diff --git a/.gitignore b/.gitignore
index ce56449..fd7a0f4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,3 +5,5 @@ venv/
*.egg-info/
dist/
build/
+.pytest_cache/
+.mypy_cache/
diff --git a/README.md b/README.md
index 982d414..7a87dac 100644
--- a/README.md
+++ b/README.md
@@ -204,59 +204,36 @@ hablar 9
Extract vocabulary from an Anki TSV export file instead of using AnkiConnect:
```shell
-saiki words --lang es --input Español.txt --field 2 --output words_es_content.txt --debug words_es_debug.tsv
-```
-
-Additional file-based options:
-
-```shell
-saiki words --lang es --input Español.txt --field 2 --include-proper-nouns
-saiki words --lang es --input Español.txt --field 2 --function-words
-saiki words --lang es --input Español.txt --field 2 --lemma-corrections my_fixes.tsv
-saiki words --lang es --input Español.txt --field 2 --bad-lemma-file bad_lemmas.txt
+saiki words --lang es --input Español.txt --field 2 --field-section first --output words_es_content.txt --debug words_es_debug.tsv
+saiki words --lang jp --input Japanese.txt --field 2 --output words_jp_content.txt
```
When `--input` is provided, `--field` specifies the 1-based column index of the
-Spanish text (default 2). The audio column (index 1) and tags column are
-automatically skipped. File-based NLP extraction is currently Spanish-specific;
-the older AnkiConnect-based `saiki words jp` flow is unchanged.
+text field (default 2). Only that column is mined; other columns such as audio
+or Anki tags are ignored. By default, all blank-line-separated sections inside
+the selected field are kept. Use `--field-section first` when your card format
+stores target-language text before a translation or note in the same field.
The file-based pipeline:
- Parses Anki `#` header lines (`#separator:tab`, `#html:true`, `#tags column:N`)
- Uses Python's `csv` module for robust TSV parsing
-- Removes `[sound:...mp3]` markers, HTML tags, and English glosses after
- `
` from the field text
-- Applies safe Spanish lemmatisation (multi-word lemmas like `ayudar yo` or
- `lavar él` are rejected; bad lemmas like `comar` -> `comer` are corrected)
+- Removes `[sound:...mp3]` markers, HTML tags, URLs, and email addresses from
+ the field text
+- Uses the configured language's spaCy model, token filter, and output format
- Tracks POS counts, surface forms, example sentences, and source line numbers
-- Tracks original spaCy lemmas and correction/fallback reasons for debugging
+- Tracks original spaCy lemmas for debugging
Output files produced:
-- `words_es_content.txt` — cleaned content vocabulary (NOUN, VERB, ADJ, ADV)
-- `words_es_debug.tsv` — per-lemma debug info
-- `words_es_proper_nouns.txt` — proper nouns (only with `--include-proper-nouns`)
-- `words_es_function_words.txt` — function words (only with `--function-words`)
-- `words_es_suspicious_tokens.txt` — lemmas that required correction
+- `words__content.txt` — cleaned vocabulary
+- `words__debug.tsv` — per-entry debug info (only with `--debug`)
Debug TSV example:
```text
-lemma count pos_counts top_surface_forms example_sentences source_lines original_lemmas lemma_statuses status
-comer 8 VERB:8 como, come, comen Yo como manzanas.; Ustedes los comen con arroz. 70,979 comar, comer corrected:comar->comer:1, ok:7
+entry count pos_counts top_surface_forms example_sentences source_lines original_lemmas
+comer 8 VERB:8 como, come, comen Yo como manzanas.; Ustedes los comen con arroz. 70,979 comer
```
-### Lint Spanish Cards
-
-Check an Anki TSV export for suspicious or awkward Spanish:
-
-```shell
-saiki lint-anki-es --input Español.txt --field 2 --output suspicious_cards_es.tsv
-```
-
-Detects known errors and rule-based suspicious patterns and produces a TSV
-report with: source line, original text, cleaned text, reason, and suggested
-fix.
-
### Compare
Compare deck vocabulary against a target list:
@@ -270,7 +247,7 @@ Normalises case and optionally strips accents for matching, so `cómo` and
`como` are treated as the same word. Use `--min-frequency` to ignore accidental
low-frequency words in the deck.
-
+### YouTube
Mine vocabulary or sentence rows from YouTube subtitles.
diff --git a/src/saiki/anki_tsv.py b/src/saiki/anki_tsv.py
index cddc7e3..36af7a4 100644
--- a/src/saiki/anki_tsv.py
+++ b/src/saiki/anki_tsv.py
@@ -67,7 +67,7 @@ def parse_anki_tsv(
``text``, ``tags`` (if *include_tags* is True), ``line_number``,
and ``raw_line``.
- Header lines (``#``-prefixed) are skipped. The actual separator
+ Leading header lines (``#``-prefixed) are skipped. The actual separator
is detected from ``#separator:`` and defaults to tab.
"""
if field_index < 1:
@@ -95,13 +95,15 @@ def parse_anki_tsv(
with open(path, "r", encoding="utf-8") as fh:
reader = csv.reader(fh, delimiter=separator)
line_number = 0
+ in_headers = True
for row in reader:
line_number += 1
# skip header rows and completely empty rows
if not row:
continue
- if row and row[0].startswith("#"):
+ if in_headers and row[0].startswith("#"):
continue
+ in_headers = False
if col_idx >= len(row):
raise ValueError(
diff --git a/src/saiki/cli.py b/src/saiki/cli.py
index 7c9041a..c5120a6 100644
--- a/src/saiki/cli.py
+++ b/src/saiki/cli.py
@@ -15,7 +15,7 @@ from .importer import (
supported_tts_backends,
synthesize_tts_sample,
)
-from .words import compare_word_files, compare_word_lists, extract_words, extract_words_from_file, lint_anki_cards_main
+from .words import compare_word_files, compare_word_lists, extract_words, extract_words_from_file
from .youtube import run_youtube
@@ -98,12 +98,13 @@ def build_parser(config: Config | None = None) -> argparse.ArgumentParser:
words.add_argument("--out", "--output", dest="out")
words.add_argument("--full-field", action="store_true")
words.add_argument("--spacy-model")
- words.add_argument("--include-proper-nouns", action="store_true", help="Include proper nouns (PROPN).")
- words.add_argument("--function-words", action="store_true", help="Extract function words separately.")
words.add_argument("--debug", help="Path for debug TSV output.")
- words.add_argument("--lemma-corrections", help="Path to badgood TSV of lemma corrections.")
- words.add_argument("--bad-lemma-file", help="Path to file listing lemmas to skip.")
- words.add_argument("--include-tags", action="store_true", help="Include tags column in parsing.")
+ words.add_argument(
+ "--field-section",
+ choices=["all", "first"],
+ default="all",
+ help="Which blank-line-separated TSV field section to mine.",
+ )
words.add_argument("--debug-min-freq", type=int, default=0,
help="Minimum frequency for debug output (default: all).")
words.add_argument("--no-clean", action="store_true",
@@ -121,12 +122,6 @@ def build_parser(config: Config | None = None) -> argparse.ArgumentParser:
compare_new.add_argument("--min-frequency", type=int, default=0,
help="Minimum frequency for deck words to count.")
- lint_es = sub.add_parser("lint-anki-es", help="Check Spanish Anki cards for suspicious patterns.")
- lint_es.add_argument("--input", required=True, help="Anki TSV export file.")
- lint_es.add_argument("--field", type=int, default=2, help="1-based column index (default 2).")
- lint_es.add_argument("--output", default="suspicious_cards_es.tsv", help="Output TSV path.")
- lint_es.add_argument("--verbose", action="store_true", help="Output all cards, not just suspicious ones.")
-
youtube = sub.add_parser("youtube", help="Mine a YouTube transcript.")
