Consolidates in-progress work across autotag, explore, and library: - autotag: beets/Picard-informed scoring engine — ID-first matching, VA handling, recommendation tiers, and a merged distance/rank cascade, with an eval harness for regression tracking. - explore: offline MusicBrainz dump import/incremental refresh replaces the legacy tier crawl; index-first local search with fuzzy matching and a dedicated ranker; disk-free guards for dump downloads. - library: artist-credit extraction and matching. - lyrics: owned-library lyric search (FTS) with LRCLIB backfill. Also: rewrite README to be user-focused, and migrate upstream to git.ljones.me/yonlu/yellowjacket. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
134 lines
3.8 KiB
Go
134 lines
3.8 KiB
Go
package autotag
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// Recommendation is a qualitative confidence tier for a group's
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// ranked candidates — the piece a raw score can't express on its
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// own. Modeled on beets' Recommendation enum: the tier starts from
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// the top candidate's absolute score and is then CAPPED by defects
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// (ambiguity with a different release group, missing/unmatched
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// tracks, thin evidence). Auto-accept (plan 011) should require
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// RecommendationStrong; the review UI can badge the rest.
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type Recommendation string
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// Recommendation tiers, weakest to strongest.
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const (
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RecommendationNone Recommendation = "none"
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RecommendationLow Recommendation = "low"
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RecommendationMedium Recommendation = "medium"
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RecommendationStrong Recommendation = "strong"
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)
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const (
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// Absolute score tiers.
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strongScoreThresh = 0.90
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mediumScoreThresh = 0.75
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// A runner-up from a DIFFERENT release group within this margin
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// of the top score makes the match ambiguous — two genuinely
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// different albums both fit, so a human should look. Editions
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// of the same release group are expected to score nearly
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// identically and never count as ambiguity.
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ambiguityMargin = 0.05
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)
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// Recommend derives the confidence tier for a ranked candidate
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// list. candidates must already be sorted best-first (the shape
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// RankCandidates returns).
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func Recommend(g Group, candidates []Candidate) Recommendation {
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if len(candidates) == 0 {
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return RecommendationNone
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}
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top := candidates[0]
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var rec Recommendation
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switch {
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case top.Score >= strongScoreThresh:
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rec = RecommendationStrong
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case top.Score >= mediumScoreThresh:
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rec = RecommendationMedium
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default:
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return RecommendationLow
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}
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// Cap: a different release group scoring within the ambiguity
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// margin means the score alone can't pick between two albums.
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if rivalWithinMargin(top, candidates[1:]) {
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rec = minRecommendation(rec, RecommendationMedium)
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}
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// Cap: missing or unmatched tracks mean the alignment itself is
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// incomplete, however good the matched tracks look (beets caps
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// these penalties at "medium" the same way).
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for _, a := range top.Alignments {
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if a.Status == AlignmentMissing || a.Status == AlignmentUnmatched {
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rec = minRecommendation(rec, RecommendationMedium)
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break
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}
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}
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// Cap: tiny folders can't corroborate a match strongly enough
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// to act on without review, whatever the arithmetic says.
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if len(g.Tracks) < evidenceFullTracks {
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rec = minRecommendation(rec, RecommendationMedium)
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}
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return rec
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}
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// rivalWithinMargin reports whether any candidate from a different
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// release group scores within ambiguityMargin of the top candidate.
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func rivalWithinMargin(top Candidate, rest []Candidate) bool {
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for _, c := range rest {
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if top.Score-c.Score > ambiguityMargin {
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// Sorted descending: everything further is farther away.
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return false
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}
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if !sameReleaseGroup(top, c) {
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return true
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}
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}
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return false
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}
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// sameReleaseGroup reports whether two candidates belong to the
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// same release group — by MBID when both carry one, by normalized
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// title + artist-credit otherwise (local candidates may lack RG
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// MBIDs).
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func sameReleaseGroup(a, b Candidate) bool {
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if a.ReleaseGroupMBID != "" && b.ReleaseGroupMBID != "" {
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return a.ReleaseGroupMBID == b.ReleaseGroupMBID
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}
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return Normalize(a.Title) == Normalize(b.Title) &&
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Normalize(a.ArtistCredit) == Normalize(b.ArtistCredit)
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}
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// recommendationRank orders tiers for min-comparison.
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func recommendationRank(r Recommendation) int {
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switch r {
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case RecommendationNone:
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return 0
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case RecommendationLow:
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return 1
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case RecommendationMedium:
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return 2
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case RecommendationStrong:
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return 3
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default:
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return 0
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}
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}
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// minRecommendation returns the weaker of two tiers.
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func minRecommendation(a, b Recommendation) Recommendation {
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if recommendationRank(a) <= recommendationRank(b) {
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return a
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}
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return b
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}
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