|
import matplotlib |
|
import torch |
|
from matplotlib import pyplot as plt |
|
|
|
matplotlib.use("Agg") |
|
|
|
|
|
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 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 init_weights(m, mean=0.0, std=0.01): |
|
classname = m.__class__.__name__ |
|
if 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 plot_mel(data, titles=None): |
|
fig, axes = plt.subplots(len(data), 1, squeeze=False) |
|
|
|
if titles is None: |
|
titles = [None for i in range(len(data))] |
|
|
|
plt.tight_layout() |
|
|
|
for i in range(len(data)): |
|
mel = data[i] |
|
|
|
if isinstance(mel, torch.Tensor): |
|
mel = mel.float().detach().cpu().numpy() |
|
|
|
axes[i][0].imshow(mel, origin="lower") |
|
axes[i][0].set_aspect(2.5, adjustable="box") |
|
axes[i][0].set_ylim(0, mel.shape[0]) |
|
axes[i][0].set_title(titles[i], fontsize="medium") |
|
axes[i][0].tick_params(labelsize="x-small", left=False, labelleft=False) |
|
axes[i][0].set_anchor("W") |
|
|
|
return fig |
|
|
|
|
|
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 = torch.clamp(x_lengths - segment_size + 1, min=0) |
|
ids_str = (torch.rand([b], device=x.device) * ids_str_max).to(dtype=torch.long) |
|
ret = slice_segments(x, ids_str, segment_size) |
|
return ret, ids_str |
|
|
|
|
|
@torch.jit.script |
|
def fused_add_tanh_sigmoid_multiply(in_act, n_channels): |
|
n_channels_int = n_channels[0] |
|
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 avg_with_mask(x, mask): |
|
assert mask.dtype == torch.float, "Mask should be float" |
|
|
|
if mask.ndim == 2: |
|
mask = mask.unsqueeze(1) |
|
|
|
if mask.shape[1] == 1: |
|
mask = mask.expand_as(x) |
|
|
|
return (x * mask).sum() / mask.sum() |
|
|
