feat: evaluate fetch results with "confidence"

.vscode/settings.json

@@ -1,3 +0,0 @@
-{
-  "python-envs.defaultEnvManager": "ms-python.python:venv"
-}

lrx_cli/cache.py

@@ -4,56 +4,21 @@ Date: 2026-03-25 10:18:03
 Description: SQLite-based lyric cache with per-source storage and TTL expiration
 """
 
-import re
 import sqlite3
 import hashlib
 import time
-import unicodedata
 from typing import Optional
 from loguru import logger
 
 from .lrc import LRCData
-from .config import DURATION_TOLERANCE_MS
-from .models import TrackMeta, LyricResult, CacheStatus
-
-# Punctuation to strip for fuzzy matching (ASCII + fullwidth + CJK brackets/symbols)
-_PUNCT_RE = re.compile(
-    r"[~!@#$%^&*()_+\-=\[\]{}|;:'\",.<>?/\\`"
-    r"～！＠＃＄％＾＆＊（）＿＋－＝【】｛｝｜；：＇＂，。＜＞？／＼｀"
-    r"「」『』《》〈〉〔〕·•‥…—–]"
+from .normalize import normalize_for_match as _normalize_for_match
+from .normalize import normalize_artist as _normalize_artist
+from .config import (
+    DURATION_TOLERANCE_MS,
+    LEGACY_CONFIDENCE_SYNCED,
+    LEGACY_CONFIDENCE_UNSYNCED,
 )
-_SPACE_RE = re.compile(r"\s+")
-# feat./ft./featuring and everything after (case-insensitive, word boundary)
-_FEAT_RE = re.compile(r"\s*(?:\bfeat\.?\b|\bft\.?\b|\bfeaturing\b).*", re.IGNORECASE)
-# Multi-artist separators: /, &, ×, x (surrounded by spaces), ;, 、, vs.
-_ARTIST_SEP_RE = re.compile(r"\s*(?:[/&;×、]|\bvs\.?\b|\bx\b)\s*", re.IGNORECASE)
-
-
-def _normalize_for_match(s: str) -> str:
-    """Normalize a string for fuzzy comparison.
-
-    Lowercases, NFKC-normalizes (fullwidth → halfwidth), strips punctuation,
-    and collapses whitespace.
-    """
-    s = unicodedata.normalize("NFKC", s).lower()
-    s = _FEAT_RE.sub("", s)
-    s = _PUNCT_RE.sub(" ", s)
-    s = _SPACE_RE.sub(" ", s).strip()
-    return s
-
-
-def _normalize_artist(s: str) -> str:
-    """Normalize an artist string: split by separators, normalize each, sort.
-
-    Splits first (on /, &, ;, ×, 、, vs., x), then strips feat./ft./featuring
-    from each part individually, so 'A feat. C / B' → ['a', 'b'] not just ['a'].
-    """
-    s = unicodedata.normalize("NFKC", s).lower()
-    parts = _ARTIST_SEP_RE.split(s)
-    normed = sorted(
-        {_normalize_for_match(p) for p in parts if _FEAT_RE.sub("", p).strip()}
-    )
-    return "\0".join(normed) if normed else _normalize_for_match(s)
+from .models import TrackMeta, LyricResult, CacheStatus
 
 
 def _generate_key(track: TrackMeta, source: str) -> str:
@@ -110,10 +75,12 @@ class CacheEngine:
                     length INTEGER
                 )
             """)
-            # Migration: add length column if missing
+            # Migrations
             cols = {r[1] for r in conn.execute("PRAGMA table_info(cache)").fetchall()}
             if "length" not in cols:
                 conn.execute("ALTER TABLE cache ADD COLUMN length INTEGER")
+            if "confidence" not in cols:
+                conn.execute("ALTER TABLE cache ADD COLUMN confidence REAL")
             conn.commit()
 
     # Read
@@ -130,7 +97,7 @@ class CacheEngine:
 
         with sqlite3.connect(self.db_path) as conn:
             row = conn.execute(
-                "SELECT status, lyrics, source, expires_at, length FROM cache WHERE key = ?",
+                "SELECT status, lyrics, source, expires_at, length, confidence FROM cache WHERE key = ?",
                 (key,),
             ).fetchone()
 
@@ -138,7 +105,7 @@ class CacheEngine:
                 logger.debug(f"Cache miss: {source} / {track.display_name()}")
                 return None
 
-            status_str, lyrics, src, expires_at, cached_length = row
+            status_str, lyrics, src, expires_at, cached_length, confidence = row
 
             # Check TTL expiration
             if expires_at and expires_at < int(time.time()):
@@ -160,15 +127,27 @@ class CacheEngine:
                 f"Cache hit: {source} / {track.display_name()} "
                 f"[{status_str}, ttl={remaining}s]"
             )
+            status = CacheStatus(status_str)
+            if confidence is None and status in (
+                CacheStatus.SUCCESS_SYNCED,
+                CacheStatus.SUCCESS_UNSYNCED,
+            ):
+                confidence = (
+                    LEGACY_CONFIDENCE_SYNCED
+                    if status == CacheStatus.SUCCESS_SYNCED
+                    else LEGACY_CONFIDENCE_UNSYNCED
+                )
+
             return LyricResult(
-                status=CacheStatus(status_str),
+                status=status,
                 lyrics=LRCData(lyrics) if lyrics else None,
                 source=src,
                 ttl=remaining,
+                confidence=confidence,
             )
 
     def get_best(self, track: TrackMeta, sources: list[str]) -> Optional[LyricResult]:
-        """Return the best cached result across *sources* (synced > unsynced).
+        """Return the best cached result across *sources* by confidence.
 
         Skips negative statuses (NOT_FOUND, NETWORK_ERROR) — those are only
         consulted per-source to avoid redundant fetches.
@@ -178,9 +157,19 @@ class CacheEngine:
             cached = self.get(track, src)
             if not cached:
                 continue
-            if cached.status == CacheStatus.SUCCESS_SYNCED:
-                return cached  # Can't do better
-            if cached.status == CacheStatus.SUCCESS_UNSYNCED and best is None:
+            if cached.status not in (
+                CacheStatus.SUCCESS_SYNCED,
+                CacheStatus.SUCCESS_UNSYNCED,
+            ):
+                continue
+            if best is None:
+                best = cached
+            else:
+                cached_conf = (
+                    cached.confidence if cached.confidence is not None else 100.0
+                )
+                best_conf = best.confidence if best.confidence is not None else 100.0
+                if cached_conf > best_conf:
                     best = cached
         return best
 
@@ -207,8 +196,8 @@ class CacheEngine:
             conn.execute(
                 """INSERT OR REPLACE INTO cache
                    (key, source, status, lyrics, created_at, expires_at,
-                    artist, title, album, length)
-                   VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""",
+                    artist, title, album, length, confidence)
+                   VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""",
                 (
                     key,
                     source,
@@ -220,6 +209,7 @@ class CacheEngine:
                     track.title,
                     track.album,
                     track.length,
+                    result.confidence,
                 ),
             )
             conn.commit()
@@ -288,7 +278,7 @@ class CacheEngine:
         """Find the best positive (synced/unsynced) cache entry for *track*.
 
