feat: added model
Browse files- config.json +53 -0
- config.py +37 -0
- dino_huvits.py +54 -0
- model.safetensors +3 -0
- module/__init__.py +561 -0
- module/resblocks.py +150 -0
- src/__init__.py +4 -0
- src/attentions.py +219 -0
- src/campplus.py +407 -0
- src/flow.py +47 -0
- src/hifi_gan.py +86 -0
- src/hubert_posterior.py +59 -0
- src/inference_hubert/__init__.py +1 -0
- src/inference_hubert/fairseq_modules.py +52 -0
- src/inference_hubert/hubert.py +281 -0
- src/vae_memory_bank.py +40 -0
- src/wespeaker_campplus/__init__.py +4 -0
- src/wespeaker_campplus/fbank_feature_extractor.py +38 -0
- src/wespeaker_campplus/pooling_layers.py +287 -0
- tools/commons.py +181 -0
- tools/transforms.py +209 -0
config.json
ADDED
@@ -0,0 +1,53 @@
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{
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"architectures": [
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"DinoHuVits"
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],
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"auto_map": {
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"AutoModel": "config.DinoHuVitsConfig"
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},
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"gin_channels": 256,
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"hidden_channels": 192,
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"hubert_downsample_channels": 192,
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"hubert_feature_channels": 768,
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"hubert_output_layer": 11,
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"inter_channels": 192,
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"model_type": "DINO-HuVITS",
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"resblock": "1",
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"resblock_dilation_sizes": [
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[
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],
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],
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1,
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5
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]
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],
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"resblock_kernel_sizes": [
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3,
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7,
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11
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],
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"torch_dtype": "float32",
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"transformers_version": "4.38.2",
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"upsample_initial_channel": 512,
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"upsample_kernel_sizes": [
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20,
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16,
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4,
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4
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],
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"upsample_rates": [
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10,
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8,
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2,
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2
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]
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}
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config.py
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from transformers import PretrainedConfig
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from typing import List
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class DinoHuVitsConfig(PretrainedConfig):
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model_type = "DinoHuVits"
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def __init__(
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self,
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inter_channels=192,
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hidden_channels=192,
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resblock="1",
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resblock_kernel_sizes=[3, 7, 11],
