paddlespeech.t2s.models.vits.duration_predictor module
Stochastic duration predictor modules in VITS.
This code is based on https://github.com/jaywalnut310/vits.
- class paddlespeech.t2s.models.vits.duration_predictor.StochasticDurationPredictor(channels: int = 192, kernel_size: int = 3, dropout_rate: float = 0.5, flows: int = 4, dds_conv_layers: int = 3, global_channels: int = -1)[source]
Bases:
Layer
Stochastic duration predictor module. This is a module of stochastic duration predictor described in `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`_. .. _`Conditional Variational Autoencoder with Adversarial Learning for End-to-End
Text-to-Speech`: https://arxiv.org/abs/2106.06103
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[, w, g, inverse, noise_scale])Calculate forward propagation. Args: x (Tensor): Input tensor (B, channels, T_text). x_mask (Tensor): Mask tensor (B, 1, T_text). w (Optional[Tensor]): Duration tensor (B, 1, T_text). g (Optional[Tensor]): Global conditioning tensor (B, channels, 1) inverse (bool): Whether to inverse the flow. noise_scale (float): Noise scale value. Returns: Tensor: If not inverse, negative log-likelihood (NLL) tensor (B,). If inverse, log-duration tensor (B, 1, T_text).
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: Tensor, x_mask: Tensor, w: Optional[Tensor] = None, g: Optional[Tensor] = None, inverse: bool = False, noise_scale: float = 1.0) Tensor [source]
Calculate forward propagation. Args:
- x (Tensor):
Input tensor (B, channels, T_text).
- x_mask (Tensor):
Mask tensor (B, 1, T_text).
- w (Optional[Tensor]):
Duration tensor (B, 1, T_text).
- g (Optional[Tensor]):
Global conditioning tensor (B, channels, 1)
- inverse (bool):
Whether to inverse the flow.
- noise_scale (float):
Noise scale value.
- Returns:
- Tensor:
If not inverse, negative log-likelihood (NLL) tensor (B,). If inverse, log-duration tensor (B, 1, T_text).