paddlespeech.t2s.modules.transformer.encoder module

class paddlespeech.t2s.modules.transformer.encoder.BaseEncoder(idim: int, attention_dim: int = 256, attention_heads: int = 4, linear_units: int = 2048, num_blocks: int = 6, dropout_rate: float = 0.1, positional_dropout_rate: float = 0.1, attention_dropout_rate: float = 0.0, input_layer: str = 'conv2d', normalize_before: bool = True, concat_after: bool = False, positionwise_layer_type: str = 'linear', positionwise_conv_kernel_size: int = 1, macaron_style: bool = False, pos_enc_layer_type: str = 'abs_pos', selfattention_layer_type: str = 'selfattn', activation_type: str = 'swish', use_cnn_module: bool = False, zero_triu: bool = False, cnn_module_kernel: int = 31, padding_idx: int = -1, stochastic_depth_rate: float = 0.0, intermediate_layers: Optional[List[int]] = None, encoder_type: str = 'transformer')

Bases: Layer

Base Encoder module.

Args:

idim (int):

Input dimension.

attention_dim (int):

Dimention of attention.

attention_heads (int):

The number of heads of multi head attention.

linear_units (int):

The number of units of position-wise feed forward.

num_blocks (int):

The number of decoder blocks.

dropout_rate (float):

Dropout rate.

positional_dropout_rate (float):

Dropout rate after adding positional encoding.

attention_dropout_rate (float):

Dropout rate in attention.

input_layer (Union[str, nn.Layer]):

Input layer type.

normalize_before (bool):

Whether to use layer_norm before the first block.

concat_after (bool):

Whether to concat attention layer's input and output. if True, additional linear will be applied. i.e. x -> x + linear(concat(x, att(x))) if False, no additional linear will be applied. i.e. x -> x + att(x)

positionwise_layer_type (str):

"linear", "conv1d", or "conv1d-linear".

positionwise_conv_kernel_size (int):

Kernel size of positionwise conv1d layer.

macaron_style (bool):

Whether to use macaron style for positionwise layer.

pos_enc_layer_type (str):

Encoder positional encoding layer type.

selfattention_layer_type (str):

Encoder attention layer type.

activation_type (str):

Encoder activation function type.

use_cnn_module (bool):

Whether to use convolution module.

zero_triu (bool):

Whether to zero the upper triangular part of attention matrix.

cnn_module_kernel (int):

Kernerl size of convolution module.

padding_idx (int):

Padding idx for input_layer=embed.

stochastic_depth_rate (float):

Maximum probability to skip the encoder layer.

intermediate_layers (Union[List[int], None]):

indices of intermediate CTC layer. indices start from 1. if not None, intermediate outputs are returned (which changes return type signature.)

encoder_type (str): "transformer", or "conformer".

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, masks)	Encode input sequence.
full_name()	Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
get_positionwise_layer([...])	Define positionwise layer.
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
get_embed
get_encoder_selfattn_layer
get_pos_enc_class
register_state_dict_hook

forward(xs, masks)

Encode input sequence.

Args:

xs (Tensor):

Input tensor (#batch, time, idim).

masks (Tensor):

Mask tensor (#batch, 1, time).

Returns:

Tensor:

Output tensor (#batch, time, attention_dim).

Tensor:

Mask tensor (#batch, 1, time).

get_embed(idim, input_layer='conv2d', attention_dim: int = 256, pos_enc_class=<class 'paddlespeech.t2s.modules.transformer.embedding.PositionalEncoding'>, dropout_rate: int = 0.1, positional_dropout_rate: int = 0.1, padding_idx: int = -1)

get_encoder_selfattn_layer(selfattention_layer_type: str = 'selfattn', attention_heads: int = 4, attention_dim: int = 256, attention_dropout_rate: float = 0.0, zero_triu: bool = False, pos_enc_layer_type: str = 'abs_pos')

get_pos_enc_class(pos_enc_layer_type: str = 'abs_pos', selfattention_layer_type: str = 'selfattn')

get_positionwise_layer(positionwise_layer_type: str = 'linear', attention_dim: int = 256, linear_units: int = 2048, dropout_rate: float = 0.1, positionwise_conv_kernel_size: int = 1, activation: Layer = ReLU())

Define positionwise layer.

class paddlespeech.t2s.modules.transformer.encoder.CNNDecoder(emb_dim: int = 256, odim: int = 80, kernel_size: int = 5, dropout_rate: float = 0.2, resblock_kernel_sizes: List[int] = [256, 256])

Bases: Layer

Much simplified decoder than the original one with Prenet.

