# 节拍与歌词对时（细节）

引擎只认两样时间：一条**恒定速度的拍网格**（`song.js` 的 `bpm` + `offset`，拍点 = `offset + n × 60/bpm`）和
**每句歌词的起止时间**（`lyrics.js` 的 `LY`）。所有打点、舞步、卡拉 OK 都由它们决定，所以动画开工前必须把它们弄对。

## analyze_audio.mjs

```bash
node <SKILLS>/paint-mv/scripts/analyze_audio.mjs song.mp3            # 可读报告
node <SKILLS>/paint-mv/scripts/analyze_audio.mjs song.mp3 --json     # JSON（new_project.mjs 存为 assets/analysis.json）
node <SKILLS>/paint-mv/scripts/analyze_audio.mjs song.mp3 --bpm=88   # 指定速度，只拟合首拍
```

做法：ffmpeg 解码成 22050 Hz 单声道 → 40 个对数频带的谱通量作起音包络 → 自相关（以 120 BPM 为中心、一个八度宽的先验）
选速度 → 在 ±4% 内拟合恒定网格，BPM 精确到 0.01、首拍精确到约 1 ms → 整数或 .5 BPM 拟合得几乎一样好时取整
（`song.js` 存的是取整后的值，首拍按它重新拟合）。

报告怎么读：
- `tempo candidates`：候选速度和相对强度。标了 **AMBIGUOUS** 表示有一个 2:1 或 3:2 关系的对手几乎一样强，
  光凭音频分不出拍子在哪一层（例如满是均匀三连音的歌，88 和 132 BPM 都成立）；脚本取最接近 95 BPM 舞步脉搏的那个。
  两种网格的拍点都踩在真实起音上，选错只是舞步快慢不同，不会失步。拿不准就请用户看一段对拍短片（见 paint-mv-render）。
- `beat 0 could also be at … ← AMBIGUOUS phase`：在半拍或三分之一拍之外还有几乎一样强的起音（反拍很重、或满是三连音），
  哪个位置才是拍头只是猜测。选错会让所有打点整体早或晚半拍/三分之一拍。同样请用户看对拍短片，用 `--offset=S` 选定。
  原作歌曲就是这种情况：首选 0.247 s，备选 0.474 / 0.02 s，原作手定的 0.21 s 最接近首选。
- `grid drift`：每 32 拍局部最佳相位相对全曲网格的偏移（ms）。稳定的歌都在几 ms 内；超过约 45 ms 会警告：
  这首歌速度不恒定（现场录音、渐快渐慢），恒定网格在那些段落会漂，那里的打点要按听感手动定时间，别用 `B(n)`。
- `bars start on beats ≡ k (mod 4)`：小节起点的猜测（按底鼓落点），置信度低时只作参考。
- `loudness per bar`：每小节响度条形图，找前奏、主歌、副歌、间奏、尾奏的边界。

精度（实测）：合成点击音轨的首拍误差 1 ms；原作歌曲测出 88 BPM（与原作一致，并正确报出 88/132 歧义），
首拍 0.247 s（原作手定 0.21 s，差 37 ms，不到 24 fps 的一帧）。需要时在 `song.js` 里微调 `offset`，一帧约 0.042 s。

## lyrics_to_ly.mjs

```bash
node <SKILLS>/paint-mv/scripts/lyrics_to_ly.mjs lyrics.lrc --out=src/lyrics.js --duration=<song.js 的 duration> [--shift=0.2]
```

| 格式 | 说明 |
|---|---|
| LRC | `[mm:ss.xx]歌词`；支持一行多个时间标签、`[offset:±ms]`、逐字 `<mm:ss.xx>` 标签（会去掉）；**空时间行 `[mm:ss.xx]` 表示上一句在此结束**；同一时间戳的第二行（常见的翻译行）会丢弃并警告 |
| SRT / VTT | 每条字幕的起止时间直接用；多行文字合成一行 |
| TSV | `起<TAB>止<TAB>歌词` 或 `起<TAB>歌词`（秒或 mm:ss.xx） |
| JSON | `[[起, 止, "歌词"]]` 或 `[{start, end, text}]` |
| 纯文本 | 拒绝，先用 align_lyrics.py 打轴 |

没有结束时间（普通 LRC）时：结束 = min(下一句开始 − 0.1 s, 开始 + max(2 s, 2 × 扫字时间))，
这样卡拉 OK 不会一直挂过间奏。实测在原作歌词上，只有开始时间的 LRC 结束时间平均偏差 0.32 s（间奏前的句子最多拖 2.5 s）；
加上空时间行标出间奏开始后偏差 ≤ 0.1 s。

