paddlespeech.t2s.models.hifigan.hifigan module

class paddlespeech.t2s.models.hifigan.hifigan.HiFiGANGenerator(in_channels: int = 80, out_channels: int = 1, channels: int = 512, global_channels: int = -1, kernel_size: int = 7, upsample_scales: List[int] = (8, 8, 2, 2), upsample_kernel_sizes: List[int] = (16, 16, 4, 4), resblock_kernel_sizes: List[int] = (3, 7, 11), resblock_dilations: List[List[int]] = [(1, 3, 5), (1, 3, 5), (1, 3, 5)], use_additional_convs: bool = True, bias: bool = True, nonlinear_activation: str = 'leakyrelu', nonlinear_activation_params: Dict[str, Any] = {'negative_slope': 0.1}, use_weight_norm: bool = True, init_type: str = 'xavier_uniform')

Bases: Layer

HiFiGAN generator module.

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.
apply_weight_norm()	Recursively apply weight normalization to all the Convolution layers in the sublayers.
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[, g])	Calculate forward propagation.
full_name()	Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
inference(c[, g])	Perform inference. Args: c (Tensor): Input tensor (T, in_channels). g (Optional[Tensor]): Global conditioning tensor (global_channels, 1). Returns: Tensor: Output tensor (T ** prod(upsample_scales), out_channels).
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.
remove_weight_norm()	Recursively remove weight normalization from all the Convolution layers in the sublayers.
reset_parameters()	Reset parameters.
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

apply_weight_norm()

Recursively apply weight normalization to all the Convolution layers in the sublayers.

forward(c, g: Optional[Tensor] = None)

Calculate forward propagation.

Args:

c (Tensor): Input tensor (B, in_channels, T). g (Optional[Tensor]): Global conditioning tensor (B, global_channels, 1).

Returns:

Tensor: Output tensor (B, out_channels, T).

inference(c, g: Optional[Tensor] = None)

Perform inference. Args:

c (Tensor):

Input tensor (T, in_channels).

g (Optional[Tensor]):

Global conditioning tensor (global_channels, 1).

Returns:

Tensor:

Output tensor (T ** prod(upsample_scales), out_channels).

remove_weight_norm()

Recursively remove weight normalization from all the Convolution layers in the sublayers.

reset_parameters()

Reset parameters. This initialization follows official implementation manner. https://github.com/jik876/hifi-gan/blob/master/models.py

class paddlespeech.t2s.models.hifigan.hifigan.HiFiGANInference(normalizer, hifigan_generator)

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(logmel)	Defines the computation performed at every call.
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(logmel)

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters:

*inputs(tuple): unpacked tuple arguments **kwargs(dict): unpacked dict arguments

class paddlespeech.t2s.models.hifigan.hifigan.HiFiGANMultiPeriodDiscriminator(periods: List[int] = [2, 3, 5, 7, 11], discriminator_params: Dict[str, Any] = {'bias': True, 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'in_channels': 1, 'kernel_sizes': [5, 3], 'max_downsample_channels': 1024, 'nonlinear_activation': 'leakyrelu', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'out_channels': 1, 'use_spectral_norm': False, 'use_weight_norm': True}, init_type: str = 'xavier_uniform')

Bases: Layer

HiFiGAN multi-period discriminator module.

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)	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(x)

Calculate forward propagation.

Args:

x (Tensor):

Input noise signal (B, 1, T).

Returns:

List: List of list of each discriminator outputs, which consists of each layer output tensors.

class paddlespeech.t2s.models.hifigan.hifigan.HiFiGANMultiScaleDiscriminator(scales: int = 3, downsample_pooling: str = 'AvgPool1D', downsample_pooling_params: Dict[str, Any] = {'kernel_size': 4, 'padding': 2, 'stride': 2}, discriminator_params: Dict[str, Any] = {'bias': True, 'channels': 128, 'downsample_scales': [2, 2, 4, 4, 1], 'in_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'max_downsample_channels': 1024, 'max_groups': 16, 'nonlinear_activation': 'leakyrelu', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'out_channels': 1}, follow_official_norm: bool = False, init_type: str = 'xavier_uniform')

Bases: Layer

HiFi-GAN multi-scale discriminator module.

