feat(enrichment): alias-aware scoring (auto-confirm non-Latin-primary artists)

Builds on the loose-search fallback: that surfaces a recording stored under a
Japanese primary name (大橋純子) via its romanized alias, but the SCORER still
compared the reference ("Junko Ohashi") against the primary only → artist
similarity 0 → below the auto floor, so it could only ever be a manual
candidate, never an auto-fill.

- mb_match: `cand_artist_sim` takes the best similarity over the candidate's
  primary name AND its `artist_aliases`; score_candidate + classify use it.
- server: `_mb_artist_aliases(id)` fetches an artist's aliases (one throttled
  lookup, process-cached — a one-artist discography costs ONE request) and
  `_alias_enrich` attaches them ONLY to promising near-misses (title agrees,
  primary artist doesn't) so a normal pass spends zero extra requests. Wired
  into both the auto-matcher (_enrich_one) and the manual search proxy.

Verified live: "Junko Ohashi / Telephone Number" → 大橋純子 candidate goes from
score 0.5 (loose-only) to 1.0 (auto-confirmable), ranked #1; "AC/DC / Highway
to Hell" unchanged at 1.0 with no alias lookup.

Stacks on #771 (feat/mb-loose-search-fallback).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

lib/mb_match.py

@@ -136,12 +136,31 @@ def _duration_int(v):
         return None
 
 
+def cand_artist_sim(song: dict, cand: dict) -> float:
+    """Best artist similarity between the song's reference artist and the
+    candidate's PRIMARY name OR any of its `artist_aliases` (romanized/alternate
+    names). MusicBrainz stores many artists under a non-Latin primary name
+    (大橋純子) with the romanized form ("Junko Ohashi") only as an alias, so a
+    reference typed/derived in romaji scores 0 against the primary but 1.0
+    against the alias. The caller (server) attaches `artist_aliases` only for
+    promising near-misses, so this is a plain max when they're present and the
+    original single comparison when they're not."""
+    best = similarity(song.get("artist"), cand.get("artist"), artist=True)
+    for alias in cand.get("artist_aliases") or []:
+        if best >= 1.0:
+            break
+        s = similarity(song.get("artist"), alias, artist=True)
+        if s > best:
+            best = s
+    return best
+
+
 def score_candidate(song: dict, cand: dict) -> float:
     """Combined confidence that MusicBrainz candidate `cand` is the song the
     chart transcribes. 0.5*artist + 0.5*title, plus small year/duration
     corroboration bonuses, capped at 1.0. Missing fields score 0 on their
     half — classify() separately refuses to auto-match without both."""
-    artist_sim = similarity(song.get("artist"), cand.get("artist"), artist=True)
+    artist_sim = cand_artist_sim(song, cand)
     title_sim = similarity(song.get("title"), cand.get("title"))
     score = 0.5 * artist_sim + 0.5 * title_sim
     sy, cy = _year_int(song.get("year")), _year_int(cand.get("year"))
@@ -168,7 +187,7 @@ def classify(song: dict, cand: dict, score: float, auto_min: float | None = None
     """
     if auto_min is None:
         auto_min = AUTO_MIN
-    artist_sim = similarity(song.get("artist"), cand.get("artist"), artist=True)
+    artist_sim = cand_artist_sim(song, cand)
     title_sim = similarity(song.get("title"), cand.get("title"))
     if (score >= auto_min and artist_sim >= AUTO_ARTIST_MIN
             and title_sim >= AUTO_TITLE_MIN):