align: let a star token absorb non-lyric audio between lines

The aligner had to explain every frame of a section with the lyrics, so
background speech or laughter between lines pulled the next line's
letters onto it, which then came out as a burst of tiny notes before the
real line was rushed to catch up. MMS_FA has a star token that matches
any audio; put one before, between and after the lines of each section,
as ctc-forced-aligner does by default, and drop the stars' spans from the
result.

src/songtrace/stages/align.py

@@ -1,12 +1,24 @@
 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
+# 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
+# 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
@@ -40,7 +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))
@@ -50,6 +62,39 @@ 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
@@ -92,9 +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]
-    aligned = iter(force_align_section(vocals, sec[0], sec[1], alignable, device)
-                   if alignable else [])
+    # A star before, between and after lines lets speech, laughter or an
+    # ad-lib there go unexplained rather than claim the next line's letters.
+    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
@@ -112,11 +168,18 @@ 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/pitch.py

@@ -6,6 +6,11 @@ import numpy as np
 from ..models import on_release
 from ..pipeline import Ctx, Stage
 
+# Separated vocal stems keep faint bleed that crepe still finds a pitch in, so
+# frames this far below the song's loud vocal level count as silence. Relative
+# rather than absolute, so it holds however loud the song was mastered.
+SILENCE_BELOW_LOUD_DB = 25.0
+
 
 @on_release
 def unload_model() -> None:
@@ -18,6 +23,11 @@ def unload_model() -> None:
             delattr(torchcrepe.infer, attr)
 
 
+def mute_silence(conf: np.ndarray, loudness: np.ndarray) -> None:
+    """Zero the confidence of frames quiet enough to be bleed, in place."""
+    conf[loudness < np.quantile(loudness, 0.9) - SILENCE_BELOW_LOUD_DB] = 0.0
+
+
 def track_pitch(vocals_path: Path, model: str, device: str):
     import torchaudio
     import torchcrepe
@@ -33,6 +43,7 @@ def track_pitch(vocals_path: Path, model: str, device: str):
         return_periodicity=True, batch_size=512)
     f0 = f0[0].cpu().numpy()
     conf = periodicity[0].cpu().numpy()
+    mute_silence(conf, torchcrepe.loudness.a_weighted(waveform, 16000, 160)[0].cpu().numpy())
     times = np.arange(len(f0)) * 0.01
     return times, f0, conf
 

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/fixtures/chart_crlf.txt

@@ -0,0 +1,36 @@
+#TITLE:Placeholder Chart
+#ARTIST:Test Artist
+#LANGUAGE:English
+#YEAR:2000
+#CREATOR:songtrace tests
+#GENRE:Test
+#COVER:Test Artist - Placeholder Chart [CO].jpg
+#MP3:Test Artist - Placeholder Chart.mp3
+#VIDEO:Test Artist - Placeholder Chart.mp4
+#BPM:217,98
+#GAP:4880
+#MEDLEYSTARTBEAT:113
+#MEDLEYENDBEAT:240
+: 0 3 16 ah
+: 5 3 20  ah
+: 11 3 23  la
+: 17 3 16  la
+: 22 28 15  ah
+: 51 2 16  doo
+: 54 31 16 ~
+- 95  
+F 113 3 18 la
+F 117 3 21  la
+F 121 3 21  la
+* 125 3 21  doo
+* 129 12 19 ~
+- 150
+: 155 4 12 Na
+R 160 4 0  na
+G 165 4 0  na
+: 170 6 14  la
+- 190
+: 194 3 16 La
+: 198 3 18  la
+* 202 10 20  ah
+E

tests/fixtures/chart_latin1.txt

@@ -0,0 +1,21 @@
+#TITLE:Café Placeholder
+#ARTIST:Tést Artist
+#LANGUAGE:English
+#GENRE:Test
+#YEAR:2000
+#MP3:Tést Artist - Café Placeholder.mp3
+#CREATOR:songtrace tests
+#BPM:256
+#GAP:49673
+: 0 4 2 Doo
+: 6 4 4  doo
+: 12 8 5  laé
+- 28 30
+: 32 4 0 Na
+: 38 4 -2  na
+* 44 12 -9  ah
+- 64
+F 70 4 3 La
+: 76 6 -5  la
+: 84 10 -9 ~
+E

