用vggish-keras从WAV文件中提取音频特征的代码并保存

以下是使用vggish-keras从WAV文件中提取音频特征的代码，并将其保存为numpy数组的示例代码：

import numpy as np
import librosa
from vggish_keras import VGGish

# Load the VGGish model
vggish = VGGish()

# Load the audio file
audio_file = 'path/to/audio.wav'
audio, sr = librosa.load(audio_file, sr=vggish.sample_rate, mono=True)

# Extract the audio features using VGGish
features = vggish.extract_features(audio)

# Save the features as a numpy array
np.save('path/to/features.npy', features)

请注意，这只是一个示例代码，您需要根据您的具体情况进行修改。

相关问题

用vggish-keras提取音频特征的代码

以下是使用vggish-keras提取音频特征的代码：

import numpy as np
import tensorflow as tf
from keras.models import Model
from keras.layers import Input, Dense, Dropout, Flatten
from keras.optimizers import Adam
from vggish_keras import VGGish

# Load VGGish model
vggish = VGGish(include_top=False, input_shape=(None, 96, 1))

# Freeze all layers except the last one
for layer in vggish.layers[:-1]:
    layer.trainable = False

# Add a dense layer and output layer
x = Flatten()(vggish.output)
x = Dense(512, activation='relu')(x)
x = Dropout(0.5)(x)
output = Dense(128, activation='softmax')(x)

# Create a new model with VGGish and the dense layers
model = Model(inputs=vggish.input, outputs=output)

# Compile the model
model.compile(optimizer=Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])

# Load audio data and preprocess it
audio_data = np.load('audio_data.npy')
audio_data = np.expand_dims(audio_data, axis=-1)

# Train the model
model.fit(audio_data, labels, epochs=10, batch_size=32)

希望对你有帮助！