n_fft =2048 值对于长度来说太小，不能使用“常量”或“空

如何解决n_fft =2048 值对于长度来说太小，不能使用“常量”或“空

警告（来自警告模块）：文件“C:\Users\Hp\AppData\Local\Programs\Python\python39\lib\site-packages\librosa\core\spectrum.py”，第 222 行警告。警告（用户警告：n_fft=2048 对于长度=0 的输入信号来说太小了回溯（最近一次调用最后一次）：文件“C:\Users\Hp\AppData\Local\Programs\Python\python39\datasetread.py”，第 72 行，在 save_mfcc(DATASET_PATH,JSON_PATH,num_segments=10) 文件“C:\Users\Hp\AppData\Local\Programs\Python\python39\datasetread.py”，第 54 行，在 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) 文件“C:\Users\Hp\AppData\Local\Programs\Python\python39\lib\site-packages\librosa\feature\spectral.py”，第 1852 行，在 mfcc S = power_to_db(melspectrogram(y=y,**kwargs)) 文件 "C:\Users\Hp\AppData\Local\Programs\Python\python39\lib\site-packages\librosa\feature\spectral.py",line 1996,in melspectrogram S,n_fft = _spectrogram( 文件“C:\Users\Hp\AppData\Local\Programs\Python\python39\lib\site-packages\librosa\core\spectrum.py”，第 2512 行，在 _spectrogram stft( 文件“C:\Users\Hp\AppData\Local\Programs\Python\python39\lib\site-packages\librosa\core\spectrum.py”，第 228 行，在 stft y = np.pad(y,int(n_fft // 2),mode=pad_mode) 文件“array_function internals>”，第 5 行，在 pad 中文件“C:\Users\Hp\AppData\Local\Programs\Python\python39\lib\site-packages\numpy\lib\arraypad.py”，第 814 行，在 pad 中引发值错误（ ValueError：无法使用“常量”或“空”以外的模式扩展空轴 0 我正在研究语音识别系统，通过这段代码我想使用 MFCC 提取特征

    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],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,num_segments=10)