skytnt commited on
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.gitattributes CHANGED
@@ -29,3 +29,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.o filter=lfs diff=lfs merge=lfs -text
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.gitignore ADDED
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MoeGoe.py ADDED
@@ -0,0 +1,119 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import sys
2
+ from torch import no_grad, LongTensor
3
+ import logging
4
+
5
+ logging.getLogger('numba').setLevel(logging.WARNING)
6
+
7
+ import commons
8
+ import utils
9
+ from models import SynthesizerTrn
10
+ from text import text_to_sequence
11
+ from mel_processing import spectrogram_torch
12
+
13
+ from scipy.io.wavfile import write
14
+
15
+ def get_text(text, hps):
16
+ text_norm = text_to_sequence(text, hps_ms.symbols, hps.data.text_cleaners)
17
+ if hps.data.add_blank:
18
+ text_norm = commons.intersperse(text_norm, 0)
19
+ text_norm = LongTensor(text_norm)
20
+ return text_norm
21
+
22
+ def ask_if_continue():
23
+ while True:
24
+ answer = input('Continue? (y/n): ')
25
+ if answer == 'y':
26
+ break
27
+ elif answer == 'n':
28
+ sys.exit(0)
29
+
30
+ def print_speakers(speakers):
31
+ print('ID\tSpeaker')
32
+ for id, name in enumerate(speakers):
33
+ print(str(id) + '\t' + name)
34
+
35
+ def get_speaker_id(message):
36
+ speaker_id = input(message)
37
+ try:
38
+ speaker_id = int(speaker_id)
39
+ except:
40
+ print(str(speaker_id) + ' is not a valid ID!')
41
+ sys.exit(1)
42
+ return speaker_id
43
+
44
+ if __name__ == '__main__':
45
+ model = input('Path of a VITS model: ')
46
+ config = input('Path of a config file: ')
47
+ try:
48
+ hps_ms = utils.get_hparams_from_file(config)
49
+ net_g_ms = SynthesizerTrn(
50
+ len(hps_ms.symbols),
51
+ hps_ms.data.filter_length // 2 + 1,
52
+ hps_ms.train.segment_size // hps_ms.data.hop_length,
53
+ n_speakers=hps_ms.data.n_speakers,
54
+ **hps_ms.model)
55
+ _ = net_g_ms.eval()
56
+ _ = utils.load_checkpoint(model, net_g_ms, None)
57
+ except:
58
+ print('Failed to load!')
59
+ sys.exit(1)
60
+
61
+ while True:
62
+ choice = input('TTS or VC? (t/v):')
63
+ if choice == 't':
64
+ text = input('Text to read: ')
65
+ try:
66
+ stn_tst = get_text(text, hps_ms)
67
+ except:
68
+ print('Invalid text!')
69
+ sys.exit(1)
70
+
71
+ print_speakers(hps_ms.speakers)
72
+ speaker_id = get_speaker_id('Speaker ID: ')
73
+
74
+ out_path = input('Path to save: ')
75
+
76
+ try:
77
+ with no_grad():
78
+ x_tst = stn_tst.unsqueeze(0)
79
+ x_tst_lengths = LongTensor([stn_tst.size(0)])
80
+ sid = LongTensor([speaker_id])
81
+ audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.cpu().float().numpy()
82
+ write(out_path, hps_ms.data.sampling_rate, audio)
83
+ except:
84
+ print('Failed to generate!')
85
+ sys.exit(1)
86
+
87
+ print('Successfully saved!')
88
+ ask_if_continue()
89
+
90
+
91
+ elif choice == 'v':
92
+ wav_path = input('Path of a WAV file (22050 Hz, 16 bits, 1 channel) to convert:\n')
93
+ print_speakers(hps_ms.speakers)
94
+ audio, sampling_rate = utils.load_wav_to_torch(wav_path)
95
+
96
+ originnal_id = get_speaker_id('Original speaker ID: ')
97
+ target_id = get_speaker_id('Target speaker ID: ')
98
+ out_path = input('Path to save: ')
99
+
100
+ y = audio / hps_ms.data.max_wav_value
101
+ y = y.unsqueeze(0)
102
+
103
+ spec = spectrogram_torch(y, hps_ms.data.filter_length,
104
+ hps_ms.data.sampling_rate, hps_ms.data.hop_length, hps_ms.data.win_length,
105
+ center=False)
106
+ spec_lengths = LongTensor([spec.size(-1)])
107
+ sid_src = LongTensor([originnal_id])
108
+
109
+ try:
110
+ with no_grad():
111
+ sid_tgt = LongTensor([target_id])
112
+ audio = net_g_ms.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][0,0].data.cpu().float().numpy()
113
+ write(out_path, hps_ms.data.sampling_rate, audio)
114
+ except:
115
+ print('Failed to generate!')
116
+ sys.exit(1)
117
+
118
+ print('Successfully saved!')
119
+ ask_if_continue()
README.md CHANGED
@@ -1,6 +1,6 @@
1
  ---
2
- title: Moe Japanese Tts
3
- emoji: πŸ¦€
4
  colorFrom: red
5
  colorTo: pink
6
  sdk: gradio
 
1
  ---
2
+ title: Moe Japanese TTS
3
+ emoji: πŸ˜ŠπŸŽ™οΈ
4
  colorFrom: red
5
  colorTo: pink
6
  sdk: gradio
app.py ADDED
@@ -0,0 +1,86 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numpy as np
2
+ import torch
3
+ from torch import no_grad, LongTensor
4
+ import commons
5
+ import utils
6
+ import gradio as gr
7
+ from models import SynthesizerTrn
8
+ from text import text_to_sequence
9
+ from mel_processing import spectrogram_torch
10
+
11
+
12
+ def get_text(text):
13
+ text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
14
+ if hps.data.add_blank:
15
+ text_norm = commons.intersperse(text_norm, 0)
16
+ text_norm = LongTensor(text_norm)
17
+ return text_norm
18
+
19
+
20
+ def tts_fn(text, speaker_id):
21
+ stn_tst = get_text(text)
22
+ with no_grad():
23
+ x_tst = stn_tst.unsqueeze(0)
24
+ x_tst_lengths = LongTensor([stn_tst.size(0)])
25
+ sid = LongTensor([speaker_id])
26
+ audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][
27
+ 0, 0].data.cpu().float().numpy()
28
+ return hps.data.sampling_rate, audio
29
+
30
+
31
+ def vc_fn(original_speaker_id, target_speaker_id, input_audio):
32
+ sampling_rate, audio = input_audio
33
+ y = torch.FloatTensor(audio.astype(np.float32)) / hps.data.max_wav_value
34
+ y = y.unsqueeze(0)
35
+
36
+ spec = spectrogram_torch(y, hps.data.filter_length,
37
+ hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
38
+ center=False)
39
+ spec_lengths = LongTensor([spec.size(-1)])
40
+ sid_src = LongTensor([original_speaker_id])
41
+ sid_tgt = LongTensor([target_speaker_id])
42
+ with no_grad():
43
+ audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
44
+ 0, 0].data.cpu().float().numpy()
45
+ return hps.data.sampling_rate, audio
46
+
47
+
48
+ if __name__ == '__main__':
49
+ config_path = "saved_model/config.json"
50
+ model_path = "saved_model/model.pth"
51
+ hps = utils.get_hparams_from_file(config_path)
52
+ model = SynthesizerTrn(
53
+ len(hps.symbols),
54
+ hps.data.filter_length // 2 + 1,
55
+ hps.train.segment_size // hps.data.hop_length,
56
+ n_speakers=hps.data.n_speakers,
57
+ **hps.model)
58
+ utils.load_checkpoint(model_path, model, None)
59
+ model.eval()
60
+
61
+ app = gr.Blocks()
62
+
63
+ with app:
64
+ gr.Markdown("# Moe Japanese TTS And Voice Conversion Using VITS Model\n\n"
65
+ "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.moegoe)\n\n"
66
+ "unofficial demo for [https://github.com/CjangCjengh/MoeGoe](https://github.com/CjangCjengh/MoeGoe)"
67
+ )
68
+ with gr.Tabs():
69
+ with gr.TabItem("TTS"):
70
+ with gr.Column():
71
+ tts_input1 = gr.TextArea(label="Text", value="こんにけは。")
72
+ tts_input2 = gr.Dropdown(label="Speaker", choices=hps.speakers, type="index")
73
+ tts_submit = gr.Button("Generate", variant="primary")
74
+ tts_output = gr.Audio(label="Output Audio")
75
+ with gr.TabItem("Voice Conversion"):
76
+ with gr.Column():
77
+ vc_input1 = gr.Dropdown(label="Original Speaker", choices=hps.speakers, type="index")
78
+ vc_input2 = gr.Dropdown(label="Target Speaker", choices=hps.speakers, type="index")
79
+ vc_input3 = gr.Audio(label="Input Audio")
80
+ vc_submit = gr.Button("Convert", variant="primary")
81
+ vc_output = gr.Audio(label="Output Audio")
82
+
83
+ tts_submit.click(tts_fn, [tts_input1, tts_input2], [tts_output])
84
+ vc_submit.click(vc_fn, [vc_input1, vc_input2, vc_input3], [vc_output])
85
+
86
+ app.launch()
attentions.py ADDED
@@ -0,0 +1,300 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import math
2
+ import torch
3
+ from torch import nn
4
+ from torch.nn import functional as F
5
+
6
+ import commons
7
+ from modules import LayerNorm
8
+
9
+
10
+ class Encoder(nn.Module):
11
