Duplicate from Plachta/VITS-Umamusume-voice-synthesizer
Browse filesCo-authored-by: ElderFrog <Plachta@users.noreply.huggingface.co>
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- .gitattributes +34 -0
- ONNXVITS_infer.py +201 -0
- ONNXVITS_inference.py +36 -0
- ONNXVITS_models.py +509 -0
- ONNXVITS_modules.py +390 -0
- ONNXVITS_to_onnx.py +31 -0
- ONNXVITS_transforms.py +196 -0
- ONNXVITS_utils.py +19 -0
- ONNX_net/G_jp/dec.onnx +3 -0
- ONNX_net/G_jp/dp.onnx +3 -0
- ONNX_net/G_jp/enc_p.onnx +3 -0
- ONNX_net/G_jp/flow.onnx +3 -0
- ONNX_net/G_trilingual/dec.onnx +3 -0
- ONNX_net/G_trilingual/dp.onnx +3 -0
- ONNX_net/G_trilingual/enc_p.onnx +3 -0
- ONNX_net/G_trilingual/flow.onnx +3 -0
- README.md +13 -0
- app.py +256 -0
- attentions.py +300 -0
- commons.py +97 -0
- configs/uma87.json +133 -0
- configs/uma_trilingual.json +202 -0
- data_utils.py +393 -0
- hubert_model.py +221 -0
- jieba/dict.txt +0 -0
- losses.py +61 -0
- mel_processing.py +101 -0
- models.py +542 -0
- modules.py +387 -0
- monotonic_align/__init__.py +19 -0
- monotonic_align/__pycache__/__init__.cpython-37.pyc +0 -0
- monotonic_align/build/lib.win-amd64-cpython-37/monotonic_align/core.cp37-win_amd64.pyd +0 -0
- monotonic_align/build/temp.win-amd64-cpython-37/Release/core.cp37-win_amd64.exp +0 -0
- monotonic_align/build/temp.win-amd64-cpython-37/Release/core.cp37-win_amd64.lib +0 -0
- monotonic_align/build/temp.win-amd64-cpython-37/Release/core.obj +0 -0
- monotonic_align/core.c +0 -0
- monotonic_align/core.pyx +42 -0
- monotonic_align/monotonic_align/core.cp37-win_amd64.pyd +0 -0
- monotonic_align/setup.py +9 -0
- pretrained_models/D_trilingual.pth +3 -0
- pretrained_models/G_jp.pth +3 -0
- pretrained_models/G_trilingual.pth +3 -0
- requirements.txt +28 -0
- text/LICENSE +19 -0
- text/__init__.py +32 -0
- text/__pycache__/__init__.cpython-37.pyc +0 -0
- text/__pycache__/cleaners.cpython-37.pyc +0 -0
- text/__pycache__/english.cpython-37.pyc +0 -0
- text/__pycache__/japanese.cpython-37.pyc +0 -0
- text/__pycache__/korean.cpython-37.pyc +0 -0
.gitattributes
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ONNXVITS_infer.py
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import torch
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import commons
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import models
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import math
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from torch import nn
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from torch.nn import functional as F
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import modules
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import attentions
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from torch.nn import Conv1d, ConvTranspose1d, Conv2d
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from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
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from commons import init_weights, get_padding
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class TextEncoder(nn.Module):
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def __init__(self,
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n_vocab,
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out_channels,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout,
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emotion_embedding):
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super().__init__()
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self.n_vocab = n_vocab
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self.out_channels = out_channels
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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.emotion_embedding = emotion_embedding
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if self.n_vocab != 0:
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self.emb = nn.Embedding(n_vocab, hidden_channels)
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if emotion_embedding:
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self.emo_proj = nn.Linear(1024, hidden_channels)
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nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
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self.encoder = attentions.Encoder(
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout)
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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
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def forward(self, x, x_lengths, emotion_embedding=None):
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if self.n_vocab != 0:
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x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
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57 |
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if emotion_embedding is not None:
|
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print("emotion added")
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59 |
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x = x + self.emo_proj(emotion_embedding.unsqueeze(1))
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x = torch.transpose(x, 1, -1) # [b, h, t]
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61 |
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x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
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x = self.encoder(x * x_mask, x_mask)
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stats = self.proj(x) * x_mask
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m, logs = torch.split(stats, self.out_channels, dim=1)
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return x, m, logs, x_mask
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69 |
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class PosteriorEncoder(nn.Module):
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def __init__(self,
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in_channels,
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out_channels,
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hidden_channels,
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75 |
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kernel_size,
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76 |
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dilation_rate,
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n_layers,
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gin_channels=0):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.hidden_channels = hidden_channels
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self.kernel_size = kernel_size
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self.dilation_rate = dilation_rate
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self.n_layers = n_layers
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86 |
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self.gin_channels = gin_channels
|
87 |
+
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88 |
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self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
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self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
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91 |
+
|
92 |
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def forward(self, x, x_lengths, g=None):
|
93 |
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x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
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x = self.pre(x) * x_mask
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x = self.enc(x, x_mask, g=g)
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stats = self.proj(x) * x_mask
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m, logs = torch.split(stats, self.out_channels, dim=1)
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98 |
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z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
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99 |
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return z, m, logs, x_mask
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101 |
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class SynthesizerTrn(models.SynthesizerTrn):
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"""
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Synthesizer for Training
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"""
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106 |
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107 |
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def __init__(self,
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108 |
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n_vocab,
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109 |
+
spec_channels,
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110 |
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segment_size,
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111 |
+
inter_channels,
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112 |
+
hidden_channels,
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113 |
+
filter_channels,
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114 |
+
n_heads,
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115 |
+
n_layers,
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116 |
+
kernel_size,
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117 |
+
p_dropout,
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118 |
+
resblock,
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119 |
+
resblock_kernel_sizes,
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120 |
+
resblock_dilation_sizes,
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121 |
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upsample_rates,
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122 |
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upsample_initial_channel,
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123 |
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upsample_kernel_sizes,
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n_speakers=0,
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gin_channels=0,
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use_sdp=True,
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emotion_embedding=False,
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ONNX_dir="./ONNX_net/",
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**kwargs):
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super().__init__(
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n_vocab,
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133 |
+
spec_channels,
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134 |
+
segment_size,
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135 |
+
inter_channels,
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136 |
+
hidden_channels,
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137 |
+
filter_channels,
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138 |
+
n_heads,
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139 |
+
n_layers,
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140 |
+
kernel_size,
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141 |
+
p_dropout,
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142 |
+
resblock,
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143 |
+
resblock_kernel_sizes,
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144 |
+
resblock_dilation_sizes,
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145 |
+
upsample_rates,
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146 |
+
upsample_initial_channel,
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147 |
+
upsample_kernel_sizes,
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148 |
+
n_speakers=n_speakers,
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149 |
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gin_channels=gin_channels,
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use_sdp=use_sdp,
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151 |
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**kwargs
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)
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self.ONNX_dir = ONNX_dir
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self.enc_p = TextEncoder(n_vocab,
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inter_channels,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout,
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emotion_embedding)
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self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
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def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None,
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emotion_embedding=None):
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from ONNXVITS_utils import runonnx
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with torch.no_grad():
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x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, emotion_embedding)
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171 |
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if self.n_speakers > 0:
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g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
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else:
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g = None
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+
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# logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
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logw = runonnx(f"{self.ONNX_dir}dp.onnx", x=x.numpy(), x_mask=x_mask.numpy(), g=g.numpy())
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logw = torch.from_numpy(logw[0])
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179 |
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w = torch.exp(logw) * x_mask * length_scale
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w_ceil = torch.ceil(w)
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182 |
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y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
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183 |
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y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
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184 |
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attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
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attn = commons.generate_path(w_ceil, attn_mask)
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+
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187 |
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m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
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188 |
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logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1,
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2) # [b, t', t], [b, t, d] -> [b, d, t']
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z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
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+
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# z = self.flow(z_p, y_mask, g=g, reverse=True)
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z = runonnx(f"{self.ONNX_dir}flow.onnx", z_p=z_p.numpy(), y_mask=y_mask.numpy(), g=g.numpy())
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z = torch.from_numpy(z[0])
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197 |
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# o = self.dec((z * y_mask)[:,:,:max_len], g=g)
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o = runonnx(f"{self.ONNX_dir}dec.onnx", z_in=(z * y_mask)[:, :, :max_len].numpy(), g=g.numpy())
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o = torch.from_numpy(o[0])
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return o, attn, y_mask, (z, z_p, m_p, logs_p)
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ONNXVITS_inference.py
ADDED
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import logging
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logging.getLogger('numba').setLevel(logging.WARNING)
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import IPython.display as ipd
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import torch
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import commons
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import utils
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7 |
+
import ONNXVITS_infer
|
8 |
+
from text import text_to_sequence
|
9 |
+
|
10 |
+
def get_text(text, hps):
|
11 |
+
text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
|
12 |
+
if hps.data.add_blank:
|
13 |
+
text_norm = commons.intersperse(text_norm, 0)
|
14 |
+
text_norm = torch.LongTensor(text_norm)
|
15 |
+
return text_norm
|
16 |
+
|
17 |
+
hps = utils.get_hparams_from_file("../vits/pretrained_models/uma87.json")
|
18 |
+
|
19 |
+
net_g = ONNXVITS_infer.SynthesizerTrn(
|
20 |
+
len(hps.symbols),
|
21 |
+
hps.data.filter_length // 2 + 1,
|
22 |
+
hps.train.segment_size // hps.data.hop_length,
|
23 |
+
n_speakers=hps.data.n_speakers,
|
24 |
+
**hps.model)
|
25 |
+
_ = net_g.eval()
|
26 |
+
|
27 |
+
_ = utils.load_checkpoint("../vits/pretrained_models/uma_1153000.pth", net_g)
|
28 |
+
|
29 |
+
text1 = get_text("おはようございます。", hps)
|
30 |
+
stn_tst = text1
|
31 |
+
with torch.no_grad():
|
32 |
+
x_tst = stn_tst.unsqueeze(0)
|
33 |
+
x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
|
34 |
+
sid = torch.LongTensor([0])
|
35 |
+
audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.cpu().float().numpy()
|
36 |
+
print(audio)
|
ONNXVITS_models.py
ADDED
@@ -0,0 +1,509 @@
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|
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|
|
|
|
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|
|
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|
|
|
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|
|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
1 |
+
import copy
|
2 |
+
import math
|
3 |
+
import torch
|
4 |
+
from torch import nn
|
5 |
+
from torch.nn import functional as F
|
6 |
+
|
7 |
+
import commons
|
8 |
+
import ONNXVITS_modules as 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 |
+
self.w = None
|
51 |
+
self.reverse = None
|
52 |
+
self.noise_scale = None
|
53 |
+
def forward(self, x, x_mask, g=None):
|
54 |
+
w = self.w
|
55 |
+
reverse = self.reverse
|
56 |
+
noise_scale = self.noise_scale
|
57 |
+
|
58 |
+
x = torch.detach(x)
|
59 |
+
x = self.pre(x)
|
60 |
+
if g is not None:
|
61 |
+
g = torch.detach(g)
|
62 |
+
x = x + self.cond(g)
|
63 |
+
x = self.convs(x, x_mask)
|
64 |
+
x = self.proj(x) * x_mask
|
65 |
+
|
66 |
+
if not reverse:
|
67 |
+
flows = self.flows
|
68 |
+
assert w is not None
|
69 |
+
|
70 |
+
logdet_tot_q = 0
|
71 |
+
h_w = self.post_pre(w)
|
72 |
+
h_w = self.post_convs(h_w, x_mask)
|
73 |
+
h_w = self.post_proj(h_w) * x_mask
|
74 |
+
e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
|
75 |
+
z_q = e_q
|
76 |
+
for flow in self.post_flows:
|
77 |
+
z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
|
78 |
+
logdet_tot_q += logdet_q
|
79 |
+
z_u, z1 = torch.split(z_q, [1, 1], 1)
|
80 |
+
u = torch.sigmoid(z_u) * x_mask
|
81 |
+
z0 = (w - u) * x_mask
|
82 |
+
logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
|
83 |
+
logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
|
84 |
+
|
85 |
+
logdet_tot = 0
|
86 |
+
z0, logdet = self.log_flow(z0, x_mask)
|
87 |
+
logdet_tot += logdet
|
88 |
+
z = torch.cat([z0, z1], 1)
|
89 |
+
for flow in flows:
|
90 |
+
z, logdet = flow(z, x_mask, g=x, reverse=reverse)
|
91 |
+
logdet_tot = logdet_tot + logdet
|
92 |
+
nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
|
93 |
+
return nll + logq # [b]
|
94 |
+
else:
|
95 |
+
flows = list(reversed(self.flows))
|
96 |
+
flows = flows[:-2] + [flows[-1]] # remove a useless vflow
|
97 |
+
z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
|
98 |
+
for flow in flows:
|
99 |
+
z = flow(z, x_mask, g=x, reverse=reverse)
|
100 |
+
z0, z1 = torch.split(z, [1, 1], 1)
|
101 |
+
logw = z0
|
102 |
+
return logw
|
103 |
+
|
104 |
+
|
105 |
+
class TextEncoder(nn.Module):
|
106 |
+
def __init__(self,
|
107 |
+
n_vocab,
|
108 |
+
out_channels,
|
109 |
+
hidden_channels,
|
110 |
+
filter_channels,
|
111 |
+
n_heads,
|
112 |
+
n_layers,
|
113 |
+
kernel_size,
|
114 |
+
p_dropout):
|
115 |
+
super().__init__()
|
116 |
+
self.n_vocab = n_vocab
|
117 |
+
self.out_channels = out_channels
|
118 |
+
self.hidden_channels = hidden_channels
|
119 |
+
self.filter_channels = filter_channels
|
120 |
+
self.n_heads = n_heads
|
121 |
+
self.n_layers = n_layers
|
122 |
+
self.kernel_size = kernel_size
|
123 |
+
self.p_dropout = p_dropout
|
124 |
+
|
125 |
+
self.emb = nn.Embedding(n_vocab, hidden_channels)
|
126 |
+
nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
|
127 |
+
|
128 |
+
self.encoder = attentions.Encoder(
|
129 |
+
hidden_channels,
|
130 |
+
filter_channels,
|
131 |
+
n_heads,
|
132 |
+
n_layers,
|
133 |
+
kernel_size,
|
134 |
+
p_dropout)
|
135 |
+
self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
136 |
+
|
137 |
+
def forward(self, x, x_lengths):
|
138 |
+
x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
|
139 |
+
x = torch.transpose(x, 1, -1) # [b, h, t]
|
140 |
+
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
141 |
+
|
142 |
+
x = self.encoder(x * x_mask, x_mask)
|
143 |
+
stats = self.proj(x) * x_mask
|
144 |
+
|
145 |
+
m, logs = torch.split(stats, self.out_channels, dim=1)
|
146 |
+
return x, m, logs, x_mask
|
147 |
+
|
148 |
+
|
149 |
+
class ResidualCouplingBlock(nn.Module):
|
150 |
+
def __init__(self,
|
151 |
+
channels,
|
152 |
+
hidden_channels,
|
153 |
+
kernel_size,
|
154 |
+
dilation_rate,
|
155 |
+
n_layers,
|
156 |
+
n_flows=4,
|
157 |
+
gin_channels=0):
|
158 |
+
super().__init__()
|
159 |
+
self.channels = channels
|
160 |
+
self.hidden_channels = hidden_channels
|
161 |
+
self.kernel_size = kernel_size
|
162 |
+
self.dilation_rate = dilation_rate
|
163 |
+
self.n_layers = n_layers
|
164 |
+
self.n_flows = n_flows
|
165 |
+
self.gin_channels = gin_channels
|
166 |
+
|
167 |
+
self.flows = nn.ModuleList()
|
168 |
+
for i in range(n_flows):
|
169 |
+
self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
|
170 |
+
self.flows.append(modules.Flip())
|
171 |
+
|
172 |
+
self.reverse = None
|
173 |
+
def forward(self, x, x_mask, g=None):
|
174 |
+
reverse = self.reverse
|
175 |
+
if not reverse:
|
176 |
+
for flow in self.flows:
|
177 |
+
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
178 |
+
else:
|
179 |
+
for flow in reversed(self.flows):
|
180 |
+
x = flow(x, x_mask, g=g, reverse=reverse)
|
181 |
+
return x
|
182 |
+
|
183 |
+
|
184 |
+
class PosteriorEncoder(nn.Module):
|
185 |
+
def __init__(self,
|
186 |
+
in_channels,
|
187 |
+
out_channels,
|
188 |
+
hidden_channels,
|
189 |
+
kernel_size,
|
190 |
+
dilation_rate,
|
191 |
+
n_layers,
|
192 |
+
gin_channels=0):
|
193 |
+
super().__init__()
|
194 |
+
self.in_channels = in_channels
|
195 |
+
self.out_channels = out_channels
|
196 |
+
self.hidden_channels = hidden_channels
|
197 |
+
self.kernel_size = kernel_size
|
198 |
+
self.dilation_rate = dilation_rate
|
199 |
+
self.n_layers = n_layers
|
200 |
+
self.gin_channels = gin_channels
|
201 |
+
|
202 |
+
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
203 |
+
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
|
204 |
+
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
205 |
+
|
206 |
+
def forward(self, x, x_lengths, g=None):
|
207 |
+
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
208 |
+
x = self.pre(x) * x_mask # x_in : [b, c, t] -> [b, h, t]
|
209 |
+
x = self.enc(x, x_mask, g=g) # x_in : [b, h, t], g : [b, h, 1], x = x_in + g
|
210 |
+
stats = self.proj(x) * x_mask
|
211 |
+
m, logs = torch.split(stats, self.out_channels, dim=1)
|
212 |
+
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
213 |
+
return z, m, logs, x_mask # z, m, logs : [b, h, t]
|
214 |
+
|
215 |
+
|
216 |
+
class Generator(torch.nn.Module):
|
217 |
+
def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
|
218 |
+
super(Generator, self).__init__()
|
219 |
+
self.num_kernels = len(resblock_kernel_sizes)
|
220 |
+
self.num_upsamples = len(upsample_rates)
|
221 |
+
self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
|
222 |
+
resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
|
223 |
+
|
224 |
+
self.ups = nn.ModuleList()
|
225 |
+
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
226 |
+
self.ups.append(weight_norm(
|
227 |
+
ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
|
228 |
+
k, u, padding=(k-u)//2)))
|
229 |
+
|
230 |
+
self.resblocks = nn.ModuleList()
|
231 |
+
for i in range(len(self.ups)):
|
232 |
+
ch = upsample_initial_channel//(2**(i+1))
|
233 |
+
for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
|
234 |
+
self.resblocks.append(resblock(ch, k, d))
|
235 |
+
|
236 |
+
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
|
237 |
+
self.ups.apply(init_weights)
|
238 |
+
|
239 |
+
if gin_channels != 0:
|
240 |
+
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
|
241 |
+
|
242 |
+
def forward(self, x, g=None):
|
243 |
+
x = self.conv_pre(x)
|
244 |
+
if g is not None:
|
245 |
+
x = x + self.cond(g)
|
246 |
+
|
247 |
+
for i in range(self.num_upsamples):
|
248 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
249 |
+
x = self.ups[i](x)
|
250 |
+
xs = None
|
251 |
+
for j in range(self.num_kernels):
|
252 |
+
if xs is None:
|
253 |
+
xs = self.resblocks[i*self.num_kernels+j](x)
|
254 |
+
else:
|
255 |
+
xs += self.resblocks[i*self.num_kernels+j](x)
|
256 |
+
x = xs / self.num_kernels
|
257 |
+
x = F.leaky_relu(x)
|
258 |
+
x = self.conv_post(x)
|
259 |
+
x = torch.tanh(x)
|
260 |
+
|
261 |
+
return x
|
262 |
+
|
263 |
+
def remove_weight_norm(self):
|
264 |
+
print('Removing weight norm...')
