.etr_start_time,
                         total_characters=self.total_char_count,
                     )
+                    intro_synth = SynthParams(
+                        tts_context=self._tts_context,
+                        stats=intro_stats,
+                        check_cancel=lambda: self.cancel_requested,
+                        on_progress=lambda pct, etr: self.progress_updated.emit(pct, etr),
+                        audio_sink=merged_sink,
+                    )
                     run_tts_segment_loop(
                         text=intro_spec.text,
+                        params=intro_synth,
                         backend=self.backend,
                         voice=loaded_intro_voice,
                         speed=self.speed,
                         split_pattern=self.split_pattern,
-                        stats=intro_stats,
-                        check_cancel=lambda: self.cancel_requested,
-                        on_progress=lambda pct, etr: self.progress_updated.emit(pct, etr),
-                        chapter_sink=None,
-                        audio_sink=merged_sink,
                     )
                     self.processed_char_count = intro_stats.processed_chars
                     current_time = intro_stats.current_time
@@ -885,12 +875,11 @@ class ConversionThread(QThread):
                     )
                     spacy_sentences = None
                     active_split_pattern = self.split_pattern
-                    spacing_pattern = r"\s*" if self.lang_code in ["z", "j"] else r"\s+"
 
                     # Pre-load spaCy model for English if it will be needed for subtitle generation
                     if (
                         use_spacy
-                        and self.lang_code in ["a", "b"]
+                        and self.lang_code in (Language.EN_US, Language.EN_GB)
                         and self.subtitle_mode in ["Sentence", "Sentence + Comma"]
                     ):
                         from abogen.spacy_utils import get_spacy_model
@@ -907,7 +896,7 @@ class ConversionThread(QThread):
                                 )
                             )
 
-                    if use_spacy and self.lang_code not in ["a", "b"]:
+                    if use_spacy and self.lang_code not in (Language.EN_US, Language.EN_GB):
                         # Non-English: use spaCy for pre-TTS segmentation
                         self.log_updated.emit(
                             ("\nUsing spaCy for sentence segmentation (pre-TTS)...", "grey")
@@ -926,15 +915,11 @@ class ConversionThread(QThread):
                                     "grey",
                                 )
                             )
-                            # For Sentence + Comma mode, still split on commas within spaCy sentences
-                            if self.subtitle_mode == "Sentence + Comma":
-                                active_split_pattern = r"(?<=[{}]){}|\n+".format(
-                                    self.PUNCTUATION_COMMAS, spacing_pattern
-                                )
-                            else:
-                                active_split_pattern = (
-                                    "\n"  # Use newline splitting for Sentence mode
-                                )
+                            # spaCy already split at sentence boundaries; the
+                            # engine only splits on newlines. Commas are never
+                            # used in the engine split pattern (Sentence +
+                            # Comma splits at commas only at subtitle time).
+                            active_split_pattern = "\n"
                         else:
                             self.log_updated.emit(
                                 ("\nspaCy: Fallback to default segmentation...", "grey")
@@ -945,10 +930,10 @@ class ConversionThread(QThread):
 
                     # Print active split pattern used by the TTS engine once for this batch
                     try:
-                        print(f"Using split pattern: {active_split_pattern!r}")
+                        logger.info(f"Using split pattern: {active_split_pattern!r}")
                     except Exception:
-                        # Print must never break processing
-                        print("Using split pattern: (unprintable)")
+                        # Logging must never break processing
+                        logger.warning("Using split pattern: (unprintable)")
 
                     for text_segment in text_segments:
                         def _qt_check_cancel() -> bool:
@@ -1016,18 +1001,26 @@ class ConversionThread(QThread):
                             total_characters=self.total_char_count,
                         )
 
+                        synth_params = SynthParams(
+                            tts_context=self._tts_context,
+                            stats=stats,
+                            check_cancel=_qt_check_cancel,
+                            on_progress=_qt_on_progress,
+                            audio_sink=merged_sink if merge_chapters_at_end else None,
+                            subtitle_mode=self.subtitle_mode,
+                            max_subtitle_words=self.max_subtitle_words,
+                            language=self.lang_code,
+                            use_spacy_segmentation=getattr(self, "use_spacy_segmentation", False),
+                        )
+
                         try:
                             synthesize_text(
                                 text=text_segment,
-                                tts_context=self._tts_context,
+                                params=synth_params,
                                 backend=self.backend,
                                 voice=loaded_voice,
                                 speed=self.speed,
-                                stats=stats,
-                                check_cancel=_qt_check_cancel,
-                                on_progress=_qt_on_progress,
                                 chapter_sink=chapter_sink,
-                                audio_sink=merged_sink if merge_chapters_at_end else None,
                                 on_segment=_qt_on_segment,
                                 split_pattern_override=active_split_pattern,
                             )
@@ -1084,7 +1077,7 @@ class ConversionThread(QThread):
             # --- Outro synthesis ---
             if merge_chapters_at_end:
                 outro_spec = resolve_outro(
-                    extract_metadata_for_file(self.file_name, self.is_direct_text),
+                    self._extracted_metadata,
                     os.path.basename(self.file_name) if self.file_name else "",
                     getattr(self, "read_closing_outro", True),
                     self.voice, self.voice, list(self.voice_cache._cache.keys()),
@@ -1098,17 +1091,20 @@ class ConversionThread(QThread):
                         etr_start_time=self.etr_start_time,
                         total_characters=self.total_char_count,
                     )
+                    outro_synth = SynthParams(
+                        tts_context=self._tts_context,
+                        stats=outro_stats,
+                        check_cancel=lambda: self.cancel_requested,
+                        on_progress=lambda pct, etr: self.progress_updated.emit(pct, etr),
+                        audio_sink=merged_sink,
+                    )
                     run_tts_segment_loop(
                         text=outro_spec.text,
+                        params=outro_synth,
                         backend=self.backend,
                         voice=loaded_outro_voice,
                         speed=self.speed,
                         split_pattern=self.split_pattern,
-                        stats=outro_stats,
-                        check_cancel=lambda: self.cancel_requested,
-                        on_progress=lambda pct, etr: self.progress_updated.emit(pct, etr),
-                        chapter_sink=None,
-                        audio_sink=merged_sink,
                     )
                     self.processed_char_count = outro_stats.processed_chars
                     current_time = outro_stats.current_time
@@ -1122,12 +1118,7 @@ class ConversionThread(QThread):
                     # Add chapters via ExportService (unified with WebUI)
                     if total_chapters > 1:
                         export_svc = ExportService()
-                        metadata_text = read_text_for_metadata(
-                            file_path=self.file_name,
-                            is_direct_text=self.is_direct_text,
-                            direct_text=self.file_name if self.is_direct_text else None,
-                        )
-                        metadata = extract_metadata_from_text(metadata_text) if metadata_text else {}
+                        metadata = dict(getattr(self, "_extracted_metadata", {}))
                         # Convert cover_path from metadata to Path if present
                         cover_path_raw = metadata.pop("cover_path", None)
                         cover_path = Path(cover_path_raw) if cover_path_raw and os.path.exists(cover_path_raw) else None
@@ -1366,33 +1357,28 @@ class ConversionThread(QThread):
             raise ValueError(f"Unsupported output format: {self.output_format}")
 
     def _extract_and_add_metadata_tags_to_ffmpeg_cmd(self):
-        """Extract metadata tags from text content and add them to ffmpeg command"""
-        # Read text for metadata extraction
-        text = read_text_for_metadata(
-            file_path=self.file_name,
-            is_direct_text=self.is_direct_text,
-            direct_text=self.file_name if self.is_direct_text else None,
-        )
-        
-        if not text:
+        """Build ffmpeg metadata args from previously extracted metadata."""
+        metadata = getattr(self, "_extracted_metadata", None)
+        if not metadata or not any(metadata.values()):
             self.log_updated.emit(
-                ("Warning: Could not read file for metadata extraction", "orange")
+                ("Warning: No metadata tags found in text", "orange")
             )
             return [], None
         
-        # Extract metadata and build ffmpeg args
         filename = self.file_name if self.is_direct_text else (
             self.display_path if self.display_path else self.file_name
         )
         
         try:
-            metadata_options, cover_path = extract_metadata_and_build_args(
-                text=text,
-                filename=filename,
+            from abogen.domain.metadata_extraction import build_ffmpeg_metadata_args, get_filename_from_path
+            actual_filename = get_filename_from_path(
+                file_path=filename,
                 display_path=getattr(self, "display_path", None),
                 from_queue=getattr(self, "from_queue", False),
             )
-            return metadata_options, cover_path
+            args = build_ffmpeg_metadata_args(metadata, actual_filename)
+            cover_path = metadata.get("cover_path")
+            return args, cover_path
         except Exception as e:
             self.log_updated.emit(
                 (f"Warning: Metadata extraction error: {e}", "orange")
@@ -1456,7 +1442,7 @@ class VoicePreviewThread(QThread):
         return os.path.join(self.cache_dir, filename)
 
     def run(self):
-        print(
+        logger.info(
             f"\nVoice: {self.voice}\nLanguage: {self.lang_code}\nSpeed: {self.speed}\nGPU: {self.use_gpu}\n"
         )
 

abogen/pyqt/conversion_adapter.py

@@ -0,0 +1,209 @@
+"""PyQt adapter: ConversionThread -> ConversionRequest.
+
+Converts a PyQt ConversionThread into a ConversionRequest that the application layer can process.
+This adapter is the bridge between the PyQt layer and the application/domain layer.
+
+The adapter is responsible for:
+- Mapping ConversionThread fields to ConversionRequest fields
+- Handling UI-specific state (signals, dialogs, cancellation)
+- Providing PipelineProvider and VoiceResolver implementations
+
+Subtitle file/timestamp special paths remain in ConversionThread.run() early return.
+"""
+
+from __future__ import annotations
+
+import os
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Optional
+
+from abogen.application.conversion_config import (
+    ChapterChunkConfig,
+    Epub3ExportConfig,
+    PronunciationConfig,
+    WordSubstitutionConfig,
+)
+from abogen.application.conversion_request import ConversionRequest
+from abogen.application.conversion_ports import ConversionCancelled, ResolvedVoice
+
+
+def build_conversion_request_from_thread(thread: Any) -> ConversionRequest:
+    """Convert a PyQt ConversionThread into a ConversionRequest.
+
+    This is the primary function that maps thread fields to ConversionRequest.
+    All fields are copied — the request is independent of the thread.
+
+    Args:
+        thread: PyQt ConversionThread instance
+
+    Returns:
+        ConversionRequest with all thread data mapped
+    """
+    # Determine source path
+    source_path = None
+    is_direct_text = getattr(thread, "is_direct_text", False)
+    if not is_direct_text and thread.file_name:
+        source_path = Path(thread.file_name)
+
+    # Determine original filename
+    original_filename = ""
+    if getattr(thread, "from_queue", False):
+        base_path = getattr(thread, "save_base_path", None) or thread.file_name
+    else:
+        base_path = getattr(thread, "display_path", None) or thread.file_name
+
+    if base_path:
+        original_filename = os.path.basename(base_path)
+
+    # Determine output folder
+    output_folder = None
+    if thread.output_folder:
+        output_folder = Path(thread.output_folder)
+
+    # Build pronunciation config
+    pronunciation = None
+    pron_overrides = getattr(thread, "pronunciation_overrides", []) or []
+    manual_overrides = getattr(thread, "manual_overrides", []) or []
+    heteronym_overrides = getattr(thread, "heteronym_overrides", []) or []
+    norm_overrides = getattr(thread, "normalization_overrides", None)
+    if pron_overrides or manual_overrides or heteronym_overrides or norm_overrides:
+        pronunciation = PronunciationConfig(
+            pronunciation_overrides=pron_overrides,
+            manual_overrides=manual_overrides,
+            heteronym_overrides=heteronym_overrides,
+            normalization_overrides=norm_overrides,
+        )
+
+    # Build epub3 config
+    epub3_export = None
+    if getattr(thread, "generate_epub3", False):
+        epub3_export = Epub3ExportConfig()
+
+    return ConversionRequest(
+        # Source
+        source_path=source_path,
+        direct_text=thread.file_name if is_direct_text else None,
+        original_filename=original_filename,
+        # TTS Settings
+        language=thread.lang_code,
+        tts_provider="kokoro",  # PyQt uses Kokoro by default
+        voice=thread.voice,
+        voice_profile=getattr(thread, "voice_profile", None),
+        speed=thread.speed,
+        use_gpu=thread.use_gpu,
+        supertonic_total_steps=getattr(thread, "supertonic_total_steps", 5),
+        # Output Format
+        output_format=thread.output_format,
+        subtitle_mode=thread.subtitle_mode,
+        subtitle_format=getattr(thread, "subtitle_format", "srt"),
+        max_subtitle_words=getattr(thread, "max_subtitle_words", 50),
+        # Save Options
+        save_mode=thread.save_option,
+        output_folder=output_folder,
+        save_chapters_separately=getattr(thread, "save_chapters_separately", False),
+        merge_chapters_at_end=getattr(thread, "merge_chapters_at_end", True),
+        separate_chapters_format=getattr(thread, "separate_chapters_format", "wav"),
+        save_as_project=getattr(thread, "save_as_project", False),
+        # Timing
+        silence_between_chapters=getattr(thread, "silence_duration", 2.0),
+        chapter_intro_delay=getattr(thread, "chapter_intro_delay", 0.0),
+        # Content Processing
+        replace_single_newlines=getattr(thread, "replace_single_newlines", False),
+        read_title_intro=getattr(thread, "read_title_intro", False),
+        read_closing_outro=getattr(thread, "read_closing_outro", True),
+        auto_prefix_chapter_titles=getattr(thread, "auto_prefix_chapter_titles", True),
+        normalize_chapter_opening_caps=thread.normalize_chapter_opening_caps,
+        # Metadata
+        metadata_tags=getattr(thread, "metadata_tags", {}) or {},
+        # Artifacts
+        cover_image_path=getattr(thread, "cover_image_path", None),
+        cover_image_mime=getattr(thread, "cover_image_mime", None),
+        # Feature configs
+        pronunciation=pronunciation,
+        epub3_export=epub3_export,
+        chapter_chunk=ChapterChunkConfig(),  # PyQt doesn't use chapter overrides from GUI
+    )
+
+
+class PyQtEvents:
+    """PyQt implementation of ConversionEvents protocol.
+
+    Wraps a ConversionThread to provide logging, progress, and cancellation.
+    """
+
+    def __init__(self, thread: Any):
+        self._thread = thread
+
+    def log(self, message: str, level: str = "info") -> None:
+        """Log a message via signal."""
+        self._thread.log_updated.emit((message, _level_to_color(level)))
+
+    def progress(self, pct: int, etr: str) -> None:
+        """Update progress via signal."""
+        self._thread.progress_updated.emit(pct, etr)
+
+    def check_cancelled(self) -> None:
+        """Check if conversion was cancelled.
+
+        Raises:
+            ConversionCancelled: If cancellation was requested
+        """
+        if self._thread.cancel_requested:
+            raise ConversionCancelled("Conversion cancelled by user")
+
+
+class PyQtPipelineProvider:
+    """PyQt implementation of PipelineProvider protocol.
+
+    Wraps the existing backend from ConversionThread.
+    """
+
+    def __init__(self, backend: Any):
+        self._backend = backend
+
+    def get(self, provider: str, language: str, use_gpu: bool) -> Any:
+        """Get a TTS backend instance.
+
+        For PyQt, this returns the pre-initialized backend.
+        """
+        return self._backend
+
+    def dispose_all(self) -> None:
+        """Dispose all backend resources."""
+        pass  # PyQt manages backend lifecycle in thread
+
+
+class PyQtVoiceResolver:
+    """PyQt implementation of VoiceResolver protocol.
+
+    Wraps load_voice_cached from the ConversionThread.
+    """
+
+    def __init__(self, thread: Any):
+        self._thread = thread
+
+    def resolve(self, voice_spec: str) -> ResolvedVoice:
+        """Resolve a voice spec into a loaded voice."""
+        from abogen.domain.voice_resolution import resolve_fallback_voice_spec
+
+        # Use thread's load_voice_cached method
+        loaded_voice = self._thread.load_voice_cached(voice_spec, self._thread.backend)
+
+        return ResolvedVoice(
+            provider="kokoro",
+            resolved_spec=voice_spec,
+            voice=loaded_voice,
+            speed=self._thread.speed,
+            supertonic_steps=getattr(self._thread, "supertonic_total_steps", 5),
+        )
+
+
+def _level_to_color(level: str) -> str:
+    """Map log level to PyQt color string."""
+    colors = {
+        "info": "grey",
+        "warning": "orange",
+        "error": "red",
+        "debug": "grey",
+    }
+    return colors.get(level, "grey")

abogen/pyqt/gui.py

@@ -5,9 +5,12 @@ import tempfile
 import platform
 import base64
 import re
+import logging
 from abogen.pyqt.queue_manager_gui import QueueManager
 from abogen.pyqt.queued_item import QueuedItem
 
+_log = logging.getLogger("abogen.gui")
+
 import abogen.hf_tracker as hf_tracker
 import hashlib  # Added for cache path generation
 from PyQt6.QtWidgets import (
@@ -70,13 +73,12 @@ from abogen.utils import (
     LoadPipelineThread,
 )
 
-from abogen.subtitle_utils import (
-    clean_text,
-    calculate_text_length,
-)
+from abogen.subtitle_utils import clean_text
+from abogen.domain.text_utils import calculate_text_length
 
 from abogen.pyqt.conversion import ConversionThread, VoicePreviewThread, PlayAudioThread, ChapterOptionsDialog, TimestampDetectionDialog
 from abogen.pyqt.book_handler import HandlerDialog
+from abogen.domain.enums import Language
 from abogen.constants import (
     PROGRAM_NAME,
     VERSION,
@@ -90,8 +92,9 @@ from abogen.constants import (
 from abogen.tts_plugin.utils import get_voices
 import threading
 from abogen.pyqt.voice_formula_gui import VoiceFormulaDialog
-from abogen.voice_profiles import load_profiles
+from abogen.voice_profiles import load_profiles, resolve_profile_language
 from abogen.domain.settings_core import all_settings_defaults
+from plugins.kokoro.engine import language_for_code, language_for_voice_id
 
 # Module-level default cache for use outside __init__
 _DEFAULTS = all_settings_defaults()
@@ -134,6 +137,28 @@ class ThreadSafeLogSignal(QObject):
         self.log_signal.emit(message)
 
 
+_UPDATE_CHECK_URL = "https://raw.githubusercontent.com/denizsafak/abogen/refs/heads/main/abogen/VERSION"
+_UPDATE_CHECK_TIMEOUT = 8  # seconds; bounds offline/DNS hangs so the GUI never blocks
+
+
+class _UpdateCheckThread(QThread):
+    """Fetch the remote VERSION file off the GUI thread."""
+
+    succeeded = pyqtSignal(str)
+    failed = pyqtSignal(str)
+
+    def run(self):
+        import urllib.request
+
+        try:
+            with urllib.request.urlopen(
+                _UPDATE_CHECK_URL, timeout=_UPDATE_CHECK_TIMEOUT
+            ) as response:
+                self.succeeded.emit(response.read().decode().strip())
+        except Exception as exc:  # offline, DNS hang, HTTP error, ...
+            self.failed.emit(str(exc))
+
+
 class IconProvider(QFileIconProvider):
     def icon(self, fileInfo):
         return super().icon(fileInfo)
@@ -399,11 +424,7 @@ class InputBox(QLabel):
         # Re-enable subtitle and replace newlines controls when cleared
         window = self.window()
         if hasattr(window, "subtitle_combo"):
-            # Only enable if language supports it
-            current_lang = getattr(window, "selected_lang", "a")
-            window.subtitle_combo.setEnabled(
-                current_lang in SUPPORTED_LANGUAGES_FOR_SUBTITLE_GENERATION
-            )
+            window.subtitle_combo.setEnabled(True)
         if hasattr(window, "replace_newlines_combo"):
             window.replace_newlines_combo.setEnabled(True)
 
@@ -842,7 +863,7 @@ class WordSubstitutionsDialog(QDialog):
             self,
         )
         instructions.setStyleSheet(
-            "padding: 10px; background-color: #f0f0f0; border-radius: 5px;"
+            f"padding: 10px; background-color: {COLORS['GREY_BACKGROUND']}; border-radius: 5px;"
         )
         instructions.setWordWrap(True)
         layout.addWidget(instructions)
@@ -943,7 +964,7 @@ class abogen(QWidget):
             self.selected_lang = None
         else:
             self.selected_voice = self.config.get("selected_voice", _d["selected_voice"])
-            self.selected_lang = self.selected_voice[0] if self.selected_voice else None
+            self.selected_lang = language_for_voice_id(self.selected_voice)
         self.is_converting = False
         self.subtitle_mode = self.config.get("subtitle_mode", _d["subtitle_mode"])
         self.max_subtitle_words = self.config.get("max_subtitle_words", _d["max_subtitle_words"])
@@ -990,7 +1011,12 @@ class abogen(QWidget):
         self.queued_items = []
         self.current_queue_index = 0
 
-        self.initUI()
+        from abogen.utils import timed_log
+        import logging
+        _startup_log = logging.getLogger("abogen.startup")
+
+        with timed_log("GUI initUI (widget building)", logger=_startup_log):
+            self.initUI()
         self.speed_slider.setValue(int(self.config.get("speed", _d["speed"]) * 100))
         self.update_speed_label()
         # Set initial selection: prefer profile, else voice
@@ -1005,13 +1031,17 @@ class abogen(QWidget):
             if self.selected_profile_name:
                 from abogen.voice_profiles import load_profiles
 
-                entry = load_profiles().get(self.selected_profile_name, {})
+                with timed_log("voice profile load", logger=_startup_log):
+                    entry = load_profiles().get(self.selected_profile_name, {})
                 if isinstance(entry, dict):
                     self.mixed_voice_state = entry.get("voices", [])
-                    self.selected_lang = entry.get("language")
+                    self.selected_lang = resolve_profile_language(entry)
                 else:
                     self.mixed_voice_state = entry
-                    self.selected_lang = entry[0][0] if entry and entry[0] else None
+                    self.selected_lang = (
+                        language_for_voice_id(entry[0]) if entry and entry[0] else Language.EN_US
+                    )
+        self.update_subtitle_options_availability()
         if self.save_option == "Choose output folder" and self.selected_output_folder:
             self.save_path_label.setText(self.selected_output_folder)
             self.save_path_row_widget.show()
@@ -1177,6 +1207,7 @@ class abogen(QWidget):
             "Sentence + Comma: Subtitles will be generated for each sentence and comma.\n"
             "Sentence + Highlighting: Subtitles with word-by-word karaoke highlighting.\n"
             "1+ word: Subtitles will be generated for each word(s).\n\n"
+            "Word-count and highlighting modes are only available for English.\n"
             "Supported languages for subtitle generation:\n"
             + "\n".join(
                 f'"{lang}" => {LANGUAGE_DESCRIPTIONS.get(lang, lang)}'
@@ -1755,8 +1786,9 @@ class abogen(QWidget):
 
     def update_subtitle_options_availability(self):
         """
-        Update the enabled state of subtitle options based on the selected language.
-        For non-English languages, only sentence-based and line-based modes are supported.
+        Update the enabled state of subtitle options based on the selected
+        language and input type. Subtitle generation works for every language,
+        but word-count and highlighting modes are only available for English.
         """
         # Check if current file is a subtitle file
         is_subtitle_input = False
@@ -1765,16 +1797,14 @@ class abogen(QWidget):
         ):
             is_subtitle_input = True
 
-        if self.selected_lang not in SUPPORTED_LANGUAGES_FOR_SUBTITLE_GENERATION:
-            self.subtitle_combo.setEnabled(False)
-            self.subtitle_format_combo.setEnabled(False)
-            return
-
         # Only enable subtitle_combo if it's NOT a subtitle input
         self.subtitle_combo.setEnabled(not is_subtitle_input)
         self.subtitle_format_combo.setEnabled(True)
 
-        is_english = self.selected_lang in ["a", "b"]
+        is_english = self.selected_lang in (
+            Language.EN_US,
+            Language.EN_GB,
+        )
 
         # Items to keep enabled for non-English
         allowed_modes = ["Disabled", "Line", "Sentence", "Sentence + Comma"]
@@ -1789,10 +1819,7 @@ class abogen(QWidget):
             if is_english:
                 item.setEnabled(True)
             else:
-                if text in allowed_modes:
-                    item.setEnabled(True)
-                else:
-                    item.setEnabled(False)
+                item.setEnabled(text in allowed_modes)
 
         # If current selection is disabled, switch to a valid one
         current_text = self.subtitle_combo.currentText()
@@ -1811,7 +1838,7 @@ class abogen(QWidget):
 
     def on_voice_changed(self, index):
         voice = self.voice_combo.itemData(index)
-        self.selected_voice, self.selected_lang = voice, voice[0]
+        self.selected_voice, self.selected_lang = voice, language_for_voice_id(voice)
         self.config["selected_voice"] = voice
         save_config(self.config)
         # Enable/disable subtitle options based on language
@@ -1828,10 +1855,12 @@ class abogen(QWidget):
             # set mixed voices and language
             if isinstance(entry, dict):
                 self.mixed_voice_state = entry.get("voices", [])
-                self.selected_lang = entry.get("language")
+                self.selected_lang = resolve_profile_language(entry)
             else:
                 self.mixed_voice_state = entry
-                self.selected_lang = entry[0][0] if entry and entry[0] else None
+                self.selected_lang = (
+                    language_for_voice_id(entry[0]) if entry and entry[0] else Language.EN_US
+                )
             self.selected_voice = None
             self.config["selected_profile_name"] = pname
             self.config.pop("selected_voice", None)
@@ -1841,7 +1870,7 @@ class abogen(QWidget):
         else:
             self.mixed_voice_state = None
             self.selected_profile_name = None
-            self.selected_voice, self.selected_lang = data, data[0]
+            self.selected_voice, self.selected_lang = data, language_for_voice_id(data)
             self.config["selected_voice"] = data
             if "selected_profile_name" in self.config:
                 del self.config["selected_profile_name"]
@@ -1852,8 +1881,9 @@ class abogen(QWidget):
         from abogen.voice_profiles import load_profiles
 
         entry = load_profiles().get(profile_name, {})
-        lang = entry.get("language") if isinstance(entry, dict) else None
-        enable = lang in SUPPORTED_LANGUAGES_FOR_SUBTITLE_GENERATION
+        enable = (
+            resolve_profile_language(entry) in SUPPORTED_LANGUAGES_FOR_SUBTITLE_GENERATION
+        )
         self.subtitle_combo.setEnabled(enable)
         self.subtitle_format_combo.setEnabled(enable)
 
@@ -2235,18 +2265,18 @@ class abogen(QWidget):
         else:
             return self.selected_voice
 
-    def get_selected_lang(self, voice_formula) -> str:
+    def get_selected_lang(self, voice_formula) -> Language:
         if self.selected_profile_name:
             from abogen.voice_profiles import load_profiles
 
             entry = load_profiles().get(self.selected_profile_name, {})
-            selected_lang = entry.get("language")
+            selected_lang = resolve_profile_language(entry)
         else:
-            selected_lang = self.selected_voice[0] if self.selected_voice else None
+            selected_lang = language_for_voice_id(self.selected_voice)
         # fallback: extract from formula if missing
         if not selected_lang:
             m = re.search(r"\b([a-z])", voice_formula)
-            selected_lang = m.group(1) if m else None
+            selected_lang = language_for_code(m.group(1)) if m else Language.EN_US
         return selected_lang
 
     def get_actual_subtitle_mode(self) -> str:
@@ -2422,7 +2452,7 @@ class abogen(QWidget):
             self.update_log((gpu_msg, gpu_ok))
             self.update_log("Loading modules...")
 
