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1113 lines
36 KiB
1113 lines
36 KiB
# Copyright (c) 2020 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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import math
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import librosa
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import numpy as np
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import paddle
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from paddle import nn
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from paddle.fluid.layers import sequence_mask
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from paddle.nn import functional as F
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from scipy import signal
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from paddlespeech.t2s.modules.nets_utils import make_non_pad_mask
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# Losses for WaveRNN
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def log_sum_exp(x):
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""" numerically stable log_sum_exp implementation that prevents overflow """
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# TF ordering
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axis = len(x.shape) - 1
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m = paddle.max(x, axis=axis)
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m2 = paddle.max(x, axis=axis, keepdim=True)
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return m + paddle.log(paddle.sum(paddle.exp(x - m2), axis=axis))
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# It is adapted from https://github.com/r9y9/wavenet_vocoder/blob/master/wavenet_vocoder/mixture.py
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def discretized_mix_logistic_loss(y_hat,
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y,
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num_classes=65536,
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log_scale_min=None,
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reduce=True):
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if log_scale_min is None:
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log_scale_min = float(np.log(1e-14))
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y_hat = y_hat.transpose([0, 2, 1])
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assert y_hat.dim() == 3
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assert y_hat.shape[1] % 3 == 0
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nr_mix = y_hat.shape[1] // 3
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# (B x T x C)
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y_hat = y_hat.transpose([0, 2, 1])
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# unpack parameters. (B, T, num_mixtures) x 3
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logit_probs = y_hat[:, :, :nr_mix]
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means = y_hat[:, :, nr_mix:2 * nr_mix]
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log_scales = paddle.clip(
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y_hat[:, :, 2 * nr_mix:3 * nr_mix], min=log_scale_min)
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# B x T x 1 -> B x T x num_mixtures
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y = y.expand_as(means)
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centered_y = paddle.cast(y, dtype=paddle.get_default_dtype()) - means
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inv_stdv = paddle.exp(-log_scales)
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plus_in = inv_stdv * (centered_y + 1. / (num_classes - 1))
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cdf_plus = F.sigmoid(plus_in)
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min_in = inv_stdv * (centered_y - 1. / (num_classes - 1))
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cdf_min = F.sigmoid(min_in)
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# log probability for edge case of 0 (before scaling)
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# equivalent: torch.log(F.sigmoid(plus_in))
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# softplus: log(1+ e^{-x})
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log_cdf_plus = plus_in - F.softplus(plus_in)
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# log probability for edge case of 255 (before scaling)
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# equivalent: (1 - F.sigmoid(min_in)).log()
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log_one_minus_cdf_min = -F.softplus(min_in)
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# probability for all other cases
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cdf_delta = cdf_plus - cdf_min
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mid_in = inv_stdv * centered_y
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# log probability in the center of the bin, to be used in extreme cases
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# (not actually used in our code)
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log_pdf_mid = mid_in - log_scales - 2. * F.softplus(mid_in)
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# TODO: cdf_delta <= 1e-5 actually can happen. How can we choose the value
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# for num_classes=65536 case? 1e-7? not sure..
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inner_inner_cond = cdf_delta > 1e-5
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inner_inner_cond = paddle.cast(
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inner_inner_cond, dtype=paddle.get_default_dtype())
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# inner_inner_out = inner_inner_cond * \
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# paddle.log(paddle.clip(cdf_delta, min=1e-12)) + \
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# (1. - inner_inner_cond) * (log_pdf_mid - np.log((num_classes - 1) / 2))
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inner_inner_out = inner_inner_cond * paddle.log(
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paddle.clip(cdf_delta, min=1e-12)) + (1. - inner_inner_cond) * (
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log_pdf_mid - np.log((num_classes - 1) / 2))
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inner_cond = y > 0.999
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inner_cond = paddle.cast(inner_cond, dtype=paddle.get_default_dtype())
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inner_out = inner_cond * log_one_minus_cdf_min + (1. - inner_cond
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) * inner_inner_out
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cond = y < -0.999
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cond = paddle.cast(cond, dtype=paddle.get_default_dtype())
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log_probs = cond * log_cdf_plus + (1. - cond) * inner_out
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log_probs = log_probs + F.log_softmax(logit_probs, -1)
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if reduce:
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return -paddle.mean(log_sum_exp(log_probs))
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else:
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return -log_sum_exp(log_probs).unsqueeze(-1)
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def sample_from_discretized_mix_logistic(y, log_scale_min=None):
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"""
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Sample from discretized mixture of logistic distributions
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Parameters
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----------
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y : Tensor
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(B, C, T)
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log_scale_min : float
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Log scale minimum value
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Returns
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----------
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Tensor
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sample in range of [-1, 1].
