model.py

## 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()]