ValueError: Exception encountered when calling layer "transformer" (type Transformer)

So I code a Transformers neural network that works as an ASR, it works, it trains good and saved the model as...

model.save("savedmodel.model")

The problem is that when I want to predict, I do this..

speech_model = load_model('D:\DOT\Speechrecognition\speechrecognitionE.model')

path = "D:\DOT\Speechrecognition\Data\LJSpeech-1.1\wavs\LJ001-0001.wav"

def path_to_audio(path):
    # spectrogram using stft
    audio = tf.io.read_file(path)
    audio, _ = tf.audio.decode_wav(audio, 1)
    audio = tf.squeeze(audio, axis=-1)
    stfts = tf.signal.stft(audio, frame_length=200, frame_step=80, fft_length=256)
    x = tf.math.pow(tf.abs(stfts), 0.5)
    # normalisation
    means = tf.math.reduce_mean(x, 1, keepdims=True)
    stddevs = tf.math.reduce_std(x, 1, keepdims=True)
    x = (x - means) / stddevs
    audio_len = tf.shape(x)[0]
    # padding to 10 seconds
    pad_len = 2754
    paddings = tf.constant([[0, pad_len], [0, 0]])
    x = tf.pad(x, paddings, "CONSTANT")[:pad_len, :]
    return x

x = path_to_audio(path)
#print(x)
speech_model.predict(x)

The path to audio function, converts the audio path to an spectrogram, in the training model it receive audio spectrograms as inputs, but it show this error..

    Traceback (most recent call last):
File "C:\Users\berna\Desktop\Programming\AI_ML_DL\Projects\DOT\DOT-alpha.py", line 72, in <module>
    speech_model.predict(x)
File "C:\Users\berna\AppData\Roaming\Python\Python39\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
    raise e.with_traceback(filtered_tb) from None
File "C:\Users\berna\AppData\Roaming\Python\Python39\site-packages\tensorflow\python\framework\func_graph.py", line 1129, in autograph_handler
    raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:

    File "C:\Users\berna\AppData\Roaming\Python\Python39\site-packages\keras\engine\training.py", line 1621, in predict_function  *
        return step_function(self, iterator)
    File "C:\Users\berna\AppData\Roaming\Python\Python39\site-packages\keras\engine\training.py", line 1611, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "C:\Users\berna\AppData\Roaming\Python\Python39\site-packages\keras\engine\training.py", line 1604, in run_step  **
        outputs = model.predict_step(data)
    File "C:\Users\berna\AppData\Roaming\Python\Python39\site-packages\keras\engine\training.py", line 1572, in predict_step
        return self(x, training=False)
    File "C:\Users\berna\AppData\Roaming\Python\Python39\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
        raise e.with_traceback(filtered_tb) from None

    ValueError: Exception encountered when calling layer "transformer" (type Transformer).

    Could not find matching concrete function to call loaded from the SavedModel. Got:
    Positional arguments (2 total):
        * Tensor("inputs:0", shape=(None, 129), dtype=float32)
        * False
    Keyword arguments: {}

    Expected these arguments to match one of the following 4 option(s):

    Option 1:
    Positional arguments (2 total):
        * [TensorSpec(shape=(None, None, 129), dtype=tf.float32, name='inputs/0'), TensorSpec(shape=(None, 199), dtype=tf.int32, name='inputs/1')]
        * False
    Keyword arguments: {}

    Option 2:
    Positional arguments (2 total):
        * [TensorSpec(shape=(None, None, 129), dtype=tf.float32, name='inputs/0'), TensorSpec(shape=(None, 199), dtype=tf.int32, name='inputs/1')]
        * True
    Keyword arguments: {}

    Option 3:
    Positional arguments (2 total):
        * [TensorSpec(shape=(None, None, 129), dtype=tf.float32, name='input_1'), TensorSpec(shape=(None, 199), dtype=tf.int32, name='input_2')]
        * False
    Keyword arguments: {}

    Option 4:
    Positional arguments (2 total):
        * [TensorSpec(shape=(None, None, 129), dtype=tf.float32, name='input_1'), TensorSpec(shape=(None, 199), dtype=tf.int32, name='input_2')]
        * True
    Keyword arguments: {}

    Call arguments received:
    • args=('tf.Tensor(shape=(None, 129), dtype=float32)',)
    • kwargs={'training': 'False'}

What does that means? what is wrong with the prediction?