Update README.md to include Supertonic TTS in PyQt6 Desktop GUI features - Supertonic 3 support

Fixes #163

.gitattributes

@@ -1,15 +0,0 @@
-*.py text eol=lf
-*.md text eol=lf
-*.yml text eol=lf
-*.yaml text eol=lf
-*.toml text eol=lf
-*.json text eol=lf
-*.txt text eol=lf
-*.html text eol=lf
-*.css text eol=lf
-*.js text eol=lf
-*.sh text eol=lf
-*.cfg text eol=lf
-*.ini text eol=lf
-*.svg text eol=lf
-*.j2 text eol=lf

.github/FUNDING.yml

@@ -1,6 +1,6 @@
 # These are supported funding model platforms
 
-github: [jborza, jeremiahsb, mohangk, k0sm0naft]
+github: [jborza, jeremiahsb, mohangk]
 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

.github/workflows/test_pip.yml

@@ -1,9 +1,7 @@
-name: CI
-run-name: CI
-
+name: pip install
+run-name: pip install
 on: 
   push:
-    branches: [main]
     paths:
       - '**.py'
       - 'pyproject.toml'
@@ -13,41 +11,23 @@ on:
       - 'pyproject.toml'
       - '.github/workflows/**'
   workflow_dispatch:
-
 jobs:
-  test:
+  install-and-run:
     strategy:
       matrix:
-        os: [ubuntu-latest, macos-14, windows-latest]
+        os: [ubuntu-latest, macos-latest, windows-latest]
         python-version: ['3.12']
       fail-fast: false
+    continue-on-error: true
     runs-on: ${{ matrix.os }}
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v7
-
+        uses: actions/checkout@v4
       - name: Set up Python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v8.3.1
-        with:
-          enable-cache: true
-          prune-cache: false
-          cache-dependency-glob: pyproject.toml
-
-      - name: Install system dependencies (Ubuntu)
-        if: runner.os == 'Linux'
-        run: sudo apt-get update && sudo apt-get install -y libegl1
-
-      - name: Install dependencies
-        run: uv pip install --system .[dev]
-        env:
-          UV_LINK_MODE: copy
-
-      - name: Run tests
-        env:
-          QT_QPA_PLATFORM: offscreen
-        run: pytest tests/ -v --tb=short
+      - name: Install from repository
+        run: python -m pip install .
+      #- name: Run abogen
+      #  run: abogen

.github/workflows/test_publish.yml

@@ -18,7 +18,7 @@ jobs:
   build:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v7
+    - uses: actions/checkout@v4
 
     - name: Login to Github Container Registry
       # Only if we need to push an image

.gitignore

@@ -39,4 +39,3 @@ dist/
 test_assets/
 dev_notes/
 .claude/
-.coverage

README.md

@@ -38,6 +38,8 @@ This method handles everything automatically - installing all dependencies inclu
 #### <b>OPTION 2: Install using uv</b>
 First, [install uv](https://docs.astral.sh/uv/getting-started/installation/) if you haven't already.
 
+The CUDA extras install both GPU-accelerated Kokoro (via PyTorch) and Supertonic (via onnxruntime-gpu).
+
 ```bash
 # For NVIDIA GPUs (CUDA 12.8) - Recommended
 uv tool install --python 3.12 abogen[cuda] --extra-index-url https://download.pytorch.org/whl/cu128 --index-strategy unsafe-best-match
@@ -65,6 +67,9 @@ venv\Scripts\activate
 # We need to use an older version of PyTorch (2.8.0) until this issue is fixed: https://github.com/pytorch/pytorch/issues/166628
 pip install torch==2.8.0+cu128 torchvision==0.23.0+cu128 torchaudio==2.8.0 --index-url https://download.pytorch.org/whl/cu128
 
+# Also install onnxruntime-gpu for Supertonic GPU acceleration:
+pip install onnxruntime-gpu
+
 # For AMD GPUs:
 # Not supported yet, because ROCm is not available on Windows. Use Linux if you have AMD GPU.
 
@@ -173,7 +178,7 @@ Abogen offers **two interfaces**, but currently they have different feature sets
 
 | Command | Interface | Features |
 |---------|-----------|----------|
-| `abogen` | PyQt6 Desktop GUI | Stable core features |
+| `abogen` | PyQt6 Desktop GUI | Stable core features + **Supertonic TTS**|
 | `abogen-web` | Flask Web UI | Core features + **Supertonic TTS**, **LLM Normalization**, **Audiobookshelf Integration** and more! |
 
 > **Note:** The Web UI is under active development. We are working to integrate these new features into the PyQt desktop app. until then, the Web UI provides the most feature-rich experience.
@@ -407,18 +412,18 @@ When Audiobookshelf sits behind Nginx Proxy Manager (NPM), make sure the API pat
 1. Create a **Proxy Host** that points to your ABS container or host (default forward port `13378`).
 2. Under the **SSL** tab, enable your certificate and tick **Force SSL** if you want HTTPS only.
 3. In the **Advanced** tab, append the snippet below so bearer tokens, client IPs, and large uploads survive the proxy hop:
-   ```nginx
-  proxy_set_header Host $host;
-  proxy_set_header X-Real-IP $remote_addr;
-  proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
-  proxy_set_header X-Forwarded-Proto $scheme;
-  proxy_set_header X-Forwarded-Host $host;
-  proxy_set_header X-Forwarded-Port $server_port;
-  proxy_set_header Authorization $http_authorization;
-  client_max_body_size 5g;
-  proxy_read_timeout 300s;
-  proxy_connect_timeout 300s;
-   ```
+```nginx
+proxy_set_header Host $host;
+proxy_set_header X-Real-IP $remote_addr;
+proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+proxy_set_header X-Forwarded-Proto $scheme;
+proxy_set_header X-Forwarded-Host $host;
+proxy_set_header X-Forwarded-Port $server_port;
+proxy_set_header Authorization $http_authorization;
+client_max_body_size 5g;
+proxy_read_timeout 300s;
+proxy_connect_timeout 300s;
+  ```
 4. Disable **Block Common Exploits** (it strips Authorization headers in some NPM builds).
 5. Enable **Websockets Support** on the main proxy screen (Audiobookshelf uses it for the web UI, and it keeps the reverse proxy configuration consistent).
 6. If you publish Audiobookshelf under a path prefix (for example `/abs`), add a **Custom Location** with `Location: /abs/` and set the **Forward Path** to `/`. That rewrite strips the `/abs` prefix before traffic reaches Audiobookshelf so `/abs/api/...` on the internet becomes `/api/...` on the backend. Use the same prefixed URL in Abogen’s “Base URL” field.

WINDOWS_INSTALL.bat

@@ -323,6 +323,13 @@ if /I "%IS_NVIDIA%"=="true" (
             pause
             exit /b
         )
+        echo Installing onnxruntime-gpu for Supertonic GPU acceleration...
+        %PYTHON_CONSOLE_PATH% -m uv pip install --system onnxruntime-gpu
+        if errorlevel 1 (
+            echo Failed to install onnxruntime-gpu.
+            pause
+            exit /b
+        )
     ) else (
         echo CUDA is available on NVIDIA GPU.
     )
@@ -348,6 +355,13 @@ if /I "%IS_NVIDIA%"=="true" (
             pause
             exit /b
         )
+        echo Installing onnxruntime-gpu for Supertonic GPU acceleration...
+        %PYTHON_CONSOLE_PATH% -m uv pip install --system onnxruntime-gpu
+        if errorlevel 1 (
+            echo Failed to install onnxruntime-gpu.
+            pause
+            exit /b
+        )
     )
 )
 

abogen/application/__init__.py

@@ -1,8 +0,0 @@
-"""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

@@ -1,62 +0,0 @@
-"""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

@@ -1,73 +0,0 @@
-"""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
-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()
-    try:
-        import 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

@@ -1,95 +0,0 @@
-"""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

@@ -1,660 +0,0 @@
-"""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

@@ -1,97 +0,0 @@
-"""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

@@ -1,393 +0,0 @@
-"""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

@@ -1,112 +0,0 @@
-"""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

@@ -1,155 +0,0 @@
-"""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

@@ -1,50 +0,0 @@
-"""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

@@ -1,250 +0,0 @@
-"""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

@@ -1,164 +0,0 @@
-"""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

@@ -1,149 +0,0 @@
-"""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

@@ -1,81 +0,0 @@
-"""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

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

abogen/constants.py

@@ -1,5 +1,4 @@
 from abogen.utils import get_version
-from abogen.domain.enums import Language
 
 # Program Information
 PROGRAM_NAME = "abogen"
@@ -17,22 +16,8 @@ SUBTITLE_FORMATS = [
     ("ass_centered_narrow", "ASS (centered narrow)"),
 ]
 
-# Language description mapping (Language enum → human-readable label).
+# Language description mapping
 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",
@@ -44,6 +29,57 @@ KOKORO_CODE_LABELS = {
     "z": "Mandarin Chinese",
 }
 
+# Mapping from Kokoro single-letter language codes to ISO 3166-1 alpha-2 country codes
+# Used for loading flag icons
+KOKORO_LANG_TO_COUNTRY = {
+    "a": "us",  # American English -> United States
+    "b": "gb",  # British English -> United Kingdom
+    "e": "es",  # Spanish -> Spain
+    "f": "fr",  # French -> France
+    "h": "in",  # Hindi -> India
+    "i": "it",  # Italian -> Italy
+    "j": "jp",  # Japanese -> Japan
+    "p": "br",  # Brazilian Portuguese -> Brazil
+    "z": "cn",  # Mandarin Chinese -> China
+}
+
+# Mapping from Supertonic ISO 639-1 language codes to ISO 3166-1 alpha-2 country codes
+# Used for loading flag icons in the Supertonic language picker
+SUPERTONIC_LANG_TO_COUNTRY = {
+    "en": "gb",
+    "ko": "kr",
+    "ja": "jp",
+    "ar": "ae",
+    "bg": "bg",
+    "cs": "cz",
+    "da": "dk",
+    "de": "de",
+    "el": "gr",
+    "es": "es",
+    "et": "ee",
+    "fi": "fi",
+    "fr": "fr",
+    "hi": "in",
+    "hr": "hr",
+    "hu": "hu",
+    "id": "id",
+    "it": "it",
+    "lt": "lt",
+    "lv": "lv",
+    "nl": "nl",
+    "pl": "pl",
+    "pt": "pt",
+    "ro": "ro",
+    "ru": "ru",
+    "sk": "sk",
+    "sl": "si",
+    "sv": "se",
+    "tr": "tr",
+    "uk": "ua",
+    "vi": "vn",
+    "na": "na",
+}
+
 # Supported sound formats
 SUPPORTED_SOUND_FORMATS = [
     "wav",
@@ -71,22 +107,82 @@ SUPPORTED_INPUT_FORMATS = [
 ]
 
 # Supported languages for subtitle generation
-# Currently, only English (EN_US, EN_GB) are supported 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
-SUPPORTED_LANGUAGES_FOR_SUBTITLE_GENERATION = [Language.EN_US, Language.EN_GB]
+# 383 English processing (unchanged)
+# 384 if self.lang_code in 'ab':
+SUPPORTED_LANGUAGES_FOR_SUBTITLE_GENERATION = list(LANGUAGE_DESCRIPTIONS.keys())
+
+# Voice and sample text constants
+VOICES_INTERNAL = [
+    "af_alloy",
+    "af_aoede",
+    "af_bella",
+    "af_heart",
+    "af_jessica",
+    "af_kore",
+    "af_nicole",
+    "af_nova",
+    "af_river",
+    "af_sarah",
+    "af_sky",
+    "am_adam",
+    "am_echo",
+    "am_eric",
+    "am_fenrir",
+    "am_liam",
+    "am_michael",
+    "am_onyx",
+    "am_puck",
+    "am_santa",
+    "bf_alice",
+    "bf_emma",
+    "bf_isabella",
+    "bf_lily",
+    "bm_daniel",
+    "bm_fable",
+    "bm_george",
+    "bm_lewis",
+    "ef_dora",
+    "em_alex",
+    "em_santa",
+    "ff_siwis",
+    "hf_alpha",
+    "hf_beta",
+    "hm_omega",
+    "hm_psi",
+    "if_sara",
+    "im_nicola",
+    "jf_alpha",
+    "jf_gongitsune",
+    "jf_nezumi",
+    "jf_tebukuro",
+    "jm_kumo",
+    "pf_dora",
+    "pm_alex",
+    "pm_santa",
+    "zf_xiaobei",
+    "zf_xiaoni",
+    "zf_xiaoxiao",
+    "zf_xiaoyi",
+    "zm_yunjian",
+    "zm_yunxi",
+    "zm_yunxia",
+    "zm_yunyang",
+]
 
 # Voice and sample text mapping
 SAMPLE_VOICE_TEXTS = {
-    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。",
+    "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。",
 }
 
 COLORS = {

abogen/domain/audio_buffer.py

@@ -1,239 +0,0 @@
-"""Audio buffer operations for audiobook generation.
-
-This module provides core audio buffer manipulation functions including:
-- Silence generation
-- Audio mixing
-- Audio normalization
-- Audio buffer resizing
-"""
-
-from __future__ import annotations
-
-from typing import Optional
-
-import numpy as np
-
-# Standard sample rate used throughout the application
-SAMPLE_RATE = 24000
-
-
-def create_silence(duration_seconds: float) -> np.ndarray:
-    """Create a silence audio buffer.
-    
-    Args:
-        duration_seconds: Duration of silence in seconds.
-        
-    Returns:
-        Numpy array of float32 zeros with length = duration_seconds * SAMPLE_RATE.
-        Returns empty array if duration is <= 0.
-    """
-    if duration_seconds <= 0:
-        return np.array([], dtype="float32")
-    
-    samples = int(round(duration_seconds * SAMPLE_RATE))
-    if samples <= 0:
-        return np.array([], dtype="float32")
-    
-    return np.zeros(samples, dtype="float32")
-
-
-def mix_audio(
-    target: np.ndarray,
-    source: np.ndarray,
-    start_sample: int,
-    end_sample: Optional[int] = None,
-) -> np.ndarray:
-    """Mix source audio into target buffer at specified position.
-    
-    This performs additive mixing (target += source). The target buffer
-    is extended if necessary to accommodate the source audio.
-    
-    Args:
-        target: The target audio buffer to mix into.
-        source: The source audio buffer to mix.
-        start_sample: Starting sample index in target buffer.
-        end_sample: Optional end sample index. If None, calculated from source length.
-        
-    Returns:
-        The target buffer (possibly extended). If target was extended, returns new array.
-    """
-    if source.size == 0:
-        return target
-    
-    if end_sample is None:
-        end_sample = start_sample + len(source)
-    
-    # Extend target buffer if needed
-    if end_sample > len(target):
-        new_length = end_sample
-        new_target = np.concatenate([
-            target,
-            np.zeros(new_length - len(target), dtype="float32")
-        ])
-        target = new_target
-    
-    # Perform the mix (additive)
-    target[start_sample:end_sample] += source
-    return target
-
-
-def normalize_audio(
-    audio: np.ndarray,
-    target_peak: float = 1.0,
-) -> np.ndarray:
-    """Normalize audio buffer to prevent clipping.
-    
-    If the audio exceeds the target peak (default 1.0), it is scaled down
-    proportionally to prevent distortion.
-    
-    Args:
-        audio: Input audio buffer.
-        target_peak: Target maximum amplitude (default 1.0).
-        
-    Returns:
-        Normalized audio buffer (new array, original is not modified).
-    """
-    if audio.size == 0:
-        return audio.copy()
-    
-    max_amplitude = float(np.abs(audio).max())
-    
-    if max_amplitude <= target_peak:
-        return audio.copy()
-    
-    # Scale down to prevent clipping
-    scale_factor = target_peak / max_amplitude
-    return (audio * scale_factor).astype("float32")
-
-
-def ensure_buffer_size(
-    buffer: np.ndarray,
-    min_samples: int,
-) -> np.ndarray:
-    """Ensure audio buffer is at least min_samples long.
-    
-    If buffer is shorter, it is extended with zeros.
-    
-    Args:
-        buffer: Input audio buffer.
-        min_samples: Minimum required length in samples.
-        
-    Returns:
-        Buffer of at least min_samples length (new array if extended).
-    """
-    if len(buffer) >= min_samples:
-        return buffer
-    
-    new_buffer = np.zeros(min_samples, dtype="float32")
-    new_buffer[:len(buffer)] = buffer
-    return new_buffer
-
-
-def concatenate_audio(*buffers: np.ndarray) -> np.ndarray:
-    """Concatenate multiple audio buffers.
-    
-    Args:
-        *buffers: Audio buffers to concatenate.
-        
-    Returns:
-        Single concatenated audio buffer.
-    """
-    non_empty = [b for b in buffers if b.size > 0]
-    if not non_empty:
-        return np.array([], dtype="float32")
-    return np.concatenate(non_empty)
-
-
-def audio_duration(audio: np.ndarray, sample_rate: int = SAMPLE_RATE) -> float:
-    """Calculate duration of audio buffer in seconds.
-    
-    Args:
-        audio: Audio buffer.
-        sample_rate: Sample rate in Hz (default SAMPLE_RATE).
-        
-    Returns:
-        Duration in seconds.
-    """
-    return len(audio) / sample_rate
-
-
-def samples_for_duration(duration_seconds: float, sample_rate: int = SAMPLE_RATE) -> int:
-    """Calculate number of samples for a given duration.
-    
-    Args:
-        duration_seconds: Duration in seconds.
-        sample_rate: Sample rate in Hz (default SAMPLE_RATE).
-        
-    Returns:
-        Number of samples (rounded to nearest integer), or 0 if duration is <= 0.
-    """
-    if duration_seconds <= 0:
-        return 0
-    return int(round(duration_seconds * sample_rate))
-
-
-def fit_audio_to_duration(
-    audio: np.ndarray,
-    target_duration: float,
-    sample_rate: int = SAMPLE_RATE,
-) -> np.ndarray:
-    """Pad or trim audio to match target duration.
-
-    Args:
-        audio: Input audio buffer.
-        target_duration: Desired duration in seconds.
-        sample_rate: Sample rate in Hz.
-
-    Returns:
-        Audio buffer of exact length target_duration * sample_rate.
-    """
-    target_samples = int(target_duration * sample_rate)
-    if len(audio) < target_samples:
-        padding = np.zeros(target_samples - len(audio), dtype="float32")
-        return np.concatenate([audio, padding])
-    return audio[:target_samples]
-
-
-def ffmpeg_time_stretch(
-    audio: np.ndarray,
-    speed_factor: float,
-    sample_rate: int = SAMPLE_RATE,
-) -> np.ndarray:
-    """Time-stretch audio using FFmpeg's atempo filter.
-
-    Args:
-        audio: Input audio buffer (float32).
-        speed_factor: Speed multiplier (>1.0 = faster).
-        sample_rate: Sample rate in Hz.
-
-    Returns:
-        Time-stretched audio buffer.
-    """
-    import math
-    import subprocess
-
-    import static_ffmpeg
-
-    if speed_factor <= 1.0 or audio.size == 0:
-        return audio
-
-    static_ffmpeg.add_paths()
-    num_stages = max(1, int(math.ceil(math.log(speed_factor) / math.log(2.0))))
-    tempo = speed_factor ** (1.0 / num_stages)
-    filter_str = ",".join([f"atempo={tempo:.6f}"] * num_stages)
-
-    proc = subprocess.Popen(
-        [
-            "ffmpeg", "-y",
-            "-f", "f32le", "-ar", str(sample_rate), "-ac", "1",
-            "-i", "pipe:0",
-            "-filter:a", filter_str,
-            "-f", "f32le", "-ar", str(sample_rate), "-ac", "1",
-            "pipe:1",
-        ],
-        stdin=subprocess.PIPE,
-        stdout=subprocess.PIPE,
-        stderr=subprocess.PIPE,
-    )
-    out, _ = proc.communicate(input=audio.tobytes())
-    return np.frombuffer(out, dtype="float32")

