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520 lines
18 KiB
520 lines
18 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Attention modules for RNN."""
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import paddle
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import paddle.nn.functional as F
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from paddle import nn
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from paddlespeech.t2s.modules.masked_fill import masked_fill
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from paddlespeech.t2s.modules.nets_utils import make_pad_mask
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def _apply_attention_constraint(e,
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last_attended_idx,
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backward_window=1,
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forward_window=3):
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"""Apply monotonic attention constraint.
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This function apply the monotonic attention constraint
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introduced in `Deep Voice 3: Scaling
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Text-to-Speech with Convolutional Sequence Learning`_.
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Parameters
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----------
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e : Tensor
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Attention energy before applying softmax (1, T).
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last_attended_idx : int
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The index of the inputs of the last attended [0, T].
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backward_window : int, optional
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Backward window size in attention constraint.
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forward_window : int, optional
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Forward window size in attetion constraint.
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Returns
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----------
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Tensor
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Monotonic constrained attention energy (1, T).
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.. _`Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning`:
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https://arxiv.org/abs/1710.07654
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"""
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if paddle.shape(e)[0] != 1:
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raise NotImplementedError(
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"Batch attention constraining is not yet supported.")
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backward_idx = last_attended_idx - backward_window
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forward_idx = last_attended_idx + forward_window
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if backward_idx > 0:
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e[:, :backward_idx] = -float("inf")
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if forward_idx < paddle.shape(e)[1]:
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e[:, forward_idx:] = -float("inf")
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return e
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class AttLoc(nn.Layer):
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"""location-aware attention module.
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Reference: Attention-Based Models for Speech Recognition
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(https://arxiv.org/pdf/1506.07503.pdf)
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Parameters
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----------
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eprojs : int
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projection-units of encoder
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dunits : int
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units of decoder
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att_dim : int
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att_dim: attention dimension
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aconv_chans : int
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channels of attention convolution
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aconv_filts : int
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filter size of attention convolution
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han_mode : bool
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flag to swith on mode of hierarchical attention and not store pre_compute_enc_h
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"""
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def __init__(self,
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eprojs,
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dunits,
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att_dim,
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aconv_chans,
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aconv_filts,
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han_mode=False):
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super().__init__()
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self.mlp_enc = nn.Linear(eprojs, att_dim)
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self.mlp_dec = nn.Linear(dunits, att_dim, bias_attr=False)
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self.mlp_att = nn.Linear(aconv_chans, att_dim, bias_attr=False)
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self.loc_conv = nn.Conv2D(
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1,
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aconv_chans,
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(1, 2 * aconv_filts + 1),
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padding=(0, aconv_filts),
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bias_attr=False, )
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self.gvec = nn.Linear(att_dim, 1)
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self.dunits = dunits
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self.eprojs = eprojs
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self.att_dim = att_dim
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self.h_length = None
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self.enc_h = None
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self.pre_compute_enc_h = None
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self.mask = None
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self.han_mode = han_mode
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def reset(self):
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"""reset states"""
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self.h_length = None
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self.enc_h = None
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self.pre_compute_enc_h = None
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self.mask = None
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def forward(
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self,
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enc_hs_pad,
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enc_hs_len,
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dec_z,
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att_prev,
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scaling=2.0,
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last_attended_idx=None,
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backward_window=1,
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forward_window=3, ):
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"""Calculate AttLoc forward propagation.
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Parameters
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----------
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enc_hs_pad : paddle.Tensor
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padded encoder hidden state (B, T_max, D_enc)
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enc_hs_len : paddle.Tensor
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padded encoder hidden state length (B)
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dec_z : paddle.Tensor dec_z
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decoder hidden state (B, D_dec)
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att_prev : paddle.Tensor
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previous attention weight (B, T_max)
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scaling : float
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scaling parameter before applying softmax
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forward_window : paddle.Tensor
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forward window size when constraining attention
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last_attended_idx : int
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index of the inputs of the last attended
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backward_window : int
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backward window size in attention constraint
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forward_window : int
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forward window size in attetion constraint
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Returns
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----------
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paddle.Tensor
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attention weighted encoder state (B, D_enc)
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paddle.Tensor
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previous attention weights (B, T_max)
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"""
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batch = len(enc_hs_pad)
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# pre-compute all h outside the decoder loop
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if self.pre_compute_enc_h is None or self.han_mode:
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# (utt, frame, hdim)
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self.enc_h = enc_hs_pad
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self.h_length = paddle.shape(self.enc_h)[1]
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# (utt, frame, att_dim)
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self.pre_compute_enc_h = self.mlp_enc(self.enc_h)
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if dec_z is None:
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dec_z = paddle.zeros([batch, self.dunits])
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else:
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dec_z = dec_z.reshape([batch, self.dunits])
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# initialize attention weight with uniform dist.