+ def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
12
+ super().__init__()
13
+ self.hidden_channels = hidden_channels
14
+ self.filter_channels = filter_channels
15
+ self.n_heads = n_heads
16
+ self.n_layers = n_layers
17
+ self.kernel_size = kernel_size
18
+ self.p_dropout = p_dropout
19
+ self.window_size = window_size
20
+
21
+ self.drop = nn.Dropout(p_dropout)
22
+ self.attn_layers = nn.ModuleList()
23
+ self.norm_layers_1 = nn.ModuleList()
24
+ self.ffn_layers = nn.ModuleList()
25
+ self.norm_layers_2 = nn.ModuleList()
26
+ for i in range(self.n_layers):
27
+ self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
28
+ self.norm_layers_1.append(LayerNorm(hidden_channels))
29
+ self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
30
+ self.norm_layers_2.append(LayerNorm(hidden_channels))
31
+
32
+ def forward(self, x, x_mask):
33
+ attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
34
+ x = x * x_mask
35
+ for i in range(self.n_layers):
36
+ y = self.attn_layers[i](x, x, attn_mask)
37
+ y = self.drop(y)
38
+ x = self.norm_layers_1[i](x + y)
39
+
40
+ y = self.ffn_layers[i](x, x_mask)
41
+ y = self.drop(y)
42
+ x = self.norm_layers_2[i](x + y)
43
+ x = x * x_mask
44
+ return x
45
+
46
+
47
+ class Decoder(nn.Module):
48
+ def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
49
+ super().__init__()
50
+ self.hidden_channels = hidden_channels
51
+ self.filter_channels = filter_channels
52
+ self.n_heads = n_heads
53
+ self.n_layers = n_layers
54
+ self.kernel_size = kernel_size
55
+ self.p_dropout = p_dropout
56
+ self.proximal_bias = proximal_bias
57
+ self.proximal_init = proximal_init
58
+
59
+ self.drop = nn.Dropout(p_dropout)
60
+ self.self_attn_layers = nn.ModuleList()
61
+ self.norm_layers_0 = nn.ModuleList()
62
+ self.encdec_attn_layers = nn.ModuleList()
63
+ self.norm_layers_1 = nn.ModuleList()
64
+ self.ffn_layers = nn.ModuleList()
65
+ self.norm_layers_2 = nn.ModuleList()
66
+ for i in range(self.n_layers):
67
+ self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
68
+ self.norm_layers_0.append(LayerNorm(hidden_channels))
69
+ self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
70
+ self.norm_layers_1.append(LayerNorm(hidden_channels))
71
+ self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
72
+ self.norm_layers_2.append(LayerNorm(hidden_channels))
73
+
74
+ def forward(self, x, x_mask, h, h_mask):
75
+ """
76
+ x: decoder input
77
+ h: encoder output
78
+ """
79
+ self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
80
+ encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
81
+ x = x * x_mask
82
+ for i in range(self.n_layers):
83
+ y = self.self_attn_layers[i](x, x, self_attn_mask)
84
+ y = self.drop(y)
85
+ x = self.norm_layers_0[i](x + y)
86
+
87
+ y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
88
+ y = self.drop(y)
89
+ x = self.norm_layers_1[i](x + y)
90
+
91
+ y = self.ffn_layers[i](x, x_mask)
92
+ y = self.drop(y)
93
+ x = self.norm_layers_2[i](x + y)
94
+ x = x * x_mask
95
+ return x
96
+
97
+
98
+ class MultiHeadAttention(nn.Module):
99
+ 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):
100
+ super().__init__()
101
+ assert channels % n_heads == 0
102
+
103
+ self.channels = channels
104
+ self.out_channels = out_channels
105
+ self.n_heads = n_heads
106
+ self.p_dropout = p_dropout
107
+ self.window_size = window_size
108
+ self.heads_share = heads_share
109
+ self.block_length = block_length
110
+ self.proximal_bias = proximal_bias
111
+ self.proximal_init = proximal_init
112
+ self.attn = None
113
+
114
+ self.k_channels = channels // n_heads
115
+ self.conv_q = nn.Conv1d(channels, channels, 1)
116
+ self.conv_k = nn.Conv1d(channels, channels, 1)
117
+ self.conv_v = nn.Conv1d(channels, channels, 1)
118
+ self.conv_o = nn.Conv1d(channels, out_channels, 1)
119
+ self.drop = nn.Dropout(p_dropout)
120
+
121
+ if window_size is not None:
122
+ n_heads_rel = 1 if heads_share else n_heads
123
+ rel_stddev = self.k_channels**-0.5
124
+ self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
125
+ self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
126
+
127
+ nn.init.xavier_uniform_(self.conv_q.weight)
128
+ nn.init.xavier_uniform_(self.conv_k.weight)
129
+ nn.init.xavier_uniform_(self.conv_v.weight)
130
+ if proximal_init:
131
+ with torch.no_grad():
132
+ self.conv_k.weight.copy_(self.conv_q.weight)
133
+ self.conv_k.bias.copy_(self.conv_q.bias)
134
+
135
+ def forward(self, x, c, attn_mask=None):
136
+ q = self.conv_q(x)
137
+ k = self.conv_k(c)
138
+ v = self.conv_v(c)
139
+
140
+ x, self.attn = self.attention(q, k, v, mask=attn_mask)
141
+
142
+ x = self.conv_o(x)
143
+ return x
144
+
145
+ def attention(self, query, key, value, mask=None):
146
+ # reshape [b, d, t] -> [b, n_h, t, d_k]
147
+ b, d, t_s, t_t = (*key.size(), query.size(2))
148
+ query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
149
+ key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
150
+ value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
151
+
152
+ scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
153
+ if self.window_size is not None:
154
+ assert t_s == t_t, "Relative attention is only available for self-attention."
155
+ key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
156
+ rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
157
+ scores_local = self._relative_position_to_absolute_position(rel_logits)
158
+ scores = scores + scores_local
159
+ if self.proximal_bias:
160
+ assert t_s == t_t, "Proximal bias is only available for self-attention."
161
+ scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
162
+ if mask is not None:
163
+ scores = scores.masked_fill(mask == 0, -1e4)
164
+ if self.block_length is not None:
165
+ assert t_s == t_t, "Local attention is only available for self-attention."
166
+ block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
167
+ scores = scores.masked_fill(block_mask == 0, -1e4)
168
+ p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
169
+ p_attn = self.drop(p_attn)
170
+ output = torch.matmul(p_attn, value)
171
+ if self.window_size is not None:
172
+ relative_weights = self._absolute_position_to_relative_position(p_attn)
173
+ value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
174
+ output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
175
+ output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
176
+ return output, p_attn
177
+
178
+ def _matmul_with_relative_values(self, x, y):
179
+ """
180
+ x: [b, h, l, m]
181
+ y: [h or 1, m, d]
182
+ ret: [b, h, l, d]
183
+ """
184
+ ret = torch.matmul(x, y.unsqueeze(0))
185
+ return ret
186
+
187
+ def _matmul_with_relative_keys(self, x, y):
188
+ """
189
+ x: [b, h, l, d]
190
+ y: [h or 1, m, d]
191
+ ret: [b, h, l, m]
192
+ """
193
+ ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
194
+ return ret
195
+
196
+ def _get_relative_embeddings(self, relative_embeddings, length):
197
+ max_relative_position = 2 * self.window_size + 1
198
+ # Pad first before slice to avoid using cond ops.
199
+ pad_length = max(length - (self.window_size + 1), 0)
200
+ slice_start_position = max((self.window_size + 1) - length, 0)
201
+ slice_end_position = slice_start_position + 2 * length - 1
202
+ if pad_length > 0:
203
+ padded_relative_embeddings = F.pad(
204
+ relative_embeddings,
205
+ commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
206
+ else:
207
+ padded_relative_embeddings = relative_embeddings
208
+ used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
209
+ return used_relative_embeddings
210
+
211
+ def _relative_position_to_absolute_position(self, x):
212
+ """
213
+ x: [b, h, l, 2*l-1]
214
+ ret: [b, h, l, l]
215
+ """
216
+ batch, heads, length, _ = x.size()
217
+ # Concat columns of pad to shift from relative to absolute indexing.
