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e96a17e
0
Parent(s):
Duplicate from alysa/Mandarin-TTS
Browse filesCo-authored-by: alysa <alysa@users.noreply.huggingface.co>
- .gitattributes +34 -0
- README.md +13 -0
- app.py +101 -0
- attentions.py +417 -0
- bert/ProsodyModel.py +75 -0
- bert/__init__.py +1 -0
- bert/config.json +19 -0
- bert/prosody_model.pt +3 -0
- bert/prosody_tool.py +426 -0
- bert/vocab.txt +0 -0
- commons.py +163 -0
- configs/bert_vits.json +50 -0
- models.py +533 -0
- modules.py +522 -0
- requirements.txt +4 -0
- text/__init__.py +447 -0
- text/symbols.py +71 -0
- transforms.py +210 -0
- utils.py +319 -0
- vits_bert_model.pth +3 -0
- vits_pinyin.py +88 -0
.gitattributes
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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title: Ctts
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emoji: 🔥
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colorFrom: blue
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colorTo: green
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sdk: gradio
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sdk_version: 3.19.1
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app_file: app.py
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pinned: false
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duplicated_from: alysa/Mandarin-TTS
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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from models import SynthesizerTrn
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from vits_pinyin import VITS_PinYin
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from text import cleaned_text_to_sequence
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from text.symbols import symbols
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import gradio as gr
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import utils
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import torch
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import argparse
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import os
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import re
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import logging
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logging.getLogger('numba').setLevel(logging.WARNING)
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limitation = os.getenv("SYSTEM") == "spaces"
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def create_calback(net_g: SynthesizerTrn, tts_front: VITS_PinYin):
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def tts_calback(text, dur_scale):
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if limitation:
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text_len = len(re.sub("\[([A-Z]{2})\]", "", text))
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max_len = 150
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if text_len > max_len:
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return "Error: Text is too long", None
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phonemes, char_embeds = tts_front.chinese_to_phonemes(text)
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input_ids = cleaned_text_to_sequence(phonemes)
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with torch.no_grad():
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x_tst = torch.LongTensor(input_ids).unsqueeze(0).to(device)
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x_tst_lengths = torch.LongTensor([len(input_ids)]).to(device)
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x_tst_prosody = torch.FloatTensor(
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char_embeds).unsqueeze(0).to(device)
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audio = net_g.infer(x_tst, x_tst_lengths, x_tst_prosody, noise_scale=0.5,
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length_scale=dur_scale)[0][0, 0].data.cpu().float().numpy()
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del x_tst, x_tst_lengths, x_tst_prosody
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return "Success", (16000, audio)
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return tts_calback
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example = [['这星球,天天有五十亿人,在错过,多幸运,有你一起看星星,在争宠,这一刻不再问为什么不再去猜测人和人,心和心,有什么不同。', 1],
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['当你说,太聪明,往往还是,会寂寞,我笑着,倾听孤单终结后的静寞,看月亮像夜空的瞳孔,静静凝视你我,和我们闹嚷的星球。', 1],
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['床前明月光,疑是地上霜,举头望明月,低头思故乡。', 1],]
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--share", action="store_true",
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default=False, help="share gradio app")
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args = parser.parse_args()
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device = torch.device("cpu")
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# pinyin
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tts_front = VITS_PinYin("./bert", device)
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# config
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hps = utils.get_hparams_from_file("./configs/bert_vits.json")
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# model
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net_g = SynthesizerTrn(
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len(symbols),
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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**hps.model)
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model_path = "vits_bert_model.pth"
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utils.load_model(model_path, net_g)
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net_g.eval()
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net_g.to(device)
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tts_calback = create_calback(net_g, tts_front)
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app = gr.Blocks()
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with app:
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gr.Markdown("# Mandarin-TTS\n\n"
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)
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with gr.Tabs():
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with gr.TabItem("TTS"):
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with gr.Row():
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with gr.Column():
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textbox = gr.TextArea(label="Text",
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placeholder="Type your sentence here (Maximum 150 words)",
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value="中文语音合成", elem_id=f"tts-input")
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duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1,
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label='速度 Speed')
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with gr.Column():
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text_output = gr.Textbox(label="Message")
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audio_output = gr.Audio(
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label="Output Audio", elem_id="tts-audio")
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btn = gr.Button("Generate!")
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btn.click(tts_calback,
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inputs=[textbox, duration_slider],
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outputs=[text_output, audio_output])
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gr.Examples(
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examples=example,
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inputs=[textbox, duration_slider],
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outputs=[text_output, audio_output],
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fn=tts_calback
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)
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app.queue(concurrency_count=3).launch(show_api=False, share=args.share)
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attentions.py
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import copy
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import math
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import numpy as np
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import torch
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from torch import nn
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from torch.nn import functional as F
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import commons
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import modules
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from modules import LayerNorm
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class Encoder(nn.Module):
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def __init__(
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self,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size=1,
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p_dropout=0.0,
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window_size=4,
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**kwargs
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):
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super().__init__()
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.window_size = window_size
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+
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self.drop = nn.Dropout(p_dropout)
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self.attn_layers = nn.ModuleList()
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self.norm_layers_1 = nn.ModuleList()
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self.ffn_layers = nn.ModuleList()
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self.norm_layers_2 = nn.ModuleList()
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for i in range(self.n_layers):
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self.attn_layers.append(
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MultiHeadAttention(
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hidden_channels,
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hidden_channels,
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n_heads,
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p_dropout=p_dropout,
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46 |
+
window_size=window_size,
|
47 |
+
)
|
48 |
+
)
|
49 |
+
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
50 |
+
self.ffn_layers.append(
|
51 |
+
FFN(
|
52 |
+
hidden_channels,
|
53 |
+
hidden_channels,
|
54 |
+
filter_channels,
|
55 |
+
kernel_size,
|
56 |
+
p_dropout=p_dropout,
|
57 |
+
)
|
58 |
+
)
|
59 |
+
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
60 |
+
|
61 |
+
def forward(self, x, x_mask):
|
62 |
+
attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
63 |
+
x = x * x_mask
|
64 |
+
for i in range(self.n_layers):
|
65 |
+
y = self.attn_layers[i](x, x, attn_mask)
|
66 |
+
y = self.drop(y)
|
67 |
+
x = self.norm_layers_1[i](x + y)
|
68 |
+
|
69 |
+
y = self.ffn_layers[i](x, x_mask)
|
70 |
+
y = self.drop(y)
|
71 |
+
x = self.norm_layers_2[i](x + y)
|
72 |
+
x = x * x_mask
|
73 |
+
return x
|
74 |
+
|
75 |
+
|
76 |
+
class Decoder(nn.Module):
|
77 |
+
def __init__(
|
78 |
+
self,
|
79 |
+
hidden_channels,
|
80 |
+
filter_channels,
|
81 |
+
n_heads,
|
82 |
+
n_layers,
|
83 |
+
kernel_size=1,
|
84 |
+
p_dropout=0.0,
|
85 |
+
proximal_bias=False,
|
86 |
+
proximal_init=True,
|
87 |
+
**kwargs
|
88 |
+
):
|
89 |
+
super().__init__()
|
90 |
+
self.hidden_channels = hidden_channels
|
91 |
+
self.filter_channels = filter_channels
|
92 |
+
self.n_heads = n_heads
|
93 |
+
self.n_layers = n_layers
|
94 |
+
self.kernel_size = kernel_size
|
95 |
+
self.p_dropout = p_dropout
|
96 |
+
self.proximal_bias = proximal_bias
|
97 |
+
self.proximal_init = proximal_init
|
98 |
+
|
99 |
+
self.drop = nn.Dropout(p_dropout)
|
100 |
+
self.self_attn_layers = nn.ModuleList()
|
101 |
+
self.norm_layers_0 = nn.ModuleList()
|
102 |
+
self.encdec_attn_layers = nn.ModuleList()
|
103 |
+
self.norm_layers_1 = nn.ModuleList()
|
104 |
+
self.ffn_layers = nn.ModuleList()
|
105 |
+
self.norm_layers_2 = nn.ModuleList()
|
106 |
+
for i in range(self.n_layers):
|
107 |
+
self.self_attn_layers.append(
|
108 |
+
MultiHeadAttention(
|
109 |
+
hidden_channels,
|
110 |
+
hidden_channels,
|
111 |
+
n_heads,
|
112 |
+
p_dropout=p_dropout,
|
113 |
+
proximal_bias=proximal_bias,
|
114 |
+
proximal_init=proximal_init,
|
115 |
+
)
|
116 |
+
)
|
117 |
+
self.norm_layers_0.append(LayerNorm(hidden_channels))
|
118 |
+
self.encdec_attn_layers.append(
|
119 |
+
MultiHeadAttention(
|
120 |
+
hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
|
121 |
+
)
|
122 |
+
)
|
123 |
+
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
124 |
+
self.ffn_layers.append(
|
125 |
+
FFN(
|
126 |
+
hidden_channels,
|
127 |
+
hidden_channels,
|
128 |
+
filter_channels,
|
129 |
+
kernel_size,
|
130 |
+
p_dropout=p_dropout,
|
131 |
+
causal=True,
|
132 |
+
)
|
133 |
+
)
|
134 |
+
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
135 |
+
|
136 |
+
def forward(self, x, x_mask, h, h_mask):
|
137 |
+
"""
|
138 |
+
x: decoder input
|
139 |
+
h: encoder output
|
140 |
+
"""
|
141 |
+
self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
|
142 |
+
device=x.device, dtype=x.dtype
|
143 |
+
)
|
144 |
+
encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
145 |
+
x = x * x_mask
|
146 |
+
for i in range(self.n_layers):
|
147 |
+
y = self.self_attn_layers[i](x, x, self_attn_mask)
|
148 |
+
y = self.drop(y)
|
149 |
+
x = self.norm_layers_0[i](x + y)
|
150 |
+
|
151 |
+
y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
|
152 |
+
y = self.drop(y)
|
153 |
+
x = self.norm_layers_1[i](x + y)
|
154 |
+
|
155 |
+
y = self.ffn_layers[i](x, x_mask)
|
156 |
+
y = self.drop(y)
|
157 |
+
x = self.norm_layers_2[i](x + y)
|
158 |
+
x = x * x_mask
|
159 |
+
return x
|
160 |
+
|
161 |
+
|
162 |
+
class MultiHeadAttention(nn.Module):
|
163 |
+
def __init__(
|
164 |
+
self,
|
165 |
+
channels,
|
166 |
+
out_channels,
|
167 |
+
n_heads,
|
168 |
+
p_dropout=0.0,
|
169 |
+
window_size=None,
|
170 |
+
heads_share=True,
|
171 |
+
block_length=None,
|
172 |
+
proximal_bias=False,
|
173 |
+
proximal_init=False,
|
174 |
+
):
|
175 |
+
super().__init__()
|
176 |
+
assert channels % n_heads == 0
|
177 |
+
|
178 |
+
self.channels = channels
|
179 |
+
self.out_channels = out_channels
|
180 |
+
self.n_heads = n_heads
|
181 |
+
self.p_dropout = p_dropout
|
182 |
+
self.window_size = window_size
|
183 |
+
self.heads_share = heads_share
|
184 |
+
self.block_length = block_length
|
185 |
+
self.proximal_bias = proximal_bias
|
186 |
+
self.proximal_init = proximal_init
|
187 |
+
self.attn = None
|
188 |
+
|
189 |
+
self.k_channels = channels // n_heads
|
190 |
+
self.conv_q = nn.Conv1d(channels, channels, 1)
|
191 |
+
self.conv_k = nn.Conv1d(channels, channels, 1)
|
192 |
+
self.conv_v = nn.Conv1d(channels, channels, 1)
|
193 |
+
self.conv_o = nn.Conv1d(channels, out_channels, 1)
|
194 |
+
self.drop = nn.Dropout(p_dropout)
|
195 |
+
|
196 |
+
if window_size is not None:
|
197 |
+
n_heads_rel = 1 if heads_share else n_heads
|
198 |
+
rel_stddev = self.k_channels**-0.5
|
199 |
+
self.emb_rel_k = nn.Parameter(
|
200 |
+
torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
|
201 |
+
* rel_stddev
|
202 |
+
)
|
203 |
+
self.emb_rel_v = nn.Parameter(
|
204 |
+
torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
|
205 |
+
* rel_stddev
|
206 |
+
)
|
207 |
+
|
208 |
+
nn.init.xavier_uniform_(self.conv_q.weight)
|
209 |
+
nn.init.xavier_uniform_(self.conv_k.weight)
|
210 |
+
nn.init.xavier_uniform_(self.conv_v.weight)
|
211 |
+
if proximal_init:
|
212 |
+
with torch.no_grad():
|
213 |
+
self.conv_k.weight.copy_(self.conv_q.weight)
|
214 |
+
self.conv_k.bias.copy_(self.conv_q.bias)
|
215 |
+
|
216 |
+
def forward(self, x, c, attn_mask=None):
|
217 |
+
q = self.conv_q(x)
|
218 |
+
k = self.conv_k(c)
|
219 |
+
v = self.conv_v(c)
|
220 |
+
|
221 |
+
x, self.attn = self.attention(q, k, v, mask=attn_mask)
|
222 |
+
|
223 |
+
x = self.conv_o(x)
|
224 |
+
return x
|
225 |
+
|
226 |
+
def attention(self, query, key, value, mask=None):
|
227 |
+
# reshape [b, d, t] -> [b, n_h, t, d_k]
|
228 |
+
b, d, t_s, t_t = (*key.size(), query.size(2))
|
229 |
+
query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
|
230 |
+
key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
231 |
+
value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
232 |
+
|
233 |
+
scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
|
234 |
+
if self.window_size is not None:
|
235 |
+
assert (
|
236 |
+
t_s == t_t
|
237 |
+
), "Relative attention is only available for self-attention."
|
238 |
+
key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
|
239 |
+
rel_logits = self._matmul_with_relative_keys(
|
240 |
+
query / math.sqrt(self.k_channels), key_relative_embeddings
|
241 |
+
)
|
242 |
+
scores_local = self._relative_position_to_absolute_position(rel_logits)
|
243 |
+
scores = scores + scores_local
|
244 |
+
if self.proximal_bias:
|
245 |
+
assert t_s == t_t, "Proximal bias is only available for self-attention."
|
246 |
+
scores = scores + self._attention_bias_proximal(t_s).to(
|
247 |
+
device=scores.device, dtype=scores.dtype
|
248 |
+
)
|
249 |
+
if mask is not None:
|
250 |
+
scores = scores.masked_fill(mask == 0, -1e4)
|
251 |
+
if self.block_length is not None:
|
252 |
+
assert (
|
253 |
+
t_s == t_t
|
254 |
+
), "Local attention is only available for self-attention."
|
255 |
+
block_mask = (
|
256 |
+
torch.ones_like(scores)
|
257 |
+
.triu(-self.block_length)
|
258 |
+
.tril(self.block_length)
|
259 |
+
)
|
260 |
+
scores = scores.masked_fill(block_mask == 0, -1e4)
|
261 |
+
p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
|
262 |
+
p_attn = self.drop(p_attn)
|
263 |
+
output = torch.matmul(p_attn, value)
|
264 |
+
if self.window_size is not None:
|
265 |
+
relative_weights = self._absolute_position_to_relative_position(p_attn)
|
266 |
+
value_relative_embeddings = self._get_relative_embeddings(
|
267 |
+
self.emb_rel_v, t_s
|
268 |
+
)
|
269 |
+
output = output + self._matmul_with_relative_values(
|
270 |
+
relative_weights, value_relative_embeddings
|
271 |
+
)
|
272 |
+
output = (
|
273 |
+
output.transpose(2, 3).contiguous().view(b, d, t_t)
|
274 |
+
) # [b, n_h, t_t, d_k] -> [b, d, t_t]
|
275 |
+
return output, p_attn
|
276 |
+
|
277 |
+
def _matmul_with_relative_values(self, x, y):
|
278 |
+
"""
|
279 |
+
x: [b, h, l, m]
|
280 |
+
y: [h or 1, m, d]
|
281 |
+
ret: [b, h, l, d]
|
282 |
+
"""
|
283 |
+
ret = torch.matmul(x, y.unsqueeze(0))
|
284 |
+
return ret
|
285 |
+
|
286 |
+
def _matmul_with_relative_keys(self, x, y):
|
287 |
+
"""
|
288 |
+
x: [b, h, l, d]
|
289 |
+
y: [h or 1, m, d]
|
290 |
+
ret: [b, h, l, m]
|
291 |
+
"""
|
292 |
+
ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
|
293 |
+
return ret
|
294 |
+
|
295 |
+
def _get_relative_embeddings(self, relative_embeddings, length):
|
296 |
+
max_relative_position = 2 * self.window_size + 1
|
297 |
+
# Pad first before slice to avoid using cond ops.
|
298 |
+
pad_length = max(length - (self.window_size + 1), 0)
|
299 |
+
slice_start_position = max((self.window_size + 1) - length, 0)
|
300 |
+
slice_end_position = slice_start_position + 2 * length - 1
|
301 |
+
if pad_length > 0:
|
302 |
+
padded_relative_embeddings = F.pad(
|
303 |
+
relative_embeddings,
|
304 |
+
commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
|
305 |
+
)
|
306 |
+
else:
|
307 |
+
padded_relative_embeddings = relative_embeddings
|
308 |
+
used_relative_embeddings = padded_relative_embeddings[
|
309 |
+
:, slice_start_position:slice_end_position
|
310 |
+
]
|
311 |
+
return used_relative_embeddings
|
312 |
+
|
313 |
+
def _relative_position_to_absolute_position(self, x):
|
314 |
+
"""
|
315 |
+
x: [b, h, l, 2*l-1]
|
316 |
+
ret: [b, h, l, l]
|
317 |
+
"""
|
318 |
+
batch, heads, length, _ = x.size()
|
319 |
+
# Concat columns of pad to shift from relative to absolute indexing.
|
320 |
+
x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
|
321 |
+
|
322 |
+
# Concat extra elements so to add up to shape (len+1, 2*len-1).
|
323 |
+
x_flat = x.view([batch, heads, length * 2 * length])
|
324 |
+
x_flat = F.pad(
|
325 |
+
x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
|
326 |
+
)
|
327 |
+
|
328 |
+
# Reshape and slice out the padded elements.
|
329 |
+
x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
|
330 |
+
:, :, :length, length - 1 :
|
331 |
+
]
|
332 |
+
return x_final
|
333 |
+
|
334 |
+
def _absolute_position_to_relative_position(self, x):
|
335 |
+
"""
|
336 |
+
x: [b, h, l, l]
|
337 |
+
ret: [b, h, l, 2*l-1]
|
338 |
+
"""
|
339 |
+
batch, heads, length, _ = x.size()
|
340 |
+
# padd along column
|
341 |
+
x = F.pad(
|
342 |
+
x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
|
343 |
+
)
|
344 |
+
x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
|
345 |
+
# add 0's in the beginning that will skew the elements after reshape
|
346 |
+
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
|
347 |
+
x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
|
348 |
+
return x_final
|
349 |
+
|
350 |
+
def _attention_bias_proximal(self, length):
|
351 |
+
"""Bias for self-attention to encourage attention to close positions.
