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huangchy
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Commit
•
44c1ee0
1
Parent(s):
3c2d7a2
init
Browse filesThis view is limited to 50 files because it contains too many changes.
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- G_250000.pth +3 -0
- __pycache__/attentions.cpython-38.pyc +0 -0
- __pycache__/commons.cpython-38.pyc +0 -0
- __pycache__/data_utils.cpython-38.pyc +0 -0
- __pycache__/mel_processing.cpython-38.pyc +0 -0
- __pycache__/models.cpython-38.pyc +0 -0
- __pycache__/modules.cpython-38.pyc +0 -0
- __pycache__/transforms.cpython-38.pyc +0 -0
- __pycache__/utils.cpython-38.pyc +0 -0
- app.py +93 -0
- attentions.py +303 -0
- avatar.webp +0 -0
- commons.py +161 -0
- configs/biaobei_base.json +53 -0
- configs/chinese_base.json +55 -0
- configs/cjke_base.json +54 -0
- configs/hoshimi_base.json +53 -0
- data_utils.py +392 -0
- header.html +23 -0
- header.webp +0 -0
- mel_processing.py +116 -0
- models.py +534 -0
- modules.py +390 -0
- monotonic_align/core.cpython-38-x86_64-linux-gnu.so +0 -0
- requirements.txt +15 -0
- text/LICENSE +19 -0
- text/__init__.py +56 -0
- text/__pycache__/__init__.cpython-38.pyc +0 -0
- text/__pycache__/cantonese.cpython-38.pyc +0 -0
- text/__pycache__/cleaners.cpython-38.pyc +0 -0
- text/__pycache__/english.cpython-38.pyc +0 -0
- text/__pycache__/japanese.cpython-38.pyc +0 -0
- text/__pycache__/korean.cpython-38.pyc +0 -0
- text/__pycache__/mandarin.cpython-38.pyc +0 -0
- text/__pycache__/ngu_dialect.cpython-38.pyc +0 -0
- text/__pycache__/sanskrit.cpython-38.pyc +0 -0
- text/__pycache__/shanghainese.cpython-38.pyc +0 -0
- text/__pycache__/symbols.cpython-38.pyc +0 -0
- text/__pycache__/thai.cpython-38.pyc +0 -0
- text/cantonese.py +59 -0
- text/cleaners.py +176 -0
- text/english.py +188 -0
- text/japanese.py +153 -0
- text/korean.py +210 -0
- text/mandarin.py +328 -0
- text/ngu_dialect.py +29 -0
- text/sanskrit.py +62 -0
- text/shanghainese.py +64 -0
- text/symbols.py +75 -0
- text/thai.py +44 -0
G_250000.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:5933249bc8e9e8d5453e21bcd287dad3c2f0e6e20beda81667932f32b43a9da6
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size 436355463
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__pycache__/attentions.cpython-38.pyc
ADDED
Binary file (9.56 kB). View file
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__pycache__/commons.cpython-38.pyc
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Binary file (5.83 kB). View file
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__pycache__/data_utils.cpython-38.pyc
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__pycache__/mel_processing.cpython-38.pyc
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Binary file (3.47 kB). View file
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__pycache__/models.cpython-38.pyc
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Binary file (15.2 kB). View file
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__pycache__/modules.cpython-38.pyc
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Binary file (11.5 kB). View file
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__pycache__/transforms.cpython-38.pyc
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Binary file (3.92 kB). View file
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__pycache__/utils.cpython-38.pyc
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Binary file (8.48 kB). View file
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app.py
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import os
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import json
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import math
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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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from torch.utils.data import DataLoader
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import commons
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import utils
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from data_utils import TextAudioLoader, TextAudioCollate, TextAudioSpeakerLoader, TextAudioSpeakerCollate
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from models import SynthesizerTrn
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from text.symbols import symbols
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from text import text_to_sequence
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import gradio as gr
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pth_path = "G_240000.pth"
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hps = utils.get_hparams_from_file("./configs/hoshimi_base.json")
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# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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device = torch.device("cpu")
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def get_text(text, hps):
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text_norm = text_to_sequence(text, hps.data.text_cleaners)
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if hps.data.add_blank:
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text_norm = commons.intersperse(text_norm, 0)
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text_norm = torch.LongTensor(text_norm)
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return text_norm
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def load_model(pth_path):
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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).to(device)
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_ = net_g.eval()
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_ = utils.load_checkpoint(pth_path, net_g, None)
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return net_g
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def list_model():
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global pth_path
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res = []
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dir = os.getcwd()
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for f in os.listdir(dir):
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if (f.startswith("D_")):
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continue
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if (f.endswith(".pth")):
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res.append(f)
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if len(f) >= len(pth_path):
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pth_path = f
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return res
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def infer(text):
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stn_tst = get_text(text, hps)
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with torch.no_grad():
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x_tst = stn_tst.unsqueeze(0).to(device)
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x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device)
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audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.float().numpy()
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return (hps.data.sampling_rate, audio)
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models = list_model()
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net_g = load_model(pth_path)
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def change_model(model):
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global pth_path
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global net_g_ms
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pth_path = model
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net_g_ms = load_model(pth_path)
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return "载入模型:"+pth_path
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app = gr.Blocks()
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with app:
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with open("header.html", "r") as f:
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gr.HTML(f.read())
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with gr.Tabs():
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with gr.TabItem("Basic"):
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choice_model = gr.Dropdown(
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choices=models, label="模型", value=pth_path)
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tts_input1 = gr.TextArea(
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label="请输入文本(目前只支持汉字和单个英文字母,也可以使用逗号、句号、感叹号、空格等常用符号来改变语调和停顿)",
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value="这里是爱喝奶茶,穿得也像奶茶魅力点是普通话二乙的星弥吼西咪,晚上齁。")
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tts_submit = gr.Button("用文本合成", variant="primary")
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tts_output = gr.Audio(label="Output")
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tts_model = gr.Markdown("")
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tts_submit.click(infer, [tts_input1], [tts_output])
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choice_model.change(change_model, inputs=[
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choice_model], outputs=[tts_model])
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app.launch()
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attentions.py
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@@ -0,0 +1,303 @@
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import copy
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import math
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3 |
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import numpy as np
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import torch
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5 |
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from torch import nn
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6 |
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from torch.nn import functional as F
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7 |
+
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8 |
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import commons
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9 |
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import modules
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10 |
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from modules import LayerNorm
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12 |
+
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class Encoder(nn.Module):
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def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
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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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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(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
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self.norm_layers_1.append(LayerNorm(hidden_channels))
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self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
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self.norm_layers_2.append(LayerNorm(hidden_channels))
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+
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def forward(self, x, x_mask):
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attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
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x = x * x_mask
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for i in range(self.n_layers):
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y = self.attn_layers[i](x, x, attn_mask)
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y = self.drop(y)
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x = self.norm_layers_1[i](x + y)
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+
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y = self.ffn_layers[i](x, x_mask)
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y = self.drop(y)
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x = self.norm_layers_2[i](x + y)
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x = x * x_mask
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return x
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class Decoder(nn.Module):
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def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
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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.proximal_bias = proximal_bias
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self.proximal_init = proximal_init
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+
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self.drop = nn.Dropout(p_dropout)
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self.self_attn_layers = nn.ModuleList()
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self.norm_layers_0 = nn.ModuleList()
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self.encdec_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.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
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self.norm_layers_0.append(LayerNorm(hidden_channels))
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self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
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self.norm_layers_1.append(LayerNorm(hidden_channels))
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self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
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self.norm_layers_2.append(LayerNorm(hidden_channels))
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76 |
+
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def forward(self, x, x_mask, h, h_mask):
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+
"""
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79 |
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x: decoder input
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80 |
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h: encoder output
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"""
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82 |
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self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
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encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
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x = x * x_mask
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for i in range(self.n_layers):
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y = self.self_attn_layers[i](x, x, self_attn_mask)
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y = self.drop(y)
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x = self.norm_layers_0[i](x + y)
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89 |
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y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
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y = self.drop(y)
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x = self.norm_layers_1[i](x + y)
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93 |
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y = self.ffn_layers[i](x, x_mask)
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y = self.drop(y)
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96 |
+
x = self.norm_layers_2[i](x + y)
|
97 |
+
x = x * x_mask
|
98 |
+
return x
|
99 |
+
|
100 |
+
|
101 |
+
class MultiHeadAttention(nn.Module):
|
102 |
+
def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
|
103 |
+
super().__init__()
|
104 |
+
assert channels % n_heads == 0
|
105 |
+
|
106 |
+
self.channels = channels
|
107 |
+
self.out_channels = out_channels
|
108 |
+
self.n_heads = n_heads
|
109 |
+
self.p_dropout = p_dropout
|
110 |
+
self.window_size = window_size
|
111 |
+
self.heads_share = heads_share
|
112 |
+
self.block_length = block_length
|
113 |
+
self.proximal_bias = proximal_bias
|
114 |
+
self.proximal_init = proximal_init
|
115 |
+
self.attn = None
|
116 |
+
|
117 |
+
self.k_channels = channels // n_heads
|
118 |
+
self.conv_q = nn.Conv1d(channels, channels, 1)
|
119 |
+
self.conv_k = nn.Conv1d(channels, channels, 1)
|
120 |
+
self.conv_v = nn.Conv1d(channels, channels, 1)
|
121 |
+
self.conv_o = nn.Conv1d(channels, out_channels, 1)
|
122 |
+
self.drop = nn.Dropout(p_dropout)
|
123 |
+
|
124 |
+
if window_size is not None:
|
125 |
+
n_heads_rel = 1 if heads_share else n_heads
|
126 |
+
rel_stddev = self.k_channels**-0.5
|
127 |
+
self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
|
128 |
+
self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
|
129 |
+
|
130 |
+
nn.init.xavier_uniform_(self.conv_q.weight)
|
131 |
+
nn.init.xavier_uniform_(self.conv_k.weight)
|
132 |
+
nn.init.xavier_uniform_(self.conv_v.weight)
|
133 |
+
if proximal_init:
|
134 |
+
with torch.no_grad():
|
135 |
+
self.conv_k.weight.copy_(self.conv_q.weight)
|
136 |
+
self.conv_k.bias.copy_(self.conv_q.bias)
|
137 |
+
|
138 |
+
def forward(self, x, c, attn_mask=None):
|
139 |
+
q = self.conv_q(x)
|
140 |
+
k = self.conv_k(c)
|
141 |
+
v = self.conv_v(c)
|
142 |
+
|
143 |
+
x, self.attn = self.attention(q, k, v, mask=attn_mask)
|
144 |
+
|
145 |
+
x = self.conv_o(x)
|
146 |
+
return x
|
147 |
+
|
148 |
+
def attention(self, query, key, value, mask=None):
|
149 |
+
# reshape [b, d, t] -> [b, n_h, t, d_k]
|
150 |
+
b, d, t_s, t_t = (*key.size(), query.size(2))
|
151 |
+
query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
|
152 |
+
key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
153 |
+
value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
154 |
+
|
155 |
+
scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
|
156 |
+
if self.window_size is not None:
|
157 |
+
assert t_s == t_t, "Relative attention is only available for self-attention."
|
158 |
+
key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
|
159 |
+
rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
|
160 |
+
scores_local = self._relative_position_to_absolute_position(rel_logits)
|
161 |
+
scores = scores + scores_local
|
162 |
+
if self.proximal_bias:
|
163 |
+
assert t_s == t_t, "Proximal bias is only available for self-attention."
|
164 |
+
scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
|
165 |
+
if mask is not None:
|
166 |
+
scores = scores.masked_fill(mask == 0, -1e4)
|
167 |
+
if self.block_length is not None:
|
168 |
+
assert t_s == t_t, "Local attention is only available for self-attention."
|
169 |
+
block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
|
170 |
+
scores = scores.masked_fill(block_mask == 0, -1e4)
|
171 |
+
p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
|
172 |
+
p_attn = self.drop(p_attn)
|
173 |
+
output = torch.matmul(p_attn, value)
|
174 |
+
if self.window_size is not None:
|
175 |
+
relative_weights = self._absolute_position_to_relative_position(p_attn)
|
176 |
+
value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
|
177 |
+
output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
|
178 |
+
output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
|
179 |
+
return output, p_attn
|
180 |
+
|
181 |
+
def _matmul_with_relative_values(self, x, y):
|
182 |
+
"""
|
183 |
+
x: [b, h, l, m]
|
184 |
+
y: [h or 1, m, d]
|
185 |
+
ret: [b, h, l, d]
|
186 |
+
"""
|
187 |
+
ret = torch.matmul(x, y.unsqueeze(0))
|
188 |
+
return ret
|
189 |
+
|
190 |
+
def _matmul_with_relative_keys(self, x, y):
|
191 |
+
"""
|
192 |
+
x: [b, h, l, d]
|
193 |
+
y: [h or 1, m, d]
|
194 |
+
ret: [b, h, l, m]
|
195 |
+
"""
|
196 |
+
ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
|
197 |
+
return ret
|
198 |
+
|
199 |
+
def _get_relative_embeddings(self, relative_embeddings, length):
|
200 |
+
max_relative_position = 2 * self.window_size + 1
|
201 |
+
# Pad first before slice to avoid using cond ops.
|
202 |
+
pad_length = max(length - (self.window_size + 1), 0)
|
203 |
+
slice_start_position = max((self.window_size + 1) - length, 0)
|
204 |
+
slice_end_position = slice_start_position + 2 * length - 1
|
205 |
+
if pad_length > 0:
|
206 |
+
padded_relative_embeddings = F.pad(
|
207 |
+
relative_embeddings,
|
208 |
+
commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
|
209 |
+
else:
|
210 |
+
padded_relative_embeddings = relative_embeddings
|
211 |
+
used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
|
212 |
+
return used_relative_embeddings
|
213 |
+
|
214 |
+
def _relative_position_to_absolute_position(self, x):
|
215 |
+
"""
|
216 |
+
x: [b, h, l, 2*l-1]
|
217 |
+
ret: [b, h, l, l]
|
218 |
+
"""
|
219 |
+
batch, heads, length, _ = x.size()
|
220 |
+
# Concat columns of pad to shift from relative to absolute indexing.
|
221 |
+
x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
|
222 |
+
|
223 |
+
# Concat extra elements so to add up to shape (len+1, 2*len-1).
|
224 |
+
x_flat = x.view([batch, heads, length * 2 * length])
|
225 |
+
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
|
226 |
+
|
227 |
+
# Reshape and slice out the padded elements.
|
228 |
+
x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
|
229 |
+
return x_final
|
230 |
+
|
231 |
+
def _absolute_position_to_relative_position(self, x):
|
232 |
+
"""
|
233 |
+
x: [b, h, l, l]
|
234 |
+
ret: [b, h, l, 2*l-1]
|
235 |
+
"""
|
236 |
+
batch, heads, length, _ = x.size()
|
237 |
+
# padd along column
|
238 |
+
x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
|
239 |
+
x_flat = x.view([batch, heads, length**2 + length*(length -1)])
|
240 |
+
# add 0's in the beginning that will skew the elements after reshape
|
241 |
+
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
|
242 |
+
x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
|
243 |
+
return x_final
|
244 |
+
|
245 |
+
def _attention_bias_proximal(self, length):
|
246 |
+
"""Bias for self-attention to encourage attention to close positions.
|
247 |
+
Args:
|
248 |
+
length: an integer scalar.
|
249 |
+
Returns:
|
250 |
+
a Tensor with shape [1, 1, length, length]
|
251 |
+
"""
|
252 |
+
r = torch.arange(length, dtype=torch.float32)
|
253 |
+
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
|
254 |
+
return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
|
255 |
+
|
256 |
+
|
257 |
+
class FFN(nn.Module):
|
258 |
+
def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
|
259 |
+
super().__init__()
|
260 |
+
self.in_channels = in_channels
|
261 |
+
self.out_channels = out_channels
|
262 |
+
self.filter_channels = filter_channels
|
263 |
+
self.kernel_size = kernel_size
|
264 |
+
self.p_dropout = p_dropout
|
265 |
+
self.activation = activation
|
266 |
+
self.causal = causal
|
267 |
+
|
268 |
+
if causal:
|
269 |
+
self.padding = self._causal_padding
|
270 |
+
else:
|
271 |
+
self.padding = self._same_padding
|
272 |
+
|
273 |
+
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
|
274 |
+
self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
|
275 |
+
self.drop = nn.Dropout(p_dropout)
|
276 |
+
|
277 |
+
def forward(self, x, x_mask):
|
278 |
+
x = self.conv_1(self.padding(x * x_mask))
|
279 |
+
if self.activation == "gelu":
|
280 |
+
x = x * torch.sigmoid(1.702 * x)
|
281 |
+
else:
|
282 |
+
x = torch.relu(x)
|
283 |
+
x = self.drop(x)
|
284 |
+
x = self.conv_2(self.padding(x * x_mask))
|
285 |
+
return x * x_mask
|
286 |
+
|
287 |
+
def _causal_padding(self, x):
|
288 |
+
if self.kernel_size == 1:
|
289 |
+
return x
|
290 |
+
pad_l = self.kernel_size - 1
|
291 |
+
pad_r = 0
|
292 |
+
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
293 |
+
x = F.pad(x, commons.convert_pad_shape(padding))
|
294 |
+
return x
|
295 |
+
|
296 |
+
def _same_padding(self, x):
|
297 |
+
if self.kernel_size == 1:
|
298 |
+
return x
|
299 |
+
pad_l = (self.kernel_size - 1) // 2
|
300 |
+
pad_r = self.kernel_size // 2
|
301 |
+
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
302 |
+
x = F.pad(x, commons.convert_pad_shape(padding))
|
303 |
+
return x
|
avatar.webp
ADDED
commons.py
ADDED
@@ -0,0 +1,161 @@
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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 += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
|
34 |
+
return kl
|
35 |
+
|
36 |
+
|
37 |
+
def rand_gumbel(shape):
|
38 |
+
"""Sample from the Gumbel distribution, protect from overflows."""