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if att_prev is None:
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# if no bias, 0 0-pad goes 0
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att_prev = 1.0 - make_pad_mask(enc_hs_len)
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att_prev = att_prev / enc_hs_len.unsqueeze(-1)
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# att_prev: (utt, frame) -> (utt, 1, 1, frame)
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# -> (utt, att_conv_chans, 1, frame)
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att_conv = self.loc_conv(att_prev.reshape([batch, 1, 1, self.h_length]))
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# att_conv: (utt, att_conv_chans, 1, frame) -> (utt, frame, att_conv_chans)
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att_conv = att_conv.squeeze(2).transpose([0, 2, 1])
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# att_conv: (utt, frame, att_conv_chans) -> (utt, frame, att_dim)
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att_conv = self.mlp_att(att_conv)
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# dec_z_tiled: (utt, frame, att_dim)
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dec_z_tiled = self.mlp_dec(dec_z).reshape([batch, 1, self.att_dim])
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# dot with gvec
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# (utt, frame, att_dim) -> (utt, frame)
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e = self.gvec(
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paddle.tanh(att_conv + self.pre_compute_enc_h +
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dec_z_tiled)).squeeze(2)
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# NOTE: consider zero padding when compute w.
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if self.mask is None:
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self.mask = make_pad_mask(enc_hs_len)
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e = masked_fill(e, self.mask, -float("inf"))
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# apply monotonic attention constraint (mainly for TTS)
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if last_attended_idx is not None:
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e = _apply_attention_constraint(e, last_attended_idx,
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backward_window, forward_window)
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w = F.softmax(scaling * e, axis=1)
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# weighted sum over frames
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# utt x hdim
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c = paddle.sum(
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self.enc_h * w.reshape([batch, self.h_length, 1]), axis=1)
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return c, w
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class AttForward(nn.Layer):
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"""Forward attention module.
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Reference
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----------
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Forward attention in sequence-to-sequence acoustic modeling for speech synthesis
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(https://arxiv.org/pdf/1807.06736.pdf)
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Parameters
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----------
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eprojs : int
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projection-units of encoder
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dunits : int
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units of decoder
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att_dim : int
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attention dimension
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aconv_chans : int
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channels of attention convolution
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aconv_filts : int
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filter size of attention convolution
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"""
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def __init__(self, eprojs, dunits, att_dim, aconv_chans, aconv_filts):
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super().__init__()
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self.mlp_enc = nn.Linear(eprojs, att_dim)
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self.mlp_dec = nn.Linear(dunits, att_dim, bias_attr=False)
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self.mlp_att = nn.Linear(aconv_chans, att_dim, bias_attr=False)
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self.loc_conv = nn.Conv2D(
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1,
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aconv_chans,
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(1, 2 * aconv_filts + 1),
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padding=(0, aconv_filts),
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bias_attr=False, )
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self.gvec = nn.Linear(att_dim, 1)
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self.dunits = dunits
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self.eprojs = eprojs
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self.att_dim = att_dim
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self.h_length = None
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self.enc_h = None
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self.pre_compute_enc_h = None
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self.mask = None
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def reset(self):
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"""reset states"""
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self.h_length = None
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self.enc_h = None
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self.pre_compute_enc_h = None
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self.mask = None
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def forward(
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self,
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enc_hs_pad,
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enc_hs_len,
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dec_z,
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att_prev,
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scaling=1.0,
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last_attended_idx=None,
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backward_window=1,
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forward_window=3, ):
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"""Calculate AttForward forward propagation.