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"""
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if log_scale_min is None:
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log_scale_min = float(np.log(1e-14))
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assert y.shape[1] % 3 == 0
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nr_mix = y.shape[1] // 3
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# (B, T, C)
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y = y.transpose([0, 2, 1])
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logit_probs = y[:, :, :nr_mix]
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# sample mixture indicator from softmax
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temp = paddle.uniform(
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logit_probs.shape, dtype=logit_probs.dtype, min=1e-5, max=1.0 - 1e-5)
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temp = logit_probs - paddle.log(-paddle.log(temp))
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argmax = paddle.argmax(temp, axis=-1)
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# (B, T) -> (B, T, nr_mix)
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one_hot = F.one_hot(argmax, nr_mix)
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one_hot = paddle.cast(one_hot, dtype=paddle.get_default_dtype())
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# select logistic parameters
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means = paddle.sum(y[:, :, nr_mix:2 * nr_mix] * one_hot, axis=-1)
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log_scales = paddle.clip(
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paddle.sum(y[:, :, 2 * nr_mix:3 * nr_mix] * one_hot, axis=-1),
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min=log_scale_min)
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# sample from logistic & clip to interval
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# we don't actually round to the nearest 8bit value when sampling
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u = paddle.uniform(means.shape, min=1e-5, max=1.0 - 1e-5)
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x = means + paddle.exp(log_scales) * (paddle.log(u) - paddle.log(1. - u))
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x = paddle.clip(x, min=-1., max=-1.)
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return x
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# Loss for new Tacotron2
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class GuidedAttentionLoss(nn.Layer):
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"""Guided attention loss function module.
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This module calculates the guided attention loss described
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in `Efficiently Trainable Text-to-Speech System Based
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on Deep Convolutional Networks with Guided Attention`_,
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which forces the attention to be diagonal.
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.. _`Efficiently Trainable Text-to-Speech System
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Based on Deep Convolutional Networks with Guided Attention`:
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https://arxiv.org/abs/1710.08969
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"""
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def __init__(self, sigma=0.4, alpha=1.0, reset_always=True):
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"""Initialize guided attention loss module.
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Parameters
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----------
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sigma : float, optional
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Standard deviation to control how close attention to a diagonal.
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alpha : float, optional
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Scaling coefficient (lambda).
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reset_always : bool, optional
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Whether to always reset masks.
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"""
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super().__init__()
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self.sigma = sigma
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self.alpha = alpha
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self.reset_always = reset_always
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self.guided_attn_masks = None
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self.masks = None
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def _reset_masks(self):
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self.guided_attn_masks = None
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self.masks = None
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def forward(self, att_ws, ilens, olens):
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"""Calculate forward propagation.
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Parameters
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----------
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att_ws : Tensor
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Batch of attention weights (B, T_max_out, T_max_in).
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ilens : Tensor(int64)
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Batch of input lenghts (B,).
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olens : Tensor(int64)
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Batch of output lenghts (B,).
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Returns
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----------
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Tensor
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Guided attention loss value.
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"""
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if self.guided_attn_masks is None:
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self.guided_attn_masks = self._make_guided_attention_masks(ilens,
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olens)
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if self.masks is None:
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self.masks = self._make_masks(ilens, olens)
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losses = self.guided_attn_masks * att_ws
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loss = paddle.mean(
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losses.masked_select(self.masks.broadcast_to(losses.shape)))
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if self.reset_always:
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self._reset_masks()
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return self.alpha * loss
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def _make_guided_attention_masks(self, ilens, olens):
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n_batches = len(ilens)
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max_ilen = max(ilens)
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max_olen = max(olens)
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guided_attn_masks = paddle.zeros((n_batches, max_olen, max_ilen))
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for idx, (ilen, olen) in enumerate(zip(ilens, olens)):
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guided_attn_masks[idx, :olen, :
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ilen] = self._make_guided_attention_mask(
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ilen, olen, self.sigma)
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return guided_attn_masks
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@staticmethod
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def _make_guided_attention_mask(ilen, olen, sigma):
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"""Make guided attention mask.
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Examples
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----------
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>>> guided_attn_mask =_make_guided_attention(5, 5, 0.4)
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>>> guided_attn_mask.shape
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[5, 5]
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>>> guided_attn_mask
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tensor([[0.0000, 0.1175, 0.3935, 0.6753, 0.8647],
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[0.1175, 0.0000, 0.1175, 0.3935, 0.6753],
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[0.3935, 0.1175, 0.0000, 0.1175, 0.3935],
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[0.6753, 0.3935, 0.1175, 0.0000, 0.1175],
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[0.8647, 0.6753, 0.3935, 0.1175, 0.0000]])
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>>> guided_attn_mask =_make_guided_attention(3, 6, 0.4)
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>>> guided_attn_mask.shape
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[6, 3]
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>>> guided_attn_mask
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tensor([[0.0000, 0.2934, 0.7506],
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[0.0831, 0.0831, 0.5422],
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[0.2934, 0.0000, 0.2934],
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[0.5422, 0.0831, 0.0831],
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[0.7506, 0.2934, 0.0000],
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[0.8858, 0.5422, 0.0831]])
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"""
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grid_x, grid_y = paddle.meshgrid(
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paddle.arange(olen), paddle.arange(ilen))
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grid_x = grid_x.cast(dtype=paddle.float32)
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grid_y = grid_y.cast(dtype=paddle.float32)
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return 1.0 - paddle.exp(-(
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(grid_y / ilen - grid_x / olen)**2) / (2 * (sigma**2)))
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@staticmethod
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def _make_masks(ilens, olens):
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"""Make masks indicating non-padded part.