-            lang_code = self.selected_lang or "a"
+            lang_code = self.selected_lang or Language.EN_US
             load_thread = LoadPipelineThread(
                 pipeline_loaded_callback, lang_code=lang_code, use_gpu=gpu_ok
             )
@@ -2753,12 +2783,12 @@ class abogen(QWidget):
                 from abogen.voice_profiles import load_profiles
 
                 entry = load_profiles().get(self.selected_profile_name, {})
-                lang_to_cache = entry.get("language")
+                lang_to_cache = resolve_profile_language(entry)
             else:
                 lang_to_cache = self.selected_lang
             if not lang_to_cache and self.mixed_voice_state:
                 lang_to_cache = (
-                    self.mixed_voice_state[0][0][0]
+                    language_for_voice_id(self.mixed_voice_state[0][0])
                     if self.mixed_voice_state and self.mixed_voice_state[0][0]
                     else None
                 )
@@ -2862,7 +2892,7 @@ class abogen(QWidget):
             )
             self.loading_movie.start()
 
-        lang = self.selected_lang or "a"
+        lang = self.selected_lang or Language.EN_US
         load_thread = LoadPipelineThread(
             self._on_pipeline_loaded_for_preview, lang_code=lang, use_gpu=self.gpu_ok
         )
@@ -2894,17 +2924,17 @@ class abogen(QWidget):
                 from abogen.voice_profiles import load_profiles
 
                 entry = load_profiles().get(self.selected_profile_name, {})
-                lang = entry.get("language")
+                lang = resolve_profile_language(entry)
             else:
                 lang = self.selected_lang
             if not lang and self.mixed_voice_state:
                 lang = (
-                    self.mixed_voice_state[0][0][0]
+                    language_for_voice_id(self.mixed_voice_state[0][0])
                     if self.mixed_voice_state and self.mixed_voice_state[0][0]
                     else None
                 )
         else:
-            lang = self.selected_voice[0]
+            lang = language_for_voice_id(self.selected_voice)
             voice = self.selected_voice
 
         # use same gpu/cpu logic as in conversion
@@ -3165,14 +3195,25 @@ class abogen(QWidget):
             save_config(self.config)
 
     def cleanup_conversion_thread(self):
-        # Stop conversion thread
+        # Stop conversion thread (bounded wait so closing never hangs)
         if (
             hasattr(self, "conversion_thread")
             and self.conversion_thread is not None
             and self.conversion_thread.isRunning()
         ):
+            _log.info("Close: stopping conversion thread")
+            start = time.perf_counter()
             self.conversion_thread.cancel()
-            self.conversion_thread.wait()
+            if not self.conversion_thread.wait(2000):
+                _log.warning("Close: conversion thread did not stop in 2s, terminating")
+                self.conversion_thread.terminate()
+                self.conversion_thread.wait(1000)
+            _log.info(
+                "Close: conversion thread stopped in %.2fs",
+                time.perf_counter() - start,
+            )
+        else:
+            _log.info("Close: no running conversion thread")
 
     def cleanup_preview_threads(self):
         # Stop preview generation thread
@@ -3181,8 +3222,13 @@ class abogen(QWidget):
             and self.preview_thread is not None
             and self.preview_thread.isRunning()
         ):
+            _log.info("Close: terminating preview thread")
+            start = time.perf_counter()
             self.preview_thread.terminate()
-            self.preview_thread.wait()
+            self.preview_thread.wait(1000)
+            _log.info(
+                "Close: preview thread stopped in %.2fs", time.perf_counter() - start
+            )
 
         # Stop audio playback thread
         if (
@@ -3190,8 +3236,13 @@ class abogen(QWidget):
             and self.play_audio_thread is not None
             and self.play_audio_thread.isRunning()
         ):
+            _log.info("Close: stopping audio playback thread")
+            start = time.perf_counter()
             self.play_audio_thread.stop()
-            self.play_audio_thread.wait()
+            self.play_audio_thread.wait(1000)
+            _log.info(
+                "Close: audio thread stopped in %.2fs", time.perf_counter() - start
+            )
 
         # Cleanup pygame mixer if initialized
         try:
@@ -3202,6 +3253,7 @@ class abogen(QWidget):
             pass
 
     def closeEvent(self, event):
+        _log.info("Close: window close requested (converting=%s)", self.is_converting)
         if self.is_converting:
             box = QMessageBox(self)
             box.setIcon(QMessageBox.Icon.Warning)
@@ -3214,16 +3266,14 @@ class abogen(QWidget):
             )
             box.setDefaultButton(QMessageBox.StandardButton.No)
             if box.exec() == QMessageBox.StandardButton.Yes:
-                from abogen import shutdown
-                shutdown.request_shutdown()
+                _log.info("Close: user confirmed exit during conversion")
                 self.cleanup_conversion_thread()
                 self.cleanup_preview_threads()
                 event.accept()
             else:
+                _log.info("Close: user cancelled exit")
                 event.ignore()
         else:
-            from abogen import shutdown
-            shutdown.request_shutdown()
             self.cleanup_conversion_thread()
             self.cleanup_preview_threads()
             event.accept()
@@ -3950,7 +4000,9 @@ Categories=AudioVideo;Audio;Utility;
                     initial_state = entry.get("voices", [])
                 else:
                     initial_state = entry
-                    self.selected_lang = entry[0][0] if entry and entry[0] else None
+                    self.selected_lang = (
+                        language_for_voice_id(entry[0]) if entry and entry[0] else Language.EN_US
+                    )
         dialog = VoiceFormulaDialog(
             self, initial_state=initial_state, selected_profile=selected_profile
         )
@@ -4047,75 +4099,85 @@ Categories=AudioVideo;Audio;Utility;
         self.check_for_updates_startup()
 
     def check_for_updates_startup(self):
-        import urllib.request
-
-        def show_update_message(remote_version, local_version):
-            msg_box = QMessageBox(self)
-            msg_box.setIcon(QMessageBox.Icon.Information)
-            msg_box.setWindowTitle("Update Available")
-            msg_box.setText(
-                f"A new version of {PROGRAM_NAME} is available! ({local_version} > {remote_version})"
-            )
-            msg_box.setInformativeText(
-                f"If you installed via pip, update by running:\n"
-                f"pip install --upgrade {PROGRAM_NAME}\n\n"
-                f"If you're using the Windows portable version, run 'WINDOWS_INSTALL.bat' again.\n\n"
-                "Alternatively, visit the GitHub repository for more information. "
-                "Would you like to view the changelog?"
-            )
-            msg_box.setStandardButtons(
-                QMessageBox.StandardButton.Yes | QMessageBox.StandardButton.No
-            )
-            msg_box.setDefaultButton(QMessageBox.StandardButton.Yes)
-            if msg_box.exec() == QMessageBox.StandardButton.Yes:
-                try:
-                    QDesktopServices.openUrl(QUrl(GITHUB_URL + "/releases/latest"))
-                except Exception:
-                    pass
-
-        # Reset flag to track if we should show "no updates" message
+        # Network I/O runs in a worker thread: urlopen without a timeout on
+        # the GUI thread froze the whole app when offline (DNS/connect can
+        # hang for minutes). Results return via signals on the GUI thread.
+        thread = getattr(self, "_update_check_thread", None)
+        if thread is not None:
+            try:
+                if thread.isRunning():
+                    return
+            except RuntimeError:
+                pass  # previous thread already finished/deleted
         show_result = (
             hasattr(self, "_show_update_check_result")
             and self._show_update_check_result
         )
         self._show_update_check_result = False
+        self._update_check_thread = _UpdateCheckThread(self)
+        self._update_check_thread.succeeded.connect(
+            lambda remote_raw: self._on_update_check_done(remote_raw, show_result)
+        )
+        self._update_check_thread.failed.connect(
+            lambda err: self._on_update_check_failed(err, show_result)
+        )
+        self._update_check_thread.finished.connect(
+            self._update_check_thread.deleteLater
+        )
+        self._update_check_thread.start()
 
+    def _on_update_check_done(self, remote_raw, show_result):
+        remote_version = remote_raw.strip()
+        local_version = VERSION
         try:
-            update_url = "https://raw.githubusercontent.com/denizsafak/abogen/refs/heads/main/abogen/VERSION"
-            with urllib.request.urlopen(update_url) as response:
-                remote_raw = response.read().decode().strip()
-            local_raw = VERSION
+            remote_num = int("".join(remote_version.split(".")))
+            local_num = int("".join(local_version.split(".")))
+        except ValueError:
+            return
+        if remote_num > local_num:
+            # Use QTimer to ensure UI is ready, then show update message.
+            QTimer.singleShot(
+                1000,
+                lambda: self._show_update_message(remote_version, local_version),
+            )
+        elif show_result:
+            QMessageBox.information(
+                self,
+                "Up to Date",
+                f"You are running the latest version of {PROGRAM_NAME} ({local_version}).",
+            )
 
-            # Parse version numbers
-            remote_version = remote_raw
-            local_version = local_raw
+    def _on_update_check_failed(self, err, show_result):
+        if show_result:
+            QMessageBox.warning(
+                self,
+                "Update Check Failed",
+                f"Could not check for updates:\n{err}",
+            )
 
+    def _show_update_message(self, remote_version, local_version):
+        msg_box = QMessageBox(self)
+        msg_box.setIcon(QMessageBox.Icon.Information)
+        msg_box.setWindowTitle("Update Available")
+        msg_box.setText(
+            f"A new version of {PROGRAM_NAME} is available! ({local_version} > {remote_version})"
+        )
+        msg_box.setInformativeText(
+            f"If you installed via pip, update by running:\n"
+            f"pip install --upgrade {PROGRAM_NAME}\n\n"
+            f"If you're using the Windows portable version, run 'WINDOWS_INSTALL.bat' again.\n\n"
+            "Alternatively, visit the GitHub repository for more information. "
+            "Would you like to view the changelog?"
+        )
+        msg_box.setStandardButtons(
+            QMessageBox.StandardButton.Yes | QMessageBox.StandardButton.No
+        )
+        msg_box.setDefaultButton(QMessageBox.StandardButton.Yes)
+        if msg_box.exec() == QMessageBox.StandardButton.Yes:
             try:
-                remote_num = int("".join(remote_version.split(".")))
-                local_num = int("".join(local_version.split(".")))
-            except ValueError as ve:
-                return
-
-            if remote_num > local_num:
-                # Use QTimer to ensure UI is ready, then show update message.
-                QTimer.singleShot(
-                    1000, lambda: show_update_message(remote_version, local_version)
-                )
-            elif show_result:
-                # Show "no updates" message if manually checking
-                QMessageBox.information(
-                    self,
-                    "Up to Date",
-                    f"You are running the latest version of {PROGRAM_NAME} ({local_version}).",
-                )
-        except Exception as e:
-            if show_result:
-                QMessageBox.warning(
-                    self,
-                    "Update Check Failed",
-                    f"Could not check for updates:\n{str(e)}",
-                )
-            pass
+                QDesktopServices.openUrl(QUrl(GITHUB_URL + "/releases/latest"))
+            except Exception:
+                pass
 
     def clear_cache_files(self):
         """Clear cache files created by the program."""
@@ -4218,8 +4280,6 @@ Categories=AudioVideo;Audio;Utility;
 
     def set_max_log_lines(self):
         """Open a dialog to set the maximum lines in the log window."""
-        from PyQt6.QtWidgets import QInputDialog
-
         value, ok = QInputDialog.getInt(
             self,
             "Max Lines in Log Window",
@@ -4241,8 +4301,6 @@ Categories=AudioVideo;Audio;Utility;
 
     def set_max_subtitle_words(self):
         """Open a dialog to set the maximum words per subtitle"""
-        from PyQt6.QtWidgets import QInputDialog
-
         current_value = self.config.get("max_subtitle_words", _DEFAULTS["max_subtitle_words"])
 
         value, ok = QInputDialog.getInt(

abogen/pyqt/main.py

@@ -1,3 +1,4 @@
+import logging
 import os
 import sys
 import platform
@@ -6,101 +7,113 @@ import platform
 from abogen import shutdown  # noqa: F401
 shutdown.register_shutdown()
 
+from abogen.utils import get_resource_path, setup_console_logging, timed_log  # noqa: E402
+
+_log = logging.getLogger("abogen.startup")
+setup_console_logging()
+
 # Fix PyTorch DLL loading issue ([WinError 1114]) on Windows before importing PyQt6
 if platform.system() == "Windows":
-    import ctypes
-    from importlib.util import find_spec
+    with timed_log("PyTorch DLLs (Windows)", logger=_log):
+        import ctypes
+        from importlib.util import find_spec
 
-    try:
-        if (
-            (spec := find_spec("torch"))
-            and spec.origin
-            and os.path.exists(
-                dll_path := os.path.join(os.path.dirname(spec.origin), "lib", "c10.dll")
-            )
-        ):
-            ctypes.CDLL(os.path.normpath(dll_path))
-    except Exception:
-        pass
+        try:
+            if (
+                (spec := find_spec("torch"))
+                and spec.origin
+                and os.path.exists(
+                    dll_path := os.path.join(os.path.dirname(spec.origin), "lib", "c10.dll")
+                )
+            ):
+                ctypes.CDLL(os.path.normpath(dll_path))
+        except Exception:
+            pass
 
 
 # Qt platform plugin detection (fixes #59)
-try:
-    from PyQt6.QtCore import QLibraryInfo
+with timed_log("Qt platform plugin detection", logger=_log):
+    try:
+        from PyQt6.QtCore import QLibraryInfo
 
-    # Get the path to the plugins directory
-    plugins = QLibraryInfo.path(QLibraryInfo.LibraryPath.PluginsPath)
+        # Get the path to the plugins directory
+        plugins = QLibraryInfo.path(QLibraryInfo.LibraryPath.PluginsPath)
 
-    # Normalize path to use the OS-native separators and absolute path
-    platform_dir = os.path.normpath(os.path.join(plugins, "platforms"))
+        # Normalize path to use the OS-native separators and absolute path
+        platform_dir = os.path.normpath(os.path.join(plugins, "platforms"))
 
-    # Ensure we work with an absolute path for clarity
-    platform_dir = os.path.abspath(platform_dir)
+        # Ensure we work with an absolute path for clarity
+        platform_dir = os.path.abspath(platform_dir)
 
-    if os.path.isdir(platform_dir):
-        os.environ["QT_QPA_PLATFORM_PLUGIN_PATH"] = platform_dir
-        print("QT_QPA_PLATFORM_PLUGIN_PATH set to:", platform_dir)
-    else:
-        print("PyQt6 platform plugins not found at", platform_dir)
-except ImportError:
-    print("PyQt6 not installed.")
+        if os.path.isdir(platform_dir):
+            os.environ["QT_QPA_PLATFORM_PLUGIN_PATH"] = platform_dir
+            _log.info("QT_QPA_PLATFORM_PLUGIN_PATH set to: %s", platform_dir)
+        else:
+            _log.warning("PyQt6 platform plugins not found at %s", platform_dir)
+    except ImportError:
+        _log.warning("PyQt6 not installed.")
 
 
 # Pre-load "libxcb-cursor" on Linux (fixes #101)
 if platform.system() == "Linux":
-    arch = platform.machine().lower()
-    lib_filename = {"x86_64": "libxcb-cursor-amd64.so.0", "amd64": "libxcb-cursor-amd64.so.0", "aarch64": "libxcb-cursor-arm64.so.0", "arm64": "libxcb-cursor-arm64.so.0"}.get(arch)
-    if lib_filename:
-        import ctypes
-        try:
-            # Try to load the system libxcb-cursor.so.0 first
-            ctypes.CDLL('libxcb-cursor.so.0', mode=ctypes.RTLD_GLOBAL)
-        except OSError:
-            # System lib not available, load the bundled version
-            lib_path = get_resource_path('abogen.libs', lib_filename)
-            if lib_path:
-                try:
-                    ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
-                except OSError:
-                    # If it fails (e.g. wrong glibc version on very old systems),
-                    # we simply ignore it and hope the system has the library.
-                    pass
+    with timed_log("libxcb-cursor preload (Linux)", logger=_log):
+        arch = platform.machine().lower()
+        lib_filename = {"x86_64": "libxcb-cursor-amd64.so.0", "amd64": "libxcb-cursor-amd64.so.0", "aarch64": "libxcb-cursor-arm64.so.0", "arm64": "libxcb-cursor-arm64.so.0"}.get(arch)
+        if lib_filename:
+            import ctypes
+            try:
+                # Try to load the system libxcb-cursor.so.0 first
+                ctypes.CDLL('libxcb-cursor.so.0', mode=ctypes.RTLD_GLOBAL)
+            except OSError:
+                # System lib not available, load the bundled version
+                lib_path = get_resource_path('abogen.libs', lib_filename)
+                if lib_path:
+                    try:
+                        ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
+                    except OSError:
+                        # If it fails (e.g. wrong glibc version on very old systems),
+                        # we simply ignore it and hope the system has the library.
+                        pass
 
 
 # Set application ID for Windows taskbar icon
 if platform.system() == "Windows":
-    try:
-        from abogen.constants import PROGRAM_NAME, VERSION
-        import ctypes
+    with timed_log("Windows AppUserModelID", logger=_log):
+        try:
+            from abogen.constants import PROGRAM_NAME, VERSION
+            import ctypes
 
-        app_id = f"{PROGRAM_NAME}.{VERSION}"
-        ctypes.windll.shell32.SetCurrentProcessExplicitAppUserModelID(app_id)
-    except Exception as e:
-        print("Warning: failed to set AppUserModelID:", e)
+            app_id = f"{PROGRAM_NAME}.{VERSION}"
+            ctypes.windll.shell32.SetCurrentProcessExplicitAppUserModelID(app_id)
+        except Exception as e:
+            _log.warning("Failed to set AppUserModelID: %s", e)
 
-from PyQt6.QtWidgets import QApplication
-from PyQt6.QtGui import QIcon
-from PyQt6.QtCore import (
-    QLibraryInfo,
-    qInstallMessageHandler,
-    QtMsgType,
-)
+with timed_log("PyQt6 imports", logger=_log):
+    from PyQt6.QtWidgets import QApplication
+    from PyQt6.QtGui import QIcon
+    from PyQt6.QtCore import (
+        QLibraryInfo,
+        qInstallMessageHandler,
+        QtMsgType,
+    )
 
 # Add the directory to Python path
 sys.path.insert(0, os.path.join(os.path.dirname(__file__)))
 
 # Set Hugging Face Hub environment variables
-os.environ["HF_HUB_DISABLE_TELEMETRY"] = "1"  # Disable Hugging Face telemetry
-os.environ["HF_HUB_ETAG_TIMEOUT"] = "10"  # Metadata request timeout (seconds)
-os.environ["HF_HUB_DOWNLOAD_TIMEOUT"] = "10"  # File download timeout (seconds)
-os.environ["HF_HUB_DISABLE_SYMLINKS_WARNING"] = "1"  # Disable symlinks warning
-from abogen.utils import load_config
-if load_config().get("disable_kokoro_internet", False):
-    print("INFO: Kokoro's internet access is disabled.")
-    os.environ["HF_HUB_OFFLINE"] = "1"  # Disable Hugging Face Hub internet access
+with timed_log("config load + HF env setup", logger=_log):
+    os.environ["HF_HUB_DISABLE_TELEMETRY"] = "1"  # Disable Hugging Face telemetry
+    os.environ["HF_HUB_ETAG_TIMEOUT"] = "10"  # Metadata request timeout (seconds)
+    os.environ["HF_HUB_DOWNLOAD_TIMEOUT"] = "10"  # File download timeout (seconds)
+    os.environ["HF_HUB_DISABLE_SYMLINKS_WARNING"] = "1"  # Disable symlinks warning
+    from abogen.utils import load_config
+    if load_config().get("disable_kokoro_internet", False):
+        _log.info("Kokoro's internet access is disabled.")
+        os.environ["HF_HUB_OFFLINE"] = "1"  # Disable Hugging Face Hub internet access
 
-from abogen.pyqt.gui import abogen
-from abogen.constants import PROGRAM_NAME, VERSION
+with timed_log("GUI module import (abogen.pyqt.gui)", logger=_log):
+    from abogen.pyqt.gui import abogen
+    from abogen.constants import PROGRAM_NAME, VERSION
 
 # Set environment variables for AMD ROCm
 os.environ["MIOPEN_FIND_MODE"] = "FAST"
@@ -118,6 +131,8 @@ def qt_message_handler(mode, context, message):
         return  # Suppress this specific message
     if "setGrabPopup called with a parent, QtWaylandClient" in message:
         return
+    if "Failed to register with host portal" in message:
+        return
 
     if mode == QtMsgType.QtWarningMsg:
         print(f"Qt Warning: {message}")
@@ -146,7 +161,11 @@ if platform.system() == "Linux":
 
 def main():
     """Main entry point for console usage."""
-    app = QApplication(sys.argv)
+    with timed_log("QApplication creation", logger=_log):
+        app = QApplication(sys.argv)
+
+    # Qt shutdown hook must be connected AFTER QApplication exists
+    shutdown.install_qt_hook()
 
     # Set application icon using get_resource_path from utils
     icon_path = get_resource_path("abogen.assets", "icon.ico")
@@ -160,9 +179,16 @@ def main():
         except AttributeError:
             pass
 
-    ex = abogen()
-    ex.show()
-    sys.exit(app.exec())
+    with timed_log("main window construction", logger=_log):
+        ex = abogen()
+    with timed_log("window show", logger=_log):
+        ex.show()
+    _log.info("App startup complete. Showing window.")
+    rc = app.exec()
+    # Restore the default Qt message handler BEFORE interpreter shutdown.
+    # A Python message handler invoked during Qt teardown segfaults (SIGSEGV).
+    qInstallMessageHandler(None)
+    sys.exit(rc)
 
 
 if __name__ == "__main__":

abogen/pyqt/queue_manager_gui.py

@@ -523,7 +523,7 @@ class QueueManager(QDialog):
         return attrs
 
     def add_files_from_paths(self, file_paths):
-        from abogen.subtitle_utils import calculate_text_length
+        from abogen.domain.text_utils import calculate_text_length
         from PyQt6.QtWidgets import QMessageBox
         import os
 

abogen/pyqt/voice_formula_gui.py

@@ -28,7 +28,6 @@ from PyQt6.QtWidgets import (
 from PyQt6.QtCore import Qt, QTimer, QPoint, QRect, QSize
 from PyQt6.QtGui import QPixmap, QIcon, QAction
 from abogen.constants import (
-    SUPPORTED_LANGUAGES_FOR_SUBTITLE_GENERATION,
     LANGUAGE_DESCRIPTIONS,
     COLORS,
 )
@@ -949,7 +948,9 @@ class VoiceFormulaDialog(QDialog):
         lang = state.get("language") if isinstance(state, dict) else None
         # apply language selection
         if lang:
-            i = self.language_combo.findData(lang)
+            from abogen.voice_profiles import resolve_profile_language
+
+            i = self.language_combo.findData(resolve_profile_language(state))
             if i >= 0:
                 self.language_combo.blockSignals(True)
                 self.language_combo.setCurrentIndex(i)
@@ -1571,9 +1572,10 @@ class VoiceFormulaDialog(QDialog):
             parent.selected_profile_name = None
             lang = self.language_combo.currentData()
             parent.selected_lang = lang
-            parent.subtitle_combo.setEnabled(
-                lang in SUPPORTED_LANGUAGES_FOR_SUBTITLE_GENERATION
-            )
+            if hasattr(parent, "update_subtitle_options_availability"):
+                parent.update_subtitle_options_availability()
+            else:
+                parent.subtitle_combo.setEnabled(True)
             # Reset start flag and trigger preview
             self._started = False
             parent.preview_voice()

abogen/shutdown.py

@@ -1,12 +1,27 @@
-"""Graceful shutdown - single module, no over-engineering."""
+"""Graceful shutdown — process-level hooks and orchestration.
+
+Responsibilities:
+- Install atexit/signal/Qt hooks
+- Stop WebUI ConversionService (worker thread)
+- Restore sleep prevention
+- Terminate child processes (ffmpeg, etc.)
+- Delegate GPU/engine/UI cleanup to application.cleanup
+
+App-layer cleanup (GPU, engines, UI callbacks) lives in application/cleanup.py.
+Per-conversion cleanup lives in run_conversion() finally block.
+"""
+
 from __future__ import annotations
 
 import atexit
-import gc
+import logging
 import signal
 import sys
+import time
 from typing import Callable
 
+_log = logging.getLogger("abogen.shutdown")
+
 _CLEANUP_FUNCS: list[Callable[[], None]] = []
 _EXECUTED = False
 
@@ -21,27 +36,24 @@ def _run_cleanups() -> None:
     if _EXECUTED:
         return
     _EXECUTED = True
+    _log.info("Shutdown: starting %d cleanup hook(s)", len(_CLEANUP_FUNCS))
     for fn in _CLEANUP_FUNCS:
+        start = time.perf_counter()
         try:
             fn()
         except Exception:
             pass
+        _log.info(
+            "Shutdown: %s done in %.2fs", fn.__name__, time.perf_counter() - start
+        )
+    _log.info("Shutdown: all cleanups finished")
 
 
-# ---- Register built-in cleanup functions ----
+# ---- Process-level cleanup functions ----
 
-# 1. Restore sleep prevention
-def _restore_sleep() -> None:
-    try:
-        from abogen.utils import prevent_sleep_end
-        prevent_sleep_end()
-    except Exception:
-        pass
 
-register_cleanup(_restore_sleep)
-
-# 2. Shutdown web UI ConversionService
-def _shutdown_conversion_service() -> None:
+def _stop_conversion_service() -> None:
+    """Stop WebUI ConversionService worker thread."""
     try:
         from abogen.webui.service import get_service
         svc = get_service()
@@ -50,50 +62,18 @@ def _shutdown_conversion_service() -> None:
     except Exception:
         pass
 
-register_cleanup(_shutdown_conversion_service)
 
-# 3. Clear TTS pipelines and GPU memory
-def _cleanup_tts_pipelines() -> None:
-    # Clear web UI pipeline cache
+def _restore_sleep() -> None:
+    """Restore system sleep prevention (caffeinate/systemd-inhibit/Windows)."""
     try:
-        from abogen.webui.conversion_runner import _PIPELINES
-        _PIPELINES.clear()
+        from abogen.utils import prevent_sleep_end
+        prevent_sleep_end()
     except Exception:
         pass
 
-    # Clear PyQt conversion thread voice cache
-    try:
-        from abogen.pyqt.conversion import ConversionThread
-        if hasattr(ConversionThread, "voice_cache"):
-            ConversionThread.voice_cache.clear()
-    except Exception:
-        pass
 
-    gc.collect()
-
-    # Release CUDA cache
-    try:
-        import torch
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-            torch.cuda.ipc_collect()
-    except Exception:
-        pass
-
-register_cleanup(_cleanup_tts_pipelines)
-
-# 4. Clear global voice cache
-def _clear_voice_cache() -> None:
-    try:
-        from abogen.voice_cache import clear_voice_cache
-        clear_voice_cache()
-    except Exception:
-        pass
-
-register_cleanup(_clear_voice_cache)
-
-# 5. Terminate child processes (ffmpeg, etc.)
 def _terminate_subprocesses() -> None:
+    """Terminate all child processes (ffmpeg, etc.)."""
     try:
         import psutil
     except Exception:
@@ -115,6 +95,20 @@ def _terminate_subprocesses() -> None:
     except Exception:
         pass
 
+
+def _app_cleanup() -> None:
+    """Delegate to application-layer cleanup (engines, GPU, UI callbacks)."""
+    try:
+        from abogen.application.cleanup import cleanup
+        cleanup()
+    except Exception:
+        pass
+
+
+# Register in execution order
+register_cleanup(_stop_conversion_service)
+register_cleanup(_app_cleanup)
+register_cleanup(_restore_sleep)
 register_cleanup(_terminate_subprocesses)
 
 
@@ -133,13 +127,19 @@ def register_shutdown() -> None:
         except Exception:
             pass
 
-    # Qt hook
+    install_qt_hook()
+
+
+def install_qt_hook() -> None:
+    """Connect Qt aboutToQuit to cleanup. Must run AFTER QApplication is created."""
     try:
         from PyQt6.QtWidgets import QApplication
 
         app = QApplication.instance()
-        if app is not None:
+        if app is not None and not getattr(app, "_abogen_cleanup_connected", False):
             app.aboutToQuit.connect(_run_cleanups)
+            app._abogen_cleanup_connected = True
+            _log.info("Shutdown: Qt aboutToQuit hook connected")
     except Exception:
         pass
 
@@ -148,13 +148,15 @@ register_shutdown._registered = False
 
 
 def _on_signal(signum: int, _frame) -> None:
+    _log.info("Shutdown: signal %s received", signum)
     _run_cleanups()
     sys.exit(0)
 
 
 def request_shutdown() -> None:
     """Programmatically trigger cleanup (e.g., from GUI closeEvent)."""
+    _log.info("Shutdown: cleanup requested")
     _run_cleanups()
 
 
-__all__ = ["register_shutdown", "request_shutdown", "register_cleanup"]
+__all__ = ["register_shutdown", "install_qt_hook", "request_shutdown", "register_cleanup"]

abogen/spacy_contraction_resolver.py

@@ -6,10 +6,9 @@ from dataclasses import dataclass
 from functools import lru_cache
 from typing import Any, Dict, Optional, Tuple
 
-try:  # pragma: no cover - optional dependency
-    import spacy
-except Exception:  # pragma: no cover - spaCy unavailable at runtime
-    spacy = None
+# spaCy is intentionally NOT imported at module level: importing it pulls in
+# thinc -> torch, which costs seconds of startup time. It is imported lazily
+# inside _load_spacy_model below.
 