|
265 |
+
for l in self.ups:
|
266 |
+
remove_weight_norm(l)
|
267 |
+
for l in self.resblocks:
|
268 |
+
l.remove_weight_norm()
|
269 |
+
|
270 |
+
|
271 |
+
class DiscriminatorP(torch.nn.Module):
|
272 |
+
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
273 |
+
super(DiscriminatorP, self).__init__()
|
274 |
+
self.period = period
|
275 |
+
self.use_spectral_norm = use_spectral_norm
|
276 |
+
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
277 |
+
self.convs = nn.ModuleList([
|
278 |
+
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
279 |
+
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
280 |
+
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
281 |
+
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
282 |
+
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
|
283 |
+
])
|
284 |
+
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
285 |
+
|
286 |
+
def forward(self, x):
|
287 |
+
fmap = []
|
288 |
+
|
289 |
+
# 1d to 2d
|
290 |
+
b, c, t = x.shape
|
291 |
+
if t % self.period != 0: # pad first
|
292 |
+
n_pad = self.period - (t % self.period)
|
293 |
+
x = F.pad(x, (0, n_pad), "reflect")
|
294 |
+
t = t + n_pad
|
295 |
+
x = x.view(b, c, t // self.period, self.period)
|
296 |
+
|
297 |
+
for l in self.convs:
|
298 |
+
x = l(x)
|
299 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
300 |
+
fmap.append(x)
|
301 |
+
x = self.conv_post(x)
|
302 |
+
fmap.append(x)
|
303 |
+
x = torch.flatten(x, 1, -1)
|
304 |
+
|
305 |
+
return x, fmap
|
306 |
+
|
307 |
+
|
308 |
+
class DiscriminatorS(torch.nn.Module):
|
309 |
+
def __init__(self, use_spectral_norm=False):
|
310 |
+
super(DiscriminatorS, self).__init__()
|
311 |
+
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
312 |
+
self.convs = nn.ModuleList([
|
313 |
+
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
|
314 |
+
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
|
315 |
+
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
|
316 |
+
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
|
317 |
+
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
|
318 |
+
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
319 |
+
])
|
320 |
+
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
321 |
+
|
322 |
+
def forward(self, x):
|
323 |
+
fmap = []
|
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 MultiPeriodDiscriminator(torch.nn.Module):
|
337 |
+
def __init__(self, use_spectral_norm=False):
|
338 |
+
super(MultiPeriodDiscriminator, self).__init__()
|
339 |
+
periods = [2,3,5,7,11]
|
340 |
+
|
341 |
+
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
|
342 |
+
discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
|
343 |
+
self.discriminators = nn.ModuleList(discs)
|
344 |
+
|
345 |
+
def forward(self, y, y_hat):
|
346 |
+
y_d_rs = []
|
347 |
+
y_d_gs = []
|
348 |
+
fmap_rs = []
|
349 |
+
fmap_gs = []
|
350 |
+
for i, d in enumerate(self.discriminators):
|
351 |
+
y_d_r, fmap_r = d(y)
|
352 |
+
y_d_g, fmap_g = d(y_hat)
|
353 |
+
y_d_rs.append(y_d_r)
|
354 |
+
y_d_gs.append(y_d_g)
|
355 |
+
fmap_rs.append(fmap_r)
|
356 |
+
fmap_gs.append(fmap_g)
|
357 |
+
|
358 |
+
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
359 |
+
|
360 |
+
|
361 |
+
|
362 |
+
class SynthesizerTrn(nn.Module):
|
363 |
+
"""
|
364 |
+
Synthesizer for Training
|
365 |
+
"""
|
366 |
+
|
367 |
+
def __init__(self,
|
368 |
+
n_vocab,
|
369 |
+
spec_channels,
|
370 |
+
segment_size,
|
371 |
+
inter_channels,
|
372 |
+
hidden_channels,
|
373 |
+
filter_channels,
|
374 |
+
n_heads,
|
375 |
+
n_layers,
|
376 |
+
kernel_size,
|
377 |
+
p_dropout,
|
378 |
+
resblock,
|
379 |
+
resblock_kernel_sizes,
|
380 |
+
resblock_dilation_sizes,
|
381 |
+
upsample_rates,
|
382 |
+
upsample_initial_channel,
|
383 |
+
upsample_kernel_sizes,
|
384 |
+
n_speakers=0,
|
385 |
+
gin_channels=0,
|
386 |
+
use_sdp=True,
|
387 |
+
**kwargs):
|
388 |
+
|
389 |
+
super().__init__()
|
390 |
+
self.n_vocab = n_vocab
|
391 |
+
self.spec_channels = spec_channels
|
392 |
+
self.inter_channels = inter_channels
|
393 |
+
self.hidden_channels = hidden_channels
|
394 |
+
self.filter_channels = filter_channels
|
395 |
+
self.n_heads = n_heads
|
396 |
+
self.n_layers = n_layers
|
397 |
+
self.kernel_size = kernel_size
|
398 |
+
self.p_dropout = p_dropout
|
399 |
+
self.resblock = resblock
|
400 |
+
self.resblock_kernel_sizes = resblock_kernel_sizes
|
401 |
+
self.resblock_dilation_sizes = resblock_dilation_sizes
|
402 |
+
self.upsample_rates = upsample_rates
|
403 |
+
self.upsample_initial_channel = upsample_initial_channel
|
404 |
+
self.upsample_kernel_sizes = upsample_kernel_sizes
|
405 |
+
self.segment_size = segment_size
|
406 |
+
self.n_speakers = n_speakers
|
407 |
+
self.gin_channels = gin_channels
|
408 |
+
|
409 |
+
self.use_sdp = use_sdp
|
410 |
+
|
411 |
+
self.enc_p = TextEncoder(n_vocab,
|
412 |
+
inter_channels,
|
413 |
+
hidden_channels,
|
414 |
+
filter_channels,
|
415 |
+
n_heads,
|
416 |
+
n_layers,
|
417 |
+
kernel_size,
|
418 |
+
p_dropout)
|
419 |
+
self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
|
420 |
+
self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
|
421 |
+
self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
|
422 |
+
|
423 |
+
self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
|
424 |
+
|
425 |
+
if n_speakers > 0:
|
426 |
+
self.emb_g = nn.Embedding(n_speakers, gin_channels)
|
427 |
+
|
428 |
+
def forward(self, x, x_lengths, sid=None, noise_scale=.667, length_scale=1, noise_scale_w=.8, max_len=None):
|
429 |
+
torch.onnx.export(
|
430 |
+
self.enc_p,
|
431 |
+
(x, x_lengths),
|
432 |
+
"ONNX_net/enc_p.onnx",
|
433 |
+
input_names=["x", "x_lengths"],
|
434 |
+
output_names=["xout", "m_p", "logs_p", "x_mask"],
|
435 |
+
dynamic_axes={
|
436 |
+
"x" : [1],
|
437 |
+
"xout" : [2],
|
438 |
+
"m_p" : [2],
|
439 |
+
"logs_p" : [2],
|
440 |
+
"x_mask" : [2]
|
441 |
+
},
|
442 |
+
verbose=True,
|
443 |
+
)
|
444 |
+
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
|
445 |
+
|
446 |
+
if self.n_speakers > 0:
|
447 |
+
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
|
448 |
+
else:
|
449 |
+
g = None
|
450 |
+
|
451 |
+
self.dp.reverse = True
|
452 |
+
self.dp.noise_scale = noise_scale_w
|
453 |
+
torch.onnx.export(
|
454 |
+
self.dp,
|
455 |
+
(x, x_mask, g),
|
456 |
+
"ONNX_net/dp.onnx",
|
457 |
+
input_names=["x", "x_mask", "g"],
|
458 |
+
output_names=["logw"],
|
459 |
+
dynamic_axes={
|
460 |
+
"x" : [2],
|
461 |
+
"x_mask" : [2],
|
462 |
+
"logw" : [2]
|
463 |
+
},
|
464 |
+
verbose=True,
|
465 |
+
)
|
466 |
+
logw = self.dp(x, x_mask, g=g)
|
467 |
+
w = torch.exp(logw) * x_mask * length_scale
|
468 |
+
w_ceil = torch.ceil(w)
|
469 |
+
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
|
470 |
+
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
|
471 |
+
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
|
472 |
+
attn = commons.generate_path(w_ceil, attn_mask)
|
473 |
+
|
474 |
+
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
|
475 |
+
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
|
476 |
+
|
477 |
+
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
|
478 |
+
|
479 |
+
self.flow.reverse = True
|
480 |
+
torch.onnx.export(
|
481 |
+
self.flow,
|
482 |
+
(z_p, y_mask, g),
|
483 |
+
"ONNX_net/flow.onnx",
|
484 |
+
input_names=["z_p", "y_mask", "g"],
|
485 |
+
output_names=["z"],
|
486 |
+
dynamic_axes={
|
487 |
+
"z_p" : [2],
|
488 |
+
"y_mask" : [2],
|
489 |
+
"z" : [2]
|
490 |
+
},
|
491 |
+
verbose=True,
|
492 |
+
)
|
493 |
+
z = self.flow(z_p, y_mask, g=g)
|
494 |
+
z_in = (z * y_mask)[:,:,:max_len]
|
495 |
+
|
496 |
+
torch.onnx.export(
|
497 |
+
self.dec,
|
498 |
+
(z_in, g),
|
499 |
+
"ONNX_net/dec.onnx",
|
500 |
+
input_names=["z_in", "g"],
|
501 |
+
output_names=["o"],
|
502 |
+
dynamic_axes={
|
503 |
+
"z_in" : [2],
|
504 |
+
"o" : [2]
|
505 |
+
},
|
506 |
+
verbose=True,
|
507 |
+
)
|
508 |
+
o = self.dec(z_in, g=g)
|
509 |
+
return o
|
ONNXVITS_modules.py
ADDED
@@ -0,0 +1,390 @@
|
|
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|
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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 ONNXVITS_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
|
ONNXVITS_to_onnx.py
ADDED
@@ -0,0 +1,31 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import ONNXVITS_models
|
2 |
+
import utils
|
3 |
+
from text import text_to_sequence
|
4 |
+
import torch
|
5 |
+
import commons
|
6 |
+
|
7 |
+
def get_text(text, hps):
|
8 |
+
text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
|
9 |
+
if hps.data.add_blank:
|
10 |
+
text_norm = commons.intersperse(text_norm, 0)
|
11 |
+
text_norm = torch.LongTensor(text_norm)
|
12 |
+
return text_norm
|
13 |
+
|
14 |
+
hps = utils.get_hparams_from_file("../vits/pretrained_models/uma87.json")
|
15 |
+
symbols = hps.symbols
|
16 |
+
net_g = ONNXVITS_models.SynthesizerTrn(
|
17 |
+
len(symbols),
|
18 |
+
hps.data.filter_length // 2 + 1,
|
19 |
+
hps.train.segment_size // hps.data.hop_length,
|
20 |
+
n_speakers=hps.data.n_speakers,
|
21 |
+
**hps.model)
|
22 |
+
_ = net_g.eval()
|
23 |
+
_ = utils.load_checkpoint("../vits/pretrained_models/uma_1153000.pth", net_g)
|
24 |
+
|
25 |
+
text1 = get_text("ありがとうございます。", hps)
|
26 |
+
stn_tst = text1
|
27 |
+
with torch.no_grad():
|
28 |
+
x_tst = stn_tst.unsqueeze(0)
|
29 |
+
x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
|
30 |
+
sid = torch.tensor([0])
|
31 |
+
o = net_g(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)
|
ONNXVITS_transforms.py
ADDED
@@ -0,0 +1,196 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
from torch.nn import functional as F
|
3 |
+
|
4 |
+
import numpy as np
|
5 |
+
|
6 |
+
|
7 |
+
DEFAULT_MIN_BIN_WIDTH = 1e-3
|
8 |
+
DEFAULT_MIN_BIN_HEIGHT = 1e-3
|
9 |
+
DEFAULT_MIN_DERIVATIVE = 1e-3
|
10 |
+
|
11 |
+
|
12 |
+
def piecewise_rational_quadratic_transform(inputs,
|
13 |
+
unnormalized_widths,
|
14 |
+
unnormalized_heights,
|
15 |
+
unnormalized_derivatives,
|
16 |
+
inverse=False,
|
17 |
+
tails=None,
|
18 |
+
tail_bound=1.,
|
19 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
20 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
21 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
22 |
+
|
23 |
+
if tails is None:
|
24 |
+
spline_fn = rational_quadratic_spline
|
25 |
+
spline_kwargs = {}
|
26 |
+
else:
|
27 |
+
spline_fn = unconstrained_rational_quadratic_spline
|
28 |
+
spline_kwargs = {
|
29 |
+
'tails': tails,
|
30 |
+
'tail_bound': tail_bound
|
31 |
+
}
|
32 |
+
|
33 |
+
outputs, logabsdet = spline_fn(
|
34 |
+
inputs=inputs,
|
35 |
+
unnormalized_widths=unnormalized_widths,
|
36 |
+
unnormalized_heights=unnormalized_heights,
|
37 |
+
unnormalized_derivatives=unnormalized_derivatives,
|
38 |
+
inverse=inverse,
|
39 |
+
min_bin_width=min_bin_width,
|
40 |
+
min_bin_height=min_bin_height,
|
41 |
+
min_derivative=min_derivative,
|
42 |
+
**spline_kwargs
|
43 |
+
)
|
44 |
+
return outputs, logabsdet
|
45 |
+
|
46 |
+
|
47 |
+
def searchsorted(bin_locations, inputs, eps=1e-6):
|
48 |
+
bin_locations[..., -1] += eps
|
49 |
+
return torch.sum(
|
50 |
+
inputs[..., None] >= bin_locations,
|
51 |
+
dim=-1
|
52 |
+
) - 1
|
53 |
+
|
54 |
+
|
55 |
+
def unconstrained_rational_quadratic_spline(inputs,
|
56 |
+
unnormalized_widths,
|
57 |
+
unnormalized_heights,
|
58 |
+
unnormalized_derivatives,
|
59 |
+
inverse=False,
|
60 |
+
tails='linear',
|
61 |
+
tail_bound=1.,
|
62 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
63 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
64 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
65 |
+
inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
|
66 |
+
outside_interval_mask = ~inside_interval_mask
|
67 |
+
|
68 |
+
outputs = torch.zeros_like(inputs)
|
69 |
+
logabsdet = torch.zeros_like(inputs)
|
70 |
+
|
71 |
+
if tails == 'linear':
|
72 |
+
#unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
|
73 |
+
unnormalized_derivatives_ = torch.zeros((1, 1, unnormalized_derivatives.size(2), unnormalized_derivatives.size(3)+2))
|
74 |
+
unnormalized_derivatives_[...,1:-1] = unnormalized_derivatives
|
75 |
+
unnormalized_derivatives = unnormalized_derivatives_
|
76 |
+
constant = np.log(np.exp(1 - min_derivative) - 1)
|
77 |
+
unnormalized_derivatives[..., 0] = constant
|
78 |
+
unnormalized_derivatives[..., -1] = constant
|
79 |
+
|
80 |
+
outputs[outside_interval_mask] = inputs[outside_interval_mask]
|
81 |
+
logabsdet[outside_interval_mask] = 0
|
82 |
+
else:
|
83 |
+
raise RuntimeError('{} tails are not implemented.'.format(tails))
|
84 |
+
|
85 |
+
outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
|
86 |
+
inputs=inputs[inside_interval_mask],
|
87 |
+
unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
|
88 |
+
unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
|
89 |
+
unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
|
90 |
+
inverse=inverse,
|
91 |
+
left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
|
92 |
+
min_bin_width=min_bin_width,
|
93 |
+
min_bin_height=min_bin_height,
|
94 |
+
min_derivative=min_derivative
|
95 |
+
)
|
96 |
+
|
97 |
+
return outputs, logabsdet
|
98 |
+
|
99 |
+
def rational_quadratic_spline(inputs,
|
100 |
+
unnormalized_widths,
|
101 |
+
unnormalized_heights,
|
102 |
+
unnormalized_derivatives,
|
103 |
+
inverse=False,
|
104 |
+
left=0., right=1., bottom=0., top=1.,
|
105 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
106 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
107 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
108 |
+
if torch.min(inputs) < left or torch.max(inputs) > right:
|
109 |
+
raise ValueError('Input to a transform is not within its domain')
|
110 |
+
|
111 |
+
num_bins = unnormalized_widths.shape[-1]
|
112 |
+
|
113 |
+
if min_bin_width * num_bins > 1.0:
|
114 |
+
raise ValueError('Minimal bin width too large for the number of bins')
|
115 |
+
if min_bin_height * num_bins > 1.0:
|
116 |
+
raise ValueError('Minimal bin height too large for the number of bins')
|
117 |
+
|
118 |
+
widths = F.softmax(unnormalized_widths, dim=-1)
|
119 |
+
widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
|
120 |
+
cumwidths = torch.cumsum(widths, dim=-1)
|
121 |
+
cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
|
122 |
+
cumwidths = (right - left) * cumwidths + left
|
123 |
+
cumwidths[..., 0] = left
|
124 |
+
cumwidths[..., -1] = right
|
125 |
+
widths = cumwidths[..., 1:] - cumwidths[..., :-1]
|
126 |
+
|
127 |
+
derivatives = min_derivative + F.softplus(unnormalized_derivatives)
|
128 |
+
|
129 |
+
heights = F.softmax(unnormalized_heights, dim=-1)
|
130 |
+
heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
|
131 |
+
cumheights = torch.cumsum(heights, dim=-1)
|
132 |
+
cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
|
133 |
+
cumheights = (top - bottom) * cumheights + bottom
|
134 |
+
cumheights[..., 0] = bottom
|
135 |
+
cumheights[..., -1] = top
|
136 |
+
heights = cumheights[..., 1:] - cumheights[..., :-1]
|
137 |
+
|
138 |
+
if inverse:
|
139 |
+
bin_idx = searchsorted(cumheights, inputs)[..., None]
|
140 |
+
else:
|
141 |
+
bin_idx = searchsorted(cumwidths, inputs)[..., None]
|
142 |
+
|
143 |
+
input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
|
144 |
+
input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
|
145 |
+
|
146 |
+
input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
|
147 |
+
delta = heights / widths
|
148 |
+
input_delta = delta.gather(-1, bin_idx)[..., 0]
|
149 |
+
|
150 |
+
input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
|
151 |
+
input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
|
152 |
+
|
153 |
+
input_heights = heights.gather(-1, bin_idx)[..., 0]
|
154 |
+
|
155 |
+
if inverse:
|
156 |
+
a = (((inputs - input_cumheights) * (input_derivatives
|
157 |
+
+ input_derivatives_plus_one
|
158 |
+
- 2 * input_delta)
|
159 |
+
+ input_heights * (input_delta - input_derivatives)))
|
160 |
+
b = (input_heights * input_derivatives
|
161 |
+
- (inputs - input_cumheights) * (input_derivatives
|
162 |
+
+ input_derivatives_plus_one
|
163 |
+
- 2 * input_delta))
|
164 |
+
c = - input_delta * (inputs - input_cumheights)
|
165 |
+
|
166 |
+
discriminant = b.pow(2) - 4 * a * c
|
167 |
+
assert (discriminant >= 0).all()
|
168 |
+
|
169 |
+
root = (2 * c) / (-b - torch.sqrt(discriminant))
|
170 |
+
outputs = root * input_bin_widths + input_cumwidths
|
171 |
+
|
172 |
+
theta_one_minus_theta = root * (1 - root)
|
173 |
+
denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
|
174 |
+
* theta_one_minus_theta)
|
175 |
+
derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
|
176 |
+
+ 2 * input_delta * theta_one_minus_theta
|
177 |
+
+ input_derivatives * (1 - root).pow(2))
|
178 |
+
logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
|
179 |
+
|
180 |
+
return outputs, -logabsdet
|
181 |
+
else:
|
182 |
+
theta = (inputs - input_cumwidths) / input_bin_widths
|
183 |
+
theta_one_minus_theta = theta * (1 - theta)
|
184 |
+
|
185 |
+
numerator = input_heights * (input_delta * theta.pow(2)
|
186 |
+
+ input_derivatives * theta_one_minus_theta)
|
187 |
+
denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
|
188 |
+
* theta_one_minus_theta)
|
189 |
+
outputs = input_cumheights + numerator / denominator
|
190 |
+
|
191 |
+
derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
|
192 |
+
+ 2 * input_delta * theta_one_minus_theta
|
193 |
+
+ input_derivatives * (1 - theta).pow(2))
|
194 |
+
logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
|
195 |
+
|
196 |
+
return outputs, logabsdet
|
ONNXVITS_utils.py
ADDED
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import numpy as np
|
3 |
+
import random
|
4 |
+
import onnxruntime as ort
|
5 |
+
def set_random_seed(seed=0):
|
6 |
+
ort.set_seed(seed)
|
7 |
+
torch.manual_seed(seed)
|
8 |
+
torch.cuda.manual_seed(seed)
|
9 |
+
torch.backends.cudnn.deterministic = True
|
10 |
+
random.seed(seed)
|
11 |
+
np.random.seed(seed)
|
12 |
+
|
13 |
+
def runonnx(model_path, **kwargs):
|
14 |
+
ort_session = ort.InferenceSession(model_path)
|
15 |
+
outputs = ort_session.run(
|
16 |
+
None,
|
17 |
+
kwargs
|
18 |
+
)
|
19 |
+
return outputs
|
ONNX_net/G_jp/dec.onnx
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:559b9b8db0784cffd24e4bb3de2ec44799beb367cbf2bfa8e2868a7744463dcb
|
3 |
+
size 58201240
|
ONNX_net/G_jp/dp.onnx
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:c4a7393ca121d3d2a21626841d9b66e49a45e6a287c88c4f707c041a58cf20bc
|
3 |
+
size 7781725
|
ONNX_net/G_jp/enc_p.onnx
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ad8b13ac90e35576ec57f97d879acf543f4d1c37785b8182c1ef48a87cda7106
|
3 |
+
size 28764539
|
ONNX_net/G_jp/flow.onnx
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:13ea432975f560f8c34fcf08fb7e6bee3278ea3e2fe97319b2c34aeb21ae657c
|
3 |
+
size 35774909
|
ONNX_net/G_trilingual/dec.onnx
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:7b51d61f0c621f0e6531f59c0322c692aef9cc0f0cbb633f890f820027cfdbec
|
3 |
+
size 58201240
|
ONNX_net/G_trilingual/dp.onnx
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:8b699d166d7c95648763fc322ee7ba7aadd7289f9c038dec81d14d98c0de3d5d
|
3 |
+
size 7781725
|
ONNX_net/G_trilingual/enc_p.onnx
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:a5a4c00c85c82f916d24fe22a46fb70f5d6da9b4e122692b4561c027a514988b
|
3 |
+
size 28786043
|
ONNX_net/G_trilingual/flow.onnx
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:7a5e74edc4d412e1857bb3a33b819d7ac4b3f6119484a775c478989b79351097
|
3 |
+
size 35774909
|
README.md
ADDED
@@ -0,0 +1,13 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
title: Multilingual Anime TTS
|
3 |
+
emoji: 🎙🐴
|
4 |
+
colorFrom: green
|
5 |
+
colorTo: gray
|
6 |
+
sdk: gradio
|
7 |
+
sdk_version: 3.7
|
8 |
+
app_file: app.py
|
9 |
+
pinned: false
|
10 |
+
duplicated_from: Plachta/VITS-Umamusume-voice-synthesizer
|
11 |
+
---
|
12 |
+
|
13 |
+
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
app.py
ADDED
@@ -0,0 +1,256 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import argparse
|
2 |
+
import json
|
3 |
+
import os
|
4 |
+
import re
|
5 |
+
import tempfile
|
6 |
+
import logging
|
7 |
+
|
8 |
+
logging.getLogger('numba').setLevel(logging.WARNING)
|
9 |
+
import librosa
|
10 |
+
import numpy as np
|
11 |
+
import torch
|
12 |
+
from torch import no_grad, LongTensor
|
13 |
+
import commons
|
14 |
+
import utils
|
15 |
+
import gradio as gr
|
16 |
+
import gradio.utils as gr_utils
|
17 |
+
import gradio.processing_utils as gr_processing_utils
|
18 |
+
import ONNXVITS_infer
|
19 |
+
import models
|
20 |
+
from text import text_to_sequence, _clean_text
|
21 |
+
from text.symbols import symbols
|
22 |
+
from mel_processing import spectrogram_torch
|
23 |
+
import psutil
|
24 |
+
from datetime import datetime
|
25 |
+
|
26 |
+
language_marks = {
|
27 |
+
"Japanese": "",
|
28 |
+
"日本語": "[JA]",
|
29 |
+
"简体中文": "[ZH]",
|
30 |
+
"English": "[EN]",
|
31 |
+
"Mix": "",
|
32 |
+
}
|
33 |
+
|
34 |
+
limitation = os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces
|
35 |
+
|
36 |
+
|
37 |
+
def create_tts_fn(model, hps, speaker_ids):
|
38 |
+
def tts_fn(text, speaker, language, speed, is_symbol):
|
39 |
+
if limitation:
|
40 |
+
text_len = len(re.sub("\[([A-Z]{2})\]", "", text))
|
41 |
+
max_len = 150
|
42 |
+
if is_symbol:
|
43 |
+
max_len *= 3
|
44 |
+
if text_len > max_len:
|
45 |
+
return "Error: Text is too long", None
|
46 |
+
if language is not None:
|
47 |
+
text = language_marks[language] + text + language_marks[language]
|
48 |
+
speaker_id = speaker_ids[speaker]
|
49 |
+
stn_tst = get_text(text, hps, is_symbol)
|
50 |
+
with no_grad():
|
51 |
+
x_tst = stn_tst.unsqueeze(0)
|
52 |
+
x_tst_lengths = LongTensor([stn_tst.size(0)])
|
53 |
+
sid = LongTensor([speaker_id])
|
54 |
+
audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8,
|
55 |
+
length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
|
56 |
+
del stn_tst, x_tst, x_tst_lengths, sid
|
57 |
+
return "Success", (hps.data.sampling_rate, audio)
|
58 |
+
|
59 |
+
return tts_fn
|
60 |
+
|
61 |
+
|
62 |
+
def create_vc_fn(model, hps, speaker_ids):
|
63 |
+
def vc_fn(original_speaker, target_speaker, input_audio):
|
64 |
+
if input_audio is None:
|
65 |
+
return "You need to upload an audio", None
|
66 |
+
sampling_rate, audio = input_audio
|
67 |
+
duration = audio.shape[0] / sampling_rate
|
68 |
+
if limitation and duration > 30:
|
69 |
+
return "Error: Audio is too long", None
|
70 |
+
original_speaker_id = speaker_ids[original_speaker]
|
71 |
+
target_speaker_id = speaker_ids[target_speaker]
|
72 |
+
|
73 |
+
audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
|
74 |
+
if len(audio.shape) > 1:
|
75 |
+
audio = librosa.to_mono(audio.transpose(1, 0))
|
76 |
+
if sampling_rate != hps.data.sampling_rate:
|
77 |
+
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate)
|
78 |
+
with no_grad():
|
79 |
+
y = torch.FloatTensor(audio)
|
80 |
+
y = y.unsqueeze(0)
|
81 |
+
spec = spectrogram_torch(y, hps.data.filter_length,
|
82 |
+
hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
|
83 |
+
center=False)
|
84 |
+
spec_lengths = LongTensor([spec.size(-1)])
|
85 |
+
sid_src = LongTensor([original_speaker_id])
|
86 |
+
sid_tgt = LongTensor([target_speaker_id])
|
87 |
+
audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
|
88 |
+
0, 0].data.cpu().float().numpy()
|
89 |
+
del y, spec, spec_lengths, sid_src, sid_tgt
|
90 |
+
return "Success", (hps.data.sampling_rate, audio)
|
91 |
+
|
92 |
+
return vc_fn
|
93 |
+
|
94 |
+
|
95 |
+
def get_text(text, hps, is_symbol):
|
96 |
+
text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
|
97 |
+
if hps.data.add_blank:
|
98 |
+
text_norm = commons.intersperse(text_norm, 0)
|
99 |
+
text_norm = LongTensor(text_norm)
|
100 |
+
return text_norm
|
101 |
+
|
102 |
+
|
103 |
+
def create_to_symbol_fn(hps):
|
104 |
+
def to_symbol_fn(is_symbol_input, input_text, temp_text):
|
105 |
+
return (_clean_text(input_text, hps.data.text_cleaners), input_text) if is_symbol_input \
|
106 |
+
else (temp_text, temp_text)
|
107 |
+
|
108 |
+
return to_symbol_fn
|
109 |
+
|
110 |
+
|
111 |
+
models_tts = []
|
112 |
+
models_vc = []
|
113 |
+
models_info = [
|
114 |
+
{
|
115 |
+
"title": "Trilingual",
|
116 |
+
"languages": ['日本語', '简体中文', 'English', 'Mix'],
|
117 |
+
"description": """
|
118 |
+
This model is trained on a mix up of Umamusume, Genshin Impact, Sanoba Witch & VCTK voice data to learn multilanguage.