         Uses exact metadata match (artist + title + album) across all sources.
-        Returns synced if available, otherwise unsynced, or None.
+        Returns the highest-confidence entry, or None.
         """
         conditions, params = self._track_where(track)
         if not conditions:
@@ -306,19 +296,34 @@ class CacheEngine:
         with sqlite3.connect(self.db_path) as conn:
             conn.row_factory = sqlite3.Row
             rows = conn.execute(
-                f"SELECT status, lyrics, source FROM cache WHERE {where} "
-                "ORDER BY CASE status WHEN ? THEN 0 ELSE 1 END LIMIT 1",
-                params + [CacheStatus.SUCCESS_SYNCED.value],
+                f"SELECT status, lyrics, source, confidence FROM cache WHERE {where} "
+                "ORDER BY COALESCE(confidence, "
+                "  CASE status WHEN ? THEN ? ELSE ? END"
+                ") DESC, created_at DESC LIMIT 1",
+                params
+                + [
+                    CacheStatus.SUCCESS_SYNCED.value,
+                    LEGACY_CONFIDENCE_SYNCED,
+                    LEGACY_CONFIDENCE_UNSYNCED,
+                ],
             ).fetchall()
 
         if not rows:
             return None
 
         row = dict(rows[0])
+        confidence = row["confidence"]
+        if confidence is None:
+            confidence = (
+                LEGACY_CONFIDENCE_SYNCED
+                if row["status"] == CacheStatus.SUCCESS_SYNCED.value
+                else LEGACY_CONFIDENCE_UNSYNCED
+            )
         return LyricResult(
             status=CacheStatus(row["status"]),
             lyrics=LRCData(row["lyrics"]) if row["lyrics"] else None,
             source="cache-search",
+            confidence=confidence,
         )
 
     # Fuzzy search
@@ -384,7 +389,8 @@ class CacheEngine:
             scored.sort(
                 key=lambda x: (
                     x[0],
-                    x[1].get("status") != CacheStatus.SUCCESS_SYNCED.value,
+                    -(x[1].get("confidence") or 0),
+                    -(x[1].get("created_at") or 0),
                 )
             )
             matches = [m for _, m in scored]

lrx_cli/cli.py

@@ -428,6 +428,7 @@ def _print_cache_row(row: dict, indent: str = "") -> None:
     created = row.get("created_at", 0)
     expires = row.get("expires_at")
     lyrics = row.get("lyrics", "")
+    confidence = row.get("confidence")
 
     name = f"{artist} - {title}" if artist and title else row.get("key", "?")
     print(f"{indent}[{source}] {name}")
@@ -450,6 +451,8 @@ def _print_cache_row(row: dict, indent: str = "") -> None:
     if lyrics:
         line_count = len(lyrics.splitlines())
         print(f"{indent}  Lyrics  : {line_count} lines")
+    if confidence is not None:
+        print(f"{indent}  Confidence: {confidence:.0f}")
 
 
 def run():

lrx_cli/config.py

@@ -38,6 +38,21 @@ TTL_NETWORK_ERROR = 3600  # 1 hour
 # Search
 DURATION_TOLERANCE_MS = 3000  # max duration mismatch for search matching
 
+# Confidence scoring weights (sum to 100)
+SCORE_W_TITLE = 40.0
+SCORE_W_ARTIST = 30.0
+SCORE_W_ALBUM = 10.0
+SCORE_W_DURATION = 10.0
+SCORE_W_SYNCED = 10.0
+
+# Confidence thresholds
+MIN_CONFIDENCE = 25.0  # below this, candidate is rejected
+HIGH_CONFIDENCE = 80.0  # at or above this, stop searching early
+
+# Legacy cache rows (no confidence stored) get a base score by sync status
+LEGACY_CONFIDENCE_SYNCED = 50.0
+LEGACY_CONFIDENCE_UNSYNCED = 40.0
+
 # Spotify related
 SPOTIFY_TOKEN_URL = "https://open.spotify.com/api/token"
 SPOTIFY_LYRICS_URL = "https://spclient.wg.spotify.com/color-lyrics/v2/track/"

lrx_cli/core.py

@@ -9,7 +9,7 @@ Fetch pipeline:
   1. Check cache for each source in the fallback sequence
   2. For sources without a valid cache hit, call the fetcher
   3. Cache every result (success, not-found, or error) per source
-  4. Return the best result (synced > unsynced > None)
+  4. Return the best result by confidence (highest wins)
 """
 
 from typing import Optional
@@ -19,7 +19,13 @@ from .fetchers import FetcherMethodType, create_fetchers
 from .fetchers.base import BaseFetcher
 from .cache import CacheEngine
 from .lrc import LRCData
-from .config import TTL_SYNCED, TTL_UNSYNCED, TTL_NOT_FOUND, TTL_NETWORK_ERROR
+from .config import (
+    TTL_SYNCED,
+    TTL_UNSYNCED,
+    TTL_NOT_FOUND,
+    TTL_NETWORK_ERROR,
+    HIGH_CONFIDENCE,
+)
 from .models import TrackMeta, LyricResult, CacheStatus
 from .enrichers import enrich_track
 
@@ -33,6 +39,18 @@ _STATUS_TTL: dict[CacheStatus, Optional[int]] = {
 }
 
 
+def _is_better(new: LyricResult, old: LyricResult) -> bool:
+    """Compare two results by confidence only.
+
+    Synced/unsynced preference is already baked into the confidence score
+    (synced bonus in scoring weights), so we don't need a separate tier.
+    None confidence = trusted = 100.
+    """
+    new_conf = new.confidence if new.confidence is not None else 100.0
+    old_conf = old.confidence if old.confidence is not None else 100.0
+    return new_conf > old_conf
+
+
 class LrcManager:
     """Main entry point for fetching lyrics with caching."""
 
@@ -72,7 +90,7 @@ class LrcManager:
         - Cache miss or unsynced → call fetcher, then cache the result
 
         After all sources are tried, returns the best result found
-        (synced > unsynced > None).
+        (highest confidence wins).
         """
         track = enrich_track(track)
         logger.info(f"Fetching lyrics for: {track.display_name()}")
@@ -81,7 +99,7 @@ class LrcManager:
         if not sequence:
             return None
 
-        # Best result seen so far (synced wins over unsynced)
+        # Best result seen so far (highest confidence wins)
         best_result: Optional[LyricResult] = None
 
         for fetcher in sequence:
@@ -91,17 +109,7 @@ class LrcManager:
             if not bypass_cache and not fetcher.self_cached:
                 cached = self.cache.get(track, source)
                 if cached:
-                    if cached.status == CacheStatus.SUCCESS_SYNCED:
-                        logger.info(f"[{source}] cache hit: synced lyrics")
-                        return cached
-                    elif cached.status == CacheStatus.SUCCESS_UNSYNCED:
-                        logger.debug(
-                            f"[{source}] cache hit: unsynced lyrics (continuing)"
-                        )
-                        if best_result is None:
-                            best_result = cached
-                        continue  # Try next source for synced
-                    elif cached.status in (
+                    if cached.status in (
                         CacheStatus.NOT_FOUND,
                         CacheStatus.NETWORK_ERROR,
                     ):
@@ -109,6 +117,23 @@ class LrcManager:
                             f"[{source}] cache hit: {cached.status.value}, skipping"
                         )
                         continue
+
+                    # Positive cache hit — apply the same confidence evaluation
+                    # as fresh fetches so that low-confidence cached results
+                    # don't block better results from later fetchers.
+                    is_trusted = (
+                        cached.confidence is None
+                        or cached.confidence >= HIGH_CONFIDENCE
+                    )
+                    logger.info(
+                        f"[{source}] cache hit: {cached.status.value}"
+                        f" (confidence={'trusted' if cached.confidence is None else f'{cached.confidence:.0f}'})"
+                    )
+                    if cached.status == CacheStatus.SUCCESS_SYNCED and is_trusted:
+                        return cached
+                    if best_result is None or _is_better(cached, best_result):
+                        best_result = cached
+                    continue
             elif not fetcher.self_cached:
                 logger.debug(f"[{source}] cache bypassed")
 
