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https://github.com/denizsafak/abogen.git
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refactor(domain): add prepare_text_for_tts — unified normalization pipeline
New function chains all three normalization stages: 1. Heteronym sentence rules (context-dependent pronunciation) 2. Pronunciation rules (token-level replacements) 3. Pipeline normalization (apostrophe, LLM) This is the single entry point that both Web UI and PyQt should call before TTS synthesis. Currently only Web UI uses it; PyQt has NO normalization — this unlocks that capability. Updated conversion_runner.emit_text to use the new function. 1038 tests pass.
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@@ -1,8 +1,15 @@
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"""Text normalization convenience helpers."""
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"""Text normalization convenience helpers.
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Provides both the simple ``normalize_text_for_pipeline`` (apostrophe + LLM only)
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and the comprehensive ``prepare_text_for_tts`` that chains all three normalization
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stages used during conversion: heteronym rules → pronunciation rules → pipeline
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normalization. The latter is the single entry point that both the Web UI and
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PyQt Desktop GUI should use.
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"""
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from __future__ import annotations
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from typing import Any, Mapping, Optional
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from typing import Any, Dict, List, Mapping, Optional
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from abogen.kokoro_text_normalization import (
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ApostropheConfig,
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@@ -28,3 +35,62 @@ def normalize_text_for_pipeline(
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runtime_settings = _apply_overrides(runtime_settings, normalization_overrides)
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apostrophe_config = build_apostrophe_config(settings=runtime_settings, base=_BASE_APOSTROPHE_CONFIG)
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return _normalize_for_pipeline(text, config=apostrophe_config, settings=runtime_settings)
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def prepare_text_for_tts(
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text: str,
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*,
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heteronym_rules: Optional[List[Dict[str, Any]]] = None,
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pronunciation_rules: Optional[List[Dict[str, Any]]] = None,
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normalization_overrides: Optional[Mapping[str, Any]] = None,
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usage_counter: Optional[Dict[str, int]] = None,
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) -> str:
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"""Apply the full text normalization pipeline before TTS synthesis.
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Chains three stages in order:
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1. Heteronym sentence rules (context-dependent pronunciation)
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2. Pronunciation rules (token-level replacements)
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3. Pipeline normalization (apostrophe handling, LLM normalization)
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This is the **single entry point** that both the Web UI conversion runner
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and the PyQt conversion thread should call before passing text to the TTS
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backend.
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Parameters
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----------
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text:
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Raw text to normalize.
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heteronym_rules:
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Compiled heteronym rules from ``compile_heteronym_sentence_rules``.
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pronunciation_rules:
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Compiled pronunciation rules from ``compile_pronunciation_rules``.
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normalization_overrides:
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User-level overrides for normalization settings (apostrophe mode, etc.).
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usage_counter:
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Mutable dict that tracks how many times each pronunciation override was
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applied. Passed through to ``apply_pronunciation_rules``.
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Returns
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-------
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str
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Fully normalized text ready for TTS.
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"""
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from abogen.domain.pronunciation import (
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apply_heteronym_sentence_rules,
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apply_pronunciation_rules,
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)
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result = str(text or "")
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if heteronym_rules:
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result = apply_heteronym_sentence_rules(result, heteronym_rules)
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if pronunciation_rules:
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result = apply_pronunciation_rules(result, pronunciation_rules, usage_counter)
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runtime_settings = get_runtime_settings()
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if normalization_overrides:
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runtime_settings = _apply_overrides(runtime_settings, normalization_overrides)
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apostrophe_config = build_apostrophe_config(settings=runtime_settings, base=_BASE_APOSTROPHE_CONFIG)
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return _normalize_for_pipeline(result, config=apostrophe_config, settings=runtime_settings)
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@@ -73,10 +73,10 @@ from abogen.domain.file_type import (
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from abogen.domain.pronunciation import (
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compile_pronunciation_rules as _compile_pronunciation_rules,
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compile_heteronym_sentence_rules as _compile_heteronym_sentence_rules,
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apply_heteronym_sentence_rules as _apply_heteronym_sentence_rules,
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apply_pronunciation_rules as _apply_pronunciation_rules,
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merge_pronunciation_overrides as _merge_pronunciation_overrides,
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)
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from abogen.domain.normalization import prepare_text_for_tts
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from abogen.domain.voice_resolution import (
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spec_to_voice_ids as _spec_to_voice_ids,
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job_voice_fallback as _job_voice_fallback,
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@@ -445,19 +445,13 @@ def run_conversion_job(job: Job) -> None:
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) -> int:
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nonlocal processed_chars, current_time
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source_text = str(text or "")
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if heteronym_sentence_rules:
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source_text = _apply_heteronym_sentence_rules(source_text, heteronym_sentence_rules)
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if pronunciation_rules:
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source_text = _apply_pronunciation_rules(
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source_text,
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pronunciation_rules,
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usage_counter,
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)
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try:
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normalized = normalize_for_pipeline(
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normalized = prepare_text_for_tts(
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source_text,
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config=apostrophe_config,
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settings=normalization_settings,
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heteronym_rules=heteronym_sentence_rules,
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pronunciation_rules=pronunciation_rules,
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normalization_overrides=getattr(job, "normalization_overrides", None),
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usage_counter=usage_counter,
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)
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except LLMClientError as exc:
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job.add_log(f"LLM normalization failed: {exc}", level="error")
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@@ -0,0 +1,147 @@
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"""Tests for domain/normalization.py — prepare_text_for_tts."""
