mirror of
https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-06-05 01:10:22 +08:00
5eed789fe7
- remove redundant attentions_onnx - shrink models_onnx - add some type note to rvc.utils
80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
from typing import List, Optional, Tuple
|
|
|
|
import torch
|
|
|
|
def call_weight_data_normal_if_Conv(m: torch.nn.Module):
|
|
classname = m.__class__.__name__
|
|
if classname.find("Conv") != -1:
|
|
mean=0.0
|
|
std=0.01
|
|
m.weight.data.normal_(mean, std)
|
|
|
|
|
|
def get_padding(kernel_size: int, dilation=1):
|
|
return int((kernel_size * dilation - dilation) / 2)
|
|
|
|
|
|
def slice_on_last_dim(
|
|
x: torch.Tensor, start_indices: List[int], segment_size=4,
|
|
) -> torch.Tensor:
|
|
new_shape = x.shape
|
|
new_shape[-1] = segment_size
|
|
ret = torch.empty(new_shape)
|
|
for i in range(x.size(0)):
|
|
idx_str = start_indices[i]
|
|
idx_end = idx_str + segment_size
|
|
ret[i, ..., :] = x[i, ..., idx_str:idx_end]
|
|
return ret
|
|
|
|
|
|
def rand_slice_segments(
|
|
x: torch.Tensor, x_lengths: int = None, segment_size=4,
|
|
) -> Tuple[torch.Tensor, List[int]]:
|
|
b, _, 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_on_last_dim(x, ids_str, segment_size)
|
|
return ret, ids_str
|
|
|
|
|
|
@torch.jit.script
|
|
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_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]:
|
|
return torch.tensor(pad_shape).flip(0).reshape(-1).int().tolist()
|
|
|
|
|
|
def sequence_mask(
|
|
length: torch.Tensor, max_length: Optional[int] = None,
|
|
) -> torch.BoolTensor:
|
|
if max_length is None:
|
|
max_length = int(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):
|
|
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
|