fix(normalization): improve punctuation handling and spacing in text normalization

fix(subtitles): enhance ellipsis and paragraph break handling in subtitle processing
feat(gui): implement background update check for new versions
test(tests): add tests for ellipsis handling and paragraph breaks preservation

abogen/domain/split_pattern.py

@@ -5,10 +5,10 @@ from __future__ import annotations
 from abogen.domain.enums import Language, SubtitleMode
 
 # Canonical punctuation sets covering all supported scripts:
-# ASCII (. ! ?), Arabic ؟, CJK (。！？), Devanagari ।
-PUNCTUATION_SENTENCE = r".!?؟。！？।"
+# ASCII (. ! ?), ellipsis (…), Arabic ؟, CJK (。！？), Devanagari ।
+PUNCTUATION_SENTENCE = r".!?…؟。！？।"
 # Commas: ASCII , CJK fullwidth ，CJK ideographic 、
-PUNCTUATION_SENTENCE_COMMA = r".!?,？。！？،，、।"
+PUNCTUATION_SENTENCE_COMMA = r".!?…,？。！？،，、।"
 PUNCTUATION_COMMAS = ",，、"
 
 

abogen/domain/subtitle_generation.py

@@ -13,6 +13,34 @@ from typing import List, Optional, Tuple
 from abogen.domain.enums import Language, SubtitleMode
 from abogen.domain.split_pattern import PUNCTUATION_SENTENCE, PUNCTUATION_SENTENCE_COMMA
 
+_CLOSING_DELIMS = "\"\"\"\"'\"”’»›)]}」』"
+
+
+def _is_sentence_boundary(
+    token: dict,
+    current_sentence: List[dict],
+    separator: str,
+) -> bool:
+    """Check whether token ends a sentence, considering closing quotes and brackets."""
+    ws = token.get("whitespace", "") or ""
+    if not ws:
+        return False
+
+    # For Line mode, a newline in whitespace or text marks line boundary
+    if separator == r"\n":
+        return "\n" in ws or "\n" in str(token.get("text", ""))
+
+    text = str(token.get("text", ""))
+    if re.search(rf"{separator}[{re.escape(_CLOSING_DELIMS)}]*$", text):
+        return True
+
+    if len(current_sentence) >= 2 and text and all(c in _CLOSING_DELIMS for c in text):
+        prev_text = str(current_sentence[-2].get("text", ""))
+        if re.search(rf"{separator}$", prev_text):
+            return True
+
+    return False
+
 
 def process_subtitle_tokens(
     tokens_with_timestamps: List[dict],
@@ -42,37 +70,55 @@ def process_subtitle_tokens(
     if not tokens_with_timestamps:
         return
 
+    if not isinstance(language, Language):
+        try:
+            language = Language.from_str(str(language))
+        except ValueError:
+            language = Language.EN_US
+
+    if isinstance(subtitle_mode, SubtitleMode):
+        subtitle_mode_str = subtitle_mode.value
+    else:
+        subtitle_mode_str = str(subtitle_mode)
+
     processed_tokens = tokens_with_timestamps
 
     # For English with spaCy enabled and sentence-based modes, use spaCy for sentence boundaries
     # spaCy is disabled when subtitle mode is "Disabled" or "Line"
     use_spacy_for_english = (
         use_spacy_segmentation
-        and subtitle_mode not in [SubtitleMode.DISABLED, SubtitleMode.LINE]
+        and subtitle_mode_str not in [SubtitleMode.DISABLED.value, SubtitleMode.LINE.value, "Disabled", "Line"]
         and language in [Language.EN_US, Language.EN_GB]
-        and subtitle_mode in [SubtitleMode.SENTENCE, SubtitleMode.SENTENCE_COMMA]
+        and subtitle_mode_str in [SubtitleMode.SENTENCE.value, SubtitleMode.SENTENCE_COMMA.value, "Sentence", "Sentence + Comma"]
     )
 
-    if subtitle_mode == SubtitleMode.SENTENCE_HIGHLIGHT:
+    if subtitle_mode_str in (SubtitleMode.SENTENCE_HIGHLIGHT.value, "Sentence + Highlighting"):
         _process_karaoke_highlighting(
             processed_tokens, subtitle_entries, max_subtitle_words, fallback_end_time
         )
-    elif subtitle_mode in [SubtitleMode.SENTENCE, SubtitleMode.SENTENCE_COMMA, SubtitleMode.LINE]:
-        if use_spacy_for_english and subtitle_mode != SubtitleMode.LINE:
+    elif subtitle_mode_str in [
+        SubtitleMode.SENTENCE.value,
+        SubtitleMode.SENTENCE_COMMA.value,
+        SubtitleMode.LINE.value,
+        "Sentence",
+        "Sentence + Comma",
+        "Line",
+    ]:
+        if use_spacy_for_english and subtitle_mode_str not in (SubtitleMode.LINE.value, "Line"):
             _process_spacy_sentences(
                 processed_tokens, subtitle_entries, max_subtitle_words,
-                subtitle_mode, language, fallback_end_time
+                subtitle_mode_str, language, fallback_end_time
             )
         else:
             _process_regex_sentences(
                 processed_tokens, subtitle_entries, max_subtitle_words,
-                subtitle_mode, fallback_end_time
+                subtitle_mode_str, fallback_end_time
             )
     else:
         # Word count-based grouping (e.g., "5" for 5-word groups)
         _process_word_count(
             processed_tokens, subtitle_entries, max_subtitle_words,
-            subtitle_mode, fallback_end_time
+            subtitle_mode_str, fallback_end_time
         )
 
 
@@ -91,10 +137,8 @@ def _process_karaoke_highlighting(
         current_sentence.append(token)
         word_count += 1
 
-        # Split sentences based on separator or word count
-        if (
-            re.search(separator, token["text"]) and token.get("whitespace") == " "
-        ) or word_count >= max_subtitle_words:
+        is_boundary = _is_sentence_boundary(token, current_sentence, separator)
+        if is_boundary or word_count >= max_subtitle_words:
             if current_sentence:
                 # Create karaoke subtitle entry for this sentence
                 start_time = current_sentence[0]["start"]
@@ -109,12 +153,17 @@ def _process_karaoke_highlighting(
                         if t.get("end") is not None and t.get("start") is not None
                         else 0.5
                     )
+                    try:
                         duration_cs = int(duration * 100)
+                    except (ValueError, OverflowError, TypeError):
+                        duration_cs = 50
                     # Add karaoke effect
-                    karaoke_text += f"{{\\kf{duration_cs}}}{t['text']}{t.get('whitespace', '') or ''}"
+                    karaoke_text += f"{{\\kf{duration_cs}}}{t.get('text', '')}{t.get('whitespace', '') or ''}"
 
