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https://github.com/denizsafak/abogen.git
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Deniz Şafak
08e2ee8b85
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
403 lines
18 KiB
Python
403 lines
18 KiB
Python
"""Comprehensive tests for subtitle generation across different models, modes, and text scenarios.
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Tests include:
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- Quotation mark handling (straight quotes, curly quotes, guillemets, dialogs)
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- No spurious spaces after opening quotes (e.g., test "word word" vs test " word word")
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- Proper sentence boundary detection for quoted dialogues (e.g., "Hello." She said.)
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- Paragraph handling and multi-line text
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- All subtitle modes (Line, Sentence, Sentence + Comma, Sentence + Highlighting, N-words)
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- Both TTS model token styles (Kokoro per-word tokens and Supertonic FakeTokens)
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- Non-English and multilingual scenarios
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"""
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import pytest
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from abogen.domain.enums import Language, SubtitleMode
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from abogen.domain.normalization import prepare_text_for_tts
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from abogen.domain.subtitle_generation import (
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process_subtitle_tokens,
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PUNCTUATION_SENTENCE,
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PUNCTUATION_SENTENCE_COMMA,
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)
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class TestQuoteNormalizationAndSpacing:
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"""Verify text normalization correctly preserves quotation mark spacing."""
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def test_straight_quote_mid_sentence_no_extra_space(self):
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"""Input 'test "word word"' should keep space before quote and no space after."""
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text = 'test "word word"'
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normalized = prepare_text_for_tts(text)
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assert '" word' not in normalized
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assert 'test "' in normalized or 'test "word' in normalized
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def test_straight_quote_at_start_no_extra_space(self):
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"""Input '"word word"' should not have a leading space after opening quote."""
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text = '"word word"'
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normalized = prepare_text_for_tts(text)
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assert not normalized.startswith('" ')
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assert normalized.startswith('"word')
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def test_dialogue_quote_spacing(self):
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"""He said, "Hello world." should preserve proper comma-space-quote-word sequence."""
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text = 'He said, "Hello world."'
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normalized = prepare_text_for_tts(text)
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assert 'said, "' in normalized or 'said,"' not in normalized
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assert '" Hello' not in normalized
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assert '"Hello' in normalized
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def test_quote_with_contraction(self):
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"""Contraction inside quotes like "Don't go!" should expand cleanly without extra spaces."""
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text = '"Don\'t go!"'
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normalized = prepare_text_for_tts(text)
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assert '" Do not' not in normalized
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assert '"Do not' in normalized or '"Don\'t' in normalized
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def test_curly_quotes_preserved(self):
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"""Curly quotes like “Hello world.” should not have spurious spacing."""
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text = '“Hello world.”'
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normalized = prepare_text_for_tts(text)
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assert '“ ' not in normalized
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assert ' ”' not in normalized
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def test_spanish_opening_punctuation(self):
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"""Spanish inverted exclamation ¡Hola! should not have space after ¡."""
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text = '¡Hola mundo!'
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normalized = prepare_text_for_tts(text)
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assert '¡ ' not in normalized
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def test_french_guillemets_spacing(self):
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"""French guillemets « Bonjour » should clean up spaces properly."""
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text = '« Bonjour »'
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normalized = prepare_text_for_tts(text)
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assert '« ' not in normalized
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assert ' »' not in normalized
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class TestKokoroPerWordTokenSubtitles:
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"""Tests using Kokoro-style per-word tokens with individual timestamps."""
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def test_quoted_phrase_subtitles(self):
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"""Tokens for 'test "word word"' produce subtitle without space after quote."""
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tokens = [
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{"start": 0.0, "end": 0.3, "text": "test", "whitespace": " "},
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{"start": 0.3, "end": 0.35, "text": '"', "whitespace": ""},
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{"start": 0.35, "end": 0.7, "text": "word", "whitespace": " "},
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{"start": 0.7, "end": 1.0, "text": "word", "whitespace": ""},
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{"start": 1.0, "end": 1.05, "text": '"', "whitespace": ""},
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]
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entries = []
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process_subtitle_tokens(
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tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence",
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language=Language.EN_US, use_spacy_segmentation=False
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)
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assert len(entries) == 1
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assert entries[0][2] == 'test "word word"'
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def test_dialogue_sentence_splitting_regex(self):
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"""Dialogue ending with ." should split into separate sentence subtitles."""