|
352 |
+
Args:
|
353 |
+
length: an integer scalar.
|
354 |
+
Returns:
|
355 |
+
a Tensor with shape [1, 1, length, length]
|
356 |
+
"""
|
357 |
+
r = torch.arange(length, dtype=torch.float32)
|
358 |
+
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
|
359 |
+
return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
|
360 |
+
|
361 |
+
|
362 |
+
class FFN(nn.Module):
|
363 |
+
def __init__(
|
364 |
+
self,
|
365 |
+
in_channels,
|
366 |
+
out_channels,
|
367 |
+
filter_channels,
|
368 |
+
kernel_size,
|
369 |
+
p_dropout=0.0,
|
370 |
+
activation=None,
|
371 |
+
causal=False,
|
372 |
+
):
|
373 |
+
super().__init__()
|
374 |
+
self.in_channels = in_channels
|
375 |
+
self.out_channels = out_channels
|
376 |
+
self.filter_channels = filter_channels
|
377 |
+
self.kernel_size = kernel_size
|
378 |
+
self.p_dropout = p_dropout
|
379 |
+
self.activation = activation
|
380 |
+
self.causal = causal
|
381 |
+
|
382 |
+
if causal:
|
383 |
+
self.padding = self._causal_padding
|
384 |
+
else:
|
385 |
+
self.padding = self._same_padding
|
386 |
+
|
387 |
+
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
|
388 |
+
self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
|
389 |
+
self.drop = nn.Dropout(p_dropout)
|
390 |
+
|
391 |
+
def forward(self, x, x_mask):
|
392 |
+
x = self.conv_1(self.padding(x * x_mask))
|
393 |
+
if self.activation == "gelu":
|
394 |
+
x = x * torch.sigmoid(1.702 * x)
|
395 |
+
else:
|
396 |
+
x = torch.relu(x)
|
397 |
+
x = self.drop(x)
|
398 |
+
x = self.conv_2(self.padding(x * x_mask))
|
399 |
+
return x * x_mask
|
400 |
+
|
401 |
+
def _causal_padding(self, x):
|
402 |
+
if self.kernel_size == 1:
|
403 |
+
return x
|
404 |
+
pad_l = self.kernel_size - 1
|
405 |
+
pad_r = 0
|
406 |
+
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
407 |
+
x = F.pad(x, commons.convert_pad_shape(padding))
|
408 |
+
return x
|
409 |
+
|
410 |
+
def _same_padding(self, x):
|
411 |
+
if self.kernel_size == 1:
|
412 |
+
return x
|
413 |
+
pad_l = (self.kernel_size - 1) // 2
|
414 |
+
pad_r = self.kernel_size // 2
|
415 |
+
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
416 |
+
x = F.pad(x, commons.convert_pad_shape(padding))
|
417 |
+
return x
|
bert/ProsodyModel.py
ADDED
@@ -0,0 +1,75 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
1 |
+
import os
|
2 |
+
import torch
|
3 |
+
import torch.nn as nn
|
4 |
+
import torch.nn.functional as F
|
5 |
+
|
6 |
+
from transformers import BertModel, BertConfig, BertTokenizer
|
7 |
+
|
8 |
+
|
9 |
+
class CharEmbedding(nn.Module):
|
10 |
+
def __init__(self, model_dir):
|
11 |
+
super().__init__()
|
12 |
+
self.tokenizer = BertTokenizer.from_pretrained(model_dir)
|
13 |
+
self.bert_config = BertConfig.from_pretrained(model_dir)
|
14 |
+
self.hidden_size = self.bert_config.hidden_size
|
15 |
+
self.bert = BertModel(self.bert_config)
|
16 |
+
self.proj = nn.Linear(self.hidden_size, 256)
|
17 |
+
self.linear = nn.Linear(256, 3)
|
18 |
+
|
19 |
+
def text2Token(self, text):
|
20 |
+
token = self.tokenizer.tokenize(text)
|
21 |
+
txtid = self.tokenizer.convert_tokens_to_ids(token)
|
22 |
+
return txtid
|
23 |
+
|
24 |
+
def forward(self, inputs_ids, inputs_masks, tokens_type_ids):
|
25 |
+
out_seq = self.bert(input_ids=inputs_ids,
|
26 |
+
attention_mask=inputs_masks,
|
27 |
+
token_type_ids=tokens_type_ids)[0]
|
28 |
+
out_seq = self.proj(out_seq)
|
29 |
+
return out_seq
|
30 |
+
|
31 |
+
|
32 |
+
class TTSProsody(object):
|
33 |
+
def __init__(self, path, device):
|
34 |
+
self.device = device
|
35 |
+
self.char_model = CharEmbedding(path)
|
36 |
+
self.char_model.load_state_dict(
|
37 |
+
torch.load(
|
38 |
+
os.path.join(path, 'prosody_model.pt'),
|
39 |
+
map_location="cpu"
|
40 |
+
),
|
41 |
+
strict=False
|
42 |
+
)
|
43 |
+
self.char_model.eval()
|
44 |
+
self.char_model.to(self.device)
|
45 |
+
|
46 |
+
def get_char_embeds(self, text):
|
47 |
+
input_ids = self.char_model.text2Token(text)
|
48 |
+
input_masks = [1] * len(input_ids)
|
49 |
+
type_ids = [0] * len(input_ids)
|
50 |
+
input_ids = torch.LongTensor([input_ids]).to(self.device)
|
51 |
+
input_masks = torch.LongTensor([input_masks]).to(self.device)
|
52 |
+
type_ids = torch.LongTensor([type_ids]).to(self.device)
|
53 |
+
|
54 |
+
with torch.no_grad():
|
55 |
+
char_embeds = self.char_model(
|
56 |
+
input_ids, input_masks, type_ids).squeeze(0).cpu()
|
57 |
+
return char_embeds
|
58 |
+
|
59 |
+
def expand_for_phone(self, char_embeds, length): # length of phones for char
|
60 |
+
assert char_embeds.size(0) == len(length)
|
61 |
+
expand_vecs = list()
|
62 |
+
for vec, leng in zip(char_embeds, length):
|
63 |
+
vec = vec.expand(leng, -1)
|
64 |
+
expand_vecs.append(vec)
|
65 |
+
expand_embeds = torch.cat(expand_vecs, 0)
|
66 |
+
assert expand_embeds.size(0) == sum(length)
|
67 |
+
return expand_embeds.numpy()
|
68 |
+
|
69 |
+
|
70 |
+
if __name__ == "__main__":
|
71 |
+
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
72 |
+
prosody = TTSProsody('./bert/', device)
|
73 |
+
while True:
|
74 |
+
text = input("请输入文本:")
|
75 |
+
prosody.get_char_embeds(text)
|
bert/__init__.py
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
from .ProsodyModel import TTSProsody
|
bert/config.json
ADDED
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"attention_probs_dropout_prob": 0.1,
|
3 |
+
"directionality": "bidi",
|
4 |
+
"hidden_act": "gelu",
|
5 |
+
"hidden_dropout_prob": 0.1,
|
6 |
+
"hidden_size": 768,
|
7 |
+
"initializer_range": 0.02,
|
8 |
+
"intermediate_size": 3072,
|
9 |
+
"max_position_embeddings": 512,
|
10 |
+
"num_attention_heads": 12,
|
11 |
+
"num_hidden_layers": 12,
|
12 |
+
"pooler_fc_size": 768,
|
13 |
+
"pooler_num_attention_heads": 12,
|
14 |
+
"pooler_num_fc_layers": 3,
|
15 |
+
"pooler_size_per_head": 128,
|
16 |
+
"pooler_type": "first_token_transform",
|
17 |
+
"type_vocab_size": 2,
|
18 |
+
"vocab_size": 21128
|
19 |
+
}
|
bert/prosody_model.pt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3accec7a0d5cbfccaa8a42b96374a91d442a69801c6a01402baae3bf06b8c015
|
3 |
+
size 409941419
|
bert/prosody_tool.py
ADDED
@@ -0,0 +1,426 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
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|
|
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|
|
|
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|
|
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|
|
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|
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|
|
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|
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|
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|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
|
|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
|
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|
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|
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|
|
|
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|
|
|
|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
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|
|
|
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|
|
|
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|
|
|
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|
|
|
|
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|
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|
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|
|
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|
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|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
def is_chinese(uchar):
|
2 |
+
if uchar >= u'\u4e00' and uchar <= u'\u9fa5':
|
3 |
+
return True
|
4 |
+
else:
|
5 |
+
return False
|
6 |
+
|
7 |
+
|
8 |
+
pinyin_dict = {
|
9 |
+
"a": ("^", "a"),
|
10 |
+
"ai": ("^", "ai"),
|
11 |
+
"an": ("^", "an"),
|
12 |
+
"ang": ("^", "ang"),
|
13 |
+
"ao": ("^", "ao"),
|
14 |
+
"ba": ("b", "a"),
|
15 |
+
"bai": ("b", "ai"),
|
16 |
+
"ban": ("b", "an"),
|
17 |
+
"bang": ("b", "ang"),
|
18 |
+
"bao": ("b", "ao"),
|
19 |
+
"be": ("b", "e"),
|
20 |
+
"bei": ("b", "ei"),
|
21 |
+
"ben": ("b", "en"),
|
22 |
+
"beng": ("b", "eng"),
|
23 |
+
"bi": ("b", "i"),
|
24 |
+
"bian": ("b", "ian"),
|
25 |
+
"biao": ("b", "iao"),
|
26 |
+
"bie": ("b", "ie"),
|
27 |
+
"bin": ("b", "in"),
|
28 |
+
"bing": ("b", "ing"),
|
29 |
+
"bo": ("b", "o"),
|
30 |
+
"bu": ("b", "u"),
|
31 |
+
"ca": ("c", "a"),
|
32 |
+
"cai": ("c", "ai"),
|
33 |
+
"can": ("c", "an"),
|
34 |
+
"cang": ("c", "ang"),
|
35 |
+
"cao": ("c", "ao"),
|
36 |
+
"ce": ("c", "e"),
|
37 |
+
"cen": ("c", "en"),
|
38 |
+
"ceng": ("c", "eng"),
|
39 |
+
"cha": ("ch", "a"),
|
40 |
+
"chai": ("ch", "ai"),
|
41 |
+
"chan": ("ch", "an"),
|
42 |
+
"chang": ("ch", "ang"),
|
43 |
+
"chao": ("ch", "ao"),
|
44 |
+
"che": ("ch", "e"),
|
45 |
+
"chen": ("ch", "en"),
|
46 |
+
"cheng": ("ch", "eng"),
|
47 |
+
"chi": ("ch", "iii"),
|
48 |
+
"chong": ("ch", "ong"),
|
49 |
+
"chou": ("ch", "ou"),
|
50 |
+
"chu": ("ch", "u"),
|
51 |
+
"chua": ("ch", "ua"),
|
52 |
+
"chuai": ("ch", "uai"),
|
53 |
+
"chuan": ("ch", "uan"),
|
54 |
+
"chuang": ("ch", "uang"),
|
55 |
+
"chui": ("ch", "uei"),
|
56 |
+
"chun": ("ch", "uen"),
|
57 |
+
"chuo": ("ch", "uo"),
|
58 |
+
"ci": ("c", "ii"),
|
59 |
+
"cong": ("c", "ong"),
|
60 |
+
"cou": ("c", "ou"),
|
61 |
+
"cu": ("c", "u"),
|
62 |
+
"cuan": ("c", "uan"),
|
63 |
+
"cui": ("c", "uei"),
|
64 |
+
"cun": ("c", "uen"),
|
65 |
+
"cuo": ("c", "uo"),
|
66 |
+
"da": ("d", "a"),
|
67 |
+
"dai": ("d", "ai"),
|
68 |
+
"dan": ("d", "an"),
|
69 |
+
"dang": ("d", "ang"),
|
70 |
+
"dao": ("d", "ao"),
|
71 |
+
"de": ("d", "e"),
|
72 |
+
"dei": ("d", "ei"),
|
73 |
+
"den": ("d", "en"),
|
74 |
+
"deng": ("d", "eng"),
|
75 |
+
"di": ("d", "i"),
|
76 |
+
"dia": ("d", "ia"),
|
77 |
+
"dian": ("d", "ian"),
|
78 |
+
"diao": ("d", "iao"),
|
79 |
+
"die": ("d", "ie"),
|
80 |
+
"ding": ("d", "ing"),
|
81 |
+
"diu": ("d", "iou"),
|
82 |
+
"dong": ("d", "ong"),
|
83 |
+
"dou": ("d", "ou"),
|
84 |
+
"du": ("d", "u"),
|
85 |
+
"duan": ("d", "uan"),
|
86 |
+
"dui": ("d", "uei"),
|
87 |
+
"dun": ("d", "uen"),
|
88 |
+
"duo": ("d", "uo"),
|
89 |
+
"e": ("^", "e"),
|
90 |
+
"ei": ("^", "ei"),
|
91 |
+
"en": ("^", "en"),
|
92 |
+
"ng": ("^", "en"),
|
93 |
+
"eng": ("^", "eng"),
|
94 |
+
"er": ("^", "er"),
|
95 |
+
"fa": ("f", "a"),
|
96 |
+
"fan": ("f", "an"),
|
97 |
+
"fang": ("f", "ang"),
|
98 |
+
"fei": ("f", "ei"),
|
99 |
+
"fen": ("f", "en"),
|
100 |
+
"feng": ("f", "eng"),
|
101 |
+
"fo": ("f", "o"),
|
102 |
+
"fou": ("f", "ou"),
|
103 |
+
"fu": ("f", "u"),
|
104 |
+
"ga": ("g", "a"),
|
105 |
+
"gai": ("g", "ai"),
|
106 |
+
"gan": ("g", "an"),
|
107 |
+
"gang": ("g", "ang"),
|
108 |
+
"gao": ("g", "ao"),
|
109 |
+
"ge": ("g", "e"),
|
110 |
+
"gei": ("g", "ei"),
|
111 |
+
"gen": ("g", "en"),
|
112 |
+
"geng": ("g", "eng"),
|
113 |
+
"gong": ("g", "ong"),
|
114 |
+
"gou": ("g", "ou"),
|
115 |
+
"gu": ("g", "u"),
|
116 |
+
"gua": ("g", "ua"),
|
117 |
+
"guai": ("g", "uai"),
|
118 |
+
"guan": ("g", "uan"),
|
119 |
+
"guang": ("g", "uang"),
|
120 |
+
"gui": ("g", "uei"),
|
121 |
+
"gun": ("g", "uen"),
|
122 |
+
"guo": ("g", "uo"),
|
123 |
+
"ha": ("h", "a"),
|
124 |
+
"hai": ("h", "ai"),
|
125 |
+
"han": ("h", "an"),
|
126 |
+
"hang": ("h", "ang"),
|
127 |
+
"hao": ("h", "ao"),
|
128 |
+
"he": ("h", "e"),
|
129 |
+
"hei": ("h", "ei"),
|
130 |
+
"hen": ("h", "en"),
|
131 |
+
"heng": ("h", "eng"),
|
132 |
+
"hong": ("h", "ong"),
|
133 |
+
"hou": ("h", "ou"),
|
134 |
+
"hu": ("h", "u"),
|
135 |
+
"hua": ("h", "ua"),
|
136 |
+
"huai": ("h", "uai"),
|
137 |
+
"huan": ("h", "uan"),
|
138 |
+
"huang": ("h", "uang"),
|
139 |
+
"hui": ("h", "uei"),
|
140 |
+
"hun": ("h", "uen"),
|
141 |
+
"huo": ("h", "uo"),
|
142 |
+
"ji": ("j", "i"),
|
143 |
+
"jia": ("j", "ia"),
|
144 |
+
"jian": ("j", "ian"),
|
145 |
+
"jiang": ("j", "iang"),
|
146 |
+
"jiao": ("j", "iao"),
|
147 |
+
"jie": ("j", "ie"),
|
148 |
+
"jin": ("j", "in"),
|
149 |
+
"jing": ("j", "ing"),
|
150 |
+
"jiong": ("j", "iong"),
|
151 |
+
"jiu": ("j", "iou"),
|
152 |
+
"ju": ("j", "v"),
|
153 |
+
"juan": ("j", "van"),
|
154 |
+
"jue": ("j", "ve"),
|
155 |
+
"jun": ("j", "vn"),
|
156 |
+
"ka": ("k", "a"),
|
157 |
+
"kai": ("k", "ai"),
|
158 |
+
"kan": ("k", "an"),
|
159 |
+
"kang": ("k", "ang"),
|
160 |
+
"kao": ("k", "ao"),
|
161 |
+
"ke": ("k", "e"),
|
162 |
+
"kei": ("k", "ei"),
|
163 |
+
"ken": ("k", "en"),
|
164 |
+
"keng": ("k", "eng"),
|
165 |
+
"kong": ("k", "ong"),
|
166 |
+
"kou": ("k", "ou"),
|
167 |
+
"ku": ("k", "u"),
|
168 |
+
"kua": ("k", "ua"),
|
169 |
+
"kuai": ("k", "uai"),
|
170 |
+
"kuan": ("k", "uan"),
|
171 |
+
"kuang": ("k", "uang"),
|
172 |
+
"kui": ("k", "uei"),
|
173 |
+
"kun": ("k", "uen"),
|
174 |
+
"kuo": ("k", "uo"),
|
175 |
+
"la": ("l", "a"),
|
176 |
+
"lai": ("l", "ai"),
|
177 |
+
"lan": ("l", "an"),
|
178 |
+
"lang": ("l", "ang"),
|
179 |
+
"lao": ("l", "ao"),
|
180 |
+
"le": ("l", "e"),
|
181 |
+
"lei": ("l", "ei"),
|
182 |
+
"leng": ("l", "eng"),
|
183 |
+
"li": ("l", "i"),
|
184 |
+
"lia": ("l", "ia"),
|
185 |
+
"lian": ("l", "ian"),
|
186 |
+
"liang": ("l", "iang"),
|
187 |
+
"liao": ("l", "iao"),
|
188 |
+
"lie": ("l", "ie"),
|
189 |
+
"lin": ("l", "in"),
|
190 |
+
"ling": ("l", "ing"),
|
191 |
+
"liu": ("l", "iou"),
|
192 |
+
"lo": ("l", "o"),
|
193 |
+
"long": ("l", "ong"),
|
194 |
+
"lou": ("l", "ou"),
|
195 |
+
"lu": ("l", "u"),
|
196 |
+
"lv": ("l", "v"),
|
197 |
+
"luan": ("l", "uan"),
|
198 |
+
"lve": ("l", "ve"),
|
199 |
+
"lue": ("l", "ve"),
|
200 |
+
"lun": ("l", "uen"),
|
201 |
+
"luo": ("l", "uo"),
|
202 |
+
"ma": ("m", "a"),
|
203 |
+
"mai": ("m", "ai"),
|
204 |
+
"man": ("m", "an"),
|
205 |
+
"mang": ("m", "ang"),
|
206 |
+
"mao": ("m", "ao"),
|
207 |
+
"me": ("m", "e"),
|
208 |
+
"mei": ("m", "ei"),
|
209 |
+
"men": ("m", "en"),
|
210 |
+
"meng": ("m", "eng"),
|
211 |
+
"mi": ("m", "i"),
|
212 |
+
"mian": ("m", "ian"),
|
213 |
+
"miao": ("m", "iao"),
|
214 |
+
"mie": ("m", "ie"),
|
215 |
+
"min": ("m", "in"),
|
216 |
+
"ming": ("m", "ing"),
|
217 |
+
"miu": ("m", "iou"),
|
218 |
+
"mo": ("m", "o"),
|
219 |
+
"mou": ("m", "ou"),
|
220 |
+
"mu": ("m", "u"),
|
221 |
+
"na": ("n", "a"),
|
222 |
+
"nai": ("n", "ai"),
|
223 |
+
"nan": ("n", "an"),
|
224 |
+
"nang": ("n", "ang"),
|
225 |
+
"nao": ("n", "ao"),
|
226 |
+
"ne": ("n", "e"),
|
227 |
+
"nei": ("n", "ei"),
|
228 |
+
"nen": ("n", "en"),
|
229 |
+
"neng": ("n", "eng"),
|
230 |
+
"ni": ("n", "i"),
|
231 |
+
"nia": ("n", "ia"),
|
232 |
+
"nian": ("n", "ian"),
|
233 |
+
