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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""Fastspeech2 related modules for paddle"""
from typing import Dict
from typing import Sequence
from typing import Tuple
import numpy
import paddle
import paddle.nn.functional as F
from paddle import nn
from typeguard import check_argument_types
from parakeet.modules.fastspeech2_transformer.attention import MultiHeadedAttention
from parakeet.modules.fastspeech2_transformer.decoder import Decoder
from parakeet.modules.fastspeech2_transformer.embedding import PositionalEncoding
from parakeet.modules.fastspeech2_transformer.embedding import ScaledPositionalEncoding
from parakeet.modules.fastspeech2_transformer.encoder import Encoder
from parakeet.modules.fastspeech2_transformer.mask import subsequent_mask
from parakeet.modules.nets_utils import initialize
from parakeet.modules.nets_utils import make_non_pad_mask
from parakeet.modules.nets_utils import make_pad_mask
from parakeet.modules.style_encoder import StyleEncoder
from parakeet.modules.tacotron2.decoder import Postnet
from parakeet.modules.tacotron2.decoder import Prenet as DecoderPrenet
from parakeet.modules.tacotron2.encoder import Encoder as EncoderPrenet
class TransformerTTS(nn.Layer):
"""TTS-Transformer module.
This is a module of text-to-speech Transformer described in `Neural Speech Synthesis
with Transformer Network`_, which convert the sequence of tokens into the sequence
of Mel-filterbanks.
.. _`Neural Speech Synthesis with Transformer Network`:
https://arxiv.org/pdf/1809.08895.pdf
Parameters
----------
idim : int
Dimension of the inputs.
odim : int
Dimension of the outputs.
embed_dim : int, optional
Dimension of character embedding.
eprenet_conv_layers : int, optional
Number of encoder prenet convolution layers.
eprenet_conv_chans : int, optional
Number of encoder prenet convolution channels.
eprenet_conv_filts : int, optional
Filter size of encoder prenet convolution.
dprenet_layers : int, optional
Number of decoder prenet layers.
dprenet_units : int, optional
Number of decoder prenet hidden units.
elayers : int, optional
Number of encoder layers.
eunits : int, optional
Number of encoder hidden units.
adim : int, optional
Number of attention transformation dimensions.
aheads : int, optional
Number of heads for multi head attention.
dlayers : int, optional
Number of decoder layers.
dunits : int, optional
Number of decoder hidden units.
postnet_layers : int, optional
Number of postnet layers.
postnet_chans : int, optional
Number of postnet channels.
postnet_filts : int, optional
Filter size of postnet.
use_scaled_pos_enc : pool, optional
Whether to use trainable scaled positional encoding.
use_batch_norm : bool, optional
Whether to use batch normalization in encoder prenet.
encoder_normalize_before : bool, optional
Whether to perform layer normalization before encoder block.
decoder_normalize_before : bool, optional
Whether to perform layer normalization before decoder block.
encoder_concat_after : bool, optional
Whether to concatenate attention layer's input and output in encoder.
decoder_concat_after : bool, optional
Whether to concatenate attention layer's input and output in decoder.
positionwise_layer_type : str, optional
Position-wise operation type.
positionwise_conv_kernel_size : int, optional
Kernel size in position wise conv 1d.
reduction_factor : int, optional
Reduction factor.
spk_embed_dim : int, optional
Number of speaker embedding dimenstions.
spk_embed_integration_type : str, optional
How to integrate speaker embedding.
use_gst : str, optional
Whether to use global style token.
gst_tokens : int, optional
The number of GST embeddings.
gst_heads : int, optional
The number of heads in GST multihead attention.
gst_conv_layers : int, optional
The number of conv layers in GST.
gst_conv_chans_list : Sequence[int], optional
List of the number of channels of conv layers in GST.
gst_conv_kernel_size : int, optional
Kernal size of conv layers in GST.
gst_conv_stride : int, optional
Stride size of conv layers in GST.
gst_gru_layers : int, optional
The number of GRU layers in GST.
gst_gru_units : int, optional
The number of GRU units in GST.
transformer_lr : float, optional
Initial value of learning rate.
transformer_warmup_steps : int, optional
Optimizer warmup steps.
transformer_enc_dropout_rate : float, optional
Dropout rate in encoder except attention and positional encoding.
transformer_enc_positional_dropout_rate : float, optional
Dropout rate after encoder positional encoding.
transformer_enc_attn_dropout_rate : float, optional
Dropout rate in encoder self-attention module.