abogen/domain/audio_helpers.py

@@ -1,118 +0,0 @@
-"""Audio helper utilities.
-
-Functions for building ffmpeg commands, converting audio formats,
-and applying chapter metadata to MP4 files.
-"""
-
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Any, Dict, List, Optional
-
-import numpy as np
-
-
-SAMPLE_RATE = 24000
-
-
-def build_ffmpeg_command(path: Path, fmt: str, metadata: Optional[Dict[str, str]] = None) -> list[str]:
-    from abogen.infrastructure.exporters import ExportService
-
-    base = [
-        "ffmpeg",
-        "-y",
-        "-f",
-        "f32le",
-        "-ar",
-        str(SAMPLE_RATE),
-        "-ac",
-        "1",
-        "-i",
-        "pipe:0",
-    ]
-    if fmt == "mp3":
-        base += ["-c:a", "libmp3lame", "-qscale:a", "2"]
-    elif fmt == "opus":
-        base += ["-c:a", "libopus", "-b:a", "24000"]
-    elif fmt == "m4b":
-        base += ["-c:a", "aac", "-q:a", "2", "-movflags", "+faststart+use_metadata_tags"]
-    else:
-        base += ["-c:a", "copy"]
-
-    if metadata:
-        svc = ExportService()
-        base.extend(svc._metadata_to_ffmpeg_args(metadata))
-    base.append(str(path))
-    return base
-
-
-def to_float32(audio_segment) -> np.ndarray:
-    if audio_segment is None:
-        return np.zeros(0, dtype="float32")
-
-    tensor = audio_segment
-    if hasattr(tensor, "detach"):
-        tensor = tensor.detach()
-    if hasattr(tensor, "cpu"):
-        try:
-            tensor = tensor.cpu()
-        except Exception:
-            pass
-    if hasattr(tensor, "numpy"):
-        return np.asarray(tensor.numpy(), dtype="float32").reshape(-1)
-    return np.asarray(tensor, dtype="float32").reshape(-1)
-
-
-def apply_m4b_chapters_with_mutagen(
-    audio_path: Path,
-    chapters: List[Dict[str, Any]],
-) -> bool:
-    """Apply chapter atoms to an MP4/M4B file using mutagen.
-
-    Returns True if chapters were written, False otherwise.
-    Raises ImportError if mutagen is not installed.
-    """
-    if not chapters:
-        return False
-
-    from fractions import Fraction
-    from mutagen.mp4 import MP4, MP4Chapter  # type: ignore[import]
-
-    mp4 = MP4(str(audio_path))
-
-    chapter_objects: List[MP4Chapter] = []
-    for index, entry in enumerate(sorted(chapters, key=lambda item: float(item.get("start") or 0.0))):
-        start_raw = entry.get("start")
-        if start_raw is None:
-            continue
-        try:
-            start_seconds = max(0.0, float(start_raw))
-        except (TypeError, ValueError):
-            continue
-
-        title_value = entry.get("title")
-        title_text = str(title_value) if title_value else f"Chapter {index + 1}"
-
-        start_fraction = Fraction(int(round(start_seconds * 1000)), 1000)
-        chapter_atom = MP4Chapter(start_fraction, title_text)
-
-        end_raw = entry.get("end")
-        if end_raw is not None:
-            try:
-                end_seconds = float(end_raw)
-            except (TypeError, ValueError):
-                end_seconds = None
-            if end_seconds is not None and end_seconds > start_seconds:
-                chapter_atom.end = Fraction(int(round(end_seconds * 1000)), 1000)
-
-        chapter_objects.append(chapter_atom)
-
-    if not chapter_objects:
-        return False
-
-    from typing import cast
-
-    mp4.chapters = cast(Any, chapter_objects)
-    mp4.save()
-
-    return True

abogen/domain/audio_sink.py

@@ -1,131 +0,0 @@
-"""Audio sink abstraction for unified audio output.
-
-Provides a context-manager-based abstraction for writing audio data
-to various output formats (WAV, FLAC via soundfile; compressed via ffmpeg).
-
-Usage:
-    with open_audio_sink(path, "wav") as sink:
-        sink.write(audio_data)
-"""
-
-from __future__ import annotations
-
-import os
-import subprocess
-import sys
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Callable, Optional
-
-import numpy as np
-
-from abogen.domain.audio_buffer import SAMPLE_RATE
-from abogen.domain.audio_helpers import build_ffmpeg_command
-
-
-@dataclass(frozen=True)
-class AudioSink:
-    """Represents an open audio output target."""
-
-    write: Callable[[np.ndarray], None]
-    close: Callable[[], None]
-
-    def __enter__(self) -> AudioSink:
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb) -> None:
-        self.close()
-
-
-def _ensure_ffmpeg() -> None:
-    """Ensure static ffmpeg binaries are on PATH."""
-    import static_ffmpeg  # type: ignore
-
-    ffmpeg_cache_root = _get_ffmpeg_cache_root()
-    platform_cache = os.path.join(ffmpeg_cache_root, sys.platform)
-    os.makedirs(platform_cache, exist_ok=True)
-    try:
-        import static_ffmpeg.run as static_ffmpeg_run  # type: ignore
-
-        static_ffmpeg_run.LOCK_FILE = os.path.join(ffmpeg_cache_root, "lock.file")
-    except Exception:
-        pass
-    static_ffmpeg.add_paths(weak=True, download_dir=platform_cache)
-
-
-def _get_ffmpeg_cache_root() -> str:
-    from abogen.utils import get_internal_cache_path
-
-    return get_internal_cache_path("ffmpeg")
-
-
-def open_audio_sink(
-    path: Path,
-    fmt: str,
-    *,
-    metadata: Optional[dict[str, str]] = None,
-    cancel_check: Optional[Callable[[], bool]] = None,
-    extra_ffmpeg_args: Optional[list[str]] = None,
-    ffmpeg_cmd: Optional[list[str]] = None,
-) -> AudioSink:
-    """Open an audio output sink for writing raw float32 PCM samples.
-
-    Args:
-        path: Output file path.
-        fmt: Output format ("wav", "flac", "mp3", "opus", "m4b").
-        metadata: Optional metadata dict (ignored when ffmpeg_cmd is provided).
-        cancel_check: Optional callable; if it returns True, writes are silently skipped.
-        extra_ffmpeg_args: Optional extra args inserted after ffmpeg header (ignored when ffmpeg_cmd is provided).
-        ffmpeg_cmd: Optional pre-built ffmpeg command list (for m4b with cover art etc.).
-
-    Returns:
-        AudioSink with write() and close() methods.
-    """
-    fmt = fmt.lower()
-
-    if fmt in {"wav", "flac"}:
-        import soundfile as sf
-
-        soundfile_obj = sf.SoundFile(
-            path,
-            mode="w",
-            samplerate=SAMPLE_RATE,
-            channels=1,
-            format=fmt.upper(),
-        )
-
-        def _write_wav(data: np.ndarray) -> None:
-            if cancel_check and cancel_check():
-                return
-            soundfile_obj.write(data)
-
-        def _close_wav() -> None:
-            soundfile_obj.close()
-
-        return AudioSink(write=_write_wav, close=_close_wav)
-
-    # Compressed formats: pipe through ffmpeg
-    _ensure_ffmpeg()
-
-    if ffmpeg_cmd is not None:
-        cmd = list(ffmpeg_cmd)
-    else:
-        cmd = build_ffmpeg_command(path, fmt, metadata=metadata)
-        if extra_ffmpeg_args:
-            cmd[2:2] = extra_ffmpeg_args
-
-    process = subprocess.Popen(
-        cmd, stdin=subprocess.PIPE, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL
-    )
-
-    def _write_compressed(data: np.ndarray) -> None:
-        if (cancel_check and cancel_check()) or process.stdin is None or process.stdin.closed:
-            return
-        process.stdin.write(data.tobytes())
-
-    def _close_compressed() -> None:
-        if process.stdin and not process.stdin.closed:
-            process.stdin.close()
-        process.wait()
-
-    return AudioSink(write=_write_compressed, close=_close_compressed)

abogen/domain/chapter_classification.py

@@ -1,131 +0,0 @@
-"""Heuristics for classifying chapters as content vs. supplements.
-
-A 'supplement' is any non-story material that a listener would typically
-skip: title page, copyright, table of contents, acknowledgements, etc.
-The scoring functions return a float; higher ⇒ more likely to be a
-supplement.  ``should_preselect_chapter`` turns that score into a
-boolean suitable for a web form default.
-"""
-
-from __future__ import annotations
-
-import re
-from typing import Any, Dict, List, Tuple
-
-# Compiled once at module load – these are immutable.
-
-_SUPPLEMENT_TITLE_PATTERNS: List[Tuple[re.Pattern[str], float]] = [
-    (re.compile(r"\btitle\s+page\b"), 3.0),
-    (re.compile(r"\bcopyright\b"), 2.4),
-    (re.compile(r"\btable\s+of\s+contents\b"), 2.8),
-    (re.compile(r"\bcontents\b"), 2.0),
-    (re.compile(r"\backnowledg(e)?ments?\b"), 2.0),
-    (re.compile(r"\bdedication\b"), 2.0),
-    (re.compile(r"\babout\s+the\s+author(s)?\b"), 2.4),
-    (re.compile(r"\balso\s+by\b"), 2.0),
-    (re.compile(r"\bpraise\s+for\b"), 2.0),
-    (re.compile(r"\bcolophon\b"), 2.2),
-    (re.compile(r"\bpublication\s+data\b"), 2.2),
-    (re.compile(r"\btranscriber'?s?\s+note\b"), 2.2),
-    (re.compile(r"\bglossary\b"), 2.2),
-    (re.compile(r"\bindex\b"), 2.0),
-    (re.compile(r"\bbibliograph(y|ies)\b"), 2.0),
-    (re.compile(r"\breferences\b"), 1.8),
-    (re.compile(r"\bappendix\b"), 1.9),
-]
-
-_CONTENT_TITLE_PATTERNS: List[re.Pattern[str]] = [
-    re.compile(r"\bchapter\b"),
-    re.compile(r"\bbook\b"),
-    re.compile(r"\bpart\b"),
-    re.compile(r"\bsection\b"),
-    re.compile(r"\bscene\b"),
-    re.compile(r"\bprologue\b"),
-    re.compile(r"\bepilogue\b"),
-    re.compile(r"\bintroduction\b"),
-    re.compile(r"\bstory\b"),
-]
-
-_SUPPLEMENT_TEXT_KEYWORDS: List[Tuple[str, float]] = [
-    ("copyright", 1.2),
-    ("all rights reserved", 1.1),
-    ("isbn", 0.9),
-    ("library of congress", 1.0),
-    ("table of contents", 1.0),
-    ("dedicated to", 0.8),
-    ("acknowledg", 0.8),
-    ("printed in", 0.6),
-    ("permission", 0.6),
-    ("publisher", 0.5),
-    ("praise for", 0.9),
-    ("also by", 0.9),
-    ("glossary", 0.8),
-    ("index", 0.8),
-    ("newsletter", 3.2),
-    ("mailing list", 2.6),
-    ("sign-up", 2.2),
-]
-
-
-def supplement_score(title: str, text: str, index: int) -> float:
-    """Return a score indicating how likely *title*/*text* is a supplement.
-
-    Higher values ⇒ more likely to be non-story material (title page,
-    copyright, acknowledgements, etc.).
-    """
-    normalized_title = (title or "").lower()
-    score = 0.0
-
-    for pattern, weight in _SUPPLEMENT_TITLE_PATTERNS:
-        if pattern.search(normalized_title):
-            score += weight
-
-    for pattern in _CONTENT_TITLE_PATTERNS:
-        if pattern.search(normalized_title):
-            score -= 2.0
-
-    stripped_text = (text or "").strip()
-    length = len(stripped_text)
-    if length <= 150:
-        score += 0.9
-    elif length <= 400:
-        score += 0.6
-    elif length <= 800:
-        score += 0.35
-
-    lowercase_text = stripped_text.lower()
-    for keyword, weight in _SUPPLEMENT_TEXT_KEYWORDS:
-        if keyword in lowercase_text:
-            score += weight
-
-    if index == 0 and score > 0:
-        score += 0.25
-
-    return score
-
-
-def should_preselect_chapter(
-    title: str,
-    text: str,
-    index: int,
-    total_count: int,
-) -> bool:
-    """Return True if the chapter should be *enabled* by default in the form.
-
-    A single chapter is always preselected.  For multi-chapter books, the
-    chapter is preselected when its supplement score is below 1.9.
-    """
-    if total_count <= 1:
-        return True
-    score = supplement_score(title, text, index)
-    return score < 1.9
-
-
-def ensure_at_least_one_chapter_enabled(chapters: List[Dict[str, Any]]) -> None:
-    """Mutate *chapters* in-place so that at least one has ``enabled=True``."""
-    if not chapters:
-        return
-    if any(chapter.get("enabled") for chapter in chapters):
-        return
-    best_index = max(range(len(chapters)), key=lambda idx: chapters[idx].get("characters", 0))
-    chapters[best_index]["enabled"] = True

abogen/domain/chapter_overrides.py

@@ -1,92 +0,0 @@
-from __future__ import annotations
-
-from typing import Any, Dict, List, Optional, Tuple
-
-from abogen.text_extractor import ExtractedChapter
-from abogen.domain.voice_utils import coerce_truthy
-
-
-def apply_chapter_overrides(
-    extracted: List[ExtractedChapter],
-    overrides: List[Dict[str, Any]],
-) -> Tuple[List[ExtractedChapter], Dict[str, str], List[str]]:
-    if not overrides:
-        return [], {}, []
-
-    selected: List[ExtractedChapter] = []
-    metadata_updates: Dict[str, str] = {}
-    diagnostics: List[str] = []
-
-    for position, payload in enumerate(overrides):
-        if not isinstance(payload, dict):
-            diagnostics.append(
-                f"Skipped chapter override at position {position + 1}: unsupported payload type {type(payload).__name__}."
-            )
-            continue
-
-        enabled = coerce_truthy(payload.get("enabled", True))
-        payload["enabled"] = enabled
-        if not enabled:
-            continue
-
-        metadata_payload = payload.get("metadata") or {}
-        if isinstance(metadata_payload, dict):
-            for key, value in metadata_payload.items():
-                if value is None:
-                    continue
-                metadata_updates[str(key)] = str(value)
-
-        base: Optional[ExtractedChapter] = None
-        idx_candidate = payload.get("index")
-        idx_normalized: Optional[int] = None
-        if isinstance(idx_candidate, int):
-            idx_normalized = idx_candidate
-        elif isinstance(idx_candidate, str):
-            try:
-                idx_normalized = int(idx_candidate)
-            except ValueError:
-                idx_normalized = None
-        if idx_normalized is not None and 0 <= idx_normalized < len(extracted):
-            base = extracted[idx_normalized]
-            payload["index"] = idx_normalized
-
-        if base is None:
-            source_title = payload.get("source_title")
-            if isinstance(source_title, str):
-                base = next((chapter for chapter in extracted if chapter.title == source_title), None)
-
-        if base is None:
-            candidate_title = payload.get("title")
-            if isinstance(candidate_title, str):
-                base = next((chapter for chapter in extracted if chapter.title == candidate_title), None)
-
-        text_override = payload.get("text")
-        if text_override is not None:
-            text_value = str(text_override)
-        elif base is not None:
-            text_value = base.text
-        else:
-            diagnostics.append(
-                f"Skipped chapter override at position {position + 1}: no text provided and no matching source chapter found."
-            )
-            continue
-
-        title_override = payload.get("title")
-        if title_override is not None:
-            title_value = str(title_override)
-        elif base is not None:
-            title_value = base.title
-        else:
-            title_value = f"Chapter {position + 1}"
-
-        if base and not payload.get("source_title"):
-            payload["source_title"] = base.title
-
-        payload["title"] = title_value
-        payload["text"] = text_value
-        payload["characters"] = len(text_value)
-        payload.setdefault("order", payload.get("order", position))
-
-        selected.append(ExtractedChapter(title=title_value, text=text_value))
-
-    return selected, metadata_updates, diagnostics

abogen/domain/chapter_titles.py

@@ -1,204 +0,0 @@
-from __future__ import annotations
-
-import re
-from typing import List, Tuple
-
-
-_HEADING_SANITIZE_RE = re.compile(r"[^a-z0-9]+")
-_HEADING_NUMBER_PREFIX_RE = re.compile(
-    r"^\s*(?P<number>(?:\d+|[ivxlcdm]+))(?P<suffix>(?:[\s.:;-].*)?)$",
-    re.IGNORECASE,
-)
-_ACRONYM_ALLOWLIST = {
-    "AI", "API", "CPU", "DIY", "GPU", "HTML", "HTTP", "HTTPS", "ID",
-    "JSON", "MP3", "MP4", "M4B", "NASA", "OCR", "PDF", "SQL", "TV",
-    "TTS", "UK", "UN", "UFO", "OK", "URL", "USA", "US", "VR",
-}
-_ROMAN_NUMERAL_CHARS = frozenset("IVXLCDM")
-_CAPS_WORD_RE = re.compile(r"[A-Z][A-Z0-9'\u2019-]*")
-
-
-def simplify_heading_text(text: str) -> str:
-    raw = str(text or "").strip().lower()
-    if not raw:
-        return ""
-    simplified = _HEADING_SANITIZE_RE.sub("", raw)
-    if simplified.startswith("chapter"):
-        simplified = simplified[7:]
-    return simplified
-
-
-def headings_equivalent(left: str, right: str) -> bool:
-    simple_left = simplify_heading_text(left)
-    simple_right = simplify_heading_text(right)
-    if not simple_left or not simple_right:
-        return False
-    if simple_left == simple_right:
-        return True
-    if simple_right.startswith(simple_left):
-        return True
-    if simple_left.startswith(simple_right):
-        return True
-    if len(simple_left) > 5 and simple_left in simple_right:
-        return True
-    return False
-
-
-def strip_duplicate_heading_line(text: str, heading: str) -> Tuple[str, bool]:
-    source_text = str(text or "")
-    if not source_text:
-        return source_text, False
-    normalized_heading = simplify_heading_text(heading)
-    if not normalized_heading:
-        return source_text, False
-    lines = source_text.splitlines()
-    new_lines: List[str] = []
-    removed = False
-    for line in lines:
-        stripped = line.strip()
-        if not removed and stripped:
-            if headings_equivalent(stripped, heading):
-                removed = True
-                continue
-        new_lines.append(line)
-    if not removed:
-        return source_text, False
-    while new_lines and not new_lines[0].strip():
-        new_lines.pop(0)
-    return "\n".join(new_lines), True
-
-
-def normalize_caps_word(word: str) -> str:
-    upper = word.upper()
-    letters = [char for char in upper if char.isalpha()]
-    if not letters:
-        return word
-    if upper in _ACRONYM_ALLOWLIST:
-        return word
-    if len(letters) <= 1:
-        return word
-    if all(char in _ROMAN_NUMERAL_CHARS for char in letters) and len(letters) <= 7:
-        return word
-
-    parts = re.split(r"(['\-\u2019])", word)
-    normalized_parts: List[str] = []
-    for part in parts:
-        if part in {"'", "-", "\u2019"}:
-            normalized_parts.append(part)
-            continue
-        if not part:
-            continue
-        normalized_parts.append(part[0].upper() + part[1:].lower())
-    return "".join(normalized_parts) or word
-
-
-def normalize_chapter_opening_caps(text: str) -> Tuple[str, bool]:
-    if not text:
-        return text, False
-
-    leading_len = len(text) - len(text.lstrip())
-    leading = text[:leading_len]
-    working = text[leading_len:]
-    if not working:
-        return text, False
-
-    builder: List[str] = []
-    pos = 0
-    changed = False
-
-    while pos < len(working):
-        char = working[pos]
-        if char in "\r\n":
-            builder.append(working[pos:])
-            pos = len(working)
-            break
-        if char.isspace():
-            builder.append(char)
-            pos += 1
-            continue
-        if char.islower():
-            builder.append(working[pos:])
-            pos = len(working)
-            break
-        if not char.isalpha():
-            builder.append(char)
-            pos += 1
-            continue
-
-        match = _CAPS_WORD_RE.match(working, pos)
-        if not match:
-            builder.append(char)
-            pos += 1
-            continue
-
-        word = match.group(0)
-        if any(ch.islower() for ch in word):
-            builder.append(working[pos:])
-            pos = len(working)
-            break
-
-        normalized = normalize_caps_word(word)
-        if normalized != word:
-            changed = True
-        builder.append(normalized)
-        pos = match.end()
-
-    if pos < len(working):
-        builder.append(working[pos:])
-
-    if not changed:
-        return text, False
-
-    return leading + "".join(builder), True
-
-
-def format_spoken_chapter_title(title: str, index: int, apply_prefix: bool) -> str:
-    base = str(title or "").strip()
-    if not base:
-        return f"Chapter {index}" if apply_prefix else ""
-    if not apply_prefix:
-        return base
-    lowered = base.lower()
-    if lowered.startswith("chapter") and (len(lowered) == 7 or not lowered[7].isalpha()):
-        return base
-    match = _HEADING_NUMBER_PREFIX_RE.match(base)
-    if match:
-        number = match.group("number") or ""
-        suffix = match.group("suffix") or ""
-        cleaned_suffix = suffix.lstrip(" .,:;-_ \t\u2013\u2014\u00b7\u2022")
-        if cleaned_suffix:
-            return f"Chapter {number}. {cleaned_suffix}"
-        return f"Chapter {number}"
-    return base
-
-
-def apply_chapter_text_transforms(
-    text: str,
-    *,
-    heading_text: str,
-    raw_title: str,
-    strip_heading: bool,
-    normalize_caps: bool,
-) -> Tuple[str, bool, bool]:
-    """Strip duplicate heading and normalize opening caps.
-
-    Returns ``(text, heading_removed, caps_changed)``.
-    The caller is responsible for state updates (pending flags, logging,
-    dict mutation, ``continue``).
-    """
-    heading_removed = False
-    caps_changed = False
-
-    if strip_heading and heading_text:
-        text, heading_removed = strip_duplicate_heading_line(text, heading_text)
-        if not heading_removed and raw_title:
-            match = _HEADING_NUMBER_PREFIX_RE.match(raw_title)
-            if match:
-                number = match.group("number")
-                if number:
-                    text, heading_removed = strip_duplicate_heading_line(text, number)
-
-    if normalize_caps and text:
-        text, caps_changed = normalize_chapter_opening_caps(text)
-
-    return text, heading_removed, caps_changed

