### 结合CNN与Transformer的文本相关语音唤醒模型 以下是一个结合卷积神经网络（CNN）和Transformer架构的Python代码实现，用于构建一个文本相关的语音唤醒模型。此模型首先通过CNN提取局部特征，随后利用Transformer捕捉全局依赖关系。 #### 1. 导入必要的库 ```python import tensorflow as tf from tensorflow.keras import layers, models ``` #### 2. 定义CNN模块 CNN部分负责从输入音频信号中提取空间上的局部特征。 ```python def cnn_module(input_shape): inputs = layers.Input(shape=input_shape) # 卷积层1 x = layers.Conv2D(64, kernel_size=(3, 3), activation='relu', padding="same")(inputs) x = layers.MaxPooling2D(pool_size=(2, 2))(x) # 卷积层2 x = layers.Conv2D(128, kernel_size=(3, 3), activation='relu', padding="same")(x) x = layers.MaxPooling2D(pool_size=(2, 2))(x) # 展平操作 x = layers.Flatten()(x) model = models.Model(inputs=inputs, outputs=x, name="CNN_Module") return model ``` #### 3. 定义Transformer模块 Transformer部分用于捕获时间序列中的长期依赖关系。 ```python class TransformerBlock(layers.Layer): def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1): super().__init__() self.att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim) self.ffn = models.Sequential([ layers.Dense(ff_dim, activation="relu"), layers.Dense(embed_dim), ]) self.layernorm1 = layers.LayerNormalization(epsilon=1e-6) self.layernorm2 = layers.LayerNormalization(epsilon=1e-6) self.dropout1 = layers.Dropout(rate) self.dropout2 = layers.Dropout(rate) def call(self, inputs, training=False): attn_output = self.att(inputs, inputs) attn_output = self.dropout1(attn_output, training=training) out1 = self.layernorm1(inputs + attn_output) ffn_output = self.ffn(out1) ffn_output = self.dropout2(ffn_output, training=training) return self.layernorm2(out1 + ffn_output) def transformer_encoder(inputs, head_size, num_heads, ff_dim, dropout_rate=0.1): x = layers.Dense(head_size)(inputs) x = TransformerBlock(head_size, num_heads, ff_dim, dropout_rate)(x) x = layers.GlobalAveragePooling1D()(x) return x ``` #### 4. 构建整体模型 将CNN模块和Transformer模块组合起来形成完整的语音唤醒模型。 ```python def build_wake_word_model(input_shape_cnn, input_shape_transformer, head_size, num_heads, ff_dim, dropout_rate=0.1): # CNN分支 cnn_input = layers.Input(shape=input_shape_cnn, name="CNN_Input") cnn_output = cnn_module(cnn_input)(cnn_input) # Transformer分支 transformer_input = layers.Input(shape=input_shape_transformer, name="Transformer_Input") transformer_output = transformer_encoder(transformer_input, head_size=head_size, num_heads=num_heads, ff_dim=ff_dim, dropout_rate=dropout_rate) # 合并两个分支 combined = layers.concatenate([cnn_output, transformer_output]) # 输出层 dense_layer = layers.Dense(128, activation='relu')(combined) output_layer = layers.Dense(1, activation='sigmoid')(dense_layer) # 创建模型 model = models.Model(inputs=[cnn_input, transformer_input], outputs=output_layer, name="Wake_Word_Model") return model ``` #### 5. 编译和训练模型 设置损失函数、优化器以及评估指标，并加载数据进行训练。 ```python input_shape_cnn = (64, 64, 1) # 假设MFCC特征形状为(64, 64, 1) input_shape_transformer = (128, 64) # 假设文本嵌入向量长度为128，维度为64 model = build_wake_word_model( input_shape_cnn, input_shape_transformer, head_size=64, num_heads=4, ff_dim=128 ) model.compile(optimizer=tf.keras.optimizers.Adam(), loss=tf.keras.losses.BinaryCrossentropy(), metrics=['accuracy']) # 打印模型结构 model.summary() ``` --- ###