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PaddleSpeech/paddlespeech/t2s/models/vits/generator.py

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# Copyright (c) 2022 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.
"""Generator module in VITS.
This code is based on https://github.com/jaywalnut310/vits.
"""
import math
from typing import List
from typing import Optional
from typing import Tuple
import numpy as np
import paddle
import paddle.nn.functional as F
from paddle import nn
from paddlespeech.t2s.models.hifigan import HiFiGANGenerator
from paddlespeech.t2s.models.vits.duration_predictor import StochasticDurationPredictor
from paddlespeech.t2s.models.vits.posterior_encoder import PosteriorEncoder
from paddlespeech.t2s.models.vits.residual_coupling import ResidualAffineCouplingBlock
from paddlespeech.t2s.models.vits.text_encoder import TextEncoder
from paddlespeech.t2s.modules.nets_utils import get_random_segments
from paddlespeech.t2s.modules.nets_utils import make_non_pad_mask
class VITSGenerator(nn.Layer):
"""Generator module in VITS.
This is a module of VITS described in `Conditional Variational Autoencoder
with Adversarial Learning for End-to-End Text-to-Speech`_.
As text encoder, we use conformer architecture instead of the relative positional
Transformer, which contains additional convolution layers.
.. _`Conditional Variational Autoencoder with Adversarial Learning for End-to-End
Text-to-Speech`: https://arxiv.org/abs/2006.04558
"""
def __init__(
self,
vocabs: int,
aux_channels: int=513,
hidden_channels: int=192,
spks: Optional[int]=None,
langs: Optional[int]=None,
spk_embed_dim: Optional[int]=None,
global_channels: int=-1,
segment_size: int=32,
text_encoder_attention_heads: int=2,
text_encoder_ffn_expand: int=4,
text_encoder_blocks: int=6,
text_encoder_positionwise_layer_type: str="conv1d",
text_encoder_positionwise_conv_kernel_size: int=1,
text_encoder_positional_encoding_layer_type: str="rel_pos",
text_encoder_self_attention_layer_type: str="rel_selfattn",
text_encoder_activation_type: str="swish",
text_encoder_normalize_before: bool=True,
text_encoder_dropout_rate: float=0.1,
text_encoder_positional_dropout_rate: float=0.0,
text_encoder_attention_dropout_rate: float=0.0,
text_encoder_conformer_kernel_size: int=7,
use_macaron_style_in_text_encoder: bool=True,
use_conformer_conv_in_text_encoder: bool=True,
decoder_kernel_size: int=7,
decoder_channels: int=512,
decoder_upsample_scales: List[int]=[8, 8, 2, 2],
decoder_upsample_kernel_sizes: List[int]=[16, 16, 4, 4],
decoder_resblock_kernel_sizes: List[int]=[3, 7, 11],
decoder_resblock_dilations: List[List[int]]=[[1, 3, 5], [1, 3, 5],
[1, 3, 5]],
use_weight_norm_in_decoder: bool=True,
posterior_encoder_kernel_size: int=5,
posterior_encoder_layers: int=16,
posterior_encoder_stacks: int=1,
posterior_encoder_base_dilation: int=1,
posterior_encoder_dropout_rate: float=0.0,
use_weight_norm_in_posterior_encoder: bool=True,
flow_flows: int=4,
flow_kernel_size: int=5,
flow_base_dilation: int=1,
flow_layers: int=4,
flow_dropout_rate: float=0.0,
use_weight_norm_in_flow: bool=True,
use_only_mean_in_flow: bool=True,
stochastic_duration_predictor_kernel_size: int=3,
stochastic_duration_predictor_dropout_rate: float=0.5,
stochastic_duration_predictor_flows: int=4,
stochastic_duration_predictor_dds_conv_layers: int=3, ):
"""Initialize VITS generator module.
