Rameau: functional harmony from notation

A text-to-text dataset and benchmark for functional harmony: Roman-numeral analysis, cadence classification, and key identification. A probabilistic common-practice grammar generates the progressions; four task framings hide the answer to increasing degrees. Chord-symbol lookup stops working after the first one.

Named for Jean-Philippe Rameau, whose Traité de l'harmonie (1722) started the discipline.

symbol_to_rn  key: C major / progression: Dm7 G7 Cmaj7   ->  ii7 V7 IM7 / cadence: PAC
notes_to_rn   key: C major / notes: D4 F4 A4 C5 | ...     ->  ii7 V7 IM7 / cadence: PAC
pcset_to_rn   key: C major / pitch classes: [2 5 9 0]|... ->  ii7 V7 IM7 / cadence: PAC
key_id        notes: D4 F4 A4 C5 | G3 B3 D4 F4 | ...      ->  C major

Configs (tasks)

Load one with load_dataset("4esv/rameau", "<config>"). Default: notes_to_rn.

config	task	rows
symbol_to_rn	key + chord symbols -> Roman numerals + cadence (easy: chord quality is given)	5,715
notes_to_rn	key + spelled notes -> Roman numerals + cadence (must read each chord)	5,715
pcset_to_rn	key + bass-first pitch-class lists -> Roman numerals + cadence (no spelling)	5,715
key_id	spelled notes, no key -> identify the key (only key-unambiguous phrases)	4,795
	total	21,940

Gold labels

Nothing is hand-annotated. The grammar (tonic -> predominant -> dominant -> cadence, with sevenths, inversions, cadential 6-4s and secondary dominants) generates each progression together with its intended analysis. Every chord is then derived two independent ways with music21: the Roman-numeral figure through the roman engine, and the printed chord symbol through the chord-symbol parser. An item is kept only if both agree on pitch-class set and bass. This release: 27,480 of 27,480 chords agree. See VERIFY.md.

Built from 845 progression shapes (key-independent), transposed across keys. All content is synthetic; no third-party corpus is redistributed.

Label convention

Roman numerals follow the feature decomposition of the DCML harmony standard (numeral / form / figbass / changes / relativeroot). We follow the notation and copy no DCML data. Major-seventh tonic is IM7; secondary dominants use / (e.g. V7/vi). Cadence codes: PAC perfect authentic, IAC imperfect authentic, HC half, DC deceptive, PC plagal.

Fields

Common to every config: input, target, key, mode, labels, cadence, analysis (per-chord DCML features), source (grammar/curated/single), category, shape_id. Plus the input representation for the config: chords (symbols), notes (spelled, bass-first), or pitch_classes (bass-first).

Accidentals are written the standard way (Bb, F#, Cb). music21 users: its parsers want - for flats, so convert b -> - in note and root names before calling ChordSymbol or Pitch.

Splits

The atomic unit is a shape, a key-independent Roman-numeral sequence. A shape hashes to exactly one split, so none of its transpositions or task framings crosses splits. The test split doubles as the benchmark. Rows: train 14,725 / validation 3,571 / test 3,644.

Known limitations

• The distribution is synthetic. Grammar output, not repertoire; chord statistics are not naturalistic.
• PAC vs IAC is decided by inversion, since there is no notated soprano. Cadence rules are strict: an HC ends on a root-position V triad (a terminal V7 is not labelled), and a DC requires a root-position dominant (V65 -> vi does not count).
• key_id keeps only cadence-terminated progressions of three or more chords whose notes contain scale degree 4 and the leading tone, so the key is uniquely determined. Without the gate, gold keys are contestable: I V7/V V in C is note-identical to IV V7 I in G, and the G reading is arguably stronger. Such phrases are excluded.
• Harmony only: no voice leading, melody, or rhythm. No modal mixture, Neapolitans, or augmented sixths yet.