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tokens = [
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{"start": 0.0, "end": 0.05, "text": '"', "whitespace": ""},
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{"start": 0.05, "end": 0.5, "text": "Hello", "whitespace": " "},
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{"start": 0.5, "end": 0.9, "text": "world.", "whitespace": ""},
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{"start": 0.9, "end": 0.95, "text": '"', "whitespace": " "},
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{"start": 0.95, "end": 1.4, "text": "She", "whitespace": " "},
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{"start": 1.4, "end": 1.8, "text": "smiled.", "whitespace": ""},
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]
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entries = []
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process_subtitle_tokens(
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tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence",
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language=Language.EN_US, use_spacy_segmentation=False
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)
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assert len(entries) == 2
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assert entries[0][2] == '"Hello world."'
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assert entries[1][2] == "She smiled."
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assert entries[0][0] == 0.0
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assert entries[0][1] == 0.95
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assert entries[1][0] == 0.95
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assert entries[1][1] == 1.8
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def test_question_exclamation_dialogue_splitting(self):
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"""Dialogue with ?" and !" should split sentences cleanly."""
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tokens = [
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{"start": 0.0, "end": 0.05, "text": '"', "whitespace": ""},
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{"start": 0.05, "end": 0.4, "text": "Why?", "whitespace": ""},
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{"start": 0.4, "end": 0.45, "text": '"', "whitespace": " "},
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{"start": 0.45, "end": 0.8, "text": "she", "whitespace": " "},
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{"start": 0.8, "end": 1.2, "text": "asked.", "whitespace": " "},
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{"start": 1.2, "end": 1.25, "text": '"', "whitespace": ""},
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{"start": 1.25, "end": 1.7, "text": "Because!", "whitespace": ""},
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{"start": 1.7, "end": 1.75, "text": '"', "whitespace": " "},
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{"start": 1.75, "end": 2.0, "text": "he", "whitespace": " "},
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{"start": 2.0, "end": 2.4, "text": "replied.", "whitespace": ""},
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]
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entries = []
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process_subtitle_tokens(
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tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence",
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language=Language.EN_US, use_spacy_segmentation=False
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)
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assert len(entries) == 4
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assert entries[0][2] == '"Why?"'
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assert entries[1][2] == "she asked."
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assert entries[2][2] == '"Because!"'
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assert entries[3][2] == "he replied."
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def test_sentence_comma_mode_with_quotes(self):
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"""Sentence + Comma mode splits at commas and sentence boundaries."""
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tokens = [
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{"start": 0.0, "end": 0.4, "text": "First,", "whitespace": " "},
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{"start": 0.4, "end": 0.8, "text": "she", "whitespace": " "},
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{"start": 0.8, "end": 1.2, "text": "said,", "whitespace": " "},
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{"start": 1.2, "end": 1.25, "text": '"', "whitespace": ""},
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{"start": 1.25, "end": 1.6, "text": "wait.", "whitespace": ""},
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{"start": 1.6, "end": 1.65, "text": '"', "whitespace": ""},
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]
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entries = []
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process_subtitle_tokens(
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tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence + Comma",
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language=Language.EN_US, use_spacy_segmentation=False
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)
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assert len(entries) >= 2
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assert "First," in entries[0][2]
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def test_karaoke_highlighting_with_quotes(self):
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"""Sentence + Highlighting generates valid karaoke tags with quotes."""
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tokens = [
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{"start": 0.0, "end": 0.05, "text": '"', "whitespace": ""},
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{"start": 0.05, "end": 0.5, "text": "Hello", "whitespace": " "},
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{"start": 0.5, "end": 0.9, "text": "world.", "whitespace": ""},
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{"start": 0.9, "end": 0.95, "text": '"', "whitespace": " "},
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{"start": 0.95, "end": 1.4, "text": "She", "whitespace": " "},
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{"start": 1.4, "end": 1.8, "text": "said.", "whitespace": ""},
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]
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entries = []
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process_subtitle_tokens(
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tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence + Highlighting",
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language=Language.EN_US, use_spacy_segmentation=False
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)
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assert len(entries) == 2
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assert '{\\kf' in entries[0][2]
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assert '{\\kf' in entries[1][2]
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assert '"' in entries[0][2]
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def test_word_count_mode_with_quotes(self):
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"""N-words mode (e.g. '3 words') groups tokens by space count."""