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Parameters
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----------
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enc_hs_pad : paddle.Tensor
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padded encoder hidden state (B, T_max, D_enc)
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enc_hs_len : list
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padded encoder hidden state length (B,)
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dec_z : paddle.Tensor
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decoder hidden state (B, D_dec)
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att_prev : paddle.Tensor
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attention weights of previous step (B, T_max)
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scaling : float
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scaling parameter before applying softmax
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last_attended_idx : int
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index of the inputs of the last attended
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backward_window : int
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backward window size in attention constraint
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forward_window : int
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forward window size in attetion constraint
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Returns
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----------
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paddle.Tensor
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attention weighted encoder state (B, D_enc)
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paddle.Tensor
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previous attention weights (B, T_max)
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"""
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batch = len(enc_hs_pad)
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# pre-compute all h outside the decoder loop
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if self.pre_compute_enc_h is None:
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self.enc_h = enc_hs_pad # utt x frame x hdim
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self.h_length = paddle.shape(self.enc_h)[1]
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# utt x frame x att_dim
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self.pre_compute_enc_h = self.mlp_enc(self.enc_h)
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if dec_z is None:
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dec_z = paddle.zeros([batch, self.dunits])
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else:
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dec_z = dec_z.reshape([batch, self.dunits])
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if att_prev is None:
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# initial attention will be [1, 0, 0, ...]
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att_prev = paddle.zeros([*paddle.shape(enc_hs_pad)[:2]])
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att_prev[:, 0] = 1.0
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# att_prev: utt x frame -> utt x 1 x 1 x frame
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# -> utt x att_conv_chans x 1 x frame
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att_conv = self.loc_conv(att_prev.reshape([batch, 1, 1, self.h_length]))
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# att_conv: utt x att_conv_chans x 1 x frame -> utt x frame x att_conv_chans
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att_conv = att_conv.squeeze(2).transpose([0, 2, 1])
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# att_conv: utt x frame x att_conv_chans -> utt x frame x att_dim
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att_conv = self.mlp_att(att_conv)
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# dec_z_tiled: utt x frame x att_dim
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dec_z_tiled = self.mlp_dec(dec_z).unsqueeze(1)
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# dot with gvec
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# utt x frame x att_dim -> utt x frame
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e = self.gvec(
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paddle.tanh(self.pre_compute_enc_h + dec_z_tiled +
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att_conv)).squeeze(2)
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# NOTE: consider zero padding when compute w.
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if self.mask is None:
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self.mask = make_pad_mask(enc_hs_len)
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e = masked_fill(e, self.mask, -float("inf"))
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# apply monotonic attention constraint (mainly for TTS)
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if last_attended_idx is not None:
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e = _apply_attention_constraint(e, last_attended_idx,
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backward_window, forward_window)
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w = F.softmax(scaling * e, axis=1)
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# forward attention
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att_prev_shift = F.pad(att_prev, (0, 0, 1, 0))[:, :-1]
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w = (att_prev + att_prev_shift) * w
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# NOTE: clip is needed to avoid nan gradient
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w = F.normalize(paddle.clip(w, 1e-6), p=1, axis=1)
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# weighted sum over flames
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# utt x hdim
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# NOTE use bmm instead of sum(*)
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c = paddle.sum(self.enc_h * w.unsqueeze(-1), axis=1)
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return c, w
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class AttForwardTA(nn.Layer):
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"""Forward attention with transition agent module.
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Reference
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----------
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Forward attention in sequence-to-sequence acoustic modeling for speech synthesis
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(https://arxiv.org/pdf/1807.06736.pdf)
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Parameters
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----------
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eunits : int
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units of encoder
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dunits : int
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units of decoder
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att_dim : int
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attention dimension
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aconv_chans : int
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channels of attention convolution
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aconv_filts : int
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filter size of attention convolution
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odim : int
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output dimension
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"""
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def __init__(self, eunits, dunits, att_dim, aconv_chans, aconv_filts, odim):
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super().__init__()
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self.mlp_enc = nn.Linear(eunits, att_dim)
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self.mlp_dec = nn.Linear(dunits, att_dim, bias_attr=False)
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self.mlp_ta = nn.Linear(eunits + dunits + odim, 1)
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self.mlp_att = nn.Linear(aconv_chans, att_dim, bias_attr=False)
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self.loc_conv = nn.Conv2D(
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1,
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aconv_chans,
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(1, 2 * aconv_filts + 1),
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padding=(0, aconv_filts),
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bias_attr=False, )
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self.gvec = nn.Linear(att_dim, 1)
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self.dunits = dunits
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self.eunits = eunits
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self.att_dim = att_dim
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self.h_length = None
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self.enc_h = None
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self.pre_compute_enc_h = None
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self.mask = None
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self.trans_agent_prob = 0.5
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def reset(self):
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self.h_length = None
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self.enc_h = None
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self.pre_compute_enc_h = None
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self.mask = None
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self.trans_agent_prob = 0.5
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def forward(
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self,
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enc_hs_pad,
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enc_hs_len,
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dec_z,
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att_prev,
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out_prev,
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scaling=1.0,
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last_attended_idx=None,
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backward_window=1,
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forward_window=3, ):
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"""Calculate AttForwardTA forward propagation.