Evaluation

Gold is deterministic, so scoring is exact match. No LLM judge. The harness in eval/ is stdlib-only and works against any OpenAI-compatible endpoint:

python eval/run_model.py --config notes_to_rn --model <model> --out preds.jsonl
python eval/score.py preds.jsonl --config notes_to_rn --split test

Metrics for the RN configs: exact (numerals and cadence both correct), labels_exact, chord_acc, cadence_acc. For key_id: exact, tonic_acc, mode_acc. Prompts are versioned in eval/prompts.py; parsing rules are in eval/README.md.

Results

Full test split, zero-shot, temperature 0, prompt v1, run 2026-07-11 via OpenRouter. Cells are exact match, with per-chord accuracy in parentheses. Raw predictions and per-run metadata are in results/.

model	symbol_to_rn	notes_to_rn	pcset_to_rn	key_id
gpt-oss-120b (reasoning low)	0.256 (0.885)	0.155 (0.778)	0.146 (0.724)	0.778
Claude Sonnet 5	0.220 (0.863)	0.066 (0.688)	0.157 (0.732)	0.823
Qwen3-235B-A22B-Instruct	0.193 (0.739)	0.016 (0.380)	0.002 (0.163)	0.719
DeepSeek-V3.2	0.151 (0.634)	0.005 (0.305)	0.001 (0.150)	0.661
Kimi-K2.5	0.142 (0.562)	0.007 (0.399)	0.009 (0.205)	0.788
Llama-3.3-70B	0.037 (0.466)	0.004 (0.307)	0.000 (0.194)	0.471

No model saturates the easiest config. Models that do not reason drop toward zero once the chord symbols disappear; the two that do degrade more slowly. Claude Sonnet 5 scores higher on pitch classes than on spelled notes, which suggests spelling rather than harmony is its bottleneck. The table cost about nine dollars in API credits.

Reasoning on vs off

Fixed test subsets rerun with reasoning enabled; n in the table. Exact match on notes_to_rn:

model	n	off	on
Kimi-K2.5	150	0.000	0.740
DeepSeek-V3.2	200	0.025	0.725
Claude Sonnet 5	100	0.090	0.520
gpt-oss-120b (low -> high)	200	0.185	0.440

The pattern holds on every config; full numbers are in results/reasoning/. With thinking enabled, per-chord accuracy reaches 0.89 to 0.98 on the hidden configs, so the remaining exact-match gap is mostly cadence and figure errors. Without thinking the benchmark measures pattern recall; with thinking it measures multi-step computation. Neither saturates it.

Reproduce

The full generation pipeline ships in this repo (src/harmony_dataset/):

uv sync && uv run pytest
uv run python -m harmony_dataset.export    # regenerates data/, README, VERIFY.md

Related work

• MusicTheoryBench (ChatMusician, 2024): 372 hand-written multiple-choice questions on broad music knowledge. Rameau is generative and machine-verified.
• Harmonic Reasoning in LLMs (Kruspe, 2024): synthetic interval, chord, and scale identification. No key context, so identification rather than functional analysis.
• Teaching LLMs Music Theory (Pond & Fujinaga, 2025): one RCM Level 6 exam in four encodings, with prompting strategies. Rameau frames the same progressions in each representation, so representation is the only variable.
• Score-based Roman-numeral analysis (Micchi et al., AugmentedNet, AnalysisGNN): specialist models trained on NC-licensed annotated corpora. Rameau targets text models and generates its own data, which is what keeps the license CC-BY.

Licensing

CC-BY-4.0. Content is generated by this repository's pipeline from music theory; the underlying facts are not copyrightable and no source corpus is redistributed.

Citation

@misc{rameau,
  title  = {Rameau: Functional Harmony from Notation (Roman Numerals, Cadence, Key)},
  author = {Stevens, Axel},
  year   = {2026},
  doi    = {10.57967/hf/9570},
  url    = {https://huggingface.co/datasets/4esv/rameau},
  note   = {Synthetic, music21-verified, DCML labels}
}