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Parameters
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----------
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ilens : Tensor(int64) or List
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Batch of lengths (B,).
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olens : Tensor(int64) or List
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Batch of lengths (B,).
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Returns
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----------
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Tensor
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Mask tensor indicating non-padded part.
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Examples
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----------
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>>> ilens, olens = [5, 2], [8, 5]
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>>> _make_mask(ilens, olens)
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tensor([[[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1]],
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[[1, 1, 0, 0, 0],
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[1, 1, 0, 0, 0],
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[1, 1, 0, 0, 0],
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[1, 1, 0, 0, 0],
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[1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]]], dtype=paddle.uint8)
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"""
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# (B, T_in)
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in_masks = make_non_pad_mask(ilens)
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# (B, T_out)
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out_masks = make_non_pad_mask(olens)
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# (B, T_out, T_in)
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return paddle.logical_and(
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out_masks.unsqueeze(-1), in_masks.unsqueeze(-2))
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class GuidedMultiHeadAttentionLoss(GuidedAttentionLoss):
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"""Guided attention loss function module for multi head attention.
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Parameters
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----------
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sigma : float, optional
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Standard deviation to controlGuidedAttentionLoss
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how close attention to a diagonal.
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alpha : float, optional
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Scaling coefficient (lambda).
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reset_always : bool, optional
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Whether to always reset masks.
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"""
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def forward(self, att_ws, ilens, olens):
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"""Calculate forward propagation.
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Parameters
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----------
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att_ws : Tensor
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Batch of multi head attention weights (B, H, T_max_out, T_max_in).
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ilens : Tensor
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Batch of input lenghts (B,).
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olens : Tensor
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Batch of output lenghts (B,).
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Returns
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----------
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Tensor
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Guided attention loss value.
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"""
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if self.guided_attn_masks is None:
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self.guided_attn_masks = (
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self._make_guided_attention_masks(ilens, olens).unsqueeze(1))
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if self.masks is None:
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self.masks = self._make_masks(ilens, olens).unsqueeze(1)
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losses = self.guided_attn_masks * att_ws
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loss = paddle.mean(
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losses.masked_select(self.masks.broadcast_to(losses.shape)))
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if self.reset_always:
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self._reset_masks()
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return self.alpha * loss
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class Tacotron2Loss(nn.Layer):
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"""Loss function module for Tacotron2."""
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def __init__(self,
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use_masking=True,
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use_weighted_masking=False,
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bce_pos_weight=20.0):
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"""Initialize Tactoron2 loss module.
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Parameters
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----------
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use_masking : bool
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Whether to apply masking for padded part in loss calculation.
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use_weighted_masking : bool
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Whether to apply weighted masking in loss calculation.
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bce_pos_weight : float
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Weight of positive sample of stop token.
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"""
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super().__init__()
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assert (use_masking != use_weighted_masking) or not use_masking
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self.use_masking = use_masking
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self.use_weighted_masking = use_weighted_masking
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# define criterions
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reduction = "none" if self.use_weighted_masking else "mean"
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self.l1_criterion = nn.L1Loss(reduction=reduction)
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self.mse_criterion = nn.MSELoss(reduction=reduction)
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self.bce_criterion = nn.BCEWithLogitsLoss(
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reduction=reduction, pos_weight=paddle.to_tensor(bce_pos_weight))
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def forward(self, after_outs, before_outs, logits, ys, stop_labels, olens):
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"""Calculate forward propagation.
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Parameters
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----------
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after_outs : Tensor
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Batch of outputs after postnets (B, Lmax, odim).
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before_outs : Tensor
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Batch of outputs before postnets (B, Lmax, odim).
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logits : Tensor
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Batch of stop logits (B, Lmax).
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ys : Tensor
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Batch of padded target features (B, Lmax, odim).
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stop_labels : Tensor(int64)
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Batch of the sequences of stop token labels (B, Lmax).
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olens : Tensor(int64)
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Batch of the lengths of each target (B,).
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Returns
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----------
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Tensor
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L1 loss value.
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Tensor
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Mean square error loss value.
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Tensor
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Binary cross entropy loss value.
