Keras：ValueError：无法将 NumPy 数组转换为张量（不支持的对象类型列表）

Posted 2023-02-16

【中文标题】Keras：ValueError：无法将 NumPy 数组转换为张量（不支持的对象类型列表）

【英文标题】：Keras: ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type list)

【发布时间】：2021-12-01 19:12:22

【问题描述】：

我正在尝试对音频信号进行分类。为此，我开始编译用于训练 CNN 的每个 .wav 文件的 MFCC，按标签拆分它们（在某些文件中，前几秒有一个声音，其余的有另一个声音）。然后我将它们分成 2.5 秒的序列，并将每个 MFCC 存储在一个自己的 json 文件中，如下所示：（由于 librosa.features.mfcc 返回一个 nd.array，我必须将其转换为列表，然后再将其存储在 json 中）

for path in wav_paths:
    # split stereo .wav-file into channels
    filename = os.path.basename(path)
    print(filename)
    audiosegment = AudioSegment.from_file(path)
    arr_mono = audiosegment.get_array_of_samples()
    audio_data = (np.asarray(arr_mono)).astype(
            np.float32)  # audio_data is array.array (int16), ndarray (float32) needed for librosa
    sample_rate = audiosegment.frame_rate

    # calculate MFCCs for whole audio
    mfcc = librosa.feature.mfcc(audio_data, sr=sample_rate, n_mfcc=n_mfcc, n_fft=framesize, hop_length=int(hop_size))

    duration = audiosegment.duration_seconds
    begin, end, event = create_dataframe.read_json(path_to_json)
    list1 = [0, begin, end, duration]  # one sound goes from 0secs to begin, the other one from begin to end and then the first one again from end to duration
    list2 = list(zip(list1, list1[1:]))  # list2=[(0, begin), (begin, end), (end, duration)
    lst_mfcc_split_by_label = []

    for from_sec, to_sec in list2:
        # get label of sequence
        label_str = create_dataframe.get_label(begin, end, event, from_sec, to_sec)
        label = create_dataframe.label_key(label_str)  # label as number between 0 and 3

        # split MFCC by label
        index_first_frame = librosa.time_to_frames(from_sec, sr=sample_rate, hop_length=hop_size)
        index_last_frame = librosa.time_to_frames(to_sec, sr=sample_rate, hop_length=hop_size)
        lst_mfcc_split_by_label = np.hsplit(mfcc, [index_first_frame,
                                                       index_last_frame + 1])  # returns list of 3 arrays (mfcc-array split at index_first_frame and index_last_frame)
        mfcc_split_by_label = lst_mfcc_split_by_label[
                1]  # returns part between index_first_frame and index_last_frame+1)

        # set size of blocks
        secs_per_split = 2.5

        # nur Blöcke betrachten, die genau secs_per_split entsprechen
        n_blocks_in_sequence = int((to_sec - from_sec)/secs_per_split)  # abrunden
        to_sec_block = n_blocks_in_sequence * secs_per_split  # end of last block of sequence

        for time in np.arange(0, to_sec_block, secs_per_split):

            # get index of frame corresponding to begin and end of block
            index_first_frame_block = librosa.time_to_frames(time, sr=sample_rate, hop_length=hop_size)
            index_last_frame_block = librosa.time_to_frames(time + 2.5, sr=sample_rate, hop_length=hop_size)

            # split
            lst_mfcc_split_in_blocks = np.hsplit(mfcc, [index_first_frame_block,
                                                            index_last_frame_block + 1])  # returns list of 3 arrays (mfcc-array split at index_first_frame and index_last_frame+1)
            mfcc_split_in_blocks = lst_mfcc_split_in_blocks[
                    1]  # returns part between index_first_frame and index_last_frame+1)

            # store label and mfcc in dict
            data["label"] = label
            data["mfcc"] = mfcc_split_in_blocks.tolist()
                
            # save MFCCs to json file
            json_filename_data = str(time) + "-" + str(time + secs_per_split) + filename + ".json"
            path_to_json_data = os.path.join(dirPath_data, json_filename_data)
            with open(path_to_json_data, "w") as fp:
                json.dump(data, fp, indent=4)

然后，在尝试拟合我的模型时（见下文），我总是收到以下错误：

ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type list).

我也收到此警告：

C:\Users\emmah\OneDrive - rwth-aachen.de\Dokumente\Uni\RWTH\13_Bachelorarbeit\BA Emma Heyen\06 - Repo\ba-emma-heyen-0\src\train_CNN.py:12: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  X = np.array(data["mfcc"])

但是当我指定dtype=object 时，它并没有改变任何东西。

这就是我加载数据并执行 Train_test_split 的方式：

def load_dataset(data_path):  
    list_data_X = []
    list_data_y = []
    files = [f for f in os.listdir(data_path) if os.path.isfile(os.path.join(data_path, f))]
    for f in files:
        path_to_json = os.path.join(data_path, f)
        with open(path_to_json, "r") as fp:
            data = json.load(fp)

        # extract inputs and targets
        X = data["mfcc"]
        y = data["label"]

        list_data_X.append(X)
        list_data_y.append(y)

    X_arr = np.array(list_data_X, dtype = object)  
    y_arr = np.array(list_data_y, dtype = object)  

    return X_arr, y_arr

def get_data_splits(data_path, test_size=0.1, test_validation=0.1):  # train_size=0.9, validation=.9*.9=.09 of all data
    # load dataset
    X, y = load_dataset_2(data_path)

    # create train/validation/test splits
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)
    X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=test_validation)

    # covert inputs from 2d to 3d arrays bc Im using a CNN
    X_train = X_train[..., np.newaxis]
    X_validation = X_validation[..., np.newaxis]
    X_test = X_test[..., np.newaxis]

    return X_train, X_validation, X_test, y_train, y_validation, y_test

然后我像这样构建我的模型：

    X_train, X_validation, X_test, y_train, y_validation, y_test = train_CNN.get_data_splits(DATA_PATH)

    # build CNN model
    input_shape = (X_train.shape[0], X_train.shape[1], X_train.shape[2])
    model = train_CNN.build_model(input_shape, learning_rate=LEARNING_RATE, num_keywords=NUM_KEYWORDS)

    # train model
    model.fit(X_train, y_train, epochs=EPOCHS, batch_size=BATCH_SIZE, validation_data=(X_validation, y_validation))

我还尝试通过附加一个包含每个段的所有 mfcc 的列表，将所有 mfcc 存储在一个 json 中，但是当我尝试训练 CNN 时，我得到了同样的错误。

我发现了很多关于完全相同或类似错误的帖子，可以通过将数组转换为 np.float32 来解决它，但在这里没有帮助。

有人知道有什么帮助吗？ 提前致谢！

【参考方案1】：

事实证明，在每个 .wav 文件的第一个 json（从 0 到 2.5）中，mfcc 向量的 len 比所有其他 json 中的 len 短一个。

仍然不知道为什么会发生这种情况，但我认为这就是我收到上述错误的原因。

【讨论】：

正如目前所写，您的答案尚不清楚。请edit 添加其他详细信息，以帮助其他人了解这如何解决所提出的问题。你可以找到更多关于如何写好答案的信息in the help center。

这种张量流错误通常是输入不是正确的数字数组的结果，而是包含大小不同的列表或数组。 ragged arrays 不是正确的张量流输入。