mirror of
https://github.com/denizsafak/abogen.git
synced 2026-09-20 11:40:57 +02:00
Artem Akymenko
f802fb2af6
- Move stored_integration_config(), build_audiobookshelf_config() to domain/settings_core.py
- Remove legacy fallback from stored_integration_config() (only config[integrations])
- Add load_audiobookshelf_config() as combined entry point
- PostConversionHooks reads config directly via stored_integration_config()
- WebUI imports from domain/settings_core instead of webui/routes/utils/settings
- Remove duplicate _build_abs_config() from PostConversionHooks
- Remove dead audiobookshelf code from infrastructure/exporters.py
- Remove duplicate audiobookshelf functions from webui/service.py
- Fix OutputFormat enum serialization in output_layout_service.py and conversion_executor.py
(f'{enum}' gave 'OutputFormat.WAV' instead of '.wav')
- Mock spaCy in test_returns_at_least_one_segment instead of loading real model
- Add 28 tests for PostConversionHooks and build_audiobookshelf_config
266 lines
8.5 KiB
Python
266 lines
8.5 KiB
Python
"""Tests for domain/conversion_pipeline.py — tts_segments, emit_text_segments."""
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from dataclasses import dataclass, field
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from typing import Any, List, Optional
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from unittest.mock import MagicMock
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import numpy as np
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import pytest
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from abogen.domain.conversion_pipeline import tts_segments, emit_text_segments, SegmentResult
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@dataclass
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class FakeSegment:
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graphemes: str
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audio: Any
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tokens: list = field(default_factory=list)
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@dataclass
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class FakeTokenObj:
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text: str
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start_ts: float
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end_ts: float
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whitespace: str = ""
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def make_backend(segments):
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"""Create a mock TTS backend that yields FakeSegments."""
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def backend(text, voice=None, speed=1.0, split_pattern=None):
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for seg in segments:
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yield seg
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return backend
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class TestEmitTextSegments:
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def test_yields_segments(self):
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audio = np.ones(24000, dtype="float32")
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segments = [FakeSegment("Hello", audio)]
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results = list(emit_text_segments(
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"Hello world",
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backend=make_backend(segments),
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voice="A",
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speed=1.0,
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split_pattern=r"\s+",
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))
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assert len(results) == 1
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assert results[0].graphemes == "Hello"
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assert results[0].duration == 1.0
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def test_skips_empty_audio(self):
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segments = [
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FakeSegment("Hello", np.ones(24000, dtype="float32")),
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FakeSegment("", np.array([], dtype="float32")),
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FakeSegment("World", np.ones(12000, dtype="float32")),
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]
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results = list(emit_text_segments(
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"test",
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backend=make_backend(segments),
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voice="A",
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speed=1.0,
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split_pattern=r"\s+",
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))
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assert len(results) == 2
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assert results[0].graphemes == "Hello"
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assert results[1].graphemes == "World"
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def test_chunk_start_increments(self):
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audio = np.ones(24000, dtype="float32")
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segments = [FakeSegment("A", audio), FakeSegment("B", audio)]
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results = list(emit_text_segments(
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"test",
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backend=make_backend(segments),
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voice="A",
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speed=1.0,
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split_pattern=r"\s+",
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current_time=5.0,
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))
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assert results[0].chunk_start == 5.0
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assert results[1].chunk_start == 6.0
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def test_tokens_extracted(self):
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token = FakeTokenObj("Hello", 0.0, 0.5, " ")
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audio = np.ones(24000, dtype="float32")
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segments = [FakeSegment("Hello", audio, [token])]
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results = list(emit_text_segments(
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"test",
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backend=make_backend(segments),
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voice="A",
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speed=1.0,
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split_pattern=r"\s+",
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current_time=2.0,
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))
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assert len(results[0].tokens) == 1
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assert results[0].tokens[0]["start"] == 2.0
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assert results[0].tokens[0]["end"] == 2.5
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assert results[0].tokens[0]["text"] == "Hello"
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def test_fake_token_fallback(self):
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"""When no tokens provided, creates a single FakeToken for the segment."""