|
119 |
+
All characters can speak English, Chinese & Japanese.\n\n
|
120 |
+
To mix multiple languages in a single sentence, wrap the corresponding part with language tokens
|
121 |
+
([JA] for Japanese, [ZH] for Chinese, [EN] for English), as shown in the examples.\n\n
|
122 |
+
这个模型在赛马娘,原神,魔女的夜宴以及VCTK数据集上混合训练以学习多种语言。
|
123 |
+
所有角色均可说中日英三语。\n\n
|
124 |
+
若需要在同一个句子中混合多种语言,使用相应的语言标记包裹句子。
|
125 |
+
(日语用[JA], 中文用[ZH], 英文用[EN]),参考Examples中的示例。
|
126 |
+
""",
|
127 |
+
"model_path": "./pretrained_models/G_trilingual.pth",
|
128 |
+
"config_path": "./configs/uma_trilingual.json",
|
129 |
+
"examples": [['你好,训练员先生,很高兴见到你。', '草上飞 Grass Wonder (Umamusume Pretty Derby)', '简体中文', 1, False],
|
130 |
+
['To be honest, I have no idea what to say as examples.', '派蒙 Paimon (Genshin Impact)', 'English',
|
131 |
+
1, False],
|
132 |
+
['授業中に出しだら,学校生活終わるですわ。', '綾地 寧々 Ayachi Nene (Sanoba Witch)', '日本語', 1, False],
|
133 |
+
['[JA]こんにちわ。[JA][ZH]你好![ZH][EN]Hello![EN]', '綾地 寧々 Ayachi Nene (Sanoba Witch)', 'Mix', 1, False]],
|
134 |
+
"onnx_dir": "./ONNX_net/G_trilingual/"
|
135 |
+
},
|
136 |
+
{
|
137 |
+
"title": "Japanese",
|
138 |
+
"languages": ["Japanese"],
|
139 |
+
"description": """
|
140 |
+
This model contains 87 characters from Umamusume: Pretty Derby, Japanese only.\n\n
|
141 |
+
这个模型包含赛马娘的所有87名角色,只能合成日语。
|
142 |
+
""",
|
143 |
+
"model_path": "./pretrained_models/G_jp.pth",
|
144 |
+
"config_path": "./configs/uma87.json",
|
145 |
+
"examples": [['お疲れ様です,トレーナーさん。', '无声铃鹿 Silence Suzuka (Umamusume Pretty Derby)', 'Japanese', 1, False],
|
146 |
+
['張り切っていこう!', '北部玄驹 Kitasan Black (Umamusume Pretty Derby)', 'Japanese', 1, False],
|
147 |
+
['何でこんなに慣れでんのよ,私のほが先に好きだっだのに。', '草上飞 Grass Wonder (Umamusume Pretty Derby)', 'Japanese', 1, False],
|
148 |
+
['授業中に出しだら,学校生活終わるですわ。', '目白麦昆 Mejiro Mcqueen (Umamusume Pretty Derby)', 'Japanese', 1, False],
|
149 |
+
['お帰りなさい,お兄様!', '米浴 Rice Shower (Umamusume Pretty Derby)', 'Japanese', 1, False],
|
150 |
+
['私の処女をもらっでください!', '米浴 Rice Shower (Umamusume Pretty Derby)', 'Japanese', 1, False]],
|
151 |
+
"onnx_dir": "./ONNX_net/G_jp/"
|
152 |
+
},
|
153 |
+
]
|
154 |
+
|
155 |
+
if __name__ == "__main__":
|
156 |
+
parser = argparse.ArgumentParser()
|
157 |
+
parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
|
158 |
+
args = parser.parse_args()
|
159 |
+
for info in models_info:
|
160 |
+
name = info['title']
|
161 |
+
lang = info['languages']
|
162 |
+
examples = info['examples']
|
163 |
+
config_path = info['config_path']
|
164 |
+
model_path = info['model_path']
|
165 |
+
description = info['description']
|
166 |
+
onnx_dir = info["onnx_dir"]
|
167 |
+
hps = utils.get_hparams_from_file(config_path)
|
168 |
+
model = ONNXVITS_infer.SynthesizerTrn(
|
169 |
+
len(hps.symbols),
|
170 |
+
hps.data.filter_length // 2 + 1,
|
171 |
+
hps.train.segment_size // hps.data.hop_length,
|
172 |
+
n_speakers=hps.data.n_speakers,
|
173 |
+
ONNX_dir=onnx_dir,
|
174 |
+
**hps.model)
|
175 |
+
utils.load_checkpoint(model_path, model, None)
|
176 |
+
model.eval()
|
177 |
+
speaker_ids = hps.speakers
|
178 |
+
speakers = list(hps.speakers.keys())
|
179 |
+
models_tts.append((name, description, speakers, lang, examples,
|
180 |
+
hps.symbols, create_tts_fn(model, hps, speaker_ids),
|
181 |
+
create_to_symbol_fn(hps)))
|
182 |
+
models_vc.append((name, description, speakers, create_vc_fn(model, hps, speaker_ids)))
|
183 |
+
app = gr.Blocks()
|
184 |
+
with app:
|
185 |
+
gr.Markdown("# English & Chinese & Japanese Anime TTS\n\n"
|
186 |
+
"![visitor badge](https://visitor-badge.glitch.me/badge?page_id=Plachta.VITS-Umamusume-voice-synthesizer)\n\n"
|
187 |
+
"Including Japanese TTS & Trilingual TTS, speakers are all anime characters. \n\n包含一个纯日语TTS和一个中日英三语TTS模型,主要为二次元角色。\n\n"
|
188 |
+
"If you have any suggestions or bug reports, feel free to open discussion in [Community](https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer/discussions).\n\n"
|
189 |
+
"若有bug反馈或建议,请在[Community](https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer/discussions)下开启一个新的Discussion。 \n\n"
|
190 |
+
)
|
191 |
+
with gr.Tabs():
|
192 |
+
with gr.TabItem("TTS"):
|
193 |
+
with gr.Tabs():
|
194 |
+
for i, (name, description, speakers, lang, example, symbols, tts_fn, to_symbol_fn) in enumerate(
|
195 |
+
models_tts):
|
196 |
+
with gr.TabItem(name):
|
197 |
+
gr.Markdown(description)
|
198 |
+
with gr.Row():
|
199 |
+
with gr.Column():
|
200 |
+
textbox = gr.TextArea(label="Text",
|
201 |
+
placeholder="Type your sentence here (Maximum 150 words)",
|
202 |
+
value="こんにちわ。", elem_id=f"tts-input")
|
203 |
+
with gr.Accordion(label="Phoneme Input", open=False):
|
204 |
+
temp_text_var = gr.Variable()
|
205 |
+
symbol_input = gr.Checkbox(value=False, label="Symbol input")
|
206 |
+
symbol_list = gr.Dataset(label="Symbol list", components=[textbox],
|
207 |
+
samples=[[x] for x in symbols],
|
208 |
+
elem_id=f"symbol-list")
|
209 |
+
symbol_list_json = gr.Json(value=symbols, visible=False)
|
210 |
+
symbol_input.change(to_symbol_fn,
|
211 |
+
[symbol_input, textbox, temp_text_var],
|
212 |
+
[textbox, temp_text_var])
|
213 |
+
symbol_list.click(None, [symbol_list, symbol_list_json], textbox,
|
214 |
+
_js=f"""
|
215 |
+
(i, symbols, text) => {{
|
216 |
+
let root = document.querySelector("body > gradio-app");
|
217 |
+
if (root.shadowRoot != null)
|
218 |
+
root = root.shadowRoot;
|
219 |
+
let text_input = root.querySelector("#tts-input").querySelector("textarea");
|
220 |
+
let startPos = text_input.selectionStart;
|
221 |
+
let endPos = text_input.selectionEnd;
|
222 |
+
let oldTxt = text_input.value;
|
223 |
+
let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos);
|
224 |
+
text_input.value = result;
|
225 |
+
let x = window.scrollX, y = window.scrollY;
|
226 |
+
text_input.focus();
|
227 |
+
text_input.selectionStart = startPos + symbols[i].length;
|
228 |
+
text_input.selectionEnd = startPos + symbols[i].length;
|
229 |
+
text_input.blur();
|
230 |
+
window.scrollTo(x, y);
|
231 |
+
|
232 |
+
text = text_input.value;
|
233 |
+
|
234 |
+
return text;
|
235 |
+
}}""")
|
236 |
+
# select character
|
237 |
+
char_dropdown = gr.Dropdown(choices=speakers, value=speakers[0], label='character')
|
238 |
+
language_dropdown = gr.Dropdown(choices=lang, value=lang[0], label='language')
|
239 |
+
duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1,
|
240 |
+
label='速度 Speed')
|
241 |
+
with gr.Column():
|
242 |
+
text_output = gr.Textbox(label="Message")
|
243 |
+
audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio")
|
244 |
+
btn = gr.Button("Generate!")
|
245 |
+
btn.click(tts_fn,
|
246 |
+
inputs=[textbox, char_dropdown, language_dropdown, duration_slider,
|
247 |
+
symbol_input],
|
248 |
+
outputs=[text_output, audio_output])
|
249 |
+
gr.Examples(
|
250 |
+
examples=example,
|
251 |
+
inputs=[textbox, char_dropdown, language_dropdown,
|
252 |
+
duration_slider, symbol_input],
|
253 |
+
outputs=[text_output, audio_output],
|
254 |
+
fn=tts_fn
|
255 |
+
)
|
256 |
+
app.queue(concurrency_count=3).launch(show_api=False, share=args.share)
|
attentions.py
ADDED
@@ -0,0 +1,300 @@
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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 torch
|
3 |
+
from torch import nn
|
4 |
+
from torch.nn import functional as F
|
5 |
+
|
6 |
+
import commons
|
7 |
+
from modules import LayerNorm
|
8 |
+
|
9 |
+
|
10 |
+
class Encoder(nn.Module):
|
11 |
+
def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
|
12 |
+
super().__init__()
|
13 |
+
self.hidden_channels = hidden_channels
|
14 |
+
self.filter_channels = filter_channels
|
15 |
+
self.n_heads = n_heads
|
16 |
+
self.n_layers = n_layers
|
17 |
+
self.kernel_size = kernel_size
|
18 |
+
self.p_dropout = p_dropout
|
19 |
+
self.window_size = window_size
|
20 |
+
|
21 |
+
self.drop = nn.Dropout(p_dropout)
|
22 |
+
self.attn_layers = nn.ModuleList()
|
23 |
+
self.norm_layers_1 = nn.ModuleList()
|
24 |
+
self.ffn_layers = nn.ModuleList()
|
25 |
+
self.norm_layers_2 = nn.ModuleList()
|
26 |
+
for i in range(self.n_layers):
|
27 |
+
self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
|
28 |
+
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
29 |
+
self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
|
30 |
+
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
31 |
+
|
32 |
+
def forward(self, x, x_mask):
|
33 |
+
attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
34 |
+
x = x * x_mask
|
35 |
+
for i in range(self.n_layers):
|
36 |
+
y = self.attn_layers[i](x, x, attn_mask)
|
37 |
+
y = self.drop(y)
|
38 |
+
x = self.norm_layers_1[i](x + y)
|
39 |
+
|
40 |
+
y = self.ffn_layers[i](x, x_mask)
|
41 |
+
y = self.drop(y)
|
42 |
+
x = self.norm_layers_2[i](x + y)
|
43 |
+
x = x * x_mask
|
44 |
+
return x
|
45 |
+
|
46 |
+
|
47 |
+
class Decoder(nn.Module):
|
48 |
+
def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
|
49 |
+
super().__init__()
|
50 |
+
self.hidden_channels = hidden_channels
|
51 |
+
self.filter_channels = filter_channels
|
52 |
+
self.n_heads = n_heads
|
53 |
+
self.n_layers = n_layers
|
54 |
+
self.kernel_size = kernel_size
|
55 |
+
self.p_dropout = p_dropout
|
56 |
+
self.proximal_bias = proximal_bias
|
57 |
+
self.proximal_init = proximal_init
|
58 |
+
|
59 |
+
self.drop = nn.Dropout(p_dropout)
|
60 |
+
self.self_attn_layers = nn.ModuleList()
|
61 |
+
self.norm_layers_0 = nn.ModuleList()
|
62 |
+
self.encdec_attn_layers = nn.ModuleList()
|
63 |
+
self.norm_layers_1 = nn.ModuleList()
|
64 |
+
self.ffn_layers = nn.ModuleList()
|
65 |
+
self.norm_layers_2 = nn.ModuleList()
|
66 |
+
for i in range(self.n_layers):
|
67 |
+
self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
|
68 |
+
self.norm_layers_0.append(LayerNorm(hidden_channels))
|
69 |
+
self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
|
70 |
+
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
71 |
+
self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
|
72 |
+
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
73 |
+
|
74 |
+
def forward(self, x, x_mask, h, h_mask):
|
75 |
+
"""
|
76 |
+
x: decoder input
|
77 |
+
h: encoder output
|
78 |
+
"""
|
79 |
+
self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
|
80 |
+
encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
81 |
+
x = x * x_mask
|
82 |
+
for i in range(self.n_layers):
|
83 |
+
y = self.self_attn_layers[i](x, x, self_attn_mask)
|
84 |
+
y = self.drop(y)
|
85 |
+
x = self.norm_layers_0[i](x + y)
|
86 |
+
|
87 |
+
y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
|
88 |
+
y = self.drop(y)
|
89 |
+
x = self.norm_layers_1[i](x + y)
|
90 |
+
|
91 |
+
y = self.ffn_layers[i](x, x_mask)
|
92 |
+
y = self.drop(y)
|
93 |
+
x = self.norm_layers_2[i](x + y)
|
94 |
+
x = x * x_mask
|
95 |
+
return x
|
96 |
+
|
97 |
+
|
98 |
+
class MultiHeadAttention(nn.Module):
|
99 |
+
def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
|
100 |
+
super().__init__()
|
101 |
+
assert channels % n_heads == 0
|
102 |
+
|
103 |
+
self.channels = channels
|
104 |
+
self.out_channels = out_channels
|
105 |
+
self.n_heads = n_heads
|
106 |
+
self.p_dropout = p_dropout
|
107 |
+
self.window_size = window_size
|
108 |
+
self.heads_share = heads_share
|
109 |
+
self.block_length = block_length
|
110 |
+
self.proximal_bias = proximal_bias
|
111 |
+
self.proximal_init = proximal_init
|
112 |
+
self.attn = None
|
113 |
+
|
114 |
+
self.k_channels = channels // n_heads
|
115 |
+
self.conv_q = nn.Conv1d(channels, channels, 1)
|
116 |
+
self.conv_k = nn.Conv1d(channels, channels, 1)
|
117 |
+
self.conv_v = nn.Conv1d(channels, channels, 1)
|
118 |
+
self.conv_o = nn.Conv1d(channels, out_channels, 1)
|
119 |
+
self.drop = nn.Dropout(p_dropout)
|
120 |
+
|
121 |
+
if window_size is not None:
|
122 |
+
n_heads_rel = 1 if heads_share else n_heads
|
123 |
+
rel_stddev = self.k_channels**-0.5
|
124 |
+
self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
|
125 |
+
self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
|
126 |
+
|
127 |
+
nn.init.xavier_uniform_(self.conv_q.weight)
|
128 |
+
nn.init.xavier_uniform_(self.conv_k.weight)
|
129 |
+
nn.init.xavier_uniform_(self.conv_v.weight)
|
130 |
+
if proximal_init:
|
131 |
+
with torch.no_grad():
|
132 |
+
self.conv_k.weight.copy_(self.conv_q.weight)
|
133 |
+
self.conv_k.bias.copy_(self.conv_q.bias)
|
134 |
+
|
135 |
+
def forward(self, x, c, attn_mask=None):
|
136 |
+
q = self.conv_q(x)
|
137 |
+
k = self.conv_k(c)
|
138 |
+
v = self.conv_v(c)
|
139 |
+
|
140 |
+
x, self.attn = self.attention(q, k, v, mask=attn_mask)
|
141 |
+
|
142 |
+
x = self.conv_o(x)
|
143 |
+
return x
|
144 |
+
|
145 |
+
def attention(self, query, key, value, mask=None):
|
146 |
+
# reshape [b, d, t] -> [b, n_h, t, d_k]
|
147 |
+
b, d, t_s, t_t = (*key.size(), query.size(2))
|
148 |
+
query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
|
149 |
+
key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
150 |
+
value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
151 |
+
|
152 |
+
scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
|
153 |
+
if self.window_size is not None:
|
154 |
+
assert t_s == t_t, "Relative attention is only available for self-attention."
