def fetch_signals()

in scripts/make_dataset.py [0:0]

• 29 lines of code
• 4 McCabe index (conditional complexity)

    def fetch_signals(self, number_of_speakers):
        # read random spealers
        self.num_of_speakers = number_of_speakers
        # (num_speakers,singlas)
        signals = np.zeros((self.num_of_speakers, self.size_of_signals))
        sig_indx = np.random.randint(
            low=0, high=self.len, size=self.num_of_speakers)
        names = list()
        for i in range(self.num_of_speakers):
            speaker = self.speakers[sig_indx[i]]
            cand_names = glob.glob(os.path.join(speaker + "/**","*.wav"))
            select_name_indx = np.random.randint(
            low=0, high=len(cand_names), size=1)
            name = cand_names[select_name_indx[0]]
            names.append(os.path.basename(name)[:-14])
            s, fs = sf.read(name)
            if len(s.shape) > 1:
                s = s[:, 0]
            s = quantize(s)

            l = len(s)
            if l > self.size_of_signals:
                noise_scale = np.std(s[0:self.size_of_signals]) / 80
                temp_noise = np.random.randn(self.size_of_signals,) * noise_scale
                signals[i] = temp_noise
                signals[i, :] = signals[i, 0:l] + s[0:self.size_of_signals]
            else:
                noise_scale = np.std(s) / 80
                temp_noise = np.random.randn(self.size_of_signals,) * noise_scale
                signals[i] = temp_noise
                signals[i, 0:l] = signals[i, 0:l] + s
        return signals, names