|
39 |
+
uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
|
40 |
+
return -torch.log(-torch.log(uniform_samples))
|
41 |
+
|
42 |
+
|
43 |
+
def rand_gumbel_like(x):
|
44 |
+
g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
|
45 |
+
return g
|
46 |
+
|
47 |
+
|
48 |
+
def slice_segments(x, ids_str, segment_size=4):
|
49 |
+
ret = torch.zeros_like(x[:, :, :segment_size])
|
50 |
+
for i in range(x.size(0)):
|
51 |
+
idx_str = ids_str[i]
|
52 |
+
idx_end = idx_str + segment_size
|
53 |
+
ret[i] = x[i, :, idx_str:idx_end]
|
54 |
+
return ret
|
55 |
+
|
56 |
+
|
57 |
+
def rand_slice_segments(x, x_lengths=None, segment_size=4):
|
58 |
+
b, d, t = x.size()
|
59 |
+
if x_lengths is None:
|
60 |
+
x_lengths = t
|
61 |
+
ids_str_max = x_lengths - segment_size + 1
|
62 |
+
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
|
63 |
+
ret = slice_segments(x, ids_str, segment_size)
|
64 |
+
return ret, ids_str
|
65 |
+
|
66 |
+
|
67 |
+
def get_timing_signal_1d(
|
68 |
+
length, channels, min_timescale=1.0, max_timescale=1.0e4):
|
69 |
+
position = torch.arange(length, dtype=torch.float)
|
70 |
+
num_timescales = channels // 2
|
71 |
+
log_timescale_increment = (
|
72 |
+
math.log(float(max_timescale) / float(min_timescale)) /
|
73 |
+
(num_timescales - 1))
|
74 |
+
inv_timescales = min_timescale * torch.exp(
|
75 |
+
torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
|
76 |
+
scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
|
77 |
+
signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
|
78 |
+
signal = F.pad(signal, [0, 0, 0, channels % 2])
|
79 |
+
signal = signal.view(1, channels, length)
|
80 |
+
return signal
|
81 |
+
|
82 |
+
|
83 |
+
def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
|
84 |
+
b, channels, length = x.size()
|
85 |
+
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
86 |
+
return x + signal.to(dtype=x.dtype, device=x.device)
|
87 |
+
|
88 |
+
|
89 |
+
def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
|
90 |
+
b, channels, length = x.size()
|
91 |
+
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
92 |
+
return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
|
93 |
+
|
94 |
+
|
95 |
+
def subsequent_mask(length):
|
96 |
+
mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
|
97 |
+
return mask
|
98 |
+
|
99 |
+
|
100 |
+
@torch.jit.script
|
101 |
+
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
102 |
+
n_channels_int = n_channels[0]
|
103 |
+
in_act = input_a + input_b
|
104 |
+
t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
105 |
+
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
106 |
+
acts = t_act * s_act
|
107 |
+
return acts
|
108 |
+
|
109 |
+
|
110 |
+
def convert_pad_shape(pad_shape):
|
111 |
+
l = pad_shape[::-1]
|
112 |
+
pad_shape = [item for sublist in l for item in sublist]
|
113 |
+
return pad_shape
|
114 |
+
|
115 |
+
|
116 |
+
def shift_1d(x):
|
117 |
+
x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
|
118 |
+
return x
|
119 |
+
|
120 |
+
|
121 |
+
def sequence_mask(length, max_length=None):
|
122 |
+
if max_length is None:
|
123 |
+
max_length = length.max()
|
124 |
+
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
|
125 |
+
return x.unsqueeze(0) < length.unsqueeze(1)
|
126 |
+
|
127 |
+
|
128 |
+
def generate_path(duration, mask):
|
129 |
+
"""
|
130 |
+
duration: [b, 1, t_x]
|
131 |
+
mask: [b, 1, t_y, t_x]
|
132 |
+
"""
|
133 |
+
device = duration.device
|
134 |
+
|
135 |
+
b, _, t_y, t_x = mask.shape
|
136 |
+
cum_duration = torch.cumsum(duration, -1)
|
137 |
+
|
138 |
+
cum_duration_flat = cum_duration.view(b * t_x)
|
139 |
+
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
|
140 |
+
path = path.view(b, t_x, t_y)
|
141 |
+
path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
|
142 |
+
path = path.unsqueeze(1).transpose(2,3) * mask
|
143 |
+
return path
|
144 |
+
|
145 |
+
|
146 |
+
def clip_grad_value_(parameters, clip_value, norm_type=2):
|
147 |
+
if isinstance(parameters, torch.Tensor):
|
148 |
+
parameters = [parameters]
|
149 |
+
parameters = list(filter(lambda p: p.grad is not None, parameters))
|
150 |
+
norm_type = float(norm_type)
|
151 |
+
if clip_value is not None:
|
152 |
+
clip_value = float(clip_value)
|
153 |
+
|
154 |
+
total_norm = 0
|
155 |
+
for p in parameters:
|
156 |
+
param_norm = p.grad.data.norm(norm_type)
|
157 |
+
total_norm += param_norm.item() ** norm_type
|
158 |
+
if clip_value is not None:
|
159 |
+
p.grad.data.clamp_(min=-clip_value, max=clip_value)
|
160 |
+
total_norm = total_norm ** (1. / norm_type)
|
161 |
+
return total_norm
|
configs/biaobei_base.json
ADDED
@@ -0,0 +1,53 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"train": {
|
3 |
+
"log_interval": 200,
|
4 |
+
"eval_interval": 10000,
|
5 |
+
"seed": 1234,
|
6 |
+
"epochs": 10000,
|
7 |
+
"learning_rate": 2e-4,
|
8 |
+
"betas": [0.8, 0.99],
|
9 |
+
"eps": 1e-9,
|
10 |
+
"batch_size": 16,
|
11 |
+
"fp16_run": true,
|
12 |
+
"lr_decay": 0.999875,
|
13 |
+
"segment_size": 8192,
|
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/biaobei_train_filelist.txt.cleaned",
|
21 |
+
"validation_files":"filelists/biaobei_val_filelist.txt.cleaned",
|
22 |
+
"text_cleaners":["chinese_cleaners"],
|
23 |
+
"max_wav_value": 32768.0,
|
24 |
+
"sampling_rate": 16000,
|
25 |
+
"filter_length": 1024,
|
26 |
+
"hop_length": 256,
|
27 |
+
"win_length": 1024,
|
28 |
+
"n_mel_channels": 80,
|
29 |
+
"mel_fmin": 0.0,
|
30 |
+
"mel_fmax": null,
|
31 |
+
"add_blank": true,
|
32 |
+
"n_speakers": 0,
|
33 |
+
"cleaned_text": true
|
34 |
+
},
|
35 |
+
"model": {
|
36 |
+
"inter_channels": 192,
|
37 |
+
"hidden_channels": 192,
|
38 |
+
"filter_channels": 768,
|
39 |
+
"n_heads": 2,
|
40 |
+
"n_layers": 6,
|
41 |
+
"kernel_size": 3,
|
42 |
+
"p_dropout": 0.1,
|
43 |
+
"resblock": "1",
|
44 |
+
"resblock_kernel_sizes": [3,7,11],
|
45 |
+
"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
|
46 |
+
"upsample_rates": [8,8,2,2],
|
47 |
+
"upsample_initial_channel": 512,
|
48 |
+
"upsample_kernel_sizes": [16,16,4,4],
|
49 |
+
"n_layers_q": 3,
|
50 |
+
"use_spectral_norm": false
|
51 |
+
},
|
52 |
+
"symbols": ["_", "\uff0c", "\u3002", "\uff01", "\uff1f", "\u2014", "\u2026", "\u3105", "\u3106", "\u3107", "\u3108", "\u3109", "\u310a", "\u310b", "\u310c", "\u310d", "\u310e", "\u310f", "\u3110", "\u3111", "\u3112", "\u3113", "\u3114", "\u3115", "\u3116", "\u3117", "\u3118", "\u3119", "\u311a", "\u311b", "\u311c", "\u311d", "\u311e", "\u311f", "\u3120", "\u3121", "\u3122", "\u3123", "\u3124", "\u3125", "\u3126", "\u3127", "\u3128", "\u3129", "\u02c9", "\u02ca", "\u02c7", "\u02cb", "\u02d9", " "]
|
53 |
+
}
|
configs/chinese_base.json
ADDED
@@ -0,0 +1,55 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"train": {
|
3 |
+
"log_interval": 200,
|
4 |
+
"eval_interval": 1000,
|
5 |
+
"seed": 1234,
|
6 |
+
"epochs": 10000,
|
7 |
+
"learning_rate": 2e-4,
|
8 |
+
"betas": [0.8, 0.99],
|
9 |
+
"eps": 1e-9,
|
10 |
+
"batch_size": 32,
|
11 |
+
"fp16_run": true,
|
12 |
+
"lr_decay": 0.999875,
|
13 |
+
"segment_size": 8192,
|
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/juzi_train_filelist.txt.cleaned",
|
21 |
+
"validation_files":"filelists/juzi_val_filelist.txt.cleaned",
|
22 |
+
"text_cleaners":["chinese_cleaners"],
|
23 |
+
"max_wav_value": 32768.0,
|
24 |
+
"sampling_rate": 22050,
|
25 |
+
"filter_length": 1024,
|
26 |
+
"hop_length": 256,
|
27 |
+
"win_length": 1024,
|
28 |
+
"n_mel_channels": 80,
|
29 |
+
"mel_fmin": 0.0,
|
30 |
+
"mel_fmax": null,
|
31 |
+
"add_blank": true,
|
32 |
+
"n_speakers": 8,
|
33 |
+
"cleaned_text": true
|
34 |
+
},
|
35 |
+
"model": {
|
36 |
+
"inter_channels": 192,
|
37 |
+
"hidden_channels": 192,
|
38 |
+
"filter_channels": 768,
|
39 |
+
"n_heads": 2,
|
40 |
+
"n_layers": 6,
|
41 |
+
"kernel_size": 3,
|
42 |
+
"p_dropout": 0.1,
|
43 |
+
"resblock": "1",
|
44 |
+
"resblock_kernel_sizes": [3,7,11],
|
45 |
+
"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
|
46 |
+
"upsample_rates": [8,8,2,2],
|
47 |
+
"upsample_initial_channel": 512,
|
48 |
+
"upsample_kernel_sizes": [16,16,4,4],
|
49 |
+
"n_layers_q": 3,
|
50 |
+
"use_spectral_norm": false,
|
51 |
+
"gin_channels": 256
|
52 |
+
},
|
53 |
+
"speakers": ["\u5c0f\u8338", "\u5510\u4e50\u541f", "\u5c0f\u6bb7", "\u82b1\u73b2", "\u8bb8\u8001\u5e08", "\u90b1\u7433", "\u4e03\u4e00", "\u516b\u56db"],
|
54 |
+
"symbols": ["_", "\uff0c", "\u3002", "\uff01", "\uff1f", "\u2014", "\u2026", "\u3105", "\u3106", "\u3107", "\u3108", "\u3109", "\u310a", "\u310b", "\u310c", "\u310d", "\u310e", "\u310f", "\u3110", "\u3111", "\u3112", "\u3113", "\u3114", "\u3115", "\u3116", "\u3117", "\u3118", "\u3119", "\u311a", "\u311b", "\u311c", "\u311d", "\u311e", "\u311f", "\u3120", "\u3121", "\u3122", "\u3123", "\u3124", "\u3125", "\u3126", "\u3127", "\u3128", "\u3129", "\u02c9", "\u02ca", "\u02c7", "\u02cb", "\u02d9", " "]
|
55 |
+
}
|
configs/cjke_base.json
ADDED
@@ -0,0 +1,54 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"train": {
|
3 |
+
"log_interval": 200,
|
4 |
+
"eval_interval": 1000,
|
5 |
+
"seed": 1234,
|
6 |
+
"epochs": 10000,
|
7 |
+
"learning_rate": 2e-4,
|
8 |
+
"betas": [0.8, 0.99],
|
9 |
+
"eps": 1e-9,
|
10 |
+
"batch_size": 32,
|
11 |
+
"fp16_run": true,
|
12 |
+
"lr_decay": 0.999875,
|
13 |
+
"segment_size": 8192,
|
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/cjke_train_filelist.txt.cleaned",
|
21 |
+
"validation_files":"filelists/cjke_val_filelist.txt.cleaned",
|
22 |
+
"text_cleaners":["cjke_cleaners2"],
|
23 |
+
"max_wav_value": 32768.0,
|
24 |
+
"sampling_rate": 22050,
|
25 |
+
"filter_length": 1024,
|
26 |
+
"hop_length": 256,
|
27 |
+
"win_length": 1024,
|
28 |
+
"n_mel_channels": 80,
|
29 |
+
"mel_fmin": 0.0,
|
30 |
+
"mel_fmax": null,
|
31 |
+
"add_blank": true,
|
32 |
+
"n_speakers": 2891,
|
33 |
+
"cleaned_text": true
|
34 |
+
},
|
35 |
+
"model": {
|
36 |
+
"inter_channels": 192,
|
37 |
+
"hidden_channels": 192,
|
38 |
+
"filter_channels": 768,
|
39 |
+
"n_heads": 2,
|
40 |
+
"n_layers": 6,
|
41 |
+
"kernel_size": 3,
|
42 |
+
"p_dropout": 0.1,
|
43 |
+
"resblock": "1",
|
44 |
+
"resblock_kernel_sizes": [3,7,11],
|
45 |
+
"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
|
46 |
+
"upsample_rates": [8,8,2,2],
|
47 |
+
"upsample_initial_channel": 512,
|
48 |
+
"upsample_kernel_sizes": [16,16,4,4],
|
49 |
+
"n_layers_q": 3,
|
50 |
+
"use_spectral_norm": false,
|
51 |
+
"gin_channels": 256
|
52 |
+
},
|
53 |
+
"symbols": ["_", ",", ".", "!", "?", "-", "~", "\u2026", "N", "Q", "a", "b", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "s", "t", "u", "v", "w", "x", "y", "z", "\u0251", "\u00e6", "\u0283", "\u0291", "\u00e7", "\u026f", "\u026a", "\u0254", "\u025b", "\u0279", "\u00f0", "\u0259", "\u026b", "\u0265", "\u0278", "\u028a", "\u027e", "\u0292", "\u03b8", "\u03b2", "\u014b", "\u0266", "\u207c", "\u02b0", "`", "^", "#", "*", "=", "\u02c8", "\u02cc", "\u2192", "\u2193", "\u2191", " "]
|
54 |
+
}
|
configs/hoshimi_base.json
ADDED
@@ -0,0 +1,53 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"train": {
|
3 |
+
"log_interval": 200,
|
4 |
+
"eval_interval": 10000,
|
5 |
+
"seed": 1234,
|
6 |
+
"epochs": 10000,
|
7 |
+
"learning_rate": 2e-4,
|
8 |
+
"betas": [0.8, 0.99],
|
9 |
+
"eps": 1e-9,
|
10 |
+
"batch_size": 16,
|
11 |
+
"fp16_run": true,
|
12 |
+
"lr_decay": 0.999875,
|
13 |
+
"segment_size": 8192,
|
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/hoshimi_train_filelist.txt.cleaned",
|
21 |
+
"validation_files":"filelists/hoshimi_val_filelist.txt.cleaned",
|
22 |
+
"text_cleaners":["chinese_cleaners"],
|
23 |
+
"max_wav_value": 32768.0,
|
24 |
+
"sampling_rate": 16000,
|
25 |
+
"filter_length": 1024,
|
26 |
+
"hop_length": 256,
|
27 |
+
"win_length": 1024,
|
28 |
+
"n_mel_channels": 80,
|
29 |
+
"mel_fmin": 0.0,
|
30 |
+
"mel_fmax": null,
|
31 |
+
"add_blank": true,
|
32 |
+
"n_speakers": 0,
|
33 |
+
"cleaned_text": true
|
34 |
+
},
|
35 |
+
"model": {
|
36 |
+
"inter_channels": 192,
|
37 |
+
"hidden_channels": 192,
|
38 |
+
"filter_channels": 768,
|
39 |
+
"n_heads": 2,
|
40 |
+
"n_layers": 6,
|
41 |
+
"kernel_size": 3,
|
42 |
+
"p_dropout": 0.1,
|
43 |
+
"resblock": "1",
|
44 |
+
"resblock_kernel_sizes": [3,7,11],
|
45 |
+
"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
|
46 |
+
"upsample_rates": [8,8,2,2],
|
47 |
+
"upsample_initial_channel": 512,
|
48 |
+
"upsample_kernel_sizes": [16,16,4,4],
|
49 |
+
"n_layers_q": 3,
|
50 |
+
"use_spectral_norm": false
|
51 |
+
},
|
52 |
+
"symbols": ["_", "\uff0c", "\u3002", "\uff01", "\uff1f", "\u2014", "\u2026", "\u3105", "\u3106", "\u3107", "\u3108", "\u3109", "\u310a", "\u310b", "\u310c", "\u310d", "\u310e", "\u310f", "\u3110", "\u3111", "\u3112", "\u3113", "\u3114", "\u3115", "\u3116", "\u3117", "\u3118", "\u3119", "\u311a", "\u311b", "\u311c", "\u311d", "\u311e", "\u311f", "\u3120", "\u3121", "\u3122", "\u3123", "\u3124", "\u3125", "\u3126", "\u3127", "\u3128", "\u3129", "\u02c9", "\u02ca", "\u02c7", "\u02cb", "\u02d9", " "]
|
53 |
+
}
|
data_utils.py
ADDED
@@ -0,0 +1,392 @@
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|
|
|
|
|
1 |
+
import time
|
2 |
+
import os
|
3 |
+
import random
|
4 |
+
import numpy as np
|
5 |
+
import torch
|
6 |
+
import torch.utils.data
|
7 |
+
|
8 |
+
import commons
|
9 |
+
from mel_processing import spectrogram_torch
|
10 |
+
from utils import load_wav_to_torch, load_filepaths_and_text
|
11 |
+
from text import text_to_sequence, cleaned_text_to_sequence
|
12 |
+
|
13 |
+
|
14 |
+
class TextAudioLoader(torch.utils.data.Dataset):
|
15 |
+
"""
|
16 |
+
1) loads audio, text pairs
|
17 |
+
2) normalizes text and converts them to sequences of integers
|
18 |
+
3) computes spectrograms from audio files.
|
19 |
+
"""
|
20 |
+
def __init__(self, audiopaths_and_text, hparams):
|
21 |
+
self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
|
22 |
+
self.text_cleaners = hparams.text_cleaners
|
23 |
+
self.max_wav_value = hparams.max_wav_value
|
24 |
+
self.sampling_rate = hparams.sampling_rate
|
25 |
+
self.filter_length = hparams.filter_length
|
26 |
+
self.hop_length = hparams.hop_length
|
27 |
+
self.win_length = hparams.win_length
|
28 |
+
self.sampling_rate = hparams.sampling_rate
|
29 |
+
|
30 |
+
self.cleaned_text = getattr(hparams, "cleaned_text", False)
|
31 |
+
|
32 |
+
self.add_blank = hparams.add_blank
|
33 |
+
self.min_text_len = getattr(hparams, "min_text_len", 1)
|
34 |
+
self.max_text_len = getattr(hparams, "max_text_len", 190)
|
35 |
+
|
36 |
+
random.seed(1234)
|
37 |
+
random.shuffle(self.audiopaths_and_text)
|
38 |
+
self._filter()
|
39 |
+
|
40 |
+
|
41 |
+
def _filter(self):
|
42 |
+
"""
|
43 |
+
Filter text & store spec lengths
|
44 |
+
"""
|
45 |
+
# Store spectrogram lengths for Bucketing
|
46 |
+
# wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
|
47 |
+
# spec_length = wav_length // hop_length
|
48 |
+
|
49 |
+
audiopaths_and_text_new = []
|
50 |
+
lengths = []
|
51 |
+
for audiopath, text in self.audiopaths_and_text:
|
52 |
+
if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
|
53 |
+
audiopaths_and_text_new.append([audiopath, text])
|
54 |
+
lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
|
55 |
+
self.audiopaths_and_text = audiopaths_and_text_new
|
56 |
+
self.lengths = lengths
|
57 |
+
|
58 |
+
def get_audio_text_pair(self, audiopath_and_text):
|
59 |
+
# separate filename and text
|
60 |
+
audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
|
61 |
+
text = self.get_text(text)
|
62 |
+
spec, wav = self.get_audio(audiopath)
|
63 |
+
return (text, spec, wav)
|
64 |
+
|
65 |
+
def get_audio(self, filename):
|
66 |
+
audio, sampling_rate = load_wav_to_torch(filename)
|
67 |
+
if sampling_rate != self.sampling_rate:
|
68 |
+
raise ValueError("{} SR doesn't match target {} SR".format(
|
69 |
+
sampling_rate, self.sampling_rate))
|
70 |
+
audio_norm = audio / self.max_wav_value
|
71 |
+
audio_norm = audio_norm.unsqueeze(0)
|
72 |
+
spec_filename = filename.replace(".wav", ".spec.pt")
|
73 |
+
if os.path.exists(spec_filename):
|
74 |
+
spec = torch.load(spec_filename)
|
75 |
+
else:
|
76 |
+
spec = spectrogram_torch(audio_norm, self.filter_length,
|
77 |
+
self.sampling_rate, self.hop_length, self.win_length,
|
78 |
+
center=False)
|
79 |
+
spec = torch.squeeze(spec, 0)
|
80 |
+
torch.save(spec, spec_filename)
|
81 |
+
return spec, audio_norm
|
82 |
+
|
83 |
+
def get_text(self, text):
|
84 |
+
if self.cleaned_text:
|
85 |
+
text_norm = cleaned_text_to_sequence(text)
|
86 |
+
else:
|
87 |
+
text_norm = text_to_sequence(text, self.text_cleaners)
|
88 |
+
if self.add_blank:
|
89 |
+
text_norm = commons.intersperse(text_norm, 0)
|
90 |
+
text_norm = torch.LongTensor(text_norm)
|
91 |
+
return text_norm
|
92 |
+
|
93 |
+
def __getitem__(self, index):
|
94 |
+
return self.get_audio_text_pair(self.audiopaths_and_text[index])
|
95 |
+
|
96 |
+
def __len__(self):
|
97 |
+
return len(self.audiopaths_and_text)
|
98 |
+
|
99 |
+
|
100 |
+
class TextAudioCollate():
|
101 |
+
""" Zero-pads model inputs and targets
|
102 |
+
"""
|
103 |
+
def __init__(self, return_ids=False):
|
104 |
+
self.return_ids = return_ids
|
105 |
+
|
106 |
+
def __call__(self, batch):
|
107 |
+
"""Collate's training batch from normalized text and aduio
|
108 |
+
PARAMS
|
109 |
+
------
|
110 |
+
batch: [text_normalized, spec_normalized, wav_normalized]
|
111 |
+
"""
|
112 |
+
# Right zero-pad all one-hot text sequences to max input length
|
113 |
+
_, ids_sorted_decreasing = torch.sort(
|
114 |
+
torch.LongTensor([x[1].size(1) for x in batch]),
|
115 |
+
dim=0, descending=True)
|
116 |
+
|
117 |
+
max_text_len = max([len(x[0]) for x in batch])
|
118 |
+
max_spec_len = max([x[1].size(1) for x in batch])
|
119 |
+
max_wav_len = max([x[2].size(1) for x in batch])
|
120 |
+
|
121 |
+
text_lengths = torch.LongTensor(len(batch))
|
122 |
+
spec_lengths = torch.LongTensor(len(batch))
|
123 |
+
wav_lengths = torch.LongTensor(len(batch))
|
124 |
+
|
125 |
+
text_padded = torch.LongTensor(len(batch), max_text_len)
|
126 |
+
spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
|
127 |
+
wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
|
128 |
+
text_padded.zero_()
|
129 |
+
spec_padded.zero_()
|
130 |
+
wav_padded.zero_()
|
131 |
+
for i in range(len(ids_sorted_decreasing)):
|
132 |
+
row = batch[ids_sorted_decreasing[i]]
|
133 |
+
|
134 |
+
text = row[0]
|
135 |
+
text_padded[i, :text.size(0)] = text
|
136 |
+
text_lengths[i] = text.size(0)
|
137 |
+
|
138 |
+
spec = row[1]
|
139 |
+
spec_padded[i, :, :spec.size(1)] = spec
|
140 |
+
spec_lengths[i] = spec.size(1)
|
141 |
+
|
142 |
+
wav = row[2]
|
143 |
+
wav_padded[i, :, :wav.size(1)] = wav
|
144 |
+
wav_lengths[i] = wav.size(1)
|
145 |
+
|
146 |
+
if self.return_ids:
|
147 |
+
return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, ids_sorted_decreasing
|
148 |
+
return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths
|
149 |
+
|
150 |
+
|
151 |
+
"""Multi speaker version"""
|
152 |
+
class TextAudioSpeakerLoader(torch.utils.data.Dataset):
|
153 |
+
"""
|
154 |
+
1) loads audio, speaker_id, text pairs
|
155 |
+
2) normalizes text and converts them to sequences of integers
|
156 |
+
3) computes spectrograms from audio files.