218
+ x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
219
+
220
+ # Concat extra elements so to add up to shape (len+1, 2*len-1).
221
+ x_flat = x.view([batch, heads, length * 2 * length])
222
+ x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
223
+
224
+ # Reshape and slice out the padded elements.
225
+ x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
226
+ return x_final
227
+
228
+ def _absolute_position_to_relative_position(self, x):
229
+ """
230
+ x: [b, h, l, l]
231
+ ret: [b, h, l, 2*l-1]
232
+ """
233
+ batch, heads, length, _ = x.size()
234
+ # padd along column
235
+ x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
236
+ x_flat = x.view([batch, heads, length**2 + length*(length -1)])
237
+ # add 0's in the beginning that will skew the elements after reshape
238
+ x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
239
+ x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
240
+ return x_final
241
+
242
+ def _attention_bias_proximal(self, length):
243
+ """Bias for self-attention to encourage attention to close positions.
244
+ Args:
245
+ length: an integer scalar.
246
+ Returns:
247
+ a Tensor with shape [1, 1, length, length]
248
+ """
249
+ r = torch.arange(length, dtype=torch.float32)
250
+ diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
251
+ return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
252
+
253
+
254
+ class FFN(nn.Module):
255
+ def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
256
+ super().__init__()
257
+ self.in_channels = in_channels
258
+ self.out_channels = out_channels
259
+ self.filter_channels = filter_channels
260
+ self.kernel_size = kernel_size
261
+ self.p_dropout = p_dropout
262
+ self.activation = activation
263
+ self.causal = causal
264
+
265
+ if causal:
266
+ self.padding = self._causal_padding
267
+ else:
268
+ self.padding = self._same_padding
269
+
270
+ self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
271
+ self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
272
+ self.drop = nn.Dropout(p_dropout)
273
+
274
+ def forward(self, x, x_mask):
275
+ x = self.conv_1(self.padding(x * x_mask))
276
+ if self.activation == "gelu":
277
+ x = x * torch.sigmoid(1.702 * x)
278
+ else:
279
+ x = torch.relu(x)
280
+ x = self.drop(x)
281
+ x = self.conv_2(self.padding(x * x_mask))
282
+ return x * x_mask
283
+
284
+ def _causal_padding(self, x):
285
+ if self.kernel_size == 1:
286
+ return x
287
+ pad_l = self.kernel_size - 1
288
+ pad_r = 0
289
+ padding = [[0, 0], [0, 0], [pad_l, pad_r]]
290
+ x = F.pad(x, commons.convert_pad_shape(padding))
291
+ return x
292
+
293
+ def _same_padding(self, x):
294
+ if self.kernel_size == 1:
295
+ return x
296
+ pad_l = (self.kernel_size - 1) // 2
297
+ pad_r = self.kernel_size // 2
298
+ padding = [[0, 0], [0, 0], [pad_l, pad_r]]
299
+ x = F.pad(x, commons.convert_pad_shape(padding))
300
+ return x
commons.py ADDED
@@ -0,0 +1,172 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import math
2
+ import torch
3
+ from torch.nn import functional as F
4
+ import torch.jit
5
+
6
+
7
+ def script_method(fn, _rcb=None):
8
+ return fn
9
+
10
+
11
+ def script(obj, optimize=True, _frames_up=0, _rcb=None):
12
+ return obj
13
+
14
+
15
+ torch.jit.script_method = script_method
16
+ torch.jit.script = script
17
+
18
+
19
+ def init_weights(m, mean=0.0, std=0.01):
20
+ classname = m.__class__.__name__
21
+ if classname.find("Conv") != -1:
22
+ m.weight.data.normal_(mean, std)
23
+
24
+
25
+ def get_padding(kernel_size, dilation=1):
26
+ return int((kernel_size*dilation - dilation)/2)
27
+
28
+
29
+ def convert_pad_shape(pad_shape):
30
+ l = pad_shape[::-1]
31
+ pad_shape = [item for sublist in l for item in sublist]
32
+ return pad_shape
33
+
34
+
35
+ def intersperse(lst, item):
36
+ result = [item] * (len(lst) * 2 + 1)
37
+ result[1::2] = lst
38
+ return result
39
+
40
+
41
+ def kl_divergence(m_p, logs_p, m_q, logs_q):
42
+ """KL(P||Q)"""
43
+ kl = (logs_q - logs_p) - 0.5
44
+ kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
45
+ return kl
46
+
47
+
48
+ def rand_gumbel(shape):
49
+ """Sample from the Gumbel distribution, protect from overflows."""
50
+ uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
51
+ return -torch.log(-torch.log(uniform_samples))
52
+
53
+
54
+ def rand_gumbel_like(x):
55
+ g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
56
+ return g
57
+
58
+
59
+ def slice_segments(x, ids_str, segment_size=4):
60
+ ret = torch.zeros_like(x[:, :, :segment_size])
61
+ for i in range(x.size(0)):
62
+ idx_str = ids_str[i]
63
+ idx_end = idx_str + segment_size
64
+ ret[i] = x[i, :, idx_str:idx_end]
65
+ return ret
66
+
67
+
68
+ def rand_slice_segments(x, x_lengths=None, segment_size=4):
69
+ b, d, t = x.size()
70
+ if x_lengths is None:
71
+ x_lengths = t
72
+ ids_str_max = x_lengths - segment_size + 1
73
+ ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
74
+ ret = slice_segments(x, ids_str, segment_size)
75
+ return ret, ids_str
76
+
77
+
78
+ def get_timing_signal_1d(
79
+ length, channels, min_timescale=1.0, max_timescale=1.0e4):
80
+ position = torch.arange(length, dtype=torch.float)
81
+ num_timescales = channels // 2
82
+ log_timescale_increment = (
83
+ math.log(float(max_timescale) / float(min_timescale)) /
84
+ (num_timescales - 1))
85
+ inv_timescales = min_timescale * torch.exp(
86
+ torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
87
+ scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
88
+ signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
89
+ signal = F.pad(signal, [0, 0, 0, channels % 2])
90
+ signal = signal.view(1, channels, length)
91
+ return signal
92
+
93
+
94
+ def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
95
+ b, channels, length = x.size()
96
+ signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
97
+ return x + signal.to(dtype=x.dtype, device=x.device)
98
+
99
+
100
+ def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
101
+ b, channels, length = x.size()
102
+ signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
103
+ return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
104
+
105
+
106
+ def subsequent_mask(length):
107
+ mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
108
+ return mask
109
+
110
+
111
+ @torch.jit.script
112
+ def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
113
+ n_channels_int = n_channels[0]
114
+ in_act = input_a + input_b
115
+ t_act = torch.tanh(in_act[:, :n_channels_int, :])
116
+ s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
117
+ acts = t_act * s_act
118
+ return acts
119
+
120
+
121
+ def convert_pad_shape(pad_shape):
122
+ l = pad_shape[::-1]
123
+ pad_shape = [item for sublist in l for item in sublist]
124
+ return pad_shape
125
+
126
+
127
+ def shift_1d(x):
128
+ x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
129
+ return x
130
+
131
+
132
+ def sequence_mask(length, max_length=None):
133
+ if max_length is None:
134
+ max_length = length.max()
135
+ x = torch.arange(max_length, dtype=length.dtype, device=length.device)
136
+ return x.unsqueeze(0) < length.unsqueeze(1)
137
+
138
+
139
+ def generate_path(duration, mask):
140
+ """
141
+ duration: [b, 1, t_x]
142
+ mask: [b, 1, t_y, t_x]
143
+ """
144
+ device = duration.device
145
+
146
+ b, _, t_y, t_x = mask.shape
147
+ cum_duration = torch.cumsum(duration, -1)
148
+
149
+ cum_duration_flat = cum_duration.view(b * t_x)
150
+ path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
151
+ path = path.view(b, t_x, t_y)
152
+ path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
153
+ path = path.unsqueeze(1).transpose(2,3) * mask
154
+ return path
155
+
156
+
157
+ def clip_grad_value_(parameters, clip_value, norm_type=2):
158
+ if isinstance(parameters, torch.Tensor):
159
+ parameters = [parameters]
160
+ parameters = list(filter(lambda p: p.grad is not None, parameters))
161
+ norm_type = float(norm_type)
162
+ if clip_value is not None:
163
+ clip_value = float(clip_value)
164
+
165
+ total_norm = 0
166
+ for p in parameters:
167
+ param_norm = p.grad.data.norm(norm_type)
168
+ total_norm += param_norm.item() ** norm_type
169
+ if clip_value is not None:
170
+ p.grad.data.clamp_(min=-clip_value, max=clip_value)
171
+ total_norm = total_norm ** (1. / norm_type)
172
+ return total_norm
mel_processing.py ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import torch.utils.data
3
+ from librosa.filters import mel as librosa_mel_fn
4
+
5
+ MAX_WAV_VALUE = 32768.0
6
+
7
+
8
+ def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
9
+ """
10
+ PARAMS
11
+ ------
12
+ C: compression factor
13
+ """
14
+ return torch.log(torch.clamp(x, min=clip_val) * C)
15
+
16
+
17
+ def dynamic_range_decompression_torch(x, C=1):
18
+ """
19
+ PARAMS
20
+ ------
21
+ C: compression factor used to compress
22
+ """
23
+ return torch.exp(x) / C
24
+
25
+
26
+ def spectral_normalize_torch(magnitudes):
27
+ output = dynamic_range_compression_torch(magnitudes)
28
+ return output
29
+
30
+
31
+ def spectral_de_normalize_torch(magnitudes):
32
+ output = dynamic_range_decompression_torch(magnitudes)
33
+ return output
34
+
35
+
36
+ mel_basis = {}
37
+ hann_window = {}
38
+
39
+
40
+ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
41
+ if torch.min(y) < -1.:
42
+ print('min value is ', torch.min(y))
43
+ if torch.max(y) > 1.:
44
+ print('max value is ', torch.max(y))
45
+
46
+ global hann_window
47
+ dtype_device = str(y.dtype) + '_' + str(y.device)
48
+ wnsize_dtype_device = str(win_size) + '_' + dtype_device
49
+ if wnsize_dtype_device not in hann_window:
50
+ hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
51
+
52
+ y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
53
+ y = y.squeeze(1)
54
+
55
+ spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
56
+ center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
57
+
58
+ spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
59
+ return spec
60
+
61
+
62
+ def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
63
+ global mel_basis
64
+ dtype_device = str(spec.dtype) + '_' + str(spec.device)
65
+ fmax_dtype_device = str(fmax) + '_' + dtype_device
66
+ if fmax_dtype_device not in mel_basis:
67
+ mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
68
+ mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
69
+ spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
70
+ spec = spectral_normalize_torch(spec)
71
+ return spec
72
+
73
+
74
+ def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
75
+ if torch.min(y) < -1.:
76
+ print('min value is ', torch.min(y))
77
+ if torch.max(y) > 1.:
78
+ print('max value is ', torch.max(y))
79
+
80
+ global mel_basis, hann_window
81
+ dtype_device = str(y.dtype) + '_' + str(y.device)
82
+ fmax_dtype_device = str(fmax) + '_' + dtype_device
83
+ wnsize_dtype_device = str(win_size) + '_' + dtype_device
84
+ if fmax_dtype_device not in mel_basis:
85
+ mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
86
+ mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
87
+ if wnsize_dtype_device not in hann_window:
88
+ hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
89
+
90
+ y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
91
+ y = y.squeeze(1)
92
+
93
+ spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
94
+ center=center, pad_mode='reflect', normalized=False, onesided=True)
95
+
96
+ spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
97
+
98
+ spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
99
+ spec = spectral_normalize_torch(spec)
100
+
101
+ return spec
models.py ADDED
@@ -0,0 +1,533 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import math
2
+ import torch
3
+ from torch import nn
4
+ from torch.nn import functional as F
5
+
6
+ import commons
7
+ import modules
8
+ import attentions
9
+ import monotonic_align
10
+
11
+ from torch.nn import Conv1d, ConvTranspose1d, Conv2d
12
+ from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
13
+ from commons import init_weights, get_padding
14
+
15
+
16
+ class StochasticDurationPredictor(nn.Module):
17
+ def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
18
+ super().__init__()
19
+ filter_channels = in_channels # it needs to be removed from future version.
20
+ self.in_channels = in_channels
21
+ self.filter_channels = filter_channels
22
+ self.kernel_size = kernel_size
23
+ self.p_dropout = p_dropout
24
+ self.n_flows = n_flows
25
+ self.gin_channels = gin_channels
26
+
27
+ self.log_flow = modules.Log()
28
+ self.flows = nn.ModuleList()
29
+ self.flows.append(modules.ElementwiseAffine(2))
30
+ for i in range(n_flows):
31
+ self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
32
+ self.flows.append(modules.Flip())
33
+
34
+ self.post_pre = nn.Conv1d(1, filter_channels, 1)
35
+ self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
36
+ self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
37
+ self.post_flows = nn.ModuleList()
38
+ self.post_flows.append(modules.ElementwiseAffine(2))
39
+ for i in range(4):
40
+ self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
41
+ self.post_flows.append(modules.Flip())
42
+
43
+ self.pre = nn.Conv1d(in_channels, filter_channels, 1)
44
+ self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
45
+ self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
46
+ if gin_channels != 0:
47
+ self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
48
+
49
+ def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
50
+ x = torch.detach(x)
51
+ x = self.pre(x)
52
+ if g is not None:
53
+ g = torch.detach(g)
54
+ x = x + self.cond(g)
55
+ x = self.convs(x, x_mask)
56
+ x = self.proj(x) * x_mask
57
+
58
+ if not reverse:
59
+ flows = self.flows
60
+ assert w is not None
61
+
62
+ logdet_tot_q = 0
63
+ h_w = self.post_pre(w)
64
+ h_w = self.post_convs(h_w, x_mask)
65
+ h_w = self.post_proj(h_w) * x_mask
66
+ e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
67
+ z_q = e_q
68
+ for flow in self.post_flows:
69
+ z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
70
+ logdet_tot_q += logdet_q
71
+ z_u, z1 = torch.split(z_q, [1, 1], 1)
72
+ u = torch.sigmoid(z_u) * x_mask
73
+ z0 = (w - u) * x_mask
74
+ logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
75
+ logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
76
+
77
+ logdet_tot = 0
78
+ z0, logdet = self.log_flow(z0, x_mask)
79
+ logdet_tot += logdet
80
+ z = torch.cat([z0, z1], 1)
81
+ for flow in flows:
82
+ z, logdet = flow(z, x_mask, g=x, reverse=reverse)
83
+ logdet_tot = logdet_tot + logdet
84
+ nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
85
+ return nll + logq # [b]
86
+ else:
87
+ flows = list(reversed(self.flows))
88
+ flows = flows[:-2] + [flows[-1]] # remove a useless vflow
89
+ z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
90
+ for flow in flows:
91
+ z = flow(z, x_mask, g=x, reverse=reverse)
92
+ z0, z1 = torch.split(z, [1, 1], 1)
93
+ logw = z0
94
+ return logw
95
+
96
+
97
+ class DurationPredictor(nn.Module):
98
+ def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
99
+ super().__init__()
100
+
101
+ self.in_channels = in_channels
102
+ self.filter_channels = filter_channels
103
+ self.kernel_size = kernel_size
104
+ self.p_dropout = p_dropout
105
+ self.gin_channels = gin_channels
106
+
107
+ self.drop = nn.Dropout(p_dropout)
108
+ self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
109
+ self.norm_1 = modules.LayerNorm(filter_channels)
110
+ self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
111
+ self.norm_2 = modules.LayerNorm(filter_channels)
112
+ self.proj = nn.Conv1d(filter_channels, 1, 1)
113
+
114
+ if gin_channels != 0:
115
+ self.cond = nn.Conv1d(gin_channels, in_channels, 1)
116
+
117
+ def forward(self, x, x_mask, g=None):
118
+ x = torch.detach(x)
119
+ if g is not None:
120
+ g = torch.detach(g)
121
+ x = x + self.cond(g)
122
+ x = self.conv_1(x * x_mask)
123
+ x = torch.relu(x)
124
+ x = self.norm_1(x)
125
+ x = self.drop(x)
126
+ x = self.conv_2(x * x_mask)
127
+ x = torch.relu(x)
128
+ x = self.norm_2(x)
129
+ x = self.drop(x)
130
+ x = self.proj(x * x_mask)
131
+ return x * x_mask
132
+
133
+
134
+ class TextEncoder(nn.Module):
135
+ def __init__(self,
136
+ n_vocab,
137
+ out_channels,
138
+ hidden_channels,
139
+ filter_channels,
140
+ n_heads,
141
+ n_layers,
142
+ kernel_size,
143
+ p_dropout):
144
+ super().__init__()
145
+ self.n_vocab = n_vocab
146
+ self.out_channels = out_channels
147
+ self.hidden_channels = hidden_channels
148
+ self.filter_channels = filter_channels
149
+ self.n_heads = n_heads
150
+ self.n_layers = n_layers
151
+ self.kernel_size = kernel_size
152
+ self.p_dropout = p_dropout
153
+
154
+ self.emb = nn.Embedding(n_vocab, hidden_channels)
155
+ nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
156
+
157
+ self.encoder = attentions.Encoder(
158
+ hidden_channels,
159
+ filter_channels,
160
+ n_heads,
161
+ n_layers,
162
+ kernel_size,
163
+ p_dropout)
164
+ self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
165
+
166
+ def forward(self, x, x_lengths):
167
+ x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
168
+ x = torch.transpose(x, 1, -1) # [b, h, t]
169
+ x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
170
+
171
+ x = self.encoder(x * x_mask, x_mask)
172
+ stats = self.proj(x) * x_mask
173
+
174
+ m, logs = torch.split(stats, self.out_channels, dim=1)
175
+ return x, m, logs, x_mask
176
+
177
+
178
+ class ResidualCouplingBlock(nn.Module):
179
+ def __init__(self,
180
+ channels,
181
+ hidden_channels,
182
+ kernel_size,
183
+ dilation_rate,
184
+ n_layers,
185
+ n_flows=4,
186
+ gin_channels=0):
187
+ super().__init__()
188
+ self.channels = channels
189
+ self.hidden_channels = hidden_channels
190
+ self.kernel_size = kernel_size
191
+ self.dilation_rate = dilation_rate
192
+ self.n_layers = n_layers
193
+ self.n_flows = n_flows
194
+ self.gin_channels = gin_channels
195
+
196
+ self.flows = nn.ModuleList()
197
+ for i in range(n_flows):
198
+ self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
199
+ self.flows.append(modules.Flip())
200
+
201
+ def forward(self, x, x_mask, g=None, reverse=False):
202
+ if not reverse:
203
+ for flow in self.flows:
204
+ x, _ = flow(x, x_mask, g=g, reverse=reverse)
205
+ else:
206
+ for flow in reversed(self.flows):
207
+ x = flow(x, x_mask, g=g, reverse=reverse)
208
+ return x
209
+
210
+
211
+ class PosteriorEncoder(nn.Module):
212
+ def __init__(self,
213
+ in_channels,
214
+ out_channels,
215
+ hidden_channels,
216
+ kernel_size,
217
+ dilation_rate,
218
+ n_layers,
219
+ gin_channels=0):
220
+ super().__init__()
221
+ self.in_channels = in_channels
222
+ self.out_channels = out_channels
223
+ self.hidden_channels = hidden_channels
224
+ self.kernel_size = kernel_size
225
+ self.dilation_rate = dilation_rate
226
+ self.n_layers = n_layers
227
+ self.gin_channels = gin_channels
228
+
229
+ self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
230
+ self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
231
+ self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
232
+
233
+ def forward(self, x, x_lengths, g=None):
234
+ x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
235
+ x = self.pre(x) * x_mask
236
+ x = self.enc(x, x_mask, g=g)
237
+ stats = self.proj(x) * x_mask
238
+ m, logs = torch.split(stats, self.out_channels, dim=1)
239
+ z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
240
+ return z, m, logs, x_mask
241
+
242
+
243
+ class Generator(torch.nn.Module):
244
+ def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
245
+ super(Generator, self).__init__()
246
+ self.num_kernels = len(resblock_kernel_sizes)
247
+ self.num_upsamples = len(upsample_rates)
248
+ self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
249
+ resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
250
+
251
+ self.ups = nn.ModuleList()
252
+ for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
253
+ self.ups.append(weight_norm(
254
+ ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
255
+ k, u, padding=(k-u)//2)))
256
+
257
+ self.resblocks = nn.ModuleList()
258
+ for i in range(len(self.ups)):
259
+ ch = upsample_initial_channel//(2**(i+1))
260
+ for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
261
+ self.resblocks.append(resblock(ch, k, d))
262
+
263
+ self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
264
+ self.ups.apply(init_weights)
265
+
266
+ if gin_channels != 0:
267
+ self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
268
+
269
+ def forward(self, x, g=None):
270
+ x = self.conv_pre(x)
271
+ if g is not None:
272
+ x = x + self.cond(g)
273
+
274
+ for i in range(self.num_upsamples):
275
+ x = F.leaky_relu(x, modules.LRELU_SLOPE)
276
+ x = self.ups[i](x)
277
+ xs = None
278
+ for j in range(self.num_kernels):
279
+ if xs is None:
280
+ xs = self.resblocks[i*self.num_kernels+j](x)
281
+ else:
282
+ xs += self.resblocks[i*self.num_kernels+j](x)
283
+ x = xs / self.num_kernels
284
+ x = F.leaky_relu(x)
285
+ x = self.conv_post(x)
286
+ x = torch.tanh(x)
287
+
288
+ return x
289
+
290
+ def remove_weight_norm(self):
291
+ print('Removing weight norm...')
292
+ for l in self.ups:
293
+ remove_weight_norm(l)
294
+ for l in self.resblocks:
295
+ l.remove_weight_norm()
296
+
297
+
298
+ class DiscriminatorP(torch.nn.Module):
299
+ def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
300
+ super(DiscriminatorP, self).__init__()
301
+ self.period = period
302
+ self.use_spectral_norm = use_spectral_norm
303
+ norm_f = weight_norm if use_spectral_norm == False else spectral_norm
304
+ self.convs = nn.ModuleList([
305
+ norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
306
+ norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
307
+ norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
308
+ norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
309
+ norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
310
+ ])
311
+ self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
312
+
313
+ def forward(self, x):
314
+ fmap = []
315
+
316
+ # 1d to 2d
317
+ b, c, t = x.shape
318
+ if t % self.period != 0: # pad first
319
+ n_pad = self.period - (t % self.period)
320
+ x = F.pad(x, (0, n_pad), "reflect")
321
+ t = t + n_pad
322
+ x = x.view(b, c, t // self.period, self.period)
323
+
324
+ for l in self.convs:
325
+ x = l(x)
326
+ x = F.leaky_relu(x, modules.LRELU_SLOPE)
327
+ fmap.append(x)
328
+ x = self.conv_post(x)
329
+ fmap.append(x)
330
+ x = torch.flatten(x, 1, -1)
331
+
332
+ return x, fmap
333
+
334
+
335
+ class DiscriminatorS(torch.nn.Module):
336
+ def __init__(self, use_spectral_norm=False):
337
+ super(DiscriminatorS, self).__init__()
338
+ norm_f = weight_norm if use_spectral_norm == False else spectral_norm
339
+ self.convs = nn.ModuleList([
340
+ norm_f(Conv1d(1, 16, 15, 1, padding=7)),
341
+ norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
342
+ norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
343
+ norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
344
+ norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
345
+ norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
346
+ ])
347
+ self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
348
+
349
+ def forward(self, x):
350
+ fmap = []
351
+
352
+ for l in self.convs:
353
+ x = l(x)
354
+ x = F.leaky_relu(x, modules.LRELU_SLOPE)
355
+ fmap.append(x)
356
+ x = self.conv_post(x)
357
+ fmap.append(x)
358
+ x = torch.flatten(x, 1, -1)
359
+
360
+ return x, fmap
361
+
362
+
363
+ class MultiPeriodDiscriminator(torch.nn.Module):
364
+ def __init__(self, use_spectral_norm=False):
365
+ super(MultiPeriodDiscriminator, self).__init__()
366
+ periods = [2,3,5,7,11]
367
+
368
+ discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
369
+ discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
370
+ self.discriminators = nn.ModuleList(discs)
371
+
372
+ def forward(self, y, y_hat):
373
+ y_d_rs = []
374
+ y_d_gs = []
375
+ fmap_rs = []
376
+ fmap_gs = []
377
+ for i, d in enumerate(self.discriminators):
378
+ y_d_r, fmap_r = d(y)
379
+ y_d_g, fmap_g = d(y_hat)
380
+ y_d_rs.append(y_d_r)
381
+ y_d_gs.append(y_d_g)
382
+ fmap_rs.append(fmap_r)
383
+ fmap_gs.append(fmap_g)
384
+
385
+ return y_d_rs, y_d_gs, fmap_rs, fmap_gs
386
+
387
+
388
+
389
+ class SynthesizerTrn(nn.Module):
390
+ """
391
+ Synthesizer for Training
392
+ """
393
+
394
+ def __init__(self,
395
+ n_vocab,
396
+ spec_channels,
397
+ segment_size,
398
+ inter_channels,
399
+ hidden_channels,
400
+ filter_channels,
401
+ n_heads,
402
+ n_layers,
403
+ kernel_size,
404
+ p_dropout,
405
+ resblock,
406
+ resblock_kernel_sizes,
407
+ resblock_dilation_sizes,
408
+ upsample_rates,
409
+ upsample_initial_channel,
410
+ upsample_kernel_sizes,
411
+ n_speakers=0,
412
+ gin_channels=0,
413
+ use_sdp=True,
414
+ **kwargs):
415
+
416
+ super().__init__()
417
+ self.n_vocab = n_vocab
418
+ self.spec_channels = spec_channels
419
+ self.inter_channels = inter_channels
420
+ self.hidden_channels = hidden_channels
421
+ self.filter_channels = filter_channels
422
+ self.n_heads = n_heads
423
+ self.n_layers = n_layers
424
+ self.kernel_size = kernel_size
425
+ self.p_dropout = p_dropout
426
+ self.resblock = resblock
427
+ self.resblock_kernel_sizes = resblock_kernel_sizes
428
+ self.resblock_dilation_sizes = resblock_dilation_sizes
429
+ self.upsample_rates = upsample_rates
430
+ self.upsample_initial_channel = upsample_initial_channel
431
+ self.upsample_kernel_sizes = upsample_kernel_sizes
432
+ self.segment_size = segment_size
433
+ self.n_speakers = n_speakers
434
+ self.gin_channels = gin_channels
435
+
436
+ self.use_sdp = use_sdp
437
+
438
+ self.enc_p = TextEncoder(n_vocab,
439
+ inter_channels,
440
+ hidden_channels,
441
+ filter_channels,
442
+ n_heads,
443
+ n_layers,
444
+ kernel_size,
445
+ p_dropout)
446
+ self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