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resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
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upsample_rates=[10, 8, 2, 2],
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upsample_initial_channel=512,
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upsample_kernel_sizes=[20, 16, 4, 4],
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gin_channels=256,
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hubert_feature_channels=768,
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hubert_downsample_channels=192,
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hubert_output_layer=11,
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**kwargs
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):
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self.inter_channels = inter_channels
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self.hidden_channels = hidden_channels
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self.resblock = resblock
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self.resblock_kernel_sizes = resblock_kernel_sizes
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self.resblock_dilation_sizes = resblock_dilation_sizes
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self.upsample_rates = upsample_rates
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self.upsample_initial_channel = upsample_initial_channel
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self.upsample_kernel_sizes = upsample_kernel_sizes
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self.gin_channels = gin_channels
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self.hubert_feature_channels = hubert_feature_channels
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self.hubert_downsample_channels = hubert_downsample_channels
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self.hubert_output_layer = hubert_output_layer
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super().__init__(**kwargs)
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dino_huvits.py
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import torch
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from transformers import PreTrainedModel
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from config import DinoHuVitsConfig
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from src import CAMPPlus, Flow, HiFiGAN, PosteriorHubert
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class DinoHuVits(PreTrainedModel):
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config_class = DinoHuVitsConfig
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def __init__(self, config: DinoHuVitsConfig):
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super().__init__(config)
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self.enc_r = CAMPPlus(embed_dim=config.gin_channels, pooling_func="TSTP")
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self.enc_q = PosteriorHubert(
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out_channels=config.inter_channels,
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feature_channels=config.hubert_feature_channels,
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downsample_channels=config.hubert_downsample_channels,
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output_layer=config.hubert_output_layer,
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)
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self.flow = Flow(
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channels=config.inter_channels,
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hidden_channels=config.hidden_channels,
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kernel_size=5,
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dilation_rate=1,
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n_layers=4,
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gin_channels=config.gin_channels,
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)
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self.dec = HiFiGAN(
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initial_channel=config.inter_channels,
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resblock=config.resblock,
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resblock_kernel_sizes=config.resblock_kernel_sizes,
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resblock_dilation_sizes=config.resblock_dilation_sizes,