|
157 |
+
"""
|
158 |
+
def __init__(self, audiopaths_sid_text, hparams):
|
159 |
+
self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
|
160 |
+
self.text_cleaners = hparams.text_cleaners
|
161 |
+
self.max_wav_value = hparams.max_wav_value
|
162 |
+
self.sampling_rate = hparams.sampling_rate
|
163 |
+
self.filter_length = hparams.filter_length
|
164 |
+
self.hop_length = hparams.hop_length
|
165 |
+
self.win_length = hparams.win_length
|
166 |
+
self.sampling_rate = hparams.sampling_rate
|
167 |
+
|
168 |
+
self.cleaned_text = getattr(hparams, "cleaned_text", False)
|
169 |
+
|
170 |
+
self.add_blank = hparams.add_blank
|
171 |
+
self.min_text_len = getattr(hparams, "min_text_len", 1)
|
172 |
+
self.max_text_len = getattr(hparams, "max_text_len", 190)
|
173 |
+
|
174 |
+
random.seed(1234)
|
175 |
+
random.shuffle(self.audiopaths_sid_text)
|
176 |
+
self._filter()
|
177 |
+
|
178 |
+
def _filter(self):
|
179 |
+
"""
|
180 |
+
Filter text & store spec lengths
|
181 |
+
"""
|
182 |
+
# Store spectrogram lengths for Bucketing
|
183 |
+
# wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
|
184 |
+
# spec_length = wav_length // hop_length
|
185 |
+
|
186 |
+
audiopaths_sid_text_new = []
|
187 |
+
lengths = []
|
188 |
+
for audiopath, sid, text in self.audiopaths_sid_text:
|
189 |
+
if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
|
190 |
+
audiopaths_sid_text_new.append([audiopath, sid, text])
|
191 |
+
lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
|
192 |
+
self.audiopaths_sid_text = audiopaths_sid_text_new
|
193 |
+
self.lengths = lengths
|
194 |
+
|
195 |
+
def get_audio_text_speaker_pair(self, audiopath_sid_text):
|
196 |
+
# separate filename, speaker_id and text
|
197 |
+
audiopath, sid, text = audiopath_sid_text[0], audiopath_sid_text[1], audiopath_sid_text[2]
|
198 |
+
text = self.get_text(text)
|
199 |
+
spec, wav = self.get_audio(audiopath)
|
200 |
+
sid = self.get_sid(sid)
|
201 |
+
return (text, spec, wav, sid)
|
202 |
+
|
203 |
+
def get_audio(self, filename):
|
204 |
+
audio, sampling_rate = load_wav_to_torch(filename)
|
205 |
+
if sampling_rate != self.sampling_rate:
|
206 |
+
raise ValueError("{} {} SR doesn't match target {} SR".format(
|
207 |
+
sampling_rate, self.sampling_rate))
|
208 |
+
audio_norm = audio / self.max_wav_value
|
209 |
+
audio_norm = audio_norm.unsqueeze(0)
|
210 |
+
spec_filename = filename.replace(".wav", ".spec.pt")
|
211 |
+
if os.path.exists(spec_filename):
|
212 |
+
spec = torch.load(spec_filename)
|
213 |
+
else:
|
214 |
+
spec = spectrogram_torch(audio_norm, self.filter_length,
|
215 |
+
self.sampling_rate, self.hop_length, self.win_length,
|
216 |
+
center=False)
|
217 |
+
spec = torch.squeeze(spec, 0)
|
218 |
+
torch.save(spec, spec_filename)
|
219 |
+
return spec, audio_norm
|
220 |
+
|
221 |
+
def get_text(self, text):
|
222 |
+
if self.cleaned_text:
|
223 |
+
text_norm = cleaned_text_to_sequence(text)
|
224 |
+
else:
|
225 |
+
text_norm = text_to_sequence(text, self.text_cleaners)
|
226 |
+
if self.add_blank:
|
227 |
+
text_norm = commons.intersperse(text_norm, 0)
|
228 |
+
text_norm = torch.LongTensor(text_norm)
|
229 |
+
return text_norm
|
230 |
+
|
231 |
+
def get_sid(self, sid):
|
232 |
+
sid = torch.LongTensor([int(sid)])
|
233 |
+
return sid
|
234 |
+
|
235 |
+
def __getitem__(self, index):
|
236 |
+
return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
|
237 |
+
|
238 |
+
def __len__(self):
|
239 |
+
return len(self.audiopaths_sid_text)
|
240 |
+
|
241 |
+
|
242 |
+
class TextAudioSpeakerCollate():
|
243 |
+
""" Zero-pads model inputs and targets
|
244 |
+
"""
|
245 |
+
def __init__(self, return_ids=False):
|
246 |
+
self.return_ids = return_ids
|
247 |
+
|
248 |
+
def __call__(self, batch):
|
249 |
+
"""Collate's training batch from normalized text, audio and speaker identities
|
250 |
+
PARAMS
|
251 |
+
------
|
252 |
+
batch: [text_normalized, spec_normalized, wav_normalized, sid]
|
253 |
+
"""
|
254 |
+
# Right zero-pad all one-hot text sequences to max input length
|
255 |
+
_, ids_sorted_decreasing = torch.sort(
|
256 |
+
torch.LongTensor([x[1].size(1) for x in batch]),
|
257 |
+
dim=0, descending=True)
|
258 |
+
|
259 |
+
max_text_len = max([len(x[0]) for x in batch])
|
260 |
+
max_spec_len = max([x[1].size(1) for x in batch])
|
261 |
+
max_wav_len = max([x[2].size(1) for x in batch])
|
262 |
+
|
263 |
+
text_lengths = torch.LongTensor(len(batch))
|
264 |
+
spec_lengths = torch.LongTensor(len(batch))
|
265 |
+
wav_lengths = torch.LongTensor(len(batch))
|
266 |
+
sid = torch.LongTensor(len(batch))
|
267 |
+
|
268 |
+
text_padded = torch.LongTensor(len(batch), max_text_len)
|
269 |
+
spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
|
270 |
+
wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
|
271 |
+
text_padded.zero_()
|
272 |
+
spec_padded.zero_()
|
273 |
+
wav_padded.zero_()
|
274 |
+
for i in range(len(ids_sorted_decreasing)):
|
275 |
+
row = batch[ids_sorted_decreasing[i]]
|
276 |
+
|
277 |
+
text = row[0]
|
278 |
+
text_padded[i, :text.size(0)] = text
|
279 |
+
text_lengths[i] = text.size(0)
|
280 |
+
|
281 |
+
spec = row[1]
|
282 |
+
spec_padded[i, :, :spec.size(1)] = spec
|
283 |
+
spec_lengths[i] = spec.size(1)
|
284 |
+
|
285 |
+
wav = row[2]
|
286 |
+
wav_padded[i, :, :wav.size(1)] = wav
|
287 |
+
wav_lengths[i] = wav.size(1)
|
288 |
+
|
289 |
+
sid[i] = row[3]
|
290 |
+
|
291 |
+
if self.return_ids:
|
292 |
+
return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, ids_sorted_decreasing
|
293 |
+
return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid
|
294 |
+
|
295 |
+
|
296 |
+
class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
|
297 |
+
"""
|
298 |
+
Maintain similar input lengths in a batch.
|
299 |
+
Length groups are specified by boundaries.
|
300 |
+
Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
|
301 |
+
|
302 |
+
It removes samples which are not included in the boundaries.
|
303 |
+
Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
|
304 |
+
"""
|
305 |
+
def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
|
306 |
+
super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
|
307 |
+
self.lengths = dataset.lengths
|
308 |
+
self.batch_size = batch_size
|
309 |
+
self.boundaries = boundaries
|
310 |
+
|
311 |
+
self.buckets, self.num_samples_per_bucket = self._create_buckets()
|
312 |
+
self.total_size = sum(self.num_samples_per_bucket)
|
313 |
+
self.num_samples = self.total_size // self.num_replicas
|
314 |
+
|
315 |
+
def _create_buckets(self):
|
316 |
+
buckets = [[] for _ in range(len(self.boundaries) - 1)]
|
317 |
+
for i in range(len(self.lengths)):
|
318 |
+
length = self.lengths[i]
|
319 |
+
idx_bucket = self._bisect(length)
|
320 |
+
if idx_bucket != -1:
|
321 |
+
buckets[idx_bucket].append(i)
|
322 |
+
|
323 |
+
for i in range(len(buckets) - 1, 0, -1):
|
324 |
+
if len(buckets[i]) == 0:
|
325 |
+
buckets.pop(i)
|
326 |
+
self.boundaries.pop(i+1)
|
327 |
+
|
328 |
+
num_samples_per_bucket = []
|
329 |
+
for i in range(len(buckets)):
|
330 |
+
len_bucket = len(buckets[i])
|
331 |
+
total_batch_size = self.num_replicas * self.batch_size
|
332 |
+
rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
|
333 |
+
num_samples_per_bucket.append(len_bucket + rem)
|
334 |
+
return buckets, num_samples_per_bucket
|
335 |
+
|
336 |
+
def __iter__(self):
|
337 |
+
# deterministically shuffle based on epoch
|
338 |
+
g = torch.Generator()
|
339 |
+
g.manual_seed(self.epoch)
|
340 |
+
|
341 |
+
indices = []
|
342 |
+
if self.shuffle:
|
343 |
+
for bucket in self.buckets:
|
344 |
+
indices.append(torch.randperm(len(bucket), generator=g).tolist())
|
345 |
+
else:
|
346 |
+
for bucket in self.buckets:
|
347 |
+
indices.append(list(range(len(bucket))))
|
348 |
+
|
349 |
+
batches = []
|
350 |
+
for i in range(len(self.buckets)):
|
351 |
+
bucket = self.buckets[i]
|
352 |
+
len_bucket = len(bucket)
|
353 |
+
ids_bucket = indices[i]
|
354 |
+
num_samples_bucket = self.num_samples_per_bucket[i]
|
355 |
+
|
356 |
+
# add extra samples to make it evenly divisible
|
357 |
+
rem = num_samples_bucket - len_bucket
|
358 |
+
ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
|
359 |
+
|
360 |
+
# subsample
|
361 |
+
ids_bucket = ids_bucket[self.rank::self.num_replicas]
|
362 |
+
|
363 |
+
# batching
|
364 |
+
for j in range(len(ids_bucket) // self.batch_size):
|
365 |
+
batch = [bucket[idx] for idx in ids_bucket[j*self.batch_size:(j+1)*self.batch_size]]
|
366 |
+
batches.append(batch)
|
367 |
+
|
368 |
+
if self.shuffle:
|
369 |
+
batch_ids = torch.randperm(len(batches), generator=g).tolist()
|
370 |
+
batches = [batches[i] for i in batch_ids]
|
371 |
+
self.batches = batches
|
372 |
+
|
373 |
+
assert len(self.batches) * self.batch_size == self.num_samples
|
374 |
+
return iter(self.batches)
|
375 |
+
|
376 |
+
def _bisect(self, x, lo=0, hi=None):
|
377 |
+
if hi is None:
|
378 |
+
hi = len(self.boundaries) - 1
|
379 |
+
|
380 |
+
if hi > lo:
|
381 |
+
mid = (hi + lo) // 2
|
382 |
+
if self.boundaries[mid] < x and x <= self.boundaries[mid+1]:
|
383 |
+
return mid
|
384 |
+
elif x <= self.boundaries[mid]:
|
385 |
+
return self._bisect(x, lo, mid)
|
386 |
+
else:
|
387 |
+
return self._bisect(x, mid + 1, hi)
|
388 |
+
else:
|
389 |
+
return -1
|
390 |
+
|
391 |
+
def __len__(self):
|
392 |
+
return self.num_samples // self.batch_size
|
header.html
ADDED
@@ -0,0 +1,23 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
|
|
|
1 |
+
<div
|
2 |
+
style="width: 100%;padding-top:116px;background-image: url('https://huggingface.co/spaces/candlend/vits-hoshimi/resolve/main/header.webp');;background-size:cover">
|
3 |
+
<div>
|
4 |
+
<div style="margin: 0px 20px;display: flex;">
|
5 |
+
<div class="bili-avatar" style="padding-top: 20px;">
|
6 |
+
<a href="https://space.bilibili.com/477342747" target="_blank">
|
7 |
+
<img style="width:60px;height:60px;border-radius:30px;max-width:60px;" title="星弥Hoshimi"
|
8 |
+
src="https://huggingface.co/spaces/candlend/vits-hoshimi/resolve/main/avatar.webp">
|
9 |
+
</a>
|
10 |
+
</div>
|
11 |
+
<div style="margin:20px;color:white">
|
12 |
+
<div style="align-items: flex-end;display: flex">
|
13 |
+
<span style="font-size: 20px;min-width:85px;">星弥Hoshimi</span>
|
14 |
+
<img style="padding-left: 3px;" title="粉丝数"
|
15 |
+
src="https://img.shields.io/badge/dynamic/json?color=orange&label=%E7%B2%89%E4%B8%9D%E6%95%B0&query=data.follower&url=https%3A%2F%2Fapi.bilibili.com%2Fx%2Frelation%2Fstat%3Fvmid%3D477342747"></img>
|
16 |
+
<img style="padding-left: 5px;"
|
17 |
+
src="https://img.shields.io/badge/VirtuaReal-%E4%BA%94%E6%9C%9F%E7%94%9F-orange"
|
18 |
+
title="五期生"></img>
|
19 |
+
</div>
|
20 |
+
</div>
|
21 |
+
</div>
|
22 |
+
</div>
|
23 |
+
</div>
|
header.webp
ADDED
mel_processing.py
ADDED
@@ -0,0 +1,116 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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 os
|
3 |
+
import random
|
4 |
+
import torch
|
5 |
+
from torch import nn
|
6 |
+
import torch.nn.functional as F
|
7 |
+
import torch.utils.data
|
8 |
+
import numpy as np
|
9 |
+
import librosa
|
10 |
+
import librosa.util as librosa_util
|
11 |
+
from librosa.util import normalize, pad_center, tiny
|
12 |
+
from scipy.signal import get_window
|
13 |
+
from scipy.io.wavfile import read
|
14 |
+
from librosa.filters import mel as librosa_mel_fn
|
15 |
+
|
16 |
+
MAX_WAV_VALUE = 32768.0
|
17 |
+
|
18 |
+
|
19 |
+
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
|
20 |
+
"""
|
21 |
+
PARAMS
|
22 |
+
------
|
23 |
+
C: compression factor
|
24 |
+
"""
|
25 |
+
return torch.log(torch.clamp(x, min=clip_val) * C)
|
26 |
+
|
27 |
+
|
28 |
+
def dynamic_range_decompression_torch(x, C=1):
|
29 |
+
"""
|
30 |
+
PARAMS
|
31 |
+
------
|
32 |
+
C: compression factor used to compress
|
33 |
+
"""
|
34 |
+
return torch.exp(x) / C
|
35 |
+
|
36 |
+
|
37 |
+
def spectral_normalize_torch(magnitudes):
|
38 |
+
output = dynamic_range_compression_torch(magnitudes)
|
39 |
+
return output
|
40 |
+
|
41 |
+
|
42 |
+
def spectral_de_normalize_torch(magnitudes):
|
43 |
+
output = dynamic_range_decompression_torch(magnitudes)
|
44 |
+
return output
|
45 |
+
|
46 |
+
|
47 |
+
mel_basis = {}
|
48 |
+
hann_window = {}
|
49 |
+
|
50 |
+
|
51 |
+
def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
|
52 |
+
if torch.min(y) < -1.:
|
53 |
+
print('min value is ', torch.min(y))
|
54 |
+
if torch.max(y) > 1.:
|
55 |
+
print('max value is ', torch.max(y))
|
56 |
+
|
57 |
+
global hann_window
|
58 |
+
dtype_device = str(y.dtype) + '_' + str(y.device)
|
59 |
+
wnsize_dtype_device = str(win_size) + '_' + dtype_device
|
60 |
+
if wnsize_dtype_device not in hann_window:
|
61 |
+
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
|
62 |
+
|
63 |
+
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
|
64 |
+
y = y.squeeze(1)
|
65 |
+
|
66 |
+
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
|
67 |
+
center=center, pad_mode='reflect', normalized=False, onesided=True)
|
68 |
+
|
69 |
+
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
|
70 |
+
return spec
|
71 |
+
|
72 |
+
|
73 |
+
def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
|
74 |
+
global mel_basis
|
75 |
+
dtype_device = str(spec.dtype) + '_' + str(spec.device)
|
76 |
+
fmax_dtype_device = str(fmax) + '_' + dtype_device
|
77 |
+
if fmax_dtype_device not in mel_basis:
|
78 |
+
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
|
79 |
+
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
|
80 |
+
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
|
81 |
+
spec = spectral_normalize_torch(spec)
|
82 |
+
return spec
|
83 |
+
|
84 |
+
|
85 |
+
def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
|
86 |
+
if torch.min(y) < -1.:
|
87 |
+
print('min value is ', torch.min(y))
|
88 |
+
if torch.max(y) > 1.:
|
89 |
+
print('max value is ', torch.max(y))
|
90 |
+
|
91 |
+
global mel_basis, hann_window
|
92 |
+
dtype_device = str(y.dtype) + '_' + str(y.device)
|
93 |
+
fmax_dtype_device = str(fmax) + '_' + dtype_device
|
94 |
+
wnsize_dtype_device = str(win_size) + '_' + dtype_device
|
95 |
+
if fmax_dtype_device not in mel_basis:
|
96 |
+
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
|
97 |
+
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
|
98 |
+
if wnsize_dtype_device not in hann_window:
|
99 |
+
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
|
100 |
+
|
101 |
+
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
|
102 |
+
y = y.squeeze(1)
|
103 |
+
|
104 |
+
# spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
|
105 |
+
# center=center, pad_mode='reflect', normalized=False, onesided=True)
|
106 |
+
with torch.autocast("cuda", enabled=False):
|
107 |
+
y = y.float()
|
108 |
+
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
|
109 |
+
center=center, pad_mode='reflect', normalized=False, onesided=True)
|
110 |
+
|
111 |
+
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
|
112 |
+
|
113 |
+
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
|
114 |
+
spec = spectral_normalize_torch(spec)
|
115 |
+
|
116 |
+
return spec
|
models.py
ADDED
@@ -0,0 +1,534 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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 |
+
import monotonic_align
|
11 |
+
|
12 |
+
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
13 |
+
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
14 |
+
from commons import init_weights, get_padding
|
15 |
+
|
16 |
+
|
17 |
+
class StochasticDurationPredictor(nn.Module):
|
18 |
+
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
|
19 |
+
super().__init__()
|
20 |
+
filter_channels = in_channels # it needs to be removed from future version.
