refactor: metadata + markers unified in shared layer

- MarkerCollector: SRP extraction from executor (observation, not execution)
- Outro marker: executor now records outro as chapter marker
- Voice format: chapter voices [{provider, voice}], chunk voice {provider, voice}
- _finalize(): build_metadata_payload() + metadata.json + record_override_usage()
- ffmetadata: voices list → comma-separated string
- EPUB3: ChunkOverlay.voice = dict, _render_chunk_inline handles dict format
- 8 new tests: multi-speaker, ffmetadata format, EPUB3 format
- Updated existing tests for new voice format

abogen/application/conversion_executor.py

@@ -10,7 +10,7 @@ from __future__ import annotations
 
 import time
 from contextlib import ExitStack
-from typing import Any, Callable, Dict, List, Optional, Tuple
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple
 
 from abogen.application.conversion_models import (
     ConversionPlan,
@@ -40,6 +40,109 @@ 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,
@@ -67,6 +170,7 @@ def execute_conversion(
     """
     request = plan.request
     result = ConversionResult(metadata=plan.metadata)
+    collector = MarkerCollector()
 
     # Determine cancellation checker
     if check_cancelled is None:
@@ -192,6 +296,9 @@ def execute_conversion(
             )
             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
@@ -285,8 +392,6 @@ def execute_conversion(
                     pending_heading_strip = True
 
             # Process body segments
-            chapter_chunk_markers: List[Dict[str, Any]] = []
-            chapter_body_start = stats.current_time
             for seg_idx, segment in enumerate(chapter.segments):
                 check_cancelled()
 
@@ -317,6 +422,9 @@ def execute_conversion(
                     seg_speed = chapter_speed
                     seg_backend = chapter_backend
 
+                # Track voice for chapter marker
+                collector.on_segment(seg_provider, seg_voice, segment.voice_spec)
+
                 seg_start_time = stats.current_time
                 local_segments, accumulated_tokens = synthesize_text(
                     text=seg_text,
@@ -353,17 +461,18 @@ def execute_conversion(
 
                 # Record chunk marker
                 if segment.source in ("chunk", "voice_marker"):
-                    chapter_chunk_markers.append({
-                        "id": segment.chunk_id,
-                        "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,
-                        "voice": segment.voice_spec,
-                        "level": segment.level or (request.chapter_chunk.chunk_level if request.chapter_chunk else "paragraph"),
-                        "characters": len(segment.text),
-                    })
+                    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:
@@ -382,15 +491,8 @@ def execute_conversion(
             if chapter_subtitle_writer:
                 chapter_subtitle_writer.close()
 
-            # Add chapter marker
-            result.chapter_markers.append({
-                "chapter_index": chapter_idx - 1,
-                "title": chapter.title,
-                "start": chapter_body_start,
-                "end": stats.current_time,
-            })
-
-            result.chunk_markers.extend(chapter_chunk_markers)
+            # Record chapter end for markers
+            collector.on_chapter_end(stats.current_time)
 
         # Process outro
         if plan.outro and plan.outro.enabled and merge_chapters:
@@ -409,6 +511,7 @@ def execute_conversion(
                     stats=stats,
                 )
 
+            outro_start = stats.current_time
             synthesize_text(
                 text=plan.outro.text,
                 params=synth,
@@ -418,9 +521,13 @@ def execute_conversion(
                 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

abogen/application/conversion_service.py

@@ -166,6 +166,39 @@ def _finalize(
         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")
+
 
 def _prepare_tts_context(
     request: ConversionRequest,

abogen/epub3/exporter.py

@@ -22,7 +22,7 @@ class ChunkOverlay:
     start: Optional[float]
     end: Optional[float]
     speaker_id: str
-    voice: Optional[str]
+    voice: Optional[Dict[str, str]]
     level: Optional[str] = None
     group_id: Optional[str] = None
 
