import torch import torch.nn as nn import torch.nn.functional as F from torchlibrosa.stft import Spectrogram, LogmelFilterBank # import torchaudio import random 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.freq_masking = torchaudio.transforms.FrequencyMasking(freq_mask_param=80) self.time_masking = torchaudio.transforms.TimeMasking(80) 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) random_aug_freq = random.uniform(0,1) random_aug_time = random.uniform(0,1) if random_aug_freq < 0.2: x = self.freq_masking(x) if random_aug_time < 0.2: x = self.time_masking(x) 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