paddlespeech.t2s.modules.upsample module

class paddlespeech.t2s.modules.upsample.ConvInUpsampleNet(upsample_scales: List[int], nonlinear_activation: Optional[str] = None, nonlinear_activation_params: Dict[str, Any] = {}, interpolate_mode: str = 'nearest', freq_axis_kernel_size: int = 1, aux_channels: int = 80, aux_context_window: int = 0, use_causal_conv: bool = False)

Bases: Layer

A Layer to upsample spectrogram composed of a convolution and an UpsampleNet.

Args:

upsample_scales (List[int]):

Upsampling factors for each strech.

nonlinear_activation (Optional[str], optional):

Activation after each convolution, by default None

nonlinear_activation_params (Dict[str, Any], optional):

Parameters passed to construct the activation, by default {}

interpolate_mode (str, optional):

Interpolation mode of the strech, by default "nearest"

freq_axis_kernel_size (int, optional):

Convolution kernel size along the frequency axis, by default 1

aux_channels (int, optional):

Feature size of the input, by default 80

aux_context_window (int, optional):

Context window of the first 1D convolution applied to the input. It related to the kernel size of the convolution, by default 0 If use causal convolution, the kernel size is window + 1, else the kernel size is 2 * window + 1.

use_causal_conv (bool, optional):

Whether to use causal padding before convolution, by default False If True, Causal padding is used along the time axis, i.e. padding amount is receptive field - 1 and 0 for before and after, respectively. If False, "same" padding is used along the time axis.

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(c)	Args:
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(c)

Args:

c (Tensor):

spectrogram. Shape (N, F, T)

Returns:

Tensors: upsampled spectrogram. Shape (N, F, T'), where T' = upsample_factor * T,

class paddlespeech.t2s.modules.upsample.Stretch2D(w_scale: int, h_scale: int, mode: str = 'nearest')

Bases: Layer

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(x)	Args:
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(x)

Args:

x (Tensor):

Shape (N, C, H, W)

Returns:

Tensor:

The stretched image. Shape (N, C, H', W'), where H'=h_scale * H, W'=w_scale * W.

class paddlespeech.t2s.modules.upsample.UpsampleNet(upsample_scales: List[int], nonlinear_activation: Optional[str] = None, nonlinear_activation_params: Dict[str, Any] = {}, interpolate_mode: str = 'nearest', freq_axis_kernel_size: int = 1, use_causal_conv: bool = False)

Bases: Layer

A Layer to upsample spectrogram by applying consecutive stretch and convolutions.

Args:

upsample_scales (List[int]):

Upsampling factors for each strech.

nonlinear_activation (Optional[str], optional):

Activation after each convolution, by default None

nonlinear_activation_params (Dict[str, Any], optional):

Parameters passed to construct the activation, by default {}

interpolate_mode (str, optional):

Interpolation mode of the strech, by default "nearest"

freq_axis_kernel_size (int, optional):

Convolution kernel size along the frequency axis, by default 1

use_causal_conv (bool, optional):

Whether to use causal padding before convolution, by default False If True, Causal padding is used along the time axis, i.e. padding amount is receptive field - 1 and 0 for before and after, respectively. If False, "same" padding is used along the time axis.

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(c)	Args:
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(c)

Args:

c (Tensor):

spectrogram. Shape (N, F, T)

Returns:

Tensor: upsampled spectrogram.

Shape (N, F, T'), where T' = upsample_factor * T,