paddlespeech.t2s.modules.tacotron2.encoder module

Tacotron2 encoder related modules.

class paddlespeech.t2s.modules.tacotron2.encoder.Encoder(idim, input_layer='embed', embed_dim=512, elayers=1, eunits=512, econv_layers=3, econv_chans=512, econv_filts=5, use_batch_norm=True, use_residual=False, dropout_rate=0.5, padding_idx=0)

Bases: Layer

Encoder module of Spectrogram prediction network.

This is a module of encoder of Spectrogram prediction network in Tacotron2, which described in Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions. This is the encoder which converts either a sequence of characters or acoustic features into the sequence of hidden states.

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(xs[, ilens])	Calculate forward propagation.
full_name()	Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
inference(x)	Inference.
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(xs, ilens=None)

Calculate forward propagation.

Args:

xs (Tensor):

Batch of the padded sequence. Either character ids (B, Tmax) or acoustic feature (B, Tmax, idim * encoder_reduction_factor). Padded value should be 0.

ilens (Tensor(int64)):

Batch of lengths of each input batch (B,).

Returns:

Tensor:

Batch of the sequences of encoder states(B, Tmax, eunits).

Tensor(int64):

Batch of lengths of each sequence (B,)

inference(x)

Inference.

Args:

x (Tensor):

The sequeunce of character ids (T,) or acoustic feature (T, idim * encoder_reduction_factor).

Returns:

Tensor: The sequences of encoder states(T, eunits).