447
+ self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
448
+ self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
449
+
450
+ if use_sdp:
451
+ self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
452
+ else:
453
+ self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
454
+
455
+ if n_speakers > 1:
456
+ self.emb_g = nn.Embedding(n_speakers, gin_channels)
457
+
458
+ def forward(self, x, x_lengths, y, y_lengths, sid=None):
459
+
460
+ x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
461
+ if self.n_speakers > 0:
462
+ g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
463
+ else:
464
+ g = None
465
+
466
+ z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
467
+ z_p = self.flow(z, y_mask, g=g)
468
+
469
+ with torch.no_grad():
470
+ # negative cross-entropy
471
+ s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
472
+ neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
473
+ 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]
474
+ 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]
475
+ neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
476
+ neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
477
+
478
+ attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
479
+ attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
480
+
481
+ w = attn.sum(2)
482
+ if self.use_sdp:
483
+ l_length = self.dp(x, x_mask, w, g=g)
484
+ l_length = l_length / torch.sum(x_mask)
485
+ else:
486
+ logw_ = torch.log(w + 1e-6) * x_mask
487
+ logw = self.dp(x, x_mask, g=g)
488
+ l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging
489
+
490
+ # expand prior
491
+ m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
492
+ logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
493
+
494
+ z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
495
+ o = self.dec(z_slice, g=g)
496
+ return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
497
+
498
+ def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
499
+ x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
500
+ if self.n_speakers > 0:
501
+ g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
502
+ else:
503
+ g = None
504
+
505
+ if self.use_sdp:
506
+ logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
507
+ else:
508
+ logw = self.dp(x, x_mask, g=g)
509
+ w = torch.exp(logw) * x_mask * length_scale
510
+ w_ceil = torch.ceil(w)
511
+ y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
512
+ y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
513
+ attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
514
+ attn = commons.generate_path(w_ceil, attn_mask)
515
+
516
+ m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
517
+ logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
518
+
519
+ z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
520
+ z = self.flow(z_p, y_mask, g=g, reverse=True)
521
+ o = self.dec((z * y_mask)[:,:,:max_len], g=g)
522
+ return o, attn, y_mask, (z, z_p, m_p, logs_p)
523
+
524
+ def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
525
+ assert self.n_speakers > 0, "n_speakers have to be larger than 0."
526
+ g_src = self.emb_g(sid_src).unsqueeze(-1)
527
+ g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
528
+ z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
529
+ z_p = self.flow(z, y_mask, g=g_src)
530
+ z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
531
+ o_hat = self.dec(z_hat * y_mask, g=g_tgt)
532
+ return o_hat, y_mask, (z, z_p, z_hat)
533
+
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 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numpy as np
2
+ import torch
3
+ from ctypes import cdll
4
+ maximum_path_c = cdll.LoadLibrary('./monotonic_align/core.dll')
5
+
6
+
7
+ def maximum_path(neg_cent, mask):
8
+ """ Cython optimized version.
9
+ neg_cent: [b, t_t, t_s]
10
+ mask: [b, t_t, t_s]
11
+ """
12
+ device = neg_cent.device
13
+ dtype = neg_cent.dtype
14
+ neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
15
+ path = np.zeros(neg_cent.shape, dtype=np.int32)
16
+
17
+ t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
18
+ t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
19
+ maximum_path_c(path, neg_cent, t_t_max, t_s_max)
20
+ return torch.from_numpy(path).to(device=device, dtype=dtype)
monotonic_align/build/temp.linux-x86_64-3.7/core.o ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:07fb98441110e755daf37fc4174ecdd627b7375ffad753037428f12b66dcb45f
3
+ size 1747240
monotonic_align/core.c ADDED
The diff for this file is too large to render. See raw diff
 
monotonic_align/core.dll ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f5411d1ec93998065bd2ffd5a39a0d06b25385deb020e30edea1b9125def7cb1
3
+ size 58880
monotonic_align/core.pyx ADDED
@@ -0,0 +1,42 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ cimport cython
2
+ from cython.parallel import prange
3
+
4
+
5
+ @cython.boundscheck(False)
6
+ @cython.wraparound(False)
7
+ cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil:
8
+ cdef int x
9
+ cdef int y
10
+ cdef float v_prev
11
+ cdef float v_cur
12
+ cdef float tmp
13
+ cdef int index = t_x - 1
14
+
15
+ for y in range(t_y):
16
+ for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
17
+ if x == y:
18
+ v_cur = max_neg_val
19
+ else:
20
+ v_cur = value[y-1, x]
21
+ if x == 0:
22
+ if y == 0:
23
+ v_prev = 0.
24
+ else:
25
+ v_prev = max_neg_val
26
+ else:
27
+ v_prev = value[y-1, x-1]
28
+ value[y, x] += max(v_prev, v_cur)
29
+
30
+ for y in range(t_y - 1, -1, -1):
31
+ path[y, index] = 1
32
+ if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
33
+ index = index - 1
34
+
35
+
36
+ @cython.boundscheck(False)
37
+ @cython.wraparound(False)
38
+ cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil:
39
+ cdef int b = paths.shape[0]
40
+ cdef int i
41
+ for i in prange(b, nogil=True):
42
+ maximum_path_each(paths[i], values[i], t_ys[i], t_xs[i])
monotonic_align/monotonic_align/core.cpython-37m-x86_64-linux-gnu.so ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ced7c27b10321e2e32b1e2ae446d399bc8fb85ea072a1f9dd16d2a95c1a192b1
3
+ size 723240
monotonic_align/setup.py ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ from distutils.core import setup
2
+ from Cython.Build import cythonize
3
+ import numpy
4
+
5
+ setup(
6
+ name = 'monotonic_align',
7
+ ext_modules = cythonize("core.pyx"),
8
+ include_dirs=[numpy.get_include()]
9
+ )
requirements.txt ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ librosa
2
+ matplotlib
3
+ numpy
4
+ phonemizer
5
+ scipy
6
+ tensorboard
7
+ torch
8
+ torchvision
9
+ Unidecode
10
+ pyopenjtalk
11
+ gradio
saved_model/config.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:abd4346c22576c4de22f8a0bef8ddaefe0c1313627f980cf6b4156dec9705b78
3
+ size 1260
saved_model/model.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:db51eaef26d99632a159d29a09d6b8ef477bf761647ce71346f377758cf1a378
3
+ size 476620095
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 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """ from https://github.com/keithito/tacotron """
2
+ from text import cleaners
3
+
4
+
5
+ def text_to_sequence(text, symbols, cleaner_names):
6
+ '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
7
+ Args:
8
+ text: string to convert to a sequence
9
+ cleaner_names: names of the cleaner functions to run the text through
10
+ Returns:
11
+ List of integers corresponding to the symbols in the text
12
+ '''
13
+ _symbol_to_id = {s: i for i, s in enumerate(symbols)}
14
+
15
+ sequence = []
16
+
17
+ clean_text = _clean_text(text, cleaner_names)
18
+ for symbol in clean_text:
19
+ if symbol not in _symbol_to_id.keys():
20
+ continue
21
+ symbol_id = _symbol_to_id[symbol]
22
+ sequence += [symbol_id]
23
+ return sequence
24
+
25
+
26
+ def _clean_text(text, cleaner_names):
27
+ for name in cleaner_names:
28
+ cleaner = getattr(cleaners, name)
29
+ if not cleaner:
30
+ raise Exception('Unknown cleaner: %s' % name)
31
+ text = cleaner(text)
32
+ return text
text/cleaners.py ADDED
@@ -0,0 +1,51 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import re
2
+ from unidecode import unidecode
3
+ import pyopenjtalk
4
+ pyopenjtalk._lazy_init()
5
+
6
+ # Regular expression matching Japanese without punctuation marks:
7
+ _japanese_characters = re.compile(r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
8
+
9
+ # Regular expression matching non-Japanese characters or punctuation marks:
10
+ _japanese_marks = re.compile(r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
11
+
12
+
13
+ def japanese_cleaners(text):
14
+ '''Pipeline for notating accent in Japanese text.'''
