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Datasets:
Alcadramin
/
signalign-bench

Tasks:
Automatic Speech Recognition
Languages:
English
French
German
Size:
n<1K
Tags:
music
lyrics
alignment
singing
benchmark
evaluation
License:
Dataset card Files
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
clean: struct<n_words: int64, coverage: double, mae_ms: double, medae_ms: double, pco_100: double, pco_200: (... 25 chars omitted)
  child 0, n_words: int64
  child 1, coverage: double
  child 2, mae_ms: double
  child 3, medae_ms: double
  child 4, pco_100: double
  child 5, pco_200: double
  child 6, pco_300: double
buried: struct<n_words: int64, coverage: double, mae_ms: double, medae_ms: double, pco_100: double, pco_200: (... 25 chars omitted)
  child 0, n_words: int64
  child 1, coverage: double
  child 2, mae_ms: double
  child 3, medae_ms: double
  child 4, pco_100: double
  child 5, pco_200: double
  child 6, pco_300: double
rap: struct<n_words: int64, coverage: double, mae_ms: double, medae_ms: double, pco_100: double, pco_200: (... 25 chars omitted)
  child 0, n_words: int64
  child 1, coverage: double
  child 2, mae_ms: double
  child 3, medae_ms: double
  child 4, pco_100: double
  child 5, pco_200: double
  child 6, pco_300: double
melisma: struct<n_words: int64, coverage: double, mae_ms: double, medae_ms: double, pco_100: double, pco_200: (... 25 chars omitted)
  child 0, n_words: int64
  child 1, coverage: double
  child 2, mae_ms: double
  child 3, medae_ms: double
  child 4, pco_100: double
  child 5, pco_200: double
  child 6, pco_300: double
held: struct<n_words: int64, coverage: double, mae_ms: double, medae_ms: double, pco_100: double, pco_200: (... 25 chars omitted)
  child 0, n_words: int64
  child 1, coverage: double
  child 2, mae_ms: double
  child 3, medae_ms: double
  child 4, pco_100: double
  child 5, pco_200: double
  child 6, pco_300: double
all: struct<n_words: int64, coverage: double, mae_ms: double, medae_ms: double, pco_100: double, pco_200: (... 25 chars omitted)
  child 0, n_words: int64
  child 1, coverage: double
  child 2, mae_ms: double
  child 3, medae_ms: double
  child 4, pco_100: double
  child 5, pco_200: double
  child 6, pco_300: double
language: string
duration: double
source: string
words: list<item: struct<text: string, start: double, end: double>>
  child 0, item: struct<text: string, start: double, end: double>
      child 0, text: string
      child 1, start: double
      child 2, end: double
cer_vs_lyrics: null
vocal_stem_path: null
asr_confidence: null
difficulty: string
audio_path: string
license: string
id: string
to
{'id': Value('string'), 'audio_path': Value('string'), 'vocal_stem_path': Value('null'), 'duration': Value('float64'), 'words': List({'text': Value('string'), 'start': Value('float64'), 'end': Value('float64')}), 'difficulty': Value('string'), 'language': Value('string'), 'asr_confidence': Value('null'), 'cer_vs_lyrics': Value('null'), 'source': Value('string'), 'license': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              clean: struct<n_words: int64, coverage: double, mae_ms: double, medae_ms: double, pco_100: double, pco_200: (... 25 chars omitted)
                child 0, n_words: int64
                child 1, coverage: double
                child 2, mae_ms: double
                child 3, medae_ms: double
                child 4, pco_100: double
                child 5, pco_200: double
                child 6, pco_300: double
              buried: struct<n_words: int64, coverage: double, mae_ms: double, medae_ms: double, pco_100: double, pco_200: (... 25 chars omitted)
                child 0, n_words: int64
                child 1, coverage: double
                child 2, mae_ms: double
                child 3, medae_ms: double
                child 4, pco_100: double
                child 5, pco_200: double
                child 6, pco_300: double
              rap: struct<n_words: int64, coverage: double, mae_ms: double, medae_ms: double, pco_100: double, pco_200: (... 25 chars omitted)
                child 0, n_words: int64
                child 1, coverage: double
                child 2, mae_ms: double
                child 3, medae_ms: double
                child 4, pco_100: double
                child 5, pco_200: double
                child 6, pco_300: double
              melisma: struct<n_words: int64, coverage: double, mae_ms: double, medae_ms: double, pco_100: double, pco_200: (... 25 chars omitted)
                child 0, n_words: int64
                child 1, coverage: double
                child 2, mae_ms: double
                child 3, medae_ms: double
                child 4, pco_100: double
                child 5, pco_200: double
                child 6, pco_300: double
              held: struct<n_words: int64, coverage: double, mae_ms: double, medae_ms: double, pco_100: double, pco_200: (... 25 chars omitted)
                child 0, n_words: int64
                child 1, coverage: double
                child 2, mae_ms: double
                child 3, medae_ms: double
                child 4, pco_100: double
                child 5, pco_200: double
                child 6, pco_300: double
              all: struct<n_words: int64, coverage: double, mae_ms: double, medae_ms: double, pco_100: double, pco_200: (... 25 chars omitted)
                child 0, n_words: int64
                child 1, coverage: double
                child 2, mae_ms: double
                child 3, medae_ms: double
                child 4, pco_100: double
                child 5, pco_200: double
                child 6, pco_300: double
              language: string
              duration: double
              source: string
              words: list<item: struct<text: string, start: double, end: double>>
                child 0, item: struct<text: string, start: double, end: double>
                    child 0, text: string
                    child 1, start: double
                    child 2, end: double
              cer_vs_lyrics: null
              vocal_stem_path: null
              asr_confidence: null
              difficulty: string
              audio_path: string
              license: string
              id: string
              to
              {'id': Value('string'), 'audio_path': Value('string'), 'vocal_stem_path': Value('null'), 'duration': Value('float64'), 'words': List({'text': Value('string'), 'start': Value('float64'), 'end': Value('float64')}), 'difficulty': Value('string'), 'language': Value('string'), 'asr_confidence': Value('null'), 'cer_vs_lyrics': Value('null'), 'source': Value('string'), 'license': Value('string')}
              because column names don't match