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tokens = [
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{"start": 0.0, "end": 0.3, "text": "One", "whitespace": " "},
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{"start": 0.3, "end": 0.35, "text": '"', "whitespace": ""},
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{"start": 0.35, "end": 0.7, "text": "two", "whitespace": " "},
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{"start": 0.7, "end": 1.0, "text": "three", "whitespace": ""},
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{"start": 1.0, "end": 1.05, "text": '"', "whitespace": " "},
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{"start": 1.05, "end": 1.4, "text": "four", "whitespace": " "},
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{"start": 1.4, "end": 1.8, "text": "five", "whitespace": " "},
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{"start": 1.8, "end": 2.2, "text": "six.", "whitespace": ""},
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]
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entries = []
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process_subtitle_tokens(
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tokens, entries, max_subtitle_words=50, subtitle_mode="3",
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language=Language.EN_US, use_spacy_segmentation=False
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)
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assert len(entries) == 2
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assert entries[0][2] == 'One "two three"'
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assert entries[1][2] == "four five six."
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class TestSupertonicAndFakeTokenSubtitles:
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"""Tests using Supertonic / non-English Kokoro FakeTokens (segment-level stubs)."""
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def test_faketoken_multi_sentence_regex_split(self):
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"""A single FakeToken containing multiple sentences should split proportionally."""
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tokens = [
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{
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"start": 0.0,
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"end": 6.0,
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"text": 'First sentence. "Second quoted sentence." Third sentence.',
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"whitespace": "",
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}
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]
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entries = []
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process_subtitle_tokens(
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tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence",
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language=Language.ES, use_spacy_segmentation=False
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)
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assert len(entries) == 3
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assert entries[0][2] == "First sentence."
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assert entries[1][2] == '"Second quoted sentence."'
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assert entries[2][2] == "Third sentence."
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assert entries[0][0] == 0.0
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assert entries[2][1] == 6.0
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def test_faketoken_multi_sentence_spacy_split(self):
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"""A single FakeToken in English with spaCy should split into separate sentences."""
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tokens = [
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{
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"start": 0.0,
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"end": 6.0,
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"text": 'The sun rose high. "Are you ready?" she asked. "Always," he replied.',
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"whitespace": "",
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}
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]
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entries = []
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process_subtitle_tokens(
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tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence",
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language=Language.EN_US, use_spacy_segmentation=True
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)
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assert len(entries) >= 2
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assert entries[0][0] == 0.0
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assert entries[-1][1] == 6.0
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for e in entries:
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assert not e[2].startswith('" ')
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def test_faketoken_single_sentence_with_quotes(self):
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"""Single sentence FakeToken preserves quotes cleanly."""
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tokens = [
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{
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"start": 1.0,
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"end": 3.5,
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"text": '"This is a single quoted thought."',
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"whitespace": "",
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}
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]
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entries = []
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process_subtitle_tokens(
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tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence",
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language=Language.FR, use_spacy_segmentation=False
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)
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assert len(entries) == 1
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assert entries[0][2] == '"This is a single quoted thought."'
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assert entries[0][0] == 1.0
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assert entries[0][1] == 3.5
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def test_line_mode_with_faketokens(self):
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"""Line mode emits one subtitle per line / segment."""
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tokens = [
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{"start": 0.0, "end": 2.0, "text": 'Line 1 with "quotes"', "whitespace": "\n"},
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{"start": 2.0, "end": 4.0, "text": 'Line 2 with "more quotes"', "whitespace": ""},
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]
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entries = []
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process_subtitle_tokens(
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tokens, entries, max_subtitle_words=50, subtitle_mode="Line",
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language=Language.EN_US, use_spacy_segmentation=False
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)
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assert len(entries) == 2
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assert entries[0][2] == 'Line 1 with "quotes"'
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assert entries[1][2] == 'Line 2 with "more quotes"'
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class TestComplexParagraphsAndEdgeCases:
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"""Tests for paragraphs, multiple newlines, and unusual punctuation combinations."""
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def test_paragraph_multi_line_token_flow(self):
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"""Text spanning paragraphs with multiple sentences."""
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tokens = [
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{"start": 0.0, "end": 0.5, "text": "Paragraph", "whitespace": " "},
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{"start": 0.5, "end": 1.0, "text": "one.", "whitespace": "\n\n"},
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{"start": 1.0, "end": 1.5, "text": "Paragraph", "whitespace": " "},
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{"start": 1.5, "end": 2.0, "text": "two.", "whitespace": ""},
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]
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entries = []
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process_subtitle_tokens(
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tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence",
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language=Language.EN_US, use_spacy_segmentation=False
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)
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assert len(entries) == 2
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assert entries[0][2] == "Paragraph one."
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assert entries[1][2] == "Paragraph two."