Args:
vocabs (int):
Input vocabulary size.
aux_channels (int):
Number of acoustic feature channels.
hidden_channels (int):
Number of hidden channels.
spks (Optional[int]):
Number of speakers. If set to > 1, assume that the
sids will be provided as the input and use sid embedding layer.
langs (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 spembs will be provided as the input.
global_channels (int):
Number of global conditioning channels.
segment_size (int):
Segment size for decoder.
text_encoder_attention_heads (int):
Number of heads in conformer block of text encoder.
text_encoder_ffn_expand (int):
Expansion ratio of FFN in conformer block of text encoder.
text_encoder_blocks (int):
Number of conformer blocks in text encoder.
text_encoder_positionwise_layer_type (str):
Position-wise layer type in conformer block of text encoder.
text_encoder_positionwise_conv_kernel_size (int):
Position-wise convolution kernel size in conformer block of text encoder.
Only used when the above layer type is conv1d or conv1d-linear.
text_encoder_positional_encoding_layer_type (str):
Positional encoding layer type in conformer block of text encoder.
text_encoder_self_attention_layer_type (str):
Self-attention layer type in conformer block of text encoder.
text_encoder_activation_type (str):
Activation function type in conformer block of text encoder.
text_encoder_normalize_before (bool):
Whether to apply layer norm before self-attention in conformer block of text encoder.
text_encoder_dropout_rate (float):
Dropout rate in conformer block of text encoder.
text_encoder_positional_dropout_rate (float):
Dropout rate for positional encoding in conformer block of text encoder.
text_encoder_attention_dropout_rate (float):
Dropout rate for attention in conformer block of text encoder.
text_encoder_conformer_kernel_size (int):
Conformer conv kernel size. It will be used when only use_conformer_conv_in_text_encoder = True.
use_macaron_style_in_text_encoder (bool):
Whether to use macaron style FFN in conformer block of text encoder.
use_conformer_conv_in_text_encoder (bool):
Whether to use covolution in conformer block of text encoder.
decoder_kernel_size (int):
Decoder kernel size.
decoder_channels (int):
Number of decoder initial channels.
decoder_upsample_scales (List[int]):
List of upsampling scales in decoder.
decoder_upsample_kernel_sizes (List[int]):
List of kernel size for upsampling layers in decoder.
decoder_resblock_kernel_sizes (List[int]):
List of kernel size for resblocks in decoder.
decoder_resblock_dilations (List[List[int]]):
List of list of dilations for resblocks in decoder.
use_weight_norm_in_decoder (bool):
Whether to apply weight normalization in decoder.
posterior_encoder_kernel_size (int):
Posterior encoder kernel size.
posterior_encoder_layers (int):
Number of layers of posterior encoder.
posterior_encoder_stacks (int):
Number of stacks of posterior encoder.
posterior_encoder_base_dilation (int):
Base dilation of posterior encoder.
posterior_encoder_dropout_rate (float):
Dropout rate for posterior encoder.
use_weight_norm_in_posterior_encoder (bool):
Whether to apply weight normalization in posterior encoder.
flow_flows (int):
Number of flows in flow.
flow_kernel_size (int):
Kernel size in flow.
flow_base_dilation (int):
Base dilation in flow.
flow_layers (int):
Number of layers in flow.
flow_dropout_rate (float):
Dropout rate in flow
use_weight_norm_in_flow (bool):
Whether to apply weight normalization in flow.
use_only_mean_in_flow (bool):
Whether to use only mean in flow.
stochastic_duration_predictor_kernel_size (int):
Kernel size in stochastic duration predictor.
stochastic_duration_predictor_dropout_rate (float):
Dropout rate in stochastic duration predictor.
stochastic_duration_predictor_flows (int):
Number of flows in stochastic duration predictor.
stochastic_duration_predictor_dds_conv_layers (int):
Number of DDS conv layers in stochastic duration predictor.
