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import math
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from torchaudio.functional import resample
from huggingface_hub import PyTorchModelHubMixin
class SEModule(nn.Module):
def __init__(self, channels : int , bottleneck : int = 128) -> None:
super(SEModule, self).__init__()
self.se = nn.Sequential(
nn.AdaptiveAvgPool1d(1),
nn.Conv1d(channels, bottleneck, kernel_size=1, padding=0),
nn.ReLU(),
# nn.BatchNorm1d(bottleneck), # I remove this layer
nn.Conv1d(bottleneck, channels, kernel_size=1, padding=0),
nn.Sigmoid(),
)
def forward(self, input : torch.Tensor) -> torch.Tensor:
x = self.se(input)
return input * x
class Bottle2neck(nn.Module):
def __init__(self, inplanes : int, planes : int, kernel_size : Optional[int] = None, dilation : Optional[int] = None, scale : int = 8) -> None:
super(Bottle2neck, self).__init__()
width = int(math.floor(planes / scale))
self.conv1 = nn.Conv1d(inplanes, width*scale, kernel_size=1)
self.bn1 = nn.BatchNorm1d(width*scale)
self.nums = scale -1
convs = []
bns = []
num_pad = math.floor(kernel_size/2)*dilation
for i in range(self.nums):
convs.append(nn.Conv1d(width, width, kernel_size=kernel_size, dilation=dilation, padding=num_pad))
bns.append(nn.BatchNorm1d(width))
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
self.conv3 = nn.Conv1d(width*scale, planes, kernel_size=1)
self.bn3 = nn.BatchNorm1d(planes)
self.relu = nn.ReLU()
self.width = width
self.se = SEModule(planes)
def forward(self, x : torch.Tensor) -> torch.Tensor:
residual = x
out = self.conv1(x)
out = self.relu(out)
out = self.bn1(out)
spx = torch.split(out, self.width, 1)
for i in range(self.nums):
if i==0:
sp = spx[i]
else:
sp = sp + spx[i]
sp = self.convs[i](sp)
sp = self.relu(sp)
sp = self.bns[i](sp)
if i==0:
out = sp
else:
out = torch.cat((out, sp), 1)
out = torch.cat((out, spx[self.nums]),1)
out = self.conv3(out)
out = self.relu(out)
out = self.bn3(out)
out = self.se(out)
out += residual
return out
class ECAPA_gender(nn.Module, PyTorchModelHubMixin):
def __init__(self, C : int = 1024):
super(ECAPA_gender, self).__init__()
self.C = C
self.conv1 = nn.Conv1d(80, C, kernel_size=5, stride=1, padding=2)
self.relu = nn.ReLU()
self.bn1 = nn.BatchNorm1d(C)
self.layer1 = Bottle2neck(C, C, kernel_size=3, dilation=2, scale=8)
self.layer2 = Bottle2neck(C, C, kernel_size=3, dilation=3, scale=8)
self.layer3 = Bottle2neck(C, C, kernel_size=3, dilation=4, scale=8)
# I fixed the shape of the output from MFA layer, that is close to the setting from ECAPA paper.
self.layer4 = nn.Conv1d(3*C, 1536, kernel_size=1)
self.attention = nn.Sequential(
nn.Conv1d(4608, 256, kernel_size=1),
nn.ReLU(),
nn.BatchNorm1d(256),
nn.Tanh(), # I add this layer
nn.Conv1d(256, 1536, kernel_size=1),
nn.Softmax(dim=2),
)
self.bn5 = nn.BatchNorm1d(3072)
self.fc6 = nn.Linear(3072, 192)
self.bn6 = nn.BatchNorm1d(192)
self.fc7 = nn.Linear(192, 2)
self.pred2gender = {0 : 'male', 1 : 'female'}
def logtorchfbank(self, x : torch.Tensor) -> torch.Tensor:
# Preemphasis
flipped_filter = torch.FloatTensor([-0.97, 1.]).unsqueeze(0).unsqueeze(0)
x = x.unsqueeze(1)
x = F.pad(x, (1, 0), 'reflect')
x = F.conv1d(x, flipped_filter).squeeze(1)
# Melspectrogram
x = torchaudio.transforms.MelSpectrogram(sample_rate=16000, n_fft=512, win_length=400, hop_length=160, \
f_min = 20, f_max = 7600, window_fn=torch.hamming_window, n_mels=80)(x) + 1e-6
# Log and normalize
x = x.log()
x = x - torch.mean(x, dim=-1, keepdim=True)
return x
def forward(self, x : torch.Tensor) -> torch.Tensor:
x = self.logtorchfbank(x)
x = self.conv1(x)
x = self.relu(x)
x = self.bn1(x)
x1 = self.layer1(x)
x2 = self.layer2(x+x1)
x3 = self.layer3(x+x1+x2)
x = self.layer4(torch.cat((x1,x2,x3),dim=1))
x = self.relu(x)
t = x.size()[-1]
global_x = torch.cat((x,torch.mean(x,dim=2,keepdim=True).repeat(1,1,t), torch.sqrt(torch.var(x,dim=2,keepdim=True).clamp(min=1e-4)).repeat(1,1,t)), dim=1)
w = self.attention(global_x)
mu = torch.sum(x * w, dim=2)
sg = torch.sqrt( ( torch.sum((x**2) * w, dim=2) - mu**2 ).clamp(min=1e-4) )
x = torch.cat((mu,sg),1)
x = self.bn5(x)
x = self.fc6(x)
x = self.bn6(x)
x = self.relu(x)
x = self.fc7(x)
return x
def load_audio(self, path : str) -> torch.Tensor:
audio, sr = torchaudio.load(path)
if sr != 16000:
audio = resample(audio, sr, 16000)
return audio
def predict(self, audio : torch.Tensor) -> torch.Tensor:
audio = self.load_audio(audio)
self.eval()
with torch.no_grad():
output = self.forward(audio)
_, pred = output.max(1)
return self.pred2gender[pred.item()] |