@@ -126,20 +151,28 @@ class LrcManager:
                 self.cache.set(track, source, result, ttl_seconds=ttl)
 
             # Evaluate result
-            if result.status == CacheStatus.SUCCESS_SYNCED:
-                logger.info(f"[{source}] got synced lyrics")
+            if result.status in (
+                CacheStatus.SUCCESS_SYNCED,
+                CacheStatus.SUCCESS_UNSYNCED,
+            ):
+                is_trusted = (
+                    result.confidence is None or result.confidence >= HIGH_CONFIDENCE
+                )
+                logger.info(
+                    f"[{source}] got {result.status.value} lyrics"
+                    f" (confidence={'trusted' if result.confidence is None else f'{result.confidence:.0f}'})"
+                )
+                # Trusted synced → return immediately
+                if result.status == CacheStatus.SUCCESS_SYNCED and is_trusted:
                     return result
-
-            if result.status == CacheStatus.SUCCESS_UNSYNCED:
-                logger.debug(f"[{source}] got unsynced lyrics (continuing)")
-                if best_result is None:
+                # Track best result by confidence
+                if best_result is None or _is_better(result, best_result):
                     best_result = result
 
             # NOT_FOUND / NETWORK_ERROR: already cached, try next
 
         # Return best available
         if best_result:
-            # Normalize unsynced lyrics: set all timestamps to [00:00.00]
             if (
                 best_result.status == CacheStatus.SUCCESS_UNSYNCED
                 and best_result.lyrics
@@ -149,10 +182,10 @@ class LrcManager:
                     lyrics=best_result.lyrics.normalize_unsynced(),
                     source=best_result.source,
                     ttl=best_result.ttl,
+                    confidence=best_result.confidence,
                 )
             logger.info(
-                f"Returning unsynced lyrics from {best_result.source} "
-                f"(no synced source found)"
+                f"Returning {best_result.status.value} lyrics from {best_result.source}"
             )
         else:
             logger.info(f"No lyrics found for {track.display_name()}")

lrx_cli/fetchers/cache_search.py

@@ -64,16 +64,26 @@ class CacheSearchFetcher(BaseFetcher):
             logger.debug(f"Cache-search: no match for {track.display_name()}")
             return None
 
-        # Pick best: prefer synced, then first available
+        # Pick best by confidence scoring
         candidates = [
             SearchCandidate(
                 item=m,
+                duration_ms=float(m["length"]) if m.get("length") else None,
                 is_synced=m.get("status") == CacheStatus.SUCCESS_SYNCED.value,
+                title=m.get("title"),
+                artist=m.get("artist"),
+                album=m.get("album"),
             )
             for m in matches
             if m.get("lyrics")
         ]
-        best = select_best(candidates, track.length)
+        best, confidence = select_best(
+            candidates,
+            track.length,
+            title=track.title,
+            artist=track.artist,
+            album=track.album,
+        )
 
         if not best:
             return None
@@ -81,10 +91,11 @@ class CacheSearchFetcher(BaseFetcher):
         status = CacheStatus(best["status"])
         logger.info(
             f"Cache-search: fuzzy hit from [{best.get('source')}] "
-            f"album={best.get('album')!r} ({status.value})"
+            f"album={best.get('album')!r} ({status.value}, confidence={confidence:.0f})"
         )
         return LyricResult(
             status=status,
             lyrics=LRCData(best["lyrics"]),
             source=self.source_name,
+            confidence=confidence,
         )

lrx_cli/fetchers/lrclib_search.py

@@ -126,10 +126,19 @@ class LrclibSearchFetcher(BaseFetcher):
                     else None,
                     is_synced=isinstance(item.get("syncedLyrics"), str)
                     and bool(item["syncedLyrics"].strip()),
+                    title=item.get("trackName"),
+                    artist=item.get("artistName"),
+                    album=item.get("albumName"),
                 )
                 for item in candidates
             ]
-            best = select_best(mapped, track.length)
+            best, confidence = select_best(
+                mapped,
+                track.length,
+                title=track.title,
+                artist=track.artist,
+                album=track.album,
+            )
             if best is None:
                 logger.debug("LRCLIB-search: no valid candidate found")
                 return LyricResult(status=CacheStatus.NOT_FOUND, ttl=TTL_NOT_FOUND)
@@ -139,20 +148,26 @@ class LrclibSearchFetcher(BaseFetcher):
 
             if isinstance(synced, str) and synced.strip():
                 lyrics = LRCData(synced)
-                logger.info(f"LRCLIB-search: got synced lyrics ({len(lyrics)} lines)")
+                logger.info(
+                    f"LRCLIB-search: got synced lyrics ({len(lyrics)} lines, confidence={confidence:.0f})"
+                )
                 return LyricResult(
                     status=CacheStatus.SUCCESS_SYNCED,
                     lyrics=lyrics,
                     source=self.source_name,
+                    confidence=confidence,
                 )
             elif isinstance(unsynced, str) and unsynced.strip():
                 lyrics = LRCData(unsynced)
-                logger.info(f"LRCLIB-search: got unsynced lyrics ({len(lyrics)} lines)")
+                logger.info(
+                    f"LRCLIB-search: got unsynced lyrics ({len(lyrics)} lines, confidence={confidence:.0f})"
+                )
                 return LyricResult(
                     status=CacheStatus.SUCCESS_UNSYNCED,
                     lyrics=lyrics,
                     source=self.source_name,
                     ttl=TTL_UNSYNCED,
+                    confidence=confidence,
                 )
             else:
                 logger.debug("LRCLIB-search: best candidate has empty lyrics")

lrx_cli/fetchers/netease.py

@@ -43,15 +43,15 @@ class NeteaseFetcher(BaseFetcher):
     def is_available(self, track: TrackMeta) -> bool:
         return bool(track.title)
 
-    def _search(self, track: TrackMeta, limit: int = 10) -> Optional[int]:
-        """Search Netease and return the best-matching song ID.
+    def _search(self, track: TrackMeta, limit: int = 10) -> tuple[Optional[int], float]:
+        """Search Netease and return the best-matching song ID with confidence.
 
