def extract_spec(dataset='train'):
    f = open(data_path + dataset + '_list.txt', 'r')

    i = 0
    for file_name in f:
        i = i + 1
        if not (i % 10):
            print(i)

        # load audio file
        file_name = file_name.rstrip('\n')
        file_path = data_path + file_name
        # print file_path
        y0, sr = librosa.load(file_path, sr=22050)
        # we use first 1 second
        half = len(y0) / 4
        y = y0[:round(half)]
        # mfcc
        mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=MFCC_DIM)
        # delta mfcc and double delta
        delta_mfcc = librosa.feature.delta(mfcc)
        ddelta_mfcc = librosa.feature.delta(mfcc, order=2)

        # STFT
        D = np.abs(librosa.core.stft(y, hop_length=512, n_fft=1024, win_length=1024))
        D_dB = librosa.amplitude_to_db(D, ref=np.max)

        # mel spectrogram
        mel_S = librosa.feature.melspectrogram(S=D, sr=sr, n_mels=128)
        S_dB = librosa.power_to_db(mel_S, ref=np.max)  # log compression

        # spectral centroid
        spec_centroid = librosa.feature.spectral_centroid(S=D)

        # concatenate all features
        features = np.concatenate([mfcc, delta_mfcc, ddelta_mfcc, spec_centroid], axis=0)

        # save mfcc as a file
        file_name = file_name.replace('.wav', '.npy')
        save_file = spec_path + file_name

        if not os.path.exists(os.path.dirname(save_file)):
            os.makedirs(os.path.dirname(save_file))
        np.save(save_file, features)

    f.close()