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PaddleSpeech/paddlespeech/t2s/models/transformer_tts/transformer_tts.py

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30 KiB

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
3 years ago
# Modified from espnet(https://github.com/espnet/espnet)
"""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 paddlespeech.t2s.modules.nets_utils import initialize
from paddlespeech.t2s.modules.nets_utils import make_non_pad_mask
from paddlespeech.t2s.modules.nets_utils import make_pad_mask
from paddlespeech.t2s.modules.style_encoder import StyleEncoder
from paddlespeech.t2s.modules.tacotron2.decoder import Postnet
from paddlespeech.t2s.modules.tacotron2.decoder import Prenet as DecoderPrenet
from paddlespeech.t2s.modules.tacotron2.encoder import Encoder as EncoderPrenet
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from paddlespeech.t2s.modules.transformer.attention import MultiHeadedAttention
from paddlespeech.t2s.modules.transformer.decoder import Decoder
from paddlespeech.t2s.modules.transformer.embedding import PositionalEncoding
from paddlespeech.t2s.modules.transformer.embedding import ScaledPositionalEncoding
from paddlespeech.t2s.modules.transformer.encoder import TransformerEncoder
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from paddlespeech.t2s.modules.transformer.mask import subsequent_mask
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
Args:
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.
"""
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 layer type
transformer_pos_enc_layer_type = "scaled_abs_pos" if self.use_scaled_pos_enc else "abs_pos"
# 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 = TransformerEncoder(
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_layer_type=transformer_pos_enc_layer_type,
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"
# get positional encoding class
pos_enc_class = (ScaledPositionalEncoding
if self.use_scaled_pos_enc else PositionalEncoding)
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,
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spk_emb: paddle.Tensor=None,
) -> Tuple[paddle.Tensor, Dict[str, paddle.Tensor], paddle.Tensor]:
"""Calculate forward propagation.
Args:
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,).
spk_emb(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
stop_labels = make_pad_mask(olens - 1)
# bool 类型无法切片
stop_labels = paddle.cast(stop_labels, dtype='float32')
stop_labels = F.pad(stop_labels, [0, 0, 0, 1], "constant", 1.0)
# calculate transformer outputs
after_outs, before_outs, logits = self._forward(xs, ilens, ys, olens,
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spk_emb)
# modifiy mod part of groundtruth
if self.reduction_factor > 1:
olens = olens - olens % self.reduction_factor
max_olen = max(olens)
ys = ys[:, :max_olen]
stop_labels = stop_labels[:, :max_olen]
stop_labels[:, -1] = 1.0 # make sure at least one frame has 1
olens_in = olens // self.reduction_factor
else:
olens_in = olens
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, stop_labels, olens, olens_in, need_dict
def _forward(
self,
xs: paddle.Tensor,
ilens: paddle.Tensor,
ys: paddle.Tensor,
olens: paddle.Tensor,
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spk_emb: 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:
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hs = self._integrate_with_spk_embed(hs, spk_emb)
# 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 // self.reduction_factor
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,
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spk_emb: 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.
Args:
text(Tensor(int64)): Input sequence of characters (T,).
speech(Tensor, optional): Feature sequence to extract style (N, idim).
spk_emb(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
# 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)
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spk_emb = None if spk_emb is None else spk_emb.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)
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outs, *_ = self._forward(xs, ilens, ys, olens, spk_emb)
# 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
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if spk_emb is not None:
spk_emb = spk_emb.unsqueeze(0)
hs = self._integrate_with_spk_embed(hs, spk_emb)
# 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.
Args:
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.
Args:
olens (Tensor(int64)): 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,
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spk_emb: paddle.Tensor) -> paddle.Tensor:
"""Integrate speaker embedding with hidden states.
Args:
hs(Tensor): Batch of hidden state sequences (B, Tmax, adim).
spk_emb(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
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spk_emb = self.projection(F.normalize(spk_emb))
hs = hs + spk_emb.unsqueeze(1)
elif self.spk_embed_integration_type == "concat":
# concat hidden states with spk embeds and then apply projection
3 years ago
spk_emb = F.normalize(spk_emb).unsqueeze(1).expand(-1, hs.shape[1],
-1)
hs = self.projection(paddle.concat([hs, spk_emb], 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