"""
super().__init__()
self.segment_size = segment_size
self.text_encoder = TextEncoder(
vocabs=vocabs,
attention_dim=hidden_channels,
attention_heads=text_encoder_attention_heads,
linear_units=hidden_channels * text_encoder_ffn_expand,
blocks=text_encoder_blocks,
positionwise_layer_type=text_encoder_positionwise_layer_type,
positionwise_conv_kernel_size=text_encoder_positionwise_conv_kernel_size,
positional_encoding_layer_type=text_encoder_positional_encoding_layer_type,
self_attention_layer_type=text_encoder_self_attention_layer_type,
activation_type=text_encoder_activation_type,
normalize_before=text_encoder_normalize_before,
dropout_rate=text_encoder_dropout_rate,
positional_dropout_rate=text_encoder_positional_dropout_rate,
attention_dropout_rate=text_encoder_attention_dropout_rate,
conformer_kernel_size=text_encoder_conformer_kernel_size,
use_macaron_style=use_macaron_style_in_text_encoder,
use_conformer_conv=use_conformer_conv_in_text_encoder, )
self.decoder = HiFiGANGenerator(
in_channels=hidden_channels,
out_channels=1,
channels=decoder_channels,
global_channels=global_channels,
kernel_size=decoder_kernel_size,
upsample_scales=decoder_upsample_scales,
upsample_kernel_sizes=decoder_upsample_kernel_sizes,
resblock_kernel_sizes=decoder_resblock_kernel_sizes,
resblock_dilations=decoder_resblock_dilations,
use_weight_norm=use_weight_norm_in_decoder, )
self.posterior_encoder = PosteriorEncoder(
in_channels=aux_channels,
out_channels=hidden_channels,
hidden_channels=hidden_channels,
kernel_size=posterior_encoder_kernel_size,
layers=posterior_encoder_layers,
stacks=posterior_encoder_stacks,
base_dilation=posterior_encoder_base_dilation,
global_channels=global_channels,
dropout_rate=posterior_encoder_dropout_rate,
use_weight_norm=use_weight_norm_in_posterior_encoder, )
self.flow = ResidualAffineCouplingBlock(
in_channels=hidden_channels,
hidden_channels=hidden_channels,
flows=flow_flows,
kernel_size=flow_kernel_size,
base_dilation=flow_base_dilation,
layers=flow_layers,
global_channels=global_channels,
dropout_rate=flow_dropout_rate,
use_weight_norm=use_weight_norm_in_flow,
use_only_mean=use_only_mean_in_flow, )
# TODO: Add deterministic version as an option
self.duration_predictor = StochasticDurationPredictor(
channels=hidden_channels,
kernel_size=stochastic_duration_predictor_kernel_size,
dropout_rate=stochastic_duration_predictor_dropout_rate,
flows=stochastic_duration_predictor_flows,
dds_conv_layers=stochastic_duration_predictor_dds_conv_layers,
global_channels=global_channels, )
self.upsample_factor = int(np.prod(decoder_upsample_scales))
self.spks = None
if spks is not None and spks > 1:
assert global_channels > 0
self.spks = spks
self.global_emb = nn.Embedding(spks, global_channels)
self.spk_embed_dim = None
if spk_embed_dim is not None and spk_embed_dim > 0:
assert global_channels > 0
self.spk_embed_dim = spk_embed_dim
self.spemb_proj = nn.Linear(spk_embed_dim, global_channels)
self.langs = None
if langs is not None and langs > 1:
assert global_channels > 0
self.langs = langs
self.lang_emb = nn.Embedding(langs, global_channels)
# delayed import
from paddlespeech.t2s.models.vits.monotonic_align import maximum_path
self.maximum_path = maximum_path
self.pad1d = nn.Pad1D(
padding=[1, 0],
mode='constant',
data_format='NLC', )
def forward(
self,
text: paddle.Tensor,
text_lengths: paddle.Tensor,
feats: paddle.Tensor,
feats_lengths: paddle.Tensor,
sids: Optional[paddle.Tensor]=None,
spembs: Optional[paddle.Tensor]=None,
lids: Optional[paddle.Tensor]=None,
) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor,
paddle.Tensor, paddle.Tensor,
Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor,
paddle.Tensor, paddle.Tensor, ], ]:
"""Calculate forward propagation.
Args:
text (Tensor):
Text index tensor (B, T_text).
text_lengths (Tensor):
Text length tensor (B,).
feats (Tensor):
Feature tensor (B, aux_channels, T_feats).
feats_lengths (Tensor):
Feature length tensor (B,).
sids (Optional[Tensor]):
Speaker index tensor (B,) or (B, 1).
spembs (Optional[Tensor]):
Speaker embedding tensor (B, spk_embed_dim).
lids (Optional[Tensor]):
Language index tensor (B,) or (B, 1).
Returns:
Tensor:
Waveform tensor (B, 1, segment_size * upsample_factor).
Tensor:
Duration negative log-likelihood (NLL) tensor (B,).
Tensor:
Monotonic attention weight tensor (B, 1, T_feats, T_text).
Tensor:
Segments start index tensor (B,).
Tensor:
Text mask tensor (B, 1, T_text).
Tensor:
Feature mask tensor (B, 1, T_feats).
tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]:
- Tensor: Posterior encoder hidden representation (B, H, T_feats).
- Tensor: Flow hidden representation (B, H, T_feats).
- Tensor: Expanded text encoder projected mean (B, H, T_feats).
- Tensor: Expanded text encoder projected scale (B, H, T_feats).
- Tensor: Posterior encoder projected mean (B, H, T_feats).
- Tensor: Posterior encoder projected scale (B, H, T_feats).
"""
# forward text encoder
x, m_p, logs_p, x_mask = self.text_encoder(text, text_lengths)
# calculate global conditioning
g = None
if self.spks is not None:
# speaker one-hot vector embedding: (B, global_channels, 1)
g = self.global_emb(paddle.reshape(sids, [-1])).unsqueeze(-1)
if self.spk_embed_dim is not None:
# pretreined speaker embedding, e.g., X-vector (B, global_channels, 1)
g_ = self.spemb_proj(F.normalize(spembs)).unsqueeze(-1)
if g is None:
g = g_
else:
g = g + g_
if self.langs is not None:
# language one-hot vector embedding: (B, global_channels, 1)
g_ = self.lang_emb(paddle.reshape(lids, [-1])).unsqueeze(-1)
if g is None:
g = g_
else:
g = g + g_
# forward posterior encoder
z, m_q, logs_q, y_mask = self.posterior_encoder(
feats, feats_lengths, g=g)
# forward flow
# (B, H, T_feats)
z_p = self.flow(z, y_mask, g=g)
# monotonic alignment search
with paddle.no_grad():
# negative cross-entropy
# (B, H, T_text)
s_p_sq_r = paddle.exp(-2 * logs_p)
# (B, 1, T_text)
tmp1 = -0.5 * math.log(2 * math.pi) - logs_p
neg_x_ent_1 = paddle.sum(
tmp1,
[1],
keepdim=True, )
# (B, T_feats, H) x (B, H, T_text) = (B, T_feats, T_text)
neg_x_ent_2 = paddle.matmul(
-0.5 * (z_p**2).transpose([0, 2, 1]),
s_p_sq_r, )
# (B, T_feats, H) x (B, H, T_text) = (B, T_feats, T_text)
neg_x_ent_3 = paddle.matmul(
z_p.transpose([0, 2, 1]),
(m_p * s_p_sq_r), )
# (B, 1, T_text)
tmp2 = -0.5 * (m_p**2) * s_p_sq_r
neg_x_ent_4 = paddle.sum(
tmp2,
[1],
keepdim=True, )