"niang": ("n", "iang"),
|
234 |
+
"niao": ("n", "iao"),
|
235 |
+
"nie": ("n", "ie"),
|
236 |
+
"nin": ("n", "in"),
|
237 |
+
"ning": ("n", "ing"),
|
238 |
+
"niu": ("n", "iou"),
|
239 |
+
"nong": ("n", "ong"),
|
240 |
+
"nou": ("n", "ou"),
|
241 |
+
"nu": ("n", "u"),
|
242 |
+
"nv": ("n", "v"),
|
243 |
+
"nuan": ("n", "uan"),
|
244 |
+
"nve": ("n", "ve"),
|
245 |
+
"nue": ("n", "ve"),
|
246 |
+
"nuo": ("n", "uo"),
|
247 |
+
"o": ("^", "o"),
|
248 |
+
"ou": ("^", "ou"),
|
249 |
+
"pa": ("p", "a"),
|
250 |
+
"pai": ("p", "ai"),
|
251 |
+
"pan": ("p", "an"),
|
252 |
+
"pang": ("p", "ang"),
|
253 |
+
"pao": ("p", "ao"),
|
254 |
+
"pe": ("p", "e"),
|
255 |
+
"pei": ("p", "ei"),
|
256 |
+
"pen": ("p", "en"),
|
257 |
+
"peng": ("p", "eng"),
|
258 |
+
"pi": ("p", "i"),
|
259 |
+
"pian": ("p", "ian"),
|
260 |
+
"piao": ("p", "iao"),
|
261 |
+
"pie": ("p", "ie"),
|
262 |
+
"pin": ("p", "in"),
|
263 |
+
"ping": ("p", "ing"),
|
264 |
+
"po": ("p", "o"),
|
265 |
+
"pou": ("p", "ou"),
|
266 |
+
"pu": ("p", "u"),
|
267 |
+
"qi": ("q", "i"),
|
268 |
+
"qia": ("q", "ia"),
|
269 |
+
"qian": ("q", "ian"),
|
270 |
+
"qiang": ("q", "iang"),
|
271 |
+
"qiao": ("q", "iao"),
|
272 |
+
"qie": ("q", "ie"),
|
273 |
+
"qin": ("q", "in"),
|
274 |
+
"qing": ("q", "ing"),
|
275 |
+
"qiong": ("q", "iong"),
|
276 |
+
"qiu": ("q", "iou"),
|
277 |
+
"qu": ("q", "v"),
|
278 |
+
"quan": ("q", "van"),
|
279 |
+
"que": ("q", "ve"),
|
280 |
+
"qun": ("q", "vn"),
|
281 |
+
"ran": ("r", "an"),
|
282 |
+
"rang": ("r", "ang"),
|
283 |
+
"rao": ("r", "ao"),
|
284 |
+
"re": ("r", "e"),
|
285 |
+
"ren": ("r", "en"),
|
286 |
+
"reng": ("r", "eng"),
|
287 |
+
"ri": ("r", "iii"),
|
288 |
+
"rong": ("r", "ong"),
|
289 |
+
"rou": ("r", "ou"),
|
290 |
+
"ru": ("r", "u"),
|
291 |
+
"rua": ("r", "ua"),
|
292 |
+
"ruan": ("r", "uan"),
|
293 |
+
"rui": ("r", "uei"),
|
294 |
+
"run": ("r", "uen"),
|
295 |
+
"ruo": ("r", "uo"),
|
296 |
+
"sa": ("s", "a"),
|
297 |
+
"sai": ("s", "ai"),
|
298 |
+
"san": ("s", "an"),
|
299 |
+
"sang": ("s", "ang"),
|
300 |
+
"sao": ("s", "ao"),
|
301 |
+
"se": ("s", "e"),
|
302 |
+
"sen": ("s", "en"),
|
303 |
+
"seng": ("s", "eng"),
|
304 |
+
"sha": ("sh", "a"),
|
305 |
+
"shai": ("sh", "ai"),
|
306 |
+
"shan": ("sh", "an"),
|
307 |
+
"shang": ("sh", "ang"),
|
308 |
+
"shao": ("sh", "ao"),
|
309 |
+
"she": ("sh", "e"),
|
310 |
+
"shei": ("sh", "ei"),
|
311 |
+
"shen": ("sh", "en"),
|
312 |
+
"sheng": ("sh", "eng"),
|
313 |
+
"shi": ("sh", "iii"),
|
314 |
+
"shou": ("sh", "ou"),
|
315 |
+
"shu": ("sh", "u"),
|
316 |
+
"shua": ("sh", "ua"),
|
317 |
+
"shuai": ("sh", "uai"),
|
318 |
+
"shuan": ("sh", "uan"),
|
319 |
+
"shuang": ("sh", "uang"),
|
320 |
+
"shui": ("sh", "uei"),
|
321 |
+
"shun": ("sh", "uen"),
|
322 |
+
"shuo": ("sh", "uo"),
|
323 |
+
"si": ("s", "ii"),
|
324 |
+
"song": ("s", "ong"),
|
325 |
+
"sou": ("s", "ou"),
|
326 |
+
"su": ("s", "u"),
|
327 |
+
"suan": ("s", "uan"),
|
328 |
+
"sui": ("s", "uei"),
|
329 |
+
"sun": ("s", "uen"),
|
330 |
+
"suo": ("s", "uo"),
|
331 |
+
"ta": ("t", "a"),
|
332 |
+
"tai": ("t", "ai"),
|
333 |
+
"tan": ("t", "an"),
|
334 |
+
"tang": ("t", "ang"),
|
335 |
+
"tao": ("t", "ao"),
|
336 |
+
"te": ("t", "e"),
|
337 |
+
"tei": ("t", "ei"),
|
338 |
+
"teng": ("t", "eng"),
|
339 |
+
"ti": ("t", "i"),
|
340 |
+
"tian": ("t", "ian"),
|
341 |
+
"tiao": ("t", "iao"),
|
342 |
+
"tie": ("t", "ie"),
|
343 |
+
"ting": ("t", "ing"),
|
344 |
+
"tong": ("t", "ong"),
|
345 |
+
"tou": ("t", "ou"),
|
346 |
+
"tu": ("t", "u"),
|
347 |
+
"tuan": ("t", "uan"),
|
348 |
+
"tui": ("t", "uei"),
|
349 |
+
"tun": ("t", "uen"),
|
350 |
+
"tuo": ("t", "uo"),
|
351 |
+
"wa": ("^", "ua"),
|
352 |
+
"wai": ("^", "uai"),
|
353 |
+
"wan": ("^", "uan"),
|
354 |
+
"wang": ("^", "uang"),
|
355 |
+
"wei": ("^", "uei"),
|
356 |
+
"wen": ("^", "uen"),
|
357 |
+
"weng": ("^", "ueng"),
|
358 |
+
"wo": ("^", "uo"),
|
359 |
+
"wu": ("^", "u"),
|
360 |
+
"xi": ("x", "i"),
|
361 |
+
"xia": ("x", "ia"),
|
362 |
+
"xian": ("x", "ian"),
|
363 |
+
"xiang": ("x", "iang"),
|
364 |
+
"xiao": ("x", "iao"),
|
365 |
+
"xie": ("x", "ie"),
|
366 |
+
"xin": ("x", "in"),
|
367 |
+
"xing": ("x", "ing"),
|
368 |
+
"xiong": ("x", "iong"),
|
369 |
+
"xiu": ("x", "iou"),
|
370 |
+
"xu": ("x", "v"),
|
371 |
+
"xuan": ("x", "van"),
|
372 |
+
"xue": ("x", "ve"),
|
373 |
+
"xun": ("x", "vn"),
|
374 |
+
"ya": ("^", "ia"),
|
375 |
+
"yan": ("^", "ian"),
|
376 |
+
"yang": ("^", "iang"),
|
377 |
+
"yao": ("^", "iao"),
|
378 |
+
"ye": ("^", "ie"),
|
379 |
+
"yi": ("^", "i"),
|
380 |
+
"yin": ("^", "in"),
|
381 |
+
"ying": ("^", "ing"),
|
382 |
+
"yo": ("^", "iou"),
|
383 |
+
"yong": ("^", "iong"),
|
384 |
+
"you": ("^", "iou"),
|
385 |
+
"yu": ("^", "v"),
|
386 |
+
"yuan": ("^", "van"),
|
387 |
+
"yue": ("^", "ve"),
|
388 |
+
"yun": ("^", "vn"),
|
389 |
+
"za": ("z", "a"),
|
390 |
+
"zai": ("z", "ai"),
|
391 |
+
"zan": ("z", "an"),
|
392 |
+
"zang": ("z", "ang"),
|
393 |
+
"zao": ("z", "ao"),
|
394 |
+
"ze": ("z", "e"),
|
395 |
+
"zei": ("z", "ei"),
|
396 |
+
"zen": ("z", "en"),
|
397 |
+
"zeng": ("z", "eng"),
|
398 |
+
"zha": ("zh", "a"),
|
399 |
+
"zhai": ("zh", "ai"),
|
400 |
+
"zhan": ("zh", "an"),
|
401 |
+
"zhang": ("zh", "ang"),
|
402 |
+
"zhao": ("zh", "ao"),
|
403 |
+
"zhe": ("zh", "e"),
|
404 |
+
"zhei": ("zh", "ei"),
|
405 |
+
"zhen": ("zh", "en"),
|
406 |
+
"zheng": ("zh", "eng"),
|
407 |
+
"zhi": ("zh", "iii"),
|
408 |
+
"zhong": ("zh", "ong"),
|
409 |
+
"zhou": ("zh", "ou"),
|
410 |
+
"zhu": ("zh", "u"),
|
411 |
+
"zhua": ("zh", "ua"),
|
412 |
+
"zhuai": ("zh", "uai"),
|
413 |
+
"zhuan": ("zh", "uan"),
|
414 |
+
"zhuang": ("zh", "uang"),
|
415 |
+
"zhui": ("zh", "uei"),
|
416 |
+
"zhun": ("zh", "uen"),
|
417 |
+
"zhuo": ("zh", "uo"),
|
418 |
+
"zi": ("z", "ii"),
|
419 |
+
"zong": ("z", "ong"),
|
420 |
+
"zou": ("z", "ou"),
|
421 |
+
"zu": ("z", "u"),
|
422 |
+
"zuan": ("z", "uan"),
|
423 |
+
"zui": ("z", "uei"),
|
424 |
+
"zun": ("z", "uen"),
|
425 |
+
"zuo": ("z", "uo"),
|
426 |
+
}
|
bert/vocab.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|
commons.py
ADDED
@@ -0,0 +1,163 @@
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
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|
|
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|
|
|
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|
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|
|
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|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
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|
|
|
|
|
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|
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 += (
|
34 |
+
0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
|
35 |
+
)
|
36 |
+
return kl
|
37 |
+
|
38 |
+
|
39 |
+
def rand_gumbel(shape):
|
40 |
+
"""Sample from the Gumbel distribution, protect from overflows."""
|
41 |
+
uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
|
42 |
+
return -torch.log(-torch.log(uniform_samples))
|
43 |
+
|
44 |
+
|
45 |
+
def rand_gumbel_like(x):
|
46 |
+
g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
|
47 |
+
return g
|
48 |
+
|
49 |
+
|
50 |
+
def slice_segments(x, ids_str, segment_size=4):
|
51 |
+
ret = torch.zeros_like(x[:, :, :segment_size])
|
52 |
+
for i in range(x.size(0)):
|
53 |
+
idx_str = ids_str[i]
|
54 |
+
idx_end = idx_str + segment_size
|
55 |
+
ret[i] = x[i, :, idx_str:idx_end]
|
56 |
+
return ret
|
57 |
+
|
58 |
+
|
59 |
+
def rand_slice_segments(x, x_lengths=None, segment_size=4):
|
60 |
+
b, d, t = x.size()
|
61 |
+
if x_lengths is None:
|
62 |
+
x_lengths = t
|
63 |
+
ids_str_max = x_lengths - segment_size + 1
|
64 |
+
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
|
65 |
+
ret = slice_segments(x, ids_str, segment_size)
|
66 |
+
return ret, ids_str
|
67 |
+
|
68 |
+
|
69 |
+
def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
|
70 |
+
position = torch.arange(length, dtype=torch.float)
|
71 |
+
num_timescales = channels // 2
|
72 |
+
log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
|
73 |
+
num_timescales - 1
|
74 |
+
)
|
75 |
+
inv_timescales = min_timescale * torch.exp(
|
76 |
+
torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
|
77 |
+
)
|
78 |
+
scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
|
79 |
+
signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
|
80 |
+
signal = F.pad(signal, [0, 0, 0, channels % 2])
|
81 |
+
signal = signal.view(1, channels, length)
|
82 |
+
return signal
|
83 |
+
|
84 |
+
|
85 |
+
def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
|
86 |
+
b, channels, length = x.size()
|
87 |
+
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
88 |
+
return x + signal.to(dtype=x.dtype, device=x.device)
|
89 |
+
|
90 |
+
|
91 |
+
def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
|
92 |
+
b, channels, length = x.size()
|
93 |
+
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
94 |
+
return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
|
95 |
+
|
96 |
+
|
97 |
+
def subsequent_mask(length):
|
98 |
+
mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
|
99 |
+
return mask
|
100 |
+
|
101 |
+
|
102 |
+
@torch.jit.script
|
103 |
+
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
104 |
+
n_channels_int = n_channels[0]
|
105 |
+
in_act = input_a + input_b
|
106 |
+
t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
107 |
+
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
108 |
+
acts = t_act * s_act
|
109 |
+
return acts
|
110 |
+
|
111 |
+
|
112 |
+
def convert_pad_shape(pad_shape):
|
113 |
+
l = pad_shape[::-1]
|
114 |
+
pad_shape = [item for sublist in l for item in sublist]
|
115 |
+
return pad_shape
|
116 |
+
|
117 |
+
|
118 |
+
def shift_1d(x):
|
119 |
+
x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
|
120 |
+
return x
|
121 |
+
|
122 |
+
|
123 |
+
def sequence_mask(length, max_length=None):
|
124 |
+
if max_length is None:
|
125 |
+
max_length = length.max()
|
126 |
+
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
|
127 |
+
return x.unsqueeze(0) < length.unsqueeze(1)
|
128 |
+
|
129 |
+
|
130 |
+
def generate_path(duration, mask):
|
131 |
+
"""
|
132 |
+
duration: [b, 1, t_x]
|
133 |
+
mask: [b, 1, t_y, t_x]
|
134 |
+
"""
|
135 |
+
device = duration.device
|
136 |
+
|
137 |
+
b, _, t_y, t_x = mask.shape
|
138 |
+
cum_duration = torch.cumsum(duration, -1)
|
139 |
+
|
140 |
+
cum_duration_flat = cum_duration.view(b * t_x)
|
141 |
+
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
|
142 |
+
path = path.view(b, t_x, t_y)
|
143 |
+
path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
|
144 |
+
path = path.unsqueeze(1).transpose(2, 3) * mask
|
145 |
+
return path
|
146 |
+
|
147 |
+
|
148 |
+
def clip_grad_value_(parameters, clip_value, norm_type=2):
|
149 |
+
if isinstance(parameters, torch.Tensor):
|
150 |
+
parameters = [parameters]
|
151 |
+
parameters = list(filter(lambda p: p.grad is not None, parameters))
|
152 |
+
norm_type = float(norm_type)
|
153 |
+
if clip_value is not None:
|
154 |
+
clip_value = float(clip_value)
|
155 |
+
|
156 |
+
total_norm = 0
|
157 |
+
for p in parameters:
|
158 |
+
param_norm = p.grad.data.norm(norm_type)
|
159 |
+
total_norm += param_norm.item() ** norm_type
|
160 |
+
if clip_value is not None:
|
161 |
+
p.grad.data.clamp_(min=-clip_value, max=clip_value)
|
162 |
+
total_norm = total_norm ** (1.0 / norm_type)
|
163 |
+
return total_norm
|
configs/bert_vits.json
ADDED
@@ -0,0 +1,50 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"train": {
|
3 |
+
"log_interval": 100,
|
4 |
+
"eval_interval": 10000,
|
5 |
+
"seed": 1234,
|
6 |
+
"epochs": 20000,
|
7 |
+
"learning_rate": 1e-4,
|
8 |
+
"betas": [0.8, 0.99],
|
9 |
+
"eps": 1e-9,
|
10 |
+
"batch_size": 8,
|
11 |
+
"fp16_run": false,
|
12 |
+
"lr_decay": 0.999875,
|
13 |
+
"segment_size": 12800,
|
14 |
+
"init_lr_ratio": 1,
|
15 |
+
"warmup_epochs": 0,
|
16 |
+
"c_mel": 45,
|
17 |
+
"c_kl": 1.0
|
18 |
+
},
|
19 |
+
"data": {
|
20 |
+
"training_files":"filelists/train.txt",
|
21 |
+
"validation_files":"filelists/valid.txt",
|
22 |
+
"max_wav_value": 32768.0,
|
23 |
+
"sampling_rate": 16000,
|
24 |
+
"filter_length": 1024,
|
25 |
+
"hop_length": 256,
|
26 |
+
"win_length": 1024,
|
27 |
+
"n_mel_channels": 80,
|
28 |
+
"mel_fmin": 0.0,
|
29 |
+
"mel_fmax": null,
|
30 |
+
"add_blank": false,
|
31 |
+
"n_speakers": 0
|
32 |
+
},
|
33 |
+
"model": {
|
34 |
+
"inter_channels": 192,
|
35 |
+
"hidden_channels": 192,
|
36 |
+
"filter_channels": 768,
|
37 |
+
"n_heads": 2,
|
38 |
+
"n_layers": 6,
|
39 |
+
"kernel_size": 3,
|
40 |
+
"p_dropout": 0.1,
|
41 |
+
"resblock": "1",
|
42 |
+
"resblock_kernel_sizes": [3,7,11],
|
43 |
+
"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
|
44 |
+
"upsample_rates": [8,8,2,2],
|
45 |
+
"upsample_initial_channel": 512,
|
46 |
+
"upsample_kernel_sizes": [16,16,4,4],
|
47 |
+
"n_layers_q": 3,
|
48 |
+
"use_spectral_norm": false
|
49 |
+
}
|
50 |
+
}
|
models.py
ADDED
@@ -0,0 +1,533 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
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|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
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|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
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|
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|
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|
|
|
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|
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|
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|
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|
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|
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|
|
|
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|
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|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 |
+
|
11 |
+
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, 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 DurationPredictor(nn.Module):
|
17 |
+
def __init__(
|
18 |
+
self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
|
19 |
+
):
|
20 |
+
super().__init__()
|
21 |
+
|
22 |
+
self.in_channels = in_channels
|
23 |
+
self.filter_channels = filter_channels
|
24 |
+
self.kernel_size = kernel_size
|
25 |
+
self.p_dropout = p_dropout
|
26 |
+
self.gin_channels = gin_channels
|
27 |
+
|
28 |
+
self.drop = nn.Dropout(p_dropout)
|
29 |
+
self.conv_1 = nn.Conv1d(
|
30 |
+
in_channels, filter_channels, kernel_size, padding=kernel_size // 2
|
31 |
+
)
