我尝试使用MFCC和HMM训练说话人识别模型，在运行时我得到以下错误

line 55, in <module>
    X = X_train[y_train == speaker]
IndexError: boolean index did not match indexed array along dimension 0; dimension is 1 but corresponding boolean dimension is 3301

也就是这行代码

X = X_train[y_train == speaker]

我试着打印数组的形状，以便更好地理解这个问题，得到了这个结果

(1, 3301)
(3301,)
[False False False ...  True  True  True]

但我似乎不能把我的头绕在如何解决它。任何提示和帮助是非常感谢。
下面是我关于上述问题的代码块

import os
import numpy as np
import scipy.io.wavfile as wav
from python_speech_features import mfcc
from hmmlearn import hmm
import matplotlib.pyplot as plt

# Define the number of MFCC features and HMM states
num_features = 13
num_states = 5

# Define the directory containing the training data
data_dir = "speech-data"

# Define the names of the speaker subdirectories in the training data directory
speakers = ["speaker1", "speaker2"]

# Create a list to hold the training feature vectors and labels
X_train = []
y_train = []

# Loop over the speaker subdirectories
for speaker in speakers:
    # Get the path to the speaker's training subdirectory
    train_dir = os.path.join(data_dir, speaker, "train")

    # Loop over the audio files in the speaker's training subdirectory
    for file in os.listdir(train_dir):
        # Get the path to the audio file
        file_path = os.path.join(train_dir, file)

        # Load the audio signal
        rate, signal = wav.read(file_path)

        # Extract MFCC features from the signal
        features = mfcc(signal, rate, numcep=num_features)

        # Append the features and label to the training lists
        X_train.append(features)
        y_train.append(speaker)

# Convert the training data to numpy arrays
X_train = np.array([X_train], dtype=object)
y_train = np.array(y_train)

print(X_train.shape)
print(y_train.shape)
print(y_train == speaker)

# Create an HMM for each speaker
models = []
for speaker in speakers:
    # Get the training feature vectors and labels for the current speaker
    X = X_train[y_train == speaker]

    # Create an HMM for the current speaker
    model = hmm.GaussianHMM(n_components=num_states)
    model.fit(X)

    # Add the HMM to the list of models
    models.append(model)

我试着重塑数组，但没有修复错误。我期待通过重塑数组的基础上，我的样本的尺寸会更好。
所以更新，我试着改变代码的基础上什么Chrysophylaxs说，并改变了它从这个

# Convert the training data to numpy arrays
X_train = np.array([X_train], dtype=object)
y_train = np.array(y_train)

print(X_train.shape)
print(y_train.shape)
print(y_train == speaker)

# Create an HMM for each speaker
models = []
for speaker in speakers:
    # Get the training feature vectors and labels for the current speaker
    X = X_train[y_train == speaker]

到这个

# Convert the training data to numpy arrays
X_train = np.array([X_train], dtype=object)
y_train = np.array(y_train)

print(X_train.shape)
print(y_train.shape)
print(y_train == speaker)

# Create an HMM for each speaker
models = []
for speaker in speakers:
    # Get the training feature vectors and labels for the current speaker
    X = X_train[:, y_train == speaker]

现在我得到一个类型错误，主要是这个
TypeError：只有size-1数组可以转换为Python标量