会给出警告的情况：句子重叠（自动截短）、短于 0.6 s（卡拉 OK 条几乎打不开）、太宽放不进卡拉 OK 条
（50px 字号下约 70 个拉丁字符或 33 个汉字，需要拆成两句）、超出歌曲时长、第一句早于 1 s。

## align_lyrics.py（只有纯文本歌词时）

```bash
uv run --python 3.12 --with faster-whisper --with zhconv python <SKILLS>/paint-mv/scripts/align_lyrics.py \
    song.mp3 lyrics.txt --out assets/lyrics.srt --lang zh --model small --words assets/words.json
```

- 歌词文本：**每行一句，按演唱顺序写全**，重复的副歌每唱一次就写一次。`[Chorus]`、`(Verse 2)`、`【副歌】`、`Chorus:` 这类段落标记会被跳过。
  歌名、作词作曲这类**不唱的行要删掉**，否则会被当成第一句去找时间。
- 做法：faster-whisper 带词级时间戳转写（从不跳过"无人声"窗口：音乐常骗过静音检测，漏掉一个 30 s 窗口就丢掉后面所有句子），
  再把已知歌词和转写结果按字符序列对齐（difflib），唱错/听错的词由邻居带出时间；听不到的句子按字数分摊到前后句之间。
  修正了 Whisper 的几个常见毛病：停顿后第一个词把停顿"吞"进自己的时长（按词长封顶）、同一个词复读（只留第一个）、
  零时长的词（成串出现时是幻觉出来的"背诵"，丢掉不用）；一句里匹配上的字挤在比人能唱的还短的时间里
  （每个汉字不到约 0.08 s、每个字母不到约 0.03 s）就当这句没听到。
- 默认不给识别器提示。`--prompt` 把歌词作为 `hotwords` 提示每个窗口：生僻词和专有名词听得更准，但识别器可能在器乐段
  "背"出歌词（实测一首中文说唱：305 个词里 138 个零时长，整段副歌被挤到同一时刻）。超过 20% 的词没有时长时脚本会警告，
  建议换另一种模式或更大的模型。
- 输出每句的听到比例；`← weak`（听到不足一半）和 `← neighbour of a weak line`（它的邻居，时间常被挤歪）要人工复核。
- `--words` 缓存转写结果：改了歌词文本再对齐只要几秒。缓存与 `--prompt`、`--model` 无关，换模式或模型时换个缓存文件名。
- 精度（实测，原作歌曲、small 模型）：句首误差中位数 0.34 s，46 句里 32 句在 0.5 s 内（带 `--prompt`：0.31 s、31 句），
  所有误差超过 1 s 的句子都被标出。
  想更准用 `--model medium` 或 `large-v3`（下载更大、更慢）；最好的办法仍是拿到现成的 LRC（音乐平台的歌词文件）。
- 首次运行会下载模型（small 约 0.5 GB）。Hugging Face 慢就设 `HF_ENDPOINT=https://hf-mirror.com`。
  繁体输出靠 zhconv 统一成简体再匹配（去掉 `--with zhconv` 也能跑，只是繁简不会互认）。

结果是 SRT，接着用 lyrics_to_ly.mjs 转成 `src/lyrics.js`（`new_project.mjs --lyrics=assets/lyrics.srt` 会自动做）。

## 中日韩歌词

模板在拉丁字体后面加了 ZCOOL KuaiLe 作中日韩字符的回退（卡拉 OK、`letter()` 都生效），启动时预加载歌词、歌名、
计量器标签和 `SONG.glyphs` 用到的字形，保证并行渲染的每一帧都是同一种字体。章节里要写不在这些文字里的汉字时，
把它们加进 `song.js` 的 `glyphs`。卡拉 OK 扫字速度按一个汉字约等于四个拉丁字母计。

#!/usr/bin/env python3
"""align_lyrics.py: time plain lyrics (one sung line per text line) against the song and write an SRT that
lyrics_to_ly.mjs turns into src/lyrics.js.

faster-whisper transcribes the song with word timestamps; the known lyrics are then matched to the transcript character
by character (difflib), so misheard words still get timed by their neighbours. Lines it could not hear are placed in the
gap between their neighbours and flagged. Run it through uv so the dependencies install on demand:

  uv run --python 3.12 --with faster-whisper --with zhconv python align_lyrics.py song.mp3 lyrics.txt \\
      --out lyrics.srt [--lang zh] [--model small] [--words words.json] [--prompt]

