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import torch
import numpy as np
import torch.nn as nn
try:
from torch.amp import autocast
torch_amp_new = True
except:
from torch.cuda.amp import autocast
torch_amp_new = False
from torchaudio.transforms import AmplitudeToDB, MelSpectrogram
class FeatureExtractor(nn.Module):
def __init__(
self,
cfg,
):
"""
Feature extraction module.
Args:
params (dict): Parameters for the spectrogram.
aug_config (dict, optional): Configuration for data augmentation. Defaults to None.
top_db (float, optional): Threshold for computing the amplitude to dB. Defaults to None.
norm (str, optional): Normalization method. Defaults to "min_max".
"""
super().__init__()
self.audio2melspec = MelSpectrogram(
n_fft=cfg.melspec.n_fft,
hop_length=cfg.melspec.hop_length,
win_length=cfg.melspec.win_length,
n_mels=cfg.melspec.n_mels,
sample_rate=cfg.audio.sample_rate,
f_min=cfg.melspec.f_min,
f_max=cfg.melspec.f_max,
power=cfg.melspec.power,
)
self.amplitude_to_db = AmplitudeToDB(top_db=cfg.melspec.top_db)
if cfg.melspec.norm == "mean_std":
self.normalizer = MeanStdNorm()
elif cfg.melspec.norm == "min_max":
self.normalizer = MinMaxNorm()
elif cfg.melspec.norm == "simple":
self.normalizer = SimpleNorm()
else:
self.normalizer = nn.Identity()
def forward(self, x):
"""
Forward pass of the feature extractor.
Args:
x (torch.Tensor): Input audio data.
Returns:
torch.Tensor: Extracted features.
"""
with (
autocast("cuda", enabled=False)
if torch_amp_new
else autocast(enabled=False)
):
melspec = self.audio2melspec(x.float())
melspec = self.amplitude_to_db(melspec)
melspec = self.normalizer(melspec)
return melspec
class MinMaxNorm(nn.Module):
def __init__(self, eps=1e-6):
"""
Module for performing min-max normalization on input data.
Args:
eps (float, optional): Small value to avoid division by zero. Defaults to 1e-6.
"""
super().__init__()
self.eps = eps
def forward(self, X):
"""
Forward pass of the min-max normalization module.
Args:
X (torch.Tensor): Input data.
Returns:
torch.Tensor: Normalized data.
"""
min_ = torch.amax(X, dim=(1, 2), keepdim=True)
max_ = torch.amin(X, dim=(1, 2), keepdim=True)
return (X - min_) / (max_ - min_ + self.eps)
class SimpleNorm(nn.Module):
def __init__(self):
"""
Module for performing simple normalization on input data.
"""
super().__init__()
def forward(self, x):
"""
Forward pass of the simple normalization module.
Args:
x (torch.Tensor): Input data.
Returns:
torch.Tensor: Normalized data.
"""
return (x - 40) / 80
class MeanStdNorm(nn.Module):
def __init__(self, eps=1e-6):
"""
Module for performing mean and standard deviation normalization on input data.
Args:
eps (float, optional): Small value to avoid division by zero. Defaults to 1e-6.
"""
super().__init__()
self.eps = eps
def forward(self, X):
"""
Forward pass of the mean and standard deviation normalization module.
Args:
X (torch.Tensor): Input data.
Returns:
torch.Tensor: Normalized data.
"""
mean = X.mean((1, 2), keepdim=True)
std = X.reshape(X.size(0), -1).std(1, keepdim=True).unsqueeze(-1)
return (X - mean) / (std + self.eps)
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