dac/nn/loss.py

import typing
from typing import List

import torch
import torch.nn.functional as F
from audiotools import AudioSignal
from audiotools import STFTParams
from torch import nn


class L1Loss(nn.L1Loss):
    """L1 Loss between AudioSignals. Defaults

    to comparing ``audio_data``, but any

    attribute of an AudioSignal can be used.



    Parameters

    ----------

    attribute : str, optional

        Attribute of signal to compare, defaults to ``audio_data``.

    weight : float, optional

        Weight of this loss, defaults to 1.0.



    Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/distance.py

    """

    def __init__(self, attribute: str = "audio_data", weight: float = 1.0, **kwargs):
        self.attribute = attribute
        self.weight = weight
        super().__init__(**kwargs)

    def forward(self, x: AudioSignal, y: AudioSignal):
        """

        Parameters

        ----------

        x : AudioSignal

            Estimate AudioSignal

        y : AudioSignal

            Reference AudioSignal



        Returns

        -------

        torch.Tensor

            L1 loss between AudioSignal attributes.

        """
        if isinstance(x, AudioSignal):
            x = getattr(x, self.attribute)
            y = getattr(y, self.attribute)
        return super().forward(x, y)


class SISDRLoss(nn.Module):
    """

    Computes the Scale-Invariant Source-to-Distortion Ratio between a batch

    of estimated and reference audio signals or aligned features.



    Parameters

    ----------

    scaling : int, optional

        Whether to use scale-invariant (True) or

        signal-to-noise ratio (False), by default True

    reduction : str, optional

        How to reduce across the batch (either 'mean',

        'sum', or none).], by default ' mean'

    zero_mean : int, optional

        Zero mean the references and estimates before

        computing the loss, by default True

    clip_min : int, optional

        The minimum possible loss value. Helps network

        to not focus on making already good examples better, by default None

    weight : float, optional

        Weight of this loss, defaults to 1.0.



    Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/distance.py

    """

    def __init__(

        self,

        scaling: int = True,

        reduction: str = "mean",

        zero_mean: int = True,

        clip_min: int = None,

        weight: float = 1.0,

    ):
        self.scaling = scaling
        self.reduction = reduction
        self.zero_mean = zero_mean
        self.clip_min = clip_min
        self.weight = weight
        super().__init__()

    def forward(self, x: AudioSignal, y: AudioSignal):
        eps = 1e-8
        # nb, nc, nt
        if isinstance(x, AudioSignal):
            references = x.audio_data
            estimates = y.audio_data
        else:
            references = x
            estimates = y

        nb = references.shape[0]
        references = references.reshape(nb, 1, -1).permute(0, 2, 1)
        estimates = estimates.reshape(nb, 1, -1).permute(0, 2, 1)

        # samples now on axis 1
        if self.zero_mean:
            mean_reference = references.mean(dim=1, keepdim=True)
            mean_estimate = estimates.mean(dim=1, keepdim=True)
        else:
            mean_reference = 0
            mean_estimate = 0

        _references = references - mean_reference
        _estimates = estimates - mean_estimate

        references_projection = (_references**2).sum(dim=-2) + eps
        references_on_estimates = (_estimates * _references).sum(dim=-2) + eps

        scale = (
            (references_on_estimates / references_projection).unsqueeze(1)
            if self.scaling
            else 1
        )

        e_true = scale * _references
        e_res = _estimates - e_true

        signal = (e_true**2).sum(dim=1)
        noise = (e_res**2).sum(dim=1)
        sdr = -10 * torch.log10(signal / noise + eps)

        if self.clip_min is not None:
            sdr = torch.clamp(sdr, min=self.clip_min)

        if self.reduction == "mean":
            sdr = sdr.mean()
        elif self.reduction == "sum":
            sdr = sdr.sum()
        return sdr


class MultiScaleSTFTLoss(nn.Module):
    """Computes the multi-scale STFT loss from [1].



    Parameters

    ----------

    window_lengths : List[int], optional

        Length of each window of each STFT, by default [2048, 512]

    loss_fn : typing.Callable, optional

        How to compare each loss, by default nn.L1Loss()

    clamp_eps : float, optional

        Clamp on the log magnitude, below, by default 1e-5

    mag_weight : float, optional

        Weight of raw magnitude portion of loss, by default 1.0

    log_weight : float, optional

        Weight of log magnitude portion of loss, by default 1.0

    pow : float, optional

        Power to raise magnitude to before taking log, by default 2.0

    weight : float, optional

        Weight of this loss, by default 1.0

    match_stride : bool, optional

        Whether to match the stride of convolutional layers, by default False



    References

    ----------



    1.  Engel, Jesse, Chenjie Gu, and Adam Roberts.

        "DDSP: Differentiable Digital Signal Processing."

        International Conference on Learning Representations. 2019.



    Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/spectral.py

    """

    def __init__(

        self,

        window_lengths: List[int] = [2048, 512],

        loss_fn: typing.Callable = nn.L1Loss(),

        clamp_eps: float = 1e-5,

        mag_weight: float = 1.0,

        log_weight: float = 1.0,

        pow: float = 2.0,

        weight: float = 1.0,

        match_stride: bool = False,

        window_type: str = None,

    ):
        super().__init__()
        self.stft_params = [
            STFTParams(
                window_length=w,
                hop_length=w // 4,
                match_stride=match_stride,
                window_type=window_type,
            )
            for w in window_lengths
        ]
        self.loss_fn = loss_fn
        self.log_weight = log_weight
        self.mag_weight = mag_weight
        self.clamp_eps = clamp_eps
        self.weight = weight
        self.pow = pow

    def forward(self, x: AudioSignal, y: AudioSignal):
        """Computes multi-scale STFT between an estimate and a reference

        signal.