transformer_dec_dropout_rate : float, optional
Dropout rate in decoder except attention & positional encoding.
transformer_dec_positional_dropout_rate : float, optional
Dropout rate after decoder positional encoding.
transformer_dec_attn_dropout_rate : float, optional
Dropout rate in deocoder self-attention module.
transformer_enc_dec_attn_dropout_rate : float, optional
Dropout rate in encoder-deocoder attention module.
init_type : str, optional
How to initialize transformer parameters.
init_enc_alpha : float, optional
Initial value of alpha in scaled pos encoding of the encoder.
init_dec_alpha : float, optional
Initial value of alpha in scaled pos encoding of the decoder.
eprenet_dropout_rate : float, optional
Dropout rate in encoder prenet.
dprenet_dropout_rate : float, optional
Dropout rate in decoder prenet.
postnet_dropout_rate : float, optional
Dropout rate in postnet.
use_masking : bool, optional
Whether to apply masking for padded part in loss calculation.
use_weighted_masking : bool, optional
Whether to apply weighted masking in loss calculation.
bce_pos_weight : float, optional
Positive sample weight in bce calculation (only for use_masking=true).
loss_type : str, optional
How to calculate loss.
use_guided_attn_loss : bool, optional
Whether to use guided attention loss.
num_heads_applied_guided_attn : int, optional
Number of heads in each layer to apply guided attention loss.
num_layers_applied_guided_attn : int, optional
Number of layers to apply guided attention loss.
List of module names to apply guided attention loss.
"""
def __init__(
self,
# network structure related
idim: int,
odim: int,
embed_dim: int=512,
eprenet_conv_layers: int=3,
eprenet_conv_chans: int=256,
eprenet_conv_filts: int=5,
dprenet_layers: int=2,
dprenet_units: int=256,
elayers: int=6,
eunits: int=1024,
adim: int=512,
aheads: int=4,
dlayers: int=6,
dunits: int=1024,
postnet_layers: int=5,
postnet_chans: int=256,
postnet_filts: int=5,
positionwise_layer_type: str="conv1d",
positionwise_conv_kernel_size: int=1,
use_scaled_pos_enc: bool=True,
use_batch_norm: bool=True,
encoder_normalize_before: bool=True,
decoder_normalize_before: bool=True,
encoder_concat_after: bool=False,
decoder_concat_after: bool=False,
reduction_factor: int=1,
spk_embed_dim: int=None,
spk_embed_integration_type: str="add",
use_gst: bool=False,
gst_tokens: int=10,
gst_heads: int=4,
gst_conv_layers: int=6,
gst_conv_chans_list: Sequence[int]=(32, 32, 64, 64, 128, 128),
gst_conv_kernel_size: int=3,
gst_conv_stride: int=2,
gst_gru_layers: int=1,
gst_gru_units: int=128,
# training related
transformer_enc_dropout_rate: float=0.1,
transformer_enc_positional_dropout_rate: float=0.1,
transformer_enc_attn_dropout_rate: float=0.1,
transformer_dec_dropout_rate: float=0.1,
transformer_dec_positional_dropout_rate: float=0.1,
transformer_dec_attn_dropout_rate: float=0.1,
transformer_enc_dec_attn_dropout_rate: float=0.1,
eprenet_dropout_rate: float=0.5,
dprenet_dropout_rate: float=0.5,
postnet_dropout_rate: float=0.5,
init_type: str="xavier_uniform",
init_enc_alpha: float=1.0,
init_dec_alpha: float=1.0,
use_guided_attn_loss: bool=True,
num_heads_applied_guided_attn: int=2,
num_layers_applied_guided_attn: int=2, ):
"""Initialize Transformer module."""