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"""
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# make mask and apply it
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if self.use_masking:
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masks = make_non_pad_mask(olens).unsqueeze(-1)
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ys = ys.masked_select(masks.broadcast_to(ys.shape))
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after_outs = after_outs.masked_select(
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masks.broadcast_to(after_outs.shape))
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before_outs = before_outs.masked_select(
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masks.broadcast_to(before_outs.shape))
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stop_labels = stop_labels.masked_select(
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masks[:, :, 0].broadcast_to(stop_labels.shape))
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logits = logits.masked_select(
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masks[:, :, 0].broadcast_to(logits.shape))
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# calculate loss
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l1_loss = self.l1_criterion(after_outs, ys) + self.l1_criterion(
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before_outs, ys)
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mse_loss = self.mse_criterion(after_outs, ys) + self.mse_criterion(
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before_outs, ys)
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bce_loss = self.bce_criterion(logits, stop_labels)
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# make weighted mask and apply it
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if self.use_weighted_masking:
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masks = make_non_pad_mask(olens).unsqueeze(-1)
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weights = masks.float() / masks.sum(axis=1, keepdim=True).float()
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out_weights = weights.divide(
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paddle.shape(ys)[0] * paddle.shape(ys)[2])
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logit_weights = weights.divide(paddle.shape(ys)[0])
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# apply weight
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l1_loss = l1_loss.multiply(out_weights)
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l1_loss = l1_loss.masked_select(masks.broadcast_to(l1_loss)).sum()
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mse_loss = mse_loss.multiply(out_weights)
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mse_loss = mse_loss.masked_select(
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masks.broadcast_to(mse_loss)).sum()
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bce_loss = bce_loss.multiply(logit_weights.squeeze(-1))
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bce_loss = bce_loss.masked_select(
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masks.squeeze(-1).broadcast_to(bce_loss)).sum()
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return l1_loss, mse_loss, bce_loss
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# Loss for Tacotron2
|
|
def attention_guide(dec_lens, enc_lens, N, T, g, dtype=None):
|
|
"""Build that W matrix. shape(B, T_dec, T_enc)
|
|
W[i, n, t] = 1 - exp(-(n/dec_lens[i] - t/enc_lens[i])**2 / (2g**2))
|
|
|
|
See also:
|
|
Tachibana, Hideyuki, Katsuya Uenoyama, and Shunsuke Aihara. 2017. “Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention.” ArXiv:1710.08969 [Cs, Eess], October. http://arxiv.org/abs/1710.08969.
|
|
"""
|
|
dtype = dtype or paddle.get_default_dtype()
|
|
dec_pos = paddle.arange(0, N).astype(dtype) / dec_lens.unsqueeze(
|
|
-1) # n/N # shape(B, T_dec)
|
|
enc_pos = paddle.arange(0, T).astype(dtype) / enc_lens.unsqueeze(
|
|
-1) # t/T # shape(B, T_enc)
|
|
W = 1 - paddle.exp(-(dec_pos.unsqueeze(-1) - enc_pos.unsqueeze(1))**2 /
|
|
(2 * g**2))
|
|
|
|
dec_mask = sequence_mask(dec_lens, maxlen=N)
|
|
enc_mask = sequence_mask(enc_lens, maxlen=T)
|
|
mask = dec_mask.unsqueeze(-1) * enc_mask.unsqueeze(1)
|
|
mask = paddle.cast(mask, W.dtype)
|
|
|
|
W *= mask
|
|
return W
|
|
|
|
|
|
def guided_attention_loss(attention_weight, dec_lens, enc_lens, g):
|
|
"""Guided attention loss, masked to excluded padding parts."""
|
|
_, N, T = attention_weight.shape
|
|
W = attention_guide(dec_lens, enc_lens, N, T, g, attention_weight.dtype)
|
|
|
|
total_tokens = (dec_lens * enc_lens).astype(W.dtype)
|
|
loss = paddle.mean(paddle.sum(W * attention_weight, [1, 2]) / total_tokens)
|
|
return loss
|
|
|
|
|
|
# Losses for GAN Vocoder
|
|
def stft(x,
|
|
fft_size,
|
|
hop_length=None,
|
|
win_length=None,
|
|
window='hann',
|
|
center=True,
|
|
pad_mode='reflect'):
|
|
"""Perform STFT and convert to magnitude spectrogram.
|
|
Parameters
|
|
----------
|
|
x : Tensor
|
|
Input signal tensor (B, T).
|
|
fft_size : int
|
|
FFT size.
|
|
hop_size : int
|
|
Hop size.
|
|
win_length : int
|
|
window : str, optional
|
|
window : str
|
|
Name of window function, see `scipy.signal.get_window` for more
|
|
details. Defaults to "hann".
|
|
center : bool, optional
|
|
center (bool, optional): Whether to pad `x` to make that the
|
|
:math:`t \times hop\\_length` at the center of :math:`t`-th frame. Default: `True`.
|
|
pad_mode : str, optional
|
|
Choose padding pattern when `center` is `True`.
|
|
Returns
|
|
----------
|
|
Tensor:
|
|
Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
|
|
"""
|
|
# calculate window
|
|
window = signal.get_window(window, win_length, fftbins=True)
|
|
window = paddle.to_tensor(window)
|
|
x_stft = paddle.signal.stft(
|
|
x,
|
|
fft_size,
|
|
hop_length,
|
|
win_length,
|
|
window=window,
|
|
center=center,
|
|
pad_mode=pad_mode)
|
|
|
|
real = x_stft.real()
|
|
imag = x_stft.imag()
|
|
|
|
return paddle.sqrt(paddle.clip(real**2 + imag**2, min=1e-7)).transpose(
|
|
[0, 2, 1])
|
|
|
|
|
|
class SpectralConvergenceLoss(nn.Layer):
|
|
"""Spectral convergence loss module."""
