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JacobLinCool/
softchart-v15

Name: softchart-v15
Rating: 2.5 (12 reviews)

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🥁 SoftChart v1.5 — unified model

One 7.94M-parameter model (bf16, ~16 MB) replacing three (slot generator + time generator + beat model, 24.5M / ~98 MB): −68% parameters, −84% disk, with measured quality parity or better on every axis.

axis	v1.5 (this model)	v1.0 dedicated	verdict
slot-mode F1 (12 songs)	0.663 / oni 0.732	0.665 / 0.727 (slot12)	parity, oni better
time-mode F1	0.682 / oni 0.721	0.659 (pr12)	better
beat grid ok-rate (const)	1.0 · p50 10.5 ms	0.93 · 10.2 ms (beatd2)	better
beat grid (variable BPM)	0.9 · p50 3.2 ms	1.0 · 4.0 ms	parity
tuplet evenness (CV)	0.0006	0.0006	parity

How the reduction was achieved (all measured, see repo REPORT):

Role folding — dual-mode training (50% slot / 50% time windows, MODE token) + a hi-res beat head. Beat supervision is masked on slot windows: they carry grid-phase input channels, and an unmasked head copies the channels instead of listening (measured collapse: 723 ms → fixed: 10.5 ms).
Factorized embeddings (ALBERT-style, E=64) — position tokens are 95% of the table; 463K → 132K.
bf16 weights — training ran bf16 autocast, so fp32 storage carried no extra information (verified: F1/grid identical within noise).

Interesting side effect: the shared trunk improved both the time mode (+2.3pp over the dedicated model) and the beat ok-rate — beat supervision appears to act as a metrical regularizer for generation, and vice versa.

Usage (drop-in for all three v1.0 roles):

python

from softchart.generate import load_hf, generate_song_slot, generate_song
from softchart.grid import fit_grid_piecewise
from softchart.tja import write_tja_slots

m = load_hf("JacobLinCool/softchart-v15")     # one model, three roles
grid = fit_grid_piecewise(m, mel)             # its own beat head drives the grid
g = generate_song_slot(m, mel, grid, "oni", level=9, density_bucket=7)
tja = write_tja_slots(g, grid, "Title", "oni", 9, "song.ogg")
# gridless fallback: generate_song(m, mel, "oni", level=9)  (time mode)

MIT-licensed. Trained from scratch on community-made charts (taiko-1000-parsed); the official game never released chart data.