|
155 |
+
key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
|
156 |
+
rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
|
157 |
+
scores_local = self._relative_position_to_absolute_position(rel_logits)
|
158 |
+
scores = scores + scores_local
|
159 |
+
if self.proximal_bias:
|
160 |
+
assert t_s == t_t, "Proximal bias is only available for self-attention."
|
161 |
+
scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
|
162 |
+
if mask is not None:
|
163 |
+
scores = scores.masked_fill(mask == 0, -1e4)
|
164 |
+
if self.block_length is not None:
|
165 |
+
assert t_s == t_t, "Local attention is only available for self-attention."
|
166 |
+
block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
|
167 |
+
scores = scores.masked_fill(block_mask == 0, -1e4)
|
168 |
+
p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
|
169 |
+
p_attn = self.drop(p_attn)
|
170 |
+
output = torch.matmul(p_attn, value)
|
171 |
+
if self.window_size is not None:
|
172 |
+
relative_weights = self._absolute_position_to_relative_position(p_attn)
|
173 |
+
value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
|
174 |
+
output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
|
175 |
+
output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
|
176 |
+
return output, p_attn
|
177 |
+
|
178 |
+
def _matmul_with_relative_values(self, x, y):
|
179 |
+
"""
|
180 |
+
x: [b, h, l, m]
|
181 |
+
y: [h or 1, m, d]
|
182 |
+
ret: [b, h, l, d]
|
183 |
+
"""
|
184 |
+
ret = torch.matmul(x, y.unsqueeze(0))
|
185 |
+
return ret
|
186 |
+
|
187 |
+
def _matmul_with_relative_keys(self, x, y):
|
188 |
+
"""
|
189 |
+
x: [b, h, l, d]
|
190 |
+
y: [h or 1, m, d]
|
191 |
+
ret: [b, h, l, m]
|
192 |
+
"""
|
193 |
+
ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
|
194 |
+
return ret
|
195 |
+
|
196 |
+
def _get_relative_embeddings(self, relative_embeddings, length):
|
197 |
+
max_relative_position = 2 * self.window_size + 1
|
198 |
+
# Pad first before slice to avoid using cond ops.
|
199 |
+
pad_length = max(length - (self.window_size + 1), 0)
|
200 |
+
slice_start_position = max((self.window_size + 1) - length, 0)
|
201 |
+
slice_end_position = slice_start_position + 2 * length - 1
|
202 |
+
if pad_length > 0:
|
203 |
+
padded_relative_embeddings = F.pad(
|
204 |
+
relative_embeddings,
|
205 |
+
commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
|
206 |
+
else:
|
207 |
+
padded_relative_embeddings = relative_embeddings
|
208 |
+
used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
|
209 |
+
return used_relative_embeddings
|
210 |
+
|
211 |
+
def _relative_position_to_absolute_position(self, x):
|
212 |
+
"""
|
213 |
+
x: [b, h, l, 2*l-1]
|
214 |
+
ret: [b, h, l, l]
|
215 |
+
"""
|
216 |
+
batch, heads, length, _ = x.size()
|
217 |
+
# Concat columns of pad to shift from relative to absolute indexing.
|
218 |
+
x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
|
219 |
+
|
220 |
+
# Concat extra elements so to add up to shape (len+1, 2*len-1).
|
221 |
+
x_flat = x.view([batch, heads, length * 2 * length])
|
222 |
+
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
|
223 |
+
|
224 |
+
# Reshape and slice out the padded elements.
|
225 |
+
x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
|
226 |
+
return x_final
|
227 |
+
|
228 |
+
def _absolute_position_to_relative_position(self, x):
|
229 |
+
"""
|
230 |
+
x: [b, h, l, l]
|
231 |
+
ret: [b, h, l, 2*l-1]
|
232 |
+
"""
|
233 |
+
batch, heads, length, _ = x.size()
|
234 |
+
# padd along column
|
235 |
+
x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
|
236 |
+
x_flat = x.view([batch, heads, length**2 + length*(length -1)])
|
237 |
+
# add 0's in the beginning that will skew the elements after reshape
|
238 |
+
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
|
239 |
+
x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
|
240 |
+
return x_final
|
241 |
+
|
242 |
+
def _attention_bias_proximal(self, length):
|
243 |
+
"""Bias for self-attention to encourage attention to close positions.
|
244 |
+
Args:
|
245 |
+
length: an integer scalar.
|
246 |
+
Returns:
|
247 |
+
a Tensor with shape [1, 1, length, length]
|
248 |
+
"""
|
249 |
+
r = torch.arange(length, dtype=torch.float32)
|
250 |
+
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
|
251 |
+
return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
|
252 |
+
|
253 |
+
|
254 |
+
class FFN(nn.Module):
|
255 |
+
def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
|
256 |
+
super().__init__()
|
257 |
+
self.in_channels = in_channels
|
258 |
+
self.out_channels = out_channels
|
259 |
+
self.filter_channels = filter_channels
|
260 |
+
self.kernel_size = kernel_size
|
261 |
+
self.p_dropout = p_dropout
|
262 |
+
self.activation = activation
|
263 |
+
self.causal = causal
|
264 |
+
|
265 |
+
if causal:
|
266 |
+
self.padding = self._causal_padding
|
267 |
+
else:
|
268 |
+
self.padding = self._same_padding
|
269 |
+
|
270 |
+
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
|
271 |
+
self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
|
272 |
+
self.drop = nn.Dropout(p_dropout)
|
273 |
+
|
274 |
+
def forward(self, x, x_mask):
|
275 |
+
x = self.conv_1(self.padding(x * x_mask))
|
276 |
+
if self.activation == "gelu":
|
277 |
+
x = x * torch.sigmoid(1.702 * x)
|
278 |
+
else:
|
279 |
+
x = torch.relu(x)
|
280 |
+
x = self.drop(x)
|
281 |
+
x = self.conv_2(self.padding(x * x_mask))
|
282 |
+
return x * x_mask
|
283 |
+
|
284 |
+
def _causal_padding(self, x):
|
285 |
+
if self.kernel_size == 1:
|
286 |
+
return x
|
287 |
+
pad_l = self.kernel_size - 1
|
288 |
+
pad_r = 0
|
289 |
+
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
290 |
+
x = F.pad(x, commons.convert_pad_shape(padding))
|
291 |
+
return x
|
292 |
+
|
293 |
+
def _same_padding(self, x):
|
294 |
+
if self.kernel_size == 1:
|
295 |
+
return x
|
296 |
+
pad_l = (self.kernel_size - 1) // 2
|
297 |
+
pad_r = self.kernel_size // 2
|
298 |
+
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
299 |
+
x = F.pad(x, commons.convert_pad_shape(padding))
|
300 |
+
return x
|
commons.py
ADDED
@@ -0,0 +1,97 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import math
|
2 |
+
import torch
|
3 |
+
from torch.nn import functional as F
|
4 |
+
import torch.jit
|
5 |
+
|
6 |
+
|
7 |
+
def script_method(fn, _rcb=None):
|
8 |
+
return fn
|
9 |
+
|
10 |
+
|
11 |
+
def script(obj, optimize=True, _frames_up=0, _rcb=None):
|
12 |
+
return obj
|
13 |
+
|
14 |
+
|
15 |
+
torch.jit.script_method = script_method
|
16 |
+
torch.jit.script = script
|
17 |
+
|
18 |
+
|
19 |
+
def init_weights(m, mean=0.0, std=0.01):
|
20 |
+
classname = m.__class__.__name__
|
21 |
+
if classname.find("Conv") != -1:
|
22 |
+
m.weight.data.normal_(mean, std)
|
23 |
+
|
24 |
+
|
25 |
+
def get_padding(kernel_size, dilation=1):
|
26 |
+
return int((kernel_size*dilation - dilation)/2)
|
27 |
+
|
28 |
+
|
29 |
+
def intersperse(lst, item):
|
30 |
+
result = [item] * (len(lst) * 2 + 1)
|
31 |
+
result[1::2] = lst
|
32 |
+
return result
|
33 |
+
|
34 |
+
|
35 |
+
def slice_segments(x, ids_str, segment_size=4):
|
36 |
+
ret = torch.zeros_like(x[:, :, :segment_size])
|
37 |
+
for i in range(x.size(0)):
|
38 |
+
idx_str = ids_str[i]
|
39 |
+
idx_end = idx_str + segment_size
|
40 |
+
ret[i] = x[i, :, idx_str:idx_end]
|
41 |
+
return ret
|
42 |
+
|
43 |
+
|
44 |
+
def rand_slice_segments(x, x_lengths=None, segment_size=4):
|
45 |
+
b, d, t = x.size()
|
46 |
+
if x_lengths is None:
|
47 |
+
x_lengths = t
|
48 |
+
ids_str_max = x_lengths - segment_size + 1
|
49 |
+
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
|
50 |
+
ret = slice_segments(x, ids_str, segment_size)
|
51 |
+
return ret, ids_str
|
52 |
+
|
53 |
+
|
54 |
+
def subsequent_mask(length):
|
55 |
+
mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
|
56 |
+
return mask
|
57 |
+
|
58 |
+
|
59 |
+
@torch.jit.script
|
60 |
+
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
61 |
+
n_channels_int = n_channels[0]
|
62 |
+
in_act = input_a + input_b
|
63 |
+
t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
64 |
+
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
65 |
+
acts = t_act * s_act
|
66 |
+
return acts
|
67 |
+
|
68 |
+
|
69 |
+
def convert_pad_shape(pad_shape):
|
70 |
+
l = pad_shape[::-1]
|
71 |
+
pad_shape = [item for sublist in l for item in sublist]
|
72 |
+
return pad_shape
|
73 |
+
|
74 |
+
|
75 |
+
def sequence_mask(length, max_length=None):
|
76 |
+
if max_length is None:
|
77 |
+
max_length = length.max()
|
78 |
+
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
|
79 |
+
return x.unsqueeze(0) < length.unsqueeze(1)
|
80 |
+
|
81 |
+
|
82 |
+
def generate_path(duration, mask):
|
83 |
+
"""
|
84 |
+
duration: [b, 1, t_x]
|
85 |
+
mask: [b, 1, t_y, t_x]
|
86 |
+
"""
|
87 |
+
device = duration.device
|
88 |
+
|
89 |
+
b, _, t_y, t_x = mask.shape
|
90 |
+
cum_duration = torch.cumsum(duration, -1)
|
91 |
+
|
92 |
+
cum_duration_flat = cum_duration.view(b * t_x)
|
93 |
+
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
|
94 |
+
path = path.view(b, t_x, t_y)
|
95 |
+
path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
|
96 |
+
path = path.unsqueeze(1).transpose(2,3) * mask
|
97 |
+
return path
|
configs/uma87.json
ADDED
@@ -0,0 +1,133 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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": 1,
|
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":"E:/uma_voice/output_train.txt.cleaned",
|
21 |
+
"validation_files":"E:/uma_voice/output_val.txt.cleaned",
|
22 |
+
"text_cleaners":["japanese_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": 87,
|
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": {"特别周 Special Week (Umamusume Pretty Derby)": 0,
|
54 |
+
"无声铃鹿 Silence Suzuka (Umamusume Pretty Derby)": 1,
|
55 |
+
"东海帝王 Tokai Teio (Umamusume Pretty Derby)": 2,
|
56 |
+
"丸善斯基 Maruzensky (Umamusume Pretty Derby)": 3,
|
57 |
+
"富士奇迹 Fuji Kiseki (Umamusume Pretty Derby)": 4,
|
58 |
+
"小栗帽 Oguri Cap (Umamusume Pretty Derby)": 5,
|
59 |
+
"黄金船 Gold Ship (Umamusume Pretty Derby)": 6,
|
60 |
+
"伏特加 Vodka (Umamusume Pretty Derby)": 7,
|
61 |
+
"大和赤骥 Daiwa Scarlet (Umamusume Pretty Derby)": 8,
|
62 |
+
"大树快车 Taiki Shuttle (Umamusume Pretty Derby)": 9,
|
63 |
+
"草上飞 Grass Wonder (Umamusume Pretty Derby)": 10,
|
64 |
+
"菱亚马逊 Hishi Amazon (Umamusume Pretty Derby)": 11,
|
65 |
+
"目白麦昆 Mejiro Mcqueen (Umamusume Pretty Derby)": 12,
|
66 |
+
"神鹰 El Condor Pasa (Umamusume Pretty Derby)": 13,
|
67 |
+
"好歌剧 T.M. Opera O (Umamusume Pretty Derby)": 14,
|
68 |
+
"成田白仁 Narita Brian (Umamusume Pretty Derby)": 15,
|
69 |
+
"鲁道夫象征 Symboli Rudolf (Umamusume Pretty Derby)": 16,
|
70 |
+
"气槽 Air Groove (Umamusume Pretty Derby)": 17,
|
71 |
+
"爱丽数码 Agnes Digital (Umamusume Pretty Derby)": 18,
|
72 |
+
"青云天空 Seiun Sky (Umamusume Pretty Derby)": 19,
|
73 |
+
"玉藻十字 Tamamo Cross (Umamusume Pretty Derby)": 20,
|
74 |
+
"美妙姿势 Fine Motion (Umamusume Pretty Derby)": 21,
|
75 |
+
"琵琶晨光 Biwa Hayahide (Umamusume Pretty Derby)": 22,
|
76 |
+
"重炮 Mayano Topgun (Umamusume Pretty Derby)": 23,
|
77 |
+
"曼城茶座 Manhattan Cafe (Umamusume Pretty Derby)": 24,
|
78 |
+
"美普波旁 Mihono Bourbon (Umamusume Pretty Derby)": 25,
|
79 |
+
"目白雷恩 Mejiro Ryan (Umamusume Pretty Derby)": 26,
|
80 |
+
"雪之美人 Yukino Bijin (Umamusume Pretty Derby)": 28,
|
81 |
+
"米浴 Rice Shower (Umamusume Pretty Derby)": 29,
|
82 |
+
"艾尼斯风神 Ines Fujin (Umamusume Pretty Derby)": 30,
|
83 |
+
"爱丽速子 Agnes Tachyon (Umamusume Pretty Derby)": 31,
|
84 |
+
"爱慕织姬 Admire Vega (Umamusume Pretty Derby)": 32,
|
85 |
+
"稻荷一 Inari One (Umamusume Pretty Derby)": 33,
|
86 |
+
"胜利奖券 Winning Ticket (Umamusume Pretty Derby)": 34,
|
87 |
+
"空中神宫 Air Shakur (Umamusume Pretty Derby)": 35,
|
88 |
+
"荣进闪耀 Eishin Flash (Umamusume Pretty Derby)": 36,
|
89 |
+
"真机伶 Curren Chan (Umamusume Pretty Derby)": 37,
|
90 |
+
"川上公主 Kawakami Princess (Umamusume Pretty Derby)": 38,
|
91 |
+
"黄金城市 Gold City (Umamusume Pretty Derby)": 39,
|
92 |
+
"樱花进王 Sakura Bakushin O (Umamusume Pretty Derby)": 40,
|
93 |
+
"采珠 Seeking the Pearl (Umamusume Pretty Derby)": 41,
|
94 |
+
"新光风 Shinko Windy (Umamusume Pretty Derby)": 42,
|
95 |
+
"东商变革 Sweep Tosho (Umamusume Pretty Derby)": 43,
|
96 |
+
"超级小溪 Super Creek (Umamusume Pretty Derby)": 44,
|
97 |
+
"醒目飞鹰 Smart Falcon (Umamusume Pretty Derby)": 45,
|
98 |
+
"荒漠英雄 Zenno Rob Roy (Umamusume Pretty Derby)": 46,
|
99 |
+
"东瀛佐敦 Tosen Jordan (Umamusume Pretty Derby)": 47,
|
100 |
+
"中山庆典 Nakayama Festa (Umamusume Pretty Derby)": 48,
|
101 |
+
"成田大进 Narita Taishin (Umamusume Pretty Derby)": 49,
|
102 |
+
"西野花 Nishino Flower (Umamusume Pretty Derby)": 50,
|
103 |
+
"春乌拉拉 Haru Urara (Umamusume Pretty Derby)": 51,
|
104 |
+
"青竹回忆 Bamboo Memory (Umamusume Pretty Derby)": 52,
|
105 |
+
"待兼福来 Matikane Fukukitaru (Umamusume Pretty Derby)": 55,
|
106 |
+
"名将怒涛 Meisho Doto (Umamusume Pretty Derby)": 57,
|
107 |
+
"目白多伯 Mejiro Dober (Umamusume Pretty Derby)": 58,
|
108 |
+
"优秀素质 Nice Nature (Umamusume Pretty Derby)": 59,
|
109 |
+
"帝王光环 King Halo (Umamusume Pretty Derby)": 60,
|
110 |
+
"待兼诗歌剧 Matikane Tannhauser (Umamusume Pretty Derby)": 61,
|
111 |
+
"生野狄杜斯 Ikuno Dictus (Umamusume Pretty Derby)": 62,
|
112 |
+
"目白善信 Mejiro Palmer (Umamusume Pretty Derby)": 63,
|
113 |
+
"大拓太阳神 Daitaku Helios (Umamusume Pretty Derby)": 64,
|
114 |
+
"双涡轮 Twin Turbo (Umamusume Pretty Derby)": 65,
|
115 |
+
"里见光钻 Satono Diamond (Umamusume Pretty Derby)": 66,
|
116 |
+
"北部玄驹 Kitasan Black (Umamusume Pretty Derby)": 67,
|
117 |
+
"樱花千代王 Sakura Chiyono O (Umamusume Pretty Derby)": 68,
|
118 |
+
"天狼星象征 Sirius Symboli (Umamusume Pretty Derby)": 69,
|
119 |
+
"目白阿尔丹 Mejiro Ardan (Umamusume Pretty Derby)": 70,
|
120 |
+
"八重无敌 Yaeno Muteki (Umamusume Pretty Derby)": 71,
|
121 |
+
"鹤丸刚志 Tsurumaru Tsuyoshi (Umamusume Pretty Derby)": 72,
|
122 |
+
"目白光明 Mejiro Bright (Umamusume Pretty Derby)": 73,
|
123 |
+
"樱花桂冠 Sakura Laurel (Umamusume Pretty Derby)": 74,
|
124 |
+
"成田路 Narita Top Road (Umamusume Pretty Derby)": 75,