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[, masks])	Encode input sequence. Args: xs (Tensor): Input tensor (#batch, time, idim). masks (Tensor): Mask tensor (#batch, 1, time). Returns: Tensor: Output tensor (#batch, time, odim).
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(xs, masks=None)

Encode input sequence. Args:

xs (Tensor):

Input tensor (#batch, time, idim).

masks (Tensor):

Mask tensor (#batch, 1, time).

Returns:

Tensor: Output tensor (#batch, time, odim).

class paddlespeech.t2s.modules.transformer.encoder.CNNPostnet(odim: int = 80, kernel_size: int = 5, dropout_rate: float = 0.2, resblock_kernel_sizes: List[int] = [256, 256])

Bases: Layer

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[, masks])	Encode input sequence. Args: xs (Tensor): Input tensor (#batch, odim, time). masks (Tensor): Mask tensor (#batch, 1, time). Returns: Tensor: Output tensor (#batch, odim, time).
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(xs, masks=None)

Encode input sequence. Args:

xs (Tensor):

Input tensor (#batch, odim, time).

masks (Tensor):

Mask tensor (#batch, 1, time).

Returns:

Tensor: Output tensor (#batch, odim, time).

class paddlespeech.t2s.modules.transformer.encoder.ConformerEncoder(idim: int, attention_dim: int = 256, attention_heads: int = 4, linear_units: int = 2048, num_blocks: int = 6, dropout_rate: float = 0.1, positional_dropout_rate: float = 0.1, attention_dropout_rate: float = 0.0, input_layer: str = 'conv2d', normalize_before: bool = True, concat_after: bool = False, positionwise_layer_type: str = 'linear', positionwise_conv_kernel_size: int = 1, macaron_style: bool = False, pos_enc_layer_type: str = 'rel_pos', selfattention_layer_type: str = 'rel_selfattn', activation_type: str = 'swish', use_cnn_module: bool = False, zero_triu: bool = False, cnn_module_kernel: int = 31, padding_idx: int = -1, stochastic_depth_rate: float = 0.0, intermediate_layers: Optional[List[int]] = None)

Bases: BaseEncoder

Conformer encoder module.

Args:

idim (int):

Input dimension.

attention_dim (int):

Dimention of attention.

attention_heads (int):

The number of heads of multi head attention.

linear_units (int):

The number of units of position-wise feed forward.

num_blocks (int):

The number of decoder blocks.

dropout_rate (float):

Dropout rate.

positional_dropout_rate (float):

Dropout rate after adding positional encoding.

attention_dropout_rate (float):

Dropout rate in attention.

input_layer (Union[str, nn.Layer]):

Input layer type.

normalize_before (bool):

Whether to use layer_norm before the first block.

concat_after (bool):

Whether to concat attention layer's input and output. if True, additional linear will be applied. i.e. x -> x + linear(concat(x, att(x))) if False, no additional linear will be applied. i.e. x -> x + att(x)

positionwise_layer_type (str):

"linear", "conv1d", or "conv1d-linear".

positionwise_conv_kernel_size (int):

Kernel size of positionwise conv1d layer.

macaron_style (bool):

Whether to use macaron style for positionwise layer.

pos_enc_layer_type (str):

Encoder positional encoding layer type.

selfattention_layer_type (str):

Encoder attention layer type.

activation_type (str):

Encoder activation function type.

use_cnn_module (bool):

Whether to use convolution module.

zero_triu (bool):

Whether to zero the upper triangular part of attention matrix.

cnn_module_kernel (int):

Kernerl size of convolution module.

padding_idx (int):

Padding idx for input_layer=embed.

stochastic_depth_rate (float):

Maximum probability to skip the encoder layer.

intermediate_layers (Union[List[int], None]):

indices of intermediate CTC layer. indices start from 1. if not None, intermediate outputs are returned (which changes return type signature.)

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, masks)	Encode input sequence.
full_name()	Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
get_positionwise_layer([...])	Define positionwise layer.
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
get_embed
get_encoder_selfattn_layer
get_pos_enc_class
register_state_dict_hook

forward(xs, masks)

Encode input sequence.