youtube.add_argument("lang", choices=choices)
youtube.add_argument("video")
@@ -195,12 +190,6 @@ def main(argv: list[str] | None = None) -> int:
if args.command == "words":
if args.input:
# File-based extraction
- if config.language_name(args.lang) != "spanish":
- print(
- "Error: TSV file-based word extraction is currently Spanish-only.",
- file=sys.stderr,
- )
- return 1
if args.field and not args.field.isdigit():
print(
f"Error: --field must be a 1-based column index (integer) "
@@ -221,25 +210,16 @@ def main(argv: list[str] | None = None) -> int:
min_freq=args.min_freq,
outdir=args.outdir,
out=args.out,
- include_proper_nouns=args.include_proper_nouns,
- function_words=args.function_words,
debug=args.debug,
- lemma_corrections=args.lemma_corrections,
- bad_lemma_file=args.bad_lemma_file,
spacy_model=args.spacy_model,
- include_tags=args.include_tags,
debug_min_freq=args.debug_min_freq,
no_clean=args.no_clean,
+ field_section=args.field_section,
)
print(f"Parsed {result['records']} records")
- print(f"Wrote {result['written']} content entries to: {result['out']}")
+ print(f"Wrote {result['written']} entries to: {result['out']}")
if result.get("debug"):
print(f"Debug output: {result['debug']}")
- if result.get("proper_nouns"):
- print(f"Proper nouns: {result['proper_nouns']}")
- if result.get("function_words"):
- print(f"Function words: {result['function_words']}")
- print(f"Suspicious tokens: {result.get('suspicious', '')}")
else:
result = extract_words(
config, args.lang, args.query, args.deck, args.field, args.min_freq,
@@ -279,24 +259,6 @@ def main(argv: list[str] | None = None) -> int:
print(f"Seen words written to: {args.seen_output}")
return 0
- if args.command == "lint-anki-es":
- if args.field < 1:
- print(
- f"Error: --field must be a 1-based column index >= 1, got: {args.field}",
- file=sys.stderr,
- )
- return 1
- result = lint_anki_cards_main(
- args.input,
- field_index=args.field,
- output_path=args.output,
- verbose=args.verbose,
- )
- print(f"Checked {result['records']} cards")
- print(f"Found {result['issues']} suspicious card(s)")
- print(f"Report written to: {result['path']}")
- return 0
-
if args.command == "youtube":
result = run_youtube(
config, args.lang, args.video, args.mode, args.top, args.no_stopwords,
diff --git a/src/saiki/config.py b/src/saiki/config.py
index d70aa7a..0e4f0b8 100644
--- a/src/saiki/config.py
+++ b/src/saiki/config.py
@@ -13,7 +13,7 @@ from typing import Any
try:
import yaml
-except Exception: # pragma: no cover - handled when config files are loaded
+except ImportError: # pragma: no cover - handled when config files are loaded
yaml = None
diff --git a/src/saiki/spanish.py b/src/saiki/spanish.py
deleted file mode 100644
index 0733326..0000000
--- a/src/saiki/spanish.py
+++ /dev/null
@@ -1,719 +0,0 @@
-"""Spanish-specific NLP utilities for vocabulary extraction and card linting."""
-
-from __future__ import annotations
-
-import html
-import re
-from collections import defaultdict
-from dataclasses import dataclass
-from typing import Any, Callable
-
-# ── Regex patterns ─────────────────────────────────────────────────
-
-SOUND_RE = re.compile(r"\[sound:[^\]]*\]")
-MEDIA_RE = re.compile(r"\[\s*(?:sound|image|media):[^\]]*\]", re.IGNORECASE)
-MEDIA_FILENAME_RE = re.compile(
- r"[^/\s]+\.(?:mp3|ogg|wav|m4a|flac|jpg|jpeg|png|gif|webp|mp4|webm)\b",
- re.IGNORECASE,
-)
-BR_RE = re.compile(r"<\s*br\s*/?\s*>", re.IGNORECASE)
-DOUBLE_BR_RE = re.compile(
- r"<\s*br\s*/?\s*>\s*<\s*br\s*/?\s*>", re.IGNORECASE
-)
-HTML_TAG_RE = re.compile(r"<[^>]+>")
-URL_RE = re.compile(r"https?://\S+|www\.\S+", re.IGNORECASE)
-EMAIL_RE = re.compile(r"\S+@\S+\.\S+")
-MULTI_WS_RE = re.compile(r"[ \t]+")
-SPANISH_WORD_RE = re.compile(r"^[a-záéíóúüñ]+$", re.IGNORECASE)
-BAD_TOKEN_TEXT_RE = re.compile(r"[\d_/@#\\|+*=<>~`^]")
-LEMMA_PUNCT_RE = re.compile(r"[.,;:!?¿¡\"'()\[\]{}<>\-_/\\|`~@#$%^&*+=\d]")
-
-_ENGLISH_PATTERNS: list[str] = [
- r"\bI\s+(?:am|have|had|will|would|could|should|was|were|did|do|don"
- r"|can|need|like|want|think|know|love|hate|see|hear|eat|drink"
- r"|go|come|take|give|make|say|tell|ask|get|put)\b",
- r"\byou\s+(?:are|have|had|will|would|could|should|were|did|do|don"
- r"|can|need|like|want|think|know|love|hate|see|hear)\b",
- r"\b(the|to|and|in|of|a|an|is|it|for|with|on|at|by|from|or|be|this|that)\b",
- r"\bsee you later\b",
- r"\bI love you\b",
- r"\bI want you\b",
- r"\bknowledge/understanding\b",
- r"\b(?:see|watch|read|write|speak|listen|learn|study|teach|explain)\s+"
- r"(?:you|him|her|it|us|them|me)\b",
-]
-ENGLISH_RE = re.compile("|".join(_ENGLISH_PATTERNS), re.IGNORECASE)
-
-# ── Lemma corrections ──────────────────────────────────────────────
-
-BUILTIN_LEMMA_CORRECTIONS: dict[str, str] = {
- "comar": "comer",
- "acabir": "acabar",
- "deliciós": "delicioso",
- "llover/llover": "llover",
-}
-
-PRIR_SURFACE_FORMS = {"pide", "pido", "piden", "pidiendo", "pedir"}
-
-
-@dataclass(frozen=True)
-class LemmaInfo:
- """A selected lemma plus audit metadata about how it was chosen."""
-
- lemma: str
- original_lemma: str
- surface: str
- status: str = "ok"
-
-# ── Known card issues (exact substring → fix → explanation) ────────
-
-KNOWN_CARD_ISSUES: list[tuple[str, str, str]] = [
- (
- "Tú sabes nada",
- "Tú no sabes nada.",
- "Missing negation 'no' before 'sabes'",
- ),
- (
- "Dulce sueños",
- "Dulces sueños.",
- "'Dulce' should agree in number with 'sueños'",
- ),
- (
- "Ella soña con viajar.",
- "Ella sueña con viajar.",
- "'Soña' → 'sueña' (present indicative of soñar)",
- ),
- (
- "Siento lastima",
- "Siento lástima",
- "'lastima' should be 'lástima' with accent",
- ),
- (
- "Tengo uno libro",
- "Tengo un libro",
- "'uno' → 'un' before singular masculine noun",
- ),
- (
- "Tengo veintiuno años",
- "Tengo veintiún años",
- "'veintiuno' → 'veintiún' before plural noun",
- ),
- (
- "Dile a tu madre que feliz cumpleaños",
- 'Dile a tu madre: "Feliz cumpleaños."',
- "Missing colon/quotes or restructured phrasing",
- ),
- (
- "La pareja está en un bote observando a los peces nadan.",
- "La pareja está en un bote observando cómo nadan los peces.",
- "Missing 'cómo' or subordinating conjunction",
- ),
- (
- "Los perros pequeños le vuelven loca.",
- "Los perros pequeños la vuelven loca.",
- "'le' → 'la' (direct object, not indirect)",
- ),
-]
-
-# ── Regex-based suspicious patterns ────────────────────────────────
-# (pattern, description, suggested_fix_or_None)
-
-SUSPICIOUS_PATTERNS: list[tuple[str, str, str | None]] = [
- (
- r"AI-generated text-to-speech",
- "Metadata contamination: AI-generated TTS label in text field",
- None,
- ),
- (
- r"\bsabes\s+nada\b(?!.*\bno\b)",
- "Missing negation: 'sabes nada' without 'no'",
- "Add 'no' before 'sabes': 'No sabes nada.'",
- ),
- (
- r"\buno\s+(?:libro|coche|casa|perro|gato|hombre|mujer|niño"
- r"|amigo|día|año|mes|semana|minuto|segundo)\b",
- "'uno' instead of 'un' before masculine singular noun",
- "Replace 'uno' with 'un'",
- ),
- (
- r"\bveintiuno\s+(?:años|días|meses|semanas|horas|minutos|segundos)\b",
- "'veintiuno' instead of 'veintiún' before plural noun",
- "Replace 'veintiuno' with 'veintiún'",
- ),
- (
- r"observando\s+a\s+.*\bnadan\b",
- "Possible missing 'cómo' in 'observando cómo ...' construction",
- "Consider adding 'cómo': 'observando cómo ...'",
- ),
- (
- r"\ble\s+vuelve\w*\s+loc[ao]s?\b",
- "'le vuelve/vuelven loco/a' may need a direct object pronoun",
- "Replace 'le' with lo/la/los/las as appropriate",
- ),
- (
- r"\ble\s+vuelven\s+loc[ao]s?\b",
- "'le vuelven loco/a' may need a direct object pronoun",
- "Replace 'le' with lo/la/los/las as appropriate",
- ),
- (
- r"\bNecesito\s+[a-záéíóúüñ]{2,}\b(?!\s+(?:un|una|el|la|los"
- r"|las|al|del|mi|tu|su|nuestro))\s*$",
- "Missing article before the noun after 'Necesito'",
- None,
- ),
- (
- r"\bQu[ée]\s+es\s+la\s+respuesta\b",
- "'Qué es la respuesta' → 'Cuál es la respuesta'",
- "Replace 'Qué' with 'Cuál'",
- ),
- (
- r"\bpeludos?\s+animales?\b",
- "'peludo(s) animal(es)' → 'animal(es) peludo(s)' (adjective placement)",
- "Place adjective after noun",
- ),
- (
- r"\bhembras\s+y\s+varones\b",
- "'hembras y varones' — consider 'niños y niñas' or 'chicos y chicas'",
- None,
- ),
- (
- r"\bt[ií]o\s+alto\s+con\s+uniforme\b",
- "'tío alto con uniforme' — check intent (Spain slang vs 'hombre')",
- None,
- ),
- (
- r"\brepresentante\s+adulto\b",
- "'representante adulto' → 'adulto responsable'",
- "Consider 'adulto responsable'",
- ),
- (
- r"\bpropiedad\s+a\s+este\s+representante\b",
- "'propiedad a este representante' — unusual phrasing",
- "Consider revising wording",
- ),
-]
-
-CONTENT_POS = frozenset({"NOUN", "VERB", "ADJ", "ADV"})
-FUNCTION_POS = frozenset(
- {"PRON", "ADP", "CCONJ", "SCONJ", "DET", "AUX", "PART", "INTJ"}
-)
-
-# ── Text cleaning ──────────────────────────────────────────────────
-
-
-def _clean_single_field_part(text: str) -> str:
- """Clean one segment of an Anki field (no English-gloss filtering)."""