@@ -273,7 +273,7 @@ class EPUB3PackageBuilder:
                     start=_safe_float(marker.get("start")),
                     end=_safe_float(marker.get("end")),
                     speaker_id=speaker_id,
-                    voice=str(voice) if voice else None,
+                    voice=voice if isinstance(voice, dict) else None,
                     level=str(level) if level else None,
                     group_id=normalized_group_id,
                 )
@@ -696,7 +696,12 @@ def _group_chunks_for_render(chunks: Sequence[ChunkOverlay]) -> List[Tuple[Optio
 def _render_chunk_inline(chunk: ChunkOverlay) -> str:
     escaped_id = html.escape(chunk.id)
     speaker_attr = f" data-speaker=\"{html.escape(chunk.speaker_id)}\"" if chunk.speaker_id else ""
-    voice_attr = f" data-voice=\"{html.escape(chunk.voice)}\"" if chunk.voice else ""
+    voice_str = None
+    if chunk.voice and isinstance(chunk.voice, dict):
+        name = chunk.voice.get("voice", "")
+        provider = chunk.voice.get("provider", "")
+        voice_str = f"{name}@{provider}" if name and provider else name or None
+    voice_attr = f" data-voice=\"{html.escape(voice_str)}\"" if voice_str else ""
     level_attr = f" data-level=\"{html.escape(chunk.level)}\"" if chunk.level else ""
     raw_text = chunk.text or ""
     escaped_text = html.escape(raw_text)

abogen/infrastructure/exporters.py

@@ -84,9 +84,14 @@ class ExportService:
             title = chapter.get("title")
             if title:
                 lines.append(f"title={self._escape_ffmetadata_value(title)}")
-            voice = chapter.get("voice")
-            if voice:
-                lines.append(f"voice={self._escape_ffmetadata_value(voice)}")
+            voices = chapter.get("voices")
+            if voices and isinstance(voices, list):
+                voice_str = ", ".join(
+                    f"{v.get('voice', '')}@{v.get('provider', '')}"
+                    for v in voices if v.get("voice")
+                )
+                if voice_str:
+                    lines.append(f"voice={self._escape_ffmetadata_value(voice_str)}")
         
         return "\n".join(lines) + "\n"
     

tests/test_conversion_executor_unified.py

@@ -622,3 +622,276 @@ class TestHeadingDedup:
 