         When ``track.length`` is available, candidates are ranked by duration
         difference and only accepted if within ``DURATION_TOLERANCE_MS``.
         """
         query = f"{track.artist or ''} {track.title or ''}".strip()
         if not query:
-            return None
+            return None, 0.0
 
         logger.debug(f"Netease: searching for '{query}' (limit={limit})")
 
@@ -70,17 +70,17 @@ class NeteaseFetcher(BaseFetcher):
                 logger.error(
                     f"Netease: search returned non-dict: {type(result).__name__}"
                 )
-                return None
+                return None, 0.0
 
             result_body = result.get("result")
             if not isinstance(result_body, dict):
                 logger.debug("Netease: search 'result' field missing or invalid")
-                return None
+                return None, 0.0
 
             songs = result_body.get("songs")
             if not isinstance(songs, list) or len(songs) == 0:
                 logger.debug("Netease: search returned 0 results")
-                return None
+                return None, 0.0
 
             logger.debug(f"Netease: search returned {len(songs)} candidates")
 
@@ -90,23 +90,37 @@ class NeteaseFetcher(BaseFetcher):
                     duration_ms=float(song["dt"])
                     if isinstance(song.get("dt"), int)
                     else None,
+                    title=song.get("name"),
+                    artist=", ".join(a.get("name", "") for a in song.get("ar", []))
+                    or None,
+                    album=(song.get("al") or {}).get("name"),
                 )
                 for song in songs
                 if isinstance(song, dict) and song.get("id") is not None
             ]
-            best_id = select_best(candidates, track.length)
+            best_id, confidence = select_best(
+                candidates,
+                track.length,
+                title=track.title,
+                artist=track.artist,
+                album=track.album,
+            )
             if best_id is not None:
-                logger.debug(f"Netease: selected id={best_id}")
-                return best_id
+                logger.debug(
+                    f"Netease: selected id={best_id} (confidence={confidence:.0f})"
+                )
+                return best_id, confidence
 
             logger.debug("Netease: no suitable candidate found")
-            return None
+            return None, 0.0
 
         except Exception as e:
             logger.error(f"Netease: search failed: {e}")
-            return None
+            return None, 0.0
 
-    def _get_lyric(self, song_id: int) -> Optional[LyricResult]:
+    def _get_lyric(
+        self, song_id: int, confidence: float = 0.0
+    ) -> Optional[LyricResult]:
         """Fetch lyrics for a given Netease song ID."""
         logger.debug(f"Netease: fetching lyrics for song_id={song_id}")
 
@@ -158,7 +172,12 @@ class NeteaseFetcher(BaseFetcher):
                 f"Netease: got {status.value} lyrics for song_id={song_id} "
                 f"({len(lrcdata)} lines)"
             )
-            return LyricResult(status=status, lyrics=lrcdata, source=self.source_name)
+            return LyricResult(
+                status=status,
+                lyrics=lrcdata,
+                source=self.source_name,
+                confidence=confidence,
+            )
 
         except Exception as e:
             logger.error(f"Netease: lyric fetch failed for song_id={song_id}: {e}")
@@ -174,9 +193,9 @@ class NeteaseFetcher(BaseFetcher):
             return None
 
         logger.info(f"Netease: fetching lyrics for {track.display_name()}")
-        song_id = self._search(track)
+        song_id, confidence = self._search(track)
         if not song_id:
             logger.debug(f"Netease: no match found for {track.display_name()}")
             return LyricResult(status=CacheStatus.NOT_FOUND, ttl=TTL_NOT_FOUND)
 
-        return self._get_lyric(song_id)
+        return self._get_lyric(song_id, confidence=confidence)

lrx_cli/fetchers/qqmusic.py

@@ -35,11 +35,11 @@ class QQMusicFetcher(BaseFetcher):
     def is_available(self, track: TrackMeta) -> bool:
         return bool(track.title) and bool(QQ_MUSIC_API_URL)
 
-    def _search(self, track: TrackMeta, limit: int = 10) -> Optional[str]:
-        """Search QQ Music and return the best-matching song MID."""
+    def _search(self, track: TrackMeta, limit: int = 10) -> tuple[Optional[str], float]:
+        """Search QQ Music and return the best-matching song MID with confidence."""
         query = f"{track.artist or ''} {track.title or ''}".strip()
         if not query:
-            return None
+            return None, 0.0
 
         logger.debug(f"QQMusic: searching for '{query}' (limit={limit})")
 
@@ -54,12 +54,12 @@ class QQMusicFetcher(BaseFetcher):
 
             if data.get("code") != 0:
                 logger.error(f"QQMusic: search API error: {data}")
-                return None
+                return None, 0.0
 
             songs = data.get("data", {}).get("list", [])
             if not songs:
                 logger.debug("QQMusic: search returned 0 results")
-                return None
+                return None, 0.0
 
             logger.debug(f"QQMusic: search returned {len(songs)} candidates")
 
@@ -69,23 +69,35 @@ class QQMusicFetcher(BaseFetcher):
                     duration_ms=float(song["interval"]) * 1000
                     if isinstance(song.get("interval"), int)
                     else None,
+                    title=song.get("name"),
+                    artist=", ".join(s.get("name", "") for s in song.get("singer", []))
+                    or None,
+                    album=(song.get("album") or {}).get("name"),
                 )
                 for song in songs
                 if isinstance(song, dict) and song.get("mid") is not None
             ]
-            best_mid = select_best(candidates, track.length)
+            best_mid, confidence = select_best(
+                candidates,
+                track.length,
+                title=track.title,
+                artist=track.artist,
+                album=track.album,
+            )
             if best_mid is not None:
-                logger.debug(f"QQMusic: selected mid={best_mid}")
-                return best_mid
+                logger.debug(
+                    f"QQMusic: selected mid={best_mid} (confidence={confidence:.0f})"
+                )
+                return best_mid, confidence
 
             logger.debug("QQMusic: no suitable candidate found")
-            return None
+            return None, 0.0
 
         except Exception as e:
             logger.error(f"QQMusic: search failed: {e}")
-            return None
+            return None, 0.0
 
-    def _get_lyric(self, mid: str) -> Optional[LyricResult]:
+    def _get_lyric(self, mid: str, confidence: float = 0.0) -> Optional[LyricResult]:
         """Fetch lyrics for a given QQ Music song MID."""
         logger.debug(f"QQMusic: fetching lyrics for mid={mid}")
 
@@ -115,7 +127,12 @@ class QQMusicFetcher(BaseFetcher):
                 f"QQMusic: got {status.value} lyrics for mid={mid} "
                 f"({len(lrcdata)} lines)"
             )
-            return LyricResult(status=status, lyrics=lrcdata, source=self.source_name)
+            return LyricResult(
+                status=status,
+                lyrics=lrcdata,
+                source=self.source_name,
+                confidence=confidence,
+            )
 
         except Exception as e:
             logger.error(f"QQMusic: lyric fetch failed for mid={mid}: {e}")
@@ -135,9 +152,9 @@ class QQMusicFetcher(BaseFetcher):
             return None
 
         logger.info(f"QQMusic: fetching lyrics for {track.display_name()}")
-        mid = self._search(track)
+        mid, confidence = self._search(track)
         if not mid:
             logger.debug(f"QQMusic: no match found for {track.display_name()}")
             return LyricResult(status=CacheStatus.NOT_FOUND, ttl=TTL_NOT_FOUND)
 
-        return self._get_lyric(mid)
+        return self._get_lyric(mid, confidence=confidence)

lrx_cli/fetchers/selection.py

@@ -2,13 +2,23 @@
 Shared candidate-selection logic for search-based fetchers.
 