|
125 |
+
"也文摄辉 Yamanin Zephyr (Umamusume Pretty Derby)": 76,
|
126 |
+
"真弓快车 Aston Machan (Umamusume Pretty Derby)": 80,
|
127 |
+
"骏川手纲 Hayakawa Tazuna (Umamusume Pretty Derby)": 81,
|
128 |
+
"小林历奇 Kopano Rickey (Umamusume Pretty Derby)": 83,
|
129 |
+
"奇锐骏 Wonder Acute (Umamusume Pretty Derby)": 85,
|
130 |
+
"秋川理事长 President Akikawa (Umamusume Pretty Derby)": 86
|
131 |
+
},
|
132 |
+
"symbols": ["_", ",", ".", "!", "?", "-", "A", "E", "I", "N", "O", "Q", "U", "a", "b", "d", "e", "f", "g", "h", "i", "j", "k", "m", "n", "o", "p", "r", "s", "t", "u", "v", "w", "y", "z", "\u0283", "\u02a7", "\u2193", "\u2191", " "]
|
133 |
+
}
|
configs/uma_trilingual.json
ADDED
@@ -0,0 +1,202 @@
|
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|
|
|
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": 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":"../CH_JA_EN_mix_voice/clipped_3_vits_trilingual_annotations.train.txt.cleaned",
|
21 |
+
"validation_files":"../CH_JA_EN_mix_voice/clipped_3_vits_trilingual_annotations.val.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": 999,
|
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 |
+
"speakers": {"特别周 Special Week (Umamusume Pretty Derby)": 0,
|
55 |
+
"无声铃鹿 Silence Suzuka (Umamusume Pretty Derby)": 1,
|
56 |
+
"东海帝王 Tokai Teio (Umamusume Pretty Derby)": 2,
|
57 |
+
"丸善斯基 Maruzensky (Umamusume Pretty Derby)": 3,
|
58 |
+
"富士奇迹 Fuji Kiseki (Umamusume Pretty Derby)": 4,
|
59 |
+
"小栗帽 Oguri Cap (Umamusume Pretty Derby)": 5,
|
60 |
+
"黄金船 Gold Ship (Umamusume Pretty Derby)": 6,
|
61 |
+
"伏特加 Vodka (Umamusume Pretty Derby)": 7,
|
62 |
+
"大和赤骥 Daiwa Scarlet (Umamusume Pretty Derby)": 8,
|
63 |
+
"大树快车 Taiki Shuttle (Umamusume Pretty Derby)": 9,
|
64 |
+
"草上飞 Grass Wonder (Umamusume Pretty Derby)": 10,
|
65 |
+
"菱亚马逊 Hishi Amazon (Umamusume Pretty Derby)": 11,
|
66 |
+
"目白麦昆 Mejiro Mcqueen (Umamusume Pretty Derby)": 12,
|
67 |
+
"神鹰 El Condor Pasa (Umamusume Pretty Derby)": 13,
|
68 |
+
"好歌剧 T.M. Opera O (Umamusume Pretty Derby)": 14,
|
69 |
+
"成田白仁 Narita Brian (Umamusume Pretty Derby)": 15,
|
70 |
+
"鲁道夫象征 Symboli Rudolf (Umamusume Pretty Derby)": 16,
|
71 |
+
"气槽 Air Groove (Umamusume Pretty Derby)": 17,
|
72 |
+
"爱丽数码 Agnes Digital (Umamusume Pretty Derby)": 18,
|
73 |
+
"青云天空 Seiun Sky (Umamusume Pretty Derby)": 19,
|
74 |
+
"玉藻十字 Tamamo Cross (Umamusume Pretty Derby)": 20,
|
75 |
+
"美妙姿势 Fine Motion (Umamusume Pretty Derby)": 21,
|
76 |
+
"琵琶晨光 Biwa Hayahide (Umamusume Pretty Derby)": 22,
|
77 |
+
"重炮 Mayano Topgun (Umamusume Pretty Derby)": 23,
|
78 |
+
"曼城茶座 Manhattan Cafe (Umamusume Pretty Derby)": 24,
|
79 |
+
"美普波旁 Mihono Bourbon (Umamusume Pretty Derby)": 25,
|
80 |
+
"目白雷恩 Mejiro Ryan (Umamusume Pretty Derby)": 26,
|
81 |
+
"雪之美人 Yukino Bijin (Umamusume Pretty Derby)": 28,
|
82 |
+
"米浴 Rice Shower (Umamusume Pretty Derby)": 29,
|
83 |
+
"艾尼斯风神 Ines Fujin (Umamusume Pretty Derby)": 30,
|
84 |
+
"爱丽速子 Agnes Tachyon (Umamusume Pretty Derby)": 31,
|
85 |
+
"爱慕织姬 Admire Vega (Umamusume Pretty Derby)": 32,
|
86 |
+
"稻荷一 Inari One (Umamusume Pretty Derby)": 33,
|
87 |
+
"胜利奖券 Winning Ticket (Umamusume Pretty Derby)": 34,
|
88 |
+
"空中神宫 Air Shakur (Umamusume Pretty Derby)": 35,
|
89 |
+
"荣进闪耀 Eishin Flash (Umamusume Pretty Derby)": 36,
|
90 |
+
"真机伶 Curren Chan (Umamusume Pretty Derby)": 37,
|
91 |
+
"川上公主 Kawakami Princess (Umamusume Pretty Derby)": 38,
|
92 |
+
"黄金城市 Gold City (Umamusume Pretty Derby)": 39,
|
93 |
+
"樱花进王 Sakura Bakushin O (Umamusume Pretty Derby)": 40,
|
94 |
+
"采珠 Seeking the Pearl (Umamusume Pretty Derby)": 41,
|
95 |
+
"新光风 Shinko Windy (Umamusume Pretty Derby)": 42,
|
96 |
+
"东商变革 Sweep Tosho (Umamusume Pretty Derby)": 43,
|
97 |
+
"超级小溪 Super Creek (Umamusume Pretty Derby)": 44,
|
98 |
+
"醒目飞鹰 Smart Falcon (Umamusume Pretty Derby)": 45,
|
99 |
+
"荒漠英雄 Zenno Rob Roy (Umamusume Pretty Derby)": 46,
|
100 |
+
"东瀛佐敦 Tosen Jordan (Umamusume Pretty Derby)": 47,
|
101 |
+
"中山庆典 Nakayama Festa (Umamusume Pretty Derby)": 48,
|
102 |
+
"成田大进 Narita Taishin (Umamusume Pretty Derby)": 49,
|
103 |
+
"西野花 Nishino Flower (Umamusume Pretty Derby)": 50,
|
104 |
+
"春乌拉拉 Haru Urara (Umamusume Pretty Derby)": 51,
|
105 |
+
"青竹回忆 Bamboo Memory (Umamusume Pretty Derby)": 52,
|
106 |
+
"待兼福来 Matikane Fukukitaru (Umamusume Pretty Derby)": 55,
|
107 |
+
"名将怒涛 Meisho Doto (Umamusume Pretty Derby)": 57,
|
108 |
+
"目白多伯 Mejiro Dober (Umamusume Pretty Derby)": 58,
|
109 |
+
"优秀素质 Nice Nature (Umamusume Pretty Derby)": 59,
|
110 |
+
"帝王光环 King Halo (Umamusume Pretty Derby)": 60,
|
111 |
+
"待兼诗歌剧 Matikane Tannhauser (Umamusume Pretty Derby)": 61,
|
112 |
+
"生野狄杜斯 Ikuno Dictus (Umamusume Pretty Derby)": 62,
|
113 |
+
"目白善信 Mejiro Palmer (Umamusume Pretty Derby)": 63,
|
114 |
+
"大拓太阳神 Daitaku Helios (Umamusume Pretty Derby)": 64,
|
115 |
+
"双涡轮 Twin Turbo (Umamusume Pretty Derby)": 65,
|
116 |
+
"里见光钻 Satono Diamond (Umamusume Pretty Derby)": 66,
|
117 |
+
"北部玄驹 Kitasan Black (Umamusume Pretty Derby)": 67,
|
118 |
+
"樱花千代王 Sakura Chiyono O (Umamusume Pretty Derby)": 68,
|
119 |
+
"天狼星象征 Sirius Symboli (Umamusume Pretty Derby)": 69,
|
120 |
+
"目白阿尔丹 Mejiro Ardan (Umamusume Pretty Derby)": 70,
|
121 |
+
"八重无敌 Yaeno Muteki (Umamusume Pretty Derby)": 71,
|
122 |
+
"鹤丸刚志 Tsurumaru Tsuyoshi (Umamusume Pretty Derby)": 72,
|
123 |
+
"目白光明 Mejiro Bright (Umamusume Pretty Derby)": 73,
|
124 |
+
"樱花桂冠 Sakura Laurel (Umamusume Pretty Derby)": 74,
|
125 |
+
"成田路 Narita Top Road (Umamusume Pretty Derby)": 75,
|
126 |
+
"也文摄辉 Yamanin Zephyr (Umamusume Pretty Derby)": 76,
|
127 |
+
"真弓快车 Aston Machan (Umamusume Pretty Derby)": 80,
|
128 |
+
"骏川手纲 Hayakawa Tazuna (Umamusume Pretty Derby)": 81,
|
129 |
+
"小林历奇 Kopano Rickey (Umamusume Pretty Derby)": 83,
|
130 |
+
"奇锐骏 Wonder Acute (Umamusume Pretty Derby)": 85,
|
131 |
+
"秋川理事长 President Akikawa (Umamusume Pretty Derby)": 86,
|
132 |
+
"綾地 寧々 Ayachi Nene (Sanoba Witch)": 87,
|
133 |
+
"因幡 めぐる Inaba Meguru (Sanoba Witch)": 88,
|
134 |
+
"椎葉 紬 Shiiba Tsumugi (Sanoba Witch)": 89,
|
135 |
+
"仮屋 和奏 Kariya Wakama (Sanoba Witch)": 90,
|
136 |
+
"戸隠 憧子 Togakushi Touko (Sanoba Witch)": 91,
|
137 |
+
"九条裟罗 Kujou Sara (Genshin Impact)": 92,
|
138 |
+
"芭芭拉 Barbara (Genshin Impact)": 93,
|
139 |
+
"派蒙 Paimon (Genshin Impact)": 94,
|
140 |
+
"荒泷一斗 Arataki Itto (Genshin Impact)": 96,
|
141 |
+
"早柚 Sayu (Genshin Impact)": 97,
|
142 |
+
"香菱 Xiangling (Genshin Impact)": 98,
|
143 |
+
"神里绫华 Kamisato Ayaka (Genshin Impact)": 99,
|
144 |
+
"重云 Chongyun (Genshin Impact)": 100,
|
145 |
+
"流浪者 Wanderer (Genshin Impact)": 102,
|
146 |
+
"优菈 Eula (Genshin Impact)": 103,
|
147 |
+
"凝光 Ningguang (Genshin Impact)": 105,
|
148 |
+
"钟离 Zhongli (Genshin Impact)": 106,
|
149 |
+
"雷电将军 Raiden Shogun (Genshin Impact)": 107,
|
150 |
+
"枫原万叶 Kaedehara Kazuha (Genshin Impact)": 108,
|
151 |
+
"赛诺 Cyno (Genshin Impact)": 109,
|
152 |
+
"诺艾尔 Noelle (Genshin Impact)": 112,
|
153 |
+
"八重神子 Yae Miko (Genshin Impact)": 113,
|
154 |
+
"凯亚 Kaeya (Genshin Impact)": 114,
|
155 |
+
"魈 Xiao (Genshin Impact)": 115,
|
156 |
+
"托马 Thoma (Genshin Impact)": 116,
|
157 |
+
"可莉 Klee (Genshin Impact)": 117,
|
158 |
+
"迪卢克 Diluc (Genshin Impact)": 120,
|
159 |
+
"夜兰 Yelan (Genshin Impact)": 121,
|
160 |
+
"鹿野院平藏 Shikanoin Heizou (Genshin Impact)": 123,
|
161 |
+
"辛焱 Xinyan (Genshin Impact)": 124,
|
162 |
+
"丽莎 Lisa (Genshin Impact)": 125,
|
163 |
+
"云堇 Yun Jin (Genshin Impact)": 126,
|
164 |
+
"坎蒂丝 Candace (Genshin Impact)": 127,
|
165 |
+
"罗莎莉亚 Rosaria (Genshin Impact)": 128,
|
166 |
+
"北斗 Beidou (Genshin Impact)": 129,
|
167 |
+
"珊瑚宫心海 Sangonomiya Kokomi (Genshin Impact)": 132,
|
168 |
+
"烟绯 Yanfei (Genshin Impact)": 133,
|
169 |
+
"久岐忍 Kuki Shinobu (Genshin Impact)": 136,
|
170 |
+
"宵宫 Yoimiya (Genshin Impact)": 139,
|
171 |
+
"安柏 Amber (Genshin Impact)": 143,
|
172 |
+
"迪奥娜 Diona (Genshin Impact)": 144,
|
173 |
+
"班尼特 Bennett (Genshin Impact)": 146,
|
174 |
+
"雷泽 Razor (Genshin Impact)": 147,
|
175 |
+
"阿贝多 Albedo (Genshin Impact)": 151,
|
176 |
+
"温迪 Venti (Genshin Impact)": 152,
|
177 |
+
"空 Player Male (Genshin Impact)": 153,
|
178 |
+
"神里绫人 Kamisato Ayato (Genshin Impact)": 154,
|
179 |
+
"琴 Jean (Genshin Impact)": 155,
|
180 |
+
"艾尔海森 Alhaitham (Genshin Impact)": 156,
|
181 |
+
"莫娜 Mona (Genshin Impact)": 157,
|
182 |
+
"妮露 Nilou (Genshin Impact)": 159,
|
183 |
+
"胡桃 Hu Tao (Genshin Impact)": 160,
|
184 |
+
"甘雨 Ganyu (Genshin Impact)": 161,
|
185 |
+
"纳西妲 Nahida (Genshin Impact)": 162,
|
186 |
+
"刻晴 Keqing (Genshin Impact)": 165,
|
187 |
+
"荧 Player Female (Genshin Impact)": 169,
|
188 |
+
"埃洛伊 Aloy (Genshin Impact)": 179,
|
189 |
+
"柯莱 Collei (Genshin Impact)": 182,
|
190 |
+
"多莉 Dori (Genshin Impact)": 184,
|
191 |
+
"提纳里 Tighnari (Genshin Impact)": 186,
|
192 |
+
"砂糖 Sucrose (Genshin Impact)": 188,
|
193 |
+
"行秋 Xingqiu (Genshin Impact)": 190,
|
194 |
+
"奥兹 Oz (Genshin Impact)": 193,
|
195 |
+
"五郎 Gorou (Genshin Impact)": 198,
|
196 |
+
"达达利亚 Tartalia (Genshin Impact)": 202,
|
197 |
+
"七七 Qiqi (Genshin Impact)": 207,
|
198 |
+
"申鹤 Shenhe (Genshin Impact)": 217,
|
199 |
+
"莱依拉 Layla (Genshin Impact)": 228,
|
200 |
+
"菲谢尔 Fishl (Genshin Impact)": 230
|
201 |
+
}
|
202 |
+
}
|
data_utils.py
ADDED
@@ -0,0 +1,393 @@
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|
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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 |
+
audiopath = "E:/uma_voice/" + audiopath
|
190 |
+
if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
|
191 |
+
audiopaths_sid_text_new.append([audiopath, sid, text])
|
192 |
+
lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
|
193 |
+
self.audiopaths_sid_text = audiopaths_sid_text_new
|
194 |
+
self.lengths = lengths
|
195 |
+
|
196 |
+
def get_audio_text_speaker_pair(self, audiopath_sid_text):
|
197 |
+
# separate filename, speaker_id and text
|
198 |
+
audiopath, sid, text = audiopath_sid_text[0], audiopath_sid_text[1], audiopath_sid_text[2]
|
199 |
+
text = self.get_text(text)
|
200 |
+
spec, wav = self.get_audio(audiopath)
|
201 |
+
sid = self.get_sid(sid)
|
202 |
+
return (text, spec, wav, sid)
|
203 |
+
|
204 |
+
def get_audio(self, filename):
|
205 |
+
audio, sampling_rate = load_wav_to_torch(filename)
|
206 |
+
if sampling_rate != self.sampling_rate:
|
207 |
+
raise ValueError("{} {} SR doesn't match target {} SR".format(
|
208 |
+
sampling_rate, self.sampling_rate))
|
209 |
+
audio_norm = audio / self.max_wav_value
|
210 |
+
audio_norm = audio_norm.unsqueeze(0)
|
211 |
+
spec_filename = filename.replace(".wav", ".spec.pt")
|
212 |
+
if os.path.exists(spec_filename):
|
213 |
+
spec = torch.load(spec_filename)
|
214 |
+
else:
|
215 |
+
spec = spectrogram_torch(audio_norm, self.filter_length,
|
216 |
+
self.sampling_rate, self.hop_length, self.win_length,
|
217 |
+
center=False)
|
218 |
+
spec = torch.squeeze(spec, 0)
|
219 |
+
torch.save(spec, spec_filename)
|
220 |
+
return spec, audio_norm
|
221 |
+
|
222 |
+
def get_text(self, text):
|
223 |
+
if self.cleaned_text:
|
224 |
+
text_norm = cleaned_text_to_sequence(text)
|
225 |
+
else:
|
226 |
+
text_norm = text_to_sequence(text, self.text_cleaners)
|
227 |
+
if self.add_blank:
|
228 |
+
text_norm = commons.intersperse(text_norm, 0)
|
229 |
+
text_norm = torch.LongTensor(text_norm)
|
230 |
+
return text_norm
|
231 |
+
|
232 |
+
def get_sid(self, sid):
|
233 |
+
sid = torch.LongTensor([int(sid)])
|
234 |
+
return sid
|
235 |
+
|
236 |
+
def __getitem__(self, index):
|
237 |
+
return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
|
238 |
+
|
239 |
+
def __len__(self):
|
240 |
+
return len(self.audiopaths_sid_text)
|
241 |
+
|
242 |
+
|
243 |
+
class TextAudioSpeakerCollate():
|
244 |
+
""" Zero-pads model inputs and targets
|
245 |
+
"""
|
246 |
+
def __init__(self, return_ids=False):
|
247 |
+
self.return_ids = return_ids
|
248 |
+
|
249 |
+
def __call__(self, batch):
|
250 |
+
"""Collate's training batch from normalized text, audio and speaker identities
|
251 |
+
PARAMS
|
252 |
+
------
|
253 |
+
batch: [text_normalized, spec_normalized, wav_normalized, sid]
|
254 |
+
"""
|
255 |
+
# Right zero-pad all one-hot text sequences to max input length
|
256 |
+
_, ids_sorted_decreasing = torch.sort(
|
257 |
+
torch.LongTensor([x[1].size(1) for x in batch]),
|
258 |
+
dim=0, descending=True)
|
259 |
+
|
260 |
+
max_text_len = max([len(x[0]) for x in batch])
|
261 |
+
max_spec_len = max([x[1].size(1) for x in batch])
|
262 |
+
max_wav_len = max([x[2].size(1) for x in batch])
|
263 |
+
|
264 |
+
text_lengths = torch.LongTensor(len(batch))
|
265 |
+
spec_lengths = torch.LongTensor(len(batch))
|
266 |
+
wav_lengths = torch.LongTensor(len(batch))
|
267 |
+
sid = torch.LongTensor(len(batch))
|
268 |
+
|
269 |
+
text_padded = torch.LongTensor(len(batch), max_text_len)
|
270 |
+
spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
|
271 |
+
wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
|
272 |
+
text_padded.zero_()
|
273 |
+
spec_padded.zero_()
|
274 |
+
wav_padded.zero_()
|
275 |
+
for i in range(len(ids_sorted_decreasing)):
|
276 |
+
row = batch[ids_sorted_decreasing[i]]
|
277 |
+
|
278 |
+
text = row[0]
|
279 |
+
text_padded[i, :text.size(0)] = text
|
280 |
+
text_lengths[i] = text.size(0)
|
281 |
+
|
282 |
+
spec = row[1]
|
283 |
+
spec_padded[i, :, :spec.size(1)] = spec
|
284 |
+
spec_lengths[i] = spec.size(1)
|
285 |
+
|
286 |
+
wav = row[2]
|
287 |
+
wav_padded[i, :, :wav.size(1)] = wav
|
288 |
+
wav_lengths[i] = wav.size(1)
|
289 |
+
|
290 |
+
sid[i] = row[3]
|
291 |
+
|
292 |
+
if self.return_ids:
|
293 |
+
return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, ids_sorted_decreasing
|
294 |
+
return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid
|
295 |
+
|
296 |
+
|
297 |
+
class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
|
298 |
+
"""
|
299 |
+
Maintain similar input lengths in a batch.
|
300 |
+
Length groups are specified by boundaries.
|
301 |
+
Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
|
302 |
+
|
303 |
+
It removes samples which are not included in the boundaries.