|
21 |
+
self.in_channels = in_channels
|
22 |
+
self.filter_channels = filter_channels
|
23 |
+
self.kernel_size = kernel_size
|
24 |
+
self.p_dropout = p_dropout
|
25 |
+
self.n_flows = n_flows
|
26 |
+
self.gin_channels = gin_channels
|
27 |
+
|
28 |
+
self.log_flow = modules.Log()
|
29 |
+
self.flows = nn.ModuleList()
|
30 |
+
self.flows.append(modules.ElementwiseAffine(2))
|
31 |
+
for i in range(n_flows):
|
32 |
+
self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
|
33 |
+
self.flows.append(modules.Flip())
|
34 |
+
|
35 |
+
self.post_pre = nn.Conv1d(1, filter_channels, 1)
|
36 |
+
self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
|
37 |
+
self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
|
38 |
+
self.post_flows = nn.ModuleList()
|
39 |
+
self.post_flows.append(modules.ElementwiseAffine(2))
|
40 |
+
for i in range(4):
|
41 |
+
self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
|
42 |
+
self.post_flows.append(modules.Flip())
|
43 |
+
|
44 |
+
self.pre = nn.Conv1d(in_channels, filter_channels, 1)
|
45 |
+
self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
|
46 |
+
self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
|
47 |
+
if gin_channels != 0:
|
48 |
+
self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
|
49 |
+
|
50 |
+
def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
|
51 |
+
x = torch.detach(x)
|
52 |
+
x = self.pre(x)
|
53 |
+
if g is not None:
|
54 |
+
g = torch.detach(g)
|
55 |
+
x = x + self.cond(g)
|
56 |
+
x = self.convs(x, x_mask)
|
57 |
+
x = self.proj(x) * x_mask
|
58 |
+
|
59 |
+
if not reverse:
|
60 |
+
flows = self.flows
|
61 |
+
assert w is not None
|
62 |
+
|
63 |
+
logdet_tot_q = 0
|
64 |
+
h_w = self.post_pre(w)
|
65 |
+
h_w = self.post_convs(h_w, x_mask)
|
66 |
+
h_w = self.post_proj(h_w) * x_mask
|
67 |
+
e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
|
68 |
+
z_q = e_q
|
69 |
+
for flow in self.post_flows:
|
70 |
+
z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
|
71 |
+
logdet_tot_q += logdet_q
|
72 |
+
z_u, z1 = torch.split(z_q, [1, 1], 1)
|
73 |
+
u = torch.sigmoid(z_u) * x_mask
|
74 |
+
z0 = (w - u) * x_mask
|
75 |
+
logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
|
76 |
+
logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
|
77 |
+
|
78 |
+
logdet_tot = 0
|
79 |
+
z0, logdet = self.log_flow(z0, x_mask)
|
80 |
+
logdet_tot += logdet
|
81 |
+
z = torch.cat([z0, z1], 1)
|
82 |
+
for flow in flows:
|
83 |
+
z, logdet = flow(z, x_mask, g=x, reverse=reverse)
|
84 |
+
logdet_tot = logdet_tot + logdet
|
85 |
+
nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
|
86 |
+
return nll + logq # [b]
|
87 |
+
else:
|
88 |
+
flows = list(reversed(self.flows))
|
89 |
+
flows = flows[:-2] + [flows[-1]] # remove a useless vflow
|
90 |
+
z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
|
91 |
+
for flow in flows:
|
92 |
+
z = flow(z, x_mask, g=x, reverse=reverse)
|
93 |
+
z0, z1 = torch.split(z, [1, 1], 1)
|
94 |
+
logw = z0
|
95 |
+
return logw
|
96 |
+
|
97 |
+
|
98 |
+
class DurationPredictor(nn.Module):
|
99 |
+
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
|
100 |
+
super().__init__()
|
101 |
+
|
102 |
+
self.in_channels = in_channels
|
103 |
+
self.filter_channels = filter_channels
|
104 |
+
self.kernel_size = kernel_size
|
105 |
+
self.p_dropout = p_dropout
|
106 |
+
self.gin_channels = gin_channels
|
107 |
+
|
108 |
+
self.drop = nn.Dropout(p_dropout)
|
109 |
+
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
|
110 |
+
self.norm_1 = modules.LayerNorm(filter_channels)
|
111 |
+
self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
|
112 |
+
self.norm_2 = modules.LayerNorm(filter_channels)
|
113 |
+
self.proj = nn.Conv1d(filter_channels, 1, 1)
|
114 |
+
|
115 |
+
if gin_channels != 0:
|
116 |
+
self.cond = nn.Conv1d(gin_channels, in_channels, 1)
|
117 |
+
|
118 |
+
def forward(self, x, x_mask, g=None):
|
119 |
+
x = torch.detach(x)
|
120 |
+
if g is not None:
|
121 |
+
g = torch.detach(g)
|
122 |
+
x = x + self.cond(g)
|
123 |
+
x = self.conv_1(x * x_mask)
|
124 |
+
x = torch.relu(x)
|
125 |
+
x = self.norm_1(x)
|
126 |
+
x = self.drop(x)
|
127 |
+
x = self.conv_2(x * x_mask)
|
128 |
+
x = torch.relu(x)
|
129 |
+
x = self.norm_2(x)
|
130 |
+
x = self.drop(x)
|
131 |
+
x = self.proj(x * x_mask)
|
132 |
+
return x * x_mask
|
133 |
+
|
134 |
+
|
135 |
+
class TextEncoder(nn.Module):
|
136 |
+
def __init__(self,
|
137 |
+
n_vocab,
|
138 |
+
out_channels,
|
139 |
+
hidden_channels,
|
140 |
+
filter_channels,
|
141 |
+
n_heads,
|
142 |
+
n_layers,
|
143 |
+
kernel_size,
|
144 |
+
p_dropout):
|
145 |
+
super().__init__()
|
146 |
+
self.n_vocab = n_vocab
|
147 |
+
self.out_channels = out_channels
|
148 |
+
self.hidden_channels = hidden_channels
|
149 |
+
self.filter_channels = filter_channels
|
150 |
+
self.n_heads = n_heads
|
151 |
+
self.n_layers = n_layers
|
152 |
+
self.kernel_size = kernel_size
|
153 |
+
self.p_dropout = p_dropout
|
154 |
+
|
155 |
+
self.emb = nn.Embedding(n_vocab, hidden_channels)
|
156 |
+
nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
|
157 |
+
|
158 |
+
self.encoder = attentions.Encoder(
|
159 |
+
hidden_channels,
|
160 |
+
filter_channels,
|
161 |
+
n_heads,
|
162 |
+
n_layers,
|
163 |
+
kernel_size,
|
164 |
+
p_dropout)
|
165 |
+
self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
166 |
+
|
167 |
+
def forward(self, x, x_lengths):
|
168 |
+
x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
|
169 |
+
x = torch.transpose(x, 1, -1) # [b, h, t]
|
170 |
+
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
171 |
+
|
172 |
+
x = self.encoder(x * x_mask, x_mask)
|
173 |
+
stats = self.proj(x) * x_mask
|
174 |
+
|
175 |
+
m, logs = torch.split(stats, self.out_channels, dim=1)
|
176 |
+
return x, m, logs, x_mask
|
177 |
+
|
178 |
+
|
179 |
+
class ResidualCouplingBlock(nn.Module):
|
180 |
+
def __init__(self,
|
181 |
+
channels,
|
182 |
+
hidden_channels,
|
183 |
+
kernel_size,
|
184 |
+
dilation_rate,
|
185 |
+
n_layers,
|
186 |
+
n_flows=4,
|
187 |
+
gin_channels=0):
|
188 |
+
super().__init__()
|
189 |
+
self.channels = channels
|
190 |
+
self.hidden_channels = hidden_channels
|
191 |
+
self.kernel_size = kernel_size
|
192 |
+
self.dilation_rate = dilation_rate
|
193 |
+
self.n_layers = n_layers
|
194 |
+
self.n_flows = n_flows
|
195 |
+
self.gin_channels = gin_channels
|
196 |
+
|
197 |
+
self.flows = nn.ModuleList()
|
198 |
+
for i in range(n_flows):
|
199 |
+
self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
|
200 |
+
self.flows.append(modules.Flip())
|
201 |
+
|
202 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
203 |
+
if not reverse:
|
204 |
+
for flow in self.flows:
|
205 |
+
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
206 |
+
else:
|
207 |
+
for flow in reversed(self.flows):
|
208 |
+
x = flow(x, x_mask, g=g, reverse=reverse)
|
209 |
+
return x
|
210 |
+
|
211 |
+
|
212 |
+
class PosteriorEncoder(nn.Module):
|
213 |
+
def __init__(self,
|
214 |
+
in_channels,
|
215 |
+
out_channels,
|
216 |
+
hidden_channels,
|
217 |
+
kernel_size,
|
218 |
+
dilation_rate,
|
219 |
+
n_layers,
|
220 |
+
gin_channels=0):
|
221 |
+
super().__init__()
|
222 |
+
self.in_channels = in_channels
|
223 |
+
self.out_channels = out_channels
|
224 |
+
self.hidden_channels = hidden_channels
|
225 |
+
self.kernel_size = kernel_size
|
226 |
+
self.dilation_rate = dilation_rate
|
227 |
+
self.n_layers = n_layers
|
228 |
+
self.gin_channels = gin_channels
|
229 |
+
|
230 |
+
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
231 |
+
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
|
232 |
+
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
233 |
+
|
234 |
+
def forward(self, x, x_lengths, g=None):
|
235 |
+
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
236 |
+
x = self.pre(x) * x_mask
|
237 |
+
x = self.enc(x, x_mask, g=g)
|
238 |
+
stats = self.proj(x) * x_mask
|
239 |
+
m, logs = torch.split(stats, self.out_channels, dim=1)
|
240 |
+
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
241 |
+
return z, m, logs, x_mask
|
242 |
+
|
243 |
+
|
244 |
+
class Generator(torch.nn.Module):
|
245 |
+
def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
|
246 |
+
super(Generator, self).__init__()
|
247 |
+
self.num_kernels = len(resblock_kernel_sizes)
|
248 |
+
self.num_upsamples = len(upsample_rates)
|
249 |
+
self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
|
250 |
+
resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
|
251 |
+
|
252 |
+
self.ups = nn.ModuleList()
|
253 |
+
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
254 |
+
self.ups.append(weight_norm(
|
255 |
+
ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
|
256 |
+
k, u, padding=(k-u)//2)))
|
257 |
+
|
258 |
+
self.resblocks = nn.ModuleList()
|
259 |
+
for i in range(len(self.ups)):
|
260 |
+
ch = upsample_initial_channel//(2**(i+1))
|
261 |
+
for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
|
262 |
+
self.resblocks.append(resblock(ch, k, d))
|
263 |
+
|
264 |
+
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
|
265 |
+
self.ups.apply(init_weights)
|
266 |
+
|
267 |
+
if gin_channels != 0:
|
268 |
+
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
|
269 |
+
|
270 |
+
def forward(self, x, g=None):
|
271 |
+
x = self.conv_pre(x)
|
272 |
+
if g is not None:
|
273 |
+
x = x + self.cond(g)
|
274 |
+
|
275 |
+
for i in range(self.num_upsamples):
|
276 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
277 |
+
x = self.ups[i](x)
|
278 |
+
xs = None
|
279 |
+
for j in range(self.num_kernels):
|
280 |
+
if xs is None:
|
281 |
+
xs = self.resblocks[i*self.num_kernels+j](x)
|
282 |
+
else:
|
283 |
+
xs += self.resblocks[i*self.num_kernels+j](x)
|
284 |
+
x = xs / self.num_kernels
|
285 |
+
x = F.leaky_relu(x)
|
286 |
+
x = self.conv_post(x)
|
287 |
+
x = torch.tanh(x)
|
288 |
+
|
289 |
+
return x
|
290 |
+
|
291 |
+
def remove_weight_norm(self):
|
292 |
+
print('Removing weight norm...')
|
293 |
+
for l in self.ups:
|
294 |
+
remove_weight_norm(l)
|
295 |
+
for l in self.resblocks:
|
296 |
+
l.remove_weight_norm()
|
297 |
+
|
298 |
+
|
299 |
+
class DiscriminatorP(torch.nn.Module):
|
300 |
+
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
301 |
+
super(DiscriminatorP, self).__init__()
|
302 |
+
self.period = period
|
303 |
+
self.use_spectral_norm = use_spectral_norm
|
304 |
+
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
305 |
+
self.convs = nn.ModuleList([
|
306 |
+
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
307 |
+
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
308 |
+
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
309 |
+
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
310 |
+
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
|
311 |
+
])
|
312 |
+
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
313 |
+
|
314 |
+
def forward(self, x):
|
315 |
+
fmap = []
|
316 |
+
|
317 |
+
# 1d to 2d
|
318 |
+
b, c, t = x.shape
|
319 |
+
if t % self.period != 0: # pad first
|
320 |
+
n_pad = self.period - (t % self.period)
|
321 |
+
x = F.pad(x, (0, n_pad), "reflect")
|
322 |
+
t = t + n_pad
|
323 |
+
x = x.view(b, c, t // self.period, self.period)
|
324 |
+
|
325 |
+
for l in self.convs:
|
326 |
+
x = l(x)
|
327 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
328 |
+
fmap.append(x)
|
329 |
+
x = self.conv_post(x)
|
330 |
+
fmap.append(x)
|
331 |
+
x = torch.flatten(x, 1, -1)
|
332 |
+
|
333 |
+
return x, fmap
|
334 |
+
|
335 |
+
|
336 |
+
class DiscriminatorS(torch.nn.Module):
|
337 |
+
def __init__(self, use_spectral_norm=False):
|
338 |
+
super(DiscriminatorS, self).__init__()
|
339 |
+
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
340 |
+
self.convs = nn.ModuleList([
|
341 |
+
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
|
342 |
+
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
|
343 |
+
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
|
344 |
+
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
|
345 |
+
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
|
346 |
+
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
347 |
+
])
|
348 |
+
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
349 |
+
|
350 |
+
def forward(self, x):
|
351 |
+
fmap = []
|
352 |
+
|
353 |
+
for l in self.convs:
|
354 |
+
x = l(x)
|
355 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
356 |
+
fmap.append(x)
|
357 |
+
x = self.conv_post(x)
|
358 |
+
fmap.append(x)
|
359 |
+
x = torch.flatten(x, 1, -1)
|
360 |
+
|
361 |
+
return x, fmap
|
362 |
+
|
363 |
+
|
364 |
+
class MultiPeriodDiscriminator(torch.nn.Module):
|
365 |
+
def __init__(self, use_spectral_norm=False):
|
366 |
+
super(MultiPeriodDiscriminator, self).__init__()
|
367 |
+
periods = [2,3,5,7,11]
|
368 |
+
|
369 |
+
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
|
370 |
+
discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
|
371 |
+
self.discriminators = nn.ModuleList(discs)
|
372 |
+
|
373 |
+
def forward(self, y, y_hat):
|
374 |
+
y_d_rs = []
|
375 |
+
y_d_gs = []
|
376 |
+
fmap_rs = []
|
377 |
+
fmap_gs = []
|
378 |
+
for i, d in enumerate(self.discriminators):
|
379 |
+
y_d_r, fmap_r = d(y)
|
380 |
+
y_d_g, fmap_g = d(y_hat)
|
381 |
+
y_d_rs.append(y_d_r)
|
382 |
+
y_d_gs.append(y_d_g)
|
383 |
+
fmap_rs.append(fmap_r)
|
384 |
+
fmap_gs.append(fmap_g)
|
385 |
+
|
386 |
+
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
387 |
+
|
388 |
+
|
389 |
+
|
390 |
+
class SynthesizerTrn(nn.Module):
|
391 |
+
"""
|
392 |
+
Synthesizer for Training
|
393 |
+
"""
|
394 |
+
|
395 |
+
def __init__(self,
|
396 |
+
n_vocab,
|
397 |
+
spec_channels,
|
398 |
+
segment_size,
|
399 |
+
inter_channels,
|
400 |
+
hidden_channels,
|
401 |
+
filter_channels,
|
402 |
+
n_heads,
|
403 |
+
n_layers,
|
404 |
+
kernel_size,
|
405 |
+
p_dropout,
|
406 |
+
resblock,
|
407 |
+
resblock_kernel_sizes,
|
408 |
+
resblock_dilation_sizes,
|
409 |
+
upsample_rates,
|
410 |
+
upsample_initial_channel,
|
411 |
+
upsample_kernel_sizes,
|
412 |
+
n_speakers=0,
|
413 |
+
gin_channels=0,
|
414 |
+
use_sdp=True,
|
415 |
+
**kwargs):
|
416 |
+
|
417 |
+
super().__init__()
|
418 |
+
self.n_vocab = n_vocab
|
419 |
+
self.spec_channels = spec_channels
|
420 |
+
self.inter_channels = inter_channels
|
421 |
+
self.hidden_channels = hidden_channels
|
422 |
+
self.filter_channels = filter_channels
|
423 |
+
self.n_heads = n_heads
|
424 |
+
self.n_layers = n_layers
|
425 |
+
self.kernel_size = kernel_size
|
426 |
+
self.p_dropout = p_dropout
|
427 |
+
self.resblock = resblock
|
428 |
+
self.resblock_kernel_sizes = resblock_kernel_sizes
|
429 |
+
self.resblock_dilation_sizes = resblock_dilation_sizes
|
430 |
+
self.upsample_rates = upsample_rates
|
431 |
+
self.upsample_initial_channel = upsample_initial_channel
|
432 |
+
self.upsample_kernel_sizes = upsample_kernel_sizes
|
433 |
+
self.segment_size = segment_size
|
434 |
+
self.n_speakers = n_speakers
|
435 |
+
self.gin_channels = gin_channels
|
436 |
+
|
437 |
+
self.use_sdp = use_sdp
|
438 |
+
|
439 |
+
self.enc_p = TextEncoder(n_vocab,
|
440 |
+
inter_channels,
|
441 |
+
hidden_channels,
|
442 |
+
filter_channels,
|
443 |
+
n_heads,
|
444 |
+
n_layers,
|
445 |
+
kernel_size,
|
446 |
+
p_dropout)
|
447 |
+
self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
|
448 |
+
self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
|
449 |
+
self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
|
450 |
+
|
451 |
+
if use_sdp:
|
452 |
+
self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
|
453 |
+
else:
|
454 |
+
self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
|
455 |
+
|
456 |
+
if n_speakers > 1:
|
457 |
+
self.emb_g = nn.Embedding(n_speakers, gin_channels)
|
458 |
+
|
459 |
+
def forward(self, x, x_lengths, y, y_lengths, sid=None):
|
460 |
+
|
461 |
+
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
|
462 |
+
if self.n_speakers > 0:
|
463 |
+
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
|
464 |
+
else:
|
465 |
+
g = None
|
466 |
+
|
467 |
+
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
|
468 |
+
z_p = self.flow(z, y_mask, g=g)
|
469 |
+
|
470 |
+
with torch.no_grad():
|
471 |
+
# negative cross-entropy
|
472 |
+
s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
|
473 |
+
neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
|
474 |
+
neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
|
475 |
+
neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
|
476 |
+
neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
|
477 |
+
neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
|
478 |
+
|
479 |
+
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
|
480 |
+
attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
|
481 |
+
|
482 |
+
w = attn.sum(2)
|
483 |
+
if self.use_sdp:
|
484 |
+
l_length = self.dp(x, x_mask, w, g=g)
|
485 |
+
l_length = l_length / torch.sum(x_mask)
|
486 |
+
else:
|
487 |
+
logw_ = torch.log(w + 1e-6) * x_mask
|
488 |
+
logw = self.dp(x, x_mask, g=g)
|
489 |
+
l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging
|
490 |
+
|
491 |
+
# expand prior
|
492 |
+
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
|
493 |
+
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
|
494 |
+
|
495 |
+
z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
|
496 |
+
o = self.dec(z_slice, g=g)
|
497 |
+
return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
|
498 |
+
|
499 |
+
def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
|
500 |
+
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
|
501 |
+
if self.n_speakers > 0:
|
502 |
+
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
|
503 |
+
else:
|
504 |
+
g = None
|
505 |
+
|
506 |
+
if self.use_sdp:
|
507 |
+
logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
|
508 |
+
else:
|
509 |
+
logw = self.dp(x, x_mask, g=g)
|
510 |
+
w = torch.exp(logw) * x_mask * length_scale
|
511 |
+
w_ceil = torch.ceil(w)
|
512 |
+
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
|
513 |
+
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
|
514 |
+
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
|
515 |
+
attn = commons.generate_path(w_ceil, attn_mask)
|
516 |
+
|
517 |
+
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
|
518 |
+
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
|
519 |
+
|
520 |
+
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
|
521 |
+
z = self.flow(z_p, y_mask, g=g, reverse=True)
|
522 |
+
o = self.dec((z * y_mask)[:,:,:max_len], g=g)
|
523 |
+
return o, attn, y_mask, (z, z_p, m_p, logs_p)
|
524 |
+
|
525 |
+
def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
|
526 |
+
assert self.n_speakers > 0, "n_speakers have to be larger than 0."
|
527 |
+
g_src = self.emb_g(sid_src).unsqueeze(-1)
|
528 |
+
g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
|
529 |
+
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
|
530 |
+
z_p = self.flow(z, y_mask, g=g_src)
|
531 |
+
z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
|
532 |
+
o_hat = self.dec(z_hat * y_mask, g=g_tgt)
|
533 |
+
return o_hat, y_mask, (z, z_p, z_hat)
|
534 |
+
|
modules.py
ADDED
@@ -0,0 +1,390 @@
|
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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__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
|
37 |
+
super().__init__()
|
38 |
+
self.in_channels = in_channels
|
39 |
+
self.hidden_channels = hidden_channels
|
40 |
+
self.out_channels = out_channels
|
41 |
+
self.kernel_size = kernel_size
|
42 |
+
self.n_layers = n_layers
|
43 |
+
self.p_dropout = p_dropout
|
44 |
+
assert n_layers > 1, "Number of layers should be larger than 0."