+                text_stripped = karaoke_text.strip()
+                if text_stripped:
                     subtitle_entries.append(
-                    (start_time, end_time, karaoke_text.strip())
+                        (start_time, end_time, text_stripped)
                     )
                 current_sentence = []
                 word_count = 0
@@ -128,9 +177,14 @@ def _process_karaoke_highlighting(
         karaoke_text = ""
         for t in current_sentence:
             duration = t["end"] - t["start"] if t.get("end") and t.get("start") else 0.5
+            try:
                 duration_cs = int(duration * 100)
-            karaoke_text += f"{{\\kf{duration_cs}}}{t['text']}{t.get('whitespace', '') or ''}"
-        subtitle_entries.append((start_time, end_time, karaoke_text.strip()))
+            except (ValueError, OverflowError, TypeError):
+                duration_cs = 50
+            karaoke_text += f"{{\\kf{duration_cs}}}{t.get('text', '')}{t.get('whitespace', '') or ''}"
+        text_stripped = karaoke_text.strip()
+        if text_stripped:
+            subtitle_entries.append((start_time, end_time, text_stripped))
 
     # Fallback for last entry
     _apply_fallback_end_time(subtitle_entries, fallback_end_time)
@@ -166,7 +220,7 @@ def _process_spacy_sentences(
     # Build full text and track character positions to token indices
     full_text = ""
     for token in tokens:
-        text_part = token["text"] + (token.get("whitespace") or "")
+        text_part = str(token.get("text", "")) + (token.get("whitespace") or "")
         full_text += text_part
 
     # Get sentence boundaries from spaCy
@@ -174,7 +228,7 @@ def _process_spacy_sentences(
     sentence_boundaries = [sent.end_char for sent in doc.sents]
 
     # For "Sentence + Comma" mode, also split on commas
-    if subtitle_mode == SubtitleMode.SENTENCE_COMMA:
+    if subtitle_mode in (SubtitleMode.SENTENCE_COMMA.value, "Sentence + Comma"):
         comma_positions = [
             i + 1 for i, c in enumerate(full_text) if c == ","
         ]
@@ -182,6 +236,56 @@ def _process_spacy_sentences(
             set(sentence_boundaries + comma_positions)
         )
 
+    # spaCy does not treat ellipsis ("...", "..", "…") as a sentence
+    # boundary ("Lorem ipsum... Lorem..." stays one sentence), so ellipsis
+    # runs followed by whitespace/end would merge into a single subtitle
+    # entry. Add explicit boundaries after them. Single dots ("Mr.") stay
+    # spaCy's responsibility so abbreviations don't regress.
+    for m in re.finditer(r"\.{2,}(?=[\s\"'”’»›)\]}]|$)|…(?=[\s\"'”’»›)\]}]|$)", full_text):
+        sentence_boundaries.append(m.end())
+    # Double newlines are paragraph breaks: always split, even when spaCy
+    # sees no sentence boundary.
+    for m in re.finditer(r"\n{2,}", full_text):
+        sentence_boundaries.append(m.end())
+    sentence_boundaries = sorted(set(sentence_boundaries))
+
+    # Multi-sentence single FakeToken handling
+    if len(tokens) == 1 and len(sentence_boundaries) > 1:
+        single = tokens[0]
+        start_time = single.get("start", 0.0) or 0.0
+        end_time = single.get("end")
+        duration = (end_time - start_time) if (end_time is not None and end_time > start_time) else 0.0
+
+        prev_pos = 0
+        cur_start = start_time
+        total_chars = max(len(full_text), 1)
+
+        for i, b_pos in enumerate(sentence_boundaries):
+            piece = full_text[prev_pos:b_pos].strip()
+            if not piece:
+                prev_pos = b_pos
+                continue
+            if i == len(sentence_boundaries) - 1:
+                cur_end = end_time if end_time is not None else (cur_start + 1.0)
+            else:
+                cur_end = cur_start + duration * len(piece) / total_chars
+            subtitle_entries.append((cur_start, cur_end, piece))
+            cur_start = cur_end
+            prev_pos = b_pos
+
+        if prev_pos < len(full_text):
+            remainder = full_text[prev_pos:].strip()
+            if remainder:
+                remainder_end = end_time
+                if remainder_end is None:
+                    remainder_end = fallback_end_time
+                if remainder_end is None:
+                    remainder_end = cur_start
+                subtitle_entries.append((cur_start, remainder_end, remainder))
+
+        _apply_fallback_end_time(subtitle_entries, fallback_end_time)
+        return
+
     # Group tokens by sentence boundaries
     current_sentence = []
     word_count = 0
@@ -191,7 +295,7 @@ def _process_spacy_sentences(
     for token in tokens:
         current_sentence.append(token)
         word_count += 1
-        text_len = len(token["text"]) + len(token.get("whitespace") or "")
+        text_len = len(str(token.get("text", ""))) + len(token.get("whitespace") or "")
         current_char_pos += text_len
 
         # Check if we've hit a sentence boundary or max words
@@ -204,15 +308,19 @@ def _process_spacy_sentences(
                 start_time = current_sentence[0]["start"]
                 end_time = current_sentence[-1]["end"]
                 sentence_text = "".join(
-                    t["text"] + (t.get("whitespace") or "")
+                    str(t.get("text", "")) + (t.get("whitespace") or "")
                     for t in current_sentence
-                )
+                ).strip()
+                if sentence_text:
                     subtitle_entries.append(
-                    (start_time, end_time, sentence_text.strip())
+                        (start_time, end_time, sentence_text)
                     )
                 current_sentence = []
                 word_count = 0
-            if at_boundary:
+            while (
+                boundary_idx < len(sentence_boundaries)
+                and current_char_pos >= sentence_boundaries[boundary_idx]
+            ):
                 boundary_idx += 1
 