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audio = np.ones(24000, dtype="float32")
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segments = [FakeSegment("Hello", audio)] # No tokens
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results = list(emit_text_segments(
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"test",
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backend=make_backend(segments),
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voice="A",
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speed=1.0,
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split_pattern=r"\s+",
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))
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assert len(results[0].tokens) == 1
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assert results[0].tokens[0]["text"] == "Hello"
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def test_empty_text(self):
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results = list(emit_text_segments(
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"",
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backend=make_backend([]),
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voice="A",
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speed=1.0,
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split_pattern=r"\s+",
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))
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assert len(results) == 0
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def test_segment_result_fields(self):
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audio = np.ones(48000, dtype="float32")
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segments = [FakeSegment("Test", audio)]
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results = list(emit_text_segments(
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"test",
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backend=make_backend(segments),
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voice="A",
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speed=1.0,
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split_pattern=r"\s+",
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))
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seg = results[0]
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assert isinstance(seg, SegmentResult)
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assert seg.graphemes == "Test"
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assert seg.duration == 2.0
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assert len(seg.audio) == 48000
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class TestTtsSegments:
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def test_yields_segments_from_normalized_text(self):
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audio = np.ones(24000, dtype="float32")
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segments = [FakeSegment("Hello", audio)]
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results = list(tts_segments(
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"Already normalized text",
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backend=make_backend(segments),
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voice="A",
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speed=1.0,
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split_pattern=r"\s+",
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))
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assert len(results) == 1
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assert results[0].graphemes == "Hello"
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def test_no_normalization_performed(self):
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"""tts_segments should NOT normalize — it passes text directly to backend."""
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received_texts = []
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def capture_backend(text, voice=None, speed=1.0, split_pattern=None):
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received_texts.append(text)
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yield FakeSegment("ok", np.ones(24000, dtype="float32"))
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list(tts_segments(
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"Raw unnormalized text",
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backend=capture_backend,
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voice="A",
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speed=1.0,
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split_pattern=r"\s+",
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))
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assert received_texts[0] == "Raw unnormalized text"
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def test_chunk_start_increments(self):
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audio = np.ones(24000, dtype="float32")
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segments = [FakeSegment("A", audio), FakeSegment("B", audio)]
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results = list(tts_segments(
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"test",
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backend=make_backend(segments),
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voice="A",
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speed=1.0,
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split_pattern=r"\s+",
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current_time=10.0,
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))
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assert results[0].chunk_start == 10.0
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assert results[1].chunk_start == 11.0
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class TestSpacyPreTtsSegmentation:
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"""Tests for spacy_pre_tts_segmentation()."""
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def test_disabled_when_use_spacy_false(self):
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from abogen.domain.conversion_pipeline import spacy_pre_tts_segmentation
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from abogen.domain.enums import Language
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segments, split = spacy_pre_tts_segmentation(
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"Hello world",
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Language.EN_US,
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"Disabled",
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use_spacy_segmentation=False,
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)
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assert segments == ["Hello world"]
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assert isinstance(split, str)
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def test_disabled_for_disabled_subtitle_mode(self):
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from abogen.domain.conversion_pipeline import spacy_pre_tts_segmentation
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from abogen.domain.enums import Language
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segments, split = spacy_pre_tts_segmentation(
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"Hello world",
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Language.FR,
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"Disabled",
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use_spacy_segmentation=True,
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)
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assert segments == ["Hello world"]
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def test_disabled_for_line_subtitle_mode(self):
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from abogen.domain.conversion_pipeline import spacy_pre_tts_segmentation
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from abogen.domain.enums import Language
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segments, split = spacy_pre_tts_segmentation(
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"Hello world",
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Language.ES,
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"Line",
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use_spacy_segmentation=True,
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)
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assert segments == ["Hello world"]
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def test_disabled_for_subtitle_input(self):
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from abogen.domain.conversion_pipeline import spacy_pre_tts_segmentation
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from abogen.domain.enums import Language
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segments, split = spacy_pre_tts_segmentation(
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"Hello world",
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Language.FR,
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"Sentence",
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is_subtitle_input=True,
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use_spacy_segmentation=True,
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)
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assert segments == ["Hello world"]
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def test_english_excluded_from_pre_tts(self):
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"""English uses spaCy only for post-TTS subtitles, not pre-TTS."""
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from abogen.domain.conversion_pipeline import spacy_pre_tts_segmentation
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from abogen.domain.enums import Language
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segments, split = spacy_pre_tts_segmentation(
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"Hello world. How are you?",
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Language.EN_US,
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"Sentence",
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use_spacy_segmentation=True,
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)
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# English should return single segment (no pre-TTS segmentation)
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assert len(segments) == 1
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def test_returns_at_least_one_segment(self):
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from unittest.mock import patch
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from abogen.domain.conversion_pipeline import spacy_pre_tts_segmentation
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from abogen.domain.enums import Language
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with patch("abogen.spacy_utils.segment_sentences", return_value=None):
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segments, split = spacy_pre_tts_segmentation(
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"",
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Language.FR,
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"Sentence",
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use_spacy_segmentation=True,
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)
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assert len(segments) >= 1
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