paddlespeech.t2s.modules.predictor.duration_predictor module

Duration predictor related modules.

class paddlespeech.t2s.modules.predictor.duration_predictor.DurationPredictor(idim, n_layers=2, n_chans=384, kernel_size=3, dropout_rate=0.1, offset=1.0)

Bases: Layer

Duration predictor module.

This is a module of duration predictor described in FastSpeech: Fast, Robust and Controllable Text to Speech. The duration predictor predicts a duration of each frame in log domain from the hidden embeddings of encoder.

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.
extra_repr()	Extra representation of this layer, you can have custom implementation of your own layer.
forward(xs[, x_masks])	Calculate forward propagation. Args: xs(Tensor): Batch of input sequences (B, Tmax, idim). x_masks(ByteTensor, optional, optional): Batch of masks indicating padded part (B, Tmax). (Default value = None).
full_name()	Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
inference(xs[, x_masks])	Inference duration. Args: xs(Tensor): Batch of input sequences (B, Tmax, idim). x_masks(Tensor(bool), optional, optional): Batch of masks indicating padded part (B, Tmax). (Default value = None).
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

forward(xs, x_masks=None)

Calculate forward propagation. Args:

xs(Tensor):

Batch of input sequences (B, Tmax, idim).

x_masks(ByteTensor, optional, optional):

Batch of masks indicating padded part (B, Tmax). (Default value = None)

Returns:

Tensor: Batch of predicted durations in log domain (B, Tmax).

inference(xs, x_masks=None)

Inference duration. Args:

xs(Tensor):

Batch of input sequences (B, Tmax, idim).

x_masks(Tensor(bool), optional, optional):

Batch of masks indicating padded part (B, Tmax). (Default value = None)

Returns:

Tensor: Batch of predicted durations in linear domain int64 (B, Tmax).

class paddlespeech.t2s.modules.predictor.duration_predictor.DurationPredictorLoss(offset=1.0, reduction='mean')

Bases: Layer

Loss function module for duration predictor.

The loss value is Calculated in log domain to make it Gaussian.

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.
extra_repr()	Extra representation of this layer, you can have custom implementation of your own layer.
forward(outputs, targets)	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

forward(outputs, targets)

Calculate forward propagation.

Args:

outputs(Tensor):

Batch of prediction durations in log domain (B, T)

targets(Tensor):

Batch of groundtruth durations in linear domain (B, T)

Returns:

Tensor: Mean squared error loss value.

Note:

outputs is in log domain but targets is in linear domain.