# (B, T_feats, T_text)
neg_x_ent = neg_x_ent_1 + neg_x_ent_2 + neg_x_ent_3 + neg_x_ent_4
# (B, 1, T_feats, T_text)
attn_mask = paddle.unsqueeze(x_mask, 2) * paddle.unsqueeze(y_mask,
-1)
# monotonic attention weight: (B, 1, T_feats, T_text)
attn = (self.maximum_path(
neg_x_ent,
attn_mask.squeeze(1), ).unsqueeze(1).detach())
# forward duration predictor
# (B, 1, T_text)
w = attn.sum(2)
dur_nll = self.duration_predictor(x, x_mask, w=w, g=g)
dur_nll = dur_nll / paddle.sum(x_mask)
# expand the length to match with the feature sequence
# (B, T_feats, T_text) x (B, T_text, H) -> (B, H, T_feats)
m_p = paddle.matmul(attn.squeeze(1),
m_p.transpose([0, 2, 1])).transpose([0, 2, 1])
# (B, T_feats, T_text) x (B, T_text, H) -> (B, H, T_feats)
logs_p = paddle.matmul(attn.squeeze(1),
logs_p.transpose([0, 2, 1])).transpose([0, 2, 1])
# get random segments
z_segments, z_start_idxs = get_random_segments(
z,
feats_lengths,
self.segment_size, )
# forward decoder with random segments
wav = self.decoder(z_segments, g=g)
return (wav, dur_nll, attn, z_start_idxs, x_mask, y_mask,
(z, z_p, m_p, logs_p, m_q, logs_q), )
def inference(
self,
text: paddle.Tensor,
text_lengths: paddle.Tensor,
feats: Optional[paddle.Tensor]=None,
feats_lengths: Optional[paddle.Tensor]=None,
sids: Optional[paddle.Tensor]=None,
spembs: Optional[paddle.Tensor]=None,
lids: Optional[paddle.Tensor]=None,
dur: Optional[paddle.Tensor]=None,
noise_scale: float=0.667,
noise_scale_dur: float=0.8,
alpha: float=1.0,
max_len: Optional[int]=None,
use_teacher_forcing: bool=False,
) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
"""Run inference.
Args:
text (Tensor):
Input text index tensor (B, T_text,).
text_lengths (Tensor):
Text length tensor (B,).
feats (Tensor):
Feature tensor (B, aux_channels, T_feats,).
feats_lengths (Tensor):
Feature length tensor (B,).
sids (Optional[Tensor]):
Speaker index tensor (B,) or (B, 1).
spembs (Optional[Tensor]):
Speaker embedding tensor (B, spk_embed_dim).
lids (Optional[Tensor]):
Language index tensor (B,) or (B, 1).
dur (Optional[Tensor]):
Ground-truth duration (B, T_text,). If provided,
skip the prediction of durations (i.e., teacher forcing).
noise_scale (float):
Noise scale parameter for flow.
noise_scale_dur (float):
Noise scale parameter for duration predictor.
alpha (float):
Alpha parameter to control the speed of generated speech.
max_len (Optional[int]):
Maximum length of acoustic feature sequence.
use_teacher_forcing (bool):
Whether to use teacher forcing.
Returns:
Tensor:
Generated waveform tensor (B, T_wav).
Tensor:
Monotonic attention weight tensor (B, T_feats, T_text).
Tensor:
Duration tensor (B, T_text).
