n_fft =2048 value is too small for length and can't extend empty axis 0 using modes other than 'constant' or 'empty

Question

this is the error

Warning (from warnings module): File "C:\Users\Hp\AppData\Local\Programs\Python\Python39\lib\site-packages\librosa\core\spectrum.py", line 222 warnings.warn( UserWarning: n_fft=2048 is too small for input signal of length=0 Traceback (most recent call last): File "C:\Users\Hp\AppData\Local\Programs\Python\Python39\datasetread.py", line 72, in save_mfcc(DATASET_PATH, JSON_PATH, num_segments=10) File "C:\Users\Hp\AppData\Local\Programs\Python\Python39\datasetread.py", line 54, in save_mfcc mfcc = librosa.feature.mfcc(signal[start_sample:finish_sample],sr=sr,n_fft=n_fft,n_mfcc=n_mfcc,hop_length=hop_length) File "C:\Users\Hp\AppData\Local\Programs\Python\Python39\lib\site-packages\librosa\feature\spectral.py", line 1852, in mfcc S = power_to_db(melspectrogram(y=y, sr=sr, **kwargs)) File "C:\Users\Hp\AppData\Local\Programs\Python\Python39\lib\site-packages\librosa\feature\spectral.py", line 1996, in melspectrogram S, n_fft = _spectrogram( File "C:\Users\Hp\AppData\Local\Programs\Python\Python39\lib\site-packages\librosa\core\spectrum.py", line 2512, in _spectrogram stft( File "C:\Users\Hp\AppData\Local\Programs\Python\Python39\lib\site-packages\librosa\core\spectrum.py", line 228, in stft y = np.pad(y, int(n_fft // 2), mode=pad_mode) File "<array_function internals>", line 5, in pad File "C:\Users\Hp\AppData\Local\Programs\Python\Python39\lib\site-packages\numpy\lib\arraypad.py", line 814, in pad raise ValueError( ValueError: can't extend empty axis 0 using modes other than 'constant' or 'empty' I am working on speech recognition system and by this code i want to extract features using MFCC

    DATASET_PATH = "F://MS//MS-4//LibriSpeech"
JSON_PATH = "data_10.json"
SAMPLE_RATE = 22050
TRACK_DURATION = 15  
SAMPLES_PER_TRACK = SAMPLE_RATE * TRACK_DURATION
def save_mfcc (dataset_path, json_path, n_mfcc=13, n_fft=2048, hop_length=512, num_segments=5):
   
    data = {
        "mapping": [ ],
        "mfcc": [ ],
        "labels": [ ]}

    num_samples_per_segment = int(SAMPLES_PER_TRACK / num_segments)
    expected_num_mfcc_vectors_per_segment = math.ceil(num_samples_per_segment / hop_length)
    for i, (dirpath, dirnames, filenames) in enumerate(os.walk(dataset_path)):
        if dirpath is not dataset_path:

            dirpath_components = os.path.split(dirpath)
            semantic_label = dirpath_components[-1]
            data["mapping"].append(semantic_label)
            print("\nProcessing: {}".format(semantic_label))
            for f in filenames:
                    file_path = os.path.join(dirpath, f)
                signal, sr = librosa.load(file_path, sr = SAMPLE_RATE)
                for s in range(num_segments):
                    start_sample = num_samples_per_segment * s
                    finish_sample = start_sample + num_samples_per_segment
                    mfcc=librosa.feature.mfcc(signal[start_sample:finish_sample],sr=sr,n_fft=n_fft,n_mfcc=n_mfcc,hop_length=hop_length)
                    mfcc = mfcc.T
                    if len(mfcc) == expected_num_mfcc_vectors_per_segment:
                        data["mfcc"].append(mfcc.tolist())
                        data["labels"].append(i-1)
                        print("{}, segment:{}".format(file_path, s+1 ))
    with open(json_path, "w") as fp:
        json.dump(data, fp, indent=4)
if __name__ == "__main__":
    save_mfcc(DATASET_PATH, JSON_PATH, num_segments=10)