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abogen/tests/test_subtitle_scenarios.py
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Deniz Şafak be74c69507 fix(subtitles): fix quotation mark spacing corruption and quoted dialogue splitting 
- kokoro_text_normalization:
  - refactor _cleanup_spacing to contextually distinguish opening vs closing
    straight quotes (" and ') so leading quotes do not have spaces added after
    them and spaces before opening quotes are preserved
  - recognize non-English opening delimiters (¡, ¿, «, 「, etc.) and closing
    delimiters (», 」, etc.) in spacing normalization
  - in normalize_apostrophes, reconstruct text using original token offsets
    to preserve inter-token whitespace instead of blindly joining with spaces
- subtitle_generation:
  - add _is_sentence_boundary to recognize sentence punctuation followed by
    closing quotes/brackets (.", !", ?") in regex and karaoke modes
  - support proportional splitting for single FakeToken multi-sentence segments
    in spaCy mode (Supertonic / non-English Kokoro)
  - support newline splitting for Line mode in FakeToken fallbacks
  - safely convert language and subtitle_mode inputs
- tests:
  - add tests/test_subtitle_scenarios.py covering quotes, dialogues,
    paragraphs, contractions, all subtitle modes, and all TTS token styles
2026-08-29 12:45:18 +03:00

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"""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."
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