ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray) error with audio features

Question

I am trying to use sound files to analyze emotion using LSTM. As an input, I use "Sentiment" which is "Negative, Positive, Neutral" and "soundFile" which is obviously sound files. My input data has 28 rows and my training set is 16 rows. Every row have 101 values. I nearly tried every solution written in the internet but can't solve this problem.

Here's my code:

#Extracts the sound features
def extract_sound_feature(audio_file, max_length=100):
    audio, sr=librosa.load(audio_file, sr=None)
    mfccs=librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=20)
    mfccs_normalized=(mfccs - mfccs.mean()) / mfccs.std()
    mfccs_padded = pad_sequences([mfccs_normalized], maxlen=max_length, padding='post', truncating='post')[0]
    return mfccs_padded

#Importing sound files
def import_sound_files(table_df, sound_df, folder_name):
    for i in range(len(table_df)):
       file_name = f"{folder_name}/{table_df['fileName'][i]}"
       features = extract_sound_feature(file_name, 20)
       temp_df=pd.DataFrame({
           'fileName': [table_df['fileName'][i]],
           'soundFile': [features]
           }) 
       sound_df=pd.concat([sound_df, temp_df], ignore_index=True)
    return sound_df

#Training LSTM model
def lstm_model_training(table_df):
    le_x = LabelEncoder()
    le_y = LabelEncoder()
    table_df['Sentiment'] = le_x.fit_transform(table_df['Sentiment'])
    table_df['Emotion'] = le_y.fit_transform(table_df['Emotion'])

    #Adding "Sentiment" and "soundFile" column together
    table_df['soundFile'] = np.array(table_df['soundFile'])
    for i in range(len(table_df['soundFile'])):
        table_df['soundFile'][i] = np.append(table_df['soundFile'][i], table_df['Sentiment'][i])
    
    x=table_df['soundFile']
    y=np.array(table_df['Emotion'])

    
    x_train, x_test, y_train, y_test= train_test_split(x, y, test_size=0.25, random_state=42)
    x_train, x_validation, y_train, y_validation= train_test_split(x_train, y_train, test_size=0.2)
    
    input_shape = (101, 1)  # Correct input shape
    model= keras.Sequential()
    #Adding layers
    model.add(keras.layers.LSTM(64, input_shape=input_shape, return_sequences=True))

    #Dense layers
    model.add(keras.layers.Dense(64, activation= 'relu'))
    model.add(keras.layers.Dropout(0.3))
    
    #Output layer
    model.add(keras.layers.Dense(10, activation='softmax'))

    #Compiling 
    optimiser = keras.optimizers.Adam(learning_rate=0.0001)
    model.compile(optimizer=optimiser,
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])

    model.summary()

    #Training
    model.fit(x_train, y_train, validation_data=(x_validation, y_validation), batch_size=32, epochs=30)
    loss, accuracy=model.evaluate(x_test, y_test, verbose=2)
    print('\nTest accuracy: ', accuracy)

    return model```