|
304 |
+
Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
|
305 |
+
"""
|
306 |
+
def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
|
307 |
+
super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
|
308 |
+
self.lengths = dataset.lengths
|
309 |
+
self.batch_size = batch_size
|
310 |
+
self.boundaries = boundaries
|
311 |
+
|
312 |
+
self.buckets, self.num_samples_per_bucket = self._create_buckets()
|
313 |
+
self.total_size = sum(self.num_samples_per_bucket)
|
314 |
+
self.num_samples = self.total_size // self.num_replicas
|
315 |
+
|
316 |
+
def _create_buckets(self):
|
317 |
+
buckets = [[] for _ in range(len(self.boundaries) - 1)]
|
318 |
+
for i in range(len(self.lengths)):
|
319 |
+
length = self.lengths[i]
|
320 |
+
idx_bucket = self._bisect(length)
|
321 |
+
if idx_bucket != -1:
|
322 |
+
buckets[idx_bucket].append(i)
|
323 |
+
|
324 |
+
for i in range(len(buckets) - 1, 0, -1):
|
325 |
+
if len(buckets[i]) == 0:
|
326 |
+
buckets.pop(i)
|
327 |
+
self.boundaries.pop(i+1)
|
328 |
+
|
329 |
+
num_samples_per_bucket = []
|
330 |
+
for i in range(len(buckets)):
|
331 |
+
len_bucket = len(buckets[i])
|
332 |
+
total_batch_size = self.num_replicas * self.batch_size
|
333 |
+
rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
|
334 |
+
num_samples_per_bucket.append(len_bucket + rem)
|
335 |
+
return buckets, num_samples_per_bucket
|
336 |
+
|
337 |
+
def __iter__(self):
|
338 |
+
# deterministically shuffle based on epoch
|
339 |
+
g = torch.Generator()
|
340 |
+
g.manual_seed(self.epoch)
|
341 |
+
|
342 |
+
indices = []
|
343 |
+
if self.shuffle:
|
344 |
+
for bucket in self.buckets:
|
345 |
+
indices.append(torch.randperm(len(bucket), generator=g).tolist())
|
346 |
+
else:
|
347 |
+
for bucket in self.buckets:
|
348 |
+
indices.append(list(range(len(bucket))))
|
349 |
+
|
350 |
+
batches = []
|
351 |
+
for i in range(len(self.buckets)):
|
352 |
+
bucket = self.buckets[i]
|
353 |
+
len_bucket = len(bucket)
|
354 |
+
ids_bucket = indices[i]
|
355 |
+
num_samples_bucket = self.num_samples_per_bucket[i]
|
356 |
+
|
357 |
+
# add extra samples to make it evenly divisible
|
358 |
+
rem = num_samples_bucket - len_bucket
|
359 |
+
ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
|
360 |
+
|
361 |
+
# subsample
|
362 |
+
ids_bucket = ids_bucket[self.rank::self.num_replicas]
|
363 |
+
|
364 |
+
# batching
|
365 |
+
for j in range(len(ids_bucket) // self.batch_size):
|
366 |
+
batch = [bucket[idx] for idx in ids_bucket[j*self.batch_size:(j+1)*self.batch_size]]
|
367 |
+
batches.append(batch)
|
368 |
+
|
369 |
+
if self.shuffle:
|
370 |
+
batch_ids = torch.randperm(len(batches), generator=g).tolist()
|
371 |
+
batches = [batches[i] for i in batch_ids]
|
372 |
+
self.batches = batches
|
373 |
+
|
374 |
+
assert len(self.batches) * self.batch_size == self.num_samples
|
375 |
+
return iter(self.batches)
|
376 |
+
|
377 |
+
def _bisect(self, x, lo=0, hi=None):
|
378 |
+
if hi is None:
|
379 |
+
hi = len(self.boundaries) - 1
|
380 |
+
|
381 |
+
if hi > lo:
|
382 |
+
mid = (hi + lo) // 2
|
383 |
+
if self.boundaries[mid] < x and x <= self.boundaries[mid+1]:
|
384 |
+
return mid
|
385 |
+
elif x <= self.boundaries[mid]:
|
386 |
+
return self._bisect(x, lo, mid)
|
387 |
+
else:
|
388 |
+
return self._bisect(x, mid + 1, hi)
|
389 |
+
else:
|
390 |
+
return -1
|
391 |
+
|
392 |
+
def __len__(self):
|
393 |
+
return self.num_samples // self.batch_size
|
hubert_model.py
ADDED
@@ -0,0 +1,221 @@
|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
|
|
|
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|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import copy
|
2 |
+
from typing import Optional, Tuple
|
3 |
+
import random
|
4 |
+
|
5 |
+
import torch
|
6 |
+
import torch.nn as nn
|
7 |
+
import torch.nn.functional as F
|
8 |
+
from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
|
9 |
+
|
10 |
+
class Hubert(nn.Module):
|
11 |
+
def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
|
12 |
+
super().__init__()
|
13 |
+
self._mask = mask
|
14 |
+
self.feature_extractor = FeatureExtractor()
|
15 |
+
self.feature_projection = FeatureProjection()
|
16 |
+
self.positional_embedding = PositionalConvEmbedding()
|
17 |
+
self.norm = nn.LayerNorm(768)
|
18 |
+
self.dropout = nn.Dropout(0.1)
|
19 |
+
self.encoder = TransformerEncoder(
|
20 |
+
nn.TransformerEncoderLayer(
|
21 |
+
768, 12, 3072, activation="gelu", batch_first=True
|
22 |
+
),
|
23 |
+
12,
|
24 |
+
)
|
25 |
+
self.proj = nn.Linear(768, 256)
|
26 |
+
|
27 |
+
self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
|
28 |
+
self.label_embedding = nn.Embedding(num_label_embeddings, 256)
|
29 |
+
|
30 |
+
def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
31 |
+
mask = None
|
32 |
+
if self.training and self._mask:
|
33 |
+
mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
|
34 |
+
x[mask] = self.masked_spec_embed.to(x.dtype)
|
35 |
+
return x, mask
|
36 |
+
|
37 |
+
def encode(
|
38 |
+
self, x: torch.Tensor, layer: Optional[int] = None
|
39 |
+
) -> Tuple[torch.Tensor, torch.Tensor]:
|
40 |
+
x = self.feature_extractor(x)
|
41 |
+
x = self.feature_projection(x.transpose(1, 2))
|
42 |
+
x, mask = self.mask(x)
|
43 |
+
x = x + self.positional_embedding(x)
|
44 |
+
x = self.dropout(self.norm(x))
|
45 |
+
x = self.encoder(x, output_layer=layer)
|
46 |
+
return x, mask
|
47 |
+
|
48 |
+
def logits(self, x: torch.Tensor) -> torch.Tensor:
|
49 |
+
logits = torch.cosine_similarity(
|
50 |
+
x.unsqueeze(2),
|
51 |
+
self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
|
52 |
+
dim=-1,
|
53 |
+
)
|
54 |
+
return logits / 0.1
|
55 |
+
|
56 |
+
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
57 |
+
x, mask = self.encode(x)
|
58 |
+
x = self.proj(x)
|
59 |
+
logits = self.logits(x)
|
60 |
+
return logits, mask
|
61 |
+
|
62 |
+
|
63 |
+
class HubertSoft(Hubert):
|
64 |
+
def __init__(self):
|
65 |
+
super().__init__()
|
66 |
+
|
67 |
+
@torch.inference_mode()
|
68 |
+
def units(self, wav: torch.Tensor) -> torch.Tensor:
|
69 |
+
wav = F.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
|
70 |
+
x, _ = self.encode(wav)
|
71 |
+
return self.proj(x)
|
72 |
+
|
73 |
+
|
74 |
+
class FeatureExtractor(nn.Module):
|
75 |
+
def __init__(self):
|
76 |
+
super().__init__()
|
77 |
+
self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
|
78 |
+
self.norm0 = nn.GroupNorm(512, 512)
|
79 |
+
self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
80 |
+
self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
81 |
+
self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
82 |
+
self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
83 |
+
self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
|
84 |
+
self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
|
85 |
+
|
86 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
87 |
+
x = F.gelu(self.norm0(self.conv0(x)))
|
88 |
+
x = F.gelu(self.conv1(x))
|
89 |
+
x = F.gelu(self.conv2(x))
|
90 |
+
x = F.gelu(self.conv3(x))
|
91 |
+
x = F.gelu(self.conv4(x))
|
92 |
+
x = F.gelu(self.conv5(x))
|
93 |
+
x = F.gelu(self.conv6(x))
|
94 |
+
return x
|
95 |
+
|
96 |
+
|
97 |
+
class FeatureProjection(nn.Module):
|
98 |
+
def __init__(self):
|
99 |
+
super().__init__()
|
100 |
+
self.norm = nn.LayerNorm(512)
|
101 |
+
self.projection = nn.Linear(512, 768)
|
102 |
+
self.dropout = nn.Dropout(0.1)
|
103 |
+
|
104 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
105 |
+
x = self.norm(x)
|
106 |
+
x = self.projection(x)
|
107 |
+
x = self.dropout(x)
|
108 |
+
return x
|
109 |
+
|
110 |
+
|
111 |
+
class PositionalConvEmbedding(nn.Module):
|
112 |
+
def __init__(self):
|
113 |
+
super().__init__()
|
114 |
+
self.conv = nn.Conv1d(
|
115 |
+
768,
|
116 |
+
768,
|
117 |
+
kernel_size=128,
|
118 |
+
padding=128 // 2,
|
119 |
+
groups=16,
|
120 |
+
)
|
121 |
+
self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
|
122 |
+
|
123 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
124 |
+
x = self.conv(x.transpose(1, 2))
|
125 |
+
x = F.gelu(x[:, :, :-1])
|
126 |
+
return x.transpose(1, 2)
|
127 |
+
|
128 |
+
|
129 |
+
class TransformerEncoder(nn.Module):
|
130 |
+
def __init__(
|
131 |
+
self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
|
132 |
+
) -> None:
|
133 |
+
super(TransformerEncoder, self).__init__()
|
134 |
+
self.layers = nn.ModuleList(
|
135 |
+
[copy.deepcopy(encoder_layer) for _ in range(num_layers)]
|
136 |
+
)
|
137 |
+
self.num_layers = num_layers
|
138 |
+
|
139 |
+
def forward(
|
140 |
+
self,
|
141 |
+
src: torch.Tensor,
|
142 |
+
mask: torch.Tensor = None,
|
143 |
+
src_key_padding_mask: torch.Tensor = None,
|
144 |
+
output_layer: Optional[int] = None,
|
145 |
+
) -> torch.Tensor:
|
146 |
+
output = src
|
147 |
+
for layer in self.layers[:output_layer]:
|
148 |
+
output = layer(
|
149 |
+
output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
|
150 |
+
)
|
151 |
+
return output
|
152 |
+
|
153 |
+
|
154 |
+
def _compute_mask(
|
155 |
+
shape: Tuple[int, int],
|
156 |
+
mask_prob: float,
|
157 |
+
mask_length: int,
|
158 |
+
device: torch.device,
|
159 |
+
min_masks: int = 0,
|
160 |
+
) -> torch.Tensor:
|
161 |
+
batch_size, sequence_length = shape
|
162 |
+
|
163 |
+
if mask_length < 1:
|
164 |
+
raise ValueError("`mask_length` has to be bigger than 0.")
|
165 |
+
|
166 |
+
if mask_length > sequence_length:
|
167 |
+
raise ValueError(
|
168 |
+
f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
|
169 |
+
)
|
170 |
+
|
171 |
+
# compute number of masked spans in batch
|
172 |
+
num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
|
173 |
+
num_masked_spans = max(num_masked_spans, min_masks)
|
174 |
+
|
175 |
+
# make sure num masked indices <= sequence_length
|
176 |
+
if num_masked_spans * mask_length > sequence_length:
|
177 |
+
num_masked_spans = sequence_length // mask_length
|
178 |
+
|
179 |
+
# SpecAugment mask to fill
|
180 |
+
mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
|
181 |
+
|
182 |
+
# uniform distribution to sample from, make sure that offset samples are < sequence_length
|
183 |
+
uniform_dist = torch.ones(
|
184 |
+
(batch_size, sequence_length - (mask_length - 1)), device=device
|
185 |
+
)
|
186 |
+
|
187 |
+
# get random indices to mask
|
188 |
+
mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
|
189 |
+
|
190 |
+
# expand masked indices to masked spans
|
191 |
+
mask_indices = (
|
192 |
+
mask_indices.unsqueeze(dim=-1)
|
193 |
+
.expand((batch_size, num_masked_spans, mask_length))
|
194 |
+
.reshape(batch_size, num_masked_spans * mask_length)
|
195 |
+
)
|
196 |
+
offsets = (
|
197 |
+
torch.arange(mask_length, device=device)[None, None, :]
|
198 |
+
.expand((batch_size, num_masked_spans, mask_length))
|
199 |
+
.reshape(batch_size, num_masked_spans * mask_length)
|
200 |
+
)
|
201 |
+
mask_idxs = mask_indices + offsets
|
202 |
+
|
203 |
+
# scatter indices to mask
|
204 |
+
mask = mask.scatter(1, mask_idxs, True)
|
205 |
+
|
206 |
+
return mask
|
207 |
+
|
208 |
+
|
209 |
+
def hubert_soft(
|
210 |
+
path: str
|
211 |
+
) -> HubertSoft:
|
212 |
+
r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
|
213 |
+
Args:
|
214 |
+
path (str): path of a pretrained model
|
215 |
+
"""
|
216 |
+
hubert = HubertSoft()
|
217 |
+
checkpoint = torch.load(path)
|
218 |
+
consume_prefix_in_state_dict_if_present(checkpoint, "module.")
|
219 |
+
hubert.load_state_dict(checkpoint)
|
220 |
+
hubert.eval()
|
221 |
+
return hubert
|
jieba/dict.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|
losses.py
ADDED
@@ -0,0 +1,61 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
from torch.nn import functional as F
|
3 |
+
|
4 |
+
import commons
|
5 |
+
|
6 |
+
|
7 |
+
def feature_loss(fmap_r, fmap_g):
|
8 |
+
loss = 0
|
9 |
+
for dr, dg in zip(fmap_r, fmap_g):
|
10 |
+
for rl, gl in zip(dr, dg):
|
11 |
+
rl = rl.float().detach()
|
12 |
+
gl = gl.float()
|
13 |
+
loss += torch.mean(torch.abs(rl - gl))
|
14 |
+
|
15 |
+
return loss * 2
|
16 |
+
|
17 |
+
|
18 |
+
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
|
19 |
+
loss = 0
|
20 |
+
r_losses = []
|
21 |
+
g_losses = []
|
22 |
+
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
|
23 |
+
dr = dr.float()
|
24 |
+
dg = dg.float()
|
25 |
+
r_loss = torch.mean((1-dr)**2)
|
26 |
+
g_loss = torch.mean(dg**2)
|
27 |
+
loss += (r_loss + g_loss)
|
28 |
+
r_losses.append(r_loss.item())
|
29 |
+
g_losses.append(g_loss.item())
|
30 |
+
|
31 |
+
return loss, r_losses, g_losses
|
32 |
+
|
33 |
+
|
34 |
+
def generator_loss(disc_outputs):
|
35 |
+
loss = 0
|
36 |
+
gen_losses = []
|
37 |
+
for dg in disc_outputs:
|
38 |
+
dg = dg.float()
|
39 |
+
l = torch.mean((1-dg)**2)
|
40 |
+
gen_losses.append(l)
|
41 |
+
loss += l
|
42 |
+
|
43 |
+
return loss, gen_losses
|
44 |
+
|
45 |
+
|
46 |
+
def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
|
47 |
+
"""
|
48 |
+
z_p, logs_q: [b, h, t_t]
|
49 |
+
m_p, logs_p: [b, h, t_t]
|
50 |
+
"""
|
51 |
+
z_p = z_p.float()
|
52 |
+
logs_q = logs_q.float()
|
53 |
+
m_p = m_p.float()
|
54 |
+
logs_p = logs_p.float()
|
55 |
+
z_mask = z_mask.float()
|
56 |
+
|
57 |
+
kl = logs_p - logs_q - 0.5
|
58 |
+
kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
|
59 |
+
kl = torch.sum(kl * z_mask)
|
60 |
+
l = kl / torch.sum(z_mask)
|
61 |
+
return l
|
mel_processing.py
ADDED
@@ -0,0 +1,101 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.utils.data
|
3 |
+
from librosa.filters import mel as librosa_mel_fn
|
4 |
+
|
5 |
+
MAX_WAV_VALUE = 32768.0
|
6 |
+
|
7 |
+
|
8 |
+
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
|
9 |
+
"""
|
10 |
+
PARAMS
|
11 |
+
------
|
12 |
+
C: compression factor
|
13 |
+
"""
|
14 |
+
return torch.log(torch.clamp(x, min=clip_val) * C)
|
15 |
+
|
16 |
+
|
17 |
+
def dynamic_range_decompression_torch(x, C=1):
|
18 |
+
"""
|
19 |
+
PARAMS
|
20 |
+
------
|
21 |
+
C: compression factor used to compress
|
22 |
+
"""
|
23 |
+
return torch.exp(x) / C
|
24 |
+
|
25 |
+
|
26 |
+
def spectral_normalize_torch(magnitudes):
|
27 |
+
output = dynamic_range_compression_torch(magnitudes)
|
28 |
+
return output
|
29 |
+
|
30 |
+
|
31 |
+
def spectral_de_normalize_torch(magnitudes):
|
32 |
+
output = dynamic_range_decompression_torch(magnitudes)
|
33 |
+
return output
|
34 |
+
|
35 |
+
|
36 |
+
mel_basis = {}
|
37 |
+
hann_window = {}
|
38 |
+
|
39 |
+
|
40 |
+
def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
|
41 |
+
if torch.min(y) < -1.:
|
42 |
+
print('min value is ', torch.min(y))
|
43 |
+
if torch.max(y) > 1.:
|
44 |
+
print('max value is ', torch.max(y))
|
45 |
+
|
46 |
+
global hann_window
|
47 |
+
dtype_device = str(y.dtype) + '_' + str(y.device)
|
48 |
+
wnsize_dtype_device = str(win_size) + '_' + dtype_device
|
49 |
+
if wnsize_dtype_device not in hann_window:
|
50 |
+
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
|
51 |
+
|
52 |
+
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
|
53 |
+
y = y.squeeze(1)
|
54 |
+
|
55 |
+
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
|
56 |
+
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
|
57 |
+
|
58 |
+
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
|
59 |
+
return spec
|
60 |
+
|
61 |
+
|
62 |
+
def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
|
63 |
+
global mel_basis
|
64 |
+
dtype_device = str(spec.dtype) + '_' + str(spec.device)
|
65 |
+
fmax_dtype_device = str(fmax) + '_' + dtype_device
|
66 |
+
if fmax_dtype_device not in mel_basis:
|
67 |
+
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
|
68 |
+
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
|
69 |
+
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
|
70 |
+
spec = spectral_normalize_torch(spec)
|
71 |
+
return spec
|
72 |
+
|
73 |
+
|
74 |
+
def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
|
75 |
+
if torch.min(y) < -1.:
|
76 |
+
print('min value is ', torch.min(y))
|
77 |
+
if torch.max(y) > 1.:
|
78 |
+
print('max value is ', torch.max(y))
|
79 |
+
|
80 |
+
global mel_basis, hann_window
|
81 |
+
dtype_device = str(y.dtype) + '_' + str(y.device)
|
82 |
+
fmax_dtype_device = str(fmax) + '_' + dtype_device
|
83 |
+
wnsize_dtype_device = str(win_size) + '_' + dtype_device
|
84 |
+
if fmax_dtype_device not in mel_basis:
|
85 |
+
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
|
86 |
+
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
|
87 |
+
if wnsize_dtype_device not in hann_window:
|
88 |
+
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
|
89 |
+
|
90 |
+
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
|
91 |
+
y = y.squeeze(1)
|
92 |
+
|
93 |
+
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
|
94 |
+
center=center, pad_mode='reflect', normalized=False, onesided=True)
|
95 |
+
|
96 |
+
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
|
97 |
+
|
98 |
+
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
|
99 |
+
spec = spectral_normalize_torch(spec)
|
100 |
+
|
101 |
+
return spec
|
models.py
ADDED
@@ -0,0 +1,542 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
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|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
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|
|
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|
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|
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|
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|
|
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|
|
|
|
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|
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|
1 |
+
import math
|
2 |
+
import torch
|
3 |
+
from torch import nn
|
4 |
+
from torch.nn import functional as F
|
5 |
+
|
6 |
+
import commons
|
7 |
+
import modules
|
8 |
+
import attentions
|
9 |
+
|
10 |
+
from torch.nn import Conv1d, ConvTranspose1d, Conv2d
|
11 |
+
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
12 |
+
from commons import init_weights, get_padding
|
13 |
+
|
14 |
+
|
15 |
+
class StochasticDurationPredictor(nn.Module):
|
16 |
+
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
|
17 |
+
super().__init__()
|
18 |
+
filter_channels = in_channels # it needs to be removed from future version.