|
|
|
|
def __init__(self):
|
|
"""Initilize spectral convergence loss module."""
|
|
super().__init__()
|
|
|
|
def forward(self, x_mag, y_mag):
|
|
"""Calculate forward propagation.
|
|
Parameters
|
|
----------
|
|
x_mag : Tensor
|
|
Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
|
|
y_mag : Tensor)
|
|
Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
|
|
Returns
|
|
----------
|
|
Tensor
|
|
Spectral convergence loss value.
|
|
"""
|
|
return paddle.norm(
|
|
y_mag - x_mag, p="fro") / paddle.clip(
|
|
paddle.norm(y_mag, p="fro"), min=1e-10)
|
|
|
|
|
|
class LogSTFTMagnitudeLoss(nn.Layer):
|
|
"""Log STFT magnitude loss module."""
|
|
|
|
def __init__(self, epsilon=1e-7):
|
|
"""Initilize los STFT magnitude loss module."""
|
|
super().__init__()
|
|
self.epsilon = epsilon
|
|
|
|
def forward(self, x_mag, y_mag):
|
|
"""Calculate forward propagation.
|
|
Parameters
|
|
----------
|
|
x_mag : Tensor
|
|
Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
|
|
y_mag : Tensor
|
|
Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
|
|
Returns
|
|
----------
|
|
Tensor
|
|
Log STFT magnitude loss value.
|
|
"""
|
|
return F.l1_loss(
|
|
paddle.log(paddle.clip(y_mag, min=self.epsilon)),
|
|
paddle.log(paddle.clip(x_mag, min=self.epsilon)))
|
|
|
|
|
|
class STFTLoss(nn.Layer):
|
|
"""STFT loss module."""
|
|
|
|
def __init__(self,
|
|
fft_size=1024,
|
|
shift_size=120,
|
|
win_length=600,
|
|
window="hann"):
|
|
"""Initialize STFT loss module."""
|
|
super().__init__()
|
|
self.fft_size = fft_size
|
|
self.shift_size = shift_size
|
|
self.win_length = win_length
|
|
self.window = window
|
|
self.spectral_convergence_loss = SpectralConvergenceLoss()
|
|
self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss()
|
|
|
|
def forward(self, x, y):
|
|
"""Calculate forward propagation.
|
|
Parameters
|
|
----------
|
|
x : Tensor
|
|
Predicted signal (B, T).
|
|
y : Tensor
|
|
Groundtruth signal (B, T).
|
|
Returns
|
|
----------
|
|
Tensor
|
|
Spectral convergence loss value.
|
|
Tensor
|
|
Log STFT magnitude loss value.
|
|
"""
|
|
x_mag = stft(x, self.fft_size, self.shift_size, self.win_length,
|
|
self.window)
|
|
y_mag = stft(y, self.fft_size, self.shift_size, self.win_length,
|
|
self.window)
|
|
sc_loss = self.spectral_convergence_loss(x_mag, y_mag)
|
|
mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag)
|
|
|
|
return sc_loss, mag_loss
|
|
|
|
|
|
class MultiResolutionSTFTLoss(nn.Layer):
|
|
"""Multi resolution STFT loss module."""
|
|
|
|
def __init__(
|
|
self,
|
|
fft_sizes=[1024, 2048, 512],
|
|
hop_sizes=[120, 240, 50],
|
|
win_lengths=[600, 1200, 240],
|
|
window="hann", ):
|
|
"""Initialize Multi resolution STFT loss module.
|
|
Parameters
|
|
----------
|
|
fft_sizes : list
|
|
List of FFT sizes.
|
|
hop_sizes : list
|
|
List of hop sizes.
|
|
win_lengths : list
|
|
List of window lengths.
|
|
window : str
|
|
Window function type.
|
|
"""
|
|
super().__init__()
|
|
assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
|
|
self.stft_losses = nn.LayerList()
|
|
for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
|
|
self.stft_losses.append(STFTLoss(fs, ss, wl, window))
|
|
|
|
def forward(self, x, y):
|
|
"""Calculate forward propagation.
|
|
Parameters
|
|
----------
|
|
x : Tensor
|
|
Predicted signal (B, T) or (B, #subband, T).
|
|
y : Tensor
|
|
Groundtruth signal (B, T) or (B, #subband, T).
|
|
Returns
|
|
----------
|
|
Tensor
|
|
Multi resolution spectral convergence loss value.
|
|
Tensor
|
|
Multi resolution log STFT magnitude loss value.
|
|
"""
|
|
if len(x.shape) == 3:
|
|
# (B, C, T) -> (B x C, T)
|
|
x = x.reshape([-1, x.shape[2]])
|
|
# (B, C, T) -> (B x C, T)
|
|
y = y.reshape([-1, y.shape[2]])
|
|
sc_loss = 0.0
|
|
mag_loss = 0.0
|
|
for f in self.stft_losses:
|
|
sc_l, mag_l = f(x, y)
|
|
sc_loss += sc_l
|
|
mag_loss += mag_l
|
|
sc_loss /= len(self.stft_losses)
|
|
mag_loss /= len(self.stft_losses)
|
|
|
|
return sc_loss, mag_loss
|
|
|
|
|
|
class GeneratorAdversarialLoss(nn.Layer):
|
|
"""Generator adversarial loss module."""
