Invalid argument: Dimension -972891 must be >= 0

I have created a data pipeline using tf.data for speech recognition using the following code snippets:

def get_waveform_and_label(file_path):
    label = tf.strings.split(file_path, os.path.sep)[-2]

    audio_binary = tf.io.read_file(file_path)
    audio, _ = tf.audio.decode_wav(audio_binary)
    waveform = tf.squeeze(audio, axis=-1)
    
    return waveform, label

def get_spectrogram(waveform):
    # Padding for files with less than 16000 samples
    # Generate zeros w.r.t how many the waveform lacks
    zero_padding = tf.zeros([16000] - tf.shape(waveform), dtype=tf.float32)

    # Concatenate audio with padding so that all audio clips will be of the same length
    waveform = tf.cast(waveform, tf.float32)
    waveform = tf.concat([waveform, zero_padding], 0)

    spectrogram = tf.signal.stft(waveform, frame_length=255, frame_step=128)
    spectrogram = tf.abs(spectrogram)

    return spectrogram

def get_spectrogram_and_label_id(audio, label):
    spectrogram = get_spectrogram(audio)
    spectrogram = tf.expand_dims(spectrogram, -1)
    
    label_id = tf.argmax(label == np.array(labels))
    label_onehot = tf.one_hot(label_id, len(labels))
    
    return spectrogram, label_onehot

files_ds = tf.data.Dataset.from_tensor_slices(files)
waveform_ds = files_ds.map(get_waveform_and_label, num_parallel_calls=tf.data.AUTOTUNE)
spectrogram_ds = waveform_ds.map(get_spectrogram_and_label_id, num_parallel_calls=tf.data.AUTOTUNE)

These snippets are borrowed from https://www.tensorflow.org/tutorials/audio/simple_audio#build_and_train_the_model.

And my model is defined as below:

import tensorflow as tf

inputs = tf.keras.layers.Input(shape=(input_shape))
x = tf.keras.layers.BatchNormalization()(inputs)

x = tf.keras.layers.Conv2D(8,13, padding='same', activation='relu', strides=1)(x)
x = tf.keras.layers.MaxPooling2D(3)(x)
x = tf.keras.layers.Dropout(0.4)(x)
x = tf.keras.layers.BatchNormalization()(x)

x = tf.keras.layers.Conv2D(32, 11, padding='same', activation='relu', strides=1)(x)
x = tf.keras.layers.MaxPooling2D(3)(x)
x = tf.keras.layers.Dropout(0.4)(x)
x = tf.keras.layers.BatchNormalization()(x)

x = tf.keras.layers.Conv2D(256, 9, padding='same', activation='relu', strides=1)(x)
x = tf.keras.layers.MaxPooling2D(3)(x)
x = tf.keras.layers.Dropout(0.4)(x)
x = tf.keras.layers.BatchNormalization()(x)

x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dense(512, activation='relu')(x)
outputs = tf.keras.layers.Dense(len(labels), activation="softmax")(x)

model = tf.keras.models.Model(inputs, outputs)

model.compile(loss="categorical_crossentropy",
              optimizer=tf.keras.optimizers.Adam(), 
              metrics=['accuracy'])
model.summary()

When I start training process this error appears after a few iterations:

> InvalidArgumentError: 2 root error(s) found.   

> (0) Invalid argument: 
> Dimension -972891 must be >= 0     [[{{node zeros}}]]     
> [[IteratorGetNext]]   
> [[categorical_crossentropy/softmax_cross_entropy_with_logits/Shape_2/_6]]

> (1) Invalid argument:  Dimension -972891 must be >= 0      [[{{node
> zeros}}]]      [[IteratorGetNext]] 0 successful operations. 0 derived
> errors ignored. [Op:__inference_train_function_6412]
> 
> Function call stack: train_function -> train_function

I have found that the issue happened in the padding step, I mean

zero_padding = tf.zeros([16000] - tf.shape(waveform), dtype=tf.float32)
waveform = tf.cast(waveform, tf.float32)
waveform = tf.concat([waveform, zero_padding], 0)

I've replaced the padding step by tf.signal.frame and the issue is resolved.