sbss.nfca.tasks.JdAviTask

class sbss.nfca.tasks.JdAviTask(encoder: Module, decoder: Module, n_fft: int, hop_length: int, n_src: int, beta: float, optimizer_config: OptimizerConfig)

Neural FastFCA task [Bando2023] implemented in PyTorch Lightning.

Similar to sbss.nfca.tasks.AviTask, this module optimizes the evidence lower bound of neural FCA, but it follows the fast variant [Bando2023] in which the spatial covariance model is factorized by an estimated joint diagonalizer Q and gain tensor g. The encoder produces amortized posterior distributions over latent variables as well as g, Q, and auxiliary spectrograms xt derived from the diagonalized mixtures. The decoder converts latent samples into power spectral densities lm; negative log-likelihood and KL penalties are then computed in the diagonalized domain and separation is performed via Wiener filtering followed by iSTFT.

Parameters:

• encoder (nn.Module) – Joint-diagonalization encoder returning a latent posterior distribution plus g, Q, and xt tensors described in [Bando2023].

• decoder (nn.Module) – Maps latent variables to power spectral densities that correspond to |S|^2 in neural FastFCA.

• n_fft (int) – FFT size used during STFT preprocessing.

• hop_length (int) – Hop length for the STFT/iSTFT pair.

• n_src (int) – Number of sources (N) modeled by the task.

• beta (float) – Weight applied to the KL term in the ELBO objective.

• optimizer_config (OptimizerConfig) – Optimizer/lightning configuration wrapper.

Returns:

• wav_sep: Time-domain separated waveform tensor [B, T].

• dump: Snapshot with xpwr, lm, z, and xt useful for inspection.

Return type:

tuple[torch.Tensor, Snapshot]

__init__(encoder: Module, decoder: Module, n_fft: int, hop_length: int, n_src: int, beta: float, optimizer_config: OptimizerConfig)