|
|
|
|
def __init__(
|
|
self,
|
|
average_by_discriminators=True,
|
|
loss_type="mse", ):
|
|
"""Initialize GeneratorAversarialLoss module."""
|
|
super().__init__()
|
|
self.average_by_discriminators = average_by_discriminators
|
|
assert loss_type in ["mse", "hinge"], f"{loss_type} is not supported."
|
|
if loss_type == "mse":
|
|
self.criterion = self._mse_loss
|
|
else:
|
|
self.criterion = self._hinge_loss
|
|
|
|
def forward(self, outputs):
|
|
"""Calcualate generator adversarial loss.
|
|
Parameters
|
|
----------
|
|
outputs: Tensor or List
|
|
Discriminator outputs or list of discriminator outputs.
|
|
Returns
|
|
----------
|
|
Tensor
|
|
Generator adversarial loss value.
|
|
"""
|
|
if isinstance(outputs, (tuple, list)):
|
|
adv_loss = 0.0
|
|
for i, outputs_ in enumerate(outputs):
|
|
if isinstance(outputs_, (tuple, list)):
|
|
# case including feature maps
|
|
outputs_ = outputs_[-1]
|
|
adv_loss += self.criterion(outputs_)
|
|
if self.average_by_discriminators:
|
|
adv_loss /= i + 1
|
|
else:
|
|
adv_loss = self.criterion(outputs)
|
|
|
|
return adv_loss
|
|
|
|
def _mse_loss(self, x):
|
|
return F.mse_loss(x, paddle.ones_like(x))
|
|
|
|
def _hinge_loss(self, x):
|
|
return -x.mean()
|
|
|
|
|
|
class DiscriminatorAdversarialLoss(nn.Layer):
|
|
"""Discriminator adversarial loss module."""
|
|
|
|
def __init__(
|
|
self,
|
|
average_by_discriminators=True,
|
|
loss_type="mse", ):
|
|
"""Initialize DiscriminatorAversarialLoss module."""
|
|
super().__init__()
|
|
self.average_by_discriminators = average_by_discriminators
|
|
assert loss_type in ["mse"], f"{loss_type} is not supported."
|
|
if loss_type == "mse":
|
|
self.fake_criterion = self._mse_fake_loss
|
|
self.real_criterion = self._mse_real_loss
|
|
|
|
def forward(self, outputs_hat, outputs):
|
|
"""Calcualate discriminator adversarial loss.
|
|
Parameters
|
|
----------
|
|
outputs_hat : Tensor or list
|
|
Discriminator outputs or list of
|
|
discriminator outputs calculated from generator outputs.
|
|
outputs : Tensor or list
|
|
Discriminator outputs or list of
|
|
discriminator outputs calculated from groundtruth.
|
|
Returns
|
|
----------
|
|
Tensor
|
|
Discriminator real loss value.
|
|
Tensor
|
|
Discriminator fake loss value.
|
|
"""
|
|
if isinstance(outputs, (tuple, list)):
|
|
real_loss = 0.0
|
|
fake_loss = 0.0
|
|
for i, (outputs_hat_,
|
|
outputs_) in enumerate(zip(outputs_hat, outputs)):
|
|
if isinstance(outputs_hat_, (tuple, list)):
|
|
# case including feature maps
|
|
outputs_hat_ = outputs_hat_[-1]
|
|
outputs_ = outputs_[-1]
|
|
real_loss += self.real_criterion(outputs_)
|
|
fake_loss += self.fake_criterion(outputs_hat_)
|
|
if self.average_by_discriminators:
|
|
fake_loss /= i + 1
|
|
real_loss /= i + 1
|
|
else:
|
|
real_loss = self.real_criterion(outputs)
|
|
fake_loss = self.fake_criterion(outputs_hat)
|
|
|
|
return real_loss, fake_loss
|
|
|
|
def _mse_real_loss(self, x):
|
|
return F.mse_loss(x, paddle.ones_like(x))
|
|
|
|
def _mse_fake_loss(self, x):
|
|
return F.mse_loss(x, paddle.zeros_like(x))
|
|
|
|
|
|
# Losses for SpeedySpeech
|
|
# Structural Similarity Index Measure (SSIM)
|
|
def gaussian(window_size, sigma):
|
|
gauss = paddle.to_tensor([
|
|
math.exp(-(x - window_size // 2)**2 / float(2 * sigma**2))
|
|
for x in range(window_size)
|
|
])
|
|
return gauss / gauss.sum()
|
|
|
|
|
|
def create_window(window_size, channel):
|
|
_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
|
|
_2D_window = paddle.matmul(_1D_window, paddle.transpose(
|
|
_1D_window, [1, 0])).unsqueeze([0, 1])
|
|
window = paddle.expand(_2D_window, [channel, 1, window_size, window_size])
|
|
return window
|
|
|
|
|
|
def _ssim(img1, img2, window, window_size, channel, size_average=True):
|
|
mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
|
|
mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
|
|
|
|
mu1_sq = mu1.pow(2)
|
|
mu2_sq = mu2.pow(2)
|
|
mu1_mu2 = mu1 * mu2
|
|
|
|
sigma1_sq = F.conv2d(
|
|
img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
|
|
sigma2_sq = F.conv2d(
|
|
img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
|
|
sigma12 = F.conv2d(
|
|
img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2
|
|
|
|
C1 = 0.01**2
|
|
C2 = 0.03**2
|
|
|
|
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) \
|
|
/ ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
|
|
|
|
if size_average:
|
|
return ssim_map.mean()
|
|
else:
|
|
return ssim_map.mean(1).mean(1).mean(1)
|
|
|
|
|
|
def ssim(img1, img2, window_size=11, size_average=True):
|
|
(_, channel, _, _) = img1.shape
|
|
window = create_window(window_size, channel)
|
|
return _ssim(img1, img2, window, window_size, channel, size_average)
|
|
|
|
|
|
def weighted_mean(input, weight):
|
|
"""Weighted mean. It can also be used as masked mean.
