Spaces:
Running
on
A10G
Running
on
A10G
File size: 2,627 Bytes
b2eb230 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
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()
|