assert check_argument_types()
super().__init__()
# store hyperparameters
self.idim = idim
self.odim = odim
self.eos = idim - 1
self.spk_embed_dim = spk_embed_dim
self.reduction_factor = reduction_factor
self.use_gst = use_gst
self.use_scaled_pos_enc = use_scaled_pos_enc
self.use_guided_attn_loss = use_guided_attn_loss
if self.use_guided_attn_loss:
if num_layers_applied_guided_attn == -1:
self.num_layers_applied_guided_attn = elayers
else:
self.num_layers_applied_guided_attn = num_layers_applied_guided_attn
if num_heads_applied_guided_attn == -1:
self.num_heads_applied_guided_attn = aheads
else:
self.num_heads_applied_guided_attn = num_heads_applied_guided_attn
if self.spk_embed_dim is not None:
self.spk_embed_integration_type = spk_embed_integration_type
# use idx 0 as padding idx
self.padding_idx = 0
# set_global_initializer 会影响后面的全局，包括 create_parameter
initialize(self, init_type)
# get positional encoding class
pos_enc_class = (ScaledPositionalEncoding
if self.use_scaled_pos_enc else PositionalEncoding)
# define transformer encoder
if eprenet_conv_layers != 0:
# encoder prenet
encoder_input_layer = nn.Sequential(
EncoderPrenet(
idim=idim,
embed_dim=embed_dim,
elayers=0,
econv_layers=eprenet_conv_layers,
econv_chans=eprenet_conv_chans,
econv_filts=eprenet_conv_filts,
use_batch_norm=use_batch_norm,
dropout_rate=eprenet_dropout_rate,
padding_idx=self.padding_idx, ),
nn.Linear(eprenet_conv_chans, adim), )
else:
encoder_input_layer = nn.Embedding(
num_embeddings=idim,
embedding_dim=adim,
padding_idx=self.padding_idx)
self.encoder = Encoder(
idim=idim,
attention_dim=adim,
attention_heads=aheads,
linear_units=eunits,
num_blocks=elayers,
input_layer=encoder_input_layer,
dropout_rate=transformer_enc_dropout_rate,
positional_dropout_rate=transformer_enc_positional_dropout_rate,
attention_dropout_rate=transformer_enc_attn_dropout_rate,
pos_enc_class=pos_enc_class,
normalize_before=encoder_normalize_before,
concat_after=encoder_concat_after,
positionwise_layer_type=positionwise_layer_type,
positionwise_conv_kernel_size=positionwise_conv_kernel_size, )
# define GST
if self.use_gst:
self.gst = StyleEncoder(
idim=odim, # the input is mel-spectrogram
gst_tokens=gst_tokens,
gst_token_dim=adim,
gst_heads=gst_heads,
conv_layers=gst_conv_layers,
conv_chans_list=gst_conv_chans_list,
conv_kernel_size=gst_conv_kernel_size,
conv_stride=gst_conv_stride,
gru_layers=gst_gru_layers,
gru_units=gst_gru_units, )
# define projection layer
if self.spk_embed_dim is not None:
if self.spk_embed_integration_type == "add":
self.projection = nn.Linear(self.spk_embed_dim, adim)
else:
self.projection = nn.Linear(adim + self.spk_embed_dim, adim)
# define transformer decoder
if dprenet_layers != 0:
# decoder prenet
decoder_input_layer = nn.Sequential(
DecoderPrenet(
idim=odim,
n_layers=dprenet_layers,
n_units=dprenet_units,
dropout_rate=dprenet_dropout_rate, ),
nn.Linear(dprenet_units, adim), )
else:
decoder_input_layer = "linear"
self.decoder = Decoder(
odim=odim, # odim is needed when no prenet is used
attention_dim=adim,
attention_heads=aheads,
linear_units=dunits,
num_blocks=dlayers,
dropout_rate=transformer_dec_dropout_rate,
positional_dropout_rate=transformer_dec_positional_dropout_rate,
self_attention_dropout_rate=transformer_dec_attn_dropout_rate,
src_attention_dropout_rate=transformer_enc_dec_attn_dropout_rate,
input_layer=decoder_input_layer,
use_output_layer=False,
pos_enc_class=pos_enc_class,
normalize_before=decoder_normalize_before,
concat_after=decoder_concat_after, )
# define final projection
self.feat_out = nn.Linear(adim, odim * reduction_factor)
self.prob_out = nn.Linear(adim, reduction_factor)
# define postnet
self.postnet = (None if postnet_layers == 0 else Postnet(
idim=idim,
odim=odim,
n_layers=postnet_layers,
n_chans=postnet_chans,
n_filts=postnet_filts,
use_batch_norm=use_batch_norm,
dropout_rate=postnet_dropout_rate, ))
# 闭合的 initialize() 中的 set_global_initializer 的作用域，防止其影响到 self._reset_parameters()
nn.initializer.set_global_initializer(None)
self._reset_parameters(
init_enc_alpha=init_enc_alpha,
init_dec_alpha=init_dec_alpha, )
def _reset_parameters(self, init_enc_alpha: float, init_dec_alpha: float):
# initialize alpha in scaled positional encoding
if self.use_scaled_pos_enc:
init_enc_alpha = paddle.to_tensor(init_enc_alpha)
self.encoder.embed[-1].alpha = paddle.create_parameter(
shape=init_enc_alpha.shape,
dtype=str(init_enc_alpha.numpy().dtype),
default_initializer=paddle.nn.initializer.Assign(
init_enc_alpha))
init_dec_alpha = paddle.to_tensor(init_dec_alpha)
self.decoder.embed[-1].alpha = paddle.create_parameter(
shape=init_dec_alpha.shape,
dtype=str(init_dec_alpha.numpy().dtype),
default_initializer=paddle.nn.initializer.Assign(
init_dec_alpha))
def forward(
self,
text: paddle.Tensor,
text_lengths: paddle.Tensor,
speech: paddle.Tensor,
speech_lengths: paddle.Tensor,
spembs: paddle.Tensor=None,
) -> Tuple[paddle.Tensor, Dict[str, paddle.Tensor], paddle.Tensor]:
"""Calculate forward propagation.
