| import math |
| import torch |
| from typing import List, Optional |
|
|
|
|
| def init_weights(m, mean=0.0, std=0.01): |
| """ |
| Initialize the weights of a module. |
| |
| Args: |
| m: The module to initialize. |
| mean: The mean of the normal distribution. |
| std: The standard deviation of the normal distribution. |
| """ |
| classname = m.__class__.__name__ |
| if classname.find("Conv") != -1: |
| m.weight.data.normal_(mean, std) |
|
|
|
|
| def get_padding(kernel_size, dilation=1): |
| """ |
| Calculate the padding needed for a convolution. |
| |
| Args: |
| kernel_size: The size of the kernel. |
| dilation: The dilation of the convolution. |
| """ |
| return int((kernel_size * dilation - dilation) / 2) |
|
|
|
|
| def convert_pad_shape(pad_shape): |
| """ |
| Convert the pad shape to a list of integers. |
| |
| Args: |
| pad_shape: The pad shape.. |
| """ |
| l = pad_shape[::-1] |
| pad_shape = [item for sublist in l for item in sublist] |
| return pad_shape |
|
|
|
|
| def kl_divergence(m_p, logs_p, m_q, logs_q): |
| """ |
| Calculate the KL divergence between two distributions. |
| |
| Args: |
| m_p: The mean of the first distribution. |
| logs_p: The log of the standard deviation of the first distribution. |
| m_q: The mean of the second distribution. |
| logs_q: The log of the standard deviation of the second distribution. |
| """ |
| kl = (logs_q - logs_p) - 0.5 |
| kl += ( |
| 0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q) |
| ) |
| return kl |
|
|
|
|
| def slice_segments( |
| x: torch.Tensor, ids_str: torch.Tensor, segment_size: int = 4, dim: int = 2 |
| ): |
| """ |
| Slice segments from a tensor, handling tensors with different numbers of dimensions. |
| |
| Args: |
| x (torch.Tensor): The tensor to slice. |
| ids_str (torch.Tensor): The starting indices of the segments. |
| segment_size (int, optional): The size of each segment. Defaults to 4. |
| dim (int, optional): The dimension to slice across (2D or 3D tensors). Defaults to 2. |
| """ |
| if dim == 2: |
| ret = torch.zeros_like(x[:, :segment_size]) |
| elif dim == 3: |
| ret = torch.zeros_like(x[:, :, :segment_size]) |
|
|
| for i in range(x.size(0)): |
| idx_str = ids_str[i].item() |
| idx_end = idx_str + segment_size |
| if dim == 2: |
| ret[i] = x[i, idx_str:idx_end] |
| else: |
| ret[i] = x[i, :, idx_str:idx_end] |
|
|
| return ret |
|
|
|
|
| def rand_slice_segments(x, x_lengths=None, segment_size=4): |
| """ |
| Randomly slice segments from a tensor. |
| |
| Args: |
| x: The tensor to slice. |
| x_lengths: The lengths of the sequences. |
| segment_size: The size of each segment. |
| """ |
| b, d, t = x.size() |
| if x_lengths is None: |
| x_lengths = t |
| ids_str_max = x_lengths - segment_size + 1 |
| ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) |
| ret = slice_segments(x, ids_str, segment_size, dim=3) |
| return ret, ids_str |
|
|
|
|
| def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4): |
| """ |
| Generate a 1D timing signal. |
| |
| Args: |
| length: The length of the signal. |
| channels: The number of channels of the signal. |
| min_timescale: The minimum timescale. |
| max_timescale: The maximum timescale. |
| """ |
| position = torch.arange(length, dtype=torch.float) |
| num_timescales = channels // 2 |
| log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / ( |
| num_timescales - 1 |
| ) |
| inv_timescales = min_timescale * torch.exp( |
| torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment |
| ) |
| scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1) |
| signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0) |
| signal = torch.nn.functional.pad(signal, [0, 0, 0, channels % 2]) |
| signal = signal.view(1, channels, length) |
| return signal |
|
|
|
|
| def subsequent_mask(length): |
| """ |
| Generate a subsequent mask. |
| |
| Args: |
| length: The length of the sequence. |
| """ |
| mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) |
| return mask |
|
|
|
|
| @torch.jit.script |
| def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): |
| """ |
| Fused add tanh sigmoid multiply operation. |
| |
| Args: |
| input_a: The first input tensor. |
| input_b: The second input tensor. |
| n_channels: The number of channels. |
| """ |
| n_channels_int = n_channels[0] |
| in_act = input_a + input_b |
| t_act = torch.tanh(in_act[:, :n_channels_int, :]) |
| s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) |
| acts = t_act * s_act |
| return acts |
|
|
|
|
| |
| def fused_add_tanh_sigmoid_multiply_no_jit(input_a, input_b, n_channels): |
| """ |
| Fused add tanh sigmoid multiply operation. |
| |
| Args: |
| input_a: The first input tensor. |
| input_b: The second input tensor. |
| n_channels: The number of channels. |
| """ |
| n_channels_int = n_channels[0] |
| in_act = input_a + input_b |
| t_act = torch.tanh(in_act[:, :n_channels_int, :]) |
| s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) |
| acts = t_act * s_act |
| return acts |
|
|
|
|
| def convert_pad_shape(pad_shape: List[List[int]]) -> List[int]: |
| """ |
| Convert the pad shape to a list of integers. |
| |
| Args: |
| pad_shape: The pad shape. |
| """ |
| return torch.tensor(pad_shape).flip(0).reshape(-1).int().tolist() |
|
|
|
|
| def sequence_mask(length: torch.Tensor, max_length: Optional[int] = None): |
| """ |
| Generate a sequence mask. |
| |
| Args: |
| length: The lengths of the sequences. |
| max_length: The maximum length of the sequences. |
| """ |
| 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 clip_grad_value(parameters, clip_value, norm_type=2): |
| """ |
| Clip the gradients of a list of parameters. |
| |
| Args: |
| parameters: The list of parameters to clip. |
| clip_value: The maximum value of the gradients. |
| norm_type: The type of norm to use for clipping. |
| """ |
| if isinstance(parameters, torch.Tensor): |
| parameters = [parameters] |
| parameters = list(filter(lambda p: p.grad is not None, parameters)) |
| norm_type = float(norm_type) |
| if clip_value is not None: |
| clip_value = float(clip_value) |
|
|
| total_norm = 0 |
| for p in parameters: |
| param_norm = p.grad.data.norm(norm_type) |
| total_norm += param_norm.item() ** norm_type |
| if clip_value is not None: |
| p.grad.data.clamp_(min=-clip_value, max=clip_value) |
| total_norm = total_norm ** (1.0 / norm_type) |
| return total_norm |
|
|
