File size: 6,531 Bytes
034e769 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import typing as tp
import numpy as np
from torchaudio.transforms import MelSpectrogram
import torch
from torch import nn
from torch.nn import functional as F
from ..modules import pad_for_conv1d
class MelSpectrogramWrapper(nn.Module):
"""Wrapper around MelSpectrogram torchaudio transform providing proper padding
and additional post-processing including log scaling.
Args:
n_mels (int): Number of mel bins.
n_fft (int): Number of fft.
hop_length (int): Hop size.
win_length (int): Window length.
n_mels (int): Number of mel bins.
sample_rate (int): Sample rate.
f_min (float or None): Minimum frequency.
f_max (float or None): Maximum frequency.
log (bool): Whether to scale with log.
normalized (bool): Whether to normalize the melspectrogram.
floor_level (float): Floor level based on human perception (default=1e-5).
"""
def __init__(self, n_fft: int = 1024, hop_length: int = 256, win_length: tp.Optional[int] = None,
n_mels: int = 80, sample_rate: float = 22050, f_min: float = 0.0, f_max: tp.Optional[float] = None,
log: bool = True, normalized: bool = False, floor_level: float = 1e-5):
super().__init__()
self.n_fft = n_fft
hop_length = int(hop_length)
self.hop_length = hop_length
self.mel_transform = MelSpectrogram(n_mels=n_mels, sample_rate=sample_rate, n_fft=n_fft, hop_length=hop_length,
win_length=win_length, f_min=f_min, f_max=f_max, normalized=normalized,
window_fn=torch.hann_window, center=False)
self.floor_level = floor_level
self.log = log
def forward(self, x):
p = int((self.n_fft - self.hop_length) // 2)
if len(x.shape) == 2:
x = x.unsqueeze(1)
x = F.pad(x, (p, p), "reflect")
# Make sure that all the frames are full.
# The combination of `pad_for_conv1d` and the above padding
# will make the output of size ceil(T / hop).
x = pad_for_conv1d(x, self.n_fft, self.hop_length)
self.mel_transform.to(x.device)
mel_spec = self.mel_transform(x)
B, C, freqs, frame = mel_spec.shape
if self.log:
mel_spec = torch.log10(self.floor_level + mel_spec)
return mel_spec.reshape(B, C * freqs, frame)
class MelSpectrogramL1Loss(torch.nn.Module):
"""L1 Loss on MelSpectrogram.
Args:
sample_rate (int): Sample rate.
n_fft (int): Number of fft.
hop_length (int): Hop size.
win_length (int): Window length.
n_mels (int): Number of mel bins.
f_min (float or None): Minimum frequency.
f_max (float or None): Maximum frequency.
log (bool): Whether to scale with log.
normalized (bool): Whether to normalize the melspectrogram.
floor_level (float): Floor level value based on human perception (default=1e-5).
"""
def __init__(self, sample_rate: int, n_fft: int = 1024, hop_length: int = 256, win_length: int = 1024,
n_mels: int = 80, f_min: float = 0.0, f_max: tp.Optional[float] = None,
log: bool = True, normalized: bool = False, floor_level: float = 1e-5):
super().__init__()
self.l1 = torch.nn.L1Loss()
self.melspec = MelSpectrogramWrapper(n_fft=n_fft, hop_length=hop_length, win_length=win_length,
n_mels=n_mels, sample_rate=sample_rate, f_min=f_min, f_max=f_max,
log=log, normalized=normalized, floor_level=floor_level)
def forward(self, x, y):
self.melspec.to(x.device)
s_x = self.melspec(x)
s_y = self.melspec(y)
return self.l1(s_x, s_y)
class MultiScaleMelSpectrogramLoss(nn.Module):
"""Multi-Scale spectrogram loss (msspec).
Args:
sample_rate (int): Sample rate.
range_start (int): Power of 2 to use for the first scale.
range_stop (int): Power of 2 to use for the last scale.
n_mels (int): Number of mel bins.
f_min (float): Minimum frequency.
f_max (float or None): Maximum frequency.
normalized (bool): Whether to normalize the melspectrogram.
alphas (bool): Whether to use alphas as coefficients or not.
floor_level (float): Floor level value based on human perception (default=1e-5).
"""
def __init__(self, sample_rate: int, range_start: int = 6, range_end: int = 11,
n_mels: int = 64, f_min: float = 0.0, f_max: tp.Optional[float] = None,
normalized: bool = False, alphas: bool = True, floor_level: float = 1e-5):
super().__init__()
l1s = list()
l2s = list()
self.alphas = list()
self.total = 0
self.normalized = normalized
for i in range(range_start, range_end):
l1s.append(
MelSpectrogramWrapper(n_fft=2 ** i, hop_length=(2 ** i) / 4, win_length=2 ** i,
n_mels=n_mels, sample_rate=sample_rate, f_min=f_min, f_max=f_max,
log=False, normalized=normalized, floor_level=floor_level))
l2s.append(
MelSpectrogramWrapper(n_fft=2 ** i, hop_length=(2 ** i) / 4, win_length=2 ** i,
n_mels=n_mels, sample_rate=sample_rate, f_min=f_min, f_max=f_max,
log=True, normalized=normalized, floor_level=floor_level))
if alphas:
self.alphas.append(np.sqrt(2 ** i - 1))
else:
self.alphas.append(1)
self.total += self.alphas[-1] + 1
self.l1s = nn.ModuleList(l1s)
self.l2s = nn.ModuleList(l2s)
def forward(self, x, y):
loss = 0.0
self.l1s.to(x.device)
self.l2s.to(x.device)
for i in range(len(self.alphas)):
s_x_1 = self.l1s[i](x)
s_y_1 = self.l1s[i](y)
s_x_2 = self.l2s[i](x)
s_y_2 = self.l2s[i](y)
loss += F.l1_loss(s_x_1, s_y_1) + self.alphas[i] * F.mse_loss(s_x_2, s_y_2)
if self.normalized:
loss = loss / self.total
return loss
|