|
45 |
+
|
46 |
+
self.conv_layers = nn.ModuleList()
|
47 |
+
self.norm_layers = nn.ModuleList()
|
48 |
+
self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
|
49 |
+
self.norm_layers.append(LayerNorm(hidden_channels))
|
50 |
+
self.relu_drop = nn.Sequential(
|
51 |
+
nn.ReLU(),
|
52 |
+
nn.Dropout(p_dropout))
|
53 |
+
for _ in range(n_layers-1):
|
54 |
+
self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
|
55 |
+
self.norm_layers.append(LayerNorm(hidden_channels))
|
56 |
+
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
57 |
+
self.proj.weight.data.zero_()
|
58 |
+
self.proj.bias.data.zero_()
|
59 |
+
|
60 |
+
def forward(self, x, x_mask):
|
61 |
+
x_org = x
|
62 |
+
for i in range(self.n_layers):
|
63 |
+
x = self.conv_layers[i](x * x_mask)
|
64 |
+
x = self.norm_layers[i](x)
|
65 |
+
x = self.relu_drop(x)
|
66 |
+
x = x_org + self.proj(x)
|
67 |
+
return x * x_mask
|
68 |
+
|
69 |
+
|
70 |
+
class DDSConv(nn.Module):
|
71 |
+
"""
|
72 |
+
Dialted and Depth-Separable Convolution
|
73 |
+
"""
|
74 |
+
def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
|
75 |
+
super().__init__()
|
76 |
+
self.channels = channels
|
77 |
+
self.kernel_size = kernel_size
|
78 |
+
self.n_layers = n_layers
|
79 |
+
self.p_dropout = p_dropout
|
80 |
+
|
81 |
+
self.drop = nn.Dropout(p_dropout)
|
82 |
+
self.convs_sep = nn.ModuleList()
|
83 |
+
self.convs_1x1 = nn.ModuleList()
|
84 |
+
self.norms_1 = nn.ModuleList()
|
85 |
+
self.norms_2 = nn.ModuleList()
|
86 |
+
for i in range(n_layers):
|
87 |
+
dilation = kernel_size ** i
|
88 |
+
padding = (kernel_size * dilation - dilation) // 2
|
89 |
+
self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
|
90 |
+
groups=channels, dilation=dilation, padding=padding
|
91 |
+
))
|
92 |
+
self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
|
93 |
+
self.norms_1.append(LayerNorm(channels))
|
94 |
+
self.norms_2.append(LayerNorm(channels))
|
95 |
+
|
96 |
+
def forward(self, x, x_mask, g=None):
|
97 |
+
if g is not None:
|
98 |
+
x = x + g
|
99 |
+
for i in range(self.n_layers):
|
100 |
+
y = self.convs_sep[i](x * x_mask)
|
101 |
+
y = self.norms_1[i](y)
|
102 |
+
y = F.gelu(y)
|
103 |
+
y = self.convs_1x1[i](y)
|
104 |
+
y = self.norms_2[i](y)
|
105 |
+
y = F.gelu(y)
|
106 |
+
y = self.drop(y)
|
107 |
+
x = x + y
|
108 |
+
return x * x_mask
|
109 |
+
|
110 |
+
|
111 |
+
class WN(torch.nn.Module):
|
112 |
+
def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
|
113 |
+
super(WN, self).__init__()
|
114 |
+
assert(kernel_size % 2 == 1)
|
115 |
+
self.hidden_channels =hidden_channels
|
116 |
+
self.kernel_size = kernel_size,
|
117 |
+
self.dilation_rate = dilation_rate
|
118 |
+
self.n_layers = n_layers
|
119 |
+
self.gin_channels = gin_channels
|
120 |
+
self.p_dropout = p_dropout
|
121 |
+
|
122 |
+
self.in_layers = torch.nn.ModuleList()
|
123 |
+
self.res_skip_layers = torch.nn.ModuleList()
|
124 |
+
self.drop = nn.Dropout(p_dropout)
|
125 |
+
|
126 |
+
if gin_channels != 0:
|
127 |
+
cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
|
128 |
+
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
|
129 |
+
|
130 |
+
for i in range(n_layers):
|
131 |
+
dilation = dilation_rate ** i
|
132 |
+
padding = int((kernel_size * dilation - dilation) / 2)
|
133 |
+
in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
|
134 |
+
dilation=dilation, padding=padding)
|
135 |
+
in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
|
136 |
+
self.in_layers.append(in_layer)
|
137 |
+
|
138 |
+
# last one is not necessary
|
139 |
+
if i < n_layers - 1:
|
140 |
+
res_skip_channels = 2 * hidden_channels
|
141 |
+
else:
|
142 |
+
res_skip_channels = hidden_channels
|
143 |
+
|
144 |
+
res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
|
145 |
+
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
|
146 |
+
self.res_skip_layers.append(res_skip_layer)
|
147 |
+
|
148 |
+
def forward(self, x, x_mask, g=None, **kwargs):
|
149 |
+
output = torch.zeros_like(x)
|
150 |
+
n_channels_tensor = torch.IntTensor([self.hidden_channels])
|
151 |
+
|
152 |
+
if g is not None:
|
153 |
+
g = self.cond_layer(g)
|
154 |
+
|
155 |
+
for i in range(self.n_layers):
|
156 |
+
x_in = self.in_layers[i](x)
|
157 |
+
if g is not None:
|
158 |
+
cond_offset = i * 2 * self.hidden_channels
|
159 |
+
g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
|
160 |
+
else:
|
161 |
+
g_l = torch.zeros_like(x_in)
|
162 |
+
|
163 |
+
acts = commons.fused_add_tanh_sigmoid_multiply(
|
164 |
+
x_in,
|
165 |
+
g_l,
|
166 |
+
n_channels_tensor)
|
167 |
+
acts = self.drop(acts)
|
168 |
+
|
169 |
+
res_skip_acts = self.res_skip_layers[i](acts)
|
170 |
+
if i < self.n_layers - 1:
|
171 |
+
res_acts = res_skip_acts[:,:self.hidden_channels,:]
|
172 |
+
x = (x + res_acts) * x_mask
|
173 |
+
output = output + res_skip_acts[:,self.hidden_channels:,:]
|
174 |
+
else:
|
175 |
+
output = output + res_skip_acts
|
176 |
+
return output * x_mask
|
177 |
+
|
178 |
+
def remove_weight_norm(self):
|
179 |
+
if self.gin_channels != 0:
|
180 |
+
torch.nn.utils.remove_weight_norm(self.cond_layer)
|
181 |
+
for l in self.in_layers:
|
182 |
+
torch.nn.utils.remove_weight_norm(l)
|
183 |
+
for l in self.res_skip_layers:
|
184 |
+
torch.nn.utils.remove_weight_norm(l)
|
185 |
+
|
186 |
+
|
187 |
+
class ResBlock1(torch.nn.Module):
|
188 |
+
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
|
189 |
+
super(ResBlock1, self).__init__()
|
190 |
+
self.convs1 = nn.ModuleList([
|
191 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
192 |
+
padding=get_padding(kernel_size, dilation[0]))),
|
193 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
194 |
+
padding=get_padding(kernel_size, dilation[1]))),
|
195 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
|
196 |
+
padding=get_padding(kernel_size, dilation[2])))
|
197 |
+
])
|
198 |
+
self.convs1.apply(init_weights)
|
199 |
+
|
200 |
+
self.convs2 = nn.ModuleList([
|
201 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
202 |
+
padding=get_padding(kernel_size, 1))),
|
203 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
204 |
+
padding=get_padding(kernel_size, 1))),
|
205 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
206 |
+
padding=get_padding(kernel_size, 1)))
|
207 |
+
])
|
208 |
+
self.convs2.apply(init_weights)
|
209 |
+
|
210 |
+
def forward(self, x, x_mask=None):
|
211 |
+
for c1, c2 in zip(self.convs1, self.convs2):
|
212 |
+
xt = F.leaky_relu(x, LRELU_SLOPE)
|
213 |
+
if x_mask is not None:
|
214 |
+
xt = xt * x_mask
|
215 |
+
xt = c1(xt)
|
216 |
+
xt = F.leaky_relu(xt, LRELU_SLOPE)
|
217 |
+
if x_mask is not None:
|
218 |
+
xt = xt * x_mask
|
219 |
+
xt = c2(xt)
|
220 |
+
x = xt + x
|
221 |
+
if x_mask is not None:
|
222 |
+
x = x * x_mask
|
223 |
+
return x
|
224 |
+
|
225 |
+
def remove_weight_norm(self):
|
226 |
+
for l in self.convs1:
|
227 |
+
remove_weight_norm(l)
|
228 |
+
for l in self.convs2:
|
229 |
+
remove_weight_norm(l)
|
230 |
+
|
231 |
+
|
232 |
+
class ResBlock2(torch.nn.Module):
|
233 |
+
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
|
234 |
+
super(ResBlock2, self).__init__()
|
235 |
+
self.convs = nn.ModuleList([
|
236 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
237 |
+
padding=get_padding(kernel_size, dilation[0]))),
|
238 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
239 |
+
padding=get_padding(kernel_size, dilation[1])))
|
240 |
+
])
|
241 |
+
self.convs.apply(init_weights)
|
242 |
+
|
243 |
+
def forward(self, x, x_mask=None):
|
244 |
+
for c in self.convs:
|
245 |
+
xt = F.leaky_relu(x, LRELU_SLOPE)
|
246 |
+
if x_mask is not None:
|
247 |
+
xt = xt * x_mask
|
248 |
+
xt = c(xt)
|
249 |
+
x = xt + x
|
250 |
+
if x_mask is not None:
|
251 |
+
x = x * x_mask
|
252 |
+
return x
|
253 |
+
|
254 |
+
def remove_weight_norm(self):
|
255 |
+
for l in self.convs:
|
256 |
+
remove_weight_norm(l)
|
257 |
+
|
258 |
+
|
259 |
+
class Log(nn.Module):
|
260 |
+
def forward(self, x, x_mask, reverse=False, **kwargs):
|
261 |
+
if not reverse:
|
262 |
+
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
|
263 |
+
logdet = torch.sum(-y, [1, 2])
|
264 |
+
return y, logdet
|
265 |
+
else:
|
266 |
+
x = torch.exp(x) * x_mask
|
267 |
+
return x
|
268 |
+
|
269 |
+
|
270 |
+
class Flip(nn.Module):
|
271 |
+
def forward(self, x, *args, reverse=False, **kwargs):
|
272 |
+
x = torch.flip(x, [1])
|
273 |
+
if not reverse:
|
274 |
+
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
|
275 |
+
return x, logdet
|
276 |
+
else:
|
277 |
+
return x
|
278 |
+
|
279 |
+
|
280 |
+
class ElementwiseAffine(nn.Module):
|
281 |
+
def __init__(self, channels):
|
282 |
+
super().__init__()
|
283 |
+
self.channels = channels
|
284 |
+
self.m = nn.Parameter(torch.zeros(channels,1))
|
285 |
+
self.logs = nn.Parameter(torch.zeros(channels,1))
|
286 |
+
|
287 |
+
def forward(self, x, x_mask, reverse=False, **kwargs):
|
288 |
+
if not reverse:
|
289 |
+
y = self.m + torch.exp(self.logs) * x
|
290 |
+
y = y * x_mask
|
291 |
+
logdet = torch.sum(self.logs * x_mask, [1,2])
|
292 |
+
return y, logdet
|
293 |
+
else:
|
294 |
+
x = (x - self.m) * torch.exp(-self.logs) * x_mask
|
295 |
+
return x
|
296 |
+
|
297 |
+
|
298 |
+
class ResidualCouplingLayer(nn.Module):
|
299 |
+
def __init__(self,
|
300 |
+
channels,
|
301 |
+
hidden_channels,
|
302 |
+
kernel_size,
|
303 |
+
dilation_rate,
|
304 |
+
n_layers,
|
305 |
+
p_dropout=0,
|
306 |
+
gin_channels=0,
|
307 |
+
mean_only=False):
|
308 |
+
assert channels % 2 == 0, "channels should be divisible by 2"
|
309 |
+
super().__init__()
|
310 |
+
self.channels = channels
|
311 |
+
self.hidden_channels = hidden_channels
|
312 |
+
self.kernel_size = kernel_size
|
313 |
+
self.dilation_rate = dilation_rate
|
314 |
+
self.n_layers = n_layers
|
315 |
+
self.half_channels = channels // 2
|
316 |
+
self.mean_only = mean_only
|
317 |
+
|
318 |
+
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
|
319 |
+
self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
|
320 |
+
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
|
321 |
+
self.post.weight.data.zero_()
|
322 |
+
self.post.bias.data.zero_()
|
323 |
+
|
324 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
325 |
+
x0, x1 = torch.split(x, [self.half_channels]*2, 1)
|
326 |
+
h = self.pre(x0) * x_mask
|
327 |
+
h = self.enc(h, x_mask, g=g)
|
328 |
+
stats = self.post(h) * x_mask
|
329 |
+
if not self.mean_only:
|
330 |
+
m, logs = torch.split(stats, [self.half_channels]*2, 1)
|
331 |
+
else:
|
332 |
+
m = stats
|
333 |
+
logs = torch.zeros_like(m)
|
334 |
+
|
335 |
+
if not reverse:
|
336 |
+
x1 = m + x1 * torch.exp(logs) * x_mask
|
337 |
+
x = torch.cat([x0, x1], 1)
|
338 |
+
logdet = torch.sum(logs, [1,2])
|
339 |
+
return x, logdet
|
340 |
+
else:
|
341 |
+
x1 = (x1 - m) * torch.exp(-logs) * x_mask
|
342 |
+
x = torch.cat([x0, x1], 1)
|
343 |
+
return x
|
344 |
+
|
345 |
+
|
346 |
+
class ConvFlow(nn.Module):
|
347 |
+
def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
|
348 |
+
super().__init__()
|
349 |
+
self.in_channels = in_channels
|
350 |
+
self.filter_channels = filter_channels
|
351 |
+
self.kernel_size = kernel_size
|
352 |
+
self.n_layers = n_layers
|
353 |
+
self.num_bins = num_bins
|
354 |
+
self.tail_bound = tail_bound
|
355 |
+
self.half_channels = in_channels // 2
|
356 |
+
|
357 |
+
self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
|
358 |
+
self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
|
359 |
+
self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
|
360 |
+
self.proj.weight.data.zero_()
|
361 |
+
self.proj.bias.data.zero_()
|
362 |
+
|
363 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
364 |
+
x0, x1 = torch.split(x, [self.half_channels]*2, 1)
|
365 |
+
h = self.pre(x0)
|
366 |
+
h = self.convs(h, x_mask, g=g)
|
367 |
+
h = self.proj(h) * x_mask
|
368 |
+
|
369 |
+
b, c, t = x0.shape
|
370 |
+
h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
|
371 |
+
|
372 |
+
unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
|
373 |
+
unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
|
374 |
+
unnormalized_derivatives = h[..., 2 * self.num_bins:]
|
375 |
+
|
376 |
+
x1, logabsdet = piecewise_rational_quadratic_transform(x1,
|
377 |
+
unnormalized_widths,
|
378 |
+
unnormalized_heights,
|
379 |
+
unnormalized_derivatives,
|
380 |
+
inverse=reverse,
|
381 |
+
tails='linear',
|
382 |
+
tail_bound=self.tail_bound
|
383 |
+
)
|
384 |
+
|
385 |
+
x = torch.cat([x0, x1], 1) * x_mask
|
386 |
+
logdet = torch.sum(logabsdet * x_mask, [1,2])
|
387 |
+
if not reverse:
|
388 |
+
return x, logdet
|
389 |
+
else:
|
390 |
+
return x
|
monotonic_align/core.cpython-38-x86_64-linux-gnu.so
ADDED
Binary file (937 kB). View file
|
|
requirements.txt
ADDED
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
Cython==0.29.21
|
2 |
+
librosa==0.8.0
|
3 |
+
matplotlib==3.3.1
|
4 |
+
numpy==1.21.6
|
5 |
+
phonemizer==2.2.1
|
6 |
+
scipy==1.5.2
|
7 |
+
tensorboard==2.3.0
|
8 |
+
torch==1.6.0
|
9 |
+
torchvision==0.7.0
|
10 |
+
Unidecode==1.1.1
|
11 |
+
pyopenjtalk==0.2.0
|
12 |
+
jamo==0.4.1
|
13 |
+
pypinyin==0.44.0
|
14 |
+
jieba==0.42.1
|
15 |
+
cn2an==0.5.17
|
text/LICENSE
ADDED
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
1 |
+
Copyright (c) 2017 Keith Ito
|
2 |
+
|
3 |
+
Permission is hereby granted, free of charge, to any person obtaining a copy
|
4 |
+
of this software and associated documentation files (the "Software"), to deal
|
5 |
+
in the Software without restriction, including without limitation the rights
|
6 |
+
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
7 |
+
copies of the Software, and to permit persons to whom the Software is
|
8 |
+
furnished to do so, subject to the following conditions:
|
9 |
+
|
10 |
+
The above copyright notice and this permission notice shall be included in
|
11 |
+
all copies or substantial portions of the Software.
|
12 |
+
|
13 |
+
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
14 |
+
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
15 |
+
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
16 |
+
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
17 |
+
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
18 |
+
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
|
19 |
+
THE SOFTWARE.
|
text/__init__.py
ADDED
@@ -0,0 +1,56 @@
|
|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
""" from https://github.com/keithito/tacotron """
|
2 |
+
from text import cleaners
|
3 |
+
from text.symbols import symbols
|
4 |
+
|
5 |
+
|
6 |
+
# Mappings from symbol to numeric ID and vice versa:
|
7 |
+
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
|
8 |
+
_id_to_symbol = {i: s for i, s in enumerate(symbols)}
|
9 |
+
|
10 |
+
|
11 |
+
def text_to_sequence(text, cleaner_names):
|
12 |
+
'''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
|
13 |
+
Args:
|
14 |
+
text: string to convert to a sequence
|
15 |
+
cleaner_names: names of the cleaner functions to run the text through
|
16 |
+
Returns:
|
17 |
+
List of integers corresponding to the symbols in the text
|
18 |
+
'''
|
19 |
+
sequence = []
|
20 |
+
|
21 |
+
clean_text = _clean_text(text, cleaner_names)
|
22 |
+
for symbol in clean_text:
|
23 |
+
if symbol not in _symbol_to_id.keys():
|
24 |
+
continue
|
25 |
+
symbol_id = _symbol_to_id[symbol]
|
26 |
+
sequence += [symbol_id]
|
27 |
+
return sequence
|
28 |
+
|
29 |
+
|
30 |
+
def cleaned_text_to_sequence(cleaned_text):
|
31 |
+
'''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
|
32 |
+
Args:
|
33 |
+
text: string to convert to a sequence
|
34 |
+
Returns:
|
35 |
+
List of integers corresponding to the symbols in the text
|
36 |
+
'''
|
37 |
+
sequence = [_symbol_to_id[symbol] for symbol in cleaned_text if symbol in _symbol_to_id.keys()]
|
38 |
+
return sequence
|
39 |
+
|
40 |
+
|
41 |
+
def sequence_to_text(sequence):
|
42 |
+
'''Converts a sequence of IDs back to a string'''
|
43 |
+
result = ''
|
44 |
+
for symbol_id in sequence:
|
45 |
+
s = _id_to_symbol[symbol_id]
|
46 |
+
result += s
|
47 |
+
return result
|
48 |
+
|
49 |
+
|
50 |
+
def _clean_text(text, cleaner_names):
|
51 |
+
for name in cleaner_names:
|
52 |
+
cleaner = getattr(cleaners, name)
|
53 |
+
if not cleaner:
|
54 |
+
raise Exception('Unknown cleaner: %s' % name)
|
55 |
+
text = cleaner(text)
|
56 |
+
return text
|
text/__pycache__/__init__.cpython-38.pyc
ADDED
Binary file (2.13 kB). View file
|
|
text/__pycache__/cantonese.cpython-38.pyc
ADDED
Binary file (1.94 kB). View file
|
|
text/__pycache__/cleaners.cpython-38.pyc
ADDED
Binary file (6.62 kB). View file
|
|
text/__pycache__/english.cpython-38.pyc
ADDED
Binary file (4.85 kB). View file
|
|
text/__pycache__/japanese.cpython-38.pyc
ADDED
Binary file (4.44 kB). View file
|
|
text/__pycache__/korean.cpython-38.pyc
ADDED
Binary file (5.71 kB). View file
|
|
text/__pycache__/mandarin.cpython-38.pyc
ADDED
Binary file (6.41 kB). View file
|
|
text/__pycache__/ngu_dialect.cpython-38.pyc
ADDED
Binary file (1.03 kB). View file
|
|
text/__pycache__/sanskrit.cpython-38.pyc
ADDED
Binary file (1.68 kB). View file
|
|
text/__pycache__/shanghainese.cpython-38.pyc
ADDED
Binary file (1.78 kB). View file
|
|
text/__pycache__/symbols.cpython-38.pyc
ADDED
Binary file (476 Bytes). View file
|
|
text/__pycache__/thai.cpython-38.pyc
ADDED
Binary file (1.44 kB). View file
|
|
text/cantonese.py
ADDED
@@ -0,0 +1,59 @@
|
|
|
|
|
|
|
|
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|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import re
|
2 |
+
import cn2an
|
3 |
+
import opencc
|
4 |
+
|
5 |
+
|
6 |
+
converter = opencc.OpenCC('jyutjyu')
|
7 |
+
|
8 |
+
# List of (Latin alphabet, ipa) pairs:
|
9 |
+
_latin_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
|
10 |
+
('A', 'ei˥'),
|
11 |
+
('B', 'biː˥'),
|
12 |
+
('C', 'siː˥'),
|
13 |
+
('D', 'tiː˥'),
|
14 |
+
('E', 'iː˥'),
|
15 |
+
('F', 'e˥fuː˨˩'),
|
16 |
+
('G', 'tsiː˥'),
|
17 |
+
('H', 'ɪk̚˥tsʰyː˨˩'),
|
18 |
+
('I', 'ɐi˥'),
|
19 |
+
('J', 'tsei˥'),
|
20 |
+
('K', 'kʰei˥'),
|
21 |
+
('L', 'e˥llou˨˩'),
|
22 |
+
('M', 'ɛːm˥'),
|
23 |
+
('N', 'ɛːn˥'),
|
24 |
+
('O', 'ou˥'),
|
25 |
+
('P', 'pʰiː˥'),
|
26 |
+
('Q', 'kʰiːu˥'),
|
27 |
+
('R', 'aː˥lou˨˩'),
|
28 |
+
('S', 'ɛː˥siː˨˩'),
|
29 |
+
('T', 'tʰiː˥'),
|
30 |
+
('U', 'juː˥'),
|
31 |
+
('V', 'wiː˥'),
|
32 |
+
('W', 'tʊk̚˥piː˥juː˥'),
|
33 |
+
('X', 'ɪk̚˥siː˨˩'),
|
34 |
+
('Y', 'waːi˥'),
|
35 |
+
('Z', 'iː˨sɛːt̚˥')
|
36 |
+
]]
|
37 |
+
|
38 |
+
|
39 |
+
def number_to_cantonese(text):
|
40 |
+
return re.sub(r'\d+(?:\.?\d+)?', lambda x: cn2an.an2cn(x.group()), text)
|
41 |
+
|
42 |
+
|
43 |
+
def latin_to_ipa(text):
|
44 |
+
for regex, replacement in _latin_to_ipa:
|
45 |
+
text = re.sub(regex, replacement, text)
|
46 |
+
return text
|
47 |
+
|
48 |
+
|
49 |
+
def cantonese_to_ipa(text):
|
50 |
+
text = number_to_cantonese(text.upper())
|
51 |
+
text = converter.convert(text).replace('-','').replace('$',' ')
|
52 |
+
text = re.sub(r'[A-Z]', lambda x: latin_to_ipa(x.group())+' ', text)
|
53 |
+
text = re.sub(r'[、;:]', ',', text)
|
54 |
+
text = re.sub(r'\s*,\s*', ', ', text)
|
55 |
+
text = re.sub(r'\s*。\s*', '. ', text)
|
56 |
+
text = re.sub(r'\s*?\s*', '? ', text)
|
57 |
+
text = re.sub(r'\s*!\s*', '! ', text)
|
58 |
+
text = re.sub(r'\s*$', '', text)
|
59 |
+
return text
|
text/cleaners.py
ADDED
@@ -0,0 +1,176 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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 re
|
2 |
+
from text.japanese import japanese_to_romaji_with_accent, japanese_to_ipa, japanese_to_ipa2, japanese_to_ipa3
|
3 |
+
from text.korean import latin_to_hangul, number_to_hangul, divide_hangul, korean_to_lazy_ipa, korean_to_ipa
|
4 |
+
from text.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo, chinese_to_romaji, chinese_to_lazy_ipa, chinese_to_ipa, chinese_to_ipa2
|
5 |
+
from text.sanskrit import devanagari_to_ipa
|
6 |
+
from text.english import english_to_lazy_ipa, english_to_ipa2, english_to_lazy_ipa2
|
7 |
+
from text.thai import num_to_thai, latin_to_thai
|
8 |
+
# from text.shanghainese import shanghainese_to_ipa
|
9 |
+
# from text.cantonese import cantonese_to_ipa
|
10 |
+
from text.ngu_dialect import ngu_dialect_to_ipa
|
11 |
+
|
12 |
+
|
13 |
+
def japanese_cleaners(text):
|
14 |
+
text = japanese_to_romaji_with_accent(text)
|
15 |
+
if re.match('[A-Za-z]', text[-1]):
|
16 |
+
text += '.'
|
17 |
+
return text
|
18 |
+
|
19 |
+
|
20 |
+
def japanese_cleaners2(text):
|
21 |
+
return japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…')
|
22 |
+
|
23 |
+
|
24 |
+
def korean_cleaners(text):
|
25 |
+
'''Pipeline for Korean text'''
|
26 |
+
text = latin_to_hangul(text)
|
27 |
+
text = number_to_hangul(text)
|
28 |
+
text = divide_hangul(text)
|
29 |
+
if re.match('[\u3131-\u3163]', text[-1]):
|
30 |
+
text += '.'
|
31 |
+
return text
|
32 |
+
|
33 |
+
|
34 |
+
def chinese_cleaners(text):
|
35 |
+
'''Pipeline for Chinese text'''
|
36 |
+
text = number_to_chinese(text)
|
37 |
+
text = chinese_to_bopomofo(text)
|
38 |
+
text = latin_to_bopomofo(text)
|
39 |
+
if re.match('[ˉˊˇˋ˙]', text[-1]):
|
40 |
+
text += '。'
|
41 |
+
return text
|
42 |
+
|
43 |
+
|
44 |
+
def zh_ja_mixture_cleaners(text):
|
45 |
+
chinese_texts = re.findall(r'\[ZH\].*?\[ZH\]', text)
|
46 |
+
japanese_texts = re.findall(r'\[JA\].*?\[JA\]', text)
|
47 |
+
for chinese_text in chinese_texts:
|
48 |
+
cleaned_text = chinese_to_romaji(chinese_text[4:-4])
|
49 |
+
text = text.replace(chinese_text, cleaned_text+' ', 1)
|
50 |
+
for japanese_text in japanese_texts:
|
51 |
+
cleaned_text = japanese_to_romaji_with_accent(
|
52 |
+
japanese_text[4:-4]).replace('ts', 'ʦ').replace('u', 'ɯ').replace('...', '…')
|
53 |
+
text = text.replace(japanese_text, cleaned_text+' ', 1)
|
54 |
+
text = text[:-1]
|
55 |
+
if re.match('[A-Za-zɯɹəɥ→↓↑]', text[-1]):
|
56 |
+
text += '.'
