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Improve Spanish Anki vocabulary tooling

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This commit is contained in:
Pawel Sarkowicz
2026-06-13 19:26:45 -04:00
parent 0c8561d25e
commit 67594835d2
6 changed files with 2027 additions and 16 deletions
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README.md
+72 -1
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@@ -199,7 +199,78 @@ hablar 9
見る (見た) 6
```
### YouTube
### Words from TSV Export
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
```
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.
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
`<br><br>` 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)
- Tracks POS counts, surface forms, example sentences, and source line numbers
- Tracks original spaCy lemmas and correction/fallback reasons 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
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
```
### 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:
```shell
saiki compare --deck-words words_es_content.txt --target-words target_es_top_1000.txt --output missing_from_deck.txt
saiki compare --deck-words words_es_content.txt --target-words target_es_top_1000.txt --min-frequency 3
```
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.
Mine vocabulary or sentence rows from YouTube subtitles.
src/saiki/anki_tsv.py
+124
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@@ -0,0 +1,124 @@
"""Parse Anki TSV export files.
Anki exports are tab-separated files with ``#`` header lines describing
the separator, HTML mode, and tag column. This module parses those
headers and extracts structured records.
"""
from __future__ import annotations
import csv
from typing import Any
SEPARATOR_ALIASES = {
"tab": "\t",
"space": " ",
"comma": ",",
"semicolon": ";",
}
def _resolve_separator(raw: str) -> str:
"""Map Anki separator names to actual delimiter characters."""
return SEPARATOR_ALIASES.get(raw.strip().lower(), raw.strip())
def parse_anki_headers(path: str) -> dict[str, Any]:
"""Read ``#`` -prefixed metadata lines from an Anki export file.
Stops at the first non-header line. Returns a dict with optional keys
``separator``, ``html``, and ``tags_column``.
"""
info: dict[str, Any] = {}
with open(path, "r", encoding="utf-8") as fh:
for line in fh:
line = line.rstrip("\n\r")
if not line.startswith("#"):
break
if line.startswith("#separator:"):
raw = line[len("#separator:"):].strip()
info["separator"] = _resolve_separator(raw)
elif line.startswith("#html:"):
info["html"] = line[len("#html:"):].strip().lower() == "true"
elif line.startswith("#tags column:"):
info["tags_column"] = line[len("#tags column:"):].strip()
return info
def parse_anki_tsv(
path: str,
field_index: int = 2,
include_tags: bool = False,
) -> list[dict[str, Any]]:
"""Parse an Anki TSV export file into structured records.
Parameters
----------
path:
Path to the Anki TSV export file.
field_index:
1-based column index of the target text field (default 2).
include_tags:
Whether to include the tags column in output records.
Returns
-------
A list of record dicts with keys:
``text``, ``tags`` (if *include_tags* is True), ``line_number``,
and ``raw_line``.
Header lines (``#``-prefixed) are skipped. The actual separator
is detected from ``#separator:`` and defaults to tab.