Args:

xs (Tensor):

Input tensor (#batch, time, idim).

masks (Tensor):

Mask tensor (#batch, 1, time).

Returns:

Tensor:

Output tensor (#batch, time, attention_dim).

Tensor:

Mask tensor (#batch, 1, time).

class paddlespeech.t2s.modules.transformer.encoder.Conv1dResidualBlock(idim: int = 256, odim: int = 256, kernel_size: int = 5, dropout_rate: float = 0.2)

Bases: Layer

Special module for simplified version of Encoder class.

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)	Encode input sequence. Args: xs (Tensor): Input tensor (#batch, idim, T). Returns: Tensor: Output tensor (#batch, odim, 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

forward(xs)

Encode input sequence. Args:

xs (Tensor):

Input tensor (#batch, idim, T).

Returns:

Tensor: Output tensor (#batch, odim, T).

class paddlespeech.t2s.modules.transformer.encoder.TransformerEncoder(idim, attention_dim: int = 256, attention_heads: int = 4, linear_units: int = 2048, num_blocks: int = 6, dropout_rate: float = 0.1, positional_dropout_rate: float = 0.1, attention_dropout_rate: float = 0.0, input_layer: str = 'conv2d', pos_enc_layer_type: str = 'abs_pos', normalize_before: bool = True, concat_after: bool = False, positionwise_layer_type: str = 'linear', positionwise_conv_kernel_size: int = 1, selfattention_layer_type: str = 'selfattn', activation_type: str = 'relu', padding_idx: int = -1)

Bases: BaseEncoder

Transformer encoder module.

Args:

idim (int):

Input dimension.

attention_dim (int):

Dimention of attention.

attention_heads (int):

The number of heads of multi head attention.

linear_units (int):

The number of units of position-wise feed forward.

num_blocks (int):

The number of decoder blocks.

dropout_rate (float):

Dropout rate.

positional_dropout_rate (float):

Dropout rate after adding positional encoding.

attention_dropout_rate (float):

Dropout rate in attention.

input_layer (Union[str, paddle.nn.Layer]):

Input layer type.

pos_enc_layer_type (str):

Encoder positional encoding layer type.

normalize_before (bool):

Whether to use layer_norm before the first block.

concat_after (bool):

Whether to concat attention layer's input and output. if True, additional linear will be applied. i.e. x -> x + linear(concat(x, att(x))) if False, no additional linear will be applied. i.e. x -> x + att(x)

positionwise_layer_type (str):

"linear", "conv1d", or "conv1d-linear".

positionwise_conv_kernel_size (int):

Kernel size of positionwise conv1d layer.

selfattention_layer_type (str):

Encoder attention layer type.

activation_type (str):

Encoder activation function type.

padding_idx (int):

Padding idx for input_layer=embed.

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, masks[, note_emb, note_dur_emb, ...])	Encoder input sequence.
forward_one_step(xs, masks[, cache])	Encode input frame.
full_name()	Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
get_positionwise_layer([...])	Define positionwise layer.
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
get_embed
get_encoder_selfattn_layer
get_pos_enc_class
register_state_dict_hook

forward(xs: Tensor, masks: Tensor, note_emb: Optional[Tensor] = None, note_dur_emb: Optional[Tensor] = None, is_slur_emb: Optional[Tensor] = None, scale: int = 16)

Encoder input sequence.

Args:

xs(Tensor):

Input tensor (#batch, time, idim).

masks(Tensor):

Mask tensor (#batch, 1, time).

note_emb(Tensor):

Input tensor (#batch, time, attention_dim).

note_dur_emb(Tensor):

Input tensor (#batch, time, attention_dim).

is_slur_emb(Tensor):

Input tensor (#batch, time, attention_dim).

Returns:

Tensor:

Output tensor (#batch, time, attention_dim).

Tensor:

Mask tensor (#batch, 1, time).

forward_one_step(xs, masks, cache=None)

Encode input frame.

Args:

xs (Tensor):

Input tensor.

masks (Tensor):

Mask tensor.

cache (List[Tensor]):

List of cache tensors.

Returns:

Tensor:

Output tensor.

Tensor:

Mask tensor.

List[Tensor]:

List of new cache tensors.