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PaddleSpeech/paddlespeech/t2s/models/tacotron2/tacotron2.py

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18 KiB

# Copyright (c) 2020 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.
"""Tacotron 2 related modules for paddle"""
import logging
from typing import Dict
from typing import Optional
from typing import Tuple
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_pad_mask
from paddlespeech.t2s.modules.tacotron2.attentions import AttForward
from paddlespeech.t2s.modules.tacotron2.attentions import AttForwardTA
from paddlespeech.t2s.modules.tacotron2.attentions import AttLoc
from paddlespeech.t2s.modules.tacotron2.decoder import Decoder
from paddlespeech.t2s.modules.tacotron2.encoder import Encoder
class Tacotron2(nn.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.
.. _`Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions`:
https://arxiv.org/abs/1712.05884
"""
def __init__(
self,
# network structure related
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: str=None,
use_batch_norm: bool=True,
use_concate: bool=True,
use_residual: bool=False,
reduction_factor: int=1,
# extra embedding related
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,
# training related
init_type: str="xavier_uniform", ):
"""Initialize Tacotron2 module.
Args:
idim (int): Dimension of the inputs.
odim (int): Dimension of the outputs.
embed_dim (int): Dimension of the token embedding.
elayers (int): Number of encoder blstm layers.
eunits (int): Number of encoder blstm units.
econv_layers (int): Number of encoder conv layers.
econv_filts (int): Number of encoder conv filter size.
econv_chans (int): Number of encoder conv filter channels.
dlayers (int): Number of decoder lstm layers.
dunits (int): Number of decoder lstm units.
prenet_layers (int): Number of prenet layers.
prenet_units (int): Number of prenet units.
postnet_layers (int): Number of postnet layers.
postnet_filts (int): Number of postnet filter size.
postnet_chans (int): Number of postnet filter channels.
output_activation (str): Name of activation function for outputs.
adim (int): Number of dimension of mlp in attention.
aconv_chans (int): Number of attention conv filter channels.
aconv_filts (int): Number of attention conv filter size.
cumulate_att_w (bool): Whether to cumulate previous attention weight.
use_batch_norm (bool): Whether to use batch normalization.
use_concate (bool): Whether to concat enc outputs w/ dec lstm outputs.
reduction_factor (int): Reduction factor.
spk_num (Optional[int]): Number of speakers. If set to > 1, assume that the
sids will be provided as the input and use sid embedding layer.
lang_num (Optional[int]): Number of languages. If set to > 1, assume that the
lids will be provided as the input and use sid embedding layer.
spk_embed_dim (Optional[int]): Speaker embedding dimension. If set to > 0,
assume that spk_emb will be provided as the input.
spk_embed_integration_type (str): How to integrate speaker embedding.
dropout_rate (float): Dropout rate.
zoneout_rate (float): Zoneout rate.
"""
assert check_argument_types()
super().__init__()
# store hyperparameters
self.idim = idim
self.odim = odim
self.eos = idim - 1
self.cumulate_att_w = cumulate_att_w
self.reduction_factor = reduction_factor
# define activation function for the final output
if output_activation is None:
self.output_activation_fn = None
elif hasattr(F, output_activation):
self.output_activation_fn = getattr(F, output_activation)
else:
raise ValueError(f"there is no such an activation function. "
f"({output_activation})")
# set padding idx
padding_idx = 0
self.padding_idx = padding_idx
# initialize parameters
initialize(self, init_type)
# define network modules
self.enc = Encoder(
idim=idim,
embed_dim=embed_dim,
elayers=elayers,
eunits=eunits,
econv_layers=econv_layers,
econv_chans=econv_chans,
econv_filts=econv_filts,
use_batch_norm=use_batch_norm,
use_residual=use_residual,
dropout_rate=dropout_rate,
padding_idx=padding_idx, )
self.spk_num = None
if spk_num is not None and spk_num > 1:
self.spk_num = spk_num
self.sid_emb = nn.Embedding(spk_num, eunits)
self.lang_num = None
if lang_num is not None and lang_num > 1:
self.lang_num = lang_num
self.lid_emb = nn.Embedding(lang_num, eunits)
self.spk_embed_dim = None
if spk_embed_dim is not None and spk_embed_dim > 0:
self.spk_embed_dim = spk_embed_dim
self.spk_embed_integration_type = spk_embed_integration_type
if self.spk_embed_dim is None:
dec_idim = eunits
elif self.spk_embed_integration_type == "concat":
dec_idim = eunits + spk_embed_dim
elif self.spk_embed_integration_type == "add":
dec_idim = eunits
self.projection = nn.Linear(self.spk_embed_dim, eunits)
else:
raise ValueError(f"{spk_embed_integration_type} is not supported.")