 # Lazy spaCy type hints to avoid a hard dependency at import time.
 Language = Any  # type: ignore[assignment]
@@ -37,7 +36,9 @@ _DEFAULT_MODEL = os.environ.get("ABOGEN_SPACY_MODEL", "en_core_web_sm")
 
 @lru_cache(maxsize=1)
 def _load_spacy_model(model: str = _DEFAULT_MODEL) -> Optional[Language]:
-    if spacy is None:
+    try:  # pragma: no cover - optional dependency
+        import spacy
+    except Exception:  # pragma: no cover - spaCy unavailable at runtime
         logger.debug("spaCy is not installed; skipping contraction disambiguation")
         return None
 

abogen/spacy_utils.py

@@ -2,24 +2,25 @@
 Lazy-loaded spaCy utilities for sentence segmentation.
 """
 
+from abogen.domain.enums import Language
+
 # Cached spaCy module and models (lazy loaded)
 _spacy = None
 _nlp_cache = {}
 
 # Language code to spaCy model mapping
 SPACY_MODELS = {
-    "a": "en_core_web_sm",  # American English
-    "b": "en_core_web_sm",  # British English
-    "e": "es_core_news_sm",  # Spanish
-    "f": "fr_core_news_sm",  # French
-    "i": "it_core_news_sm",  # Italian
-    "p": "pt_core_news_sm",  # Brazilian Portuguese
-    "z": "zh_core_web_sm",  # Mandarin Chinese
-    "j": "ja_core_news_sm",  # Japanese
-    "h": "xx_sent_ud_sm",  # Hindi (multi-language model)
+    Language.EN_US: "en_core_web_sm",
+    Language.EN_GB: "en_core_web_sm",
+    Language.ES: "es_core_news_sm",
+    Language.FR: "fr_core_news_sm",
+    Language.IT: "it_core_news_sm",
+    Language.PT_BR: "pt_core_news_sm",
+    Language.ZH: "zh_core_web_sm",
+    Language.JA: "ja_core_news_sm",
+    Language.HI: "xx_sent_ud_sm",
 }
 
-
 def _load_spacy():
     """Lazy load spaCy module."""
     global _spacy
@@ -33,13 +34,12 @@ def _load_spacy():
     return _spacy
 
 
-def get_spacy_model(lang_code, log_callback=None):
+def get_spacy_model(language: Language, log_callback=None):
     """
-    Get or load a spaCy model for the given language code.
-    Downloads the model automatically if not available.
+    Get or load a spaCy model for the given language.
 
     Args:
-        lang_code: Language code (a, b, e, f, etc.)
+        language: Language enum value.
         log_callback: Optional function to log messages
 
     Returns:
@@ -47,25 +47,26 @@ def get_spacy_model(lang_code, log_callback=None):
     """
 
     def log(msg, is_error=False):
-        # Prefer GUI log callback when provided to avoid spamming stdout.
         if log_callback:
             color = "red" if is_error else "grey"
             try:
                 log_callback((msg, color))
             except Exception:
-                # Fallback to printing if callback misbehaves
                 print(msg)
         else:
             print(msg)
 
-    # Check if model is cached
-    if lang_code in _nlp_cache:
-        return _nlp_cache[lang_code]
+    if not isinstance(language, Language):
+        raise TypeError(
+            f"language must be Language enum, got {type(language).__name__}: {language!r}"
+        )
 
-    # Check if language is supported
-    model_name = SPACY_MODELS.get(lang_code)
+    if language in _nlp_cache:
+        return _nlp_cache[language]
+
+    model_name = SPACY_MODELS.get(language)
     if not model_name:
-        log(f"\nspaCy: No model mapping for language '{lang_code}'...")
+        log(f"\nspaCy: No model mapping for language '{language}'...")
         return None
 
     # Lazy load spaCy
@@ -89,7 +90,7 @@ def get_spacy_model(lang_code, log_callback=None):
         if "parser" not in nlp.pipe_names and "sentencizer" not in nlp.pipe_names:
             nlp.add_pipe("sentencizer")
 
-        _nlp_cache[lang_code] = nlp
+        _nlp_cache[language] = nlp
         return nlp
     except OSError:
         # Model not found, attempt download
@@ -106,7 +107,7 @@ def get_spacy_model(lang_code, log_callback=None):
             if "parser" not in nlp.pipe_names and "sentencizer" not in nlp.pipe_names:
                 nlp.add_pipe("sentencizer")
 
-            _nlp_cache[lang_code] = nlp
+            _nlp_cache[language] = nlp
             log(f"spaCy model '{model_name}' downloaded and loaded")
             return nlp
         except Exception as e:
@@ -120,19 +121,19 @@ def get_spacy_model(lang_code, log_callback=None):
         return None
 
 
-def segment_sentences(text, lang_code, log_callback=None):
+def segment_sentences(text, language: Language, log_callback=None):
     """
     Segment text into sentences using spaCy.
 
     Args:
         text: Text to segment
-        lang_code: Language code
+        language: Language enum value
         log_callback: Optional function to log messages
 
     Returns:
         List of sentence strings, or None if spaCy unavailable
     """
-    nlp = get_spacy_model(lang_code, log_callback)
+    nlp = get_spacy_model(language, log_callback)
     if nlp is None:
         return None
 

abogen/speaker_configs.py

@@ -4,7 +4,7 @@ import json
 import os
 from typing import Any, Dict, List, Optional
 
-from abogen.constants import LANGUAGE_DESCRIPTIONS
+from abogen.constants import KOKORO_CODE_LABELS
 from abogen.utils import get_user_config_path
 
 _CONFIG_WRAPPER_KEY = "abogen_speaker_configs"
@@ -163,4 +163,4 @@ def list_configs() -> List[Dict[str, Any]]:
 
 def describe_language(code: str) -> str:
     code = (code or "a").lower()
-    return LANGUAGE_DESCRIPTIONS.get(code, code.upper())
+    return KOKORO_CODE_LABELS.get(code, code.upper())

abogen/subtitle_utils.py

@@ -1,7 +1,7 @@
 import re
-import platform
 from abogen.utils import detect_encoding, load_config
 from abogen.constants import SAMPLE_VOICE_TEXTS
+from abogen.domain.enums import Language
 
 # Pre-compile frequently used regex patterns for better performance
 _METADATA_TAG_PATTERN = re.compile(r"<<METADATA_[^:]+:[^>]*>>")
@@ -23,13 +23,6 @@ _VTT_NOTE_PATTERN = re.compile(r"NOTE\s*\n.*?(?=\n\n|$)", re.DOTALL)
 _DOUBLE_NEWLINE_SPLIT_PATTERN = re.compile(r"\n\s*\n")
 _VTT_TIMESTAMP_PATTERN = re.compile(r"([\d:.]+)\s*-->\s*([\d:.]+)")
 _TIMESTAMP_ONLY_PATTERN = re.compile(r"^(\d{1,2}:\d{2}:\d{2}(?:[.,]\d{1,3})?)$")
-_WINDOWS_ILLEGAL_CHARS_PATTERN = re.compile(r'[<>:"/\\|?*]')
-_CONTROL_CHARS_PATTERN = re.compile(r"[\x00-\x1f]")
-_LINUX_CONTROL_CHARS_PATTERN = re.compile(
-    r"[\x01-\x1f]"
-)  # Linux: exclude \x00 for separate handling
-_MACOS_ILLEGAL_CHARS_PATTERN = re.compile(r"[:]")
-_LINUX_ILLEGAL_CHARS_PATTERN = re.compile(r"[/\x00]")
 
 
 def clean_subtitle_text(text):
@@ -41,17 +34,6 @@ def clean_subtitle_text(text):
     return text.strip()
 
 
-def calculate_text_length(text):
-    # Use pre-compiled patterns for better performance
-    # Ignore chapter markers, voice markers, and metadata patterns in a single pass
-    text = _CHAPTER_MARKER_PATTERN.sub("", text)
-    text = _VOICE_MARKER_PATTERN.sub("", text)
-    text = _METADATA_TAG_PATTERN.sub("", text)
-    # Ignore newlines and leading/trailing spaces
-    text = text.replace("\n", "").strip()
-    # Calculate character count
-    char_count = len(text)
-    return char_count
 
 
 def clean_text(text, *args, **kwargs):
@@ -396,189 +378,27 @@ def parse_ass_file(file_path):
     return subtitles
 
 
-def get_sample_voice_text(lang_code):
-    return SAMPLE_VOICE_TEXTS.get(lang_code, SAMPLE_VOICE_TEXTS["a"])
-
-
-def sanitize_name_for_os(name, is_folder=True):
-    """
-    Sanitize a filename or folder name based on the operating system.
+def get_sample_voice_text(language):
+    """Get sample voice text for a language.
 
     Args:
-        name: The name to sanitize
-        is_folder: Whether this is a folder name (default: True)
-
-    Returns:
-        Sanitized name safe for the current OS
+        language: Language enum value or string (for backward compatibility).
     """
-    if not name:
-        return "audiobook"
-
-    system = platform.system()
-
-    if system == "Windows":
-        # Windows illegal characters: < > : " / \ | ? *
-        # Also can't end with space or dot
-        # Use pre-compiled pattern for better performance
-        sanitized = _WINDOWS_ILLEGAL_CHARS_PATTERN.sub("_", name)
-        # Remove control characters (0-31)
-        sanitized = _CONTROL_CHARS_PATTERN.sub("_", sanitized)
-        # Remove trailing spaces and dots
-        sanitized = sanitized.rstrip(". ")
-        # Windows reserved names (CON, PRN, AUX, NUL, COM1-9, LPT1-9)
-        reserved = (
-            ["CON", "PRN", "AUX", "NUL"]
-            + [f"COM{i}" for i in range(1, 10)]
-            + [f"LPT{i}" for i in range(1, 10)]
-        )
-        if sanitized.upper() in reserved or sanitized.upper().split(".")[0] in reserved:
-            sanitized = f"_{sanitized}"
-    elif system == "Darwin":  # macOS
-        # macOS illegal characters: : (colon is converted to / by the system)
-        # Also can't start with dot (hidden file) for folders typically
-        # Use pre-compiled pattern for better performance
-        sanitized = _MACOS_ILLEGAL_CHARS_PATTERN.sub("_", name)
-        # Remove control characters
-        sanitized = _CONTROL_CHARS_PATTERN.sub("_", sanitized)
-        # Avoid leading dot for folders (creates hidden folders)
-        if is_folder and sanitized.startswith("."):
-            sanitized = "_" + sanitized[1:]
-    else:  # Linux and others
-        # Linux illegal characters: / and null character
-        # Though / is illegal, most other chars are technically allowed
-        # Use pre-compiled pattern for better performance
-        sanitized = _LINUX_ILLEGAL_CHARS_PATTERN.sub("_", name)
-        # Remove other control characters for safety (excluding \x00 which is already handled)
-        sanitized = _LINUX_CONTROL_CHARS_PATTERN.sub("_", sanitized)
-        # Avoid leading dot for folders (creates hidden folders)
-        if is_folder and sanitized.startswith("."):
-            sanitized = "_" + sanitized[1:]
-
-    # Ensure the name is not empty after sanitization
-    if not sanitized or sanitized.strip() == "":
-        sanitized = "audiobook"
-
-    # Limit length to 255 characters (common limit across filesystems)
-    if len(sanitized) > 255:
-        sanitized = sanitized[:255].rstrip(". ")
-
-    return sanitized
+    if isinstance(language, str):
+        try:
+            language = Language.from_str(language)
+        except (ValueError, AttributeError):
+            language = Language.EN_US
+    return SAMPLE_VOICE_TEXTS.get(language, SAMPLE_VOICE_TEXTS[Language.EN_US])
 
 
-def validate_voice_name(voice_name):
-    """Validate voice name against available voices (case-insensitive).
-    Handles both single voices and formulas like 'af_heart*0.5 + am_echo*0.5'.
+# Backward-compatible re-exports — canonical location is domain/output_paths.py
+from abogen.domain.output_paths import sanitize_name_for_os  # noqa: E402, F401
 
-    Args:
-        voice_name: Voice name or formula string to validate
-
-    Returns:
-        Tuple of (is_valid, invalid_voice_name):
-            - is_valid: True if all voices in the name/formula are valid
-            - invalid_voice_name: The first invalid voice found, or None if all valid
-    """
-    from abogen.tts_plugin.utils import get_voices
-
-    # Create case-insensitive lookup set (done once per call)
-    voice_lookup_lower = {v.lower() for v in get_voices("kokoro")}
-    voice_name = voice_name.strip()
-
-    # Check if it's a formula (contains *)
-    if "*" in voice_name:
-        # Extract voice names from formula
-        voices = voice_name.split("+")
-        for term in voices:
-            if "*" in term:
-                base_voice = term.split("*")[0].strip()
-                # Case-insensitive comparison
-                if base_voice.lower() not in voice_lookup_lower:
-                    return False, base_voice
-        return True, None
-    else:
-        # Single voice - case-insensitive comparison
-        if voice_name.lower() not in voice_lookup_lower:
-            return False, voice_name
-        return True, None
-
-
-def split_text_by_voice_markers(text, default_voice):
-    """Split text by voice markers, returning list of (voice, text) tuples.
-
-    IMPORTANT: Returns the last voice used so it can persist across chapters.
-    Voice names are normalized to lowercase to match canonical voice names.
-
-    Args:
-        text: Text potentially containing <<VOICE:name>> markers
-        default_voice: Voice to use if no markers found or before first marker
-
-    Returns:
-        Tuple of (segments_list, last_voice_used, valid_count, invalid_count):
-            - segments_list: List of (voice_name, segment_text) tuples
-            - last_voice_used: The voice that should continue into next chapter
-            - valid_count: Number of valid voice markers processed
-            - invalid_count: Number of invalid voice markers skipped
-    """
-    from abogen.tts_plugin.utils import get_voices
-
-    voice_splits = list(_VOICE_MARKER_SEARCH_PATTERN.finditer(text))
-
-    if not voice_splits:
-        # No voice markers, return entire text with default voice
-        return [(default_voice, text)], default_voice, 0, 0
-
-    segments = []
-    current_voice = default_voice
-    valid_markers = 0
-    invalid_markers = 0
-
-    # Text before first marker uses default voice
-    first_start = voice_splits[0].start()
-    if first_start > 0:
-        intro_text = text[:first_start].strip()
-        if intro_text:
-            segments.append((current_voice, intro_text))
-
-    # Process each voice marker
-    for idx, match in enumerate(voice_splits):
-        voice_name = match.group(1).strip()
-        start = match.end()
-        end = voice_splits[idx + 1].start() if idx + 1 < len(voice_splits) else len(text)
-        segment_text = text[start:end].strip()
-
-        # Validate voice name
-        is_valid, invalid_voice = validate_voice_name(voice_name)
-        if is_valid:
-            # Normalize to lowercase to match canonical form
-            # Handle both single voices and formulas
-            if "*" in voice_name:
-                # Normalize each voice in the formula
-                normalized_parts = []
-                for part in voice_name.split("+"):
-                    part = part.strip()
-                    if "*" in part:
-                        voice_part, weight = part.split("*", 1)
-                        # Find the canonical (lowercase) voice name
-                        voice_part_lower = voice_part.strip().lower()
-                        canonical_voice = next(
-                            (v for v in get_voices("kokoro") if v.lower() == voice_part_lower),
-                            voice_part.strip()
-                        )
-                        normalized_parts.append(f"{canonical_voice}*{weight.strip()}")
-                current_voice = " + ".join(normalized_parts)
-            else:
-                # Find the canonical (lowercase) voice name
-                voice_name_lower = voice_name.lower()
-                current_voice = next(
-                    (v for v in get_voices("kokoro") if v.lower() == voice_name_lower),
-                    voice_name
-                )
-            valid_markers += 1
-        else:
-            # Invalid voice - stay with previous voice
-            invalid_markers += 1
-
-        if segment_text:
-            segments.append((current_voice, segment_text))
-
-    # Return segments, last voice, and counts
-    return segments, current_voice, valid_markers, invalid_markers
+# Backward-compatible re-exports — canonical location is domain/voice_markers.py
+from abogen.domain.voice_markers import (  # noqa: E402, F401
+    validate_voice_name,
+    split_text_by_voice_markers,
+    _VOICE_MARKER_PATTERN,
+    _VOICE_MARKER_SEARCH_PATTERN,
+)

abogen/text_extractor.py

@@ -16,7 +16,8 @@ import markdown  # type: ignore[import]
 from bs4 import BeautifulSoup, NavigableString  # type: ignore[import]
 from ebooklib import epub  # type: ignore[import]
 
-from .utils import calculate_text_length, clean_text, detect_encoding
+from .utils import clean_text, detect_encoding
+from .domain.text_utils import calculate_text_length
 
 logger = logging.getLogger(__name__)
 

abogen/tts_plugin/loader.py

@@ -12,7 +12,7 @@ The loader does NOT:
 
 from __future__ import annotations
 
-import importlib
+import importlib.util
 import re
 import sys
 import types

abogen/tts_plugin/plugin_manager.py

@@ -8,7 +8,7 @@ Usage:
     from abogen.tts_plugin.plugin_manager import get_plugin_manager
 
     manager = get_plugin_manager()
-    engine = manager.create_engine("kokoro", lang_code="a", device="cpu")
+    engine = manager.create_engine("kokoro", language=Language.EN_US, device="cpu")
     session = engine.create_session()
     try:
         result = session.synthesize("Hello world")
@@ -42,8 +42,11 @@ class PluginManager:
 
         plugins_path = Path(plugins_dir)
         if not plugins_path.exists():
-            self._loaded = True
-            return
+            if plugins_dir == "plugins":
+                plugins_path = Path(__file__).resolve().parent.parent.parent / "plugins"
+            if not plugins_path.exists():
+                self._loaded = True
+                return
 
         for entry in plugins_path.iterdir():
             if entry.is_dir() and (entry / "__init__.py").exists():

abogen/tts_plugin/types.py

@@ -9,6 +9,8 @@ from __future__ import annotations
 from dataclasses import dataclass, field
 from typing import Any, Mapping
 
+from abogen.domain.enums import Language
+
 
 @dataclass(frozen=True)
 class AudioFormat:
@@ -77,6 +79,44 @@ class SynthesisRequest:
     format: AudioFormat
 
 
+@dataclass(frozen=True)
+class TokenTiming:
+    """Per-token timing within a synthesized segment.
+
+    Attributes:
+        text: Token text.
+        whitespace: Whitespace following the token ("" if none).
+        start: Start time in seconds (relative to segment start).
+        end: End time in seconds (relative to segment start).
+    """
+
+    text: str
+    whitespace: str = ""
+    start: float = 0.0
+    end: float = 0.0
+
+
+@dataclass(frozen=True)
+class AudioSegment:
+    """One contiguous synthesized segment (sentence-level chunk).
+
+    Engines that split the input text (via ``split_pattern``) expose each
+    chunk as its own AudioSegment so hosts can report per-sentence progress
+    and build subtitles from per-token timings.
+
+    Attributes:
+        graphemes: The text this segment was synthesized from.
+        audio: Raw float32 PCM audio bytes for this segment.
+        sample_rate: Sample rate of ``audio``.
+        tokens: Per-token timing details, when the engine provides them.
+    """
+
+    graphemes: str
+    audio: bytes
+    sample_rate: int
+    tokens: tuple[TokenTiming, ...] = ()
+
+
 @dataclass(frozen=True)
 class SynthesizedAudio:
     """Immutable value object for synthesized audio result.
@@ -85,11 +125,15 @@ class SynthesizedAudio:
         data: Raw audio bytes.
         format: Audio format of the result.
         duration: Duration of the audio.
+        segments: Per-segment details when the engine split the text into
+            sentence-level chunks (empty for engines that only produce a
+            single merged result).
     """
 
     data: bytes
     format: AudioFormat
     duration: Duration
+    segments: tuple[AudioSegment, ...] = ()
 
 
 @dataclass(frozen=True)
@@ -103,9 +147,9 @@ class EngineConfig:
 
     Attributes:
         device: Device to use (e.g., "cpu", "cuda:0").
-        lang_code: Language code for the engine (e.g., "a" for Kokoro English).
-            Plugins that do not require a language code ignore this field.
+        language: Language enum value. The engine converts to its internal
+            format internally — callers never see engine-specific codes.
     """
 
     device: str = "cpu"
-    lang_code: str = "a"
+    language: Language = Language.EN_US

abogen/tts_plugin/utils.py

@@ -10,6 +10,7 @@ from typing import Any, Iterator
 
 import numpy as np
 
+from abogen.domain.enums import Language
 from abogen.tts_plugin.plugin_manager import get_plugin_manager
 
 
@@ -123,7 +124,7 @@ class Pipeline:
 
     Presents the same interface that old callers expect::
 
-        pipeline = create_pipeline("kokoro", lang_code="a", device="cpu")
+        pipeline = create_pipeline("kokoro", language=Language.EN_US, device="cpu")
         for segment in pipeline(text, voice="af_nova", speed=1.0):
             audio = segment.audio
     """
@@ -168,17 +169,46 @@ class Pipeline:
         )
 
         result = session.synthesize(request)
-        audio_array = np.frombuffer(result.data, dtype=np.float32)
 
-        from dataclasses import dataclass
+        from dataclasses import dataclass, field
+
+        @dataclass
+        class Token:
+            text: str
+            whitespace: str = ""
+            start_ts: float = 0.0
+            end_ts: float = 0.0
 
         @dataclass
         class Segment:
             graphemes: str
             audio: np.ndarray
+            tokens: list[Any] = field(default_factory=list)
 
+        if result.segments:
+            for seg in result.segments:
+                audio_array = np.frombuffer(seg.audio, dtype=np.float32)
+                tokens = [
+                    Token(
+                        text=tok.text,
+                        whitespace=tok.whitespace,
+                        start_ts=tok.start,
+                        end_ts=tok.end,
+                    )
+                    for tok in seg.tokens
+                ]
+                yield Segment(graphemes=seg.graphemes, audio=audio_array, tokens=tokens)
+            return
+
+        audio_array = np.frombuffer(result.data, dtype=np.float32)
         yield Segment(graphemes=text, audio=audio_array)
 
+    def load_single_voice(self, voice_name: str) -> Any:
+        engine_pipeline = getattr(self._engine, '_pipeline', None)
+        if engine_pipeline is not None and hasattr(engine_pipeline, 'load_single_voice'):
+            return engine_pipeline.load_single_voice(voice_name)
+        raise AttributeError(f"load_single_voice not available on {type(self._engine).__name__}")
+
     def dispose(self) -> None:
         if self._session is not None:
             try:
@@ -194,7 +224,7 @@ class Pipeline:
 def create_pipeline(
     plugin_id: str,
     *,
-    lang_code: str = "a",
+    language: Language = Language.EN_US,
     device: str = "cpu",
 ) -> Pipeline:
     """Create a callable TTS pipeline via the Plugin Architecture.
@@ -205,7 +235,7 @@ def create_pipeline(
 
     Args:
         plugin_id: Plugin identifier (e.g., "kokoro", "supertonic").
-        lang_code: Language code for the engine.
+        language: Language enum value (app-layer type, not engine-specific).
         device: Device to use (e.g., "cpu", "cuda:0").
 
