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import math | |
import torch | |
from torch.nn import functional as F | |
def slice_pitch_segments(x, ids_str, segment_size=4): | |
ret = torch.zeros_like(x[:, :segment_size]) | |
for i in range(x.size(0)): | |
idx_str = ids_str[i] | |
idx_end = idx_str + segment_size | |
ret[i] = x[i, idx_str:idx_end] | |
return ret | |
def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4): | |
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) | |
ret_pitch = slice_pitch_segments(pitch, ids_str, segment_size) | |
return ret, ret_pitch, ids_str | |
def init_weights(m, mean=0.0, std=0.01): | |
classname = m.__class__.__name__ | |
if "Depthwise_Separable" in classname: | |
m.depth_conv.weight.data.normal_(mean, std) | |
m.point_conv.weight.data.normal_(mean, std) | |
elif classname.find("Conv") != -1: | |
m.weight.data.normal_(mean, std) | |
def get_padding(kernel_size, dilation=1): | |
return int((kernel_size*dilation - dilation)/2) | |
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 intersperse(lst, item): | |
result = [item] * (len(lst) * 2 + 1) | |
result[1::2] = lst | |
return result | |
def kl_divergence(m_p, logs_p, m_q, logs_q): | |
"""KL(P||Q)""" | |
kl = (logs_q - logs_p) - 0.5 | |
kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q) | |
return kl | |
def rand_gumbel(shape): | |
"""Sample from the Gumbel distribution, protect from overflows.""" | |
uniform_samples = torch.rand(shape) * 0.99998 + 0.00001 | |
return -torch.log(-torch.log(uniform_samples)) | |
def rand_gumbel_like(x): | |
g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device) | |
return g | |
def slice_segments(x, ids_str, segment_size=4): | |
ret = torch.zeros_like(x[:, :, :segment_size]) | |
for i in range(x.size(0)): | |
idx_str = ids_str[i] | |
idx_end = idx_str + segment_size | |
ret[i] = x[i, :, idx_str:idx_end] | |
return ret | |
def rand_slice_segments(x, x_lengths=None, segment_size=4): | |
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) | |
return ret, ids_str | |
def rand_spec_segments(x, x_lengths=None, segment_size=4): | |
b, d, t = x.size() | |
if x_lengths is None: | |
x_lengths = t | |
ids_str_max = x_lengths - segment_size | |
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) | |
ret = slice_segments(x, ids_str, segment_size) | |
return ret, ids_str | |
def get_timing_signal_1d( | |
length, channels, min_timescale=1.0, max_timescale=1.0e4): | |
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 = F.pad(signal, [0, 0, 0, channels % 2]) | |
signal = signal.view(1, channels, length) | |
return signal | |
def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4): | |
b, channels, length = x.size() | |
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) | |
return x + signal.to(dtype=x.dtype, device=x.device) | |
def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1): | |
b, channels, length = x.size() | |
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) | |
return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis) | |
def subsequent_mask(length): | |
mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) | |
return mask | |
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 shift_1d(x): | |
x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] | |
return x | |
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 generate_path(duration, mask): | |
""" | |
duration: [b, 1, t_x] | |
mask: [b, 1, t_y, t_x] | |
""" | |
b, _, t_y, t_x = mask.shape | |
cum_duration = torch.cumsum(duration, -1) | |
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 - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] | |
path = path.unsqueeze(1).transpose(2,3) * mask | |
return path | |
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. / norm_type) | |
return total_norm | |