paddlespeech.t2s.models.wavernn.wavernn_updater module
- class paddlespeech.t2s.models.wavernn.wavernn_updater.WaveRNNEvaluator(model: Layer, criterion: Layer, dataloader: Optimizer, output_dir: Optional[Path] = None, valid_generate_loader=None, config=None)[source]
Bases:
StandardEvaluator- Attributes:
- name
Methods
__call__([trainer])Main action of the extention.
finalize(trainer)Action that is executed when training is done.
initialize(trainer)Action that is executed once to get the corect trainer state.
on_error(trainer, exc, tb)Handles the error raised during training before finalization.
evaluate
evaluate_core
gen_valid_samples
- class paddlespeech.t2s.models.wavernn.wavernn_updater.WaveRNNUpdater(model: Layer, optimizer: Optimizer, criterion: Layer, dataloader: DataLoader, init_state=None, output_dir: Optional[Path] = None, mode='RAW')[source]
Bases:
StandardUpdater- Attributes:
updates_per_epochNumber of updater per epoch, determined by the length of the dataloader.
Methods
new_epoch()Start a new epoch.
read_batch()Read a batch from the data loader, auto renew when data is exhausted.
set_state_dict(state_dict)Set state dict for a Updater.
state_dict()State dict of a Updater, model, optimizer and updater state are included.
update_core(batch)A simple case for a training step.
load
save
update
- update_core(batch)[source]
A simple case for a training step. Basic assumptions are: Single model; Single optimizer; A batch from the dataloader is just the input of the model; The model return a single loss, or a dict containing serval losses. Parameters updates at every batch, no gradient accumulation.