无法将大小为 486 的数组重塑为形状 (1,1)

Question

我创建了一个通过说话来预测情绪的模型！当我尝试获取语音功能时出现错误

cannot reshape array of size 486 into shape (1,1)

我尝试了不同的重塑但没有任何效果！如果我更改 (1, -1) 中的重塑，我会遇到另一个错误

ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 162, 1), found shape=(None, 486)

这是我的模型：

    # scaling our data with sklearn's Standard scaler
    scaler = StandardScaler()
    x_train = scaler.fit_transform(x_train)
    x_test = scaler.transform(x_test)
    x_train.shape, y_train.shape, x_test.shape, y_test.shape

    # making our data compatible to model.
    x_train = np.expand_dims(x_train, axis=2)
    x_test = np.expand_dims(x_test, axis=2)
    x_train.shape, y_train.shape, x_test.shape, y_test.shape

    model=Sequential()
    model.add(Conv1D(256, kernel_size=5, strides=1, padding='same', activation='relu', input_shape=(x_train.shape[1], 1)))
    model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))

    model.add(Conv1D(256, kernel_size=5, strides=1, padding='same', activation='relu'))
    model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))

    model.add(Conv1D(128, kernel_size=5, strides=1, padding='same', activation='relu'))
    model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))
    model.add(Dropout(0.2))

    model.add(Conv1D(64, kernel_size=5, strides=1, padding='same', activation='relu'))
    model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))

    model.add(Flatten())
    model.add(Dense(units=32, activation='relu'))
    model.add(Dropout(0.3))

    model.add(Dense(units=8, activation='softmax'))
    model.compile(optimizer = 'adam' , loss = 'categorical_crossentropy' , metrics = ['accuracy'])

    model.summary()

    rlrp = ReduceLROnPlateau(monitor='loss', factor=0.4, verbose=0, patience=100, min_lr=0.0000001)
    history=model.fit(x_train, y_train, batch_size=23, epochs=50, validation_data=(x_test, y_test), callbacks=[rlrp])

这是提取特征函数：


    def extract_features(data, **kwargs):
        # ZCR
        result = np.array([])
        zcr = np.mean(librosa.feature.zero_crossing_rate(y=data).T, axis=0)
        result=np.hstack((result, zcr)) # stacking horizontally

        # Chroma_stft
        stft = np.abs(librosa.stft(data))
        chroma_stft = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T, axis=0)
        result = np.hstack((result, chroma_stft)) # stacking horizontally

        # MFCC
        mfcc = np.mean(librosa.feature.mfcc(y=data, sr=sample_rate).T, axis=0)
        result = np.hstack((result, mfcc)) # stacking horizontally

        # Root Mean Square Value
        rms = np.mean(librosa.feature.rms(y=data).T, axis=0)
        result = np.hstack((result, rms)) # stacking horizontally

        # MelSpectogram
        mel = np.mean(librosa.feature.melspectrogram(y=data, sr=sample_rate).T, axis=0)
        result = np.hstack((result, mel)) # stacking horizontally
        
        return result

    def get_features(path):
        # duration and offset are used to take care of the no audio in start and the ending of each audio files as seen above.
        data, sample_rate = librosa.load(path, duration=2.5, offset=0.6)
        
        # without augmentation
        res1 = extract_features(data)
        result = np.array(res1)
        
        # data with noise
        noise_data = noise(data)
        res2 = extract_features(noise_data)
        result = np.vstack((result, res2)) # stacking vertically
        
        # data with stretching and pitching
        new_data = stretch(data)
        data_stretch_pitch = pitch(new_data, sample_rate)
        res3 = extract_features(data_stretch_pitch)
        result = np.vstack((result, res3)) # stacking vertically
        
        return result

这里是我遇到错误的主要地方：


    if __name__ == "__main__":
        # load the saved model (after training)
        
        print("Please talk")
        filename = "test.wav"
        # record the file (start talking)
        record_to_file(filename)
        # extract features and reshape it
        features =get_features(filename).reshape(1, -1)
        # predict
        result = model.predict(features)[0]
        # show the result !
        print("result:", result)

对这个错误有什么想法吗？

Answer 1

IIUC，你的错误来自 features 的形状，也许这对你有帮助。

例如，您 features 如下所示：

features = np.random.rand(1, 486)
# features.shape
# (1, 486)

然后你需要将这个 features 拆分为 three 部分：

features = np.array_split(features, 3, axis=1)
features_0 = features[0] # shape : (1, 162)
features_1 = features[1] # shape : (1, 162)
features_2 = features[2] # shape : (1, 162)

然后 expand_dims 并进行如下预测：

features_0 = np.expand_dims(features_0, axis=2)
result = model.predict(features_0)[0]

无法将大小为 486 的数组重塑为形状 (1,1)

cannot reshape array of size 486 into shape (1,1)

python

machine-learning

deep-learning

keras

tensorflow