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)	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(x)

Calculate forward propagation.

Args:

x (Tensor):

Input noise signal (B, 1, T).

Returns:

List: List of list of each discriminator outputs, which consists of each layer output tensors.

class paddlespeech.t2s.models.hifigan.hifigan.HiFiGANMultiScaleMultiPeriodDiscriminator(scales: int = 3, scale_downsample_pooling: str = 'AvgPool1D', scale_downsample_pooling_params: Dict[str, Any] = {'kernel_size': 4, 'padding': 2, 'stride': 2}, scale_discriminator_params: Dict[str, Any] = {'bias': True, 'channels': 128, 'downsample_scales': [2, 2, 4, 4, 1], 'in_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'max_downsample_channels': 1024, 'max_groups': 16, 'nonlinear_activation': 'leakyrelu', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'out_channels': 1}, follow_official_norm: bool = True, periods: List[int] = [2, 3, 5, 7, 11], period_discriminator_params: Dict[str, Any] = {'bias': True, 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'in_channels': 1, 'kernel_sizes': [5, 3], 'max_downsample_channels': 1024, 'nonlinear_activation': 'leakyrelu', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'out_channels': 1, 'use_spectral_norm': False, 'use_weight_norm': True}, init_type: str = 'xavier_uniform')

Bases: Layer

HiFi-GAN multi-scale + multi-period discriminator module.

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)	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(x)

Calculate forward propagation.

Args:

x (Tensor):

Input noise signal (B, 1, T).

Returns:

List:

List of list of each discriminator outputs, which consists of each layer output tensors. Multi scale and multi period ones are concatenated.

class paddlespeech.t2s.models.hifigan.hifigan.HiFiGANPeriodDiscriminator(in_channels: int = 1, out_channels: int = 1, period: int = 3, kernel_sizes: List[int] = [5, 3], channels: int = 32, downsample_scales: List[int] = [3, 3, 3, 3, 1], max_downsample_channels: int = 1024, bias: bool = True, nonlinear_activation: str = 'leakyrelu', nonlinear_activation_params: Dict[str, Any] = {'negative_slope': 0.1}, use_weight_norm: bool = True, use_spectral_norm: bool = False, init_type: str = 'xavier_uniform')

Bases: Layer

HiFiGAN period discriminator module.

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.
apply_spectral_norm()	Apply spectral normalization module from all of the layers.
apply_weight_norm()	Recursively apply weight normalization to all the Convolution layers in the sublayers.
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)	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

apply_spectral_norm()

Apply spectral normalization module from all of the layers.

apply_weight_norm()

Recursively apply weight normalization to all the Convolution layers in the sublayers.

forward(x)

Calculate forward propagation.

Args:

c (Tensor):

Input tensor (B, in_channels, T).

Returns:

list: List of each layer's tensors.

class paddlespeech.t2s.models.hifigan.hifigan.HiFiGANScaleDiscriminator(in_channels: int = 1, out_channels: int = 1, kernel_sizes: List[int] = [15, 41, 5, 3], channels: int = 128, max_downsample_channels: int = 1024, max_groups: int = 16, bias: bool = True, downsample_scales: List[int] = [2, 2, 4, 4, 1], nonlinear_activation: str = 'leakyrelu', nonlinear_activation_params: Dict[str, Any] = {'negative_slope': 0.1}, use_weight_norm: bool = True, use_spectral_norm: bool = False, init_type: str = 'xavier_uniform')

Bases: Layer

HiFi-GAN scale discriminator module.

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.
apply_spectral_norm()	Apply spectral normalization module from all of the layers.
apply_weight_norm()	Recursively apply weight normalization to all the Convolution layers in the sublayers.
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)	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

apply_spectral_norm()

Apply spectral normalization module from all of the layers.

apply_weight_norm()

Recursively apply weight normalization to all the Convolution layers in the sublayers.

forward(x)

Calculate forward propagation.

Args:

x (Tensor): Input noise signal (B, 1, T).

Returns:

List: List of output tensors of each layer.