--words caches the transcript, so re-aligning after fixing the lyrics text takes seconds instead of minutes.
Section markers such as [Chorus], (Verse 2), 【副歌】 or "Chorus:" are skipped. Models download from Hugging Face on first
use (small ~0.5 GB, medium ~1.5 GB, large-v3 ~3 GB); set HF_ENDPOINT=https://hf-mirror.com if that is slow.
"""
import argparse
import difflib
import json
import os
import re
import statistics
import sys

CJK = re.compile(r'[\u3040-\u30ff\u3400-\u9fff\uf900-\ufaff\uac00-\ud7af]')
MARKER = re.compile(r'^\s*(\[[^\]]*\]|\([^)]*\)|【[^】]*】|(verse|chorus|bridge|intro|outro|hook|pre-chorus)\s*\d*\s*:?)\s*$', re.I)

try:
    from zhconv import convert as _zh
    def simplify(s): return _zh(s, 'zh-cn')
except ImportError:
    def simplify(s): return s


def norm_chars(s):
    """Characters that take part in matching: CJK characters and lowercase letters/digits."""
    s = simplify(s).lower()
    return [c for c in s if CJK.match(c) or c.isalnum()]


def read_lines(path):
    lines = []
    with open(path, encoding='utf-8') as f:
        for raw in f:
            t = raw.strip()
            if not t or MARKER.match(t):
                continue
            if norm_chars(t):
                lines.append(t)
    return lines


def transcribe(audio, model_name, lang, device, hint):
    from faster_whisper import WhisperModel
    model = WhisperModel(model_name, device=device, compute_type='int8' if device in ('cpu', 'auto') else 'float16',
                         cpu_threads=os.cpu_count() or 4)
    # Music fools the no-speech detector, and one skipped window loses every line after it: never skip. `hotwords`
    # (only with --prompt) re-primes every 30 s window with the lyrics; it helps with odd words but can make the model
    # recite the whole lyric sheet over an instrumental, so it is off by default.
    segments, info = model.transcribe(audio, language=lang, word_timestamps=True, vad_filter=False, beam_size=5,
                                      condition_on_previous_text=False, no_speech_threshold=None, hotwords=hint)
    words = [(w.start, w.end, w.word) for s in segments for w in (s.words or [])]
    return words, info


def align(lines, words):
    # lyric side: every matchable character with its line; transcript side: characters timed by spreading each word
    L, owner = [], []
    for i, t in enumerate(lines):
        for c in norm_chars(t):
            L.append(c)
            owner.append(i)
    A, at, last = [], [], None
    for s, e, w in words:
        cs = norm_chars(w)
        # a word with no duration carries no timing (and whole runs of them are hallucinated recitation); "just, just,
        # just, just" is Whisper looping on a word: keep the first
        if not cs or e - s < .02 or cs == last:
            continue
        last = cs
        # Whisper stretches the first word after a pause back over the pause: keep its end, cap how long it can last
        cjk = sum(1 for c in cs if CJK.match(c))
        cap = .7 * cjk + (.4 + .1 * (len(cs) - cjk) if len(cs) > cjk else 0)
        s = max(s, e - cap)
        for k, c in enumerate(cs):
            A.append(c)
            at.append((s + (e - s) * k / len(cs), s + (e - s) * (k + 1) / len(cs)))
    hit = {}
    for i, j, n in difflib.SequenceMatcher(None, L, A, autojunk=False).get_matching_blocks():
        for k in range(n):
            hit[i + k] = at[j + k]
    durs = [b - a for a, b in hit.values() if b > a]
    cdur = statistics.median(durs) if durs else .25
    first = {}
    for idx, ln in enumerate(owner):
        first.setdefault(ln, idx)
    out = []
    for ln, text in enumerate(lines):
        i0 = first[ln]
        n = sum(1 for o in owner if o == ln)
        m = [(k - i0, hit[k]) for k in range(i0, i0 + n) if k in hit]
        if m:   # drop matches that wandered far from the line's median time
            med = statistics.median(t[0] for _, t in m)
            m = [(k, t) for k, t in m if abs(t[0] - med) < 6]
        cov = len(m) / n
        if cov >= .2:
            (k0, t0), (k1, t1) = m[0], m[-1]
            # nobody sings faster than ~0.08 s per CJK character or ~0.03 s per letter: a tighter match landed on a
            # garbled stretch
            if t1[1] - t0[0] < sum(.08 if CJK.match(c) else .03 for c in L[i0 + k0:i0 + k1 + 1]):
                cov = 0
        if cov < .2:
            out.append([None, None, text, cov])
            continue
        out.append([max(0, t0[0] - k0 * cdur), t1[1] + (n - 1 - k1) * cdur, text, cov])
    # unheard lines: share the gap between the known neighbours in proportion to their length
    i = 0
    while i < len(out):
        if out[i][0] is not None:
            i += 1
            continue
        j = i
        while j < len(out) and out[j][0] is None:
            j += 1
        a = out[i - 1][1] if i else 0.0
        b = out[j][0] if j < len(out) else a + sum(len(r[2]) for r in out[i:j]) * cdur * 1.5
        w = [len(norm_chars(r[2])) for r in out[i:j]]
        x = a
        for r, k in zip(out[i:j], w):
            span = (b - a) * k / sum(w)
            r[0], r[1] = x + .05, x + span - .05
            x += span
        i = j
    # no overlaps, nothing shorter than 0.5 s
    for k in range(len(out)):
        if k and out[k][0] < out[k - 1][1] + .05:
            out[k][0] = out[k - 1][1] + .05
        out[k][1] = max(out[k][1], out[k][0] + .5)
    for k in range(len(out) - 1):
        if out[k][1] > out[k + 1][0] - .05:
            out[k][1] = max(out[k][0] + .3, out[k + 1][0] - .05)
    return out


