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import numpy as np
import torch
import torch.nn as nn
class SingleWindowDisc(nn.Module):
def __init__(self, time_length, freq_length=80, kernel=(3, 3), c_in=1, hidden_size=128):
super().__init__()
padding = (kernel[0] // 2, kernel[1] // 2)
self.model = nn.ModuleList([
nn.Sequential(*[
nn.Conv2d(c_in, hidden_size, kernel, (2, 2), padding),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout2d(0.25),
nn.BatchNorm2d(hidden_size, 0.8)
]),
nn.Sequential(*[
nn.Conv2d(hidden_size, hidden_size, kernel, (2, 2), padding),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout2d(0.25),
nn.BatchNorm2d(hidden_size, 0.8)
]),
nn.Sequential(*[
nn.Conv2d(hidden_size, hidden_size, kernel, (2, 2), padding),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout2d(0.25),
]),
])
ds_size = (time_length // 2 ** 3, (freq_length + 7) // 2 ** 3)
self.adv_layer = nn.Linear(hidden_size * ds_size[0] * ds_size[1], 1)
def forward(self, x):
"""
:param x: [B, C, T, n_bins]
:return: validity: [B, 1], h: List of hiddens
"""
h = []
for l in self.model:
x = l(x)
h.append(x)
x = x.view(x.shape[0], -1)
validity = self.adv_layer(x) # [B, 1]
return validity, h
class MultiWindowDiscriminator(nn.Module):
def __init__(self, time_lengths, freq_length=80, kernel=(3, 3), c_in=1, hidden_size=128):
super(MultiWindowDiscriminator, self).__init__()
self.win_lengths = time_lengths
self.discriminators = nn.ModuleList()
for time_length in time_lengths:
self.discriminators += [SingleWindowDisc(time_length, freq_length, kernel, c_in=c_in, hidden_size=hidden_size)]
def forward(self, x, x_len, start_frames_wins=None):
'''
Args:
x (tensor): input mel, (B, c_in, T, n_bins).
x_length (tensor): len of per mel. (B,).
Returns:
tensor : (B).
'''
validity = []
if start_frames_wins is None:
start_frames_wins = [None] * len(self.discriminators)
h = []
for i, start_frames in zip(range(len(self.discriminators)), start_frames_wins):
x_clip, start_frames = self.clip(x, x_len, self.win_lengths[i], start_frames) # (B, win_length, C)
start_frames_wins[i] = start_frames
if x_clip is None:
continue
x_clip, h_ = self.discriminators[i](x_clip)
h += h_
validity.append(x_clip)
if len(validity) != len(self.discriminators):
return None, start_frames_wins, h
validity = sum(validity) # [B]
return validity, start_frames_wins, h
def clip(self, x, x_len, win_length, start_frames=None):
'''Ramdom clip x to win_length.
Args:
x (tensor) : (B, c_in, T, n_bins).
cond (tensor) : (B, T, H).
x_len (tensor) : (B,).
win_length (int): target clip length
Returns:
(tensor) : (B, c_in, win_length, n_bins).
'''
T_start = 0
T_end = x_len.max() - win_length
if T_end < 0:
return None, None, start_frames
T_end = T_end.item()
if start_frames is None:
start_frame = np.random.randint(low=T_start, high=T_end + 1)
start_frames = [start_frame] * x.size(0)
else:
start_frame = start_frames[0]
x_batch = x[:, :, start_frame: start_frame + win_length]
return x_batch, start_frames
class Discriminator(nn.Module):
def __init__(self, time_lengths=[32, 64, 128], freq_length=80, kernel=(3, 3), c_in=1,
hidden_size=128):
super(Discriminator, self).__init__()
self.time_lengths = time_lengths
self.discriminator = MultiWindowDiscriminator(
freq_length=freq_length,
time_lengths=time_lengths,
kernel=kernel,
c_in=c_in, hidden_size=hidden_size
)
def forward(self, x, start_frames_wins=None):
"""
:param x: [B, T, 80]
:param return_y_only:
:return:
"""
if len(x.shape) == 3:
x = x[:, None, :, :] # [B,1,T,80]
x_len = x.sum([1, -1]).ne(0).int().sum([-1])
ret = {'y_c': None, 'y': None}
ret['y'], start_frames_wins, ret['h'] = self.discriminator(
x, x_len, start_frames_wins=start_frames_wins)
ret['start_frames_wins'] = start_frames_wins
return ret
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