|
|
|
|
Parameters
|
|
-----------
|
|
input : Tensor
|
|
The input tensor.
|
|
weight : Tensor
|
|
The weight tensor with broadcastable shape with the input.
|
|
|
|
Returns
|
|
----------
|
|
Tensor [shape=(1,)]
|
|
Weighted mean tensor with the same dtype as input.
|
|
"""
|
|
weight = paddle.cast(weight, input.dtype)
|
|
# paddle.Tensor.size is different with torch.size() and has been overrided in s2t.__init__
|
|
broadcast_ratio = input.numel() / weight.numel()
|
|
return paddle.sum(input * weight) / (paddle.sum(weight) * broadcast_ratio)
|
|
|
|
|
|
def masked_l1_loss(prediction, target, mask):
|
|
"""Compute maksed L1 loss.
|
|
|
|
Parameters
|
|
----------
|
|
prediction : Tensor
|
|
The prediction.
|
|
target : Tensor
|
|
The target. The shape should be broadcastable to ``prediction``.
|
|
mask : Tensor
|
|
The mask. The shape should be broadcatable to the broadcasted shape of
|
|
``prediction`` and ``target``.
|
|
|
|
Returns
|
|
-------
|
|
Tensor [shape=(1,)]
|
|
The masked L1 loss.
|
|
"""
|
|
abs_error = F.l1_loss(prediction, target, reduction='none')
|
|
loss = weighted_mean(abs_error, mask)
|
|
return loss
|
|
|
|
|
|
class MelSpectrogram(nn.Layer):
|
|
"""Calculate Mel-spectrogram."""
|
|
|
|
def __init__(
|
|
self,
|
|
fs=22050,
|
|
fft_size=1024,
|
|
hop_size=256,
|
|
win_length=None,
|
|
window="hann",
|
|
num_mels=80,
|
|
fmin=80,
|
|
fmax=7600,
|
|
center=True,
|
|
normalized=False,
|
|
onesided=True,
|
|
eps=1e-10,
|
|
log_base=10.0, ):
|
|
"""Initialize MelSpectrogram module."""
|
|
super().__init__()
|
|
self.fft_size = fft_size
|
|
if win_length is None:
|
|
self.win_length = fft_size
|
|
else:
|
|
self.win_length = win_length
|
|
self.hop_size = hop_size
|
|
self.center = center
|
|
self.normalized = normalized
|
|
self.onesided = onesided
|
|
|
|
if window is not None and not hasattr(signal.windows, f"{window}"):
|
|
raise ValueError(f"{window} window is not implemented")
|
|
self.window = window
|
|
self.eps = eps
|
|
|
|
fmin = 0 if fmin is None else fmin
|
|
fmax = fs / 2 if fmax is None else fmax
|
|
melmat = librosa.filters.mel(
|
|
sr=fs,
|
|
n_fft=fft_size,
|
|
n_mels=num_mels,
|
|
fmin=fmin,
|
|
fmax=fmax, )
|
|
|
|
self.melmat = paddle.to_tensor(melmat.T)
|
|
self.stft_params = {
|
|
"n_fft": self.fft_size,
|
|
"win_length": self.win_length,
|
|
"hop_length": self.hop_size,
|
|
"center": self.center,
|
|
"normalized": self.normalized,
|
|
"onesided": self.onesided,
|
|
}
|
|
|
|
self.log_base = log_base
|
|
if self.log_base is None:
|
|
self.log = paddle.log
|
|
elif self.log_base == 2.0:
|
|
self.log = paddle.log2
|
|
elif self.log_base == 10.0:
|
|
self.log = paddle.log10
|
|
else:
|
|
raise ValueError(f"log_base: {log_base} is not supported.")
|
|
|
|
def forward(self, x):
|
|
"""Calculate Mel-spectrogram.
