src/songtrace/stages/align.py

@@ -12,6 +12,8 @@ from .transcribe import (classify_lyrics_match, load_transcript, lyrics_similari
                          recovered_spans, vocal_sections)
 
 SIMILARITY_THRESHOLD = 0.4
+# MMS_FA's wildcard token: any audio the transcript does not cover
+STAR = "*"
 # Singing is audio within this much of the stem's loud level; separation
 # bleed sits far below it.
 SINGING_BELOW_LOUD_DB = 25.0
@@ -50,7 +52,7 @@ def force_align_section(vocals_path: Path, start: float, end: float,
         waveform[:, lo:hi], sr, bundle.sample_rate)
     # MMS_FA only models a-z and apostrophe; anything else becomes the star token.
     vocab = bundle.get_dict()
-    tokens = ["".join(c if c in vocab else "*" for c in t) for t in tokens]
+    tokens = ["".join(c if c in vocab else STAR for c in t) for t in tokens]
     with torch.inference_mode():
         emission, _ = _get_model(device)(waveform.to(device))
         spans = bundle.get_aligner()(emission[0], bundle.get_tokenizer()(tokens))
@@ -135,9 +137,20 @@ def _enforce_monotonic(rows: list[dict]) -> None:
 def _align_section(vocals: Path, sec: tuple[float, float], words: list[tuple[str, int]],
                    language: str | None, device: str) -> list[dict]:
     tokens = [alignment_token(w, language) for w, _ in words]
-    alignable = [t for t in tokens if t]
+    # A star before, between and after lines lets speech, laughter or an
-    aligned = iter(force_align_section(vocals, sec[0], sec[1], alignable, device)
+    # ad-lib there go unexplained rather than claim the next line's letters.
-                   if alignable else [])
+    seq, prev_line = [], None
+    for (_, line_index), token in zip(words, tokens):
+        if not token:
+            continue
+        if line_index != prev_line:
+            seq.append(STAR)
+            prev_line = line_index
+        seq.append(token)
+    if seq:
+        seq.append(STAR)
+    spans = force_align_section(vocals, sec[0], sec[1], seq, device) if seq else []
+    aligned = iter(span for span, token in zip(spans, seq) if token != STAR)
     rows, blank = [], (None, None, 0.0)
     for (word, line_index), token in zip(words, tokens):
         start, end, score = next(aligned, blank) if token else blank

tests/test_align.py

@@ -213,3 +213,25 @@ def test_lines_whisper_missed_are_aligned_in_the_untranscribed_singing(
     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"] == []
+
+
+def test_stars_bracket_each_line_and_take_no_words(tmp_path, monkeypatch):
+    lyrics = "one two\n...\nthree four"
+    transcript = [{"start": 0.0, "end": 10.0, "text": "one two three four"}]
+    seen = []
+
+    def spy(vocals_path, start, end, tokens, device):
+        seen.append(tokens)
+        # each star claims a long stretch, so a word given a star's span shows
+        spans = [(start + i, start + i + (5.0 if t == "*" else 0.5), 0.9)
+                 for i, t in enumerate(tokens)]
+        return spans
+
+    monkeypatch.setattr(align, "force_align_section", spy)
+    ctx = make_ctx(tmp_path, lyrics, transcript)
+    align.align_stage().run(ctx)
+    rows = json.loads((ctx.work / "alignment.json").read_text())
+    assert seen == [["*", "one", "two", "*", "three", "four", "*"]]
+    timed = [r for r in rows if r["word"] != "..."]
+    assert all(r["end"] - r["start"] == pytest.approx(0.5) for r in timed)
+    check_invariants(rows, lyrics)