|
32 |
+
self.norm_1 = modules.LayerNorm(filter_channels)
|
33 |
+
self.conv_2 = nn.Conv1d(
|
34 |
+
filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
|
35 |
+
)
|
36 |
+
self.norm_2 = modules.LayerNorm(filter_channels)
|
37 |
+
self.proj = nn.Conv1d(filter_channels, 1, 1)
|
38 |
+
|
39 |
+
if gin_channels != 0:
|
40 |
+
self.cond = nn.Conv1d(gin_channels, in_channels, 1)
|
41 |
+
|
42 |
+
def forward(self, x, x_mask, g=None):
|
43 |
+
x = torch.detach(x)
|
44 |
+
if g is not None:
|
45 |
+
g = torch.detach(g)
|
46 |
+
x = x + self.cond(g)
|
47 |
+
x = self.conv_1(x * x_mask)
|
48 |
+
x = torch.relu(x)
|
49 |
+
x = self.norm_1(x)
|
50 |
+
x = self.drop(x)
|
51 |
+
x = self.conv_2(x * x_mask)
|
52 |
+
x = torch.relu(x)
|
53 |
+
x = self.norm_2(x)
|
54 |
+
x = self.drop(x)
|
55 |
+
x = self.proj(x * x_mask)
|
56 |
+
return x * x_mask
|
57 |
+
|
58 |
+
|
59 |
+
class TextEncoder(nn.Module):
|
60 |
+
def __init__(
|
61 |
+
self,
|
62 |
+
n_vocab,
|
63 |
+
out_channels,
|
64 |
+
hidden_channels,
|
65 |
+
filter_channels,
|
66 |
+
n_heads,
|
67 |
+
n_layers,
|
68 |
+
kernel_size,
|
69 |
+
p_dropout,
|
70 |
+
):
|
71 |
+
super().__init__()
|
72 |
+
self.n_vocab = n_vocab
|
73 |
+
self.out_channels = out_channels
|
74 |
+
self.hidden_channels = hidden_channels
|
75 |
+
self.filter_channels = filter_channels
|
76 |
+
self.n_heads = n_heads
|
77 |
+
self.n_layers = n_layers
|
78 |
+
self.kernel_size = kernel_size
|
79 |
+
self.p_dropout = p_dropout
|
80 |
+
|
81 |
+
self.emb = nn.Embedding(n_vocab, hidden_channels)
|
82 |
+
self.emb_bert = nn.Linear(256, hidden_channels)
|
83 |
+
nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
|
84 |
+
|
85 |
+
self.encoder = attentions.Encoder(
|
86 |
+
hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
|
87 |
+
)
|
88 |
+
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
89 |
+
|
90 |
+
def forward(self, x, x_lengths, bert):
|
91 |
+
x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
|
92 |
+
b = self.emb_bert(bert)
|
93 |
+
x = x + b
|
94 |
+
x = torch.transpose(x, 1, -1) # [b, h, t]
|
95 |
+
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
|
96 |
+
x.dtype
|
97 |
+
)
|
98 |
+
|
99 |
+
x = self.encoder(x * x_mask, x_mask)
|
100 |
+
stats = self.proj(x) * x_mask
|
101 |
+
|
102 |
+
m, logs = torch.split(stats, self.out_channels, dim=1)
|
103 |
+
return x, m, logs, x_mask
|
104 |
+
|
105 |
+
|
106 |
+
class ResidualCouplingBlock(nn.Module):
|
107 |
+
def __init__(
|
108 |
+
self,
|
109 |
+
channels,
|
110 |
+
hidden_channels,
|
111 |
+
kernel_size,
|
112 |
+
dilation_rate,
|
113 |
+
n_layers,
|
114 |
+
n_flows=4,
|
115 |
+
gin_channels=0,
|
116 |
+
):
|
117 |
+
super().__init__()
|
118 |
+
self.channels = channels
|
119 |
+
self.hidden_channels = hidden_channels
|
120 |
+
self.kernel_size = kernel_size
|
121 |
+
self.dilation_rate = dilation_rate
|
122 |
+
self.n_layers = n_layers
|
123 |
+
self.n_flows = n_flows
|
124 |
+
self.gin_channels = gin_channels
|
125 |
+
|
126 |
+
self.flows = nn.ModuleList()
|
127 |
+
for i in range(n_flows):
|
128 |
+
self.flows.append(
|
129 |
+
modules.ResidualCouplingLayer(
|
130 |
+
channels,
|
131 |
+
hidden_channels,
|
132 |
+
kernel_size,
|
133 |
+
dilation_rate,
|
134 |
+
n_layers,
|
135 |
+
gin_channels=gin_channels,
|
136 |
+
mean_only=True,
|
137 |
+
)
|
138 |
+
)
|
139 |
+
self.flows.append(modules.Flip())
|
140 |
+
|
141 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
142 |
+
if not reverse:
|
143 |
+
for flow in self.flows:
|
144 |
+
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
145 |
+
else:
|
146 |
+
for flow in reversed(self.flows):
|
147 |
+
x = flow(x, x_mask, g=g, reverse=reverse)
|
148 |
+
return x
|
149 |
+
|
150 |
+
def remove_weight_norm(self):
|
151 |
+
for i in range(self.n_flows):
|
152 |
+
self.flows[i * 2].remove_weight_norm()
|
153 |
+
|
154 |
+
|
155 |
+
class PosteriorEncoder(nn.Module):
|
156 |
+
def __init__(
|
157 |
+
self,
|
158 |
+
in_channels,
|
159 |
+
out_channels,
|
160 |
+
hidden_channels,
|
161 |
+
kernel_size,
|
162 |
+
dilation_rate,
|
163 |
+
n_layers,
|
164 |
+
gin_channels=0,
|
165 |
+
):
|
166 |
+
super().__init__()
|
167 |
+
self.in_channels = in_channels
|
168 |
+
self.out_channels = out_channels
|
169 |
+
self.hidden_channels = hidden_channels
|
170 |
+
self.kernel_size = kernel_size
|
171 |
+
self.dilation_rate = dilation_rate
|
172 |
+
self.n_layers = n_layers
|
173 |
+
self.gin_channels = gin_channels
|
174 |
+
|
175 |
+
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
176 |
+
self.enc = modules.WN(
|
177 |
+
hidden_channels,
|
178 |
+
kernel_size,
|
179 |
+
dilation_rate,
|
180 |
+
n_layers,
|
181 |
+
gin_channels=gin_channels,
|
182 |
+
)
|
183 |
+
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
184 |
+
|
185 |
+
def forward(self, x, x_lengths, g=None):
|
186 |
+
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
|
187 |
+
x.dtype
|
188 |
+
)
|
189 |
+
x = self.pre(x) * x_mask
|
190 |
+
x = self.enc(x, x_mask, g=g)
|
191 |
+
stats = self.proj(x) * x_mask
|
192 |
+
m, logs = torch.split(stats, self.out_channels, dim=1)
|
193 |
+
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
194 |
+
return z, m, logs, x_mask
|
195 |
+
|
196 |
+
def remove_weight_norm(self):
|
197 |
+
self.enc.remove_weight_norm()
|
198 |
+
|
199 |
+
|
200 |
+
class Generator(torch.nn.Module):
|
201 |
+
def __init__(
|
202 |
+
self,
|
203 |
+
initial_channel,
|
204 |
+
resblock,
|
205 |
+
resblock_kernel_sizes,
|
206 |
+
resblock_dilation_sizes,
|
207 |
+
upsample_rates,
|
208 |
+
upsample_initial_channel,
|
209 |
+
upsample_kernel_sizes,
|
210 |
+
gin_channels=0,
|
211 |
+
):
|
212 |
+
super(Generator, self).__init__()
|
213 |
+
self.num_kernels = len(resblock_kernel_sizes)
|
214 |
+
self.num_upsamples = len(upsample_rates)
|
215 |
+
self.conv_pre = Conv1d(
|
216 |
+
initial_channel, upsample_initial_channel, 7, 1, padding=3
|
217 |
+
)
|
218 |
+
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
|
219 |
+
|
220 |
+
self.ups = nn.ModuleList()
|
221 |
+
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
222 |
+
self.ups.append(
|
223 |
+
weight_norm(
|
224 |
+
ConvTranspose1d(
|
225 |
+
upsample_initial_channel // (2**i),
|
226 |
+
upsample_initial_channel // (2 ** (i + 1)),
|
227 |
+
k,
|
228 |
+
u,
|
229 |
+
padding=(k - u) // 2,
|
230 |
+
)
|
231 |
+
)
|
232 |
+
)
|
233 |
+
|
234 |
+
self.resblocks = nn.ModuleList()
|
235 |
+
for i in range(len(self.ups)):
|
236 |
+
ch = upsample_initial_channel // (2 ** (i + 1))
|
237 |
+
for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
|
238 |
+
self.resblocks.append(resblock(ch, k, d))
|
239 |
+
|
240 |
+
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
|
241 |
+
self.ups.apply(init_weights)
|
242 |
+
|
243 |
+
if gin_channels != 0:
|
244 |
+
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
|
245 |
+
|
246 |
+
def forward(self, x, g=None):
|
247 |
+
x = self.conv_pre(x)
|
248 |
+
if g is not None:
|
249 |
+
x = x + self.cond(g)
|
250 |
+
|
251 |
+
for i in range(self.num_upsamples):
|
252 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
253 |
+
x = self.ups[i](x)
|
254 |
+
xs = None
|
255 |
+
for j in range(self.num_kernels):
|
256 |
+
if xs is None:
|
257 |
+
xs = self.resblocks[i * self.num_kernels + j](x)
|
258 |
+
else:
|
259 |
+
xs += self.resblocks[i * self.num_kernels + j](x)
|
260 |
+
x = xs / self.num_kernels
|
261 |
+
x = F.leaky_relu(x)
|
262 |
+
x = self.conv_post(x)
|
263 |
+
x = torch.tanh(x)
|
264 |
+
|
265 |
+
return x
|
266 |
+
|
267 |
+
def remove_weight_norm(self):
|
268 |
+
for l in self.ups:
|
269 |
+
remove_weight_norm(l)
|
270 |
+
for l in self.resblocks:
|
271 |
+
l.remove_weight_norm()
|
272 |
+
remove_weight_norm(self.conv_pre)
|
273 |
+
remove_weight_norm(self.conv_post)
|
274 |
+
|
275 |
+
|
276 |
+
class DiscriminatorP(torch.nn.Module):
|
277 |
+
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
278 |
+
super(DiscriminatorP, self).__init__()
|
279 |
+
self.period = period
|
280 |
+
self.use_spectral_norm = use_spectral_norm
|
281 |
+
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
282 |
+
self.convs = nn.ModuleList(
|
283 |
+
[
|
284 |
+
norm_f(
|
285 |
+
Conv2d(
|
286 |
+
1,
|
287 |
+
32,
|
288 |
+
(kernel_size, 1),
|
289 |
+
(stride, 1),
|
290 |
+
padding=(get_padding(kernel_size, 1), 0),
|
291 |
+
)
|
292 |
+
),
|
293 |
+
norm_f(
|
294 |
+
Conv2d(
|
295 |
+
32,
|
296 |
+
128,
|
297 |
+
(kernel_size, 1),
|
298 |
+
(stride, 1),
|
299 |
+
padding=(get_padding(kernel_size, 1), 0),
|
300 |
+
)
|
301 |
+
),
|
302 |
+
norm_f(
|
303 |
+
Conv2d(
|
304 |
+
128,
|
305 |
+
512,
|
306 |
+
(kernel_size, 1),
|
307 |
+
(stride, 1),
|
308 |
+
padding=(get_padding(kernel_size, 1), 0),
|
309 |
+
)
|
310 |
+
),
|
311 |
+
norm_f(
|
312 |
+
Conv2d(
|
313 |
+
512,
|
314 |
+
1024,
|
315 |
+
(kernel_size, 1),
|
316 |
+
(stride, 1),
|
317 |
+
padding=(get_padding(kernel_size, 1), 0),
|
318 |
+
)
|
319 |
+
),
|
320 |
+
norm_f(
|
321 |
+
Conv2d(
|
322 |
+
1024,
|
323 |
+
1024,
|
324 |
+
(kernel_size, 1),
|
325 |
+
1,
|
326 |
+
padding=(get_padding(kernel_size, 1), 0),
|
327 |
+
)
|
328 |
+
),
|
329 |
+
]
|
330 |
+
)
|
331 |
+
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
332 |
+
|
333 |
+
def forward(self, x):
|
334 |
+
fmap = []
|
335 |
+
|
336 |
+
# 1d to 2d
|
337 |
+
b, c, t = x.shape
|
338 |
+
if t % self.period != 0: # pad first
|
339 |
+
n_pad = self.period - (t % self.period)
|
340 |
+
x = F.pad(x, (0, n_pad), "reflect")
|
341 |
+
t = t + n_pad
|
342 |
+
x = x.view(b, c, t // self.period, self.period)
|
343 |
+
|
344 |
+
for l in self.convs:
|
345 |
+
x = l(x)
|
346 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
347 |
+
fmap.append(x)
|
348 |
+
x = self.conv_post(x)
|
349 |
+
fmap.append(x)
|
350 |
+
x = torch.flatten(x, 1, -1)
|
351 |
+
|
352 |
+
return x, fmap
|
353 |
+
|
354 |
+
|
355 |
+
class DiscriminatorS(torch.nn.Module):
|
356 |
+
def __init__(self, use_spectral_norm=False):
|
357 |
+
super(DiscriminatorS, self).__init__()
|
358 |
+
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
359 |
+
self.convs = nn.ModuleList(
|
360 |
+
[
|
361 |
+
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
|
362 |
+
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
|
363 |
+
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
|
364 |
+
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
|
365 |
+
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
|
366 |
+
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
367 |
+
]
|
368 |
+
)
|
369 |
+
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
370 |
+
|
371 |
+
def forward(self, x):
|
372 |
+
fmap = []
|
373 |
+
|
374 |
+
for l in self.convs:
|
375 |
+
x = l(x)
|
376 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
377 |
+
fmap.append(x)
|
378 |
+
x = self.conv_post(x)
|
379 |
+
fmap.append(x)
|
380 |
+
x = torch.flatten(x, 1, -1)
|
381 |
+
|
382 |
+
return x, fmap
|
383 |
+
|
384 |
+
|
385 |
+
class MultiPeriodDiscriminator(torch.nn.Module):
|
386 |
+
def __init__(self, use_spectral_norm=False):
|
387 |
+
super(MultiPeriodDiscriminator, self).__init__()
|
388 |
+
periods = [2, 3, 5, 7, 11]
|
389 |
+
|
390 |
+
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
|
391 |
+
discs = discs + [
|
392 |
+
DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
|
393 |
+
]
|
394 |
+
self.discriminators = nn.ModuleList(discs)
|
395 |
+
|
396 |
+
def forward(self, y, y_hat):
|
397 |
+
y_d_rs = []
|
398 |
+
y_d_gs = []
|
399 |
+
fmap_rs = []
|
400 |
+
fmap_gs = []
|
401 |
+
for i, d in enumerate(self.discriminators):
|
402 |
+
y_d_r, fmap_r = d(y)
|
403 |
+
y_d_g, fmap_g = d(y_hat)
|
404 |
+
y_d_rs.append(y_d_r)
|
405 |
+
y_d_gs.append(y_d_g)
|
406 |
+
fmap_rs.append(fmap_r)
|
407 |
+
fmap_gs.append(fmap_g)
|
408 |
+
|
409 |
+
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
410 |
+
|
411 |
+
|
412 |
+
class SynthesizerTrn(nn.Module):
|
413 |
+
"""
|
414 |
+
Synthesizer for Training
|
415 |
+
"""
|
416 |
+
|
417 |
+
def __init__(
|
418 |
+
self,
|
419 |
+
n_vocab,
|
420 |
+
spec_channels,
|
421 |
+
segment_size,
|
422 |
+
inter_channels,
|
423 |
+
hidden_channels,
|
424 |
+
filter_channels,
|
425 |
+
n_heads,
|
426 |
+
n_layers,
|
427 |
+
kernel_size,
|
428 |
+
p_dropout,
|
429 |
+
resblock,
|
430 |
+
resblock_kernel_sizes,
|
431 |
+
resblock_dilation_sizes,
|
432 |
+
upsample_rates,
|
433 |
+
upsample_initial_channel,
|
434 |
+
upsample_kernel_sizes,
|
435 |
+
n_speakers=0,
|
436 |
+
gin_channels=0,
|
437 |
+
use_sdp=False,
|
438 |
+
**kwargs
|
439 |
+
):
|
440 |
+
|
441 |
+
super().__init__()
|
442 |
+
self.n_vocab = n_vocab
|
443 |
+
self.spec_channels = spec_channels
|
444 |
+
self.inter_channels = inter_channels
|
445 |
+
self.hidden_channels = hidden_channels
|
446 |
+
self.filter_channels = filter_channels
|
447 |
+
self.n_heads = n_heads
|
448 |
+
self.n_layers = n_layers
|
449 |
+
self.kernel_size = kernel_size
|
450 |
+
self.p_dropout = p_dropout
|
451 |
+
self.resblock = resblock
|
452 |
+
self.resblock_kernel_sizes = resblock_kernel_sizes
|
453 |
+
self.resblock_dilation_sizes = resblock_dilation_sizes
|
454 |
+
self.upsample_rates = upsample_rates
|
455 |
+
self.upsample_initial_channel = upsample_initial_channel
|
456 |
+
self.upsample_kernel_sizes = upsample_kernel_sizes
|
457 |
+
self.segment_size = segment_size
|
458 |
+
self.n_speakers = n_speakers
|
459 |
+
self.gin_channels = gin_channels
|
460 |
+
|
461 |
+
self.enc_p = TextEncoder(
|
462 |
+
n_vocab,
|
463 |
+
inter_channels,
|
464 |
+
hidden_channels,
|
465 |
+
filter_channels,
|
466 |
+
n_heads,
|
467 |
+
n_layers,
|
468 |
+
kernel_size,
|
469 |
+
p_dropout,
|
470 |
+
)
|
471 |
+
self.dec = Generator(
|
472 |
+
inter_channels,
|
473 |
+
resblock,
|
474 |
+
resblock_kernel_sizes,
|
475 |
+
resblock_dilation_sizes,
|
476 |
+
upsample_rates,
|
477 |
+
upsample_initial_channel,
|
478 |
+
upsample_kernel_sizes,
|
479 |
+
gin_channels=gin_channels,
|
480 |
+
)
|
481 |
+
self.enc_q = PosteriorEncoder(
|
482 |
+
spec_channels,
|
483 |
+
inter_channels,
|
484 |
+
hidden_channels,
|
485 |
+
5,
|
486 |
+
1,
|
487 |
+
16,
|
488 |
+
gin_channels=gin_channels,
|
489 |
+
)
|
490 |
+
self.flow = ResidualCouplingBlock(
|
491 |
+
inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels
|
492 |
+
)
|
493 |
+
self.dp = DurationPredictor(
|
494 |
+
hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
|
495 |
+
)
|
496 |
+
if n_speakers > 1:
|
497 |
+
self.emb_g = nn.Embedding(n_speakers, gin_channels)
|
498 |
+
|
499 |
+
def remove_weight_norm(self):
|
500 |
+
print("Removing weight norm...")