if atype == "location":
att = AttLoc(dec_idim, dunits, adim, aconv_chans, aconv_filts)
elif atype == "forward":
att = AttForward(dec_idim, dunits, adim, aconv_chans, aconv_filts)
if self.cumulate_att_w:
logging.warning("cumulation of attention weights is disabled "
"in forward attention.")
self.cumulate_att_w = False
elif atype == "forward_ta":
att = AttForwardTA(dec_idim, dunits, adim, aconv_chans, aconv_filts,
odim)
if self.cumulate_att_w:
logging.warning("cumulation of attention weights is disabled "
"in forward attention.")
self.cumulate_att_w = False
else:
raise NotImplementedError("Support only location or forward")
self.dec = Decoder(
idim=dec_idim,
odim=odim,
att=att,
dlayers=dlayers,
dunits=dunits,
prenet_layers=prenet_layers,
prenet_units=prenet_units,
postnet_layers=postnet_layers,
postnet_chans=postnet_chans,
postnet_filts=postnet_filts,
output_activation_fn=self.output_activation_fn,
cumulate_att_w=self.cumulate_att_w,
use_batch_norm=use_batch_norm,
use_concate=use_concate,
dropout_rate=dropout_rate,
zoneout_rate=zoneout_rate,
reduction_factor=reduction_factor, )
nn.initializer.set_global_initializer(None)
def forward(
self,
text: paddle.Tensor,
text_lengths: paddle.Tensor,
speech: paddle.Tensor,
speech_lengths: paddle.Tensor,
spk_emb: Optional[paddle.Tensor]=None,
spk_id: Optional[paddle.Tensor]=None,
lang_id: Optional[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, 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.
"""
text = text[:, :text_lengths.max()]
speech = speech[:, :speech_lengths.max()]
batch_size = paddle.shape(text)[0]
# Add eos at the last of sequence
xs = F.pad(text, [0, 0, 0, 1], "constant", self.padding_idx)
for i, l in enumerate(text_lengths):
xs[i, l] = self.eos
ilens = text_lengths + 1
ys = speech
olens = speech_lengths
# 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 tacotron2 outputs
after_outs, before_outs, logits, att_ws = self._forward(
xs=xs,
ilens=ilens,
ys=ys,
olens=olens,
spk_emb=spk_emb,
spk_id=spk_id,
lang_id=lang_id, )
# modify mod part of groundtruth
if self.reduction_factor > 1:
assert olens.ge(self.reduction_factor).all(
), "Output length must be greater than or equal to reduction factor."