- text = BR_RE.sub(" ", text)
- text = HTML_TAG_RE.sub("", text)
- text = html.unescape(text)
- text = URL_RE.sub("", text)
- text = EMAIL_RE.sub("", text)
- return text.strip()
-
-
-def _looks_like_english_gloss(text: str) -> bool:
- """Heuristic: does *text* read like an English gloss/translation?"""
- stripped = text.strip().strip("()[]（）【】")
- if not stripped:
- return True
- return bool(ENGLISH_RE.search(stripped))
-
-
-def clean_anki_field_text(text: str) -> str:
- """Clean Anki field text for Spanish NLP.
-
- * Removes ``[sound:...]`` and ``[media:...]`` markers.
- * Splits on ``
`` to separate Spanish from English glosses.
- * Strips HTML tags and unescapes entities.
- * Removes URLs, emails.
- * Drops segments that look like English translations.
- * Normalises whitespace.
- * Preserves Spanish accents and meaningful content.
- """
- if not text:
- return ""
-
- text = SOUND_RE.sub("", text)
- text = MEDIA_RE.sub("", text)
-
- # Split on double
to isolate Spanish from glosses
- parts = DOUBLE_BR_RE.split(text)
- cleaned_parts: list[str] = []
- for i, part in enumerate(parts):
- cleaned = _clean_single_field_part(part)
- if not cleaned:
- continue
- if i > 0 and _looks_like_english_gloss(cleaned):
- continue
- cleaned_parts.append(cleaned)
-
- result = " ".join(cleaned_parts)
- result = MULTI_WS_RE.sub(" ", result).strip()
- return result
-
-
-# ── Safe lemmatisation ─────────────────────────────────────────────
-
-
-def load_lemma_corrections(path: str) -> dict[str, str]:
- """Load a ``badgood`` TSV of manual lemma fixes."""
- corrections: dict[str, str] = {}
- with open(path, "r", encoding="utf-8") as fh:
- for line in fh:
- line = line.strip()
- if not line or line.startswith("#"):
- continue
- parts = line.split("\t")
- if len(parts) >= 2:
- bad = parts[0].strip().lower()
- good = parts[1].strip().lower()
- if bad and good:
- corrections[bad] = good
- return corrections
-
-
-def load_bad_lemmas(path: str) -> set[str]:
- """Load a list of lemmas (one per line) to skip and report."""
- bad: set[str] = set()
- with open(path, "r", encoding="utf-8") as fh:
- for line in fh:
- lemma = line.strip()
- if lemma and not lemma.startswith("#"):
- bad.add(lemma.lower())
- return bad
-
-
-def safe_spanish_lemma(
- token,
- extra_corrections: dict[str, str] | None = None,
-) -> str:
- """Return only the selected lemma for callers that do not need metadata."""
- return safe_spanish_lemma_info(token, extra_corrections=extra_corrections).lemma
-
-
-def safe_spanish_lemma_info(
- token,
- extra_corrections: dict[str, str] | None = None,
-) -> LemmaInfo:
- """Return a safe Spanish lemma for a spaCy token.
-
- Falls back to ``token.text.lower()`` when the spaCy lemma is empty,
- multi-word, contains punctuation, or looks non-Spanish. Applies a
- built-in and optionally an external correction map, and records why
- fallback/correction happened.
- """
- manual = dict(BUILTIN_LEMMA_CORRECTIONS)
- if extra_corrections:
- manual.update({k.lower(): v.lower() for k, v in extra_corrections.items()})
-
- lemma = (token.lemma_ or "").strip().lower()
- surface = (token.text or "").strip().lower()
-
- # Manual correction map wins
- if lemma in manual:
- return LemmaInfo(
- manual[lemma],
- original_lemma=lemma,
- surface=surface,
- status=f"corrected:{lemma}->{manual[lemma]}",
- )
-
- # Empty or one-char fallback
- if not lemma or len(lemma) <= 1:
- return LemmaInfo(
- surface or lemma,
- original_lemma=lemma,
- surface=surface,
- status="fallback_empty_or_short_lemma",
- )
-
- # Multi-word lemma → suspicious spaCy output
- if " " in lemma:
- return LemmaInfo(
- surface,
- original_lemma=lemma,
- surface=surface,
- status="fallback_multi_word_lemma",
- )
-
- # Punctuation or digits in lemma → fall back
- if LEMMA_PUNCT_RE.search(lemma):
- return LemmaInfo(
- surface,
- original_lemma=lemma,
- surface=surface,
- status="fallback_bad_lemma_characters",
- )
-
- # Special case: "prir" might be spaCy failing on pedir forms
- if lemma == "prir":
- if surface in PRIR_SURFACE_FORMS:
- return LemmaInfo(
- "pedir",
- original_lemma=lemma,
- surface=surface,
- status="corrected:prir->pedir",
- )
- # otherwise return surface so it shows up in debug as suspicious
- return LemmaInfo(
- surface,
- original_lemma=lemma,
- surface=surface,
- status="suspicious_prir",
- )
-
- if not SPANISH_WORD_RE.match(lemma):
- return LemmaInfo(
- surface,
- original_lemma=lemma,
- surface=surface,
- status="fallback_non_spanish_lemma",
- )
-
- return LemmaInfo(lemma, original_lemma=lemma, surface=surface)
-
-
-# ── Token filters ──────────────────────────────────────────────────
-
-
-def spanish_content_filter(
- token,
- include_proper_nouns: bool = False,
-) -> bool:
- """Return True for Spanish content words (NOUN, VERB, ADJ, ADV).
-
- Excludes punctuation, digits, symbols, URLs, emails, media
- filenames, and one-character junk.
- """
- text = (token.text or "").strip()
- if not text:
- return False
- if token.is_punct or token.is_space or token.is_digit:
- return False
- if token.is_currency:
- return False
- if token.like_url or token.like_email:
- return False
- if MEDIA_FILENAME_RE.search(text):
- return False
- if BAD_TOKEN_TEXT_RE.search(text):
- return False
- if len(text) <= 1 and text not in ("a", "y", "e", "o", "u", "él"):
- return False
- pos = getattr(token, "pos_", "") or ""
- if pos in CONTENT_POS:
- return True
- if include_proper_nouns and pos == "PROPN":
- return True
- return False
-
-
-def spanish_function_word_filter(token) -> bool:
- """Return True for Spanish function words (PRON, ADP, CCONJ, …)."""
- text = (token.text or "").strip()
- if not text:
- return False
- if token.is_punct or token.is_space or token.like_url or token.like_email:
- return False
- if MEDIA_FILENAME_RE.search(text) or BAD_TOKEN_TEXT_RE.search(text):
- return False
- pos = getattr(token, "pos_", "") or ""
- return pos in FUNCTION_POS
-
-
-# ── Suspicious-card detection ──────────────────────────────────────
-
-
-def check_card_for_issues(text: str, cleaned: str) -> list[dict[str, Any]]:
- """Check a single card's text for suspicious patterns.
-
- Returns a list of issue dicts (normally 0 or 1 per card, but one
- card can match multiple patterns).
- """
- issues: list[dict[str, Any]] = []
- seen_reasons: set[str] = set()
-
- for bad_text, fix, explanation in KNOWN_CARD_ISSUES:
- if bad_text in text:
- if explanation not in seen_reasons:
- seen_reasons.add(explanation)
- issues.append({
- "reason": explanation,
- "suggested_fix": fix,
- })
-
- for pattern, description, fix in SUSPICIOUS_PATTERNS:
- if re.search(pattern, text, re.IGNORECASE):
- if description not in seen_reasons:
- seen_reasons.add(description)
- issues.append({
- "reason": description,
- "suggested_fix": fix or "",
- })
-
- # Check for English after
- parts = DOUBLE_BR_RE.split(text)
- for part in parts[1:]:
- cleaned_part = _clean_single_field_part(part)
- if _looks_like_english_gloss(cleaned_part):
- desc = "English gloss after
"
- if desc not in seen_reasons:
- seen_reasons.add(desc)
- issues.append({"reason": desc, "suggested_fix": ""})
-
- # Check for missing final punctuation in full-sentence cards
- stripped_text = text.rstrip()
- if (
- stripped_text
- and not stripped_text.endswith((".", "!", "?", "…", '"', "'"))
- and stripped_text[0].isupper()
- and len(stripped_text.split()) >= 3
- ):
- desc = "Missing final punctuation for full-sentence card"
- if desc not in seen_reasons:
- seen_reasons.add(desc)
- issues.append({"reason": desc, "suggested_fix": ""})
-
- return issues
-
-
-def lint_anki_cards(
- records: list[dict[str, Any]],
- verbose: bool = False,
-) -> list[dict[str, Any]]:
- """Run all suspicious-card checks across parsed Anki records.