|
501 |
+
self.dec.remove_weight_norm()
|
502 |
+
self.flow.remove_weight_norm()
|
503 |
+
self.enc_q.remove_weight_norm()
|
504 |
+
|
505 |
+
|
506 |
+
def infer(self, x, x_lengths, bert, sid=None, noise_scale=1, length_scale=1, max_len=None):
|
507 |
+
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, bert)
|
508 |
+
if self.n_speakers > 0:
|
509 |
+
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
|
510 |
+
else:
|
511 |
+
g = None
|
512 |
+
|
513 |
+
logw = self.dp(x, x_mask, g=g)
|
514 |
+
w = torch.exp(logw) * x_mask * length_scale
|
515 |
+
w_ceil = torch.ceil(w)
|
516 |
+
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
|
517 |
+
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(
|
518 |
+
x_mask.dtype
|
519 |
+
)
|
520 |
+
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
|
521 |
+
attn = commons.generate_path(w_ceil, attn_mask)
|
522 |
+
|
523 |
+
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
|
524 |
+
1, 2
|
525 |
+
) # [b, t', t], [b, t, d] -> [b, d, t']
|
526 |
+
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
|
527 |
+
1, 2
|
528 |
+
) # [b, t', t], [b, t, d] -> [b, d, t']
|
529 |
+
|
530 |
+
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
|
531 |
+
z = self.flow(z_p, y_mask, g=g, reverse=True)
|
532 |
+
o = self.dec((z * y_mask)[:, :, :max_len], g=g)
|
533 |
+
return o, attn, y_mask, (z, z_p, m_p, logs_p)
|
modules.py
ADDED
@@ -0,0 +1,522 @@
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|
|
|
|
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__(
|
37 |
+
self,
|
38 |
+
in_channels,
|
39 |
+
hidden_channels,
|
40 |
+
out_channels,
|
41 |
+
kernel_size,
|
42 |
+
n_layers,
|
43 |
+
p_dropout,
|
44 |
+
):
|
45 |
+
super().__init__()
|
46 |
+
self.in_channels = in_channels
|
47 |
+
self.hidden_channels = hidden_channels
|
48 |
+
self.out_channels = out_channels
|
49 |
+
self.kernel_size = kernel_size
|
50 |
+
self.n_layers = n_layers
|
51 |
+
self.p_dropout = p_dropout
|
52 |
+
assert n_layers > 1, "Number of layers should be larger than 0."
|
53 |
+
|
54 |
+
self.conv_layers = nn.ModuleList()
|
55 |
+
self.norm_layers = nn.ModuleList()
|
56 |
+
self.conv_layers.append(
|
57 |
+
nn.Conv1d(
|
58 |
+
in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
|
59 |
+
)
|
60 |
+
)
|
61 |
+
self.norm_layers.append(LayerNorm(hidden_channels))
|
62 |
+
self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
|
63 |
+
for _ in range(n_layers - 1):
|
64 |
+
self.conv_layers.append(
|
65 |
+
nn.Conv1d(
|
66 |
+
hidden_channels,
|
67 |
+
hidden_channels,
|
68 |
+
kernel_size,
|
69 |
+
padding=kernel_size // 2,
|
70 |
+
)
|
71 |
+
)
|
72 |
+
self.norm_layers.append(LayerNorm(hidden_channels))
|
73 |
+
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
74 |
+
self.proj.weight.data.zero_()
|
75 |
+
self.proj.bias.data.zero_()
|
76 |
+
|
77 |
+
def forward(self, x, x_mask):
|
78 |
+
x_org = x
|
79 |
+
for i in range(self.n_layers):
|
80 |
+
x = self.conv_layers[i](x * x_mask)
|
81 |
+
x = self.norm_layers[i](x)
|
82 |
+
x = self.relu_drop(x)
|
83 |
+
x = x_org + self.proj(x)
|
84 |
+
return x * x_mask
|
85 |
+
|
86 |
+
|
87 |
+
class DDSConv(nn.Module):
|
88 |
+
"""
|
89 |
+
Dialted and Depth-Separable Convolution
|
90 |
+
"""
|
91 |
+
|
92 |
+
def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
|
93 |
+
super().__init__()
|
94 |
+
self.channels = channels
|
95 |
+
self.kernel_size = kernel_size
|
96 |
+
self.n_layers = n_layers
|
97 |
+
self.p_dropout = p_dropout
|
98 |
+
|
99 |
+
self.drop = nn.Dropout(p_dropout)
|
100 |
+
self.convs_sep = nn.ModuleList()
|
101 |
+
self.convs_1x1 = nn.ModuleList()
|
102 |
+
self.norms_1 = nn.ModuleList()
|
103 |
+
self.norms_2 = nn.ModuleList()
|
104 |
+
for i in range(n_layers):
|
105 |
+
dilation = kernel_size**i
|
106 |
+
padding = (kernel_size * dilation - dilation) // 2
|
107 |
+
self.convs_sep.append(
|
108 |
+
nn.Conv1d(
|
109 |
+
channels,
|
110 |
+
channels,
|
111 |
+
kernel_size,
|
112 |
+
groups=channels,
|
113 |
+
dilation=dilation,
|
114 |
+
padding=padding,
|
115 |
+
)
|
116 |
+
)
|
117 |
+
self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
|
118 |
+
self.norms_1.append(LayerNorm(channels))
|
119 |
+
self.norms_2.append(LayerNorm(channels))
|
120 |
+
|
121 |
+
def forward(self, x, x_mask, g=None):
|
122 |
+
if g is not None:
|
123 |
+
x = x + g
|
124 |
+
for i in range(self.n_layers):
|
125 |
+
y = self.convs_sep[i](x * x_mask)
|
126 |
+
y = self.norms_1[i](y)
|
127 |
+
y = F.gelu(y)
|
128 |
+
y = self.convs_1x1[i](y)
|
129 |
+
y = self.norms_2[i](y)
|
130 |
+
y = F.gelu(y)
|
131 |
+
y = self.drop(y)
|
132 |
+
x = x + y
|
133 |
+
return x * x_mask
|
134 |
+
|
135 |
+
|
136 |
+
class WN(torch.nn.Module):
|
137 |
+
def __init__(
|
138 |
+
self,
|
139 |
+
hidden_channels,
|
140 |
+
kernel_size,
|
141 |
+
dilation_rate,
|
142 |
+
n_layers,
|
143 |
+
gin_channels=0,
|
144 |
+
p_dropout=0,
|
145 |
+
):
|
146 |
+
super(WN, self).__init__()
|
147 |
+
assert kernel_size % 2 == 1
|
148 |
+
self.hidden_channels = hidden_channels
|
149 |
+
self.kernel_size = (kernel_size,)
|
150 |
+
self.dilation_rate = dilation_rate
|
151 |
+
self.n_layers = n_layers
|
152 |
+
self.gin_channels = gin_channels
|
153 |
+
self.p_dropout = p_dropout
|
154 |
+
|
155 |
+
self.in_layers = torch.nn.ModuleList()
|
156 |
+
self.res_skip_layers = torch.nn.ModuleList()
|
157 |
+
self.drop = nn.Dropout(p_dropout)
|
158 |
+
|
159 |
+
if gin_channels != 0:
|
160 |
+
cond_layer = torch.nn.Conv1d(
|
161 |
+
gin_channels, 2 * hidden_channels * n_layers, 1
|
162 |
+
)
|
163 |
+
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
|
164 |
+
|
165 |
+
for i in range(n_layers):
|
166 |
+
dilation = dilation_rate**i
|
167 |
+
padding = int((kernel_size * dilation - dilation) / 2)
|
168 |
+
in_layer = torch.nn.Conv1d(
|
169 |
+
hidden_channels,
|
170 |
+
2 * hidden_channels,
|
171 |
+
kernel_size,
|
172 |
+
dilation=dilation,
|
173 |
+
padding=padding,
|
174 |
+
)
|
175 |
+
in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
|
176 |
+
self.in_layers.append(in_layer)
|
177 |
+
|
178 |
+
# last one is not necessary
|
179 |
+
if i < n_layers - 1:
|
180 |
+
res_skip_channels = 2 * hidden_channels
|
181 |
+
else:
|
182 |
+
res_skip_channels = hidden_channels
|
183 |
+
|
184 |
+
res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
|
185 |
+
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
|
186 |
+
self.res_skip_layers.append(res_skip_layer)
|
187 |
+
|
188 |
+
def forward(self, x, x_mask, g=None, **kwargs):
|
189 |
+
output = torch.zeros_like(x)
|
190 |
+
n_channels_tensor = torch.IntTensor([self.hidden_channels])
|
191 |
+
|
192 |
+
if g is not None:
|
193 |
+
g = self.cond_layer(g)
|
194 |
+
|
195 |
+
for i in range(self.n_layers):
|
196 |
+
x_in = self.in_layers[i](x)
|
197 |
+
if g is not None:
|
198 |
+
cond_offset = i * 2 * self.hidden_channels
|
199 |
+
g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
|
200 |
+
else:
|
201 |
+
g_l = torch.zeros_like(x_in)
|
202 |
+
|
203 |
+
acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
|
204 |
+
acts = self.drop(acts)
|
205 |
+
|
206 |
+
res_skip_acts = self.res_skip_layers[i](acts)
|
207 |
+
if i < self.n_layers - 1:
|
208 |
+
res_acts = res_skip_acts[:, : self.hidden_channels, :]
|
209 |
+
x = (x + res_acts) * x_mask
|
210 |
+
output = output + res_skip_acts[:, self.hidden_channels :, :]
|
211 |
+
else:
|
212 |
+
output = output + res_skip_acts
|
213 |
+
return output * x_mask
|
214 |
+
|
215 |
+
def remove_weight_norm(self):
|
216 |
+
if self.gin_channels != 0:
|
217 |
+
torch.nn.utils.remove_weight_norm(self.cond_layer)
|
218 |
+
for l in self.in_layers:
|
219 |
+
torch.nn.utils.remove_weight_norm(l)
|
220 |
+
for l in self.res_skip_layers:
|
221 |
+
torch.nn.utils.remove_weight_norm(l)
|
222 |
+
|
223 |
+
|
224 |
+
class ResBlock1(torch.nn.Module):
|
225 |
+
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
|
226 |
+
super(ResBlock1, self).__init__()
|
227 |
+
self.convs1 = nn.ModuleList(
|
228 |
+
[
|
229 |
+
weight_norm(
|
230 |
+
Conv1d(
|
231 |
+
channels,
|
232 |
+
channels,
|
233 |
+
kernel_size,
|
234 |
+
1,
|
235 |
+
dilation=dilation[0],
|
236 |
+
padding=get_padding(kernel_size, dilation[0]),
|
237 |
+
)
|
238 |
+
),
|
239 |
+
weight_norm(
|
240 |
+
Conv1d(
|
241 |
+
channels,
|
242 |
+
channels,
|
243 |
+
kernel_size,
|
244 |
+
1,
|
245 |
+
dilation=dilation[1],
|
246 |
+
padding=get_padding(kernel_size, dilation[1]),
|
247 |
+
)
|
248 |
+
),
|
249 |
+
weight_norm(
|
250 |
+
Conv1d(
|
251 |
+
channels,
|
252 |
+
channels,
|
253 |
+
kernel_size,
|
254 |
+
1,
|
255 |
+
dilation=dilation[2],
|
256 |
+
padding=get_padding(kernel_size, dilation[2]),
|
257 |
+
)
|
258 |
+
),
|
259 |
+
]
|
260 |
+
)
|
261 |
+
self.convs1.apply(init_weights)
|
262 |
+
|
263 |
+
self.convs2 = nn.ModuleList(
|
264 |
+
[
|
265 |
+
weight_norm(
|
266 |
+
Conv1d(
|
267 |
+
channels,
|
268 |
+
channels,
|
269 |
+
kernel_size,
|
270 |
+
1,
|
271 |
+
dilation=1,
|
272 |
+
padding=get_padding(kernel_size, 1),
|
273 |
+
)
|
274 |
+
),
|
275 |
+
weight_norm(
|
276 |
+
Conv1d(
|
277 |
+
channels,
|
278 |
+
channels,
|
279 |
+
kernel_size,
|
280 |
+
1,
|
281 |
+
dilation=1,
|
282 |
+
padding=get_padding(kernel_size, 1),
|
283 |
+
)
|
284 |
+
),
|
285 |
+
weight_norm(
|
286 |
+
Conv1d(
|
287 |
+
channels,
|
288 |
+
channels,
|
289 |
+
kernel_size,
|
290 |
+
1,
|
291 |
+
dilation=1,
|
292 |
+
padding=get_padding(kernel_size, 1),
|
293 |
+
)
|
294 |
+
),
|
295 |
+
]
|
296 |
+
)
|
297 |
+
self.convs2.apply(init_weights)
|
298 |
+
|
299 |
+
def forward(self, x, x_mask=None):
|
300 |
+
for c1, c2 in zip(self.convs1, self.convs2):
|
301 |
+
xt = F.leaky_relu(x, LRELU_SLOPE)
|
302 |
+
if x_mask is not None:
|
303 |
+
xt = xt * x_mask
|
304 |
+
xt = c1(xt)
|
305 |
+
xt = F.leaky_relu(xt, LRELU_SLOPE)
|
306 |
+
if x_mask is not None:
|
307 |
+
xt = xt * x_mask
|
308 |
+
xt = c2(xt)
|
309 |
+
x = xt + x
|
310 |
+
if x_mask is not None:
|
311 |
+
x = x * x_mask
|
312 |
+
return x
|
313 |
+
|
314 |
+
def remove_weight_norm(self):
|
315 |
+
for l in self.convs1:
|
316 |
+
remove_weight_norm(l)
|
317 |
+
for l in self.convs2:
|
318 |
+
remove_weight_norm(l)
|
319 |
+
|
320 |
+
|
321 |
+
class ResBlock2(torch.nn.Module):
|
322 |
+
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
|
323 |
+
super(ResBlock2, self).__init__()
|
324 |
+
self.convs = nn.ModuleList(
|
325 |
+
[
|
326 |
+
weight_norm(
|
327 |
+
Conv1d(
|
328 |
+
channels,
|
329 |
+
channels,
|
330 |
+
kernel_size,
|
331 |
+
1,
|
332 |
+
dilation=dilation[0],
|
333 |
+
padding=get_padding(kernel_size, dilation[0]),
|
334 |
+
)
|
335 |
+
),
|
336 |
+
weight_norm(
|
337 |
+
Conv1d(
|
338 |
+
channels,
|
339 |
+
channels,
|
340 |
+
kernel_size,
|
341 |
+
1,
|
342 |
+
dilation=dilation[1],
|
343 |
+
padding=get_padding(kernel_size, dilation[1]),
|
344 |
+
)
|
345 |
+
),
|
346 |
+
]
|
347 |
+
)
|
348 |
+
self.convs.apply(init_weights)
|
349 |
+
|
350 |
+
def forward(self, x, x_mask=None):
|
351 |
+
for c in self.convs:
|
352 |
+
xt = F.leaky_relu(x, LRELU_SLOPE)
|
353 |
+
if x_mask is not None:
|
354 |
+
xt = xt * x_mask
|
355 |
+
xt = c(xt)
|
356 |
+
x = xt + x
|
357 |
+
if x_mask is not None:
|
358 |
+
x = x * x_mask
|
359 |
+
return x
|
360 |
+
|
361 |
+
def remove_weight_norm(self):
|
362 |
+
for l in self.convs:
|
363 |
+
remove_weight_norm(l)
|
364 |
+
|
365 |
+
|
366 |
+
class Log(nn.Module):
|
367 |
+
def forward(self, x, x_mask, reverse=False, **kwargs):
|
368 |
+
if not reverse:
|
369 |
+
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
|
370 |
+
logdet = torch.sum(-y, [1, 2])
|
371 |
+
return y, logdet
|
372 |
+
else:
|
373 |
+
x = torch.exp(x) * x_mask
|
374 |
+
return x
|
375 |
+
|
376 |
+
|
377 |
+
class Flip(nn.Module):
|
378 |
+
def forward(self, x, *args, reverse=False, **kwargs):
|
379 |
+
x = torch.flip(x, [1])
|
380 |
+
if not reverse:
|
381 |
+
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
|
382 |
+
return x, logdet
|
383 |
+
else:
|
384 |
+
return x
|
385 |
+
|
386 |
+
|
387 |
+
class ElementwiseAffine(nn.Module):
|
388 |
+
def __init__(self, channels):
|
389 |
+
super().__init__()
|
390 |
+
self.channels = channels
|
391 |
+
self.m = nn.Parameter(torch.zeros(channels, 1))
|
392 |
+
self.logs = nn.Parameter(torch.zeros(channels, 1))
|
393 |
+
|
394 |
+
def forward(self, x, x_mask, reverse=False, **kwargs):
|
395 |
+
if not reverse:
|
396 |
+
y = self.m + torch.exp(self.logs) * x
|
397 |
+
y = y * x_mask
|
398 |
+
logdet = torch.sum(self.logs * x_mask, [1, 2])
|
399 |
+
return y, logdet
|
400 |
+
else:
|
401 |
+
x = (x - self.m) * torch.exp(-self.logs) * x_mask
|
402 |
+
return x
|
403 |
+
|
404 |
+
|
405 |
+
class ResidualCouplingLayer(nn.Module):
|
406 |
+
def __init__(
|
407 |
+
self,
|
408 |
+
channels,
|
409 |
+
hidden_channels,
|
410 |
+
kernel_size,
|
411 |
+
dilation_rate,
|
412 |
+
n_layers,
|
413 |
+
p_dropout=0,
|
414 |
+
gin_channels=0,
|
415 |
+
mean_only=False,
|
416 |
+
):
|
417 |
+
assert channels % 2 == 0, "channels should be divisible by 2"
|
418 |
+
super().__init__()
|
419 |
+
self.channels = channels
|
420 |
+
self.hidden_channels = hidden_channels
|
421 |
+
self.kernel_size = kernel_size
|
422 |
+
