fix: review cleanup — imports, _FakeToken, use_spacy_segmentation

- Move pronunciation imports from inside run() to top-level imports
- Extract _FakeToken to module level (was redefined every loop iteration)
- use_spacy_segmentation now mirrors PyQt logic: pass the flag,
  let process_subtitle_tokens filter by language internally

abogen/pyqt/conversion.py

@@ -39,6 +39,11 @@ from abogen.domain.subtitle_generation import process_subtitle_tokens
 from abogen.domain.voice_loader import load_voice_cached
 from abogen.domain.progress import calc_etr_str
 from abogen.domain.normalization import prepare_text_for_tts
+from abogen.domain.pronunciation import (
+    compile_pronunciation_rules,
+    compile_heteronym_sentence_rules,
+    merge_pronunciation_overrides,
+)
 from abogen.domain.metadata_extraction import (
     extract_metadata_and_build_args,
     read_text_for_metadata,
@@ -537,11 +542,6 @@ class ConversionThread(QThread):
                 )
 
             # --- Compile normalization rules (heteronym + pronunciation) ---
-            from abogen.domain.pronunciation import (
-                compile_pronunciation_rules,
-                compile_heteronym_sentence_rules,
-                merge_pronunciation_overrides,
-            )
             pronunciation_overrides = merge_pronunciation_overrides(
                 getattr(self, "pronunciation_overrides", None),
                 getattr(self, "manual_overrides", None),

abogen/webui/conversion_runner.py

@@ -135,6 +135,16 @@ SPLIT_PATTERN = r"\n+"  # Kept for backward compatibility; prefer get_split_patt
 SAMPLE_RATE = 24000
 
 
+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 = ""
+
+
 class _JobCancelled(Exception):
     """Raised internally to abort a conversion when the client cancels."""
 
@@ -534,12 +544,6 @@ def run_conversion_job(job: Job) -> None:
 
                         # Fallback for languages without token support: create a single token
                         if not tokens_list and graphemes:
-                            class _FakeToken:
-                                def __init__(self, text, start, end):
-                                    self.text = text
-                                    self.start_ts = start
-                                    self.end_ts = end
-                                    self.whitespace = ""
                             tokens_list = [_FakeToken(graphemes, 0, duration)]
 
                         for tok in tokens_list:
@@ -555,6 +559,7 @@ def run_conversion_job(job: Job) -> None:
 
                 # Flush accumulated tokens through process_subtitle_tokens
                 if subtitle_writer and audio_sink and accumulated_tokens:
+                    _use_spacy = job.subtitle_mode not in ("Disabled", "Line")
                     new_entries: List[tuple] = []
                     process_subtitle_tokens(
                         accumulated_tokens,
@@ -562,7 +567,7 @@ def run_conversion_job(job: Job) -> None:
                         job.max_subtitle_words,
                         job.subtitle_mode,
                         job.language,
-                        use_spacy_segmentation=False,
+                        use_spacy_segmentation=_use_spacy,
                         fallback_end_time=current_time,
                     )
                     for start, end, text in new_entries: