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  1. .gitattributes +1 -0
  2. __pycache__/attentions.cpython-38.pyc +0 -0
  3. __pycache__/commons.cpython-38.pyc +0 -0
  4. __pycache__/data_utils.cpython-38.pyc +0 -0
  5. __pycache__/mel_processing.cpython-38.pyc +0 -0
  6. __pycache__/models.cpython-38.pyc +0 -0
  7. __pycache__/modules.cpython-38.pyc +0 -0
  8. __pycache__/pqmf.cpython-38.pyc +0 -0
  9. __pycache__/stft.cpython-38.pyc +0 -0
  10. __pycache__/transforms.cpython-38.pyc +0 -0
  11. __pycache__/utils.cpython-38.pyc +0 -0
  12. attentions.py +303 -0
  13. commons.py +161 -0
  14. data_utils.py +392 -0
  15. fig/proposed_model.png +3 -0
  16. inference.py +158 -0
  17. losses.py +71 -0
  18. mel_processing.py +112 -0
  19. models.py +730 -0
  20. modules.py +390 -0
  21. monotonic_align/__init__.py +19 -0
  22. monotonic_align/__pycache__/__init__.cpython-37.pyc +0 -0
  23. monotonic_align/__pycache__/__init__.cpython-38.pyc +0 -0
  24. monotonic_align/__pycache__/core.cpython-37.pyc +0 -0
  25. monotonic_align/__pycache__/core.cpython-38.pyc +0 -0
  26. monotonic_align/build/lib.win-amd64-3.8/monotonic_align/core.cp38-win_amd64.pyd +0 -0
  27. monotonic_align/build/temp.win-amd64-3.8/Release/core.cp38-win_amd64.exp +0 -0
  28. monotonic_align/build/temp.win-amd64-3.8/Release/core.cp38-win_amd64.lib +0 -0
  29. monotonic_align/build/temp.win-amd64-3.8/Release/core.obj +0 -0
  30. monotonic_align/core.c +0 -0
  31. monotonic_align/core.py +35 -0
  32. monotonic_align/core.pyx +44 -0
  33. monotonic_align/monotonic_align/core.cp38-win_amd64.pyd +0 -0
  34. monotonic_align/setup.py +10 -0
  35. pqmf.py +116 -0
  36. preprocess.py +25 -0
  37. requirements.txt +10 -0
  38. stft.py +209 -0
  39. stft_loss.py +136 -0
  40. text/LICENSE +19 -0
  41. text/__init__.py +56 -0
  42. text/__pycache__/__init__.cpython-38.pyc +0 -0
  43. text/__pycache__/cleaners.cpython-38.pyc +0 -0
  44. text/__pycache__/japanese.cpython-38.pyc +0 -0
  45. text/__pycache__/mandarin.cpython-38.pyc +0 -0
  46. text/__pycache__/symbols.cpython-38.pyc +0 -0
  47. text/cleaners.py +175 -0
  48. text/japanese.py +153 -0
  49. text/mandarin.py +328 -0
  50. text/symbols.py +67 -0
.gitattributes CHANGED
@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
32
  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
34
  *tfevents* filter=lfs diff=lfs merge=lfs -text
35
+ fig/proposed_model.png filter=lfs diff=lfs merge=lfs -text
__pycache__/attentions.cpython-38.pyc ADDED
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__pycache__/commons.cpython-38.pyc ADDED
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__pycache__/data_utils.cpython-38.pyc ADDED
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__pycache__/mel_processing.cpython-38.pyc ADDED
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attentions.py ADDED
@@ -0,0 +1,303 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import copy
2
+ import math
3
+ import numpy as np
4
+ import torch
5
+ from torch import nn
6
+ from torch.nn import functional as F
7
+
8
+ import commons
9
+ import modules
10
+ from modules import LayerNorm
11
+
12
+
13
+ class Encoder(nn.Module):
14
+ def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
15
+ super().__init__()
16
+ self.hidden_channels = hidden_channels
17
+ self.filter_channels = filter_channels
18
+ self.n_heads = n_heads
19
+ self.n_layers = n_layers
20
+ self.kernel_size = kernel_size
21
+ self.p_dropout = p_dropout
22
+ self.window_size = window_size
23
+
24
+ self.drop = nn.Dropout(p_dropout)
25
+ self.attn_layers = nn.ModuleList()
26
+ self.norm_layers_1 = nn.ModuleList()
27
+ self.ffn_layers = nn.ModuleList()
28
+ self.norm_layers_2 = nn.ModuleList()
29
+ for i in range(self.n_layers):
30
+ self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
31
+ self.norm_layers_1.append(LayerNorm(hidden_channels))
32
+ self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
33
+ self.norm_layers_2.append(LayerNorm(hidden_channels))
34
+
35
+ def forward(self, x, x_mask):
36
+ attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
37
+ x = x * x_mask
38
+ for i in range(self.n_layers):
39
+ y = self.attn_layers[i](x, x, attn_mask)
40
+ y = self.drop(y)
41
+ x = self.norm_layers_1[i](x + y)
42
+
43
+ y = self.ffn_layers[i](x, x_mask)
44
+ y = self.drop(y)
45
+ x = self.norm_layers_2[i](x + y)
46
+ x = x * x_mask
47
+ return x
48
+
49
+
50
+ class Decoder(nn.Module):
51
+ def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
52
+ super().__init__()
53
+ self.hidden_channels = hidden_channels
54
+ self.filter_channels = filter_channels
55
+ self.n_heads = n_heads
56
+ self.n_layers = n_layers
57
+ self.kernel_size = kernel_size
58
+ self.p_dropout = p_dropout
59
+ self.proximal_bias = proximal_bias
60
+ self.proximal_init = proximal_init
61
+
62
+ self.drop = nn.Dropout(p_dropout)
63
+ self.self_attn_layers = nn.ModuleList()
64
+ self.norm_layers_0 = nn.ModuleList()
65
+ self.encdec_attn_layers = nn.ModuleList()
66
+ self.norm_layers_1 = nn.ModuleList()
67
+ self.ffn_layers = nn.ModuleList()
68
+ self.norm_layers_2 = nn.ModuleList()
69
+ for i in range(self.n_layers):
70
+ self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
71
+ self.norm_layers_0.append(LayerNorm(hidden_channels))
72
+ self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
73
+ self.norm_layers_1.append(LayerNorm(hidden_channels))
74
+ self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
75
+ self.norm_layers_2.append(LayerNorm(hidden_channels))
76
+
77
+ def forward(self, x, x_mask, h, h_mask):
78
+ """
79
+ x: decoder input
80
+ h: encoder output
81
+ """
82
+ self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
83
+ encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
84
+ x = x * x_mask
85
+ for i in range(self.n_layers):
86
+ y = self.self_attn_layers[i](x, x, self_attn_mask)
87
+ y = self.drop(y)
88
+ x = self.norm_layers_0[i](x + y)
89
+
90
+ y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
91
+ y = self.drop(y)
92
+ x = self.norm_layers_1[i](x + y)
93
+
94
+ y = self.ffn_layers[i](x, x_mask)
95
+ y = self.drop(y)
96
+ x = self.norm_layers_2[i](x + y)
97
+ x = x * x_mask
98
+ return x
99
+
100
+
101
+ class MultiHeadAttention(nn.Module):
102
+ def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
103
+ super().__init__()
104
+ assert channels % n_heads == 0
105
+
106
+ self.channels = channels
107
+ self.out_channels = out_channels
108
+ self.n_heads = n_heads
109
+ self.p_dropout = p_dropout
110
+ self.window_size = window_size
111
+ self.heads_share = heads_share
112
+ self.block_length = block_length
113
+ self.proximal_bias = proximal_bias
114
+ self.proximal_init = proximal_init
115
+ self.attn = None
116
+
117
+ self.k_channels = channels // n_heads
118
+ self.conv_q = nn.Conv1d(channels, channels, 1)
119
+ self.conv_k = nn.Conv1d(channels, channels, 1)
120
+ self.conv_v = nn.Conv1d(channels, channels, 1)
121
+ self.conv_o = nn.Conv1d(channels, out_channels, 1)
122
+ self.drop = nn.Dropout(p_dropout)
123
+
124
+ if window_size is not None:
125
+ n_heads_rel = 1 if heads_share else n_heads
126
+ rel_stddev = self.k_channels**-0.5
127
+ self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
128
+ self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
129
+
130
+ nn.init.xavier_uniform_(self.conv_q.weight)
131
+ nn.init.xavier_uniform_(self.conv_k.weight)
132
+ nn.init.xavier_uniform_(self.conv_v.weight)
133
+ if proximal_init:
134
+ with torch.no_grad():
135
+ self.conv_k.weight.copy_(self.conv_q.weight)
136
+ self.conv_k.bias.copy_(self.conv_q.bias)
137
+
138
+ def forward(self, x, c, attn_mask=None):
139
+ q = self.conv_q(x)
140
+ k = self.conv_k(c)
141
+ v = self.conv_v(c)
142
+
143
+ x, self.attn = self.attention(q, k, v, mask=attn_mask)
144
+
145
+ x = self.conv_o(x)
146
+ return x
147
+
148
+ def attention(self, query, key, value, mask=None):
149
+ # reshape [b, d, t] -> [b, n_h, t, d_k]
150
+ b, d, t_s, t_t = (*key.size(), query.size(2))
151
+ query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
152
+ key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
153
+ value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
154
+
155
+ scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
156
+ if self.window_size is not None:
157
+ assert t_s == t_t, "Relative attention is only available for self-attention."
158
+ key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
159
+ rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
160
+ scores_local = self._relative_position_to_absolute_position(rel_logits)
161
+ scores = scores + scores_local
162
+ if self.proximal_bias:
163
+ assert t_s == t_t, "Proximal bias is only available for self-attention."
164
+ scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
165
+ if mask is not None:
166
+ scores = scores.masked_fill(mask == 0, -1e4)
167
+ if self.block_length is not None:
168
+ assert t_s == t_t, "Local attention is only available for self-attention."
169
+ block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
170
+ scores = scores.masked_fill(block_mask == 0, -1e4)
171
+ p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
172
+ p_attn = self.drop(p_attn)
173
+ output = torch.matmul(p_attn, value)
174
+ if self.window_size is not None:
175
+ relative_weights = self._absolute_position_to_relative_position(p_attn)
176
+ value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
177
+ output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
178
+ output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
179
+ return output, p_attn
180
+
181
+ def _matmul_with_relative_values(self, x, y):
182
+ """
183
+ x: [b, h, l, m]
184
+ y: [h or 1, m, d]
185
+ ret: [b, h, l, d]
186
+ """
187
+ ret = torch.matmul(x, y.unsqueeze(0))
188
+ return ret
189
+
190
+ def _matmul_with_relative_keys(self, x, y):
191
+ """
192
+ x: [b, h, l, d]
193
+ y: [h or 1, m, d]
194
+ ret: [b, h, l, m]
195
+ """
196
+ ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
197
+ return ret
198
+
199
+ def _get_relative_embeddings(self, relative_embeddings, length):
200
+ max_relative_position = 2 * self.window_size + 1
201
+ # Pad first before slice to avoid using cond ops.
202
+ pad_length = max(length - (self.window_size + 1), 0)
203
+ slice_start_position = max((self.window_size + 1) - length, 0)
204
+ slice_end_position = slice_start_position + 2 * length - 1
205
+ if pad_length > 0:
206
+ padded_relative_embeddings = F.pad(
207
+ relative_embeddings,
208
+ commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
209
+ else:
210
+ padded_relative_embeddings = relative_embeddings
211
+ used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
212
+ return used_relative_embeddings
213
+
214
+ def _relative_position_to_absolute_position(self, x):
215
+ """
216
+ x: [b, h, l, 2*l-1]
217
+ ret: [b, h, l, l]
218
+ """
219
+ batch, heads, length, _ = x.size()
220
+ # Concat columns of pad to shift from relative to absolute indexing.
221
+ x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
222
+
223
+ # Concat extra elements so to add up to shape (len+1, 2*len-1).
224
+ x_flat = x.view([batch, heads, length * 2 * length])
225
+ x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
226
+
227
+ # Reshape and slice out the padded elements.
228
+ x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
229
+ return x_final
230
+
231
+ def _absolute_position_to_relative_position(self, x):
232
+ """
233
+ x: [b, h, l, l]
234
+ ret: [b, h, l, 2*l-1]
235
+ """
236
+ batch, heads, length, _ = x.size()
237
+ # padd along column
238
+ x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
239
+ x_flat = x.view([batch, heads, length**2 + length*(length -1)])
240
+ # add 0's in the beginning that will skew the elements after reshape
241
+ x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
242
+ x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
243
+ return x_final
244
+
245
+ def _attention_bias_proximal(self, length):
246
+ """Bias for self-attention to encourage attention to close positions.
247
+ Args:
248
+ length: an integer scalar.
249
+ Returns:
250
+ a Tensor with shape [1, 1, length, length]
251
+ """
252
+ r = torch.arange(length, dtype=torch.float32)
253
+ diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
254
+ return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
255
+
256
+
257
+ class FFN(nn.Module):
258
+ def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
259
+ super().__init__()
260
+ self.in_channels = in_channels
261
+ self.out_channels = out_channels
262
+ self.filter_channels = filter_channels
263
+ self.kernel_size = kernel_size
264
+ self.p_dropout = p_dropout
265
+ self.activation = activation
266
+ self.causal = causal
267
+
268
+ if causal:
269
+ self.padding = self._causal_padding
270
+ else:
271
+ self.padding = self._same_padding
272
+
273
+ self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
274
+ self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
275
+ self.drop = nn.Dropout(p_dropout)
276
+
277
+ def forward(self, x, x_mask):
278
+ x = self.conv_1(self.padding(x * x_mask))
279
+ if self.activation == "gelu":
280
+ x = x * torch.sigmoid(1.702 * x)
281
+ else:
282
+ x = torch.relu(x)
283
+ x = self.drop(x)
284
+ x = self.conv_2(self.padding(x * x_mask))
285
+ return x * x_mask
286
+
287
+ def _causal_padding(self, x):
288
+ if self.kernel_size == 1:
289
+ return x
290
+ pad_l = self.kernel_size - 1
291
+ pad_r = 0
292
+ padding = [[0, 0], [0, 0], [pad_l, pad_r]]
293
+ x = F.pad(x, commons.convert_pad_shape(padding))
294
+ return x
295
+
296
+ def _same_padding(self, x):
297
+ if self.kernel_size == 1:
298
+ return x
299
+ pad_l = (self.kernel_size - 1) // 2
300
+ pad_r = self.kernel_size // 2
301
+ padding = [[0, 0], [0, 0], [pad_l, pad_r]]
302
+ x = F.pad(x, commons.convert_pad_shape(padding))
303
+ return x
commons.py ADDED
@@ -0,0 +1,161 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import math
2
+ import numpy as np
3
+ import torch
4
+ from torch import nn
5
+ from torch.nn import functional as F
6
+
7
+
8
+ def init_weights(m, mean=0.0, std=0.01):
9
+ classname = m.__class__.__name__
10
+ if classname.find("Conv") != -1:
11
+ m.weight.data.normal_(mean, std)
12
+
13
+
14
+ def get_padding(kernel_size, dilation=1):
15
+ return int((kernel_size*dilation - dilation)/2)
16
+
17
+
18
+ def convert_pad_shape(pad_shape):
19
+ l = pad_shape[::-1]
20
+ pad_shape = [item for sublist in l for item in sublist]
21
+ return pad_shape
22
+
23
+
24
+ def intersperse(lst, item):
25
+ result = [item] * (len(lst) * 2 + 1)
26
+ result[1::2] = lst
27
+ return result
28
+
29
+
30
+ def kl_divergence(m_p, logs_p, m_q, logs_q):
31
+ """KL(P||Q)"""
32
+ kl = (logs_q - logs_p) - 0.5
33
+ kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
34
+ return kl
35
+
36
+
37
+ def rand_gumbel(shape):
38
+ """Sample from the Gumbel distribution, protect from overflows."""
39
+ uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
40
+ return -torch.log(-torch.log(uniform_samples))
41
+
42
+
43
+ def rand_gumbel_like(x):
44
+ g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
45
+ return g
46
+
47
+
48
+ def slice_segments(x, ids_str, segment_size=4):
49
+ ret = torch.zeros_like(x[:, :, :segment_size])
50
+ for i in range(x.size(0)):
51
+ idx_str = ids_str[i]
52
+ idx_end = idx_str + segment_size
53
+ ret[i] = x[i, :, idx_str:idx_end]
54
+ return ret
55
+
56
+
57
+ def rand_slice_segments(x, x_lengths=None, segment_size=4):
58
+ b, d, t = x.size()
59
+ if x_lengths is None:
60
+ x_lengths = t
61
+ ids_str_max = x_lengths - segment_size + 1
62
+ ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
63
+ ret = slice_segments(x, ids_str, segment_size)
64
+ return ret, ids_str
65
+
66
+
67
+ def get_timing_signal_1d(
68
+ length, channels, min_timescale=1.0, max_timescale=1.0e4):
69
+ position = torch.arange(length, dtype=torch.float)
70
+ num_timescales = channels // 2
71
+ log_timescale_increment = (
72
+ math.log(float(max_timescale) / float(min_timescale)) /
73
+ (num_timescales - 1))
74
+ inv_timescales = min_timescale * torch.exp(
75
+ torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
76
+ scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
77
+ signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
78
+ signal = F.pad(signal, [0, 0, 0, channels % 2])
79
+ signal = signal.view(1, channels, length)
80
+ return signal
81
+
82
+
83
+ def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
84
+ b, channels, length = x.size()
85
+ signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
86
+ return x + signal.to(dtype=x.dtype, device=x.device)
87
+
88
+
89
+ def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
90
+ b, channels, length = x.size()
91
+ signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
92
+ return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
93
+
94
+
95
+ def subsequent_mask(length):
96
+ mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
97
+ return mask
98
+
99
+
100
+ @torch.jit.script
101
+ def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
102
+ n_channels_int = n_channels[0]
103
+ in_act = input_a + input_b
104
+ t_act = torch.tanh(in_act[:, :n_channels_int, :])
105
+ s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
106
+ acts = t_act * s_act
107
+ return acts
108
+
109
+
110
+ def convert_pad_shape(pad_shape):
111
+ l = pad_shape[::-1]
112
+ pad_shape = [item for sublist in l for item in sublist]
113
+ return pad_shape
114
+
115
+
116
+ def shift_1d(x):
117
+ x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
118
+ return x
119
+
120
+
121
+ def sequence_mask(length, max_length=None):
122
+ if max_length is None:
123
+ max_length = length.max()
124
+ x = torch.arange(max_length, dtype=length.dtype, device=length.device)
125
+ return x.unsqueeze(0) < length.unsqueeze(1)
126
+
127
+
128
+ def generate_path(duration, mask):
129
+ """
130
+ duration: [b, 1, t_x]
131
+ mask: [b, 1, t_y, t_x]
132
+ """
133
+ device = duration.device
134
+
135
+ b, _, t_y, t_x = mask.shape
136
+ cum_duration = torch.cumsum(duration, -1)
137
+
138
+ cum_duration_flat = cum_duration.view(b * t_x)
139
+ path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
140
+ path = path.view(b, t_x, t_y)
141
+ path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
142
+ path = path.unsqueeze(1).transpose(2,3) * mask
143
+ return path
144
+
145
+
146
+ def clip_grad_value_(parameters, clip_value, norm_type=2):
147
+ if isinstance(parameters, torch.Tensor):
148
+ parameters = [parameters]
149
+ parameters = list(filter(lambda p: p.grad is not None, parameters))
150
+ norm_type = float(norm_type)
151
+ if clip_value is not None:
152
+ clip_value = float(clip_value)
153
+
154
+ total_norm = 0
155
+ for p in parameters:
156
+ param_norm = p.grad.data.norm(norm_type)
157
+ total_norm += param_norm.item() ** norm_type
158
+ if clip_value is not None:
159
+ p.grad.data.clamp_(min=-clip_value, max=clip_value)
160
+ total_norm = total_norm ** (1. / norm_type)
161
+ return total_norm
data_utils.py ADDED
@@ -0,0 +1,392 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import time
2
+ import os
3
+ import random
4
+ import numpy as np
5
+ import torch
6
+ import torch.utils.data
7
+
8
+ import commons
9
+ from mel_processing import spectrogram_torch
10
+ from utils import load_wav_to_torch, load_filepaths_and_text
11
+ from text import text_to_sequence, cleaned_text_to_sequence
12
+
13
+
14
+ class TextAudioLoader(torch.utils.data.Dataset):
15
+ """
16
+ 1) loads audio, text pairs
17
+ 2) normalizes text and converts them to sequences of integers
18
+ 3) computes spectrograms from audio files.
19
+ """
20
+ def __init__(self, audiopaths_and_text, hparams):
21
+ self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
22
+ self.text_cleaners = hparams.text_cleaners
23
+ self.max_wav_value = hparams.max_wav_value
24
+ self.sampling_rate = hparams.sampling_rate
25
+ self.filter_length = hparams.filter_length
26
+ self.hop_length = hparams.hop_length
27
+ self.win_length = hparams.win_length
28
+ self.sampling_rate = hparams.sampling_rate
29
+
30
+ self.cleaned_text = getattr(hparams, "cleaned_text", False)
31
+
32
+ self.add_blank = hparams.add_blank
33
+ self.min_text_len = getattr(hparams, "min_text_len", 1)
34
+ self.max_text_len = getattr(hparams, "max_text_len", 190)
35
+
36
+ random.seed(1234)
37
+ random.shuffle(self.audiopaths_and_text)
38
+ self._filter()
39
+
40
+
41
+ def _filter(self):
42
+ """
43
+ Filter text & store spec lengths
44
+ """
45
+ # Store spectrogram lengths for Bucketing
46
+ # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
47
+ # spec_length = wav_length // hop_length
48
+
49
+ audiopaths_and_text_new = []
50
+ lengths = []
51
+ for audiopath, text in self.audiopaths_and_text:
52
+ if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
53
+ audiopaths_and_text_new.append([audiopath, text])
54
+ lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
55
+ self.audiopaths_and_text = audiopaths_and_text_new
56
+ self.lengths = lengths
57
+
58
+ def get_audio_text_pair(self, audiopath_and_text):
59
+ # separate filename and text
60
+ audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
61
+ text = self.get_text(text)
62
+ spec, wav = self.get_audio(audiopath)
63
+ return (text, spec, wav)
64
+
65
+ def get_audio(self, filename):
66
+ audio, sampling_rate = load_wav_to_torch(filename)
67
+ if sampling_rate != self.sampling_rate:
68
+ raise ValueError("{} {} SR doesn't match target {} SR".format(
69
+ sampling_rate, self.sampling_rate))
70
+ audio_norm = audio / self.max_wav_value
71
+ audio_norm = audio_norm.unsqueeze(0)
72
+ spec_filename = filename.replace(".wav", ".spec.pt")
73
+ if os.path.exists(spec_filename):
74
+ spec = torch.load(spec_filename)
75
+ else:
76
+ spec = spectrogram_torch(audio_norm, self.filter_length,
77
+ self.sampling_rate, self.hop_length, self.win_length,
78
+ center=False)
79
+ spec = torch.squeeze(spec, 0)
80
+ torch.save(spec, spec_filename)
81
+ return spec, audio_norm
82
+
83
+ def get_text(self, text):
84
+ if self.cleaned_text:
85
+ text_norm = cleaned_text_to_sequence(text)
86
+ else:
87
+ text_norm = text_to_sequence(text, self.text_cleaners)
88
+ if self.add_blank:
89
+ text_norm = commons.intersperse(text_norm, 0)
90
+ text_norm = torch.LongTensor(text_norm)
91
+ return text_norm
92
+
93
+ def __getitem__(self, index):
94
+ return self.get_audio_text_pair(self.audiopaths_and_text[index])
95
+
96
+ def __len__(self):
97
+ return len(self.audiopaths_and_text)
98
+
99
+
100
+ class TextAudioCollate():
101
+ """ Zero-pads model inputs and targets
102
+ """
103
+ def __init__(self, return_ids=False):
104
+ self.return_ids = return_ids
105
+
106
+ def __call__(self, batch):
107
+ """Collate's training batch from normalized text and aduio
108
+ PARAMS
109
+ ------
110
+ batch: [text_normalized, spec_normalized, wav_normalized]
111
+ """
112
+ # Right zero-pad all one-hot text sequences to max input length
113
+ _, ids_sorted_decreasing = torch.sort(
114
+ torch.LongTensor([x[1].size(1) for x in batch]),
115
+ dim=0, descending=True)
116
+
117
+ max_text_len = max([len(x[0]) for x in batch])
118
+ max_spec_len = max([x[1].size(1) for x in batch])
119
+ max_wav_len = max([x[2].size(1) for x in batch])
120
+
121
+ text_lengths = torch.LongTensor(len(batch))
122
+ spec_lengths = torch.LongTensor(len(batch))
123
+ wav_lengths = torch.LongTensor(len(batch))
124
+
125
+ text_padded = torch.LongTensor(len(batch), max_text_len)
126
+ spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
127
+ wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
128
+ text_padded.zero_()
129
+ spec_padded.zero_()
130
+ wav_padded.zero_()
131
+ for i in range(len(ids_sorted_decreasing)):
132
+ row = batch[ids_sorted_decreasing[i]]
133
+
134
+ text = row[0]
135
+ text_padded[i, :text.size(0)] = text
136
+ text_lengths[i] = text.size(0)
137
+
138
+ spec = row[1]
139
+ spec_padded[i, :, :spec.size(1)] = spec
140
+ spec_lengths[i] = spec.size(1)
141
+
142
+ wav = row[2]
143
+ wav_padded[i, :, :wav.size(1)] = wav
144
+ wav_lengths[i] = wav.size(1)
145
+
146
+ if self.return_ids:
147
+ return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, ids_sorted_decreasing
148
+ return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths
149
+
150
+
151
+ """Multi speaker version"""
152
+ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
153
+ """
154
+ 1) loads audio, speaker_id, text pairs
155
+ 2) normalizes text and converts them to sequences of integers
156
+ 3) computes spectrograms from audio files.
157
+ """
158
+ def __init__(self, audiopaths_sid_text, hparams):
159
+ self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
160
+ self.text_cleaners = hparams.text_cleaners
161
+ self.max_wav_value = hparams.max_wav_value
162
+ self.sampling_rate = hparams.sampling_rate
163
+ self.filter_length = hparams.filter_length
164
+ self.hop_length = hparams.hop_length
165
+ self.win_length = hparams.win_length
166
+ self.sampling_rate = hparams.sampling_rate
167
+
168
+ self.cleaned_text = getattr(hparams, "cleaned_text", False)
169
+
170
+ self.add_blank = hparams.add_blank
171
+ self.min_text_len = getattr(hparams, "min_text_len", 1)
172
+ self.max_text_len = getattr(hparams, "max_text_len", 190)
173
+
174
+ random.seed(1234)
175
+ random.shuffle(self.audiopaths_sid_text)
176
+ self._filter()
177
+
178
+ def _filter(self):
179
+ """
180
+ Filter text & store spec lengths
181
+ """
182
+ # Store spectrogram lengths for Bucketing
183
+ # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
184
+ # spec_length = wav_length // hop_length
185
+
186
+ audiopaths_sid_text_new = []
187
+ lengths = []
188
+ for audiopath, sid, text in self.audiopaths_sid_text:
189
+ if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
190
+ audiopaths_sid_text_new.append([audiopath, sid, text])
191
+ lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
192
+ self.audiopaths_sid_text = audiopaths_sid_text_new
193
+ self.lengths = lengths
194
+
195
+ def get_audio_text_speaker_pair(self, audiopath_sid_text):
196
+ # separate filename, speaker_id and text
197
+ audiopath, sid, text = audiopath_sid_text[0], audiopath_sid_text[1], audiopath_sid_text[2]
198
+ text = self.get_text(text)
199
+ spec, wav = self.get_audio(audiopath)
200
+ sid = self.get_sid(sid)
201
+ return (text, spec, wav, sid)
202
+
203
+ def get_audio(self, filename):
204
+ audio, sampling_rate = load_wav_to_torch(filename)
205
+ if sampling_rate != self.sampling_rate:
206
+ raise ValueError("{} {} SR doesn't match target {} SR".format(
207
+ sampling_rate, self.sampling_rate))
208
+ audio_norm = audio / self.max_wav_value
209
+ audio_norm = audio_norm.unsqueeze(0)
210
+ spec_filename = filename.replace(".wav", ".spec.pt")
211
+ if os.path.exists(spec_filename):
212
+ spec = torch.load(spec_filename)
213
+ else:
214
+ spec = spectrogram_torch(audio_norm, self.filter_length,
215
+ self.sampling_rate, self.hop_length, self.win_length,
216
+ center=False)
217
+ spec = torch.squeeze(spec, 0)
218
+ torch.save(spec, spec_filename)
219
+ return spec, audio_norm
220
+
221
+ def get_text(self, text):
222
+ if self.cleaned_text:
223
+ text_norm = cleaned_text_to_sequence(text)
224
+ else:
225
+ text_norm = text_to_sequence(text, self.text_cleaners)
226
+ if self.add_blank:
227
+ text_norm = commons.intersperse(text_norm, 0)
228
+ text_norm = torch.LongTensor(text_norm)
229
+ return text_norm
230
+
231
+ def get_sid(self, sid):
232
+ sid = torch.LongTensor([int(sid)])
233
+ return sid
234
+
235
+ def __getitem__(self, index):
236
+ return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
237
+
238
+ def __len__(self):
239
+ return len(self.audiopaths_sid_text)
240
+
241
+
242
+ class TextAudioSpeakerCollate():
243
+ """ Zero-pads model inputs and targets
244
+ """
245
+ def __init__(self, return_ids=False):
246
+ self.return_ids = return_ids
247
+
248
+ def __call__(self, batch):
249
+ """Collate's training batch from normalized text, audio and speaker identities
250
+ PARAMS
251
+ ------
252
+ batch: [text_normalized, spec_normalized, wav_normalized, sid]
253
+ """
254
+ # Right zero-pad all one-hot text sequences to max input length
255
+ _, ids_sorted_decreasing = torch.sort(
256
+ torch.LongTensor([x[1].size(1) for x in batch]),
257
+ dim=0, descending=True)
258
+
259
+ max_text_len = max([len(x[0]) for x in batch])
260
+ max_spec_len = max([x[1].size(1) for x in batch])
261
+ max_wav_len = max([x[2].size(1) for x in batch])
262
+
263
+ text_lengths = torch.LongTensor(len(batch))
264
+ spec_lengths = torch.LongTensor(len(batch))
265
+ wav_lengths = torch.LongTensor(len(batch))
266
+ sid = torch.LongTensor(len(batch))
267
+
268
+ text_padded = torch.LongTensor(len(batch), max_text_len)
269
+ spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
270
+ wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
271
+ text_padded.zero_()
272
+ spec_padded.zero_()
273
+ wav_padded.zero_()
274
+ for i in range(len(ids_sorted_decreasing)):
275
+ row = batch[ids_sorted_decreasing[i]]
276
+
277
+ text = row[0]
278
+ text_padded[i, :text.size(0)] = text
279
+ text_lengths[i] = text.size(0)
280
+
281
+ spec = row[1]
282
+ spec_padded[i, :, :spec.size(1)] = spec
283
+ spec_lengths[i] = spec.size(1)
284
+
285
+ wav = row[2]
286
+ wav_padded[i, :, :wav.size(1)] = wav
287
+ wav_lengths[i] = wav.size(1)
288
+
289
+ sid[i] = row[3]
290
+
291
+ if self.return_ids:
292
+ return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, ids_sorted_decreasing
293
+ return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid
294
+
295
+
296
+ class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
297
+ """
298
+ Maintain similar input lengths in a batch.
299
+ Length groups are specified by boundaries.
300
+ Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
301
+
302
+ It removes samples which are not included in the boundaries.
303
+ Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
304
+ """
305
+ def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
306
+ super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
307
+ self.lengths = dataset.lengths
308
+ self.batch_size = batch_size
309
+ self.boundaries = boundaries
310
+
311
+ self.buckets, self.num_samples_per_bucket = self._create_buckets()
312
+ self.total_size = sum(self.num_samples_per_bucket)
313
+ self.num_samples = self.total_size // self.num_replicas
314
+
315
+ def _create_buckets(self):
316
+ buckets = [[] for _ in range(len(self.boundaries) - 1)]
317
+ for i in range(len(self.lengths)):
318
+ length = self.lengths[i]
319
+ idx_bucket = self._bisect(length)
320
+ if idx_bucket != -1:
321
+ buckets[idx_bucket].append(i)
322
+
323
+ for i in range(len(buckets) - 1, 0, -1):
324
+ if len(buckets[i]) == 0:
325
+ buckets.pop(i)
326
+ self.boundaries.pop(i+1)
327
+
328
+ num_samples_per_bucket = []
329
+ for i in range(len(buckets)):
330
+ len_bucket = len(buckets[i])
331
+ total_batch_size = self.num_replicas * self.batch_size
332
+ rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
333
+ num_samples_per_bucket.append(len_bucket + rem)
334
+ return buckets, num_samples_per_bucket
335
+
336
+ def __iter__(self):
337
+ # deterministically shuffle based on epoch
338
+ g = torch.Generator()
339
+ g.manual_seed(self.epoch)
340
+
341
+ indices = []
342
+ if self.shuffle:
343
+ for bucket in self.buckets:
344
+ indices.append(torch.randperm(len(bucket), generator=g).tolist())
345
+ else:
346
+ for bucket in self.buckets:
347
+ indices.append(list(range(len(bucket))))
348
+
349
+ batches = []
350
+ for i in range(len(self.buckets)):
351
+ bucket = self.buckets[i]
352
+ len_bucket = len(bucket)
353
+ ids_bucket = indices[i]
354
+ num_samples_bucket = self.num_samples_per_bucket[i]
355
+
356
+ # add extra samples to make it evenly divisible
357
+ rem = num_samples_bucket - len_bucket
358
+ ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
359
+
360
+ # subsample
361
+ ids_bucket = ids_bucket[self.rank::self.num_replicas]
362
+
363
+ # batching
364
+ for j in range(len(ids_bucket) // self.batch_size):
365
+ batch = [bucket[idx] for idx in ids_bucket[j*self.batch_size:(j+1)*self.batch_size]]
366
+ batches.append(batch)
367
+
368
+ if self.shuffle:
369
+ batch_ids = torch.randperm(len(batches), generator=g).tolist()
370
+ batches = [batches[i] for i in batch_ids]
371
+ self.batches = batches
372
+
373
+ assert len(self.batches) * self.batch_size == self.num_samples
374
+ return iter(self.batches)
375
+
376
+ def _bisect(self, x, lo=0, hi=None):
377
+ if hi is None:
378
+ hi = len(self.boundaries) - 1
379
+
380
+ if hi > lo:
381
+ mid = (hi + lo) // 2
382
+ if self.boundaries[mid] < x and x <= self.boundaries[mid+1]:
383
+ return mid
384
+ elif x <= self.boundaries[mid]:
385
+ return self._bisect(x, lo, mid)
386
+ else:
387
+ return self._bisect(x, mid + 1, hi)
388
+ else:
389
+ return -1
390
+
391
+ def __len__(self):
392
+ return self.num_samples // self.batch_size
fig/proposed_model.png ADDED

Git LFS Details

  • SHA256: ad9921b88eb890f87bf334e839586034d89429991e24217c77faf49611c71a64
  • Pointer size: 132 Bytes
  • Size of remote file: 1.02 MB
inference.py ADDED
@@ -0,0 +1,158 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import time
2
+ import matplotlib.pyplot as plt
3
+ import IPython.display as ipd
4
+ import re
5
+ import os
6
+ import json
7
+ import math
8
+ import torch
9
+ from torch import nn
10
+ from torch.nn import functional as F
11
+ from torch.utils.data import DataLoader
12
+ import gradio as gr
13
+ import commons
14
+ import utils
15
+ from data_utils import TextAudioLoader, TextAudioCollate, TextAudioSpeakerLoader, TextAudioSpeakerCollate
16
+ from models import SynthesizerTrn
17
+ from text.symbols import symbols
18
+ from text import text_to_sequence
19
+ import unicodedata
20
+ from scipy.io.wavfile import write
21
+ def get_text(text, hps):
22
+ text_norm = text_to_sequence(text, hps.data.text_cleaners)
23
+ if hps.data.add_blank:
24
+ text_norm = commons.intersperse(text_norm, 0)
25
+ text_norm = torch.LongTensor(text_norm)
26
+ return text_norm
27
+
28
+
29
+ def get_label(text, label):
30
+ if f'[{label}]' in text:
31
+ return True, text.replace(f'[{label}]', '')
32
+ else:
33
+ return False, text
34
+
35
+
36
+
37
+
38
+
39
+ def selection(speaker):
40
+ if speaker == "高咲侑":
41
+ spk = 0
42
+ return spk
43
+
44
+ elif speaker == "歩夢":
45
+ spk = 1
46
+ return spk
47
+
48
+ elif speaker == "かすみ":
49
+ spk = 2
50
+ return spk
51
+
52
+ elif speaker == "しずく":
53
+ spk = 3
54
+ return spk
55
+
56
+ elif speaker == "果林":
57
+ spk = 4
58
+ return spk
59
+
60
+ elif speaker == "愛":
61
+ spk = 5
62
+ return spk
63
+
64
+ elif speaker == "彼方":
65
+ spk = 6
66
+ return spk
67
+
68
+ elif speaker == "せつ菜":
69
+ spk = 7
70
+ return spk
71
+ elif speaker == "エマ":
72
+ spk = 8
73
+ return spk
74
+ elif speaker == "璃奈":
75
+ spk = 9
76
+ return spk
77
+ elif speaker == "栞子":
78
+ spk = 10
79
+ return spk
80
+ elif speaker == "ランジュ":
81
+ spk = 11
82
+ return spk
83
+ elif speaker == "ミア":
84
+ spk = 12
85
+ return spk
86
+ elif speaker == "三色绘恋1":
87
+ spk = 13
88
+ return spk
89
+ elif speaker == "三色绘恋2":
90
+ spk = 15
91
+ elif speaker == "派蒙":
92
+ spk = 16
93
+ return spk
94
+
95
+ def sle(language,tts_input0):
96
+ if language == "中文":
97
+ tts_input1 = "[ZH]" + tts_input0.replace('\n','。').replace(' ',',') + "[ZH]"
98
+ return tts_input1
99
+ if language == "英文":
100
+ tts_input1 = "[EN]" + tts_input0.replace('\n','.').replace(' ',',') + "[EN]"
101
+ return tts_input1
102
+ elif language == "日文":
103
+ tts_input1 = "[JA]" + tts_input0.replace('\n','。').replace(' ',',') + "[JA]"
104
+ return tts_input1
105
+ def infer(language,text,speaker_id, n_scale= 0.667,n_scale_w = 0.8, l_scale = 1 ):
106
+ speaker_id = int(selection(speaker_id))
107
+ stn_tst = get_text(sle(language,text), hps_ms)
108
+ with torch.no_grad():
109
+ x_tst = stn_tst.unsqueeze(0).to(dev)
110
+ x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(dev)
111
+ sid = torch.LongTensor([speaker_id]).to(dev)
112
+ t1 = time.time()
113
+ audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=n_scale, noise_scale_w=n_scale_w, length_scale=l_scale)[0][0,0].data.cpu().float().numpy()
114
+ t2 = time.time()
115
+ spending_time = "推理时间:"+str(t2-t1)+"s"
116
+ print(spending_time)
117
+ return (hps_ms.data.sampling_rate, audio)
118
+ lan = ["中文","日文","英文"]
119
+ idols = ["高咲侑","歩夢","かすみ","しずく","果林","愛","彼方","せつ菜","璃奈","栞子","エマ","ランジュ","ミア","三色绘恋1","三色绘恋2","派蒙"]
120
+
121
+
122
+
123
+ Device = input("设置运行时类型")
124
+ if Device == "cpu":
125
+ dev = torch.device("cpu")
126
+ else:
127
+ dev = torch.device("cuda:0")
128
+ hps_ms = utils.get_hparams_from_file("C:/Users/24293/机器学习-/MB-iSTFT-VITS/logs/nijigaku/config.json")
129
+ net_g_ms = SynthesizerTrn(
130
+ len(symbols),
131
+ hps_ms.data.filter_length // 2 + 1,
132
+ hps_ms.train.segment_size // hps_ms.data.hop_length,
133
+ n_speakers=hps_ms.data.n_speakers,
134
+ **hps_ms.model).to(dev)
135
+ _ = net_g_ms.eval()
136
+
137
+ _ = utils.load_checkpoint("C:/Users/24293/机器学习-/MB-iSTFT-VITS/logs/nijigaku/G_1330000.pth", net_g_ms, None)
138
+
139
+ app = gr.Blocks()
140
+
141
+
142
+
143
+ with app:
144
+ with gr.Tabs():
145
+
146
+ with gr.TabItem("Basic"):
147
+
148
+ tts_input1 = gr.TextArea(label="旧模型", value="一次審査、二次審査、それぞれの欄に記入をお願いします。")
149
+ language = gr.Dropdown(label="选择语言",choices=lan, value="日文", interactive=True)
150
+ para_input1 = gr.Slider(minimum= 0.01,maximum=1.0,label="更改噪声比例", value=0.667)
151
+ para_input2 = gr.Slider(minimum= 0.01,maximum=1.0,label="更改噪声偏差", value=0.8)
152
+ para_input3 = gr.Slider(minimum= 0.1,maximum=10,label="更改时间比例", value=1)
153
+ tts_submit = gr.Button("Generate", variant="primary")
154
+ speaker1 = gr.Dropdown(label="选择说话人",choices=idols, value="かすみ", interactive=True)
155
+ tts_output2 = gr.Audio(label="Output")
156
+ tts_submit.click(infer, [language,tts_input1,speaker1,para_input1,para_input2,para_input3], [tts_output2])
157
+ #app.launch(share=True)
158
+ app.launch()
losses.py ADDED
@@ -0,0 +1,71 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ from torch.nn import functional as F
3
+ from stft_loss import MultiResolutionSTFTLoss
4
+
5
+
6
+ import commons
7
+
8
+
9
+ def feature_loss(fmap_r, fmap_g):
10
+ loss = 0
11
+ for dr, dg in zip(fmap_r, fmap_g):
12
+ for rl, gl in zip(dr, dg):
13
+ rl = rl.float().detach()
14
+ gl = gl.float()
15
+ loss += torch.mean(torch.abs(rl - gl))
16
+
17
+ return loss * 2
18
+
19
+
20
+ def discriminator_loss(disc_real_outputs, disc_generated_outputs):
21
+ loss = 0
22
+ r_losses = []
23
+ g_losses = []
24
+ for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
25
+ dr = dr.float()
26
+ dg = dg.float()
27
+ r_loss = torch.mean((1-dr)**2)
28
+ g_loss = torch.mean(dg**2)
29
+ loss += (r_loss + g_loss)
30
+ r_losses.append(r_loss.item())
31
+ g_losses.append(g_loss.item())
32
+
33
+ return loss, r_losses, g_losses
34
+
35
+
36
+ def generator_loss(disc_outputs):
37
+ loss = 0
38
+ gen_losses = []
39
+ for dg in disc_outputs:
40
+ dg = dg.float()
41
+ l = torch.mean((1-dg)**2)
42
+ gen_losses.append(l)
43
+ loss += l
44
+
45
+ return loss, gen_losses
46
+
47
+
48
+ def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
49
+ """
50
+ z_p, logs_q: [b, h, t_t]
51
+ m_p, logs_p: [b, h, t_t]
52
+ """
53
+ z_p = z_p.float()
54
+ logs_q = logs_q.float()
55
+ m_p = m_p.float()
56
+ logs_p = logs_p.float()
57
+ z_mask = z_mask.float()
58
+
59
+ kl = logs_p - logs_q - 0.5
60
+ kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
61
+ kl = torch.sum(kl * z_mask)
62
+ l = kl / torch.sum(z_mask)
63
+ return l
64
+
65
+ def subband_stft_loss(h, y_mb, y_hat_mb):
66
+ sub_stft_loss = MultiResolutionSTFTLoss(h.train.fft_sizes, h.train.hop_sizes, h.train.win_lengths)
67
+ y_mb = y_mb.view(-1, y_mb.size(2))
68
+ y_hat_mb = y_hat_mb.view(-1, y_hat_mb.size(2))
69
+ sub_sc_loss, sub_mag_loss = sub_stft_loss(y_hat_mb[:, :y_mb.size(-1)], y_mb)
70
+ return sub_sc_loss+sub_mag_loss
71
+
mel_processing.py ADDED
@@ -0,0 +1,112 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import math
2
+ import os
3
+ import random
4
+ import torch
5
+ from torch import nn
6
+ import torch.nn.functional as F
7
+ import torch.utils.data
8
+ import numpy as np
9
+ import librosa
10
+ import librosa.util as librosa_util
11
+ from librosa.util import normalize, pad_center, tiny
12
+ from scipy.signal import get_window
13
+ from scipy.io.wavfile import read
14
+ from librosa.filters import mel as librosa_mel_fn
15
+
16
+ MAX_WAV_VALUE = 32768.0
17
+
18
+
19
+ def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
20
+ """
21
+ PARAMS
22
+ ------
23
+ C: compression factor
24
+ """
25
+ return torch.log(torch.clamp(x, min=clip_val) * C)
26
+
27
+
28
+ def dynamic_range_decompression_torch(x, C=1):
29
+ """
30
+ PARAMS
31
+ ------
32
+ C: compression factor used to compress
33
+ """
34
+ return torch.exp(x) / C
35
+
36
+
37
+ def spectral_normalize_torch(magnitudes):
38
+ output = dynamic_range_compression_torch(magnitudes)
39
+ return output
40
+
41
+
42
+ def spectral_de_normalize_torch(magnitudes):
43
+ output = dynamic_range_decompression_torch(magnitudes)
44
+ return output
45
+
46
+
47
+ mel_basis = {}
48
+ hann_window = {}
49
+
50
+
51
+ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
52
+ if torch.min(y) < -1.:
53
+ print('min value is ', torch.min(y))
54
+ if torch.max(y) > 1.:
55
+ print('max value is ', torch.max(y))
56
+
57
+ global hann_window
58
+ dtype_device = str(y.dtype) + '_' + str(y.device)
59
+ wnsize_dtype_device = str(win_size) + '_' + dtype_device
60
+ if wnsize_dtype_device not in hann_window:
61
+ hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
62
+
63
+ y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
64
+ y = y.squeeze(1)
65
+
66
+ spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
67
+ center=center, pad_mode='reflect', normalized=False, onesided=True)
68
+
69
+ spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
70
+ return spec
71
+
72
+
73
+ def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
74
+ global mel_basis
75
+ dtype_device = str(spec.dtype) + '_' + str(spec.device)
76
+ fmax_dtype_device = str(fmax) + '_' + dtype_device
77
+ if fmax_dtype_device not in mel_basis:
78
+ mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
79
+ mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
80
+ spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
81
+ spec = spectral_normalize_torch(spec)
82
+ return spec
83
+
84
+
85
+ def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
86
+ if torch.min(y) < -1.:
87
+ print('min value is ', torch.min(y))
88
+ if torch.max(y) > 1.:
89
+ print('max value is ', torch.max(y))
90
+
91
+ global mel_basis, hann_window
92
+ dtype_device = str(y.dtype) + '_' + str(y.device)
93
+ fmax_dtype_device = str(fmax) + '_' + dtype_device
94
+ wnsize_dtype_device = str(win_size) + '_' + dtype_device
95
+ if fmax_dtype_device not in mel_basis:
96
+ mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
97
+ mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
98
+ if wnsize_dtype_device not in hann_window:
99
+ hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
100
+
101
+ y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
102
+ y = y.squeeze(1)
103
+
104
+ spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
105
+ center=center, pad_mode='reflect', normalized=False, onesided=True)
106
+
107
+ spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
108
+
109
+ spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
110
+ spec = spectral_normalize_torch(spec)
111
+
112
+ return spec
models.py ADDED
@@ -0,0 +1,730 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import copy
2
+ import math
3
+ import torch
4
+ from torch import nn
5
+ from torch.nn import functional as F
6
+
7
+ import commons
8
+ import modules
9
+ import attentions
10
+ import monotonic_align
11
+
12
+ from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
13
+ from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
14
+ from commons import init_weights, get_padding
15
+ from pqmf import PQMF
16
+ from stft import TorchSTFT
17
+ import math
18
+
19
+
20
+ class StochasticDurationPredictor(nn.Module):
21
+ def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
22
+ super().__init__()
23
+ filter_channels = in_channels # it needs to be removed from future version.
24
+ self.in_channels = in_channels
25
+ self.filter_channels = filter_channels
26
+ self.kernel_size = kernel_size
27
+ self.p_dropout = p_dropout
28
+ self.n_flows = n_flows
29
+ self.gin_channels = gin_channels
30
+
31
+ self.log_flow = modules.Log()
32
+ self.flows = nn.ModuleList()
33
+ self.flows.append(modules.ElementwiseAffine(2))
34
+ for i in range(n_flows):
35
+ self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
36
+ self.flows.append(modules.Flip())
37
+
38
+ self.post_pre = nn.Conv1d(1, filter_channels, 1)
39
+ self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
40
+ self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
41
+ self.post_flows = nn.ModuleList()
42
+ self.post_flows.append(modules.ElementwiseAffine(2))
43
+ for i in range(4):
44
+ self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
45
+ self.post_flows.append(modules.Flip())
46
+
47
+ self.pre = nn.Conv1d(in_channels, filter_channels, 1)
48
+ self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
49
+ self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
50
+ if gin_channels != 0:
51
+ self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
52
+
53
+ def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
54
+ x = torch.detach(x)
55
+ x = self.pre(x)
56
+ if g is not None:
57
+ g = torch.detach(g)
58
+ x = x + self.cond(g)
59
+ x = self.convs(x, x_mask)
60
+ x = self.proj(x) * x_mask
61
+
62
+ if not reverse:
63
+ flows = self.flows
64
+ assert w is not None
65
+
66
+ logdet_tot_q = 0
67
+ h_w = self.post_pre(w)
68
+ h_w = self.post_convs(h_w, x_mask)
69
+ h_w = self.post_proj(h_w) * x_mask
70
+ e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
71
+ z_q = e_q
72
+ for flow in self.post_flows:
73
+ z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
74
+ logdet_tot_q += logdet_q
75
+ z_u, z1 = torch.split(z_q, [1, 1], 1)
76
+ u = torch.sigmoid(z_u) * x_mask
77
+ z0 = (w - u) * x_mask
78
+ logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
79
+ logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
80
+
81
+ logdet_tot = 0
82
+ z0, logdet = self.log_flow(z0, x_mask)
83
+ logdet_tot += logdet
84
+ z = torch.cat([z0, z1], 1)
85
+ for flow in flows:
86
+ z, logdet = flow(z, x_mask, g=x, reverse=reverse)
87
+ logdet_tot = logdet_tot + logdet
88
+ nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
89
+ return nll + logq # [b]
90
+ else:
91
+ flows = list(reversed(self.flows))
92
+ flows = flows[:-2] + [flows[-1]] # remove a useless vflow
93
+ z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
94
+ for flow in flows:
95
+ z = flow(z, x_mask, g=x, reverse=reverse)
96
+ z0, z1 = torch.split(z, [1, 1], 1)
97
+ logw = z0
98
+ return logw
99
+
100
+
101
+ class DurationPredictor(nn.Module):
102
+ def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
103
+ super().__init__()
104
+
105
+ self.in_channels = in_channels
106
+ self.filter_channels = filter_channels
107
+ self.kernel_size = kernel_size
108
+ self.p_dropout = p_dropout
109
+ self.gin_channels = gin_channels
110
+
111
+ self.drop = nn.Dropout(p_dropout)
112
+ self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
113
+ self.norm_1 = modules.LayerNorm(filter_channels)
114
+ self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
115
+ self.norm_2 = modules.LayerNorm(filter_channels)
116
+ self.proj = nn.Conv1d(filter_channels, 1, 1)
117
+
118
+ if gin_channels != 0:
119
+ self.cond = nn.Conv1d(gin_channels, in_channels, 1)
120
+
121
+ def forward(self, x, x_mask, g=None):
122
+ x = torch.detach(x)
123
+ if g is not None:
124
+ g = torch.detach(g)
125
+ x = x + self.cond(g)
126
+ x = self.conv_1(x * x_mask)
127
+ x = torch.relu(x)
128
+ x = self.norm_1(x)
129
+ x = self.drop(x)
130
+ x = self.conv_2(x * x_mask)
131
+ x = torch.relu(x)
132
+ x = self.norm_2(x)
133
+ x = self.drop(x)
134
+ x = self.proj(x * x_mask)
135
+ return x * x_mask
136
+
137
+
138
+ class TextEncoder(nn.Module):
139
+ def __init__(self,
140
+ n_vocab,
141
+ out_channels,
142
+ hidden_channels,
143
+ filter_channels,
144
+ n_heads,
145
+ n_layers,
146
+ kernel_size,
147
+ p_dropout):
148
+ super().__init__()
149
+ self.n_vocab = n_vocab
150
+ self.out_channels = out_channels
151
+ self.hidden_channels = hidden_channels
152
+ self.filter_channels = filter_channels
153
+ self.n_heads = n_heads
154
+ self.n_layers = n_layers
155
+ self.kernel_size = kernel_size
156
+ self.p_dropout = p_dropout
157
+
158
+ self.emb = nn.Embedding(n_vocab, hidden_channels)
159
+ nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
160
+
161
+ self.encoder = attentions.Encoder(
162
+ hidden_channels,
163
+ filter_channels,
164
+ n_heads,
165
+ n_layers,
166
+ kernel_size,
167
+ p_dropout)
168
+ self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
169
+
170
+ def forward(self, x, x_lengths):
171
+ x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
172
+ x = torch.transpose(x, 1, -1) # [b, h, t]
173
+ x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
174
+
175
+ x = self.encoder(x * x_mask, x_mask)
176
+ stats = self.proj(x) * x_mask
177
+
178
+ m, logs = torch.split(stats, self.out_channels, dim=1)
179
+ return x, m, logs, x_mask
180
+
181
+
182
+ class ResidualCouplingBlock(nn.Module):
183
+ def __init__(self,
184
+ channels,
185
+ hidden_channels,
186
+ kernel_size,
187
+ dilation_rate,
188
+ n_layers,
189
+ n_flows=4,
190
+ gin_channels=0):
191
+ super().__init__()
192
+ self.channels = channels
193
+ self.hidden_channels = hidden_channels
194
+ self.kernel_size = kernel_size
195
+ self.dilation_rate = dilation_rate
196
+ self.n_layers = n_layers
197
+ self.n_flows = n_flows
198
+ self.gin_channels = gin_channels
199
+
200
+ self.flows = nn.ModuleList()
201
+ for i in range(n_flows):
202
+ self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
203
+ self.flows.append(modules.Flip())
204
+
205
+ def forward(self, x, x_mask, g=None, reverse=False):
206
+ if not reverse:
207
+ for flow in self.flows:
208
+ x, _ = flow(x, x_mask, g=g, reverse=reverse)
209
+ else:
210
+ for flow in reversed(self.flows):
211
+ x = flow(x, x_mask, g=g, reverse=reverse)
212
+ return x
213
+
214
+
215
+ class PosteriorEncoder(nn.Module):
216
+ def __init__(self,
217
+ in_channels,
218
+ out_channels,
219
+ hidden_channels,
220
+ kernel_size,
221
+ dilation_rate,
222
+ n_layers,
223
+ gin_channels=0):
224
+ super().__init__()
225
+ self.in_channels = in_channels
226
+ self.out_channels = out_channels
227
+ self.hidden_channels = hidden_channels
228
+ self.kernel_size = kernel_size
229
+ self.dilation_rate = dilation_rate
230
+ self.n_layers = n_layers
231
+ self.gin_channels = gin_channels
232
+
233
+ self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
234
+ self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
235
+ self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
236
+
237
+ def forward(self, x, x_lengths, g=None):
238
+ x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
239
+ x = self.pre(x) * x_mask
240
+ x = self.enc(x, x_mask, g=g)
241
+ stats = self.proj(x) * x_mask
242
+ m, logs = torch.split(stats, self.out_channels, dim=1)
243
+ z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
244
+ return z, m, logs, x_mask
245
+
246
+ class iSTFT_Generator(torch.nn.Module):
247
+ def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size, gin_channels=0):
248
+ super(iSTFT_Generator, self).__init__()
249
+ # self.h = h
250
+ self.gen_istft_n_fft = gen_istft_n_fft
251
+ self.gen_istft_hop_size = gen_istft_hop_size
252
+
253
+ self.num_kernels = len(resblock_kernel_sizes)
254
+ self.num_upsamples = len(upsample_rates)
255
+ self.conv_pre = weight_norm(Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3))
256
+ resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
257
+
258
+ self.ups = nn.ModuleList()
259
+ for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
260
+ self.ups.append(weight_norm(
261
+ ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
262
+ k, u, padding=(k-u)//2)))
263
+
264
+ self.resblocks = nn.ModuleList()
265
+ for i in range(len(self.ups)):
266
+ ch = upsample_initial_channel//(2**(i+1))
267
+ for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
268
+ self.resblocks.append(resblock(ch, k, d))
269
+
270
+ self.post_n_fft = self.gen_istft_n_fft
271
+ self.conv_post = weight_norm(Conv1d(ch, self.post_n_fft + 2, 7, 1, padding=3))
272
+ self.ups.apply(init_weights)
273
+ self.conv_post.apply(init_weights)
274
+ self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))
275
+ self.stft = TorchSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size, win_length=self.gen_istft_n_fft)
276
+ def forward(self, x, g=None):
277
+
278
+ x = self.conv_pre(x)
279
+ for i in range(self.num_upsamples):
280
+ x = F.leaky_relu(x, modules.LRELU_SLOPE)
281
+ x = self.ups[i](x)
282
+ xs = None
283
+ for j in range(self.num_kernels):
284
+ if xs is None:
285
+ xs = self.resblocks[i*self.num_kernels+j](x)
286
+ else:
287
+ xs += self.resblocks[i*self.num_kernels+j](x)
288
+ x = xs / self.num_kernels
289
+ x = F.leaky_relu(x)
290
+ x = self.reflection_pad(x)
291
+ x = self.conv_post(x)
292
+ spec = torch.exp(x[:,:self.post_n_fft // 2 + 1, :])
293
+ phase = math.pi*torch.sin(x[:, self.post_n_fft // 2 + 1:, :])
294
+ out = self.stft.inverse(spec, phase).to(x.device)
295
+ return out, None
296
+
297
+ def remove_weight_norm(self):
298
+ print('Removing weight norm...')
299
+ for l in self.ups:
300
+ remove_weight_norm(l)
301
+ for l in self.resblocks:
302
+ l.remove_weight_norm()
303
+ remove_weight_norm(self.conv_pre)
304
+ remove_weight_norm(self.conv_post)
305
+
306
+
307
+ class Multiband_iSTFT_Generator(torch.nn.Module):
308
+ def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size, subbands, gin_channels=0):
309
+ super(Multiband_iSTFT_Generator, self).__init__()
310
+ # self.h = h
311
+ self.subbands = subbands
312
+ self.num_kernels = len(resblock_kernel_sizes)
313
+ self.num_upsamples = len(upsample_rates)
314
+ self.conv_pre = weight_norm(Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3))
315
+ resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
316
+
317
+ self.ups = nn.ModuleList()
318
+ for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
319
+ self.ups.append(weight_norm(
320
+ ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
321
+ k, u, padding=(k-u)//2)))
322
+
323
+ self.resblocks = nn.ModuleList()
324
+ for i in range(len(self.ups)):
325
+ ch = upsample_initial_channel//(2**(i+1))
326
+ for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
327
+ self.resblocks.append(resblock(ch, k, d))
328
+
329
+ self.post_n_fft = gen_istft_n_fft
330
+ self.ups.apply(init_weights)
331
+ self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))
332
+ self.reshape_pixelshuffle = []
333
+
334
+ self.subband_conv_post = weight_norm(Conv1d(ch, self.subbands*(self.post_n_fft + 2), 7, 1, padding=3))
335
+
336
+ self.subband_conv_post.apply(init_weights)
337
+
338
+ self.gen_istft_n_fft = gen_istft_n_fft
339
+ self.gen_istft_hop_size = gen_istft_hop_size
340
+
341
+
342
+ def forward(self, x, g=None):
343
+ stft = TorchSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size, win_length=self.gen_istft_n_fft).to(x.device)
344
+ pqmf = PQMF(x.device)
345
+
346
+ x = self.conv_pre(x)#[B, ch, length]
347
+
348
+ for i in range(self.num_upsamples):
349
+ x = F.leaky_relu(x, modules.LRELU_SLOPE)
350
+ x = self.ups[i](x)
351
+
352
+
353
+ xs = None
354
+ for j in range(self.num_kernels):
355
+ if xs is None:
356
+ xs = self.resblocks[i*self.num_kernels+j](x)
357
+ else:
358
+ xs += self.resblocks[i*self.num_kernels+j](x)
359
+ x = xs / self.num_kernels
360
+
361
+ x = F.leaky_relu(x)
362
+ x = self.reflection_pad(x)
363
+ x = self.subband_conv_post(x)
364
+ x = torch.reshape(x, (x.shape[0], self.subbands, x.shape[1]//self.subbands, x.shape[-1]))
365
+
366
+ spec = torch.exp(x[:,:,:self.post_n_fft // 2 + 1, :])
367
+ phase = math.pi*torch.sin(x[:,:, self.post_n_fft // 2 + 1:, :])
368
+
369
+ y_mb_hat = stft.inverse(torch.reshape(spec, (spec.shape[0]*self.subbands, self.gen_istft_n_fft // 2 + 1, spec.shape[-1])), torch.reshape(phase, (phase.shape[0]*self.subbands, self.gen_istft_n_fft // 2 + 1, phase.shape[-1])))
370
+ y_mb_hat = torch.reshape(y_mb_hat, (x.shape[0], self.subbands, 1, y_mb_hat.shape[-1]))
371
+ y_mb_hat = y_mb_hat.squeeze(-2)
372
+
373
+ y_g_hat = pqmf.synthesis(y_mb_hat)
374
+
375
+ return y_g_hat, y_mb_hat
376
+
377
+ def remove_weight_norm(self):
378
+ print('Removing weight norm...')
379
+ for l in self.ups:
380
+ remove_weight_norm(l)
381
+ for l in self.resblocks:
382
+ l.remove_weight_norm()
383
+
384
+
385
+ class Multistream_iSTFT_Generator(torch.nn.Module):
386
+ def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size, subbands, gin_channels=0):
387
+ super(Multistream_iSTFT_Generator, self).__init__()
388
+ # self.h = h
389
+ self.subbands = subbands
390
+ self.num_kernels = len(resblock_kernel_sizes)
391
+ self.num_upsamples = len(upsample_rates)
392
+ self.conv_pre = weight_norm(Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3))
393
+ resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
394
+
395
+ self.ups = nn.ModuleList()
396
+ for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
397
+ self.ups.append(weight_norm(
398
+ ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
399
+ k, u, padding=(k-u)//2)))
400
+
401
+ self.resblocks = nn.ModuleList()
402
+ for i in range(len(self.ups)):
403
+ ch = upsample_initial_channel//(2**(i+1))
404
+ for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
405
+ self.resblocks.append(resblock(ch, k, d))
406
+
407
+ self.post_n_fft = gen_istft_n_fft
408
+ self.ups.apply(init_weights)
409
+ self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))
410
+ self.reshape_pixelshuffle = []
411
+
412
+ self.subband_conv_post = weight_norm(Conv1d(ch, self.subbands*(self.post_n_fft + 2), 7, 1, padding=3))
413
+
414
+ self.subband_conv_post.apply(init_weights)
415
+
416
+ self.gen_istft_n_fft = gen_istft_n_fft
417
+ self.gen_istft_hop_size = gen_istft_hop_size
418
+
419
+ updown_filter = torch.zeros((self.subbands, self.subbands, self.subbands)).float()
420
+ for k in range(self.subbands):
421
+ updown_filter[k, k, 0] = 1.0
422
+ self.register_buffer("updown_filter", updown_filter)
423
+ self.multistream_conv_post = weight_norm(Conv1d(4, 1, kernel_size=63, bias=False, padding=get_padding(63, 1)))
424
+ self.multistream_conv_post.apply(init_weights)
425
+
426
+
427
+
428
+ def forward(self, x, g=None):
429
+ stft = TorchSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size, win_length=self.gen_istft_n_fft).to(x.device)
430
+ # pqmf = PQMF(x.device)
431
+
432
+ x = self.conv_pre(x)#[B, ch, length]
433
+
434
+ for i in range(self.num_upsamples):
435
+
436
+
437
+ x = F.leaky_relu(x, modules.LRELU_SLOPE)
438
+ x = self.ups[i](x)
439
+
440
+
441
+ xs = None
442
+ for j in range(self.num_kernels):
443
+ if xs is None:
444
+ xs = self.resblocks[i*self.num_kernels+j](x)
445
+ else:
446
+ xs += self.resblocks[i*self.num_kernels+j](x)
447
+ x = xs / self.num_kernels
448
+
449
+ x = F.leaky_relu(x)
450
+ x = self.reflection_pad(x)
451
+ x = self.subband_conv_post(x)
452
+ x = torch.reshape(x, (x.shape[0], self.subbands, x.shape[1]//self.subbands, x.shape[-1]))
453
+
454
+ spec = torch.exp(x[:,:,:self.post_n_fft // 2 + 1, :])
455
+ phase = math.pi*torch.sin(x[:,:, self.post_n_fft // 2 + 1:, :])
456
+
457
+ y_mb_hat = stft.inverse(torch.reshape(spec, (spec.shape[0]*self.subbands, self.gen_istft_n_fft // 2 + 1, spec.shape[-1])), torch.reshape(phase, (phase.shape[0]*self.subbands, self.gen_istft_n_fft // 2 + 1, phase.shape[-1])))
458
+ y_mb_hat = torch.reshape(y_mb_hat, (x.shape[0], self.subbands, 1, y_mb_hat.shape[-1]))
459
+ y_mb_hat = y_mb_hat.squeeze(-2)
460
+
461
+ y_mb_hat = F.conv_transpose1d(y_mb_hat, self.updown_filter.to(x.device) * self.subbands, stride=self.subbands)
462
+
463
+ y_g_hat = self.multistream_conv_post(y_mb_hat)
464
+
465
+ return y_g_hat, y_mb_hat
466
+
467
+ def remove_weight_norm(self):
468
+ print('Removing weight norm...')
469
+ for l in self.ups:
470
+ remove_weight_norm(l)
471
+ for l in self.resblocks:
472
+ l.remove_weight_norm()
473
+
474
+
475
+ class DiscriminatorP(torch.nn.Module):
476
+ def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
477
+ super(DiscriminatorP, self).__init__()
478
+ self.period = period
479
+ self.use_spectral_norm = use_spectral_norm
480
+ norm_f = weight_norm if use_spectral_norm == False else spectral_norm
481
+ self.convs = nn.ModuleList([
482
+ norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
483
+ norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
484
+ norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
485
+ norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
486
+ norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
487
+ ])
488
+ self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
489
+
490
+ def forward(self, x):
491
+ fmap = []
492
+
493
+ # 1d to 2d
494
+ b, c, t = x.shape
495
+ if t % self.period != 0: # pad first
496
+ n_pad = self.period - (t % self.period)
497
+ x = F.pad(x, (0, n_pad), "reflect")
498
+ t = t + n_pad
499
+ x = x.view(b, c, t // self.period, self.period)
500
+
501
+ for l in self.convs:
502
+ x = l(x)
503
+ x = F.leaky_relu(x, modules.LRELU_SLOPE)
504
+ fmap.append(x)
505
+ x = self.conv_post(x)
506
+ fmap.append(x)
507
+ x = torch.flatten(x, 1, -1)
508
+
509
+ return x, fmap
510
+
511
+
512
+ class DiscriminatorS(torch.nn.Module):
513
+ def __init__(self, use_spectral_norm=False):
514
+ super(DiscriminatorS, self).__init__()
515
+ norm_f = weight_norm if use_spectral_norm == False else spectral_norm
516
+ self.convs = nn.ModuleList([
517
+ norm_f(Conv1d(1, 16, 15, 1, padding=7)),
518
+ norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
519
+ norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
520
+ norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
521
+ norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
522
+ norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
523
+ ])
524
+ self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
525
+
526
+ def forward(self, x):
527
+ fmap = []
528
+
529
+ for l in self.convs:
530
+ x = l(x)
531
+ x = F.leaky_relu(x, modules.LRELU_SLOPE)
532
+ fmap.append(x)
533
+ x = self.conv_post(x)
534
+ fmap.append(x)
535
+ x = torch.flatten(x, 1, -1)
536
+
537
+ return x, fmap
538
+
539
+
540
+ class MultiPeriodDiscriminator(torch.nn.Module):
541
+ def __init__(self, use_spectral_norm=False):
542
+ super(MultiPeriodDiscriminator, self).__init__()
543
+ periods = [2,3,5,7,11]
544
+
545
+ discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
546
+ discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
547
+ self.discriminators = nn.ModuleList(discs)
548
+
549
+ def forward(self, y, y_hat):
550
+ y_d_rs = []
551
+ y_d_gs = []
552
+ fmap_rs = []
553
+ fmap_gs = []
554
+ for i, d in enumerate(self.discriminators):
555
+ y_d_r, fmap_r = d(y)
556
+ y_d_g, fmap_g = d(y_hat)
557
+ y_d_rs.append(y_d_r)
558
+ y_d_gs.append(y_d_g)
559
+ fmap_rs.append(fmap_r)
560
+ fmap_gs.append(fmap_g)
561
+
562
+ return y_d_rs, y_d_gs, fmap_rs, fmap_gs
563
+
564
+
565
+
566
+ class SynthesizerTrn(nn.Module):
567
+ """
568
+ Synthesizer for Training
569
+ """
570
+
571
+ def __init__(self,
572
+ n_vocab,
573
+ spec_channels,
574
+ segment_size,
575
+ inter_channels,
576
+ hidden_channels,
577
+ filter_channels,
578
+ n_heads,
579
+ n_layers,
580
+ kernel_size,
581
+ p_dropout,
582
+ resblock,
583
+ resblock_kernel_sizes,
584
+ resblock_dilation_sizes,
585
+ upsample_rates,
586
+ upsample_initial_channel,
587
+ upsample_kernel_sizes,
588
+ gen_istft_n_fft,
589
+ gen_istft_hop_size,
590
+ n_speakers=0,
591
+ gin_channels=0,
592
+ use_sdp=False,
593
+ ms_istft_vits=False,
594
+ mb_istft_vits = False,
595
+ subbands = False,
596
+ istft_vits=False,
597
+ **kwargs):
598
+
599
+ super().__init__()
600
+ self.n_vocab = n_vocab
601
+ self.spec_channels = spec_channels
602
+ self.inter_channels = inter_channels
603
+ self.hidden_channels = hidden_channels
604
+ self.filter_channels = filter_channels
605
+ self.n_heads = n_heads
606
+ self.n_layers = n_layers
607
+ self.kernel_size = kernel_size
608
+ self.p_dropout = p_dropout
609
+ self.resblock = resblock
610
+ self.resblock_kernel_sizes = resblock_kernel_sizes
611
+ self.resblock_dilation_sizes = resblock_dilation_sizes
612
+ self.upsample_rates = upsample_rates
613
+ self.upsample_initial_channel = upsample_initial_channel
614
+ self.upsample_kernel_sizes = upsample_kernel_sizes
615
+ self.segment_size = segment_size
616
+ self.n_speakers = n_speakers
617
+ self.gin_channels = gin_channels
618
+ self.ms_istft_vits = ms_istft_vits
619
+ self.mb_istft_vits = mb_istft_vits
620
+ self.istft_vits = istft_vits
621
+
622
+ self.use_sdp = use_sdp
623
+
624
+ self.enc_p = TextEncoder(n_vocab,
625
+ inter_channels,
626
+ hidden_channels,
627
+ filter_channels,
628
+ n_heads,
629
+ n_layers,
630
+ kernel_size,
631
+ p_dropout)
632
+ if mb_istft_vits == True:
633
+ print('Mutli-band iSTFT VITS')
634
+ self.dec = Multiband_iSTFT_Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size, subbands, gin_channels=gin_channels)
635
+ elif ms_istft_vits == True:
636
+ print('Mutli-stream iSTFT VITS')
637
+ self.dec = Multistream_iSTFT_Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size, subbands, gin_channels=gin_channels)
638
+ elif istft_vits == True:
639
+ print('iSTFT-VITS')
640
+ self.dec = iSTFT_Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size, gin_channels=gin_channels)
641
+ else:
642
+ print('Decoder Error in json file')
643
+
644
+ self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
645
+ self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
646
+
647
+ if use_sdp:
648
+ self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
649
+ else:
650
+ self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
651
+
652
+ if n_speakers > 1:
653
+ self.emb_g = nn.Embedding(n_speakers, gin_channels)
654
+
655
+ def forward(self, x, x_lengths, y, y_lengths, sid=None):
656
+
657
+ x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
658
+ if self.n_speakers > 0:
659
+ g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
660
+ else:
661
+ g = None
662
+
663
+ z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
664
+ z_p = self.flow(z, y_mask, g=g)
665
+
666
+ with torch.no_grad():
667
+ # negative cross-entropy
668
+ s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
669
+ neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
670
+ neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
671
+ neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
672
+ neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
673
+ neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
674
+
675
+ attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
676
+ attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
677
+
678
+ w = attn.sum(2)
679
+ if self.use_sdp:
680
+ l_length = self.dp(x, x_mask, w, g=g)
681
+ l_length = l_length / torch.sum(x_mask)
682
+ else:
683
+ logw_ = torch.log(w + 1e-6) * x_mask
684
+ logw = self.dp(x, x_mask, g=g)
685
+ l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging
686
+
687
+ # expand prior
688
+ m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
689
+ logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
690
+
691
+ z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
692
+ o, o_mb = self.dec(z_slice, g=g)
693
+ return o, o_mb, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
694
+
695
+ def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
696
+ x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
697
+ if self.n_speakers > 0:
698
+ g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
699
+ else:
700
+ g = None
701
+
702
+ if self.use_sdp:
703
+ logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
704
+ else:
705
+ logw = self.dp(x, x_mask, g=g)
706
+ w = torch.exp(logw) * x_mask * length_scale
707
+ w_ceil = torch.ceil(w)
708
+ y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
709
+ y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
710
+ attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
711
+ attn = commons.generate_path(w_ceil, attn_mask)
712
+
713
+ m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
714
+ logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
715
+
716
+ z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
717
+ z = self.flow(z_p, y_mask, g=g, reverse=True)
718
+ o, o_mb = self.dec((z * y_mask)[:,:,:max_len], g=g)
719
+ return o, o_mb, attn, y_mask, (z, z_p, m_p, logs_p)
720
+
721
+ def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
722
+ assert self.n_speakers > 0, "n_speakers have to be larger than 0."
723
+ g_src = self.emb_g(sid_src).unsqueeze(-1)
724
+ g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
725
+ z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
726
+ z_p = self.flow(z, y_mask, g=g_src)
727
+ z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
728
+ o_hat, o_hat_mb = self.dec(z_hat * y_mask, g=g_tgt)
729
+ return o_hat, o_hat_mb, y_mask, (z, z_p, z_hat)
730
+
modules.py ADDED
@@ -0,0 +1,390 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import copy
2
+ import math
3
+ import numpy as np
4
+ import scipy
5
+ import torch
6
+ from torch import nn
7
+ from torch.nn import functional as F
8
+
9
+ from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
10
+ from torch.nn.utils import weight_norm, remove_weight_norm
11
+
12
+ import commons
13
+ from commons import init_weights, get_padding
14
+ from transforms import piecewise_rational_quadratic_transform
15
+
16
+
17
+ LRELU_SLOPE = 0.1
18
+
19
+
20
+ class LayerNorm(nn.Module):
21
+ def __init__(self, channels, eps=1e-5):
22
+ super().__init__()
23
+ self.channels = channels
24
+ self.eps = eps
25
+
26
+ self.gamma = nn.Parameter(torch.ones(channels))
27
+ self.beta = nn.Parameter(torch.zeros(channels))
28
+
29
+ def forward(self, x):
30
+ x = x.transpose(1, -1)
31
+ x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
32
+ return x.transpose(1, -1)
33
+
34
+
35
+ class ConvReluNorm(nn.Module):
36
+ def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
37
+ super().__init__()
38
+ self.in_channels = in_channels
39
+ self.hidden_channels = hidden_channels
40
+ self.out_channels = out_channels
41
+ self.kernel_size = kernel_size
42
+ self.n_layers = n_layers
43
+ self.p_dropout = p_dropout
44
+ assert n_layers > 1, "Number of layers should be larger than 0."
45
+
46
+ self.conv_layers = nn.ModuleList()
47
+ self.norm_layers = nn.ModuleList()
48
+ self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
49
+ self.norm_layers.append(LayerNorm(hidden_channels))
50
+ self.relu_drop = nn.Sequential(
51
+ nn.ReLU(),
52
+ nn.Dropout(p_dropout))
53
+ for _ in range(n_layers-1):
54
+ self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
55
+ self.norm_layers.append(LayerNorm(hidden_channels))
56
+ self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
57
+ self.proj.weight.data.zero_()
58
+ self.proj.bias.data.zero_()
59
+
60
+ def forward(self, x, x_mask):
61
+ x_org = x
62
+ for i in range(self.n_layers):
63
+ x = self.conv_layers[i](x * x_mask)
64
+ x = self.norm_layers[i](x)
65
+ x = self.relu_drop(x)
66
+ x = x_org + self.proj(x)
67
+ return x * x_mask
68
+
69
+
70
+ class DDSConv(nn.Module):
71
+ """
72
+ Dialted and Depth-Separable Convolution
73
+ """
74
+ def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
75
+ super().__init__()
76
+ self.channels = channels
77
+ self.kernel_size = kernel_size
78
+ self.n_layers = n_layers
79
+ self.p_dropout = p_dropout
80
+
81
+ self.drop = nn.Dropout(p_dropout)
82
+ self.convs_sep = nn.ModuleList()
83
+ self.convs_1x1 = nn.ModuleList()
84
+ self.norms_1 = nn.ModuleList()
85
+ self.norms_2 = nn.ModuleList()
86
+ for i in range(n_layers):
87
+ dilation = kernel_size ** i
88
+ padding = (kernel_size * dilation - dilation) // 2
89
+ self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
90
+ groups=channels, dilation=dilation, padding=padding
91
+ ))
92
+ self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
93
+ self.norms_1.append(LayerNorm(channels))
94
+ self.norms_2.append(LayerNorm(channels))
95
+
96
+ def forward(self, x, x_mask, g=None):
97
+ if g is not None:
98
+ x = x + g
99
+ for i in range(self.n_layers):
100
+ y = self.convs_sep[i](x * x_mask)
101
+ y = self.norms_1[i](y)
102
+ y = F.gelu(y)
103
+ y = self.convs_1x1[i](y)
104
+ y = self.norms_2[i](y)
105
+ y = F.gelu(y)
106
+ y = self.drop(y)
107
+ x = x + y
108
+ return x * x_mask
109
+
110
+
111
+ class WN(torch.nn.Module):
112
+ def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
113
+ super(WN, self).__init__()
114
+ assert(kernel_size % 2 == 1)
115
+ self.hidden_channels =hidden_channels
116
+ self.kernel_size = kernel_size,
117
+ self.dilation_rate = dilation_rate
118
+ self.n_layers = n_layers
119
+ self.gin_channels = gin_channels
120
+ self.p_dropout = p_dropout
121
+
122
+ self.in_layers = torch.nn.ModuleList()
123
+ self.res_skip_layers = torch.nn.ModuleList()
124
+ self.drop = nn.Dropout(p_dropout)
125
+
126
+ if gin_channels != 0:
127
+ cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
128
+ self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
129
+
130
+ for i in range(n_layers):
131
+ dilation = dilation_rate ** i
132
+ padding = int((kernel_size * dilation - dilation) / 2)
133
+ in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
134
+ dilation=dilation, padding=padding)
135
+ in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
136
+ self.in_layers.append(in_layer)
137
+
138
+ # last one is not necessary
139
+ if i < n_layers - 1:
140
+ res_skip_channels = 2 * hidden_channels
141
+ else:
142
+ res_skip_channels = hidden_channels
143
+
144
+ res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
145
+ res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
146
+ self.res_skip_layers.append(res_skip_layer)
147
+
148
+ def forward(self, x, x_mask, g=None, **kwargs):
149
+ output = torch.zeros_like(x)
150
+ n_channels_tensor = torch.IntTensor([self.hidden_channels])
151
+
152
+ if g is not None:
153
+ g = self.cond_layer(g)
154
+
155
+ for i in range(self.n_layers):
156
+ x_in = self.in_layers[i](x)
157
+ if g is not None:
158
+ cond_offset = i * 2 * self.hidden_channels
159
+ g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
160
+ else:
161
+ g_l = torch.zeros_like(x_in)
162
+
163
+ acts = commons.fused_add_tanh_sigmoid_multiply(
164
+ x_in,
165
+ g_l,
166
+ n_channels_tensor)
167
+ acts = self.drop(acts)
168
+
169
+ res_skip_acts = self.res_skip_layers[i](acts)
170
+ if i < self.n_layers - 1:
171
+ res_acts = res_skip_acts[:,:self.hidden_channels,:]
172
+ x = (x + res_acts) * x_mask
173
+ output = output + res_skip_acts[:,self.hidden_channels:,:]
174
+ else:
175
+ output = output + res_skip_acts
176
+ return output * x_mask
177
+
178
+ def remove_weight_norm(self):
179
+ if self.gin_channels != 0:
180
+ torch.nn.utils.remove_weight_norm(self.cond_layer)
181
+ for l in self.in_layers:
182
+ torch.nn.utils.remove_weight_norm(l)
183
+ for l in self.res_skip_layers:
184
+ torch.nn.utils.remove_weight_norm(l)
185
+
186
+
187
+ class ResBlock1(torch.nn.Module):
188
+ def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
189
+ super(ResBlock1, self).__init__()
190
+ self.convs1 = nn.ModuleList([
191
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
192
+ padding=get_padding(kernel_size, dilation[0]))),
193
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
194
+ padding=get_padding(kernel_size, dilation[1]))),
195
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
196
+ padding=get_padding(kernel_size, dilation[2])))
197
+ ])
198
+ self.convs1.apply(init_weights)
199
+
200
+ self.convs2 = nn.ModuleList([
201
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
202
+ padding=get_padding(kernel_size, 1))),
203
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
204
+ padding=get_padding(kernel_size, 1))),
205
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
206
+ padding=get_padding(kernel_size, 1)))
207
+ ])
208
+ self.convs2.apply(init_weights)
209
+
210
+ def forward(self, x, x_mask=None):
211
+ for c1, c2 in zip(self.convs1, self.convs2):
212
+ xt = F.leaky_relu(x, LRELU_SLOPE)
213
+ if x_mask is not None:
214
+ xt = xt * x_mask
215
+ xt = c1(xt)
216
+ xt = F.leaky_relu(xt, LRELU_SLOPE)
217
+ if x_mask is not None:
218
+ xt = xt * x_mask
219
+ xt = c2(xt)
220
+ x = xt + x
221
+ if x_mask is not None:
222
+ x = x * x_mask
223
+ return x
224
+
225
+ def remove_weight_norm(self):
226
+ for l in self.convs1:
227
+ remove_weight_norm(l)
228
+ for l in self.convs2:
229
+ remove_weight_norm(l)
230
+
231
+
232
+ class ResBlock2(torch.nn.Module):
233
+ def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
234
+ super(ResBlock2, self).__init__()
235
+ self.convs = nn.ModuleList([
236
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
237
+ padding=get_padding(kernel_size, dilation[0]))),
238
+ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
239
+ padding=get_padding(kernel_size, dilation[1])))
240
+ ])
241
+ self.convs.apply(init_weights)
242
+
243
+ def forward(self, x, x_mask=None):
244
+ for c in self.convs:
245
+ xt = F.leaky_relu(x, LRELU_SLOPE)
246
+ if x_mask is not None:
247
+ xt = xt * x_mask
248
+ xt = c(xt)
249
+ x = xt + x
250
+ if x_mask is not None:
251
+ x = x * x_mask
252
+ return x
253
+
254
+ def remove_weight_norm(self):
255
+ for l in self.convs:
256
+ remove_weight_norm(l)
257
+
258
+
259
+ class Log(nn.Module):
260
+ def forward(self, x, x_mask, reverse=False, **kwargs):
261
+ if not reverse:
262
+ y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
263
+ logdet = torch.sum(-y, [1, 2])
264
+ return y, logdet
265
+ else:
266
+ x = torch.exp(x) * x_mask
267
+ return x
268
+
269
+
270
+ class Flip(nn.Module):
271
+ def forward(self, x, *args, reverse=False, **kwargs):
272
+ x = torch.flip(x, [1])
273
+ if not reverse:
274
+ logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
275
+ return x, logdet
276
+ else:
277
+ return x
278
+
279
+
280
+ class ElementwiseAffine(nn.Module):
281
+ def __init__(self, channels):
282
+ super().__init__()
283
+ self.channels = channels
284
+ self.m = nn.Parameter(torch.zeros(channels,1))
285
+ self.logs = nn.Parameter(torch.zeros(channels,1))
286
+
287
+ def forward(self, x, x_mask, reverse=False, **kwargs):
288
+ if not reverse:
289
+ y = self.m + torch.exp(self.logs) * x
290
+ y = y * x_mask
291
+ logdet = torch.sum(self.logs * x_mask, [1,2])
292
+ return y, logdet
293
+ else:
294
+ x = (x - self.m) * torch.exp(-self.logs) * x_mask
295
+ return x
296
+
297
+
298
+ class ResidualCouplingLayer(nn.Module):
299
+ def __init__(self,
300
+ channels,
301
+ hidden_channels,
302
+ kernel_size,
303
+ dilation_rate,
304
+ n_layers,
305
+ p_dropout=0,
306
+ gin_channels=0,
307
+ mean_only=False):
308
+ assert channels % 2 == 0, "channels should be divisible by 2"
309
+ super().__init__()
310
+ self.channels = channels
311
+ self.hidden_channels = hidden_channels
312
+ self.kernel_size = kernel_size
313
+ self.dilation_rate = dilation_rate
314
+ self.n_layers = n_layers
315
+ self.half_channels = channels // 2
316
+ self.mean_only = mean_only
317
+
318
+ self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
319
+ self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
320
+ self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
321
+ self.post.weight.data.zero_()
322
+ self.post.bias.data.zero_()
323
+
324
+ def forward(self, x, x_mask, g=None, reverse=False):
325
+ x0, x1 = torch.split(x, [self.half_channels]*2, 1)
326
+ h = self.pre(x0) * x_mask
327
+ h = self.enc(h, x_mask, g=g)
328
+ stats = self.post(h) * x_mask
329
+ if not self.mean_only:
330
+ m, logs = torch.split(stats, [self.half_channels]*2, 1)
331
+ else:
332
+ m = stats
333
+ logs = torch.zeros_like(m)
334
+
335
+ if not reverse:
336
+ x1 = m + x1 * torch.exp(logs) * x_mask
337
+ x = torch.cat([x0, x1], 1)
338
+ logdet = torch.sum(logs, [1,2])
339
+ return x, logdet
340
+ else:
341
+ x1 = (x1 - m) * torch.exp(-logs) * x_mask
342
+ x = torch.cat([x0, x1], 1)
343
+ return x
344
+
345
+
346
+ class ConvFlow(nn.Module):
347
+ def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
348
+ super().__init__()
349
+ self.in_channels = in_channels
350
+ self.filter_channels = filter_channels
351
+ self.kernel_size = kernel_size
352
+ self.n_layers = n_layers
353
+ self.num_bins = num_bins
354
+ self.tail_bound = tail_bound
355
+ self.half_channels = in_channels // 2
356
+
357
+ self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
358
+ self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
359
+ self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
360
+ self.proj.weight.data.zero_()
361
+ self.proj.bias.data.zero_()
362
+
363
+ def forward(self, x, x_mask, g=None, reverse=False):
364
+ x0, x1 = torch.split(x, [self.half_channels]*2, 1)
365
+ h = self.pre(x0)
366
+ h = self.convs(h, x_mask, g=g)
367
+ h = self.proj(h) * x_mask
368
+
369
+ b, c, t = x0.shape
370
+ h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
371
+
372
+ unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
373
+ unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
374
+ unnormalized_derivatives = h[..., 2 * self.num_bins:]
375
+
376
+ x1, logabsdet = piecewise_rational_quadratic_transform(x1,
377
+ unnormalized_widths,
378
+ unnormalized_heights,
379
+ unnormalized_derivatives,
380
+ inverse=reverse,
381
+ tails='linear',
382
+ tail_bound=self.tail_bound
383
+ )
384
+
385
+ x = torch.cat([x0, x1], 1) * x_mask
386
+ logdet = torch.sum(logabsdet * x_mask, [1,2])
387
+ if not reverse:
388
+ return x, logdet
389
+ else:
390
+ return x
monotonic_align/__init__.py ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from numpy import zeros, int32, float32
2
+ from torch import from_numpy
3
+
4
+ from .core import maximum_path_jit
5
+
6
+ def maximum_path(neg_cent, mask):
7
+ """ numba optimized version.
8
+ neg_cent: [b, t_t, t_s]
9
+ mask: [b, t_t, t_s]
10
+ """
11
+ device = neg_cent.device
12
+ dtype = neg_cent.dtype
13
+ neg_cent = neg_cent.data.cpu().numpy().astype(float32)
14
+ path = zeros(neg_cent.shape, dtype=int32)
15
+
16
+ t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
17
+ t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
18
+ maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
19
+ return from_numpy(path).to(device=device, dtype=dtype)
monotonic_align/__pycache__/__init__.cpython-37.pyc ADDED
Binary file (822 Bytes). View file
 
monotonic_align/__pycache__/__init__.cpython-38.pyc ADDED
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monotonic_align/__pycache__/core.cpython-37.pyc ADDED
Binary file (995 Bytes). View file
 
monotonic_align/__pycache__/core.cpython-38.pyc ADDED
Binary file (968 Bytes). View file
 
monotonic_align/build/lib.win-amd64-3.8/monotonic_align/core.cp38-win_amd64.pyd ADDED
Binary file (123 kB). View file
 
monotonic_align/build/temp.win-amd64-3.8/Release/core.cp38-win_amd64.exp ADDED
Binary file (755 Bytes). View file
 
monotonic_align/build/temp.win-amd64-3.8/Release/core.cp38-win_amd64.lib ADDED
Binary file (1.94 kB). View file
 
monotonic_align/build/temp.win-amd64-3.8/Release/core.obj ADDED
Binary file (723 kB). View file
 
monotonic_align/core.c ADDED
The diff for this file is too large to render. See raw diff
 
monotonic_align/core.py ADDED
@@ -0,0 +1,35 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numba
2
+
3
+
4
+ @numba.jit(numba.void(numba.int32[:,:,::1], numba.float32[:,:,::1], numba.int32[::1], numba.int32[::1]), nopython=True, nogil=True)
5
+ def maximum_path_jit(paths, values, t_ys, t_xs):
6
+ b = paths.shape[0]
7
+ max_neg_val=-1e9
8
+ for i in range(int(b)):
9
+ path = paths[i]
10
+ value = values[i]
11
+ t_y = t_ys[i]
12
+ t_x = t_xs[i]
13
+
14
+ v_prev = v_cur = 0.0
15
+ index = t_x - 1
16
+
17
+ for y in range(t_y):
18
+ for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
19
+ if x == y:
20
+ v_cur = max_neg_val
21
+ else:
22
+ v_cur = value[y-1, x]
23
+ if x == 0:
24
+ if y == 0:
25
+ v_prev = 0.
26
+ else:
27
+ v_prev = max_neg_val
28
+ else:
29
+ v_prev = value[y-1, x-1]
30
+ value[y, x] += max(v_prev, v_cur)
31
+
32
+ for y in range(t_y - 1, -1, -1):
33
+ path[y, index] = 1
34
+ if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
35
+ index = index - 1
monotonic_align/core.pyx ADDED
@@ -0,0 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # cython:language_level=3
2
+
3
+ cimport cython
4
+ from cython.parallel import prange
5
+
6
+
7
+ @cython.boundscheck(False)
8
+ @cython.wraparound(False)
9
+ cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil:
10
+ cdef int x
11
+ cdef int y
12
+ cdef float v_prev
13
+ cdef float v_cur
14
+ cdef float tmp
15
+ cdef int index = t_x - 1
16
+
17
+ for y in range(t_y):
18
+ for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
19
+ if x == y:
20
+ v_cur = max_neg_val
21
+ else:
22
+ v_cur = value[y-1, x]
23
+ if x == 0:
24
+ if y == 0:
25
+ v_prev = 0.
26
+ else:
27
+ v_prev = max_neg_val
28
+ else:
29
+ v_prev = value[y-1, x-1]
30
+ value[y, x] += max(v_prev, v_cur)
31
+
32
+ for y in range(t_y - 1, -1, -1):
33
+ path[y, index] = 1
34
+ if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
35
+ index = index - 1
36
+
37
+
38
+ @cython.boundscheck(False)
39
+ @cython.wraparound(False)
40
+ cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil:
41
+ cdef int b = paths.shape[0]
42
+ cdef int i
43
+ for i in prange(b, nogil=True):
44
+ maximum_path_each(paths[i], values[i], t_ys[i], t_xs[i])
monotonic_align/monotonic_align/core.cp38-win_amd64.pyd ADDED
Binary file (123 kB). View file
 
monotonic_align/setup.py ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ # cython:language_level=3
2
+ from distutils.core import setup
3
+ from Cython.Build import cythonize
4
+ import numpy
5
+
6
+ setup(
7
+ name = 'monotonic_align',
8
+ ext_modules = cythonize("core.pyx"),
9
+ include_dirs=[numpy.get_include()]
10
+ )
pqmf.py ADDED
@@ -0,0 +1,116 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+
3
+ # Copyright 2020 Tomoki Hayashi
4
+ # MIT License (https://opensource.org/licenses/MIT)
5
+
6
+ """Pseudo QMF modules."""
7
+
8
+ import numpy as np
9
+ import torch
10
+ import torch.nn.functional as F
11
+
12
+ from scipy.signal import kaiser
13
+
14
+
15
+ def design_prototype_filter(taps=62, cutoff_ratio=0.15, beta=9.0):
16
+ """Design prototype filter for PQMF.
17
+ This method is based on `A Kaiser window approach for the design of prototype
18
+ filters of cosine modulated filterbanks`_.
19
+ Args:
20
+ taps (int): The number of filter taps.
21
+ cutoff_ratio (float): Cut-off frequency ratio.
22
+ beta (float): Beta coefficient for kaiser window.
23
+ Returns:
24
+ ndarray: Impluse response of prototype filter (taps + 1,).
25
+ .. _`A Kaiser window approach for the design of prototype filters of cosine modulated filterbanks`:
26
+ https://ieeexplore.ieee.org/abstract/document/681427
27
+ """
28
+ # check the arguments are valid
29
+ assert taps % 2 == 0, "The number of taps mush be even number."
30
+ assert 0.0 < cutoff_ratio < 1.0, "Cutoff ratio must be > 0.0 and < 1.0."
31
+
32
+ # make initial filter
33
+ omega_c = np.pi * cutoff_ratio
34
+ with np.errstate(invalid='ignore'):
35
+ h_i = np.sin(omega_c * (np.arange(taps + 1) - 0.5 * taps)) \
36
+ / (np.pi * (np.arange(taps + 1) - 0.5 * taps))
37
+ h_i[taps // 2] = np.cos(0) * cutoff_ratio # fix nan due to indeterminate form
38
+
39
+ # apply kaiser window
40
+ w = kaiser(taps + 1, beta)
41
+ h = h_i * w
42
+
43
+ return h
44
+
45
+
46
+ class PQMF(torch.nn.Module):
47
+ """PQMF module.
48
+ This module is based on `Near-perfect-reconstruction pseudo-QMF banks`_.
49
+ .. _`Near-perfect-reconstruction pseudo-QMF banks`:
50
+ https://ieeexplore.ieee.org/document/258122
51
+ """
52
+
53
+ def __init__(self, device, subbands=4, taps=62, cutoff_ratio=0.15, beta=9.0):
54
+ """Initilize PQMF module.
55
+ Args:
56
+ subbands (int): The number of subbands.
57
+ taps (int): The number of filter taps.
58
+ cutoff_ratio (float): Cut-off frequency ratio.
59
+ beta (float): Beta coefficient for kaiser window.
60
+ """
61
+ super(PQMF, self).__init__()
62
+
63
+ # define filter coefficient
64
+ h_proto = design_prototype_filter(taps, cutoff_ratio, beta)
65
+ h_analysis = np.zeros((subbands, len(h_proto)))
66
+ h_synthesis = np.zeros((subbands, len(h_proto)))
67
+ for k in range(subbands):
68
+ h_analysis[k] = 2 * h_proto * np.cos(
69
+ (2 * k + 1) * (np.pi / (2 * subbands)) *
70
+ (np.arange(taps + 1) - ((taps - 1) / 2)) +
71
+ (-1) ** k * np.pi / 4)
72
+ h_synthesis[k] = 2 * h_proto * np.cos(
73
+ (2 * k + 1) * (np.pi / (2 * subbands)) *
74
+ (np.arange(taps + 1) - ((taps - 1) / 2)) -
75
+ (-1) ** k * np.pi / 4)
76
+
77
+ # convert to tensor
78
+ analysis_filter = torch.from_numpy(h_analysis).float().unsqueeze(1).to(device)
79
+ synthesis_filter = torch.from_numpy(h_synthesis).float().unsqueeze(0).to(device)
80
+
81
+ # register coefficients as beffer
82
+ self.register_buffer("analysis_filter", analysis_filter)
83
+ self.register_buffer("synthesis_filter", synthesis_filter)
84
+
85
+ # filter for downsampling & upsampling
86
+ updown_filter = torch.zeros((subbands, subbands, subbands)).float().to(device)
87
+ for k in range(subbands):
88
+ updown_filter[k, k, 0] = 1.0
89
+ self.register_buffer("updown_filter", updown_filter)
90
+ self.subbands = subbands
91
+
92
+ # keep padding info
93
+ self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0)
94
+
95
+ def analysis(self, x):
96
+ """Analysis with PQMF.
97
+ Args:
98
+ x (Tensor): Input tensor (B, 1, T).
99
+ Returns:
100
+ Tensor: Output tensor (B, subbands, T // subbands).
101
+ """
102
+ x = F.conv1d(self.pad_fn(x), self.analysis_filter)
103
+ return F.conv1d(x, self.updown_filter, stride=self.subbands)
104
+
105
+ def synthesis(self, x):
106
+ """Synthesis with PQMF.
107
+ Args:
108
+ x (Tensor): Input tensor (B, subbands, T // subbands).
109
+ Returns:
110
+ Tensor: Output tensor (B, 1, T).
111
+ """
112
+ # NOTE(kan-bayashi): Power will be dreased so here multipy by # subbands.
113
+ # Not sure this is the correct way, it is better to check again.
114
+ # TODO(kan-bayashi): Understand the reconstruction procedure
115
+ x = F.conv_transpose1d(x, self.updown_filter * self.subbands, stride=self.subbands)
116
+ return F.conv1d(self.pad_fn(x), self.synthesis_filter)
preprocess.py ADDED
@@ -0,0 +1,25 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import argparse
2
+ import text
3
+ from utils import load_filepaths_and_text
4
+
5
+ if __name__ == '__main__':
6
+ parser = argparse.ArgumentParser()
7
+ parser.add_argument("--out_extension", default="cleaned")
8
+ parser.add_argument("--text_index", default=1, type=int)
9
+ parser.add_argument("--filelists", nargs="+", default=["filelists/ljs_audio_text_val_filelist.txt", "filelists/ljs_audio_text_test_filelist.txt"])
10
+ parser.add_argument("--text_cleaners", nargs="+", default=["english_cleaners2"])
11
+
12
+ args = parser.parse_args()
13
+
14
+
15
+ for filelist in args.filelists:
16
+ print("START:", filelist)
17
+ filepaths_and_text = load_filepaths_and_text(filelist)
18
+ for i in range(len(filepaths_and_text)):
19
+ original_text = filepaths_and_text[i][args.text_index]
20
+ cleaned_text = text._clean_text(original_text, args.text_cleaners)
21
+ filepaths_and_text[i][args.text_index] = cleaned_text
22
+
23
+ new_filelist = filelist + "." + args.out_extension
24
+ with open(new_filelist, "w", encoding="utf-8") as f:
25
+ f.writelines(["|".join(x) + "\n" for x in filepaths_and_text])
requirements.txt ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ Cython==0.29.21
2
+ librosa==0.8.0
3
+ matplotlib==3.3.1
4
+ numpy==1.18.5
5
+ phonemizer==2.2.1
6
+ scipy==1.5.2
7
+ tensorboard==2.3.0
8
+ torch==1.6.0
9
+ torchvision==0.7.0
10
+ Unidecode==1.1.1
stft.py ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ BSD 3-Clause License
3
+ Copyright (c) 2017, Prem Seetharaman
4
+ All rights reserved.
5
+ * Redistribution and use in source and binary forms, with or without
6
+ modification, are permitted provided that the following conditions are met:
7
+ * Redistributions of source code must retain the above copyright notice,
8
+ this list of conditions and the following disclaimer.
9
+ * Redistributions in binary form must reproduce the above copyright notice, this
10
+ list of conditions and the following disclaimer in the
11
+ documentation and/or other materials provided with the distribution.
12
+ * Neither the name of the copyright holder nor the names of its
13
+ contributors may be used to endorse or promote products derived from this
14
+ software without specific prior written permission.
15
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
16
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
17
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
18
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
19
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
20
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
21
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
22
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
23
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
24
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
25
+ """
26
+
27
+ import torch
28
+ import numpy as np
29
+ import torch.nn.functional as F
30
+ from torch.autograd import Variable
31
+ from scipy.signal import get_window
32
+ from librosa.util import pad_center, tiny
33
+ import librosa.util as librosa_util
34
+
35
+ def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
36
+ n_fft=800, dtype=np.float32, norm=None):
37
+ """
38
+ # from librosa 0.6
39
+ Compute the sum-square envelope of a window function at a given hop length.
40
+ This is used to estimate modulation effects induced by windowing
41
+ observations in short-time fourier transforms.
42
+ Parameters
43
+ ----------
44
+ window : string, tuple, number, callable, or list-like
45
+ Window specification, as in `get_window`
46
+ n_frames : int > 0
47
+ The number of analysis frames
48
+ hop_length : int > 0
49
+ The number of samples to advance between frames
50
+ win_length : [optional]
51
+ The length of the window function. By default, this matches `n_fft`.
52
+ n_fft : int > 0
53
+ The length of each analysis frame.
54
+ dtype : np.dtype
55
+ The data type of the output
56
+ Returns
57
+ -------
58
+ wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
59
+ The sum-squared envelope of the window function
60
+ """
61
+ if win_length is None:
62
+ win_length = n_fft
63
+
64
+ n = n_fft + hop_length * (n_frames - 1)
65
+ x = np.zeros(n, dtype=dtype)
66
+
67
+ # Compute the squared window at the desired length
68
+ win_sq = get_window(window, win_length, fftbins=True)
69
+ win_sq = librosa_util.normalize(win_sq, norm=norm)**2
70
+ win_sq = librosa_util.pad_center(win_sq, n_fft)
71
+
72
+ # Fill the envelope
73
+ for i in range(n_frames):
74
+ sample = i * hop_length
75
+ x[sample:min(n, sample + n_fft)] += win_sq[:max(0, min(n_fft, n - sample))]
76
+ return x
77
+
78
+
79
+ class STFT(torch.nn.Module):
80
+ """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft"""
81
+ def __init__(self, filter_length=800, hop_length=200, win_length=800,
82
+ window='hann'):
83
+ super(STFT, self).__init__()
84
+ self.filter_length = filter_length
85
+ self.hop_length = hop_length
86
+ self.win_length = win_length
87
+ self.window = window
88
+ self.forward_transform = None
89
+ scale = self.filter_length / self.hop_length
90
+ fourier_basis = np.fft.fft(np.eye(self.filter_length))
91
+
92
+ cutoff = int((self.filter_length / 2 + 1))
93
+ fourier_basis = np.vstack([np.real(fourier_basis[:cutoff, :]),
94
+ np.imag(fourier_basis[:cutoff, :])])
95
+
96
+ forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
97
+ inverse_basis = torch.FloatTensor(
98
+ np.linalg.pinv(scale * fourier_basis).T[:, None, :])
99
+
100
+ if window is not None:
101
+ assert(filter_length >= win_length)
102
+ # get window and zero center pad it to filter_length
103
+ fft_window = get_window(window, win_length, fftbins=True)
104
+ fft_window = pad_center(fft_window, filter_length)
105
+ fft_window = torch.from_numpy(fft_window).float()
106
+
107
+ # window the bases
108
+ forward_basis *= fft_window
109
+ inverse_basis *= fft_window
110
+
111
+ self.register_buffer('forward_basis', forward_basis.float())
112
+ self.register_buffer('inverse_basis', inverse_basis.float())
113
+
114
+ def transform(self, input_data):
115
+ num_batches = input_data.size(0)
116
+ num_samples = input_data.size(1)
117
+
118
+ self.num_samples = num_samples
119
+
120
+ # similar to librosa, reflect-pad the input
121
+ input_data = input_data.view(num_batches, 1, num_samples)
122
+ input_data = F.pad(
123
+ input_data.unsqueeze(1),
124
+ (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0),
125
+ mode='reflect')
126
+ input_data = input_data.squeeze(1)
127
+
128
+ forward_transform = F.conv1d(
129
+ input_data,
130
+ Variable(self.forward_basis, requires_grad=False),
131
+ stride=self.hop_length,
132
+ padding=0)
133
+
134
+ cutoff = int((self.filter_length / 2) + 1)
135
+ real_part = forward_transform[:, :cutoff, :]
136
+ imag_part = forward_transform[:, cutoff:, :]
137
+
138
+ magnitude = torch.sqrt(real_part**2 + imag_part**2)
139
+ phase = torch.autograd.Variable(
140
+ torch.atan2(imag_part.data, real_part.data))
141
+
142
+ return magnitude, phase
143
+
144
+ def inverse(self, magnitude, phase):
145
+ recombine_magnitude_phase = torch.cat(
146
+ [magnitude*torch.cos(phase), magnitude*torch.sin(phase)], dim=1)
147
+
148
+ inverse_transform = F.conv_transpose1d(
149
+ recombine_magnitude_phase,
150
+ Variable(self.inverse_basis, requires_grad=False),
151
+ stride=self.hop_length,
152
+ padding=0)
153
+
154
+ if self.window is not None:
155
+ window_sum = window_sumsquare(
156
+ self.window, magnitude.size(-1), hop_length=self.hop_length,
157
+ win_length=self.win_length, n_fft=self.filter_length,
158
+ dtype=np.float32)
159
+ # remove modulation effects
160
+ approx_nonzero_indices = torch.from_numpy(
161
+ np.where(window_sum > tiny(window_sum))[0])
162
+ window_sum = torch.autograd.Variable(
163
+ torch.from_numpy(window_sum), requires_grad=False)
164
+ window_sum = window_sum.to(inverse_transform.device()) if magnitude.is_cuda else window_sum
165
+ inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices]
166
+
167
+ # scale by hop ratio
168
+ inverse_transform *= float(self.filter_length) / self.hop_length
169
+
170
+ inverse_transform = inverse_transform[:, :, int(self.filter_length/2):]
171
+ inverse_transform = inverse_transform[:, :, :-int(self.filter_length/2):]
172
+
173
+ return inverse_transform
174
+
175
+ def forward(self, input_data):
176
+ self.magnitude, self.phase = self.transform(input_data)
177
+ reconstruction = self.inverse(self.magnitude, self.phase)
178
+ return reconstruction
179
+
180
+
181
+ class TorchSTFT(torch.nn.Module):
182
+ def __init__(self, filter_length=800, hop_length=200, win_length=800, window='hann'):
183
+ super().__init__()
184
+ self.filter_length = filter_length
185
+ self.hop_length = hop_length
186
+ self.win_length = win_length
187
+ self.window = torch.from_numpy(get_window(window, win_length, fftbins=True).astype(np.float32))
188
+
189
+ def transform(self, input_data):
190
+ forward_transform = torch.stft(
191
+ input_data,
192
+ self.filter_length, self.hop_length, self.win_length, window=self.window,
193
+ return_complex=True)
194
+
195
+ return torch.abs(forward_transform), torch.angle(forward_transform)
196
+
197
+ def inverse(self, magnitude, phase):
198
+ inverse_transform = torch.istft(
199
+ magnitude * torch.exp(phase * 1j),
200
+ self.filter_length, self.hop_length, self.win_length, window=self.window.to(magnitude.device))
201
+
202
+ return inverse_transform.unsqueeze(-2) # unsqueeze to stay consistent with conv_transpose1d implementation
203
+
204
+ def forward(self, input_data):
205
+ self.magnitude, self.phase = self.transform(input_data)
206
+ reconstruction = self.inverse(self.magnitude, self.phase)
207
+ return reconstruction
208
+
209
+
stft_loss.py ADDED
@@ -0,0 +1,136 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+
3
+ # Copyright 2019 Tomoki Hayashi
4
+ # MIT License (https://opensource.org/licenses/MIT)
5
+
6
+ """STFT-based Loss modules."""
7
+
8
+ import torch
9
+ import torch.nn.functional as F
10
+
11
+
12
+ def stft(x, fft_size, hop_size, win_length, window):
13
+ """Perform STFT and convert to magnitude spectrogram.
14
+ Args:
15
+ x (Tensor): Input signal tensor (B, T).
16
+ fft_size (int): FFT size.
17
+ hop_size (int): Hop size.
18
+ win_length (int): Window length.
19
+ window (str): Window function type.
20
+ Returns:
21
+ Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
22
+ """
23
+ x_stft = torch.stft(x, fft_size, hop_size, win_length, window.to(x.device))
24
+ real = x_stft[..., 0]
25
+ imag = x_stft[..., 1]
26
+
27
+ # NOTE(kan-bayashi): clamp is needed to avoid nan or inf
28
+ return torch.sqrt(torch.clamp(real ** 2 + imag ** 2, min=1e-7)).transpose(2, 1)
29
+
30
+
31
+ class SpectralConvergengeLoss(torch.nn.Module):
32
+ """Spectral convergence loss module."""
33
+
34
+ def __init__(self):
35
+ """Initilize spectral convergence loss module."""
36
+ super(SpectralConvergengeLoss, self).__init__()
37
+
38
+ def forward(self, x_mag, y_mag):
39
+ """Calculate forward propagation.
40
+ Args:
41
+ x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
42
+ y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
43
+ Returns:
44
+ Tensor: Spectral convergence loss value.
45
+ """
46
+ return torch.norm(y_mag - x_mag, p="fro") / torch.norm(y_mag, p="fro")
47
+
48
+
49
+ class LogSTFTMagnitudeLoss(torch.nn.Module):
50
+ """Log STFT magnitude loss module."""
51
+
52
+ def __init__(self):
53
+ """Initilize los STFT magnitude loss module."""
54
+ super(LogSTFTMagnitudeLoss, self).__init__()
55
+
56
+ def forward(self, x_mag, y_mag):
57
+ """Calculate forward propagation.
58
+ Args:
59
+ x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
60
+ y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
61
+ Returns:
62
+ Tensor: Log STFT magnitude loss value.
63
+ """
64
+ return F.l1_loss(torch.log(y_mag), torch.log(x_mag))
65
+
66
+
67
+ class STFTLoss(torch.nn.Module):
68
+ """STFT loss module."""
69
+
70
+ def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window"):
71
+ """Initialize STFT loss module."""
72
+ super(STFTLoss, self).__init__()
73
+ self.fft_size = fft_size
74
+ self.shift_size = shift_size
75
+ self.win_length = win_length
76
+ self.window = getattr(torch, window)(win_length)
77
+ self.spectral_convergenge_loss = SpectralConvergengeLoss()
78
+ self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss()
79
+
80
+ def forward(self, x, y):
81
+ """Calculate forward propagation.
82
+ Args:
83
+ x (Tensor): Predicted signal (B, T).
84
+ y (Tensor): Groundtruth signal (B, T).
85
+ Returns:
86
+ Tensor: Spectral convergence loss value.
87
+ Tensor: Log STFT magnitude loss value.
88
+ """
89
+ x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window)
90
+ y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window)
91
+ sc_loss = self.spectral_convergenge_loss(x_mag, y_mag)
92
+ mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag)
93
+
94
+ return sc_loss, mag_loss
95
+
96
+
97
+ class MultiResolutionSTFTLoss(torch.nn.Module):
98
+ """Multi resolution STFT loss module."""
99
+
100
+ def __init__(self,
101
+ fft_sizes=[1024, 2048, 512],
102
+ hop_sizes=[120, 240, 50],
103
+ win_lengths=[600, 1200, 240],
104
+ window="hann_window"):
105
+ """Initialize Multi resolution STFT loss module.
106
+ Args:
107
+ fft_sizes (list): List of FFT sizes.
108
+ hop_sizes (list): List of hop sizes.
109
+ win_lengths (list): List of window lengths.
110
+ window (str): Window function type.
111
+ """
112
+ super(MultiResolutionSTFTLoss, self).__init__()
113
+ assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
114
+ self.stft_losses = torch.nn.ModuleList()
115
+ for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
116
+ self.stft_losses += [STFTLoss(fs, ss, wl, window)]
117
+
118
+ def forward(self, x, y):
119
+ """Calculate forward propagation.
120
+ Args:
121
+ x (Tensor): Predicted signal (B, T).
122
+ y (Tensor): Groundtruth signal (B, T).
123
+ Returns:
124
+ Tensor: Multi resolution spectral convergence loss value.
125
+ Tensor: Multi resolution log STFT magnitude loss value.
126
+ """
127
+ sc_loss = 0.0
128
+ mag_loss = 0.0
129
+ for f in self.stft_losses:
130
+ sc_l, mag_l = f(x, y)
131
+ sc_loss += sc_l
132
+ mag_loss += mag_l
133
+ sc_loss /= len(self.stft_losses)
134
+ mag_loss /= len(self.stft_losses)
135
+
136
+ return sc_loss, mag_loss
text/LICENSE ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Copyright (c) 2017 Keith Ito
2
+
3
+ Permission is hereby granted, free of charge, to any person obtaining a copy
4
+ of this software and associated documentation files (the "Software"), to deal
5
+ in the Software without restriction, including without limitation the rights
6
+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
7
+ copies of the Software, and to permit persons to whom the Software is
8
+ furnished to do so, subject to the following conditions:
9
+
10
+ The above copyright notice and this permission notice shall be included in
11
+ all copies or substantial portions of the Software.
12
+
13
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
14
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
15
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
16
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
17
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
18
+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
19
+ THE SOFTWARE.
text/__init__.py ADDED
@@ -0,0 +1,56 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """ from https://github.com/keithito/tacotron """
2
+ from text import cleaners
3
+ from text.symbols import symbols
4
+
5
+
6
+ # Mappings from symbol to numeric ID and vice versa:
7
+ _symbol_to_id = {s: i for i, s in enumerate(symbols)}
8
+ _id_to_symbol = {i: s for i, s in enumerate(symbols)}
9
+
10
+
11
+ def text_to_sequence(text, cleaner_names):
12
+ '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
13
+ Args:
14
+ text: string to convert to a sequence
15
+ cleaner_names: names of the cleaner functions to run the text through
16
+ Returns:
17
+ List of integers corresponding to the symbols in the text
18
+ '''
19
+ sequence = []
20
+
21
+ clean_text = _clean_text(text, cleaner_names)
22
+ for symbol in clean_text:
23
+ if symbol not in _symbol_to_id.keys():
24
+ continue
25
+ symbol_id = _symbol_to_id[symbol]
26
+ sequence += [symbol_id]
27
+ return sequence
28
+
29
+
30
+ def cleaned_text_to_sequence(cleaned_text):
31
+ '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
32
+ Args:
33
+ text: string to convert to a sequence
34
+ Returns:
35
+ List of integers corresponding to the symbols in the text
36
+ '''
37
+ sequence = [_symbol_to_id[symbol] for symbol in cleaned_text if symbol in _symbol_to_id.keys()]
38
+ return sequence
39
+
40
+
41
+ def sequence_to_text(sequence):
42
+ '''Converts a sequence of IDs back to a string'''
43
+ result = ''
44
+ for symbol_id in sequence:
45
+ s = _id_to_symbol[symbol_id]
46
+ result += s
47
+ return result
48
+
49
+
50
+ def _clean_text(text, cleaner_names):
51
+ for name in cleaner_names:
52
+ cleaner = getattr(cleaners, name)
53
+ if not cleaner:
54
+ raise Exception('Unknown cleaner: %s' % name)
55
+ text = cleaner(text)
56
+ return text
text/__pycache__/__init__.cpython-38.pyc ADDED
Binary file (2.15 kB). View file
 
text/__pycache__/cleaners.cpython-38.pyc ADDED
Binary file (6.36 kB). View file
 
text/__pycache__/japanese.cpython-38.pyc ADDED
Binary file (4.47 kB). View file
 
text/__pycache__/mandarin.cpython-38.pyc ADDED
Binary file (6.44 kB). View file
 
text/__pycache__/symbols.cpython-38.pyc ADDED
Binary file (432 Bytes). View file
 
text/cleaners.py ADDED
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1
+ import re
2
+ from text.japanese import japanese_to_romaji_with_accent, japanese_to_ipa, japanese_to_ipa2, japanese_to_ipa3
3
+ from text.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo, chinese_to_romaji, chinese_to_lazy_ipa, chinese_to_ipa, chinese_to_ipa2
4
+ # from text.sanskrit import devanagari_to_ipa
5
+ # from text.english import english_to_lazy_ipa, english_to_ipa2, english_to_lazy_ipa2
6
+ # from text.thai import num_to_thai, latin_to_thai
7
+ # from text.shanghainese import shanghainese_to_ipa
8
+ # from text.cantonese import cantonese_to_ipa
9
+ # from text.ngu_dialect import ngu_dialect_to_ipa
10
+
11
+
12
+ def japanese_cleaners(text):
13
+ text = japanese_to_romaji_with_accent(text)
14
+ if re.match('[A-Za-z]', text[-1]):
15
+ text += '.'
16
+ return text
17
+
18
+
19
+ def japanese_cleaners2(text):
20
+ return japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…')
21
+
22
+
23
+ def korean_cleaners(text):
24
+ '''Pipeline for Korean text'''
25
+ text = latin_to_hangul(text)
26
+ text = number_to_hangul(text)
27
+ text = divide_hangul(text)
28
+ if re.match('[\u3131-\u3163]', text[-1]):
29
+ text += '.'
30
+ return text
31
+
32
+
33
+ def chinese_cleaners(text):
34
+ '''Pipeline for Chinese text'''
35
+ text = number_to_chinese(text)
36
+ text = chinese_to_bopomofo(text)
37
+ text = latin_to_bopomofo(text)
38
+ if re.match('[ˉˊˇˋ˙]', text[-1]):
39
+ text += '。'
40
+ return text
41
+
42
+
43
+ def zh_ja_mixture_cleaners(text):
44
+ chinese_texts = re.findall(r'\[ZH\].*?\[ZH\]', text)
45
+ japanese_texts = re.findall(r'\[JA\].*?\[JA\]', text)
46
+ for chinese_text in chinese_texts:
47
+ cleaned_text = chinese_to_romaji(chinese_text[4:-4])
48
+ text = text.replace(chinese_text, cleaned_text+' ', 1)
49
+ for japanese_text in japanese_texts:
50
+ cleaned_text = japanese_to_romaji_with_accent(
51
+ japanese_text[4:-4]).replace('ts', 'ʦ').replace('u', 'ɯ').replace('...', '…')
52
+ text = text.replace(japanese_text, cleaned_text+' ', 1)
53
+ text = text[:-1]
54
+ if re.match('[A-Za-zɯɹəɥ→↓↑]', text[-1]):
55
+ text += '.'
56
+ return text
57
+
58
+
59
+ def sanskrit_cleaners(text):
60
+ text = text.replace('॥', '।').replace('ॐ', 'ओम्')
61
+ if text[-1] != '।':
62
+ text += ' ।'
63
+ return text
64
+
65
+
66
+ def cjks_cleaners(text):
67
+ chinese_texts = re.findall(r'\[ZH\].*?\[ZH\]', text)
68
+ japanese_texts = re.findall(r'\[JA\].*?\[JA\]', text)
69
+ korean_texts = re.findall(r'\[KO\].*?\[KO\]', text)
70
+ sanskrit_texts = re.findall(r'\[SA\].*?\[SA\]', text)
71
+ english_texts = re.findall(r'\[EN\].*?\[EN\]', text)
72
+ for chinese_text in chinese_texts:
73
+ cleaned_text = chinese_to_lazy_ipa(chinese_text[4:-4])
74
+ text = text.replace(chinese_text, cleaned_text+' ', 1)
75
+ for japanese_text in japanese_texts:
76
+ cleaned_text = japanese_to_ipa(japanese_text[4:-4])
77
+ text = text.replace(japanese_text, cleaned_text+' ', 1)
78
+ for korean_text in korean_texts:
79
+ cleaned_text = korean_to_lazy_ipa(korean_text[4:-4])
80
+ text = text.replace(korean_text, cleaned_text+' ', 1)
81
+ for sanskrit_text in sanskrit_texts:
82
+ cleaned_text = devanagari_to_ipa(sanskrit_text[4:-4])
83
+ text = text.replace(sanskrit_text, cleaned_text+' ', 1)
84
+ for english_text in english_texts:
85
+ cleaned_text = english_to_lazy_ipa(english_text[4:-4])
86
+ text = text.replace(english_text, cleaned_text+' ', 1)
87
+ text = text[:-1]
88
+ if re.match(r'[^\.,!\?\-…~]', text[-1]):
89
+ text += '.'
90
+ return text
91
+
92
+
93
+ def cjke_cleaners(text):
94
+ chinese_texts = re.findall(r'\[ZH\].*?\[ZH\]', text)
95
+ japanese_texts = re.findall(r'\[JA\].*?\[JA\]', text)
96
+ korean_texts = re.findall(r'\[KO\].*?\[KO\]', text)
97
+ english_texts = re.findall(r'\[EN\].*?\[EN\]', text)
98
+ for chinese_text in chinese_texts:
99
+ cleaned_text = chinese_to_lazy_ipa(chinese_text[4:-4])
100
+ cleaned_text = cleaned_text.replace(
101
+ 'ʧ', 'tʃ').replace('ʦ', 'ts').replace('ɥan', 'ɥæn')
102
+ text = text.replace(chinese_text, cleaned_text+' ', 1)
103
+ for japanese_text in japanese_texts:
104
+ cleaned_text = japanese_to_ipa(japanese_text[4:-4])
105
+ cleaned_text = cleaned_text.replace('ʧ', 'tʃ').replace(
106
+ 'ʦ', 'ts').replace('ɥan', 'ɥæn').replace('ʥ', 'dz')
107
+ text = text.replace(japanese_text, cleaned_text+' ', 1)
108
+ for korean_text in korean_texts:
109
+ cleaned_text = korean_to_ipa(korean_text[4:-4])
110
+ text = text.replace(korean_text, cleaned_text+' ', 1)
111
+ for english_text in english_texts:
112
+ cleaned_text = english_to_ipa2(english_text[4:-4])
113
+ cleaned_text = cleaned_text.replace('ɑ', 'a').replace(
114
+ 'ɔ', 'o').replace('ɛ', 'e').replace('ɪ', 'i').replace('ʊ', 'u')
115
+ text = text.replace(english_text, cleaned_text+' ', 1)
116
+ text = text[:-1]
117
+ if re.match(r'[^\.,!\?\-…~]', text[-1]):
118
+ text += '.'
119
+ return text
120
+
121
+
122
+ def cjke_cleaners2(text):
123
+ chinese_texts = re.findall(r'\[ZH\].*?\[ZH\]', text)
124
+ japanese_texts = re.findall(r'\[JA\].*?\[JA\]', text)
125
+ korean_texts = re.findall(r'\[KO\].*?\[KO\]', text)
126
+ english_texts = re.findall(r'\[EN\].*?\[EN\]', text)
127
+ for chinese_text in chinese_texts:
128
+ cleaned_text = chinese_to_ipa(chinese_text[4:-4])
129
+ text = text.replace(chinese_text, cleaned_text+' ', 1)
130
+ for japanese_text in japanese_texts:
131
+ cleaned_text = japanese_to_ipa2(japanese_text[4:-4])
132
+ text = text.replace(japanese_text, cleaned_text+' ', 1)
133
+ for korean_text in korean_texts:
134
+ cleaned_text = korean_to_ipa(korean_text[4:-4])
135
+ text = text.replace(korean_text, cleaned_text+' ', 1)
136
+ for english_text in english_texts:
137
+ cleaned_text = english_to_ipa2(english_text[4:-4])
138
+ text = text.replace(english_text, cleaned_text+' ', 1)
139
+ text = text[:-1]
140
+ if re.match(r'[^\.,!\?\-…~]', text[-1]):
141
+ text += '.'
142
+ return text
143
+
144
+
145
+ def thai_cleaners(text):
146
+ text = num_to_thai(text)
147
+ text = latin_to_thai(text)
148
+ return text
149
+
150
+
151
+ def shanghainese_cleaners(text):
152
+ text = shanghainese_to_ipa(text)
153
+ if re.match(r'[^\.,!\?\-…~]', text[-1]):
154
+ text += '.'
155
+ return text
156
+
157
+
158
+ def chinese_dialect_cleaners(text):
159
+ text = re.sub(r'\[MD\](.*?)\[MD\]',
160
+ lambda x: chinese_to_ipa2(x.group(1))+' ', text)
161
+ text = re.sub(r'\[TW\](.*?)\[TW\]',
162
+ lambda x: chinese_to_ipa2(x.group(1), True)+' ', text)
163
+ text = re.sub(r'\[JA\](.*?)\[JA\]',
164
+ lambda x: japanese_to_ipa3(x.group(1)).replace('Q', 'ʔ')+' ', text)
165
+ text = re.sub(r'\[SH\](.*?)\[SH\]', lambda x: shanghainese_to_ipa(x.group(1)).replace('1', '˥˧').replace('5',
166
+ '˧˧˦').replace('6', '˩˩˧').replace('7', '˥').replace('8', '˩˨').replace('ᴀ', 'ɐ').replace('ᴇ', 'e')+' ', text)
167
+ text = re.sub(r'\[GD\](.*?)\[GD\]',
168
+ lambda x: cantonese_to_ipa(x.group(1))+' ', text)
169
+ text = re.sub(r'\[EN\](.*?)\[EN\]',
170
+ lambda x: english_to_lazy_ipa2(x.group(1))+' ', text)
171
+ text = re.sub(r'\[([A-Z]{2})\](.*?)\[\1\]', lambda x: ngu_dialect_to_ipa(x.group(2), x.group(
172
+ 1)).replace('ʣ', 'dz').replace('ʥ', 'dʑ').replace('ʦ', 'ts').replace('ʨ', 'tɕ')+' ', text)
173
+ text = re.sub(r'\s+$', '', text)
174
+ text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
175
+ return text
text/japanese.py ADDED
@@ -0,0 +1,153 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import re
2
+ from unidecode import unidecode
3
+ import pyopenjtalk
4
+
5
+
6
+ # Regular expression matching Japanese without punctuation marks:
7
+ _japanese_characters = re.compile(
8
+ r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
9
+
10
+ # Regular expression matching non-Japanese characters or punctuation marks:
11
+ _japanese_marks = re.compile(
12
+ r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
13
+
14
+ # List of (symbol, Japanese) pairs for marks:
15
+ _symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
16
+ ('%', 'パーセント')
17
+ ]]
18
+
19
+ # List of (romaji, ipa) pairs for marks:
20
+ _romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
21
+ ('ts', 'ʦ'),
22
+ ('u', 'ɯ'),
23
+ ('j', 'ʥ'),
24
+ ('y', 'j'),
25
+ ('ni', 'n^i'),
26
+ ('nj', 'n^'),
27
+ ('hi', 'çi'),
28
+ ('hj', 'ç'),
29
+ ('f', 'ɸ'),
30
+ ('I', 'i*'),
31
+ ('U', 'ɯ*'),
32
+ ('r', 'ɾ')
33
+ ]]
34
+
35
+ # List of (romaji, ipa2) pairs for marks:
36
+ _romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
37
+ ('u', 'ɯ'),
38
+ ('ʧ', 'tʃ'),
39
+ ('j', 'dʑ'),
40
+ ('y', 'j'),
41
+ ('ni', 'n^i'),
42
+ ('nj', 'n^'),
43
+ ('hi', 'çi'),
44
+ ('hj', 'ç'),
45
+ ('f', 'ɸ'),
46
+ ('I', 'i*'),
47
+ ('U', 'ɯ*'),
48
+ ('r', 'ɾ')
49
+ ]]
50
+
51
+ # List of (consonant, sokuon) pairs:
52
+ _real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
53
+ (r'Q([↑↓]*[kg])', r'k#\1'),
54
+ (r'Q([↑↓]*[tdjʧ])', r't#\1'),
55
+ (r'Q([↑↓]*[sʃ])', r's\1'),
56
+ (r'Q([↑↓]*[pb])', r'p#\1')
57
+ ]]
58
+
59
+ # List of (consonant, hatsuon) pairs:
60
+ _real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
61
+ (r'N([↑↓]*[pbm])', r'm\1'),
62
+ (r'N([↑↓]*[ʧʥj])', r'n^\1'),
63
+ (r'N([↑↓]*[tdn])', r'n\1'),
64
+ (r'N([↑↓]*[kg])', r'ŋ\1')
65
+ ]]
66
+
67
+
68
+ def symbols_to_japanese(text):
69
+ for regex, replacement in _symbols_to_japanese:
70
+ text = re.sub(regex, replacement, text)
71
+ return text
72
+
73
+
74
+ def japanese_to_romaji_with_accent(text):
75
+ '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
76
+ text = symbols_to_japanese(text)
77
+ sentences = re.split(_japanese_marks, text)
78
+ marks = re.findall(_japanese_marks, text)
79
+ text = ''
80
+ for i, sentence in enumerate(sentences):
81
+ if re.match(_japanese_characters, sentence):
82
+ if text != '':
83
+ text += ' '
84
+ labels = pyopenjtalk.extract_fullcontext(sentence)
85
+ for n, label in enumerate(labels):
86
+ phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
87
+ if phoneme not in ['sil', 'pau']:
88
+ text += phoneme.replace('ch', 'ʧ').replace('sh',
89
+ 'ʃ').replace('cl', 'Q')
90
+ else:
91
+ continue
92
+ # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
93
+ a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
94
+ a2 = int(re.search(r"\+(\d+)\+", label).group(1))
95
+ a3 = int(re.search(r"\+(\d+)/", label).group(1))
96
+ if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
97
+ a2_next = -1
98
+ else:
99
+ a2_next = int(
100
+ re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
101
+ # Accent phrase boundary
102
+ if a3 == 1 and a2_next == 1:
103
+ text += ' '
104
+ # Falling
105
+ elif a1 == 0 and a2_next == a2 + 1:
106
+ text += '↓'
107
+ # Rising
108
+ elif a2 == 1 and a2_next == 2:
109
+ text += '↑'
110
+ if i < len(marks):
111
+ text += unidecode(marks[i]).replace(' ', '')
112
+ return text
113
+
114
+
115
+ def get_real_sokuon(text):
116
+ for regex, replacement in _real_sokuon:
117
+ text = re.sub(regex, replacement, text)
118
+ return text
119
+
120
+
121
+ def get_real_hatsuon(text):
122
+ for regex, replacement in _real_hatsuon:
123
+ text = re.sub(regex, replacement, text)
124
+ return text
125
+
126
+
127
+ def japanese_to_ipa(text):
128
+ text = japanese_to_romaji_with_accent(text).replace('...', '…')
129
+ text = re.sub(
130
+ r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
131
+ text = get_real_sokuon(text)
132
+ text = get_real_hatsuon(text)
133
+ for regex, replacement in _romaji_to_ipa:
134
+ text = re.sub(regex, replacement, text)
135
+ return text
136
+
137
+
138
+ def japanese_to_ipa2(text):
139
+ text = japanese_to_romaji_with_accent(text).replace('...', '…')
140
+ text = get_real_sokuon(text)
141
+ text = get_real_hatsuon(text)
142
+ for regex, replacement in _romaji_to_ipa2:
143
+ text = re.sub(regex, replacement, text)
144
+ return text
145
+
146
+
147
+ def japanese_to_ipa3(text):
148
+ text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
149
+ 'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
150
+ text = re.sub(
151
+ r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
152
+ text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
153
+ return text
text/mandarin.py ADDED
@@ -0,0 +1,328 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import sys
3
+ import re
4
+ from pypinyin import lazy_pinyin, BOPOMOFO
5
+ import jieba
6
+ import cn2an
7
+
8
+
9
+ # List of (Latin alphabet, bopomofo) pairs:
10
+ _latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
11
+ ('a', 'ㄟˉ'),
12
+ ('b', 'ㄅㄧˋ'),
13
+ ('c', 'ㄙㄧˉ'),
14
+ ('d', 'ㄉㄧˋ'),
15
+ ('e', 'ㄧˋ'),
16
+ ('f', 'ㄝˊㄈㄨˋ'),
17
+ ('g', 'ㄐㄧˋ'),
18
+ ('h', 'ㄝˇㄑㄩˋ'),
19
+ ('i', 'ㄞˋ'),
20
+ ('j', 'ㄐㄟˋ'),
21
+ ('k', 'ㄎㄟˋ'),
22
+ ('l', 'ㄝˊㄛˋ'),
23
+ ('m', 'ㄝˊㄇㄨˋ'),
24
+ ('n', 'ㄣˉ'),
25
+ ('o', 'ㄡˉ'),
26
+ ('p', 'ㄆㄧˉ'),
27
+ ('q', 'ㄎㄧㄡˉ'),
28
+ ('r', 'ㄚˋ'),
29
+ ('s', 'ㄝˊㄙˋ'),
30
+ ('t', 'ㄊㄧˋ'),
31
+ ('u', 'ㄧㄡˉ'),
32
+ ('v', 'ㄨㄧˉ'),
33
+ ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
34
+ ('x', 'ㄝˉㄎㄨˋㄙˋ'),
35
+ ('y', 'ㄨㄞˋ'),
36
+ ('z', 'ㄗㄟˋ')
37
+ ]]
38
+
39
+ # List of (bopomofo, romaji) pairs:
40
+ _bopomofo_to_romaji = [(re.compile('%s' % x[0]), x[1]) for x in [
41
+ ('ㄅㄛ', 'p⁼wo'),
42
+ ('ㄆㄛ', 'pʰwo'),
43
+ ('ㄇㄛ', 'mwo'),
44
+ ('ㄈㄛ', 'fwo'),
45
+ ('ㄅ', 'p⁼'),
46
+ ('ㄆ', 'pʰ'),
47
+ ('ㄇ', 'm'),
48
+ ('ㄈ', 'f'),
49
+ ('ㄉ', 't⁼'),
50
+ ('ㄊ', 'tʰ'),
51
+ ('ㄋ', 'n'),
52
+ ('ㄌ', 'l'),
53
+ ('ㄍ', 'k⁼'),
54
+ ('ㄎ', 'kʰ'),
55
+ ('ㄏ', 'h'),
56
+ ('ㄐ', 'ʧ⁼'),
57
+ ('ㄑ', 'ʧʰ'),
58
+ ('ㄒ', 'ʃ'),
59
+ ('ㄓ', 'ʦ`⁼'),
60
+ ('ㄔ', 'ʦ`ʰ'),
61
+ ('ㄕ', 's`'),
62
+ ('ㄖ', 'ɹ`'),
63
+ ('ㄗ', 'ʦ⁼'),
64
+ ('ㄘ', 'ʦʰ'),
65
+ ('ㄙ', 's'),
66
+ ('ㄚ', 'a'),
67
+ ('ㄛ', 'o'),
68
+ ('ㄜ', 'ə'),
69
+ ('ㄝ', 'e'),
70
+ ('ㄞ', 'ai'),
71
+ ('ㄟ', 'ei'),
72
+ ('ㄠ', 'au'),
73
+ ('ㄡ', 'ou'),
74
+ ('ㄧㄢ', 'yeNN'),
75
+ ('ㄢ', 'aNN'),
76
+ ('ㄧㄣ', 'iNN'),
77
+ ('ㄣ', 'əNN'),
78
+ ('ㄤ', 'aNg'),
79
+ ('ㄧㄥ', 'iNg'),
80
+ ('ㄨㄥ', 'uNg'),
81
+ ('ㄩㄥ', 'yuNg'),
82
+ ('ㄥ', 'əNg'),
83
+ ('ㄦ', 'əɻ'),
84
+ ('ㄧ', 'i'),
85
+ ('ㄨ', 'u'),
86
+ ('ㄩ', 'ɥ'),
87
+ ('ˉ', '→'),
88
+ ('ˊ', '↑'),
89
+ ('ˇ', '↓↑'),
90
+ ('ˋ', '↓'),
91
+ ('˙', ''),
92
+ (',', ','),
93
+ ('。', '.'),
94
+ ('!', '!'),
95
+ ('?', '?'),
96
+ ('—', '-')
97
+ ]]
98
+
99
+ # List of (romaji, ipa) pairs:
100
+ _romaji_to_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
101
+ ('ʃy', 'ʃ'),
102
+ ('ʧʰy', 'ʧʰ'),
103
+ ('ʧ⁼y', 'ʧ⁼'),
104
+ ('NN', 'n'),
105
+ ('Ng', 'ŋ'),
106
+ ('y', 'j'),
107
+ ('h', 'x')
108
+ ]]
109
+
110
+ # List of (bopomofo, ipa) pairs:
111
+ _bopomofo_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
112
+ ('ㄅㄛ', 'p⁼wo'),
113
+ ('ㄆㄛ', 'pʰwo'),
114
+ ('ㄇㄛ', 'mwo'),
115
+ ('ㄈㄛ', 'fwo'),
116
+ ('ㄅ', 'p⁼'),
117
+ ('ㄆ', 'pʰ'),
118
+ ('ㄇ', 'm'),
119
+ ('ㄈ', 'f'),
120
+ ('ㄉ', 't⁼'),
121
+ ('ㄊ', 'tʰ'),
122
+ ('ㄋ', 'n'),
123
+ ('ㄌ', 'l'),
124
+ ('ㄍ', 'k⁼'),
125
+ ('ㄎ', 'kʰ'),
126
+ ('ㄏ', 'x'),
127
+ ('ㄐ', 'tʃ⁼'),
128
+ ('ㄑ', 'tʃʰ'),
129
+ ('ㄒ', 'ʃ'),
130
+ ('ㄓ', 'ts`⁼'),
131
+ ('ㄔ', 'ts`ʰ'),
132
+ ('ㄕ', 's`'),
133
+ ('ㄖ', 'ɹ`'),
134
+ ('ㄗ', 'ts⁼'),
135
+ ('ㄘ', 'tsʰ'),
136
+ ('ㄙ', 's'),
137
+ ('ㄚ', 'a'),
138
+ ('ㄛ', 'o'),
139
+ ('ㄜ', 'ə'),
140
+ ('ㄝ', 'ɛ'),
141
+ ('ㄞ', 'aɪ'),
142
+ ('ㄟ', 'eɪ'),
143
+ ('ㄠ', 'ɑʊ'),
144
+ ('ㄡ', 'oʊ'),
145
+ ('ㄧㄢ', 'jɛn'),
146
+ ('ㄩㄢ', 'ɥæn'),
147
+ ('ㄢ', 'an'),
148
+ ('ㄧㄣ', 'in'),
149
+ ('ㄩㄣ', 'ɥn'),
150
+ ('ㄣ', 'ən'),
151
+ ('ㄤ', 'ɑŋ'),
152
+ ('ㄧㄥ', 'iŋ'),
153
+ ('ㄨㄥ', 'ʊŋ'),
154
+ ('ㄩㄥ', 'jʊŋ'),
155
+ ('ㄥ', 'əŋ'),
156
+ ('ㄦ', 'əɻ'),
157
+ ('ㄧ', 'i'),
158
+ ('ㄨ', 'u'),
159
+ ('ㄩ', 'ɥ'),
160
+ ('ˉ', '→'),
161
+ ('ˊ', '↑'),
162
+ ('ˇ', '↓↑'),
163
+ ('ˋ', '↓'),
164
+ ('˙', ''),
165
+ (',', ','),
166
+ ('。', '.'),
167
+ ('!', '!'),
168
+ ('?', '?'),
169
+ ('—', '-')
170
+ ]]
171
+
172
+ # List of (bopomofo, ipa2) pairs:
173
+ _bopomofo_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
174
+ ('ㄅㄛ', 'pwo'),
175
+ ('ㄆㄛ', 'pʰwo'),
176
+ ('ㄇㄛ', 'mwo'),
177
+ ('ㄈㄛ', 'fwo'),
178
+ ('ㄅ', 'p'),
179
+ ('ㄆ', 'pʰ'),
180
+ ('ㄇ', 'm'),
181
+ ('ㄈ', 'f'),
182
+ ('ㄉ', 't'),
183
+ ('ㄊ', 'tʰ'),
184
+ ('ㄋ', 'n'),
185
+ ('ㄌ', 'l'),
186
+ ('ㄍ', 'k'),
187
+ ('ㄎ', 'kʰ'),
188
+ ('ㄏ', 'h'),
189
+ ('ㄐ', 'tɕ'),
190
+ ('ㄑ', 'tɕʰ'),
191
+ ('ㄒ', 'ɕ'),
192
+ ('ㄓ', 'tʂ'),
193
+ ('ㄔ', 'tʂʰ'),
194
+ ('ㄕ', 'ʂ'),
195
+ ('ㄖ', 'ɻ'),
196
+ ('ㄗ', 'ts'),
197
+ ('ㄘ', 'tsʰ'),
198
+ ('ㄙ', 's'),
199
+ ('ㄚ', 'a'),
200
+ ('ㄛ', 'o'),
201
+ ('ㄜ', 'ɤ'),
202
+ ('ㄝ', 'ɛ'),
203
+ ('ㄞ', 'aɪ'),
204
+ ('ㄟ', 'eɪ'),
205
+ ('ㄠ', 'ɑʊ'),
206
+ ('ㄡ', 'oʊ'),
207
+ ('ㄧㄢ', 'jɛn'),
208
+ ('ㄩㄢ', 'yæn'),
209
+ ('ㄢ', 'an'),
210
+ ('ㄧㄣ', 'in'),
211
+ ('ㄩㄣ', 'yn'),
212
+ ('ㄣ', 'ən'),
213
+ ('ㄤ', 'ɑŋ'),
214
+ ('ㄧㄥ', 'iŋ'),
215
+ ('ㄨㄥ', 'ʊŋ'),
216
+ ('ㄩㄥ', 'jʊŋ'),
217
+ ('ㄥ', 'ɤŋ'),
218
+ ('ㄦ', 'əɻ'),
219
+ ('ㄧ', 'i'),
220
+ ('ㄨ', 'u'),
221
+ ('ㄩ', 'y'),
222
+ ('ˉ', '˥'),
223
+ ('ˊ', '˧˥'),
224
+ ('ˇ', '˨˩˦'),
225
+ ('ˋ', '˥˩'),
226
+ ('˙', ''),
227
+ (',', ','),
228
+ ('。', '.'),
229
+ ('!', '!'),
230
+ ('?', '?'),
231
+ ('—', '-')
232
+ ]]
233
+
234
+
235
+ def number_to_chinese(text):
236
+ numbers = re.findall(r'\d+(?:\.?\d+)?', text)
237
+ for number in numbers:
238
+ text = text.replace(number, cn2an.an2cn(number), 1)
239
+ return text
240
+
241
+
242
+ def chinese_to_bopomofo(text, taiwanese=False):
243
+ text = text.replace('、', ',').replace(';', ',').replace(':', ',')
244
+ words = jieba.lcut(text, cut_all=False)
245
+ text = ''
246
+ for word in words:
247
+ bopomofos = lazy_pinyin(word, BOPOMOFO)
248
+ if not re.search('[\u4e00-\u9fff]', word):
249
+ text += word
250
+ continue
251
+ for i in range(len(bopomofos)):
252
+ bopomofos[i] = re.sub(r'([\u3105-\u3129])$', r'\1ˉ', bopomofos[i])
253
+ if text != '':
254
+ text += ' '
255
+ if taiwanese:
256
+ text += '#'+'#'.join(bopomofos)
257
+ else:
258
+ text += ''.join(bopomofos)
259
+ return text
260
+
261
+
262
+ def latin_to_bopomofo(text):
263
+ for regex, replacement in _latin_to_bopomofo:
264
+ text = re.sub(regex, replacement, text)
265
+ return text
266
+
267
+
268
+ def bopomofo_to_romaji(text):
269
+ for regex, replacement in _bopomofo_to_romaji:
270
+ text = re.sub(regex, replacement, text)
271
+ return text
272
+
273
+
274
+ def bopomofo_to_ipa(text):
275
+ for regex, replacement in _bopomofo_to_ipa:
276
+ text = re.sub(regex, replacement, text)
277
+ return text
278
+
279
+
280
+ def bopomofo_to_ipa2(text):
281
+ for regex, replacement in _bopomofo_to_ipa2:
282
+ text = re.sub(regex, replacement, text)
283
+ return text
284
+
285
+
286
+ def chinese_to_romaji(text):
287
+ text = number_to_chinese(text)
288
+ text = chinese_to_bopomofo(text)
289
+ text = latin_to_bopomofo(text)
290
+ text = bopomofo_to_romaji(text)
291
+ text = re.sub('i([aoe])', r'y\1', text)
292
+ text = re.sub('u([aoəe])', r'w\1', text)
293
+ text = re.sub('([ʦsɹ]`[⁼ʰ]?)([→↓↑ ]+|$)',
294
+ r'\1ɹ`\2', text).replace('ɻ', 'ɹ`')
295
+ text = re.sub('([ʦs][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text)
296
+ return text
297
+
298
+
299
+ def chinese_to_lazy_ipa(text):
300
+ text = chinese_to_romaji(text)
301
+ for regex, replacement in _romaji_to_ipa:
302
+ text = re.sub(regex, replacement, text)
303
+ return text
304
+
305
+
306
+ def chinese_to_ipa(text):
307
+ text = number_to_chinese(text)
308
+ text = chinese_to_bopomofo(text)
309
+ text = latin_to_bopomofo(text)
310
+ text = bopomofo_to_ipa(text)
311
+ text = re.sub('i([aoe])', r'j\1', text)
312
+ text = re.sub('u([aoəe])', r'w\1', text)
313
+ text = re.sub('([sɹ]`[⁼ʰ]?)([→↓↑ ]+|$)',
314
+ r'\1ɹ`\2', text).replace('ɻ', 'ɹ`')
315
+ text = re.sub('([s][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text)
316
+ return text
317
+
318
+
319
+ def chinese_to_ipa2(text, taiwanese=False):
320
+ text = number_to_chinese(text)
321
+ text = chinese_to_bopomofo(text, taiwanese)
322
+ text = latin_to_bopomofo(text)
323
+ text = bopomofo_to_ipa2(text)
324
+ text = re.sub(r'i([aoe])', r'j\1', text)
325
+ text = re.sub(r'u([aoəe])', r'w\1', text)
326
+ text = re.sub(r'([ʂɹ]ʰ?)([˩˨˧˦˥ ]+|$)', r'\1ʅ\2', text)
327
+ text = re.sub(r'(sʰ?)([˩˨˧˦˥ ]+|$)', r'\1ɿ\2', text)
328
+ return text
text/symbols.py ADDED
@@ -0,0 +1,67 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ '''
2
+ Defines the set of symbols used in text input to the model.
3
+ '''
4
+ _pad = '_'
5
+ _punctuation = ',.!?-~…'
6
+ _letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
7
+ '''
8
+ # japanese_cleaners2
9
+ _pad = '_'
10
+ _punctuation = ',.!?-~…'
11
+ _letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
12
+ '''
13
+
14
+ '''# korean_cleaners
15
+ _pad = '_'
16
+ _punctuation = ',.!?…~'
17
+ _letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
18
+ '''
19
+
20
+ '''# chinese_cleaners
21
+ _pad = '_'
22
+ _punctuation = ',。!?—…'
23
+ _letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
24
+ '''
25
+
26
+
27
+ '''# sanskrit_cleaners
28
+ _pad = '_'
29
+ _punctuation = '।'
30
+ _letters = 'ँंःअआइईउऊऋएऐओऔकखगघङचछजझञटठडढणतथदधनपफबभमयरलळवशषसहऽािीुूृॄेैोौ्ॠॢ '
31
+ '''
32
+
33
+ '''# cjks_cleaners
34
+ _pad = '_'
35
+ _punctuation = ',.!?-~…'
36
+ _letters = 'NQabdefghijklmnopstuvwxyzʃʧʥʦɯɹəɥçɸɾβŋɦː⁼ʰ`^#*=→↓↑ '
37
+ '''
38
+
39
+ '''# thai_cleaners
40
+ _pad = '_'
41
+ _punctuation = '.!? '
42
+ _letters = 'กขฃคฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลวศษสหฬอฮฯะัาำิีึืุูเแโใไๅๆ็่้๊๋์'
43
+ '''
44
+
45
+ '''# cjke_cleaners2
46
+ _pad = '_'
47
+ _punctuation = ',.!?-~…'
48
+ _letters = 'NQabdefghijklmnopstuvwxyzɑæʃʑçɯɪɔɛɹðəɫɥɸʊɾʒθβŋɦ⁼ʰ`^#*=ˈˌ→↓↑ '
49
+ '''
50
+
51
+ '''# shanghainese_cleaners
52
+ _pad = '_'
53
+ _punctuation = ',.!?…'
54
+ _letters = 'abdfghiklmnopstuvyzøŋȵɑɔɕəɤɦɪɿʑʔʰ̩̃ᴀᴇ15678 '
55
+ '''
56
+
57
+ '''# chinese_dialect_cleaners
58
+ _pad = '_'
59
+ _punctuation = ',.!?~…─'
60
+ _letters = '#Nabdefghijklmnoprstuvwxyzæçøŋœȵɐɑɒɓɔɕɗɘəɚɛɜɣɤɦɪɭɯɵɷɸɻɾɿʂʅʊʋʌʏʑʔʦʮʰʷˀː˥˦˧˨˩̥̩̃̚αᴀᴇ↑↓∅ⱼ '
61
+ '''
62
+
63
+ # Export all symbols:
64
+ symbols = [_pad] + list(_punctuation) + list(_letters)
65
+
66
+ # Special symbol ids
67
+ SPACE_ID = symbols.index(" ")