Python 3.x 元组索引超出范围-训练音频模型

我试图分析一个音频文件，并根据提取的特征对系统进行训练，但在拟合模型时出现了一个错误，即“元组索引超出范围”。我已经在print语句旁边的注释中提供了我正在使用的所有数组的形状。在定义模型时，您能帮助我理解如何定义尺寸吗

如果需要更多的细节，请告诉我

import glob
import numpy as np
import pandas as pd
import random
import librosa
import librosa.display
import glob
import os
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from tensorflow.keras.layers import LSTM, Dense, Dropout, Flatten
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint

X, sample_rate = librosa.load(r'C:\Users\Sumanth\Desktop\voice\Speaker-275-3.wav', res_type='kaiser_fast')
print(X.shape) # Shape is (439238,)

#extracting the MFCC feature from Audio signal
mfccs = librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40)
print(mfccs.shape) # Shape is (40, 858)

#manually assigning the label as 275
z = np.asarray(275)

#Validation data
val_x, sample_rate = librosa.load(r'C:\Users\Sumanth\Desktop\voice\Speaker-275-2.wav', res_type='kaiser_fast')
print(val_x.shape) # Shape is (292826,)

val_y=np.asarray(275)

#Building the model
model = Sequential()
model.add(Dense(256, input_shape=(858,),activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(275,activation='softmax'))
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')
#training our model
model.fit(mfccs, z, epochs=5, validation_data=(val_x, val_y))

---

-------------------错误------------------------------------------------------

---

索引器错误回溯（最近一次调用）
在里面
40模型。编译（loss='classifical_crossentropy'，metrics=['accurity']，optimizer='adam'）
41#训练我们的模型
--->42模型拟合（MFCC，z，历元=5，验证数据=（val_x，val_y））
43
44
~\AppData\Roaming\Python\Python37\site packages\tensorflow\u core\Python\keras\engine\training.py适合（self、x、y、批大小、历元、冗余、回调、验证拆分、验证数据、无序排列、类权重、样本权重、初始历元、每历元步数、验证步骤、验证频率、最大队列大小、工作者、使用多处理、**kwargs）
726最大队列大小=最大队列大小，
727名工人=工人，
-->728使用多处理=使用多处理）
729
730 def评估（自我，
~\AppData\Roaming\Python\Python37\site packages\tensorflow\u core\Python\keras\engine\training\u v2.py适合（self、model、x、y、批大小、epoch、verbose、回调、验证拆分、验证数据、无序、类权重、样本权重、初始epoch、每epoch步数、验证步数、验证频率、**kwargs）
222验证数据=验证数据，
223验证步骤=验证步骤，
-->224分销（策略=策略）
225
226总样本数=\u获取\u总样本数\u（训练\u数据\u适配器）
~\AppData\Roaming\Python\Python37\site packages\tensorflow\u core\Python\keras\engine\training\u v2.py进程内\u训练输入（模型、x、y、批量大小、历元、样本权重、类权重、每历元步长、验证拆分、验证数据、验证步骤、无序排列、分布策略、最大队列大小、工人、使用多处理）
545最大队列大小=最大队列大小，
546名工人=工人，
-->547使用\多处理=使用\多处理）
548 val_适配器=无
549如果验证数据：
~\AppData\Roaming\Python\Python37\site packages\tensorflow\u core\Python\keras\engine\training\u v2.py进程内输入（模型、x、y、批次大小、历代、样本权重、类权重、随机、步骤、分布策略、最大队列大小、工作人员、使用多处理）
592批次大小=批次大小，
593检查步骤=错误，
-->594步=步）
595适配器=适配器(
596 x，
~\AppData\Roaming\Python\Python37\site packages\tensorflow\u core\Python\keras\engine\training.py in\u-standard\u-user\u数据（self、x、y、sample\u-weight、class\u-weight、batch\u-size、check\u-steps、steps\u-name、steps、validation\u-split、shuffle、extract\u-tensors\u-from\u-dataset）
2532#检查所有阵列的长度是否相同。
2533如果不是自我分配策略：
->2534训练工具。检查数组长度（x、y、样本权重）
2535如果self.\u是网络而不是self.run\u：
2536#额外检查，以避免用户错误地使用不当损耗FN。
~\AppData\Roaming\Python\Python37\site packages\tensorflow\u core\Python\keras\engine\training\u utils.py检查数组长度（输入、目标、权重）
661
662集合x=集合长度（输入）
-->663集合长度=集合长度（目标）
664套长度=套长度（重量）
665如果len（set_x）>1：
~\AppData\Roaming\Python\Python37\site packages\tensorflow\u core\Python\keras\engine\training\u utils.py，长度为x
656返回集([
657 y形[0]
-->x中y的658
659如果y不是无且不是张量或复合张量（y）
660       ])
（.0）中的~\AppData\Roaming\Python\Python37\site packages\tensorflow\u core\Python\keras\engine\training\u utils.py
657 y形[0]
x中y的658
-->659如果y不是无且不是张量或复合张量（y）
660       ])
661
索引器错误：元组索引超出范围