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import pytest
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from unittest.mock import patch, MagicMock
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from abogen.domain.normalization import prepare_text_for_tts, normalize_text_for_pipeline
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class TestPrepareTextForTts:
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"""Tests for the comprehensive TTS text preparation pipeline."""
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def test_empty_text(self):
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result = prepare_text_for_tts("")
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assert result == ""
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def test_none_text(self):
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result = prepare_text_for_tts(None)
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assert result == ""
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def test_passthrough_no_rules(self):
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result = prepare_text_for_tts("Hello world")
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assert isinstance(result, str)
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assert len(result) > 0
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def test_heteronym_rules_applied(self):
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from abogen.domain.pronunciation import compile_heteronym_sentence_rules
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overrides = [
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{
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"token": "read",
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"pronunciation": "red",
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"context": "past tense",
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}
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]
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rules = compile_heteronym_sentence_rules(overrides)
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if rules:
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result = prepare_text_for_tts("I will read the book", heteronym_rules=rules)
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assert isinstance(result, str)
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def test_pronunciation_rules_applied(self):
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from abogen.domain.pronunciation import compile_pronunciation_rules
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overrides = [
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{
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"token": "epub",
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"pronunciation": "ee-pub",
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"normalized": "epub",
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}
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]
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rules = compile_pronunciation_rules(overrides)
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result = prepare_text_for_tts(
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"This is an epub file",
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pronunciation_rules=rules,
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)
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assert "ee-pub" in result
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def test_usage_counter_tracks_pronunciation(self):
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from abogen.domain.pronunciation import compile_pronunciation_rules
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overrides = [
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{
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"token": "data",
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"pronunciation": "day-ta",
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"normalized": "data",
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}
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]
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rules = compile_pronunciation_rules(overrides)
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counter = {}
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prepare_text_for_tts(
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"The data is here and the data is there",
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pronunciation_rules=rules,
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usage_counter=counter,
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)
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assert counter.get("data", 0) >= 1
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def test_combined_heteronym_and_pronunciation(self):
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from abogen.domain.pronunciation import (
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compile_heteronym_sentence_rules,
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compile_pronunciation_rules,
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)
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heteronym_overrides = [
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{
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"token": "lead",
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"pronunciation": "led",
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"context": "metal",
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}
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]
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pronunciation_overrides = [
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{
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"token": "gif",
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"pronunciation": "jif",
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"normalized": "gif",
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}
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]
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h_rules = compile_heteronym_sentence_rules(heteronym_overrides)
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p_rules = compile_pronunciation_rules(pronunciation_overrides)
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result = prepare_text_for_tts(
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"A lead gif",
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heteronym_rules=h_rules if h_rules else None,
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pronunciation_rules=p_rules,
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)
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assert isinstance(result, str)
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@patch("abogen.domain.normalization.get_runtime_settings")
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def test_normalization_overrides_passed_through(self, mock_settings):
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mock_settings.return_value = {
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"normalization_apostrophe_mode": "spacy",
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"normalization_enabled": True,
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}
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result = prepare_text_for_tts(
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"It's a test",
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normalization_overrides={"normalization_enabled": False},
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)
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assert isinstance(result, str)
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def test_pronunciation_rules_empty(self):
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result = prepare_text_for_tts("Hello", pronunciation_rules=[])
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assert isinstance(result, str)
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def test_heteronym_rules_empty(self):
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result = prepare_text_for_tts("Hello", heteronym_rules=[])
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assert isinstance(result, str)
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class TestNormalizeTextForPipeline:
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"""Tests for the simpler normalization function."""
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def test_basic_normalization(self):
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result = normalize_text_for_pipeline("It's a test")
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assert isinstance(result, str)
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assert len(result) > 0
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def test_empty_text(self):
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result = normalize_text_for_pipeline("")
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assert result == ""
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@patch("abogen.domain.normalization.get_runtime_settings")
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def test_with_overrides(self, mock_settings):
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mock_settings.return_value = {
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"normalization_apostrophe_mode": "spacy",
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}
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result = normalize_text_for_pipeline(
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"It's a test",
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normalization_overrides={"normalization_apostrophe_mode": "spacy"},
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)
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assert isinstance(result, str)
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