Parameters
----------
text : Tensor(int64)
Batch of padded character ids (B, Tmax).
text_lengths : Tensor(int64)
Batch of lengths of each input batch (B,).
speech : Tensor
Batch of padded target features (B, Lmax, odim).
speech_lengths : Tensor(int64)
Batch of the lengths of each target (B,).
spembs : Tensor, optional
Batch of speaker embeddings (B, spk_embed_dim).
Returns
----------
Tensor
Loss scalar value.
Dict
Statistics to be monitored.
"""
# input of embedding must be int64
text_lengths = paddle.cast(text_lengths, 'int64')
# Add eos at the last of sequence
text = numpy.pad(text.numpy(), ((0, 0), (0, 1)), 'constant')
xs = paddle.to_tensor(text, dtype='int64')
for i, l in enumerate(text_lengths):
xs[i, l] = self.eos
ilens = text_lengths + 1
ys = speech
olens = paddle.cast(speech_lengths, 'int64')
# make labels for stop prediction
labels = make_pad_mask(olens - 1)
labels = numpy.pad(
labels.numpy(), ((0, 0), (0, 1)), 'constant', constant_values=1.0)
labels = paddle.to_tensor(labels)
labels = paddle.cast(labels, dtype="float32")
# labels = F.pad(labels, [0, 1], "constant", 1.0)
# calculate transformer outputs
after_outs, before_outs, logits = self._forward(xs, ilens, ys, olens,
spembs)
# modifiy mod part of groundtruth
if self.reduction_factor > 1:
olens = paddle.to_tensor(
[olen - olen % self.reduction_factor for olen in olens.numpy()])
max_olen = max(olens)
ys = ys[:, :max_olen]
labels = labels[:, :max_olen]
labels[:, -1] = 1.0 # make sure at least one frame has 1
need_dict = {}
need_dict['encoder'] = self.encoder
need_dict['decoder'] = self.decoder
need_dict[
'num_heads_applied_guided_attn'] = self.num_heads_applied_guided_attn
need_dict[
'num_layers_applied_guided_attn'] = self.num_layers_applied_guided_attn
need_dict['use_scaled_pos_enc'] = self.use_scaled_pos_enc
return after_outs, before_outs, logits, ys, labels, olens, ilens, need_dict
def _forward(
self,
xs: paddle.Tensor,
ilens: paddle.Tensor,
ys: paddle.Tensor,
olens: paddle.Tensor,
spembs: paddle.Tensor,
) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
# forward encoder
x_masks = self._source_mask(ilens)
hs, h_masks = self.encoder(xs, x_masks)
# integrate with GST
if self.use_gst:
style_embs = self.gst(ys)
hs = hs + style_embs.unsqueeze(1)
# integrate speaker embedding
if self.spk_embed_dim is not None:
hs = self._integrate_with_spk_embed(hs, spembs)
# thin out frames for reduction factor (B, Lmax, odim) -> (B, Lmax//r, odim)
if self.reduction_factor > 1:
ys_in = ys[:, self.reduction_factor - 1::self.reduction_factor]
olens_in = olens.new(
[olen // self.reduction_factor for olen in olens])
else:
ys_in, olens_in = ys, olens
# add first zero frame and remove last frame for auto-regressive
ys_in = self._add_first_frame_and_remove_last_frame(ys_in)
# forward decoder
y_masks = self._target_mask(olens_in)
zs, _ = self.decoder(ys_in, y_masks, hs, h_masks)
# (B, Lmax//r, odim * r) -> (B, Lmax//r * r, odim)
before_outs = self.feat_out(zs).reshape([zs.shape[0], -1, self.odim])
# (B, Lmax//r, r) -> (B, Lmax//r * r)
logits = self.prob_out(zs).reshape([zs.shape[0], -1])
# postnet -> (B, Lmax//r * r, odim)
if self.postnet is None:
after_outs = before_outs
else:
after_outs = before_outs + self.postnet(
before_outs.transpose([0, 2, 1])).transpose([0, 2, 1])
return after_outs, before_outs, logits
def inference(
self,
text: paddle.Tensor,
speech: paddle.Tensor=None,
spembs: paddle.Tensor=None,
threshold: float=0.5,
minlenratio: float=0.0,
maxlenratio: float=10.0,
use_teacher_forcing: bool=False,
) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
"""Generate the sequence of features given the sequences of characters.
