| import sys |
| import copy |
| import torch |
|
|
| def _check_model_old_version(model): |
| if hasattr(model.WN[0], 'res_layers') or hasattr(model.WN[0], 'cond_layers'): |
| return True |
| else: |
| return False |
|
|
|
|
| def _update_model_res_skip(old_model, new_model): |
| for idx in range(0, len(new_model.WN)): |
| wavenet = new_model.WN[idx] |
| n_channels = wavenet.n_channels |
| n_layers = wavenet.n_layers |
| wavenet.res_skip_layers = torch.nn.ModuleList() |
| for i in range(0, n_layers): |
| if i < n_layers - 1: |
| res_skip_channels = 2*n_channels |
| else: |
| res_skip_channels = n_channels |
| res_skip_layer = torch.nn.Conv1d(n_channels, res_skip_channels, 1) |
| skip_layer = torch.nn.utils.remove_weight_norm(wavenet.skip_layers[i]) |
| if i < n_layers - 1: |
| res_layer = torch.nn.utils.remove_weight_norm(wavenet.res_layers[i]) |
| res_skip_layer.weight = torch.nn.Parameter(torch.cat([res_layer.weight, skip_layer.weight])) |
| res_skip_layer.bias = torch.nn.Parameter(torch.cat([res_layer.bias, skip_layer.bias])) |
| else: |
| res_skip_layer.weight = torch.nn.Parameter(skip_layer.weight) |
| res_skip_layer.bias = torch.nn.Parameter(skip_layer.bias) |
| res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') |
| wavenet.res_skip_layers.append(res_skip_layer) |
| del wavenet.res_layers |
| del wavenet.skip_layers |
|
|
| def _update_model_cond(old_model, new_model): |
| for idx in range(0, len(new_model.WN)): |
| wavenet = new_model.WN[idx] |
| n_channels = wavenet.n_channels |
| n_layers = wavenet.n_layers |
| n_mel_channels = wavenet.cond_layers[0].weight.shape[1] |
| cond_layer = torch.nn.Conv1d(n_mel_channels, 2*n_channels*n_layers, 1) |
| cond_layer_weight = [] |
| cond_layer_bias = [] |
| for i in range(0, n_layers): |
| _cond_layer = torch.nn.utils.remove_weight_norm(wavenet.cond_layers[i]) |
| cond_layer_weight.append(_cond_layer.weight) |
| cond_layer_bias.append(_cond_layer.bias) |
| cond_layer.weight = torch.nn.Parameter(torch.cat(cond_layer_weight)) |
| cond_layer.bias = torch.nn.Parameter(torch.cat(cond_layer_bias)) |
| cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') |
| wavenet.cond_layer = cond_layer |
| del wavenet.cond_layers |
|
|
| def update_model(old_model): |
| if not _check_model_old_version(old_model): |
| return old_model |
| new_model = copy.deepcopy(old_model) |
| if hasattr(old_model.WN[0], 'res_layers'): |
| _update_model_res_skip(old_model, new_model) |
| if hasattr(old_model.WN[0], 'cond_layers'): |
| _update_model_cond(old_model, new_model) |
| return new_model |
|
|
| if __name__ == '__main__': |
| old_model_path = sys.argv[1] |
| new_model_path = sys.argv[2] |
| model = torch.load(old_model_path, map_location='cpu') |
| model['model'] = update_model(model['model']) |
| torch.save(model, new_model_path) |
| |
|
|
