feat: align with upstream 1.5.9.9
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import { parseTTML, parseYrc } from '@applemusic-like-lyrics/lyric'
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import { cleanTTMLTranslations, parseQRCLyric, parseSmartLrc } from './lyricParser'
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const MIN_CONTENT_SCORE = 0.82
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const MAX_ANCHOR_DIFF_MS = 3000
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const MAX_P90_DIFF_MS = 2500
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const MAX_TIMELINE_DRIFT_MS = 4000
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const MIN_TIMELINE_RATIO = 0.985
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const MAX_TIMELINE_RATIO = 1.015
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const MIN_LINE_SIMILARITY = 0.68
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const METADATA_LINE_RE =
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/^(?:作词|作曲|编曲|制作人|混音|母带|录音|词|曲|composer|lyricist|arranger|producer|mixed by)\s*[:：]/i
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export type LyricMatchData = {
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lrc?: string
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yrc?: string
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ttml?: string
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}
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type ComparableLine = {
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text: string
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startTime: number
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endTime: number
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}
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type MatchAnchor = {
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referenceStart: number
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candidateStart: number
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}
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export type LyricMatchMetrics = {
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durationDiffMs?: number
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durationLimitMs?: number
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contentScore?: number
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anchorCount?: number
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timelineCoverage?: number
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withinTimeRatio?: number
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p90DiffMs?: number
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timelineDriftMs?: number
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timelineRatio?: number
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}
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export type LyricMatchDecision = {
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status: 'accepted' | 'rejected' | 'uncertain'
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reason: string
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metrics: LyricMatchMetrics
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}
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/** 归一化歌词正文，仅用于跨来源一致性比较 */
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const normalizeText = (text: string): string =>
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text
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.normalize('NFKC')
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.toLowerCase()
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.replace(/[\p{P}\p{S}\s]+/gu, '')
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/** 将不同解析器的行结构转为一致的比较结构 */
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const toComparableLines = (lines: any[]): ComparableLine[] =>
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lines.flatMap((line) => {
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if (line?.isBG) return []
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const raw = Array.isArray(line?.words)
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? line.words
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.map((word: any) => word?.word ?? word?.content ?? '')
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.join('')
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.trim()
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: String(line?.content ?? '').trim()
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if (!raw || METADATA_LINE_RE.test(raw)) return []
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const text = normalizeText(raw)
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if (text.length < 2) return []
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const startTime = Number(line?.startTime ?? line?.time ?? 0)
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const endTime = Number(line?.endTime ?? startTime)
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return [{ text, startTime, endTime }]
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})
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/** 按格式优先级解析歌词正文 */
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const parseComparableLyrics = (data: LyricMatchData): ComparableLine[] => {
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if (data.ttml?.trim()) {
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try {
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const parsed = parseTTML(cleanTTMLTranslations(data.ttml)) as any
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const lines = toComparableLines(parsed?.lines ?? [])
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if (lines.length > 0) return lines
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} catch {
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// 继续尝试逐字或普通歌词
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}
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}
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if (data.yrc?.trim()) {
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const raw = data.yrc.trim()
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try {
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if (raw.startsWith('<')) {
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const lines = toComparableLines(parseQRCLyric(raw))
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if (lines.length > 0) return lines
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} else {
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const lines = toComparableLines(parseYrc(raw) ?? [])
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if (lines.length > 0) return lines
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}
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} catch {
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// 继续回退到通用 LRC
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}
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}
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if (data.lrc?.trim()) {
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try {
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return toComparableLines(parseSmartLrc(data.lrc).lines)
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} catch {
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return []
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}
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}
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return []
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}
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/** 计算两个短文本的归一化编辑相似度 */
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const textSimilarity = (left: string, right: string): number => {
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if (left === right) return 1
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if (!left || !right) return 0
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if (left.length < right.length) return textSimilarity(right, left)
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const previous = new Uint16Array(right.length + 1)
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const current = new Uint16Array(right.length + 1)
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for (let j = 0; j <= right.length; j++) previous[j] = j
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for (let i = 1; i <= left.length; i++) {
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current[0] = i
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for (let j = 1; j <= right.length; j++) {
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current[j] = Math.min(
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previous[j] + 1,
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current[j - 1] + 1,
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previous[j - 1] + (left[i - 1] === right[j - 1] ? 0 : 1)
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)
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}
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previous.set(current)
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}
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return 1 - previous[right.length] / Math.max(left.length, right.length)
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}
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const spanLength = (lines: ComparableLine[], start: number, count: number): number =>
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lines[start].text.length + (count === 2 ? lines[start + 1].text.length : 0)
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const joinSpan = (lines: ComparableLine[], start: number, count: number): string =>
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count === 1 ? lines[start].text : lines[start].text + lines[start + 1].text
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/** 有序对齐歌词行，并允许相邻两行互相合并 */
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const alignLines = (
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reference: ComparableLine[],
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candidate: ComparableLine[]
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): { score: number; anchors: MatchAnchor[] } => {
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const width = candidate.length + 1
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const size = (reference.length + 1) * width
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const scores = new Float64Array(size)
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const prevReference = new Uint8Array(size)
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const prevCandidate = new Uint8Array(size)
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const update = (
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fromReference: number,
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fromCandidate: number,
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referenceCount: number,
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candidateCount: number,
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addedScore: number
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) => {
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const nextReference = fromReference + referenceCount
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const nextCandidate = fromCandidate + candidateCount
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const from = fromReference * width + fromCandidate
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const next = nextReference * width + nextCandidate