Parameters
----------
text : Tensor(int64)
Input sequence of characters (T,).
speech : Tensor, optional
Feature sequence to extract style (N, idim).
spembs : Tensor, optional
Speaker embedding vector (spk_embed_dim,).
threshold : float, optional
Threshold in inference.
minlenratio : float, optional
Minimum length ratio in inference.
maxlenratio : float, optional
Maximum length ratio in inference.
use_teacher_forcing : bool, optional
Whether to use teacher forcing.
Returns
----------
Tensor
Output sequence of features (L, odim).
Tensor
Output sequence of stop probabilities (L,).
Tensor
Encoder-decoder (source) attention weights (#layers, #heads, L, T).
"""
# input of embedding must be int64
y = speech
spemb = spembs
# add eos at the last of sequence
text = numpy.pad(
text.numpy(), (0, 1), 'constant', constant_values=self.eos)
x = paddle.to_tensor(text, dtype='int64')
# inference with teacher forcing
if use_teacher_forcing:
assert speech is not None, "speech must be provided with teacher forcing."
# get teacher forcing outputs
xs, ys = x.unsqueeze(0), y.unsqueeze(0)
spembs = None if spemb is None else spemb.unsqueeze(0)
ilens = paddle.to_tensor(
[xs.shape[1]], dtype=paddle.int64, place=xs.place)
olens = paddle.to_tensor(
[ys.shape[1]], dtype=paddle.int64, place=ys.place)
outs, *_ = self._forward(xs, ilens, ys, olens, spembs)
# get attention weights
att_ws = []
for i in range(len(self.decoder.decoders)):
att_ws += [self.decoder.decoders[i].src_attn.attn]
# (B, L, H, T_out, T_in)
att_ws = paddle.stack(att_ws, axis=1)
return outs[0], None, att_ws[0]
# forward encoder
xs = x.unsqueeze(0)
hs, _ = self.encoder(xs, None)
# integrate GST
if self.use_gst:
style_embs = self.gst(y.unsqueeze(0))
hs = hs + style_embs.unsqueeze(1)
# integrate speaker embedding
if self.spk_embed_dim is not None:
spembs = spemb.unsqueeze(0)
hs = self._integrate_with_spk_embed(hs, spembs)
# set limits of length
maxlen = int(hs.shape[1] * maxlenratio / self.reduction_factor)
minlen = int(hs.shape[1] * minlenratio / self.reduction_factor)
# initialize
idx = 0
ys = paddle.zeros([1, 1, self.odim])
outs, probs = [], []
# forward decoder step-by-step
z_cache = None
while True:
# update index
idx += 1
# calculate output and stop prob at idx-th step
y_masks = subsequent_mask(idx).unsqueeze(0)
z, z_cache = self.decoder.forward_one_step(
ys, y_masks, hs, cache=z_cache) # (B, adim)
outs += [
self.feat_out(z).reshape([self.reduction_factor, self.odim])
] # [(r, odim), ...]
probs += [F.sigmoid(self.prob_out(z))[0]] # [(r), ...]
# update next inputs
ys = paddle.concat(
(ys, outs[-1][-1].reshape([1, 1, self.odim])),
axis=1) # (1, idx + 1, odim)
# get attention weights
att_ws_ = []
for name, m in self.named_sublayers():
if isinstance(m, MultiHeadedAttention) and "src" in name:
# [(#heads, 1, T),...]
att_ws_ += [m.attn[0, :, -1].unsqueeze(1)]
if idx == 1:
att_ws = att_ws_
else:
# [(#heads, l, T), ...]
att_ws = [
paddle.concat([att_w, att_w_], axis=1)
for att_w, att_w_ in zip(att_ws, att_ws_)
]
# check whether to finish generation
if sum(paddle.cast(probs[-1] >= threshold,
'int64')) > 0 or idx >= maxlen:
# check mininum length
if idx < minlen:
continue
# (L, odim) -> (1, L, odim) -> (1, odim, L)
outs = (paddle.concat(outs, axis=0).unsqueeze(0).transpose(
[0, 2, 1]))
if self.postnet is not None:
# (1, odim, L)
outs = outs + self.postnet(outs)
# (L, odim)
outs = outs.transpose([0, 2, 1]).squeeze(0)
probs = paddle.concat(probs, axis=0)
break
# concatenate attention weights -> (#layers, #heads, L, T)
att_ws = paddle.stack(att_ws, axis=0)
return outs, probs, att_ws
def _add_first_frame_and_remove_last_frame(
self, ys: paddle.Tensor) -> paddle.Tensor:
ys_in = paddle.concat(
[paddle.zeros((ys.shape[0], 1, ys.shape[2])), ys[:, :-1]], axis=1)
return ys_in
def _source_mask(self, ilens: paddle.Tensor) -> paddle.Tensor:
"""Make masks for self-attention.
Parameters
----------
ilens : Tensor
Batch of lengths (B,).
Returns
-------
Tensor
Mask tensor for self-attention.
dtype=paddle.bool
Examples
-------
>>> ilens = [5, 3]
>>> self._source_mask(ilens)
tensor([[[1, 1, 1, 1, 1],
[1, 1, 1, 0, 0]]]) bool
"""
x_masks = make_non_pad_mask(ilens)
return x_masks.unsqueeze(-2)
def _target_mask(self, olens: paddle.Tensor) -> paddle.Tensor:
"""Make masks for masked self-attention.
Parameters
----------
olens : LongTensor
Batch of lengths (B,).
Returns
----------
Tensor
Mask tensor for masked self-attention.
Examples
----------
>>> olens = [5, 3]
>>> self._target_mask(olens)
tensor([[[1, 0, 0, 0, 0],
[1, 1, 0, 0, 0],
[1, 1, 1, 0, 0],
[1, 1, 1, 1, 0],
[1, 1, 1, 1, 1]],
[[1, 0, 0, 0, 0],
[1, 1, 0, 0, 0],
[1, 1, 1, 0, 0],
[1, 1, 1, 0, 0],
[1, 1, 1, 0, 0]]], dtype=paddle.uint8)
"""
y_masks = make_non_pad_mask(olens)
s_masks = subsequent_mask(y_masks.shape[-1]).unsqueeze(0)
return paddle.logical_and(y_masks.unsqueeze(-2), s_masks)
def _integrate_with_spk_embed(self,
hs: paddle.Tensor,
spembs: paddle.Tensor) -> paddle.Tensor:
"""Integrate speaker embedding with hidden states.
Parameters
----------
hs : Tensor
Batch of hidden state sequences (B, Tmax, adim).
spembs : Tensor
Batch of speaker embeddings (B, spk_embed_dim).
Returns
----------
Tensor
Batch of integrated hidden state sequences (B, Tmax, adim).
"""
if self.spk_embed_integration_type == "add":
# apply projection and then add to hidden states
spembs = self.projection(F.normalize(spembs))
hs = hs + spembs.unsqueeze(1)
elif self.spk_embed_integration_type == "concat":
# concat hidden states with spk embeds and then apply projection
spembs = F.normalize(spembs).unsqueeze(1).expand(-1, hs.shape[1],
-1)
hs = self.projection(paddle.concat([hs, spembs], axis=-1))
else:
raise NotImplementedError("support only add or concat.")
return hs
class TransformerTTSInference(nn.Layer):
def __init__(self, normalizer, model):
super().__init__()
self.normalizer = normalizer
self.acoustic_model = model
def forward(self, text, spk_id=None):
normalized_mel = self.acoustic_model.inference(text)[0]
logmel = self.normalizer.inverse(normalized_mel)
return logmel
class TransformerTTSLoss(nn.Layer):
"""Loss function module for Tacotron2."""
def __init__(self,
use_masking=True,
use_weighted_masking=False,
bce_pos_weight=5.0):
"""Initialize Tactoron2 loss module.
Parameters
----------
use_masking : bool
Whether to apply masking for padded part in loss calculation.
use_weighted_masking : bool
Whether to apply weighted masking in loss calculation.
bce_pos_weight : float
Weight of positive sample of stop token.