abogen/domain/chunk_utils.py

@@ -1,76 +0,0 @@
-"""Chunk processing utilities.
-
-Functions for grouping chunks, recording override usage, and selecting
-text for TTS synthesis.
-"""
-
-from __future__ import annotations
-
-from collections import defaultdict
-from typing import Any, Dict, Iterable, Mapping
-
-from abogen.domain.enums import Language
-from abogen.pronunciation_store import increment_usage
-
-
-def safe_int(value: Any, default: int = 0) -> int:
-    try:
-        return int(value)
-    except (TypeError, ValueError):
-        return default
-
-
-def group_chunks_by_chapter(chunks: Iterable[Dict[str, Any]]) -> Dict[int, List[Dict[str, Any]]]:
-    grouped: Dict[int, List[Dict[str, Any]]] = defaultdict(list)
-    for entry in chunks or []:
-        if not isinstance(entry, dict):
-            continue
-        try:
-            chapter_index = int(entry.get("chapter_index", 0))
-        except (TypeError, ValueError):
-            chapter_index = 0
-        grouped[chapter_index].append(dict(entry))
-
-    for chapter_index, items in grouped.items():
-        items.sort(key=lambda payload: safe_int(payload.get("chunk_index")))
-
-    return grouped
-
-
-def record_override_usage(
-    job: Any,
-    usage_counter: Mapping[str, int],
-    token_map: Mapping[str, str],
-) -> None:
-    if not usage_counter:
-        return
-
-    language = getattr(job, "language", Language.EN_US) or Language.EN_US
-    for normalized, amount in usage_counter.items():
-        if amount <= 0:
-            continue
-        token_value = token_map.get(normalized, normalized)
-        try:
-            increment_usage(language=language, token=token_value, amount=int(amount))
-        except Exception:  # pragma: no cover - defensive logging
-            job.add_log(f"Failed to record usage for override {token_value}", level="warning")
-
-
-def chunk_text_for_tts(entry: Mapping[str, Any]) -> str:
-    """Choose the best source text for synthesis.
-
-    We must prefer the raw chunk text (``text`` / ``original_text``) so
-    manual/pronunciation overrides can match against the original tokens
-    (e.g. censored words like ``Unfu*k``).  ``normalized_text`` may have
-    already been run through ``normalize_for_pipeline``, which can remove
-    punctuation and prevent overrides from triggering.
-    """
-
-    if not isinstance(entry, Mapping):
-        return ""
-    return str(
-        entry.get("text")
-        or entry.get("original_text")
-        or entry.get("normalized_text")
-        or ""
-    ).strip()

abogen/domain/config_types.py

@@ -1,52 +0,0 @@
-"""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

@@ -1,241 +0,0 @@
-"""Shared TTS iteration loop used by both WebUI and PyQt conversion runners.
-
-The core pattern is identical across both UIs:
-
-    for seg in tts_segments(text, backend, voice, speed, split_pattern, current_time):
-        check_cancel()
-        update_progress(seg)
-        write_audio(seg, sink)
-        accumulate_subtitles(seg)
-
-After the loop, the caller processes accumulated subtitle tokens.
-
-This module provides ``run_tts_segment_loop`` which encapsulates that
-iteration, and ``synthesize_text`` which adds normalization on top —
-the single entry point both UIs should call for text-to-speech.
-"""
-
-from __future__ import annotations
-
-import time
-from dataclasses import dataclass, field
-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
-
-
-class CancelChecker(Protocol):
-    """Returns True if conversion has been cancelled."""
-    def __call__(self) -> bool: ...
-
-
-@dataclass
-class SegmentStats:
-    """Running statistics updated per TTS segment."""
-    processed_chars: int = 0
-    current_time: float = 0.0
-    etr_start_time: float = field(default_factory=time.time)
-    total_characters: int = 0
-
-
-@dataclass
-class SegmentInfo:
-    """Read-only info about a TTS segment, passed to on_segment callback."""
-    graphemes: str
-    audio: Any
-    tokens: list
-    duration: float
-    chunk_start: float
-
-
-def run_tts_segment_loop(
-    *,
-    text: str,
-    params: SynthParams,
-    backend: Any,
-    voice: Any,
-    speed: float,
-    split_pattern: str,
-    total_steps: Optional[int] = None,
-    chapter_sink: Optional[AudioSink] = None,
-    preview_callback: Optional[Callable[[str], None]] = None,
-    on_segment: Optional[Callable[[SegmentInfo], None]] = None,
-) -> 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.
-        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.
-
-    Returns:
-        Tuple of (segment_count, accumulated_subtitle_tokens).
-        The caller is responsible for processing subtitle tokens via
-        ``process_subtitle_tokens`` and writing entries to subtitle writers.
-    """
-    local_segments = 0
-    accumulated_tokens: list[dict] = []
-
-    for seg in tts_segments(
-        text,
-        backend=backend,
-        voice=voice,
-        speed=speed,
-        split_pattern=split_pattern,
-        current_time=params.stats.current_time,
-        total_steps=total_steps,
-    ):
-        if params.check_cancel():
-            break
-
-        local_segments += 1
-        params.stats.processed_chars += len(seg.graphemes)
-
-        # Progress
-        if params.stats.total_characters:
-            percent = min(int(params.stats.processed_chars / params.stats.total_characters * 100), 99)
-        else:
-            percent = 0 if params.stats.processed_chars == 0 else 99
-
-        etr_str = calc_etr_str(
-            time.time() - params.stats.etr_start_time,
-            params.stats.processed_chars,
-            params.stats.total_characters,
-        )
-        params.on_progress(percent, etr_str)
-
-        # Preview / log
-        if preview_callback:
-            preview_callback(seg.graphemes or "[silence]")
-
-        # Per-segment callback (for callers needing segment-level access)
-        if on_segment:
-            info = SegmentInfo(
-                graphemes=seg.graphemes,
-                audio=seg.audio,
-                tokens=list(seg.tokens) if seg.tokens else [],
-                duration=seg.duration,
-                chunk_start=getattr(seg, "chunk_start", params.stats.current_time),
-            )
-            on_segment(info)
-
-        # Write audio
-        if chapter_sink:
-            chapter_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 params.subtitle_mode != SubtitleMode.DISABLED and seg.tokens:
-            accumulated_tokens.extend(seg.tokens)
-
-        # Update timing
-        if params.audio_sink:
-            params.stats.current_time += seg.duration
-
-    return local_segments, accumulated_tokens
-
-
-def process_and_write_subtitles(
-    accumulated_tokens: list[dict],
-    subtitle_writer: Any,
-    *,
-    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.
-
-    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,
-        words,
-        mode_str,
-        language,
-        use_spacy_segmentation=use_spacy_segmentation,
-        fallback_end_time=fallback_end_time,
-    )
-    for start, end, text in new_entries:
-        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,
-    params: SynthParams,
-    backend: Any,
-    voice: Any,
-    speed: float,
-    total_steps: Optional[int] = None,
-    chapter_sink: Optional[AudioSink] = None,
-    preview_callback: Optional[Callable[[str], None]] = None,
-    on_segment: Optional[Callable[[SegmentInfo], None]] = None,
-    split_pattern_override: Optional[str] = None,
-) -> tuple[int, list]:
-    """Normalize text and run TTS — the single entry point for both UIs.
-
-    Combines TTSContext.normalize() + run_tts_segment_loop() into one call.
-    UI-specific concerns (provider resolution, progress display) stay in the UI.
-    """
-    normalized = params.tts_context.normalize(text)
-    return run_tts_segment_loop(
-        text=normalized,
-        params=params,
-        backend=backend,
-        voice=voice,
-        speed=speed,
-        total_steps=total_steps,
-        split_pattern=split_pattern_override or params.tts_context.split_pattern,
-        chapter_sink=chapter_sink,
-        preview_callback=preview_callback,
-        on_segment=on_segment,
-    )

abogen/domain/conversion_pipeline.py

@@ -1,357 +0,0 @@
-"""Shared TTS emission pipeline.
-
-Provides the core TTS emission loop used by both WebUI and PyQt conversion runners.
-The caller handles audio I/O, progress reporting, and subtitle writing.
-"""
-
-from __future__ import annotations
-
-import logging
-from dataclasses import dataclass, field
-
-from abogen.domain.enums import Language, SubtitleMode
-from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple
-
-import numpy as np
-
-from abogen.domain.audio_helpers import to_float32
-from abogen.domain.normalization import prepare_text_for_tts
-from abogen.domain.tokens import FakeToken
-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 PUNCTUATION_COMMAS, 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...")
-
-    # Compute split_pattern override based on subtitle mode
-    spacing_pattern = r"\s*" if lang_enum in _CJK_LANGS else r"\s+"
-
-    if subtitle_mode_str == "Sentence + Comma":
-        active_split = r"(?<=[{}]){}|\n+".format(PUNCTUATION_COMMAS, spacing_pattern)
-    else:
-        # Sentence mode: spaCy already split, only split on newlines
-        active_split = "\n"
-
-    return spacy_sentences, active_split
-
-
-def _to_language_enum(lang_code: Any) -> Language:
-    """Convert lang_code to Language enum."""
-    if isinstance(lang_code, Language):
-        return lang_code
-    try:
-        return Language.from_str(str(lang_code))
-    except (ValueError, AttributeError):
-        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:
-    """One TTS segment emitted by the pipeline."""
-    graphemes: str
-    audio: np.ndarray
-    duration: float
-    chunk_start: float
-    tokens: List[Dict[str, Any]] = field(default_factory=list)
-
-
-def tts_segments(
-    text: str,
-    *,
-    backend: Any,
-    voice: Any,
-    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.
-
-    Use this when you've already normalized the text yourself (e.g. after
-    spaCy sentence segmentation). For raw text, use emit_text_segments() instead.
-
-    Args:
-        text: Already-normalized text to synthesize.
-        backend: TTS pipeline callable.
-        voice: Resolved voice.
-        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.
-    """
-    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
-
-    for segment in segment_iter:
-        graphemes_raw = getattr(segment, "graphemes", "") or ""
-        graphemes = graphemes_raw.strip()
-
-        audio = to_float32(getattr(segment, "audio", None))
-        if audio.size == 0:
-            continue
-
-        duration = len(audio) / SAMPLE_RATE
-
-        tokens_list = getattr(segment, "tokens", [])
-        if not tokens_list and graphemes:
-            tokens_list = [FakeToken(graphemes, 0, duration)]
-
-        tokens = [
-            {
-                "start": chunk_start + (tok.start_ts or 0),
-                "end": chunk_start + (tok.end_ts or 0),
-                "text": tok.text,
-                "whitespace": tok.whitespace,
-            }
-            for tok in tokens_list
-        ]
-
-        yield SegmentResult(
-            graphemes=graphemes,
-            audio=audio,
-            duration=duration,
-            chunk_start=chunk_start,
-            tokens=tokens,
-        )
-
-        chunk_start += duration
-
-
-def emit_text_segments(
-    text: str,
-    *,
-    backend: Any,
-    voice: Any,
-    speed: float,
-    split_pattern: str,
-    current_time: float = 0.0,
-    total_steps: Optional[int] = None,
-    # normalization
-    heteronym_rules: Any = None,
-    pronunciation_rules: Any = None,
-    normalization_overrides: Any = None,
-    usage_counter: Optional[Dict[str, int]] = None,
-) -> Iterator[SegmentResult]:
-    """Normalize text and yield SegmentResults from the TTS backend.
-
-    This is the innermost TTS emission loop shared by both UIs. It handles:
-    1. Text normalization (heteronym + pronunciation rules)
-    2. TTS backend invocation
-    3. Segment iteration with token extraction
-
-    The caller is responsible for:
-    - Writing audio to sinks
-    - Accumulating tokens for subtitle processing
-    - Progress tracking and cancellation
-    - Error handling
-
-    Args:
-        text: Raw text to synthesize.
-        backend: TTS pipeline callable (kokoro or supertonic).
-        voice: Resolved voice for TTS.
-        speed: TTS speed multiplier.
-        split_pattern: Regex pattern for sentence splitting.
-        current_time: Current position in the audio timeline (seconds).
-        heteronym_rules: Compiled heteronym rules.
-        pronunciation_rules: Compiled pronunciation rules.
-        normalization_overrides: User normalization overrides.
-        usage_counter: Counter for normalization statistics.
-
-    Yields:
-        SegmentResult for each non-empty TTS segment.
-    """
-    source_text = str(text or "")
-    normalized = prepare_text_for_tts(
-        source_text,
-        heteronym_rules=heteronym_rules,
-        pronunciation_rules=pronunciation_rules,
-        normalization_overrides=normalization_overrides,
-        usage_counter=usage_counter,
-    )
-
-    yield from tts_segments(
-        normalized,
-        backend=backend,
-        voice=voice,
-        speed=speed,
-        split_pattern=split_pattern,
-        current_time=current_time,
-        total_steps=total_steps,
-    )
-
-
-def emit_text_to_sinks(
-    text: str,
-    *,
-    backend: Any,
-    voice: Any,
-    speed: float,
-    split_pattern: str,
-    current_time: float = 0.0,
-    # sinks
-    audio_sink: Any = None,
-    chapter_sink: Any = None,
-    # subtitle
-    subtitle_writer: Any = None,
-    subtitle_mode: str = "Disabled",
-    subtitle_lang: Language = Language.EN_US,
-    max_subtitle_words: int = 50,
-    use_spacy_segmentation: bool = True,
-    # normalization
-    heteronym_rules: Any = None,
-    pronunciation_rules: Any = None,
-    normalization_overrides: Any = None,
-    usage_counter: Optional[Dict[str, int]] = None,
-) -> tuple[int, float, List[Dict[str, Any]]]:
-    """Emit TTS audio for text, writing to sinks and collecting subtitle tokens.
-
-    Convenience wrapper around emit_text_segments() that handles audio writing
-    and token accumulation. Returns stats for the caller to update progress.
-
-    Returns:
-        Tuple of (segments_emitted, new_current_time, accumulated_tokens).
-    """
-    from abogen.domain.subtitle_generation import process_subtitle_tokens
-
-    segments_emitted = 0
-    accumulated_tokens: List[Dict[str, Any]] = []
-
-    for seg in emit_text_segments(
-        text,
-        backend=backend,
-        voice=voice,
-        speed=speed,
-        split_pattern=split_pattern,
-        current_time=current_time,
-        heteronym_rules=heteronym_rules,
-        pronunciation_rules=pronunciation_rules,
-        normalization_overrides=normalization_overrides,
-        usage_counter=usage_counter,
-    ):
-        segments_emitted += 1
-
-        # Write audio
-        if chapter_sink:
-            chapter_sink.write(seg.audio)
-        if audio_sink:
-            audio_sink.write(seg.audio)
-
-        # Collect tokens
-        accumulated_tokens.extend(seg.tokens)
-
-    # Flush subtitle tokens
-    if subtitle_writer and accumulated_tokens:
-        _use_spacy = subtitle_mode not in (SubtitleMode.DISABLED, SubtitleMode.LINE)
-        new_entries: List[tuple] = []
-        process_subtitle_tokens(
-            accumulated_tokens,
-            new_entries,
-            max_subtitle_words,
-            subtitle_mode,
-            subtitle_lang,
-            use_spacy_segmentation=_use_spacy,
-            fallback_end_time=current_time + sum(t["end"] - t["start"] for t in accumulated_tokens if accumulated_tokens),
-        )
-        for start, end, text_entry in new_entries:
-            subtitle_writer.write_entry(start=start, end=end, text=text_entry)
-
-    new_time = current_time
-    if accumulated_tokens:
-        new_time = max(t["end"] for t in accumulated_tokens)
-
-    return segments_emitted, new_time, accumulated_tokens

abogen/domain/device.py

@@ -1,31 +0,0 @@
-from __future__ import annotations
-
-import platform as _platform
-
-
-def select_device() -> str:
-    """Return the best available compute device (``"mps"``, ``"cuda"``, or ``"cpu"``).
-
-    Checks ``torch`` availability at runtime so this can be called from
-    any context without requiring torch at import time.
-    """
-    try:
-        import torch  # type: ignore[import-not-found]
-    except Exception:
-        return "cpu"
-
-    system = _platform.system()
-    if system == "Darwin" and _platform.processor() == "arm":
-        try:
-            if torch.backends.mps.is_available():  # type: ignore[union-attr]
-                return "mps"
-        except Exception:
-            pass
-        return "cpu"
-
-    try:
-        if torch.cuda.is_available():  # type: ignore[union-attr]
-            return "cuda"
-    except Exception:
-        pass
-    return "cpu"

abogen/domain/enums.py

@@ -1,227 +0,0 @@
-"""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 generates timestamped tokens for subtitles."""
-        return self in (self.EN_US, self.EN_GB)
-
-    @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/file_type.py

@@ -1,136 +0,0 @@
-from __future__ import annotations
-
-import re
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Any, Dict, List, Tuple
-
-from abogen.text_extractor import ExtractedChapter
-
-
-_SIGNIFICANT_LENGTH_THRESHOLDS: Dict[str, int] = {"epub": 1000, "markdown": 500}
-_MIN_SHORT_CONTENT: Dict[str, int] = {"epub": 240, "markdown": 160}
-_STRUCTURAL_KEYWORDS = (
-    "preface",
-    "prologue",
-    "introduction",
-    "foreword",
-    "epilogue",
-    "afterword",
-    "appendix",
-    "acknowledgment",
-    "acknowledgement",
-)
-_STRUCTURAL_MIN_LENGTH = 120
-_MAX_SHORT_CHAPTERS = 2
-
-
-@dataclass
-class ChapterFilterResult:
-    kept: List[ExtractedChapter]
-    skipped: List[Tuple[str, int]]
-
-
-def infer_file_type(path: Path) -> str:
-    suffix = path.suffix.lower()
-    if suffix == ".epub":
-        return "epub"
-    if suffix in {".md", ".markdown"}:
-        return "markdown"
-    if suffix == ".pdf":
-        return "pdf"
-    if suffix == ".txt":
-        return "text"
-    return suffix.lstrip(".") or "text"
-
-
-def looks_structural(title: str) -> bool:
-    lowered = title.strip().lower()
-    if not lowered:
-        return False
-    return any(keyword in lowered for keyword in _STRUCTURAL_KEYWORDS)
-
-
-def chapter_label(file_type: str) -> str:
-    return "chapters" if file_type.lower() in {"epub", "markdown"} else "pages"
-
-
-def auto_select_relevant_chapters(
-    chapters: List[ExtractedChapter],
-    file_type: str,
-) -> ChapterFilterResult:
-    if not chapters:
-        return ChapterFilterResult(kept=[], skipped=[])
-
-    normalized = file_type.lower()
-    threshold = _SIGNIFICANT_LENGTH_THRESHOLDS.get(normalized, 0)
-    min_short = _MIN_SHORT_CONTENT.get(normalized, 0)
-
-    kept: List[ExtractedChapter] = []
-    skipped: List[Tuple[str, int]] = []
-    short_kept = 0
-
-    for chapter in chapters:
-        stripped = chapter.text.strip()
-        length = len(stripped)
-        if length == 0:
-            skipped.append((chapter.title, length))
-            continue
-
-        keep = False
-        if threshold == 0:
-            keep = True
-        elif length >= threshold:
-            keep = True
-        elif not kept:
-            keep = True
-        elif min_short and length >= min_short and short_kept < _MAX_SHORT_CHAPTERS:
-            keep = True
-            short_kept += 1
-        elif looks_structural(chapter.title) and length >= _STRUCTURAL_MIN_LENGTH:
-            keep = True
-
-        if keep:
-            kept.append(chapter)
-        else:
-            skipped.append((chapter.title, length))
-
-    if kept:
-        return ChapterFilterResult(kept=kept, skipped=skipped)
-
-    longest_idx = None
-    longest_length = 0
-    for idx, chapter in enumerate(chapters):
-        stripped = chapter.text.strip()
-        if stripped and len(stripped) > longest_length:
-            longest_length = len(stripped)
-            longest_idx = idx
-
-    if longest_idx is not None:
-        longest = chapters[longest_idx]
-        fallback_skipped = [
-            (chapter.title, len(chapter.text.strip()))
-            for idx, chapter in enumerate(chapters)
-            if idx != longest_idx and chapter.text.strip()
-        ]
-        return ChapterFilterResult(kept=[longest], skipped=fallback_skipped)
-
-    return ChapterFilterResult(kept=[], skipped=skipped)
-
-
-def update_metadata_for_chapter_count(
-    metadata: Dict[str, Any], count: int, file_type: str
-) -> None:
-    if not metadata or count <= 0:
-        return
-
-    label = "Chapters" if file_type.lower() in {"epub", "markdown"} else "Pages"
-    metadata["chapter_count"] = str(count)
-
-    pattern = re.compile(r"\(\d+\s+(Chapters?|Pages?)\)")
-    replacement = f"({count} {label})"
-    for key in ("album", "ALBUM"):
-        value = metadata.get(key)
-        if not isinstance(value, str):
-            continue
-        metadata[key] = pattern.sub(replacement, value)

