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import typing
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from typing import List
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import torch
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import torch.nn.functional as F
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from audiotools import AudioSignal
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from audiotools import STFTParams
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from torch import nn
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class L1Loss(nn.L1Loss):
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"""L1 Loss between AudioSignals. Defaults
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to comparing ``audio_data``, but any
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attribute of an AudioSignal can be used.
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Parameters
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----------
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attribute : str, optional
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Attribute of signal to compare, defaults to ``audio_data``.
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weight : float, optional
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Weight of this loss, defaults to 1.0.
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Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/distance.py
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"""
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def __init__(self, attribute: str = "audio_data", weight: float = 1.0, **kwargs):
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self.attribute = attribute
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self.weight = weight
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super().__init__(**kwargs)
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def forward(self, x: AudioSignal, y: AudioSignal):
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"""
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Parameters
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----------
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x : AudioSignal
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Estimate AudioSignal
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y : AudioSignal
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Reference AudioSignal
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Returns
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-------
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torch.Tensor
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L1 loss between AudioSignal attributes.
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"""
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if isinstance(x, AudioSignal):
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x = getattr(x, self.attribute)
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y = getattr(y, self.attribute)
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return super().forward(x, y)
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class SISDRLoss(nn.Module):
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"""
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Computes the Scale-Invariant Source-to-Distortion Ratio between a batch
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of estimated and reference audio signals or aligned features.
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Parameters
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----------
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scaling : int, optional
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Whether to use scale-invariant (True) or
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signal-to-noise ratio (False), by default True
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reduction : str, optional
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How to reduce across the batch (either 'mean',
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'sum', or none).], by default ' mean'
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zero_mean : int, optional
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Zero mean the references and estimates before
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computing the loss, by default True
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clip_min : int, optional
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The minimum possible loss value. Helps network
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to not focus on making already good examples better, by default None
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weight : float, optional
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Weight of this loss, defaults to 1.0.
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Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/distance.py
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"""
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def __init__(
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self,
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scaling: int = True,
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reduction: str = "mean",
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zero_mean: int = True,
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clip_min: int = None,
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weight: float = 1.0,
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):
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self.scaling = scaling
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self.reduction = reduction
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self.zero_mean = zero_mean
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self.clip_min = clip_min
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self.weight = weight
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super().__init__()
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def forward(self, x: AudioSignal, y: AudioSignal):
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eps = 1e-8
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if isinstance(x, AudioSignal):
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references = x.audio_data
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estimates = y.audio_data
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else:
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references = x
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estimates = y
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nb = references.shape[0]
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references = references.reshape(nb, 1, -1).permute(0, 2, 1)
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estimates = estimates.reshape(nb, 1, -1).permute(0, 2, 1)
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if self.zero_mean:
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mean_reference = references.mean(dim=1, keepdim=True)
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mean_estimate = estimates.mean(dim=1, keepdim=True)
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else:
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mean_reference = 0
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mean_estimate = 0
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_references = references - mean_reference
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_estimates = estimates - mean_estimate
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references_projection = (_references**2).sum(dim=-2) + eps
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references_on_estimates = (_estimates * _references).sum(dim=-2) + eps
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scale = (
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(references_on_estimates / references_projection).unsqueeze(1)
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if self.scaling
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else 1
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)
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e_true = scale * _references
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e_res = _estimates - e_true
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signal = (e_true**2).sum(dim=1)
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noise = (e_res**2).sum(dim=1)
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sdr = -10 * torch.log10(signal / noise + eps)
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if self.clip_min is not None:
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sdr = torch.clamp(sdr, min=self.clip_min)
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if self.reduction == "mean":
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sdr = sdr.mean()
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elif self.reduction == "sum":
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sdr = sdr.sum()
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return sdr
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class MultiScaleSTFTLoss(nn.Module):
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"""Computes the multi-scale STFT loss from [1].
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Parameters
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----------
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window_lengths : List[int], optional
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Length of each window of each STFT, by default [2048, 512]
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loss_fn : typing.Callable, optional
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How to compare each loss, by default nn.L1Loss()
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clamp_eps : float, optional
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Clamp on the log magnitude, below, by default 1e-5
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mag_weight : float, optional
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Weight of raw magnitude portion of loss, by default 1.0
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log_weight : float, optional
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Weight of log magnitude portion of loss, by default 1.0
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pow : float, optional
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Power to raise magnitude to before taking log, by default 2.0
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weight : float, optional
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Weight of this loss, by default 1.0
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match_stride : bool, optional
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Whether to match the stride of convolutional layers, by default False
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References
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----------
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1. Engel, Jesse, Chenjie Gu, and Adam Roberts.
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"DDSP: Differentiable Digital Signal Processing."
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International Conference on Learning Representations. 2019.
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Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/spectral.py
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"""
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def __init__(
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self,
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window_lengths: List[int] = [2048, 512],
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loss_fn: typing.Callable = nn.L1Loss(),
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clamp_eps: float = 1e-5,
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mag_weight: float = 1.0,
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log_weight: float = 1.0,
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pow: float = 2.0,
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weight: float = 1.0,
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match_stride: bool = False,
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window_type: str = None,
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):
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super().__init__()
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self.stft_params = [
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STFTParams(
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window_length=w,
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hop_length=w // 4,
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match_stride=match_stride,
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window_type=window_type,
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)
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for w in window_lengths
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]
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self.loss_fn = loss_fn
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self.log_weight = log_weight
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self.mag_weight = mag_weight
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self.clamp_eps = clamp_eps
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self.weight = weight
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self.pow = pow
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def forward(self, x: AudioSignal, y: AudioSignal):
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"""Computes multi-scale STFT between an estimate and a reference
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signal.