-
- When *verbose* is True every record is returned, not just those
- with issues.
-
- Returns a list of issue dicts with keys:
- ``source_line``, ``original_text``, ``cleaned_text``,
- ``reason``, ``suggested_fix``.
- """
- results: list[dict[str, Any]] = []
- for record in records:
- text = record.get("text", "")
- if not text:
- if verbose:
- results.append({
- "source_line": record.get("line_number", ""),
- "original_text": record.get("raw_line", ""),
- "cleaned_text": "",
- "reason": "empty field",
- "suggested_fix": "",
- })
- continue
- cleaned = clean_anki_field_text(text)
- issues = check_card_for_issues(text, cleaned)
- if verbose or issues:
- if issues:
- combined_reason = "; ".join(
- sorted({i["reason"] for i in issues})
- )
- suggested = ""
- for i in issues:
- if i.get("suggested_fix"):
- suggested = i["suggested_fix"]
- break
- else:
- combined_reason = ""
- suggested = ""
- results.append({
- "source_line": record.get("line_number", ""),
- "original_text": record.get("raw_line", text),
- "cleaned_text": cleaned,
- "reason": combined_reason,
- "suggested_fix": suggested,
- })
-
- return results
-
-
-# ── Detailed extraction helpers ─────────────────────────────────────
-
-
-def extract_detailed_counts(
- texts: list[dict[str, Any]],
- nlp,
- token_filter: Callable,
- lemma_fn: Callable,
- max_examples: int = 3,
- clean: bool = True,
-) -> dict[str, dict[str, Any]]:
- """Build detailed per-lemma statistics from a list of ``{text, line_number}`` records.
-
- When *clean* is False the raw text is passed to the NLP pipeline
- without going through ``clean_anki_field_text``.
-
- Returns ::
-
- {lemma: {"count": int,
- "pos_counts": {POS: int, …},
- "surface_forms": [str, …],
- "example_sentences": [str, …],
- "source_lines": [str, …]}}
- """
- stats: dict[str, dict[str, Any]] = {}
-
- for record in texts:
- raw = record.get("text", "")
- line_number = record.get("line_number", 0)
- if not raw:
- continue
- txt = clean_anki_field_text(raw) if clean else raw.strip()
- if not txt:
- continue
-
- doc = nlp(txt)
- for token in doc:
- if not token_filter(token):
- continue
- lemma_value = lemma_fn(token)
- if lemma_value is None:
- continue
- if isinstance(lemma_value, LemmaInfo):
- lemma = lemma_value.lemma
- original_lemma = lemma_value.original_lemma
- lemma_status = lemma_value.status
- else:
- lemma = str(lemma_value)
- original_lemma = (getattr(token, "lemma_", "") or "").strip().lower()
- lemma_status = "ok"
- if not lemma:
- continue
-
- if lemma not in stats:
- stats[lemma] = {
- "count": 0,
- "pos_counts": defaultdict(int),
- "surface_forms": set(),
- "original_lemmas": set(),
- "lemma_statuses": defaultdict(int),
- "example_sentences": [],
- "source_lines": [],
- }
-
- s = stats[lemma]
- s["count"] += 1
- s["pos_counts"][token.pos_] += 1
- s["surface_forms"].add(token.text.lower())
- if original_lemma:
- s["original_lemmas"].add(original_lemma)
- s["lemma_statuses"][lemma_status] += 1
- if len(s["example_sentences"]) < max_examples:
- sent_text = txt
- if sent_text not in s["example_sentences"]:
- s["example_sentences"].append(sent_text)
- if line_number:
- s["source_lines"].append(str(line_number))
-
- # Convert defaultdicts/sets to plain types for serialisation
- for lemma, s in stats.items():
- s["pos_counts"] = dict(s["pos_counts"])
- s["surface_forms"] = sorted(s["surface_forms"])
- s["original_lemmas"] = sorted(s.get("original_lemmas", []))
- s["lemma_statuses"] = dict(s.get("lemma_statuses", {}))
- s["source_lines"] = list(dict.fromkeys(s["source_lines"])) # dedup, preserve order
-
- return stats
-
-
-# ── Output helpers ─────────────────────────────────────────────────
-
-
-def write_debug_tsv(
- stats: dict[str, dict[str, Any]],
- path: str,
- bad_lemmas: set[str] | None = None,
- min_freq: int = 0,
-) -> None:
- """Write a debug TSV with columns::
-
- lemma count pos_counts top_surface_forms example_sentences source_lines
-
- Appends a special line per lemma in *bad_lemmas* with an
- ``(EXCLUDED)`` marker. When *min_freq* > 0 only lemmas with
- count >= min_freq are included.
- """
- import os
-
- os.makedirs(os.path.dirname(os.path.abspath(path)) or ".", exist_ok=True)
-
- bad = bad_lemmas or set()
-
- with open(path, "w", encoding="utf-8") as fh:
- fh.write(
- "lemma\tcount\tpos_counts\ttop_surface_forms"
- "\texample_sentences\tsource_lines\toriginal_lemmas"
- "\tlemma_statuses\tstatus\n"
- )
- sorted_lemmas = sorted(stats.items(), key=lambda x: (-x[1]["count"], x[0]))
- for lemma, s in sorted_lemmas:
- if min_freq > 0 and s["count"] < min_freq:
- continue
- pos_counts_str = ", ".join(
- f"{pos}:{cnt}"
- for pos, cnt in sorted(
- s["pos_counts"].items(), key=lambda x: -x[1]
- )
- )
- surfaces_str = ", ".join(s["surface_forms"])
- examples_str = "; ".join(s["example_sentences"])
- lines_str = ", ".join(s["source_lines"])
- original_lemmas_str = ", ".join(s.get("original_lemmas", []))
- lemma_statuses_str = ", ".join(
- f"{status}:{cnt}"
- for status, cnt in sorted(
- s.get("lemma_statuses", {}).items(),
- key=lambda x: (-x[1], x[0]),
- )
- )
-
- status = "EXCLUDED" if lemma in bad else ""
- fh.write(
- f"{lemma}\t{s['count']}\t{pos_counts_str}\t{surfaces_str}"
- f"\t{examples_str}\t{lines_str}\t{original_lemmas_str}"
- f"\t{lemma_statuses_str}\t{status}\n"
- )
-
-
-def write_suspicious_tokens(
- stats: dict[str, dict[str, Any]],
- path: str,
- bad_lemmas: set[str] | None = None,
-) -> None:
- """Write a file of suspicious lemmas (multi-word, punctuation, etc.)
-
- Any lemma that was corrected, fell back from a suspicious spaCy lemma,
- or appears in the user blocklist is listed here.
- """
- import os
-
- os.makedirs(os.path.dirname(os.path.abspath(path)) or ".", exist_ok=True)
- bad = bad_lemmas or set()
-
- with open(path, "w", encoding="utf-8") as fh:
- fh.write("lemma\tcount\tsurface_forms\toriginal_lemmas\tnote\n")
- fh.write(
- "# Lemmas that required correction or fallback are listed here\n"
- )
- for lemma, s in sorted(stats.items(), key=lambda x: (-x[1]["count"], x[0])):
- statuses = {
- status: count
- for status, count in s.get("lemma_statuses", {}).items()
- if status != "ok"
- }
- if lemma in bad:
- statuses["blocked_bad_lemma"] = s["count"]
- if not statuses:
- continue
- surfaces_str = ", ".join(s["surface_forms"])
- originals_str = ", ".join(s.get("original_lemmas", []))
- note = ", ".join(
- f"{status}:{count}"
- for status, count in sorted(statuses.items())
- )
- fh.write(
- f"{lemma}\t{s['count']}\t{surfaces_str}"
- f"\t{originals_str}\t{note}\n"
- )
diff --git a/src/saiki/words.py b/src/saiki/words.py
index 02ac565..2e144ec 100644
--- a/src/saiki/words.py
+++ b/src/saiki/words.py
@@ -2,9 +2,9 @@
from __future__ import annotations
-import logging
+import html
import os
-from collections import Counter
+from collections import Counter, defaultdict
import regex as re
from typing import Any, Callable
@@ -12,18 +12,6 @@ from typing import Any, Callable
from .ankiconnect import anki_request
from .anki_tsv import parse_anki_tsv
from .config import Config
-from .spanish import (
- clean_anki_field_text,
- extract_detailed_counts,
- lint_anki_cards,
- load_bad_lemmas,
- load_lemma_corrections,
- safe_spanish_lemma_info,
- spanish_content_filter,
- spanish_function_word_filter,
- write_debug_tsv,
- write_suspicious_tokens,
-)
from .text import extract_first_visible_line, extract_visible_text, normalize_word_key
ACCENT_MAP = str.maketrans("áéíóúüñÁÉÍÓÚÜÑ", "aeiouunAEIOUUN")
@@ -38,19 +26,67 @@ JAPANESE_GRAMMAR_EXCLUDE = {
"て", "た", "ます", "れる", "てる", "ぬ", "ん", "しまう", "いる", "ない", "なる", "ある", "だ", "です",
}
JAPANESE_ALLOWED_POS = {"NOUN", "PROPN", "VERB", "ADJ"}
+SPANISH_ALLOWED_POS = {"NOUN", "VERB", "ADJ", "ADV"}
+FIELD_SECTION_CHOICES = {"all", "first"}
-
-def setup_logging(logfile: str) -> None:
- """Configure file logging for word extraction scripts."""
