path: root/dnn/torch/osce/stndrd/evaluation/lace_loss_metric.py

"""
/* Copyright (c) 2023 Amazon
   Written by Jan Buethe */
/*
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   are met:

   - Redistributions of source code must retain the above copyright
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   notice, this list of conditions and the following disclaimer in the
   documentation and/or other materials provided with the distribution.

   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
   ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
   LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
   A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
   OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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*/
"""

"""STFT-based Loss modules."""

import torch
import torch.nn.functional as F
from torch import nn
import numpy as np
import torchaudio


def get_window(win_name, win_length, *args, **kwargs):
    window_dict = {
        'bartlett_window'   : torch.bartlett_window,
        'blackman_window'   : torch.blackman_window,
        'hamming_window'    : torch.hamming_window,
        'hann_window'       : torch.hann_window,
        'kaiser_window'     : torch.kaiser_window
    }

    if not win_name in window_dict:
        raise ValueError()

    return window_dict[win_name](win_length, *args, **kwargs)


def stft(x, fft_size, hop_size, win_length, window):
    """Perform STFT and convert to magnitude spectrogram.
    Args:
        x (Tensor): Input signal tensor (B, T).
        fft_size (int): FFT size.
        hop_size (int): Hop size.
        win_length (int): Window length.
        window (str): Window function type.
    Returns:
        Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
    """

    win = get_window(window, win_length).to(x.device)
    x_stft = torch.stft(x, fft_size, hop_size, win_length, win, return_complex=True)


    return torch.clamp(torch.abs(x_stft), min=1e-7)

def spectral_convergence_loss(Y_true, Y_pred):
    dims=list(range(1, len(Y_pred.shape)))
    return torch.mean(torch.norm(torch.abs(Y_true) - torch.abs(Y_pred), p="fro", dim=dims) / (torch.norm(Y_pred, p="fro", dim=dims) + 1e-6))


def log_magnitude_loss(Y_true, Y_pred):
    Y_true_log_abs = torch.log(torch.abs(Y_true) + 1e-15)
    Y_pred_log_abs = torch.log(torch.abs(Y_pred) + 1e-15)

    return torch.mean(torch.abs(Y_true_log_abs - Y_pred_log_abs))

def spectral_xcorr_loss(Y_true, Y_pred):
    Y_true = Y_true.abs()
    Y_pred = Y_pred.abs()
    dims=list(range(1, len(Y_pred.shape)))
    xcorr = torch.sum(Y_true * Y_pred, dim=dims) / torch.sqrt(torch.sum(Y_true ** 2, dim=dims) * torch.sum(Y_pred ** 2, dim=dims) + 1e-9)

    return 1 - xcorr.mean()



class MRLogMelLoss(nn.Module):
    def __init__(self,
                 fft_sizes=[512, 256, 128, 64],
                 overlap=0.5,
                 fs=16000,
                 n_mels=18
                 ):

        self.fft_sizes  = fft_sizes
        self.overlap    = overlap
        self.fs         = fs
        self.n_mels     = n_mels

        super().__init__()

        self.mel_specs = []
        for fft_size in fft_sizes:
            hop_size = int(round(fft_size * (1 - self.overlap)))

            n_mels = self.n_mels
            if fft_size < 128:
                n_mels //= 2

            self.mel_specs.append(torchaudio.transforms.MelSpectrogram(fs, fft_size, hop_length=hop_size, n_mels=n_mels))

        for i, mel_spec in enumerate(self.mel_specs):
            self.add_module(f'mel_spec_{i+1}', mel_spec)

    def forward(self, y_true, y_pred):

        loss = torch.zeros(1, device=y_true.device)

        for mel_spec in self.mel_specs:
            Y_true = mel_spec(y_true)
            Y_pred = mel_spec(y_pred)
            loss = loss + log_magnitude_loss(Y_true, Y_pred)

        loss = loss / len(self.mel_specs)

        return loss

def create_weight_matrix(num_bins, bins_per_band=10):
    m = torch.zeros((num_bins, num_bins), dtype=torch.float32)

    r0 = bins_per_band // 2
    r1 = bins_per_band - r0

    for i in range(num_bins):
        i0 = max(i - r0, 0)
        j0 = min(i + r1, num_bins)

        m[i, i0: j0] += 1

        if i < r0:
            m[i, :r0 - i] += 1

        if i > num_bins - r1:
            m[i, num_bins - r1 - i:] += 1

    return m / bins_per_band

def weighted_spectral_convergence(Y_true, Y_pred, w):

    # calculate sfm based weights
    logY = torch.log(torch.abs(Y_true) + 1e-9)
    Y = torch.abs(Y_true)

    avg_logY = torch.matmul(logY.transpose(1, 2), w)
    avg_Y = torch.matmul(Y.transpose(1, 2), w)

    sfm = torch.exp(avg_logY) / (avg_Y + 1e-9)

    weight = (torch.relu(1 - sfm) ** .5).transpose(1, 2)

    loss = torch.mean(
        torch.mean(weight * torch.abs(torch.abs(Y_true) - torch.abs(Y_pred)), dim=[1, 2])
        / (torch.mean( weight * torch.abs(Y_true), dim=[1, 2]) + 1e-9)
    )