     # Add remaining tokens
@@ -220,11 +328,12 @@ def _process_spacy_sentences(
         start_time = current_sentence[0]["start"]
         end_time = current_sentence[-1]["end"]
         sentence_text = "".join(
-            t["text"] + (t.get("whitespace") or "")
+            str(t.get("text", "")) + (t.get("whitespace") or "")
             for t in current_sentence
-        )
+        ).strip()
+        if sentence_text:
             subtitle_entries.append(
-            (start_time, end_time, sentence_text.strip())
+                (start_time, end_time, sentence_text)
             )
 
     # Fallback for last entry
@@ -240,9 +349,9 @@ def _process_regex_sentences(
 ) -> None:
     """Process tokens using regex for sentence boundary detection."""
     # Define separator pattern based on mode
-    if subtitle_mode == SubtitleMode.LINE:
+    if subtitle_mode in (SubtitleMode.LINE.value, "Line"):
         separator = r"\n"
-    elif subtitle_mode == SubtitleMode.SENTENCE:
+    elif subtitle_mode in (SubtitleMode.SENTENCE.value, "Sentence"):
         separator = rf"[{PUNCTUATION_SENTENCE}]"
     else:  # Sentence + Comma
         separator = rf"[{PUNCTUATION_SENTENCE_COMMA}]"
@@ -255,21 +364,21 @@ def _process_regex_sentences(
         word_count += 1
 
         # Split sentences based on separator or word count
-        if (
-            re.search(separator, token["text"]) and token.get("whitespace") == " "
-        ) or word_count >= max_subtitle_words:
+        is_boundary = _is_sentence_boundary(token, current_sentence, separator)
+        if is_boundary or word_count >= max_subtitle_words:
             if current_sentence:
                 # Create subtitle entry for this sentence
                 start_time = current_sentence[0]["start"]
                 end_time = current_sentence[-1]["end"]
 
-                # Simplified text joining logic
-                sentence_text = ""
-                for t in current_sentence:
-                    sentence_text += t["text"] + (t.get("whitespace") or "")
+                sentence_text = "".join(
+                    str(t.get("text", "")) + (t.get("whitespace") or "")
+                    for t in current_sentence
+                ).strip()
 
+                if sentence_text:
                     subtitle_entries.append(
-                    (start_time, end_time, sentence_text.strip())
+                        (start_time, end_time, sentence_text)
                     )
                 current_sentence = []
                 word_count = 0
@@ -279,23 +388,39 @@ def _process_regex_sentences(
         start_time = current_sentence[0]["start"]
         end_time = current_sentence[-1]["end"]
 
-        sentence_text = ""
-        for t in current_sentence:
-            sentence_text += t["text"] + (t.get("whitespace") or "")
-        sentence_text = sentence_text.strip()
+        sentence_text = "".join(
+            str(t.get("text", "")) + (t.get("whitespace") or "")
+            for t in current_sentence
+        ).strip()
 
         if len(current_sentence) == 1:
-            parts = re.split(rf"(?<={separator})\s+", sentence_text)
+            split_pat = (
+                r"\n+"
+                if separator == r"\n"
+                else rf"(?<={separator})\s+|(?<={separator}[{re.escape(_CLOSING_DELIMS)}])\s+"
+            )
+            parts = [p.strip() for p in re.split(split_pat, sentence_text) if p.strip()]
             if len(parts) > 1:
-                d = end_time - start_time
+                d = (end_time - start_time) if (end_time is not None and start_time is not None and end_time > start_time) else 0.0
+                total_len = max(len(sentence_text), 1)
+                cur_s = start_time if start_time is not None else 0.0
                 for i, p in enumerate(parts):
-                    e = end_time if i == len(parts) - 1 else start_time + d * len(p) / len(sentence_text)
-                    subtitle_entries.append((start_time, e, p.strip()))
-                    start_time = e
+                    if i == len(parts) - 1 and end_time is not None:
+                        e = end_time
+                    else:
+                        e = cur_s + d * len(p) / total_len
+                    subtitle_entries.append((cur_s, e, p))
+                    cur_s = e
                 current_sentence = []
 
-        if current_sentence:
-            subtitle_entries.append((start_time, end_time, sentence_text))
+        if current_sentence and sentence_text:
+            safe_start = start_time if start_time is not None else 0.0
+            safe_end = end_time
+            if safe_end is None:
+                safe_end = fallback_end_time
+            if safe_end is None:
+                safe_end = safe_start
+            subtitle_entries.append((safe_start, safe_end, sentence_text))
 
     # Fallback for last entry
     _apply_fallback_end_time(subtitle_entries, fallback_end_time)
@@ -328,14 +453,15 @@ def _process_word_count(
             # Split after counting N spaces
             if space_count >= word_count:
                 text = "".join(
-                    t["text"] + (t.get("whitespace") or "")
+                    str(t.get("text", "")) + (t.get("whitespace") or "")
                     for t in current_group
-                )
+                ).strip()
+                if text:
                     subtitle_entries.append(
                         (
                             current_group[0]["start"],
                             current_group[-1]["end"],
-                        text.strip(),
+                            text,
                         )
                     )
                 current_group = []
@@ -344,10 +470,11 @@ def _process_word_count(
     # Add any remaining tokens
     if current_group:
         text = "".join(
-            t["text"] + (t.get("whitespace") or "") for t in current_group
-        )
+            str(t.get("text", "")) + (t.get("whitespace") or "") for t in current_group
+        ).strip()
+        if text:
             subtitle_entries.append(
-            (current_group[0]["start"], current_group[-1]["end"], text.strip())
+                (current_group[0]["start"], current_group[-1]["end"], text)
             )
 
     # Fallback for last entry

abogen/kokoro_text_normalization.py

@@ -672,6 +672,15 @@ def tokenize_with_spans(text: str) -> List[Tuple[str, int, int]]:
     ]
 
 
+_OPENING_PUNCTUATION_CHARS = "«‹“‘([{¡¿「『"
+_CLOSING_PUNCTUATION_CHARS = "»›”’)]}」』"
+_STANDARD_PUNCTUATION_CHARS = ",.;:!?%"
+
+_OPENING_PUNCT_CLASS = re.escape(_OPENING_PUNCTUATION_CHARS)
+_CLOSING_PUNCT_CLASS = re.escape(_CLOSING_PUNCTUATION_CHARS)
+_STANDARD_PUNCT_CLASS = re.escape(_STANDARD_PUNCTUATION_CHARS)
+
+
 def _cleanup_spacing(text: str) -> str:
     if not text:
         return text
@@ -679,22 +688,39 @@ def _cleanup_spacing(text: str) -> str:
     for marker in ("\ufeff", "\u200b", "\u200c", "\u200d", "\u2060"):
         text = text.replace(marker, "")
 