olens = olens - olens % self.reduction_factor
max_out = max(olens)
ys = ys[:, :max_out]
stop_labels = stop_labels[:, :max_out]
stop_labels = paddle.scatter(stop_labels, 1,
(olens - 1).unsqueeze(1), 1.0)
olens_in = olens // self.reduction_factor
else:
olens_in = olens
return after_outs, before_outs, logits, ys, stop_labels, olens, att_ws, olens_in
def _forward(
self,
xs: paddle.Tensor,
ilens: paddle.Tensor,
ys: paddle.Tensor,
olens: paddle.Tensor,
spk_emb: paddle.Tensor,
spk_id: paddle.Tensor,
lang_id: paddle.Tensor,
) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
hs, hlens = self.enc(xs, ilens)
if self.spk_num is not None:
sid_embs = self.sid_emb(spk_id.reshape([-1]))
hs = hs + sid_embs.unsqueeze(1)
if self.lang_num is not None:
lid_embs = self.lid_emb(lang_id.reshape([-1]))
hs = hs + lid_embs.unsqueeze(1)
if self.spk_embed_dim is not None:
hs = self._integrate_with_spk_embed(hs, spk_emb)
return self.dec(hs, hlens, ys)
def inference(
self,
text: paddle.Tensor,
speech: Optional[paddle.Tensor]=None,
spk_emb: Optional[paddle.Tensor]=None,
spk_id: Optional[paddle.Tensor]=None,
lang_id: Optional[paddle.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, paddle.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).
"""
x = text
y = speech
# add eos at the last of sequence
x = F.pad(x, [0, 1], "constant", self.eos)
# inference with teacher forcing
if use_teacher_forcing:
assert speech is not None, "speech must be provided with teacher forcing."
xs, ys = x.unsqueeze(0), y.unsqueeze(0)
spk_emb = None if spk_emb is None else spk_emb.unsqueeze(0)
ilens = paddle.shape(xs)[1]
olens = paddle.shape(ys)[1]
outs, _, _, att_ws = self._forward(
xs=xs,
ilens=ilens,
ys=ys,
olens=olens,
spk_emb=spk_emb,
spk_id=spk_id,
lang_id=lang_id, )
return dict(feat_gen=outs[0], att_w=att_ws[0])
# inference
h = self.enc.inference(x)
if self.spk_num is not None:
sid_emb = self.sid_emb(spk_id.reshape([-1]))
h = h + sid_emb
if self.lang_num is not None:
lid_emb = self.lid_emb(lang_id.reshape([-1]))
h = h + lid_emb
if self.spk_embed_dim is not None:
hs, spk_emb = h.unsqueeze(0), spk_emb.unsqueeze(0)
h = self._integrate_with_spk_embed(hs, spk_emb)[0]
out, prob, att_w = self.dec.inference(
h,
threshold=threshold,
minlenratio=minlenratio,
maxlenratio=maxlenratio,
use_att_constraint=use_att_constraint,
backward_window=backward_window,
forward_window=forward_window, )
return dict(feat_gen=out, prob=prob, att_w=att_w)
def _integrate_with_spk_embed(self,
hs: paddle.Tensor,
spk_emb: paddle.Tensor) -> paddle.Tensor:
"""Integrate speaker embedding with hidden states.
Args:
hs (Tensor): Batch of hidden state sequences (B, Tmax, eunits).
spk_emb (Tensor): Batch of speaker embeddings (B, spk_embed_dim).
Returns:
Tensor: Batch of integrated hidden state sequences (B, Tmax, eunits) if
integration_type is "add" else (B, Tmax, eunits + spk_embed_dim).
"""
if self.spk_embed_integration_type == "add":
# apply projection and then add to hidden states
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
spk_emb = F.normalize(spk_emb).unsqueeze(1).expand(
shape=[-1, paddle.shape(hs)[1], -1])
hs = paddle.concat([hs, spk_emb], axis=-1)
else:
raise NotImplementedError("support only add or concat.")
return hs
class Tacotron2Inference(nn.Layer):
def __init__(self, normalizer, model):
super().__init__()
self.normalizer = normalizer
self.acoustic_model = model
def forward(self, text, spk_id=None, spk_emb=None):
out = self.acoustic_model.inference(
text, spk_id=spk_id, spk_emb=spk_emb)
normalized_mel = out["feat_gen"]
logmel = self.normalizer.inverse(normalized_mel)
return logmel