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 +172,66 @@ 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"] == []
+
+
+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)

tests/test_notes.py

@@ -78,7 +78,7 @@ def test_syllabify():
     assert syllabify("reminds", "English") == ["re", "minds"]
     assert syllabify("a", "English") == ["a"]
     assert syllabify("xyzzy", None) == ["xyzzy"]
-    assert syllabify("She's", "English") == ["She's"]
+    assert syllabify("It's", "English") == ["It's"]
 
 
 def test_syllabify_preserves_word_characters():
@@ -96,12 +96,12 @@ def test_syllabify_language_without_pyphen_dict_is_not_mishandled_as_english():
 def test_build_notes_basic_line():
     times, f0, conf = flat_pitch()
     rows = [
-        {"word": "She's", "start": 1.0, "end": 1.5, "score": 0.9, "line_index": 0},
+        {"word": "It's", "start": 1.0, "end": 1.5, "score": 0.9, "line_index": 0},
         {"word": "got", "start": 1.6, "end": 2.0, "score": 0.9, "line_index": 0},
         {"word": "Now", "start": 5.0, "end": 5.5, "score": 0.9, "line_index": 1},
     ]
     notes, breaks, warnings = build_notes(rows, times, f0, conf, "English")
-    assert notes[0].text == "She's"          # line-initial: no leading space
+    assert notes[0].text == "It's"          # line-initial: no leading space
     assert notes[1].text == " got"           # mid-line: leading space
     assert notes[2].text == "Now"            # new line: no leading space
     assert len(breaks) == 1 and 2.0 <= breaks[0] <= 5.0
@@ -278,7 +278,7 @@ def test_notes_stage_writes_json(tmp_path):
     times, f0, conf = flat_pitch()
     np.savez(work / "pitch.npz", times=times, f0=f0, conf=conf)
     (work / "alignment.json").write_text(json.dumps([
-        {"word": "She's", "start": 1.0, "end": 1.5, "score": 0.9, "line_index": 0},
+        {"word": "It's", "start": 1.0, "end": 1.5, "score": 0.9, "line_index": 0},
         {"word": "gone", "start": 3.0, "end": 3.5, "score": 0.9, "line_index": 1},
     ]))
     ctx = Ctx(inp=SongInput(title="T", artist="A", lyrics_text="x", language="English"),
@@ -287,7 +287,7 @@ def test_notes_stage_writes_json(tmp_path):
     assert stage.outputs(ctx) == [work / "notes_sec.json"]
     stage.run(ctx)
     data = json.loads((work / "notes_sec.json").read_text())
-    assert [n["text"] for n in data["notes"]] == ["She's", "gone"]
+    assert [n["text"] for n in data["notes"]] == ["It's", "gone"]
     assert data["breaks"] == [2.25]
     assert data["warnings"] == []
     assert TimedNote(**data["notes"][0]).pitch == 0

tests/test_pitch.py

@@ -17,3 +17,21 @@ def test_pitch_stage_writes_npz(tmp_path, monkeypatch):
     data = np.load(work / "pitch.npz")
     assert np.allclose(data["f0"], 220.0)
     assert data["times"].shape == data["conf"].shape
+
+
+def test_mute_silence_drops_bleed_but_keeps_quiet_singing():
+    # 60 frames of singing around -30 dB, one quiet sung frame at -50 dB, and
+    # 40 frames of bleed at -60 dB: only the bleed is more than 25 dB below
+    # the song's loud level.
+    loudness = np.array([-30.0] * 60 + [-50.0] + [-60.0] * 40)
+    conf = np.full_like(loudness, 0.8)
+    pitch.mute_silence(conf, loudness)
+    assert (conf[:61] == 0.8).all()
+    assert (conf[61:] == 0.0).all()
+
+
+def test_mute_silence_is_relative_to_the_songs_level():
+    loudness = np.array([-45.0] * 60 + [-75.0] * 40)
+    conf = np.full_like(loudness, 0.8)
+    pitch.mute_silence(conf, loudness)
+    assert (conf[:60] == 0.8).all() and (conf[60:] == 0.0).all()

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] == []

tests/test_ultrastar.py

@@ -10,8 +10,8 @@ def load(name):
     return (FIXTURES / name).read_text(encoding="utf-8", errors="replace")
 
 def test_parse_headers_and_notes():
-    song = parse_ultrastar(load("sanctuary.txt"))
-    assert get_header(song, "artist") == "Utada Hikaru"
+    song = parse_ultrastar(load("chart_crlf.txt"))
+    assert get_header(song, "artist") == "Test Artist"
     assert parse_bpm(get_header(song, "BPM")) == 217.98
     first = song.body[0]
     assert first == Note(kind=":", beat=0, length=3, pitch=16, text="ah")
@@ -19,16 +19,16 @@ def test_parse_headers_and_notes():
     assert any(n.kind == "F" for n in song.body if isinstance(n, Note))
 
 def test_note_text_keeps_leading_space():
-    song = parse_ultrastar(load("sanctuary.txt"))
+    song = parse_ultrastar(load("chart_crlf.txt"))
     notes = [n for n in song.body if isinstance(n, Note)]
     assert notes[1].text == " ah"
 
 def test_negative_pitch():
-    song = parse_ultrastar(load("sweet_child.txt"))
+    song = parse_ultrastar(load("chart_latin1.txt"))
     assert any(n.pitch == -9 for n in song.body if isinstance(n, Note))
 
 def test_round_trip():
-    for name in ("sweet_child.txt", "sanctuary.txt"):
+    for name in ("chart_latin1.txt", "chart_crlf.txt"):
         song = parse_ultrastar(load(name))
         again = parse_ultrastar(serialize_ultrastar(song))
         assert again == song