 Each fetcher maps its API-specific results to SearchCandidate, then calls
-select_best() which handles duration filtering and synced preference uniformly.
+select_best() which scores candidates by metadata similarity, duration
+proximity, and sync status.
 """
 
 from dataclasses import dataclass
 from typing import Generic, Optional, TypeVar
 
-from ..config import DURATION_TOLERANCE_MS
+from ..config import (
+    DURATION_TOLERANCE_MS,
+    SCORE_W_TITLE as _W_TITLE,
+    SCORE_W_ARTIST as _W_ARTIST,
+    SCORE_W_ALBUM as _W_ALBUM,
+    SCORE_W_DURATION as _W_DURATION,
+    SCORE_W_SYNCED as _W_SYNCED,
+    MIN_CONFIDENCE,
+)
+from ..normalize import normalize_for_match, normalize_artist
 
 T = TypeVar("T")
 
@@ -21,48 +31,138 @@ class SearchCandidate(Generic[T]):
         item: The original API-specific object (dict, ID, etc.)
         duration_ms: Track duration in milliseconds, or None if unknown.
         is_synced: Whether this candidate is known to have synced lyrics.
+        title: Candidate track title for similarity scoring.
+        artist: Candidate artist name for similarity scoring.
+        album: Candidate album name for similarity scoring.
     """
 
     item: T
     duration_ms: Optional[float] = None
     is_synced: bool = False
+    title: Optional[str] = None
+    artist: Optional[str] = None
+    album: Optional[str] = None
+
+
+def _text_similarity(a: str, b: str) -> float:
+    """Compare two normalized strings. Returns 0.0-1.0."""
+    if a == b:
+        return 1.0
+    if not a or not b:
+        return 0.0
+    # Containment: one is a substring of the other (e.g. "My Love" vs "My Love (Album Version)")
+    if a in b or b in a:
+        return min(len(a), len(b)) / max(len(a), len(b))
+    return 0.0
+
+
+def _score_candidate(
+    c: SearchCandidate[T],
+    ref_title: Optional[str],
+    ref_artist: Optional[str],
+    ref_album: Optional[str],
+    ref_length_ms: Optional[int],
+) -> float:
+    """Score a candidate from 0-100 based on metadata match quality.
+
+    Scoring works in two tiers:
+
+    1. **Metadata score** — computed from fields available on *both* sides,
+       then rescaled to fill the 0-90 range so that missing fields don't
+       inflate the score.  Fields missing on both sides are simply excluded
+       from the calculation (neutral).  Fields present on only one side
+       contribute 0 to the numerator but their weight still counts in the
+       denominator (penalty for asymmetric absence).
+
+    2. **Synced bonus** — a flat 10 pts, always applied independently.
+
+    Field weights (before rescaling):
+      - Title:    40
+      - Artist:   30
+      - Album:    10
+      - Duration: 10
+    """
+    raw = 0.0
+    available_weight = 0.0
+
+    # Title
+    if ref_title is not None or c.title is not None:
+        available_weight += _W_TITLE
+        if ref_title is not None and c.title is not None:
+            raw += _W_TITLE * _text_similarity(
+                normalize_for_match(ref_title), normalize_for_match(c.title)
+            )
+    # else both None → excluded
+
+    # Artist
+    if ref_artist is not None or c.artist is not None:
+        available_weight += _W_ARTIST
+        if ref_artist is not None and c.artist is not None:
+            na = normalize_artist(ref_artist)
+            nb = normalize_artist(c.artist)
+            if na == nb:
+                raw += _W_ARTIST
+            else:
+                raw += _W_ARTIST * _text_similarity(
+                    normalize_for_match(ref_artist), normalize_for_match(c.artist)
+                )
+
+    # Album
+    if ref_album is not None or c.album is not None:
+        available_weight += _W_ALBUM
+        if ref_album is not None and c.album is not None:
+            raw += _W_ALBUM * _text_similarity(
+                normalize_for_match(ref_album), normalize_for_match(c.album)
+            )
+
+    # Duration
+    if ref_length_ms is not None or c.duration_ms is not None:
+        available_weight += _W_DURATION
+        if ref_length_ms is not None and c.duration_ms is not None:
+            diff = abs(c.duration_ms - ref_length_ms)
+            if diff <= DURATION_TOLERANCE_MS:
+                raw += _W_DURATION * (1.0 - diff / DURATION_TOLERANCE_MS)
+
+    # Rescale metadata to 0-90 range
+    _MAX_METADATA = _W_TITLE + _W_ARTIST + _W_ALBUM + _W_DURATION  # 90
+    if available_weight > 0:
+        metadata_score = (raw / available_weight) * _MAX_METADATA
+    else:
+        # No comparable fields at all — only synced bonus matters
+        metadata_score = 0.0
+
+    # Synced bonus (always 10 pts, independent of metadata)
+    synced_score = _W_SYNCED if c.is_synced else 0.0
+
+    return metadata_score + synced_score
 
 
 def select_best(
     candidates: list[SearchCandidate[T]],
     track_length_ms: Optional[int] = None,
-    tolerance_ms: float = DURATION_TOLERANCE_MS,
-) -> Optional[T]:
-    """Pick the best candidate by duration proximity and sync preference.
+    *,
+    title: Optional[str] = None,
+    artist: Optional[str] = None,
+    album: Optional[str] = None,
+    min_confidence: float = MIN_CONFIDENCE,
+) -> tuple[Optional[T], float]:
+    """Pick the best candidate by confidence scoring.
 
-    When track_length_ms is available:
-      - Filter by tolerance_ms
-      - Pick closest duration, prefer synced at equal distance
-    When track_length_ms is unavailable:
-      - Pick first synced candidate, or first overall
+    Returns (item, score). Item is None if no candidate scores above min_confidence.
     """
-    if track_length_ms is not None:
-        best: Optional[SearchCandidate[T]] = None
-        best_diff = float("inf")
+    if not candidates:
+        return None, 0.0
+
+    best_item: Optional[T] = None
+    best_score = -1.0
 
     for c in candidates:
-            if c.duration_ms is None:
-                continue
-            diff = abs(c.duration_ms - track_length_ms)
-            if diff > tolerance_ms:
-                continue
-            if diff < best_diff or (
-                diff == best_diff
-                and c.is_synced
-                and (best is None or not best.is_synced)
-            ):
-                best_diff = diff
-                best = c
+        s = _score_candidate(c, title, artist, album, track_length_ms)
+        if s > best_score:
+            best_score = s
+            best_item = c.item
 
-        return best.item if best is not None else None
+    if best_score < min_confidence:
+        return None, best_score
 
-    # No duration — prefer synced, fallback to first
-    for c in candidates:
-        if c.is_synced:
-            return c.item
-    return candidates[0].item if candidates else None
+    return best_item, best_score

lrx_cli/models.py

@@ -62,3 +62,6 @@ class LyricResult:
     lyrics: Optional[LRCData] = None
     source: Optional[str] = None  # Which fetcher produced this result
     ttl: Optional[int] = None  # Hint for cache TTL (seconds)
+    confidence: Optional[float] = (
+        None  # 0-100 selection confidence (None = exact/trusted)
+    )