def srt_time(s):
    ms = int(round(s * 1000))
    return f'{ms // 3600000:02d}:{ms // 60000 % 60:02d}:{ms // 1000 % 60:02d},{ms % 1000:03d}'


def main():
    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
    ap.add_argument('audio')
    ap.add_argument('lyrics', help='UTF-8 text, one sung line per line')
    ap.add_argument('--out', default='lyrics.srt')
    ap.add_argument('--lang', default=None, help='language code (zh, en, ja, ...); detected when omitted')
    ap.add_argument('--model', default='small', help='tiny | base | small | medium | large-v3')
    ap.add_argument('--device', default='auto', help='auto | cpu | cuda')
    ap.add_argument('--prompt', action='store_true', help='prime the recogniser with the lyrics (hotwords): better on odd '
                                                          'words, but it can recite lyrics over instrumentals')
    ap.add_argument('--words', help='cache of the transcript (JSON): reused if it exists, written otherwise, so '
                                    'realigning edited lyrics skips the slow transcription')
    a = ap.parse_args()

    lines = read_lines(a.lyrics)
    if not lines:
        sys.exit(f'no lyric lines in {a.lyrics}')
    if a.words and os.path.exists(a.words):
        with open(a.words, encoding='utf-8') as f:
            words = [tuple(w) for w in json.load(f)]
        print(f'reusing {len(words)} transcribed words from {a.words}', file=sys.stderr)
    else:
        hint = ' '.join(dict.fromkeys(lines)) if a.prompt else None   # choruses once; the model keeps what fits
        print(f'transcribing {a.audio} with faster-whisper {a.model} …', file=sys.stderr)
        words, info = transcribe(a.audio, a.model, a.lang, a.device, hint)
        print(f'  language {info.language} ({info.language_probability:.2f}), {len(words)} words heard', file=sys.stderr)
        if a.words:
            with open(a.words, 'w', encoding='utf-8') as f:
                json.dump(words, f, ensure_ascii=False)
    flat = sum(1 for s, e, _ in words if e - s < .02)
    if words and flat > .2 * len(words):
        print(f'  warning: {flat} of {len(words)} words have no duration (hallucinated or garbled stretches); '
              f'those were ignored. If lines look wrong, retry {"without" if a.prompt else "with"} --prompt or a larger --model.',
              file=sys.stderr)
    rows = align(lines, words)
    with open(a.out, 'w', encoding='utf-8') as f:
        for i, (s, e, t, _) in enumerate(rows, 1):
            f.write(f'{i}\n{srt_time(s)} --> {srt_time(e)}\n{t}\n\n')
    # a weakly heard line also skews its neighbours (the recogniser smears their words over the gap): check those too
    weak = {i for i, r in enumerate(rows) if r[3] < .5}
    check = weak | {j for i in weak for j in (i - 1, i + 1) if 0 <= j < len(rows)}
    print(f'{len(rows)} lines → {a.out}; {len(weak)} weakly heard, {len(check)} to check by ear:', file=sys.stderr)
    for i, (s, e, t, c) in enumerate(rows):
        flag = '  ← weak' if i in weak else '  ← neighbour of a weak line' if i in check else ''
        print(f'  {s:7.2f} – {e:7.2f}  {c:4.0%}  {t}{flag}', file=sys.stderr)


if __name__ == '__main__':
    main()

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{
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