"""
if field_index < 1:
raise ValueError(f"field_index must be 1-based and >= 1, got {field_index}")
header = parse_anki_headers(path)
separator = header.get("separator", "\t")
records: list[dict[str, Any]] = []
col_idx = field_index - 1 # convert to 0-based
tags_column: int | None = None
if include_tags and "tags_column" in header:
try:
tags_column = int(header["tags_column"]) - 1
except ValueError as exc:
raise ValueError(
f"Invalid #tags column value: {header['tags_column']!r}"
) from exc
if tags_column < 0:
raise ValueError(
f"#tags column must be 1-based and >= 1, got {header['tags_column']!r}"
)
with open(path, "r", encoding="utf-8") as fh:
reader = csv.reader(fh, delimiter=separator)
line_number = 0
for row in reader:
line_number += 1
# skip header rows and completely empty rows
if not row:
continue
if row and row[0].startswith("#"):
continue
if col_idx >= len(row):
raise ValueError(
f"Line {line_number}: field index {field_index} not found "
f"in row with {len(row)} column(s)"
)
text = row[col_idx].strip()
record: dict[str, Any] = {
"text": text,
"line_number": line_number,
"raw_line": separator.join(row),
}
if include_tags and tags_column is not None:
record["tags"] = (
row[tags_column].strip() if tags_column < len(row) else ""
)
records.append(record)
return records
src/saiki/cli.py
+133 -5
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@@ -15,7 +15,7 @@ from .importer import (
supported_tts_backends,
synthesize_tts_sample,
)
from .words import compare_word_files, extract_words
from .words import compare_word_files, compare_word_lists, extract_words, extract_words_from_file, lint_anki_cards_main
from .youtube import run_youtube
@@ -85,22 +85,48 @@ def build_parser(config: Config | None = None) -> argparse.ArgumentParser:
audio.add_argument("--media-dir")
audio.add_argument("--copy-only-new", action="store_true")
words = sub.add_parser("words", help="Extract frequent words from Anki.")
words.add_argument("lang", choices=choices)
words = sub.add_parser("words", help="Extract frequent words from Anki or from a TSV export file.")
words.add_argument("lang", nargs="?", choices=choices)
words.add_argument("--lang", dest="lang_option", choices=choices, help="Language code.")
words.add_argument("--input", help="Anki TSV export file (instead of AnkiConnect).")
group = words.add_mutually_exclusive_group()
group.add_argument("--query")
group.add_argument("--deck", action="append")
words.add_argument("--field")
words.add_argument("--field", help="Anki field name (AnkiConnect) or 1-based column index (TSV file).")
words.add_argument("--min-freq", type=int, default=2)
words.add_argument("--outdir")
words.add_argument("--out")
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 bad<TAB>good 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("--debug-min-freq", type=int, default=0,
help="Minimum frequency for debug output (default: all).")
words.add_argument("--no-clean", action="store_true",
help="Skip Anki field text cleaning before NLP.")
compare = sub.add_parser("compare-words", help="Print words in source that are not in known.")
compare.add_argument("source")
compare.add_argument("known")
compare_new = sub.add_parser("compare", help="Compare deck vocabulary against a target list.")
compare_new.add_argument("--deck-words", required=True, help="Path to deck word list.")
compare_new.add_argument("--target-words", required=True, help="Path to target vocabulary list.")
compare_new.add_argument("--output", help="Write missing words to this file.")
compare_new.add_argument("--seen-output", help="Write seen words to this file.")
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")
@@ -144,6 +170,19 @@ def main(argv: list[str] | None = None) -> int:
parser = build_parser(config)
args = parser.parse_args(argv)
if args.command == "words":
lang_option = getattr(args, "lang_option", None)
if args.lang and lang_option and args.lang != lang_option:
print(
f"Error: conflicting language values: {args.lang!r} and {lang_option!r}",
file=sys.stderr,
)
return 2
args.lang = lang_option or args.lang
if not args.lang:
print("Error: words requires a language, e.g. 'saiki words es' or 'saiki words --lang es'", file=sys.stderr)
return 2
if args.command == "audio":
result = extract_audio(config, args.lang, args.outdir, args.media_dir, args.copy_only_new, args.concat)
print(f"Copied {result['copied']} files")
@@ -154,6 +193,54 @@ def main(argv: list[str] | None = None) -> int:
return 0
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) "
f"when --input is used, got: {args.field!r}",
file=sys.stderr,
)
return 1
field_index = int(args.field) if args.field else 2
