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664 lines
21 KiB
664 lines
21 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 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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# Loss for Tacotron2
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def attention_guide(dec_lens, enc_lens, N, T, g, dtype=None):
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"""Build that W matrix. shape(B, T_dec, T_enc)
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W[i, n, t] = 1 - exp(-(n/dec_lens[i] - t/enc_lens[i])**2 / (2g**2))
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See also:
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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.
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"""
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dtype = dtype or paddle.get_default_dtype()
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dec_pos = paddle.arange(0, N).astype(dtype) / dec_lens.unsqueeze(
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-1) # n/N # shape(B, T_dec)
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enc_pos = paddle.arange(0, T).astype(dtype) / enc_lens.unsqueeze(
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-1) # t/T # shape(B, T_enc)
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W = 1 - paddle.exp(-(dec_pos.unsqueeze(-1) - enc_pos.unsqueeze(1))**2 /
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(2 * g**2))
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dec_mask = sequence_mask(dec_lens, maxlen=N)
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enc_mask = sequence_mask(enc_lens, maxlen=T)
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mask = dec_mask.unsqueeze(-1) * enc_mask.unsqueeze(1)
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mask = paddle.cast(mask, W.dtype)
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W *= mask
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return W
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def guided_attention_loss(attention_weight, dec_lens, enc_lens, g):
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"""Guided attention loss, masked to excluded padding parts."""
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_, N, T = attention_weight.shape
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W = attention_guide(dec_lens, enc_lens, N, T, g, attention_weight.dtype)
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total_tokens = (dec_lens * enc_lens).astype(W.dtype)
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loss = paddle.mean(paddle.sum(W * attention_weight, [1, 2]) / total_tokens)
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return loss
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# Losses for GAN Vocoder
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def stft(x,
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fft_size,
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hop_length=None,
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win_length=None,
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window='hann',
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center=True,
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pad_mode='reflect'):
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"""Perform STFT and convert to magnitude spectrogram.
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Parameters
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----------
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x : Tensor
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Input signal tensor (B, T).
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fft_size : int
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FFT size.
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hop_size : int
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Hop size.
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win_length : int
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window : str, optional
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window : str
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Name of window function, see `scipy.signal.get_window` for more
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details. Defaults to "hann".
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center : bool, optional
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center (bool, optional): Whether to pad `x` to make that the
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:math:`t \times hop\_length` at the center of :math:`t`-th frame. Default: `True`.
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pad_mode : str, optional
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Choose padding pattern when `center` is `True`.
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Returns
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----------
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Tensor:
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Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
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"""
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# calculate window
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window = signal.get_window(window, win_length, fftbins=True)
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window = paddle.to_tensor(window)
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x_stft = paddle.signal.stft(
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x,
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fft_size,
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hop_length,
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win_length,
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window=window,
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center=center,
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pad_mode=pad_mode)
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real = x_stft.real()
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imag = x_stft.imag()
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return paddle.sqrt(paddle.clip(real**2 + imag**2, min=1e-7)).transpose(
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[0, 2, 1])
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class SpectralConvergenceLoss(nn.Layer):
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"""Spectral convergence loss module."""
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def __init__(self):
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"""Initilize spectral convergence loss module."""
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super().__init__()
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def forward(self, x_mag, y_mag):
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"""Calculate forward propagation.
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Parameters
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----------
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x_mag : Tensor
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Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
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y_mag : Tensor)
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Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
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Returns
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----------
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Tensor
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Spectral convergence loss value.
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"""
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return paddle.norm(
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y_mag - x_mag, p="fro") / paddle.clip(
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paddle.norm(y_mag, p="fro"), min=1e-10)
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class LogSTFTMagnitudeLoss(nn.Layer):
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"""Log STFT magnitude loss module."""
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def __init__(self, epsilon=1e-7):
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"""Initilize los STFT magnitude loss module."""
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super().__init__()
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self.epsilon = epsilon
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def forward(self, x_mag, y_mag):
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"""Calculate forward propagation.
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Parameters
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----------
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x_mag : Tensor
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Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
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y_mag : Tensor
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Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
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Returns
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----------
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Tensor
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Log STFT magnitude loss value.
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"""
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return F.l1_loss(
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paddle.log(paddle.clip(y_mag, min=self.epsilon)),
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paddle.log(paddle.clip(x_mag, min=self.epsilon)))
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class STFTLoss(nn.Layer):
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"""STFT loss module."""
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def __init__(self,
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fft_size=1024,
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shift_size=120,
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win_length=600,
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window="hann"):
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"""Initialize STFT loss module."""
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super().__init__()
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self.fft_size = fft_size
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self.shift_size = shift_size
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self.win_length = win_length
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self.window = window
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self.spectral_convergence_loss = SpectralConvergenceLoss()
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self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss()
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def forward(self, x, y):
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"""Calculate forward propagation.