-    # Collapse spaces before closing punctuation.
-    text = re.sub(r"\s+([,.;:!?%])", r"\1", text)
-    text = re.sub(r"\s+([’\"”»›)\]\}])", r"\1", text)
+    # Collapse spaces before standard punctuation and unambiguous closing quotes/brackets.
+    text = re.sub(rf"\s+([{_STANDARD_PUNCT_CLASS}])", r"\1", text)
+    text = re.sub(rf"\s+([{_CLOSING_PUNCT_CLASS}])", r"\1", text)
 
-    # Remove spaces directly after opening punctuation/quotes.
-    text = re.sub(r"([«‹“‘\"'(\[\{])\s+", r"\1", text)
+    # Remove spaces directly after unambiguous opening punctuation/quotes.
+    text = re.sub(rf"([{_OPENING_PUNCT_CLASS}])\s+", r"\1", text)
+
+    # Handle ambiguous straight quotes (\", ')
+    # 1. Remove spaces directly after opening straight quotes:
+    #    e.g. ' \" word' -> ' \"word', '^\" word' -> '\"word', '(\" word' -> '(\"word'
+    text = re.sub(rf"(^|[\s{_OPENING_PUNCT_CLASS}])([\"\'])\s+", r"\1\2", text)
+    # 2. Collapse spaces directly before closing straight quotes:
+    #    e.g. 'word \" ' -> 'word\" ', 'word \".' -> 'word\".'
+    text = re.sub(rf"\s+([\"\'])([\s{_STANDARD_PUNCT_CLASS}{_CLOSING_PUNCT_CLASS}]|$)", r"\1\2", text)
 
     # Ensure spaces exist after sentence punctuation when followed by a word/quote.
-    text = re.sub(r"([,.;:!?%])(?![\s”'\"’»›)])", r"\1 ", text)
-    text = re.sub(r"([”\"’])(?![\s.,;:!?\"”’»›)])", r"\1 ", text)
+    # Runs of punctuation ("...", "?!?", "!!") must stay together: no space
+    # inside the run, only after it ("a...b" -> "a... b").
+    text = re.sub(rf"([{_STANDARD_PUNCT_CLASS}])(?![\s{_STANDARD_PUNCT_CLASS}{_CLOSING_PUNCT_CLASS}\"\'”’»›)])", r"\1 ", text)
+    # Ensure space after unambiguous closing quote when followed by a word (e.g. '”Next' -> '” Next')
+    text = re.sub(rf"([{_CLOSING_PUNCT_CLASS}])(?![\s{_STANDARD_PUNCT_CLASS}{_CLOSING_PUNCT_CLASS}\"\'”’»›)])", r"\1 ", text)
+    # Straight double quote closing (preceded by non-whitespace) followed directly by a word/number/opening
+    text = re.sub(rf"(\S\")([A-Za-z0-9{_OPENING_PUNCT_CLASS}])", r"\1 \2", text)
+    # Straight single quote closing (preceded by punctuation, not internal word apostrophe) followed by a word
+    text = re.sub(rf"([{_STANDARD_PUNCT_CLASS}{_CLOSING_PUNCT_CLASS}]\')([A-Za-z0-9{_OPENING_PUNCT_CLASS}])", r"\1 \2", text)
 
     # Tighten hyphen/em dash spacing between word characters.
     text = re.sub(r"(?<=\w)\s*([-–—])\s*(?=\w)", r"\1", text)
 
-    # Normalize multiple spaces.
-    text = re.sub(r"\s{2,}", " ", text)
+    # Normalize multiple spaces, preserving paragraph breaks (double
+    # newlines must survive so the TTS engine can split on them).
+    text = re.sub(r"[^\S\n]{2,}", " ", text)
+    text = re.sub(r"\n{3,}", "\n\n", text)
     return text.strip()
 
 
@@ -1622,8 +1648,18 @@ def normalize_apostrophes(
         results.append((tok, category, norm))
         normalized_tokens.append(norm)
 
-    filtered = [token for token in normalized_tokens if token]
-    normalized_text = _cleanup_spacing(" ".join(filtered))
+    out_pieces: List[str] = []
+    last_end = 0
+    for (tok, start, end), norm in zip(token_entries, normalized_tokens):
+        if start > last_end:
+            out_pieces.append(text[last_end:start])
+        out_pieces.append(norm)
+        last_end = end
+    if last_end < len(text):
+        out_pieces.append(text[last_end:])
+
+    reconstructed = "".join(out_pieces)
+    normalized_text = _cleanup_spacing(reconstructed)
     return normalized_text, results
 
 
@@ -1824,7 +1860,10 @@ def _normalize_grouped_numbers(text: str, cfg: ApostropheConfig) -> str:
         for digit in trimmed_fraction:
             if not digit.isdigit():
                 return token
+            try:
                 digit_words.append(_DIGIT_WORDS[int(digit)])
+            except (ValueError, IndexError):
+                return token
 
         spoken = f"{integer_words} point {' '.join(digit_words)}"
         return f"minus {spoken}" if is_negative else spoken
@@ -1846,18 +1885,27 @@ def _normalize_grouped_numbers(text: str, cfg: ApostropheConfig) -> str:
             # Magnitude case: $2.5 million -> two point five million dollars
             if "." in amount_str:
                 integer_part, fraction_part = amount_str.split(".", 1)
+                try:
                     integer_val = int(integer_part)
+                except ValueError:
+                    return match.group(0)
                 integer_words = _int_to_words(integer_val, language)
 
                 # Spell out fraction digits
                 digit_words = []
                 for digit in fraction_part:
                     if digit.isdigit():
+                        try:
                             digit_words.append(_DIGIT_WORDS[int(digit)])
+                        except (ValueError, IndexError):
+                            return match.group(0)
 
                 amount_spoken = f"{integer_words} point {' '.join(digit_words)}"
             else:
+                try:
                     amount_spoken = _int_to_words(int(amount), language)
+                except (ValueError, OverflowError):
+                    return match.group(0)
 
             currency_names = {
                 "$": "dollars",

abogen/pyqt/gui.py

@@ -137,6 +137,28 @@ class ThreadSafeLogSignal(QObject):
         self.log_signal.emit(message)
 
