| | |
| | | means = np.array(means_list).astype(np.float) |
| | | vars = np.array(vars_list).astype(np.float) |
| | | cmvn = np.array([means, vars]) |
| | | cmvn = torch.as_tensor(cmvn) |
| | | cmvn = torch.as_tensor(cmvn, dype=torch.float32) |
| | | return cmvn |
| | | |
| | | |
| | |
| | | dtype = inputs.dtype |
| | | frame, dim = inputs.shape |
| | | |
| | | means = np.tile(cmvn[0:1, :dim], (frame, 1)) |
| | | vars = np.tile(cmvn[1:2, :dim], (frame, 1)) |
| | | inputs += torch.from_numpy(means).type(dtype).to(device) |
| | | inputs *= torch.from_numpy(vars).type(dtype).to(device) |
| | | means = cmvn[0:1, :dim] |
| | | vars = cmvn[1:2, :dim] |
| | | inputs += means.to(device) |
| | | inputs *= vars.to(device) |
| | | |
| | | return inputs.type(torch.float32) |
| | | |