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Parameters
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----------
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x : Tensor
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Predicted signal (B, T).
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y : Tensor
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Groundtruth signal (B, T).
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Returns
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----------
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Tensor
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Spectral convergence loss value.
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Tensor
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Log STFT magnitude loss value.
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"""
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x_mag = stft(x, self.fft_size, self.shift_size, self.win_length,
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self.window)
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y_mag = stft(y, self.fft_size, self.shift_size, self.win_length,
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self.window)
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sc_loss = self.spectral_convergence_loss(x_mag, y_mag)
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mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag)
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return sc_loss, mag_loss
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class MultiResolutionSTFTLoss(nn.Layer):
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"""Multi resolution STFT loss module."""
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def __init__(
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self,
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fft_sizes=[1024, 2048, 512],
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hop_sizes=[120, 240, 50],
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win_lengths=[600, 1200, 240],
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window="hann", ):
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"""Initialize Multi resolution STFT loss module.
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Parameters
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----------
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fft_sizes : list
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List of FFT sizes.
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hop_sizes : list
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List of hop sizes.
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win_lengths : list
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List of window lengths.
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window : str
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Window function type.
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"""
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super().__init__()
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assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
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self.stft_losses = nn.LayerList()
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for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
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self.stft_losses.append(STFTLoss(fs, ss, wl, window))
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def forward(self, x, y):
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"""Calculate forward propagation.
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Parameters
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----------
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x : Tensor
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Predicted signal (B, T) or (B, #subband, T).
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y : Tensor
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Groundtruth signal (B, T) or (B, #subband, T).
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Returns
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----------
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Tensor
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Multi resolution spectral convergence loss value.
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Tensor
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Multi resolution log STFT magnitude loss value.
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"""
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if len(x.shape) == 3:
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# (B, C, T) -> (B x C, T)
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x = x.reshape([-1, x.shape[2]])
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# (B, C, T) -> (B x C, T)
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y = y.reshape([-1, y.shape[2]])
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sc_loss = 0.0
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mag_loss = 0.0
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for f in self.stft_losses:
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sc_l, mag_l = f(x, y)
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sc_loss += sc_l
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mag_loss += mag_l
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sc_loss /= len(self.stft_losses)
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mag_loss /= len(self.stft_losses)
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return sc_loss, mag_loss
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class GeneratorAdversarialLoss(nn.Layer):
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"""Generator adversarial loss module."""
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def __init__(
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self,
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average_by_discriminators=True,
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loss_type="mse", ):
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"""Initialize GeneratorAversarialLoss module."""
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super().__init__()
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self.average_by_discriminators = average_by_discriminators
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assert loss_type in ["mse", "hinge"], f"{loss_type} is not supported."
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if loss_type == "mse":
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self.criterion = self._mse_loss
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else:
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self.criterion = self._hinge_loss
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def forward(self, outputs):
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"""Calcualate generator adversarial loss.
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Parameters
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----------
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outputs: Tensor or List
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Discriminator outputs or list of discriminator outputs.
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Returns
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----------
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Tensor
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Generator adversarial loss value.
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"""
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if isinstance(outputs, (tuple, list)):
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adv_loss = 0.0
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for i, outputs_ in enumerate(outputs):
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if isinstance(outputs_, (tuple, list)):
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# case including feature maps
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outputs_ = outputs_[-1]
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adv_loss += self.criterion(outputs_)
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if self.average_by_discriminators:
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adv_loss /= i + 1
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else:
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adv_loss = self.criterion(outputs)
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return adv_loss
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def _mse_loss(self, x):
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return F.mse_loss(x, paddle.ones_like(x))
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def _hinge_loss(self, x):
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return -x.mean()
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class DiscriminatorAdversarialLoss(nn.Layer):
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"""Discriminator adversarial loss module."""
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def __init__(
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self,
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average_by_discriminators=True,
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loss_type="mse", ):
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"""Initialize DiscriminatorAversarialLoss module."""
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super().__init__()
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self.average_by_discriminators = average_by_discriminators
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assert loss_type in ["mse"], f"{loss_type} is not supported."
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if loss_type == "mse":
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self.fake_criterion = self._mse_fake_loss
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self.real_criterion = self._mse_real_loss
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def forward(self, outputs_hat, outputs):
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"""Calcualate discriminator adversarial loss.
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Parameters
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----------
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outputs_hat : Tensor or list
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Discriminator outputs or list of
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discriminator outputs calculated from generator outputs.
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outputs : Tensor or list
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Discriminator outputs or list of
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discriminator outputs calculated from groundtruth.
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Returns
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----------
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Tensor
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Discriminator real loss value.
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Tensor
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Discriminator fake loss value.