 
+_UPDATE_CHECK_URL = "https://raw.githubusercontent.com/denizsafak/abogen/refs/heads/main/abogen/VERSION"
+_UPDATE_CHECK_TIMEOUT = 8  # seconds; bounds offline/DNS hangs so the GUI never blocks
+
+
+class _UpdateCheckThread(QThread):
+    """Fetch the remote VERSION file off the GUI thread."""
+
+    succeeded = pyqtSignal(str)
+    failed = pyqtSignal(str)
+
+    def run(self):
+        import urllib.request
+
+        try:
+            with urllib.request.urlopen(
+                _UPDATE_CHECK_URL, timeout=_UPDATE_CHECK_TIMEOUT
+            ) as response:
+                self.succeeded.emit(response.read().decode().strip())
+        except Exception as exc:  # offline, DNS hang, HTTP error, ...
+            self.failed.emit(str(exc))
+
+
 class IconProvider(QFileIconProvider):
     def icon(self, fileInfo):
         return super().icon(fileInfo)
@@ -4077,9 +4099,63 @@ Categories=AudioVideo;Audio;Utility;
         self.check_for_updates_startup()
 
     def check_for_updates_startup(self):
-        import urllib.request
+        # Network I/O runs in a worker thread: urlopen without a timeout on
+        # the GUI thread froze the whole app when offline (DNS/connect can
+        # hang for minutes). Results return via signals on the GUI thread.
+        thread = getattr(self, "_update_check_thread", None)
+        if thread is not None:
+            try:
+                if thread.isRunning():
+                    return
+            except RuntimeError:
+                pass  # previous thread already finished/deleted
+        show_result = (
+            hasattr(self, "_show_update_check_result")
+            and self._show_update_check_result
+        )
+        self._show_update_check_result = False
+        self._update_check_thread = _UpdateCheckThread(self)
+        self._update_check_thread.succeeded.connect(
+            lambda remote_raw: self._on_update_check_done(remote_raw, show_result)
+        )
+        self._update_check_thread.failed.connect(
+            lambda err: self._on_update_check_failed(err, show_result)
+        )
+        self._update_check_thread.finished.connect(
+            self._update_check_thread.deleteLater
+        )
+        self._update_check_thread.start()
 
-        def show_update_message(remote_version, local_version):
+    def _on_update_check_done(self, remote_raw, show_result):
+        remote_version = remote_raw.strip()
+        local_version = VERSION
+        try:
+            remote_num = int("".join(remote_version.split(".")))
+            local_num = int("".join(local_version.split(".")))
+        except ValueError:
+            return
+        if remote_num > local_num:
+            # Use QTimer to ensure UI is ready, then show update message.
+            QTimer.singleShot(
+                1000,
+                lambda: self._show_update_message(remote_version, local_version),
+            )
+        elif show_result:
+            QMessageBox.information(
+                self,
+                "Up to Date",
+                f"You are running the latest version of {PROGRAM_NAME} ({local_version}).",
+            )
+
+    def _on_update_check_failed(self, err, show_result):
+        if show_result:
+            QMessageBox.warning(
+                self,
+                "Update Check Failed",
+                f"Could not check for updates:\n{err}",
+            )
+
+    def _show_update_message(self, remote_version, local_version):
         msg_box = QMessageBox(self)
         msg_box.setIcon(QMessageBox.Icon.Information)
         msg_box.setWindowTitle("Update Available")
@@ -4103,50 +4179,6 @@ Categories=AudioVideo;Audio;Utility;
             except Exception:
                 pass
 
-        # Reset flag to track if we should show "no updates" message
-        show_result = (
-            hasattr(self, "_show_update_check_result")
-            and self._show_update_check_result
-        )
-        self._show_update_check_result = False
-
-        try:
-            update_url = "https://raw.githubusercontent.com/denizsafak/abogen/refs/heads/main/abogen/VERSION"
-            with urllib.request.urlopen(update_url) as response:
-                remote_raw = response.read().decode().strip()
-            local_raw = VERSION
-
-            # Parse version numbers
-            remote_version = remote_raw
-            local_version = local_raw
-
-            try:
-                remote_num = int("".join(remote_version.split(".")))
-                local_num = int("".join(local_version.split(".")))
-            except ValueError as ve:
-                return
-
-            if remote_num > local_num:
-                # Use QTimer to ensure UI is ready, then show update message.
-                QTimer.singleShot(
-                    1000, lambda: show_update_message(remote_version, local_version)
-                )
-            elif show_result:
-                # Show "no updates" message if manually checking
-                QMessageBox.information(
-                    self,
-                    "Up to Date",
-                    f"You are running the latest version of {PROGRAM_NAME} ({local_version}).",
-                )
-        except Exception as e:
-            if show_result:
-                QMessageBox.warning(
-                    self,
-                    "Update Check Failed",
-                    f"Could not check for updates:\n{str(e)}",
-                )
-            pass
-
     def clear_cache_files(self):
         """Clear cache files created by the program."""
         import glob
@@ -4248,8 +4280,6 @@ Categories=AudioVideo;Audio;Utility;
 
     def set_max_log_lines(self):
         """Open a dialog to set the maximum lines in the log window."""
-        from PyQt6.QtWidgets import QInputDialog
-
         value, ok = QInputDialog.getInt(
             self,
             "Max Lines in Log Window",
@@ -4271,8 +4301,6 @@ Categories=AudioVideo;Audio;Utility;
 
     def set_max_subtitle_words(self):
         """Open a dialog to set the maximum words per subtitle"""
-        from PyQt6.QtWidgets import QInputDialog
-
         current_value = self.config.get("max_subtitle_words", _DEFAULTS["max_subtitle_words"])
 
         value, ok = QInputDialog.getInt(

tests/test_regression_fixes.py

@@ -90,3 +90,10 @@ def test_manual_override_normalization():
     assert normalize_manual_override_token("The") == "the"
     assert normalize_manual_override_token("  A  ") == "a"
     assert normalize_manual_override_token("word") == "word"
+
+
+def test_paragraph_breaks_and_ellipsis_preserved():
+    normalized = normalize("Test. Lorem ipsum...\n\nLorem...\n\nLorem ...")
+    assert "\n\n" in normalized
+    assert ". . ." not in normalized
+    assert "Lorem..." in normalized