             # Both segments should be synthesized (heading + 2 body segments)
             assert result.total_segments >= 2
+
+
+class TestMarkerCollector:
+    """Tests for MarkerCollector."""
+
+    def test_chapter_marker_has_voices_list(self):
+        """Chapter markers should have 'voices' as list of dicts."""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            req = ConversionRequest(direct_text="Hello", voice="M1")
+            plan = ConversionPlan(
+                request=req,
+                metadata={},
+                chapters=[
+                    ChapterPlan(
+                        index=1,
+                        title="Chapter 1",
+                        original_title="Chapter 1",
+                        body_text="Body text",
+                        segments=[
+                            SegmentPlan(
+                                text="Body text",
+                                voice_spec="M1",
+                                kind="body",
+                                source="chapter",
+                            ),
+                        ],
+                        voice_spec="M1",
+                    )
+                ],
+                output_layout=OutputLayout(
+                    parent_dir=Path(tmpdir),
+                    audio_dir=Path(tmpdir),
+                ),
+            )
+
+            events = FakeEvents()
+            pipeline = FakePipelineProvider()
+            resolver = FakeVoiceResolver()
+            tts_context = TTSContext()
+
+            result = execute_conversion(plan, events, pipeline, resolver, tts_context)
+
+            assert len(result.chapter_markers) == 1
+            marker = result.chapter_markers[0]
+            assert "voices" in marker
+            assert isinstance(marker["voices"], list)
+            assert len(marker["voices"]) == 1
+            assert marker["voices"][0]["provider"] == "kokoro"
+            assert marker["voices"][0]["voice"] == "M1"
+
+    def test_outro_marker_recorded(self):
+        """Outro should be recorded as a chapter marker."""
+        from abogen.application.conversion_models import IntroOutroSpec
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            req = ConversionRequest(direct_text="Hello", voice="M1")
+            plan = ConversionPlan(
+                request=req,
+                metadata={},
+                chapters=[
+                    ChapterPlan(
+                        index=1,
+                        title="Chapter 1",
+                        original_title="Chapter 1",
+                        body_text="Body text",
+                        segments=[
+                            SegmentPlan(
+                                text="Body text",
+                                voice_spec="M1",
+                                kind="body",
+                                source="chapter",
+                            ),
+                        ],
+                        voice_spec="M1",
+                    )
+                ],
+                outro=IntroOutroSpec(
+                    enabled=True,
+                    text="Thanks for listening",
+                    voice_spec="M1",
+                    kind="outro",
+                ),
+                output_layout=OutputLayout(
+                    parent_dir=Path(tmpdir),
+                    audio_dir=Path(tmpdir),
+                ),
+            )
+
+            events = FakeEvents()
+            pipeline = FakePipelineProvider()
+            resolver = FakeVoiceResolver()
+            tts_context = TTSContext()
+
+            result = execute_conversion(plan, events, pipeline, resolver, tts_context)
+
+            # Should have chapter marker + outro marker
+            assert len(result.chapter_markers) == 2
+            outro_marker = result.chapter_markers[1]
+            assert outro_marker["title"] == "Outro"
+            assert "start" in outro_marker
+            assert "end" in outro_marker
+            assert outro_marker["end"] > outro_marker["start"]
+
+    def test_chunk_marker_voice_is_dict(self):
+        """Chunk markers should have 'voice' as dict with provider."""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            req = ConversionRequest(direct_text="Hello", voice="M1")
+            plan = ConversionPlan(
+                request=req,
+                metadata={},
+                chapters=[
+                    ChapterPlan(
+                        index=1,
+                        title="Chapter 1",
+                        original_title="Chapter 1",
+                        body_text="Body text",
+                        segments=[
+                            SegmentPlan(
+                                text="Body text",
+                                voice_spec="M1",
+                                kind="body",
+                                source="chunk",
+                                chunk_id="chunk_001",
+                                chunk_index=0,
+                                speaker_id="narrator",
+                                level="paragraph",
+                            ),
+                        ],
+                        voice_spec="M1",
+                    )
+                ],
+                output_layout=OutputLayout(
+                    parent_dir=Path(tmpdir),
+                    audio_dir=Path(tmpdir),
+                ),
+            )
+
+            events = FakeEvents()
+            pipeline = FakePipelineProvider()
+            resolver = FakeVoiceResolver()
+            tts_context = TTSContext()
+
+            result = execute_conversion(plan, events, pipeline, resolver, tts_context)
+
+            assert len(result.chunk_markers) == 1
+            chunk = result.chunk_markers[0]
+            assert isinstance(chunk["voice"], dict)
+            assert chunk["voice"]["provider"] == "kokoro"
+            assert chunk["voice"]["voice"] == "M1"
+
+    def test_multi_speaker_collects_unique_voices(self):
+        """Multi-speaker chapters should collect all unique voices."""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            req = ConversionRequest(direct_text="Hello", voice="M1")
+            plan = ConversionPlan(
+                request=req,
+                metadata={},
+                chapters=[
+                    ChapterPlan(
+                        index=1,
+                        title="Chapter 1",
+                        original_title="Chapter 1",
+                        body_text="Body text",
+                        segments=[
+                            SegmentPlan(
+                                text="Narrator speaks",
+                                voice_spec="M1",
+                                kind="body",
+                                source="chapter",
+                            ),
+                            SegmentPlan(
+                                text="Character speaks",
+                                voice_spec="F1",
+                                kind="body",
+                                source="chapter",
+                            ),
+                        ],
+                        voice_spec="M1",
+                    )
+                ],
+                output_layout=OutputLayout(
+                    parent_dir=Path(tmpdir),
+                    audio_dir=Path(tmpdir),
+                ),
+            )
+
+            events = FakeEvents()
+            pipeline = FakePipelineProvider()
+            resolver = FakeVoiceResolver()
+            tts_context = TTSContext()
+
+            result = execute_conversion(plan, events, pipeline, resolver, tts_context)
+
+            marker = result.chapter_markers[0]
+            assert len(marker["voices"]) == 2
+            voice_specs = {v["voice"] for v in marker["voices"]}
+            assert "M1" in voice_specs
+            assert "F1" in voice_specs
+
+
+class TestFfmetadataVoiceFormat:
+    """Tests for ffmetadata rendering with new voice format."""
+
+    def test_render_ffmetadata_with_voices_list(self):
+        """ffmetadata should render voices list as comma-separated string."""
+        from abogen.infrastructure.exporters import ExportService
+
+        svc = ExportService()
+        chapters = [
+            {
+                "title": "Chapter 1",
+                "start": 0.0,
+                "end": 60.0,
+                "voices": [
+                    {"provider": "kokoro", "voice": "M1"},
+                    {"provider": "kokoro", "voice": "F1"},
+                ],
+            }
+        ]
+        content = svc.render_ffmetadata({}, chapters)
+        assert "voice=M1@kokoro, F1@kokoro" in content
+
+    def test_render_ffmetadata_with_empty_voices(self):
+        """ffmetadata should handle empty voices list."""
+        from abogen.infrastructure.exporters import ExportService
+
+        svc = ExportService()
+        chapters = [
+            {
+                "title": "Chapter 1",
+                "start": 0.0,
+                "end": 60.0,
+                "voices": [],
+            }
+        ]
+        content = svc.render_ffmetadata({}, chapters)
+        assert "voice=" not in content
+
+
+class TestEpub3VoiceFormat:
+    """Tests for EPUB3 voice handling with new format."""
+
+    def test_chunk_overlay_voice_is_dict(self):
+        """ChunkOverlay should accept voice as dict."""
+        from abogen.epub3.exporter import ChunkOverlay
+
+        overlay = ChunkOverlay(
+            id="test",
+            text="hello",
+            original_text=None,
+            start=0.0,
+            end=1.0,
+            speaker_id="narrator",
+            voice={"provider": "kokoro", "voice": "M1"},
+        )
+        assert isinstance(overlay.voice, dict)
+        assert overlay.voice["provider"] == "kokoro"
+
+    def test_render_chunk_inline_with_voice_dict(self):
+        """_render_chunk_inline should render voice dict as data-voice attribute."""
+        from abogen.epub3.exporter import ChunkOverlay, _render_chunk_inline
+
+        overlay = ChunkOverlay(
+            id="chunk_001",
+            text="Hello world",
+            original_text=None,
+            start=0.0,
+            end=1.0,
+            speaker_id="narrator",
+            voice={"provider": "kokoro", "voice": "M1"},
+        )
+        html = _render_chunk_inline(overlay)
+        assert 'data-voice="M1@kokoro"' in html