        Parameters

        ----------

        x : AudioSignal

            Estimate signal

        y : AudioSignal

            Reference signal



        Returns

        -------

        torch.Tensor

            Multi-scale STFT loss.

        """
        loss = 0.0
        for s in self.stft_params:
            x.stft(s.window_length, s.hop_length, s.window_type)
            y.stft(s.window_length, s.hop_length, s.window_type)
            loss += self.log_weight * self.loss_fn(
                x.magnitude.clamp(self.clamp_eps).pow(self.pow).log10(),
                y.magnitude.clamp(self.clamp_eps).pow(self.pow).log10(),
            )
            loss += self.mag_weight * self.loss_fn(x.magnitude, y.magnitude)
        return loss


class MelSpectrogramLoss(nn.Module):
    """Compute distance between mel spectrograms. Can be used

    in a multi-scale way.



    Parameters

    ----------

    n_mels : List[int]

        Number of mels per STFT, by default [150, 80],

    window_lengths : List[int], optional

        Length of each window of each STFT, by default [2048, 512]

    loss_fn : typing.Callable, optional

        How to compare each loss, by default nn.L1Loss()

    clamp_eps : float, optional

        Clamp on the log magnitude, below, by default 1e-5

    mag_weight : float, optional

        Weight of raw magnitude portion of loss, by default 1.0

    log_weight : float, optional

        Weight of log magnitude portion of loss, by default 1.0

    pow : float, optional

        Power to raise magnitude to before taking log, by default 2.0

    weight : float, optional

        Weight of this loss, by default 1.0

    match_stride : bool, optional

        Whether to match the stride of convolutional layers, by default False



    Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/spectral.py

    """

    def __init__(

        self,

        n_mels: List[int] = [150, 80],

        window_lengths: List[int] = [2048, 512],

        loss_fn: typing.Callable = nn.L1Loss(),

        clamp_eps: float = 1e-5,

        mag_weight: float = 1.0,

        log_weight: float = 1.0,

        pow: float = 2.0,

        weight: float = 1.0,

        match_stride: bool = False,

        mel_fmin: List[float] = [0.0, 0.0],

        mel_fmax: List[float] = [None, None],

        window_type: str = None,

    ):
        super().__init__()
        self.stft_params = [
            STFTParams(
                window_length=w,
                hop_length=w // 4,
                match_stride=match_stride,
                window_type=window_type,
            )
            for w in window_lengths
        ]
        self.n_mels = n_mels
        self.loss_fn = loss_fn
        self.clamp_eps = clamp_eps
        self.log_weight = log_weight
        self.mag_weight = mag_weight
        self.weight = weight
        self.mel_fmin = mel_fmin
        self.mel_fmax = mel_fmax
        self.pow = pow

    def forward(self, x: AudioSignal, y: AudioSignal):
        """Computes mel loss between an estimate and a reference

        signal.



        Parameters

        ----------

        x : AudioSignal

            Estimate signal

        y : AudioSignal

            Reference signal



        Returns

        -------

        torch.Tensor

            Mel loss.

        """
        loss = 0.0
        for n_mels, fmin, fmax, s in zip(
            self.n_mels, self.mel_fmin, self.mel_fmax, self.stft_params
        ):
            kwargs = {
                "window_length": s.window_length,
                "hop_length": s.hop_length,
                "window_type": s.window_type,
            }
            x_mels = x.mel_spectrogram(n_mels, mel_fmin=fmin, mel_fmax=fmax, **kwargs)
            y_mels = y.mel_spectrogram(n_mels, mel_fmin=fmin, mel_fmax=fmax, **kwargs)

            loss += self.log_weight * self.loss_fn(
                x_mels.clamp(self.clamp_eps).pow(self.pow).log10(),
                y_mels.clamp(self.clamp_eps).pow(self.pow).log10(),
            )
            loss += self.mag_weight * self.loss_fn(x_mels, y_mels)
        return loss


class GANLoss(nn.Module):
    """

    Computes a discriminator loss, given a discriminator on

    generated waveforms/spectrograms compared to ground truth

    waveforms/spectrograms. Computes the loss for both the

    discriminator and the generator in separate functions.

    """

    def __init__(self, discriminator):
        super().__init__()
        self.discriminator = discriminator

    def forward(self, fake, real):
        d_fake = self.discriminator(fake.audio_data)
        d_real = self.discriminator(real.audio_data)
        return d_fake, d_real

    def discriminator_loss(self, fake, real):
        d_fake, d_real = self.forward(fake.clone().detach(), real)

        loss_d = 0
        for x_fake, x_real in zip(d_fake, d_real):
            loss_d += torch.mean(x_fake[-1] ** 2)
            loss_d += torch.mean((1 - x_real[-1]) ** 2)
        return loss_d

    def generator_loss(self, fake, real):
        d_fake, d_real = self.forward(fake, real)

        loss_g = 0
        for x_fake in d_fake:
            loss_g += torch.mean((1 - x_fake[-1]) ** 2)

        loss_feature = 0

        for i in range(len(d_fake)):
            for j in range(len(d_fake[i]) - 1):
                loss_feature += F.l1_loss(d_fake[i][j], d_real[i][j].detach())
        return loss_g, loss_feature