15
+ '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
16
+ sentences = re.split(_japanese_marks, text)
17
+ marks = re.findall(_japanese_marks, text)
18
+ text = ''
19
+ for i, sentence in enumerate(sentences):
20
+ if re.match(_japanese_characters, sentence):
21
+ if text!='':
22
+ text+=' '
23
+ labels = pyopenjtalk.extract_fullcontext(sentence)
24
+ for n, label in enumerate(labels):
25
+ phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
26
+ if phoneme not in ['sil','pau']:
27
+ text += phoneme.replace('ch','Κ§').replace('sh','Κƒ').replace('cl','Q')
28
+ else:
29
+ continue
30
+ n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
31
+ a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
32
+ a2 = int(re.search(r"\+(\d+)\+", label).group(1))
33
+ a3 = int(re.search(r"\+(\d+)/", label).group(1))
34
+ if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil','pau']:
35
+ a2_next=-1
36
+ else:
37
+ a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
38
+ # Accent phrase boundary
39
+ if a3 == 1 and a2_next == 1:
40
+ text += ' '
41
+ # Falling
42
+ elif a1 == 0 and a2_next == a2 + 1 and a2 != n_moras:
43
+ text += '↓'
44
+ # Rising
45
+ elif a2 == 1 and a2_next == 2:
46
+ text += '↑'
47
+ if i<len(marks):
48
+ text += unidecode(marks[i]).replace(' ','')
49
+ if re.match('[A-Za-z]',text[-1]):
50
+ text += '.'
51
+ return text
transforms.py ADDED
@@ -0,0 +1,193 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ from torch.nn import functional as F
3
+
4
+ import numpy as np
5
+
6
+
7
+ DEFAULT_MIN_BIN_WIDTH = 1e-3
8
+ DEFAULT_MIN_BIN_HEIGHT = 1e-3
9
+ DEFAULT_MIN_DERIVATIVE = 1e-3
10
+
11
+
12
+ def piecewise_rational_quadratic_transform(inputs,
13
+ unnormalized_widths,
14
+ unnormalized_heights,
15
+ unnormalized_derivatives,
16
+ inverse=False,
17
+ tails=None,
18
+ tail_bound=1.,
19
+ min_bin_width=DEFAULT_MIN_BIN_WIDTH,
20
+ min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
21
+ min_derivative=DEFAULT_MIN_DERIVATIVE):
22
+
23
+ if tails is None:
24
+ spline_fn = rational_quadratic_spline
25
+ spline_kwargs = {}
26
+ else:
27
+ spline_fn = unconstrained_rational_quadratic_spline
28
+ spline_kwargs = {
29
+ 'tails': tails,
30
+ 'tail_bound': tail_bound
31
+ }
32
+
33
+ outputs, logabsdet = spline_fn(
34
+ inputs=inputs,
35
+ unnormalized_widths=unnormalized_widths,
36
+ unnormalized_heights=unnormalized_heights,
37
+ unnormalized_derivatives=unnormalized_derivatives,
38
+ inverse=inverse,
39
+ min_bin_width=min_bin_width,
40
+ min_bin_height=min_bin_height,
41
+ min_derivative=min_derivative,
42
+ **spline_kwargs
43
+ )
44
+ return outputs, logabsdet
45
+
46
+
47
+ def searchsorted(bin_locations, inputs, eps=1e-6):
48
+ bin_locations[..., -1] += eps
49
+ return torch.sum(
50
+ inputs[..., None] >= bin_locations,
51
+ dim=-1
52
+ ) - 1
53
+
54
+
55
+ def unconstrained_rational_quadratic_spline(inputs,
56
+ unnormalized_widths,
57
+ unnormalized_heights,
58
+ unnormalized_derivatives,
59
+ inverse=False,
60
+ tails='linear',
61
+ tail_bound=1.,
62
+ min_bin_width=DEFAULT_MIN_BIN_WIDTH,
63
+ min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
64
+ min_derivative=DEFAULT_MIN_DERIVATIVE):
65
+ inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
66
+ outside_interval_mask = ~inside_interval_mask
67
+
68
+ outputs = torch.zeros_like(inputs)
69
+ logabsdet = torch.zeros_like(inputs)
70
+
71
+ if tails == 'linear':
72
+ unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
73
+ constant = np.log(np.exp(1 - min_derivative) - 1)
74
+ unnormalized_derivatives[..., 0] = constant
75
+ unnormalized_derivatives[..., -1] = constant
76
+
77
+ outputs[outside_interval_mask] = inputs[outside_interval_mask]
78
+ logabsdet[outside_interval_mask] = 0
79
+ else:
80
+ raise RuntimeError('{} tails are not implemented.'.format(tails))
81
+
82
+ outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
83
+ inputs=inputs[inside_interval_mask],
84
+ unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
85
+ unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
86
+ unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
87
+ inverse=inverse,
88
+ left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
89
+ min_bin_width=min_bin_width,
90
+ min_bin_height=min_bin_height,
91
+ min_derivative=min_derivative
92
+ )
93
+
94
+ return outputs, logabsdet
95
+
96
+ def rational_quadratic_spline(inputs,
97
+ unnormalized_widths,
98
+ unnormalized_heights,
99
+ unnormalized_derivatives,
100
+ inverse=False,
101
+ left=0., right=1., bottom=0., top=1.,
102
+ min_bin_width=DEFAULT_MIN_BIN_WIDTH,
103
+ min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
104
+ min_derivative=DEFAULT_MIN_DERIVATIVE):
105
+ if torch.min(inputs) < left or torch.max(inputs) > right:
106
+ raise ValueError('Input to a transform is not within its domain')
107
+
108
+ num_bins = unnormalized_widths.shape[-1]
109
+
110
+ if min_bin_width * num_bins > 1.0:
111
+ raise ValueError('Minimal bin width too large for the number of bins')
112
+ if min_bin_height * num_bins > 1.0:
113
+ raise ValueError('Minimal bin height too large for the number of bins')
114
+
115
+ widths = F.softmax(unnormalized_widths, dim=-1)
116
+ widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
117
+ cumwidths = torch.cumsum(widths, dim=-1)
118
+ cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
119
+ cumwidths = (right - left) * cumwidths + left
120
+ cumwidths[..., 0] = left
121
+ cumwidths[..., -1] = right
122
+ widths = cumwidths[..., 1:] - cumwidths[..., :-1]
123
+
124
+ derivatives = min_derivative + F.softplus(unnormalized_derivatives)
125
+
126
+ heights = F.softmax(unnormalized_heights, dim=-1)
127
+ heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
128
+ cumheights = torch.cumsum(heights, dim=-1)
129
+ cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
130
+ cumheights = (top - bottom) * cumheights + bottom
131
+ cumheights[..., 0] = bottom
132
+ cumheights[..., -1] = top
133
+ heights = cumheights[..., 1:] - cumheights[..., :-1]
134
+
135
+ if inverse:
136
+ bin_idx = searchsorted(cumheights, inputs)[..., None]
137
+ else:
138
+ bin_idx = searchsorted(cumwidths, inputs)[..., None]
139
+
140
+ input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
141
+ input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
142
+
143
+ input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
144
+ delta = heights / widths
145
+ input_delta = delta.gather(-1, bin_idx)[..., 0]
146
+
147
+ input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
148
+ input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
149
+
150
+ input_heights = heights.gather(-1, bin_idx)[..., 0]
151
+
152
+ if inverse:
153
+ a = (((inputs - input_cumheights) * (input_derivatives
154
+ + input_derivatives_plus_one
155
+ - 2 * input_delta)
156
+ + input_heights * (input_delta - input_derivatives)))
157
+ b = (input_heights * input_derivatives
158
+ - (inputs - input_cumheights) * (input_derivatives
159
+ + input_derivatives_plus_one
160
+ - 2 * input_delta))
161
+ c = - input_delta * (inputs - input_cumheights)
162
+
163
+ discriminant = b.pow(2) - 4 * a * c
164
+ assert (discriminant >= 0).all()
165
+
166
+ root = (2 * c) / (-b - torch.sqrt(discriminant))
167
+ outputs = root * input_bin_widths + input_cumwidths
168
+
169
+ theta_one_minus_theta = root * (1 - root)
170
+ denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
171
+ * theta_one_minus_theta)
172
+ derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
173
+ + 2 * input_delta * theta_one_minus_theta
174
+ + input_derivatives * (1 - root).pow(2))