lrx_cli/normalize.py

@@ -0,0 +1,47 @@
+"""
+Shared text normalization utilities for fuzzy matching.
+
+Used by cache key generation, cache search, and candidate selection scoring.
+"""
+
+import re
+import unicodedata
+
+# Punctuation to strip for fuzzy matching (ASCII + fullwidth + CJK brackets/symbols)
+_PUNCT_RE = re.compile(
+    r"[~!@#$%^&*()_+\-=\[\]{}|;:'\",.<>?/\\`"
+    r"～！＠＃＄％＾＆＊（）＿＋－＝【】｛｝｜；：＇＂，。＜＞？／＼｀"
+    r"「」『』《》〈〉〔〕·•‥…—–]"
+)
+_SPACE_RE = re.compile(r"\s+")
+# feat./ft./featuring and everything after (case-insensitive, word boundary)
+_FEAT_RE = re.compile(r"\s*(?:\bfeat\.?\b|\bft\.?\b|\bfeaturing\b).*", re.IGNORECASE)
+# Multi-artist separators: /, &, ×, x (surrounded by spaces), ;, 、, vs.
+_ARTIST_SEP_RE = re.compile(r"\s*(?:[/&;×、]|\bvs\.?\b|\bx\b)\s*", re.IGNORECASE)
+
+
+def normalize_for_match(s: str) -> str:
+    """Normalize a string for fuzzy comparison.
+
+    Lowercases, NFKC-normalizes (fullwidth → halfwidth), strips punctuation,
+    and collapses whitespace.
+    """
+    s = unicodedata.normalize("NFKC", s).lower()
+    s = _FEAT_RE.sub("", s)
+    s = _PUNCT_RE.sub(" ", s)
+    s = _SPACE_RE.sub(" ", s).strip()
+    return s
+
+
+def normalize_artist(s: str) -> str:
+    """Normalize an artist string: split by separators, normalize each, sort.
+
+    Splits first (on /, &, ;, ×, 、, vs., x), then strips feat./ft./featuring
+    from each part individually, so 'A feat. C / B' → ['a', 'b'] not just ['a'].
+    """
+    s = unicodedata.normalize("NFKC", s).lower()
+    parts = _ARTIST_SEP_RE.split(s)
+    normed = sorted(
+        {normalize_for_match(p) for p in parts if _FEAT_RE.sub("", p).strip()}
+    )
+    return "\0".join(normed) if normed else normalize_for_match(s)

tests/test_cache.py

@@ -8,11 +8,10 @@ import pytest
 from lrx_cli.cache import (
     CacheEngine,
     _generate_key,
-    _normalize_artist,
-    _normalize_for_match,
 )
 from lrx_cli.config import DURATION_TOLERANCE_MS
 from lrx_cli.models import CacheStatus, LyricResult, TrackMeta
+from lrx_cli.lrc import LRCData
 
 
 def _track(
@@ -39,7 +38,7 @@ def _result(
     lyrics: str | None,
     source: str,
 ) -> LyricResult:
-    return LyricResult(status=status, lyrics=lyrics, source=source)
+    return LyricResult(status=status, lyrics=LRCData(lyrics), source=source)
 
 
 @pytest.fixture
@@ -48,22 +47,6 @@ def cache_db(tmp_path: Path) -> CacheEngine:
     return CacheEngine(str(db_path))
 
 
-def test_normalize_for_match_covers_nfkc_punct_feat_and_whitespace() -> None:
-    text = "  Ｔｅｓｔ！  feat. SOMEONE  "
-
-    normalized = _normalize_for_match(text)
-
-    assert normalized == "test"
-
-
-def test_normalize_artist_splits_separators_and_sorts_parts() -> None:
-    artist = "B / A feat. C; D vs. E × F 、 G"
-
-    normalized = _normalize_artist(artist)
-
-    assert normalized == "a\0b\0d\0e\0f\0g"
-
-
 def test_generate_key_uses_spotify_trackid_and_url_fallback() -> None:
     spotify_track = _track(
         trackid="abc123", artist=None, title=None, album=None, length=None
@@ -157,7 +140,7 @@ def test_get_backfills_missing_length_when_track_provides_it(
     assert row[0] == 200000
 
 
-def test_get_best_prefers_synced_over_unsynced_and_negative(
+def test_get_best_prefers_higher_confidence_and_skips_negative(
     cache_db: CacheEngine,
 ) -> None:
     track = _track()
@@ -314,7 +297,7 @@ def test_search_by_meta_fuzzy_rules_and_duration_sorting(cache_db: CacheEngine)
     sources = [r["source"] for r in rows]
     assert "negative" not in sources
     assert "far-len" not in sources
-    # Sorted by duration diff, then synced before unsynced for equal diff.
+    # Sorted by duration diff, then confidence for equal diff.
     assert sources[0] == "seed"
     assert sources[1] == "close-synced"
     assert sources[2] == "close-unsynced"

tests/test_normalize.py

@@ -0,0 +1,19 @@
+from __future__ import annotations
+
+from lrx_cli.normalize import normalize_for_match, normalize_artist
+
+
+def test_normalize_for_match_covers_nfkc_punct_feat_and_whitespace() -> None:
+    text = "  Ｔｅｓｔ！  feat. SOMEONE  "
+
+    normalized = normalize_for_match(text)
+
+    assert normalized == "test"
+
+
+def test_normalize_artist_splits_separators_and_sorts_parts() -> None:
+    artist = "B / A feat. C; D vs. E × F 、 G"
+
+    normalized = normalize_artist(artist)
+
+    assert normalized == "a\0b\0d\0e\0f\0g"

tests/test_selection.py

@@ -1,92 +1,422 @@
 from __future__ import annotations
 
-from lrx_cli.fetchers.selection import SearchCandidate, select_best
+from lrx_cli.fetchers.selection import (
+    SearchCandidate,
+    select_best,
+    _score_candidate,
+    _text_similarity,
+    MIN_CONFIDENCE,
+)
 