请添加完整回溯我已经编辑了我的帖子以添加完整回溯。谢谢你好@MatiasValdenegro，你能帮我解决这个错误吗？请添加完整的回溯我已经编辑了我的帖子来添加完整的回溯。谢谢你好@MatiasValdenegro，你能帮我解决这个错误吗？

IndexError                                Traceback (most recent call last)
<ipython-input-31-adaf98404d0e> in <module>
     40 model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')
     41 #training our model
---> 42 model.fit(mfccs, z, epochs=5, validation_data=(val_x, val_y))
     43 
     44 

~\AppData\Roaming\Python\Python37\site-packages\tensorflow_core\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    726         max_queue_size=max_queue_size,
    727         workers=workers,
--> 728         use_multiprocessing=use_multiprocessing)
    729 
    730   def evaluate(self,

~\AppData\Roaming\Python\Python37\site-packages\tensorflow_core\python\keras\engine\training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
    222           validation_data=validation_data,
    223           validation_steps=validation_steps,
--> 224           distribution_strategy=strategy)
    225 
    226       total_samples = _get_total_number_of_samples(training_data_adapter)

~\AppData\Roaming\Python\Python37\site-packages\tensorflow_core\python\keras\engine\training_v2.py in _process_training_inputs(model, x, y, batch_size, epochs, sample_weights, class_weights, steps_per_epoch, validation_split, validation_data, validation_steps, shuffle, distribution_strategy, max_queue_size, workers, use_multiprocessing)
    545         max_queue_size=max_queue_size,
    546         workers=workers,
--> 547         use_multiprocessing=use_multiprocessing)
    548     val_adapter = None
    549     if validation_data:

~\AppData\Roaming\Python\Python37\site-packages\tensorflow_core\python\keras\engine\training_v2.py in _process_inputs(model, x, y, batch_size, epochs, sample_weights, class_weights, shuffle, steps, distribution_strategy, max_queue_size, workers, use_multiprocessing)
    592         batch_size=batch_size,
    593         check_steps=False,
--> 594         steps=steps)
    595   adapter = adapter_cls(
    596       x,

~\AppData\Roaming\Python\Python37\site-packages\tensorflow_core\python\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset)
   2532       # Check that all arrays have the same length.
   2533       if not self._distribution_strategy:
-> 2534         training_utils.check_array_lengths(x, y, sample_weights)
   2535         if self._is_graph_network and not self.run_eagerly:
   2536           # Additional checks to avoid users mistakenly using improper loss fns.

~\AppData\Roaming\Python\Python37\site-packages\tensorflow_core\python\keras\engine\training_utils.py in check_array_lengths(inputs, targets, weights)
    661 
    662   set_x = set_of_lengths(inputs)
--> 663   set_y = set_of_lengths(targets)
    664   set_w = set_of_lengths(weights)
    665   if len(set_x) > 1:

~\AppData\Roaming\Python\Python37\site-packages\tensorflow_core\python\keras\engine\training_utils.py in set_of_lengths(x)
    656       return set([
    657           y.shape[0]
--> 658           for y in x
    659           if y is not None and not is_tensor_or_composite_tensor(y)
    660       ])

~\AppData\Roaming\Python\Python37\site-packages\tensorflow_core\python\keras\engine\training_utils.py in <listcomp>(.0)
    657           y.shape[0]
    658           for y in x
--> 659           if y is not None and not is_tensor_or_composite_tensor(y)
    660       ])
    661 

IndexError: tuple index out of range