abogen/domain/intro_outro.py

@@ -1,83 +0,0 @@
-"""Intro/outro text building and voice resolution for audiobook conversion.
-
-Both UIs (WebUI and Desktop) need to:
-1. Build intro/outro text from book metadata
-2. Resolve which voice to use for intro/outro synthesis
-
-This module provides the shared domain logic. The actual TTS synthesis
-and audio writing remain UI-specific.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Any, Dict, Optional
-
-from abogen.domain.title_builder import build_title_intro_text, build_outro_text
-from abogen.domain.voice_resolution import resolve_fallback_voice_spec
-
-
-@dataclass
-class IntroOutroSpec:
-    """Resolved intro or outro specification ready for TTS synthesis."""
-    text: str
-    voice_spec: str
-    enabled: bool
-
-
-def resolve_intro(
-    metadata: Optional[Dict[str, Any]],
-    original_filename: str,
-    read_title_intro: bool,
-    base_voice_spec: str,
-    job_voice: str,
-    voice_cache_keys: list[str],
-) -> IntroOutroSpec:
-    """Resolve the intro specification from job settings and metadata.
-
-    Returns an IntroOutroSpec with text and voice_spec populated,
-    or enabled=False if intro is disabled or text cannot be built.
-    """
-    if not read_title_intro:
-        return IntroOutroSpec(text="", voice_spec="", enabled=False)
-
-    text = build_title_intro_text(metadata, original_filename)
-    if not text:
-        return IntroOutroSpec(text="", voice_spec="", enabled=False)
-
-    voice_spec = resolve_fallback_voice_spec(
-        base_voice_spec, job_voice, voice_cache_keys
-    )
-    if not voice_spec:
-        return IntroOutroSpec(text=text, voice_spec="", enabled=False)
-
-    return IntroOutroSpec(text=text, voice_spec=voice_spec, enabled=True)
-
-
-def resolve_outro(
-    metadata: Optional[Dict[str, Any]],
-    original_filename: str,
-    read_closing_outro: bool,
-    base_voice_spec: str,
-    job_voice: str,
-    voice_cache_keys: list[str],
-) -> IntroOutroSpec:
-    """Resolve the outro specification from job settings and metadata.
-
-    Returns an IntroOutroSpec with text and voice_spec populated,
-    or enabled=False if outro is disabled or text cannot be built.
-    """
-    if not read_closing_outro:
-        return IntroOutroSpec(text="", voice_spec="", enabled=False)
-
-    text = build_outro_text(metadata, original_filename)
-    if not text:
-        return IntroOutroSpec(text="", voice_spec="", enabled=False)
-
-    voice_spec = resolve_fallback_voice_spec(
-        base_voice_spec, job_voice, voice_cache_keys
-    )
-    if not voice_spec:
-        return IntroOutroSpec(text=text, voice_spec="", enabled=False)
-
-    return IntroOutroSpec(text=text, voice_spec=voice_spec, enabled=True)

abogen/domain/metadata_extraction.py

@@ -1,503 +0,0 @@
-"""Metadata extraction and processing utilities.
-
-This module provides functions for extracting metadata from text content,
-formatting metadata tags for TTS embedding, and generating ffmpeg metadata arguments.
-"""
-
-from __future__ import annotations
-
-import datetime
-import logging
-import os
-import re
-import uuid
-from typing import Any, Dict, List, Optional, Tuple
-
-logger = logging.getLogger(__name__)
-
-
-def extract_metadata_from_text(text: str) -> Dict[str, Optional[str]]:
-    """Extract metadata tags from text content.
-    
-    Looks for tags in format: <<METADATA_KEY:value>>
-    
-    Supported tags:
-    - TITLE, ARTIST, ALBUM, YEAR
-    - ALBUM_ARTIST, COMPOSER, GENRE
-    - COVER_PATH
-    
-    Args:
-        text: Text content to search for metadata tags.
-        
-    Returns:
-        Dictionary with extracted metadata values (None if not found).
-    """
-    metadata = {}
-    
-    patterns = {
-        "title": r"<<METADATA_TITLE:([^>]*)>>",
-        "artist": r"<<METADATA_ARTIST:([^>]*)>>",
-        "album": r"<<METADATA_ALBUM:([^>]*)>>",
-        "year": r"<<METADATA_YEAR:([^>]*)>>",
-        "album_artist": r"<<METADATA_ALBUM_ARTIST:([^>]*)>>",
-        "composer": r"<<METADATA_COMPOSER:([^>]*)>>",
-        "genre": r"<<METADATA_GENRE:([^>]*)>>",
-        "cover_path": r"<<METADATA_COVER_PATH:([^>]*)>>",
-    }
-    
-    for key, pattern in patterns.items():
-        match = re.search(pattern, text)
-        if match:
-            metadata[key] = match.group(1).strip()
-        else:
-            metadata[key] = None
-    
-    return metadata
-
-
-def get_filename_from_path(
-    file_path: str,
-    display_path: Optional[str] = None,
-    from_queue: bool = False,
-) -> str:
-    """Extract filename (without extension) from path.
-    
-    Args:
-        file_path: The file path to extract from.
-        display_path: Optional display path (used if from_queue is False).
-        from_queue: Whether the file is from queue.
-        
-    Returns:
-        Filename without extension.
-    """
-    if from_queue:
-        base_path = file_path
-    else:
-        base_path = display_path if display_path else file_path
-    
-    filename = os.path.splitext(os.path.basename(base_path))[0]
-    return filename
-
-
-def build_ffmpeg_metadata_args(
-    metadata: Dict[str, Optional[str]],
-    filename: str,
-) -> List[str]:
-    """Build ffmpeg metadata arguments from metadata dictionary.
-    
-    Args:
-        metadata: Dictionary with metadata keys and values.
-        filename: Fallback filename for title/album if not specified.
-        
-    Returns:
-        List of ffmpeg metadata arguments.
-    """
-    args = []
-    
-    # Default values
-    defaults = {
-        "title": filename,
-        "artist": "Unknown",
-        "album": filename,
-        "date": str(datetime.datetime.now().year),
-        "album_artist": "Unknown",
-        "composer": "Narrator",
-        "genre": "Audiobook",
-    }
-    
-    # Map of metadata keys to ffmpeg metadata keys
-    key_mapping = {
-        "title": "title",
-        "artist": "artist",
-        "album": "album",
-        "year": "date",  # year -> date for ffmpeg
-        "album_artist": "album_artist",
-        "composer": "composer",
-        "genre": "genre",
-    }
-    
-    for metadata_key, ffmpeg_key in key_mapping.items():
-        value = metadata.get(metadata_key)
-        if value is None:
-            value = defaults.get(metadata_key, "")
-        if value:
-            args.extend(["-metadata", f"{ffmpeg_key}={value}"])
-    
-    return args
-
-
-def extract_metadata_and_build_args(
-    text: str,
-    filename: str,
-    display_path: Optional[str] = None,
-    from_queue: bool = False,
-) -> Tuple[List[str], Optional[str]]:
-    """Extract metadata from text and build ffmpeg arguments.
-    
-    Convenience function that combines extract_metadata_from_text and
-    build_ffmpeg_metadata_args.
-    
-    Args:
-        text: Text content to search for metadata tags.
-        filename: Fallback filename for title/album.
-        display_path: Optional display path.
-        from_queue: Whether the file is from queue.
-        
-    Returns:
-        Tuple of (ffmpeg_metadata_args, cover_path).
-    """
-    metadata = extract_metadata_from_text(text)
-    cover_path = metadata.get("cover_path")
-    
-    # Get actual filename from path
-    actual_filename = get_filename_from_path(
-        file_path=filename,
-        display_path=display_path,
-        from_queue=from_queue,
-    )
-    
-    args = build_ffmpeg_metadata_args(metadata, actual_filename)
-    return args, cover_path
-
-
-def read_text_for_metadata(
-    file_path: str,
-    is_direct_text: bool,
-    direct_text: Optional[str] = None,
-    encoding: Optional[str] = None,
-) -> str:
-    """Read text content for metadata extraction.
-    
-    Args:
-        file_path: Path to file (or text if is_direct_text).
-        is_direct_text: Whether file_path contains direct text.
-        direct_text: Optional direct text (used if is_direct_text).
-        encoding: File encoding (detected if not provided).
-        
-    Returns:
-        Text content for metadata extraction.
-    """
-    if is_direct_text:
-        return direct_text or file_path
-    
-    # Read from file
-    actual_path = direct_text if direct_text else file_path
-    
-    try:
-        if encoding is None:
-            from abogen.utils import detect_encoding
-            encoding = detect_encoding(actual_path)
-        
-        with open(actual_path, "r", encoding=encoding, errors="replace") as f:
-            return f.read()
-    except Exception:
-        return ""
-
-
-def extract_metadata_for_file(
-    file_path: str,
-    is_direct_text: bool = False,
-) -> Dict[str, Optional[str]]:
-    """Extract metadata dict from a file or direct text.
-
-    Convenience function combining read_text_for_metadata + extract_metadata_from_text.
-    Returns empty dict on any error.
-    """
-    try:
-        text = read_text_for_metadata(
-            file_path=file_path,
-            is_direct_text=is_direct_text,
-            direct_text=file_path if is_direct_text else None,
-        )
-        if text:
-            return extract_metadata_from_text(text) or {}
-    except Exception:
-        pass
-    return {}
-
-
-def format_metadata_tags(
-    metadata: Dict[str, Any],
-    filename: str,
-    chapter_count: int,
-    file_type: str,
-    cover_bytes: Optional[bytes] = None,
-    cache_dir: Optional[str] = None,
-) -> str:
-    """Format metadata tags for insertion into TTS text.
-
-    Builds <<METADATA_KEY:value>> tags that are later parsed by
-    extract_metadata_from_text() and fed to ffmpeg.
-
-    Args:
-        metadata: Dict with keys like 'title', 'authors' (list),
-            'publication_year', 'description', 'cover_image' (bytes).
-        filename: Fallback filename (without extension) for title/album.
-        chapter_count: Number of chapters/pages.
-        file_type: 'epub', 'pdf', or 'markdown'.
-        cover_bytes: Optional cover image bytes to save to cache.
-        cache_dir: Directory for cover cache (uses default if None).
-
-    Returns:
-        Newline-joined string of <<METADATA_KEY:value>> tags.
-    """
-    title = metadata.get("title") or filename
-    authors = metadata.get("authors") or ["Unknown"]
-    authors_text = ", ".join(authors) if isinstance(authors, list) else str(authors)
-    year = metadata.get("publication_year") or str(datetime.datetime.now().year)
-
-    chapter_label = "Chapters" if file_type in ("epub", "markdown") else "Pages"
-    chapter_text = f"{chapter_count} {chapter_label}"
-
-    tags = [
-        f"<<METADATA_TITLE:{title}>>",
-        f"<<METADATA_ARTIST:{authors_text}>>",
-        f"<<METADATA_ALBUM:{title} ({chapter_text})>>",
-        f"<<METADATA_YEAR:{year}>>",
-        f"<<METADATA_ALBUM_ARTIST:{authors_text}>>",
-        f"<<METADATA_COMPOSER:Narrator>>",
-        f"<<METADATA_GENRE:Audiobook>>",
-    ]
-
-    cover_path = _save_cover_to_cache(cover_bytes, cache_dir)
-    if cover_path:
-        tags.append(f"<<METADATA_COVER_PATH:{cover_path}>>")
-
-    return "\n".join(tags)
-
-
-def _save_cover_to_cache(
-    cover_bytes: Optional[bytes],
-    cache_dir: Optional[str] = None,
-) -> Optional[str]:
-    """Save cover image bytes to cache directory.
-
-    Args:
-        cover_bytes: Raw image bytes (e.g. JPEG/PNG).
-        cache_dir: Directory to save to. If None, returns None.
-
-    Returns:
-        Normalized path to saved cover file, or None on failure.
-    """
-    if not cover_bytes:
-        return None
-    if cache_dir is None:
-        return None
-
-    try:
-        cover_path = os.path.join(cache_dir, f"cover_{uuid.uuid4()}.jpg")
-        cover_path = os.path.normpath(cover_path)
-        with open(cover_path, "wb") as f:
-            f.write(cover_bytes)
-        return cover_path
-    except Exception as e:
-        logger.warning("Failed to save cover image: %s", e)
-        return None
-
-
-def extract_book_metadata_epub(book: Any) -> Dict[str, Any]:
-    """Extract metadata from an opened ebooklib EPUB book.
-
-    Args:
-        book: An opened ebooklib EPUB book object.
-
-    Returns:
-        Dict with keys: title, authors, description, publisher,
-        publication_year, cover_image (bytes or None).
-    """
-    import ebooklib
-
-    metadata: Dict[str, Any] = {
-        "title": None,
-        "authors": [],
-        "description": None,
-        "cover_image": None,
-        "publisher": None,
-        "publication_year": None,
-    }
-
-    try:
-        title_items = book.get_metadata("DC", "title")
-        if title_items and len(title_items) > 0:
-            metadata["title"] = title_items[0][0]
-    except Exception as e:
-        logger.warning("Error extracting title metadata: %s", e)
-
-    try:
-        author_items = book.get_metadata("DC", "creator")
-        if author_items:
-            metadata["authors"] = [
-                author[0] for author in author_items if len(author) > 0
-            ]
-    except Exception as e:
-        logger.warning("Error extracting author metadata: %s", e)
-
-    try:
-        desc_items = book.get_metadata("DC", "description")
-        if desc_items and len(desc_items) > 0:
-            metadata["description"] = desc_items[0][0]
-    except Exception as e:
-        logger.warning("Error extracting description metadata: %s", e)
-
-    try:
-        publisher_items = book.get_metadata("DC", "publisher")
-        if publisher_items and len(publisher_items) > 0:
-            metadata["publisher"] = publisher_items[0][0]
-    except Exception as e:
-        logger.warning("Error extracting publisher metadata: %s", e)
-
-    try:
-        date_items = book.get_metadata("DC", "date")
-        if date_items and len(date_items) > 0:
-            date_str = date_items[0][0]
-            year_match = re.search(r"\b(19|20)\d{2}\b", date_str)
-            if year_match:
-                metadata["publication_year"] = year_match.group(0)
-            else:
-                metadata["publication_year"] = date_str
-    except Exception as e:
-        logger.warning("Error extracting publication date metadata: %s", e)
-
-    for item in book.get_items_of_type(ebooklib.ITEM_COVER):
-        metadata["cover_image"] = item.get_content()
-        break
-
-    if not metadata["cover_image"]:
-        for item in book.get_items_of_type(ebooklib.ITEM_IMAGE):
-            if "cover" in item.get_name().lower():
-                metadata["cover_image"] = item.get_content()
-                break
-
-    return metadata
-
-
-def extract_book_metadata_pdf(pdf_doc: Any) -> Dict[str, Any]:
-    """Extract metadata from an opened PyMuPDF document.
-
-    Args:
-        pdf_doc: An opened fitz.Document object.
-
-    Returns:
-        Dict with keys: title, authors, description, publisher,
-        publication_year, cover_image (bytes or None).
-    """
-    metadata: Dict[str, Any] = {
-        "title": None,
-        "authors": [],
-        "description": None,
-        "cover_image": None,
-        "publisher": None,
-        "publication_year": None,
-    }
-
-    pdf_info = pdf_doc.metadata
-    if pdf_info:
-        metadata["title"] = pdf_info.get("title", None)
-        author = pdf_info.get("author", None)
-        if author:
-            metadata["authors"] = [author]
-        metadata["description"] = pdf_info.get("subject", None)
-        keywords = pdf_info.get("keywords", None)
-        if keywords:
-            if metadata["description"]:
-                metadata["description"] += f"\n\nKeywords: {keywords}"
-            else:
-                metadata["description"] = f"Keywords: {keywords}"
-        metadata["publisher"] = pdf_info.get("creator", None)
-
-        if "creationDate" in pdf_info:
-            date_str = pdf_info["creationDate"]
-            year_match = re.search(r"D:(\d{4})", date_str)
-            if year_match:
-                metadata["publication_year"] = year_match.group(1)
-        elif "modDate" in pdf_info:
-            date_str = pdf_info["modDate"]
-            year_match = re.search(r"D:(\d{4})", date_str)
-            if year_match:
-                metadata["publication_year"] = year_match.group(1)
-
-    if len(pdf_doc) > 0:
-        try:
-            import fitz
-            pix = pdf_doc[0].get_pixmap(matrix=fitz.Matrix(2, 2))
-            metadata["cover_image"] = pix.tobytes("png")
-        except Exception:
-            pass
-
-    return metadata
-
-
-def extract_book_metadata_markdown(
-    markdown_text: str,
-    markdown_toc: Optional[List[Dict[str, Any]]] = None,
-) -> Dict[str, Any]:
-    """Extract metadata from markdown frontmatter and first heading.
-
-    Args:
-        markdown_text: Raw markdown text content.
-        markdown_toc: Optional table of contents list (each item has
-            'level' and 'name' keys).
-
-    Returns:
-        Dict with keys: title, authors, description, publication_year.
-        cover_image is always None for markdown.
-    """
-    metadata: Dict[str, Any] = {
-        "title": None,
-        "authors": [],
-        "description": None,
-        "cover_image": None,
-        "publisher": None,
-        "publication_year": None,
-    }
-
-    if not markdown_text:
-        return metadata
-
-    frontmatter_match = re.match(
-        r"^---\s*\n(.*?)\n---\s*\n", markdown_text, re.DOTALL
-    )
-    if frontmatter_match:
-        try:
-            frontmatter = frontmatter_match.group(1)
-            title_match = re.search(
-                r"^title:\s*(.+)$", frontmatter, re.MULTILINE | re.IGNORECASE
-            )
-            if title_match:
-                metadata["title"] = title_match.group(1).strip().strip("\"'")
-
-            author_match = re.search(
-                r"^author:\s*(.+)$", frontmatter, re.MULTILINE | re.IGNORECASE
-            )
-            if author_match:
-                metadata["authors"] = [
-                    author_match.group(1).strip().strip("\"'")
-                ]
-
-            desc_match = re.search(
-                r"^description:\s*(.+)$", frontmatter, re.MULTILINE | re.IGNORECASE
-            )
-            if desc_match:
-                metadata["description"] = (
-                    desc_match.group(1).strip().strip("\"'")
-                )
-
-            date_match = re.search(
-                r"^date:\s*(.+)$", frontmatter, re.MULTILINE | re.IGNORECASE
-            )
-            if date_match:
-                date_str = date_match.group(1).strip().strip("\"'")
-                year_match = re.search(r"\b(19|20)\d{2}\b", date_str)
-                if year_match:
-                    metadata["publication_year"] = year_match.group(0)
-        except Exception as e:
-            logger.warning("Error parsing markdown frontmatter: %s", e)
-
-    if not metadata["title"] and markdown_toc:
-        first_h1 = next(
-            (h for h in markdown_toc if h.get("level") == 1), None
-        )
-        if first_h1:
-            metadata["title"] = first_h1.get("name")
-
-    return metadata