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Parameters
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----------
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x : AudioSignal
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Estimate signal
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y : AudioSignal
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Reference signal
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Returns
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-------
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torch.Tensor
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Multi-scale STFT loss.
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"""
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loss = 0.0
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for s in self.stft_params:
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x.stft(s.window_length, s.hop_length, s.window_type)
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y.stft(s.window_length, s.hop_length, s.window_type)
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loss += self.log_weight * self.loss_fn(
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x.magnitude.clamp(self.clamp_eps).pow(self.pow).log10(),
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y.magnitude.clamp(self.clamp_eps).pow(self.pow).log10(),
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)
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loss += self.mag_weight * self.loss_fn(x.magnitude, y.magnitude)
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return loss
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class MelSpectrogramLoss(nn.Module):
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"""Compute distance between mel spectrograms. Can be used
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in a multi-scale way.
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Parameters
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----------
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n_mels : List[int]
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Number of mels per STFT, by default [150, 80],
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window_lengths : List[int], optional
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Length of each window of each STFT, by default [2048, 512]
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loss_fn : typing.Callable, optional
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How to compare each loss, by default nn.L1Loss()
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clamp_eps : float, optional
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Clamp on the log magnitude, below, by default 1e-5
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mag_weight : float, optional
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Weight of raw magnitude portion of loss, by default 1.0
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log_weight : float, optional
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Weight of log magnitude portion of loss, by default 1.0
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pow : float, optional
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Power to raise magnitude to before taking log, by default 2.0
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weight : float, optional
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Weight of this loss, by default 1.0
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match_stride : bool, optional
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Whether to match the stride of convolutional layers, by default False
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Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/spectral.py
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"""
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def __init__(
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self,
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n_mels: List[int] = [150, 80],
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window_lengths: List[int] = [2048, 512],
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loss_fn: typing.Callable = nn.L1Loss(),
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clamp_eps: float = 1e-5,
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mag_weight: float = 1.0,
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log_weight: float = 1.0,
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pow: float = 2.0,
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weight: float = 1.0,
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match_stride: bool = False,
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mel_fmin: List[float] = [0.0, 0.0],
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mel_fmax: List[float] = [None, None],
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window_type: str = None,
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):
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super().__init__()
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self.stft_params = [
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STFTParams(
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window_length=w,
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hop_length=w // 4,
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match_stride=match_stride,
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window_type=window_type,
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)
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for w in window_lengths
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]
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self.n_mels = n_mels
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self.loss_fn = loss_fn
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self.clamp_eps = clamp_eps
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self.log_weight = log_weight
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self.mag_weight = mag_weight
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self.weight = weight
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self.mel_fmin = mel_fmin
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self.mel_fmax = mel_fmax
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self.pow = pow
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def forward(self, x: AudioSignal, y: AudioSignal):
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"""Computes mel loss between an estimate and a reference
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signal.
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Parameters
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----------
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x : AudioSignal
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Estimate signal
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y : AudioSignal
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Reference signal
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Returns
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-------
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torch.Tensor
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Mel loss.
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"""
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loss = 0.0
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for n_mels, fmin, fmax, s in zip(
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self.n_mels, self.mel_fmin, self.mel_fmax, self.stft_params
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):
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kwargs = {
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"window_length": s.window_length,
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"hop_length": s.hop_length,
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"window_type": s.window_type,
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}
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x_mels = x.mel_spectrogram(n_mels, mel_fmin=fmin, mel_fmax=fmax, **kwargs)
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y_mels = y.mel_spectrogram(n_mels, mel_fmin=fmin, mel_fmax=fmax, **kwargs)
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loss += self.log_weight * self.loss_fn(
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x_mels.clamp(self.clamp_eps).pow(self.pow).log10(),
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y_mels.clamp(self.clamp_eps).pow(self.pow).log10(),
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)
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loss += self.mag_weight * self.loss_fn(x_mels, y_mels)
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return loss
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class GANLoss(nn.Module):
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"""
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Computes a discriminator loss, given a discriminator on
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generated waveforms/spectrograms compared to ground truth
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waveforms/spectrograms. Computes the loss for both the
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discriminator and the generator in separate functions.
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"""
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def __init__(self, discriminator):
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super().__init__()
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self.discriminator = discriminator
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def forward(self, fake, real):
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d_fake = self.discriminator(fake.audio_data)
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d_real = self.discriminator(real.audio_data)
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return d_fake, d_real
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def discriminator_loss(self, fake, real):
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d_fake, d_real = self.forward(fake.clone().detach(), real)
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loss_d = 0
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for x_fake, x_real in zip(d_fake, d_real):
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loss_d += torch.mean(x_fake[-1] ** 2)
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loss_d += torch.mean((1 - x_real[-1]) ** 2)
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return loss_d
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def generator_loss(self, fake, real):
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d_fake, d_real = self.forward(fake, real)
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loss_g = 0
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for x_fake in d_fake:
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loss_g += torch.mean((1 - x_fake[-1]) ** 2)
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loss_feature = 0
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for i in range(len(d_fake)):
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for j in range(len(d_fake[i]) - 1):
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loss_feature += F.l1_loss(d_fake[i][j], d_real[i][j].detach())
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return loss_g, loss_feature
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