"""
super().__init__()
assert (use_masking != use_weighted_masking) or not use_masking
self.use_masking = use_masking
self.use_weighted_masking = use_weighted_masking
# define criterions
reduction = "none" if self.use_weighted_masking else "mean"
self.l1_criterion = nn.L1Loss(reduction=reduction)
self.mse_criterion = nn.MSELoss(reduction=reduction)
self.bce_criterion = nn.BCEWithLogitsLoss(
reduction=reduction, pos_weight=paddle.to_tensor(bce_pos_weight))
def forward(self, after_outs, before_outs, logits, ys, labels, olens):
"""Calculate forward propagation.
Parameters
----------
after_outs : Tensor
Batch of outputs after postnets (B, Lmax, odim).
before_outs : Tensor
Batch of outputs before postnets (B, Lmax, odim).
logits : Tensor
Batch of stop logits (B, Lmax).
ys : Tensor
Batch of padded target features (B, Lmax, odim).
labels : LongTensor
Batch of the sequences of stop token labels (B, Lmax).
olens : LongTensor
Batch of the lengths of each target (B,).
Returns
----------
Tensor
L1 loss value.
Tensor
Mean square error loss value.
Tensor
Binary cross entropy loss value.
"""
# make mask and apply it
if self.use_masking:
masks = make_non_pad_mask(olens).unsqueeze(-1)
ys = ys.masked_select(masks.broadcast_to(ys.shape))
after_outs = after_outs.masked_select(
masks.broadcast_to(after_outs.shape))
before_outs = before_outs.masked_select(
masks.broadcast_to(before_outs.shape))
# Operator slice does not have kernel for data_type[bool]
tmp_masks = paddle.cast(masks, dtype='int64')
tmp_masks = tmp_masks[:, :, 0]
tmp_masks = paddle.cast(tmp_masks, dtype='bool')
labels = labels.masked_select(tmp_masks.broadcast_to(labels.shape))
logits = logits.masked_select(tmp_masks.broadcast_to(logits.shape))
# calculate loss
l1_loss = self.l1_criterion(after_outs, ys) + self.l1_criterion(
before_outs, ys)
mse_loss = self.mse_criterion(after_outs, ys) + self.mse_criterion(
before_outs, ys)
bce_loss = self.bce_criterion(logits, labels)
# make weighted mask and apply it
if self.use_weighted_masking:
masks = make_non_pad_mask(olens).unsqueeze(-1)
weights = masks.float() / masks.sum(dim=1, keepdim=True).float()
out_weights = weights.div(ys.shape[0] * ys.shape[2])
logit_weights = weights.div(ys.shape[0])
# apply weight
l1_loss = l1_loss.multiply(out_weights)
l1_loss = l1_loss.masked_select(
masks.broadcast_to(l1_loss.shape)).sum()
mse_loss = mse_loss.multiply(out_weights)
mse_loss = mse_loss.masked_select(
masks.broadcast_to(mse_loss.shape)).sum()
bce_loss = bce_loss.multiply(logit_weights.squeeze(-1))
bce_loss = bce_loss.masked_select(
masks.squeeze(-1).broadcast_to(bce_loss.shape)).sum()
return l1_loss, mse_loss, bce_loss
class GuidedAttentionLoss(nn.Layer):
"""Guided attention loss function module.
This module calculates the guided attention loss described
in `Efficiently Trainable Text-to-Speech System Based
on Deep Convolutional Networks with Guided Attention`_,
which forces the attention to be diagonal.
.. _`Efficiently Trainable Text-to-Speech System
Based on Deep Convolutional Networks with Guided Attention`:
https://arxiv.org/abs/1710.08969
"""
def __init__(self, sigma=0.4, alpha=1.0, reset_always=True):
"""Initialize guided attention loss module.
Parameters
----------
sigma : float, optional
Standard deviation to control how close attention to a diagonal.
alpha : float, optional
Scaling coefficient (lambda).
reset_always : bool, optional
Whether to always reset masks.
"""
super(GuidedAttentionLoss, self).__init__()
self.sigma = sigma
self.alpha = alpha
self.reset_always = reset_always
self.guided_attn_masks = None
self.masks = None
def _reset_masks(self):
self.guided_attn_masks = None
self.masks = None
def forward(self, att_ws, ilens, olens):
"""Calculate forward propagation.
Parameters
----------
att_ws : Tensor
Batch of attention weights (B, T_max_out, T_max_in).
ilens : LongTensor
Batch of input lenghts (B,).
olens : LongTensor
Batch of output lenghts (B,).
Returns
----------
Tensor
Guided attention loss value.