|
|
Parameters
|
|
----------
|
|
x : Tensor
|
|
Input waveform tensor (B, T) or (B, 1, T).
|
|
Returns
|
|
----------
|
|
Tensor
|
|
Mel-spectrogram (B, #mels, #frames).
|
|
"""
|
|
if len(x.shape) == 3:
|
|
# (B, C, T) -> (B*C, T)
|
|
x = x.reshape([-1, paddle.shape(x)[2]])
|
|
|
|
if self.window is not None:
|
|
# calculate window
|
|
window = signal.get_window(
|
|
self.window, self.win_length, fftbins=True)
|
|
window = paddle.to_tensor(window)
|
|
else:
|
|
window = None
|
|
|
|
x_stft = paddle.signal.stft(x, window=window, **self.stft_params)
|
|
real = x_stft.real()
|
|
imag = x_stft.imag()
|
|
# (B, #freqs, #frames) -> (B, $frames, #freqs)
|
|
real = real.transpose([0, 2, 1])
|
|
imag = imag.transpose([0, 2, 1])
|
|
x_power = real**2 + imag**2
|
|
x_amp = paddle.sqrt(paddle.clip(x_power, min=self.eps))
|
|
x_mel = paddle.matmul(x_amp, self.melmat)
|
|
x_mel = paddle.clip(x_mel, min=self.eps)
|
|
|
|
return self.log(x_mel).transpose([0, 2, 1])
|
|
|
|
|
|
class MelSpectrogramLoss(nn.Layer):
|
|
"""Mel-spectrogram loss."""
|
|
|
|
def __init__(
|
|
self,
|
|
fs=22050,
|
|
fft_size=1024,
|
|
hop_size=256,
|
|
win_length=None,
|
|
window="hann",
|
|
num_mels=80,
|
|
fmin=80,
|
|
fmax=7600,
|
|
center=True,
|
|
normalized=False,
|
|
onesided=True,
|
|
eps=1e-10,
|
|
log_base=10.0, ):
|
|
"""Initialize Mel-spectrogram loss."""
|
|
super().__init__()
|
|
self.mel_spectrogram = MelSpectrogram(
|
|
fs=fs,
|
|
fft_size=fft_size,
|
|
hop_size=hop_size,
|
|
win_length=win_length,
|
|
window=window,
|
|
num_mels=num_mels,
|
|
fmin=fmin,
|
|
fmax=fmax,
|
|
center=center,
|
|
normalized=normalized,
|
|
onesided=onesided,
|
|
eps=eps,
|
|
log_base=log_base, )
|
|
|
|
def forward(self, y_hat, y):
|
|
"""Calculate Mel-spectrogram loss.
|
|
Parameters
|
|
----------
|
|
y_hat : Tensor
|
|
Generated single tensor (B, 1, T).
|
|
y : Tensor
|
|
Groundtruth single tensor (B, 1, T).
|
|
Returns
|
|
----------
|
|
Tensor
|
|
Mel-spectrogram loss value.
|
|
"""
|
|
mel_hat = self.mel_spectrogram(y_hat)
|
|
mel = self.mel_spectrogram(y)
|
|
mel_loss = F.l1_loss(mel_hat, mel)
|
|
|
|
return mel_loss
|
|
|
|
|
|
class FeatureMatchLoss(nn.Layer):
|
|
"""Feature matching loss module."""
|
|
|
|
def __init__(
|
|
self,
|
|
average_by_layers=True,
|
|
average_by_discriminators=True,
|
|
include_final_outputs=False, ):
|
|
"""Initialize FeatureMatchLoss module."""
|
|
super().__init__()
|
|
self.average_by_layers = average_by_layers
|
|
self.average_by_discriminators = average_by_discriminators
|
|
self.include_final_outputs = include_final_outputs
|
|
|
|
def forward(self, feats_hat, feats):
|
|
"""Calcualate feature matching loss.
|
|
Parameters
|
|
----------
|
|
feats_hat : list
|
|
List of list of discriminator outputs
|
|
calcuated from generater outputs.
|
|
feats : list
|
|
List of list of discriminator outputs
|
|
calcuated from groundtruth.
|
|
Returns
|
|
----------
|
|
Tensor
|
|
Feature matching loss value.
|
|
|
|
"""
|
|
feat_match_loss = 0.0
|
|
for i, (feats_hat_, feats_) in enumerate(zip(feats_hat, feats)):
|
|
feat_match_loss_ = 0.0
|
|
if not self.include_final_outputs:
|
|
feats_hat_ = feats_hat_[:-1]
|
|
feats_ = feats_[:-1]
|
|
for j, (feat_hat_, feat_) in enumerate(zip(feats_hat_, feats_)):
|
|
feat_match_loss_ += F.l1_loss(feat_hat_, feat_.detach())
|
|
if self.average_by_layers:
|
|
feat_match_loss_ /= j + 1
|
|
feat_match_loss += feat_match_loss_
|
|
if self.average_by_discriminators:
|
|
feat_match_loss /= i + 1
|
|
|
|
return feat_match_loss
|