if field_index < 1:
print(
f"Error: --field must be a 1-based column index >= 1, got: {field_index}",
file=sys.stderr,
)
return 1
result = extract_words_from_file(
config, args.lang, args.input,
field_index=field_index,
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,
)
print(f"Parsed {result['records']} records")
print(f"Wrote {result['written']} content 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,
args.outdir, args.out, args.full_field, args.spacy_model,
@@ -169,6 +256,47 @@ def main(argv: list[str] | None = None) -> int:
print(line)
return 0
if args.command == "compare":
result = compare_word_lists(
args.deck_words,
args.target_words,
output_path=args.output,
min_frequency=args.min_frequency,
seen_output_path=args.seen_output,
)
print(f"Total missing from deck: {result['total_missing']}")
seen_count = len(result["seen"])
print(f"Total seen in deck: {seen_count}")
if result["missing"]:
print("Missing words:")
for w in result["missing"][:20]:
print(f" {w}")
if len(result["missing"]) > 20:
print(f" ... and {len(result['missing']) - 20} more")
if args.output:
print(f"Missing words written to: {args.output}")
if args.seen_output:
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,
src/saiki/spanish.py
+719
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@@ -0,0 +1,719 @@
"""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 ``<br><br>`` 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 <br> 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 ``bad<TAB>good`` 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 <br><br>
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 <br><br>"
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"
)
src/saiki/words.py
+324 -1
View File
@@ -5,14 +5,29 @@ from __future__ import annotations
import logging
import os
from collections import Counter
from typing import Callable
import regex as re
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")
JAPANESE_CHAR_RE = re.compile(r"[\p{Script=Hiragana}\p{Script=Katakana}\p{Script=Han}ー]+")
JAPANESE_PARTICLES = {
"は", "が", "を", "に", "へ", "で", "と", "や", "も", "から", "まで", "より", "ば", "なら",
@@ -202,3 +217,311 @@ def extract_words(
counter = extract_counts(notes, field_name, nlp, profile["token_filter"], profile["output_format"], full_field)
written = write_counts(counter, out_path, min_freq)
return {"query": search_query, "notes": len(notes), "unique": len(counter), "written": written, "out": out_path}
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()
items = [
(lemma, data["count"])
for lemma, data in stats.items()
if data["count"] >= min_freq and lemma not in bad
]
items.sort(key=lambda x: (-x[1], x[0]))
os.makedirs(os.path.dirname(os.path.abspath(path)), exist_ok=True)
with open(path, "w", encoding="utf-8") as f:
for word, freq in items:
f.write(f"{word} {freq}\n")
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,
input_path: str,
field_index: int = 2,
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,
) -> dict[str, Any]:
"""Extract frequent Spanish vocabulary from an Anki TSV export file.
Parameters
----------
config:
Saiki configuration.
lang:
Language code (e.g. ``"es"``).
input_path:
Path to the Anki TSV export file.
field_index:
1-based column index of the Spanish text (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.
Returns
-------
A dict with keys ``records``, ``stats``, ``written``, ``out``,
``debug``, ``proper_nouns``, ``function_words``, ``suspicious``.
"""
language_bucket = config.language_name(lang)
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),
clean=not no_clean,
)
written = _write_content_words(stats, out_path, min_freq, bad_lemmas=bad_lemmas)
result: dict[str, Any] = {
"records": len(records),
"stats": stats,
"written": written,
"out": out_path,
}
# 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)
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)
def read_word_file_normalized(path: str) -> dict[str, set[str]]:
"""Read a word-frequency file returning ``{normalised: {display_forms}}``.
Also returns a flat set of normalised forms.
"""
display_map: dict[str, set[str]] = {}
with open(os.path.expanduser(path), "r", encoding="utf-8") as fh:
for line in fh:
stripped = line.strip()
if not stripped:
continue
word = stripped.rsplit(" ", 1)[0]
key = normalize_word_for_comparison(word)
display_map.setdefault(key, set()).add(word)
return display_map
def compare_word_lists(
deck_words_path: str,
target_words_path: str,
output_path: str | None = None,
min_frequency: int = 0,
seen_output_path: str | None = None,
) -> dict[str, Any]:
"""Compare a deck word list against a target vocabulary list.