|
57 |
+
return text
|
58 |
+
|
59 |
+
|
60 |
+
def sanskrit_cleaners(text):
|
61 |
+
text = text.replace('॥', '।').replace('ॐ', 'ओम्')
|
62 |
+
if text[-1] != '।':
|
63 |
+
text += ' ।'
|
64 |
+
return text
|
65 |
+
|
66 |
+
|
67 |
+
def cjks_cleaners(text):
|
68 |
+
chinese_texts = re.findall(r'\[ZH\].*?\[ZH\]', text)
|
69 |
+
japanese_texts = re.findall(r'\[JA\].*?\[JA\]', text)
|
70 |
+
korean_texts = re.findall(r'\[KO\].*?\[KO\]', text)
|
71 |
+
sanskrit_texts = re.findall(r'\[SA\].*?\[SA\]', text)
|
72 |
+
english_texts = re.findall(r'\[EN\].*?\[EN\]', text)
|
73 |
+
for chinese_text in chinese_texts:
|
74 |
+
cleaned_text = chinese_to_lazy_ipa(chinese_text[4:-4])
|
75 |
+
text = text.replace(chinese_text, cleaned_text+' ', 1)
|
76 |
+
for japanese_text in japanese_texts:
|
77 |
+
cleaned_text = japanese_to_ipa(japanese_text[4:-4])
|
78 |
+
text = text.replace(japanese_text, cleaned_text+' ', 1)
|
79 |
+
for korean_text in korean_texts:
|
80 |
+
cleaned_text = korean_to_lazy_ipa(korean_text[4:-4])
|
81 |
+
text = text.replace(korean_text, cleaned_text+' ', 1)
|
82 |
+
for sanskrit_text in sanskrit_texts:
|
83 |
+
cleaned_text = devanagari_to_ipa(sanskrit_text[4:-4])
|
84 |
+
text = text.replace(sanskrit_text, cleaned_text+' ', 1)
|
85 |
+
for english_text in english_texts:
|
86 |
+
cleaned_text = english_to_lazy_ipa(english_text[4:-4])
|
87 |
+
text = text.replace(english_text, cleaned_text+' ', 1)
|
88 |
+
text = text[:-1]
|
89 |
+
if re.match(r'[^\.,!\?\-…~]', text[-1]):
|
90 |
+
text += '.'
|
91 |
+
return text
|
92 |
+
|
93 |
+
|
94 |
+
def cjke_cleaners(text):
|
95 |
+
chinese_texts = re.findall(r'\[ZH\].*?\[ZH\]', text)
|
96 |
+
japanese_texts = re.findall(r'\[JA\].*?\[JA\]', text)
|
97 |
+
korean_texts = re.findall(r'\[KO\].*?\[KO\]', text)
|
98 |
+
english_texts = re.findall(r'\[EN\].*?\[EN\]', text)
|
99 |
+
for chinese_text in chinese_texts:
|
100 |
+
cleaned_text = chinese_to_lazy_ipa(chinese_text[4:-4])
|
101 |
+
cleaned_text = cleaned_text.replace(
|
102 |
+
'ʧ', 'tʃ').replace('ʦ', 'ts').replace('ɥan', 'ɥæn')
|
103 |
+
text = text.replace(chinese_text, cleaned_text+' ', 1)
|
104 |
+
for japanese_text in japanese_texts:
|
105 |
+
cleaned_text = japanese_to_ipa(japanese_text[4:-4])
|
106 |
+
cleaned_text = cleaned_text.replace('ʧ', 'tʃ').replace(
|
107 |
+
'ʦ', 'ts').replace('ɥan', 'ɥæn').replace('ʥ', 'dz')
|
108 |
+
text = text.replace(japanese_text, cleaned_text+' ', 1)
|
109 |
+
for korean_text in korean_texts:
|
110 |
+
cleaned_text = korean_to_ipa(korean_text[4:-4])
|
111 |
+
text = text.replace(korean_text, cleaned_text+' ', 1)
|
112 |
+
for english_text in english_texts:
|
113 |
+
cleaned_text = english_to_ipa2(english_text[4:-4])
|
114 |
+
cleaned_text = cleaned_text.replace('ɑ', 'a').replace(
|
115 |
+
'ɔ', 'o').replace('ɛ', 'e').replace('ɪ', 'i').replace('ʊ', 'u')
|
116 |
+
text = text.replace(english_text, cleaned_text+' ', 1)
|
117 |
+
text = text[:-1]
|
118 |
+
if re.match(r'[^\.,!\?\-…~]', text[-1]):
|
119 |
+
text += '.'
|
120 |
+
return text
|
121 |
+
|
122 |
+
|
123 |
+
def cjke_cleaners2(text):
|
124 |
+
chinese_texts = re.findall(r'\[ZH\].*?\[ZH\]', text)
|
125 |
+
japanese_texts = re.findall(r'\[JA\].*?\[JA\]', text)
|
126 |
+
korean_texts = re.findall(r'\[KO\].*?\[KO\]', text)
|
127 |
+
english_texts = re.findall(r'\[EN\].*?\[EN\]', text)
|
128 |
+
for chinese_text in chinese_texts:
|
129 |
+
cleaned_text = chinese_to_ipa(chinese_text[4:-4])
|
130 |
+
text = text.replace(chinese_text, cleaned_text+' ', 1)
|
131 |
+
for japanese_text in japanese_texts:
|
132 |
+
cleaned_text = japanese_to_ipa2(japanese_text[4:-4])
|
133 |
+
text = text.replace(japanese_text, cleaned_text+' ', 1)
|
134 |
+
for korean_text in korean_texts:
|
135 |
+
cleaned_text = korean_to_ipa(korean_text[4:-4])
|
136 |
+
text = text.replace(korean_text, cleaned_text+' ', 1)
|
137 |
+
for english_text in english_texts:
|
138 |
+
cleaned_text = english_to_ipa2(english_text[4:-4])
|
139 |
+
text = text.replace(english_text, cleaned_text+' ', 1)
|
140 |
+
text = text[:-1]
|
141 |
+
if re.match(r'[^\.,!\?\-…~]', text[-1]):
|
142 |
+
text += '.'
|
143 |
+
return text
|
144 |
+
|
145 |
+
|
146 |
+
def thai_cleaners(text):
|
147 |
+
text = num_to_thai(text)
|
148 |
+
text = latin_to_thai(text)
|
149 |
+
return text
|
150 |
+
|
151 |
+
|
152 |
+
def shanghainese_cleaners(text):
|
153 |
+
text = shanghainese_to_ipa(text)
|
154 |
+
if re.match(r'[^\.,!\?\-…~]', text[-1]):
|
155 |
+
text += '.'
|
156 |
+
return text
|
157 |
+
|
158 |
+
|
159 |
+
def chinese_dialect_cleaners(text):
|
160 |
+
text = re.sub(r'\[MD\](.*?)\[MD\]',
|
161 |
+
lambda x: chinese_to_ipa2(x.group(1))+' ', text)
|
162 |
+
text = re.sub(r'\[TW\](.*?)\[TW\]',
|
163 |
+
lambda x: chinese_to_ipa2(x.group(1), True)+' ', text)
|
164 |
+
text = re.sub(r'\[JA\](.*?)\[JA\]',
|
165 |
+
lambda x: japanese_to_ipa3(x.group(1)).replace('Q', 'ʔ')+' ', text)
|
166 |
+
text = re.sub(r'\[SH\](.*?)\[SH\]', lambda x: shanghainese_to_ipa(x.group(1)).replace('1', '˥˧').replace('5',
|
167 |
+
'˧˧˦').replace('6', '˩˩˧').replace('7', '˥').replace('8', '˩˨').replace('ᴀ', 'ɐ').replace('ᴇ', 'e')+' ', text)
|
168 |
+
text = re.sub(r'\[GD\](.*?)\[GD\]',
|
169 |
+
lambda x: cantonese_to_ipa(x.group(1))+' ', text)
|
170 |
+
text = re.sub(r'\[EN\](.*?)\[EN\]',
|
171 |
+
lambda x: english_to_lazy_ipa2(x.group(1))+' ', text)
|
172 |
+
text = re.sub(r'\[([A-Z]{2})\](.*?)\[\1\]', lambda x: ngu_dialect_to_ipa(x.group(2), x.group(
|
173 |
+
1)).replace('ʣ', 'dz').replace('ʥ', 'dʑ').replace('ʦ', 'ts').replace('ʨ', 'tɕ')+' ', text)
|
174 |
+
text = re.sub(r'\s+$', '', text)
|
175 |
+
text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
|
176 |
+
return text
|
text/english.py
ADDED
@@ -0,0 +1,188 @@
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
""" from https://github.com/keithito/tacotron """
|
2 |
+
|
3 |
+
'''
|
4 |
+
Cleaners are transformations that run over the input text at both training and eval time.
|
5 |
+
|
6 |
+
Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
|
7 |
+
hyperparameter. Some cleaners are English-specific. You'll typically want to use:
|
8 |
+
1. "english_cleaners" for English text
|
9 |
+
2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
|
10 |
+
the Unidecode library (https://pypi.python.org/pypi/Unidecode)
|
11 |
+
3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
|
12 |
+
the symbols in symbols.py to match your data).
|
13 |
+
'''
|
14 |
+
|
15 |
+
|
16 |
+
# Regular expression matching whitespace:
|
17 |
+
|
18 |
+
|
19 |
+
import re
|
20 |
+
import inflect
|
21 |
+
from unidecode import unidecode
|
22 |
+
import eng_to_ipa as ipa
|
23 |
+
_inflect = inflect.engine()
|
24 |
+
_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
|
25 |
+
_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
|
26 |
+
_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
|
27 |
+
_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
|
28 |
+
_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
|
29 |
+
_number_re = re.compile(r'[0-9]+')
|
30 |
+
|
31 |
+
# List of (regular expression, replacement) pairs for abbreviations:
|
32 |
+
_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
|
33 |
+
('mrs', 'misess'),
|
34 |
+
('mr', 'mister'),
|
35 |
+
('dr', 'doctor'),
|
36 |
+
('st', 'saint'),
|
37 |
+
('co', 'company'),
|
38 |
+
('jr', 'junior'),
|
39 |
+
('maj', 'major'),
|
40 |
+
('gen', 'general'),
|
41 |
+
('drs', 'doctors'),
|
42 |
+
('rev', 'reverend'),
|
43 |
+
('lt', 'lieutenant'),
|
44 |
+
('hon', 'honorable'),
|
45 |
+
('sgt', 'sergeant'),
|
46 |
+
('capt', 'captain'),
|
47 |
+
('esq', 'esquire'),
|
48 |
+
('ltd', 'limited'),
|
49 |
+
('col', 'colonel'),
|
50 |
+
('ft', 'fort'),
|
51 |
+
]]
|
52 |
+
|
53 |
+
|
54 |
+
# List of (ipa, lazy ipa) pairs:
|
55 |
+
_lazy_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
|
56 |
+
('r', 'ɹ'),
|
57 |
+
('æ', 'e'),
|
58 |
+
('ɑ', 'a'),
|
59 |
+
('ɔ', 'o'),
|
60 |
+
('ð', 'z'),
|
61 |
+
('θ', 's'),
|
62 |
+
('ɛ', 'e'),
|
63 |
+
('ɪ', 'i'),
|
64 |
+
('ʊ', 'u'),
|
65 |
+
('ʒ', 'ʥ'),
|
66 |
+
('ʤ', 'ʥ'),
|
67 |
+
('ˈ', '↓'),
|
68 |
+
]]
|
69 |
+
|
70 |
+
# List of (ipa, lazy ipa2) pairs:
|
71 |
+
_lazy_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
|
72 |
+
('r', 'ɹ'),
|
73 |
+
('ð', 'z'),
|
74 |
+
('θ', 's'),
|
75 |
+
('ʒ', 'ʑ'),
|
76 |
+
('ʤ', 'dʑ'),
|
77 |
+
('ˈ', '↓'),
|
78 |
+
]]
|
79 |
+
|
80 |
+
# List of (ipa, ipa2) pairs
|
81 |
+
_ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
|
82 |
+
('r', 'ɹ'),
|
83 |
+
('ʤ', 'dʒ'),
|
84 |
+
('ʧ', 'tʃ')
|
85 |
+
]]
|
86 |
+
|
87 |
+
|
88 |
+
def expand_abbreviations(text):
|
89 |
+
for regex, replacement in _abbreviations:
|
90 |
+
text = re.sub(regex, replacement, text)
|
91 |
+
return text
|
92 |
+
|
93 |
+
|
94 |
+
def collapse_whitespace(text):
|
95 |
+
return re.sub(r'\s+', ' ', text)
|
96 |
+
|
97 |
+
|
98 |
+
def _remove_commas(m):
|
99 |
+
return m.group(1).replace(',', '')
|
100 |
+
|
101 |
+
|
102 |
+
def _expand_decimal_point(m):
|
103 |
+
return m.group(1).replace('.', ' point ')
|
104 |
+
|
105 |
+
|
106 |
+
def _expand_dollars(m):
|
107 |
+
match = m.group(1)
|
108 |
+
parts = match.split('.')
|
109 |
+
if len(parts) > 2:
|
110 |
+
return match + ' dollars' # Unexpected format
|
111 |
+
dollars = int(parts[0]) if parts[0] else 0
|
112 |
+
cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
|
113 |
+
if dollars and cents:
|
114 |
+
dollar_unit = 'dollar' if dollars == 1 else 'dollars'
|
115 |
+
cent_unit = 'cent' if cents == 1 else 'cents'
|
116 |
+
return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
|
117 |
+
elif dollars:
|
118 |
+
dollar_unit = 'dollar' if dollars == 1 else 'dollars'
|
119 |
+
return '%s %s' % (dollars, dollar_unit)
|
120 |
+
elif cents:
|
121 |
+
cent_unit = 'cent' if cents == 1 else 'cents'
|
122 |
+
return '%s %s' % (cents, cent_unit)
|
123 |
+
else:
|
124 |
+
return 'zero dollars'
|
125 |
+
|
126 |
+
|
127 |
+
def _expand_ordinal(m):
|
128 |
+
return _inflect.number_to_words(m.group(0))
|
129 |
+
|
130 |
+
|
131 |
+
def _expand_number(m):
|
132 |
+
num = int(m.group(0))
|
133 |
+
if num > 1000 and num < 3000:
|
134 |
+
if num == 2000:
|
135 |
+
return 'two thousand'
|
136 |
+
elif num > 2000 and num < 2010:
|
137 |
+
return 'two thousand ' + _inflect.number_to_words(num % 100)
|
138 |
+
elif num % 100 == 0:
|
139 |
+
return _inflect.number_to_words(num // 100) + ' hundred'
|
140 |
+
else:
|
141 |
+
return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
|
142 |
+
else:
|
143 |
+
return _inflect.number_to_words(num, andword='')
|
144 |
+
|
145 |
+
|
146 |
+
def normalize_numbers(text):
|
147 |
+
text = re.sub(_comma_number_re, _remove_commas, text)
|
148 |
+
text = re.sub(_pounds_re, r'\1 pounds', text)
|
149 |
+
text = re.sub(_dollars_re, _expand_dollars, text)
|
150 |
+
text = re.sub(_decimal_number_re, _expand_decimal_point, text)
|
151 |
+
text = re.sub(_ordinal_re, _expand_ordinal, text)
|
152 |
+
text = re.sub(_number_re, _expand_number, text)
|
153 |
+
return text
|
154 |
+
|
155 |
+
|
156 |
+
def mark_dark_l(text):
|
157 |
+
return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
|
158 |
+
|
159 |
+
|
160 |
+
def english_to_ipa(text):
|
161 |
+
text = unidecode(text).lower()
|
162 |
+
text = expand_abbreviations(text)
|
163 |
+
text = normalize_numbers(text)
|
164 |
+
phonemes = ipa.convert(text)
|
165 |
+
phonemes = collapse_whitespace(phonemes)
|
166 |
+
return phonemes
|
167 |
+
|
168 |
+
|
169 |
+
def english_to_lazy_ipa(text):
|
170 |
+
text = english_to_ipa(text)
|
171 |
+
for regex, replacement in _lazy_ipa:
|
172 |
+
text = re.sub(regex, replacement, text)
|
173 |
+
return text
|
174 |
+
|
175 |
+
|
176 |
+
def english_to_ipa2(text):
|
177 |
+
text = english_to_ipa(text)
|
178 |
+
text = mark_dark_l(text)
|
179 |
+
for regex, replacement in _ipa_to_ipa2:
|
180 |
+
text = re.sub(regex, replacement, text)
|
181 |
+
return text.replace('...', '…')
|
182 |
+
|
183 |
+
|
184 |
+
def english_to_lazy_ipa2(text):
|
185 |
+
text = english_to_ipa(text)
|
186 |
+
for regex, replacement in _lazy_ipa2:
|
187 |
+
text = re.sub(regex, replacement, text)
|
188 |
+
return text
|
text/japanese.py
ADDED
@@ -0,0 +1,153 @@
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|
|
|
|
|
1 |
+
import re
|
2 |
+
from unidecode import unidecode
|
3 |
+
import pyopenjtalk
|
4 |
+
|
5 |
+
|
6 |
+
# Regular expression matching Japanese without punctuation marks:
|
7 |
+
_japanese_characters = re.compile(
|
8 |
+
r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
|
9 |
+
|
10 |
+
# Regular expression matching non-Japanese characters or punctuation marks:
|
11 |
+
_japanese_marks = re.compile(
|
12 |
+
r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
|
13 |
+
|
14 |
+
# List of (symbol, Japanese) pairs for marks:
|
15 |
+
_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
|
16 |
+
('%', 'パーセント')
|
17 |
+
]]
|
18 |
+
|
19 |
+
# List of (romaji, ipa) pairs for marks:
|
20 |
+
_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
|
21 |
+
('ts', 'ʦ'),
|
22 |
+
('u', 'ɯ'),
|
23 |
+
('j', 'ʥ'),
|
24 |
+
('y', 'j'),
|
25 |
+
('ni', 'n^i'),
|
26 |
+
('nj', 'n^'),
|
27 |
+
('hi', 'çi'),
|
28 |
+
('hj', 'ç'),
|
29 |
+
('f', 'ɸ'),
|
30 |
+
('I', 'i*'),
|
31 |
+
('U', 'ɯ*'),
|
32 |
+
('r', 'ɾ')
|
33 |
+
]]
|
34 |
+
|
35 |
+
# List of (romaji, ipa2) pairs for marks:
|
36 |
+
_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
|
37 |
+
('u', 'ɯ'),
|
38 |
+
('ʧ', 'tʃ'),
|
39 |
+
('j', 'dʑ'),
|
40 |
+
('y', 'j'),
|
41 |
+
('ni', 'n^i'),
|
42 |
+
('nj', 'n^'),
|
43 |
+
('hi', 'çi'),
|
44 |
+
('hj', 'ç'),
|
45 |
+
('f', 'ɸ'),
|
46 |
+
('I', 'i*'),
|
47 |
+
('U', 'ɯ*'),
|
48 |
+
('r', 'ɾ')
|
49 |
+
]]
|
50 |
+
|
51 |
+
# List of (consonant, sokuon) pairs:
|
52 |
+
_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
|
53 |
+
(r'Q([↑↓]*[kg])', r'k#\1'),
|
54 |
+
(r'Q([↑↓]*[tdjʧ])', r't#\1'),
|
55 |
+
(r'Q([↑↓]*[sʃ])', r's\1'),
|
56 |
+
(r'Q([↑↓]*[pb])', r'p#\1')
|
57 |
+
]]
|
58 |
+
|
59 |
+
# List of (consonant, hatsuon) pairs:
|
60 |
+
_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
|
61 |
+
(r'N([↑↓]*[pbm])', r'm\1'),
|
62 |
+
(r'N([↑↓]*[ʧʥj])', r'n^\1'),
|
63 |
+
(r'N([↑↓]*[tdn])', r'n\1'),
|
64 |
+
(r'N([↑↓]*[kg])', r'ŋ\1')
|
65 |
+
]]
|
66 |
+
|
67 |
+
|
68 |
+
def symbols_to_japanese(text):
|
69 |
+
for regex, replacement in _symbols_to_japanese:
|
70 |
+
text = re.sub(regex, replacement, text)
|
71 |
+
return text
|
72 |
+
|
73 |
+
|
74 |
+
def japanese_to_romaji_with_accent(text):
|
75 |
+
'''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
|
76 |
+
text = symbols_to_japanese(text)
|
77 |
+
sentences = re.split(_japanese_marks, text)
|
78 |
+
marks = re.findall(_japanese_marks, text)
|
79 |
+
text = ''
|
80 |
+
for i, sentence in enumerate(sentences):
|
81 |
+
if re.match(_japanese_characters, sentence):
|
82 |
+
if text != '':
|
83 |
+
text += ' '
|
84 |
+
labels = pyopenjtalk.extract_fullcontext(sentence)
|
85 |
+
for n, label in enumerate(labels):
|
86 |
+
phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
|
87 |
+
if phoneme not in ['sil', 'pau']:
|
88 |
+
text += phoneme.replace('ch', 'ʧ').replace('sh',
|
89 |
+
'ʃ').replace('cl', 'Q')
|
90 |
+
else:
|
91 |
+
continue
|
92 |
+
# n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
|
93 |
+
a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
|
94 |
+
a2 = int(re.search(r"\+(\d+)\+", label).group(1))
|
95 |
+
a3 = int(re.search(r"\+(\d+)/", label).group(1))
|
96 |
+
if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
|
97 |
+
a2_next = -1
|
98 |
+
else:
|
99 |
+
a2_next = int(
|
100 |
+
re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
|
101 |
+
# Accent phrase boundary
|
102 |
+
if a3 == 1 and a2_next == 1:
|
103 |
+
text += ' '
|
104 |
+
# Falling
|
105 |
+
elif a1 == 0 and a2_next == a2 + 1:
|
106 |
+
text += '↓'
|
107 |
+
# Rising
|
108 |
+
elif a2 == 1 and a2_next == 2:
|
109 |
+
text += '↑'
|
110 |
+
if i < len(marks):
|
111 |
+
text += unidecode(marks[i]).replace(' ', '')
|
112 |
+
return text
|
113 |
+
|
114 |
+
|
115 |
+
def get_real_sokuon(text):
|
116 |
+
for regex, replacement in _real_sokuon:
|
117 |
+
text = re.sub(regex, replacement, text)
|
118 |
+
return text
|
119 |
+
|
120 |
+
|
121 |
+
def get_real_hatsuon(text):
|
122 |
+
for regex, replacement in _real_hatsuon:
|
123 |
+
text = re.sub(regex, replacement, text)
|
124 |
+
return text
|
125 |
+
|
126 |
+
|
127 |
+
def japanese_to_ipa(text):
|
128 |
+
text = japanese_to_romaji_with_accent(text).replace('...', '…')
|
129 |
+
text = re.sub(
|
130 |
+
r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
|
131 |
+
text = get_real_sokuon(text)
|
132 |
+
text = get_real_hatsuon(text)
|
133 |
+
for regex, replacement in _romaji_to_ipa:
|
134 |
+
text = re.sub(regex, replacement, text)
|
135 |
+
return text
|
136 |
+
|
137 |
+
|
138 |
+
def japanese_to_ipa2(text):
|
139 |
+
text = japanese_to_romaji_with_accent(text).replace('...', '…')
|
140 |
+
text = get_real_sokuon(text)
|
141 |
+
text = get_real_hatsuon(text)
|
142 |
+
for regex, replacement in _romaji_to_ipa2:
|
143 |
+
text = re.sub(regex, replacement, text)
|
144 |
+
return text
|
145 |
+
|
146 |
+
|
147 |
+
def japanese_to_ipa3(text):
|
148 |
+
text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
|
149 |
+
'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
|
150 |
+
text = re.sub(
|
151 |
+
r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
|
152 |
+
text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
|
153 |
+
return text
|
text/korean.py
ADDED
@@ -0,0 +1,210 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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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 re
