align: keep lines Whisper missed when their singing went untranscribed

A line with nothing in the transcript was always dropped as not sung. When
Whisper skips a passage (one song lost a whole verse, 50s of clear singing
with no segments), that drops lines that were sung. Find singing in the
vocal stem that no segment covers; a run of unmatched lines with such
singing between its heard neighbours is kept, and that singing joins the
alignment sections so those lines have somewhere to go. Only singing that
kept a line is added, so an untranscribed ad-lib draws no lines into it.

src/songtrace/stages/align.py

@@ -1,12 +1,22 @@
 import json
 from pathlib import Path
 
+import numpy as np
+import soundfile as sf
+
 from ..lyrics import alignment_token, normalize_lyrics
 from ..models import on_release
 from ..pipeline import Ctx, PipelineError, Stage
-from .transcribe import classify_lyrics_match, load_transcript, lyrics_similarity, vocal_sections
+from ..whisper_client import TranscriptSegment
+from .transcribe import (classify_lyrics_match, load_transcript, lyrics_similarity,
+                         recovered_spans, vocal_sections)
 
 SIMILARITY_THRESHOLD = 0.4
+# Singing is audio within this much of the stem's loud level; separation
+# bleed sits far below it.
+SINGING_BELOW_LOUD_DB = 25.0
+# Shorter stretches are breaths, ad-libs or Whisper's loose segment edges.
+MIN_UNHEARD_SEC = 3.0
 
 # a stage aligns section by section, so the model is cached across the calls
 # within one run and dropped at the end of it
@@ -50,6 +60,39 @@ def force_align_section(vocals_path: Path, start: float, end: float,
              sum(t.score for t in s) / len(s)) for s in spans]
 
 
+def untranscribed_singing(vocals_path: Path, segments,
+                          frame: float = 0.05) -> list[tuple[float, float]]:
+    """Stretches of the vocal stem with singing in them but no transcript."""
+    data, sr = sf.read(str(vocals_path), dtype="float32", always_2d=True)
+    hop = int(sr * frame)
+    n = len(data) // hop
+    if n == 0:
+        return []
+    rms = np.sqrt((data[:n * hop].mean(1).reshape(n, hop) ** 2).mean(1))
+    db = 20 * np.log10(rms + 1e-10)
+    sung = db >= np.quantile(db, 0.9) - SINGING_BELOW_LOUD_DB
+    times = np.arange(n) * frame
+    for s in segments:
+        sung[(times >= min(s.start, s.end)) & (times < max(s.start, s.end))] = False
+    spans, start = [], None
+    for t, on in zip(times, sung):
+        if on and start is None:
+            start = float(t)
+        elif not on and start is not None:
+            spans.append((start, float(t)))
+            start = None
+    if start is not None:
+        spans.append((start, float(times[-1] + frame)))
+    # a breath or a quiet syllable must not split one unsung passage in two
+    merged: list[list[float]] = []
+    for a, b in spans:
+        if merged and a - merged[-1][1] < 1.0:
+            merged[-1][1] = b
+        else:
+            merged.append([a, b])
+    return [(a, b) for a, b in merged if b - a >= MIN_UNHEARD_SEC]
+
+
 def _assign_lines_to_sections(line_indices: list[int], sections) -> list[list[int]]:
     """Split matched lyric line indices across vocal sections proportionally to section duration."""
     total = sum(b - a for a, b in sections) or 1.0
@@ -112,11 +155,18 @@ def _run(ctx: Ctx) -> None:
         raise PipelineError(
             f"lyrics don't match audio (similarity {sim:.2f}) — "
             "wrong lyrics or wrong recording?")
-    # normalised so an inverted span can never collapse a section onto one instant
-    sections = [(min(a, b), max(a, b)) for a, b in vocal_sections(segments)]
-    dropped_lines, missing_spans = classify_lyrics_match(segments, ref)
-    matched = [i for i in range(len(ref.lines)) if i not in set(dropped_lines)]
     vocals = ctx.work / "vocals.wav"
+    unheard = untranscribed_singing(vocals, segments)
+    dropped_lines, missing_spans = classify_lyrics_match(segments, ref,
+                                                         unheard=unheard)
+    # lines Whisper missed are aligned against the singing it had no text for;
+    # only that singing, so an untranscribed ad-lib cannot draw lines into it
+    recovered = [TranscriptSegment(a, b, "")
+                 for a, b in recovered_spans(segments, ref, unheard)]
+    # normalised so an inverted span can never collapse a section onto one instant
+    sections = [(min(a, b), max(a, b))
+                for a, b in vocal_sections(segments + recovered)]
+    matched = [i for i in range(len(ref.lines)) if i not in set(dropped_lines)]
     rows = []
     for sec, line_idxs in zip(sections, _assign_lines_to_sections(matched, sections)):
         words = [(w, li) for li in line_idxs for w in ref.lines[li]]

src/songtrace/stages/transcribe.py

@@ -39,28 +39,74 @@ def lyrics_similarity(segments, ref: RefLyrics) -> float:
     return matcher.ratio()
 