     Returns:
@@ -229,7 +259,7 @@ def create_pipeline(
         })(),
     )
 
-    config = EngineConfig(device=device, lang_code=lang_code)
+    config = EngineConfig(device=device, language=language)
 
     engine = manager.create_engine(plugin_id, context=ctx, model_path=None, config=config)
     return Pipeline(engine)

abogen/utils.py

@@ -6,7 +6,9 @@ import re
 import shutil
 import subprocess
 import sys
+import time
 import warnings
+from contextlib import contextmanager
 from threading import Thread
 from typing import Dict, Optional
 
@@ -14,6 +16,8 @@ from functools import lru_cache
 
 from dotenv import load_dotenv, find_dotenv
 
+logger = logging.getLogger(__name__)
+
 
 def _load_environment() -> None:
     explicit_path = os.environ.get("ABOGEN_ENV_FILE")
@@ -29,6 +33,125 @@ _load_environment()
 
 warnings.filterwarnings("ignore")
 
+# --- Console log colorization via rich (mirrors AutoSubSync's approach) ---
+
+try:  # rich is a declared dependency, but degrade gracefully if unavailable
+    from rich.console import Console
+    from rich.highlighter import NullHighlighter
+    from rich.logging import RichHandler
+
+    _RICH_AVAILABLE = True
+except Exception:  # pragma: no cover - fallback to plain logging
+    Console = None
+    NullHighlighter = None
+    RichHandler = None
+    _RICH_AVAILABLE = False
+
+
+def _console_supports_color() -> bool:
+    if os.environ.get("NO_COLOR"):
+        return False
+    try:
+        return bool(sys.stderr.isatty())
+    except Exception:
+        return False
+
+
+_RICH_CONSOLE = None
+if Console is not None:
+    try:
+        _RICH_CONSOLE = Console(stderr=True, no_color=not _console_supports_color())
+    except Exception:  # pragma: no cover - defensive
+        _RICH_CONSOLE = None
+
+
+_ANSI_ESCAPE_RE = re.compile(r"\x1b\[[0-9;]*[A-Za-z]")
+
+if RichHandler is not None:
+
+    class RichConsoleHandler(RichHandler):
+        """RichHandler with default settings, except raw ANSI escapes are
+        stripped from messages first (werkzeug colorizes its own log lines
+        when attached to a TTY; without this they render as literal "[36m"
+        fragments)."""
+
+        def emit(self, record):
+            # Werkzeug logs its dev-server banner at INFO but hardcodes a
+            # "WARNING: " prefix into the message text. Promote the record so
+            # the level tag matches the content.
+            try:
+                message = _ANSI_ESCAPE_RE.sub("", record.getMessage())
+            except Exception:  # pragma: no cover - defensive
+                message = ""
+            if record.levelno < logging.WARNING and message.startswith("WARNING: "):
+                record.levelno = logging.WARNING
+                record.levelname = "WARNING"
+            super().emit(record)
+
+        def render_message(self, record, message):
+            message = _ANSI_ESCAPE_RE.sub("", message)
+            if message.startswith("WARNING: "):
+                message = message[len("WARNING: ") :]
+            return super().render_message(record, message)
+
+else:  # pragma: no cover - rich unavailable fallback
+    RichConsoleHandler = None  # type: ignore[assignment, misc]
+
+
+def console_handler(show_level=True):
+    """Build a colored console handler. Rich's RichHandler when available
+    (no timestamps, colored level tags), plain StreamHandler otherwise."""
+    if _RICH_CONSOLE is not None and RichConsoleHandler is not None:
+        return RichConsoleHandler(
+            console=_RICH_CONSOLE,
+            show_path=False,
+            show_time=False,
+            rich_tracebacks=True,
+        )
+    handler = logging.StreamHandler(sys.stderr)
+    prefix = "%(levelname)s - " if show_level else ""
+    handler.setFormatter(logging.Formatter(f"{prefix}%(message)s"))
+    return handler
+
+
+def setup_console_logging(level=logging.INFO):
+    """Configure the root logger once with a colored console handler."""
+    root = logging.getLogger()
+    if not root.handlers:
+        root.addHandler(console_handler())
+    root.setLevel(level)
+
+
+@contextmanager
+def timed_log(label, logger=None, level=logging.INFO):
+    """Context manager that logs the wall-clock time a block of code takes.
+
+    Used to surface which load/startup steps are slow. The elapsed time is
+    colorized: green < 1s, yellow 1-5s, red > 5s.
+    """
+    log = logger or logging.getLogger(__name__)
+    start = time.perf_counter()
+    try:
+        yield
+    finally:
+        elapsed = time.perf_counter() - start
+        if _RICH_AVAILABLE and _RICH_CONSOLE is not None and not _RICH_CONSOLE.no_color:
+            if elapsed >= 5.0:
+                color = "red"
+            elif elapsed >= 1.0:
+                color = "yellow"
+            else:
+                color = "green"
+            log.log(
+                level,
+                "Loaded %s in %s",
+                f"[cyan]{label}[/cyan]",
+                f"[{color}]{elapsed:.2f}s[/{color}]",
+                extra={"markup": True, "highlighter": NullHighlighter()},
+            )
+        else:
+            log.log(level, "Loaded %s in %.2fs", label, elapsed)
+
 
 def detect_encoding(file_path):
     try:
@@ -320,10 +443,6 @@ default_encoding = sys.getfilesystemencoding()
 
 
 def create_process(cmd, stdin=None, text=True, capture_output=False):
-    import logging
-
-    logger = logging.getLogger(__name__)
-
     # Configure root logger to output to console if not already configured
     root = logging.getLogger()
     if not root.handlers:
@@ -372,8 +491,8 @@ def create_process(cmd, stdin=None, text=True, capture_output=False):
             }
         )
 
-    # Print the command being executed
-    print(f"Executing: {cmd if isinstance(cmd, str) else ' '.join(cmd)}")
+    # Log the command being executed
+    logger.info(f"Executing: {cmd if isinstance(cmd, str) else ' '.join(cmd)}")
 
     proc = subprocess.Popen(cmd, **kwargs)
 
@@ -428,19 +547,6 @@ def save_config(config):
         pass
 
 
-def calculate_text_length(text):
-    # Ignore chapter markers
-    text = re.sub(r"<<CHAPTER_MARKER:.*?>>", "", text)
-    # Ignore metadata patterns
-    text = re.sub(r"<<METADATA_[^:]+:[^>]*>>", "", text)
-    # Ignore newlines
-    text = text.replace("\n", "")
-    # Ignore leading/trailing spaces
-    text = text.strip()
-    # Calculate character count
-    char_count = len(text)
-    return char_count
-
 
 def get_gpu_acceleration(enabled):
     try:
@@ -507,7 +613,7 @@ def prevent_sleep_start():
             )
         else:
             # Non-systemd distro or systemd tools not installed: skip inhibition rather than crash
-            print(
+            logger.warning(
                 "systemd-inhibit not found: skipping sleep inhibition on this Linux system."
             )
 
@@ -540,9 +646,13 @@ class LoadPipelineThread(Thread):
         try:
             from abogen.domain.pipeline_factory import create_pipeline_for_job
 
-            backend = create_pipeline_for_job(
-                "kokoro", language=self.lang_code, use_gpu=self.use_gpu
-            )
+            with timed_log(
+                f"TTS pipeline (lang={self.lang_code}, gpu={self.use_gpu})",
+                logger=logging.getLogger("abogen.startup"),
+            ):
+                backend = create_pipeline_for_job(
+                    "kokoro", language=self.lang_code, use_gpu=self.use_gpu
+                )
             self.callback(backend, None)
         except Exception as e:
             self.callback(None, str(e))

abogen/voice_profiles.py

@@ -2,6 +2,7 @@ import json
 import os
 from typing import Any, Dict, Iterable, List, Tuple
 
+from abogen.domain.enums import Language
 from abogen.tts_plugin.utils import get_voices, is_plugin_registered
 from abogen.utils import get_user_config_path
 
@@ -176,13 +177,35 @@ def save_profile(name: str, *, language: str, voices: Iterable) -> None:
         raise ValueError("At least one voice with a weight above zero is required")
 
     if not language:
-        language = "a"
+        language = Language.EN_US
 
     profiles = load_profiles()
     profiles[name] = {"provider": "kokoro", "language": language, "voices": normalized}
     save_profiles(profiles)
 
 
+def resolve_profile_language(entry: Any) -> Language:
+    """Resolve a profile's stored language to a Language enum.
+
+    New profiles store ISO codes (Language enum values); legacy profiles may
+    store kokoro letter codes ("a", "b", ...). Unparseable values fall back
+    to EN_US.
+    """
+
+    raw = entry.get("language") if isinstance(entry, dict) else None
+    if isinstance(raw, Language):
+        return raw
+    text = str(raw or "").strip()
+    if not text:
+        return Language.EN_US
+    try:
+        return Language.from_str(text)
+    except ValueError:
+        from plugins.kokoro.engine import language_for_code
+
+        return language_for_code(text)
+
+
 def remove_profile(name: str) -> None:
     delete_profile(name)
 

abogen/webui/app.py

@@ -9,11 +9,19 @@ from flask import Flask
 
 from abogen import shutdown  # noqa: F401
 shutdown.register_shutdown()
-from abogen.utils import get_user_cache_path, get_user_output_path, get_user_settings_dir
+from abogen.utils import (
+    get_user_cache_path,
+    get_user_output_path,
+    get_user_settings_dir,
+    setup_console_logging,
+    timed_log,
+)
 
 from .conversion_runner import run_conversion_job
 from .service import build_service
 
+_logger = logging.getLogger("abogen.startup")
+
 
 class _SuppressSuccessfulAccessFilter(logging.Filter):
     """Filter out successful (HTTP 200) werkzeug access logs."""
@@ -29,6 +37,13 @@ class _SuppressSuccessfulAccessFilter(logging.Filter):
         return " 200 " not in message and " 201 " not in message and " 204 " not in message
 
 
+class _SuppressPhonemizerWarnings(logging.Filter):
+    """Suppress phonemizer word-count-mismatch warnings (normal behavior)."""
+
+    def filter(self, record: logging.LogRecord) -> bool:  # pragma: no cover - small utility
+        return "words count mismatch" not in record.getMessage()
+
+
 _access_log_filter_attached = False
 
 
@@ -72,63 +87,78 @@ def _get_secret_key() -> str:
 
 
 def create_app(config: Optional[dict[str, Any]] = None) -> Flask:
-    uploads_dir, outputs_dir = _default_dirs()
+    with timed_log("default directories", logger=_logger):
+        uploads_dir, outputs_dir = _default_dirs()
 
-    app = Flask(
-        __name__,
-        static_folder="static",
-        template_folder="templates",
-    )
-    base_config = {
-        "SECRET_KEY": _get_secret_key(),
-        "UPLOAD_FOLDER": str(uploads_dir),
-        "OUTPUT_FOLDER": str(outputs_dir),
-        "MAX_CONTENT_LENGTH": 1024 * 1024 * 400,  # 400 MB uploads
-        # Large books can submit four form fields per chapter. Werkzeug's
-        # defaults reject those requests before the wizard route can process
-        # them, even though the encoded payload is much smaller than the upload
-        # limit above.
-        "MAX_FORM_MEMORY_SIZE": 10 * 1024 * 1024,
-        "MAX_FORM_PARTS": 10_000,
-    }
-    if config:
-        base_config.update(config)
-    app.config.update(base_config)
+    with timed_log("Flask app creation + config", logger=_logger):
+        app = Flask(
+            __name__,
+            static_folder="static",
+            template_folder="templates",
+        )
+        base_config = {
+            "SECRET_KEY": _get_secret_key(),
+            "UPLOAD_FOLDER": str(uploads_dir),
+            "OUTPUT_FOLDER": str(outputs_dir),
+            "MAX_CONTENT_LENGTH": 1024 * 1024 * 400,  # 400 MB uploads
+            # Large books can submit four form fields per chapter. Werkzeug's
+            # defaults reject those requests before the wizard route can process
+            # them, even though the encoded payload is much smaller than the upload
+            # limit above.
+            "MAX_FORM_MEMORY_SIZE": 10 * 1024 * 1024,
+            "MAX_FORM_PARTS": 10_000,
+        }
+        if config:
+            base_config.update(config)
+        app.config.update(base_config)
 
-    service = build_service(
-        runner=run_conversion_job,
-        output_root=Path(app.config["OUTPUT_FOLDER"]),
-        uploads_root=Path(app.config["UPLOAD_FOLDER"]),
-    )
+    with timed_log("conversion service (incl. queue state load)", logger=_logger):
+        service = build_service(
+            runner=run_conversion_job,
+            output_root=Path(app.config["OUTPUT_FOLDER"]),
+            uploads_root=Path(app.config["UPLOAD_FOLDER"]),
+        )
     app.extensions["conversion_service"] = service
 
-    from abogen.webui.routes import (
-        main_bp,
-        jobs_bp,
-        settings_bp,
-        voices_bp,
-        entities_bp,
-        books_bp,
-        api_bp,
-    )
+    with timed_log("blueprint registration", logger=_logger):
+        from abogen.webui.routes import (
+            main_bp,
+            jobs_bp,
+            settings_bp,
+            voices_bp,
+            entities_bp,
+            books_bp,
+            api_bp,
+        )
 
-    app.register_blueprint(main_bp)
-    app.register_blueprint(jobs_bp, url_prefix="/jobs")
-    app.register_blueprint(settings_bp, url_prefix="/settings")
-    app.register_blueprint(voices_bp, url_prefix="/voices")
-    app.register_blueprint(entities_bp, url_prefix="/overrides")
-    app.register_blueprint(books_bp, url_prefix="/find-books")
-    app.register_blueprint(api_bp, url_prefix="/api")
+        app.register_blueprint(main_bp)
+        app.register_blueprint(jobs_bp, url_prefix="/jobs")
+        app.register_blueprint(settings_bp, url_prefix="/settings")
+        app.register_blueprint(voices_bp, url_prefix="/voices")
+        app.register_blueprint(entities_bp, url_prefix="/overrides")
+        app.register_blueprint(books_bp, url_prefix="/find-books")
+        app.register_blueprint(api_bp, url_prefix="/api")
 
     global _access_log_filter_attached
     if not _access_log_filter_attached:
         logging.getLogger("werkzeug").addFilter(_SuppressSuccessfulAccessFilter())
+        logging.getLogger("phonemizer").addFilter(_SuppressPhonemizerWarnings())
         _access_log_filter_attached = True
 
     return app
 
 
 def main() -> None:
+    setup_console_logging()
+    # Route Flask's dev-server banner through our logger instead of click.echo.
+    import flask.cli as flask_cli
+
+    def _show_server_banner(debug, app_import_path):
+        _logger.info(" * Serving Flask app %r", app_import_path)
+        _logger.info(" * Debug mode: %s", "on" if debug else "off")
+
+    flask_cli.show_server_banner = _show_server_banner
+
     app = create_app()
     host = os.environ.get("ABOGEN_HOST", "0.0.0.0")
     port = int(os.environ.get("ABOGEN_PORT", "8808"))

abogen/webui/debug_tts_runner.py

@@ -14,9 +14,12 @@ from abogen.kokoro_text_normalization import normalize_for_pipeline
 from abogen.normalization_settings import build_apostrophe_config
 from abogen.text_extractor import extract_from_path
 from abogen.voice_cache import ensure_voice_assets
-from abogen.webui.conversion_runner import SAMPLE_RATE, _select_device, _to_float32, _spec_to_voice_ids
+from abogen.domain.device import select_device as _select_device
+from abogen.domain.audio_helpers import to_float32 as _to_float32, SAMPLE_RATE
+from abogen.domain.voice_resolution import spec_to_voice_ids as _spec_to_voice_ids
 from abogen.domain.voice_loader import resolve_voice
 from abogen.domain.split_pattern import get_split_pattern
+from abogen.domain.enums import Language
 from abogen.tts_plugin.utils import create_pipeline
 
 
@@ -43,11 +46,18 @@ def _resolve_voice_setting(value: str) -> tuple[str, Optional[str], Optional[str
     return resolve_voice_setting(value)
 
 
-def _load_pipeline(language: str, use_gpu: bool) -> Any:
-    device = "cpu"
-    if use_gpu:
-        device = _select_device()
-    return create_pipeline("kokoro", lang_code=language, device=device)
+def _load_pipeline(language: Language, use_gpu: bool) -> Any:
+    import logging
+    from abogen.utils import timed_log
+
+    with timed_log(
+        f"TTS pipeline (lang={language}, gpu={use_gpu})",
+        logger=logging.getLogger("abogen.startup"),
+    ):
+        device = "cpu"
+        if use_gpu:
+            device = _select_device()
+        return create_pipeline("kokoro", language=language, device=device)
 
 
 def _extract_cases_from_text(text: str) -> List[Tuple[str, str]]:
@@ -127,32 +137,14 @@ def run_debug_tts_wavs(
     if missing:
         raise RuntimeError(f"Debug EPUB missing expected codes: {', '.join(missing)}")
 
-    language = str(settings.get("language") or "a").strip() or "a"
-    # Kokoro's KPipeline expects short language codes like "a" (American English),
-    # but older settings may store ISO-like values such as "en".
-    language_aliases = {
-        "en": "a",
-        "en-us": "a",
-        "en_us": "a",
-        "en-gb": "b",
-        "en_gb": "b",
-        "es": "e",
-        "es-es": "e",
-        "fr": "f",
-        "fr-fr": "f",
-        "hi": "h",
-        "it": "i",
-        "pt": "p",
-        "pt-br": "p",
-        "ja": "j",
-        "jp": "j",
-        "zh": "z",
-        "zh-cn": "z",
-    }
-    language = language_aliases.get(language.lower(), language)
+    raw_language = str(settings.get("language") or "en-US").strip() or "en-US"
+    try:
+        language = Language.from_str(raw_language)
+    except ValueError:
+        language = Language.EN_US
     voice_spec = str(settings.get("default_voice") or "").strip()
     use_gpu = bool(settings.get("use_gpu", False))
-    speed = float(settings.get("default_speed", 1.0) or 1.0)
+    speed = float(settings.get("default_speed") or 1.0)
 
     # Settings may store "profile:<name>" which is not a Kokoro voice ID.
     # Resolve it to a concrete voice formula (e.g. "af_heart*0.5+...") so Kokoro
@@ -162,7 +154,10 @@ def run_debug_tts_wavs(
         if resolved_voice:
             voice_spec = resolved_voice
         if profile_language:
-            language = str(profile_language).strip() or language
+            try:
+                language = Language.from_str(str(profile_language).strip()) or language
+            except (ValueError, AttributeError):
+                pass
     except Exception:
         # Voice profile resolution is best-effort; fall back to raw voice_spec.
         pass

abogen/webui/routes/api.py

@@ -6,6 +6,7 @@ from pathlib import Path
 from flask import Blueprint, request, jsonify, send_file, url_for, current_app
 from flask.typing import ResponseReturnValue
 
+from abogen.domain.enums import Language
 from abogen.webui.routes.utils.settings import (
     load_settings,
     load_integration_settings,
@@ -47,6 +48,21 @@ from werkzeug.utils import secure_filename
 
 api_bp = Blueprint("api", __name__)
 
+
+def _parse_language(value: Any) -> Language:
+    """Parse a frontend language value to Language enum.
+
+    This is the API boundary — frontend sends strings, backend parses
+    to Language enum. No engine-specific codes leak outside the engine.
+    """
+    if isinstance(value, Language):
+        return value
+    try:
+        return Language.from_str(str(value or "").strip())
+    except (ValueError, AttributeError):
+        return Language.EN_US
+
+
 # --- Voice Profile Routes ---
 
 @api_bp.get("/voice-profiles")
@@ -152,7 +168,7 @@ def api_export_voice_profiles() -> ResponseReturnValue:
 def api_voice_profiles_preview() -> ResponseReturnValue:
     payload = request.get_json(force=True, silent=True) or {}
     text = str(payload.get("text") or "").strip() or "Hello world"
-    language = str(payload.get("language") or "a").strip().lower() or "a"
+    language = _parse_language(payload.get("language"))
     speed = coerce_float(payload.get("speed"), 1.0)
     max_seconds = coerce_float(payload.get("max_seconds"), 8.0)
 
@@ -168,6 +184,11 @@ def api_voice_profiles_preview() -> ResponseReturnValue:
     voice_spec = ""
     resolved_provider = provider or "kokoro"
 
+    current_app.logger.info(
+        "[preview] provider=%s language=%s speed=%.2f profile=%s formula=%s",
+        resolved_provider, language, speed, profile_name or "-", formula or "-",
+    )
+
     profiles = load_profiles()
     if resolved_provider == "supertonic" and not profile_name:
         voice_spec = str(payload.get("voice") or payload.get("supertonic_voice") or "M1").strip() or "M1"
@@ -186,7 +207,7 @@ def api_voice_profiles_preview() -> ResponseReturnValue:
             speed = float(normalized_entry.get("speed") or speed)
         else:
             voice_spec = formula_from_profile(normalized_entry) or ""
-            language = str(normalized_entry.get("language") or language)
+            language = _parse_language(normalized_entry.get("language") or language)
     elif formula:
         voice_spec = formula
         resolved_provider = "kokoro"
@@ -198,7 +219,13 @@ def api_voice_profiles_preview() -> ResponseReturnValue:
         voice_spec = formula_from_profile(normalized_entry) or ""
         resolved_provider = "kokoro"
 
+    current_app.logger.info(
+        "[preview] resolved: provider=%s voice_spec=%s",
+        resolved_provider, voice_spec[:80] if voice_spec else "-",
+    )
+
     if not voice_spec:
+        current_app.logger.warning("[preview] empty voice_spec, returning 400")
         return jsonify({"error": "Unable to resolve preview voice"}), 400
 
     try:
@@ -213,6 +240,7 @@ def api_voice_profiles_preview() -> ResponseReturnValue:
             max_seconds=max_seconds,
         )
     except Exception as exc:
+        current_app.logger.exception("[preview] synthesis failed: %s", exc)
         return jsonify({"error": str(exc)}), 500
 
 @api_bp.post("/speaker-preview")
@@ -221,7 +249,7 @@ def api_speaker_preview() -> ResponseReturnValue:
     pending_id = str(payload.get("pending_id") or "").strip()
     text = payload.get("text", "Hello world")
     voice = payload.get("voice", "af_heart")
-    language = payload.get("language", "a")
+    language = _parse_language(payload.get("language"))
     speed_value = payload.get("speed")
     speed = coerce_float(speed_value, 1.0)
     tts_provider = str(payload.get("tts_provider") or "").strip().lower()
@@ -576,7 +604,7 @@ def api_entity_pronunciation_preview() -> ResponseReturnValue:
     token = payload.get("token", "").strip()
     pronunciation = payload.get("pronunciation", "").strip()
     voice = payload.get("voice", "").strip()
-    language = payload.get("language", "a").strip()
+    language = _parse_language(payload.get("language"))
     
     if not token and not pronunciation:
         return jsonify({"error": "Token or pronunciation required"}), 400

abogen/webui/routes/jobs.py

@@ -8,6 +8,8 @@ from flask.typing import ResponseReturnValue
 
 from abogen.webui.service import (
     JobStatus,
+)
+from abogen.domain.metadata_helpers import (
     build_audiobookshelf_metadata,
     load_audiobookshelf_chapters,
 )
@@ -19,9 +21,9 @@ from abogen.webui.routes.utils.epub import (
     locate_job_epub,
     locate_job_audio,
 )
-from abogen.webui.routes.utils.settings import (
-    stored_integration_config,
+from abogen.domain.settings_core import (
     build_audiobookshelf_config,
+    stored_integration_config,
 )
 from abogen.webui.routes.utils.common import existing_paths
 from abogen.infrastructure.exporters import ExportService

abogen/webui/routes/settings.py

@@ -7,6 +7,7 @@ from flask import Blueprint, current_app, render_template, request, redirect, ur
 from flask.typing import ResponseReturnValue
 
 from abogen.webui.routes.utils.settings import (
+    load_integration_settings,
     load_settings,
     save_settings,
     SAVE_MODE_LABELS,

abogen/webui/routes/utils/form.py

@@ -1,14 +1,12 @@
+import logging
 import time
 import uuid
 from typing import Any, Dict, Iterable, List, Mapping, Optional, Tuple, cast
 from flask import request, render_template, jsonify
 from flask.typing import ResponseReturnValue
 
-from abogen.domain.chapter_classification import (
-    supplement_score,
-    should_preselect_chapter,
-    ensure_at_least_one_chapter_enabled,
-)
+from abogen.domain.enums import Language
+from abogen.application.chapter_selection import build_chapter_payload
 from abogen.webui.service import PendingJob, JobStatus
 from abogen.webui.routes.utils.service import get_service
 from abogen.tts_plugin.utils import is_plugin_registered
@@ -24,17 +22,21 @@ from abogen.webui.routes.utils.settings import (
     audiobookshelf_manual_available,
 )
 from abogen.webui.routes.utils.voice import (
+    inject_recommended_voices,
     parse_voice_formula,
+    template_options,
+)
+from abogen.domain.speaker_metadata import prepare_speaker_metadata
+from abogen.domain.metadata_helpers import expand_metadata_aliases
+from abogen.domain.voice_resolution import (
     formula_from_profile,
     resolve_voice_setting,
     resolve_voice_choice,
-    prepare_speaker_metadata,
-    template_options,
 )
 from abogen.webui.routes.utils.entity import sync_pronunciation_overrides
 from abogen.webui.routes.utils.epub import job_download_flags
 from abogen.webui.routes.utils.common import split_profile_spec, extract_checkbox
-from abogen.utils import calculate_text_length
+from abogen.domain.text_utils import calculate_text_length
 from abogen.voice_profiles import serialize_profiles, normalize_profile_entry
 from abogen.chunking import ChunkLevel, build_chunks_for_chapters
 from abogen.tts_plugin.utils import get_default_voice
@@ -346,7 +348,10 @@ def apply_book_step_form(
     language_fallback = pending.language or settings.get("language", "en")
     raw_language = (form.get("language") or language_fallback or "en").strip()
     if raw_language:
-        pending.language = raw_language
+        try:
+            pending.language = Language.from_str(raw_language)
+        except (ValueError, AttributeError):
+            pending.language = Language.EN_US
 
     subtitle_mode = (form.get("subtitle_mode") or pending.subtitle_mode or "Disabled").strip()
     if subtitle_mode:
@@ -513,7 +518,10 @@ def apply_book_step_form(
     )
 
     if resolved_language:
-        pending.language = resolved_language
+        try:
+            pending.language = Language.from_str(str(resolved_language))
+        except (ValueError, AttributeError):
+            pass  # keep existing language
 
     if profile_selection == "__formula" and custom_formula_raw:
         pending.voice = custom_formula_raw
@@ -535,35 +543,27 @@ def apply_book_step_form(
     if "meta_subtitle" in form:
         pending.metadata_tags["subtitle"] = str(form.get("meta_subtitle", "")).strip()
 