- os.makedirs(os.path.dirname(os.path.abspath(logfile)), exist_ok=True)
- logging.basicConfig(filename=logfile, level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
-
+SOUND_RE = re.compile(r"\[sound:[^\]]*\]")
+MEDIA_RE = re.compile(r"\[\s*(?:sound|image|media):[^\]]*\]", re.IGNORECASE)
+BR_RE = re.compile(r"<\s*br\s*/?\s*>", re.IGNORECASE)
+DOUBLE_BR_RE = re.compile(
+ r"<\s*br\s*/?\s*>\s*<\s*br\s*/?\s*>", re.IGNORECASE
+)
+HTML_TAG_RE = re.compile(r"<[^>]+>")
+URL_RE = re.compile(r"https?://\S+|www\.\S+", re.IGNORECASE)
+EMAIL_RE = re.compile(r"\S+@\S+\.\S+")
+MULTI_WS_RE = re.compile(r"[ \t]+")
def build_query_from_decks(decks: list[str]) -> str:
"""Build an Anki search query that matches any configured deck."""
return " OR ".join(f'deck:"{d}"' for d in decks)
+def _clean_single_field_part(text: str) -> str:
+ """Clean one segment of an Anki field before NLP."""
+ text = BR_RE.sub(" ", text)
+ text = HTML_TAG_RE.sub("", text)
+ text = html.unescape(text)
+ text = URL_RE.sub("", text)
+ text = EMAIL_RE.sub("", text)
+ return text.strip()
+
+
+def clean_anki_field_text(text: str | None, field_section: str = "all") -> str:
+ """Clean Anki field text before NLP.
+
+ Removes media markers, strips HTML/URLs/emails, normalizes spaces, and can
+ optionally keep only the first blank-line-separated field section.
+ """
+ if not text:
+ return ""
+ if field_section not in FIELD_SECTION_CHOICES:
+ raise ValueError(
+ f"field_section must be one of {sorted(FIELD_SECTION_CHOICES)}, "
+ f"got {field_section!r}"
+ )
+
+ text = SOUND_RE.sub("", text)
+ text = MEDIA_RE.sub("", text)
+
+ parts = DOUBLE_BR_RE.split(text)
+ if field_section == "first":
+ parts = parts[:1]
+
+ cleaned_parts: list[str] = []
+ for part in parts:
+ cleaned = _clean_single_field_part(part)
+ if not cleaned:
+ continue
+ cleaned_parts.append(cleaned)
+
+ result = " ".join(cleaned_parts)
+ return MULTI_WS_RE.sub(" ", result).strip()
+
+
def japanese_filter(token) -> bool:
"""Return whether a spaCy token is useful Japanese vocabulary.
@@ -74,7 +110,13 @@ def japanese_filter(token) -> bool:
def spanish_filter(token) -> bool:
"""Return whether a spaCy token is useful Spanish vocabulary."""
- return bool(getattr(token, "is_alpha", False)) and not bool(getattr(token, "is_stop", False))
+ if not bool(getattr(token, "is_alpha", False)):
+ return False
+ if bool(getattr(token, "is_stop", False)):
+ return False
+ if bool(getattr(token, "like_url", False)) or bool(getattr(token, "like_email", False)):
+ return False
+ return getattr(token, "pos_", None) in SPANISH_ALLOWED_POS
def spanish_format(token) -> str:
@@ -219,18 +261,109 @@ def extract_words(
return {"query": search_query, "notes": len(notes), "unique": len(counter), "written": written, "out": out_path}
+def extract_detailed_counts(
+ texts: list[dict[str, Any]],
+ nlp,
+ token_filter: Callable,
+ output_format: Callable,
+ max_examples: int = 3,
+ clean: bool = True,
+ field_section: str = "all",
+) -> dict[str, dict[str, Any]]:
+ """Build detailed per-entry statistics from parsed TSV records."""
+ stats: dict[str, dict[str, Any]] = {}
+
+ for record in texts:
+ raw = record.get("text", "")
+ line_number = record.get("line_number", 0)
+ if not raw:
+ continue
+ text = clean_anki_field_text(raw, field_section=field_section) if clean else raw.strip()
+ if not text:
+ continue
+
+ for token in nlp(text):
+ if not token_filter(token):
+ continue
+ key = str(output_format(token)).strip()
+ if not key:
+ continue
+
+ if key not in stats:
+ stats[key] = {
+ "count": 0,
+ "pos_counts": defaultdict(int),
+ "surface_forms": set(),
+ "original_lemmas": set(),
+ "example_sentences": [],
+ "source_lines": [],
+ }
+
+ item = stats[key]
+ item["count"] += 1
+ item["pos_counts"][getattr(token, "pos_", "") or ""] += 1
+ item["surface_forms"].add((getattr(token, "text", "") or "").lower())
+ original_lemma = (getattr(token, "lemma_", "") or "").strip().lower()
+ if original_lemma:
+ item["original_lemmas"].add(original_lemma)
+ if len(item["example_sentences"]) < max_examples and text not in item["example_sentences"]:
+ item["example_sentences"].append(text)
+ if line_number:
+ item["source_lines"].append(str(line_number))
+
+ for item in stats.values():
+ item["pos_counts"] = dict(item["pos_counts"])
+ item["surface_forms"] = sorted(item["surface_forms"])
+ item["original_lemmas"] = sorted(item["original_lemmas"])
+ item["source_lines"] = list(dict.fromkeys(item["source_lines"]))
+
+ return stats
+
+
+def write_debug_tsv(
+ stats: dict[str, dict[str, Any]],
+ path: str,
+ min_freq: int = 0,
+) -> None:
+ """Write a debug TSV for detailed extraction statistics."""
+ os.makedirs(os.path.dirname(os.path.abspath(path)) or ".", exist_ok=True)
+
+ with open(path, "w", encoding="utf-8") as fh:
+ fh.write(
+ "entry\tcount\tpos_counts\ttop_surface_forms"
+ "\texample_sentences\tsource_lines\toriginal_lemmas\n"
+ )
+ sorted_entries = sorted(stats.items(), key=lambda x: (-x[1]["count"], x[0]))
+ for entry, item in sorted_entries:
+ if min_freq > 0 and item["count"] < min_freq:
+ continue
+ pos_counts = ", ".join(
+ f"{pos}:{count}"
+ for pos, count in sorted(
+ item["pos_counts"].items(), key=lambda x: -x[1]
+ )
+ if pos
+ )
+ surfaces = ", ".join(item["surface_forms"])
+ examples = "; ".join(item["example_sentences"])
+ lines = ", ".join(item["source_lines"])
+ original_lemmas = ", ".join(item.get("original_lemmas", []))
+ fh.write(
+ f"{entry}\t{item['count']}\t{pos_counts}\t{surfaces}"
+ f"\t{examples}\t{lines}\t{original_lemmas}\n"
+ )
+
+
def _write_content_words(
stats: dict[str, dict[str, Any]],
path: str,
min_freq: int,
- bad_lemmas: set[str] | None = None,
) -> int:
- """Write a sorted ``lemma count`` file from detailed stats."""
- bad = bad_lemmas or set()
+ """Write a sorted ``entry count`` file from detailed stats."""
items = [
- (lemma, data["count"])
- for lemma, data in stats.items()
- if data["count"] >= min_freq and lemma not in bad
+ (entry, data["count"])
+ for entry, data in stats.items()
+ if data["count"] >= min_freq
]
items.sort(key=lambda x: (-x[1], x[0]))
os.makedirs(os.path.dirname(os.path.abspath(path)), exist_ok=True)
@@ -240,19 +373,6 @@ def _write_content_words(
return len(items)
-def _write_function_words(
- stats: dict[str, dict[str, Any]],
- path: str,
-) -> int:
- """Write a function-word frequency file from detailed stats."""