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upsample_rates=config.upsample_rates,
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upsample_initial_channel=config.upsample_initial_channel,
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upsample_kernel_sizes=config.upsample_kernel_sizes,
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gin_channels=config.gin_channels,
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)
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def forward(
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self, content: torch.Tensor, lengths: torch.Tensor, reference: torch.Tensor
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):
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g_src = self.__get_style_embedding(content)
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g_tgt = self.__get_style_embedding(reference)
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z, _, _, y_mask = self.enc_q(content, lengths, g=g_src)
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z_p = self.flow(z, y_mask, g=g_src)
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z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
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o_hat = self.dec(z_hat * y_mask, g=g_tgt)
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return o_hat, y_mask
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def __get_style_embedding(self, wavefrom: torch.Tensor):
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g = self.enc_r(wavefrom) # [b, h, 1]
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g = torch.nn.functional.normalize(g, dim=1)
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return g.unsqueeze(-1)
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model.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:ecb11979e09bbd727f5fac56234beb2f25ee5d7ab572eebecd5cf061f538eef7
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size 513863296
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module/__init__.py
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1 |
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import copy
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2 |
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import math
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4 |
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import numpy as np
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+
import scipy
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6 |
+
import torch
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7 |
+
from torch import nn
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8 |
+
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
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9 |
+
from torch.nn import functional as F
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10 |
+
from torch.nn.utils import remove_weight_norm, weight_norm
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11 |
+
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12 |
+
from tools import commons
|
13 |
+
from tools.commons import get_padding, init_weights
|
14 |
+
from tools.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).contiguous()
|
31 |
+
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
|
32 |
+
return x.transpose(1, -1).contiguous()
|
33 |
+
|
34 |
+
|
35 |
+
class ConvReluNorm(nn.Module):
|
36 |
+
def __init__(
|
37 |
+
self,
|
38 |
+
in_channels,
|
39 |
+
hidden_channels,
|
40 |
+
out_channels,
|
41 |
+
kernel_size,
|
42 |
+
n_layers,
|
43 |
+
p_dropout,
|
44 |
+
):
|
45 |
+
super().__init__()
|
46 |
+
self.in_channels = in_channels
|
47 |
+
self.hidden_channels = hidden_channels
|
48 |
+
self.out_channels = out_channels
|
49 |
+
self.kernel_size = kernel_size
|
50 |
+
self.n_layers = n_layers
|
51 |
+
self.p_dropout = p_dropout
|
52 |
+
assert n_layers > 1, "Number of layers should be larger than 0."
|
53 |
+
|
54 |
+
self.conv_layers = nn.ModuleList()
|
55 |
+
self.norm_layers = nn.ModuleList()
|
56 |
+
self.conv_layers.append(
|
57 |
+
nn.Conv1d(
|
58 |
+
in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
|
59 |
+
)
|
60 |
+
)
|
61 |
+
self.norm_layers.append(LayerNorm(hidden_channels))
|
62 |
+
self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
|
63 |
+
for _ in range(n_layers - 1):
|
64 |
+
self.conv_layers.append(
|
65 |
+
nn.Conv1d(
|
66 |
+
hidden_channels,
|
67 |
+
hidden_channels,
|
68 |
+
kernel_size,
|
69 |
+
padding=kernel_size // 2,
|
70 |
+
)
|
71 |
+
)
|
72 |
+
self.norm_layers.append(LayerNorm(hidden_channels))
|
73 |
+
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
74 |
+
self.proj.weight.data.zero_()
|
75 |
+
self.proj.bias.data.zero_()
|
76 |
+
|
77 |
+
def forward(self, x, x_mask):
|
78 |
+
x_org = x
|
79 |
+
for i in range(self.n_layers):
|
80 |
+
x = self.conv_layers[i](x * x_mask)
|
81 |
+
x = self.norm_layers[i](x)
|
82 |
+
x = self.relu_drop(x)
|
83 |
+
x = x_org + self.proj(x)
|
84 |
+
return x * x_mask
|
85 |
+
|
86 |
+
|
87 |
+
class DDSConv(nn.Module):
|
88 |
+
"""
|
89 |
+
Dialted and Depth-Separable Convolution
|
90 |
+
"""
|
91 |
+
|
92 |
+
def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
|
93 |
+
super().__init__()
|
94 |
+
self.channels = channels
|
95 |
+
self.kernel_size = kernel_size
|
96 |
+
self.n_layers = n_layers
|
97 |
+
self.p_dropout = p_dropout
|
98 |
+
|
99 |
+
self.drop = nn.Dropout(p_dropout)
|
100 |
+
self.convs_sep = nn.ModuleList()
|
101 |
+
self.convs_1x1 = nn.ModuleList()
|
102 |
+
self.norms_2 = nn.ModuleList()
|
103 |
+
for i in range(n_layers):
|
104 |
+
dilation = kernel_size**i
|
105 |
+
|
106 |
+
padding = (kernel_size * dilation - dilation) // 2
|
107 |
+
conv = nn.Conv1d(
|
108 |
+
channels,
|
109 |
+
channels,
|
110 |
+
kernel_size,
|
111 |
+
groups=channels,
|
112 |
+
dilation=dilation,
|
113 |
+
padding=padding,
|
114 |
+
)
|
115 |
+
self.convs_sep.append(conv)
|
116 |
+
self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
|
117 |
+
self.norms_2.append(LayerNorm(channels))
|
118 |
+
|
119 |
+
def forward(self, x, x_mask, g=None):
|
120 |
+
if g is not None:
|
121 |
+
x = x + g
|
122 |
+
for i in range(self.n_layers):
|
123 |
+
y = self.convs_sep[i](x * x_mask)
|
124 |
+
y = F.gelu(y)
|
125 |
+
y = self.convs_1x1[i](y)
|
126 |
+
y = self.norms_2[i](y)
|
127 |
+
y = F.gelu(y)
|
128 |
+
y = self.drop(y)
|
129 |
+
x = x + y
|
130 |
+
return x * x_mask
|
131 |
+
|
132 |
+
|
133 |
+
class WN(torch.nn.Module):
|
134 |
+
def __init__(
|
135 |
+
self,
|
136 |
+
hidden_channels,
|
137 |
+
kernel_size,
|
138 |
+
dilation_rate,
|
139 |
+
n_layers,
|
140 |
+
gin_channels=0,
|
141 |
+
p_dropout=0,
|
142 |
+
):
|
143 |
+
super(WN, self).__init__()
|
144 |
+
assert kernel_size % 2 == 1
|
145 |
+
self.hidden_channels = hidden_channels
|
146 |
+
self.kernel_size = (kernel_size,)
|
147 |
+
self.dilation_rate = dilation_rate
|
148 |
+
self.n_layers = n_layers
|
149 |
+
self.gin_channels = gin_channels
|
150 |
+
self.p_dropout = p_dropout
|
151 |
+
|
152 |
+
self.in_layers = torch.nn.ModuleList()
|
153 |
+
self.res_skip_layers = torch.nn.ModuleList()
|
154 |
+
self.drop = nn.Dropout(p_dropout)
|
155 |
+
|
156 |
+
if gin_channels != 0:
|
157 |
+
cond_layer = torch.nn.Conv1d(
|
158 |
+
gin_channels, 2 * hidden_channels * n_layers, 1
|
159 |
+
)
|
160 |
+
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
|
161 |
+
|
162 |
+
for i in range(n_layers):
|
163 |
+
dilation = dilation_rate**i
|
164 |
+
padding = int((kernel_size * dilation - dilation) / 2)
|
165 |
+
|
166 |
+
in_layer = Conv1d(
|
167 |
+
hidden_channels,
|
168 |
+
2 * hidden_channels,
|
169 |
+
kernel_size,
|
170 |
+
padding=padding,
|
171 |
+
dilation=dilation,
|
172 |
+
)
|
173 |
+
in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
|
174 |
+
self.in_layers.append(in_layer)
|
175 |
+
|
176 |
+
# last one is not necessary
|
177 |
+
if i < n_layers - 1:
|
178 |
+
res_skip_channels = 2 * hidden_channels
|
179 |
+
else:
|
180 |
+
res_skip_channels = hidden_channels
|
181 |
+
|
182 |
+
res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
|
183 |
+
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
|
184 |
+
self.res_skip_layers.append(res_skip_layer)
|
185 |
+
|
186 |
+
def forward(self, x, x_mask, g=None, **kwargs):
|
187 |
+
output = torch.zeros_like(x)
|
188 |
+
n_channels_tensor = torch.IntTensor([self.hidden_channels])
|
189 |
+
|
190 |
+
if g is not None:
|
191 |
+
g = self.cond_layer(g)
|
192 |
+
|
193 |
+
for i in range(self.n_layers):
|
194 |
+
x_in = self.in_layers[i](x)
|
195 |
+
if g is not None:
|
196 |
+
cond_offset = i * 2 * self.hidden_channels
|
197 |
+
g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
|
198 |
+
else:
|
199 |
+
g_l = torch.zeros_like(x_in)
|
200 |
+
|
201 |
+
acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
|
202 |
+
acts = self.drop(acts)
|
203 |
+
|
204 |
+
res_skip_acts = self.res_skip_layers[i](acts)