|
19 |
+
self.in_channels = in_channels
|
20 |
+
self.filter_channels = filter_channels
|
21 |
+
self.kernel_size = kernel_size
|
22 |
+
self.p_dropout = p_dropout
|
23 |
+
self.n_flows = n_flows
|
24 |
+
self.gin_channels = gin_channels
|
25 |
+
|
26 |
+
self.log_flow = modules.Log()
|
27 |
+
self.flows = nn.ModuleList()
|
28 |
+
self.flows.append(modules.ElementwiseAffine(2))
|
29 |
+
for i in range(n_flows):
|
30 |
+
self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
|
31 |
+
self.flows.append(modules.Flip())
|
32 |
+
|
33 |
+
self.post_pre = nn.Conv1d(1, filter_channels, 1)
|
34 |
+
self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
|
35 |
+
self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
|
36 |
+
self.post_flows = nn.ModuleList()
|
37 |
+
self.post_flows.append(modules.ElementwiseAffine(2))
|
38 |
+
for i in range(4):
|
39 |
+
self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
|
40 |
+
self.post_flows.append(modules.Flip())
|
41 |
+
|
42 |
+
self.pre = nn.Conv1d(in_channels, filter_channels, 1)
|
43 |
+
self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
|
44 |
+
self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
|
45 |
+
if gin_channels != 0:
|
46 |
+
self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
|
47 |
+
|
48 |
+
def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
|
49 |
+
x = torch.detach(x)
|
50 |
+
x = self.pre(x)
|
51 |
+
if g is not None:
|
52 |
+
g = torch.detach(g)
|
53 |
+
x = x + self.cond(g)
|
54 |
+
x = self.convs(x, x_mask)
|
55 |
+
x = self.proj(x) * x_mask
|
56 |
+
|
57 |
+
if not reverse:
|
58 |
+
flows = self.flows
|
59 |
+
assert w is not None
|
60 |
+
|
61 |
+
logdet_tot_q = 0
|
62 |
+
h_w = self.post_pre(w)
|
63 |
+
h_w = self.post_convs(h_w, x_mask)
|
64 |
+
h_w = self.post_proj(h_w) * x_mask
|
65 |
+
e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
|
66 |
+
z_q = e_q
|
67 |
+
for flow in self.post_flows:
|
68 |
+
z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
|
69 |
+
logdet_tot_q += logdet_q
|
70 |
+
z_u, z1 = torch.split(z_q, [1, 1], 1)
|
71 |
+
u = torch.sigmoid(z_u) * x_mask
|
72 |
+
z0 = (w - u) * x_mask
|
73 |
+
logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
|
74 |
+
logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
|
75 |
+
|
76 |
+
logdet_tot = 0
|
77 |
+
z0, logdet = self.log_flow(z0, x_mask)
|
78 |
+
logdet_tot += logdet
|
79 |
+
z = torch.cat([z0, z1], 1)
|
80 |
+
for flow in flows:
|
81 |
+
z, logdet = flow(z, x_mask, g=x, reverse=reverse)
|
82 |
+
logdet_tot = logdet_tot + logdet
|
83 |
+
nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
|
84 |
+
return nll + logq # [b]
|
85 |
+
else:
|
86 |
+
flows = list(reversed(self.flows))
|
87 |
+
flows = flows[:-2] + [flows[-1]] # remove a useless vflow
|
88 |
+
z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
|
89 |
+
for flow in flows:
|
90 |
+
z = flow(z, x_mask, g=x, reverse=reverse)
|
91 |
+
z0, z1 = torch.split(z, [1, 1], 1)
|
92 |
+
logw = z0
|
93 |
+
return logw
|
94 |
+
|
95 |
+
|
96 |
+
class DurationPredictor(nn.Module):
|
97 |
+
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
|
98 |
+
super().__init__()
|
99 |
+
|
100 |
+
self.in_channels = in_channels
|
101 |
+
self.filter_channels = filter_channels
|
102 |
+
self.kernel_size = kernel_size
|
103 |
+
self.p_dropout = p_dropout
|
104 |
+
self.gin_channels = gin_channels
|
105 |
+
|
106 |
+
self.drop = nn.Dropout(p_dropout)
|
107 |
+
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
|
108 |
+
self.norm_1 = modules.LayerNorm(filter_channels)
|
109 |
+
self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
|
110 |
+
self.norm_2 = modules.LayerNorm(filter_channels)
|
111 |
+
self.proj = nn.Conv1d(filter_channels, 1, 1)
|
112 |
+
|
113 |
+
if gin_channels != 0:
|
114 |
+
self.cond = nn.Conv1d(gin_channels, in_channels, 1)
|
115 |
+
|
116 |
+
def forward(self, x, x_mask, g=None):
|
117 |
+
x = torch.detach(x)
|
118 |
+
if g is not None:
|
119 |
+
g = torch.detach(g)
|
120 |
+
x = x + self.cond(g)
|
121 |
+
x = self.conv_1(x * x_mask)
|
122 |
+
x = torch.relu(x)
|
123 |
+
x = self.norm_1(x)
|
124 |
+
x = self.drop(x)
|
125 |
+
x = self.conv_2(x * x_mask)
|
126 |
+
x = torch.relu(x)
|
127 |
+
x = self.norm_2(x)
|
128 |
+
x = self.drop(x)
|
129 |
+
x = self.proj(x * x_mask)
|
130 |
+
return x * x_mask
|
131 |
+
|
132 |
+
|
133 |
+
class TextEncoder(nn.Module):
|
134 |
+
def __init__(self,
|
135 |
+
n_vocab,
|
136 |
+
out_channels,
|
137 |
+
hidden_channels,
|
138 |
+
filter_channels,
|
139 |
+
n_heads,
|
140 |
+
n_layers,
|
141 |
+
kernel_size,
|
142 |
+
p_dropout,
|
143 |
+
emotion_embedding):
|
144 |
+
super().__init__()
|
145 |
+
self.n_vocab = n_vocab
|
146 |
+
self.out_channels = out_channels
|
147 |
+
self.hidden_channels = hidden_channels
|
148 |
+
self.filter_channels = filter_channels
|
149 |
+
self.n_heads = n_heads
|
150 |
+
self.n_layers = n_layers
|
151 |
+
self.kernel_size = kernel_size
|
152 |
+
self.p_dropout = p_dropout
|
153 |
+
self.emotion_embedding = emotion_embedding
|
154 |
+
|
155 |
+
if self.n_vocab!=0:
|
156 |
+
self.emb = nn.Embedding(n_vocab, hidden_channels)
|
157 |
+
if emotion_embedding:
|
158 |
+
self.emotion_emb = nn.Linear(1024, hidden_channels)
|
159 |
+
nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
|
160 |
+
|
161 |
+
self.encoder = attentions.Encoder(
|
162 |
+
hidden_channels,
|
163 |
+
filter_channels,
|
164 |
+
n_heads,
|
165 |
+
n_layers,
|
166 |
+
kernel_size,
|
167 |
+
p_dropout)
|
168 |
+
self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
169 |
+
|
170 |
+
def forward(self, x, x_lengths, emotion_embedding=None):
|
171 |
+
if self.n_vocab!=0:
|
172 |
+
x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
|
173 |
+
if emotion_embedding is not None:
|
174 |
+
x = x + self.emotion_emb(emotion_embedding.unsqueeze(1))
|
175 |
+
x = torch.transpose(x, 1, -1) # [b, h, t]
|
176 |
+
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
177 |
+
|
178 |
+
x = self.encoder(x * x_mask, x_mask)
|
179 |
+
stats = self.proj(x) * x_mask
|
180 |
+
|
181 |
+
m, logs = torch.split(stats, self.out_channels, dim=1)
|
182 |
+
return x, m, logs, x_mask
|
183 |
+
|
184 |
+
|
185 |
+
class ResidualCouplingBlock(nn.Module):
|
186 |
+
def __init__(self,
|
187 |
+
channels,
|
188 |
+
hidden_channels,
|
189 |
+
kernel_size,
|
190 |
+
dilation_rate,
|
191 |
+
n_layers,
|
192 |
+
n_flows=4,
|
193 |
+
gin_channels=0):
|
194 |
+
super().__init__()
|
195 |
+
self.channels = channels
|
196 |
+
self.hidden_channels = hidden_channels
|
197 |
+
self.kernel_size = kernel_size
|
198 |
+
self.dilation_rate = dilation_rate
|
199 |
+
self.n_layers = n_layers
|
200 |
+
self.n_flows = n_flows
|
201 |
+
self.gin_channels = gin_channels
|
202 |
+
|
203 |
+
self.flows = nn.ModuleList()
|
204 |
+
for i in range(n_flows):
|
205 |
+
self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
|
206 |
+
self.flows.append(modules.Flip())
|
207 |
+
|
208 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
209 |
+
if not reverse:
|
210 |
+
for flow in self.flows:
|
211 |
+
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
212 |
+
else:
|
213 |
+
for flow in reversed(self.flows):
|
214 |
+
x = flow(x, x_mask, g=g, reverse=reverse)
|
215 |
+
return x
|
216 |
+
|
217 |
+
|
218 |
+
class PosteriorEncoder(nn.Module):
|
219 |
+
def __init__(self,
|
220 |
+
in_channels,
|
221 |
+
out_channels,
|
222 |
+
hidden_channels,
|
223 |
+
kernel_size,
|
224 |
+
dilation_rate,
|
225 |
+
n_layers,
|
226 |
+
gin_channels=0):
|
227 |
+
super().__init__()
|
228 |
+
self.in_channels = in_channels
|
229 |
+
self.out_channels = out_channels
|
230 |
+
self.hidden_channels = hidden_channels
|
231 |
+
self.kernel_size = kernel_size
|
232 |
+
self.dilation_rate = dilation_rate
|
233 |
+
self.n_layers = n_layers
|
234 |
+
self.gin_channels = gin_channels
|
235 |
+
|
236 |
+
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
237 |
+
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
|
238 |
+
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
239 |
+
|
240 |
+
def forward(self, x, x_lengths, g=None):
|
241 |
+
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
242 |
+
x = self.pre(x) * x_mask
|
243 |
+
x = self.enc(x, x_mask, g=g)
|
244 |
+
stats = self.proj(x) * x_mask
|
245 |
+
m, logs = torch.split(stats, self.out_channels, dim=1)
|
246 |
+
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
247 |
+
return z, m, logs, x_mask
|
248 |
+
|
249 |
+
|
250 |
+
class Generator(torch.nn.Module):
|
251 |
+
def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
|
252 |
+
super(Generator, self).__init__()
|
253 |
+
self.num_kernels = len(resblock_kernel_sizes)
|
254 |
+
self.num_upsamples = len(upsample_rates)
|
255 |
+
self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
|
256 |
+
resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
|
257 |
+
|
258 |
+
self.ups = nn.ModuleList()
|
259 |
+
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
260 |
+
self.ups.append(weight_norm(
|
261 |
+
ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
|
262 |
+
k, u, padding=(k-u)//2)))
|
263 |
+
|
264 |
+
self.resblocks = nn.ModuleList()
|
265 |
+
for i in range(len(self.ups)):
|
266 |
+
ch = upsample_initial_channel//(2**(i+1))
|
267 |
+
for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
|
268 |
+
self.resblocks.append(resblock(ch, k, d))
|
269 |
+
|
270 |
+
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
|
271 |
+
self.ups.apply(init_weights)
|
272 |
+
|
273 |
+
if gin_channels != 0:
|
274 |
+
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
|
275 |
+
|
276 |
+
def forward(self, x, g=None):
|
277 |
+
x = self.conv_pre(x)
|
278 |
+
if g is not None:
|
279 |
+
x = x + self.cond(g)
|
280 |
+
|
281 |
+
for i in range(self.num_upsamples):
|
282 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
283 |
+
x = self.ups[i](x)
|
284 |
+
xs = None
|
285 |
+
for j in range(self.num_kernels):
|
286 |
+
if xs is None:
|
287 |
+
xs = self.resblocks[i*self.num_kernels+j](x)
|
288 |
+
else:
|
289 |
+
xs += self.resblocks[i*self.num_kernels+j](x)
|
290 |
+
x = xs / self.num_kernels
|
291 |
+
x = F.leaky_relu(x)
|
292 |
+
x = self.conv_post(x)
|
293 |
+
x = torch.tanh(x)
|
294 |
+
|
295 |
+
return x
|
296 |
+
|
297 |
+
def remove_weight_norm(self):
|
298 |
+
print('Removing weight norm...')
|
299 |
+
for l in self.ups:
|
300 |
+
remove_weight_norm(l)
|
301 |
+
for l in self.resblocks:
|
302 |
+
l.remove_weight_norm()
|
303 |
+
|
304 |
+
|
305 |
+
class DiscriminatorP(torch.nn.Module):
|
306 |
+
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
307 |
+
super(DiscriminatorP, self).__init__()
|
308 |
+
self.period = period
|
309 |
+
self.use_spectral_norm = use_spectral_norm
|
310 |
+
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
311 |
+
self.convs = nn.ModuleList([
|
312 |
+
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
313 |
+
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
314 |
+
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
315 |
+
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
316 |
+
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
|
317 |
+
])
|
318 |
+
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
319 |
+
|
320 |
+
def forward(self, x):
|
321 |
+
fmap = []
|
322 |
+
|
323 |
+
# 1d to 2d
|
324 |
+
b, c, t = x.shape
|
325 |
+
if t % self.period != 0: # pad first
|
326 |
+
n_pad = self.period - (t % self.period)
|
327 |
+
x = F.pad(x, (0, n_pad), "reflect")
|
328 |
+
t = t + n_pad
|
329 |
+
x = x.view(b, c, t // self.period, self.period)
|
330 |
+
|
331 |
+
for l in self.convs:
|
332 |
+
x = l(x)
|
333 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
334 |
+
fmap.append(x)
|
335 |
+
x = self.conv_post(x)
|
336 |
+
fmap.append(x)
|
337 |
+
x = torch.flatten(x, 1, -1)
|
338 |
+
|
339 |
+
return x, fmap
|
340 |
+
|
341 |
+
|
342 |
+
class DiscriminatorS(torch.nn.Module):
|
343 |
+
def __init__(self, use_spectral_norm=False):
|
344 |
+
super(DiscriminatorS, self).__init__()
|
345 |
+
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
346 |
+
self.convs = nn.ModuleList([
|
347 |
+
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
|
348 |
+
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
|
349 |
+
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
|
350 |
+
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
|
351 |
+
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
|
352 |
+
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
353 |
+
])
|
354 |
+
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
355 |
+
|
356 |
+
def forward(self, x):
|
357 |
+
fmap = []
|
358 |
+
|
359 |
+
for l in self.convs:
|
360 |
+
x = l(x)
|
361 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
362 |
+
fmap.append(x)
|
363 |
+
x = self.conv_post(x)
|
364 |
+
fmap.append(x)
|
365 |
+
x = torch.flatten(x, 1, -1)
|
366 |
+
|
367 |
+
return x, fmap
|
368 |
+
|
369 |
+
|
370 |
+
class MultiPeriodDiscriminator(torch.nn.Module):
|
371 |
+
def __init__(self, use_spectral_norm=False):
|
372 |
+
super(MultiPeriodDiscriminator, self).__init__()
|
373 |
+
periods = [2,3,5,7,11]
|
374 |
+
|
375 |
+
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
|
376 |
+
discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
|
377 |
+
self.discriminators = nn.ModuleList(discs)
|
378 |
+
|
379 |
+
def forward(self, y, y_hat):
|
380 |
+
y_d_rs = []
|
381 |
+
y_d_gs = []
|
382 |
+
fmap_rs = []
|
383 |
+
fmap_gs = []
|
384 |
+
for i, d in enumerate(self.discriminators):
|
385 |
+
y_d_r, fmap_r = d(y)
|
386 |
+
y_d_g, fmap_g = d(y_hat)
|
387 |
+
y_d_rs.append(y_d_r)
|
388 |
+
y_d_gs.append(y_d_g)
|
389 |
+
fmap_rs.append(fmap_r)
|
390 |
+
fmap_gs.append(fmap_g)
|
391 |
+
|
392 |
+
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
393 |
+
|
394 |
+
|
395 |
+
|
396 |
+
class SynthesizerTrn(nn.Module):
|
397 |
+
"""
|
398 |
+
Synthesizer for Training
|
399 |
+
"""
|
400 |
+
|
401 |
+
def __init__(self,
|
402 |
+
n_vocab,
|
403 |
+
spec_channels,
|
404 |
+
segment_size,
|
405 |
+
inter_channels,
|
406 |
+
hidden_channels,
|
407 |
+
filter_channels,
|
408 |
+
n_heads,
|
409 |
+
n_layers,
|
410 |
+
kernel_size,
|
411 |
+
p_dropout,
|
412 |
+
resblock,
|
413 |
+
resblock_kernel_sizes,
|
414 |
+
resblock_dilation_sizes,
|
415 |
+
upsample_rates,
|
416 |
+
upsample_initial_channel,
|
417 |
+
upsample_kernel_sizes,
|
418 |
+
n_speakers=0,
|
419 |
+
gin_channels=0,
|
420 |
+
use_sdp=True,
|
421 |
+
emotion_embedding=False,
|
422 |
+
**kwargs):
|
423 |
+
|
424 |
+
super().__init__()
|
425 |
+
self.n_vocab = n_vocab
|
426 |
+
self.spec_channels = spec_channels
|
427 |
+
self.inter_channels = inter_channels
|
428 |
+
self.hidden_channels = hidden_channels
|
429 |
+
self.filter_channels = filter_channels
|
430 |
+
self.n_heads = n_heads
|
431 |
+
self.n_layers = n_layers
|
432 |
+
self.kernel_size = kernel_size
|
433 |
+
self.p_dropout = p_dropout
|
434 |
+
self.resblock = resblock
|
435 |
+
self.resblock_kernel_sizes = resblock_kernel_sizes
|
436 |
+
self.resblock_dilation_sizes = resblock_dilation_sizes
|
437 |
+
self.upsample_rates = upsample_rates
|
438 |
+
self.upsample_initial_channel = upsample_initial_channel
|
439 |
+
self.upsample_kernel_sizes = upsample_kernel_sizes
|
440 |
+
self.segment_size = segment_size
|
441 |
+
self.n_speakers = n_speakers
|
442 |
+
self.gin_channels = gin_channels
|
443 |
+
|
444 |
+
self.use_sdp = use_sdp
|
445 |
+
|
446 |
+
self.enc_p = TextEncoder(n_vocab,
|
447 |
+
inter_channels,
|
448 |
+
hidden_channels,
|
449 |
+
filter_channels,
|
450 |
+
n_heads,
|
451 |
+
n_layers,
|
452 |
+
kernel_size,
|
453 |
+
p_dropout,
|
454 |
+
emotion_embedding)
|
455 |
+
self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
|
456 |
+
self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
|
457 |
+
self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
|
458 |
+
|
459 |
+
if use_sdp:
|
460 |
+
self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
|
461 |
+
else:
|
462 |
+
self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
|
463 |
+
|
464 |
+
if n_speakers > 1:
|
465 |
+
self.emb_g = nn.Embedding(n_speakers, gin_channels)
|
466 |
+
|
467 |
+
def forward(self, x, x_lengths, y, y_lengths, sid=None):
|
468 |
+
|
469 |
+
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
|
470 |
+
if self.n_speakers > 0:
|
471 |
+
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
|
472 |
+
else:
|
473 |
+
g = None
|
474 |
+
|
475 |
+
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
|
476 |
+
z_p = self.flow(z, y_mask, g=g)
|
477 |
+
|
478 |
+
with torch.no_grad():
|
479 |
+
# negative cross-entropy
|
480 |
+
s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
|
481 |
+
neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
|
482 |
+
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]
|
483 |
+
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]
|
484 |
+
neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
|
485 |
+
neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
|
486 |
+
|
487 |
+
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
|
488 |
+
attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
|
489 |
+
|
490 |
+
w = attn.sum(2)
|
491 |
+
if self.use_sdp:
|
492 |
+
l_length = self.dp(x, x_mask, w, g=g)
|
493 |
+
l_length = l_length / torch.sum(x_mask)
|
494 |
+
else:
|
495 |
+
logw_ = torch.log(w + 1e-6) * x_mask
|
496 |
+
logw = self.dp(x, x_mask, g=g)
|
497 |
+
l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging
|
498 |
+
|
499 |
+
# expand prior
|
500 |
+
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
|
501 |
+
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
|
502 |
+
|
503 |
+
z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
|
504 |
+
o = self.dec(z_slice, g=g)
|
505 |
+
return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
|
506 |
+
|
507 |
+
def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None, emotion_embedding=None):
|
508 |
+
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, emotion_embedding)
|
509 |
+
if self.n_speakers > 0:
|
510 |
+
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
|
511 |
+
else:
|
512 |
+
g = None
|
513 |
+
|
514 |
+
if self.use_sdp:
|
515 |
+
logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
|
516 |
+
else:
|
517 |
+
logw = self.dp(x, x_mask, g=g)
|
518 |
+
w = torch.exp(logw) * x_mask * length_scale
|
519 |
+
w_ceil = torch.ceil(w)
|
520 |
+
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
|
521 |
+
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
|
522 |
+
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
|
523 |
+
attn = commons.generate_path(w_ceil, attn_mask)
|
524 |
+
|
525 |
+
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
|
526 |
+
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
|
527 |
+
|
528 |
+
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
|
529 |
+
z = self.flow(z_p, y_mask, g=g, reverse=True)
|
530 |
+
o = self.dec((z * y_mask)[:,:,:max_len], g=g)
|
531 |
+
return o, attn, y_mask, (z, z_p, m_p, logs_p)
|
532 |
+
|
533 |
+
def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
|
534 |
+
assert self.n_speakers > 0, "n_speakers have to be larger than 0."
|
535 |
+
g_src = self.emb_g(sid_src).unsqueeze(-1)
|
536 |
+
g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
|
537 |
+
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
|
538 |
+
z_p = self.flow(z, y_mask, g=g_src)
|
539 |
+
z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
|
540 |
+
o_hat = self.dec(z_hat * y_mask, g=g_tgt)
|
541 |
+
return o_hat, y_mask, (z, z_p, z_hat)
|
542 |
+
|
modules.py
ADDED
@@ -0,0 +1,387 @@
|
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|
1 |
+
import math
|
2 |
+
import torch
|
3 |
+
from torch import nn
|
4 |
+
from torch.nn import functional as F
|
5 |
+
|
6 |
+
from torch.nn import Conv1d
|
7 |
+
from torch.nn.utils import weight_norm, remove_weight_norm
|
8 |
+
|
9 |
+
import commons
|
10 |
+
from commons import init_weights, get_padding
|
11 |
+
from transforms import piecewise_rational_quadratic_transform
|
12 |
+
|
13 |
+
|
14 |
+
LRELU_SLOPE = 0.1
|
15 |
+
|
16 |
+
|
17 |
+
class LayerNorm(nn.Module):
|
18 |
+
def __init__(self, channels, eps=1e-5):
|
19 |
+
super().__init__()
|
20 |
+
self.channels = channels
|
21 |
+
self.eps = eps
|
22 |
+
|
23 |
+
self.gamma = nn.Parameter(torch.ones(channels))
|
24 |
+
self.beta = nn.Parameter(torch.zeros(channels))
|
25 |
+
|
26 |
+
def forward(self, x):
|
27 |
+
x = x.transpose(1, -1)
|
28 |
+
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
|
29 |
+
return x.transpose(1, -1)
|
30 |
+
|
31 |
+
|
32 |
+
class ConvReluNorm(nn.Module):
|
33 |
+
def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
|
34 |
+
super().__init__()
|
35 |
+
self.in_channels = in_channels
|
36 |
+
self.hidden_channels = hidden_channels
|
37 |
+
self.out_channels = out_channels
|
38 |
+
self.kernel_size = kernel_size
|
39 |
+
self.n_layers = n_layers
|
40 |
+
self.p_dropout = p_dropout
|
41 |
+
assert n_layers > 1, "Number of layers should be larger than 0."
