paddlespeech.t2s.modules.residual_block module
- class paddlespeech.t2s.modules.residual_block.HiFiGANResidualBlock(kernel_size: int = 3, channels: int = 512, dilations: List[int] = (1, 3, 5), bias: bool = True, use_additional_convs: bool = True, nonlinear_activation: str = 'leakyrelu', nonlinear_activation_params: Dict[str, Any] = {'negative_slope': 0.1})[source]
Bases:
Layer
Residual block module in HiFiGAN.
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. Args: x (Tensor): Input tensor (B, channels, T). Returns: Tensor: Output tensor (B, channels, T).
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
- class paddlespeech.t2s.modules.residual_block.WaveNetResidualBlock(kernel_size: int = 3, residual_channels: int = 64, gate_channels: int = 128, skip_channels: int = 64, aux_channels: int = 80, dropout: float = 0.0, dilation: int = 1, bias: bool = True, use_causal_conv: bool = False)[source]
Bases:
Layer
A gated activation unit composed of an 1D convolution, a gated tanh unit and parametric redidual and skip connections. For more details, refer to WaveNet: A Generative Model for Raw Audio.
- Args:
- kernel_size (int, optional):
Kernel size of the 1D convolution, by default 3
- residual_channels (int, optional):
Feature size of the residual output(and also the input), by default 64
- gate_channels (int, optional):
Output feature size of the 1D convolution, by default 128
- skip_channels (int, optional):
Feature size of the skip output, by default 64
- aux_channels (int, optional):
Feature size of the auxiliary input (e.g. spectrogram), by default 80
- dropout (float, optional):
Probability of the dropout before the 1D convolution, by default 0.
- dilation (int, optional):
Dilation of the 1D convolution, by default 1
- bias (bool, optional):
Whether to use bias in the 1D convolution, by default True
- use_causal_conv (bool, optional):
Whether to use causal padding for the 1D convolution, by default False
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, 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(x, c)[source]
- Args:
- x (Tensor):
the input features. Shape (N, C_res, T)
- c (Tensor):
the auxiliary input. Shape (N, C_aux, T)
- Returns:
- res (Tensor):
Shape (N, C_res, T), the residual output, which is used as the input of the next ResidualBlock in a stack of ResidualBlocks.
- skip (Tensor):
Shape (N, C_skip, T), the skip output, which is collected among each layer in a stack of ResidualBlocks.