|
205 |
+
if i < self.n_layers - 1:
|
206 |
+
res_acts = res_skip_acts[:, : self.hidden_channels, :]
|
207 |
+
x = (x + res_acts) * x_mask
|
208 |
+
output = output + res_skip_acts[:, self.hidden_channels :, :]
|
209 |
+
else:
|
210 |
+
output = output + res_skip_acts
|
211 |
+
return output * x_mask
|
212 |
+
|
213 |
+
def remove_weight_norm(self):
|
214 |
+
if self.gin_channels != 0:
|
215 |
+
torch.nn.utils.remove_weight_norm(self.cond_layer)
|
216 |
+
for l in self.in_layers:
|
217 |
+
torch.nn.utils.remove_weight_norm(l)
|
218 |
+
for l in self.res_skip_layers:
|
219 |
+
torch.nn.utils.remove_weight_norm(l)
|
220 |
+
|
221 |
+
|
222 |
+
class Log(nn.Module):
|
223 |
+
def forward(self, x, x_mask, reverse=False, **kwargs):
|
224 |
+
if not reverse:
|
225 |
+
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
|
226 |
+
logdet = torch.sum(-y, [1, 2])
|
227 |
+
return y, logdet
|
228 |
+
else:
|
229 |
+
x = torch.exp(x) * x_mask
|
230 |
+
return x
|
231 |
+
|
232 |
+
|
233 |
+
class Flip(nn.Module):
|
234 |
+
def forward(self, x, *args, reverse=False, **kwargs):
|
235 |
+
x = torch.flip(x, [1])
|
236 |
+
if not reverse:
|
237 |
+
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
|
238 |
+
return x, logdet
|
239 |
+
else:
|
240 |
+
return x
|
241 |
+
|
242 |
+
|
243 |
+
class ElementwiseAffine(nn.Module):
|
244 |
+
def __init__(self, channels):
|
245 |
+
super().__init__()
|
246 |
+
self.channels = channels
|
247 |
+
self.m = nn.Parameter(torch.zeros(channels, 1))
|
248 |
+
self.logs = nn.Parameter(torch.zeros(channels, 1))
|
249 |
+
|
250 |
+
def forward(self, x, x_mask, reverse=False, **kwargs):
|
251 |
+
if not reverse:
|
252 |
+
y = self.m + torch.exp(self.logs) * x
|
253 |
+
y = y * x_mask
|
254 |
+
logdet = torch.sum(self.logs * x_mask, [1, 2])
|
255 |
+
return y, logdet
|
256 |
+
else:
|
257 |
+
x = (x - self.m) * torch.exp(-self.logs) * x_mask
|
258 |
+
return x
|
259 |
+
|
260 |
+
|
261 |
+
class ResidualCouplingLayer(nn.Module):
|
262 |
+
def __init__(
|
263 |
+
self,
|
264 |
+
channels,
|
265 |
+
hidden_channels,
|
266 |
+
kernel_size,
|
267 |
+
dilation_rate,
|
268 |
+
n_layers,
|
269 |
+
p_dropout=0,
|
270 |
+
gin_channels=0,
|
271 |
+
mean_only=False,
|
272 |
+
):
|
273 |
+
assert channels % 2 == 0, "channels should be divisible by 2"
|
274 |
+
super().__init__()
|
275 |
+
self.channels = channels
|
276 |
+
self.hidden_channels = hidden_channels
|
277 |
+
self.kernel_size = kernel_size
|
278 |
+
self.dilation_rate = dilation_rate
|
279 |
+
self.n_layers = n_layers
|
280 |
+
self.half_channels = channels // 2
|
281 |
+
self.mean_only = mean_only
|
282 |
+
|
283 |
+
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
|
284 |
+
self.enc = WN(
|
285 |
+
hidden_channels,
|
286 |
+
kernel_size,
|
287 |
+
dilation_rate,
|
288 |
+
n_layers,
|
289 |
+
p_dropout=p_dropout,
|
290 |
+
gin_channels=gin_channels,
|
291 |
+
)
|
292 |
+
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
|
293 |
+
self.post.weight.data.zero_()
|
294 |
+
self.post.bias.data.zero_()
|
295 |
+
|
296 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
297 |
+
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
|
298 |
+
h = self.pre(x0) * x_mask
|
299 |
+
h = self.enc(h, x_mask, g=g)
|
300 |
+
stats = self.post(h) * x_mask
|
301 |
+
if not self.mean_only:
|
302 |
+
m, logs = torch.split(stats, [self.half_channels] * 2, 1)
|
303 |
+
else:
|
304 |
+
m = stats
|
305 |
+
logs = torch.zeros_like(m)
|
306 |
+
|
307 |
+
if not reverse:
|
308 |
+
x1 = m + x1 * torch.exp(logs) * x_mask
|
309 |
+
x = torch.cat([x0, x1], 1)
|
310 |
+
logdet = torch.sum(logs, [1, 2])
|
311 |
+
return x, logdet
|
312 |
+
else:
|
313 |
+
x1 = (x1 - m) * torch.exp(-logs) * x_mask
|
314 |
+
x = torch.cat([x0, x1], 1)
|
315 |
+
return x
|
316 |
+
|
317 |
+
|
318 |
+
class ConvFlow(nn.Module):
|
319 |
+
def __init__(
|
320 |
+
self,
|
321 |
+
in_channels,
|
322 |
+
filter_channels,
|
323 |
+
kernel_size,
|
324 |
+
n_layers,
|
325 |
+
num_bins=10,
|
326 |
+
tail_bound=5.0,
|
327 |
+
):
|
328 |
+
super().__init__()
|
329 |
+
self.in_channels = in_channels
|
330 |
+
self.filter_channels = filter_channels
|
331 |
+
self.kernel_size = kernel_size
|
332 |
+
self.n_layers = n_layers
|
333 |
+
self.num_bins = num_bins
|
334 |
+
self.tail_bound = tail_bound
|
335 |
+
self.half_channels = in_channels // 2
|
336 |
+
|
337 |
+
self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
|
338 |
+
self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