- os.makedirs(os.path.dirname(os.path.abspath(path)), exist_ok=True)
- items = sorted(stats.items(), key=lambda x: (-x[1]["count"], x[0]))
- with open(path, "w", encoding="utf-8") as f:
- for word, data in items:
- f.write(f"{word} {data['count']}\n")
- return len(items)
-
-
def extract_words_from_file(
config: Config,
lang: str,
@@ -261,84 +381,64 @@ def extract_words_from_file(
min_freq: int = 2,
outdir: str | None = None,
out: str | None = None,
- include_proper_nouns: bool = False,
- function_words: bool = False,
debug: str | None = None,
- lemma_corrections: str | None = None,
- bad_lemma_file: str | None = None,
spacy_model: str | None = None,
- include_tags: bool = False,
debug_min_freq: int = 0,
no_clean: bool = False,
+ field_section: str = "all",
) -> dict[str, Any]:
- """Extract frequent Spanish vocabulary from an Anki TSV export file.
+ """Extract frequent vocabulary from an Anki TSV export file.
Parameters
----------
config:
Saiki configuration.
lang:
- Language code (e.g. ``"es"``).
+ Language code (e.g. ``"es"`` or ``"jp"``).
input_path:
Path to the Anki TSV export file.
field_index:
- 1-based column index of the Spanish text (default 2).
+ 1-based column index of the text field (default 2).
min_freq:
Minimum frequency to include in output.
outdir:
Output directory (defaults to config word output root).
out:
Output filename or path for content words.
- include_proper_nouns:
- Include ``PROPN`` tokens.
- function_words:
- Also extract a separate function-word list.
debug:
Path for the debug TSV.
- lemma_corrections:
- Path to a ``bad\\tgood`` TSV of lemma corrections.
- bad_lemma_file:
- Path to a list of lemmas to skip.
spacy_model:
SpaCy model name override.
+ field_section:
+ Which blank-line-separated field section to mine when cleaning:
+ ``"all"`` or ``"first"``.
Returns
-------
- A dict with keys ``records``, ``stats``, ``written``, ``out``,
- ``debug``, ``proper_nouns``, ``function_words``, ``suspicious``.
+ A dict with keys ``records``, ``stats``, ``written``, ``out``, and
+ optional debug/supporting output paths.
"""
language_bucket = config.language_name(lang)
+ profile = LANGUAGE_PROFILES[language_bucket]
out_dir = os.path.expanduser(outdir) if outdir else os.path.join(config.word_output_root, language_bucket)
out_path = os.path.expanduser(out) if out else os.path.join(out_dir, f"words_{lang}_content.txt")
model_name = spacy_model or str(config.language(lang).get("word_model"))
nlp = load_spacy_model(model_name)
- corrections = None
- if lemma_corrections:
- corrections = load_lemma_corrections(lemma_corrections)
- bad_lemmas = None
- if bad_lemma_file:
- bad_lemmas = load_bad_lemmas(bad_lemma_file)
-
records = parse_anki_tsv(
input_path,
field_index=field_index,
- include_tags=include_tags,
)
if not records:
return {"records": 0, "stats": {}, "written": 0, "out": out_path}
- # Build token filter
- def _content_filter(token) -> bool:
- return spanish_content_filter(token, include_proper_nouns=include_proper_nouns)
-
stats = extract_detailed_counts(
- records, nlp, _content_filter,
- lambda t: safe_spanish_lemma_info(t, extra_corrections=corrections),
+ records, nlp, profile["token_filter"], profile["output_format"],
clean=not no_clean,
+ field_section=field_section,
)
- written = _write_content_words(stats, out_path, min_freq, bad_lemmas=bad_lemmas)
+ written = _write_content_words(stats, out_path, min_freq)
result: dict[str, Any] = {
"records": len(records),
@@ -350,81 +450,12 @@ def extract_words_from_file(
# Debug output
if debug:
debug_path = os.path.expanduser(debug)
- write_debug_tsv(stats, debug_path, bad_lemmas=bad_lemmas, min_freq=debug_min_freq)
+ write_debug_tsv(stats, debug_path, min_freq=debug_min_freq)
result["debug"] = debug_path
- # Proper nouns
- if include_proper_nouns:
- proper_path = os.path.join(out_dir, f"words_{lang}_proper_nouns.txt")
- proper_items = [
- (lemma, data["count"])
- for lemma, data in stats.items()
- if data["pos_counts"].get("PROPN", 0) > 0
- ]
- proper_items.sort(key=lambda x: (-x[1], x[0]))
- os.makedirs(os.path.dirname(os.path.abspath(proper_path)), exist_ok=True)
- with open(proper_path, "w", encoding="utf-8") as f:
- for w, c in proper_items:
- f.write(f"{w} {c}\n")
- result["proper_nouns"] = proper_path
-
- # Function words (separate extraction pass)
- if function_words:
- fw_path = os.path.join(out_dir, f"words_{lang}_function_words.txt")
- fw_stats = extract_detailed_counts(
- records, nlp, spanish_function_word_filter,
- lambda t: safe_spanish_lemma_info(t, extra_corrections=corrections),
- )
- fw_written = _write_function_words(fw_stats, fw_path)
- result["function_words"] = fw_path
- result["function_words_written"] = fw_written
-
- # Suspicious tokens
- susp_path = os.path.join(out_dir, f"words_{lang}_suspicious_tokens.txt")
- write_suspicious_tokens(stats, susp_path, bad_lemmas=bad_lemmas)
- result["suspicious"] = susp_path
-
return result
-def lint_anki_cards_main(
- input_path: str,
- field_index: int = 2,
- output_path: str = "suspicious_cards_es.tsv",
- verbose: bool = False,
-) -> dict[str, Any]:
- """Read an Anki TSV export and produce a suspicious-card report.
-
- When *verbose* is True every record is written to the report, not
- only those with issues.
-
- Returns ``{"records": …, "issues": …, "path": output_path}``.
- """
- records = parse_anki_tsv(input_path, field_index=field_index)
- issues = lint_anki_cards(records, verbose=verbose)
-
- os.makedirs(os.path.dirname(os.path.abspath(output_path)) or ".", exist_ok=True)
- with open(output_path, "w", encoding="utf-8") as fh:
- fh.write(
- "source_line\toriginal_text\tcleaned_text\treason\tsuggested_fix\n"
- )
- for issue in issues:
- fh.write(
- f"{issue['source_line']}\t"
- f"{_tsv_escape(issue['original_text'])}\t"
- f"{_tsv_escape(issue['cleaned_text'])}\t"
- f"{_tsv_escape(issue['reason'])}\t"
- f"{_tsv_escape(issue['suggested_fix'])}\n"
- )
-
- return {"records": len(records), "issues": len(issues), "path": output_path}
-
-
-def _tsv_escape(value: str) -> str:
- """Escape a value for TSV output (backslash-escape tabs and newlines)."""
- return value.replace("\\", "\\\\").replace("\t", "\\t").replace("\n", "\\n")
-
-
def normalize_word_for_comparison(word: str) -> str:
"""Normalise a word for file-comparison: lowercase, strip accents."""
return word.lower().translate(ACCENT_MAP)
diff --git a/tests/test_core.py b/tests/test_core.py
index 4ae5d83..00a4fc1 100644
--- a/tests/test_core.py
+++ b/tests/test_core.py
@@ -26,27 +26,18 @@ from saiki.importer import (
synthesize_tts_sample,
supported_tts_backends,
)
-from saiki.spanish import (
- BUILTIN_LEMMA_CORRECTIONS,
- check_card_for_issues,
- clean_anki_field_text,
- extract_detailed_counts,
- lint_anki_cards,
- load_lemma_corrections,
- safe_spanish_lemma,
- safe_spanish_lemma_info,
- spanish_content_filter,
- spanish_function_word_filter,
- write_debug_tsv,
- write_suspicious_tokens,
-)
from saiki.text import extract_first_visible_line, extract_visible_text
from saiki.words import (
build_query_from_decks,
+ clean_anki_field_text,
compare_word_files,
compare_word_lists,
+ extract_detailed_counts,
+ extract_words_from_file,
normalize_word_for_comparison,
read_word_file,
+ spanish_filter,
+ write_debug_tsv,
)
from saiki.youtube import TranscriptLine, extract_video_id, sentence_vocab, write_sentence_export
@@ -177,6 +168,15 @@ class AnkiTsvTests(unittest.TestCase):
self.assertEqual(records[0]["line_number"], 2)
self.assertEqual(records[1]["line_number"], 3)
+ def test_parse_tsv_preserves_hash_data_rows_after_headers(self):
+ with tempfile.TemporaryDirectory() as tmp:
+ path = os.path.join(tmp, "test.txt")
+ with open(path, "w", encoding="utf-8") as f:
+ f.write("#separator:tab\nalpha\tbeta\n#hashtag\tvalue\n")
+ records = parse_anki_tsv(path, field_index=1)
+ self.assertEqual([r["text"] for r in records], ["alpha", "#hashtag"])
+ self.assertEqual(records[1]["line_number"], 3)
+
def test_parse_tsv_include_tags(self):
with tempfile.TemporaryDirectory() as tmp:
path = os.path.join(tmp, "test.txt")
@@ -215,17 +215,19 @@ class CliValidationTests(unittest.TestCase):
self.assertEqual(code, 1)
self.assertIn("1-based", stderr.getvalue())
- def test_words_input_is_spanish_only(self):
+ def test_words_input_accepts_japanese_tsv(self):
from io import StringIO
with patch("saiki.cli.importlib.metadata.version", return_value="0"):
- with patch("sys.stderr", new_callable=StringIO) as stderr:
- code = main(["words", "jp", "--input", "deck.tsv", "--field", "2"])
- self.assertEqual(code, 1)
- self.assertIn("Spanish-only", stderr.getvalue())
+ with patch("saiki.cli.extract_words_from_file") as extract:
+ extract.return_value = {"records": 0, "written": 0, "out": "out.txt"}
+ with patch("sys.stdout", new_callable=StringIO):
+ code = main(["words", "jp", "--input", "deck.tsv", "--field", "2"])
+ self.assertEqual(code, 0)
+ extract.assert_called_once()
-class SpanishTextCleaningTests(unittest.TestCase):
+class AnkiFieldCleaningTests(unittest.TestCase):
def test_remove_sound_marker(self):
self.assertEqual(
clean_anki_field_text("[sound:es_001.mp3] Hola mundo"),
@@ -236,17 +238,23 @@ class SpanishTextCleaningTests(unittest.TestCase):
result = clean_anki_field_text("Para el lunes...")