|
2 |
+
from jamo import h2j, j2hcj
|
3 |
+
import ko_pron
|
4 |
+
|
5 |
+
|
6 |
+
# This is a list of Korean classifiers preceded by pure Korean numerals.
|
7 |
+
_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
|
8 |
+
|
9 |
+
# List of (hangul, hangul divided) pairs:
|
10 |
+
_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
|
11 |
+
('ㄳ', 'ㄱㅅ'),
|
12 |
+
('ㄵ', 'ㄴㅈ'),
|
13 |
+
('ㄶ', 'ㄴㅎ'),
|
14 |
+
('ㄺ', 'ㄹㄱ'),
|
15 |
+
('ㄻ', 'ㄹㅁ'),
|
16 |
+
('ㄼ', 'ㄹㅂ'),
|
17 |
+
('ㄽ', 'ㄹㅅ'),
|
18 |
+
('ㄾ', 'ㄹㅌ'),
|
19 |
+
('ㄿ', 'ㄹㅍ'),
|
20 |
+
('ㅀ', 'ㄹㅎ'),
|
21 |
+
('ㅄ', 'ㅂㅅ'),
|
22 |
+
('ㅘ', 'ㅗㅏ'),
|
23 |
+
('ㅙ', 'ㅗㅐ'),
|
24 |
+
('ㅚ', 'ㅗㅣ'),
|
25 |
+
('ㅝ', 'ㅜㅓ'),
|
26 |
+
('ㅞ', 'ㅜㅔ'),
|
27 |
+
('ㅟ', 'ㅜㅣ'),
|
28 |
+
('ㅢ', 'ㅡㅣ'),
|
29 |
+
('ㅑ', 'ㅣㅏ'),
|
30 |
+
('ㅒ', 'ㅣㅐ'),
|
31 |
+
('ㅕ', 'ㅣㅓ'),
|
32 |
+
('ㅖ', 'ㅣㅔ'),
|
33 |
+
('ㅛ', 'ㅣㅗ'),
|
34 |
+
('ㅠ', 'ㅣㅜ')
|
35 |
+
]]
|
36 |
+
|
37 |
+
# List of (Latin alphabet, hangul) pairs:
|
38 |
+
_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
|
39 |
+
('a', '에이'),
|
40 |
+
('b', '비'),
|
41 |
+
('c', '시'),
|
42 |
+
('d', '디'),
|
43 |
+
('e', '이'),
|
44 |
+
('f', '에프'),
|
45 |
+
('g', '지'),
|
46 |
+
('h', '에이치'),
|
47 |
+
('i', '아이'),
|
48 |
+
('j', '제이'),
|
49 |
+
('k', '케이'),
|
50 |
+
('l', '엘'),
|
51 |
+
('m', '엠'),
|
52 |
+
('n', '엔'),
|
53 |
+
('o', '오'),
|
54 |
+
('p', '피'),
|
55 |
+
('q', '큐'),
|
56 |
+
('r', '아르'),
|
57 |
+
('s', '에스'),
|
58 |
+
('t', '티'),
|
59 |
+
('u', '유'),
|
60 |
+
('v', '브이'),
|
61 |
+
('w', '더블유'),
|
62 |
+
('x', '엑스'),
|
63 |
+
('y', '와이'),
|
64 |
+
('z', '제트')
|
65 |
+
]]
|
66 |
+
|
67 |
+
# List of (ipa, lazy ipa) pairs:
|
68 |
+
_ipa_to_lazy_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
|
69 |
+
('t͡ɕ','ʧ'),
|
70 |
+
('d͡ʑ','ʥ'),
|
71 |
+
('ɲ','n^'),
|
72 |
+
('ɕ','ʃ'),
|
73 |
+
('ʷ','w'),
|
74 |
+
('ɭ','l`'),
|
75 |
+
('ʎ','ɾ'),
|
76 |
+
('ɣ','ŋ'),
|
77 |
+
('ɰ','ɯ'),
|
78 |
+
('ʝ','j'),
|
79 |
+
('ʌ','ə'),
|
80 |
+
('ɡ','g'),
|
81 |
+
('\u031a','#'),
|
82 |
+
('\u0348','='),
|
83 |
+
('\u031e',''),
|
84 |
+
('\u0320',''),
|
85 |
+
('\u0339','')
|
86 |
+
]]
|
87 |
+
|
88 |
+
|
89 |
+
def latin_to_hangul(text):
|
90 |
+
for regex, replacement in _latin_to_hangul:
|
91 |
+
text = re.sub(regex, replacement, text)
|
92 |
+
return text
|
93 |
+
|
94 |
+
|
95 |
+
def divide_hangul(text):
|
96 |
+
text = j2hcj(h2j(text))
|
97 |
+
for regex, replacement in _hangul_divided:
|
98 |
+
text = re.sub(regex, replacement, text)
|
99 |
+
return text
|
100 |
+
|
101 |
+
|
102 |
+
def hangul_number(num, sino=True):
|
103 |
+
'''Reference https://github.com/Kyubyong/g2pK'''
|
104 |
+
num = re.sub(',', '', num)
|
105 |
+
|
106 |
+
if num == '0':
|
107 |
+
return '영'
|
108 |
+
if not sino and num == '20':
|
109 |
+
return '스무'
|
110 |
+
|
111 |
+
digits = '123456789'
|
112 |
+
names = '일이삼사오육칠팔구'
|
113 |
+
digit2name = {d: n for d, n in zip(digits, names)}
|
114 |
+
|
115 |
+
modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉'
|
116 |
+
decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔'
|
117 |
+
digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())}
|
118 |
+
digit2dec = {d: dec for d, dec in zip(digits, decimals.split())}
|
119 |
+
|
120 |
+
spelledout = []
|
121 |
+
for i, digit in enumerate(num):
|
122 |
+
i = len(num) - i - 1
|
123 |
+
if sino:
|
124 |
+
if i == 0:
|
125 |
+
name = digit2name.get(digit, '')
|
126 |
+
elif i == 1:
|
127 |
+
name = digit2name.get(digit, '') + '십'
|
128 |
+
name = name.replace('일십', '십')
|
129 |
+
else:
|
130 |
+
if i == 0:
|
131 |
+
name = digit2mod.get(digit, '')
|
132 |
+
elif i == 1:
|
133 |
+
name = digit2dec.get(digit, '')
|
134 |
+
if digit == '0':
|
135 |
+
if i % 4 == 0:
|
136 |
+
last_three = spelledout[-min(3, len(spelledout)):]
|
137 |
+
if ''.join(last_three) == '':
|
138 |
+
spelledout.append('')
|
139 |
+
continue
|
140 |
+
else:
|
141 |
+
spelledout.append('')
|
142 |
+
continue
|
143 |
+
if i == 2:
|
144 |
+
name = digit2name.get(digit, '') + '백'
|
145 |
+
name = name.replace('일백', '백')
|
146 |
+
elif i == 3:
|
147 |
+
name = digit2name.get(digit, '') + '천'
|
148 |
+
name = name.replace('일천', '천')
|
149 |
+
elif i == 4:
|
150 |
+
name = digit2name.get(digit, '') + '만'
|
151 |
+
name = name.replace('일만', '만')
|
152 |
+
elif i == 5:
|
153 |
+
name = digit2name.get(digit, '') + '십'
|
154 |
+
name = name.replace('일십', '십')
|
155 |
+
elif i == 6:
|
156 |
+
name = digit2name.get(digit, '') + '백'
|
157 |
+
name = name.replace('일백', '백')
|
158 |
+
elif i == 7:
|
159 |
+
name = digit2name.get(digit, '') + '천'
|
160 |
+
name = name.replace('일천', '천')
|
161 |
+
elif i == 8:
|
162 |
+
name = digit2name.get(digit, '') + '억'
|
163 |
+
elif i == 9:
|
164 |
+
name = digit2name.get(digit, '') + '십'
|
165 |
+
elif i == 10:
|
166 |
+
name = digit2name.get(digit, '') + '백'
|
167 |
+
elif i == 11:
|
168 |
+
name = digit2name.get(digit, '') + '천'
|
169 |
+
elif i == 12:
|
170 |
+
name = digit2name.get(digit, '') + '조'
|
171 |
+
elif i == 13:
|
172 |
+
name = digit2name.get(digit, '') + '십'
|
173 |
+
elif i == 14:
|
174 |
+
name = digit2name.get(digit, '') + '백'
|
175 |
+
elif i == 15:
|
176 |
+
name = digit2name.get(digit, '') + '천'
|
177 |
+
spelledout.append(name)
|
178 |
+
return ''.join(elem for elem in spelledout)
|
179 |
+
|
180 |
+
|
181 |
+
def number_to_hangul(text):
|
182 |
+
'''Reference https://github.com/Kyubyong/g2pK'''
|
183 |
+
tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text))
|
184 |
+
for token in tokens:
|
185 |
+
num, classifier = token
|
186 |
+
if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers:
|
187 |
+
spelledout = hangul_number(num, sino=False)
|
188 |
+
else:
|
189 |
+
spelledout = hangul_number(num, sino=True)
|
190 |
+
text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}')
|
191 |
+
# digit by digit for remaining digits
|
192 |
+
digits = '0123456789'
|
193 |
+
names = '영일이삼사오육칠팔구'
|
194 |
+
for d, n in zip(digits, names):
|
195 |
+
text = text.replace(d, n)
|
196 |
+
return text
|
197 |
+
|
198 |
+
|
199 |
+
def korean_to_lazy_ipa(text):
|
200 |
+
text = latin_to_hangul(text)
|
201 |
+
text = number_to_hangul(text)
|
202 |
+
text=re.sub('[\uac00-\ud7af]+',lambda x:ko_pron.romanise(x.group(0),'ipa').split('] ~ [')[0],text)
|
203 |
+
for regex, replacement in _ipa_to_lazy_ipa:
|
204 |
+
text = re.sub(regex, replacement, text)
|
205 |
+
return text
|
206 |
+
|
207 |
+
|
208 |
+
def korean_to_ipa(text):
|
209 |
+
text = korean_to_lazy_ipa(text)
|
210 |
+
return text.replace('ʧ','tʃ').replace('ʥ','dʑ')
|
text/mandarin.py
ADDED
@@ -0,0 +1,328 @@
|
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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 sys
|
3 |
+
import re
|
4 |
+
from pypinyin import lazy_pinyin, BOPOMOFO
|
5 |
+
import jieba
|
6 |
+
import cn2an
|
7 |
+
|
8 |
+
|
9 |
+
# List of (Latin alphabet, bopomofo) pairs:
|
10 |
+
_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
|
11 |
+
('a', 'ㄟˉ'),
|
12 |
+
('b', 'ㄅㄧˋ'),
|
13 |
+
('c', 'ㄙㄧˉ'),
|
14 |
+
('d', 'ㄉㄧˋ'),
|
15 |
+
('e', 'ㄧˋ'),
|
16 |
+
('f', 'ㄝˊㄈㄨˋ'),
|
17 |
+
('g', 'ㄐㄧˋ'),
|
18 |
+
('h', 'ㄝˇㄑㄩˋ'),
|
19 |
+
('i', 'ㄞˋ'),
|
20 |
+
('j', 'ㄐㄟˋ'),
|
21 |
+
('k', 'ㄎㄟˋ'),
|
22 |
+
('l', 'ㄝˊㄛˋ'),
|
23 |
+
('m', 'ㄝˊㄇㄨˋ'),
|
24 |
+
('n', 'ㄣˉ'),
|
25 |
+
('o', 'ㄡˉ'),
|
26 |
+
('p', 'ㄆㄧˉ'),
|
27 |
+
('q', 'ㄎㄧㄡˉ'),
|
28 |
+
('r', 'ㄚˋ'),
|
29 |
+
('s', 'ㄝˊㄙˋ'),
|
30 |
+
('t', 'ㄊㄧˋ'),
|
31 |
+
('u', 'ㄧㄡˉ'),
|
32 |
+
('v', 'ㄨㄧˉ'),
|
33 |
+
('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
|
34 |
+
('x', 'ㄝˉㄎㄨˋㄙˋ'),
|
35 |
+
('y', 'ㄨㄞˋ'),
|
36 |
+
('z', 'ㄗㄟˋ')
|
37 |
+
]]
|
38 |
+
|
39 |
+
# List of (bopomofo, romaji) pairs:
|
40 |
+
_bopomofo_to_romaji = [(re.compile('%s' % x[0]), x[1]) for x in [
|
41 |
+
('ㄅㄛ', 'p⁼wo'),
|
42 |
+
('ㄆㄛ', 'pʰwo'),
|
43 |
+
('ㄇㄛ', 'mwo'),
|
44 |
+
('ㄈㄛ', 'fwo'),
|
45 |
+
('ㄅ', 'p⁼'),
|
46 |
+
('ㄆ', 'pʰ'),
|
47 |
+
('ㄇ', 'm'),
|
48 |
+
('ㄈ', 'f'),
|
49 |
+
('ㄉ', 't⁼'),
|
50 |
+
('ㄊ', 'tʰ'),
|
51 |
+
('ㄋ', 'n'),
|
52 |
+
('ㄌ', 'l'),
|
53 |
+
('ㄍ', 'k⁼'),
|
54 |
+
('ㄎ', 'kʰ'),
|
55 |
+
('ㄏ', 'h'),
|
56 |
+
('ㄐ', 'ʧ⁼'),
|
57 |
+
('ㄑ', 'ʧʰ'),
|
58 |
+
('ㄒ', 'ʃ'),
|
59 |
+
('ㄓ', 'ʦ`⁼'),
|
60 |
+
('ㄔ', 'ʦ`ʰ'),
|
61 |
+
('ㄕ', 's`'),
|
62 |
+
('ㄖ', 'ɹ`'),
|
63 |
+
('ㄗ', 'ʦ⁼'),
|
64 |
+
('ㄘ', 'ʦʰ'),
|
65 |
+
('ㄙ', 's'),
|
66 |
+
('ㄚ', 'a'),
|
67 |
+
('ㄛ', 'o'),
|
68 |
+
('ㄜ', 'ə'),
|
69 |
+
('ㄝ', 'e'),
|
70 |
+
('ㄞ', 'ai'),
|
71 |
+
('ㄟ', 'ei'),
|
72 |
+
('ㄠ', 'au'),
|
73 |
+
('ㄡ', 'ou'),
|
74 |
+
('ㄧㄢ', 'yeNN'),
|
75 |
+
('ㄢ', 'aNN'),
|
76 |
+
('ㄧㄣ', 'iNN'),
|
77 |
+
('ㄣ', 'əNN'),
|
78 |
+
('ㄤ', 'aNg'),
|
79 |
+
('ㄧㄥ', 'iNg'),
|
80 |
+
('ㄨㄥ', 'uNg'),
|
81 |
+
('ㄩㄥ', 'yuNg'),
|
82 |
+
('ㄥ', 'əNg'),
|
83 |
+
('ㄦ', 'əɻ'),
|
84 |
+
('ㄧ', 'i'),
|
85 |
+
('ㄨ', 'u'),
|
86 |
+
('ㄩ', 'ɥ'),
|
87 |
+
('ˉ', '→'),
|
88 |
+
('ˊ', '↑'),
|
89 |
+
('ˇ', '↓↑'),
|
90 |
+
('ˋ', '↓'),
|
91 |
+
('˙', ''),
|
92 |
+
(',', ','),
|
93 |
+
('。', '.'),
|
94 |
+
('!', '!'),
|
95 |
+
('?', '?'),
|
96 |
+
('—', '-')
|
97 |
+
]]
|
98 |
+
|
99 |
+
# List of (romaji, ipa) pairs:
|
100 |
+
_romaji_to_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
|
101 |
+
('ʃy', 'ʃ'),
|
102 |
+
('ʧʰy', 'ʧʰ'),
|
103 |
+
('ʧ⁼y', 'ʧ⁼'),
|
104 |
+
('NN', 'n'),
|
105 |
+
('Ng', 'ŋ'),
|
106 |
+
('y', 'j'),
|
107 |
+
('h', 'x')
|
108 |
+
]]
|
109 |
+
|
110 |
+
# List of (bopomofo, ipa) pairs:
|
111 |
+
_bopomofo_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
|
112 |
+
('ㄅㄛ', 'p⁼wo'),
|
113 |
+
('ㄆㄛ', 'pʰwo'),
|
114 |
+
('ㄇㄛ', 'mwo'),
|
115 |
+
('ㄈㄛ', 'fwo'),
|
116 |
+
('ㄅ', 'p⁼'),
|
117 |
+
('ㄆ', 'pʰ'),
|
118 |
+
('ㄇ', 'm'),
|
119 |
+
('ㄈ', 'f'),
|
120 |
+
('ㄉ', 't⁼'),
|
121 |
+
('ㄊ', 'tʰ'),
|
122 |
+
('ㄋ', 'n'),
|
123 |
+
('ㄌ', 'l'),
|
124 |
+
('ㄍ', 'k⁼'),
|
125 |
+
('ㄎ', 'kʰ'),
|
126 |
+
('ㄏ', 'x'),
|
127 |
+
('ㄐ', 'tʃ⁼'),
|
128 |
+
('ㄑ', 'tʃʰ'),
|
129 |
+
('ㄒ', 'ʃ'),
|
130 |
+
('ㄓ', 'ts`⁼'),
|
131 |
+
('ㄔ', 'ts`ʰ'),
|
132 |
+
('ㄕ', 's`'),
|
133 |
+
('ㄖ', 'ɹ`'),
|
134 |
+
('ㄗ', 'ts⁼'),
|
135 |
+
('ㄘ', 'tsʰ'),
|
136 |
+
('ㄙ', 's'),
|
137 |
+
('ㄚ', 'a'),
|
138 |
+
('ㄛ', 'o'),
|
139 |
+
('ㄜ', 'ə'),
|
140 |
+
('ㄝ', 'ɛ'),
|
141 |
+
('ㄞ', 'aɪ'),
|
142 |
+
('ㄟ', 'eɪ'),
|
143 |
+
('ㄠ', 'ɑʊ'),
|
144 |
+
('ㄡ', 'oʊ'),
|
145 |
+
('ㄧㄢ', 'jɛn'),
|
146 |
+
('ㄩㄢ', 'ɥæn'),
|
147 |
+
('ㄢ', 'an'),
|
148 |
+
('ㄧㄣ', 'in'),
|
149 |
+
('ㄩㄣ', 'ɥn'),
|
150 |
+
('ㄣ', 'ən'),
|
151 |
+
('ㄤ', 'ɑŋ'),
|
152 |
+
('ㄧㄥ', 'iŋ'),
|
153 |
+
('ㄨㄥ', 'ʊŋ'),
|
154 |
+
('ㄩㄥ', 'jʊŋ'),
|
155 |
+
('ㄥ', 'əŋ'),
|
156 |
+
('ㄦ', 'əɻ'),
|
157 |
+
('ㄧ', 'i'),
|
158 |
+
('ㄨ', 'u'),
|
159 |
+
('ㄩ', 'ɥ'),
|
160 |
+
('ˉ', '→'),
|
161 |
+
('ˊ', '↑'),
|
162 |
+
('ˇ', '↓↑'),
|
163 |
+
('ˋ', '↓'),
|
164 |
+
('˙', ''),
|
165 |
+
(',', ','),
|
166 |
+
('。', '.'),
|
167 |
+
('!', '!'),
|
168 |
+
('?', '?'),
|
169 |
+
('—', '-')
|
170 |
+
]]
|
171 |
+
|
172 |
+
# List of (bopomofo, ipa2) pairs:
|
173 |
+
_bopomofo_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
|
174 |
+
('ㄅㄛ', 'pwo'),
|
175 |
+
('ㄆㄛ', 'pʰwo'),
|
176 |
+
('ㄇㄛ', 'mwo'),
|
177 |
+
('ㄈㄛ', 'fwo'),
|
178 |
+
('ㄅ', 'p'),
|
179 |
+
('ㄆ', 'pʰ'),
|
180 |
+
('ㄇ', 'm'),
|
181 |
+
('ㄈ', 'f'),
|
182 |
+
('ㄉ', 't'),
|
183 |
+
('ㄊ', 'tʰ'),
|
184 |
+
('ㄋ', 'n'),
|
185 |
+
('ㄌ', 'l'),
|
186 |
+
('ㄍ', 'k'),
|
187 |
+
('ㄎ', 'kʰ'),
|
188 |
+
('ㄏ', 'h'),
|
189 |
+
('ㄐ', 'tɕ'),
|
190 |
+
('ㄑ', 'tɕʰ'),
|
191 |
+
('ㄒ', 'ɕ'),
|
192 |
+
('ㄓ', 'tʂ'),
|
193 |
+
('ㄔ', 'tʂʰ'),
|
194 |
+
('ㄕ', 'ʂ'),
|
195 |
+
('ㄖ', 'ɻ'),
|
196 |
+
('ㄗ', 'ts'),
|
197 |
+
('ㄘ', 'tsʰ'),
|
198 |
+
('ㄙ', 's'),
|
199 |
+
('ㄚ', 'a'),
|
200 |
+
('ㄛ', 'o'),
|
201 |
+
('ㄜ', 'ɤ'),
|
202 |
+
('ㄝ', 'ɛ'),
|
203 |
+
('ㄞ', 'aɪ'),
|
204 |
+
('ㄟ', 'eɪ'),
|
205 |
+
('ㄠ', 'ɑʊ'),
|
206 |
+
('ㄡ', 'oʊ'),
|
207 |
+
('ㄧㄢ', 'jɛn'),
|
208 |
+
('ㄩㄢ', 'yæn'),
|
209 |
+
('ㄢ', 'an'),
|
210 |
+
('ㄧㄣ', 'in'),
|
211 |
+
('ㄩㄣ', 'yn'),
|
212 |
+
('ㄣ', 'ən'),
|
213 |
+
('ㄤ', 'ɑŋ'),
|
214 |
+
('ㄧㄥ', 'iŋ'),
|
215 |
+
('ㄨㄥ', 'ʊŋ'),
|
216 |
+
('ㄩㄥ', 'jʊŋ'),
|
217 |
+
('ㄥ', 'ɤŋ'),
|
218 |
+
('ㄦ', 'əɻ'),
|
219 |
+
('ㄧ', 'i'),
|
220 |
+
('ㄨ', 'u'),
|
221 |
+
('ㄩ', 'y'),
|
222 |
+
('ˉ', '˥'),
|
223 |
+
('ˊ', '˧˥'),
|
224 |
+
('ˇ', '˨˩˦'),
|
225 |
+
('ˋ', '˥˩'),
|
226 |
+
('˙', ''),
|
227 |
+
(',', ','),
|
228 |
+
('。', '.'),
|
229 |
+
('!', '!'),
|
230 |
+
('?', '?'),
|
231 |
+
('—', '-')
|
232 |
+
]]
|
233 |