tests/test_exporters.py

@@ -69,9 +69,9 @@ class TestRenderFfmetadata:
         assert "title=Ch 1" in result
 
     def test_renders_voice_in_chapter(self):
-        chapters = [{"start": 0.0, "end": 5.0, "voice": "af_heart"}]
+        chapters = [{"start": 0.0, "end": 5.0, "voices": [{"provider": "kokoro", "voice": "af_heart"}]}]
         result = self.svc.render_ffmetadata({}, chapters)
-        assert "voice=af_heart" in result
+        assert "voice=af_heart@kokoro" in result
 
     def test_skips_chapters_without_times(self):
         chapters = [{"title": "No times"}]

tests/test_ffmetadata.py

@@ -14,7 +14,7 @@ def test_render_ffmetadata_includes_chapters(tmp_path):
         "publisher": "ACME=Corp",
     }
     chapters = [
-        {"start": 0.0, "end": 5.0, "title": "Intro", "voice": "voice_a"},
+        {"start": 0.0, "end": 5.0, "title": "Intro", "voices": [{"provider": "kokoro", "voice": "voice_a"}]},
         {"start": 5.0, "end": 12.345, "title": "Chapter 2"},
     ]
 
@@ -28,7 +28,7 @@ def test_render_ffmetadata_includes_chapters(tmp_path):
     assert rendered.count("[CHAPTER]") == 2
     assert "START=0" in rendered
     assert "END=5000" in rendered
-    assert "voice=voice_a" in rendered
+    assert "voice=voice_a@kokoro" in rendered
 
     audio_path = tmp_path / "book.m4b"
     metadata_path = svc.write_ffmetadata_file(audio_path, metadata, chapters)