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const score = scores[from] + addedScore
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if (score <= scores[next]) return
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scores[next] = score
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prevReference[next] = referenceCount
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prevCandidate[next] = candidateCount
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}
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for (let i = 0; i <= reference.length; i++) {
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for (let j = 0; j <= candidate.length; j++) {
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if (i < reference.length) update(i, j, 1, 0, 0)
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if (j < candidate.length) update(i, j, 0, 1, 0)
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for (let referenceCount = 1; referenceCount <= 2; referenceCount++) {
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if (i + referenceCount > reference.length) break
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for (let candidateCount = 1; candidateCount <= 2; candidateCount++) {
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if (j + candidateCount > candidate.length) break
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const referenceLength = spanLength(reference, i, referenceCount)
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const candidateLength = spanLength(candidate, j, candidateCount)
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if (
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Math.min(referenceLength, candidateLength) /
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Math.max(referenceLength, candidateLength) <
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MIN_LINE_SIMILARITY
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) {
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continue
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}
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const referenceText = joinSpan(reference, i, referenceCount)
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const candidateText = joinSpan(candidate, j, candidateCount)
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const similarity = textSimilarity(referenceText, candidateText)
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if (similarity < MIN_LINE_SIMILARITY) continue
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update(
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i,
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j,
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referenceCount,
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candidateCount,
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Math.min(referenceText.length, candidateText.length) * similarity
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)
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}
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}
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}
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}
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const anchors: MatchAnchor[] = []
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let i = reference.length
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let j = candidate.length
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while (i > 0 || j > 0) {
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const index = i * width + j
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const referenceCount = prevReference[index]
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const candidateCount = prevCandidate[index]
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if (referenceCount === 0 && candidateCount === 0) break
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const previousI = i - referenceCount
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const previousJ = j - candidateCount
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if (referenceCount > 0 && candidateCount > 0) {
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anchors.push({
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referenceStart: reference[previousI].startTime,
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candidateStart: candidate[previousJ].startTime
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})
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}
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i = previousI
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j = previousJ
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}
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anchors.reverse()
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return { score: scores[size - 1], anchors }
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}
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/** 检测高阶歌词是否与基准歌词属于同一录音版本 */
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export const evaluateLyricDataMatch = (
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referenceData: LyricMatchData,
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candidateData: LyricMatchData,
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referenceDuration?: number,
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candidateDuration?: number
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): LyricMatchDecision => {
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const metrics: LyricMatchMetrics = {}
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if (referenceDuration && candidateDuration) {
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metrics.durationDiffMs = Math.abs(referenceDuration - candidateDuration)
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metrics.durationLimitMs = Math.max(8000, referenceDuration * 0.04)
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if (metrics.durationDiffMs > metrics.durationLimitMs) {
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return { status: 'rejected', reason: 'duration_mismatch', metrics }
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}
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}
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const reference = parseComparableLyrics(referenceData)
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const candidate = parseComparableLyrics(candidateData)
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if (reference.length < 3 || candidate.length < 3) {
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return { status: 'uncertain', reason: 'insufficient_lines', metrics }
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}
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const aligned = alignLines(reference, candidate)
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const referenceChars = reference.reduce((sum, line) => sum + line.text.length, 0)
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const candidateChars = candidate.reduce((sum, line) => sum + line.text.length, 0)
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metrics.contentScore = (2 * aligned.score) / (referenceChars + candidateChars)
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if (metrics.contentScore < MIN_CONTENT_SCORE) {
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return { status: 'rejected', reason: 'content_mismatch', metrics }
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}
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const anchors = aligned.anchors.filter(
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(anchor) => Number.isFinite(anchor.referenceStart) && Number.isFinite(anchor.candidateStart)
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)
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metrics.anchorCount = anchors.length
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const lastReference = reference[reference.length - 1]
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const timelineDuration =
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referenceDuration || lastReference.endTime || lastReference.startTime || 0
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const minAnchors = timelineDuration > 0 && timelineDuration < 60000 ? 3 : 6
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if (anchors.length < minAnchors) {
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return { status: 'uncertain', reason: 'insufficient_anchors', metrics }
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}
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const first = anchors[0]
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const last = anchors[anchors.length - 1]
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const referenceSpan = last.referenceStart - first.referenceStart
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const candidateSpan = last.candidateStart - first.candidateStart
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metrics.timelineCoverage = timelineDuration > 0 ? referenceSpan / timelineDuration : 0
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const minimumCoverage = timelineDuration > 0 && timelineDuration < 60000 ? 0.35 : 0.5
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if (metrics.timelineCoverage < minimumCoverage) {
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return { status: 'uncertain', reason: 'insufficient_timeline_coverage', metrics }
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}
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const differences = anchors
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.map((anchor) => Math.abs(anchor.candidateStart - anchor.referenceStart))
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.sort((left, right) => left - right)
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metrics.withinTimeRatio =
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differences.filter((difference) => difference <= MAX_ANCHOR_DIFF_MS).length / differences.length
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metrics.p90DiffMs = differences[Math.max(0, Math.ceil(differences.length * 0.9) - 1)]
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const firstOffset = first.candidateStart - first.referenceStart
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const lastOffset = last.candidateStart - last.referenceStart
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metrics.timelineDriftMs = Math.abs(lastOffset - firstOffset)
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metrics.timelineRatio = referenceSpan > 0 ? candidateSpan / referenceSpan : 0
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if (
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metrics.withinTimeRatio < 0.8 ||
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metrics.p90DiffMs > MAX_P90_DIFF_MS ||
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metrics.timelineDriftMs > MAX_TIMELINE_DRIFT_MS ||
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metrics.timelineRatio < MIN_TIMELINE_RATIO ||
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metrics.timelineRatio > MAX_TIMELINE_RATIO
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) {
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return { status: 'rejected', reason: 'timeline_mismatch', metrics }
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}
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return { status: 'accepted', reason: 'matched', metrics }
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}
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