"""
# encoder
x, m_p, logs_p, x_mask = self.text_encoder(text, text_lengths)
g = None
if self.spks is not None:
# (B, global_channels, 1)
g = self.global_emb(paddle.reshape(sids, [-1])).unsqueeze(-1)
if self.spk_embed_dim is not None:
# (B, global_channels, 1)
g_ = self.spemb_proj(F.normalize(spembs.unsqueeze(0))).unsqueeze(-1)
if g is None:
g = g_
else:
g = g + g_
if self.langs is not None:
# (B, global_channels, 1)
g_ = self.lang_emb(paddle.reshape(lids, [-1])).unsqueeze(-1)
if g is None:
g = g_
else:
g = g + g_
if use_teacher_forcing:
# forward posterior encoder
z, m_q, logs_q, y_mask = self.posterior_encoder(
feats, feats_lengths, g=g)
# forward flow
# (B, H, T_feats)
z_p = self.flow(z, y_mask, g=g)
# monotonic alignment search
# (B, H, T_text)
s_p_sq_r = paddle.exp(-2 * logs_p)
# (B, 1, T_text)
tmp3 = -0.5 * math.log(2 * math.pi) - logs_p
neg_x_ent_1 = paddle.sum(
tmp3,
[1],
keepdim=True, )
# (B, T_feats, H) x (B, H, T_text) = (B, T_feats, T_text)
neg_x_ent_2 = paddle.matmul(
-0.5 * (z_p**2).transpose([0, 2, 1]),
s_p_sq_r, )
# (B, T_feats, H) x (B, H, T_text) = (B, T_feats, T_text)
neg_x_ent_3 = paddle.matmul(
z_p.transpose([0, 2, 1]),
(m_p * s_p_sq_r), )
# (B, 1, T_text)
tmp4 = -0.5 * (m_p**2) * s_p_sq_r
neg_x_ent_4 = paddle.sum(
tmp4,
[1],
keepdim=True, )
# (B, T_feats, T_text)
neg_x_ent = neg_x_ent_1 + neg_x_ent_2 + neg_x_ent_3 + neg_x_ent_4
# (B, 1, T_feats, T_text)
attn_mask = paddle.unsqueeze(x_mask, 2) * paddle.unsqueeze(y_mask,
-1)
# monotonic attention weight: (B, 1, T_feats, T_text)
attn = self.maximum_path(
neg_x_ent,
attn_mask.squeeze(1), ).unsqueeze(1)
# (B, 1, T_text)
dur = attn.sum(2)
# forward decoder with random segments
wav = self.decoder(z * y_mask, g=g)
else:
# duration
if dur is None:
logw = self.duration_predictor(
x,
x_mask,
g=g,
inverse=True,
noise_scale=noise_scale_dur, )
w = paddle.exp(logw) * x_mask * alpha
dur = paddle.ceil(w)
y_lengths = paddle.cast(
paddle.clip(paddle.sum(dur, [1, 2]), min=1), dtype='int64')
y_mask = make_non_pad_mask(y_lengths).unsqueeze(1)
tmp_a = paddle.cast(paddle.unsqueeze(x_mask, 2), dtype='int64')
tmp_b = paddle.cast(paddle.unsqueeze(y_mask, -1), dtype='int64')
attn_mask = tmp_a * tmp_b
attn = self._generate_path(dur, attn_mask)
# expand the length to match with the feature sequence
# (B, T_feats, T_text) x (B, T_text, H) -> (B, H, T_feats)
m_p = paddle.matmul(
attn.squeeze(1),
m_p.transpose([0, 2, 1]), ).transpose([0, 2, 1])
# (B, T_feats, T_text) x (B, T_text, H) -> (B, H, T_feats)
logs_p = paddle.matmul(
attn.squeeze(1),
logs_p.transpose([0, 2, 1]), ).transpose([0, 2, 1])
# decoder
z_p = m_p + paddle.randn(
paddle.shape(m_p)) * paddle.exp(logs_p) * noise_scale
z = self.flow(z_p, y_mask, g=g, inverse=True)
wav = self.decoder((z * y_mask)[:, :, :max_len], g=g)
return wav.squeeze(1), attn.squeeze(1), dur.squeeze(1)
def voice_conversion(
self,
feats: paddle.Tensor=None,
feats_lengths: paddle.Tensor=None,
sids_src: Optional[paddle.Tensor]=None,
sids_tgt: Optional[paddle.Tensor]=None,
spembs_src: Optional[paddle.Tensor]=None,
spembs_tgt: Optional[paddle.Tensor]=None,
lids: Optional[paddle.Tensor]=None, ) -> paddle.Tensor:
"""Run voice conversion.