self.dilation_rate = dilation_rate
|
423 |
+
self.n_layers = n_layers
|
424 |
+
self.half_channels = channels // 2
|
425 |
+
self.mean_only = mean_only
|
426 |
+
|
427 |
+
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
|
428 |
+
self.enc = WN(
|
429 |
+
hidden_channels,
|
430 |
+
kernel_size,
|
431 |
+
dilation_rate,
|
432 |
+
n_layers,
|
433 |
+
p_dropout=p_dropout,
|
434 |
+
gin_channels=gin_channels,
|
435 |
+
)
|
436 |
+
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
|
437 |
+
self.post.weight.data.zero_()
|
438 |
+
self.post.bias.data.zero_()
|
439 |
+
|
440 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
441 |
+
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
|
442 |
+
h = self.pre(x0) * x_mask
|
443 |
+
h = self.enc(h, x_mask, g=g)
|
444 |
+
stats = self.post(h) * x_mask
|
445 |
+
if not self.mean_only:
|
446 |
+
m, logs = torch.split(stats, [self.half_channels] * 2, 1)
|
447 |
+
else:
|
448 |
+
m = stats
|
449 |
+
logs = torch.zeros_like(m)
|
450 |
+
|
451 |
+
if not reverse:
|
452 |
+
x1 = m + x1 * torch.exp(logs) * x_mask
|
453 |
+
x = torch.cat([x0, x1], 1)
|
454 |
+
logdet = torch.sum(logs, [1, 2])
|
455 |
+
return x, logdet
|
456 |
+
else:
|
457 |
+
x1 = (x1 - m) * torch.exp(-logs) * x_mask
|
458 |
+
x = torch.cat([x0, x1], 1)
|
459 |
+
return x
|
460 |
+
|
461 |
+
def remove_weight_norm(self):
|
462 |
+
self.enc.remove_weight_norm()
|
463 |
+
|
464 |
+
|
465 |
+
class ConvFlow(nn.Module):
|
466 |
+
def __init__(
|
467 |
+
self,
|
468 |
+
in_channels,
|
469 |
+
filter_channels,
|
470 |
+
kernel_size,
|
471 |
+
n_layers,
|
472 |
+
num_bins=10,
|
473 |
+
tail_bound=5.0,
|
474 |
+
):
|
475 |
+
super().__init__()
|
476 |
+
self.in_channels = in_channels
|
477 |
+
self.filter_channels = filter_channels
|
478 |
+
self.kernel_size = kernel_size
|
479 |
+
self.n_layers = n_layers
|
480 |
+
self.num_bins = num_bins
|
481 |
+
self.tail_bound = tail_bound
|
482 |
+
self.half_channels = in_channels // 2
|
483 |
+
|
484 |
+
self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
|
485 |
+
self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
|
486 |
+
self.proj = nn.Conv1d(
|
487 |
+
filter_channels, self.half_channels * (num_bins * 3 - 1), 1
|
488 |
+
)
|
489 |
+
self.proj.weight.data.zero_()
|
490 |
+
self.proj.bias.data.zero_()
|
491 |
+
|
492 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
493 |
+
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
|
494 |
+
h = self.pre(x0)
|
495 |
+
h = self.convs(h, x_mask, g=g)
|
496 |
+
h = self.proj(h) * x_mask
|
497 |
+
|
498 |
+
b, c, t = x0.shape
|
499 |
+
h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
|
500 |
+
|
501 |
+
unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
|
502 |
+
unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
|
503 |
+
self.filter_channels
|
504 |
+
)
|
505 |
+
unnormalized_derivatives = h[..., 2 * self.num_bins :]
|
506 |
+
|
507 |
+
x1, logabsdet = piecewise_rational_quadratic_transform(
|
508 |
+
x1,
|
509 |
+
unnormalized_widths,
|
510 |
+
unnormalized_heights,
|
511 |
+
unnormalized_derivatives,
|
512 |
+
inverse=reverse,
|
513 |
+
tails="linear",
|
514 |
+
tail_bound=self.tail_bound,
|
515 |
+
)
|
516 |
+
|
517 |
+
x = torch.cat([x0, x1], 1) * x_mask
|
518 |
+
logdet = torch.sum(logabsdet * x_mask, [1, 2])
|
519 |
+
if not reverse:
|
520 |
+
return x, logdet
|
521 |
+
else:
|
522 |
+
return x
|
requirements.txt
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
torch
|
2 |
+
scipy
|
3 |
+
transformers
|
4 |
+
pypinyin
|
text/__init__.py
ADDED
@@ -0,0 +1,447 @@
|
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|
1 |
+
from text.symbols import symbols
|
2 |
+
|
3 |
+
|
4 |
+
# Mappings from symbol to numeric ID and vice versa:
|
5 |
+
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
|
6 |
+
_id_to_symbol = {i: s for i, s in enumerate(symbols)}
|
7 |
+
|
8 |
+
|
9 |
+
def cleaned_text_to_sequence(cleaned_text):
|
10 |
+
"""Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
|
11 |
+
Args:
|
12 |
+
text: string to convert to a sequence
|
13 |
+
Returns:
|
14 |
+
List of integers corresponding to the symbols in the text
|
15 |
+
"""
|
16 |
+
sequence = [_symbol_to_id[symbol] for symbol in cleaned_text.split()]
|
17 |
+
return sequence
|
18 |
+
|
19 |
+
|
20 |
+
def sequence_to_text(sequence):
|
21 |
+
"""Converts a sequence of IDs back to a string"""
|
22 |
+
result = ""
|
23 |
+
for symbol_id in sequence:
|
24 |
+
s = _id_to_symbol[symbol_id]
|
25 |
+
result += s
|
26 |
+
return result
|
27 |
+
|
28 |
+
|
29 |
+
pinyin_dict = {
|
30 |
+
"a": ("^", "a"),
|
31 |
+
"ai": ("^", "ai"),
|
32 |
+
"an": ("^", "an"),
|
33 |
+
"ang": ("^", "ang"),
|
34 |
+
"ao": ("^", "ao"),
|
35 |
+
"ba": ("b", "a"),
|
36 |
+
"bai": ("b", "ai"),
|
37 |
+
"ban": ("b", "an"),
|
38 |
+
"bang": ("b", "ang"),
|
39 |
+
"bao": ("b", "ao"),
|
40 |
+
"be": ("b", "e"),
|
41 |
+
"bei": ("b", "ei"),
|
42 |
+
"ben": ("b", "en"),
|
43 |
+
"beng": ("b", "eng"),
|
44 |
+
"bi": ("b", "i"),
|
45 |
+
"bian": ("b", "ian"),
|
46 |
+
"biao": ("b", "iao"),
|
47 |
+
"bie": ("b", "ie"),
|
48 |
+
"bin": ("b", "in"),
|
49 |
+
"bing": ("b", "ing"),
|
50 |
+
"bo": ("b", "o"),
|
51 |
+
"bu": ("b", "u"),
|
52 |
+
"ca": ("c", "a"),
|
53 |
+
"cai": ("c", "ai"),
|
54 |
+
"can": ("c", "an"),
|
55 |
+
"cang": ("c", "ang"),
|
56 |
+
"cao": ("c", "ao"),
|
57 |
+
"ce": ("c", "e"),
|
58 |
+
"cen": ("c", "en"),
|
59 |
+
"ceng": ("c", "eng"),
|
60 |
+
"cha": ("ch", "a"),
|
61 |
+
"chai": ("ch", "ai"),
|
62 |
+
"chan": ("ch", "an"),
|
63 |
+
"chang": ("ch", "ang"),
|
64 |
+
"chao": ("ch", "ao"),
|
65 |
+
"che": ("ch", "e"),
|
66 |
+
"chen": ("ch", "en"),
|
67 |
+
"cheng": ("ch", "eng"),
|
68 |
+
"chi": ("ch", "iii"),
|
69 |
+
"chong": ("ch", "ong"),
|
70 |
+
"chou": ("ch", "ou"),
|
71 |
+
"chu": ("ch", "u"),
|
72 |
+
"chua": ("ch", "ua"),
|
73 |
+
"chuai": ("ch", "uai"),
|
74 |
+
"chuan": ("ch", "uan"),
|
75 |
+
"chuang": ("ch", "uang"),
|
76 |
+
"chui": ("ch", "uei"),
|
77 |
+
"chun": ("ch", "uen"),
|
78 |
+
"chuo": ("ch", "uo"),
|
79 |
+
"ci": ("c", "ii"),
|
80 |
+
"cong": ("c", "ong"),
|
81 |
+
"cou": ("c", "ou"),
|
82 |
+
"cu": ("c", "u"),
|
83 |
+
"cuan": ("c", "uan"),
|
84 |
+
"cui": ("c", "uei"),
|
85 |
+
"cun": ("c", "uen"),
|
86 |
+
"cuo": ("c", "uo"),
|
87 |
+
"da": ("d", "a"),
|
88 |
+
"dai": ("d", "ai"),
|
89 |
+
"dan": ("d", "an"),
|
90 |
+
"dang": ("d", "ang"),
|
91 |
+
"dao": ("d", "ao"),
|
92 |
+
"de": ("d", "e"),
|
93 |
+
"dei": ("d", "ei"),
|
94 |
+
"den": ("d", "en"),
|
95 |
+
"deng": ("d", "eng"),
|
96 |
+
"di": ("d", "i"),
|
97 |
+
"dia": ("d", "ia"),
|
98 |
+
"dian": ("d", "ian"),
|
99 |
+
"diao": ("d", "iao"),
|
100 |
+
"die": ("d", "ie"),
|
101 |
+
"ding": ("d", "ing"),
|
102 |
+
"diu": ("d", "iou"),
|
103 |
+
"dong": ("d", "ong"),
|
104 |
+
"dou": ("d", "ou"),
|
105 |
+
"du": ("d", "u"),
|
106 |
+
"duan": ("d", "uan"),
|
107 |
+
"dui": ("d", "uei"),
|
108 |
+
"dun": ("d", "uen"),
|
109 |
+
"duo": ("d", "uo"),
|
110 |
+
"e": ("^", "e"),
|
111 |
+
"ei": ("^", "ei"),
|
112 |
+
"en": ("^", "en"),
|
113 |
+
"ng": ("^", "en"),
|
114 |
+
"eng": ("^", "eng"),
|
115 |
+
"er": ("^", "er"),
|
116 |
+
"fa": ("f", "a"),
|
117 |
+
"fan": ("f", "an"),
|
118 |
+
"fang": ("f", "ang"),
|
119 |
+
"fei": ("f", "ei"),
|
120 |
+
"fen": ("f", "en"),
|
121 |
+
"feng": ("f", "eng"),
|
122 |
+
"fo": ("f", "o"),
|
123 |
+
"fou": ("f", "ou"),
|
124 |
+
"fu": ("f", "u"),
|
125 |
+
"ga": ("g", "a"),
|
126 |
+
"gai": ("g", "ai"),
|
127 |
+
"gan": ("g", "an"),
|
128 |
+
"gang": ("g", "ang"),
|
129 |
+
"gao": ("g", "ao"),
|
130 |
+
"ge": ("g", "e"),
|
131 |
+
"gei": ("g", "ei"),
|
132 |
+
"gen": ("g", "en"),
|
133 |
+
"geng": ("g", "eng"),
|
134 |
+
"gong": ("g", "ong"),
|
135 |
+
"gou": ("g", "ou"),
|
136 |
+
"gu": ("g", "u"),
|
137 |
+
"gua": ("g", "ua"),
|
138 |
+
"guai": ("g", "uai"),
|
139 |
+
"guan": ("g", "uan"),
|
140 |
+
"guang": ("g", "uang"),
|
141 |
+
"gui": ("g", "uei"),
|
142 |
+
"gun": ("g", "uen"),
|
143 |
+
"guo": ("g", "uo"),
|
144 |
+
"ha": ("h", "a"),
|
145 |
+
"hai": ("h", "ai"),
|
146 |
+
"han": ("h", "an"),
|
147 |
+
"hang": ("h", "ang"),
|
148 |
+
"hao": ("h", "ao"),
|
149 |
+
"he": ("h", "e"),
|
150 |
+
"hei": ("h", "ei"),
|
151 |
+
"hen": ("h", "en"),
|
152 |
+
"heng": ("h", "eng"),
|
153 |
+
"hong": ("h", "ong"),
|
154 |
+
"hou": ("h", "ou"),
|
155 |
+
"hu": ("h", "u"),
|
156 |
+
"hua": ("h", "ua"),
|
157 |
+
"huai": ("h", "uai"),
|
158 |
+
"huan": ("h", "uan"),
|
159 |
+
"huang": ("h", "uang"),
|
160 |
+
"hui": ("h", "uei"),
|
161 |
+
"hun": ("h", "uen"),
|
162 |
+
"huo": ("h", "uo"),
|
163 |
+
"ji": ("j", "i"),
|
164 |
+
"jia": ("j", "ia"),
|
165 |
+
"jian": ("j", "ian"),
|
166 |
+
"jiang": ("j", "iang"),
|
167 |
+
"jiao": ("j", "iao"),
|
168 |
+
"jie": ("j", "ie"),
|
169 |
+
"jin": ("j", "in"),
|
170 |
+
"jing": ("j", "ing"),
|
171 |
+
"jiong": ("j", "iong"),
|
172 |
+
"jiu": ("j", "iou"),
|
173 |
+
"ju": ("j", "v"),
|
174 |
+
"juan": ("j", "van"),
|
175 |
+
"jue": ("j", "ve"),
|
176 |
+
"jun": ("j", "vn"),
|
177 |
+
"ka": ("k", "a"),
|
178 |
+
"kai": ("k", "ai"),
|
179 |
+
"kan": ("k", "an"),
|
180 |
+
"kang": ("k", "ang"),
|
181 |
+
"kao": ("k", "ao"),
|
182 |
+
"ke": ("k", "e"),
|
183 |
+
"kei": ("k", "ei"),
|
184 |
+
"ken": ("k", "en"),
|
185 |
+
"keng": ("k", "eng"),
|
186 |
+
"kong": ("k", "ong"),
|
187 |
+
"kou": ("k", "ou"),
|
188 |
+
"ku": ("k", "u"),
|
189 |
+
"kua": ("k", "ua"),
|
190 |
+
"kuai": ("k", "uai"),
|
191 |
+
"kuan": ("k", "uan"),
|
192 |
+
"kuang": ("k", "uang"),
|
193 |
+
"kui": ("k", "uei"),
|
194 |
+
"kun": ("k", "uen"),
|
195 |
+
"kuo": ("k", "uo"),
|
196 |
+
"la": ("l", "a"),
|
197 |
+
"lai": ("l", "ai"),
|
198 |
+
"lan": ("l", "an"),
|
199 |
+
"lang": ("l", "ang"),
|
200 |
+
"lao": ("l", "ao"),
|
201 |
+
"le": ("l", "e"),
|
202 |
+
"lei": ("l", "ei"),
|
203 |
+
"leng": ("l", "eng"),
|
204 |
+
"li": ("l", "i"),
|
205 |
+
"lia": ("l", "ia"),
|
206 |
+
"lian": ("l", "ian"),
|
207 |
+
"liang": ("l", "iang"),
|
208 |
+
"liao": ("l", "iao"),
|
209 |
+
"lie": ("l", "ie"),
|
210 |
+
"lin": ("l", "in"),
|
211 |
+
"ling": ("l", "ing"),
|
212 |
+
"liu": ("l", "iou"),
|
213 |
+
"lo": ("l", "o"),
|
214 |
+
"long": ("l", "ong"),
|
215 |
+
"lou": ("l", "ou"),
|
216 |
+
"lu": ("l", "u"),
|
217 |
+
"lv": ("l", "v"),
|
218 |
+
"luan": ("l", "uan"),
|
219 |
+
"lve": ("l", "ve"),
|
220 |
+
"lue": ("l", "ve"),
|
221 |
+
"lun": ("l", "uen"),
|
222 |
+
"luo": ("l", "uo"),
|
223 |
+
"ma": ("m", "a"),
|
224 |
+
"mai": ("m", "ai"),
|
225 |
+
"man": ("m", "an"),
|
226 |
+
"mang": ("m", "ang"),
|
227 |
+
"mao": ("m", "ao"),
|
228 |
+
"me": ("m", "e"),
|
229 |
+
"mei": ("m", "ei"),
|
230 |
+
"men": ("m", "en"),
|
231 |
+
"meng": ("m", "eng"),
|
232 |
+
"mi": ("m", "i"),
|
233 |
+
"mian": ("m", "ian"),
|
234 |
+
"miao": ("m", "iao"),
|
235 |
+
"mie": ("m", "ie"),
|
236 |
+
"min": ("m", "in"),
|
237 |
+
"ming": ("m", "ing"),
|
238 |
+
"miu": ("m", "iou"),
|
239 |
+
"mo": ("m", "o"),
|
240 |
+
"mou": ("m", "ou"),
|
241 |
+
"mu": ("m", "u"),
|
242 |
+
"na": ("n", "a"),
|
243 |
+
"nai": ("n", "ai"),
|
244 |
+
"nan": ("n", "an"),
|
245 |
+
"nang": ("n", "ang"),
|
246 |
+
"nao": ("n", "ao"),
|
247 |
+
"ne": ("n", "e"),
|
248 |
+
"nei": ("n", "ei"),
|
249 |
+
"nen": ("n", "en"),
|
250 |
+
"neng": ("n", "eng"),
|
251 |
+
"ni": ("n", "i"),
|
252 |
+
"nia": ("n", "ia"),
|
253 |
+
"nian": ("n", "ian"),
|
254 |
+
"niang": ("n", "iang"),
|
255 |
+
"niao": ("n", "iao"),
|
256 |
+
"nie": ("n", "ie"),
|
257 |
+
"nin": ("n", "in"),
|
258 |
+
"ning": ("n", "ing"),
|
259 |
+
"niu": ("n", "iou"),
|
260 |
+
"nong": ("n", "ong"),
|
261 |
+
"nou": ("n", "ou"),
|
262 |
+
"nu": ("n", "u"),
|
263 |
+
"nv": ("n", "v"),
|
264 |
+
"nuan": ("n", "uan"),
|
265 |
+
"nve": ("n", "ve"),
|
266 |
+
"nue": ("n", "ve"),
|
267 |
+
"nuo": ("n", "uo"),
|
268 |
+
"o": ("^", "o"),
|
269 |
+
"ou": ("^", "ou"),
|
270 |
+
"pa": ("p", "a"),
|
271 |
+
"pai": ("p", "ai"),
|
272 |
+
"pan": ("p", "an"),
|
273 |
+
"pang": ("p", "ang"),
|
274 |
+
"pao": ("p", "ao"),
|
275 |
+
"pe": ("p", "e"),
|
276 |
+
"pei": ("p", "ei"),
|
277 |
+
"pen": ("p", "en"),
|
278 |
+
"peng": ("p", "eng"),
|
279 |
+
"pi": ("p", "i"),
|
280 |
+
"pian": ("p", "ian"),
|
281 |
+
"piao": ("p", "iao"),
|
282 |
+
"pie": ("p", "ie"),
|
283 |
+
"pin": ("p", "in"),
|
284 |
+
"ping": ("p", "ing"),
|
285 |
+
"po": ("p", "o"),
|
286 |
+
"pou": ("p", "ou"),
|
287 |
+
"pu": ("p", "u"),
|
288 |
+
"qi": ("q", "i"),
|
289 |
+
"qia": ("q", "ia"),
|
290 |
+
"qian": ("q", "ian"),
|
291 |
+
"qiang": ("q", "iang"),
|
292 |
+
"qiao": ("q", "iao"),
|
293 |
+
"qie": ("q", "ie"),
|
294 |
+
"qin": ("q", "in"),
|
295 |
+
"qing": ("q", "ing"),
|
296 |
+
"qiong": ("q", "iong"),
|
297 |
+
"qiu": ("q", "iou"),
|
298 |
+
"qu": ("q", "v"),
|
299 |
+
"quan": ("q", "van"),
|
300 |
+
"que": ("q", "ve"),
|
301 |
+
"qun": ("q", "vn"),
|
302 |
+
"ran": ("r", "an"),
|
303 |
+
"rang": ("r", "ang"),
|
304 |
+
"rao": ("r", "ao"),
|
305 |
+
"re": ("r", "e"),
|
306 |
+
"ren": ("r", "en"),
|
307 |
+
"reng": ("r", "eng"),
|
308 |
+
"ri": ("r", "iii"),
|
309 |
+
"rong": ("r", "ong"),
|
310 |
+
"rou": ("r", "ou"),
|
311 |
+
"ru": ("r", "u"),
|
312 |
+
"rua": ("r", "ua"),
|
313 |
+
"ruan": ("r", "uan"),
|
314 |
+
"rui": ("r", "uei"),
|
315 |
+
"run": ("r", "uen"),
|