+    # Collect user-editable metadata fields that have concept aliases
+    user_metadata: Dict[str, str] = {}
     if "meta_author" in form:
-        authors = str(form.get("meta_author", "")).strip()
-        pending.metadata_tags["authors"] = authors
-        pending.metadata_tags["author"] = authors
-
+        user_metadata["author"] = str(form.get("meta_author", "")).strip()
     if "meta_series" in form:
-        series = str(form.get("meta_series", "")).strip()
-        pending.metadata_tags["series"] = series
-        pending.metadata_tags["series_name"] = series
-        pending.metadata_tags["seriesname"] = series
-        pending.metadata_tags["series_title"] = series
-        pending.metadata_tags["seriestitle"] = series
-        # If user manually edits series, update opds_series too so it persists
-        if "opds_series" in pending.metadata_tags:
-            pending.metadata_tags["opds_series"] = series
-
+        user_metadata["series"] = str(form.get("meta_series", "")).strip()
     if "meta_series_index" in form:
-        idx = str(form.get("meta_series_index", "")).strip()
-        pending.metadata_tags["series_index"] = idx
-        pending.metadata_tags["series_sequence"] = idx
+        user_metadata["series_index"] = str(form.get("meta_series_index", "")).strip()
+    if "meta_description" in form:
+        user_metadata["description"] = str(form.get("meta_description", "")).strip()
+
+    if user_metadata:
+        expanded = expand_metadata_aliases(user_metadata)
+        pending.metadata_tags.update(expanded)
+        # If user manually edits series, update opds_series too so it persists
+        if "meta_series" in form and "opds_series" in pending.metadata_tags:
+            pending.metadata_tags["opds_series"] = expanded.get("series", "")
 
     if "meta_publisher" in form:
         pending.metadata_tags["publisher"] = str(form.get("meta_publisher", "")).strip()
 
-    if "meta_description" in form:
-        desc = str(form.get("meta_description", "")).strip()
-        pending.metadata_tags["description"] = desc
-        pending.metadata_tags["summary"] = desc
-
     if coerce_bool(form.get("remove_cover"), False):
         pending.cover_image_path = None
         pending.cover_image_mime = None
@@ -637,36 +637,13 @@ def build_pending_job_from_extraction(
         getattr(extraction, "combined_text", "")
     )
     chapters_source = getattr(extraction, "chapters", []) or []
-    total_chapter_count = len(chapters_source)
-    chapters_payload: List[Dict[str, Any]] = []
-    for index, chapter in enumerate(chapters_source):
-        enabled = should_preselect_chapter(chapter.title, chapter.text, index, total_chapter_count)
-        chapters_payload.append(
-            {
-                "id": f"{index:04d}",
-                "index": index,
-                "title": chapter.title,
-                "text": chapter.text,
-                "characters": calculate_text_length(chapter.text),
-                "enabled": enabled,
-            }
-        )
+    chapters_payload = build_chapter_payload(chapters_source, source_name=original_name)
 
-    if not chapters_payload:
-        chapters_payload.append(
-            {
-                "id": "0000",
-                "index": 0,
-                "title": original_name,
-                "text": "",
-                "characters": 0,
-                "enabled": True,
-            }
-        )
-
-    ensure_at_least_one_chapter_enabled(chapters_payload)
-
-    language = str(form.get("language") or "a").strip() or "a"
+    raw_language = str(form.get("language") or "a").strip() or "a"
+    try:
+        language = Language.from_str(raw_language)
+    except (ValueError, AttributeError):
+        language = Language.EN_US
     profiles_map = dict(profiles) if isinstance(profiles, Mapping) else dict(profiles or {})
     default_voice_setting = settings.get("default_voice") or ""
     resolved_default_voice, inferred_profile, inferred_language = resolve_voice_setting(
@@ -768,6 +745,7 @@ def build_pending_job_from_extraction(
         run_analysis=initial_analysis,
         speaker_config=speaker_config_payload,
         apply_config=bool(speaker_config_payload),
+        inject_recommended=inject_recommended_voices,
     )
 
     normalization_overrides = {}
@@ -783,6 +761,11 @@ def build_pending_job_from_extraction(
         else:
             normalization_overrides[key] = default_val
 
+    logging.info(
+        "[form] Creating PendingJob: language=%s voice=%s speed=%.2f provider=%s",
+        language, voice, speed, settings.get("tts_provider", "kokoro"),
+    )
+
     pending = PendingJob(
         id=uuid.uuid4().hex,
         original_filename=original_name,
@@ -826,6 +809,8 @@ def build_pending_job_from_extraction(
         analysis_requested=initial_analysis,
     )
 
+    apply_book_step_form(pending, form, settings=settings, profiles=profiles_map)
+
     return PendingBuildResult(
         pending=pending,
         selected_speaker_config=selected_speaker_config or None,

abogen/webui/routes/utils/settings.py

@@ -2,7 +2,6 @@ import os
 from typing import Any, Dict, Mapping, Optional
 
 from abogen.integrations.calibre_opds import CalibreOPDSClient
-from abogen.integrations.audiobookshelf import AudiobookshelfConfig
 from abogen.utils import load_config, save_config
 from abogen.domain.settings_core import (
     CHUNK_LEVEL_OPTIONS,
@@ -11,6 +10,7 @@ from abogen.domain.settings_core import (
     SAVE_MODE_LABELS,
     _NORMALIZATION_BOOLEAN_KEYS,
     _NORMALIZATION_STRING_KEYS,
+    build_audiobookshelf_config,
     coerce_bool,
     coerce_float,
     coerce_int,
@@ -18,6 +18,7 @@ from abogen.domain.settings_core import (
     load_settings,
     llm_ready,
     settings_defaults,
+    stored_integration_config,
 )
 
 _NORMALIZATION_GROUPS = [
@@ -124,20 +125,8 @@ def load_integration_settings() -> Dict[str, Dict[str, Any]]:
     return integrations
 
 
-def stored_integration_config(name: str) -> Dict[str, Any]:
-    cfg = load_config() or {}
-    # Check under "integrations" first (new structure)
-    integrations = cfg.get("integrations")
-    if isinstance(integrations, Mapping):
-        entry = integrations.get(name)
-        if isinstance(entry, Mapping):
-            return dict(entry)
-    
-    # Fallback to top-level (legacy structure)
-    entry = cfg.get(name)
-    if isinstance(entry, Mapping):
-        return dict(entry)
-    return {}
+# stored_integration_config and build_audiobookshelf_config are imported from
+# abogen.domain.settings_core — single source of truth for integration config.
 
 
 def calibre_settings_from_payload(payload: Mapping[str, Any]) -> Dict[str, Any]:
@@ -305,30 +294,6 @@ def audiobookshelf_settings_from_payload(payload: Mapping[str, Any]) -> Dict[str
     }
 
 
-def build_audiobookshelf_config(settings: Mapping[str, Any]) -> Optional[AudiobookshelfConfig]:
-    base_url = str(settings.get("base_url") or "").strip()
-    api_token = str(settings.get("api_token") or "").strip()
-    library_id = str(settings.get("library_id") or "").strip()
-    if not (base_url and api_token and library_id):
-        return None
-    try:
-        timeout = float(settings.get("timeout", 3600.0))
-    except (TypeError, ValueError):
-        timeout = 3600.0
-    return AudiobookshelfConfig(
-        base_url=base_url,
-        api_token=api_token,
-        library_id=library_id,
-        collection_id=(str(settings.get("collection_id") or "").strip() or None),
-        folder_id=(str(settings.get("folder_id") or "").strip() or None),
-        verify_ssl=coerce_bool(settings.get("verify_ssl"), True),
-        send_cover=coerce_bool(settings.get("send_cover"), True),
-        send_chapters=coerce_bool(settings.get("send_chapters"), True),
-        send_subtitles=coerce_bool(settings.get("send_subtitles"), False),
-        timeout=timeout,
-    )
-
-
 def calibre_integration_enabled(
     integrations: Optional[Mapping[str, Any]] = None,
 ) -> bool:

abogen/webui/routes/utils/synthesize.py

@@ -1,4 +1,5 @@
 import io
+import logging
 import threading
 from typing import Any, Dict, Iterable, List, Mapping, Optional, Tuple
 import numpy as np
@@ -6,8 +7,15 @@ import soundfile as sf
 from flask import current_app, send_file
 from flask.typing import ResponseReturnValue
 
+from abogen.domain.audio_helpers import to_float32
 from abogen.domain.device import select_device as _select_device
+from abogen.domain.enums import Language
 from abogen.domain.split_pattern import get_split_pattern
+from abogen.domain.pronunciation import (
+    merge_pronunciation_overrides,
+    compile_pronunciation_rules,
+    apply_pronunciation_rules,
+)
 
 
 SAMPLE_RATE = 24000
@@ -27,7 +35,7 @@ def clear_preview_pipelines() -> None:
         _preview_pipelines.clear()
 
 
-def _resolve_pipeline(language: str, use_gpu: bool) -> Tuple[Any, bool]:
+def _resolve_pipeline(language: Language, use_gpu: bool) -> Tuple[Any, bool]:
     devices: List[str] = ["cpu"]
     if use_gpu:
         preferred = _select_device()
@@ -37,29 +45,33 @@ def _resolve_pipeline(language: str, use_gpu: bool) -> Tuple[Any, bool]:
     last_error: Optional[Exception] = None
     for device in devices:
         try:
+            logging.info("[preview] Trying device=%s for language=%s", device, language)
             return get_preview_pipeline(language, device), device != "cpu"
         except Exception as exc:
             last_error = exc
+            logging.warning("[preview] Device %s failed: %s", device, exc)
 
     raise RuntimeError("Preview pipeline is unavailable") from last_error
 
 
-def get_preview_pipeline(language: str, device: str) -> Any:
+def get_preview_pipeline(language: Language, device: str) -> Any:
     key = (language, device)
     with _preview_pipeline_lock:
         pipeline = _preview_pipelines.get(key)
         if pipeline is not None:
+            logging.info("[preview] Using cached pipeline for %s/%s", language, device)
             return pipeline
         from abogen.tts_plugin.utils import create_pipeline
 
-        pipeline = create_pipeline("kokoro", lang_code=language, device=device)
+        logging.info("[preview] Creating pipeline: provider=kokoro language=%s device=%s", language, device)
+        pipeline = create_pipeline("kokoro", language=language, device=device)
         _preview_pipelines[key] = pipeline
         return pipeline
 
 def generate_preview_audio(
     text: str,
     voice_spec: str,
-    language: str,
+    language: Language,
     speed: float,
     use_gpu: bool,
     tts_provider: str = "kokoro",
@@ -79,8 +91,6 @@ def generate_preview_audio(
     source_text = text
     if pronunciation_overrides or manual_overrides or speakers:
         try:
-            from abogen.webui import conversion_runner as runner
-
             class _PreviewJob:
                 def __init__(self):
                     self.language = language
@@ -90,9 +100,9 @@ def generate_preview_audio(
                     self.pronunciation_overrides = list(pronunciation_overrides or [])
 
             job = _PreviewJob()
-            merged = runner._merge_pronunciation_overrides(job)
-            rules = runner._compile_pronunciation_rules(merged)
-            source_text = runner._apply_pronunciation_rules(source_text, rules)
+            merged = merge_pronunciation_overrides(job)
+            rules = compile_pronunciation_rules(merged)
+            source_text = apply_pronunciation_rules(source_text, rules)
         except Exception:
             current_app.logger.exception("Preview override application failed; using raw text")
             source_text = text
@@ -107,12 +117,12 @@ def generate_preview_audio(
             current_app.logger.exception("Preview normalization failed; using raw text")
             normalized_text = source_text
 
-    preview_split = get_split_pattern(str(language or "a"), "Disabled")
+    preview_split = get_split_pattern(language, "Disabled")
 
     if provider == "supertonic":
         from abogen.tts_plugin.utils import create_pipeline
 
-        pipeline = create_pipeline("supertonic")
+        pipeline = create_pipeline("supertonic", language=language)
         segments = pipeline(
             normalized_text,
             voice=voice_spec,
@@ -127,9 +137,9 @@ def generate_preview_audio(
 
         voice_choice: Any = voice_spec
         if voice_spec and "*" in voice_spec:
-            from abogen.voice_formulas import get_new_voice
+            from abogen.domain.voice_loader import resolve_voice
 
-            voice_choice = get_new_voice(pipeline, voice_spec, pipeline_uses_gpu)
+            voice_choice = resolve_voice(voice_spec, pipeline, pipeline_uses_gpu)
 
         segments = pipeline(
             normalized_text,
@@ -146,7 +156,7 @@ def generate_preview_audio(
         graphemes = getattr(segment, "graphemes", "").strip()
         if not graphemes:
             continue
-        audio = _to_float32(getattr(segment, "audio", None))
+        audio = to_float32(getattr(segment, "audio", None))
         if audio.size == 0:
             continue
         remaining = max_samples - accumulated
@@ -170,7 +180,7 @@ def generate_preview_audio(
 def synthesize_preview(
     text: str,
     voice_spec: str,
-    language: str,
+    language: Language,
     speed: float,
     use_gpu: bool,
     tts_provider: str = "kokoro",

abogen/webui/routes/utils/voice.py

@@ -1,9 +1,7 @@
 from typing import Any, Dict, Iterable, List, Mapping, Optional, Tuple, cast
 
 from abogen.speaker_configs import slugify_label
-from abogen.speaker_analysis import analyze_speakers
 from abogen.webui.routes.utils.settings import load_settings, settings_defaults, _DEFAULT_ANALYSIS_THRESHOLD, _CHUNK_LEVEL_OPTIONS, _APOSTROPHE_MODE_OPTIONS, _NORMALIZATION_GROUPS
-from abogen.webui.routes.utils.common import split_profile_spec
 from abogen.voice_profiles import (
     load_profiles,
     serialize_profiles,
@@ -18,282 +16,8 @@ from abogen.constants import (
 )
 from abogen.tts_plugin.utils import get_voices
 from abogen.speaker_configs import list_configs
-
-
-def build_narrator_roster(
-    voice: str,
-    voice_profile: Optional[str],
-    existing: Optional[Mapping[str, Any]] = None,
-) -> Dict[str, Any]:
-    roster: Dict[str, Any] = {
-        "narrator": {
-            "id": "narrator",
-            "label": "Narrator",
-            "voice": voice,
-        }
-    }
-    if voice_profile:
-        roster["narrator"]["voice_profile"] = voice_profile
-    existing_entry: Optional[Mapping[str, Any]] = None
-    if existing is not None:
-        existing_entry = existing.get("narrator") if isinstance(existing, Mapping) else None
-    if isinstance(existing_entry, Mapping):
-        roster_entry = roster["narrator"]
-        for key in ("label", "voice", "voice_profile", "voice_formula", "pronunciation"):
-            value = existing_entry.get(key)
-            if value is not None and value != "":
-                roster_entry[key] = value
-    return roster
-
-
-def build_speaker_roster(
-    analysis: Dict[str, Any],
-    base_voice: str,
-    voice_profile: Optional[str],
-    existing: Optional[Mapping[str, Any]] = None,
-    order: Optional[Iterable[str]] = None,
-) -> Dict[str, Any]:
-    roster = build_narrator_roster(base_voice, voice_profile, existing)
-    existing_map: Dict[str, Any] = dict(existing) if isinstance(existing, Mapping) else {}
-    speakers = analysis.get("speakers", {}) if isinstance(analysis, dict) else {}
-    ordered_ids: Iterable[str]
-    if order is not None:
-        ordered_ids = [sid for sid in order if sid in speakers]
-    else:
-        ordered_ids = speakers.keys()
-
-    for speaker_id in ordered_ids:
-        payload = speakers.get(speaker_id, {})
-        if speaker_id == "narrator":
-            continue
-        if isinstance(payload, Mapping) and payload.get("suppressed"):
-            continue
-        previous = existing_map.get(speaker_id)
-        roster[speaker_id] = {
-            "id": speaker_id,
-            "label": payload.get("label") or speaker_id.replace("_", " ").title(),
-            "analysis_confidence": payload.get("confidence"),
-            "analysis_count": payload.get("count"),
-            "gender": payload.get("gender", "unknown"),
-        }
-        detected_gender = payload.get("detected_gender")
-        if detected_gender:
-            roster[speaker_id]["detected_gender"] = detected_gender
-        samples = payload.get("sample_quotes")
-        if isinstance(samples, list):
-            roster[speaker_id]["sample_quotes"] = samples
-        if isinstance(previous, Mapping):
-            for key in ("voice", "voice_profile", "voice_formula", "resolved_voice", "pronunciation"):
-                value = previous.get(key)
-                if value is not None and value != "":
-                    roster[speaker_id][key] = value
-            if "sample_quotes" not in roster[speaker_id]:
-                prev_samples = previous.get("sample_quotes")
-                if isinstance(prev_samples, list):
-                    roster[speaker_id]["sample_quotes"] = prev_samples
-            if "detected_gender" not in roster[speaker_id]:
-                prev_detected = previous.get("detected_gender")
-                if isinstance(prev_detected, str) and prev_detected:
-                    roster[speaker_id]["detected_gender"] = prev_detected
-    return roster
-
-
-def match_configured_speaker(
-    config_speakers: Mapping[str, Any],
-    roster_id: str,
-    roster_label: str,
-) -> Optional[Mapping[str, Any]]:
-    if not config_speakers:
-        return None
-    entry = config_speakers.get(roster_id)
-    if entry:
-        return cast(Mapping[str, Any], entry)
-    slug = slugify_label(roster_label)
-    if slug != roster_id and slug in config_speakers:
-        return cast(Mapping[str, Any], config_speakers[slug])
-    lower_label = roster_label.strip().lower()
-    for record in config_speakers.values():
-        if not isinstance(record, Mapping):
-            continue
-        if str(record.get("label", "")).strip().lower() == lower_label:
-            return record
-    return None
-
-
-def apply_speaker_config_to_roster(
-    roster: Mapping[str, Any],
-    config: Optional[Mapping[str, Any]],
-    *,
-    persist_changes: bool = False,
-    fallback_languages: Optional[Iterable[str]] = None,
-) -> Tuple[Dict[str, Any], List[str], Optional[Dict[str, Any]]]:
-    if not isinstance(roster, Mapping):
-        effective_languages = [code for code in (fallback_languages or []) if isinstance(code, str) and code]
-        return {}, effective_languages, None
-    updated_roster: Dict[str, Any] = {key: dict(value) for key, value in roster.items() if isinstance(value, Mapping)}
-    if not config:
-        effective_languages = [code for code in (fallback_languages or []) if isinstance(code, str) and code]
-        return updated_roster, effective_languages, None
-
-    speakers_map = config.get("speakers")
-    if not isinstance(speakers_map, Mapping):
-        effective_languages = [code for code in (fallback_languages or []) if isinstance(code, str) and code]
-        return updated_roster, effective_languages, None
-
-    config_languages = config.get("languages")
-    if isinstance(config_languages, list):
-        allowed_languages = [code for code in config_languages if isinstance(code, str) and code]
-    else:
-        allowed_languages = []
-    if not allowed_languages and fallback_languages:
-        allowed_languages = [code for code in fallback_languages if isinstance(code, str) and code]
-
-    default_voice = config.get("default_voice") if isinstance(config.get("default_voice"), str) else ""
-    used_voices = {entry.get("resolved_voice") or entry.get("voice") for entry in updated_roster.values()} - {None}
-    narrator_voice = ""
-    narrator_entry = updated_roster.get("narrator") if isinstance(updated_roster, Mapping) else None
-    if isinstance(narrator_entry, Mapping):
-        narrator_voice = str(
-            narrator_entry.get("resolved_voice")
-            or narrator_entry.get("default_voice")
-            or ""
-        ).strip()
-        if narrator_voice:
-            used_voices.add(narrator_voice)
-
-    config_changed = False
-    new_config_payload: Dict[str, Any] = {
-        "language": config.get("language", "a"),
-        "languages": allowed_languages,
-        "default_voice": default_voice,
-        "speakers": dict(speakers_map),
-        "version": config.get("version", 1),
-        "notes": config.get("notes", ""),
-    }
-
-    speakers_payload = new_config_payload["speakers"]
-
-    for speaker_id, roster_entry in updated_roster.items():
-        if speaker_id == "narrator":
-            continue
-        label = str(roster_entry.get("label") or speaker_id)
-        config_entry = match_configured_speaker(speakers_map, speaker_id, label)
-        if config_entry is None:
-            continue
-        voice_id = str(config_entry.get("voice") or "").strip()
-        voice_profile = str(config_entry.get("voice_profile") or "").strip()
-        voice_formula = str(config_entry.get("voice_formula") or "").strip()
-        resolved_voice = str(config_entry.get("resolved_voice") or "").strip()
-        languages = config_entry.get("languages") if isinstance(config_entry.get("languages"), list) else []
-        chosen_voice = resolved_voice or voice_formula or voice_id or roster_entry.get("voice")
-        usable_languages = languages or allowed_languages
-
-        if chosen_voice:
-            roster_entry["resolved_voice"] = chosen_voice
-            roster_entry["voice"] = chosen_voice if not voice_profile and not voice_formula else roster_entry.get("voice", chosen_voice)
-        if voice_profile:
-            roster_entry["voice_profile"] = voice_profile
-        if voice_formula:
-            roster_entry["voice_formula"] = voice_formula
-            roster_entry["resolved_voice"] = voice_formula
-        if not voice_formula and not voice_profile and resolved_voice:
-            roster_entry["resolved_voice"] = resolved_voice
-        roster_entry["config_languages"] = usable_languages or []
-
-        if chosen_voice:
-            used_voices.add(chosen_voice)
-
-        # persist updates back to config payload if required
-        if persist_changes:
-            slug = config_entry.get("id") or slugify_label(label)
-            speakers_payload[slug] = {
-                "id": slug,
-                "label": label,
-                "gender": config_entry.get("gender", "unknown"),
-                "voice": voice_id,
-                "voice_profile": voice_profile,
-                "voice_formula": voice_formula,
-                "resolved_voice": roster_entry.get("resolved_voice", resolved_voice or voice_id),
-                "languages": usable_languages,
-            }
-
-    new_config = new_config_payload if (persist_changes and config_changed) else None
-    return updated_roster, allowed_languages, new_config
-
-
-def filter_voice_catalog(
-    catalog: Iterable[Mapping[str, Any]],
-    *,
-    gender: str,
-    allowed_languages: Optional[Iterable[str]] = None,
-) -> List[str]:
-    allowed_set = {code.lower() for code in (allowed_languages or []) if isinstance(code, str) and code}
-    gender_normalized = (gender or "unknown").lower()
-    gender_code = ""
-    if gender_normalized == "male":
-        gender_code = "m"
-    elif gender_normalized == "female":
-        gender_code = "f"
-
-    matches: List[str] = []
-    seen: set[str] = set()
-
-    def _consider(entry: Mapping[str, Any]) -> None:
-        voice_id = entry.get("id")
-        if not isinstance(voice_id, str) or not voice_id:
-            return
-        if voice_id in seen:
-            return
-        seen.add(voice_id)
-        matches.append(voice_id)
-
-    primary: List[Mapping[str, Any]] = []
-    fallback: List[Mapping[str, Any]] = []
-    for entry in catalog:
-        if not isinstance(entry, Mapping):
-            continue
-        voice_lang = str(entry.get("language", "")).lower()
-        voice_gender_code = str(entry.get("gender_code", "")).lower()
-        if allowed_set and voice_lang not in allowed_set:
-            continue
-        if gender_code and voice_gender_code != gender_code:
-            fallback.append(entry)
-            continue
-        primary.append(entry)
-
-    for entry in primary:
-        _consider(entry)
-
-    if not matches:
-        for entry in fallback:
-            _consider(entry)
-
-    if not matches:
-        for entry in catalog:
-            if isinstance(entry, Mapping):
-                _consider(entry)
-
-    return matches
-
-
-def build_voice_catalog() -> List[Dict[str, str]]:
-    catalog: List[Dict[str, str]] = []
-    gender_map = {"f": "Female", "m": "Male"}
-    for voice_id in get_voices("kokoro"):
-        prefix, _, rest = voice_id.partition("_")
-        language_code = prefix[0] if prefix else "a"
-        gender_code = prefix[1] if len(prefix) > 1 else ""
-        catalog.append(
-            {
-                "id": voice_id,
-                "language": language_code,
-                "language_label": LANGUAGE_DESCRIPTIONS.get(language_code, language_code.upper()),
-                "gender": gender_map.get(gender_code, "Unknown"),
-                "gender_code": gender_code,
-                "display_name": rest.replace("_", " ").title() if rest else voice_id,
-            }
-        )
-    return catalog
+from abogen.domain.voice_resolution import formula_from_profile
+from abogen.domain.voice_catalog import build_voice_catalog, filter_voice_catalog
 
 
 def inject_recommended_voices(
@@ -385,177 +109,6 @@ def extract_speaker_config_form(form: Mapping[str, Any]) -> Tuple[str, Dict[str,
     return name, payload, errors
 