"""
if self.guided_attn_masks is None:
self.guided_attn_masks = self._make_guided_attention_masks(ilens,
olens)
if self.masks is None:
self.masks = self._make_masks(ilens, olens)
losses = self.guided_attn_masks * att_ws
loss = paddle.mean(
losses.masked_select(self.masks.broadcast_to(losses.shape)))
if self.reset_always:
self._reset_masks()
return self.alpha * loss
def _make_guided_attention_masks(self, ilens, olens):
n_batches = len(ilens)
max_ilen = max(ilens)
max_olen = max(olens)
guided_attn_masks = paddle.zeros((n_batches, max_olen, max_ilen))
for idx, (ilen, olen) in enumerate(zip(ilens, olens)):
ilen = int(ilen)
olen = int(olen)
guided_attn_masks[idx, :olen, :
ilen] = self._make_guided_attention_mask(
ilen, olen, self.sigma)
return guided_attn_masks
@staticmethod
def _make_guided_attention_mask(ilen, olen, sigma):
"""Make guided attention mask.
Examples
----------
>>> guided_attn_mask =_make_guided_attention(5, 5, 0.4)
>>> guided_attn_mask.shape
[5, 5]
>>> guided_attn_mask
tensor([[0.0000, 0.1175, 0.3935, 0.6753, 0.8647],
[0.1175, 0.0000, 0.1175, 0.3935, 0.6753],
[0.3935, 0.1175, 0.0000, 0.1175, 0.3935],
[0.6753, 0.3935, 0.1175, 0.0000, 0.1175],
[0.8647, 0.6753, 0.3935, 0.1175, 0.0000]])
>>> guided_attn_mask =_make_guided_attention(3, 6, 0.4)
>>> guided_attn_mask.shape
[6, 3]
>>> guided_attn_mask
tensor([[0.0000, 0.2934, 0.7506],
[0.0831, 0.0831, 0.5422],
[0.2934, 0.0000, 0.2934],
[0.5422, 0.0831, 0.0831],
[0.7506, 0.2934, 0.0000],
[0.8858, 0.5422, 0.0831]])
"""
grid_x, grid_y = paddle.meshgrid(
paddle.arange(olen), paddle.arange(ilen))
grid_x = grid_x.cast(dtype=paddle.float32)
grid_y = grid_y.cast(dtype=paddle.float32)
return 1.0 - paddle.exp(-(
(grid_y / ilen - grid_x / olen)**2) / (2 * (sigma**2)))
@staticmethod
def _make_masks(ilens, olens):
"""Make masks indicating non-padded part.
Parameters
----------
ilens (LongTensor or List): Batch of lengths (B,).
olens (LongTensor or List): Batch of lengths (B,).
Returns
----------
Tensor
Mask tensor indicating non-padded part.
Examples
----------
>>> ilens, olens = [5, 2], [8, 5]
>>> _make_mask(ilens, olens)
tensor([[[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1]],
[[1, 1, 0, 0, 0],
[1, 1, 0, 0, 0],
[1, 1, 0, 0, 0],
[1, 1, 0, 0, 0],
[1, 1, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]], dtype=paddle.uint8)
"""
# (B, T_in)
in_masks = make_non_pad_mask(ilens)
# (B, T_out)
out_masks = make_non_pad_mask(olens)
# (B, T_out, T_in)
return paddle.logical_and(
out_masks.unsqueeze(-1), in_masks.unsqueeze(-2))
class GuidedMultiHeadAttentionLoss(GuidedAttentionLoss):
"""Guided attention loss function module for multi head attention.
Parameters
----------
sigma : float, optional
Standard deviation to controlGuidedAttentionLoss
how close attention to a diagonal.
alpha : float, optional
Scaling coefficient (lambda).
reset_always : bool, optional
Whether to always reset masks.
"""
def forward(self, att_ws, ilens, olens):
"""Calculate forward propagation.
Parameters
----------
att_ws : Tensor
Batch of multi head attention weights (B, H, T_max_out, T_max_in).
ilens : Tensor
Batch of input lenghts (B,).
olens : Tensor
Batch of output lenghts (B,).
Returns
----------
Tensor
Guided attention loss value.
"""
if self.guided_attn_masks is None:
self.guided_attn_masks = (
self._make_guided_attention_masks(ilens, olens).unsqueeze(1))
if self.masks is None:
self.masks = self._make_masks(ilens, olens).unsqueeze(1)
losses = self.guided_attn_masks * att_ws
loss = paddle.mean(
losses.masked_select(self.masks.broadcast_to(losses.shape)))
if self.reset_always:
self._reset_masks()
return self.alpha * loss