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import torch
import torch.nn as nn
import torch.nn.functional as F
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
def get_audio_encoder(name: str):
if name == "Cnn14":
return Cnn14
else:
raise Exception('The audio encoder name {} is incorrect or not supported'.format(name))
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(ConvBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels=in_channels,
out_channels=out_channels,
kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1), bias=False)
self.conv2 = nn.Conv2d(in_channels=out_channels,
out_channels=out_channels,
kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1), bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.bn2 = nn.BatchNorm2d(out_channels)
def forward(self, input, pool_size=(2, 2), pool_type='avg'):
x = input
x = F.relu_(self.bn1(self.conv1(x)))
x = F.relu_(self.bn2(self.conv2(x)))
if pool_type == 'max':
x = F.max_pool2d(x, kernel_size=pool_size)
elif pool_type == 'avg':
x = F.avg_pool2d(x, kernel_size=pool_size)
elif pool_type == 'avg+max':
x1 = F.avg_pool2d(x, kernel_size=pool_size)
x2 = F.max_pool2d(x, kernel_size=pool_size)
x = x1 + x2
else:
raise Exception('Incorrect argument!')
return x
class ConvBlock5x5(nn.Module):
def __init__(self, in_channels, out_channels):
super(ConvBlock5x5, self).__init__()
self.conv1 = nn.Conv2d(in_channels=in_channels,
out_channels=out_channels,
kernel_size=(5, 5), stride=(1, 1),
padding=(2, 2), bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
def forward(self, input, pool_size=(2, 2), pool_type='avg'):
x = input
x = F.relu_(self.bn1(self.conv1(x)))
if pool_type == 'max':
x = F.max_pool2d(x, kernel_size=pool_size)
elif pool_type == 'avg':
x = F.avg_pool2d(x, kernel_size=pool_size)
elif pool_type == 'avg+max':
x1 = F.avg_pool2d(x, kernel_size=pool_size)
x2 = F.max_pool2d(x, kernel_size=pool_size)
x = x1 + x2
else:
raise Exception('Incorrect argument!')
return x
class AttBlock(nn.Module):
def __init__(self, n_in, n_out, activation='linear', temperature=1.):
super(AttBlock, self).__init__()
self.activation = activation
self.temperature = temperature
self.att = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True)
self.cla = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True)
self.bn_att = nn.BatchNorm1d(n_out)
def forward(self, x):
# x: (n_samples, n_in, n_time)
norm_att = torch.softmax(torch.clamp(self.att(x), -10, 10), dim=-1)
cla = self.nonlinear_transform(self.cla(x))
x = torch.sum(norm_att * cla, dim=2)
return x, norm_att, cla
def nonlinear_transform(self, x):
if self.activation == 'linear':
return x
elif self.activation == 'sigmoid':
return torch.sigmoid(x)
class Cnn14(nn.Module):
def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
fmax, classes_num, out_emb):
super(Cnn14, self).__init__()
window = 'hann'
center = True
pad_mode = 'reflect'
ref = 1.0
amin = 1e-10
top_db = None
# Spectrogram extractor
self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
win_length=window_size, window=window, center=center, pad_mode=pad_mode,
freeze_parameters=True)
# Logmel feature extractor
self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db,
freeze_parameters=True)
self.bn0 = nn.BatchNorm2d(64)
self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)
self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048)
# out_emb is 2048 for best Cnn14
self.fc1 = nn.Linear(2048, out_emb, bias=True)
self.fc_audioset = nn.Linear(out_emb, classes_num, bias=True)
def forward(self, input, mixup_lambda=None):
"""
Input: (batch_size, data_length)
"""
x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins)
x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins)
x = x.transpose(1, 3)
x = self.bn0(x)
x = x.transpose(1, 3)
x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = torch.mean(x, dim=3)
(x1, _) = torch.max(x, dim=2)
x2 = torch.mean(x, dim=2)
x = x1 + x2
x = F.dropout(x, p=0.5, training=self.training)
x = F.relu_(self.fc1(x))
embedding = F.dropout(x, p=0.5, training=self.training)
clipwise_output = torch.sigmoid(self.fc_audioset(x))
output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding}
return output_dict |