+
|
234 |
+
|
235 |
+
def number_to_chinese(text):
|
236 |
+
numbers = re.findall(r'\d+(?:\.?\d+)?', text)
|
237 |
+
for number in numbers:
|
238 |
+
text = text.replace(number, cn2an.an2cn(number), 1)
|
239 |
+
return text
|
240 |
+
|
241 |
+
|
242 |
+
def chinese_to_bopomofo(text, taiwanese=False):
|
243 |
+
text = text.replace('、', ',').replace(';', ',').replace(':', ',')
|
244 |
+
words = jieba.lcut(text, cut_all=False)
|
245 |
+
text = ''
|
246 |
+
for word in words:
|
247 |
+
bopomofos = lazy_pinyin(word, BOPOMOFO)
|
248 |
+
if not re.search('[\u4e00-\u9fff]', word):
|
249 |
+
text += word
|
250 |
+
continue
|
251 |
+
for i in range(len(bopomofos)):
|
252 |
+
bopomofos[i] = re.sub(r'([\u3105-\u3129])$', r'\1ˉ', bopomofos[i])
|
253 |
+
if text != '':
|
254 |
+
text += ' '
|
255 |
+
if taiwanese:
|
256 |
+
text += '#'+'#'.join(bopomofos)
|
257 |
+
else:
|
258 |
+
text += ''.join(bopomofos)
|
259 |
+
return text
|
260 |
+
|
261 |
+
|
262 |
+
def latin_to_bopomofo(text):
|
263 |
+
for regex, replacement in _latin_to_bopomofo:
|
264 |
+
text = re.sub(regex, replacement, text)
|
265 |
+
return text
|
266 |
+
|
267 |
+
|
268 |
+
def bopomofo_to_romaji(text):
|
269 |
+
for regex, replacement in _bopomofo_to_romaji:
|
270 |
+
text = re.sub(regex, replacement, text)
|
271 |
+
return text
|
272 |
+
|
273 |
+
|
274 |
+
def bopomofo_to_ipa(text):
|
275 |
+
for regex, replacement in _bopomofo_to_ipa:
|
276 |
+
text = re.sub(regex, replacement, text)
|
277 |
+
return text
|
278 |
+
|
279 |
+
|
280 |
+
def bopomofo_to_ipa2(text):
|
281 |
+
for regex, replacement in _bopomofo_to_ipa2:
|
282 |
+
text = re.sub(regex, replacement, text)
|
283 |
+
return text
|
284 |
+
|
285 |
+
|
286 |
+
def chinese_to_romaji(text):
|
287 |
+
text = number_to_chinese(text)
|
288 |
+
text = chinese_to_bopomofo(text)
|
289 |
+
text = latin_to_bopomofo(text)
|
290 |
+
text = bopomofo_to_romaji(text)
|
291 |
+
text = re.sub('i([aoe])', r'y\1', text)
|
292 |
+
text = re.sub('u([aoəe])', r'w\1', text)
|
293 |
+
text = re.sub('([ʦsɹ]`[⁼ʰ]?)([→↓↑ ]+|$)',
|
294 |
+
r'\1ɹ`\2', text).replace('ɻ', 'ɹ`')
|
295 |
+
text = re.sub('([ʦs][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text)
|
296 |
+
return text
|
297 |
+
|
298 |
+
|
299 |
+
def chinese_to_lazy_ipa(text):
|
300 |
+
text = chinese_to_romaji(text)
|
301 |
+
for regex, replacement in _romaji_to_ipa:
|
302 |
+
text = re.sub(regex, replacement, text)
|
303 |
+
return text
|
304 |
+
|
305 |
+
|
306 |
+
def chinese_to_ipa(text):
|
307 |
+
text = number_to_chinese(text)
|
308 |
+
text = chinese_to_bopomofo(text)
|
309 |
+
text = latin_to_bopomofo(text)
|
310 |
+
text = bopomofo_to_ipa(text)
|
311 |
+
text = re.sub('i([aoe])', r'j\1', text)
|
312 |
+
text = re.sub('u([aoəe])', r'w\1', text)
|
313 |
+
text = re.sub('([sɹ]`[⁼ʰ]?)([→↓↑ ]+|$)',
|
314 |
+
r'\1ɹ`\2', text).replace('ɻ', 'ɹ`')
|
315 |
+
text = re.sub('([s][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text)
|
316 |
+
return text
|
317 |
+
|
318 |
+
|
319 |
+
def chinese_to_ipa2(text, taiwanese=False):
|
320 |
+
text = number_to_chinese(text)
|
321 |
+
text = chinese_to_bopomofo(text, taiwanese)
|
322 |
+
text = latin_to_bopomofo(text)
|
323 |
+
text = bopomofo_to_ipa2(text)
|
324 |
+
text = re.sub(r'i([aoe])', r'j\1', text)
|
325 |
+
text = re.sub(r'u([aoəe])', r'w\1', text)
|
326 |
+
text = re.sub(r'([ʂɹ]ʰ?)([˩˨˧˦˥ ]+|$)', r'\1ʅ\2', text)
|
327 |
+
text = re.sub(r'(sʰ?)([˩˨˧˦˥ ]+|$)', r'\1ɿ\2', text)
|
328 |
+
return text
|
text/ngu_dialect.py
ADDED
@@ -0,0 +1,29 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import re
|
2 |
+
import opencc
|
3 |
+
|
4 |
+
|
5 |
+
dialects = {'SZ': 'suzhou', 'WX': 'wuxi', 'CZ': 'changzhou', 'HZ': 'hangzhou',
|
6 |
+
'SX': 'shaoxing', 'NB': 'ningbo', 'JJ': 'jingjiang', 'YX': 'yixing',
|
7 |
+
'JD': 'jiading', 'ZR': 'zhenru', 'PH': 'pinghu', 'TX': 'tongxiang',
|
8 |
+
'JS': 'jiashan', 'XS': 'xiashi', 'LP': 'linping', 'XS': 'xiaoshan',
|
9 |
+
'FY': 'fuyang', 'RA': 'ruao', 'CX': 'cixi', 'SM': 'sanmen', 'TT': 'tiantai'}
|
10 |
+
|
11 |
+
converters = {}
|
12 |
+
|
13 |
+
for dialect in dialects.values():
|
14 |
+
try:
|
15 |
+
converters[dialect] = opencc.OpenCC(dialect)
|
16 |
+
except:
|
17 |
+
pass
|
18 |
+
|
19 |
+
|
20 |
+
def ngu_dialect_to_ipa(text, dialect):
|
21 |
+
dialect = dialects[dialect]
|
22 |
+
text = converters[dialect].convert(text).replace('$',' ')
|
23 |
+
text = re.sub(r'[、;:]', ',', text)
|
24 |
+
text = re.sub(r'\s*,\s*', ', ', text)
|
25 |
+
text = re.sub(r'\s*。\s*', '. ', text)
|
26 |
+
text = re.sub(r'\s*?\s*', '? ', text)
|
27 |
+
text = re.sub(r'\s*!\s*', '! ', text)
|
28 |
+
text = re.sub(r'\s*$', '', text)
|
29 |
+
return text
|
text/sanskrit.py
ADDED
@@ -0,0 +1,62 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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 re
|
2 |
+
from indic_transliteration import sanscript
|
3 |
+
|
4 |
+
|
5 |
+
# List of (iast, ipa) pairs:
|
6 |
+
_iast_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
|
7 |
+
('a', 'ə'),
|
8 |
+
('ā', 'aː'),
|
9 |
+
('ī', 'iː'),
|
10 |
+
('ū', 'uː'),
|
11 |
+
('ṛ', 'ɹ`'),
|
12 |
+
('ṝ', 'ɹ`ː'),
|
13 |
+
('ḷ', 'l`'),
|
14 |
+
('ḹ', 'l`ː'),
|
15 |
+
('e', 'eː'),
|
16 |
+
('o', 'oː'),
|
17 |
+
('k', 'k⁼'),
|
18 |
+
('k⁼h', 'kʰ'),
|
19 |
+
('g', 'g⁼'),
|
20 |
+
('g⁼h', 'gʰ'),
|
21 |
+
('ṅ', 'ŋ'),
|
22 |
+
('c', 'ʧ⁼'),
|
23 |
+
('ʧ⁼h', 'ʧʰ'),
|
24 |
+
('j', 'ʥ⁼'),
|
25 |
+
('ʥ⁼h', 'ʥʰ'),
|
26 |
+
('ñ', 'n^'),
|
27 |
+
('ṭ', 't`⁼'),
|
28 |
+
('t`⁼h', 't`ʰ'),
|
29 |
+
('ḍ', 'd`⁼'),
|
30 |
+
('d`⁼h', 'd`ʰ'),
|
31 |
+
('ṇ', 'n`'),
|
32 |
+
('t', 't⁼'),
|
33 |
+
('t⁼h', 'tʰ'),
|
34 |
+
('d', 'd⁼'),
|
35 |
+
('d⁼h', 'dʰ'),
|
36 |
+
('p', 'p⁼'),
|
37 |
+
('p⁼h', 'pʰ'),
|
38 |
+
('b', 'b⁼'),
|
39 |
+
('b⁼h', 'bʰ'),
|
40 |
+
('y', 'j'),
|
41 |
+
('ś', 'ʃ'),
|
42 |
+
('ṣ', 's`'),
|
43 |
+
('r', 'ɾ'),
|
44 |
+
('l̤', 'l`'),
|
45 |
+
('h', 'ɦ'),
|
46 |
+
("'", ''),
|
47 |
+
('~', '^'),
|
48 |
+
('ṃ', '^')
|
49 |
+
]]
|
50 |
+
|
51 |
+
|
52 |
+
def devanagari_to_ipa(text):
|
53 |
+
text = text.replace('ॐ', 'ओम्')
|
54 |
+
text = re.sub(r'\s*।\s*$', '.', text)
|
55 |
+
text = re.sub(r'\s*।\s*', ', ', text)
|
56 |
+
text = re.sub(r'\s*॥', '.', text)
|
57 |
+
text = sanscript.transliterate(text, sanscript.DEVANAGARI, sanscript.IAST)
|
58 |
+
for regex, replacement in _iast_to_ipa:
|
59 |
+
text = re.sub(regex, replacement, text)
|
60 |
+
text = re.sub('(.)[`ː]*ḥ', lambda x: x.group(0)
|
61 |
+
[:-1]+'h'+x.group(1)+'*', text)
|
62 |
+
return text
|
text/shanghainese.py
ADDED
@@ -0,0 +1,64 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os, sys, re
|
2 |
+
import cn2an
|
3 |
+
import opencc
|
4 |
+
|
5 |
+
|
6 |
+
converter = opencc.OpenCC('zaonhe')
|
7 |
+
|
8 |
+
# List of (Latin alphabet, ipa) pairs:
|
9 |
+
_latin_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
|
10 |
+
('A', 'ᴇ'),
|
11 |
+
('B', 'bi'),
|
12 |
+
('C', 'si'),
|
13 |
+
('D', 'di'),
|
14 |
+
('E', 'i'),
|
15 |
+
('F', 'ᴇf'),
|
16 |
+
('G', 'dʑi'),
|
17 |
+
('H', 'ᴇtɕʰ'),
|
18 |
+
('I', 'ᴀi'),
|
19 |
+
('J', 'dʑᴇ'),
|
20 |
+
('K', 'kʰᴇ'),
|
21 |
+
('L', 'ᴇl'),
|
22 |
+
('M', 'ᴇm'),
|
23 |
+
('N', 'ᴇn'),
|
24 |
+
('O', 'o'),
|
25 |
+
('P', 'pʰi'),
|
26 |
+
('Q', 'kʰiu'),
|
27 |
+
('R', 'ᴀl'),
|
28 |
+
('S', 'ᴇs'),
|
29 |
+
('T', 'tʰi'),
|
30 |
+
('U', 'ɦiu'),
|
31 |
+
('V', 'vi'),
|
32 |
+
('W', 'dᴀbɤliu'),
|
33 |
+
('X', 'ᴇks'),
|
34 |
+
('Y', 'uᴀi'),
|
35 |
+
('Z', 'zᴇ')
|
36 |
+
]]
|
37 |
+
|
38 |
+
|
39 |
+
def _number_to_shanghainese(num):
|
40 |
+
num = cn2an.an2cn(num).replace('一十','十').replace('二十', '廿').replace('二', '两')
|
41 |
+
return re.sub(r'(?:(?:^|[^三四五六七八九])十|廿)两', lambda x: x.group()[:-1]+'二', num)
|
42 |
+
|
43 |
+
|
44 |
+
def number_to_shanghainese(text):
|
45 |
+
return re.sub(r'\d+(?:\.?\d+)?', lambda x: _number_to_shanghainese(x.group()), text)
|
46 |
+
|
47 |
+
|
48 |
+
def latin_to_ipa(text):
|
49 |
+
for regex, replacement in _latin_to_ipa:
|
50 |
+
text = re.sub(regex, replacement, text)
|
51 |
+
return text
|
52 |
+
|
53 |
+
|
54 |
+
def shanghainese_to_ipa(text):
|
55 |
+
text = number_to_shanghainese(text.upper())
|
56 |
+
text = converter.convert(text).replace('-','').replace('$',' ')
|
57 |
+
text = re.sub(r'[A-Z]', lambda x: latin_to_ipa(x.group())+' ', text)
|
58 |
+
text = re.sub(r'[、;:]', ',', text)
|
59 |
+
text = re.sub(r'\s*,\s*', ', ', text)
|
60 |
+
text = re.sub(r'\s*。\s*', '. ', text)
|
61 |
+
text = re.sub(r'\s*?\s*', '? ', text)
|
62 |
+
text = re.sub(r'\s*!\s*', '! ', text)
|
63 |
+
text = re.sub(r'\s*$', '', text)
|
64 |
+
return text
|
text/symbols.py
ADDED
@@ -0,0 +1,75 @@
|
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|
|
1 |
+
'''
|
2 |
+
Defines the set of symbols used in text input to the model.
|
3 |
+
'''
|
4 |
+
|
5 |
+
'''# japanese_cleaners
|
6 |
+
_pad = '_'
|
7 |
+
_punctuation = ',.!?-'
|
8 |
+
_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
|
9 |
+
'''
|
10 |
+
|
11 |
+
'''# japanese_cleaners2
|
12 |
+
_pad = '_'
|
13 |
+
_punctuation = ',.!?-~…'
|
14 |
+
_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
|
15 |
+
'''
|
16 |
+
|
17 |
+
'''# korean_cleaners
|
18 |
+
_pad = '_'
|
19 |
+
_punctuation = ',.!?…~'
|
20 |
+
_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
|
21 |
+
'''
|
22 |
+
|
23 |
+
# chinese_cleaners
|
24 |
+
_pad = '_'
|
25 |
+
_punctuation = ',。!?—…'
|
26 |
+
_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
|
27 |
+
|
28 |
+
|
29 |
+
'''# zh_ja_mixture_cleaners
|
30 |
+
_pad = '_'
|
31 |
+
_punctuation = ',.!?-~…'
|
32 |
+
_letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
|
33 |
+
'''
|
34 |
+
|
35 |
+
'''# sanskrit_cleaners
|
36 |
+
_pad = '_'
|
37 |
+
_punctuation = '।'
|
38 |
+
_letters = 'ँंःअआइईउऊऋएऐओऔकखगघङचछजझञटठडढणतथदधनपफबभमयरलळवशषसहऽािीुूृॄेैोौ्ॠॢ '
|
39 |
+
'''
|
40 |
+
|
41 |
+
'''# cjks_cleaners
|
42 |
+
_pad = '_'
|
43 |
+
_punctuation = ',.!?-~…'
|
44 |
+
_letters = 'NQabdefghijklmnopstuvwxyzʃʧʥʦɯɹəɥçɸɾβŋɦː⁼ʰ`^#*=→↓↑ '
|
45 |
+
'''
|
46 |
+
|
47 |
+
'''# thai_cleaners
|
48 |
+
_pad = '_'
|
49 |
+
_punctuation = '.!? '
|
50 |
+
_letters = 'กขฃคฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลวศษสหฬอฮฯะัาำิีึืุูเแโใไๅๆ็่้๊๋์'
|
51 |
+
'''
|
52 |
+
|
53 |
+
'''# cjke_cleaners2
|
54 |
+
_pad = '_'
|
55 |
+
_punctuation = ',.!?-~…'
|
56 |
+
_letters = 'NQabdefghijklmnopstuvwxyzɑæʃʑçɯɪɔɛɹðəɫɥɸʊɾʒθβŋɦ⁼ʰ`^#*=ˈˌ→↓↑ '
|
57 |
+
'''
|
58 |
+
|
59 |
+
'''# shanghainese_cleaners
|
60 |
+
_pad = '_'
|
61 |
+
_punctuation = ',.!?…'
|
62 |
+
_letters = 'abdfghiklmnopstuvyzøŋȵɑɔɕəɤɦɪɿʑʔʰ̩̃ᴀᴇ15678 '
|
63 |
+
'''
|
64 |
+
|
65 |
+
'''# chinese_dialect_cleaners
|
66 |
+
_pad = '_'
|
67 |
+
_punctuation = ',.!?~…─'
|
68 |
+
_letters = '#Nabdefghijklmnoprstuvwxyzæçøŋœȵɐɑɒɓɔɕɗɘəɚɛɜɣɤɦɪɭɯɵɷɸɻɾɿʂʅʊʋʌʏʑʔʦʮʰʷˀː˥˦˧˨˩̥̩̃̚αᴀᴇ↑↓∅ⱼ '
|
69 |
+
'''
|
70 |
+
|
71 |
+
# Export all symbols:
|
72 |
+
symbols = [_pad] + list(_punctuation) + list(_letters)
|
73 |
+
|
74 |
+
# Special symbol ids
|
75 |
+
SPACE_ID = symbols.index(" ")
|
text/thai.py
ADDED
@@ -0,0 +1,44 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import re
|
2 |
+
from num_thai.thainumbers import NumThai
|
3 |
+
|
4 |
+
|
5 |
+
num = NumThai()
|
6 |
+
|
7 |
+
# List of (Latin alphabet, Thai) pairs:
|
8 |
+
_latin_to_thai = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
|
9 |
+
('a', 'เอ'),
|
10 |
+
('b','บี'),
|
11 |
+
('c','ซี'),
|
12 |
+
('d','ดี'),
|
13 |
+
('e','อี'),
|
14 |
+
('f','เอฟ'),
|
15 |
+
('g','จี'),
|
16 |
+
('h','เอช'),
|
17 |
+
('i','ไอ'),
|
18 |
+
('j','เจ'),
|
19 |
+
('k','เค'),
|
20 |
+
('l','แอล'),
|
21 |
+
('m','เอ็ม'),
|
22 |
+
('n','เอ็น'),
|
23 |
+
('o','โอ'),
|
24 |
+
('p','พี'),
|
25 |
+
('q','คิว'),
|
26 |
+
('r','แอร์'),
|
27 |
+
('s','เอส'),
|
28 |
+
('t','ที'),
|
29 |
+
('u','ยู'),
|
30 |
+
('v','วี'),
|
31 |
+
('w','ดับเบิลยู'),
|
32 |
+
('x','เอ็กซ์'),
|
33 |
+
('y','วาย'),
|
34 |
+
('z','ซี')
|
35 |
+
]]
|
36 |
+
|
37 |
+
|
38 |
+
def num_to_thai(text):
|
39 |
+
return re.sub(r'(?:\d+(?:,?\d+)?)+(?:\.\d+(?:,?\d+)?)?', lambda x: ''.join(num.NumberToTextThai(float(x.group(0).replace(',', '')))), text)
|
40 |
+
|
41 |
+
def latin_to_thai(text):
|
42 |
+
for regex, replacement in _latin_to_thai:
|
43 |
+
text = re.sub(regex, replacement, text)
|
44 |
+
return text
|