self.assertEqual(result, "Para el lunes...")
- def test_english_after_brbr_removed(self):
- result = clean_anki_field_text("hasta luego
(see you later)")
- self.assertEqual(result, "hasta luego")
+ def test_double_br_sections_are_preserved_by_default(self):
+ result = clean_anki_field_text("Target sentence
(Translation sentence)")
+ self.assertEqual(result, "Target sentence (Translation sentence)")
- def test_english_gloss_after_brbr_removed(self):
- result = clean_anki_field_text("Te quiero.
I love you / I want you")
- self.assertEqual(result, "Te quiero.")
+ def test_first_field_section_drops_later_sections_without_phrase_rules(self):
+ result = clean_anki_field_text("Target sentence
Translation sentence")
+ self.assertEqual(result, "Target sentence Translation sentence")
- def test_spanish_after_brbr_preserved(self):
- result = clean_anki_field_text("Hola
Otra línea")
- self.assertEqual(result, "Hola Otra línea")
+ result = clean_anki_field_text(
+ "Target sentence
Translation sentence",
+ field_section="first",
+ )
+ self.assertEqual(result, "Target sentence")
+
+ def test_later_field_sections_are_preserved_by_default(self):
+ result = clean_anki_field_text("First line
Second line")
+ self.assertEqual(result, "First line Second line")
def test_normalize_whitespace(self):
result = clean_anki_field_text(" Hola mundo ")
@@ -283,230 +291,13 @@ class MockToken:
self.sent = None
-class SpanishLemmaTests(unittest.TestCase):
- def test_builtin_correction_comar(self):
- token = MockToken(text="como", lemma="comar")
- self.assertEqual(safe_spanish_lemma(token), "comer")
+class DetailedCountsTests(unittest.TestCase):
+ def test_spanish_filter_uses_content_pos(self):
+ self.assertTrue(spanish_filter(MockToken(text="casa", lemma="casa", pos="NOUN")))
+ self.assertTrue(spanish_filter(MockToken(text="rápido", lemma="rápido", pos="ADV")))
+ self.assertFalse(spanish_filter(MockToken(text="Madrid", lemma="Madrid", pos="PROPN")))
+ self.assertFalse(spanish_filter(MockToken(text="para", lemma="para", pos="ADP")))
- def test_builtin_correction_acabir(self):
- token = MockToken(text="acabas", lemma="acabir")
- self.assertEqual(safe_spanish_lemma(token), "acabar")
-
- def test_builtin_correction_delicius(self):
- token = MockToken(text="delicioso", lemma="deliciós")
- self.assertEqual(safe_spanish_lemma(token), "delicioso")
-
- def test_builtin_correction_llover_slash(self):
- token = MockToken(text="llueve", lemma="llover/llover")
- self.assertEqual(safe_spanish_lemma(token), "llover")
-
- def test_multi_word_lemma_falls_back(self):
- token = MockToken(text="ayudar", lemma="ayudar yo")
- self.assertEqual(safe_spanish_lemma(token), "ayudar")
-
- def test_multi_word_lemma_lavar_el(self):
- token = MockToken(text="lava", lemma="lavar él")
- self.assertEqual(safe_spanish_lemma(token), "lava")
-
- def test_prir_corrected_when_source_is_pedir(self):
- for form in ["pide", "pido", "piden", "pidiendo", "pedir"]:
- token = MockToken(text=form, lemma="prir")
- self.assertEqual(safe_spanish_lemma(token), "pedir")
-
- def test_prir_not_corrected_for_unknown_form(self):
- token = MockToken(text="prir", lemma="prir")
- self.assertEqual(safe_spanish_lemma(token), "prir")
-
- def test_empty_lemma_falls_back(self):
- token = MockToken(text="hola", lemma="")
- self.assertEqual(safe_spanish_lemma(token), "hola")
-
- def test_lemma_with_punctuation_falls_back(self):
- token = MockToken(text="comiendo", lemma="comiendo,")
- self.assertEqual(safe_spanish_lemma(token), "comiendo")
-
- def test_normal_lemma_passes_through(self):
- token = MockToken(text="comiendo", lemma="comer")
- self.assertEqual(safe_spanish_lemma(token), "comer")
-
- def test_lemma_info_tracks_correction_reason(self):
- token = MockToken(text="como", lemma="comar")
- info = safe_spanish_lemma_info(token)
- self.assertEqual(info.lemma, "comer")
- self.assertEqual(info.original_lemma, "comar")
- self.assertIn("corrected", info.status)
-
- def test_non_spanish_lemma_falls_back(self):
- token = MockToken(text="hola", lemma="hello_world")
- info = safe_spanish_lemma_info(token)
- self.assertEqual(info.lemma, "hola")
- self.assertEqual(info.status, "fallback_bad_lemma_characters")
-
- def test_extra_corrections_override_builtin(self):
- extra = {"comar": "comprar"}
- token = MockToken(text="como", lemma="comar")
- self.assertEqual(
- safe_spanish_lemma(token, extra_corrections=extra),
- "comprar",
- )
-
- def test_load_lemma_corrections(self):
- with tempfile.TemporaryDirectory() as tmp:
- path = os.path.join(tmp, "corrections.tsv")
- with open(path, "w", encoding="utf-8") as f:
- f.write("malo\tbueno\nfeo\tbonito\n")
- corrections = load_lemma_corrections(path)
- self.assertEqual(corrections, {"malo": "bueno", "feo": "bonito"})
-
- def test_load_lemma_corrections_skips_comments(self):
- with tempfile.TemporaryDirectory() as tmp:
- path = os.path.join(tmp, "corrections.tsv")
- with open(path, "w", encoding="utf-8") as f:
- f.write("# comment\nmalo\tbueno\n")
- corrections = load_lemma_corrections(path)
- self.assertEqual(corrections, {"malo": "bueno"})
-
-
-class SpanishFilterTests(unittest.TestCase):
- def test_content_filter_keeps_noun(self):
- token = MockToken(text="casa", lemma="casa", pos="NOUN")
- self.assertTrue(spanish_content_filter(token))
-
- def test_content_filter_keeps_verb(self):
- token = MockToken(text="come", lemma="comer", pos="VERB")
- self.assertTrue(spanish_content_filter(token))
-
- def test_content_filter_keeps_adj(self):
- token = MockToken(text="grande", lemma="grande", pos="ADJ")
- self.assertTrue(spanish_content_filter(token))
-
- def test_content_filter_keeps_adv(self):
- token = MockToken(text="bien", lemma="bien", pos="ADV")
- self.assertTrue(spanish_content_filter(token))
-
- def test_content_filter_excludes_propn_by_default(self):
- token = MockToken(text="Madrid", lemma="Madrid", pos="PROPN")
- self.assertFalse(spanish_content_filter(token))
-
- def test_content_filter_includes_propn_with_flag(self):
- token = MockToken(text="Madrid", lemma="Madrid", pos="PROPN")
- self.assertTrue(
- spanish_content_filter(token, include_proper_nouns=True)
- )
-
- def test_content_filter_excludes_punct(self):
- token = MockToken(text=".", lemma=".", pos="PUNCT", is_punct=True)
- self.assertFalse(spanish_content_filter(token))
-
- def test_content_filter_excludes_digit(self):
- token = MockToken(text="123", lemma="123", pos="NUM", is_digit=True)
- self.assertFalse(spanish_content_filter(token))
-
- def test_content_filter_excludes_url(self):
- token = MockToken(
- text="http://x.com", lemma="http://x.com", pos="X", like_url=True
- )
- self.assertFalse(spanish_content_filter(token))
-
- def test_content_filter_excludes_media_filename(self):
- token = MockToken(text="es_001.mp3", lemma="es_001.mp3", pos="NOUN")
- self.assertFalse(spanish_content_filter(token))
-
- def test_content_filter_excludes_one_char_non_vowel(self):
- token = MockToken(text="x", lemma="x", pos="NOUN")
- self.assertFalse(spanish_content_filter(token))
-
- def test_function_word_filter_keeps_preposition(self):
- token = MockToken(text="para", lemma="para", pos="ADP")
- self.assertTrue(spanish_function_word_filter(token))
-
- def test_function_word_filter_keeps_conjunction(self):
- token = MockToken(text="y", lemma="y", pos="CCONJ")
- self.assertTrue(spanish_function_word_filter(token))
-
- def test_function_word_filter_rejects_noun(self):
- token = MockToken(text="casa", lemma="casa", pos="NOUN")
- self.assertFalse(spanish_function_word_filter(token))
-
-
-class SpanishLintTests(unittest.TestCase):
- def _check(self, text: str) -> list:
- return check_card_for_issues(text, clean_anki_field_text(text))
-
- def test_detect_tu_sabes_nada(self):
- issues = self._check("Tú sabes nada")
- self.assertTrue(any("Missing negation" in i["reason"] for i in issues))
-
- def test_detect_dulce_suenos(self):
- issues = self._check("Dulce sueños")
- self.assertTrue(
- any("agree in number" in i["reason"] for i in issues)
- )
-
- def test_detect_ella_sona(self):
- issues = self._check("Ella soña con viajar.")
