paddlespeech.t2s.models.tacotron2.tacotron2 module

Tacotron 2 related modules for paddle

class paddlespeech.t2s.models.tacotron2.tacotron2.Tacotron2(idim: int, odim: int, embed_dim: int = 512, elayers: int = 1, eunits: int = 512, econv_layers: int = 3, econv_chans: int = 512, econv_filts: int = 5, atype: str = 'location', adim: int = 512, aconv_chans: int = 32, aconv_filts: int = 15, cumulate_att_w: bool = True, dlayers: int = 2, dunits: int = 1024, prenet_layers: int = 2, prenet_units: int = 256, postnet_layers: int = 5, postnet_chans: int = 512, postnet_filts: int = 5, output_activation: Optional[str] = None, use_batch_norm: bool = True, use_concate: bool = True, use_residual: bool = False, reduction_factor: int = 1, spk_num: Optional[int] = None, lang_num: Optional[int] = None, spk_embed_dim: Optional[int] = None, spk_embed_integration_type: str = 'concat', dropout_rate: float = 0.5, zoneout_rate: float = 0.1, init_type: str = 'xavier_uniform')

Bases: Layer

Tacotron2 module for end-to-end text-to-speech.

This is a module of Spectrogram prediction network in Tacotron2 described in Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions, which converts the sequence of characters into the sequence of Mel-filterbanks.

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(text, text_lengths, speech, ...[, ...])	Calculate forward propagation.
full_name()	Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
inference(text[, speech, spk_emb, spk_id, ...])	Generate the sequence of features given the sequences of characters.
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(text: Tensor, text_lengths: Tensor, speech: Tensor, speech_lengths: Tensor, spk_emb: Optional[Tensor] = None, spk_id: Optional[Tensor] = None, lang_id: Optional[Tensor] = None) → Tuple[Tensor, Dict[str, Tensor], Tensor]

Calculate forward propagation.

Args:

text (Tensor(int64)):

Batch of padded character ids (B, T_text).

text_lengths (Tensor(int64)):

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

speech (Tensor):

Batch of padded target features (B, T_feats, odim).

speech_lengths (Tensor(int64)):

Batch of the lengths of each target (B,).

spk_emb (Optional[Tensor]):

Batch of speaker embeddings (B, spk_embed_dim).

spk_id (Optional[Tensor]):

Batch of speaker IDs (B, 1).

lang_id (Optional[Tensor]):

Batch of language IDs (B, 1).

Returns:

Tensor:

Loss scalar value.

Dict:

Statistics to be monitored.

Tensor:

Weight value if not joint training else model outputs.

inference(text: Tensor, speech: Optional[Tensor] = None, spk_emb: Optional[Tensor] = None, spk_id: Optional[Tensor] = None, lang_id: Optional[Tensor] = None, threshold: float = 0.5, minlenratio: float = 0.0, maxlenratio: float = 10.0, use_att_constraint: bool = False, backward_window: int = 1, forward_window: int = 3, use_teacher_forcing: bool = False) → Dict[str, Tensor]

Generate the sequence of features given the sequences of characters.

Args:

text (Tensor(int64)):

Input sequence of characters (T_text,).

speech (Optional[Tensor]):

Feature sequence to extract style (N, idim).

spk_emb (ptional[Tensor]):

Speaker embedding (spk_embed_dim,).

spk_id (Optional[Tensor]):

Speaker ID (1,).

lang_id (Optional[Tensor]):

Language ID (1,).

threshold (float):

Threshold in inference.

minlenratio (float):

Minimum length ratio in inference.

maxlenratio (float):

Maximum length ratio in inference.

use_att_constraint (bool):

Whether to apply attention constraint.

backward_window (int):

Backward window in attention constraint.

forward_window (int):

Forward window in attention constraint.

use_teacher_forcing (bool):

Whether to use teacher forcing.

Returns:

Dict[str, Tensor] Output dict including the following items:

• feat_gen (Tensor): Output sequence of features (T_feats, odim).

• prob (Tensor): Output sequence of stop probabilities (T_feats,).

• att_w (Tensor): Attention weights (T_feats, T).

class paddlespeech.t2s.models.tacotron2.tacotron2.Tacotron2Inference(normalizer, model)

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(text[, spk_id, spk_emb])	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

forward(text, spk_id=None, spk_emb=None)

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

Parameters:

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