abogen/domain/metadata_helpers.py

@@ -1,496 +0,0 @@
-from __future__ import annotations
-
-import json
-import math
-import re
-from pathlib import Path
-from typing import Any, Dict, List, Mapping, Optional, Tuple
-
-
-_SERIES_NAME_KEYS = (
-    "series",
-    "series_name",
-    "series_title",
-)
-_SERIES_NUMBER_KEYS = (
-    "series_index",
-    "series_position",
-    "series_sequence",
-    "book_number",
-    "series_number",
-)
-_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] = {}
-    if not values:
-        return normalized
-    for key, value in values.items():
-        if value is None:
-            continue
-        text = str(value).strip()
-        if not text:
-            continue
-        normalized[str(key).casefold()] = text
-    return normalized
-
-
-def format_author_sentence(raw: Optional[str]) -> str:
-    if raw is None:
-        return ""
-    normalized = str(raw).strip()
-    if not normalized:
-        return ""
-    lowered = normalized.casefold()
-    if lowered in {"unknown", "various"}:
-        return ""
-
-    working = normalized.replace("&", " and ")
-    segments = [segment.strip() for segment in working.split(",") if segment.strip()]
-    tokens: List[str] = []
-
-    if segments:
-        for segment in segments:
-            parts = [part.strip() for part in re.split(r"\band\b", segment, flags=re.IGNORECASE) if part.strip()]
-            if parts:
-                tokens.extend(parts)
-            else:
-                tokens.append(segment)
-    else:
-        parts = [part.strip() for part in re.split(r"\band\b", working, flags=re.IGNORECASE) if part.strip()]
-        tokens.extend(parts or [normalized])
-
-    cleaned = [token for token in tokens if token and token.casefold() not in {"unknown", "various"}]
-    if not cleaned:
-        return ""
-    if len(cleaned) == 1:
-        return f"By {cleaned[0]}"
-    if len(cleaned) == 2:
-        return f"By {cleaned[0]} and {cleaned[1]}"
-    return f"By {', '.join(cleaned[:-1])}, and {cleaned[-1]}"
-
-
-def ensure_sentence(text: str) -> str:
-    cleaned = text.strip()
-    if not cleaned:
-        return ""
-    if cleaned[-1] in ".!?":
-        return cleaned
-    return f"{cleaned}."
-
-
-def normalize_series_number(value: Any) -> Optional[str]:
-    text = str(value or "").strip()
-    if not text:
-        return None
-    candidate = text.replace(",", ".")
-    if candidate.replace(".", "", 1).isdigit():
-        if "." in candidate:
-            normalized = candidate.rstrip("0").rstrip(".")
-            return normalized or "0"
-        try:
-            return str(int(candidate))
-        except ValueError:
-            pass
-    match = _SERIES_NUMBER_RE.search(candidate)
-    if not match:
-        return None
-    normalized = match.group(0)
-    if "." in normalized:
-        normalized = normalized.rstrip("0").rstrip(".")
-        return normalized or "0"
-    try:
-        return str(int(normalized))
-    except ValueError:
-        return normalized
-
-
-def extract_series_metadata(values: Mapping[str, str]) -> Tuple[Optional[str], Optional[str]]:
-    series_name: Optional[str] = None
-    for key in _SERIES_NAME_KEYS:
-        raw = values.get(key)
-        if raw:
-            cleaned = str(raw).strip()
-            if cleaned:
-                series_name = cleaned
-                break
-
-    series_number: Optional[str] = None
-    for key in _SERIES_NUMBER_KEYS:
-        raw = values.get(key)
-        if raw is None:
-            continue
-        normalized = normalize_series_number(raw)
-        if normalized:
-            series_number = normalized
-            break
-
-    return series_name, series_number
-
-
-def format_series_sentence(series_name: Optional[str], series_number: Optional[str]) -> str:
-    if not series_name or not series_number:
-        return ""
-    name = series_name.strip()
-    number = series_number.strip()
-    if not name or not number:
-        return ""
-    article = "the " if not name.lower().startswith("the ") else ""
-    phrase = f"Book {number} of {article}{name}"
-    return re.sub(r"\s+", " ", phrase).strip()
-
-
-_PEOPLE_SPLIT_RE = re.compile(r"[;,/&]|\band\b", re.IGNORECASE)
-_LIST_SPLIT_RE = re.compile(r"[;,\n]")
-_SERIES_SEQUENCE_TAG_KEYS: Tuple[str, ...] = (
-    "series_index",
-    "series_position",
-    "series_sequence",
-    "series_number",
-    "seriesnumber",
-    "book_number",
-    "booknumber",
-)
-
-
-def normalize_metadata_casefold(values: Optional[Mapping[str, Any]]) -> Dict[str, Any]:
-    normalized: Dict[str, Any] = {}
-    if not values:
-        return normalized
-    for key, value in values.items():
-        if value is None:
-            continue
-        key_text = str(key).strip().lower()
-        if not key_text:
-            continue
-        if isinstance(value, (list, tuple, set)):
-            normalized[key_text] = value
-        else:
-            text = str(value).strip()
-            if text:
-                normalized[key_text] = text
-    return normalized
-
-
-def split_people_field(raw: Any) -> List[str]:
-    if raw is None:
-        return []
-    if isinstance(raw, (list, tuple, set)):
-        results: List[str] = []
-        for item in raw:
-            results.extend(split_people_field(item))
-        return results
-    text = str(raw or "").strip()
-    if not text:
-        return []
-    tokens = [_token.strip() for _token in _PEOPLE_SPLIT_RE.split(text) if _token.strip()]
-    seen: set[str] = set()
-    ordered: List[str] = []
-    for token in tokens:
-        key = token.casefold()
-        if key in seen:
-            continue
-        seen.add(key)
-        ordered.append(token)
-    return ordered
-
-
-def split_simple_list(raw: Any) -> List[str]:
-    if raw is None:
-        return []
-    if isinstance(raw, (list, tuple, set)):
-        results: List[str] = []
-        for item in raw:
-            results.extend(split_simple_list(item))
-        return results
-    text = str(raw or "").strip()
-    if not text:
-        return []
-    tokens = [_token.strip() for _token in _LIST_SPLIT_RE.split(text) if _token.strip()]
-    seen: set[str] = set()
-    ordered: List[str] = []
-    for token in tokens:
-        key = token.casefold()
-        if key in seen:
-            continue
-        seen.add(key)
-        ordered.append(token)
-    return ordered
-
-
-def first_nonempty(*values: Any) -> Optional[str]:
-    for value in values:
-        if value is None:
-            continue
-        if isinstance(value, (list, tuple, set)):
-            items = list(value)
-            if not items:
-                continue
-            value = items[0]
-        text = str(value).strip()
-        if text:
-            return text
-    return None
-
-
-def extract_year(raw: Optional[str]) -> Optional[int]:
-    if not raw:
-        return None
-    text = str(raw).strip()
-    if not text:
-        return None
-    match = re.search(r"(19|20)\d{2}", text)
-    if match:
-        try:
-            return int(match.group(0))
-        except ValueError:
-            return None
-    try:
-        parsed = int(text)
-    except ValueError:
-        return None
-    if 0 < parsed < 3000:
-        return parsed
-    return None
-
-
-def normalize_series_sequence(raw: Any) -> Optional[str]:
-    if raw is None:
-        return None
-    if isinstance(raw, (int, float)):
-        if isinstance(raw, float) and (math.isnan(raw) or math.isinf(raw)):
-            return None
-        text = str(raw)
-    else:
-        text = str(raw).strip()
-    if not text:
-        return None
-    candidate = text.replace(",", ".")
-    match = _SERIES_NUMBER_RE.search(candidate)
-    if not match:
-        return None
-    normalized = match.group(0)
-    if "." in normalized:
-        normalized = normalized.rstrip("0").rstrip(".")
-        if not normalized:
-            normalized = "0"
-        return normalized
-    try:
-        return str(int(normalized))
-    except ValueError:
-        cleaned = normalized.lstrip("0")
-        return cleaned or "0"
-
-
-def build_audiobookshelf_metadata(
-    tags: Mapping[str, Any],
-    *,
-    language: str = "",
-    filename: str = "",
-) -> Dict[str, Any]:
-    normalized = normalize_metadata_casefold(tags)
-    title = first_nonempty(
-        normalized.get("title"),
-        normalized.get("book_title"),
-        normalized.get("name"),
-        normalized.get("album"),
-        filename,
-    )
-    authors = split_people_field(
-        normalized.get("authors")
-        or normalized.get("author")
-        or normalized.get("album_artist")
-        or normalized.get("artist")
-    )
-    narrators = split_people_field(normalized.get("narrators") or normalized.get("narrator"))
-    description = first_nonempty(
-        normalized.get("description"), normalized.get("summary"), normalized.get("comment")
-    )
-    genres = split_simple_list(normalized.get("genre"))
-    keywords = split_simple_list(normalized.get("tags") or normalized.get("keywords"))
-    lang = first_nonempty(normalized.get("language"), normalized.get("lang")) or language or ""
-    series_name = first_nonempty(
-        normalized.get("series"),
-        normalized.get("series_name"),
-        normalized.get("seriesname"),
-        normalized.get("series_title"),
-        normalized.get("seriestitle"),
-    )
-
-    series_sequence = None
-    for key in _SERIES_SEQUENCE_TAG_KEYS:
-        raw_value = normalized.get(key)
-        seq = normalize_series_sequence(raw_value)
-        if seq:
-            series_sequence = seq
-            break
-    if not series_name:
-        series_sequence = None
-
-    data: Dict[str, Any] = {
-        "title": title,
-        "subtitle": normalized.get("subtitle"),
-        "authors": authors,
-        "narrators": narrators,
-        "description": description,
-        "publisher": normalized.get("publisher"),
-        "genres": genres,
-        "tags": keywords,
-        "language": lang,
-        "publishedYear": extract_year(
-            normalized.get("published")
-            or normalized.get("publication_year")
-            or normalized.get("date")
-            or normalized.get("year")
-        ),
-        "seriesName": series_name,
-        "seriesSequence": series_sequence,
-        "isbn": first_nonempty(normalized.get("isbn"), normalized.get("asin")),
-    }
-    published_date = first_nonempty(
-        normalized.get("published"), normalized.get("publication_date"), normalized.get("date")
-    )
-    if published_date:
-        data["publishedDate"] = published_date
-
-    rating_text = first_nonempty(normalized.get("rating"), normalized.get("my_rating"))
-    if rating_text:
-        try:
-            data["rating"] = float(str(rating_text).strip())
-        except ValueError:
-            pass
-        rating_max_text = first_nonempty(
-            normalized.get("rating_max"), normalized.get("rating_scale")
-        )
-        if rating_max_text:
-            try:
-                data["ratingMax"] = float(str(rating_max_text).strip())
-            except ValueError:
-                pass
-
-    cleaned: Dict[str, Any] = {}
-    for key, value in data.items():
-        if value is None:
-            continue
-        if isinstance(value, str) and not value.strip():
-            continue
-        if isinstance(value, (list, tuple)) and not value:
-            continue
-        cleaned[key] = value
-    return cleaned
-
-
-def load_audiobookshelf_chapters(
-    metadata_path: Path,
-) -> Optional[List[Dict[str, Any]]]:
-    if not metadata_path.exists():
-        return None
-    try:
-        payload = json.loads(metadata_path.read_text(encoding="utf-8"))
-    except (OSError, json.JSONDecodeError):
-        return None
-    chapters = payload.get("chapters")
-    if not isinstance(chapters, list):
-        return None
-    cleaned: List[Dict[str, Any]] = []
-    for entry in chapters:
-        if not isinstance(entry, Mapping):
-            continue
-        title = first_nonempty(entry.get("title"), entry.get("original_title"))
-        start = entry.get("start")
-        end = entry.get("end")
-        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_merge.py

@@ -1,23 +0,0 @@
-from __future__ import annotations
-
-from typing import Any, Dict, Optional
-
-
-def merge_metadata(
-    extracted: Optional[Dict[str, Any]],
-    overrides: Optional[Dict[str, Any]],
-) -> Dict[str, str]:
-    merged: Dict[str, str] = {}
-    if extracted:
-        for key, value in extracted.items():
-            if value is None:
-                continue
-            merged[str(key)] = str(value)
-    if overrides:
-        for key, value in overrides.items():
-            key_str = str(key)
-            if value is None:
-                merged.pop(key_str, None)
-            else:
-                merged[key_str] = str(value)
-    return merged

abogen/domain/metadata_overrides.py

@@ -1,56 +0,0 @@
-"""OPDS metadata normalization.
-
-Normalizes metadata keys from various OPDS/Calibre sources into
-a canonical set of overrides for the audiobook conversion pipeline.
-"""
-
-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 all
-    concept aliases expanded.
-
-    Args:
-        metadata_payload: Raw metadata dict from OPDS/Calibre import.
-
-    Returns:
-        Dict with all canonical metadata key aliases expanded.
-    """
-    def _stringify(value: Any) -> str:
-        if value is None:
-            return ""
-        if isinstance(value, (list, tuple, set)):
-            parts = [str(item).strip() for item in value if item is not None]
-            return ", ".join(part for part in parts if part)
-        return str(value).strip()
-
-    # 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
-
-        # 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
-
-    return expand_metadata_aliases(normalized_input)

abogen/domain/normalization.py

@@ -1,245 +0,0 @@
-"""Text normalization convenience helpers.
-
-Provides both the simple ``normalize_text_for_pipeline`` (apostrophe + LLM only)
-and the comprehensive ``prepare_text_for_tts`` that chains all three normalization
-stages used during conversion: heteronym rules → pronunciation rules → pipeline
-normalization.  The latter is the single entry point that both the Web UI and
-PyQt Desktop GUI should use.
-
-Also provides ``TTSContext`` — a dataclass bundling all pre-compiled normalization
-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, 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,
-)
-from abogen.normalization_settings import (
-    build_apostrophe_config,
-    get_runtime_settings,
-    apply_overrides as _apply_overrides,
-)
-
-_BASE_APOSTROPHE_CONFIG = ApostropheConfig()
-
-
-@dataclass
-class TTSContext:
-    """Bundles pre-compiled normalization resources for TTS processing.
-
-    Created once per conversion job and passed to ``prepare_text_for_tts``
-    instead of threading 5 separate parameters.
-    """
-
-    split_pattern: str = r"(?<=[.!?\-])\s+"
-    pronunciation_rules: Optional[List[Dict[str, Any]]] = None
-    heteronym_rules: Optional[List[Dict[str, Any]]] = None
-    normalization_overrides: Optional[Mapping[str, Any]] = None
-    usage_counter: Dict[str, int] = field(default_factory=dict)
-
-    def normalize(self, text: str) -> str:
-        """Shorthand: normalize text using this context's compiled rules."""
-        return prepare_text_for_tts(
-            text,
-            heteronym_rules=self.heteronym_rules,
-            pronunciation_rules=self.pronunciation_rules,
-            normalization_overrides=self.normalization_overrides,
-            usage_counter=self.usage_counter,
-        )
-
-
-def normalize_text_for_pipeline(
-    text: str,
-    *,
-    normalization_overrides: Optional[Mapping[str, Any]] = None,
-) -> str:
-    """Normalize text using runtime settings with optional overrides."""
-    runtime_settings = get_runtime_settings()
-    if normalization_overrides:
-        runtime_settings = _apply_overrides(runtime_settings, normalization_overrides)
-    apostrophe_config = build_apostrophe_config(settings=runtime_settings, base=_BASE_APOSTROPHE_CONFIG)
-    return _normalize_for_pipeline(text, config=apostrophe_config, settings=runtime_settings)
-
-
-def prepare_text_for_tts(
-    text: str,
-    *,
-    heteronym_rules: Optional[List[Dict[str, Any]]] = None,
-    pronunciation_rules: Optional[List[Dict[str, Any]]] = None,
-    normalization_overrides: Optional[Mapping[str, Any]] = None,
-    usage_counter: Optional[Dict[str, int]] = None,
-) -> str:
-    """Apply the full text normalization pipeline before TTS synthesis.
-
-    Chains three stages in order:
-      1. Heteronym sentence rules (context-dependent pronunciation)
-      2. Pronunciation rules (token-level replacements)
-      3. Pipeline normalization (apostrophe handling, LLM normalization)
-
-    This is the **single entry point** that both the Web UI conversion runner
-    and the PyQt conversion thread should call before passing text to the TTS
-    backend.
-
-    Parameters
-    ----------
-    text:
-        Raw text to normalize.
-    heteronym_rules:
-        Compiled heteronym rules from ``compile_heteronym_sentence_rules``.
-    pronunciation_rules:
-        Compiled pronunciation rules from ``compile_pronunciation_rules``.
-    normalization_overrides:
-        User-level overrides for normalization settings (apostrophe mode, etc.).
-    usage_counter:
-        Mutable dict that tracks how many times each pronunciation override was
-        applied.  Passed through to ``apply_pronunciation_rules``.
-
-    Returns
-    -------
-    str
-        Fully normalized text ready for TTS.
-    """
-    from abogen.domain.pronunciation import (
-        apply_heteronym_sentence_rules,
-        apply_pronunciation_rules,
-    )
-
-    result = str(text or "")
-
-    if heteronym_rules:
-        result = apply_heteronym_sentence_rules(result, heteronym_rules)
-
-    if pronunciation_rules:
-        result = apply_pronunciation_rules(result, pronunciation_rules, usage_counter)
-
-    runtime_settings = get_runtime_settings()
-    if normalization_overrides:
-        runtime_settings = _apply_overrides(runtime_settings, normalization_overrides)
-    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

@@ -1,226 +0,0 @@
-"""Output path resolution utilities.
-
-Pure functions for resolving output directories, building file paths,
-and computing project folder layouts.
-"""
-
-from __future__ import annotations
-
-import os
-import platform
-import re
-from datetime import datetime
-from pathlib import Path
-from typing import Callable, List, Optional, Tuple
-
-from abogen.text_extractor import ExtractedChapter
-
-
-_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"}
-    | {f"COM{i}" for i in range(1, 10)}
-    | {f"LPT{i}" for i in range(1, 10)}
-)
-
-
-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:
-        sanitized = f"chapter_{index:02d}"
-    return sanitized[:80]
-
-
-def sanitize_filename_for_chapter(title: str, index: int, max_len: int = 80) -> str:
-    """Sanitize a chapter name for use as a filename component.
-
-    Combines character sanitization, OS safety, and smart truncation
-    at word boundaries. Prepends zero-padded index prefix.
-
-    Args:
-        title: Raw chapter title.
-        index: 1-based chapter number for prefix.
-        max_len: Maximum length of the sanitized portion (excluding prefix).
-
-    Returns:
-        Sanitized string like "01_the_beginning".
-    """
-    # Remove non-word/non-space/non-hyphen chars, then collapse spaces/hyphens
-    sanitized = re.sub(r"[^\w\s\-]", "", title)
-    sanitized = re.sub(r"[\s\-]+", "_", sanitized).strip("_")
-
-    if not sanitized:
-        sanitized = f"chapter_{index:02d}"
-
-    # OS-specific sanitization
-    system = platform.system()
-    if system == "Windows":
-        sanitized = _WINDOWS_ILLEGAL_CHARS_RE.sub("_", sanitized)
-        sanitized = sanitized.rstrip(". ")
-        base = sanitized.split(".")[0].upper()
-        if base in _RESERVED_NAMES:
-            sanitized = f"_{sanitized}"
-    # Linux: only NUL is truly illegal, but control chars are problematic
-    sanitized = _UNIX_CONTROL_CHARS_RE.sub("_", sanitized)
-
-    # Smart truncation at word boundary
-    if len(sanitized) > max_len:
-        pos = sanitized[:max_len].rfind("_")
-        sanitized = sanitized[: pos if pos > 0 else max_len].rstrip("_")
-
-    return f"{index:02d}_{sanitized}"
-
-
-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"
-
-
-def output_timestamp_token() -> str:
-    return datetime.now().strftime("%Y%m%d-%H%M%S")
-
-
-def build_output_path(directory: Path, original_name: str, extension: str) -> Path:
-    sanitized = sanitize_output_stem(original_name)
-    return directory / f"{sanitized}.{extension}"
-
-
-def apply_newline_policy(chapters: List[ExtractedChapter], replace_single_newlines: bool) -> None:
-    if not replace_single_newlines:
-        return
-    newline_regex = re.compile(r"(?<!\n)\n(?!\n)")
-    for chapter in chapters:
-        chapter.text = newline_regex.sub(" ", chapter.text)
-
-
-from abogen.domain.enums import SaveMode
-
-
-def resolve_output_directory(
-    *,
-    save_mode: str,
-    stored_path: Path,
-    output_folder: Optional[str],
-    desktop_dir: Optional[Path],
-    user_output_path: Optional[Path],
-    user_cache_outputs: Optional[Path],
-) -> Path:
-    if save_mode in (SaveMode.SAVE_TO_DESKTOP, "Save to Desktop") and desktop_dir:
-        return desktop_dir
-    if save_mode in (SaveMode.SAVE_NEXT_TO_INPUT, "Save next to input file"):
-        return stored_path.parent
-    if save_mode in (SaveMode.CHOOSE_OUTPUT_FOLDER, "Choose output folder") and output_folder:
-        return Path(output_folder)
-    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(".")
-
-
-def resolve_project_layout(
-    *,
-    original_filename: str,
-    save_as_project: bool,
-    base_dir: Path,
-    timestamp_fn: Callable[[], str] = output_timestamp_token,
-    sanitize_fn: Callable[[str, int], str] = sanitize_output_stem,
-) -> Tuple[Path, Path, Path, Optional[Path]]:
-    sanitized = sanitize_fn(original_filename, 0)
-    folder_name = f"{timestamp_fn()}_{sanitized}"
-    project_root = base_dir / folder_name
-    project_root.mkdir(parents=True, exist_ok=True)
-
-    if save_as_project:
-        audio_dir = project_root / "audio"
-        subtitle_dir = project_root / "subtitles"
-        metadata_dir = project_root / "metadata"
-        for directory in (audio_dir, subtitle_dir, metadata_dir):
-            directory.mkdir(parents=True, exist_ok=True)
-        return project_root, audio_dir, subtitle_dir, metadata_dir
-
-    return project_root, project_root, project_root, None
-
-
-def resolve_unique_path(
-    parent_dir: str,
-    base_name: str,
-    extension: str,
-    allowed_extensions: Optional[set] = None,
-) -> str:
-    """Find a unique file path by appending _2, _3, etc. on collision.
-
-    Args:
-        parent_dir: Directory to check for collisions.
-        base_name: Base filename (without extension).
-        extension: File extension (without dot).
-        allowed_extensions: Set of extensions to check against.
-            If None, checks any existing file/dir with same name.
-
-    Returns:
-        Full path without extension (e.g. "/path/to/name_2").
-    """
-    sanitized = sanitize_name_for_os(base_name, is_folder=True)
-    counter = 1
-    while True:
-        suffix = f"_{counter}" if counter > 1 else ""
-        candidate = os.path.join(parent_dir, f"{sanitized}{suffix}")
-        if allowed_extensions is not None:
-            file_parts = (os.path.splitext(f) for f in os.listdir(parent_dir))
-            clash = any(
-                name == f"{sanitized}{suffix}"
-                and ext[1:].lower() in allowed_extensions
-                for name, ext in file_parts
-            )
-        else:
-            clash = os.path.exists(candidate)
-        if not clash:
-            return candidate
-        counter += 1