175
+ logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
176
+
177
+ return outputs, -logabsdet
178
+ else:
179
+ theta = (inputs - input_cumwidths) / input_bin_widths
180
+ theta_one_minus_theta = theta * (1 - theta)
181
+
182
+ numerator = input_heights * (input_delta * theta.pow(2)
183
+ + input_derivatives * theta_one_minus_theta)
184
+ denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
185
+ * theta_one_minus_theta)
186
+ outputs = input_cumheights + numerator / denominator
187
+
188
+ derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
189
+ + 2 * input_delta * theta_one_minus_theta
190
+ + input_derivatives * (1 - theta).pow(2))
191
+ logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
192
+
193
+ return outputs, logabsdet
utils.py ADDED
@@ -0,0 +1,226 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import glob
3
+ import sys
4
+ import argparse
5
+ import logging
6
+ import json
7
+ import subprocess
8
+ import numpy as np
9
+ from scipy.io.wavfile import read
10
+ import torch
11
+
12
+ MATPLOTLIB_FLAG = False
13
+
14
+ logging.basicConfig(stream=sys.stdout, level=logging.ERROR)
15
+ logger = logging
16
+
17
+
18
+ def load_checkpoint(checkpoint_path, model, optimizer=None):
19
+ assert os.path.isfile(checkpoint_path)
20
+ checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
21
+ iteration = checkpoint_dict['iteration']
22
+ learning_rate = checkpoint_dict['learning_rate']
23
+ if optimizer is not None:
24
+ optimizer.load_state_dict(checkpoint_dict['optimizer'])
25
+ saved_state_dict = checkpoint_dict['model']
26
+ if hasattr(model, 'module'):
27
+ state_dict = model.module.state_dict()
28
+ else:
29
+ state_dict = model.state_dict()
30
+ new_state_dict= {}
31
+ for k, v in state_dict.items():
32
+ try:
33
+ new_state_dict[k] = saved_state_dict[k]
34
+ except:
35
+ logger.info("%s is not in the checkpoint" % k)
36
+ new_state_dict[k] = v
37
+ if hasattr(model, 'module'):
38
+ model.module.load_state_dict(new_state_dict)
39
+ else:
40
+ model.load_state_dict(new_state_dict)
41
+ logger.info("Loaded checkpoint '{}' (iteration {})" .format(
42
+ checkpoint_path, iteration))
43
+ return model, optimizer, learning_rate, iteration
44
+
45
+
46
+ def plot_spectrogram_to_numpy(spectrogram):
47
+ global MATPLOTLIB_FLAG
48
+ if not MATPLOTLIB_FLAG:
49
+ import matplotlib
50
+ matplotlib.use("Agg")
51
+ MATPLOTLIB_FLAG = True
52
+ mpl_logger = logging.getLogger('matplotlib')
53
+ mpl_logger.setLevel(logging.WARNING)
54
+ import matplotlib.pylab as plt
55
+ import numpy as np
56
+
57
+ fig, ax = plt.subplots(figsize=(10,2))
58
+ im = ax.imshow(spectrogram, aspect="auto", origin="lower",
59
+ interpolation='none')
60
+ plt.colorbar(im, ax=ax)
61
+ plt.xlabel("Frames")
62
+ plt.ylabel("Channels")
63
+ plt.tight_layout()
64
+
65
+ fig.canvas.draw()
66
+ data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
67
+ data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
68
+ plt.close()
69
+ return data
70
+
71
+
72
+ def plot_alignment_to_numpy(alignment, info=None):
73
+ global MATPLOTLIB_FLAG
74
+ if not MATPLOTLIB_FLAG:
75
+ import matplotlib
76
+ matplotlib.use("Agg")
77
+ MATPLOTLIB_FLAG = True
78
+ mpl_logger = logging.getLogger('matplotlib')
79
+ mpl_logger.setLevel(logging.WARNING)
80
+ import matplotlib.pylab as plt
81
+ import numpy as np
82
+
83
+ fig, ax = plt.subplots(figsize=(6, 4))
84
+ im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
85
+ interpolation='none')
86
+ fig.colorbar(im, ax=ax)
87
+ xlabel = 'Decoder timestep'
88
+ if info is not None:
89
+ xlabel += '\n\n' + info
90
+ plt.xlabel(xlabel)
91
+ plt.ylabel('Encoder timestep')
92
+ plt.tight_layout()
93
+
94
+ fig.canvas.draw()
95
+ data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
96
+ data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
97
+ plt.close()
98
+ return data
99
+
100
+
101
+ def load_wav_to_torch(full_path):
102
+ sampling_rate, data = read(full_path)
103
+ return torch.FloatTensor(data.astype(np.float32)), sampling_rate
104
+
105
+
106
+ def load_filepaths_and_text(filename, split="|"):
107
+ with open(filename, encoding='utf-8') as f:
108
+ filepaths_and_text = [line.strip().split(split) for line in f]
109
+ return filepaths_and_text
110
+
111
+
112
+ def get_hparams(init=True):
113
+ parser = argparse.ArgumentParser()
114
+ parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
115
+ help='JSON file for configuration')
116
+ parser.add_argument('-m', '--model', type=str, required=True,
117
+ help='Model name')
118
+
119
+ args = parser.parse_args()
120
+ model_dir = os.path.join("./logs", args.model)
121
+
122
+ if not os.path.exists(model_dir):
123
+ os.makedirs(model_dir)
124
+
125
+ config_path = args.config
126
+ config_save_path = os.path.join(model_dir, "config.json")
127
+ if init:
128
+ with open(config_path, "r") as f:
129
+ data = f.read()
130
+ with open(config_save_path, "w") as f:
131
+ f.write(data)
132
+ else:
133
+ with open(config_save_path, "r") as f:
134
+ data = f.read()
135
+ config = json.loads(data)
136
+
137
+ hparams = HParams(**config)
138
+ hparams.model_dir = model_dir
139
+ return hparams
140
+
141
+
142
+ def get_hparams_from_dir(model_dir):
143
+ config_save_path = os.path.join(model_dir, "config.json")
144
+ with open(config_save_path, "r") as f:
145
+ data = f.read()
146
+ config = json.loads(data)
147
+
148
+ hparams =HParams(**config)
149
+ hparams.model_dir = model_dir
150
+ return hparams
151
+
152
+
153
+ def get_hparams_from_file(config_path):
154
+ with open(config_path, "r") as f:
155
+ data = f.read()
156
+ config = json.loads(data)
157
+
158
+ hparams =HParams(**config)
159
+ return hparams
160
+
161
+
162
+ def check_git_hash(model_dir):
163
+ source_dir = os.path.dirname(os.path.realpath(__file__))
164
+ if not os.path.exists(os.path.join(source_dir, ".git")):
165
+ logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
166
+ source_dir
167
+ ))
168
+ return
169
+
170
+ cur_hash = subprocess.getoutput("git rev-parse HEAD")
171
+
172
+ path = os.path.join(model_dir, "githash")
173
+ if os.path.exists(path):
174
+ saved_hash = open(path).read()
175
+ if saved_hash != cur_hash:
176
+ logger.warn("git hash values are different. {}(saved) != {}(current)".format(
177
+ saved_hash[:8], cur_hash[:8]))
178
+ else:
179
+ open(path, "w").write(cur_hash)
180
+
181
+
182
+ def get_logger(model_dir, filename="train.log"):
183
+ global logger
184
+ logger = logging.getLogger(os.path.basename(model_dir))
185
+ logger.setLevel(logging.DEBUG)
186
+
187
+ formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
188
+ if not os.path.exists(model_dir):
189
+ os.makedirs(model_dir)
190
+ h = logging.FileHandler(os.path.join(model_dir, filename))
191
+ h.setLevel(logging.DEBUG)
192
+ h.setFormatter(formatter)
193
+ logger.addHandler(h)
194
+ return logger
195
+
196
+
197
+ class HParams():
198
+ def __init__(self, **kwargs):
199
+ for k, v in kwargs.items():
200
+ if type(v) == dict:
201
+ v = HParams(**v)
202
+ self[k] = v
203
+
204
+ def keys(self):
205
+ return self.__dict__.keys()
206
+
207
+ def items(self):
208
+ return self.__dict__.items()
209
+
210
+ def values(self):
211
+ return self.__dict__.values()
212
+
213
+ def __len__(self):
214
+ return len(self.__dict__)
215
+
216
+ def __getitem__(self, key):
217
+ return getattr(self, key)
218
+
219
+ def __setitem__(self, key, value):
220
+ return setattr(self, key, value)
221
+
222
+ def __contains__(self, key):
223
+ return key in self.__dict__
224
+
225
+ def __repr__(self):
226
+ return self.__dict__.__repr__()