2c6ce0ebfa9537246878e8fb9144e0c879fca17d,cube/models/vocoder.py,BeeCoder,_predict_one,#BeeCoder#,97

Before Change

        for w, b, hid in zip(self.softmax_w, self.softmax_b, output_hidden):
            logits = w.expr(update=True) * hid + b.expr(update=True)
            if runtime:
                logits = dy.softmax(logits)
            output.append(logits)
        return output

    def learn(self, wave, mgc, batch_size):

After Change

        self.model.populate(output_base + ".network")

    def _predict_one(self, mgc, last_fft=None, runtime=True):
        if last_fft is None:
            last_fft_real = np.zeros(self.FFT_SIZE)
            last_fft_imag = np.zeros(self.FFT_SIZE)
        else:
            last_fft_real = np.real(last_fft)
            last_fft_imag = np.imag(last_fft)

        hidden = dy.concatenate([dy.inputVector(mgc), dy.inputVector(last_fft_real), dy.inputVector(last_fft_imag)])

        for w, b in zip(self.hidden_w, self.hidden_b):
            hidden = dy.tanh(w.expr(update=True) * hidden + b.expr(update=True))

Instances