abogen/domain/pipeline_factory.py

@@ -1,117 +0,0 @@
-"""Pipeline creation, caching and lifecycle management.
-
-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
-
-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
-
-
-def resolve_device(use_gpu: bool) -> str:
-    """Determine compute device from job and global config flags."""
-    from abogen.utils import load_config
-
-    cfg = load_config()
-    if use_gpu and cfg.get("use_gpu", True):
-        return select_device()
-    return "cpu"
-
-
-def create_pipeline_for_job(
-    provider: str,
-    language: Language,
-    use_gpu: bool,
-) -> Any:
-    """Create a TTS pipeline with proper device selection.
-
-    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", language=language)
-
-    device = resolve_device(use_gpu)
-    return create_pipeline("kokoro", language=language, device=device)
-
-
-def dispose_pipelines(pipelines: Dict[str, Any]) -> None:
-    """Dispose all pipelines in a dict and clear it."""
-    for p in pipelines.values():
-        try:
-            p.dispose()
-        except Exception:
-            pass
-    pipelines.clear()
-
-
-class PipelinePool:
-    """Cache and manage TTS pipelines by provider.
-
-    Usage::
-
-        pool = PipelinePool()
-        backend = pool.get("kokoro", Language.EN_US, use_gpu=True)
-        # ... use backend ...
-        pool.dispose_all()
-    """
-
-    def __init__(self) -> None:
-        self._pipelines: Dict[str, Any] = {}
-        self._voice_cache_initialized = False
-
-    def get(
-        self,
-        provider: str,
-        language: Language,
-        use_gpu: bool,
-        *,
-        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 enum (app-layer type).
-            use_gpu: Whether GPU acceleration is requested.
-            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):
-            provider = "kokoro"
-
-        existing = self._pipelines.get(provider)
-        if existing is not None:
-            return existing
-
-        pipeline = create_pipeline_for_job(provider, language, use_gpu)
-        self._pipelines[provider] = pipeline
-
-        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
-
-    def dispose_all(self) -> None:
-        """Dispose all cached pipelines."""
-        dispose_pipelines(self._pipelines)
-        self._voice_cache_initialized = False

abogen/domain/progress.py

@@ -1,72 +0,0 @@
-from __future__ import annotations
-
-"""Progress and ETR (estimated time remaining) calculation.
-
-Shared by Web UI and PyQt desktop GUI. Pure math, no UI dependencies.
-"""
-import time
-from dataclasses import dataclass, field
-
-
-@dataclass
-class ProgressTracker:
-    """Tracks character-based progress with ETR calculation.
-
-    Usage:
-        tracker = ProgressTracker(total_chars=50000)
-        # ... as processing occurs:
-        tracker.update(chars_done=5000)
-        print(tracker.etr_str)   # "00:04:30"
-        print(tracker.percent)   # 10
-    """
-    total_chars: int
-    _start_time: float = field(default_factory=time.time, repr=False)
-    _chars_done: int = field(default=0, repr=False)
-
-    def update(self, chars_done: int) -> None:
-        self._chars_done = chars_done
-
-    @property
-    def percent(self) -> int:
-        if self.total_chars <= 0:
-            return 0
-        return min(int(self._chars_done / self.total_chars * 100), 99)
-
-    @property
-    def etr_str(self) -> str:
-        elapsed = time.time() - self._start_time
-        if self._chars_done <= 0 or elapsed <= 0.5:
-            return "Processing..."
-        avg_time_per_char = elapsed / self._chars_done
-        remaining = self.total_chars - self._chars_done
-        if remaining <= 0:
-            return "00:00:00"
-        secs = avg_time_per_char * remaining
-        h = int(secs // 3600)
-        m = int((secs % 3600) // 60)
-        s = int(secs % 60)
-        return f"{h:02d}:{m:02d}:{s:02d}"
-
-
-def calc_etr_str(elapsed: float, done: int, total: int) -> str:
-    """Standalone ETR string calculation (matches PyQt original logic).
-
-    Args:
-        elapsed: seconds since processing started
-        done: items/characters processed so far
-        total: total items/characters to process
-
-    Returns:
-        ETR string like "01:23:45" or "Processing..."
-    """
-    if done <= 0 or elapsed <= 0.5:
-        return "Processing..."
-    avg_time_per_item = elapsed / done
-    remaining = total - done
-    if remaining <= 0:
-        return "00:00:00"
-    secs = avg_time_per_item * remaining
-    h = int(secs // 3600)
-    m = int((secs % 3600) // 60)
-    s = int(secs % 60)
-    return f"{h:02d}:{m:02d}:{s:02d}"

abogen/domain/pronunciation.py

@@ -1,270 +0,0 @@
-"""Pronunciation rule compilation and application.
-
-Pure functions for compiling token-level and sentence-level pronunciation
-overrides into regex patterns, applying them to text, and merging multiple
-override sources with precedence rules.
-"""
-
-from __future__ import annotations
-
-import re
-from typing import Any, Dict, Iterable, List, Mapping, Optional
-
-from abogen.entity_analysis import normalize_token as normalize_entity_token
-from abogen.entity_analysis import normalize_manual_override_token
-
-
-def compile_pronunciation_rules(
-    overrides: Optional[Iterable[Mapping[str, Any]]],
-) -> List[Dict[str, Any]]:
-    if not overrides:
-        return []
-
-    candidates: List[Dict[str, Any]] = []
-    seen: set[str] = set()
-
-    for entry in overrides:
-        if not isinstance(entry, Mapping):
-            continue
-        pronunciation_value = str(entry.get("pronunciation") or "").strip()
-        if not pronunciation_value:
-            continue
-
-        token_values: List[str] = []
-        token_raw = entry.get("token")
-        if token_raw:
-            token_value = str(token_raw).strip()
-            if token_value:
-                token_values.append(token_value)
-        normalized_raw = entry.get("normalized")
-        if normalized_raw:
-            normalized_value = str(normalized_raw).strip()
-            if normalized_value:
-                token_values.append(normalized_value)
-        if token_raw and not token_values:
-            fallback = normalize_entity_token(str(token_raw))
-            if fallback:
-                token_values.append(fallback)
-
-        if not token_values:
-            continue
-
-        usage_normalized = str(entry.get("normalized") or "").strip()
-        if not usage_normalized and token_values:
-            usage_normalized = normalize_entity_token(token_values[0]) or token_values[0]
-        usage_token = str(entry.get("token") or token_values[0])
-
-        for token_value in token_values:
-            key = token_value.casefold()
-            if key in seen:
-                continue
-            seen.add(key)
-            candidates.append(
-                {
-                    "token": token_value,
-                    "normalized": usage_normalized,
-                    "replacement": pronunciation_value,
-                }
-            )
-
-    if not candidates:
-        return []
-
-    candidates.sort(key=lambda item: len(item["token"]), reverse=True)
-    compiled: List[Dict[str, Any]] = []
-    for candidate in candidates:
-        token_value = candidate["token"]
-        pronunciation_value = candidate["replacement"]
-        escaped = re.escape(token_value)
-        pattern = re.compile(rf"(?i)(?<!\w){escaped}(?P<possessive>'s|\u2019s|\u2019)?(?!\w)")
-        compiled.append(
-            {
-                "pattern": pattern,
-                "replacement": pronunciation_value,
-                "normalized": candidate.get("normalized") or token_value,
-                "token": candidate.get("token") or token_value,
-            }
-        )
-
-    return compiled
-
-
-def compile_heteronym_sentence_rules(
-    overrides: Optional[Iterable[Mapping[str, Any]]],
-) -> List[Dict[str, Any]]:
-    if not overrides:
-        return []
-
-    compiled: List[Dict[str, Any]] = []
-    seen: set[str] = set()
-
-    for entry in overrides:
-        if not isinstance(entry, Mapping):
-            continue
-        sentence = str(entry.get("sentence") or "").strip()
-        if not sentence:
-            continue
-        choice = str(entry.get("choice") or "").strip()
-        if not choice:
-            continue
-
-        replacement_sentence = ""
-        options = entry.get("options")
-        if isinstance(options, list):
-            for opt in options:
-                if not isinstance(opt, Mapping):
-                    continue
-                if str(opt.get("key") or "").strip() == choice:
-                    replacement_sentence = str(opt.get("replacement_sentence") or "").strip()
-                    break
-        if not replacement_sentence:
-            continue
-
-        rule_key = f"{sentence}\n{choice}".casefold()
-        if rule_key in seen:
-            continue
-        seen.add(rule_key)
-
-        parts = [p for p in re.split(r"\s+", sentence) if p]
-        if not parts:
-            continue
-        pattern_text = r"\s+".join(re.escape(p) for p in parts)
-        pattern = re.compile(pattern_text)
-        compiled.append({"pattern": pattern, "replacement": replacement_sentence})
-
-    compiled.sort(key=lambda item: len(item["pattern"].pattern), reverse=True)
-    return compiled
-
-
-def apply_heteronym_sentence_rules(text: str, rules: List[Dict[str, Any]]) -> str:
-    if not text or not rules:
-        return text
-    result = text
-    for rule in rules:
-        pattern = rule["pattern"]
-        replacement = rule["replacement"]
-        result = pattern.sub(replacement, result)
-    return result
-
-
-def apply_pronunciation_rules(
-    text: str,
-    rules: List[Dict[str, Any]],
-    usage_counter: Optional[Dict[str, int]] = None,
-) -> str:
-    if not text or not rules:
-        return text
-
-    result = text
-    for rule in rules:
-        pattern = rule["pattern"]
-        pronunciation_value = rule["replacement"]
-        usage_key = str(rule.get("normalized") or "").strip()
-
-        def _replacement(match: re.Match[str]) -> str:
-            suffix = match.group("possessive") or ""
-            if usage_counter is not None and usage_key:
-                usage_counter[usage_key] = usage_counter.get(usage_key, 0) + 1
-            return pronunciation_value + suffix
-
-        result = pattern.sub(_replacement, result)
-
-    return result
-
-
-def merge_pronunciation_overrides(job: Any) -> List[Dict[str, Any]]:
-    """Return pronunciation override entries, ensuring manual overrides are included.
-
-    Pending jobs keep both ``manual_overrides`` and ``pronunciation_overrides``, but the
-    latter can be stale if the UI didn't resync before enqueue. During conversion,
-    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]] = {}
-
-    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):
-                continue
-            token_value = str(entry.get("token") or "").strip()
-            pronunciation_value = str(entry.get("pronunciation") or "").strip()
-            if not token_value or not pronunciation_value:
-                continue
-            normalized = str(entry.get("normalized") or "").strip() or normalize_entity_token(token_value)
-            if not normalized:
-                continue
-            collected[normalized] = {
-                "token": token_value,
-                "normalized": normalized,
-                "pronunciation": pronunciation_value,
-                "voice": str(entry.get("voice") or "").strip() or None,
-                "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": _get("language"),
-            }
-
-    speakers = _get("speakers")
-    if isinstance(speakers, dict):
-        for payload in speakers.values():
-            if not isinstance(payload, Mapping):
-                continue
-            token_value = str(payload.get("token") or "").strip()
-            pronunciation_value = str(payload.get("pronunciation") or "").strip()
-            if not token_value or not pronunciation_value:
-                continue
-            normalized = normalize_entity_token(token_value)
-            if not normalized:
-                continue
-            collected[normalized] = {
-                "token": token_value,
-                "normalized": normalized,
-                "pronunciation": pronunciation_value,
-                "voice": str(
-                    payload.get("resolved_voice")
-                    or payload.get("voice")
-                    or _get("voice", "")
-                ).strip()
-                or None,
-                "notes": None,
-                "context": None,
-                "source": "speaker",
-                "language": _get("language"),
-            }
-
-    manual = _get("manual_overrides")
-    if isinstance(manual, list):
-        for entry in manual:
-            if not isinstance(entry, Mapping):
-                continue
-            token_value = str(entry.get("token") or "").strip()
-            pronunciation_value = str(entry.get("pronunciation") or "").strip()
-            if not token_value or not pronunciation_value:
-                continue
-            normalized = str(entry.get("normalized") or "").strip() or normalize_manual_override_token(token_value)
-            if not normalized:
-                continue
-            collected[normalized] = {
-                "token": token_value,
-                "normalized": normalized,
-                "pronunciation": pronunciation_value,
-                "voice": str(entry.get("voice") or "").strip() or None,
-                "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": _get("language"),
-            }
-
-    return list(collected.values())

