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from transformers import PreTrainedModel, HubertModel |
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import torch.nn as nn |
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import torch |
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from .configuration_emotion_classifier import EmotionClassifierConfig |
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class EmotionClassifierHuBERT(PreTrainedModel): |
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config_class = EmotionClassifierConfig |
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def __init__(self, config): |
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super().__init__(config) |
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self.hubert = HubertModel.from_pretrained("facebook/hubert-large-ls960-ft") |
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self.conv1 = nn.Conv1d(in_channels=1024, out_channels=512, kernel_size=3, padding=1) |
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self.conv2 = nn.Conv1d(in_channels=512, out_channels=256, kernel_size=3, padding=1) |
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self.transformer_encoder = nn.TransformerEncoderLayer(d_model=256, nhead=8) |
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self.bilstm = nn.LSTM(input_size=256, hidden_size=config.hidden_size_lstm, num_layers=2, batch_first=True, bidirectional=True) |
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self.fc = nn.Linear(config.hidden_size_lstm * 2, config.num_classes) |
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def forward(self, x): |
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with torch.no_grad(): |
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features = self.hubert(x).last_hidden_state |
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features = features.transpose(1, 2) |
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x = torch.relu(self.conv1(features)) |
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x = torch.relu(self.conv2(x)) |
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x = x.transpose(1, 2) |
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x = self.transformer_encoder(x) |
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x, _ = self.bilstm(x) |
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x = self.fc(x[:, -1, :]) |
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return x |