- self.assertTrue(
- any("present indicative" in i["reason"] for i in issues)
- )
-
- def test_detect_tengo_uno_libro(self):
- issues = self._check("Tengo uno libro en la mesa.")
- self.assertTrue(
- any("instead of 'un'" in i["reason"] for i in issues)
- )
-
- def test_detect_observando_nadan(self):
- issues = self._check(
- "La pareja está en un bote observando a los peces nadan."
- )
- self.assertTrue(
- any("'cómo'" in i["reason"] for i in issues)
- )
-
- def test_detect_le_vuelven_loca(self):
- issues = self._check("Los perros pequeños le vuelven loca.")
- self.assertTrue(
- any("direct object" in i["reason"] for i in issues)
- )
-
- def test_detect_ai_generated_tts(self):
- issues = self._check("AI-generated text-to-speech")
- self.assertTrue(
- any("Metadata contamination" in i["reason"] for i in issues)
- )
-
- def test_detect_necesito_missing_article(self):
- issues = self._check("Necesito jardinero")
- self.assertTrue(
- any("Missing article" in i["reason"] for i in issues)
- )
-
- def test_detect_le_vuelven_loco(self):
- issues = self._check("Estos problemas le vuelven loco.")
- self.assertTrue(
- any("direct object" in i["reason"] or "direct object pronoun" in i["reason"] for i in issues)
- )
-
- def test_veintiuno_anos(self):
- issues = self._check("Tengo veintiuno años de edad.")
- self.assertTrue(
- any("veintiún" in i["suggested_fix"] for i in issues)
- )
-
- def test_lint_anki_cards_multiple_records(self):
- records = [
- {"text": "Hola mundo", "line_number": 1, "raw_line": "Hola mundo"},
- {"text": "Tú sabes nada", "line_number": 2, "raw_line": "Tú sabes nada"},
- {"text": "Ella soña con viajar.", "line_number": 3, "raw_line": "Ella soña con viajar."},
- ]
- results = lint_anki_cards(records)
- self.assertEqual(len(results), 2)
- reasons = results[0]["reason"] + results[1]["reason"]
- self.assertIn("Missing negation", reasons)
- self.assertIn("present indicative", reasons)
-
-
-class SpanishDetailedCountsTests(unittest.TestCase):
def test_extract_detailed_counts_basic(self):
records = [
{"text": "Yo como manzanas.", "line_number": 1},
@@ -525,7 +316,7 @@ class SpanishDetailedCountsTests(unittest.TestCase):
if "como" in text:
return MockDoc([
MockToken(text="Yo", lemma="yo", pos="PRON"),
- MockToken(text="como", lemma="comar", pos="VERB"),
+ MockToken(text="como", lemma="comer", pos="VERB"),
MockToken(text="manzanas", lemma="manzana", pos="NOUN"),
MockToken(text=".", lemma=".", pos="PUNCT", is_punct=True),
])
@@ -540,12 +331,10 @@ class SpanishDetailedCountsTests(unittest.TestCase):
records,
MockNLP(),
lambda t: t.pos_ in {"NOUN", "VERB", "ADJ", "ADV"},
- safe_spanish_lemma,
+ lambda t: (t.lemma_ or t.text).lower(),
)
- # "comar" should be corrected to "comer"
self.assertIn("comer", stats)
- # "comer" should have count 2 (comes + como -> comer)
self.assertEqual(stats["comer"]["count"], 2)
self.assertIn("manzana", stats)
self.assertIn("pan", stats)
@@ -571,27 +360,6 @@ class SpanishDetailedCountsTests(unittest.TestCase):
self.assertIn("Yo como.", content)
self.assertIn("1, 2, 3", content)
- def test_suspicious_tokens_output(self):
- stats = {
- "comer": {
- "count": 3,
- "pos_counts": {"VERB": 3},
- "surface_forms": ["como", "comes"],
- "original_lemmas": ["comar", "comer"],
- "lemma_statuses": {"corrected:comar->comer": 1, "ok": 2},
- "example_sentences": ["Yo como."],
- "source_lines": ["1"],
- }
- }
- with tempfile.TemporaryDirectory() as tmp:
- path = os.path.join(tmp, "suspicious.tsv")
- write_suspicious_tokens(stats, path)
- with open(path, "r", encoding="utf-8") as f:
- content = f.read()
- self.assertIn("comer", content)
- self.assertIn("comar", content)
- self.assertIn("corrected:comar->comer", content)
-
def test_debug_tsv_min_freq_filters(self):
stats = {
"comer": {"count": 5, "pos_counts": {"VERB": 5},
@@ -638,29 +406,69 @@ class SpanishDetailedCountsTests(unittest.TestCase):
lambda t: t.text.lower(),
clean=True,
)
- # With clean=True, the sound marker is stripped → empty text → no stats
+ # With clean=True, the sound marker is stripped, so there is no text to count.
self.assertEqual(len(stats_clean), 0)
- def test_lint_anki_cards_verbose_includes_all(self):
- records = [
- {"text": "Hola mundo", "line_number": 1, "raw_line": "Hola mundo"},
- {"text": "Tú sabes nada", "line_number": 2, "raw_line": "Tú sabes nada"},
- ]
- # Without verbose – only suspicious
- results = lint_anki_cards(records, verbose=False)
- self.assertEqual(len(results), 1)
+ def test_extract_detailed_counts_can_use_first_field_section(self):
+ class MockDoc:
+ def __init__(self, tokens):
+ self.tokens = tokens
+ def __iter__(self):
+ return iter(self.tokens)
- # With verbose – both records
- results = lint_anki_cards(records, verbose=True)
- self.assertEqual(len(results), 2)
+ class MockNLP:
+ def __call__(self, text):
+ return MockDoc([
+ MockToken(text=part, lemma=part.lower(), pos="NOUN")
+ for part in text.split()
+ ])
- def test_lint_anki_cards_verbose_empty_field(self):
- records = [
- {"text": "", "line_number": 1, "raw_line": ""},
- ]
- results = lint_anki_cards(records, verbose=True)
- self.assertEqual(len(results), 1)
- self.assertEqual(results[0]["reason"], "empty field")
+ records = [{"text": "Target
Translation", "line_number": 1}]
+ stats = extract_detailed_counts(
+ records,
+ MockNLP(),
+ lambda t: True,
+ lambda t: t.lemma_,
+ field_section="first",
+ )
+ self.assertIn("target", stats)
+ self.assertNotIn("translation", stats)
+
+ def test_extract_words_from_file_uses_language_profile(self):
+ class MockDoc:
+ def __init__(self, tokens):
+ self.tokens = tokens
+ def __iter__(self):
+ return iter(self.tokens)
+
+ class MockNLP:
+ def __call__(self, text):
+ return MockDoc([
+ MockToken(text="猫", lemma="猫", pos="NOUN"),
+ MockToken(text="です", lemma="です", pos="AUX"),
+ ])
+
+ with tempfile.TemporaryDirectory() as tmp:
+ source = os.path.join(tmp, "deck.tsv")
+ out = os.path.join(tmp, "words_jp.txt")
+ with open(source, "w", encoding="utf-8") as f:
+ f.write("#separator:tab\n[sound:jp_001.mp3]\t猫です。\n")
+
+ with patch("saiki.words.load_spacy_model", return_value=MockNLP()):
+ result = extract_words_from_file(
+ Config(deepcopy(DEFAULT_CONFIG)),
+ "jp",
+ source,
+ out=out,
+ min_freq=1,
+ )
+
+ with open(out, "r", encoding="utf-8") as f:
+ content = f.read()
+
+ self.assertEqual(result["records"], 1)
+ self.assertIn("猫 1", content)
+ self.assertNotIn("です", content)
class CompareWordListsTests(unittest.TestCase):