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def test_nested_quotes_and_parentheses(self):
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"""Sentence with nested quotes and parentheses: He said, "(Wait) 'now'!" """
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tokens = [
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{"start": 0.0, "end": 0.3, "text": "He", "whitespace": " "},
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{"start": 0.3, "end": 0.6, "text": "said,", "whitespace": " "},
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{"start": 0.6, "end": 0.65, "text": '"', "whitespace": ""},
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{"start": 0.65, "end": 0.7, "text": "(", "whitespace": ""},
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{"start": 0.7, "end": 1.0, "text": "Wait", "whitespace": ""},
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{"start": 1.0, "end": 1.05, "text": ")", "whitespace": " "},
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{"start": 1.05, "end": 1.1, "text": "'", "whitespace": ""},
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{"start": 1.1, "end": 1.4, "text": "now", "whitespace": ""},
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{"start": 1.4, "end": 1.45, "text": "'!", "whitespace": ""},
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{"start": 1.45, "end": 1.5, "text": '"', "whitespace": ""},
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]
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entries = []
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process_subtitle_tokens(
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tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence",
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language=Language.EN_US, use_spacy_segmentation=False
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)
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assert len(entries) == 1
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assert entries[0][2] == 'He said, "(Wait) \'now\'!"'
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def test_trailing_quotes_and_ellipsis(self):
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"""Sentence ending with ellipsis and quote: "I wonder..." """
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tokens = [
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{"start": 0.0, "end": 0.05, "text": '"', "whitespace": ""},
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{"start": 0.05, "end": 0.3, "text": "I", "whitespace": " "},
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{"start": 0.3, "end": 0.8, "text": "wonder...", "whitespace": ""},
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{"start": 0.8, "end": 0.85, "text": '"', "whitespace": " "},
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{"start": 0.85, "end": 1.2, "text": "he", "whitespace": " "},
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{"start": 1.2, "end": 1.6, "text": "mused.", "whitespace": ""},
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]
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entries = []
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process_subtitle_tokens(
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tokens, entries, max_subtitle_words=50, subtitle_mode="Sentence",
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language=Language.EN_US, use_spacy_segmentation=False
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)
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assert len(entries) == 2
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assert entries[0][2] == '"I wonder..."'
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assert entries[1][2] == "he mused."
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class TestEllipsisAndParagraphBreaks:
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"""Regression: '...' sentences merged into one entry; '\\n\\n' flattened.
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spaCy does not treat ellipsis as a sentence boundary, so
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'Lorem ipsum... Lorem...' stayed a single subtitle entry. And
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_cleanup_spacing collapsed paragraph breaks before TTS.
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"""
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@staticmethod
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def _tok(text_ws, dur=0.5):
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toks, t = [], 0.0
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for text, ws in text_ws:
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toks.append({"start": t, "end": t + dur, "text": text, "whitespace": ws})
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t += dur
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return toks, t
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def test_spacy_splits_ellipsis_sentences(self):
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# Kokoro-style tokens: '...' arrives as 3 dot tokens.
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toks, end = self._tok([
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("Test", ""), (".", " "), ("Lorem", " "), ("ipsum", ""),
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(".", ""), (".", ""), (".", " "),
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("Lorem", ""), (".", ""), (".", ""), (".", ""),
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])
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entries = []
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process_subtitle_tokens(
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toks, entries, max_subtitle_words=50, subtitle_mode="Sentence",
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language=Language.EN_US, use_spacy_segmentation=True,
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fallback_end_time=end,
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)
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assert [e[2] for e in entries] == ["Test.", "Lorem ipsum...", "Lorem..."]
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def test_spacy_keeps_abbreviations_intact(self):
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toks, end = self._tok([
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("Mr.", " "), ("Smith", " "), ("went", " "), ("home", ""),
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(".", " "), ("He", " "), ("slept", ""), (".", ""),
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])
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entries = []
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process_subtitle_tokens(
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toks, entries, max_subtitle_words=50, subtitle_mode="Sentence",
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language=Language.EN_US, use_spacy_segmentation=True,
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fallback_end_time=end,
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)
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assert [e[2] for e in entries] == ["Mr. Smith went home.", "He slept."]
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def test_spacy_splits_faketoken_ellipsis(self):
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entries = []
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process_subtitle_tokens(
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[{"start": 0.0, "end": 3.0,
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"text": "Lorem ipsum... Lorem... Lorem...", "whitespace": ""}],
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entries, max_subtitle_words=50, subtitle_mode="Sentence",
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language=Language.EN_US, use_spacy_segmentation=True,
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fallback_end_time=3.0,
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)
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assert [e[2] for e in entries] == ["Lorem ipsum...", "Lorem...", "Lorem..."]
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