 
-def prepare_speaker_metadata(
-    *,
-    chapters: List[Dict[str, Any]],
-    chunks: List[Dict[str, Any]],
-    analysis_chunks: Optional[List[Dict[str, Any]]] = None,
-    voice: str,
-    voice_profile: Optional[str],
-    threshold: int,
-    existing_roster: Optional[Mapping[str, Any]] = None,
-    run_analysis: bool = True,
-    speaker_config: Optional[Mapping[str, Any]] = None,
-    apply_config: bool = False,
-    persist_config: bool = False,
-) -> tuple[List[Dict[str, Any]], Dict[str, Any], Dict[str, Any], List[str], Optional[Dict[str, Any]]]:
-    chunk_list = [dict(chunk) for chunk in chunks]
-    analysis_source = [dict(chunk) for chunk in (analysis_chunks or chunks)]
-    threshold_value = max(1, int(threshold))
-    analysis_enabled = run_analysis
-    settings_state = load_settings()
-    global_random_languages = [
-        code
-        for code in settings_state.get("speaker_random_languages", [])
-        if isinstance(code, str) and code
-    ]
-
-    if not analysis_enabled:
-        for chunk in chunk_list:
-            chunk["speaker_id"] = "narrator"
-            chunk["speaker_label"] = "Narrator"
-        analysis_payload = {
-            "version": "1.0",
-            "narrator": "narrator",
-            "assignments": {str(chunk.get("id")): "narrator" for chunk in chunk_list},
-            "speakers": {
-                "narrator": {
-                    "id": "narrator",
-                    "label": "Narrator",
-                    "count": len(chunk_list),
-                    "confidence": "low",
-                    "sample_quotes": [],
-                    "suppressed": False,
-                }
-            },
-            "suppressed": [],
-            "stats": {
-                "total_chunks": len(chunk_list),
-                "explicit_chunks": 0,
-                "active_speakers": 0,
-                "unique_speakers": 1,
-                "suppressed": 0,
-            },
-        }
-        roster = build_narrator_roster(voice, voice_profile, existing_roster)
-        narrator_pron = roster["narrator"].get("pronunciation")
-        if narrator_pron:
-            analysis_payload["speakers"]["narrator"]["pronunciation"] = narrator_pron
-        return chunk_list, roster, analysis_payload, [], None
-
-    analysis_result = analyze_speakers(
-        chapters,
-        analysis_source,
-        threshold=threshold_value,
-        max_speakers=0,
-    )
-    analysis_payload = analysis_result.to_dict()
-    speakers_payload = analysis_payload.get("speakers", {})
-    ordered_ids = [
-        sid
-        for sid, meta in sorted(
-            (
-                (sid, meta)
-                for sid, meta in speakers_payload.items()
-                if sid != "narrator" and isinstance(meta, Mapping) and not meta.get("suppressed")
-            ),
-            key=lambda item: item[1].get("count", 0),
-            reverse=True,
-        )
-    ]
-    analysis_payload["ordered_speakers"] = ordered_ids
-    assignments = analysis_payload.get("assignments", {})
-    suppressed_ids = analysis_payload.get("suppressed", [])
-    suppressed_details: List[Dict[str, Any]] = []
-    speakers_payload = analysis_payload.get("speakers", {})
-    if isinstance(suppressed_ids, Iterable):
-        for suppressed_id in suppressed_ids:
-            speaker_meta = speakers_payload.get(suppressed_id) if isinstance(speakers_payload, dict) else None
-            if isinstance(speaker_meta, dict):
-                suppressed_details.append(
-                    {
-                        "id": suppressed_id,
-                        "label": speaker_meta.get("label")
-                        or str(suppressed_id).replace("_", " ").title(),
-                        "pronunciation": speaker_meta.get("pronunciation"),
-                    }
-                )
-            else:
-                suppressed_details.append(
-                    {
-                        "id": suppressed_id,
-                        "label": str(suppressed_id).replace("_", " ").title(),
-                        "pronunciation": None,
-                    }
-                )
-    analysis_payload["suppressed_details"] = suppressed_details
-    roster = build_speaker_roster(
-        analysis_payload,
-        voice,
-        voice_profile,
-        existing=existing_roster,
-        order=analysis_payload.get("ordered_speakers"),
-    )
-    applied_languages: List[str] = []
-    updated_config: Optional[Dict[str, Any]] = None
-    if apply_config and speaker_config:
-        roster, applied_languages, updated_config = apply_speaker_config_to_roster(
-            roster,
-            speaker_config,
-            persist_changes=persist_config,
-            fallback_languages=global_random_languages,
-        )
-        speakers_payload = analysis_payload.get("speakers")
-        if isinstance(speakers_payload, dict):
-            for roster_id, roster_payload in roster.items():
-                speaker_meta = speakers_payload.get(roster_id)
-                if isinstance(speaker_meta, dict):
-                    for key in ("voice", "voice_profile", "voice_formula", "resolved_voice"):
-                        value = roster_payload.get(key)
-                        if value:
-                            speaker_meta[key] = value
-    effective_languages: List[str] = []
-    if applied_languages:
-        effective_languages = applied_languages
-    elif isinstance(analysis_payload.get("config_languages"), list):
-        effective_languages = [
-            code for code in analysis_payload.get("config_languages", []) if isinstance(code, str) and code
-        ]
-    elif global_random_languages:
-        effective_languages = list(global_random_languages)
-
-    if effective_languages:
-        analysis_payload["config_languages"] = effective_languages
-    speakers_payload = analysis_payload.get("speakers")
-    if isinstance(speakers_payload, dict):
-        for roster_id, roster_payload in roster.items():
-            if roster_id in speakers_payload and isinstance(roster_payload, dict):
-                pronunciation_value = roster_payload.get("pronunciation")
-                if pronunciation_value:
-                    speakers_payload[roster_id]["pronunciation"] = pronunciation_value
-
-    fallback_languages = effective_languages or []
-    inject_recommended_voices(roster, fallback_languages=fallback_languages)
-
-    for chunk in chunk_list:
-        chunk_id = str(chunk.get("id"))
-        speaker_id = assignments.get(chunk_id, "narrator")
-        chunk["speaker_id"] = speaker_id
-        speaker_meta = roster.get(speaker_id)
-        chunk["speaker_label"] = speaker_meta.get("label") if isinstance(speaker_meta, dict) else speaker_id
-
-    return chunk_list, roster, analysis_payload, applied_languages, updated_config
-
-
-def formula_from_profile(entry: Dict[str, Any]) -> Optional[str]:
-    from abogen.voice_formulas import pairs_to_formula
-
-    voices = entry.get("voices") or []
-    if not voices:
-        return None
-    return pairs_to_formula(voices)
-
-
 def template_options() -> Dict[str, Any]:
     current_settings = load_settings()
     profiles = serialize_profiles()
@@ -576,7 +129,7 @@ def template_options() -> Dict[str, Any]:
         )
     voice_catalog = build_voice_catalog()
     return {
-        "languages": LANGUAGE_DESCRIPTIONS,
+        "languages": {lang.value: label for lang, label in LANGUAGE_DESCRIPTIONS.items()},
         "voices": get_voices("kokoro"),
         "subtitle_formats": SUBTITLE_FORMATS,
         "supported_langs_for_subs": SUPPORTED_LANGUAGES_FOR_SUBTITLE_GENERATION,
@@ -601,83 +154,6 @@ def template_options() -> Dict[str, Any]:
     }
 
 
-def resolve_profile_voice(
-    profile_name: Optional[str],
-    *,
-    profiles: Optional[Mapping[str, Any]] = None,
-) -> tuple[str, Optional[str]]:
-    if not profile_name:
-        return "", None
-    source = profiles if isinstance(profiles, Mapping) else None
-    if source is None:
-        source = load_profiles()
-    entry = source.get(profile_name) if isinstance(source, Mapping) else None
-    if not isinstance(entry, Mapping):
-        return "", None
-    formula = formula_from_profile(dict(entry)) or ""
-    language = entry.get("language") if isinstance(entry.get("language"), str) else None
-    if isinstance(language, str):
-        language = language.strip().lower() or None
-    return formula, language
-
-
-def resolve_voice_setting(
-    value: Any,
-    *,
-    profiles: Optional[Mapping[str, Any]] = None,
-) -> tuple[str, Optional[str], Optional[str]]:
-    base_spec, profile_name = split_profile_spec(value)
-    if profile_name:
-        formula, language = resolve_profile_voice(profile_name, profiles=profiles)
-        return formula or "", profile_name, language
-    return base_spec, None, None
-
-
-def resolve_voice_choice(
-    language: str,
-    base_voice: str,
-    profile_name: str,
-    custom_formula: str,
-    profiles: Dict[str, Any],
-) -> tuple[str, str, Optional[str]]:
-    resolved_voice = base_voice
-    resolved_language = language
-    selected_profile = None
-
-    if profile_name:
-        from abogen.voice_profiles import normalize_profile_entry
-
-        entry_raw = profiles.get(profile_name)
-        entry = normalize_profile_entry(entry_raw)
-        provider = str((entry or {}).get("provider") or "").strip().lower()
-
-        # Provider-aware behavior:
-        # - Kokoro profiles typically represent mixes (formula strings).
-        # - SuperTonic profiles represent a discrete voice id + settings.
-        #   In that case, we return a speaker reference so downstream can
-        #   resolve provider per-speaker and allow mixed-provider casting.
-        if provider == "supertonic":
-            resolved_voice = f"speaker:{profile_name}"
-            selected_profile = profile_name
-            profile_language = (entry or {}).get("language")
-            if profile_language:
-                resolved_language = str(profile_language)
-        else:
-            formula = formula_from_profile(entry or {}) if entry else None
-            if formula:
-                resolved_voice = formula
-                selected_profile = profile_name
-                profile_language = (entry or {}).get("language")
-                if profile_language:
-                    resolved_language = profile_language
-
-    if custom_formula:
-        resolved_voice = custom_formula
-        selected_profile = None
-
-    return resolved_voice, resolved_language, selected_profile
-
-
 def parse_voice_formula(formula: str) -> List[tuple[str, float]]:
     voices = parse_formula_terms(formula)
     total = sum(weight for _, weight in voices)

abogen/webui/routes/voices.py

@@ -2,11 +2,14 @@ from typing import Any, Dict, List, Optional
 from flask import Blueprint, render_template, request, jsonify, abort, flash, redirect, url_for
 from flask.typing import ResponseReturnValue
 
+from abogen.domain.enums import Language
 from abogen.webui.routes.utils.voice import (
     template_options,
+    parse_voice_formula,
+)
+from abogen.domain.voice_resolution import (
     resolve_voice_setting,
     resolve_voice_choice,
-    parse_voice_formula,
 )
 from abogen.webui.routes.utils.settings import load_settings, coerce_bool
 from abogen.webui.routes.utils.synthesize import synthesize_preview
@@ -39,7 +42,7 @@ def test_voice() -> ResponseReturnValue:
         return synthesize_preview(
             text=text,
             voice_spec=voice,
-            language="a", # Default language
+            language=Language.EN_US,
             speed=speed,
             use_gpu=use_gpu,
         )

abogen/webui/service.py

@@ -14,24 +14,11 @@ from enum import Enum
 from pathlib import Path
 from typing import Any, Callable, Dict, Iterable, List, Optional, Mapping
 
-from abogen.utils import get_internal_cache_path, get_user_settings_dir, load_config
-from abogen.voice_cache import bootstrap_voice_cache
-from abogen.integrations.audiobookshelf import (
-    AudiobookshelfClient,
-    AudiobookshelfConfig,
-    AudiobookshelfUploadError,
-)
-from abogen.domain.metadata_helpers import (
-    normalize_metadata_casefold as _normalize_metadata_casefold,
-    split_people_field as _split_people_field,
-    split_simple_list as _split_simple_list,
-    first_nonempty as _first_nonempty,
-    extract_year as _extract_year,
-    normalize_series_sequence as _normalize_series_sequence,
-    build_audiobookshelf_metadata as _build_abs_metadata,
-    load_audiobookshelf_chapters as _load_abs_chapters,
-    _SERIES_SEQUENCE_TAG_KEYS,
-)
+from abogen.domain.metadata_helpers import normalize_metadata_map
+
+from abogen.domain.enums import Language
+from abogen.utils import console_handler, get_internal_cache_path, get_user_settings_dir
+
 
 
 def _create_set_event() -> threading.Event:
@@ -45,9 +32,7 @@ STATE_VERSION = 8
 
 _JOB_LOGGER = logging.getLogger("abogen.jobs")
 if not _JOB_LOGGER.handlers:
-    handler = logging.StreamHandler(sys.stdout)
-    handler.setFormatter(logging.Formatter("%(asctime)s [%(levelname)s] %(message)s", "%Y-%m-%d %H:%M:%S"))
-    _JOB_LOGGER.addHandler(handler)
+    _JOB_LOGGER.addHandler(console_handler())
     _JOB_LOGGER.propagate = False
 _JOB_LOGGER.setLevel(logging.DEBUG)
 
@@ -105,7 +90,7 @@ class Job:
     id: str
     original_filename: str
     stored_path: Path
-    language: str
+    language: Language
     voice: str
     speed: float
     use_gpu: bool
@@ -265,23 +250,6 @@ class Job:
         }
 
 
-def build_audiobookshelf_metadata(job: Job) -> Dict[str, Any]:
-    filename = Path(job.original_filename or "").stem or job.original_filename or "Audiobook"
-    return _build_abs_metadata(
-        job.metadata_tags,
-        language=job.language or "",
-        filename=filename,
-    )
-
-
-def load_audiobookshelf_chapters(job: Job) -> Optional[List[Dict[str, Any]]]:
-    metadata_ref = job.result.artifacts.get("metadata")
-    if not metadata_ref:
-        return None
-    metadata_path = metadata_ref if isinstance(metadata_ref, Path) else Path(str(metadata_ref))
-    return _load_abs_chapters(metadata_path)
-
-
 def _existing_paths(paths: Iterable[Any]) -> List[Path]:
     resolved: List[Path] = []
     for item in paths:
@@ -296,7 +264,7 @@ class PendingJob:
     id: str
     original_filename: str
     stored_path: Path
-    language: str
+    language: Language
     voice: str
     speed: float
     use_gpu: bool
@@ -367,7 +335,6 @@ class ConversionService:
         self._pending_jobs: Dict[str, PendingJob] = {}
         self._state_path = self._determine_state_path()
         self._ensure_directories()
-        self._bootstrap_voice_cache()
         self._load_state()
 
     # Public API ---------------------------------------------------------
@@ -384,7 +351,7 @@ class ConversionService:
         *,
         original_filename: str,
         stored_path: Path,
-        language: str,
+        language: Language,
         voice: str,
         speed: float,
         tts_provider: str = "kokoro",
@@ -428,7 +395,7 @@ class ConversionService:
         normalization_overrides: Optional[Mapping[str, Any]] = None,
     ) -> Job:
         job_id = uuid.uuid4().hex
-        normalized_metadata = self._normalize_metadata_tags(metadata_tags)
+        normalized_metadata = normalize_metadata_map(metadata_tags)
         normalized_chapters = self._normalize_chapters(chapters)
         normalized_chunks = self._normalize_chunks(chunks)
         if total_characters <= 0 and normalized_chapters:
@@ -697,23 +664,6 @@ class ConversionService:
         self._uploads_root.mkdir(parents=True, exist_ok=True)
         self._state_path.parent.mkdir(parents=True, exist_ok=True)
 
-    def _bootstrap_voice_cache(self) -> None:
-        try:
-            downloaded, errors = bootstrap_voice_cache(
-                on_progress=lambda msg: _JOB_LOGGER.debug("[voice cache] %s", msg)
-            )
-        except RuntimeError as exc:
-            _JOB_LOGGER.warning("Voice cache bootstrap skipped: %s", exc)
-            return
-
-        if downloaded:
-            count = len(downloaded)
-            suffix = "s" if count != 1 else ""
-            _JOB_LOGGER.info("Voice cache ready: downloaded %d new asset%s.", count, suffix)
-        if errors:
-            for voice_id, message in errors.items():
-                _JOB_LOGGER.warning("Voice cache failed for %s: %s", voice_id, message)
-
     def _ensure_worker(self) -> None:
         with self._lock:
             if self._worker_thread and self._worker_thread.is_alive():
@@ -780,7 +730,6 @@ class ConversionService:
             elif job.status != JobStatus.FAILED:
                 job.status = JobStatus.COMPLETED
                 job.add_log("Job completed", level="success")
-                self._post_completion_hooks(job)
             job.finished_at = time.time()
         finally:
             job.pause_event.set()
@@ -801,105 +750,6 @@ class ConversionService:
             self._queue.remove(job_id)
         self._update_queue_positions_locked()
 
-    def _post_completion_hooks(self, job: Job) -> None:
-        try:
-            self._maybe_send_to_audiobookshelf(job)
-        except AudiobookshelfUploadError as exc:
-            job.add_log(f"Audiobookshelf upload failed: {exc}", level="error")
-        except Exception as exc:  # pragma: no cover - defensive guard
-            job.add_log(f"Audiobookshelf integration error: {exc}", level="error")
-
-    def _maybe_send_to_audiobookshelf(self, job: Job) -> None:
-        cfg = load_config() or {}
-        integration_cfg = cfg.get("audiobookshelf")
-        if not isinstance(integration_cfg, Mapping):
-            return
-        enabled = self._coerce_bool(integration_cfg.get("enabled"), False)
-        auto_send = self._coerce_bool(integration_cfg.get("auto_send"), False)
-        if not (enabled and auto_send):
-            return
-
-        base_url = str(integration_cfg.get("base_url") or "").strip()
-        api_token = str(integration_cfg.get("api_token") or "").strip()
-        library_id = str(integration_cfg.get("library_id") or "").strip()
-        folder_id = str(integration_cfg.get("folder_id") or "").strip()
-        if not base_url or not api_token or not library_id:
-            job.add_log(
-                "Audiobookshelf upload skipped: configure base URL, API token, and library ID first.",
-                level="warning",
-            )
-            return
-        if not folder_id:
-            job.add_log(
-                "Audiobookshelf upload skipped: enter the folder name or ID in the Audiobookshelf settings.",
-                level="warning",
-            )
-            return
-
-        audio_ref = job.result.audio_path
-        audio_path = audio_ref if isinstance(audio_ref, Path) else Path(str(audio_ref)) if audio_ref else None
-        if not audio_path or not audio_path.exists():
-            job.add_log("Audiobookshelf upload skipped: audio output not found.", level="warning")
-            return
-
-        timeout_raw = integration_cfg.get("timeout", 3600.0)
-        try:
-            timeout_value = float(timeout_raw)
-        except (TypeError, ValueError):
-            timeout_value = 3600.0
-
-        config = AudiobookshelfConfig(
-            base_url=base_url,
-            api_token=api_token,
-            library_id=library_id,
-            collection_id=(str(integration_cfg.get("collection_id") or "").strip() or None),
-            folder_id=folder_id,
-            verify_ssl=self._coerce_bool(integration_cfg.get("verify_ssl"), True),
-            send_cover=self._coerce_bool(integration_cfg.get("send_cover"), True),
-            send_chapters=self._coerce_bool(integration_cfg.get("send_chapters"), True),
-            send_subtitles=self._coerce_bool(integration_cfg.get("send_subtitles"), False),
-            timeout=timeout_value,
-        )
-
-        cover_ref = job.cover_image_path
-        cover_path = None
-        if config.send_cover and cover_ref:
-            cover_candidate = cover_ref if isinstance(cover_ref, Path) else Path(str(cover_ref))
-            if cover_candidate.exists():
-                cover_path = cover_candidate
-
-        subtitles = _existing_paths(job.result.subtitle_paths) if config.send_subtitles else None
-        chapters = load_audiobookshelf_chapters(job) if config.send_chapters else None
-        metadata = build_audiobookshelf_metadata(job)
-
-        client = AudiobookshelfClient(config)
-
-        display_title = metadata.get("title") or audio_path.stem
-        try:
-            existing_items = client.find_existing_items(display_title, folder_id=config.folder_id)
-        except AudiobookshelfUploadError as exc:
-            job.add_log(f"Audiobookshelf lookup failed: {exc}", level="error")
-            return
-
-        if existing_items:
-            job.add_log(
-                f"Removing existing Audiobookshelf item(s) for '{display_title}' before upload.",
-                level="info",
-            )
-            try:
-                client.delete_items(existing_items)
-            except Exception as exc:
-                job.add_log(f"Failed to remove existing item(s): {exc}", level="warning")
-
-        client.upload_audiobook(
-            audio_path,
-            metadata=metadata,
-            cover_path=cover_path,
-            chapters=chapters,
-            subtitles=subtitles,
-        )
-        job.add_log("Audiobookshelf upload queued.", level="info")
-
     # Persistence ------------------------------------------------------
     def _serialize_job(self, job: Job) -> Dict[str, Any]:
         result_audio = str(job.result.audio_path) if job.result.audio_path else None
@@ -910,7 +760,7 @@ class ConversionService:
             "id": job.id,
             "original_filename": job.original_filename,
             "stored_path": str(job.stored_path),
-            "language": job.language,
+            "language": job.language.value if isinstance(job.language, Language) else str(job.language),
             "tts_provider": getattr(job, "tts_provider", "kokoro"),
             "voice": job.voice,
             "speed": job.speed,
@@ -1026,11 +876,19 @@ class ConversionService:
         stored_path = Path(payload["stored_path"])
         output_folder_raw = payload.get("output_folder")
         output_folder = Path(output_folder_raw) if output_folder_raw else None
+        raw_lang = payload.get("language", "")
+        if isinstance(raw_lang, Language):
+            language = raw_lang
+        else:
+            try:
+                language = Language.from_str(str(raw_lang or "").strip())
+            except (ValueError, AttributeError):
+                language = Language.EN_US
         job = Job(
             id=payload["id"],
             original_filename=payload["original_filename"],
             stored_path=stored_path,
-            language=payload.get("language", "a"),
+            language=language,
             tts_provider=str(payload.get("tts_provider") or "kokoro"),
             voice=payload.get("voice", ""),
             speed=float(payload.get("speed", 1.0)),
@@ -1177,20 +1035,6 @@ class ConversionService:
         except (TypeError, ValueError):
             return None
 
-    @staticmethod
-    def _normalize_metadata_tags(values: Optional[Mapping[str, Any]]) -> Dict[str, str]:
-        if not values:
-            return {}
-        normalized: Dict[str, str] = {}
-        for key, raw_value in values.items():
-            if raw_value is None:
-                continue
-            key_str = str(key).strip()
-            if not key_str:
-                continue
-            normalized[key_str] = str(raw_value)
-        return normalized
-
     @classmethod
     def _normalize_chapters(cls, chapters: Optional[Iterable[Any]]) -> List[Dict[str, Any]]:
         if not chapters:
@@ -1267,7 +1111,7 @@ class ConversionService:
             entry["enabled"] = enabled
 
             metadata_payload = raw_dict.get("metadata") or raw_dict.get("metadata_tags")
-            normalized_metadata = cls._normalize_metadata_tags(metadata_payload)
+            normalized_metadata = normalize_metadata_map(metadata_payload)
             if normalized_metadata:
                 entry["metadata"] = normalized_metadata
 

abogen/webui/services/settings_service.py

@@ -29,8 +29,8 @@ def apply_form_to_settings(current: dict, form: Mapping[str, Any]) -> dict:
         DEFAULT_ANALYSIS_THRESHOLD,
         _NORMALIZATION_BOOLEAN_KEYS,
         _NORMALIZATION_STRING_KEYS,
+        stored_integration_config,
     )
-    from abogen.webui.routes.utils.settings import stored_integration_config
     from abogen.webui.routes.utils.common import extract_checkbox
     from abogen.utils import load_config
     # General settings

abogen/webui/static/dashboard.js

@@ -452,6 +452,9 @@ const initDashboard = () => {
         return;
       }
       openUploadModal(dropzone);
+      if (sourceFileInput) {
+        sourceFileInput.click();
+      }
     });
 
     dropzone.addEventListener("keydown", (event) => {

plugins/kokoro/__init__.py

@@ -165,6 +165,7 @@ def create_engine(
     """
     try:
         KPipeline = _load_kpipeline()
+        from plugins.kokoro.engine import engine_language
 
         # Determine repo_id from model_path or use default
         repo_id = "hexgrad/Kokoro-82M"
@@ -172,8 +173,9 @@ def create_engine(
             # If a specific model path is provided, use it as repo_id
             repo_id = str(model_path)
 
+        kokoro_code = engine_language(config.language)
         pipeline = KPipeline(
-            lang_code=config.lang_code,
+            lang_code=kokoro_code,
             repo_id=repo_id,
             device=config.device,
         )

plugins/kokoro/engine.py

@@ -2,6 +2,10 @@
 
 This module adapts the existing Kokoro backend to the new Engine/EngineSession
 protocol. It wraps the KokoroBackend without modifying it.
+
+Language mapping: this is the engine's responsibility. The engine knows
+which languages it supports and converts Language enum → internal format.
+Callers outside this module never see engine-specific codes.
 """
 
 from __future__ import annotations
@@ -11,15 +15,18 @@ from typing import Any
 
 import numpy as np
 
+from abogen.domain.enums import Language
 from abogen.tts_plugin.capabilities import VoiceLister
 from abogen.tts_plugin.engine import Engine, EngineSession
 from abogen.tts_plugin.errors import EngineError
 from abogen.tts_plugin.manifest import VoiceManifest
 from abogen.tts_plugin.types import (
     AudioFormat,
+    AudioSegment,
     Duration,
     SynthesisRequest,
     SynthesizedAudio,
+    TokenTiming,
 )
 
 logger = logging.getLogger(__name__)
@@ -27,6 +34,69 @@ logger = logging.getLogger(__name__)
 # Sample rate for Kokoro audio
 _KOKORO_SAMPLE_RATE = 24000
 
+# Engine-internal language mapping: Language enum → kokoro code.
+# ONLY visible inside this module — callers never see kokoro codes.
+_KOKORO_LANG_MAP: dict[Language, str] = {
+    Language.EN_US: "a",
+    Language.EN_GB: "b",
+    Language.ES: "e",
+    Language.FR: "f",
+    Language.HI: "h",
+    Language.IT: "i",
+    Language.JA: "j",
+    Language.PT_BR: "p",
+    Language.ZH: "z",
+}
+
+# Reverse mapping: engine-internal code → Language enum.
+# Used by voice catalog and other places that need to convert
+# engine codes back to Language enum (e.g. voice ID prefix extraction).
+_CODE_TO_LANGUAGE: dict[str, Language] = {v: k for k, v in _KOKORO_LANG_MAP.items()}
+
+
+def supported_languages() -> list[Language]:
+    """Return the list of Language enum values this engine supports.
+
+    This is the engine's responsibility — the engine knows which
+    languages it supports and exposes them as Language enum values.
+    UI layers query this to populate language selectors.
+    """
+    return list(_KOKORO_LANG_MAP.keys())
+
+
+def engine_language(lang: Language) -> str:
+    """Map a Language enum to the engine's internal code.
+
+    This is the engine's responsibility — the engine owns the mapping
+    between Language enum and its internal format. Callers pass Language
+    enum; the engine converts internally. The returned string is ONLY
+    used inside the engine implementation.
+    """
+    return _KOKORO_LANG_MAP.get(lang, "a")
+
+
+def language_for_code(code: str | None) -> Language:
+    """Map a kokoro engine language code (single letter) to a Language enum.
+
+    Used to resolve legacy data such as old profile files that stored
+    kokoro letter codes. This is kokoro-specific knowledge that stays
+    inside the engine. Unparseable values fall back to EN_US.
+    """
+    letter = str(code or "").strip()[:1].lower()
+    if letter in _CODE_TO_LANGUAGE:
+        return _CODE_TO_LANGUAGE[letter]
+    return Language.EN_US
+
+
+def language_for_voice_id(voice_id: str | None) -> Language:
+    """Determine which Language a voice belongs to from its voice ID.
+
+    Kokoro voice IDs encode language as a prefix (e.g. "af_heart" → "a" → EN_US).
+    This is kokoro-specific knowledge that stays inside the engine.
+    Callers pass a voice ID string; the engine returns a Language enum.
+    """
+    return language_for_code(voice_id)
+
 
 class KokoroSession:
     """EngineSession implementation for Kokoro.
@@ -49,7 +119,9 @@ class KokoroSession:
             speed = request.parameters.values.get("speed", 1.0)
             split_pattern = request.parameters.values.get("split_pattern", None)
 