Methods

__init__(encoder, decoder, n_fft, ...)
add_module(name, module)	Add a child module to the current module.
all_gather(data[, group, sync_grads])	Gather tensors or collections of tensors from multiple processes.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
backward(loss, *args, **kwargs)	Called to perform backward on the loss returned in training_step().
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
children()	Return an iterator over immediate children modules.
clip_gradients(optimizer[, ...])	Handles gradient clipping internally.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
configure_callbacks()	Configure model-specific callbacks.
configure_gradient_clipping(optimizer[, ...])	Perform gradient clipping for the optimizer parameters.
configure_model()	Hook to create modules in a strategy and precision aware context.
configure_optimizers()	Configures the optimizer and scheduler for training.
configure_sharded_model()	Deprecated.
cpu()	See torch.nn.Module.cpu().
cuda([device])	Moves all model parameters and buffers to the GPU.
double()	See torch.nn.Module.double().
eval()	Set the module in evaluation mode.
extra_repr()	Return the extra representation of the module.
float()	See torch.nn.Module.float().
forward(wav[, out_ch])	Same as torch.nn.Module.forward().
freeze()	Freeze all params for inference.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_extra_state()	Return any extra state to include in the module's state_dict.
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
half()	See torch.nn.Module.half().
ipu([device])	Move all model parameters and buffers to the IPU.
load_from_checkpoint(checkpoint_path[, ...])	Primary way of loading a model from a checkpoint.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
log(name, value[, prog_bar, logger, ...])	Log a key, value pair.
log_dict(dictionary[, prog_bar, logger, ...])	Log a dictionary of values at once.
lr_scheduler_step(scheduler, metric)	Override this method to adjust the default way the Trainer calls each scheduler.
lr_schedulers()	Returns the learning rate scheduler(s) that are being used during training.
manual_backward(loss, *args, **kwargs)	Call this directly from your training_step() when doing optimizations manually.
modules()	Return an iterator over all modules in the network.
mtia([device])	Move all model parameters and buffers to the MTIA.
named_buffers([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
on_after_backward()	Called after loss.backward() and before optimizers are stepped.
on_after_batch_transfer(batch, dataloader_idx)	Override to alter or apply batch augmentations to your batch after it is transferred to the device.
on_before_backward(loss)	Called before loss.backward().
on_before_batch_transfer(batch, dataloader_idx)	Override to alter or apply batch augmentations to your batch before it is transferred to the device.
on_before_optimizer_step(optimizer)	Called before optimizer.step().
on_before_zero_grad(optimizer)	Called after training_step() and before optimizer.zero_grad().
on_fit_end()	Called at the very end of fit.
on_fit_start()	Called at the very beginning of fit.
on_load_checkpoint(checkpoint)	Called by Lightning to restore your model.
on_predict_batch_end(outputs, batch, batch_idx)	Called in the predict loop after the batch.
on_predict_batch_start(batch, batch_idx[, ...])	Called in the predict loop before anything happens for that batch.
on_predict_end()	Called at the end of predicting.
on_predict_epoch_end()	Called at the end of predicting.
on_predict_epoch_start()	Called at the beginning of predicting.
on_predict_model_eval()	Called when the predict loop starts.
on_predict_start()	Called at the beginning of predicting.
on_save_checkpoint(checkpoint)	Called by Lightning when saving a checkpoint to give you a chance to store anything else you might want to save.
on_test_batch_end(outputs, batch, batch_idx)	Called in the test loop after the batch.
on_test_batch_start(batch, batch_idx[, ...])	Called in the test loop before anything happens for that batch.
on_test_end()	Called at the end of testing.
on_test_epoch_end()	Called in the test loop at the very end of the epoch.
on_test_epoch_start()	Called in the test loop at the very beginning of the epoch.
on_test_model_eval()	Called when the test loop starts.
on_test_model_train()	Called when the test loop ends.
on_test_start()	Called at the beginning of testing.
on_train_batch_end(outputs, batch, batch_idx)	Called in the training loop after the batch.
on_train_batch_start(batch, batch_idx)	Called in the training loop before anything happens for that batch.
on_train_end()	Called at the end of training before logger experiment is closed.
on_train_epoch_end()	Called in the training loop at the very end of the epoch.
on_train_epoch_start()	Called in the training loop at the very beginning of the epoch.
on_train_start()	Called at the beginning of training after sanity check.
on_validation_batch_end(outputs, batch, ...)	Called in the validation loop after the batch.
on_validation_batch_start(batch, batch_idx)	Called in the validation loop before anything happens for that batch.
on_validation_end()	Called at the end of validation.
on_validation_epoch_end()	Called in the validation loop at the very end of the epoch.
on_validation_epoch_start()	Called in the validation loop at the very beginning of the epoch.
on_validation_model_eval()	Called when the validation loop starts.
on_validation_model_train()	Called when the validation loop ends.
on_validation_model_zero_grad()	Called by the training loop to release gradients before entering the validation loop.
on_validation_start()	Called at the beginning of validation.
optimizer_step(epoch, batch_idx, optimizer)	Override this method to adjust the default way the Trainer calls the optimizer.
optimizer_zero_grad(epoch, batch_idx, optimizer)	Override this method to change the default behaviour of optimizer.zero_grad().
optimizers([use_pl_optimizer])	Returns the optimizer(s) that are being used during training.
parameters([recurse])	Return an iterator over module parameters.
predict_dataloader()	An iterable or collection of iterables specifying prediction samples.
predict_step(*args, **kwargs)	Step function called during predict().
prepare_data()	Use this to download and prepare data.
print(*args, **kwargs)	Prints only from process 0.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post-hook to be run after module's load_state_dict() is called.
register_load_state_dict_pre_hook(hook)	Register a pre-hook to be run before module's load_state_dict() is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_post_hook(hook)	Register a post-hook for the state_dict() method.
register_state_dict_pre_hook(hook)	Register a pre-hook for the state_dict() method.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
save_hyperparameters(*args[, ignore, frame, ...])	Save arguments to hparams attribute.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_submodule(target, module[, strict])	Set the submodule given by target if it exists, otherwise throw an error.
setup(stage)	Called at the beginning of fit (train + validate), validate, test, or predict.
share_memory()	See torch.Tensor.share_memory_().
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
teardown(stage)	Called at the end of fit (train + validate), validate, test, or predict.
test_dataloader()	An iterable or collection of iterables specifying test samples.
test_step(*args, **kwargs)	Operates on a single batch of data from the test set.
to(*args, **kwargs)	See torch.nn.Module.to().
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
to_onnx([file_path, input_sample])	Saves the model in ONNX format.
to_tensorrt([file_path, input_sample, ir, ...])	Export the model to ScriptModule or GraphModule using TensorRT compile backend.
to_torchscript([file_path, method, ...])	By default compiles the whole model to a ScriptModule.
toggle_optimizer(optimizer)	Makes sure only the gradients of the current optimizer's parameters are calculated in the training step to prevent dangling gradients in multiple-optimizer setup.
toggled_optimizer(optimizer)	Makes sure only the gradients of the current optimizer's parameters are calculated in the training step to prevent dangling gradients in multiple-optimizer setup.
train([mode])	Set the module in training mode.
train_dataloader()	An iterable or collection of iterables specifying training samples.
training_step(wav, batch_idx[, log_prefix])	Here you compute and return the training loss and some additional metrics for e.g. the progress bar or logger.
transfer_batch_to_device(batch, device, ...)	Override this hook if your DataLoader returns tensors wrapped in a custom data structure.
type(dst_type)	See torch.nn.Module.type().
unfreeze()	Unfreeze all parameters for training.
untoggle_optimizer(optimizer)	Resets the state of required gradients that were toggled with toggle_optimizer().
val_dataloader()	An iterable or collection of iterables specifying validation samples.
validation_step(wav, batch_idx)	Operates on a single batch of data from the validation set.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

CHECKPOINT_HYPER_PARAMS_KEY
CHECKPOINT_HYPER_PARAMS_NAME
CHECKPOINT_HYPER_PARAMS_TYPE
T_destination
automatic_optimization	If set to False you are responsible for calling .backward(), .step(), .zero_grad().
call_super_init
current_epoch	The current epoch in the Trainer, or 0 if not attached.
device
device_mesh	Strategies like ModelParallelStrategy will create a device mesh that can be accessed in the configure_model() hook to parallelize the LightningModule.
dtype
dump_patches
example_input_array	The example input array is a specification of what the module can consume in the forward() method.
fabric
global_rank	The index of the current process across all nodes and devices.
global_step	Total training batches seen across all epochs.
hparams	The collection of hyperparameters saved with save_hyperparameters().
hparams_initial	The collection of hyperparameters saved with save_hyperparameters().
local_rank	The index of the current process within a single node.
logger	Reference to the logger object in the Trainer.
loggers	Reference to the list of loggers in the Trainer.
on_gpu	Returns True if this model is currently located on a GPU.
strict_loading	Determines how Lightning loads this model using .load_state_dict(..., strict=model.strict_loading).
trainer
training