Parameters
----------
deck_words_path:
Path to the deck's extracted word list (``lemma count`` format).
target_words_path:
Path to the target vocabulary list (same format).
output_path:
If given, write missing-from-deck words here.
min_frequency:
Only consider deck words with frequency >= this value.
seen_output_path:
If given, write seen-in-deck words here.
Returns
-------
``{"missing": [str], "seen": [str], "total_missing": int}``
"""
# Read target words
target_map = read_word_file_normalized(target_words_path)
target_keys = set(target_map.keys())
# Read deck words, optionally filtering by frequency
deck_map: dict[str, set[str]] = {}
with open(os.path.expanduser(deck_words_path), "r", encoding="utf-8") as fh:
for line in fh:
stripped = line.strip()
if not stripped:
continue
parts = stripped.rsplit(" ", 1)
word = parts[0]
freq = int(parts[1]) if len(parts) > 1 and parts[1].isdigit() else 1
if freq < min_frequency:
continue
key = normalize_word_for_comparison(word)
deck_map.setdefault(key, set()).add(word)
deck_keys = set(deck_map.keys())
missing_keys = target_keys - deck_keys
seen_keys = target_keys & deck_keys
missing_entries: list[str] = []
for key in sorted(missing_keys):
for display in sorted(target_map[key]):
missing_entries.append(display)
seen_entries: list[str] = []
for key in sorted(seen_keys):
for display in sorted(target_map[key]):
seen_entries.append(display)
if output_path:
out = os.path.expanduser(output_path)
os.makedirs(os.path.dirname(os.path.abspath(out)) or ".", exist_ok=True)
with open(out, "w", encoding="utf-8") as fh:
for entry in missing_entries:
fh.write(f"{entry}\n")
if seen_output_path:
seen_out = os.path.expanduser(seen_output_path)
os.makedirs(os.path.dirname(os.path.abspath(seen_out)) or ".", exist_ok=True)
with open(seen_out, "w", encoding="utf-8") as fh:
for entry in seen_entries:
fh.write(f"{entry}\n")
return {
"missing": missing_entries,
"seen": seen_entries,
"total_missing": len(missing_entries),
}
tests/test_core.py
+647 -1
View File
@@ -12,7 +12,9 @@ SRC_DIR = Path(__file__).resolve().parents[1] / "src"
if str(SRC_DIR) not in sys.path:
sys.path.insert(0, str(SRC_DIR))
from saiki.anki_tsv import parse_anki_headers, parse_anki_tsv
from saiki.audio import build_playlist, resolve_media_paths
from saiki.cli import build_parser, main
from saiki.config import Config, DEFAULT_CONFIG, deep_merge
from saiki.importer import (
PreparedTtsBackend,
@@ -24,8 +26,28 @@ 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, compare_word_files, read_word_file
from saiki.words import (
build_query_from_decks,
compare_word_files,
compare_word_lists,
normalize_word_for_comparison,
read_word_file,
)
from saiki.youtube import TranscriptLine, extract_video_id, sentence_vocab, write_sentence_export
@@ -81,6 +103,630 @@ class WordsTests(unittest.TestCase):
self.assertEqual(compare_word_files(source, known), ["hablar 2"])
class AnkiTsvTests(unittest.TestCase):
def test_parse_headers_separator_tab(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\n#html:true\n#tags column:3\ncol1\tcol2\tcol3\n")
headers = parse_anki_headers(path)
self.assertEqual(headers["separator"], "\t")
self.assertTrue(headers["html"])
self.assertEqual(headers["tags_column"], "3")
def test_parse_headers_default_separator(self):
with tempfile.TemporaryDirectory() as tmp:
path = os.path.join(tmp, "test.txt")
with open(path, "w", encoding="utf-8") as f:
f.write("# some comment\ncol1\tcol2\n")
headers = parse_anki_headers(path)
self.assertNotIn("separator", headers)
def test_parse_tsv_skips_header_lines(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\n#html:true\nA\tB\tC\nD\tE\tF\n")
records = parse_anki_tsv(path, field_index=2)
self.assertEqual(len(records), 2)
self.assertEqual(records[0]["text"], "B")
self.assertEqual(records[1]["text"], "E")
def test_parse_tsv_field_index_1_based(self):
with tempfile.TemporaryDirectory() as tmp:
path = os.path.join(tmp, "test.txt")
with open(path, "w", encoding="utf-8") as f:
f.write("alpha\tbeta\tgamma\n")
records = parse_anki_tsv(path, field_index=1)
self.assertEqual(records[0]["text"], "alpha")
records = parse_anki_tsv(path, field_index=3)
self.assertEqual(records[0]["text"], "gamma")
def test_parse_tsv_default_field_2_ignores_sound_field(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\n#tags column:3\n")
f.write("[sound:es_001.mp3]\tYo como manzanas.\tanki-tag\n")
records = parse_anki_tsv(path)
self.assertEqual(records[0]["text"], "Yo como manzanas.")