 
-def test_picks_closest_duration_within_tolerance() -> None:
-    candidates = [
-        SearchCandidate(item="far", duration_ms=10000.0),
-        SearchCandidate(item="close", duration_ms=5100.0),
-        SearchCandidate(item="exact", duration_ms=5000.0),
+def test_text_similarity_exact() -> None:
+    assert _text_similarity("my love", "my love") == 1.0
+
+
+def test_text_similarity_empty() -> None:
+    assert _text_similarity("", "anything") == 0.0
+    assert _text_similarity("anything", "") == 0.0
+
+
+def test_text_similarity_no_overlap() -> None:
+    assert _text_similarity("hello", "world") == 0.0
+
+
+def test_text_similarity_containment() -> None:
+    # "my love" is contained in "my love album version"
+    score = _text_similarity("my love", "my love album version")
+    assert 0.0 < score < 1.0
+    assert score == len("my love") / len("my love album version")
+
+
+def test_score_perfect_match() -> None:
+    """Exact metadata + close duration + synced = 100."""
+    c = SearchCandidate(
+        item="x",
+        duration_ms=232000.0,
+        is_synced=True,
+        title="My Love",
+        artist="Westlife",
+        album="Coast To Coast",
+    )
+    score = _score_candidate(c, "My Love", "Westlife", "Coast To Coast", 232000)
+    assert score == 100.0
+
+
+def test_score_no_metadata_match() -> None:
+    """Completely wrong metadata should score very low."""
+    c = SearchCandidate(
+        item="x",
+        duration_ms=192000.0,
+        is_synced=True,
+        title="Let My Love Be Your Pillow (Live)",
+        artist="Ronnie Milsap",
+        album="The Essential Ronnie Milsap",
+    )
+    score = _score_candidate(c, "My Love", "Westlife", "Coast To Coast", 232000)
+    assert score < MIN_CONFIDENCE
+
+
+def test_score_missing_both_sides_neutral() -> None:
+    """If neither ref nor candidate has any field, only synced bonus applies."""
+    c = SearchCandidate(item="x", is_synced=True)
+    score = _score_candidate(c, None, None, None, None)
+    # No comparable fields → metadata = 0, synced = 10
+    assert score == 10.0
+
+
+def test_score_missing_one_side_gives_zero_for_field() -> None:
+    """If ref has title but candidate doesn't, title gets 0 and weight still counts."""
+    c = SearchCandidate(item="x", title=None, is_synced=True)
+    # Only title is in play (weight=40), candidate missing → raw=0, rescaled=0, + synced=10
+    score = _score_candidate(c, "My Love", None, None, None)
+    assert score == 10.0
+
+
+def test_score_synced_bonus() -> None:
+    """Synced adds 10 points."""
+    base = SearchCandidate(item="x", title="My Love", is_synced=False)
+    synced = SearchCandidate(item="x", title="My Love", is_synced=True)
+    diff = _score_candidate(synced, "My Love", None, None, None) - _score_candidate(
+        base, "My Love", None, None, None
+    )
+    assert diff == 10.0
+
+
+def test_score_duration_linear_decay() -> None:
+    """Duration score decays linearly; ratios between exact/half/edge are preserved."""
+    exact = SearchCandidate(item="x", duration_ms=232000.0)
+    score_exact = _score_candidate(exact, None, None, None, 232000)
+
+    half_tol = SearchCandidate(item="x", duration_ms=232000.0 + 1500.0)
+    score_half = _score_candidate(half_tol, None, None, None, 232000)
+
+    at_tol = SearchCandidate(item="x", duration_ms=232000.0 + 3000.0)
+    score_edge = _score_candidate(at_tol, None, None, None, 232000)
+
+    # Only duration is comparable → rescaled to fill 0-90
+    # exact=90, half=45, edge=0
+    assert score_exact == 90.0
+    assert score_half == 45.0
+    assert score_edge == 0.0
+
+
+def test_score_case_insensitive_title() -> None:
+    c = SearchCandidate(item="x", title="my love")
+    s1 = _score_candidate(c, "My Love", None, None, None)
+    s2 = _score_candidate(c, "my love", None, None, None)
+    assert s1 == s2
+
+
+def test_score_artist_normalization() -> None:
+    """'Westlife feat. Someone' should still match 'Westlife'."""
+    c = SearchCandidate(item="x", artist="Westlife feat. Someone")
+    # normalize_artist strips feat. → both become "westlife"
+    score = _score_candidate(c, None, "Westlife", None, None)
+    assert score >= 30.0  # full artist weight (30) when both None on other fields
+
+
+# Reference track: Westlife - My Love, album Coast To Coast, ~232s
+_REF_TITLE = "My Love"
+_REF_ARTIST = "Westlife"
+_REF_ALBUM = "Coast To Coast"
+_REF_LENGTH = 232000  # ms
+
+
+def _lrclib_candidates() -> list[SearchCandidate[dict]]:
+    """Fixtures from real LRCLIB search results."""
+    raw = [
+        {
+            "trackName": "My Love",
+            "artistName": "Westlife",
+            "albumName": "null",
+            "duration": 232.0,
+            "synced": True,
+        },
+        {
+            "trackName": "My Love",
+            "artistName": "Westlife",
+            "albumName": "null",
+            "duration": 180.0,
+            "synced": True,
+        },
+        {
+            "trackName": "My love",
+            "artistName": "Westlife",
+            "albumName": "moments",
+            "duration": 235.327,
+            "synced": True,
+        },
+        {
+            "trackName": "My Love",
+            "artistName": "Westlife",
+            "albumName": "Unbreakable",
+            "duration": 233.026,
+            "synced": True,
+        },
+        {
+            "trackName": "My Love",
+            "artistName": "Westlife",
+            "albumName": "Coast To Coast",
+            "duration": 231.847,
+            "synced": True,
+        },
+        {
+            "trackName": "Hello My Love",
+            "artistName": "Westlife",
+            "albumName": "Spectrum",
+            "duration": 216.0,
+            "synced": True,
+        },
+        {
+            "trackName": "My Love",
+            "artistName": "Westlife",
+            "albumName": "Hitzone 13",
+            "duration": 231.0,
+            "synced": True,
+        },
     ]
-    assert select_best(candidates, track_length_ms=5000) == "exact"
-
-
-def test_filters_out_candidates_beyond_tolerance() -> None:
-    candidates = [
-        SearchCandidate(item="too_far", duration_ms=100000.0),
+    return [
+        SearchCandidate(
+            item=r,
+            duration_ms=r["duration"] * 1000,
+            is_synced=r["synced"],
+            title=r["trackName"],
+            artist=r["artistName"],
+            album=r["albumName"],
+        )
+        for r in raw
     ]
-    assert select_best(candidates, track_length_ms=5000, tolerance_ms=2000) is None
 
 
-def test_prefers_synced_at_equal_duration() -> None:
-    candidates = [
-        SearchCandidate(item="unsynced", duration_ms=5000.0, is_synced=False),
-        SearchCandidate(item="synced", duration_ms=5000.0, is_synced=True),
+def _lrclib_noisy_candidates() -> list[SearchCandidate[dict]]:
+    """Fixtures from LRCLIB title-only search — lots of wrong artists."""
+    raw = [
+        {
+            "trackName": "Let My Love Be Your Pillow (Live)",
+            "artistName": "Ronnie Milsap",
+            "albumName": "The Essential Ronnie Milsap",
+            "duration": 192.0,
+            "synced": True,
+        },
+        {
+            "trackName": "My Love",
+            "artistName": "Little Texas",
+            "albumName": "Big Time",
+            "duration": 248.0,
+            "synced": True,
+        },
+        {
+            "trackName": "My Love (Album Version)",
+            "artistName": "Little Texas",
+            "albumName": "Greatest Hits",
+            "duration": 248.0,
+            "synced": True,
+        },
+        {
+            "trackName": "My Love - Digitally Remastered '89",
+            "artistName": "Sonny James",
+            "albumName": "Capitol Collectors Series",
+            "duration": 169.0,
+            "synced": False,
+        },
+        {
+            "trackName": "My Love",
+            "artistName": "Westlife",
+            "albumName": "Coast To Coast",
+            "duration": 231.847,
+            "synced": True,
+        },
     ]
-    assert select_best(candidates, track_length_ms=5000) == "synced"
-
-
-def test_closer_duration_wins_over_synced() -> None:
-    candidates = [
-        SearchCandidate(item="synced_far", duration_ms=6000.0, is_synced=True),
-        SearchCandidate(item="unsynced_close", duration_ms=5001.0, is_synced=False),
+    return [
+        SearchCandidate(
+            item=r,
+            duration_ms=r["duration"] * 1000,
+            is_synced=r["synced"],
+            title=r["trackName"],
+            artist=r["artistName"],
+            album=r["albumName"],
+        )
+        for r in raw
     ]
-    assert select_best(candidates, track_length_ms=5000) == "unsynced_close"
 