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signalign-bench v0.1

The first difficulty-bucketed word-level lyrics alignment benchmark for sung vocals.

Existing lyrics-alignment evaluation reports one blended number. Singing fails aligners in different ways — fast rap, melisma, held notes, vocals buried in the mix — and a single number hides all of it. This benchmark tags every track with its dominant difficulty and reports metrics per bucket.

Maintained at: https://github.com/alcadramin/signalign (evaluation harness, baselines, protocol).

Contents

File What
jamendolyrics.jsonl 79 tracks, word-level gold onsets/offsets, difficulty bucket, language, per-track license
buckets.csv The raw human bucket annotations (id → difficulty)
results/ Baseline system scores (raw JSON, per bucket)

Schema per record: id, audio_path (relative to the jamendolyrics dataset root), duration, words[{text,start,end}], difficulty, language, source, license.

Difficulty buckets

Assigned by ear by a human annotator (single annotator, v0.1 — annotator: alca, 2026-08-14). One dominant label per track; ties broken by fixed priority rap > melisma > held > buried.

bucket tracks meaning
clean 46 clear vocal, plain delivery
buried 13 vocal low in the mix / heavy accompaniment
rap 11 fast dense delivery
melisma 5 multi-pitch syllables, vocal runs
held 4 delivery built from long sustained notes

Audio

Not re-hosted. Word timings and buckets reference the jamendolyrics/jamendolyrics dataset (same track ids / file names):

hf download jamendolyrics/jamendolyrics --repo-type dataset --local-dir data/jamendolyrics

Note: top-level mp3/ entries in that download may materialize as text pointer files; real audio lives under subsets/<lang>/mp3/.

Metrics protocol

Defined and implemented in eval/score.py (single source of truth): per-bucket coverage, MAE, MedAE (matched words, ms), PCO@{100,200,300}ms with all gold words as denominator (a dropped word counts as a miss). Word matching = LCS over normalized text.

Baseline results (v0.1)

Demucs htdemucs vocal stem → forced alignment. Full tables in results/.

system lyrics input coverage MAE MedAE PCO@100
wav2vec2-base-960h (speech) + gold lyrics yes 1.00 150ms 41ms 79.7%
WhisperX medium (ASR path) no 0.56 2187ms 54ms 38.9%

Per-bucket highlights: rap fails catastrophically not gradually (42ms MedAE, 214ms MAE, lyrics-informed); melisma smears onsets (worst PCO@100); held notes are the easiest bucket, not the hardest; WhisperX drops 44% of sung words. Whisper language ID misdetected a German rap track as Khmer.

Limitations

  • Single annotator, track-level buckets (v0.2 targets segment-level).
  • 79 tracks, 4 European languages; no extreme-technique or a-cappella-rap hard cases yet (planned additions).
  • Baseline alignments use an English character vocabulary after ASCII folding for all languages.

Citation 

@misc{signalign-bench,
  title = {signalign-bench: a difficulty-bucketed word-level lyrics
           alignment benchmark for sung vocals},
  author = {alca},
  year = {2026},
  url = {https://huggingface.co/datasets/Alcadramin/signalign-bench}
}

Please also cite the underlying JamendoLyrics dataset (Stoller et al.; Durand et al., see the jamendolyrics dataset card).
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