abogen/domain/settings_core.py

@@ -1,641 +0,0 @@
-"""Shared settings core.
-
-Defines the SETTINGS_REGISTRY — the single source of truth for all settings.
-Every setting has a key, type, default, validation rules, and UI scope.
-Both Web UI and Desktop GUI must reference this registry.
-"""
-
-from __future__ import annotations
-
-import os
-import re
-from dataclasses import dataclass
-from typing import Any, Callable, Dict, Mapping, Optional
-
-from abogen.constants import (
-    KOKORO_CODE_LABELS,
-    SUBTITLE_FORMATS,
-    SUPPORTED_SOUND_FORMATS,
-)
-from abogen.tts_plugin.utils import get_default_voice
-from abogen.normalization_settings import (
-    DEFAULT_LLM_PROMPT,
-    environment_llm_defaults,
-)
-
-
-# ── Schema ───────────────────────────────────────────────────────────
-
-@dataclass(frozen=True)
-class Setting:
-    """Contract for a single setting.
-
-    Attributes:
-        key: Config dict key (e.g. "output_format").
-        type_: Python type (bool, int, float, str, list).
-        default: Default value or callable returning one.
-        min_value: Minimum for numeric types.
-        max_value: Maximum for numeric types.
-        valid_values: Allowed values for str types (None = any).
-        gui_only: True if only used by PyQt Desktop GUI.
-        web_only: True if only used by Web UI.
-        normalizer: Optional callable(value, default) -> normalized_value.
-        description: Human-readable explanation.
-    """
-    key: str
-    type_: type
-    default: Any
-    min_value: float | None = None
-    max_value: float | None = None
-    valid_values: tuple[Any, ...] | None = None
-    gui_only: bool = False
-    web_only: bool = False
-    normalizer: Callable | None = None
-    description: str = ""
-
-    def coerce(self, value: Any, fallback: Any | None = None) -> Any:
-        """Coerce value to the declared type, returning fallback on failure."""
-        fb = fallback if fallback is not None else self.default
-        if self.type_ is bool:
-            if isinstance(value, bool):
-                return value
-            if isinstance(value, str):
-                return value.lower() in {"true", "1", "yes", "on"}
-            if value is None:
-                return fb
-            return bool(value)
-        if self.type_ is int:
-            try:
-                v = int(value)
-            except (TypeError, ValueError):
-                return fb
-            if self.min_value is not None:
-                v = max(int(self.min_value), v)
-            if self.max_value is not None:
-                v = min(int(self.max_value), v)
-            return v
-        if self.type_ is float:
-            try:
-                v = float(value)
-            except (TypeError, ValueError):
-                return fb
-            if self.min_value is not None:
-                v = max(self.min_value, v)
-            if self.max_value is not None:
-                v = min(self.max_value, v)
-            return v
-        if self.type_ is str:
-            if isinstance(value, str):
-                v = value.strip()
-                if self.valid_values and v not in self.valid_values:
-                    return fb
-                return v
-            return fb
-        if self.type_ is list:
-            if isinstance(value, (list, tuple, set)):
-                return list(value)
-            return fb
-        return value
-
-
-# ── Normalizers (used by Setting.normalizer) ─────────────────────────
-
-def _norm_save_mode(value: Any, default: str) -> str:
-    if isinstance(value, str):
-        if value in SAVE_MODE_LABELS:
-            return value
-        if value in LEGACY_SAVE_MODE_MAP:
-            return LEGACY_SAVE_MODE_MAP[value]
-    return default
-
-
-def _norm_voice_spec(value: Any, default: str) -> str:
-    if isinstance(value, str):
-        text = value.strip()
-        if not text:
-            return default
-        spec, profile_name = split_profile_spec(text)
-        if profile_name:
-            return f"speaker:{profile_name}"
-        return spec
-    return default
-
-
-def _norm_speaker_spec(value: Any, default: str) -> str:
-    if isinstance(value, str):
-        text = value.strip()
-        if not text:
-            return ""
-        spec, profile_name = split_profile_spec(text)
-        if profile_name:
-            return f"speaker:{profile_name}"
-        return spec
-    return ""
-
-
-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 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 KOKORO_CODE_LABELS]
-    return default
-
-
-def _norm_stripped_str(value: Any, default: str) -> str:
-    return str(value or "").strip()
-
-
-def _norm_prompt(value: Any, default: str) -> str:
-    candidate = str(value or "").strip()
-    return candidate if candidate else default
-
-
-# ── Registry ─────────────────────────────────────────────────────────
-
-def _default_output_format() -> str:
-    return "wav"
-
-
-def _default_save_mode() -> str:
-    return "default_output" if has_output_override() else "save_next_to_input"
-
-
-def _default_llm(key: str) -> str:
-    return environment_llm_defaults().get(key, "")
-
-
-SETTINGS_REGISTRY: list[Setting] = [
-    # ── Core output ──────────────────────────────────────────────
-    Setting("output_format", str, "wav",
-            valid_values=tuple(SUPPORTED_SOUND_FORMATS),
-            description="Audio output format"),
-    Setting("subtitle_format", str, "srt",
-            valid_values=tuple(item[0] for item in SUBTITLE_FORMATS),
-            description="Subtitle file format"),
-    Setting("save_mode", str, _default_save_mode,
-            normalizer=_norm_save_mode,
-            description="Where to save output files"),
-    Setting("separate_chapters_format", str, "wav",
-            valid_values=("wav", "flac", "mp3", "opus"),
-            description="Format for separately saved chapters"),
-    Setting("chunk_level", str, "paragraph",
-            valid_values=("paragraph", "sentence"),
-            description="Text chunking granularity"),
-
-    # ── Voice ────────────────────────────────────────────────────
-    Setting("default_speaker", str, "",
-            normalizer=_norm_speaker_spec,
-            description="Default speaker name"),
-    Setting("default_voice", str, lambda: get_default_voice("kokoro"),
-            normalizer=_norm_voice_spec,
-            description="Default TTS voice"),
-    Setting("speed", float, 1.0, min_value=0.5, max_value=3.0,
-            gui_only=True,
-            description="TTS speed multiplier"),
-    Setting("supertonic_total_steps", int, 5, min_value=2, max_value=15,
-            description="SuperTonic processing steps"),
-    Setting("supertonic_speed", float, 1.0, min_value=0.7, max_value=2.0,
-            description="SuperTonic speed"),
-
-    # ── Chapter handling ─────────────────────────────────────────
-    Setting("silence_between_chapters", float, 2.0, min_value=0.0,
-            description="Silence gap between chapters (seconds)"),
-    Setting("chapter_intro_delay", float, 0.5, min_value=0.0,
-            description="Delay after chapter heading (seconds)"),
-    Setting("read_title_intro", bool, False,
-            description="Read chapter title as intro"),
-    Setting("read_closing_outro", bool, True,
-            description="Read closing/outro text"),
-    Setting("normalize_chapter_opening_caps", bool, True,
-            description="Normalize chapter opening caps"),
-    Setting("auto_prefix_chapter_titles", bool, True,
-            description="Auto-prefix chapter titles"),
-    Setting("save_chapters_separately", bool, False,
-            description="Save each chapter as separate file"),
-    Setting("merge_chapters_at_end", bool, True,
-            description="Merge chapters into single file"),
-    Setting("save_as_project", bool, False,
-            description="Save as editable project"),
-    Setting("generate_epub3", bool, False,
-            description="Generate EPUB3 output"),
-
-    # ── GPU / performance ────────────────────────────────────────
-    Setting("use_gpu", bool, True,
-            description="Use GPU acceleration"),
-
-    # ── Text processing ──────────────────────────────────────────
-    Setting("replace_single_newlines", bool, False,
-            description="Replace single newlines with spaces"),
-    Setting("max_subtitle_words", int, 50, min_value=1, max_value=500,
-            description="Max words per subtitle"),
-    Setting("enable_entity_recognition", bool, True,
-            description="Enable entity recognition"),
-
-    # ── Speaker analysis ─────────────────────────────────────────
-    Setting("speaker_analysis_threshold", int, 3, min_value=1, max_value=25,
-            description="Speaker analysis threshold"),
-    Setting("speaker_pronunciation_sentence", str, "This is {{name}} speaking.",
-            description="Template for pronunciation samples"),
-    Setting("speaker_random_languages", list, [],
-            normalizer=_norm_language_list,
-            description="Languages for random speaker assignment"),
-
-    # ── LLM ──────────────────────────────────────────────────────
-    Setting("llm_base_url", str, lambda: _default_llm("llm_base_url"),
-            normalizer=_norm_stripped_str,
-            description="LLM API base URL"),
-    Setting("llm_api_key", str, lambda: _default_llm("llm_api_key"),
-            normalizer=_norm_stripped_str,
-            description="LLM API key"),
-    Setting("llm_model", str, lambda: _default_llm("llm_model"),
-            normalizer=_norm_stripped_str,
-            description="LLM model name"),
-    Setting("llm_timeout", float, lambda: _default_llm("llm_timeout") or 30.0,
-            min_value=1.0,
-            description="LLM request timeout"),
-    Setting("llm_prompt", str, lambda: _default_llm("llm_prompt") or DEFAULT_LLM_PROMPT,
-            normalizer=_norm_prompt,
-            description="LLM normalization prompt"),
-    Setting("llm_context_mode", str, lambda: _default_llm("llm_context_mode") or "sentence",
-            valid_values=("sentence",),
-            description="LLM context mode"),
-
-    # ── Normalization (booleans) ─────────────────────────────────
-    Setting("normalization_numbers", bool, True,
-            description="Convert grouped numbers to words"),
-    Setting("normalization_currency", bool, True,
-            description="Convert currency symbols"),
-    Setting("normalization_footnotes", bool, True,
-            description="Remove footnote indicators"),
-    Setting("normalization_titles", bool, True,
-            description="Expand titles and suffixes"),
-    Setting("normalization_terminal", bool, True,
-            description="Ensure terminal punctuation"),
-    Setting("normalization_phoneme_hints", bool, True,
-            description="Add phoneme hints for possessives"),
-    Setting("normalization_caps_quotes", bool, True,
-            description="Convert ALL CAPS in quotes"),
-    Setting("normalization_internet_slang", bool, False,
-            description="Expand internet slang"),
-    Setting("normalization_apostrophes_contractions", bool, True,
-            description="Expand contractions"),
-    Setting("normalization_apostrophes_plural_possessives", bool, True,
-            description="Collapse plural possessives"),
-    Setting("normalization_apostrophes_sibilant_possessives", bool, True,
-            description="Mark sibilant possessives"),
-    Setting("normalization_apostrophes_decades", bool, True,
-            description="Expand decades"),
-    Setting("normalization_apostrophes_leading_elisions", bool, True,
-            description="Expand leading elisions"),
-    Setting("normalization_contraction_aux_be", bool, True,
-            description="Expand auxiliary 'be'"),
-    Setting("normalization_contraction_aux_have", bool, True,
-            description="Expand auxiliary 'have'"),
-    Setting("normalization_contraction_modal_will", bool, True,
-            description="Expand modal 'will'"),
-    Setting("normalization_contraction_modal_would", bool, True,
-            description="Expand modal 'would'"),
-    Setting("normalization_contraction_negation_not", bool, True,
-            description="Expand negation 'not'"),
-    Setting("normalization_contraction_let_us", bool, True,
-            description="Expand 'let's'"),
-
-    # ── Normalization (strings) ──────────────────────────────────
-    Setting("normalization_apostrophe_mode", str, "spacy",
-            valid_values=("off", "spacy", "llm"),
-            description="Apostrophe handling mode"),
-    Setting("normalization_numbers_year_style", str, "american",
-            valid_values=("american", "off"),
-            description="Year style for number normalization"),
-
-    # ── PyQt GUI-only ────────────────────────────────────────────
-    Setting("theme", str, "system",
-            gui_only=True,
-            description="UI theme"),
-    Setting("check_updates", bool, True,
-            gui_only=True,
-            description="Check for updates on startup"),
-    Setting("subtitle_mode", str, "Sentence",
-            gui_only=True,
-            description="Subtitle display mode"),
-    Setting("selected_format", str, "wav",
-            gui_only=True,
-            description="Last selected audio format"),
-    Setting("selected_voice", str, "af_heart",
-            gui_only=True,
-            description="Last selected voice"),
-    Setting("selected_profile_name", str, None,
-            gui_only=True,
-            description="Last selected profile name"),
-    Setting("log_window_max_lines", int, 2000, min_value=100,
-            gui_only=True,
-            description="Max lines in log window"),
-    Setting("use_silent_gaps", bool, True,
-            gui_only=True,
-            description="Use silent gaps between chunks"),
-    Setting("subtitle_speed_method", str, "tts",
-            gui_only=True,
-            valid_values=("tts", "ffmpeg"),
-            description="Speed adjustment method for subtitles"),
-    Setting("use_spacy_segmentation", bool, True,
-            gui_only=True,
-            description="Use spaCy for sentence segmentation"),
-    Setting("word_substitutions_enabled", bool, False,
-            gui_only=True,
-            description="Enable word substitutions"),
-    Setting("word_substitutions_list", str, "",
-            gui_only=True,
-            description="Word substitutions list"),
-    Setting("case_sensitive_substitutions", bool, False,
-            gui_only=True,
-            description="Case-sensitive substitutions"),
-    Setting("replace_all_caps", bool, False,
-            gui_only=True,
-            description="Replace ALL CAPS text"),
-    Setting("replace_numerals", bool, False,
-            gui_only=True,
-            description="Replace numerals with words"),
-    Setting("fix_nonstandard_punctuation", bool, False,
-            gui_only=True,
-            description="Fix nonstandard punctuation"),
-    Setting("queue_override_settings", bool, False,
-            gui_only=True,
-            description="Override settings per queue item"),
-    Setting("disable_kokoro_internet", bool, False,
-            description="Disable Kokoro internet access"),
-]
-
-
-# ── Registry helpers ─────────────────────────────────────────────────
-
-_REGISTRY_BY_KEY: dict[str, Setting] = {s.key: s for s in SETTINGS_REGISTRY}
-
-SETTING_KEYS: frozenset[str] = frozenset(_REGISTRY_BY_KEY.keys())
-GUI_ONLY_KEYS: frozenset[str] = frozenset(s.key for s in SETTINGS_REGISTRY if s.gui_only)
-WEB_ONLY_KEYS: frozenset[str] = frozenset(s.key for s in SETTINGS_REGISTRY if s.web_only)
-SHARED_KEYS: frozenset[str] = SETTING_KEYS - GUI_ONLY_KEYS - WEB_ONLY_KEYS
-
-BOOLEAN_SETTINGS: frozenset[str] = frozenset(s.key for s in SETTINGS_REGISTRY if s.type_ is bool)
-FLOAT_SETTINGS: frozenset[str] = frozenset(s.key for s in SETTINGS_REGISTRY if s.type_ is float)
-INT_SETTINGS: frozenset[str] = frozenset(s.key for s in SETTINGS_REGISTRY if s.type_ is int)
-
-# Backward-compatible aliases (used by existing code)
-_NORMALIZATION_BOOLEAN_KEYS: frozenset[str] = frozenset(
-    s.key for s in SETTINGS_REGISTRY
-    if s.type_ is bool and s.key.startswith("normalization_")
-)
-_NORMALIZATION_STRING_KEYS: frozenset[str] = frozenset(
-    s.key for s in SETTINGS_REGISTRY
-    if s.type_ is str and s.key.startswith("normalization_")
-)
-
-
-def get_setting(key: str) -> Setting | None:
-    """Look up a setting by key."""
-    return _REGISTRY_BY_KEY.get(key)
-
-
-def has_output_override() -> bool:
-    return bool(os.environ.get("ABOGEN_OUTPUT_DIR") or os.environ.get("ABOGEN_OUTPUT_ROOT"))
-
-
-# ── Defaults ─────────────────────────────────────────────────────────
-
-def settings_defaults() -> Dict[str, Any]:
-    """Default values for all shared settings (excludes gui_only)."""
-    result: Dict[str, Any] = {}
-    for s in SETTINGS_REGISTRY:
-        if s.gui_only:
-            continue
-        result[s.key] = s.default() if callable(s.default) else s.default
-    return result
-
-
-def all_settings_defaults() -> Dict[str, Any]:
-    """Default values for ALL settings (including gui_only)."""
-    result: Dict[str, Any] = {}
-    for s in SETTINGS_REGISTRY:
-        result[s.key] = s.default() if callable(s.default) else s.default
-    return result
-
-
-def load_settings() -> Dict[str, Any]:
-    """Load and normalize settings from config file."""
-    from abogen.utils import load_config
-    defaults = settings_defaults()
-    cfg = load_config() or {}
-    settings: Dict[str, Any] = {}
-    for key, default in defaults.items():
-        raw_value = cfg.get(key, default)
-        settings[key] = normalize_setting_value(key, raw_value, defaults)
-    return settings
-
-
-# ── Normalization (delegates to Setting.coerce) ──────────────────────
-
-def normalize_setting_value(key: str, value: Any, defaults: Dict[str, Any]) -> Any:
-    """Normalize a single setting value using the registry schema."""
-    setting = _REGISTRY_BY_KEY.get(key)
-    if setting is None:
-        return value if value is not None else defaults.get(key)
-
-    fallback = defaults.get(key, setting.default() if callable(setting.default) else setting.default)
-
-    if setting.normalizer is not None:
-        return setting.normalizer(value, fallback)
-
-    return setting.coerce(value, fallback)
-
-
-def validate_setting(key: str, value: Any) -> tuple[bool, str]:
-    """Validate a setting value against its schema. Returns (ok, error_message)."""
-    setting = _REGISTRY_BY_KEY.get(key)
-    if setting is None:
-        return False, f"Unknown setting: {key}"
-    if setting.type_ is str and setting.valid_values is not None:
-        v = str(value or "").strip()
-        if v and v not in setting.valid_values:
-            return False, f"Invalid value '{v}' for {key}. Allowed: {setting.valid_values}"
-    if setting.type_ is int:
-        try:
-            iv = int(value)
-        except (TypeError, ValueError):
-            return False, f"Invalid integer value for {key}: {value!r}"
-        if setting.min_value is not None and iv < setting.min_value:
-            return False, f"{key} must be >= {setting.min_value}, got {iv}"
-        if setting.max_value is not None and iv > setting.max_value:
-            return False, f"{key} must be <= {setting.max_value}, got {iv}"
-    if setting.type_ is float:
-        try:
-            fv = float(value)
-        except (TypeError, ValueError):
-            return False, f"Invalid float value for {key}: {value!r}"
-        if setting.min_value is not None and fv < setting.min_value:
-            return False, f"{key} must be >= {setting.min_value}, got {fv}"
-        if setting.max_value is not None and fv > setting.max_value:
-            return False, f"{key} must be <= {setting.max_value}, got {fv}"
-    return True, ""
-
-
-# ── Constants (backward-compatible) ──────────────────────────────────
-
-SAVE_MODE_LABELS = {
-    "save_next_to_input": "Save next to input file",
-    "save_to_desktop": "Save to Desktop",
-    "choose_output_folder": "Choose output folder",
-    "default_output": "Use default save location",
-}
-
-LEGACY_SAVE_MODE_MAP = {label: key for key, label in SAVE_MODE_LABELS.items()}
-
-CHUNK_LEVEL_OPTIONS = [
-    {"value": "paragraph", "label": "Paragraphs"},
-    {"value": "sentence", "label": "Sentences"},
-]
-
-CHUNK_LEVEL_VALUES = frozenset(option["value"] for option in CHUNK_LEVEL_OPTIONS)
-
-DEFAULT_ANALYSIS_THRESHOLD = 3
-
-
-# ── Coercion helpers (backward-compatible, delegate to Setting.coerce) ──
-
-def coerce_bool(value: Any, default: bool) -> bool:
-    return Setting("_", bool, default).coerce(value, default)
-
-
-def coerce_float(value: Any, default: float) -> float:
-    return Setting("_", float, default).coerce(value, default)
-
-
-def coerce_int(value: Any, default: int, *, minimum: int = 1, maximum: int = 200) -> int:
-    return Setting("_", int, default, min_value=minimum, max_value=maximum).coerce(value, default)
-
-
-def split_profile_spec(value: Any) -> tuple[str, str | None]:
-    """Split 'speaker:Name' or 'profile:Name' into (raw, name)."""
-    text = str(value or "").strip()
-    if not text:
-        return "", None
-    lowered = text.lower()
-    if lowered.startswith("profile:") or lowered.startswith("speaker:"):
-        _, _, remainder = text.partition(":")
-        name = remainder.strip()
-        return "", name or None
-    return text, None
-
-
-def normalize_save_mode(value: Any, default: str) -> str:
-    return _norm_save_mode(value, default)
-
-
-# ── LLM helpers ──────────────────────────────────────────────────────
-
-_PROMPT_TOKEN_RE = re.compile(r"{{\s*([a-zA-Z0-9_]+)\s*}}")
-
-
-def llm_ready(settings: Mapping[str, Any]) -> bool:
-    base_url = str(settings.get("llm_base_url") or "").strip()
-    return bool(base_url)
-
-
-def render_prompt_template(template: str, context: Mapping[str, str]) -> str:
-    if not template:
-        return ""
-
-    def _replace(match: re.Match[str]) -> str:
-        key = match.group(1)
-        return context.get(key, "")
-
-    return _PROMPT_TOKEN_RE.sub(_replace, template)
-
-
-# ── Integration defaults ─────────────────────────────────────────────
-
-def integration_defaults() -> Dict[str, Dict[str, Any]]:
-    """Default values for integration settings."""
-    return {
-        "calibre_opds": {
-            "enabled": False,
-            "base_url": "",
-            "username": "",
-            "password": "",
-            "verify_ssl": True,
-        },
-        "audiobookshelf": {
-            "enabled": False,
-            "base_url": "",
-            "api_token": "",
-            "library_id": "",
-            "collection_id": "",
-            "folder_id": "",
-            "verify_ssl": True,
-            "send_cover": True,
-            "send_chapters": True,
-            "send_subtitles": False,
-            "auto_send": False,
-            "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

@@ -1,381 +0,0 @@
-"""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

@@ -1,49 +0,0 @@
-from __future__ import annotations
-
-"""Unified split pattern logic extracted from 3 copies."""
-
-from abogen.domain.enums import Language, SubtitleMode
-
-# Canonical punctuation sets covering all supported scripts:
-# ASCII (. ! ?), Arabic ؟, CJK (。！？), Devanagari ।
-PUNCTUATION_SENTENCE = r".!?؟。！？।"
-# Commas: ASCII , CJK fullwidth ，CJK ideographic 、
-PUNCTUATION_SENTENCE_COMMA = r".!?,？。！？،，、।"
-PUNCTUATION_COMMAS = ",，、"
-
-
-def get_split_pattern(language: Language, subtitle_mode: str) -> str:
-    """Get the appropriate split pattern based on language and subtitle mode.
-
-    Args:
-        language: Language enum value.
-        subtitle_mode: Subtitle mode ("Sentence", "Sentence + Comma", "Line", etc.)
-
-    Returns:
-        Split pattern string
-    """
-    try:
-        mode = SubtitleMode.from_str(subtitle_mode) if not isinstance(subtitle_mode, SubtitleMode) else subtitle_mode
-    except ValueError:
-        mode = SubtitleMode.DISABLED
-
-    # For English, always use newline splitting only
-    if language in (Language.EN_US, Language.EN_GB):
-        return "\n"
-
-    # Determine spacing pattern based on language
-    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 mode in (SubtitleMode.DISABLED, SubtitleMode.LINE) and language.is_cjk:
-        return rf"(?<=[{PUNCTUATION_SENTENCE}]){spacing}|\n+"
-
-    if mode == SubtitleMode.LINE:
-        return "\n"
-    elif mode == SubtitleMode.SENTENCE:
-        return rf"(?<=[{PUNCTUATION_SENTENCE}]){spacing}|\n+"
-    elif mode == SubtitleMode.SENTENCE_COMMA:
-        return rf"(?<=[{PUNCTUATION_SENTENCE_COMMA}]){spacing}|\n+"
-    else:
-        return r"\n+"

