paddlespeech.t2s.modules.pqmf module

Pseudo QMF modules.

class paddlespeech.t2s.modules.pqmf.PQMF(subbands=4, taps=62, cutoff_ratio=0.142, beta=9.0)[source]

Bases: Layer

PQMF module. This module is based on Near-perfect-reconstruction pseudo-QMF banks. .. Near-perfect-reconstruction pseudo-QMF banks:

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.

analysis(x)

Analysis with PQMF. Args: x (Tensor): Input tensor (B, 1, T). Returns: Tensor: Output tensor (B, subbands, T // subbands).

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)

Defines the computation performed at every call.

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.

synthesis(x)

Synthesis with PQMF. Args: x (Tensor): Input tensor (B, subbands, T // subbands). Returns: Tensor: Output tensor (B, 1, T).

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
analysis(x)[source]

Analysis with PQMF. Args:

x (Tensor):

Input tensor (B, 1, T).

Returns:

Tensor: Output tensor (B, subbands, T // subbands).

forward(x)[source]

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters:

*inputs(tuple): unpacked tuple arguments **kwargs(dict): unpacked dict arguments

synthesis(x)[source]

Synthesis with PQMF. Args:

x (Tensor):

Input tensor (B, subbands, T // subbands).

Returns:

Tensor: Output tensor (B, 1, T).

paddlespeech.t2s.modules.pqmf.design_prototype_filter(taps=62, cutoff_ratio=0.142, beta=9.0)[source]

Design prototype filter for PQMF. This method is based on A Kaiser window approach for the design of prototype filters of cosine modulated filterbanks.

Args:
taps (int):

The number of filter taps.

cutoff_ratio (float):

Cut-off frequency ratio.

beta (float):

Beta coefficient for kaiser window.

Returns:
ndarray:

Impluse response of prototype filter (taps + 1,).