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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(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):
factor = torch.scalar_tensor(2).pow(num_downsamplings_in_unet)
length = (length / factor).ceil() * factor
if not torch.onnx.is_in_onnx_export():
return length.int().item()
else:
return length
def convert_pad_shape(pad_shape):
inverted_shape = pad_shape[::-1]
pad_shape = [item for sublist in inverted_shape 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, int)):
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, int)):
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
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