 
-def test_skips_candidates_without_duration_when_track_length_given() -> None:
-    candidates = [
-        SearchCandidate(item="no_dur", duration_ms=None),
-        SearchCandidate(item="has_dur", duration_ms=5000.0),
+def _netease_candidates() -> list[SearchCandidate[int]]:
+    """Fixtures from real Netease search results."""
+    raw = [
+        {
+            "id": 2080607,
+            "name": "My Love",
+            "artist": "Westlife",
+            "album": "Unbreakable, Vol. 1 - The Greatest Hits",
+            "dt": 231941,
+        },
+        {
+            "id": 2080749,
+            "name": "My Love (Radio Edit)",
+            "artist": "Westlife",
+            "album": "World Of Our Own - No. 1 Hits Plus (EP)",
+            "dt": 232920,
+        },
+        {
+            "id": 29809886,
+            "name": "My Love (Live)",
+            "artist": "Westlife",
+            "album": "The Farewell Tour: Live at Croke Park",
+            "dt": 262000,
+        },
+        {
+            "id": 572412968,
+            "name": "My Love",
+            "artist": "Westlife",
+            "album": "Pure... Love",
+            "dt": 231000,
+        },
+        {
+            "id": 20707713,
+            "name": "You Raise Me Up",
+            "artist": "Westlife",
+            "album": "You Raise Me Up",
+            "dt": 241116,
+        },
     ]
-    assert select_best(candidates, track_length_ms=5000) == "has_dur"
-
-
-def test_returns_none_when_all_lack_duration_and_track_length_given() -> None:
-    candidates = [
-        SearchCandidate(item="a", duration_ms=None),
-        SearchCandidate(item="b", duration_ms=None),
+    return [
+        SearchCandidate(
+            item=r["id"],
+            duration_ms=float(r["dt"]),
+            title=r["name"],
+            artist=r["artist"],
+            album=r["album"],
+        )
+        for r in raw
     ]
-    assert select_best(candidates, track_length_ms=5000) is None
 
 
-def test_prefers_synced_when_no_track_length() -> None:
-    candidates = [
-        SearchCandidate(item="unsynced", is_synced=False),
-        SearchCandidate(item="synced", is_synced=True),
-    ]
-    assert select_best(candidates, track_length_ms=None) == "synced"
+def test_lrclib_picks_exact_album_match() -> None:
+    """With full metadata, should pick the Coast To Coast entry."""
+    candidates = _lrclib_candidates()
+    best, score = select_best(
+        candidates,
+        _REF_LENGTH,
+        title=_REF_TITLE,
+        artist=_REF_ARTIST,
+        album=_REF_ALBUM,
+    )
+    assert best is not None
+    assert best["albumName"] == "Coast To Coast"
+    assert score >= MIN_CONFIDENCE
 
 
-def test_falls_back_to_first_when_none_synced() -> None:
-    candidates = [
-        SearchCandidate(item="first"),
-        SearchCandidate(item="second"),
-    ]
-    assert select_best(candidates, track_length_ms=None) == "first"
+def test_lrclib_rejects_wrong_title() -> None:
+    """'Hello My Love' should not beat 'My Love' entries."""
+    candidates = _lrclib_candidates()
+    best, _ = select_best(
+        candidates,
+        _REF_LENGTH,
+        title=_REF_TITLE,
+        artist=_REF_ARTIST,
+        album=_REF_ALBUM,
+    )
+    assert best is not None
+    assert best["trackName"] != "Hello My Love"
+
+
+def test_lrclib_noisy_picks_westlife() -> None:
+    """In noisy title-only results, artist matching should filter to Westlife."""
+    candidates = _lrclib_noisy_candidates()
+    best, _ = select_best(
+        candidates,
+        _REF_LENGTH,
+        title=_REF_TITLE,
+        artist=_REF_ARTIST,
+        album=_REF_ALBUM,
+    )
+    assert best is not None
+    assert best["artistName"] == "Westlife"
+
+
+def test_lrclib_noisy_rejects_all_without_ref_artist() -> None:
+    """Without ref artist, wrong-artist candidates may still win, but right title should rank higher."""
+    candidates = _lrclib_noisy_candidates()
+    best, _ = select_best(
+        candidates,
+        _REF_LENGTH,
+        title=_REF_TITLE,
+    )
+    # Should pick a "My Love" over "Let My Love Be Your Pillow"
+    assert best is not None
+    assert "My Love" == best["trackName"] or best["trackName"].startswith("My Love")
+
+
+def test_netease_picks_closest_duration() -> None:
+    candidates = _netease_candidates()
+    best, _ = select_best(
+        candidates,
+        _REF_LENGTH,
+        title=_REF_TITLE,
+        artist=_REF_ARTIST,
+        album=_REF_ALBUM,
+    )
+    # 2080607 has dt=231941 (diff=59ms), closest to 232000
+    assert best == 2080607
+
+
+def test_netease_rejects_wrong_title() -> None:
+    """'You Raise Me Up' should not be selected."""
+    candidates = _netease_candidates()
+    best, _ = select_best(
+        candidates,
+        _REF_LENGTH,
+        title=_REF_TITLE,
+        artist=_REF_ARTIST,
+    )
+    assert best != 20707713
+
+
+def test_netease_without_ref_metadata_rejects_below_confidence() -> None:
+    """Without any ref metadata, candidates with one-sided fields score low and get rejected."""
+    candidates = _netease_candidates()
+    best, _ = select_best(candidates, _REF_LENGTH)
+    # Candidates have title/artist/album but ref has None for all → 0 for text fields
+    # Only duration (max 10) contributes → below MIN_CONFIDENCE (25)
+    assert best is None
+
+
+# --- Edge cases ---
 
 
 def test_empty_candidates_returns_none() -> None:
-    assert select_best([], track_length_ms=5000) is None
-    assert select_best([], track_length_ms=None) is None
+    assert select_best([], track_length_ms=5000) == (None, 0.0)
+    assert select_best([], track_length_ms=None) == (None, 0.0)
 
 
-def test_single_candidate_within_tolerance() -> None:
-    candidates = [SearchCandidate(item="only", duration_ms=5000.0)]
-    assert select_best(candidates, track_length_ms=5000) == "only"
-
-
-def test_single_candidate_beyond_tolerance() -> None:
-    candidates = [SearchCandidate(item="only", duration_ms=99999.0)]
-    assert select_best(candidates, track_length_ms=5000, tolerance_ms=1000) is None
+def test_all_below_min_confidence_returns_none() -> None:
+    """If all candidates score below threshold, return None."""
+    candidates = [
+        SearchCandidate(
+            item="x",
+            title="Completely Different Song",
+            artist="Unknown Artist",
+            album="Unknown Album",
+            duration_ms=999999.0,
+        ),
+    ]
+    result, _ = select_best(
+        candidates,
+        232000,
+        title="My Love",
+        artist="Westlife",
+        album="Coast To Coast",
+        min_confidence=90.0,
+    )
+    assert result is None
 
 
 def test_generic_type_preserved() -> None:
-    """select_best returns the same type as SearchCandidate.item."""
-    int_candidates = [SearchCandidate(item=42, duration_ms=5000.0)]
-    assert select_best(int_candidates, track_length_ms=5000) == 42
+    int_candidates = [SearchCandidate(item=42, duration_ms=5000.0, title="x")]
+    best, _ = select_best(int_candidates, 5000, title="x")
+    assert best == 42
 
-    dict_candidates = [SearchCandidate(item={"id": 1}, duration_ms=5000.0)]
-    result = select_best(dict_candidates, track_length_ms=5000)
-    assert result == {"id": 1}
+    dict_candidates = [SearchCandidate(item={"id": 1}, title="x")]
+    best, _ = select_best(dict_candidates, title="x")
+    assert best == {"id": 1}