paddlespeech.t2s.modules.predictor.length_regulator module

Length regulator related modules.

class paddlespeech.t2s.modules.predictor.length_regulator.LengthRegulator(pad_value=0.0)

Bases: Layer

Length regulator module for feed-forward Transformer.

This is a module of length regulator described in FastSpeech: Fast, Robust and Controllable Text to Speech. The length regulator expands char or phoneme-level embedding features to frame-level by repeating each feature based on the corresponding predicted durations.

Methods

__call__(*inputs, **kwargs)	Call self as a function.
add_parameter(name, parameter)	Adds a Parameter instance.
add_sublayer(name, sublayer)	Adds a sub Layer instance.
apply(fn)	Applies fn recursively to every sublayer (as returned by .sublayers()) as well as self.
buffers([include_sublayers])	Returns a list of all buffers from current layer and its sub-layers.
children()	Returns an iterator over immediate children layers.
clear_gradients()	Clear the gradients of all parameters for this layer.
create_parameter(shape[, attr, dtype, ...])	Create parameters for this layer.
create_tensor([name, persistable, dtype])	Create Tensor for this layer.
create_variable([name, persistable, dtype])	Create Tensor for this layer.
eval()	Sets this Layer and all its sublayers to evaluation mode.
expand(encodings, durations)	encodings: (B, T, C) durations: (B, T)
expand_numpy(encodings, durations)	encodings: (B, T, C) durations: (B, T)
extra_repr()	Extra representation of this layer, you can have custom implementation of your own layer.
forward(xs, ds[, alpha, is_inference])	Calculate forward propagation.
full_name()	Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
load_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
named_buffers([prefix, include_sublayers])	Returns an iterator over all buffers in the Layer, yielding tuple of name and Tensor.
named_children()	Returns an iterator over immediate children layers, yielding both the name of the layer as well as the layer itself.
named_parameters([prefix, include_sublayers])	Returns an iterator over all parameters in the Layer, yielding tuple of name and parameter.
named_sublayers([prefix, include_self, ...])	Returns an iterator over all sublayers in the Layer, yielding tuple of name and sublayer.
parameters([include_sublayers])	Returns a list of all Parameters from current layer and its sub-layers.
register_buffer(name, tensor[, persistable])	Registers a tensor as buffer into the layer.
register_forward_post_hook(hook)	Register a forward post-hook for Layer.
register_forward_pre_hook(hook)	Register a forward pre-hook for Layer.
set_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
set_state_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
state_dict([destination, include_sublayers, ...])	Get all parameters and persistable buffers of current layer and its sub-layers.
sublayers([include_self])	Returns a list of sub layers.
to([device, dtype, blocking])	Cast the parameters and buffers of Layer by the give device, dtype and blocking.
to_static_state_dict([destination, ...])	Get all parameters and buffers of current layer and its sub-layers.
train()	Sets this Layer and all its sublayers to training mode.

backward
register_state_dict_hook

expand(encodings: Tensor, durations: Tensor) → Tensor

encodings: (B, T, C) durations: (B, T)

expand_numpy(encodings: Tensor, durations: Tensor) → Tensor

encodings: (B, T, C) durations: (B, T)

forward(xs, ds, alpha=1.0, is_inference=False)

Calculate forward propagation.

Args:

xs (Tensor):

Batch of sequences of char or phoneme embeddings (B, Tmax, D).

ds (Tensor(int64)):

Batch of durations of each frame (B, T).

alpha (float, optional):

Alpha value to control speed of speech.

Returns:

Tensor: replicated input tensor based on durations (B, T*, D).