316 |
+
"ruo": ("r", "uo"),
|
317 |
+
"sa": ("s", "a"),
|
318 |
+
"sai": ("s", "ai"),
|
319 |
+
"san": ("s", "an"),
|
320 |
+
"sang": ("s", "ang"),
|
321 |
+
"sao": ("s", "ao"),
|
322 |
+
"se": ("s", "e"),
|
323 |
+
"sen": ("s", "en"),
|
324 |
+
"seng": ("s", "eng"),
|
325 |
+
"sha": ("sh", "a"),
|
326 |
+
"shai": ("sh", "ai"),
|
327 |
+
"shan": ("sh", "an"),
|
328 |
+
"shang": ("sh", "ang"),
|
329 |
+
"shao": ("sh", "ao"),
|
330 |
+
"she": ("sh", "e"),
|
331 |
+
"shei": ("sh", "ei"),
|
332 |
+
"shen": ("sh", "en"),
|
333 |
+
"sheng": ("sh", "eng"),
|
334 |
+
"shi": ("sh", "iii"),
|
335 |
+
"shou": ("sh", "ou"),
|
336 |
+
"shu": ("sh", "u"),
|
337 |
+
"shua": ("sh", "ua"),
|
338 |
+
"shuai": ("sh", "uai"),
|
339 |
+
"shuan": ("sh", "uan"),
|
340 |
+
"shuang": ("sh", "uang"),
|
341 |
+
"shui": ("sh", "uei"),
|
342 |
+
"shun": ("sh", "uen"),
|
343 |
+
"shuo": ("sh", "uo"),
|
344 |
+
"si": ("s", "ii"),
|
345 |
+
"song": ("s", "ong"),
|
346 |
+
"sou": ("s", "ou"),
|
347 |
+
"su": ("s", "u"),
|
348 |
+
"suan": ("s", "uan"),
|
349 |
+
"sui": ("s", "uei"),
|
350 |
+
"sun": ("s", "uen"),
|
351 |
+
"suo": ("s", "uo"),
|
352 |
+
"ta": ("t", "a"),
|
353 |
+
"tai": ("t", "ai"),
|
354 |
+
"tan": ("t", "an"),
|
355 |
+
"tang": ("t", "ang"),
|
356 |
+
"tao": ("t", "ao"),
|
357 |
+
"te": ("t", "e"),
|
358 |
+
"tei": ("t", "ei"),
|
359 |
+
"teng": ("t", "eng"),
|
360 |
+
"ti": ("t", "i"),
|
361 |
+
"tian": ("t", "ian"),
|
362 |
+
"tiao": ("t", "iao"),
|
363 |
+
"tie": ("t", "ie"),
|
364 |
+
"ting": ("t", "ing"),
|
365 |
+
"tong": ("t", "ong"),
|
366 |
+
"tou": ("t", "ou"),
|
367 |
+
"tu": ("t", "u"),
|
368 |
+
"tuan": ("t", "uan"),
|
369 |
+
"tui": ("t", "uei"),
|
370 |
+
"tun": ("t", "uen"),
|
371 |
+
"tuo": ("t", "uo"),
|
372 |
+
"wa": ("^", "ua"),
|
373 |
+
"wai": ("^", "uai"),
|
374 |
+
"wan": ("^", "uan"),
|
375 |
+
"wang": ("^", "uang"),
|
376 |
+
"wei": ("^", "uei"),
|
377 |
+
"wen": ("^", "uen"),
|
378 |
+
"weng": ("^", "ueng"),
|
379 |
+
"wo": ("^", "uo"),
|
380 |
+
"wu": ("^", "u"),
|
381 |
+
"xi": ("x", "i"),
|
382 |
+
"xia": ("x", "ia"),
|
383 |
+
"xian": ("x", "ian"),
|
384 |
+
"xiang": ("x", "iang"),
|
385 |
+
"xiao": ("x", "iao"),
|
386 |
+
"xie": ("x", "ie"),
|
387 |
+
"xin": ("x", "in"),
|
388 |
+
"xing": ("x", "ing"),
|
389 |
+
"xiong": ("x", "iong"),
|
390 |
+
"xiu": ("x", "iou"),
|
391 |
+
"xu": ("x", "v"),
|
392 |
+
"xuan": ("x", "van"),
|
393 |
+
"xue": ("x", "ve"),
|
394 |
+
"xun": ("x", "vn"),
|
395 |
+
"ya": ("^", "ia"),
|
396 |
+
"yan": ("^", "ian"),
|
397 |
+
"yang": ("^", "iang"),
|
398 |
+
"yao": ("^", "iao"),
|
399 |
+
"ye": ("^", "ie"),
|
400 |
+
"yi": ("^", "i"),
|
401 |
+
"yin": ("^", "in"),
|
402 |
+
"ying": ("^", "ing"),
|
403 |
+
"yo": ("^", "iou"),
|
404 |
+
"yong": ("^", "iong"),
|
405 |
+
"you": ("^", "iou"),
|
406 |
+
"yu": ("^", "v"),
|
407 |
+
"yuan": ("^", "van"),
|
408 |
+
"yue": ("^", "ve"),
|
409 |
+
"yun": ("^", "vn"),
|
410 |
+
"za": ("z", "a"),
|
411 |
+
"zai": ("z", "ai"),
|
412 |
+
"zan": ("z", "an"),
|
413 |
+
"zang": ("z", "ang"),
|
414 |
+
"zao": ("z", "ao"),
|
415 |
+
"ze": ("z", "e"),
|
416 |
+
"zei": ("z", "ei"),
|
417 |
+
"zen": ("z", "en"),
|
418 |
+
"zeng": ("z", "eng"),
|
419 |
+
"zha": ("zh", "a"),
|
420 |
+
"zhai": ("zh", "ai"),
|
421 |
+
"zhan": ("zh", "an"),
|
422 |
+
"zhang": ("zh", "ang"),
|
423 |
+
"zhao": ("zh", "ao"),
|
424 |
+
"zhe": ("zh", "e"),
|
425 |
+
"zhei": ("zh", "ei"),
|
426 |
+
"zhen": ("zh", "en"),
|
427 |
+
"zheng": ("zh", "eng"),
|
428 |
+
"zhi": ("zh", "iii"),
|
429 |
+
"zhong": ("zh", "ong"),
|
430 |
+
"zhou": ("zh", "ou"),
|
431 |
+
"zhu": ("zh", "u"),
|
432 |
+
"zhua": ("zh", "ua"),
|
433 |
+
"zhuai": ("zh", "uai"),
|
434 |
+
"zhuan": ("zh", "uan"),
|
435 |
+
"zhuang": ("zh", "uang"),
|
436 |
+
"zhui": ("zh", "uei"),
|
437 |
+
"zhun": ("zh", "uen"),
|
438 |
+
"zhuo": ("zh", "uo"),
|
439 |
+
"zi": ("z", "ii"),
|
440 |
+
"zong": ("z", "ong"),
|
441 |
+
"zou": ("z", "ou"),
|
442 |
+
"zu": ("z", "u"),
|
443 |
+
"zuan": ("z", "uan"),
|
444 |
+
"zui": ("z", "uei"),
|
445 |
+
"zun": ("z", "uen"),
|
446 |
+
"zuo": ("z", "uo"),
|
447 |
+
}
|
text/symbols.py
ADDED
@@ -0,0 +1,71 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
_pause = ["sil", "eos", "sp", "#0", "#1", "#2", "#3"]
|
2 |
+
|
3 |
+
_initials = [
|
4 |
+
"^",
|
5 |
+
"b",
|
6 |
+
"c",
|
7 |
+
"ch",
|
8 |
+
"d",
|
9 |
+
"f",
|
10 |
+
"g",
|
11 |
+
"h",
|
12 |
+
"j",
|
13 |
+
"k",
|
14 |
+
"l",
|
15 |
+
"m",
|
16 |
+
"n",
|
17 |
+
"p",
|
18 |
+
"q",
|
19 |
+
"r",
|
20 |
+
"s",
|
21 |
+
"sh",
|
22 |
+
"t",
|
23 |
+
"x",
|
24 |
+
"z",
|
25 |
+
"zh",
|
26 |
+
]
|
27 |
+
|
28 |
+
_tones = ["1", "2", "3", "4", "5"]
|
29 |
+
|
30 |
+
_finals = [
|
31 |
+
"a",
|
32 |
+
"ai",
|
33 |
+
"an",
|
34 |
+
"ang",
|
35 |
+
"ao",
|
36 |
+
"e",
|
37 |
+
"ei",
|
38 |
+
"en",
|
39 |
+
"eng",
|
40 |
+
"er",
|
41 |
+
"i",
|
42 |
+
"ia",
|
43 |
+
"ian",
|
44 |
+
"iang",
|
45 |
+
"iao",
|
46 |
+
"ie",
|
47 |
+
"ii",
|
48 |
+
"iii",
|
49 |
+
"in",
|
50 |
+
"ing",
|
51 |
+
"iong",
|
52 |
+
"iou",
|
53 |
+
"o",
|
54 |
+
"ong",
|
55 |
+
"ou",
|
56 |
+
"u",
|
57 |
+
"ua",
|
58 |
+
"uai",
|
59 |
+
"uan",
|
60 |
+
"uang",
|
61 |
+
"uei",
|
62 |
+
"uen",
|
63 |
+
"ueng",
|
64 |
+
"uo",
|
65 |
+
"v",
|
66 |
+
"van",
|
67 |
+
"ve",
|
68 |
+
"vn",
|
69 |
+
]
|
70 |
+
|
71 |
+
symbols = _pause + _initials + [i + j for i in _finals for j in _tones]
|
transforms.py
ADDED
@@ -0,0 +1,210 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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(
|
13 |
+
inputs,
|
14 |
+
unnormalized_widths,
|
15 |
+
unnormalized_heights,
|
16 |
+
unnormalized_derivatives,
|
17 |
+
inverse=False,
|
18 |
+
tails=None,
|
19 |
+
tail_bound=1.0,
|
20 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
21 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
22 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE,
|
23 |
+
):
|
24 |
+
|
25 |
+
if tails is None:
|
26 |
+
spline_fn = rational_quadratic_spline
|
27 |
+
spline_kwargs = {}
|
28 |
+
else:
|
29 |
+
spline_fn = unconstrained_rational_quadratic_spline
|
30 |
+
spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
|
31 |
+
|
32 |
+
outputs, logabsdet = spline_fn(
|
33 |
+
inputs=inputs,
|
34 |
+
unnormalized_widths=unnormalized_widths,
|
35 |
+
unnormalized_heights=unnormalized_heights,
|
36 |
+
unnormalized_derivatives=unnormalized_derivatives,
|
37 |
+
inverse=inverse,
|
38 |
+
min_bin_width=min_bin_width,
|
39 |
+
min_bin_height=min_bin_height,
|
40 |
+
min_derivative=min_derivative,
|
41 |
+
**spline_kwargs
|
42 |
+
)
|
43 |
+
return outputs, logabsdet
|
44 |
+
|
45 |
+
|
46 |
+
def searchsorted(bin_locations, inputs, eps=1e-6):
|
47 |
+
bin_locations[..., -1] += eps
|
48 |
+
return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
|
49 |
+
|
50 |
+
|
51 |
+
def unconstrained_rational_quadratic_spline(
|
52 |
+
inputs,
|
53 |
+
unnormalized_widths,
|
54 |
+
unnormalized_heights,
|
55 |
+
unnormalized_derivatives,
|
56 |
+
inverse=False,
|
57 |
+
tails="linear",
|
58 |
+
tail_bound=1.0,
|
59 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
60 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
61 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE,
|
62 |
+
):
|
63 |
+
inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
|
64 |
+
outside_interval_mask = ~inside_interval_mask
|
65 |
+
|
66 |
+
outputs = torch.zeros_like(inputs)
|
67 |
+
logabsdet = torch.zeros_like(inputs)
|
68 |
+
|
69 |
+
if tails == "linear":
|
70 |
+
unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
|
71 |
+
constant = np.log(np.exp(1 - min_derivative) - 1)
|
72 |
+
unnormalized_derivatives[..., 0] = constant
|
73 |
+
unnormalized_derivatives[..., -1] = constant
|
74 |
+
|
75 |
+
outputs[outside_interval_mask] = inputs[outside_interval_mask]
|
76 |
+
logabsdet[outside_interval_mask] = 0
|
77 |
+
else:
|
78 |
+
raise RuntimeError("{} tails are not implemented.".format(tails))
|
79 |
+
|
80 |
+
(
|
81 |
+
outputs[inside_interval_mask],
|
82 |
+
logabsdet[inside_interval_mask],
|
83 |
+
) = rational_quadratic_spline(
|
84 |
+
inputs=inputs[inside_interval_mask],
|
85 |
+
unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
|
86 |
+
unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
|
87 |
+
unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
|
88 |
+
inverse=inverse,
|
89 |
+
left=-tail_bound,
|
90 |
+
right=tail_bound,
|
91 |
+
bottom=-tail_bound,
|
92 |
+
top=tail_bound,
|
93 |
+
min_bin_width=min_bin_width,
|
94 |
+
min_bin_height=min_bin_height,
|
95 |
+
min_derivative=min_derivative,
|
96 |
+
)
|
97 |
+
|
98 |
+
return outputs, logabsdet
|
99 |
+
|
100 |
+
|
101 |
+
def rational_quadratic_spline(
|
102 |
+
inputs,
|
103 |
+
unnormalized_widths,
|
104 |
+
unnormalized_heights,
|
105 |
+
unnormalized_derivatives,
|
106 |
+
inverse=False,
|
107 |
+
left=0.0,
|
108 |
+
right=1.0,
|
109 |
+
bottom=0.0,
|
110 |
+
top=1.0,
|
111 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
112 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
113 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE,
|
114 |
+
):
|
115 |
+
if torch.min(inputs) < left or torch.max(inputs) > right:
|
116 |
+
raise ValueError("Input to a transform is not within its domain")
|
117 |
+
|
118 |
+
num_bins = unnormalized_widths.shape[-1]
|
119 |
+
|
120 |
+
if min_bin_width * num_bins > 1.0:
|
121 |
+
raise ValueError("Minimal bin width too large for the number of bins")
|
122 |
+
if min_bin_height * num_bins > 1.0:
|
123 |
+
raise ValueError("Minimal bin height too large for the number of bins")
|
124 |
+
|
125 |
+
widths = F.softmax(unnormalized_widths, dim=-1)
|
126 |
+
widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
|
127 |
+
cumwidths = torch.cumsum(widths, dim=-1)
|
128 |
+
cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
|
129 |
+
cumwidths = (right - left) * cumwidths + left
|
130 |
+
cumwidths[..., 0] = left
|
131 |
+
cumwidths[..., -1] = right
|
132 |
+
widths = cumwidths[..., 1:] - cumwidths[..., :-1]
|
133 |
+
|
134 |
+
derivatives = min_derivative + F.softplus(unnormalized_derivatives)
|
135 |
+
|
136 |
+
heights = F.softmax(unnormalized_heights, dim=-1)
|
137 |
+
heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
|
138 |
+
cumheights = torch.cumsum(heights, dim=-1)
|
139 |
+
cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
|
140 |
+
cumheights = (top - bottom) * cumheights + bottom
|
141 |
+
cumheights[..., 0] = bottom
|
142 |
+
cumheights[..., -1] = top
|
143 |
+
heights = cumheights[..., 1:] - cumheights[..., :-1]
|
144 |
+
|
145 |
+
if inverse:
|
146 |
+
bin_idx = searchsorted(cumheights, inputs)[..., None]
|
147 |
+
else:
|
148 |
+
bin_idx = searchsorted(cumwidths, inputs)[..., None]
|
149 |
+
|
150 |
+
input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
|
151 |
+
input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
|
152 |
+
|
153 |
+
input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
|
154 |
+
delta = heights / widths
|
155 |
+
input_delta = delta.gather(-1, bin_idx)[..., 0]
|
156 |
+
|
157 |
+
input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
|
158 |
+
input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
|
159 |
+
|
160 |
+
input_heights = heights.gather(-1, bin_idx)[..., 0]
|
161 |
+
|
162 |
+
if inverse:
|
163 |
+
a = (inputs - input_cumheights) * (
|
164 |
+
input_derivatives + input_derivatives_plus_one - 2 * input_delta
|
165 |
+
) + input_heights * (input_delta - input_derivatives)
|
166 |
+
b = input_heights * input_derivatives - (inputs - input_cumheights) * (
|
167 |
+
input_derivatives + input_derivatives_plus_one - 2 * input_delta
|
168 |
+
)
|
169 |
+
c = -input_delta * (inputs - input_cumheights)
|
170 |
+
|
171 |
+
discriminant = b.pow(2) - 4 * a * c
|
172 |
+
assert (discriminant >= 0).all()
|
173 |
+
|
174 |
+
root = (2 * c) / (-b - torch.sqrt(discriminant))
|
175 |
+
outputs = root * input_bin_widths + input_cumwidths
|
176 |
+
|
177 |
+
theta_one_minus_theta = root * (1 - root)
|
178 |
+
denominator = input_delta + (
|
179 |
+
(input_derivatives + input_derivatives_plus_one - 2 * input_delta)
|
180 |
+
* theta_one_minus_theta
|
181 |
+
)
|
182 |
+
derivative_numerator = input_delta.pow(2) * (
|
183 |
+
input_derivatives_plus_one * root.pow(2)
|
184 |
+
+ 2 * input_delta * theta_one_minus_theta
|
185 |
+
+ input_derivatives * (1 - root).pow(2)
|
186 |
+
)
|
187 |
+
logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
|
188 |
+
|
189 |
+
return outputs, -logabsdet
|
190 |
+
else:
|
191 |
+
theta = (inputs - input_cumwidths) / input_bin_widths
|
192 |
+
theta_one_minus_theta = theta * (1 - theta)
|
193 |
+
|
194 |
+
numerator = input_heights * (
|
195 |
+
input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
|
196 |
+
)
|
197 |
+
denominator = input_delta + (
|
198 |
+
(input_derivatives + input_derivatives_plus_one - 2 * input_delta)
|
199 |
+
* theta_one_minus_theta
|
200 |
+
)
|
201 |
+
outputs = input_cumheights + numerator / denominator
|
202 |
+
|
203 |
+
derivative_numerator = input_delta.pow(2) * (
|
204 |
+
input_derivatives_plus_one * theta.pow(2)
|
205 |
+
+ 2 * input_delta * theta_one_minus_theta
|
206 |
+
+ input_derivatives * (1 - theta).pow(2)
|
207 |
+