+            sample_rate = _KOKORO_SAMPLE_RATE
             audio_parts: list[np.ndarray] = []
+            segments: list[AudioSegment] = []
             for segment in self._pipeline(
                 request.text,
                 voice=voice,
@@ -59,7 +131,28 @@ class KokoroSession:
                 audio = segment.audio
                 if hasattr(audio, "numpy"):
                     audio = audio.numpy()
-                audio_parts.append(np.asarray(audio, dtype="float32"))
+                audio = np.asarray(audio, dtype="float32")
+                if audio.size == 0:
+                    continue
+                audio_parts.append(audio)
+
+                tokens = tuple(
+                    TokenTiming(
+                        text=str(tok.text),
+                        whitespace=str(tok.whitespace or ""),
+                        start=float(tok.start_ts or 0.0),
+                        end=float(tok.end_ts or 0.0),
+                    )
+                    for tok in (getattr(segment, "tokens", None) or [])
+                )
+                segments.append(
+                    AudioSegment(
+                        graphemes=str(getattr(segment, "graphemes", "") or ""),
+                        audio=audio.tobytes(),
+                        sample_rate=sample_rate,
+                        tokens=tokens,
+                    )
+                )
 
             if not audio_parts:
                 return SynthesizedAudio(
@@ -70,12 +163,13 @@ class KokoroSession:
 
             combined = np.concatenate(audio_parts).astype("float32", copy=False)
             audio_bytes = combined.tobytes()
-            duration_seconds = len(combined) / _KOKORO_SAMPLE_RATE
+            duration_seconds = len(combined) / sample_rate
 
             return SynthesizedAudio(
                 data=audio_bytes,
                 format=AudioFormat(mime="audio/wav", extension="wav"),
                 duration=Duration(seconds=duration_seconds),
+                segments=tuple(segments),
             )
         except EngineError:
             raise

plugins/supertonic/__init__.py

@@ -32,11 +32,12 @@ from abogen.tts_plugin.types import EngineConfig
 from .engine import SuperTonicEngine
 
 
-def _load_supertonic_pipeline() -> Any:
+def _load_supertonic_pipeline(language: Any = None) -> Any:
     """Lazy-load SuperTonic dependencies and create pipeline."""
     from plugins.supertonic.pipeline import SupertonicPipeline
 
     return SupertonicPipeline(
+        language=language,
         sample_rate=24000,
         auto_download=True,
         total_steps=5,
@@ -128,7 +129,7 @@ def create_engine(
         EngineError: On failure. Cleans up partially created resources.
     """
     try:
-        pipeline = _load_supertonic_pipeline()
+        pipeline = _load_supertonic_pipeline(language=config.language)
         engine = SuperTonicEngine(pipeline)
         return engine
     except Exception as e:

plugins/supertonic/engine.py

@@ -12,12 +12,14 @@ from typing import Any
 
 import numpy as np
 
+from abogen.domain.enums import Language
 from abogen.tts_plugin.capabilities import VoiceLister
 from abogen.tts_plugin.engine import Engine, EngineSession
 from abogen.tts_plugin.errors import EngineError
 from abogen.tts_plugin.manifest import VoiceManifest
 from abogen.tts_plugin.types import (
     AudioFormat,
+    AudioSegment,
     Duration,
     SynthesisRequest,
     SynthesizedAudio,
@@ -28,6 +30,61 @@ logger = logging.getLogger(__name__)
 # Sample rate for SuperTonic audio
 _SUPERTONIC_SAMPLE_RATE = 24000
 
+# Engine-internal language mapping: Language enum → Supertonic ISO 639-1 code.
+_SUPERTONIC_LANG_MAP: dict[Language, str] = {
+    Language.EN_US: "en",
+    Language.EN_GB: "en",
+    Language.AR: "ar",
+    Language.BG: "bg",
+    Language.CS: "cs",
+    Language.DA: "da",
+    Language.DE: "de",
+    Language.EL: "el",
+    Language.ES: "es",
+    Language.ET: "et",
+    Language.FI: "fi",
+    Language.FR: "fr",
+    Language.HI: "hi",
+    Language.HR: "hr",
+    Language.HU: "hu",
+    Language.ID: "id",
+    Language.IT: "it",
+    Language.JA: "ja",
+    Language.KO: "ko",
+    Language.LT: "lt",
+    Language.LV: "lv",
+    Language.NL: "nl",
+    Language.PL: "pl",
+    Language.PT_BR: "pt",
+    Language.RO: "ro",
+    Language.RU: "ru",
+    Language.SK: "sk",
+    Language.SL: "sl",
+    Language.SV: "sv",
+    Language.TR: "tr",
+    Language.UK: "uk",
+    Language.VI: "vi",
+}
+
+
+def supported_languages() -> list[Language]:
+    """Return the list of Language enum values this engine supports."""
+    return list(_SUPERTONIC_LANG_MAP.keys())
+
+
+def engine_language(lang: Language) -> str:
+    """Map a Language enum to the engine's internal ISO 639-1 code.
+
+    Raises ValueError for unsupported languages.
+    """
+    result = _SUPERTONIC_LANG_MAP.get(lang)
+    if result is None:
+        raise ValueError(
+            f"Supertonic does not support language: {lang!r}. "
+            f"Supported: {supported_languages()}"
+        )
+    return result
+
 
 class SuperTonicSession:
     """EngineSession implementation for SuperTonic.
@@ -57,6 +114,7 @@ class SuperTonicSession:
                 total_steps = int(total_steps)
 
             audio_parts: list[np.ndarray] = []
+            segments: list[AudioSegment] = []
             for segment in self._pipeline(
                 request.text,
                 voice=voice,
@@ -64,7 +122,17 @@ class SuperTonicSession:
                 split_pattern=split_pattern,
                 total_steps=total_steps,
             ):
-                audio_parts.append(segment.audio)
+                audio = np.asarray(segment.audio, dtype="float32")
+                if audio.size == 0:
+                    continue
+                audio_parts.append(audio)
+                segments.append(
+                    AudioSegment(
+                        graphemes=str(getattr(segment, "graphemes", "") or ""),
+                        audio=audio.tobytes(),
+                        sample_rate=self._pipeline.sample_rate,
+                    )
+                )
 
             if not audio_parts:
                 return SynthesizedAudio(
@@ -83,6 +151,7 @@ class SuperTonicSession:
                 data=audio_bytes,
                 format=AudioFormat(mime="audio/wav", extension="wav"),
                 duration=Duration(seconds=duration_seconds),
+                segments=tuple(segments),
             )
         except EngineError:
             raise

plugins/supertonic/pipeline.py

@@ -158,6 +158,7 @@ class SupertonicPipeline:
     def __init__(
         self,
         *,
+        language: Any = None,
         sample_rate: int,
         auto_download: bool = True,
         total_steps: int = 5,
@@ -167,6 +168,13 @@ class SupertonicPipeline:
         self.total_steps = int(total_steps)
         self.max_chunk_length = int(max_chunk_length)
 
+        # Resolve language to ISO 639-1 code for Supertonic
+        if language is not None:
+            from plugins.supertonic.engine import engine_language
+            self._lang = engine_language(language)
+        else:
+            self._lang = "en"
+
         _configure_supertonic_gpu()
 
         try:
@@ -212,6 +220,7 @@ class SupertonicPipeline:
                         max_chunk_length=self.max_chunk_length,
                         silence_duration=0.0,
                         verbose=False,
+                        lang=self._lang,
                     )
                     break
                 except ValueError as exc:

pyproject.toml

@@ -50,6 +50,7 @@ dependencies = [
   "num2words>=0.5.13",
   "httpx>=0.27.0",
   "PyQt6>=6.5.0",
+  "rich>=13.0.0",
 ]
 
 classifiers = [

tests/contracts/test_integration.py

@@ -14,6 +14,8 @@ from unittest.mock import MagicMock, patch
 
 import numpy as np
 
+from abogen.domain.enums import Language
+
 from abogen.tts_plugin.engine import Engine, EngineSession
 from abogen.tts_plugin.errors import EngineError
 from abogen.tts_plugin.plugin_manager import PluginManager, get_plugin_manager, reset_plugin_manager
@@ -328,7 +330,7 @@ class TestRegression:
         manager._loaded = True
         
         with patch("abogen.tts_plugin.utils.get_plugin_manager", return_value=manager):
-            backend = create_pipeline("mock_tts", lang_code="a", device="cpu")
+            backend = create_pipeline("mock_tts", language=Language.EN_US, device="cpu")
             
             # Old interface: pipeline(text, voice=..., speed=..., split_pattern=...)
             segments = list(backend(

tests/contracts/test_plugin_manager_contract.py

@@ -3,6 +3,7 @@
 import pytest
 from unittest.mock import MagicMock, patch
 
+from abogen.domain.enums import Language
 from abogen.tts_plugin.plugin_manager import PluginManager, get_plugin_manager, reset_plugin_manager
 from abogen.tts_plugin.utils import Pipeline, create_pipeline
 from abogen.tts_plugin.engine import Engine, EngineSession
@@ -175,7 +176,7 @@ class TestCreatePipelineCompat:
             mock_engine = FakeEngine()
             mock_manager.create_engine.return_value = mock_engine
             
-            backend = create_pipeline("kokoro", lang_code="a", device="cpu")
+            backend = create_pipeline("kokoro", language=Language.EN_US, device="cpu")
             
             assert callable(backend)
             mock_manager.create_engine.assert_called_once()
@@ -185,7 +186,7 @@ class TestCreatePipelineCompat:
             assert call_args.kwargs["model_path"] is None
             assert isinstance(call_args.kwargs["config"], EngineConfig)
             assert call_args.kwargs["config"].device == "cpu"
-            assert call_args.kwargs["config"].lang_code == "a"
+            assert call_args.kwargs["config"].language == Language.EN_US
     
     def test_create_pipeline_raises_for_unknown_plugin(self):
         """create_pipeline raises KeyError for unknown plugins."""

tests/contracts/test_types_contract.py

@@ -8,6 +8,7 @@ These tests verify that value objects satisfy the architectural requirements:
 
 import pytest
 
+from abogen.domain.enums import Language
 from abogen.tts_plugin.types import (
     AudioFormat,
     Duration,
@@ -192,23 +193,23 @@ class TestEngineConfigContract:
         config = EngineConfig(device="cuda:0")
         assert config.device == "cuda:0"
 
-    def test_default_lang_code(self) -> None:
+    def test_default_language(self) -> None:
         config = EngineConfig()
-        assert config.lang_code == "a"
+        assert config.language == Language.EN_US
 
-    def test_custom_lang_code(self) -> None:
-        config = EngineConfig(lang_code="j")
-        assert config.lang_code == "j"
+    def test_custom_language(self) -> None:
+        config = EngineConfig(language=Language.JA)
+        assert config.language == Language.JA
 
     def test_immutability(self) -> None:
         config = EngineConfig()
         with pytest.raises(AttributeError):
             config.device = "cuda:0"  # type: ignore[misc]
 
-    def test_immutability_lang_code(self) -> None:
+    def test_immutability_language(self) -> None:
         config = EngineConfig()
         with pytest.raises(AttributeError):
-            config.lang_code = "j"  # type: ignore[misc]
+            config.language = Language.JA  # type: ignore[misc]
 
     def test_unknown_keys_ignored_per_spec(self) -> None:
         """Architecture spec: Unknown keys are ignored (no error).
@@ -225,11 +226,11 @@ class TestEngineConfigContract:
         EngineConfig may contain fields that are not relevant to every plugin.
         Plugins MUST ignore fields they do not need, not raise on them.
         """
-        config = EngineConfig(device="cuda:0", lang_code="j")
+        config = EngineConfig(device="cuda:0", language=Language.JA)
         assert config.device == "cuda:0"
-        assert config.lang_code == "j"
+        assert config.language == Language.JA
         # A plugin that only needs device simply reads config.device
-        # and ignores config.lang_code — this must not raise.
+        # and ignores config.language — this must not raise.
 
     def test_engine_config_contains_engine_instance_configuration(self) -> None:
         """Architecture Amendment #1: EngineConfig definition.
@@ -238,7 +239,7 @@ class TestEngineConfigContract:
         Engine instance is created and that remain constant throughout
         the lifetime of that Engine.
         """
-        config = EngineConfig(device="cpu", lang_code="a")
+        config = EngineConfig(device="cpu", language=Language.EN_US)
         # Both fields are init-time, immutable, engine-scoped.
         assert config.device == "cpu"
-        assert config.lang_code == "a"
+        assert config.language == Language.EN_US

tests/test_application_chapter_selection.py

@@ -0,0 +1,102 @@
+"""Tests for application/chapter_selection.py."""
+
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+from dataclasses import dataclass
+from abogen.application.chapter_selection import build_chapter_payload
+
+
+@dataclass
+class FakeChapter:
+    title: str
+    text: str
+
+
+class TestBuildChapterPayload:
+    def test_empty_chapters(self):
+        result = build_chapter_payload([], source_name="book.txt")
+        assert len(result) == 1
+        assert result[0]["id"] == "0000"
+        assert result[0]["title"] == "book.txt"
+        assert result[0]["text"] == ""
+        assert result[0]["characters"] == 0
+        assert result[0]["enabled"] is True
+
+    def test_single_chapter_always_enabled(self):
+        chapters = [FakeChapter("Chapter 1", "Once upon a time.")]
+        result = build_chapter_payload(chapters)
+        assert len(result) == 1
+        assert result[0]["title"] == "Chapter 1"
+        assert result[0]["enabled"] is True
+        assert result[0]["index"] == 0
+        assert result[0]["id"] == "0000"
+
+    def test_content_chapters_preselected(self):
+        chapters = [
+            FakeChapter("Chapter 1", "The story begins with a long enough text to pass the threshold."),
+            FakeChapter("Chapter 2", "The story continues with another substantial chunk of text."),
+        ]
+        result = build_chapter_payload(chapters)
+        assert all(ch["enabled"] for ch in result)
+
+    def test_supplement_not_preselected(self):
+        chapters = [
+            FakeChapter("Chapter 1", "The story begins with a long enough text to pass the threshold."),
+            FakeChapter("Title Page", ""),
+            FakeChapter("Copyright", "All rights reserved."),
+            FakeChapter("Table of Contents", ""),
+            FakeChapter("Chapter 2", "The story continues with another substantial chunk of text."),
+        ]
+        result = build_chapter_payload(chapters)
+        titles_enabled = {ch["title"]: ch["enabled"] for ch in result}
+        assert titles_enabled["Chapter 1"] is True
+        assert titles_enabled["Chapter 2"] is True
+        assert titles_enabled["Title Page"] is False
+        assert titles_enabled["Copyright"] is False
+        assert titles_enabled["Table of Contents"] is False
+
+    def test_at_least_one_enabled(self):
+        chapters = [
+            FakeChapter("Title Page", ""),
+            FakeChapter("Copyright", "All rights reserved."),
+        ]
+        result = build_chapter_payload(chapters)
+        assert any(ch["enabled"] for ch in result)
+
+    def test_characters_calculated(self):
+        chapters = [FakeChapter("Ch1", "Hello world")]
+        result = build_chapter_payload(chapters)
+        assert result[0]["characters"] == 11
+
+    def test_ids_are_zero_padded(self):
+        chapters = [FakeChapter(f"Ch{i}", f"text {i}") for i in range(5)]
+        result = build_chapter_payload(chapters)
+        ids = [ch["id"] for ch in result]
+        assert ids == ["0000", "0001", "0002", "0003", "0004"]
+
+    def test_indices_are_sequential(self):
+        chapters = [FakeChapter(f"Ch{i}", f"text {i}") for i in range(3)]
+        result = build_chapter_payload(chapters)
+        indices = [ch["index"] for ch in result]
+        assert indices == [0, 1, 2]
+
+    def test_source_name_used_for_empty(self):
+        result = build_chapter_payload([], source_name="mybook.epub")
+        assert result[0]["title"] == "mybook.epub"
+
+    def test_default_source_name(self):
+        result = build_chapter_payload([])
+        assert result[0]["title"] == ""
+
+    def test_none_title_and_text(self):
+        class BadChapter:
+            def __init__(self):
+                self.title = None
+                self.text = None
+
+        result = build_chapter_payload([BadChapter()])
+        assert result[0]["title"] == ""
+        assert result[0]["text"] == ""
+        assert result[0]["enabled"] is True  # single chapter always enabled