tests/test_subtitle_scenarios.py

@@ -0,0 +1,402 @@
+"""Comprehensive tests for subtitle generation across different models, modes, and text scenarios.
+
+Tests include:
+- Quotation mark handling (straight quotes, curly quotes, guillemets, dialogs)
+- No spurious spaces after opening quotes (e.g., test "word word" vs test " word word")
+- Proper sentence boundary detection for quoted dialogues (e.g., "Hello." She said.)
+- Paragraph handling and multi-line text
+- All subtitle modes (Line, Sentence, Sentence + Comma, Sentence + Highlighting, N-words)
+- Both TTS model token styles (Kokoro per-word tokens and Supertonic FakeTokens)
+- Non-English and multilingual scenarios
+"""
+
+import pytest
+
+from abogen.domain.enums import Language, SubtitleMode
+from abogen.domain.normalization import prepare_text_for_tts
+from abogen.domain.subtitle_generation import (
+    process_subtitle_tokens,
+    PUNCTUATION_SENTENCE,
+    PUNCTUATION_SENTENCE_COMMA,
+)
+
+
+class TestQuoteNormalizationAndSpacing:
+    """Verify text normalization correctly preserves quotation mark spacing."""
+
+    def test_straight_quote_mid_sentence_no_extra_space(self):
+        """Input 'test "word word"' should keep space before quote and no space after."""
+        text = 'test "word word"'
+        normalized = prepare_text_for_tts(text)
+        assert '" word' not in normalized
+        assert 'test "' in normalized or 'test "word' in normalized
+
+    def test_straight_quote_at_start_no_extra_space(self):
+        """Input '"word word"' should not have a leading space after opening quote."""
+        text = '"word word"'
+        normalized = prepare_text_for_tts(text)
+        assert not normalized.startswith('" ')
+        assert normalized.startswith('"word')
+
+    def test_dialogue_quote_spacing(self):
+        """He said, "Hello world." should preserve proper comma-space-quote-word sequence."""
+        text = 'He said, "Hello world."'
+        normalized = prepare_text_for_tts(text)
+        assert 'said, "' in normalized or 'said,"' not in normalized
+        assert '" Hello' not in normalized
+        assert '"Hello' in normalized
+
+    def test_quote_with_contraction(self):
+        """Contraction inside quotes like "Don't go!" should expand cleanly without extra spaces."""
+        text = '"Don\'t go!"'
+        normalized = prepare_text_for_tts(text)
+        assert '" Do not' not in normalized
+        assert '"Do not' in normalized or '"Don\'t' in normalized
+
+    def test_curly_quotes_preserved(self):
+        """Curly quotes like “Hello world.” should not have spurious spacing."""
+        text = '“Hello world.”'
+        normalized = prepare_text_for_tts(text)
+        assert '“ ' not in normalized
+        assert ' ”' not in normalized
+
+    def test_spanish_opening_punctuation(self):
+        """Spanish inverted exclamation ¡Hola! should not have space after ¡."""
+        text = '¡Hola mundo!'
+        normalized = prepare_text_for_tts(text)
+        assert '¡ ' not in normalized
+
+    def test_french_guillemets_spacing(self):
+        """French guillemets « Bonjour » should clean up spaces properly."""
+        text = '« Bonjour »'
+        normalized = prepare_text_for_tts(text)
+        assert '« ' not in normalized
+        assert ' »' not in normalized
+
+
+class TestKokoroPerWordTokenSubtitles:
+    """Tests using Kokoro-style per-word tokens with individual timestamps."""
+
+    def test_quoted_phrase_subtitles(self):
+        """Tokens for 'test "word word"' produce subtitle without space after quote."""
+        tokens = [
+            {"start": 0.0, "end": 0.3, "text": "test", "whitespace": " "},
+            {"start": 0.3, "end": 0.35, "text": '"', "whitespace": ""},
+            {"start": 0.35, "end": 0.7, "text": "word", "whitespace": " "},
+            {"start": 0.7, "end": 1.0, "text": "word", "whitespace": ""},
+            {"start": 1.0, "end": 1.05, "text": '"', "whitespace": ""},
+        ]
+        entries = []
+        process_subtitle_tokens(
+            tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence",
+            language=Language.EN_US, use_spacy_segmentation=False
+        )
+        assert len(entries) == 1
+        assert entries[0][2] == 'test "word word"'
+
+    def test_dialogue_sentence_splitting_regex(self):
+        """Dialogue ending with ." should split into separate sentence subtitles."""
+        tokens = [
+            {"start": 0.0, "end": 0.05, "text": '"', "whitespace": ""},
+            {"start": 0.05, "end": 0.5, "text": "Hello", "whitespace": " "},
+            {"start": 0.5, "end": 0.9, "text": "world.", "whitespace": ""},
+            {"start": 0.9, "end": 0.95, "text": '"', "whitespace": " "},
+            {"start": 0.95, "end": 1.4, "text": "She", "whitespace": " "},
+            {"start": 1.4, "end": 1.8, "text": "smiled.", "whitespace": ""},
+        ]
+        entries = []
+        process_subtitle_tokens(
+            tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence",
+            language=Language.EN_US, use_spacy_segmentation=False
+        )
+        assert len(entries) == 2
+        assert entries[0][2] == '"Hello world."'
+        assert entries[1][2] == "She smiled."
+        assert entries[0][0] == 0.0
+        assert entries[0][1] == 0.95
+        assert entries[1][0] == 0.95
+        assert entries[1][1] == 1.8
+
+    def test_question_exclamation_dialogue_splitting(self):
+        """Dialogue with ?" and !" should split sentences cleanly."""
+        tokens = [
+            {"start": 0.0, "end": 0.05, "text": '"', "whitespace": ""},
+            {"start": 0.05, "end": 0.4, "text": "Why?", "whitespace": ""},
+            {"start": 0.4, "end": 0.45, "text": '"', "whitespace": " "},
+            {"start": 0.45, "end": 0.8, "text": "she", "whitespace": " "},
+            {"start": 0.8, "end": 1.2, "text": "asked.", "whitespace": " "},
+            {"start": 1.2, "end": 1.25, "text": '"', "whitespace": ""},
+            {"start": 1.25, "end": 1.7, "text": "Because!", "whitespace": ""},
+            {"start": 1.7, "end": 1.75, "text": '"', "whitespace": " "},
