paddlespeech.t2s.models.melgan.melgan module
MelGAN Modules.
- class paddlespeech.t2s.models.melgan.melgan.MelGANDiscriminator(in_channels: int = 1, out_channels: int = 1, kernel_sizes: List[int] = [5, 3], channels: int = 16, max_downsample_channels: int = 1024, bias: bool = True, downsample_scales: List[int] = [4, 4, 4, 4], nonlinear_activation: str = 'leakyrelu', nonlinear_activation_params: Dict[str, Any] = {'negative_slope': 0.2}, pad: str = 'Pad1D', pad_params: Dict[str, Any] = {'mode': 'reflect'}, init_type: str = 'xavier_uniform')[source]
Bases:
Layer
MelGAN discriminator module.
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)Calculate forward propagation. Args: x (Tensor): Input noise signal (B, 1, T). Returns: List: List of output tensors of each layer (for feat_match_loss).
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
- class paddlespeech.t2s.models.melgan.melgan.MelGANGenerator(in_channels: int = 80, out_channels: int = 1, kernel_size: int = 7, channels: int = 512, bias: bool = True, upsample_scales: List[int] = [8, 8, 2, 2], stack_kernel_size: int = 3, stacks: int = 3, nonlinear_activation: str = 'leakyrelu', nonlinear_activation_params: Dict[str, Any] = {'negative_slope': 0.2}, pad: str = 'Pad1D', pad_params: Dict[str, Any] = {'mode': 'reflect'}, use_final_nonlinear_activation: bool = True, use_weight_norm: bool = True, use_causal_conv: bool = False, init_type: str = 'xavier_uniform')[source]
Bases:
Layer
MelGAN generator module.
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.Recursively apply weight normalization to all the Convolution layers in the sublayers.
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
(c)Calculate forward propagation.
full_name
()Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
inference
(c)Perform 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.
Recursively remove weight normalization from all the Convolution layers in the sublayers.
Reset parameters.
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
- apply_weight_norm()[source]
Recursively apply weight normalization to all the Convolution layers in the sublayers.
- forward(c)[source]
Calculate forward propagation.
- Args:
- c (Tensor):
Input tensor (B, in_channels, T).
- Returns:
Tensor: Output tensor (B, out_channels, T ** prod(upsample_scales)).
- inference(c)[source]
Perform inference.
- Args:
- c (Union[Tensor, ndarray]):
Input tensor (T, in_channels).
- Returns:
Tensor: Output tensor (out_channels*T ** prod(upsample_scales), 1).
- remove_weight_norm()[source]
Recursively remove weight normalization from all the Convolution layers in the sublayers.
- reset_parameters()[source]
Reset parameters. This initialization follows official implementation manner. https://github.com/descriptinc/melgan-neurips/blob/master/mel2wav/modules.py
- class paddlespeech.t2s.models.melgan.melgan.MelGANInference(normalizer, melgan_generator)[source]
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
(logmel)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.
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
- class paddlespeech.t2s.models.melgan.melgan.MelGANMultiScaleDiscriminator(in_channels: int = 1, out_channels: int = 1, scales: int = 3, downsample_pooling: str = 'AvgPool1D', downsample_pooling_params: Dict[str, Any] = {'exclusive': True, 'kernel_size': 4, 'padding': 1, 'stride': 2}, kernel_sizes: List[int] = [5, 3], channels: int = 16, max_downsample_channels: int = 1024, bias: bool = True, downsample_scales: List[int] = [4, 4, 4, 4], nonlinear_activation: str = 'leakyrelu', nonlinear_activation_params: Dict[str, Any] = {'negative_slope': 0.2}, pad: str = 'Pad1D', pad_params: Dict[str, Any] = {'mode': 'reflect'}, use_weight_norm: bool = True, init_type: str = 'xavier_uniform')[source]
Bases:
Layer
MelGAN multi-scale discriminator module.
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.Recursively apply weight normalization to all the Convolution layers in the sublayers.
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)Calculate forward propagation. Args: x (Tensor): Input noise signal (B, 1, T). Returns: List: List of list of each discriminator outputs, which consists of each layer output tensors.
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.
Recursively remove weight normalization from all the Convolution layers in the sublayers.
Reset parameters.
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
- apply_weight_norm()[source]
Recursively apply weight normalization to all the Convolution layers in the sublayers.
- forward(x)[source]
Calculate forward propagation. Args:
- x (Tensor):
Input noise signal (B, 1, T).
- Returns:
List: List of list of each discriminator outputs, which consists of each layer output tensors.
- remove_weight_norm()[source]
Recursively remove weight normalization from all the Convolution layers in the sublayers.
- reset_parameters()[source]
Reset parameters. This initialization follows official implementation manner. https://github.com/descriptinc/melgan-neurips/blob/master/mel2wav/modules.py