Views
No views yet
chirality on PyPI), a piano-fingering pipeline combining
ergonomic rule sets, statistical models, and reinforcement learning.| Path | What | Human match (M_gen) |
|---|---|---|
weights/parncutt.json | Path-difference learned Parncutt rule weights (Radisavljevic & Driessen 2004, trained on PIG) | 65.4 — best non-human system |
hmm/param_FHMM{1,2,3}.txt | Nakamura et al. (2020) fingering HMMs, orders 1–3, trained on the PIG miscellaneous subset | 61.6 / 64.3 / 64.4 |
ppo/conv_jacobs.pt | PPO conv policy, jacobs reward | 44.8 (right hand) |
ppo/decoder_hmm_nll.pt | PPO decoder policy, HMM-NLL reward | 43.2 (right hand) |
ppo/transformer_hmm_nll.pt | PPO transformer policy, HMM-NLL reward | 42.2 (right hand) |
ppo/simple_hmm_nll.pt | PPO MLP policy, HMM-NLL reward | 39.9 (right hand) |
ppo/conv_mixed_hmm.pt | PPO conv policy, mixed rules+HMM reward | 39.5 (right hand) |
docs/experiments_summary.md in
the code repository.1pip install chirality
2chirality fetch-models # downloads this repo's models into ./data
3chirality annotate --input score.mei --output ./annotated --weights --both
4chirality annotate --input score.mei --output ./annotated --hmm --order 2chirality fetch-models --ppo also pulls the PPO checkpoints.chirality test --checkpoint ppo/<name>.pt --policy <arch> or chirality annotate --model ....