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import torch
import torchaudio
import torch.nn as nn
import hearbaseline
import hearbaseline.vggish
import hearbaseline.wav2vec2
import wav2clip_hear
import panns_hear
import torch.nn.functional as F
from remfx.utils import init_bn, init_layer
class PANNs(torch.nn.Module):
def __init__(
self, num_classes: int, sample_rate: float, hidden_dim: int = 256
) -> None:
super().__init__()
self.num_classes = num_classes
self.model = panns_hear.load_model("hear2021-panns_hear.pth")
self.resample = torchaudio.transforms.Resample(
orig_freq=sample_rate, new_freq=32000
)
self.proj = torch.nn.Sequential(
torch.nn.Linear(2048, hidden_dim),
torch.nn.ReLU(),
torch.nn.Linear(hidden_dim, hidden_dim),
torch.nn.ReLU(),
torch.nn.Linear(hidden_dim, num_classes),
)
def forward(self, x: torch.Tensor, **kwargs):
with torch.no_grad():
x = self.resample(x)
embed = panns_hear.get_scene_embeddings(x.view(x.shape[0], -1), self.model)
return self.proj(embed)
class Wav2CLIP(nn.Module):
def __init__(
self,
num_classes: int,
sample_rate: float,
hidden_dim: int = 256,
) -> None:
super().__init__()
self.num_classes = num_classes
self.model = wav2clip_hear.load_model("")
self.resample = torchaudio.transforms.Resample(
orig_freq=sample_rate, new_freq=16000
)
self.proj = torch.nn.Sequential(
torch.nn.Linear(512, hidden_dim),
torch.nn.ReLU(),
torch.nn.Linear(hidden_dim, hidden_dim),
torch.nn.ReLU(),
torch.nn.Linear(hidden_dim, num_classes),
)
def forward(self, x: torch.Tensor, **kwargs):
with torch.no_grad():
x = self.resample(x)
embed = wav2clip_hear.get_scene_embeddings(
x.view(x.shape[0], -1), self.model
)
return self.proj(embed)
class VGGish(nn.Module):
def __init__(
self,
num_classes: int,
sample_rate: float,
hidden_dim: int = 256,
):
super().__init__()
self.num_classes = num_classes
self.resample = torchaudio.transforms.Resample(
orig_freq=sample_rate, new_freq=16000
)
self.model = hearbaseline.vggish.load_model()
self.proj = torch.nn.Sequential(
torch.nn.Linear(128, hidden_dim),
torch.nn.ReLU(),
torch.nn.Linear(hidden_dim, hidden_dim),
torch.nn.ReLU(),
torch.nn.Linear(hidden_dim, num_classes),
)
def forward(self, x: torch.Tensor, **kwargs):
with torch.no_grad():
x = self.resample(x)
embed = hearbaseline.vggish.get_scene_embeddings(
x.view(x.shape[0], -1), self.model
)
return self.proj(embed)
class wav2vec2(nn.Module):
def __init__(
self,
num_classes: int,
sample_rate: float,
hidden_dim: int = 256,
):
super().__init__()
self.num_classes = num_classes
self.resample = torchaudio.transforms.Resample(
orig_freq=sample_rate, new_freq=16000
)
self.model = hearbaseline.wav2vec2.load_model()
self.proj = torch.nn.Sequential(
torch.nn.Linear(1024, hidden_dim),
torch.nn.ReLU(),
torch.nn.Linear(hidden_dim, hidden_dim),
torch.nn.ReLU(),
torch.nn.Linear(hidden_dim, num_classes),
)
def forward(self, x: torch.Tensor, **kwargs):
with torch.no_grad():
x = self.resample(x)
embed = hearbaseline.wav2vec2.get_scene_embeddings(
x.view(x.shape[0], -1), self.model
)
return self.proj(embed)
# adapted from https://github.com/qiuqiangkong/audioset_tagging_cnn/blob/master/pytorch/models.py
class Cnn14(nn.Module):
def __init__(
self,
num_classes: int,
sample_rate: float,
model_sample_rate: float,
n_fft: int = 1024,
hop_length: int = 256,
n_mels: int = 128,
specaugment: bool = False,
):
super().__init__()
self.num_classes = num_classes
self.n_fft = n_fft
self.hop_length = hop_length
self.sample_rate = sample_rate
self.model_sample_rate = model_sample_rate
self.specaugment = specaugment
window = torch.hann_window(n_fft)
self.register_buffer("window", window)
self.melspec = torchaudio.transforms.MelSpectrogram(
model_sample_rate,
n_fft,
hop_length=hop_length,
n_mels=n_mels,
)
self.bn0 = nn.BatchNorm2d(n_mels)
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)
self.fc1 = nn.Linear(2048, 2048, bias=True)
self.heads = torch.nn.ModuleList()
for _ in range(num_classes):
self.heads.append(nn.Linear(2048, 1, bias=True))
self.init_weight()
if sample_rate != model_sample_rate:
self.resample = torchaudio.transforms.Resample(
orig_freq=sample_rate, new_freq=model_sample_rate
)
if self.specaugment:
self.freq_mask = torchaudio.transforms.FrequencyMasking(64, True)
self.time_mask = torchaudio.transforms.TimeMasking(128, True)
def init_weight(self):
init_bn(self.bn0)
init_layer(self.fc1)
def forward(self, x: torch.Tensor, train: bool = False):
"""
Input: (batch_size, data_length)"""
if self.sample_rate != self.model_sample_rate:
x = self.resample(x)
x = self.melspec(x)
if self.specaugment and train:
x = self.freq_mask(x)
x = self.time_mask(x)
# apply standardization
x = (x - x.mean(dim=(2, 3), keepdim=True)) / x.std(dim=(2, 3), keepdim=True)
x = self.conv_block1(x, pool_size=(2, 2), pool_type="avg")
x = F.dropout(x, p=0.2, training=train)
x = self.conv_block2(x, pool_size=(2, 2), pool_type="avg")
x = F.dropout(x, p=0.2, training=train)
x = self.conv_block3(x, pool_size=(2, 2), pool_type="avg")
x = F.dropout(x, p=0.2, training=train)
x = self.conv_block4(x, pool_size=(2, 2), pool_type="avg")
x = F.dropout(x, p=0.2, training=train)
x = self.conv_block5(x, pool_size=(2, 2), pool_type="avg")
x = F.dropout(x, p=0.2, training=train)
x = self.conv_block6(x, pool_size=(1, 1), pool_type="avg")
x = F.dropout(x, p=0.2, training=train)
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=train)
x = F.relu_(self.fc1(x))
outputs = []
for head in self.heads:
outputs.append(torch.sigmoid(head(x)))
return outputs
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)
self.init_weight()
def init_weight(self):
init_layer(self.conv1)
init_layer(self.conv2)
init_bn(self.bn1)
init_bn(self.bn2)
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
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