I am trying to analyse an Audio file and train the system based on that extracted features but I am getting an error while Fitting the model saying "Tuple Index out of Range". I have provided the shapes of all the arrays I am using in the comment next to the print statement. Could you help me understand how to define the sizes while defining the model.
Please let me know if any more details are required.
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))
-------------------ERROR------------------------------------------------------
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
