PearlLeeStudio/TheArtist-MusicTransformer-ft-pop67

TheArtist Music Transformer — F2 (Pop 5K Mix)

Jazz-adapted chord model with a 5,000-sequence pop rehearsal buffer. Calibration point that the paper finds is dominated by F3 on every axis.

One of six checkpoints released alongside the paper Empirical Study of Pop and Jazz Mix Ratios for Genre-Adaptive Chord Generation (Lee, 2026). See the collection overview at PearlLeeStudio/TheArtist-MusicTransformer-pop-baseline.

Model summary

Field	Value
Architecture	Music Transformer with relative positional attention
Parameters	25,661,440
Vocabulary size	351 tokens
Max sequence length	256
d_model / heads / FFN / layers	512 / 8 / 2048 / 8
Fine-tune resumed from	Phase 0 pop baseline
Best epoch	4

Training data

All 1,513 jazz training sequences plus 5,000 pop rehearsal sequences (seed 42). Pop:jazz ≈ 3.3:1.

Evaluation (held-out per-genre test sets)

Metric	Pop test	Jazz test
Top-1 accuracy	84.07%	79.90%
Top-5 accuracy	97.04%	92.14%
Perplexity	1.75	2.33
Δ vs. Phase 0 baseline	−0.17	+7.04

F2 is dominated by F3 on every axis. It is released for reproducibility of the saturation curve described in the paper (see paper §6.1, §7.3) but is not the recommended choice for any operating point. Prefer F3 for the balanced setting, F1 for pop-leaning, or F4 for jazz-leaning.

Intended use

Reference checkpoint for replication and saturation-curve analysis. Not recommended as a default for chord-composition workflows.

Usage

import torch
from huggingface_hub import hf_hub_download
from model import MusicTransformer
from tokenizer import ChordTokenizer

ckpt_path = hf_hub_download(
    repo_id="PearlLeeStudio/TheArtist-MusicTransformer-ft-pop67",
    filename="best.pt",
)
tokenizer = ChordTokenizer()
ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=False)

model = MusicTransformer(
    vocab_size=tokenizer.vocab_size,
    d_model=512, n_heads=8, d_ff=2048, n_layers=8,
    max_seq_len=256, dropout=0.0, pad_id=tokenizer.pad_id,
)
model.load_state_dict(ckpt["model_state_dict"])
model.eval()

Training-data licenses

Dataset	License
Chordonomicon	Public (user-generated)
McGill Billboard	CC0
Jazz Harmony Treebank	Public
JazzStandards (iReal Pro)	Community redistribution
Weimar Jazz Database	ODbL
JAAH	Research-use public

Citation

Preprint: arXiv:2605.04998.

@misc{lee2026chordmix,
  title         = {Empirical Study of Pop and Jazz Mix Ratios for Genre-Adaptive Chord Generation},
  author        = {Lee, Jinju},
  year          = {2026},
  eprint        = {2605.04998},
  archivePrefix = {arXiv}
}