self.assertNotIn("tags", records[0])
def test_parse_tsv_rejects_zero_field_index(self):
with tempfile.TemporaryDirectory() as tmp:
path = os.path.join(tmp, "test.txt")
with open(path, "w", encoding="utf-8") as f:
f.write("alpha\tbeta\tgamma\n")
with self.assertRaises(ValueError):
parse_anki_tsv(path, field_index=0)
def test_parse_tsv_rejects_missing_field(self):
with tempfile.TemporaryDirectory() as tmp:
path = os.path.join(tmp, "test.txt")
with open(path, "w", encoding="utf-8") as f:
f.write("alpha\tbeta\n")
with self.assertRaises(ValueError):
parse_anki_tsv(path, field_index=3)
def test_parse_tsv_line_numbers(self):
with tempfile.TemporaryDirectory() as tmp:
path = os.path.join(tmp, "test.txt")
with open(path, "w", encoding="utf-8") as f:
f.write("#header\nuno\tdos\nthree\tfour\n")
records = parse_anki_tsv(path, field_index=2)
self.assertEqual(records[0]["line_number"], 2)
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")
with open(path, "w", encoding="utf-8") as f:
f.write("#tags column:3\nx\ty\ttag_a\n")
records = parse_anki_tsv(path, field_index=2, include_tags=True)
self.assertEqual(records[0]["tags"], "tag_a")
def test_parse_tsv_without_tags(self):
with tempfile.TemporaryDirectory() as tmp:
path = os.path.join(tmp, "test.txt")
with open(path, "w", encoding="utf-8") as f:
f.write("#tags column:3\nx\ty\ttag_a\n")
records = parse_anki_tsv(path, field_index=2, include_tags=False)
self.assertNotIn("tags", records[0])
class CliValidationTests(unittest.TestCase):
"""Test CLI-level validation against the real parser/main paths."""
def test_words_accepts_lang_option_and_output_alias(self):
with patch("saiki.cli.importlib.metadata.version", return_value="0"):
parser = build_parser()
args = parser.parse_args([
"words", "--lang", "es", "--input", "deck.tsv", "--output", "out.txt"
])
self.assertEqual(args.lang_option, "es")
self.assertEqual(args.out, "out.txt")
def test_words_input_rejects_zero_field(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", "--lang", "es", "--input", "deck.tsv", "--field", "0"])
self.assertEqual(code, 1)
self.assertIn("1-based", stderr.getvalue())
def test_words_input_is_spanish_only(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())
class SpanishTextCleaningTests(unittest.TestCase):
def test_remove_sound_marker(self):
self.assertEqual(
clean_anki_field_text("[sound:es_001.mp3] Hola mundo"),
"Hola mundo",
)
def test_strip_html_and_unescape(self):
result = clean_anki_field_text("<span>Para el lunes...</span>")
self.assertEqual(result, "Para el lunes...")
def test_english_after_brbr_removed(self):
result = clean_anki_field_text("hasta luego<br><br>(see you later)")
self.assertEqual(result, "hasta luego")
def test_english_gloss_after_brbr_removed(self):
result = clean_anki_field_text("Te quiero.<br><br>I love you / I want you")
self.assertEqual(result, "Te quiero.")
