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

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).