 
-def classify_lyrics_match(segments, ref: RefLyrics, pad: float = 1.0
+def _line_times(segments, matcher, seg_of, line_of) -> dict[int, tuple[float, float]]:
+    """For each reference line that was heard, the start of the first and the
+    end of the last transcript segment its words were heard in. Words in a
+    'replace' block are paired with heard words proportionally."""
+    times: dict[int, tuple[float, float]] = {}
+    for tag, ai1, ai2, bj1, bj2 in matcher.get_opcodes():
+        if tag not in ("equal", "replace"):
+            continue
+        for j in range(bj1, bj2):
+            seg = segments[seg_of[ai1 + (j - bj1) * (ai2 - ai1) // (bj2 - bj1)]]
+            a, b = times.get(line_of[j], (seg.start, seg.end))
+            times[line_of[j]] = (min(a, seg.start), max(b, seg.end))
+    return times
+
+
+def _unheard_runs(segments, ref: RefLyrics, unheard
+                  ) -> tuple[list[list[int]], list[list[tuple[float, float]]]]:
+    """Runs of reference lines with nothing heard for them, each with the
+    untranscribed singing between the heard lines either side of it."""
+    matcher, seg_of, line_of = match_lyrics(segments, ref)
+    times = _line_times(segments, matcher, seg_of, line_of)
+    alignable = set(line_of)
+    runs, spans, run, prev_end = [], [], [], 0.0
+
+    def close(next_start: float) -> None:
+        if run:
+            runs.append(list(run))
+            spans.append([(max(a, prev_end), min(b, next_start)) for a, b in unheard
+                          if a < next_start and b > prev_end])
+            run.clear()
+
+    for li in range(len(ref.lines)):
+        if li in times:
+            close(times[li][0])
+            prev_end = times[li][1]
+        elif li in alignable:
+            run.append(li)
+    close(float("inf"))
+    return runs, spans
+
+
+def recovered_spans(segments, ref: RefLyrics, unheard) -> list[tuple[float, float]]:
+    """The untranscribed singing that stands in for lines Whisper missed, so
+    alignment can place them there."""
+    return _merge_spans([sp for spans in _unheard_runs(segments, ref, unheard)[1]
+                         for sp in spans])
+
+
+def classify_lyrics_match(segments, ref: RefLyrics, pad: float = 1.0, unheard=()
                           ) -> tuple[list[int], list[tuple[float, float]]]:
     """Split reference lines into matched vs. dropped, and flag sung spans no
     reference line explains.
 
     dropped_lines: reference line indices covered only by 'insert' opcodes
-    (present in the lyrics, never heard). missing_spans: the padded, merged
-    bounds of the heard segments behind 'delete' opcodes (heard, but matching
-    no reference word). A segment can also hold matched lines, so a span
-    marks where to look, not words to discard.
-    """
-    matcher, seg_of, line_of = match_lyrics(segments, ref)
-    covered = [False] * len(line_of)
-    for tag, _, _, bj1, bj2 in matcher.get_opcodes():
-        if tag in ("equal", "replace"):
-            for j in range(bj1, bj2):
-                covered[j] = True
-    by_line = {}
-    for j, li in enumerate(line_of):
-        by_line.setdefault(li, []).append(covered[j])
-    dropped_lines = sorted(li for li, cov in by_line.items() if not any(cov))
+    (present in the lyrics, never heard), unless `unheard` (sung spans the
+    transcript has no text for) has singing between the heard lines either
+    side of them: then Whisper missed them, not the singer.
 
+    missing_spans: the padded, merged bounds of the heard segments behind
+    'delete' opcodes (heard, but matching no reference word). A segment can
+    also hold matched lines, so a span marks where to look, not words to
+    discard.
+    """
+    runs, run_spans = _unheard_runs(segments, ref, unheard)
+    dropped_lines = sorted(li for run, sp in zip(runs, run_spans) if not sp
+                           for li in run)
+
+    matcher, seg_of, _ = match_lyrics(segments, ref)
     spans = []
     for tag, ai1, ai2, _, _ in matcher.get_opcodes():
         if tag != "delete":

tests/test_align.py

@@ -1,11 +1,23 @@
 import json
 
+import numpy as np
 import pytest
+import soundfile as sf
 
 from songtrace.config import Config
 from songtrace.lyrics import normalize_lyrics
 from songtrace.pipeline import Ctx, PipelineError, SongInput
 from songtrace.stages import align
+from songtrace.whisper_client import TranscriptSegment
+
+REAL_UNTRANSCRIBED_SINGING = align.untranscribed_singing
+
+
+@pytest.fixture(autouse=True)
+def no_untranscribed_singing(monkeypatch):
+    """The tests have no vocals.wav; those about missed singing set their own."""
+    monkeypatch.setattr(align, "untranscribed_singing", lambda vocals, segments: [])
+
 
