tempo_module¶

PyTorch Lightning module for tempo estimation.

Classes

TempoModule(model[, loss, classification, ...])

LightningModule wrapping a tempo regression or classification model.

class TempoModule(model, loss='absolute', classification=None, lr=0.001, weight_decay=0.0001, check_val_every_n_epoch=1)[source]¶

Bases: LightningModule

LightningModule wrapping a tempo regression or classification model.

Three loss modes are supported:

"absolute" — plain MAE between predicted and true BPM.
"relative" — octave-invariant MAE.
"classification" — softmax over BPM bins with a Gaussian soft target. Requires the classification config section.

For classification mode the model’s n_outputs is overridden to classification.n_bins automatically.

Parameters:

model (DictConfig) – DictConfig for instantiating the backbone.
loss (str) – Loss name — "absolute", "relative", or "classification".
classification (DictConfig | None) – Required when loss == "classification". Must have bpm_min, bpm_max, n_bins, sigma.
lr (float) – Learning rate.
weight_decay (float) – L2 regularisation.
check_val_every_n_epoch (int) – How often the trainer actually runs validation (cfg.trainer.check_val_every_n_epoch). The ReduceLROnPlateau scheduler needs this as its frequency — Lightning otherwise tries to step it (and read val/loss) every epoch regardless of how often validation runs, raising MisconfigurationException on any epoch without a fresh value.