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| """ from https://github.com/jaywalnut310/glow-tts """ | |
| import numpy as np | |
| import torch | |
| def sequence_mask(length, max_length=None): | |
| if max_length is None: | |
| max_length = length.max() | |
| x = torch.arange(int(max_length), dtype=length.dtype, device=length.device) | |
| return x.unsqueeze(0) < length.unsqueeze(1) | |
| def fix_len_compatibility(length, num_downsamplings_in_unet=2): | |
| while True: | |
| if length % (2**num_downsamplings_in_unet) == 0: | |
| return length | |
| length += 1 | |
| def convert_pad_shape(pad_shape): | |
| l = pad_shape[::-1] | |
| pad_shape = [item for sublist in l for item in sublist] | |
| return pad_shape | |
| def generate_path(duration, mask): | |
| device = duration.device | |
| b, t_x, t_y = mask.shape | |
| cum_duration = torch.cumsum(duration, 1) | |
| path = torch.zeros(b, t_x, t_y, dtype=mask.dtype).to(device=device) | |
| cum_duration_flat = cum_duration.view(b * t_x) | |
| path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) | |
| path = path.view(b, t_x, t_y) | |
| path = path - torch.nn.functional.pad(path, convert_pad_shape([[0, 0], | |
| [1, 0], [0, 0]]))[:, :-1] | |
| path = path * mask | |
| return path | |
| def duration_loss(logw, logw_, lengths): | |
| loss = torch.sum((logw - logw_)**2) / torch.sum(lengths) | |
| return loss | |
| def normalize(data, mu, std): | |
| if not isinstance(mu, float): | |
| if isinstance(mu, list): | |
| mu = torch.tensor(mu, dtype=data.dtype, device=data.device) | |
| elif isinstance(mu, torch.Tensor): | |
| mu = mu.to(data.device) | |
| elif isinstance(mu, np.ndarray): | |
| mu = torch.from_numpy(mu).to(data.device) | |
| mu = mu.unsqueeze(-1) | |
| if not isinstance(std, float): | |
| if isinstance(std, list): | |
| std = torch.tensor(std, dtype=data.dtype, device=data.device) | |
| elif isinstance(std, torch.Tensor): | |
| std = std.to(data.device) | |
| elif isinstance(std, np.ndarray): | |
| std = torch.from_numpy(std).to(data.device) | |
| std = std.unsqueeze(-1) | |
| return (data - mu) / std | |
| def denormalize(data, mu, std): | |
| if not isinstance(mu, float): | |
| if isinstance(mu, list): | |
| mu = torch.tensor(mu, dtype=data.dtype, device=data.device) | |
| elif isinstance(mu, torch.Tensor): | |
| mu = mu.to(data.device) | |
| elif isinstance(mu, np.ndarray): | |
| mu = torch.from_numpy(mu).to(data.device) | |
| mu = mu.unsqueeze(-1) | |
| if not isinstance(std, float): | |
| if isinstance(std, list): | |
| std = torch.tensor(std, dtype=data.dtype, device=data.device) | |
| elif isinstance(std, torch.Tensor): | |
| std = std.to(data.device) | |
| elif isinstance(std, np.ndarray): | |
| std = torch.from_numpy(std).to(data.device) | |
| std = std.unsqueeze(-1) | |
| return data * std + mu | |