+            {"start": 1.75, "end": 2.0, "text": "he", "whitespace": " "},
+            {"start": 2.0, "end": 2.4, "text": "replied.", "whitespace": ""},
+        ]
+        entries = []
+        process_subtitle_tokens(
+            tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence",
+            language=Language.EN_US, use_spacy_segmentation=False
+        )
+        assert len(entries) == 4
+        assert entries[0][2] == '"Why?"'
+        assert entries[1][2] == "she asked."
+        assert entries[2][2] == '"Because!"'
+        assert entries[3][2] == "he replied."
+
+    def test_sentence_comma_mode_with_quotes(self):
+        """Sentence + Comma mode splits at commas and sentence boundaries."""
+        tokens = [
+            {"start": 0.0, "end": 0.4, "text": "First,", "whitespace": " "},
+            {"start": 0.4, "end": 0.8, "text": "she", "whitespace": " "},
+            {"start": 0.8, "end": 1.2, "text": "said,", "whitespace": " "},
+            {"start": 1.2, "end": 1.25, "text": '"', "whitespace": ""},
+            {"start": 1.25, "end": 1.6, "text": "wait.", "whitespace": ""},
+            {"start": 1.6, "end": 1.65, "text": '"', "whitespace": ""},
+        ]
+        entries = []
+        process_subtitle_tokens(
+            tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence + Comma",
+            language=Language.EN_US, use_spacy_segmentation=False
+        )
+        assert len(entries) >= 2
+        assert "First," in entries[0][2]
+
+    def test_karaoke_highlighting_with_quotes(self):
+        """Sentence + Highlighting generates valid karaoke tags with quotes."""
+        tokens = [
+            {"start": 0.0, "end": 0.05, "text": '"', "whitespace": ""},
+            {"start": 0.05, "end": 0.5, "text": "Hello", "whitespace": " "},
+            {"start": 0.5, "end": 0.9, "text": "world.", "whitespace": ""},
+            {"start": 0.9, "end": 0.95, "text": '"', "whitespace": " "},
+            {"start": 0.95, "end": 1.4, "text": "She", "whitespace": " "},
+            {"start": 1.4, "end": 1.8, "text": "said.", "whitespace": ""},
+        ]
+        entries = []
+        process_subtitle_tokens(
+            tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence + Highlighting",
+            language=Language.EN_US, use_spacy_segmentation=False
+        )
+        assert len(entries) == 2
+        assert '{\\kf' in entries[0][2]
+        assert '{\\kf' in entries[1][2]
+        assert '"' in entries[0][2]
+
+    def test_word_count_mode_with_quotes(self):
+        """N-words mode (e.g. '3 words') groups tokens by space count."""
+        tokens = [
+            {"start": 0.0, "end": 0.3, "text": "One", "whitespace": " "},
+            {"start": 0.3, "end": 0.35, "text": '"', "whitespace": ""},
+            {"start": 0.35, "end": 0.7, "text": "two", "whitespace": " "},
+            {"start": 0.7, "end": 1.0, "text": "three", "whitespace": ""},
+            {"start": 1.0, "end": 1.05, "text": '"', "whitespace": " "},
+            {"start": 1.05, "end": 1.4, "text": "four", "whitespace": " "},
+            {"start": 1.4, "end": 1.8, "text": "five", "whitespace": " "},
+            {"start": 1.8, "end": 2.2, "text": "six.", "whitespace": ""},
+        ]
+        entries = []
+        process_subtitle_tokens(
+            tokens, entries, max_subtitle_words=50, subtitle_mode="3",
+            language=Language.EN_US, use_spacy_segmentation=False
+        )
+        assert len(entries) == 2
+        assert entries[0][2] == 'One "two three"'
+        assert entries[1][2] == "four five six."
+
+
+class TestSupertonicAndFakeTokenSubtitles:
+    """Tests using Supertonic / non-English Kokoro FakeTokens (segment-level stubs)."""
+
+    def test_faketoken_multi_sentence_regex_split(self):
+        """A single FakeToken containing multiple sentences should split proportionally."""
+        tokens = [
+            {
+                "start": 0.0,
+                "end": 6.0,
+                "text": 'First sentence. "Second quoted sentence." Third sentence.',
+                "whitespace": "",
+            }
+        ]
+        entries = []
+        process_subtitle_tokens(
+            tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence",
+            language=Language.ES, use_spacy_segmentation=False
+        )
+        assert len(entries) == 3
+        assert entries[0][2] == "First sentence."
+        assert entries[1][2] == '"Second quoted sentence."'
+        assert entries[2][2] == "Third sentence."
+        assert entries[0][0] == 0.0
+        assert entries[2][1] == 6.0
+
+    def test_faketoken_multi_sentence_spacy_split(self):
+        """A single FakeToken in English with spaCy should split into separate sentences."""
+        tokens = [
+            {
+                "start": 0.0,
+                "end": 6.0,
+                "text": 'The sun rose high. "Are you ready?" she asked. "Always," he replied.',
+                "whitespace": "",
+            }
+        ]
+        entries = []
+        process_subtitle_tokens(
+            tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence",
+            language=Language.EN_US, use_spacy_segmentation=True
+        )
+        assert len(entries) >= 2
+        assert entries[0][0] == 0.0
+        assert entries[-1][1] == 6.0
+        for e in entries:
+            assert not e[2].startswith('" ')
+
+    def test_faketoken_single_sentence_with_quotes(self):
+        """Single sentence FakeToken preserves quotes cleanly."""
+        tokens = [
+            {
+                "start": 1.0,
+                "end": 3.5,
+                "text": '"This is a single quoted thought."',
+                "whitespace": "",
+            }
+        ]
+        entries = []
+        process_subtitle_tokens(
+            tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence",
+            language=Language.FR, use_spacy_segmentation=False
+        )
+        assert len(entries) == 1
+        assert entries[0][2] == '"This is a single quoted thought."'
+        assert entries[0][0] == 1.0
