def predict(modelpath, noisylistpath):
    model = load_model(modelpath)
    FRAMESIZE = 512
    OVERLAP = 256
    FFTSIZE = 512
    RATE = 16000
    FRAMEWIDTH = 2
    FBIN = ((FRAMESIZE // 2) + 1)
    noisylistpath = noisylistpath
    with open(noisylistpath, 'r') as f:
        for line in f:
            print(line)
            filename = line.split('/')[(- 1)][:]
            print(filename)
            (y, sr) = librosa.load(line[:], sr=RATE)
            training_data = np.empty((10000, FBIN, ((FRAMEWIDTH * 2) + 1)))
            (Sxx, phase, mean, std) = make_spectrum_phase(y, FRAMESIZE, OVERLAP, FFTSIZE)
            idx = 0
            for i in range(FRAMEWIDTH, (Sxx.shape[1] - FRAMEWIDTH)):
                training_data[idx, :, :] = Sxx[:, (i - FRAMEWIDTH):((i + FRAMEWIDTH) + 1)]
                idx = (idx + 1)
            X_train = training_data[:idx]
            X_train = np.reshape(X_train, (idx, (- 1)))
            predict = model.predict(X_train)
            count = 0
            for i in range(FRAMEWIDTH, (Sxx.shape[1] - FRAMEWIDTH)):
                Sxx[:, i] = predict[count]
                count += 1
            Sxx[:, :FRAMEWIDTH] = ((Sxx[:, :FRAMEWIDTH] * std) + mean)
            Sxx[:, (- FRAMEWIDTH):] = ((Sxx[:, (- FRAMEWIDTH):] * std) + mean)
            recons_y = recons_spec_phase(Sxx, phase)
            output = librosa.util.fix_length(recons_y, y.shape[0])
            wav.write('static/wav/enhanced.wav', RATE, np.int16((output * 32767)))
            return os.path.join('static', 'wav', 'enhanced.wav')
def predict(modelpath, noisylistpath):
    model = load_model(modelpath)
    FRAMESIZE = 512
    OVERLAP = 256
    FFTSIZE = 512
    RATE = 16000
    FRAMEWIDTH = 2
    FBIN = ((FRAMESIZE // 2) + 1)
    noisylistpath = noisylistpath
    with open(noisylistpath, 'r') as f:
        for line in f:
            print(line)
            filename = line.split('/')[(- 1)][:]
            print(filename)
            (y, sr) = librosa.load(line[:], sr=RATE)
            training_data = np.empty((10000, FBIN, ((FRAMEWIDTH * 2) + 1)))
            (Sxx, phase, mean, std) = make_spectrum_phase(y, FRAMESIZE, OVERLAP, FFTSIZE)
            idx = 0
            for i in range(FRAMEWIDTH, (Sxx.shape[1] - FRAMEWIDTH)):
                training_data[idx, :, :] = Sxx[:, (i - FRAMEWIDTH):((i + FRAMEWIDTH) + 1)]
                idx = (idx + 1)
            X_train = training_data[:idx]
            X_train = np.reshape(X_train, (idx, (- 1)))
            predict = model.predict(X_train)
            count = 0
            for i in range(FRAMEWIDTH, (Sxx.shape[1] - FRAMEWIDTH)):
                Sxx[:, i] = predict[count]
                count += 1
            Sxx[:, :FRAMEWIDTH] = ((Sxx[:, :FRAMEWIDTH] * std) + mean)
            Sxx[:, (- FRAMEWIDTH):] = ((Sxx[:, (- FRAMEWIDTH):] * std) + mean)
            recons_y = recons_spec_phase(Sxx, phase)
            output = librosa.util.fix_length(recons_y, y.shape[0])
            wav.write('static/wav/enhanced.wav', RATE, np.int16((output * 32767)))
            return os.path.join('static', 'wav', 'enhanced.wav')