abogen/domain/subtitle_generation.py

@@ -1,366 +0,0 @@
-"""Subtitle generation utilities for audiobook generation.
-
-This module provides functions for processing TTS tokens into subtitle entries
-according to various subtitle modes (Line, Sentence, Sentence + Comma,
-Sentence + Highlighting).
-"""
-
-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
-
-
-def process_subtitle_tokens(
-    tokens_with_timestamps: List[dict],
-    subtitle_entries: List[Tuple[float, float, str]],
-    max_subtitle_words: int,
-    subtitle_mode: str,
-    language: Language,
-    use_spacy_segmentation: bool = False,
-    fallback_end_time: Optional[float] = None,
-) -> None:
-    """Process TTS tokens into subtitle entries according to the subtitle mode.
-    
-    This function modifies subtitle_entries in-place by appending new entries.
-    
-    Args:
-        tokens_with_timestamps: List of token dictionaries with 'start', 'end', 'text',
-            and 'whitespace' keys.
-        subtitle_entries: List to append subtitle entries to (modified in-place).
-            Each entry is a tuple of (start_time, end_time, text).
-        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.
-        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
-
-    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 [SubtitleMode.DISABLED, SubtitleMode.LINE]
-        and language in [Language.EN_US, Language.EN_GB]
-        and subtitle_mode in [SubtitleMode.SENTENCE, SubtitleMode.SENTENCE_COMMA]
-    )
-
-    if subtitle_mode == SubtitleMode.SENTENCE_HIGHLIGHT:
-        _process_karaoke_highlighting(
-            processed_tokens, subtitle_entries, max_subtitle_words, fallback_end_time
-        )
-    elif subtitle_mode in [SubtitleMode.SENTENCE, SubtitleMode.SENTENCE_COMMA, SubtitleMode.LINE]:
-        if use_spacy_for_english and subtitle_mode != SubtitleMode.LINE:
-            _process_spacy_sentences(
-                processed_tokens, subtitle_entries, max_subtitle_words,
-                subtitle_mode, language, fallback_end_time
-            )
-        else:
-            _process_regex_sentences(
-                processed_tokens, subtitle_entries, max_subtitle_words,
-                subtitle_mode, 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
-        )
-
-
-def _process_karaoke_highlighting(
-    tokens: List[dict],
-    subtitle_entries: List[Tuple[float, float, str]],
-    max_subtitle_words: int,
-    fallback_end_time: Optional[float],
-) -> None:
-    """Process tokens for Sentence + Highlighting mode (karaoke effect)."""
-    separator = rf"[{PUNCTUATION_SENTENCE}]"
-    current_sentence = []
-    word_count = 0
-
-    for token in tokens:
-        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:
-            if current_sentence:
-                # Create karaoke subtitle entry for this sentence
-                start_time = current_sentence[0]["start"]
-                end_time = current_sentence[-1]["end"]
-
-                # Generate karaoke text with timing
-                karaoke_text = ""
-                for t in current_sentence:
-                    # Calculate duration in centiseconds
-                    duration = (
-                        t["end"] - t["start"]
-                        if t.get("end") is not None and t.get("start") is not None
-                        else 0.5
-                    )
-                    duration_cs = int(duration * 100)
-                    # Add karaoke effect
-                    karaoke_text += f"{{\\kf{duration_cs}}}{t['text']}{t.get('whitespace', '') or ''}"
-
-                subtitle_entries.append(
-                    (start_time, end_time, karaoke_text.strip())
-                )
-                current_sentence = []
-                word_count = 0
-
-    # Add any remaining tokens as a sentence
-    if current_sentence:
-        start_time = current_sentence[0]["start"]
-        end_time = current_sentence[-1]["end"]
-
-        # Generate karaoke text for remaining tokens
-        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()))
-
-    # Fallback for last entry
-    _apply_fallback_end_time(subtitle_entries, fallback_end_time)
-
-
-def _process_spacy_sentences(
-    tokens: List[dict],
-    subtitle_entries: List[Tuple[float, float, str]],
-    max_subtitle_words: int,
-    subtitle_mode: str,
-    language: Language,
-    fallback_end_time: Optional[float],
-) -> None:
-    """Process tokens using spaCy for sentence boundary detection."""
-    try:
-        from abogen.spacy_utils import get_spacy_model
-    except ImportError:
-        # Fall back to regex if spaCy is not available
-        _process_regex_sentences(
-            tokens, subtitle_entries, max_subtitle_words,
-            subtitle_mode, fallback_end_time
-        )
-        return
-
-    nlp = get_spacy_model(language)
-    if not nlp:
-        _process_regex_sentences(
-            tokens, subtitle_entries, max_subtitle_words,
-            subtitle_mode, fallback_end_time
-        )
-        return
-
-    # Build full text and track character positions to token indices
-    full_text = ""
-    for token in tokens:
-        text_part = token["text"] + (token.get("whitespace") or "")
-        full_text += text_part
-
-    # Get sentence boundaries from spaCy
-    doc = nlp(full_text)
-    sentence_boundaries = [sent.end_char for sent in doc.sents]
-
-    # For "Sentence + Comma" mode, also split on commas
-    if subtitle_mode == SubtitleMode.SENTENCE_COMMA:
-        comma_positions = [
-            i + 1 for i, c in enumerate(full_text) if c == ","
-        ]
-        sentence_boundaries = sorted(
-            set(sentence_boundaries + comma_positions)
-        )
-
-    # Group tokens by sentence boundaries
-    current_sentence = []
-    word_count = 0
-    current_char_pos = 0
-    boundary_idx = 0
-
-    for token in tokens:
-        current_sentence.append(token)
-        word_count += 1
-        text_len = len(token["text"]) + len(token.get("whitespace") or "")
-        current_char_pos += text_len
-
-        # Check if we've hit a sentence boundary or max words
-        at_boundary = (
-            boundary_idx < len(sentence_boundaries)
-            and current_char_pos >= sentence_boundaries[boundary_idx]
-        )
-        if at_boundary or word_count >= max_subtitle_words:
-            if current_sentence:
-                start_time = current_sentence[0]["start"]
-                end_time = current_sentence[-1]["end"]
-                sentence_text = "".join(
-                    t["text"] + (t.get("whitespace") or "")
-                    for t in current_sentence
-                )
-                subtitle_entries.append(
-                    (start_time, end_time, sentence_text.strip())
-                )
-                current_sentence = []
-                word_count = 0
-            if at_boundary:
-                boundary_idx += 1
-
-    # Add remaining tokens
-    if current_sentence:
-        start_time = current_sentence[0]["start"]
-        end_time = current_sentence[-1]["end"]
-        sentence_text = "".join(
-            t["text"] + (t.get("whitespace") or "")
-            for t in current_sentence
-        )
-        subtitle_entries.append(
-            (start_time, end_time, sentence_text.strip())
-        )
-
-    # Fallback for last entry
-    _apply_fallback_end_time(subtitle_entries, fallback_end_time)
-
-
-def _process_regex_sentences(
-    tokens: List[dict],
-    subtitle_entries: List[Tuple[float, float, str]],
-    max_subtitle_words: int,
-    subtitle_mode: str,
-    fallback_end_time: Optional[float],
-) -> None:
-    """Process tokens using regex for sentence boundary detection."""
-    # Define separator pattern based on mode
-    if subtitle_mode == SubtitleMode.LINE:
-        separator = r"\n"
-    elif subtitle_mode == SubtitleMode.SENTENCE:
-        separator = rf"[{PUNCTUATION_SENTENCE}]"
-    else:  # Sentence + Comma
-        separator = rf"[{PUNCTUATION_SENTENCE_COMMA}]"
-
-    current_sentence = []
-    word_count = 0
-
-    for token in tokens:
-        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:
-            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 "")
-
-                subtitle_entries.append(
-                    (start_time, end_time, sentence_text.strip())
-                )
-                current_sentence = []
-                word_count = 0
-
-    # 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"]
-
-        sentence_text = ""
-        for t in current_sentence:
-            sentence_text += t["text"] + (t.get("whitespace") or "")
-        sentence_text = sentence_text.strip()
-
-        if len(current_sentence) == 1:
-            parts = re.split(rf"(?<={separator})\s+", sentence_text)
-            if len(parts) > 1:
-                d = end_time - start_time
-                for i, p in enumerate(parts):
-                    e = end_time if i == len(parts) - 1 else start_time + d * len(p) / len(sentence_text)
-                    subtitle_entries.append((start_time, e, p.strip()))
-                    start_time = e
-                current_sentence = []
-
-        if current_sentence:
-            subtitle_entries.append((start_time, end_time, sentence_text))
-
-    # Fallback for last entry
-    _apply_fallback_end_time(subtitle_entries, fallback_end_time)
-
-
-def _process_word_count(
-    tokens: List[dict],
-    subtitle_entries: List[Tuple[float, float, str]],
-    max_subtitle_words: int,
-    subtitle_mode: str,
-    fallback_end_time: Optional[float],
-) -> None:
-    """Process tokens by counting spaces (word count mode)."""
-    try:
-        word_count = int(subtitle_mode.split()[0])
-        word_count = min(word_count, max_subtitle_words)
-    except (ValueError, IndexError):
-        word_count = 1
-
-    current_group = []
-    space_count = 0
-
-    for token in tokens:
-        current_group.append(token)
-
-        # Count spaces after tokens (in the whitespace field)
-        if token.get("whitespace", "") == " ":
-            space_count += 1
-
-            # Split after counting N spaces
-            if space_count >= word_count:
-                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(),
-                    )
-                )
-                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())
-        )
-
-    # Fallback for last entry
-    _apply_fallback_end_time(subtitle_entries, fallback_end_time)
-
-
-def _apply_fallback_end_time(
-    subtitle_entries: List[Tuple[float, float, str]],
-    fallback_end_time: Optional[float],
-) -> None:
-    """Apply fallback end time to the last entry if needed."""
-    if subtitle_entries and fallback_end_time is not None:
-        last_entry = subtitle_entries[-1]
-        start, end, text = last_entry
-        if end is None or end <= start or end <= 0:
-            subtitle_entries[-1] = (start, fallback_end_time, text)

abogen/domain/subtitle_processor.py

@@ -1,278 +0,0 @@
-"""Subtitle-to-audio processing pipeline.
-
-Converts subtitle files (SRT/ASS/VTT/timestamp text) into audio by
-generating TTS for each entry and mixing into a buffer.
-"""
-
-from __future__ import annotations
-
-import logging
-import time
-from dataclasses import dataclass
-from typing import Any, Callable, List, Optional, Tuple
-
-import numpy as np
-
-from abogen.domain.audio_buffer import (
-    fit_audio_to_duration,
-    ffmpeg_time_stretch,
-    mix_audio,
-    normalize_audio,
-    SAMPLE_RATE,
-)
-from abogen.domain.audio_helpers import to_float32
-from abogen.domain.progress import calc_etr_str
-from abogen.subtitle_utils import (
-    parse_ass_file,
-    parse_srt_file,
-    parse_vtt_file,
-    parse_timestamp_text_file,
-)
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class SubtitleEntry:
-    """A single subtitle entry with timing."""
-    start: float
-    end: Optional[float]
-    text: str
-
-
-def parse_subtitle_file(
-    file_path: str,
-    is_timestamp_text: bool = False,
-) -> List[Tuple[float, Optional[float], str]]:
-    """Parse a subtitle file into (start, end, text) tuples.
-
-    Args:
-        file_path: Path to subtitle file.
-        is_timestamp_text: Whether to treat as timestamp text file.
-
-    Returns:
-        List of (start_time, end_time, text) tuples.
-    """
-    if is_timestamp_text:
-        return parse_timestamp_text_file(file_path)
-
-    import os
-    ext = os.path.splitext(file_path)[1].lower()
-    if ext == ".srt":
-        return parse_srt_file(file_path)
-    elif ext == ".vtt":
-        return parse_vtt_file(file_path)
-    else:
-        return parse_ass_file(file_path)
-
-
-def format_time_range(
-    start: float,
-    end: Optional[float],
-    is_auto_end: bool = False,
-) -> str:
-    """Format a time range for display in logs.
-
-    Args:
-        start: Start time in seconds.
-        end: End time in seconds, or None.
-        is_auto_end: Whether end time is auto-detected.
-
-    Returns:
-        Formatted string like "00:01:23,456 - 00:01:25,789" or "00:01:23 - AUTO".
-    """
-    def _fmt(seconds: float) -> str:
-        h = int(seconds // 3600)
-        m = int(seconds % 3600 // 60)
-        s = int(seconds % 60)
-        ms = int((seconds - int(seconds)) * 1000)
-        result = f"{h:02d}:{m:02d}:{s:02d}"
-        if ms > 0:
-            result += f",{ms:03d}"
-        return result
-
-    if is_auto_end or end is None:
-        return f"{_fmt(start)} - AUTO"
-    return f"{_fmt(start)} - {_fmt(end)}"
-
-
-def speed_up_audio(
-    audio: np.ndarray,
-    speed_factor: float,
-    method: str = "tts",
-    *,
-    backend: Any = None,
-    text: str = "",
-    voice: Any = None,
-    base_speed: float = 1.0,
-    sample_rate: int = SAMPLE_RATE,
-) -> np.ndarray:
-    """Speed up audio to fit a time window.
-
-    Args:
-        audio: Input audio buffer.
-        speed_factor: Required speed multiplier.
-        method: "ffmpeg" for time-stretch, "tts" for regeneration.
-        backend: TTS backend (required if method="tts").
-        text: Text to regenerate (required if method="tts").
-        voice: Voice to use for regeneration.
-        base_speed: Base speed for TTS.
-        sample_rate: Sample rate.
-
-    Returns:
-        Speed-adjusted audio buffer.
-    """
-    if speed_factor <= 1.0:
-        return audio
-
-    if method == "ffmpeg":
-        logger.info("FFmpeg time-stretch: %.2fx", speed_factor)
-        return ffmpeg_time_stretch(audio, speed_factor, sample_rate)
-
-    # TTS regeneration
-    if backend is None:
-        return audio
-    new_speed = base_speed * speed_factor
-    logger.info("Regenerating at %.2fx speed", new_speed)
-    results = [
-        r for r in backend(text, voice=voice, speed=new_speed, split_pattern=None)
-    ]
-    chunks = [r.audio for r in results]
-    if not chunks:
-        return audio
-    return np.concatenate([to_float32(c) for c in chunks])
-
-
-def process_subtitle_entries(
-    subtitles: List[Tuple[float, Optional[float], str]],
-    *,
-    backend: Any,
-    voice: Any,
-    speed: float = 1.0,
-    cancel_check: Callable[[], bool] = lambda: False,
-    log_callback: Optional[Callable[[str], None]] = None,
-    progress_callback: Optional[Callable[[int, str], None]] = None,
-    replace_newlines: bool = True,
-    use_gaps: bool = False,
-    is_timestamp_text: bool = False,
-    subtitle_speed_method: str = "tts",
-    sample_rate: int = SAMPLE_RATE,
-) -> np.ndarray:
-    """Process subtitle entries: generate TTS for each and mix into buffer.
-
-    This is the core domain logic for subtitle-to-audio conversion.
-    UI-specific concerns (signals, widgets) are handled via callbacks.
-
-    Args:
-        subtitles: List of (start, end, text) tuples.
-        backend: TTS pipeline callable.
-        voice: Resolved voice for TTS.
-        speed: TTS speed.
-        cancel_check: Returns True if processing should stop.
-        log_callback: Called with log messages.
-        progress_callback: Called with (percent, etr_string).
-        replace_newlines: Replace \\n with spaces in text.
-        use_gaps: Whether to use silent gaps between subtitles.
-        is_timestamp_text: Whether input is timestamp text.
-        subtitle_speed_method: "ffmpeg" or "tts" for speed adjustment.
-        sample_rate: Audio sample rate.
-
-    Returns:
-        Mixed audio buffer (float32).
-    """
-    if not subtitles:
-        return np.array([], dtype="float32")
-
-    max_end = max((end for _, end, _ in subtitles if end is not None), default=0)
-    buffer_samples = int(max_end * sample_rate) + sample_rate
-    audio_buffer = np.zeros(buffer_samples, dtype="float32")
-    etr_start = time.time()
-    total = len(subtitles)
-
-    for idx, (start_time, end_time, text) in enumerate(subtitles, 1):
-        if cancel_check():
-            break
-
-        processed_text = text.replace("\n", " ") if replace_newlines else text
-        next_start = (
-            subtitles[idx][0]
-            if (use_gaps and idx < total)
-            else float("inf")
-        )
-        subtitle_duration = None if end_time is None else end_time - start_time
-
-        is_auto_end = is_timestamp_text or (use_gaps and idx == total) or end_time is None
-        if log_callback:
-            log_callback(
-                f"\n[{idx}/{total}] {format_time_range(start_time, end_time, is_auto_end)}: {processed_text}"
-            )
-
-        # Generate TTS
-        results = [
-            r for r in backend(
-                processed_text, voice=voice, speed=speed, split_pattern=None
-            )
-            if not cancel_check()
-        ]
-        if cancel_check():
-            break
-
-        audio_chunks = [r.audio for r in results]
-        full_audio = (
-            np.concatenate([to_float32(a) for a in audio_chunks])
-            if audio_chunks
-            else np.zeros(int((subtitle_duration or 0) * sample_rate), dtype="float32")
-        )
-        audio_duration = len(full_audio) / sample_rate
-
-        # Timing adjustment
-        if is_timestamp_text:
-            end_time = start_time + audio_duration
-            subtitle_duration = audio_duration
-        elif use_gaps:
-            end_time = min(start_time + audio_duration, next_start)
-            subtitle_duration = end_time - start_time
-        elif subtitle_duration is None:
-            subtitle_duration = audio_duration
-            end_time = start_time + audio_duration
-
-        # Speed up if needed
-        speedup_threshold = next_start - start_time if use_gaps else subtitle_duration
-        if audio_duration > speedup_threshold and speedup_threshold > 0:
-            speed_factor = audio_duration / speedup_threshold
-            full_audio = speed_up_audio(
-                full_audio, speed_factor,
-                method=subtitle_speed_method,
-                backend=backend, text=processed_text,
-                voice=voice, base_speed=speed,
-                sample_rate=sample_rate,
-            )
-            audio_duration = len(full_audio) / sample_rate
-
-        # Adjust duration after speed change
-        if use_gaps:
-            end_time = min(start_time + audio_duration, next_start)
-            subtitle_duration = end_time - start_time
-        elif subtitle_duration is None:
-            subtitle_duration = audio_duration
-            end_time = start_time + audio_duration
-
-        # Pad or trim to subtitle duration
-        full_audio = fit_audio_to_duration(full_audio, subtitle_duration, sample_rate)
-
-        # Mix into buffer
-        start_sample = int(start_time * sample_rate)
-        audio_buffer = mix_audio(audio_buffer, full_audio, start_sample)
-
-        # Progress
-        if progress_callback:
-            percent = min(int(idx / total * 100), 99)
-            etr = calc_etr_str(time.time() - etr_start, idx, total)
-            progress_callback(percent, etr)
-
-    # Normalize if needed
-    if np.abs(audio_buffer).max() > 1.0:
-        logger.info("Normalizing audio (peak: %.2f)", np.abs(audio_buffer).max())
-        audio_buffer = normalize_audio(audio_buffer)
-
-    return audio_buffer

abogen/domain/text_chapters.py

@@ -1,59 +0,0 @@
-"""Chapter parsing from raw text.
-
-Provides a unified function for splitting text by chapter markers,
-used by both WebUI and PyQt conversion runners.
-"""
-
-from __future__ import annotations
-
-import re
-from typing import List, Tuple
-
-from abogen.subtitle_utils import clean_text
-
-
-_CHAPTER_MARKER_RE = re.compile(r"<<CHAPTER_MARKER:(.*?)>>", re.IGNORECASE)
-
-
-def parse_chapters_from_text(
-    text: str,
-    default_title: str = "text",
-    clean: bool = True,
-) -> List[Tuple[str, str]]:
-    """Split raw text into chapters using chapter marker patterns.
-
-    Preserves content before the first marker as "Introduction" if present.
-    Optionally applies clean_text() to each chapter segment.
-
-    Args:
-        text: Raw text possibly containing <<CHAPTER_MARKER:Title>> markers.
-        default_title: Fallback title when no markers are found.
-        clean: Whether to apply clean_text() to each segment.
-
-    Returns:
-        List of (title, text) tuples.
-    """
-    matches = list(_CHAPTER_MARKER_RE.finditer(text))
-    if not matches:
-        cleaned = clean_text(text) if clean else text
-        return [(default_title, cleaned)]
-
-    chapters: List[Tuple[str, str]] = []
-
-    # Preserve content before first marker as "Introduction"
-    first_start = matches[0].start()
-    if first_start > 0:
-        intro_text = text[:first_start].strip()
-        if intro_text:
-            chapters.append(("Introduction", clean_text(intro_text) if clean else intro_text))
-
-    for idx, match in enumerate(matches):
-        start = match.end()
-        end = matches[idx + 1].start() if idx + 1 < len(matches) else len(text)
-        chapter_name = match.group(1).strip() or default_title
-        chapter_text = text[start:end].strip()
-        if clean:
-            chapter_text = clean_text(chapter_text)
-        chapters.append((chapter_name, chapter_text))
-
-    return chapters

abogen/domain/text_utils.py

@@ -1,22 +0,0 @@
-"""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

@@ -1,97 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Any, List, Mapping, Optional
-
-from .metadata_helpers import (
-    ensure_sentence,
-    extract_series_metadata,
-    format_author_sentence,
-    format_series_sentence,
-    normalize_metadata_map,
-)
-
-
-def build_title_intro_text(
-    metadata: Optional[Mapping[str, Any]],
-    fallback_basename: str,
-) -> str:
-    """Build the title introduction text from metadata."""
-    normalized = normalize_metadata_map(metadata)
-    fallback_title = Path(fallback_basename).stem if fallback_basename else ""
-    title = (
-        normalized.get("title")
-        or normalized.get("book_title")
-        or normalized.get("album")
-        or fallback_title
-    )
-    if not title:
-        title = fallback_title
-    subtitle = normalized.get("subtitle") or normalized.get("sub_title")
-    if subtitle and title and subtitle.casefold() == title.casefold():
-        subtitle = ""
-
-    author_value = ""
-    for candidate in ("artist", "album_artist", "author", "authors", "writer", "composer"):
-        value = normalized.get(candidate)
-        if value:
-            author_value = value
-            break
-
-    series_name, series_number = extract_series_metadata(normalized)
-    series_sentence = format_series_sentence(series_name, series_number)
-
-    sentences: List[str] = []
-    if series_sentence:
-        sentences.append(ensure_sentence(series_sentence))
-    if title:
-        sentences.append(ensure_sentence(title))
-    if subtitle:
-        sentences.append(ensure_sentence(subtitle))
-    author_sentence = format_author_sentence(author_value)
-    if author_sentence:
-        sentences.append(ensure_sentence(author_sentence))
-    return " ".join(sentences).strip()
-
-
-def build_outro_text(
-    metadata: Optional[Mapping[str, Any]],
-    fallback_basename: str,
-) -> str:
-    """Build the outro/closing text from metadata."""
-    normalized = normalize_metadata_map(metadata)
-    fallback_title = Path(fallback_basename).stem if fallback_basename else ""
-    title = (
-        normalized.get("title")
-        or normalized.get("book_title")
-        or normalized.get("album")
-        or fallback_title
-    )
-    author_value = ""
-    for candidate in ("authors", "author", "album_artist", "artist", "writer", "composer"):
-        value = normalized.get(candidate)
-        if value:
-            author_value = value
-            break
-    author_sentence = format_author_sentence(author_value)
-    authors_fragment = (
-        author_sentence[3:].strip() if author_sentence.lower().startswith("by ") else author_sentence.strip()
-    )
-
-    if title and authors_fragment:
-        closing_line = f"The end of {title} from {authors_fragment}"
-    elif title:
-        closing_line = f"The end of {title}"
-    elif authors_fragment:
-        closing_line = f"The end from {authors_fragment}"
-    else:
-        closing_line = "The end"
-
-    series_name, series_number = extract_series_metadata(normalized)
-    series_sentence = format_series_sentence(series_name, series_number)
-
-    sentences: List[str] = [ensure_sentence(closing_line)]
-    if series_sentence:
-        sentences.append(ensure_sentence(series_sentence))
-
-    return " ".join(sentence for sentence in sentences if sentence).strip()

abogen/domain/tokens.py

@@ -1,13 +0,0 @@
-"""Shared token stubs for TTS processing."""
-
-from __future__ import annotations
-
-
-class FakeToken:
-    """Minimal token stub for languages without per-word token support."""
-
-    def __init__(self, text: str, start: float, end: float):
-        self.text = text
-        self.start_ts = start
-        self.end_ts = end
-        self.whitespace = ""

abogen/domain/voice_catalog.py

@@ -1,112 +0,0 @@
-"""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