def play_rnn_OLA(model, path, n, win, format='rov', args=None, translation=True, lpf=True): net = model(*args) net.load_state_dict(torch.load(path)) x = net.generate(1, n).detach().squeeze().numpy() x = iflatten_complex_data_with(x, 31) x = pad_data_zeros(x, get_single_side_frequency().shape[0]) x = ifft_data(x) x = iwindow(x, win + 0.001, 0.9) x = istandardize_data(x) x = overlap_and_add_data(x) if lpf: x = low_pass_filter(x) write_one_file('example', x, format=format) cmd = CMD + PAR1 + 'example' + PAR2 if translation: cmd += TRANSLATION os.system(cmd) return x
def example(self): # Generate an example with numpy array data type x = self.generate(1).detach().cpu() x = np.array(x) x = iflatten_complex_data_with(x, 16) x = pad_data_zeros(x, get_single_side_frequency().shape[0]) x = ifft_data(x) # x = iwindow(x, WIN, 0.8) return x[0]
def example(self, r): # Generate an example with numpy array data type global dim x = self.generate(1, r).detach().cpu() x = x.squeeze().unsqueeze(0) x = np.array(x) x = iflatten_complex_data_with(x, 31) x = pad_data_zeros(x, get_single_side_frequency().shape[0]) x = ifft_data(x) # x = iwindow(x, WIN, 0.8) return x[0]