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