tests/test_application_coverage.py

@@ -0,0 +1,753 @@
+"""Tests for conversion_service.py, output_layout_service.py, and executor gaps.
+
+Covers the remaining untested code in the application layer.
+"""
+
+import tempfile
+from pathlib import Path
+from typing import Any, List, Optional
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+import pytest
+
+from abogen.application.conversion_config import (
+    CoverConfig,
+    PronunciationConfig,
+    SaveConfig,
+    SubtitleConfig,
+)
+from abogen.application.conversion_request import ConversionRequest
+from abogen.application.conversion_models import (
+    ChapterPlan,
+    ConversionPlan,
+    IntroOutroSpec,
+    OutputLayout,
+    SegmentPlan,
+)
+from abogen.application.conversion_ports import ResolvedVoice
+from abogen.domain.normalization import TTSContext
+
+
+# ─── Fake implementations (shared with executor tests) ─────────────
+
+
+class FakeAudioSink:
+    def __init__(self):
+        self.written: List[np.ndarray] = []
+        self.closed = False
+
+    def write(self, audio: np.ndarray) -> None:
+        self.written.append(audio)
+
+    def close(self) -> None:
+        self.closed = True
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *args):
+        self.close()
+
+
+class FakeBackend:
+    def __init__(self):
+        self.synthesized: List[str] = []
+
+    def __call__(self, text: str, *, voice: Any, speed: float = 1.0, split_pattern: str = "", **kwargs: Any) -> List:
+        self.synthesized.append(text)
+
+        class FakeSegment:
+            def __init__(self, text: str):
+                self.graphemes = text
+                self.audio = np.zeros(2400, dtype=np.float32)
+                self.tokens = []
+
+        return [FakeSegment(text)]
+
+
+class FakeEvents:
+    def __init__(self):
+        self.logs = []
+        self.progress_calls = []
+        self.cancelled = False
+
+    def log(self, message: str, level: str = "info") -> None:
+        self.logs.append((message, level))
+
+    def progress(self, pct: int, etr: str) -> None:
+        self.progress_calls.append((pct, etr))
+
+    def check_cancelled(self) -> None:
+        if self.cancelled:
+            raise RuntimeError("Conversion cancelled")
+
+
+class FakePipelineProvider:
+    def __init__(self):
+        self.backends = {}
+
+    def get(self, provider: str, language: str, use_gpu: bool) -> FakeBackend:
+        key = f"{provider}:{language}"
+        if key not in self.backends:
+            self.backends[key] = FakeBackend()
+        return self.backends[key]
+
+    def dispose_all(self) -> None:
+        self.backends.clear()
+
+
+class FakeVoiceResolver:
+    def __init__(self):
+        self.resolved_specs = []
+
+    def resolve(self, voice_spec: str) -> ResolvedVoice:
+        self.resolved_specs.append(voice_spec)
+        return ResolvedVoice(
+            provider="kokoro",
+            resolved_spec=voice_spec,
+            voice=voice_spec,
+            speed=1.0,
+            supertonic_steps=5,
+        )
+
+
+@pytest.fixture(autouse=True)
+def _mock_pool_and_resolver():
+    """Mock PipelinePool and _create_voice_resolver for all service tests."""
+    fake_pool = FakePipelineProvider()
+    fake_resolver = FakeVoiceResolver()
+    with patch(
+        "abogen.domain.pipeline_factory.PipelinePool",
+        return_value=fake_pool,
+    ), patch(
+        "abogen.domain.voice_loader.VoiceCache",
+    ), patch(
+        "abogen.application.conversion_service._create_voice_resolver",
+        return_value=fake_resolver,
+    ):
+        yield
+
+
+# ─── Tests for conversion_service.py ───────────────────────────────
+
+
+class TestConversionService:
+    """Tests for the ConversionService.run_conversion function."""
+
+    def test_simple_conversion(self):
+        """Simple text conversion through the service."""
+        from abogen.application.conversion_service import run_conversion
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            req = ConversionRequest(
+                direct_text="Hello world",
+                voice="M1",
+                save=SaveConfig(mode="custom_folder", output_folder=Path(tmpdir)),
+            )
+            events = FakeEvents()
+
+            result = run_conversion(req, events)
+
+            assert result is not None
+            assert result.audio_path is not None
+            assert result.audio_path.exists()
+
+    def test_service_logs_pipeline_preparation(self):
+        """Service logs pipeline preparation step."""
+        from abogen.application.conversion_service import run_conversion
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            req = ConversionRequest(
+                direct_text="Hello",
+                voice="M1",
+                save=SaveConfig(mode="custom_folder", output_folder=Path(tmpdir)),
+            )
+            events = FakeEvents()
+
+            result = run_conversion(req, events)
+
+            log_messages = [msg for msg, _ in events.logs]
+            assert any("Preparing conversion pipeline" in msg for msg in log_messages)
+            assert any("Building conversion plan" in msg for msg in log_messages)
+            assert any("Starting conversion" in msg for msg in log_messages)
+            assert any("Conversion complete" in msg for msg in log_messages)
+
+    def test_service_handles_cancellation(self):
+        """Service propagates cancellation from events."""
+        from abogen.application.conversion_service import run_conversion
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            req = ConversionRequest(
+                direct_text="Hello",
+                voice="M1",
+                save=SaveConfig(mode="custom_folder", output_folder=Path(tmpdir)),
+            )
+            events = FakeEvents()
+            events.cancelled = True
+
+            with pytest.raises(RuntimeError, match="Conversion cancelled"):
+                run_conversion(req, events)
+
+    def test_service_handles_empty_text(self):
+        """Service raises ValueError for empty text."""
+        from abogen.application.conversion_service import run_conversion
+
+        req = ConversionRequest(direct_text="", voice="M1")
+        events = FakeEvents()
+
+        with pytest.raises(ValueError, match="No text content"):
+            run_conversion(req, events)
+
+    def test_service_multi_chapter(self):
+        """Service handles multi-chapter conversion."""
+        from abogen.application.conversion_service import run_conversion
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            req = ConversionRequest(
+                direct_text="<<CHAPTER_MARKER:Ch1>>\nText A\n<<CHAPTER_MARKER:Ch2>>\nText B",
+                voice="M1",
+                save=SaveConfig(mode="custom_folder", output_folder=Path(tmpdir)),
+            )
+            events = FakeEvents()
+
+            result = run_conversion(req, events)
+
+            assert result.total_chapters == 2
+
+    def test_service_with_intro_outro(self):
+        """Service handles intro/outro."""
+        from abogen.application.conversion_service import run_conversion
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            req = ConversionRequest(
+                direct_text="Body text",
+                voice="M1",
+                save=SaveConfig(mode="custom_folder", output_folder=Path(tmpdir)),
+                read_title_intro=True,
+                read_closing_outro=True,
+                metadata_tags={"title": "Test Book", "author": "Author"},
+            )
+            events = FakeEvents()
+
+            result = run_conversion(req, events)
+
+            assert result is not None
+
+    def test_service_error_logs_failure(self):
+        """Service logs error when conversion fails."""
+        from abogen.application.conversion_service import run_conversion
+
+        req = ConversionRequest(direct_text="Hello", voice="M1")
+        events = FakeEvents()
+
+        # Mock build_conversion_plan to raise an error
+        with patch("abogen.application.conversion_service.build_conversion_plan", side_effect=RuntimeError("Test error")):
+            with pytest.raises(RuntimeError, match="Test error"):
+                run_conversion(req, events)
+
+            log_messages = [msg for msg, _ in events.logs]
+            assert any("Conversion failed" in msg for msg in log_messages)
+
+    def test_tts_context_applies_normalization_overrides(self):
+        """Service applies normalization_overrides from request to apostrophe config."""
+        from abogen.application.conversion_service import run_conversion
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            req = ConversionRequest(
+                direct_text="Hello",
+                voice="M1",
+                save=SaveConfig(mode="custom_folder", output_folder=Path(tmpdir)),
+                pronunciation=PronunciationConfig(
+                    normalization_overrides={"normalization_numbers": False},
+                ),
+            )
+            events = FakeEvents()
+
+            result = run_conversion(req, events)
+            assert result is not None
+
+    def test_tts_context_rejects_unconfigured_llm_mode(self):
+        """Service raises RuntimeError if LLM apostrophe mode is selected but unconfigured."""
+        from abogen.application.conversion_service import run_conversion
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            req = ConversionRequest(
+                direct_text="Hello",
+                voice="M1",
+                save=SaveConfig(mode="custom_folder", output_folder=Path(tmpdir)),
+                pronunciation=PronunciationConfig(
+                    normalization_overrides={"normalization_apostrophe_mode": "llm"},
+                ),
+            )
+            events = FakeEvents()
+
+            with pytest.raises(RuntimeError, match="LLM.*apostrophe"):
+                run_conversion(req, events)
+
+    def test_usage_counter_populated_in_result(self):
+        """usage_counter is created and accessible in result."""
+        from abogen.application.conversion_service import run_conversion
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            req = ConversionRequest(
+                direct_text="Hello",
+                voice="M1",
+                save=SaveConfig(mode="custom_folder", output_folder=Path(tmpdir)),
+            )
+            events = FakeEvents()
+
+            result = run_conversion(req, events)
+            assert hasattr(result, "usage_counter")
+            assert isinstance(result.usage_counter, dict)
+
+
+# ─── Tests for output_layout_service.py ─────────────────────────────
+
+
+class TestOutputLayoutService:
+    """Tests for the output_layout_service module."""
+
+    def test_resolve_output_layout_custom_folder(self):
+        """Output layout with custom folder."""
+        from abogen.application.output_layout_service import resolve_output_layout
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            req = ConversionRequest(
+                direct_text="Hello",
+                voice="M1",
+                save=SaveConfig(mode="custom_folder", output_folder=Path(tmpdir)),
+            )
+            layout = resolve_output_layout(req)
+
+            assert layout.parent_dir == Path(tmpdir)
+            assert layout.audio_dir == Path(tmpdir)
+
+    def test_resolve_output_layout_source_path(self):
+        """Output layout from source path."""
+        from abogen.application.output_layout_service import resolve_output_layout
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            source = Path(tmpdir) / "test.txt"
+            source.write_text("Hello")
+            req = ConversionRequest(
+                source_path=source,
+                voice="M1",
+                save=SaveConfig(mode="save_next_to_input"),
+            )
+            layout = resolve_output_layout(req)
+
+            assert layout.parent_dir == Path(tmpdir)
+
+    def test_resolve_output_layout_project(self):
+        """Output layout with save_as_project."""
+        from abogen.application.output_layout_service import resolve_output_layout
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            req = ConversionRequest(
+                direct_text="Hello",
+                voice="M1",
+                save=SaveConfig(
+                    mode="custom_folder",
+                    output_folder=Path(tmpdir),
+                    save_as_project=True,
+                ),
+                original_filename="test.wav",
+            )
+            layout = resolve_output_layout(req)
+
+            assert layout.project_root is not None
+            assert layout.audio_dir is not None
+
+    def test_resolve_merged_path(self):
+        """Resolve merged output path."""
+        from abogen.application.output_layout_service import resolve_merged_path
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            layout = OutputLayout(
+                parent_dir=Path(tmpdir),
+                audio_dir=Path(tmpdir),
+            )
+            req = ConversionRequest(
+                direct_text="Hello",
+                voice="M1",
+                original_filename="book.wav",
+                output_format="wav",
+            )
+            path = resolve_merged_path(layout, req)
+
+            assert path.name == "book.wav"
+            assert path.parent == Path(tmpdir)
+
+    def test_resolve_chapter_path(self):
+        """Resolve chapter output path."""
+        from abogen.application.output_layout_service import resolve_chapter_path
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            layout = OutputLayout(
+                parent_dir=Path(tmpdir),
+                audio_dir=Path(tmpdir),
+            )
+            req = ConversionRequest(
+                direct_text="Hello",
+                voice="M1",
+                save=SaveConfig(separate_chapters_format="wav"),
+            )
+            path = resolve_chapter_path(layout, req, "Chapter 1", 1)
+
+            assert "01" in path.name
+            assert path.suffix == ".wav"
+
+    def test_resolve_chapter_path_empty_title(self):
+        """Resolve chapter path with empty title."""
+        from abogen.application.output_layout_service import resolve_chapter_path
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            layout = OutputLayout(
+                parent_dir=Path(tmpdir),
+                audio_dir=Path(tmpdir),
+            )
+            req = ConversionRequest(
+                direct_text="Hello",
+                voice="M1",
+                save=SaveConfig(separate_chapters_format="wav"),
+            )
+            path = resolve_chapter_path(layout, req, "", 3)
+
+            assert "chapter_3" in path.name
+
+    def test_should_merge_output_m4b(self):
+        """m4b format forces merge."""
+        from abogen.application.output_layout_service import should_merge_output
+
+        req = ConversionRequest(
+            direct_text="Hello",
+            voice="M1",
+            output_format="m4b",
+            save=SaveConfig(merge_chapters_at_end=False),
+        )
+        assert should_merge_output(req) is True
+
+    def test_should_merge_output_no_separate(self):
+        """No separate chapters means merge."""
+        from abogen.application.output_layout_service import should_merge_output
+
+        req = ConversionRequest(
+            direct_text="Hello",
+            voice="M1",
+            save=SaveConfig(save_chapters_separately=False),
+        )
+        assert should_merge_output(req) is True
+
+    def test_should_merge_output_separate_and_merge(self):
+        """Separate chapters + merge_at_end means merge."""
+        from abogen.application.output_layout_service import should_merge_output
+
+        req = ConversionRequest(
+            direct_text="Hello",
+            voice="M1",
+            save=SaveConfig(
+                save_chapters_separately=True,
+                merge_chapters_at_end=True,
+            ),
+        )
+        assert should_merge_output(req) is True
+
+    def test_should_merge_output_separate_no_merge(self):
+        """Separate chapters + no merge_at_end means no merge."""
+        from abogen.application.output_layout_service import should_merge_output
+
+        req = ConversionRequest(
+            direct_text="Hello",
+            voice="M1",
+            save=SaveConfig(
+                save_chapters_separately=True,
+                merge_chapters_at_end=False,
+            ),
+        )
+        assert should_merge_output(req) is False
+
+
+# ─── Tests for executor gaps ────────────────────────────────────────
+
+
+class TestExecutorGaps:
+    """Tests for uncovered executor branches."""
+
+    def test_executor_no_layout_raises(self):
+        """Executor raises ValueError without output_layout."""
+        from abogen.application.conversion_executor import execute_conversion
+
+        req = ConversionRequest(direct_text="Hello", voice="M1")
+        plan = ConversionPlan(
+            request=req,
+            metadata={},
+            chapters=[],
+            output_layout=None,
+        )
+        events = FakeEvents()
+        pipeline = FakePipelineProvider()
+        resolver = FakeVoiceResolver()
+        tts_context = TTSContext()
+
+        with pytest.raises(ValueError, match="output_layout"):
+            execute_conversion(plan, events, pipeline, resolver, tts_context)
+
+    @patch("subprocess.Popen")
+    def test_executor_m4b_forces_merge(self, mock_popen):
+        """Executor forces merge for m4b format."""
+        from abogen.application.conversion_executor import execute_conversion
+
+        mock_proc = mock_popen.return_value
+        mock_proc.returncode = 0
+        mock_proc.wait.return_value = 0
+        mock_proc.stdin = MagicMock()
+        mock_proc.communicate.return_value = (b"", b"")
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            req = ConversionRequest(
+                direct_text="Hello",
+                voice="M1",
+                save=SaveConfig(
+                    mode="custom_folder",
+                    output_folder=Path(tmpdir),
+                    save_chapters_separately=True,
+                    merge_chapters_at_end=False,
+                ),
+                output_format="m4b",
+            )
+            plan = ConversionPlan(
+                request=req,
+                metadata={},
+                chapters=[
+                    ChapterPlan(
+                        index=1,
+                        title="text",
+                        original_title="text",
+                        body_text="Hello",
+                        segments=[
+                            SegmentPlan(text="Hello", voice_spec="M1", kind="body", source="chapter")
+                        ],
+                        voice_spec="M1",
+                    )
+                ],
+                output_layout=OutputLayout(
+                    parent_dir=Path(tmpdir),
+                    audio_dir=Path(tmpdir),
+                ),
+            )
+
+            events = FakeEvents()
+            pipeline = FakePipelineProvider()
+            resolver = FakeVoiceResolver()
+            tts_context = TTSContext()
+
+            result = execute_conversion(plan, events, pipeline, resolver, tts_context)
+
+            assert result.audio_path is not None
+            assert result.audio_path.suffix == ".m4b"
+
+    def test_executor_separate_chapters(self):
+        """Executor creates separate chapter files."""
+        from abogen.application.conversion_executor import execute_conversion
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            req = ConversionRequest(
+                direct_text="Hello",
+                voice="M1",
+                save=SaveConfig(
+                    mode="custom_folder",
+                    output_folder=Path(tmpdir),
+                    save_chapters_separately=True,
+                    merge_chapters_at_end=True,
+                ),
+            )
+            plan = ConversionPlan(
+                request=req,
+                metadata={},
+                chapters=[
+                    ChapterPlan(
+                        index=1,
+                        title="Chapter 1",
+                        original_title="Chapter 1",
+                        body_text="Text A",
+                        segments=[
+                            SegmentPlan(text="Text A", voice_spec="M1", kind="body", source="chapter")
+                        ],
+                        voice_spec="M1",
+                    ),
+                    ChapterPlan(
+                        index=2,
+                        title="Chapter 2",
+                        original_title="Chapter 2",
+                        body_text="Text B",
+                        segments=[
+                            SegmentPlan(text="Text B", voice_spec="M1", kind="body", source="chapter")
+                        ],
+                        voice_spec="M1",
+                    ),
+                ],
+                output_layout=OutputLayout(
+                    parent_dir=Path(tmpdir),
+                    audio_dir=Path(tmpdir),
+                ),
+            )
+
+            events = FakeEvents()
+            pipeline = FakePipelineProvider()
+            resolver = FakeVoiceResolver()
+            tts_context = TTSContext()
+
+            result = execute_conversion(plan, events, pipeline, resolver, tts_context)
+
+            assert len(result.chapter_paths) == 2
+
+    def test_executor_no_intro_outro(self):
+        """Executor works without intro/outro."""
+        from abogen.application.conversion_executor import execute_conversion
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            req = ConversionRequest(
+                direct_text="Hello",
+                voice="M1",
+                save=SaveConfig(mode="custom_folder", output_folder=Path(tmpdir)),
+            )
+            plan = ConversionPlan(
+                request=req,
+                metadata={},
+                chapters=[
+                    ChapterPlan(
+                        index=1,
+                        title="text",
+                        original_title="text",
+                        body_text="Hello",
+                        segments=[
+                            SegmentPlan(text="Hello", voice_spec="M1", kind="body", source="chapter")
+                        ],
+                        voice_spec="M1",
+                    )
+                ],
+                intro=None,
+                outro=None,
+                output_layout=OutputLayout(
+                    parent_dir=Path(tmpdir),
+                    audio_dir=Path(tmpdir),
+                ),
+            )
+
+            events = FakeEvents()
+            pipeline = FakePipelineProvider()
+            resolver = FakeVoiceResolver()
+            tts_context = TTSContext()
+
+            result = execute_conversion(plan, events, pipeline, resolver, tts_context)
+
+            assert result is not None
+            log_messages = [msg for msg, _ in events.logs]
+            assert not any("Title intro" in msg for msg in log_messages)
+            assert not any("Closing outro" in msg for msg in log_messages)
+
+    def test_executor_voice_fallback_on_error(self):
+        """Executor falls back to base voice on resolution error."""
+        from abogen.application.conversion_executor import execute_conversion
+
+        class FailingVoiceResolver:
+            def resolve(self, voice_spec: str) -> ResolvedVoice:
+                if voice_spec == "F1":
+                    raise ValueError("Voice not found")
+                return ResolvedVoice(
+                    provider="kokoro",
+                    resolved_spec=voice_spec,
+                    voice=voice_spec,
+                    speed=1.0,
+                    supertonic_steps=5,
+                )
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            req = ConversionRequest(
+                direct_text="Hello",
+                voice="M1",
+                save=SaveConfig(mode="custom_folder", output_folder=Path(tmpdir)),
+            )
+            plan = ConversionPlan(
+                request=req,
+                metadata={},
+                chapters=[
+                    ChapterPlan(
+                        index=1,
+                        title="text",
+                        original_title="text",
+                        body_text="Hello",
+                        segments=[
+                            SegmentPlan(text="Hello", voice_spec="M1", kind="body", source="chapter")
+                        ],
+                        voice_spec="M1",
+                    )
+                ],
+                output_layout=OutputLayout(
+                    parent_dir=Path(tmpdir),
+                    audio_dir=Path(tmpdir),
+                ),
+            )
+
+            events = FakeEvents()
+            pipeline = FakePipelineProvider()
+            resolver = FailingVoiceResolver()
+            tts_context = TTSContext()
+
+            result = execute_conversion(plan, events, pipeline, resolver, tts_context)
+
+            assert result is not None
+
+    def test_executor_silence_between_chapters(self):
+        """Executor adds silence between chapters."""
+        from abogen.application.conversion_executor import execute_conversion
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            req = ConversionRequest(
+                direct_text="Hello",
+                voice="M1",
+                save=SaveConfig(mode="custom_folder", output_folder=Path(tmpdir)),
+                silence_between_chapters=1.0,
+            )
+            plan = ConversionPlan(
+                request=req,
+                metadata={},
+                chapters=[
+                    ChapterPlan(
+                        index=1,
+                        title="Ch1",
+                        original_title="Ch1",
+                        body_text="Text A",
+                        segments=[
+                            SegmentPlan(text="Text A", voice_spec="M1", kind="body", source="chapter")
+                        ],
+                        voice_spec="M1",
+                    ),
+                    ChapterPlan(
+                        index=2,
+                        title="Ch2",
+                        original_title="Ch2",
+                        body_text="Text B",
+                        segments=[
+                            SegmentPlan(text="Text B", voice_spec="M1", kind="body", source="chapter")
+                        ],
+                        voice_spec="M1",
+                    ),
+                ],
+                output_layout=OutputLayout(
+                    parent_dir=Path(tmpdir),
+                    audio_dir=Path(tmpdir),
+                ),
+            )
+
+            events = FakeEvents()
+            pipeline = FakePipelineProvider()
+            resolver = FakeVoiceResolver()
+            tts_context = TTSContext()
+
+            result = execute_conversion(plan, events, pipeline, resolver, tts_context)
+
+            assert result is not None
+            # Check that audio was written (silence + speech)
+            assert len(pipeline.backends) > 0

tests/test_behavioral_regression.py

@@ -928,10 +928,11 @@ class TestValueObjectsBehavioral:
 
     def test_engine_config_defaults(self) -> None:
         from abogen.tts_plugin.types import EngineConfig
+        from abogen.domain.enums import Language
 
         config = EngineConfig()
         assert config.device == "cpu"
-        assert config.lang_code == "a"
+        assert config.language == Language.EN_US
 
     def test_parameter_values_defaults(self) -> None:
         pv = ParameterValues()

tests/test_chapter_overrides.py

@@ -1,195 +0,0 @@
-from __future__ import annotations
-
-import sys
-import types
-
-
-def _install_dependency_stubs() -> None:
-    if "ebooklib" not in sys.modules:
-        ebooklib_stub = types.ModuleType("ebooklib")
-        epub_stub = types.ModuleType("ebooklib.epub")
-        setattr(ebooklib_stub, "epub", epub_stub)
-        sys.modules["ebooklib"] = ebooklib_stub
-        sys.modules["ebooklib.epub"] = epub_stub
-
-    if "dotenv" not in sys.modules:
-        dotenv_stub = types.ModuleType("dotenv")
-
-        def _noop(*_, **__):
-            return None
-
-        setattr(dotenv_stub, "load_dotenv", _noop)
-        setattr(dotenv_stub, "find_dotenv", lambda *_, **__: "")
-        sys.modules["dotenv"] = dotenv_stub
-
-    if "numpy" not in sys.modules:
-        numpy_stub = types.ModuleType("numpy")
-
-        class _DummyArray(list):
-            pass
-
-        def _zeros(shape, dtype=None):
-            size = 1
-            if isinstance(shape, int):
-                size = shape
-            elif shape:
-                size = 1
-                for dimension in shape:
-                    size *= int(dimension)
-            return [0.0] * size
-
-        setattr(numpy_stub, "ndarray", _DummyArray)
-        setattr(numpy_stub, "zeros", _zeros)
-        setattr(numpy_stub, "float32", "float32")
-        setattr(numpy_stub, "array", lambda data, dtype=None: data)
-        setattr(numpy_stub, "asarray", lambda data, dtype=None: data)
-        setattr(
-            numpy_stub,
-            "concatenate",
-            lambda seq, axis=0: sum((list(item) for item in seq), []),
-        )
-        sys.modules["numpy"] = numpy_stub
-
-    if "soundfile" not in sys.modules:
-        soundfile_stub = types.ModuleType("soundfile")
-
-        class _DummySoundFile:
-            def __init__(self, *_, **__):
-                pass
-
-            def write(self, *_args, **_kwargs):
-                return None
-
-            def close(self):
-                return None
-
-        setattr(soundfile_stub, "SoundFile", _DummySoundFile)
-        setattr(soundfile_stub, "write", lambda *_args, **_kwargs: None)
-        sys.modules["soundfile"] = soundfile_stub
-
-    if "fitz" not in sys.modules:
-        sys.modules["fitz"] = types.ModuleType("fitz")
-
-    if "markdown" not in sys.modules:
-        markdown_stub = types.ModuleType("markdown")
-
-        class _DummyMarkdown:
-            def __init__(self, *_, **__):
-                pass
-
-            def convert(self, text: str) -> str:
-                return text
-
-        setattr(markdown_stub, "Markdown", _DummyMarkdown)
-        sys.modules["markdown"] = markdown_stub
-
-    if "bs4" not in sys.modules:
-        bs4_stub = types.ModuleType("bs4")
-
-        class _DummySoup:
-            def __init__(self, *_, **__):
-                pass
-
-            def select(self, *_, **__):
-                return []
-
-            def find_all(self, *_, **__):
-                return []
-
-        setattr(bs4_stub, "BeautifulSoup", _DummySoup)
-        setattr(bs4_stub, "NavigableString", str)
-        sys.modules["bs4"] = bs4_stub
-
-
-_install_dependency_stubs()
-
-from abogen.text_extractor import ExtractedChapter
-from abogen.webui.conversion_runner import _apply_chapter_overrides, _merge_metadata
-
-
-def _sample_chapters() -> list[ExtractedChapter]:
-    return [
-        ExtractedChapter(title="Chapter 1", text="Original one"),
-        ExtractedChapter(title="Chapter 2", text="Original two"),
-        ExtractedChapter(title="Chapter 3", text="Original three"),
-    ]
-
-
-def test_apply_chapter_overrides_with_custom_text() -> None:
-    overrides = [
-        {"index": 0, "enabled": True, "title": "Intro", "text": "Hello world"},
-        {"index": 1, "enabled": False},
-    ]
-
-    selected, metadata, diagnostics = _apply_chapter_overrides(
-        _sample_chapters(), overrides
-    )
-
-    assert len(selected) == 1
-    assert selected[0].title == "Intro"
-    assert selected[0].text == "Hello world"
-    assert overrides[0]["characters"] == len("Hello world")
-    assert metadata == {}
-    assert diagnostics == []
-
-
-def test_apply_chapter_overrides_uses_original_content_when_text_missing() -> None:
-    overrides = [
-        {"index": 1, "enabled": True},
-    ]
-
-    selected, metadata, diagnostics = _apply_chapter_overrides(
-        _sample_chapters(), overrides
-    )
-
-    assert len(selected) == 1
-    assert selected[0].title == "Chapter 2"
-    assert selected[0].text == "Original two"
-    assert overrides[0]["text"] == "Original two"
-    assert overrides[0]["characters"] == len("Original two")
-    assert metadata == {}
-    assert diagnostics == []
-
-
-def test_apply_chapter_overrides_collects_metadata_updates() -> None:
-    overrides = [
-        {
-            "index": 2,
-            "enabled": True,
-            "metadata": {"artist": "Test Author", "year": 2024},
-        }
-    ]
-
-    selected, metadata, diagnostics = _apply_chapter_overrides(
-        _sample_chapters(), overrides
-    )
-
-    assert len(selected) == 1
-    assert metadata == {"artist": "Test Author", "year": "2024"}
-    assert diagnostics == []
-
-
-def test_apply_chapter_overrides_reports_diagnostics_for_invalid_payload() -> None:
-    overrides = [
-        {"enabled": True, "title": "Missing"},
-    ]
-
-    selected, metadata, diagnostics = _apply_chapter_overrides(
-        _sample_chapters(), overrides
-    )
-
-    assert selected == []
-    assert metadata == {}
-    assert diagnostics and "Skipped chapter override" in diagnostics[0]
-
-
-def test_merge_metadata_prefers_overrides_and_drops_none_values() -> None:
-    extracted = {"title": "Original", "artist": "Someone"}
-    overrides = {"artist": "Another", "genre": "Fiction", "year": None}
-
-    merged = _merge_metadata(extracted, overrides)
-
-    assert merged["title"] == "Original"
-    assert merged["artist"] == "Another"
-    assert merged["genre"] == "Fiction"
-    assert "year" not in merged

tests/test_conversion_chapter_titles.py

@@ -1,240 +0,0 @@
-import sys
-import types
-
-if "soundfile" not in sys.modules:
-    soundfile_stub = types.ModuleType("soundfile")
-
-    class _SoundFileStub:  # pragma: no cover - placeholder to satisfy imports
-        def __init__(self, *args: object, **kwargs: object) -> None:
-            raise RuntimeError("soundfile is not installed in the test environment")
-
-    soundfile_stub.SoundFile = _SoundFileStub  # type: ignore[attr-defined]
-    sys.modules["soundfile"] = soundfile_stub
-
-if "static_ffmpeg" not in sys.modules:
-    sys.modules["static_ffmpeg"] = types.ModuleType("static_ffmpeg")
-
-if "ebooklib" not in sys.modules:
-    ebooklib_stub = types.ModuleType("ebooklib")
-    ebooklib_epub_stub = types.ModuleType("ebooklib.epub")
-    ebooklib_stub.epub = ebooklib_epub_stub  # type: ignore[attr-defined]
-    sys.modules["ebooklib"] = ebooklib_stub
-    sys.modules["ebooklib.epub"] = ebooklib_epub_stub
-
-if "fitz" not in sys.modules:
-    sys.modules["fitz"] = types.ModuleType("fitz")
-
-if "markdown" not in sys.modules:
-    markdown_stub = types.ModuleType("markdown")
-
-    class _MarkdownStub:
-        def __init__(self, *args: object, **kwargs: object) -> None:
-            self.toc_tokens = []
-
-        def convert(self, text: str) -> str:
-            return text
-
-    markdown_stub.Markdown = _MarkdownStub  # type: ignore[attr-defined]
-    sys.modules["markdown"] = markdown_stub
-
-if "bs4" not in sys.modules:
-    bs4_stub = types.ModuleType("bs4")
-
-    class _BeautifulSoupStub:
-        def __init__(self, *args: object, **kwargs: object) -> None:
-            self._text = ""
-
-        def find(self, *args: object, **kwargs: object) -> None:
-            return None
-
-        def get_text(self) -> str:
-            return self._text
-
-        def decompose(self) -> None:  # pragma: no cover - compatibility shim
-            return None
-
-    class _NavigableStringStub(str):
-        pass
-
-    bs4_stub.BeautifulSoup = _BeautifulSoupStub  # type: ignore[attr-defined]
-    bs4_stub.NavigableString = _NavigableStringStub  # type: ignore[attr-defined]
-    sys.modules["bs4"] = bs4_stub
-
-
-from abogen.webui.conversion_runner import (
-    _format_spoken_chapter_title,
-    _headings_equivalent,
-    _normalize_chapter_opening_caps,
-    _strip_duplicate_heading_line,
-)
-
-
-def test_format_spoken_chapter_title_adds_prefix() -> None:
-    assert _format_spoken_chapter_title("1: A Tale", 1, True) == "Chapter 1. A Tale"
-
-
-def test_format_spoken_chapter_title_respects_existing_prefix() -> None:
-    assert (
-        _format_spoken_chapter_title("Chapter 2: Story", 2, True) == "Chapter 2: Story"
-    )
-
-
-def test_format_spoken_chapter_title_handles_empty_title() -> None:
-    assert _format_spoken_chapter_title("", 4, True) == "Chapter 4"
-
-
-def test_format_spoken_chapter_title_trims_delimiters() -> None:
-    assert (
-        _format_spoken_chapter_title("7 - Into the Wild", 7, True)
-        == "Chapter 7. Into the Wild"
-    )
-
-
-def test_headings_equivalent_ignores_case_and_prefix() -> None:
-    assert _headings_equivalent("1: The House", "Chapter 1: The House")
-
-
-def test_strip_duplicate_heading_line_removes_first_match() -> None:
-    text, removed = _strip_duplicate_heading_line(
-        "Chapter 3: Intro\nBody text", "Chapter 3: Intro"
-    )
-    assert removed is True
-    assert text.strip() == "Body text"
-
-
-def test_normalize_chapter_opening_caps_basic_title() -> None:
-    normalized, changed = _normalize_chapter_opening_caps("ALL CAPS TITLE")
-    assert normalized == "All Caps Title"
-    assert changed is True
-
-
-def test_normalize_chapter_opening_caps_respects_acronyms() -> None:
-    normalized, changed = _normalize_chapter_opening_caps("NASA MISSION LOG")
-    assert normalized == "NASA Mission Log"
-    assert changed is True
-
-
-def test_normalize_chapter_opening_caps_handles_roman_numerals() -> None:
-    normalized, changed = _normalize_chapter_opening_caps("IV. THE RETURN")
-    assert normalized == "IV. The Return"
-    assert changed is True
-
-
-def test_normalize_chapter_opening_caps_keeps_mixed_case() -> None:
-    normalized, changed = _normalize_chapter_opening_caps("Already Mixed Case")
-    assert normalized == "Already Mixed Case"
-    assert changed is False
-
-
-class TestApplyChapterTextTransforms:
-    """Tests for the combined heading-strip + opening-caps helper."""
-
-    def test_both_enabled_heading_matches(self) -> None:
-        from abogen.domain.chapter_titles import apply_chapter_text_transforms
-
-        text, heading_removed, caps_changed = apply_chapter_text_transforms(
-            "Chapter 1: The Beginning\nBody text here",
-            heading_text="Chapter 1: The Beginning",
-            raw_title="Chapter 1: The Beginning",
-            strip_heading=True,
-            normalize_caps=True,
-        )
-        assert heading_removed is True
-        assert "Body text here" in text
-        assert "Chapter 1" not in text
-
-    def test_heading_fallback_to_number(self) -> None:
-        from abogen.domain.chapter_titles import apply_chapter_text_transforms
-
-        text, heading_removed, caps_changed = apply_chapter_text_transforms(
-            "1. The Beginning\nBody text",
-            heading_text="Chapter 1: The Beginning",
-            raw_title="1: The Beginning",
-            strip_heading=True,
-            normalize_caps=False,
-        )
-        assert heading_removed is True
-        assert "Body text" in text
-
-    def test_only_heading_strip(self) -> None:
-        from abogen.domain.chapter_titles import apply_chapter_text_transforms
-
-        text, heading_removed, caps_changed = apply_chapter_text_transforms(
-            "Chapter 1: Title\nBody text",
-            heading_text="Chapter 1: Title",
-            raw_title="",
-            strip_heading=True,
-            normalize_caps=False,
-        )
-        assert heading_removed is True
-        assert caps_changed is False
-
-    def test_only_opening_caps(self) -> None:
-        from abogen.domain.chapter_titles import apply_chapter_text_transforms
-
-        text, heading_removed, caps_changed = apply_chapter_text_transforms(
-            "ALL CAPS START OF CHAPTER",
-            heading_text="Chapter 1",
-            raw_title="",
-            strip_heading=False,
-            normalize_caps=True,
-        )
-        assert heading_removed is False
-        assert caps_changed is True
-        assert text == "All Caps Start Of Chapter"
-
-    def test_both_disabled_no_change(self) -> None:
-        from abogen.domain.chapter_titles import apply_chapter_text_transforms
-
-        original = "Some text here"
-        text, heading_removed, caps_changed = apply_chapter_text_transforms(
-            original,
-            heading_text="Chapter 1",
-            raw_title="",
-            strip_heading=False,
-            normalize_caps=False,
-        )
-        assert text == original
-        assert heading_removed is False
-        assert caps_changed is False
-
-    def test_heading_not_matching(self) -> None:
-        from abogen.domain.chapter_titles import apply_chapter_text_transforms
-
-        text, heading_removed, caps_changed = apply_chapter_text_transforms(
-            "Completely different text",
-            heading_text="Chapter 1: Title",
-            raw_title="",
-            strip_heading=True,
-            normalize_caps=False,
-        )
-        assert heading_removed is False
-        assert text == "Completely different text"
-
-    def test_empty_text(self) -> None:
-        from abogen.domain.chapter_titles import apply_chapter_text_transforms
-
-        text, heading_removed, caps_changed = apply_chapter_text_transforms(
-            "",
-            heading_text="Chapter 1",
-            raw_title="",
-            strip_heading=True,
-            normalize_caps=True,
-        )
-        assert text == ""
-        assert heading_removed is False
-        assert caps_changed is False
-
-    def test_both_enabled_text_only_has_caps(self) -> None:
-        from abogen.domain.chapter_titles import apply_chapter_text_transforms
-
-        text, heading_removed, caps_changed = apply_chapter_text_transforms(
-            "NASA MISSION LOG",
-            heading_text="Chapter 1",
-            raw_title="",
-            strip_heading=True,
-            normalize_caps=True,
-        )
-        assert heading_removed is False
-        assert caps_changed is True
-        assert text == "NASA Mission Log"