--- license: other license_name: per-track-cc license_details: >- Word timing annotations derive from JamendoLyrics and carry each song's original Creative Commons license (BY / BY-SA / BY-ND / BY-NC / BY-NC-SA / BY-NC-ND — see the `license` field per record). Difficulty bucket annotations (buckets.csv) are original work released under CC-BY-4.0. Audio is NOT redistributed here; fetch it from the jamendolyrics dataset. task_categories: - automatic-speech-recognition language: - en - fr - de - es tags: - music - lyrics - alignment - singing - benchmark - evaluation pretty_name: signalign-bench size_categories: - n<1K --- # 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](https://huggingface.co/datasets/jamendolyrics/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](https://huggingface.co/datasets/jamendolyrics/jamendolyrics) dataset (same track ids / file names): ```bash 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//mp3/`. ## Metrics protocol Defined and implemented in [`eval/score.py`](https://github.com/alcadramin/signalign) (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).