+        assert entries[0][1] == 3.5
+
+    def test_line_mode_with_faketokens(self):
+        """Line mode emits one subtitle per line / segment."""
+        tokens = [
+            {"start": 0.0, "end": 2.0, "text": 'Line 1 with "quotes"', "whitespace": "\n"},
+            {"start": 2.0, "end": 4.0, "text": 'Line 2 with "more quotes"', "whitespace": ""},
+        ]
+        entries = []
+        process_subtitle_tokens(
+            tokens, entries, max_subtitle_words=50, subtitle_mode="Line",
+            language=Language.EN_US, use_spacy_segmentation=False
+        )
+        assert len(entries) == 2
+        assert entries[0][2] == 'Line 1 with "quotes"'
+        assert entries[1][2] == 'Line 2 with "more quotes"'
+
+
+class TestComplexParagraphsAndEdgeCases:
+    """Tests for paragraphs, multiple newlines, and unusual punctuation combinations."""
+
+    def test_paragraph_multi_line_token_flow(self):
+        """Text spanning paragraphs with multiple sentences."""
+        tokens = [
+            {"start": 0.0, "end": 0.5, "text": "Paragraph", "whitespace": " "},
+            {"start": 0.5, "end": 1.0, "text": "one.", "whitespace": "\n\n"},
+            {"start": 1.0, "end": 1.5, "text": "Paragraph", "whitespace": " "},
+            {"start": 1.5, "end": 2.0, "text": "two.", "whitespace": ""},
+        ]
+        entries = []
+        process_subtitle_tokens(
+            tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence",
+            language=Language.EN_US, use_spacy_segmentation=False
+        )
+        assert len(entries) == 2
+        assert entries[0][2] == "Paragraph one."
+        assert entries[1][2] == "Paragraph two."
+
+    def test_nested_quotes_and_parentheses(self):
+        """Sentence with nested quotes and parentheses: He said, "(Wait) 'now'!" """
+        tokens = [
+            {"start": 0.0, "end": 0.3, "text": "He", "whitespace": " "},
+            {"start": 0.3, "end": 0.6, "text": "said,", "whitespace": " "},
+            {"start": 0.6, "end": 0.65, "text": '"', "whitespace": ""},
+            {"start": 0.65, "end": 0.7, "text": "(", "whitespace": ""},
+            {"start": 0.7, "end": 1.0, "text": "Wait", "whitespace": ""},
+            {"start": 1.0, "end": 1.05, "text": ")", "whitespace": " "},
+            {"start": 1.05, "end": 1.1, "text": "'", "whitespace": ""},
+            {"start": 1.1, "end": 1.4, "text": "now", "whitespace": ""},
+            {"start": 1.4, "end": 1.45, "text": "'!", "whitespace": ""},
+            {"start": 1.45, "end": 1.5, "text": '"', "whitespace": ""},
+        ]
+        entries = []
+        process_subtitle_tokens(
+            tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence",
+            language=Language.EN_US, use_spacy_segmentation=False
+        )
+        assert len(entries) == 1
+        assert entries[0][2] == 'He said, "(Wait) \'now\'!"'
+
+    def test_trailing_quotes_and_ellipsis(self):
+        """Sentence ending with ellipsis and quote: "I wonder..." """
+        tokens = [
+            {"start": 0.0, "end": 0.05, "text": '"', "whitespace": ""},
+            {"start": 0.05, "end": 0.3, "text": "I", "whitespace": " "},
+            {"start": 0.3, "end": 0.8, "text": "wonder...", "whitespace": ""},
+            {"start": 0.8, "end": 0.85, "text": '"', "whitespace": " "},
+            {"start": 0.85, "end": 1.2, "text": "he", "whitespace": " "},
+            {"start": 1.2, "end": 1.6, "text": "mused.", "whitespace": ""},
+        ]
+        entries = []
+        process_subtitle_tokens(
+            tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence",
+            language=Language.EN_US, use_spacy_segmentation=False
+        )
+        assert len(entries) == 2
+        assert entries[0][2] == '"I wonder..."'
+        assert entries[1][2] == "he mused."
+
+
+class TestEllipsisAndParagraphBreaks:
+    """Regression: '...' sentences merged into one entry; '\\n\\n' flattened.
+
+    spaCy does not treat ellipsis as a sentence boundary, so
+    'Lorem ipsum... Lorem...' stayed a single subtitle entry. And
+    _cleanup_spacing collapsed paragraph breaks before TTS.
+    """
+
+    @staticmethod
+    def _tok(text_ws, dur=0.5):
+        toks, t = [], 0.0
+        for text, ws in text_ws:
+            toks.append({"start": t, "end": t + dur, "text": text, "whitespace": ws})
+            t += dur
+        return toks, t
+
+    def test_spacy_splits_ellipsis_sentences(self):
+        # Kokoro-style tokens: '...' arrives as 3 dot tokens.
+        toks, end = self._tok([
+            ("Test", ""), (".", " "), ("Lorem", " "), ("ipsum", ""),
+            (".", ""), (".", ""), (".", " "),
+            ("Lorem", ""), (".", ""), (".", ""), (".", ""),
+        ])
+        entries = []
+        process_subtitle_tokens(
+            toks, entries, max_subtitle_words=50, subtitle_mode="Sentence",
+            language=Language.EN_US, use_spacy_segmentation=True,
+            fallback_end_time=end,
+        )
+        assert [e[2] for e in entries] == ["Test.", "Lorem ipsum...", "Lorem..."]
+
+    def test_spacy_keeps_abbreviations_intact(self):
+        toks, end = self._tok([
+            ("Mr.", " "), ("Smith", " "), ("went", " "), ("home", ""),
+            (".", " "), ("He", " "), ("slept", ""), (".", ""),
+        ])
+        entries = []
+        process_subtitle_tokens(
+            toks, entries, max_subtitle_words=50, subtitle_mode="Sentence",
+            language=Language.EN_US, use_spacy_segmentation=True,
+            fallback_end_time=end,
+        )
+        assert [e[2] for e in entries] == ["Mr. Smith went home.", "He slept."]
+
+    def test_spacy_splits_faketoken_ellipsis(self):
+        entries = []
+        process_subtitle_tokens(
+            [{"start": 0.0, "end": 3.0,
+              "text": "Lorem ipsum... Lorem... Lorem...", "whitespace": ""}],
+            entries, max_subtitle_words=50, subtitle_mode="Sentence",
+            language=Language.EN_US, use_spacy_segmentation=True,
+            fallback_end_time=3.0,
+        )
+        assert [e[2] for e in entries] == ["Lorem ipsum...", "Lorem...", "Lorem..."]