Args:
feats (Tensor):
Feature tensor (B, aux_channels, T_feats,).
feats_lengths (Tensor):
Feature length tensor (B,).
sids_src (Optional[Tensor]):
Speaker index tensor of source feature (B,) or (B, 1).
sids_tgt (Optional[Tensor]):
Speaker index tensor of target feature (B,) or (B, 1).
spembs_src (Optional[Tensor]):
Speaker embedding tensor of source feature (B, spk_embed_dim).
spembs_tgt (Optional[Tensor]):
Speaker embedding tensor of target feature (B, spk_embed_dim).
lids (Optional[Tensor]):
Language index tensor (B,) or (B, 1).
Returns:
Tensor:
Generated waveform tensor (B, T_wav).
"""
# encoder
g_src = None
g_tgt = None
if self.spks is not None:
# (B, global_channels, 1)
g_src = self.global_emb(
paddle.reshape(sids_src, [-1])).unsqueeze(-1)
g_tgt = self.global_emb(
paddle.reshape(sids_tgt, [-1])).unsqueeze(-1)
if self.spk_embed_dim is not None:
# (B, global_channels, 1)
g_src_ = self.spemb_proj(
F.normalize(spembs_src.unsqueeze(0))).unsqueeze(-1)
if g_src is None:
g_src = g_src_
else:
g_src = g_src + g_src_
# (B, global_channels, 1)
g_tgt_ = self.spemb_proj(
F.normalize(spembs_tgt.unsqueeze(0))).unsqueeze(-1)
if g_tgt is None:
g_tgt = g_tgt_
else:
g_tgt = g_tgt + g_tgt_
if self.langs is not None:
# (B, global_channels, 1)
g_ = self.lang_emb(paddle.reshape(lids, [-1])).unsqueeze(-1)
if g_src is None:
g_src = g_
else:
g_src = g_src + g_
if g_tgt is None:
g_tgt = g_
else:
g_tgt = g_tgt + g_
# forward posterior encoder
z, m_q, logs_q, y_mask = self.posterior_encoder(
feats, feats_lengths, g=g_src)
# forward flow
# (B, H, T_feats)
z_p = self.flow(z, y_mask, g=g_src)
# decoder
z_hat = self.flow(z_p, y_mask, g=g_tgt, inverse=True)
wav = self.decoder(z_hat * y_mask, g=g_tgt)
return wav.squeeze(1)
def _generate_path(self, dur: paddle.Tensor,
mask: paddle.Tensor) -> paddle.Tensor:
"""Generate path a.k.a. monotonic attention.
Args:
dur (Tensor):
Duration tensor (B, 1, T_text).
mask (Tensor):
Attention mask tensor (B, 1, T_feats, T_text).
Returns:
Tensor:
Path tensor (B, 1, T_feats, T_text).
"""
b, _, t_y, t_x = paddle.shape(mask)
cum_dur = paddle.cumsum(dur, -1)
cum_dur_flat = paddle.reshape(cum_dur, [b * t_x])
path = paddle.arange(t_y, dtype=dur.dtype)
path = path.unsqueeze(0) < cum_dur_flat.unsqueeze(1)
path = paddle.reshape(path, [b, t_x, t_y])
'''
path will be like (t_x = 3, t_y = 5):
[[[1., 1., 0., 0., 0.], [[[1., 1., 0., 0., 0.],
[1., 1., 1., 1., 0.], --> [0., 0., 1., 1., 0.],
[1., 1., 1., 1., 1.]]] [0., 0., 0., 0., 1.]]]
'''
path = paddle.cast(path, dtype='float32')
pad_tmp = self.pad1d(path)[:, :-1]
path = path - pad_tmp
return path.unsqueeze(1).transpose([0, 1, 3, 2]) * mask
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