mirror of
https://github.com/denizsafak/abogen.git
synced 2026-09-20 11:40:57 +02:00
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
This commit is contained in:
Artem Akymenko
@@ -622,3 +622,276 @@ class TestHeadingDedup:
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# Both segments should be synthesized (heading + 2 body segments)
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assert result.total_segments >= 2
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class TestMarkerCollector:
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"""Tests for MarkerCollector."""
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def test_chapter_marker_has_voices_list(self):
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"""Chapter markers should have 'voices' as list of dicts."""
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with tempfile.TemporaryDirectory() as tmpdir:
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req = ConversionRequest(direct_text="Hello", voice="M1")
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plan = ConversionPlan(
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request=req,
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metadata={},
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chapters=[
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ChapterPlan(
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index=1,
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title="Chapter 1",
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original_title="Chapter 1",
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body_text="Body text",
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segments=[
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SegmentPlan(
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text="Body text",
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voice_spec="M1",
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kind="body",
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source="chapter",
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),
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],
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voice_spec="M1",
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)
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],
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output_layout=OutputLayout(
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parent_dir=Path(tmpdir),
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audio_dir=Path(tmpdir),
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),
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)
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events = FakeEvents()
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pipeline = FakePipelineProvider()
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resolver = FakeVoiceResolver()
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tts_context = TTSContext()
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result = execute_conversion(plan, events, pipeline, resolver, tts_context)
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assert len(result.chapter_markers) == 1
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marker = result.chapter_markers[0]
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assert "voices" in marker
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assert isinstance(marker["voices"], list)
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assert len(marker["voices"]) == 1
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assert marker["voices"][0]["provider"] == "kokoro"
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assert marker["voices"][0]["voice"] == "M1"
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def test_outro_marker_recorded(self):
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"""Outro should be recorded as a chapter marker."""
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from abogen.application.conversion_models import IntroOutroSpec
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with tempfile.TemporaryDirectory() as tmpdir:
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req = ConversionRequest(direct_text="Hello", voice="M1")
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plan = ConversionPlan(
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request=req,
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metadata={},
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chapters=[
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ChapterPlan(
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index=1,
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title="Chapter 1",
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original_title="Chapter 1",
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body_text="Body text",
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segments=[
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SegmentPlan(
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text="Body text",
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voice_spec="M1",
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kind="body",
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source="chapter",
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),
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],
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voice_spec="M1",
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)
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],
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outro=IntroOutroSpec(
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enabled=True,
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text="Thanks for listening",
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voice_spec="M1",
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kind="outro",
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),
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output_layout=OutputLayout(
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parent_dir=Path(tmpdir),
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audio_dir=Path(tmpdir),
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),
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)
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events = FakeEvents()
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pipeline = FakePipelineProvider()
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resolver = FakeVoiceResolver()
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tts_context = TTSContext()
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result = execute_conversion(plan, events, pipeline, resolver, tts_context)
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# Should have chapter marker + outro marker
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assert len(result.chapter_markers) == 2
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outro_marker = result.chapter_markers[1]
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assert outro_marker["title"] == "Outro"
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assert "start" in outro_marker
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assert "end" in outro_marker
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assert outro_marker["end"] > outro_marker["start"]
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def test_chunk_marker_voice_is_dict(self):
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"""Chunk markers should have 'voice' as dict with provider."""
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with tempfile.TemporaryDirectory() as tmpdir:
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req = ConversionRequest(direct_text="Hello", voice="M1")
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plan = ConversionPlan(
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request=req,
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metadata={},
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chapters=[
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ChapterPlan(
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index=1,
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title="Chapter 1",
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original_title="Chapter 1",
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body_text="Body text",
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segments=[
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SegmentPlan(
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text="Body text",
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voice_spec="M1",
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kind="body",
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source="chunk",
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chunk_id="chunk_001",
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chunk_index=0,
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speaker_id="narrator",
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level="paragraph",
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),
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],
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voice_spec="M1",
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)
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],
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output_layout=OutputLayout(
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parent_dir=Path(tmpdir),
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audio_dir=Path(tmpdir),
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),
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)
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events = FakeEvents()
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pipeline = FakePipelineProvider()
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resolver = FakeVoiceResolver()
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tts_context = TTSContext()
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result = execute_conversion(plan, events, pipeline, resolver, tts_context)
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assert len(result.chunk_markers) == 1
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chunk = result.chunk_markers[0]
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assert isinstance(chunk["voice"], dict)
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assert chunk["voice"]["provider"] == "kokoro"
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assert chunk["voice"]["voice"] == "M1"
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def test_multi_speaker_collects_unique_voices(self):
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"""Multi-speaker chapters should collect all unique voices."""
