paddlespeech.t2s.models.vits.wavenet.wavenet module
- class paddlespeech.t2s.models.vits.wavenet.wavenet.WaveNet(in_channels: int = 1, out_channels: int = 1, kernel_size: int = 3, layers: int = 30, stacks: int = 3, base_dilation: int = 2, residual_channels: int = 64, aux_channels: int = -1, gate_channels: int = 128, skip_channels: int = 64, global_channels: int = -1, dropout_rate: float = 0.0, bias: bool = True, use_weight_norm: bool = True, use_first_conv: bool = False, use_last_conv: bool = False, scale_residual: bool = False, scale_skip_connect: bool = False)[source]
Bases:
Layer
WaveNet with global conditioning.
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[, x_mask, c, g])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.
apply_weight_norm
backward
register_state_dict_hook
remove_weight_norm
- forward(x: Tensor, x_mask: Optional[Tensor] = None, c: Optional[Tensor] = None, g: Optional[Tensor] = None) Tensor [source]
Calculate forward propagation.
- Args:
- x (Tensor):
Input noise signal (B, 1, T) if use_first_conv else (B, residual_channels, T).
- x_mask (Optional[Tensor]):
Mask tensor (B, 1, T).
- c (Optional[Tensor]):
Local conditioning features (B, aux_channels, T).
- g (Optional[Tensor]):
Global conditioning features (B, global_channels, 1).
- Returns:
- Tensor:
Output tensor (B, out_channels, T) if use_last_conv else(B, residual_channels, T).