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| # Prédit 33% environ partout (dans le cas 3 classes) | |
| # class EmotionClassifier(nn.Module): | |
| # def __init__(self, feature_dim, num_labels): | |
| # super(EmotionClassifier, self).__init__() | |
| # self.fc1 = nn.Linear(feature_dim, 256) | |
| # self.relu = nn.ReLU() | |
| # self.dropout = nn.Dropout(0.3) | |
| # self.fc2 = nn.Linear(256, num_labels) | |
| # def forward(self, x): | |
| # x = self.fc1(x) | |
| # x = self.relu(x) | |
| # x = self.dropout(x) | |
| # return self.fc2(x) | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| class Attention(nn.Module): | |
| """Mécanisme d’attention permettant de pondérer l’importance des caractéristiques audio""" | |
| def __init__(self, hidden_dim): | |
| super(Attention, self).__init__() | |
| self.attention_weights = nn.Linear(hidden_dim, 1) | |
| def forward(self, lstm_output): | |
| # lstm_output: (batch_size, sequence_length, hidden_dim) | |
| attention_scores = self.attention_weights(lstm_output) # (batch_size, sequence_length, 1) | |
| attention_weights = torch.softmax(attention_scores, dim=1) # Normalisation softmax | |
| weighted_output = lstm_output * attention_weights # Pondération des features | |
| return weighted_output.sum(dim=1) # Somme pondérée sur la séquence | |
| class EmotionClassifier(nn.Module): | |
| """Modèle de classification des émotions basé sur BiLSTM et attention""" | |
| def __init__(self, feature_dim, num_labels, hidden_dim=128): | |
| super(EmotionClassifier, self).__init__() | |
| self.lstm = nn.LSTM(feature_dim, hidden_dim, batch_first=True, bidirectional=True) | |
| self.attention = Attention(hidden_dim * 2) # Bidirectionnel → hidden_dim * 2 | |
| self.fc = nn.Linear(hidden_dim * 2, num_labels) # Couche de classification finale | |
| def forward(self, x): | |
| lstm_out, _ = self.lstm(x) # (batch_size, sequence_length, hidden_dim*2) | |
| attention_out = self.attention(lstm_out) # (batch_size, hidden_dim*2) | |
| logits = self.fc(attention_out) # (batch_size, num_labels) | |
| return logits | |