|
42 |
+
|
43 |
+
self.conv_layers = nn.ModuleList()
|
44 |
+
self.norm_layers = nn.ModuleList()
|
45 |
+
self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
|
46 |
+
self.norm_layers.append(LayerNorm(hidden_channels))
|
47 |
+
self.relu_drop = nn.Sequential(
|
48 |
+
nn.ReLU(),
|
49 |
+
nn.Dropout(p_dropout))
|
50 |
+
for _ in range(n_layers-1):
|
51 |
+
self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
|
52 |
+
self.norm_layers.append(LayerNorm(hidden_channels))
|
53 |
+
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
54 |
+
self.proj.weight.data.zero_()
|
55 |
+
self.proj.bias.data.zero_()
|
56 |
+
|
57 |
+
def forward(self, x, x_mask):
|
58 |
+
x_org = x
|
59 |
+
for i in range(self.n_layers):
|
60 |
+
x = self.conv_layers[i](x * x_mask)
|
61 |
+
x = self.norm_layers[i](x)
|
62 |
+
x = self.relu_drop(x)
|
63 |
+
x = x_org + self.proj(x)
|
64 |
+
return x * x_mask
|
65 |
+
|
66 |
+
|
67 |
+
class DDSConv(nn.Module):
|
68 |
+
"""
|
69 |
+
Dialted and Depth-Separable Convolution
|
70 |
+
"""
|
71 |
+
def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
|
72 |
+
super().__init__()
|
73 |
+
self.channels = channels
|
74 |
+
self.kernel_size = kernel_size
|
75 |
+
self.n_layers = n_layers
|
76 |
+
self.p_dropout = p_dropout
|
77 |
+
|
78 |
+
self.drop = nn.Dropout(p_dropout)
|
79 |
+
self.convs_sep = nn.ModuleList()
|
80 |
+
self.convs_1x1 = nn.ModuleList()
|
81 |
+
self.norms_1 = nn.ModuleList()
|
82 |
+
self.norms_2 = nn.ModuleList()
|
83 |
+
for i in range(n_layers):
|
84 |
+
dilation = kernel_size ** i
|
85 |
+
padding = (kernel_size * dilation - dilation) // 2
|
86 |
+
self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
|
87 |
+
groups=channels, dilation=dilation, padding=padding
|
88 |
+
))
|
89 |
+
self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
|
90 |
+
self.norms_1.append(LayerNorm(channels))
|
91 |
+
self.norms_2.append(LayerNorm(channels))
|
92 |
+
|
93 |
+
def forward(self, x, x_mask, g=None):
|
94 |
+
if g is not None:
|
95 |
+
x = x + g
|
96 |
+
for i in range(self.n_layers):
|
97 |
+
y = self.convs_sep[i](x * x_mask)
|
98 |
+
y = self.norms_1[i](y)
|
99 |
+
y = F.gelu(y)
|
100 |
+
y = self.convs_1x1[i](y)
|
101 |
+
y = self.norms_2[i](y)
|
102 |
+
y = F.gelu(y)
|
103 |
+
y = self.drop(y)
|
104 |
+
x = x + y
|
105 |
+
return x * x_mask
|
106 |
+
|
107 |
+
|
108 |
+
class WN(torch.nn.Module):
|
109 |
+
def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
|
110 |
+
super(WN, self).__init__()
|
111 |
+
assert(kernel_size % 2 == 1)
|
112 |
+
self.hidden_channels =hidden_channels
|
113 |
+
self.kernel_size = kernel_size,
|
114 |
+
self.dilation_rate = dilation_rate
|
115 |
+
self.n_layers = n_layers
|
116 |
+
self.gin_channels = gin_channels
|
117 |
+
self.p_dropout = p_dropout
|
118 |
+
|
119 |
+
self.in_layers = torch.nn.ModuleList()
|
120 |
+
self.res_skip_layers = torch.nn.ModuleList()
|
121 |
+
self.drop = nn.Dropout(p_dropout)
|
122 |
+
|
123 |
+
if gin_channels != 0:
|
124 |
+
cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
|
125 |
+
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
|
126 |
+
|
127 |
+
for i in range(n_layers):
|
128 |
+
dilation = dilation_rate ** i
|
129 |
+
padding = int((kernel_size * dilation - dilation) / 2)
|
130 |
+
in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
|
131 |
+
dilation=dilation, padding=padding)
|
132 |
+
in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
|
133 |
+
self.in_layers.append(in_layer)
|
134 |
+
|
135 |
+
# last one is not necessary
|
136 |
+
if i < n_layers - 1:
|
137 |
+
res_skip_channels = 2 * hidden_channels
|
138 |
+
else:
|
139 |
+
res_skip_channels = hidden_channels
|
140 |
+
|
141 |
+
res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
|
142 |
+
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
|
143 |
+
self.res_skip_layers.append(res_skip_layer)
|
144 |
+
|
145 |
+
def forward(self, x, x_mask, g=None, **kwargs):
|
146 |
+
output = torch.zeros_like(x)
|
147 |
+
n_channels_tensor = torch.IntTensor([self.hidden_channels])
|
148 |
+
|
149 |
+
if g is not None:
|
150 |
+
g = self.cond_layer(g)
|
151 |
+
|
152 |
+
for i in range(self.n_layers):
|
153 |
+
x_in = self.in_layers[i](x)
|
154 |
+
if g is not None:
|
155 |
+
cond_offset = i * 2 * self.hidden_channels
|
156 |
+
g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
|
157 |
+
else:
|
158 |
+
g_l = torch.zeros_like(x_in)
|
159 |
+
|
160 |
+
acts = commons.fused_add_tanh_sigmoid_multiply(
|
161 |
+
x_in,
|
162 |
+
g_l,
|
163 |
+
n_channels_tensor)
|
164 |
+
acts = self.drop(acts)
|
165 |
+
|
166 |
+
res_skip_acts = self.res_skip_layers[i](acts)
|
167 |
+
if i < self.n_layers - 1:
|
168 |
+
res_acts = res_skip_acts[:,:self.hidden_channels,:]
|
169 |
+
x = (x + res_acts) * x_mask
|
170 |
+
output = output + res_skip_acts[:,self.hidden_channels:,:]
|
171 |
+
else:
|
172 |
+
output = output + res_skip_acts
|
173 |
+
return output * x_mask
|
174 |
+
|
175 |
+
def remove_weight_norm(self):
|
176 |
+
if self.gin_channels != 0:
|
177 |
+
torch.nn.utils.remove_weight_norm(self.cond_layer)
|
178 |
+
for l in self.in_layers:
|
179 |
+
torch.nn.utils.remove_weight_norm(l)
|
180 |
+
for l in self.res_skip_layers:
|
181 |
+
torch.nn.utils.remove_weight_norm(l)
|
182 |
+
|
183 |
+
|
184 |
+
class ResBlock1(torch.nn.Module):
|
185 |
+
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
|
186 |
+
super(ResBlock1, self).__init__()
|
187 |
+
self.convs1 = nn.ModuleList([
|
188 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
189 |
+
padding=get_padding(kernel_size, dilation[0]))),
|
190 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
191 |
+
padding=get_padding(kernel_size, dilation[1]))),
|
192 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
|
193 |
+
padding=get_padding(kernel_size, dilation[2])))
|
194 |
+
])
|
195 |
+
self.convs1.apply(init_weights)
|
196 |
+
|
197 |
+
self.convs2 = nn.ModuleList([
|
198 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
199 |
+
padding=get_padding(kernel_size, 1))),
|
200 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
201 |
+
padding=get_padding(kernel_size, 1))),
|
202 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
203 |
+
padding=get_padding(kernel_size, 1)))
|
204 |
+
])
|
205 |
+
self.convs2.apply(init_weights)
|
206 |
+
|
207 |
+
def forward(self, x, x_mask=None):
|
208 |
+
for c1, c2 in zip(self.convs1, self.convs2):
|
209 |
+
xt = F.leaky_relu(x, LRELU_SLOPE)
|
210 |
+
if x_mask is not None:
|
211 |
+
xt = xt * x_mask
|
212 |
+
xt = c1(xt)
|
213 |
+
xt = F.leaky_relu(xt, LRELU_SLOPE)
|
214 |
+
if x_mask is not None:
|
215 |
+
xt = xt * x_mask
|
216 |
+
xt = c2(xt)
|
217 |
+
x = xt + x
|
218 |
+
if x_mask is not None:
|
219 |
+
x = x * x_mask
|
220 |
+
return x
|
221 |
+
|
222 |
+
def remove_weight_norm(self):
|
223 |
+
for l in self.convs1:
|
224 |
+
remove_weight_norm(l)
|
225 |
+
for l in self.convs2:
|
226 |
+
remove_weight_norm(l)
|
227 |
+
|
228 |
+
|
229 |
+
class ResBlock2(torch.nn.Module):
|
230 |
+
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
|
231 |
+
super(ResBlock2, self).__init__()
|
232 |
+
self.convs = nn.ModuleList([
|
233 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
234 |
+
padding=get_padding(kernel_size, dilation[0]))),
|
235 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
236 |
+
padding=get_padding(kernel_size, dilation[1])))
|
237 |
+
])
|
238 |
+
self.convs.apply(init_weights)
|
239 |
+
|
240 |
+
def forward(self, x, x_mask=None):
|
241 |
+
for c in self.convs:
|
242 |
+
xt = F.leaky_relu(x, LRELU_SLOPE)
|
243 |
+
if x_mask is not None:
|
244 |
+
xt = xt * x_mask
|
245 |
+
xt = c(xt)
|
246 |
+
x = xt + x
|
247 |
+
if x_mask is not None:
|
248 |
+
x = x * x_mask
|
249 |
+
return x
|
250 |
+
|
251 |
+
def remove_weight_norm(self):
|
252 |
+
for l in self.convs:
|
253 |
+
remove_weight_norm(l)
|
254 |
+
|
255 |
+
|
256 |
+
class Log(nn.Module):
|
257 |
+
def forward(self, x, x_mask, reverse=False, **kwargs):
|
258 |
+
if not reverse:
|
259 |
+
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
|
260 |
+
logdet = torch.sum(-y, [1, 2])
|
261 |
+
return y, logdet
|
262 |
+
else:
|
263 |
+
x = torch.exp(x) * x_mask
|
264 |
+
return x
|
265 |
+
|
266 |
+
|
267 |
+
class Flip(nn.Module):
|
268 |
+
def forward(self, x, *args, reverse=False, **kwargs):
|
269 |
+
x = torch.flip(x, [1])
|
270 |
+
if not reverse:
|
271 |
+
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
|
272 |
+
return x, logdet
|
273 |
+
else:
|
274 |
+
return x
|
275 |
+
|
276 |
+
|
277 |
+
class ElementwiseAffine(nn.Module):
|
278 |
+
def __init__(self, channels):
|
279 |
+
super().__init__()
|
280 |
+
self.channels = channels
|
281 |
+
self.m = nn.Parameter(torch.zeros(channels,1))
|
282 |
+
self.logs = nn.Parameter(torch.zeros(channels,1))
|
283 |
+
|
284 |
+
def forward(self, x, x_mask, reverse=False, **kwargs):
|
285 |
+
if not reverse:
|
286 |
+
y = self.m + torch.exp(self.logs) * x
|
287 |
+
y = y * x_mask
|
288 |
+
logdet = torch.sum(self.logs * x_mask, [1,2])
|
289 |
+
return y, logdet
|
290 |
+
else:
|
291 |
+
x = (x - self.m) * torch.exp(-self.logs) * x_mask
|
292 |
+
return x
|
293 |
+
|
294 |
+
|
295 |
+
class ResidualCouplingLayer(nn.Module):
|
296 |
+
def __init__(self,
|
297 |
+
channels,
|
298 |
+
hidden_channels,
|
299 |
+
kernel_size,
|
300 |
+
dilation_rate,
|
301 |
+
n_layers,
|
302 |
+
p_dropout=0,
|
303 |
+
gin_channels=0,
|
304 |
+
mean_only=False):
|
305 |
+
assert channels % 2 == 0, "channels should be divisible by 2"
|
306 |
+
super().__init__()
|
307 |
+
self.channels = channels
|
308 |
+
self.hidden_channels = hidden_channels
|
309 |
+
self.kernel_size = kernel_size
|
310 |
+
self.dilation_rate = dilation_rate
|
311 |
+
self.n_layers = n_layers
|
312 |
+
self.half_channels = channels // 2
|
313 |
+
self.mean_only = mean_only
|
314 |
+
|
315 |
+
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
|
316 |
+
self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
|
317 |
+
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
|
318 |
+
self.post.weight.data.zero_()
|
319 |
+
self.post.bias.data.zero_()
|
320 |
+
|
321 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
322 |
+
x0, x1 = torch.split(x, [self.half_channels]*2, 1)
|
323 |
+
h = self.pre(x0) * x_mask
|
324 |
+
h = self.enc(h, x_mask, g=g)
|
325 |
+
stats = self.post(h) * x_mask
|
326 |
+
if not self.mean_only:
|
327 |
+
m, logs = torch.split(stats, [self.half_channels]*2, 1)
|
328 |
+
else:
|
329 |
+
m = stats
|
330 |
+
logs = torch.zeros_like(m)
|
331 |
+
|
332 |
+
if not reverse:
|
333 |
+
x1 = m + x1 * torch.exp(logs) * x_mask
|
334 |
+
x = torch.cat([x0, x1], 1)
|
335 |
+
logdet = torch.sum(logs, [1,2])
|
336 |
+
return x, logdet
|
337 |
+
else:
|
338 |
+
x1 = (x1 - m) * torch.exp(-logs) * x_mask
|
339 |
+
x = torch.cat([x0, x1], 1)
|
340 |
+
return x
|
341 |
+
|
342 |
+
|
343 |
+
class ConvFlow(nn.Module):
|
344 |
+
def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
|
345 |
+
super().__init__()
|
346 |
+
self.in_channels = in_channels
|
347 |
+
self.filter_channels = filter_channels
|
348 |
+
self.kernel_size = kernel_size
|
349 |
+
self.n_layers = n_layers
|
350 |
+
self.num_bins = num_bins
|
351 |
+
self.tail_bound = tail_bound
|
352 |
+
self.half_channels = in_channels // 2
|
353 |
+
|
354 |
+
self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
|
355 |
+
self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
|
356 |
+
self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
|
357 |
+
self.proj.weight.data.zero_()
|
358 |
+
self.proj.bias.data.zero_()
|
359 |
+
|
360 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
361 |
+
x0, x1 = torch.split(x, [self.half_channels]*2, 1)
|
362 |
+
h = self.pre(x0)
|
363 |
+
h = self.convs(h, x_mask, g=g)
|
364 |
+
h = self.proj(h) * x_mask
|
365 |
+
|
366 |
+
b, c, t = x0.shape
|
367 |
+
h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
|
368 |
+
|
369 |
+
unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
|
370 |
+
unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
|
371 |
+
unnormalized_derivatives = h[..., 2 * self.num_bins:]
|
372 |
+
|
373 |
+
x1, logabsdet = piecewise_rational_quadratic_transform(x1,
|
374 |
+
unnormalized_widths,
|
375 |
+
unnormalized_heights,
|
376 |
+
unnormalized_derivatives,
|
377 |
+
inverse=reverse,
|
378 |
+
tails='linear',
|
379 |
+
tail_bound=self.tail_bound
|
380 |
+
)
|
381 |
+
|
382 |
+
x = torch.cat([x0, x1], 1) * x_mask
|
383 |
+
logdet = torch.sum(logabsdet * x_mask, [1,2])
|
384 |
+
if not reverse:
|
385 |
+
return x, logdet
|
386 |
+
else:
|
387 |
+
return x
|
monotonic_align/__init__.py
ADDED
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import numpy as np
|
2 |
+
import torch
|
3 |
+
from .monotonic_align.core import maximum_path_c
|
4 |
+
|
5 |
+
|
6 |
+
def maximum_path(neg_cent, mask):
|
7 |
+
""" Cython optimized version.
|
8 |
+
neg_cent: [b, t_t, t_s]
|
9 |
+
mask: [b, t_t, t_s]
|
10 |
+
"""
|
11 |
+
device = neg_cent.device
|
12 |
+
dtype = neg_cent.dtype
|
13 |
+
neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
|
14 |
+
path = np.zeros(neg_cent.shape, dtype=np.int32)
|
15 |
+
|
16 |
+
t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
|
17 |
+
t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
|
18 |
+
maximum_path_c(path, neg_cent, t_t_max, t_s_max)
|
19 |
+
return torch.from_numpy(path).to(device=device, dtype=dtype)
|
monotonic_align/__pycache__/__init__.cpython-37.pyc
ADDED
Binary file (765 Bytes). View file
|
|
monotonic_align/build/lib.win-amd64-cpython-37/monotonic_align/core.cp37-win_amd64.pyd
ADDED
Binary file (120 kB). View file
|
|
monotonic_align/build/temp.win-amd64-cpython-37/Release/core.cp37-win_amd64.exp
ADDED
Binary file (697 Bytes). View file
|
|
monotonic_align/build/temp.win-amd64-cpython-37/Release/core.cp37-win_amd64.lib
ADDED
Binary file (1.94 kB). View file
|
|
monotonic_align/build/temp.win-amd64-cpython-37/Release/core.obj
ADDED
Binary file (848 kB). View file
|
|
monotonic_align/core.c
ADDED
The diff for this file is too large to render.
See raw diff
|
|
monotonic_align/core.pyx
ADDED
@@ -0,0 +1,42 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
cimport cython
|
2 |
+
from cython.parallel import prange
|
3 |
+
|
4 |
+
|
5 |
+
@cython.boundscheck(False)
|
6 |
+
@cython.wraparound(False)
|
7 |
+
cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil:
|
8 |
+
cdef int x
|
9 |
+
cdef int y
|
10 |
+
cdef float v_prev
|
11 |
+
cdef float v_cur
|
12 |
+
cdef float tmp
|
13 |
+
cdef int index = t_x - 1
|
14 |
+
|
15 |
+
for y in range(t_y):
|
16 |
+
for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
|
17 |
+
if x == y:
|
18 |
+
v_cur = max_neg_val
|
19 |
+
else:
|
20 |
+
v_cur = value[y-1, x]
|
21 |
+
if x == 0:
|
22 |
+
if y == 0:
|
23 |
+
v_prev = 0.
|
24 |
+
else:
|
25 |
+
v_prev = max_neg_val
|
26 |
+
else:
|
27 |
+
v_prev = value[y-1, x-1]
|
28 |
+
value[y, x] += max(v_prev, v_cur)
|
29 |
+
|
30 |
+
for y in range(t_y - 1, -1, -1):
|
31 |
+
path[y, index] = 1
|
32 |
+
if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
|
33 |
+
index = index - 1
|
34 |
+
|
35 |
+
|
36 |
+
@cython.boundscheck(False)
|
37 |
+
@cython.wraparound(False)
|
38 |
+
cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil:
|
39 |
+
cdef int b = paths.shape[0]
|
40 |
+
cdef int i
|
41 |
+
for i in prange(b, nogil=True):
|
42 |
+
maximum_path_each(paths[i], values[i], t_ys[i], t_xs[i])
|
monotonic_align/monotonic_align/core.cp37-win_amd64.pyd
ADDED
Binary file (120 kB). View file
|
|
monotonic_align/setup.py
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from distutils.core import setup
|
2 |
+
from Cython.Build import cythonize
|
3 |
+
import numpy
|
4 |
+
|
5 |
+
setup(
|
6 |
+
name = 'monotonic_align',
|
7 |
+
ext_modules = cythonize("core.pyx"),
|
8 |
+
include_dirs=[numpy.get_include()]
|
9 |
+
)
|
pretrained_models/D_trilingual.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:920fb34bf3dc466e761996e787952a7bd53d4f6699561e9141bcbcf4f3f34092
|
3 |
+
size 187024867
|
pretrained_models/G_jp.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:4a53f4eb6bf8226b3fb4a3b31436235f697692f5566039ce3491b80af9a9567a
|
3 |
+
size 158962765
|
pretrained_models/G_trilingual.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:651381372bd0936fa90cdcfd75c8c4c231edd341a180466fc65dd3f75c5e4d70
|
3 |
+
size 159918157
|
requirements.txt
ADDED
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
numba
|
2 |
+
librosa
|
3 |
+
matplotlib
|
4 |
+
numpy
|
5 |
+
phonemizer
|
6 |
+
scipy
|
7 |
+
tensorboard
|
8 |
+
torch
|
9 |
+
torchvision
|
10 |
+
torchaudio
|
11 |
+
unidecode
|
12 |
+
pyopenjtalk>=0.3.0
|
13 |
+
jamo
|
14 |
+
pypinyin
|
15 |
+
ko_pron
|
16 |
+
jieba
|
17 |
+
cn2an
|
18 |
+
protobuf
|
19 |
+
inflect
|
20 |
+
eng_to_ipa
|
21 |
+
ko_pron
|
22 |
+
indic_transliteration
|
23 |
+
num_thai
|
24 |
+
opencc
|
25 |
+
onnx
|
26 |
+
onnxruntime
|
27 |
+
psutil
|
28 |
+
gradio
|
text/LICENSE
ADDED
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
Copyright (c) 2017 Keith Ito
|
2 |
+
|
3 |
+
Permission is hereby granted, free of charge, to any person obtaining a copy
|
4 |
+
of this software and associated documentation files (the "Software"), to deal
|
5 |
+
in the Software without restriction, including without limitation the rights
|
6 |
+
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
7 |
+
copies of the Software, and to permit persons to whom the Software is
|
8 |
+
furnished to do so, subject to the following conditions:
|
9 |
+
|
10 |
+
The above copyright notice and this permission notice shall be included in
|
11 |
+
all copies or substantial portions of the Software.
|
12 |
+
|
13 |
+
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
14 |
+
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
15 |
+
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
16 |
+
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
17 |
+
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
18 |
+
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
|
19 |
+
THE SOFTWARE.
|
text/__init__.py
ADDED
@@ -0,0 +1,32 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
""" from https://github.com/keithito/tacotron """
|
2 |
+
from text import cleaners
|
3 |
+
|
4 |
+
|
5 |
+
def text_to_sequence(text, symbols, cleaner_names):
|
6 |
+
'''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
|
7 |
+
Args:
|
8 |
+
text: string to convert to a sequence
|
9 |
+
cleaner_names: names of the cleaner functions to run the text through
|
10 |
+
Returns:
|
11 |
+
List of integers corresponding to the symbols in the text
|
12 |
+
'''
|
13 |
+
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
|
14 |
+
|
15 |
+
sequence = []
|
16 |
+
|
17 |
+
clean_text = _clean_text(text, cleaner_names)
|
18 |
+
for symbol in clean_text:
|
19 |
+
if symbol not in _symbol_to_id.keys():
|
20 |
+
continue
|
21 |
+
symbol_id = _symbol_to_id[symbol]
|
22 |
+
sequence += [symbol_id]
|
23 |
+
return sequence
|
24 |
+
|
25 |
+
|
26 |
+
def _clean_text(text, cleaner_names):
|
27 |
+
for name in cleaner_names:
|
28 |
+
cleaner = getattr(cleaners, name)
|
29 |
+
if not cleaner:
|
30 |
+
raise Exception('Unknown cleaner: %s' % name)
|
31 |
+
text = cleaner(text)
|
32 |
+
return text
|
text/__pycache__/__init__.cpython-37.pyc
ADDED
Binary file (1.19 kB). View file
|
|
text/__pycache__/cleaners.cpython-37.pyc
ADDED
Binary file (7.74 kB). View file
|
|
text/__pycache__/english.cpython-37.pyc
ADDED
Binary file (4.93 kB). View file
|
|
text/__pycache__/japanese.cpython-37.pyc
ADDED
Binary file (4.61 kB). View file
|
|
text/__pycache__/korean.cpython-37.pyc
ADDED
Binary file (5.75 kB). View file
|
|