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"""
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if isinstance(outputs, (tuple, list)):
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real_loss = 0.0
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fake_loss = 0.0
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for i, (outputs_hat_,
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outputs_) in enumerate(zip(outputs_hat, outputs)):
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if isinstance(outputs_hat_, (tuple, list)):
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# case including feature maps
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outputs_hat_ = outputs_hat_[-1]
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outputs_ = outputs_[-1]
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real_loss += self.real_criterion(outputs_)
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fake_loss += self.fake_criterion(outputs_hat_)
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if self.average_by_discriminators:
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fake_loss /= i + 1
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real_loss /= i + 1
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else:
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real_loss = self.real_criterion(outputs)
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fake_loss = self.fake_criterion(outputs_hat)
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return real_loss, fake_loss
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def _mse_real_loss(self, x):
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return F.mse_loss(x, paddle.ones_like(x))
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def _mse_fake_loss(self, x):
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return F.mse_loss(x, paddle.zeros_like(x))
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# Losses for SpeedySpeech
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# Structural Similarity Index Measure (SSIM)
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def gaussian(window_size, sigma):
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gauss = paddle.to_tensor([
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math.exp(-(x - window_size // 2)**2 / float(2 * sigma**2))
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for x in range(window_size)
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])
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return gauss / gauss.sum()
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def create_window(window_size, channel):
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_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
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_2D_window = paddle.matmul(_1D_window, paddle.transpose(
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_1D_window, [1, 0])).unsqueeze([0, 1])
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window = paddle.expand(_2D_window, [channel, 1, window_size, window_size])
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return window
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def _ssim(img1, img2, window, window_size, channel, size_average=True):
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mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
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mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
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mu1_sq = mu1.pow(2)
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mu2_sq = mu2.pow(2)
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mu1_mu2 = mu1 * mu2
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sigma1_sq = F.conv2d(
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img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
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sigma2_sq = F.conv2d(
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img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
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sigma12 = F.conv2d(
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img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2
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C1 = 0.01**2
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C2 = 0.03**2
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ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) \
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/ ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
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if size_average:
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return ssim_map.mean()
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else:
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return ssim_map.mean(1).mean(1).mean(1)
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def ssim(img1, img2, window_size=11, size_average=True):
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(_, channel, _, _) = img1.shape
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window = create_window(window_size, channel)
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return _ssim(img1, img2, window, window_size, channel, size_average)
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def weighted_mean(input, weight):
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"""Weighted mean. It can also be used as masked mean.
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Parameters
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-----------
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input : Tensor
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The input tensor.
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weight : Tensor
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The weight tensor with broadcastable shape with the input.
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Returns
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----------
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Tensor [shape=(1,)]
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Weighted mean tensor with the same dtype as input.
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"""
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weight = paddle.cast(weight, input.dtype)
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broadcast_ratio = input.size / weight.size
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return paddle.sum(input * weight) / (paddle.sum(weight) * broadcast_ratio)
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def masked_l1_loss(prediction, target, mask):
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"""Compute maksed L1 loss.
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Parameters
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----------
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prediction : Tensor
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The prediction.
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target : Tensor
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The target. The shape should be broadcastable to ``prediction``.
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mask : Tensor
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The mask. The shape should be broadcatable to the broadcasted shape of
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``prediction`` and ``target``.
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Returns
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-------
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Tensor [shape=(1,)]
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The masked L1 loss.
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"""
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abs_error = F.l1_loss(prediction, target, reduction='none')
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loss = weighted_mean(abs_error, mask)
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return loss
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class MelSpectrogram(nn.Layer):
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"""Calculate Mel-spectrogram."""
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def __init__(
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self,
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fs=22050,
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fft_size=1024,
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hop_size=256,
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win_length=None,
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window="hann",
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num_mels=80,
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fmin=80,
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fmax=7600,
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center=True,
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normalized=False,
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onesided=True,
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eps=1e-10,
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log_base=10.0, ):
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"""Initialize MelSpectrogram module."""
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super().__init__()
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self.fft_size = fft_size
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if win_length is None:
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self.win_length = fft_size
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else:
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self.win_length = win_length
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self.hop_size = hop_size
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self.center = center
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self.normalized = normalized
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self.onesided = onesided
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if window is not None and not hasattr(signal.windows, f"{window}"):
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raise ValueError(f"{window} window is not implemented")
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self.window = window
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self.eps = eps
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fmin = 0 if fmin is None else fmin
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fmax = fs / 2 if fmax is None else fmax
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melmat = librosa.filters.mel(
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sr=fs,
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n_fft=fft_size,
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n_mels=num_mels,
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fmin=fmin,
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fmax=fmax, )
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self.melmat = paddle.to_tensor(melmat.T)
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self.stft_params = {
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"n_fft": self.fft_size,
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"win_length": self.win_length,
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"hop_length": self.hop_size,
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"center": self.center,
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"normalized": self.normalized,
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"onesided": self.onesided,
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}
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self.log_base = log_base
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|
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
|