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Parameters
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----------
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enc_hs_pad : paddle.Tensor
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padded encoder hidden state (B, Tmax, eunits)
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enc_hs_len : list paddle.Tensor
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padded encoder hidden state length (B,)
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dec_z : paddle.Tensor
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decoder hidden state (B, dunits)
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att_prev : paddle.Tensor
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attention weights of previous step (B, T_max)
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out_prev : paddle.Tensor
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decoder outputs of previous step (B, odim)
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scaling : float
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scaling parameter before applying softmax
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last_attended_idx : int
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index of the inputs of the last attended
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backward_window : int
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backward window size in attention constraint
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forward_window : int
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forward window size in attetion constraint
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Returns
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----------
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paddle.Tensor
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attention weighted encoder state (B, dunits)
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paddle.Tensor
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previous attention weights (B, Tmax)
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"""
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batch = len(enc_hs_pad)
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# pre-compute all h outside the decoder loop
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if self.pre_compute_enc_h is None:
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self.enc_h = enc_hs_pad # utt x frame x hdim
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self.h_length = paddle.shape(self.enc_h)[1]
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# utt x frame x att_dim
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self.pre_compute_enc_h = self.mlp_enc(self.enc_h)
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if dec_z is None:
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dec_z = paddle.zeros([batch, self.dunits])
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else:
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dec_z = dec_z.reshape([batch, self.dunits])
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if att_prev is None:
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# initial attention will be [1, 0, 0, ...]
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att_prev = paddle.zeros([*paddle.shape(enc_hs_pad)[:2]])
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att_prev[:, 0] = 1.0
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# att_prev: utt x frame -> utt x 1 x 1 x frame
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# -> utt x att_conv_chans x 1 x frame
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att_conv = self.loc_conv(att_prev.reshape([batch, 1, 1, self.h_length]))
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# att_conv: utt x att_conv_chans x 1 x frame -> utt x frame x att_conv_chans
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att_conv = att_conv.squeeze(2).transpose([0, 2, 1])
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# att_conv: utt x frame x att_conv_chans -> utt x frame x att_dim
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att_conv = self.mlp_att(att_conv)
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# dec_z_tiled: utt x frame x att_dim
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dec_z_tiled = self.mlp_dec(dec_z).reshape([batch, 1, self.att_dim])
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# dot with gvec
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# utt x frame x att_dim -> utt x frame
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e = self.gvec(
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paddle.tanh(att_conv + self.pre_compute_enc_h +
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dec_z_tiled)).squeeze(2)
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# NOTE consider zero padding when compute w.
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if self.mask is None:
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self.mask = make_pad_mask(enc_hs_len)
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e = masked_fill(e, self.mask, -float("inf"))
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# apply monotonic attention constraint (mainly for TTS)
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if last_attended_idx is not None:
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e = _apply_attention_constraint(e, last_attended_idx,
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backward_window, forward_window)
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w = F.softmax(scaling * e, axis=1)
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# forward attention
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# att_prev_shift = F.pad(att_prev.unsqueeze(0), (1, 0), data_format='NCL').squeeze(0)[:, :-1]
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att_prev_shift = F.pad(att_prev, (0, 0, 1, 0))[:, :-1]
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w = (self.trans_agent_prob * att_prev +
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(1 - self.trans_agent_prob) * att_prev_shift) * w
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# NOTE: clip is needed to avoid nan gradient
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w = F.normalize(paddle.clip(w, 1e-6), p=1, axis=1)
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|
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# weighted sum over flames
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# utt x hdim
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# NOTE use bmm instead of sum(*)
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c = paddle.sum(
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self.enc_h * w.reshape([batch, self.h_length, 1]), axis=1)
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|
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# update transition agent prob
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self.trans_agent_prob = F.sigmoid(
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self.mlp_ta(paddle.concat([c, out_prev, dec_z], axis=1)))
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|
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return c, w
|