def test_spanish_after_brbr_preserved(self):
result = clean_anki_field_text("Hola<br><br>Otra línea")
self.assertEqual(result, "Hola Otra línea")
def test_normalize_whitespace(self):
result = clean_anki_field_text(" Hola mundo ")
self.assertEqual(result, "Hola mundo")
def test_br_variants(self):
result = clean_anki_field_text("Line1<br/>Line2<br />Line3")
self.assertEqual(result, "Line1 Line2 Line3")
def test_preserves_accents(self):
result = clean_anki_field_text("camión corazón")
self.assertEqual(result, "camión corazón")
def test_cleaned_text_empty_input(self):
self.assertEqual(clean_anki_field_text(""), "")
self.assertEqual(clean_anki_field_text(None), "")
class MockToken:
"""Minimal mock of a spaCy token for lemmatisation tests."""
def __init__(self, text="", lemma="", pos="", **kwargs):
self.text = text
self.lemma_ = lemma
self.pos_ = pos
self.is_stop = kwargs.get("is_stop", False)
self.is_alpha = kwargs.get("is_alpha", True)
self.is_punct = kwargs.get("is_punct", False)
self.is_space = kwargs.get("is_space", False)
self.is_digit = kwargs.get("is_digit", False)
self.is_currency = kwargs.get("is_currency", False)
self.like_url = kwargs.get("like_url", False)
self.like_email = kwargs.get("like_email", False)
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")
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},
{"text": "Tú comes pan.", "line_number": 2},
]
class MockDoc:
def __init__(self, tokens):
self.tokens = tokens
def __iter__(self):
return iter(self.tokens)
class MockNLP:
def __call__(self, text):
if "como" in text:
return MockDoc([
MockToken(text="Yo", lemma="yo", pos="PRON"),
MockToken(text="como", lemma="comar", pos="VERB"),
MockToken(text="manzanas", lemma="manzana", pos="NOUN"),
MockToken(text=".", lemma=".", pos="PUNCT", is_punct=True),
])
return MockDoc([
MockToken(text="Tú", lemma="tú", pos="PRON"),
MockToken(text="comes", lemma="comer", pos="VERB"),
MockToken(text="pan", lemma="pan", pos="NOUN"),
MockToken(text=".", lemma=".", pos="PUNCT", is_punct=True),
])
stats = extract_detailed_counts(
records,
MockNLP(),
lambda t: t.pos_ in {"NOUN", "VERB", "ADJ", "ADV"},
safe_spanish_lemma,
)
# "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)
def test_debug_tsv_output(self):
stats = {
"comer": {
"count": 3,
"pos_counts": {"VERB": 3},
"surface_forms": ["como", "comes", "come"],
"example_sentences": ["Yo como.", "Tú comes."],
"source_lines": ["1", "2", "3"],
}
}
with tempfile.TemporaryDirectory() as tmp:
path = os.path.join(tmp, "debug.tsv")
write_debug_tsv(stats, path)
with open(path, "r", encoding="utf-8") as f:
content = f.read()
self.assertIn("comer", content)
self.assertIn("VERB:3", content)
self.assertIn("como, comes, come", content)
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},
"surface_forms": ["come"], "example_sentences": ["Yo come."],
"source_lines": ["1"]},
"hablar": {"count": 2, "pos_counts": {"VERB": 2},
"surface_forms": ["habla"], "example_sentences": ["Él habla."],
"source_lines": ["2"]},
}
with tempfile.TemporaryDirectory() as tmp:
path = os.path.join(tmp, "debug.tsv")
write_debug_tsv(stats, path, min_freq=3)
with open(path, "r", encoding="utf-8") as f:
content = f.read()
self.assertIn("comer", content)
self.assertNotIn("hablar", content)
def test_extract_detailed_counts_no_clean(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="[sound:x.mp3]", lemma="[sound:x.mp3]", pos="X"),
