Upload model.py

model.py

@@ -0,0 +1,174 @@
+## This script is based on the https://github.com/TaoRuijie/ECAPA-TDNN/blob/main/model.py
+## I made some changes to the original code for training a binary classifier.
+
+from typing import Optional
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import torchaudio
+from torchaudio.transforms 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).to(x.device)
+        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).to(x.device)(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:
+            resampler = Resample(orig_freq=sr, new_freq=16000)
+            audio = resampler(audio)
+        return audio.mean(dim=0, keepdim=True)  # Convert to mono if stereo
+    
+    def predict(self, audio : torch.Tensor, device: torch.device) -> torch.Tensor:
+        audio = self.load_audio(audio)
+        audio = audio.to(device)
+        self.eval()
+
+        with torch.no_grad():
+            output = self.forward(audio)
+            _, pred = output.max(1)
+        return self.pred2gender[pred.item()]