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with tempfile.TemporaryDirectory() as tmpdir:
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req = ConversionRequest(direct_text="Hello", voice="M1")
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plan = ConversionPlan(
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request=req,
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metadata={},
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chapters=[
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ChapterPlan(
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index=1,
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title="Chapter 1",
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original_title="Chapter 1",
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body_text="Body text",
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segments=[
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SegmentPlan(
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text="Narrator speaks",
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voice_spec="M1",
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kind="body",
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source="chapter",
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),
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SegmentPlan(
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text="Character speaks",
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voice_spec="F1",
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kind="body",
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source="chapter",
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),
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],
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voice_spec="M1",
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)
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],
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output_layout=OutputLayout(
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parent_dir=Path(tmpdir),
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audio_dir=Path(tmpdir),
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),
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)
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events = FakeEvents()
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pipeline = FakePipelineProvider()
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resolver = FakeVoiceResolver()
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tts_context = TTSContext()
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result = execute_conversion(plan, events, pipeline, resolver, tts_context)
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marker = result.chapter_markers[0]
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assert len(marker["voices"]) == 2
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voice_specs = {v["voice"] for v in marker["voices"]}
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assert "M1" in voice_specs
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assert "F1" in voice_specs
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class TestFfmetadataVoiceFormat:
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"""Tests for ffmetadata rendering with new voice format."""
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def test_render_ffmetadata_with_voices_list(self):
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"""ffmetadata should render voices list as comma-separated string."""
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from abogen.infrastructure.exporters import ExportService
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svc = ExportService()
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chapters = [
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{
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"title": "Chapter 1",
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"start": 0.0,
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"end": 60.0,
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"voices": [
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{"provider": "kokoro", "voice": "M1"},
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{"provider": "kokoro", "voice": "F1"},
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],
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}
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]
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content = svc.render_ffmetadata({}, chapters)
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assert "voice=M1@kokoro, F1@kokoro" in content
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def test_render_ffmetadata_with_empty_voices(self):
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"""ffmetadata should handle empty voices list."""
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from abogen.infrastructure.exporters import ExportService
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svc = ExportService()
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chapters = [
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{
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"title": "Chapter 1",
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"start": 0.0,
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"end": 60.0,
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"voices": [],
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}
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]
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content = svc.render_ffmetadata({}, chapters)
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assert "voice=" not in content
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class TestEpub3VoiceFormat:
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"""Tests for EPUB3 voice handling with new format."""
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def test_chunk_overlay_voice_is_dict(self):
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"""ChunkOverlay should accept voice as dict."""
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from abogen.epub3.exporter import ChunkOverlay
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overlay = ChunkOverlay(
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id="test",
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text="hello",
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original_text=None,
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start=0.0,
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end=1.0,
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speaker_id="narrator",
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voice={"provider": "kokoro", "voice": "M1"},
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)
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assert isinstance(overlay.voice, dict)
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assert overlay.voice["provider"] == "kokoro"
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def test_render_chunk_inline_with_voice_dict(self):
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"""_render_chunk_inline should render voice dict as data-voice attribute."""
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from abogen.epub3.exporter import ChunkOverlay, _render_chunk_inline
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overlay = ChunkOverlay(
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id="chunk_001",
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text="Hello world",
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original_text=None,
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start=0.0,
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end=1.0,
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speaker_id="narrator",
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voice={"provider": "kokoro", "voice": "M1"},
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)
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html = _render_chunk_inline(overlay)
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assert 'data-voice="M1@kokoro"' in html
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@@ -69,9 +69,9 @@ class TestRenderFfmetadata:
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assert "title=Ch 1" in result
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def test_renders_voice_in_chapter(self):
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chapters = [{"start": 0.0, "end": 5.0, "voice": "af_heart"}]
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chapters = [{"start": 0.0, "end": 5.0, "voices": [{"provider": "kokoro", "voice": "af_heart"}]}]
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result = self.svc.render_ffmetadata({}, chapters)
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assert "voice=af_heart" in result
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assert "voice=af_heart@kokoro" in result
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def test_skips_chapters_without_times(self):
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chapters = [{"title": "No times"}]
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@@ -14,7 +14,7 @@ def test_render_ffmetadata_includes_chapters(tmp_path):
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"publisher": "ACME=Corp",
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}
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chapters = [
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{"start": 0.0, "end": 5.0, "title": "Intro", "voice": "voice_a"},
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{"start": 0.0, "end": 5.0, "title": "Intro", "voices": [{"provider": "kokoro", "voice": "voice_a"}]},
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{"start": 5.0, "end": 12.345, "title": "Chapter 2"},
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]
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@@ -28,7 +28,7 @@ def test_render_ffmetadata_includes_chapters(tmp_path):
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assert rendered.count("[CHAPTER]") == 2
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assert "START=0" in rendered
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assert "END=5000" in rendered
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assert "voice=voice_a" in rendered
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assert "voice=voice_a@kokoro" in rendered
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audio_path = tmp_path / "book.m4b"
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metadata_path = svc.write_ffmetadata_file(audio_path, metadata, chapters)
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