])
records = [{"text": "[sound:x.mp3]", "line_number": 1}]
stats = extract_detailed_counts(
records, MockNLP(),
lambda t: True,
lambda t: t.text.lower(),
clean=False,
)
# With clean=False, [sound:x.mp3] is NOT stripped
self.assertIn("[sound:x.mp3]", stats)
stats_clean = extract_detailed_counts(
records, MockNLP(),
lambda t: True,
lambda t: t.text.lower(),
clean=True,
)
# With clean=True, the sound marker is stripped → empty text → no stats
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)
# With verbose – both records
results = lint_anki_cards(records, verbose=True)
self.assertEqual(len(results), 2)
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")
class CompareWordListsTests(unittest.TestCase):
def test_normalize_word_for_comparison(self):
self.assertEqual(normalize_word_for_comparison("camión"), "camion")
self.assertEqual(normalize_word_for_comparison("Corazón"), "corazon")
self.assertEqual(normalize_word_for_comparison("ESPAÑOL"), "espanol")
def test_compare_word_lists_identifies_missing(self):
with tempfile.TemporaryDirectory() as tmp:
deck = os.path.join(tmp, "deck.txt")
target = os.path.join(tmp, "target.txt")
with open(deck, "w", encoding="utf-8") as f:
f.write("comer 5\nhablar 3\n")
with open(target, "w", encoding="utf-8") as f:
f.write("comer 10\nvivir 8\ncorrer 6\n")
result = compare_word_lists(deck, target)
self.assertIn("vivir", result["missing"])
self.assertIn("correr", result["missing"])
self.assertNotIn("comer", result["missing"])
def test_compare_word_lists_with_min_frequency(self):
with tempfile.TemporaryDirectory() as tmp:
deck = os.path.join(tmp, "deck.txt")
target = os.path.join(tmp, "target.txt")
with open(deck, "w", encoding="utf-8") as f:
f.write("comer 1\nhablar 5\n")
with open(target, "w", encoding="utf-8") as f:
f.write("comer 10\nhablar 10\n")
result = compare_word_lists(deck, target, min_frequency=3)
# "comer" has freq 1 < 3, so it's excluded from deck entirely
# meaning it should be "missing" from deck's perspective
self.assertIn("comer", result["missing"])
self.assertNotIn("hablar", result["missing"])
self.assertIn("hablar", result["seen"])
def test_compare_word_lists_writes_output(self):
with tempfile.TemporaryDirectory() as tmp:
deck = os.path.join(tmp, "deck.txt")
target = os.path.join(tmp, "target.txt")
out = os.path.join(tmp, "output.txt")
with open(deck, "w", encoding="utf-8") as f:
f.write("comer 5\n")
with open(target, "w", encoding="utf-8") as f:
f.write("comer 10\nvivir 8\n")
result = compare_word_lists(deck, target, output_path=out)
self.assertEqual(result["total_missing"], 1)
with open(out, "r", encoding="utf-8") as f:
self.assertIn("vivir", f.read())
def test_compare_word_lists_writes_seen_output(self):
with tempfile.TemporaryDirectory() as tmp:
deck = os.path.join(tmp, "deck.txt")
target = os.path.join(tmp, "target.txt")
seen_out = os.path.join(tmp, "seen.txt")
with open(deck, "w", encoding="utf-8") as f:
f.write("comer 5\n")
with open(target, "w", encoding="utf-8") as f:
f.write("comer 10\nvivir 8\n")
result = compare_word_lists(deck, target, seen_output_path=seen_out)
self.assertIn("comer", result["seen"])
with open(seen_out, "r", encoding="utf-8") as f:
self.assertIn("comer", f.read())
self.assertNotIn("vivir", f.read())
class YoutubeTests(unittest.TestCase):
def test_extract_video_id(self):
self.assertEqual(extract_video_id("https://youtu.be/abc123"), "abc123")