)
|
208 |
+
logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
|
209 |
+
|
210 |
+
return outputs, logabsdet
|
utils.py
ADDED
@@ -0,0 +1,319 @@
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|
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|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
|
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|
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|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
|
|
|
|
|
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|
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|
|
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|
|
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|
|
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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.DEBUG)
|
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(
|
42 |
+
"Loaded checkpoint '{}' (iteration {})".format(checkpoint_path, iteration)
|
43 |
+
)
|
44 |
+
return model, optimizer, learning_rate, iteration
|
45 |
+
|
46 |
+
|
47 |
+
def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
|
48 |
+
logger.info(
|
49 |
+
"Saving model and optimizer state at iteration {} to {}".format(
|
50 |
+
iteration, checkpoint_path
|
51 |
+
)
|
52 |
+
)
|
53 |
+
if hasattr(model, "module"):
|
54 |
+
state_dict = model.module.state_dict()
|
55 |
+
else:
|
56 |
+
state_dict = model.state_dict()
|
57 |
+
torch.save(
|
58 |
+
{
|
59 |
+
"model": state_dict,
|
60 |
+
"iteration": iteration,
|
61 |
+
"optimizer": optimizer.state_dict(),
|
62 |
+
"learning_rate": learning_rate,
|
63 |
+
},
|
64 |
+
checkpoint_path,
|
65 |
+
)
|
66 |
+
|
67 |
+
|
68 |
+
def load_model(checkpoint_path, model):
|
69 |
+
assert os.path.isfile(checkpoint_path)
|
70 |
+
checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
|
71 |
+
saved_state_dict = checkpoint_dict["model"]
|
72 |
+
if hasattr(model, "module"):
|
73 |
+
state_dict = model.module.state_dict()
|
74 |
+
else:
|
75 |
+
state_dict = model.state_dict()
|
76 |
+
new_state_dict = {}
|
77 |
+
for k, v in state_dict.items():
|
78 |
+
try:
|
79 |
+
new_state_dict[k] = saved_state_dict[k]
|
80 |
+
except:
|
81 |
+
logger.info("%s is not in the checkpoint" % k)
|
82 |
+
new_state_dict[k] = v
|
83 |
+
if hasattr(model, "module"):
|
84 |
+
model.module.load_state_dict(new_state_dict)
|
85 |
+
else:
|
86 |
+
model.load_state_dict(new_state_dict)
|
87 |
+
return model
|
88 |
+
|
89 |
+
|
90 |
+
def save_model(model, checkpoint_path):
|
91 |
+
if hasattr(model, 'module'):
|
92 |
+
state_dict = model.module.state_dict()
|
93 |
+
else:
|
94 |
+
state_dict = model.state_dict()
|
95 |
+
torch.save({'model': state_dict}, checkpoint_path)
|
96 |
+
|
97 |
+
|
98 |
+
def summarize(
|
99 |
+
writer,
|
100 |
+
global_step,
|
101 |
+
scalars={},
|
102 |
+
histograms={},
|
103 |
+
images={},
|
104 |
+
audios={},
|
105 |
+
audio_sampling_rate=22050,
|
106 |
+
):
|
107 |
+
for k, v in scalars.items():
|
108 |
+
writer.add_scalar(k, v, global_step)
|
109 |
+
for k, v in histograms.items():
|
110 |
+
writer.add_histogram(k, v, global_step)
|
111 |
+
for k, v in images.items():
|
112 |
+
writer.add_image(k, v, global_step, dataformats="HWC")
|
113 |
+
for k, v in audios.items():
|
114 |
+
writer.add_audio(k, v, global_step, audio_sampling_rate)
|
115 |
+
|
116 |
+
|
117 |
+
def latest_checkpoint_path(dir_path, regex="G_*.pth"):
|
118 |
+
f_list = glob.glob(os.path.join(dir_path, regex))
|
119 |
+
f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
|
120 |
+
x = f_list[-1]
|
121 |
+
print(x)
|
122 |
+
return x
|
123 |
+
|
124 |
+
|
125 |
+
def plot_spectrogram_to_numpy(spectrogram):
|
126 |
+
global MATPLOTLIB_FLAG
|
127 |
+
if not MATPLOTLIB_FLAG:
|
128 |
+
import matplotlib
|
129 |
+
|
130 |
+
matplotlib.use("Agg")
|
131 |
+
MATPLOTLIB_FLAG = True
|
132 |
+
mpl_logger = logging.getLogger("matplotlib")
|
133 |
+
mpl_logger.setLevel(logging.WARNING)
|
134 |
+
import matplotlib.pylab as plt
|
135 |
+
import numpy as np
|
136 |
+
|
137 |
+
fig, ax = plt.subplots(figsize=(10, 2))
|
138 |
+
im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
|
139 |
+
plt.colorbar(im, ax=ax)
|
140 |
+
plt.xlabel("Frames")
|
141 |
+
plt.ylabel("Channels")
|
142 |
+
plt.tight_layout()
|
143 |
+
|
144 |
+
fig.canvas.draw()
|
145 |
+
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
|
146 |
+
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
147 |
+
plt.close()
|
148 |
+
return data
|
149 |
+
|
150 |
+
|
151 |
+
def plot_alignment_to_numpy(alignment, info=None):
|
152 |
+
global MATPLOTLIB_FLAG
|
153 |
+
if not MATPLOTLIB_FLAG:
|
154 |
+
import matplotlib
|
155 |
+
|
156 |
+
matplotlib.use("Agg")
|
157 |
+
MATPLOTLIB_FLAG = True
|
158 |
+
mpl_logger = logging.getLogger("matplotlib")
|
159 |
+
mpl_logger.setLevel(logging.WARNING)
|
160 |
+
import matplotlib.pylab as plt
|
161 |
+
import numpy as np
|
162 |
+
|
163 |
+
fig, ax = plt.subplots(figsize=(6, 4))
|
164 |
+
im = ax.imshow(
|
165 |
+
alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
|
166 |
+
)
|
167 |
+
fig.colorbar(im, ax=ax)
|
168 |
+
xlabel = "Decoder timestep"
|
169 |
+
if info is not None:
|
170 |
+
xlabel += "\n\n" + info
|
171 |
+
plt.xlabel(xlabel)
|
172 |
+
plt.ylabel("Encoder timestep")
|
173 |
+
plt.tight_layout()
|
174 |
+
|
175 |
+
fig.canvas.draw()
|
176 |
+
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
|
177 |
+
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
178 |
+
plt.close()
|
179 |
+
return data
|
180 |
+
|
181 |
+
|
182 |
+
def load_wav_to_torch(full_path):
|
183 |
+
sampling_rate, data = read(full_path)
|
184 |
+
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
|
185 |
+
|
186 |
+
|
187 |
+
def load_filepaths_and_text(filename, split="|"):
|
188 |
+
with open(filename, encoding="utf-8") as f:
|
189 |
+
filepaths_and_text = []
|
190 |
+
for line in f:
|
191 |
+
path_text = line.strip().split(split)
|
192 |
+
filepaths_and_text.append(path_text)
|
193 |
+
return filepaths_and_text
|
194 |
+
|
195 |
+
|
196 |
+
def get_hparams(init=True):
|
197 |
+
parser = argparse.ArgumentParser()
|
198 |
+
parser.add_argument(
|
199 |
+
"-c",
|
200 |
+
"--config",
|
201 |
+
type=str,
|
202 |
+
default="./configs/bert_vits.json",
|
203 |
+
help="JSON file for configuration",
|
204 |
+
)
|
205 |
+
parser.add_argument("-m", "--model", type=str, required=True, help="Model name")
|
206 |
+
|
207 |
+
args = parser.parse_args()
|
208 |
+
model_dir = os.path.join("./logs", args.model)
|
209 |
+
|
210 |
+
if not os.path.exists(model_dir):
|
211 |
+
os.makedirs(model_dir)
|
212 |
+
|
213 |
+
config_path = args.config
|
214 |
+
config_save_path = os.path.join(model_dir, "config.json")
|
215 |
+
if init:
|
216 |
+
with open(config_path, "r") as f:
|
217 |
+
data = f.read()
|
218 |
+
with open(config_save_path, "w") as f:
|
219 |
+
f.write(data)
|
220 |
+
else:
|
221 |
+
with open(config_save_path, "r") as f:
|
222 |
+
data = f.read()
|
223 |
+
config = json.loads(data)
|
224 |
+
|
225 |
+
hparams = HParams(**config)
|
226 |
+
hparams.model_dir = model_dir
|
227 |
+
return hparams
|
228 |
+
|
229 |
+
|
230 |
+
def get_hparams_from_dir(model_dir):
|
231 |
+
config_save_path = os.path.join(model_dir, "config.json")
|
232 |
+
with open(config_save_path, "r") as f:
|
233 |
+
data = f.read()
|
234 |
+
config = json.loads(data)
|
235 |
+
|
236 |
+
hparams = HParams(**config)
|
237 |
+
hparams.model_dir = model_dir
|
238 |
+
return hparams
|
239 |
+
|
240 |
+
|
241 |
+
def get_hparams_from_file(config_path):
|
242 |
+
with open(config_path, "r") as f:
|
243 |
+
data = f.read()
|
244 |
+
config = json.loads(data)
|
245 |
+
|
246 |
+
hparams = HParams(**config)
|
247 |
+
return hparams
|
248 |
+
|
249 |
+
|
250 |
+
def check_git_hash(model_dir):
|
251 |
+
source_dir = os.path.dirname(os.path.realpath(__file__))
|
252 |
+
if not os.path.exists(os.path.join(source_dir, ".git")):
|
253 |
+
logger.warn(
|
254 |
+
"{} is not a git repository, therefore hash value comparison will be ignored.".format(
|
255 |
+
source_dir
|
256 |
+
)
|
257 |
+
)
|
258 |
+
return
|
259 |
+
|
260 |
+
cur_hash = subprocess.getoutput("git rev-parse HEAD")
|
261 |
+
|
262 |
+
path = os.path.join(model_dir, "githash")
|
263 |
+
if os.path.exists(path):
|
264 |
+
saved_hash = open(path).read()
|
265 |
+
if saved_hash != cur_hash:
|
266 |
+
logger.warn(
|
267 |
+
"git hash values are different. {}(saved) != {}(current)".format(
|
268 |
+
saved_hash[:8], cur_hash[:8]
|
269 |
+
)
|
270 |
+
)
|
271 |
+
else:
|
272 |
+
open(path, "w").write(cur_hash)
|
273 |
+
|
274 |
+
|
275 |
+
def get_logger(model_dir, filename="train.log"):
|
276 |
+
global logger
|
277 |
+
logger = logging.getLogger(os.path.basename(model_dir))
|
278 |
+
logger.setLevel(logging.DEBUG)
|
279 |
+
|
280 |
+
formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
|
281 |
+
if not os.path.exists(model_dir):
|
282 |
+
os.makedirs(model_dir)
|
283 |
+
h = logging.FileHandler(os.path.join(model_dir, filename))
|
284 |
+
h.setLevel(logging.DEBUG)
|
285 |
+
h.setFormatter(formatter)
|
286 |
+
logger.addHandler(h)
|
287 |
+
return logger
|
288 |
+
|
289 |
+
|
290 |
+
class HParams:
|
291 |
+
def __init__(self, **kwargs):
|
292 |
+
for k, v in kwargs.items():
|
293 |
+
if type(v) == dict:
|
294 |
+
v = HParams(**v)
|
295 |
+
self[k] = v
|
296 |
+
|
297 |
+
def keys(self):
|
298 |
+
return self.__dict__.keys()
|
299 |
+
|
300 |
+
def items(self):
|
301 |
+
return self.__dict__.items()
|
302 |
+
|
303 |
+
def values(self):
|
304 |
+
return self.__dict__.values()
|
305 |
+
|
306 |
+
def __len__(self):
|
307 |
+
return len(self.__dict__)
|
308 |
+
|
309 |
+
def __getitem__(self, key):
|
310 |
+
return getattr(self, key)
|
311 |
+
|
312 |
+
def __setitem__(self, key, value):
|
313 |
+
return setattr(self, key, value)
|
314 |
+
|
315 |
+
def __contains__(self, key):
|
316 |
+
return key in self.__dict__
|
317 |
+
|
318 |
+
def __repr__(self):
|
319 |
+
return self.__dict__.__repr__()
|
vits_bert_model.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:be0dcf53ffcd49d51fd9a710338a9ff7eed60d0c26ccbb03ebd5a9175f20dc39
|
3 |
+
size 141822877
|
vits_pinyin.py
ADDED
@@ -0,0 +1,88 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import re
|
2 |
+
|
3 |
+
from pypinyin import Style
|
4 |
+
from pypinyin.contrib.neutral_tone import NeutralToneWith5Mixin
|
5 |
+
from pypinyin.converter import DefaultConverter
|
6 |
+
from pypinyin.core import Pinyin
|
7 |
+
|
8 |
+
from text import pinyin_dict
|
9 |
+
from bert import TTSProsody
|
10 |
+
|
11 |
+
|
12 |
+
class MyConverter(NeutralToneWith5Mixin, DefaultConverter):
|
13 |
+
pass
|
14 |
+
|
15 |
+
|
16 |
+
def is_chinese(uchar):
|
17 |
+
if uchar >= u'\u4e00' and uchar <= u'\u9fa5':
|
18 |
+
return True
|
19 |
+
else:
|
20 |
+
return False
|
21 |
+
|
22 |
+
|
23 |
+
def clean_chinese(text: str):
|
24 |
+
text = text.strip()
|
25 |
+
text_clean = []
|
26 |
+
for char in text:
|
27 |
+
if (is_chinese(char)):
|
28 |
+
text_clean.append(char)
|
29 |
+
else:
|
30 |
+
if len(text_clean) > 1 and is_chinese(text_clean[-1]):
|
31 |
+
text_clean.append(',')
|
32 |
+
text_clean = ''.join(text_clean).strip(',')
|
33 |
+
return text_clean
|
34 |
+
|
35 |
+
|
36 |
+
class VITS_PinYin:
|
37 |
+
def __init__(self, bert_path, device):
|
38 |
+
self.pinyin_parser = Pinyin(MyConverter())
|
39 |
+
self.prosody = TTSProsody(bert_path, device)
|
40 |
+
|
41 |
+
def get_phoneme4pinyin(self, pinyins):
|
42 |
+
result = []
|
43 |
+
count_phone = []
|
44 |
+
for pinyin in pinyins:
|
45 |
+
if pinyin[:-1] in pinyin_dict:
|
46 |
+
tone = pinyin[-1]
|
47 |
+
a = pinyin[:-1]
|
48 |
+
a1, a2 = pinyin_dict[a]
|
49 |
+
result += [a1, a2 + tone]
|
50 |
+
count_phone.append(2)
|
51 |
+
return result, count_phone
|
52 |
+
|
53 |
+
def chinese_to_phonemes(self, text):
|
54 |
+
text = clean_chinese(text)
|
55 |
+
phonemes = ["sil"]
|
56 |
+
chars = ['[PAD]']
|
57 |
+
count_phone = []
|
58 |
+
count_phone.append(1)
|
59 |
+
for subtext in text.split(","):
|
60 |
+
if (len(subtext) == 0):
|
61 |
+
continue
|
62 |
+
pinyins = self.correct_pinyin_tone3(subtext)
|
63 |
+
sub_p, sub_c = self.get_phoneme4pinyin(pinyins)
|
64 |
+
phonemes.extend(sub_p)
|
65 |
+
phonemes.append("sp")
|
66 |
+
count_phone.extend(sub_c)
|
67 |
+
count_phone.append(1)
|
68 |
+
chars.append(subtext)
|
69 |
+
chars.append(',')
|
70 |
+
phonemes.append("sil")
|
71 |
+
count_phone.append(1)
|
72 |
+
chars.append('[PAD]')
|
73 |
+
chars = "".join(chars)
|
74 |
+
char_embeds = self.prosody.get_char_embeds(chars)
|
75 |
+
char_embeds = self.prosody.expand_for_phone(char_embeds, count_phone)
|
76 |
+
return " ".join(phonemes), char_embeds
|
77 |
+
|
78 |
+
def correct_pinyin_tone3(self, text):
|
79 |
+
pinyin_list = [p[0] for p in self.pinyin_parser.pinyin(
|
80 |
+
text, style=Style.TONE3, strict=False, neutral_tone_with_five=True)]
|
81 |
+
if len(pinyin_list) >= 2:
|
82 |
+
for i in range(1, len(pinyin_list)):
|
83 |
+
try:
|
84 |
+
if re.findall(r'\d', pinyin_list[i-1])[0] == '3' and re.findall(r'\d', pinyin_list[i])[0] == '3':
|
85 |
+
pinyin_list[i-1] = pinyin_list[i-1].replace('3', '2')
|
86 |
+
except IndexError:
|
87 |
+
pass
|
88 |
+
return pinyin_list
|