How to use the spectral.Spectral function in spectral

@Memoize
def do_fbank(fname):
    try:
        with open(fname[:-3] + 'npy', 'rb') as rfb:
            fb = np.load(rfb)
    except IOError:
        srate, sound = wavfile.read(fname)
        fbanks = Spectral(nfilt=N_FBANKS,    # nb of filters in mel bank
                     alpha=0.97,             # pre-emphasis
                     do_dct=False,           # we do not want MFCCs
                     fs=srate,               # sampling rate
                     frate=FBANKS_RATE,      # frame rate
                     wlen=FBANKS_WINDOW,     # window length
                     nfft=1024,              # length of dft
                     do_deltas=False,       # speed
                     do_deltasdeltas=False  # acceleration
                     )
        fb = np.array(fbanks.transform(sound), dtype='float32')
        #with open(fname.split('.')[0] + '.npy', 'wb') as wfb:
        #    np.save(wfb, fb)
    print "did:", fname
    #print fb.shape
    return fb

gammatonefname = bdir+'/'+fname[:-4]+'_gamma.npy'
                tmp_snd = loadsound(wavfname)
                gamma_cf = erbspace(20*Hz, 20*kHz, N_GAMMATONES_FILTERS)
                gamma_fb = Gammatone(tmp_snd, gamma_cf)
                with open(gammatonefname, 'w') as o_f:
                    npsave(o_f, gamma_fb.process())
            if spectrograms:
                powerspec, _, _, _ = specgram(sound, NFFT=int(srate
                    * SPECGRAM_WINDOW), Fs=srate, noverlap=int(srate
                        * SPECGRAM_OVERLAP)) # TODO
                specgramfname = bdir+'/'+fname[:-4]+'_specgram.npy'
                with open(specgramfname, 'w') as o_f:
                    npsave(o_f, powerspec.T)
            if filterbanks:
                # convert to Mel filterbanks
                fbanks = Spectral(nfilt=N_FBANKS,      # nb of filters in mel bank
                             alpha=0.97,               # pre-emphasis
                             do_dct=False,             # we do not want MFCCs
                             compression='log',
                             fs=srate,                 # sampling rate
                             #lowerf=50,                # lower frequency
                             frate=FBANKS_RATE,        # frame rate
                             wlen=FBANKS_WINDOW,       # window length
                             nfft=1024,                # length of dft
                             do_deltas=False,          # speed
                             do_deltasdeltas=False     # acceleration
                             )
                fbank = fbanks.transform(sound)
                fbanksfname = bdir+'/'+fname[:-4]+'_fbanks.npy'
                with open(fbanksfname, 'w') as o_f:
                    npsave(o_f, fbank)
            # TODO wavelets scattergrams / scalograms

tmp_snd = loadsound(wavfname)
                gamma_cf = erbspace(20*Hz, 20*kHz, N_GAMMATONES_FILTERS)
                gamma_fb = Gammatone(tmp_snd, gamma_cf)
                with open(gammatonefname, 'w') as o_f:
                    npsave(o_f, gamma_fb.process())
            if spectrograms:
                powerspec, _, _, _ = specgram(sound, NFFT=int(srate
                    * SPECGRAM_WINDOW), Fs=srate, noverlap=int(srate
                        * SPECGRAM_OVERLAP)) # TODO
                specgramfname = bdir+'/'+fname[:-4]+'_specgram.npy'
                with open(specgramfname, 'w') as o_f:
                    npsave(o_f, powerspec.T)
            if filterbanks:
                # convert to Mel filterbanks
                if fbanks == None: # assume parameters are fixed
                    fbanks = Spectral(nfilt=N_FBANKS,    # nb of filters in mel bank
                                 alpha=0.97,             # pre-emphasis
                                 do_dct=False,           # we do not want MFCCs
                                 fs=srate,               # sampling rate
                                 frate=FBANKS_RATE,      # frame rate
                                 wlen=FBANKS_WINDOW,     # window length
                                 nfft=1024,              # length of dft
                                 do_deltas=False,       # speed
                                 do_deltasdeltas=False  # acceleration
                                 )
                fbank = fbanks.transform(sound)[0]  # first dimension is for
                                                    # deltas & deltasdeltas
                fbanksfname = bdir+'/'+fname[:-4]+'_fbanks.npy'
                with open(fbanksfname, 'w') as o_f:
                    npsave(o_f, fbank)
            # TODO wavelets scattergrams / scalograms
            print "dealt with file", wavfname

@Memoize
def do_fbank(fname):
    fn = bdir + fname + '.wav'
    try:
        with open(fn[:-3] + 'npy', 'rb') as rfb:
            fb = np.load(rfb)
    except IOError:
        srate, sound = wavfile.read(fn)
        fbanks = Spectral(nfilt=N_FBANKS,    # nb of filters in mel bank
                     alpha=0.97,             # pre-emphasis
                     do_dct=False,           # we do not want MFCCs
                     fs=srate,               # sampling rate
                     frate=FBANKS_RATE,      # frame rate
                     wlen=FBANKS_WINDOW,     # window length
                     nfft=1024,              # length of dft
                     do_deltas=False,       # speed
                     do_deltasdeltas=False  # acceleration
                     )
        fb = np.array(fbanks.transform(sound), dtype='float32')
    print "did:", fn
    #print fb.shape
    return fb