 LYRICS = "it's one two three\nfour five six seven eight"
 
@@ -160,3 +172,44 @@ def test_align_flags_missing_span_for_unmatched_singing(tmp_path, monkeypatch):
     meta = json.loads((tmp_path / "w" / "align_meta.json").read_text())
     assert meta["dropped_lines"] == []
     assert meta["missing_spans"] == [[19.0, 24.0]]
+
+
+def _tone(seconds, sr, level):
+    t = np.arange(int(seconds * sr)) / sr
+    return level * np.sin(2 * np.pi * 220 * t)
+
+
+def test_untranscribed_singing_finds_sung_audio_the_transcript_skips(
+        tmp_path):
+    sr = 16000
+    rng = np.random.default_rng(0)
+    audio = np.concatenate([
+        _tone(2, sr, 0.3),           # 0-2s: sung and transcribed
+        np.zeros(2 * sr),            # 2-4s: silence
+        _tone(5, sr, 0.3),           # 4-9s: sung, never transcribed
+        np.zeros(3 * sr),            # 9-12s: silence
+        _tone(1, sr, 0.3),           # 12-13s: too short to count
+        np.zeros(3 * sr),
+    ]) + 0.001 * rng.standard_normal(16 * sr)   # separation bleed
+    path = tmp_path / "vocals.wav"
+    sf.write(path, audio.astype(np.float32), sr)
+    segments = [TranscriptSegment(0.0, 2.0, "one two")]
+    spans = REAL_UNTRANSCRIBED_SINGING(path, segments)
+    assert len(spans) == 1
+    assert spans[0][0] == pytest.approx(4.0, abs=0.1)
+    assert spans[0][1] == pytest.approx(9.0, abs=0.1)
+
+
+def test_lines_whisper_missed_are_aligned_in_the_untranscribed_singing(
+        tmp_path, monkeypatch):
+    lyrics = "one two three\nfour five six\nseven eight nine"
+    transcript = [{"start": 1.0, "end": 3.0, "text": "one two three"},
+                  {"start": 40.0, "end": 42.0, "text": "seven eight nine"}]
+    monkeypatch.setattr(align, "untranscribed_singing",
+                        lambda vocals, segments: [(20.0, 26.0)])
+    rows = run_align(tmp_path, monkeypatch, lyrics, transcript)
+    check_invariants(rows, lyrics)
+    middle = [r for r in rows if r["line_index"] == 1]
+    assert all(19.0 <= r["start"] and r["end"] <= 27.0 for r in middle)
+    meta = json.loads((tmp_path / "w" / "align_meta.json").read_text())
+    assert meta["dropped_lines"] == []

tests/test_transcribe.py

@@ -1,6 +1,7 @@
 from songtrace.lyrics import normalize_lyrics
 from songtrace.whisper_client import TranscriptSegment
-from songtrace.stages.transcribe import classify_lyrics_match, lyrics_similarity, vocal_sections
+from songtrace.stages.transcribe import (classify_lyrics_match, lyrics_similarity,
+                                         recovered_spans, vocal_sections)
 
 def seg(s, e, t=""):
     return TranscriptSegment(start=s, end=e, text=t)
@@ -64,3 +65,31 @@ def test_classify_lyrics_match_scopes_missing_span_to_adlib_not_whole_section():
     assert missing == [(5.0, 9.0)]
     for a, b in missing:
         assert not (a <= 1.0 < b)        # matched line's start (1.0) untouched
+
+
+THREE_LINES = "one two three\nfour five six\nseven eight nine"
+
+
+def test_lines_whisper_missed_are_kept_when_singing_went_untranscribed():
+    ref = normalize_lyrics(THREE_LINES)
+    segs = [seg(0, 2, "one two three"), seg(20, 22, "seven eight nine")]
+    assert classify_lyrics_match(segs, ref)[0] == [1]
+    dropped, _ = classify_lyrics_match(segs, ref, unheard=[(8.0, 14.0)])
+    assert dropped == []
+
+
+def test_untranscribed_singing_elsewhere_does_not_keep_a_line():
+    ref = normalize_lyrics(THREE_LINES)
+    segs = [seg(0, 2, "one two three"), seg(20, 22, "seven eight nine")]
+    dropped, _ = classify_lyrics_match(segs, ref, unheard=[(30.0, 35.0)])
+    assert dropped == [1]
+
+
+def test_recovered_spans_are_the_singing_between_heard_neighbours():
+    ref = normalize_lyrics(THREE_LINES)
+    segs = [seg(10, 12, "four five six"), seg(20, 22, "seven eight nine")]
+    unheard = [(3.0, 9.0), (14.0, 18.0), (30.0, 35.0)]
+    # line 0 comes before anything heard, so only the singing before line 1
+    # can stand in for it; the other spans have no missing line around them
+    assert recovered_spans(segs, ref, unheard) == [(3.0, 9.0)]
+    assert classify_lyrics_match(segs, ref, unheard=unheard)[0] == []