    return loss

def gen_filterbank(N, Fs=16000):
    in_freq = (np.arange(N+1, dtype='float32')/N*Fs/2)[None,:]
    out_freq = (np.arange(N, dtype='float32')/N*Fs/2)[:,None]
    #ERB from B.C.J Moore, An Introduction to the Psychology of Hearing, 5th Ed., page 73.
    ERB_N = 24.7 + .108*in_freq
    delta = np.abs(in_freq-out_freq)/ERB_N
    center = (delta<.5).astype('float32')
    R = -12*center*delta**2 + (1-center)*(3-12*delta)
    RE = 10.**(R/10.)
    norm = np.sum(RE, axis=1)
    RE = RE/norm[:, np.newaxis]
    return torch.from_numpy(RE)

def smooth_log_mag(Y_true, Y_pred, filterbank):
    Y_true_smooth = torch.matmul(filterbank, torch.abs(Y_true))
    Y_pred_smooth = torch.matmul(filterbank, torch.abs(Y_pred))

    loss = torch.abs(
        torch.log(Y_true_smooth + 1e-9) - torch.log(Y_pred_smooth + 1e-9)
    )

    loss = loss.mean()

    return loss

class MRSTFTLoss(nn.Module):
    def __init__(self,
                 fft_sizes=[2048, 1024, 512, 256, 128, 64],
                 overlap=0.5,
                 window='hann_window',
                 fs=16000,
                 log_mag_weight=0,
                 sc_weight=0,
                 wsc_weight=0,
                 smooth_log_mag_weight=2,
                 sxcorr_weight=1):
        super().__init__()

        self.fft_sizes = fft_sizes
        self.overlap = overlap
        self.window = window
        self.log_mag_weight = log_mag_weight
        self.sc_weight = sc_weight
        self.wsc_weight = wsc_weight
        self.smooth_log_mag_weight = smooth_log_mag_weight
        self.sxcorr_weight = sxcorr_weight
        self.fs = fs

        # weights for SFM weighted spectral convergence loss
        self.wsc_weights = torch.nn.ParameterDict()
        for fft_size in fft_sizes:
            width = min(11, int(1000 * fft_size / self.fs + .5))
            width += width % 2
            self.wsc_weights[str(fft_size)] = torch.nn.Parameter(
                create_weight_matrix(fft_size // 2 + 1, width),
                requires_grad=False
            )

        # filterbanks for smooth log magnitude loss
        self.filterbanks = torch.nn.ParameterDict()
        for fft_size in fft_sizes:
            self.filterbanks[str(fft_size)] = torch.nn.Parameter(
                gen_filterbank(fft_size//2),
                requires_grad=False
            )


    def __call__(self, y_true, y_pred):


        lm_loss = torch.zeros(1, device=y_true.device)
        sc_loss = torch.zeros(1, device=y_true.device)
        wsc_loss = torch.zeros(1, device=y_true.device)
        slm_loss = torch.zeros(1, device=y_true.device)
        sxcorr_loss = torch.zeros(1, device=y_true.device)

        for fft_size in self.fft_sizes:
            hop_size = int(round(fft_size * (1 - self.overlap)))
            win_size = fft_size

            Y_true = stft(y_true, fft_size, hop_size, win_size, self.window)
            Y_pred = stft(y_pred, fft_size, hop_size, win_size, self.window)

            if self.log_mag_weight > 0:
                lm_loss = lm_loss + log_magnitude_loss(Y_true, Y_pred)

            if self.sc_weight > 0:
                sc_loss = sc_loss + spectral_convergence_loss(Y_true, Y_pred)

            if self.wsc_weight > 0:
                wsc_loss = wsc_loss + weighted_spectral_convergence(Y_true, Y_pred, self.wsc_weights[str(fft_size)])

            if self.smooth_log_mag_weight > 0:
                slm_loss = slm_loss + smooth_log_mag(Y_true, Y_pred, self.filterbanks[str(fft_size)])

            if self.sxcorr_weight > 0:
                sxcorr_loss = sxcorr_loss + spectral_xcorr_loss(Y_true, Y_pred)


        total_loss = (self.log_mag_weight * lm_loss + self.sc_weight * sc_loss
                + self.wsc_weight * wsc_loss + self.smooth_log_mag_weight * slm_loss
                + self.sxcorr_weight * sxcorr_loss) / len(self.fft_sizes)

        return total_loss


def td_l2_norm(y_true, y_pred):
    dims = list(range(1, len(y_true.shape)))

    loss = torch.mean((y_true - y_pred) ** 2, dim=dims) / (torch.mean(y_pred ** 2, dim=dims) ** .5 + 1e-6)

    return loss.mean()


class LaceLoss(nn.Module):
    def __init__(self):
        super().__init__()


        self.stftloss = MRSTFTLoss(log_mag_weight=0, sc_weight=0, wsc_weight=0, smooth_log_mag_weight=2, sxcorr_weight=1)


    def forward(self, x, y):
        specloss = self.stftloss(x, y)
        phaseloss = td_l2_norm(x, y)
        total_loss = (specloss + 10 * phaseloss) / 13

        return total_loss

    def compare(self, x_ref, x_deg):
        # trim items to same size
        n = min(len(x_ref), len(x_deg))
        x_ref = x_ref[:n].copy()
        x_deg = x_deg[:n].copy()

        # pre-emphasis
        x_ref[1:] -= 0.85 * x_ref[:-1]
        x_deg[1:] -= 0.85 * x_deg[:-1]

        device = next(iter(self.parameters())).device

        x = torch.from_numpy(x_ref).to(device)
        y = torch.from_numpy(x_deg).to(device)

        with torch.no_grad():
            dist = 10 * self.forward(x, y)

        return dist.cpu().numpy().item()


lace_loss = LaceLoss()
device = 'cuda' if torch.cuda.is_available() else 'cpu'
lace_loss.to(device)

def compare(x, y):

    return lace_loss.compare(x, y)