paddlespeech.s2t.modules.conformer_convolution module

ConvolutionModule definition.

class paddlespeech.s2t.modules.conformer_convolution.ConvolutionModule(channels: int, kernel_size: int = 15, activation: Layer = ReLU(), norm: str = 'batch_norm', causal: bool = False, bias: bool = True, adaptive_scale: bool = False, init_weights: bool = False)

Bases: Layer

ConvolutionModule in Conformer model.

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[, mask_pad, stop_gradient, cache, ...])	Compute convolution module. Args: x (paddle.Tensor): Input tensor (#batch, time, channels). mask_pad (paddle.Tensor): used for batch padding (#batch, 1, time), (0, 0, 0) means fake mask. cache (paddle.Tensor): left context cache, it is only used in causal convolution (#batch, channels, cache_t), (0, 0, 0) meas fake cache. Returns: paddle.Tensor: Output tensor (#batch, time, channels). paddle.Tensor: Output cache tensor (#batch, channels, 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
init_weights
register_state_dict_hook

forward(x: ~paddle.Tensor, mask_pad: ~paddle.Tensor = Tensor(shape=[0, 0, 0], dtype=bool, place=Place(cpu), stop_gradient=True,        []), cache: ~paddle.Tensor = Tensor(shape=[0, 0, 0, 0], dtype=float32, place=Place(cpu), stop_gradient=True,        [])) → Tuple[Tensor, Tensor]

Compute convolution module. Args:

x (paddle.Tensor): Input tensor (#batch, time, channels). mask_pad (paddle.Tensor): used for batch padding (#batch, 1, time),

(0, 0, 0) means fake mask.

cache (paddle.Tensor): left context cache, it is only

used in causal convolution (#batch, channels, cache_t), (0, 0, 0) meas fake cache.

Returns:

paddle.Tensor: Output tensor (#batch, time, channels). paddle.Tensor: Output cache tensor (#batch, channels, time')

init_weights()