|
339 |
+
self.proj = nn.Conv1d(
|
340 |
+
filter_channels, self.half_channels * (num_bins * 3 - 1), 1
|
341 |
+
)
|
342 |
+
self.proj.weight.data.zero_()
|
343 |
+
self.proj.bias.data.zero_()
|
344 |
+
|
345 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
346 |
+
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
|
347 |
+
|
348 |
+
h = self.pre(x0)
|
349 |
+
h = self.convs(h, x_mask, g=g)
|
350 |
+
h = self.proj(h) * x_mask
|
351 |
+
|
352 |
+
b, c, t = x0.shape
|
353 |
+
h = (
|
354 |
+
h.reshape(b, c, -1, t).permute(0, 1, 3, 2).contiguous()
|
355 |
+
) # [b, cx?, t] -> [b, c, t, ?]
|
356 |
+
|
357 |
+
unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
|
358 |
+
unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
|
359 |
+
self.filter_channels
|
360 |
+
)
|
361 |
+
unnormalized_derivatives = h[..., 2 * self.num_bins :]
|
362 |
+
|
363 |
+
x1, logabsdet = piecewise_rational_quadratic_transform(
|
364 |
+
x1,
|
365 |
+
unnormalized_widths,
|
366 |
+
unnormalized_heights,
|
367 |
+
unnormalized_derivatives,
|
368 |
+
inverse=reverse,
|
369 |
+
tails="linear",
|
370 |
+
tail_bound=self.tail_bound,
|
371 |
+
)
|
372 |
+
|
373 |
+
x = torch.cat([x0, x1], 1) * x_mask
|
374 |
+
logdet = torch.sum(logabsdet * x_mask, [1, 2])
|
375 |
+
if not reverse:
|
376 |
+
return x, logdet
|
377 |
+
else:
|
378 |
+
return x
|
379 |
+
|
380 |
+
|
381 |
+
class LinearNorm(nn.Module):
|
382 |
+
def __init__(
|
383 |
+
self,
|
384 |
+
in_channels,
|
385 |
+
out_channels,
|
386 |
+
bias=True,
|
387 |
+
spectral_norm=False,
|
388 |
+
):
|
389 |
+
super(LinearNorm, self).__init__()
|
390 |
+
self.fc = nn.Linear(in_channels, out_channels, bias)
|
391 |
+
|
392 |
+
if spectral_norm:
|
393 |
+
self.fc = nn.utils.spectral_norm(self.fc)
|
394 |
+
|
395 |
+
def forward(self, input):
|
396 |
+
out = self.fc(input)
|
397 |
+
return out
|
398 |
+
|
399 |
+
|
400 |
+
class Mish(nn.Module):
|
401 |
+
def __init__(self):
|
402 |
+
super(Mish, self).__init__()
|
403 |
+
|
404 |
+
def forward(self, x):
|
405 |
+
return x * torch.tanh(F.softplus(x))
|
406 |
+
|
407 |
+
|
408 |
+
class LinearNorm(nn.Module):
|
409 |
+
def __init__(
|
410 |
+
self,
|
411 |
+
in_channels,
|
412 |
+
out_channels,
|
413 |
+
bias=True,
|
414 |
+
spectral_norm=False,
|
415 |
+
):
|
416 |
+
super(LinearNorm, self).__init__()
|
417 |
+
self.fc = nn.Linear(in_channels, out_channels, bias)
|
418 |
+
|
419 |
+
if spectral_norm:
|
420 |
+
self.fc = nn.utils.spectral_norm(self.fc)
|
421 |
+
|
422 |
+
def forward(self, input):
|
423 |
+
out = self.fc(input)
|
424 |
+
return out
|
425 |
+
|
426 |
+
|
427 |
+
class ConvNorm(nn.Module):
|
428 |
+
def __init__(
|
429 |
+
self,
|
430 |
+
in_channels,
|
431 |
+
out_channels,
|
432 |
+
kernel_size=1,
|
433 |
+
stride=1,
|
434 |
+
padding=None,
|
435 |
+
dilation=1,
|
436 |
+
bias=True,
|
437 |
+
spectral_norm=False,
|
438 |
+
):
|
439 |
+
super(ConvNorm, self).__init__()
|
440 |
+
|
441 |
+
if padding is None:
|
442 |
+
assert kernel_size % 2 == 1
|
443 |
+
padding = int(dilation * (kernel_size - 1) / 2)
|
444 |
+
|
445 |
+
self.conv = torch.nn.Conv1d(
|
446 |
+
in_channels,
|
447 |
+
out_channels,
|
448 |
+
kernel_size=kernel_size,
|
449 |
+
stride=stride,
|
450 |
+
padding=padding,
|
451 |
+
dilation=dilation,
|
452 |
+
bias=bias,
|
453 |
+
)
|
454 |
+
|
455 |
+
if spectral_norm:
|
456 |
+
self.conv = nn.utils.spectral_norm(self.conv)
|
457 |
+
|
458 |
+
def forward(self, input):
|
459 |
+
out = self.conv(input)
|
460 |
+
return out
|
461 |
+
|
462 |
+
|
463 |
+
class MultiHeadAttention(nn.Module):
|
464 |
+
"""Multi-Head Attention module"""
|
465 |
+
|
466 |
+
def __init__(self, n_head, d_model, d_k, d_v, dropout=0.0, spectral_norm=False):
|
467 |
+
super().__init__()
|
468 |
+
|
469 |
+
self.n_head = n_head
|
470 |
+
self.d_k = d_k
|
471 |
+
self.d_v = d_v
|
472 |
+
|
473 |
+
self.w_qs = nn.Linear(d_model, n_head * d_k)
|
474 |
+
self.w_ks = nn.Linear(d_model, n_head * d_k)
|
475 |
+
self.w_vs = nn.Linear(d_model, n_head * d_v)
|
476 |
+
|
477 |
+
self.attention = ScaledDotProductAttention(
|
478 |
+
temperature=np.power(d_model, 0.5), dropout=dropout
|
479 |
+
)
|
480 |
+
|
481 |
+
self.fc = nn.Linear(n_head * d_v, d_model)
|
482 |
+
self.dropout = nn.Dropout(dropout)
|
483 |
+
|
484 |
+
if spectral_norm:
|
485 |
+
self.w_qs = nn.utils.spectral_norm(self.w_qs)
|
486 |
+
self.w_ks = nn.utils.spectral_norm(self.w_ks)
|
487 |
+
self.w_vs = nn.utils.spectral_norm(self.w_vs)
|
488 |
+
self.fc = nn.utils.spectral_norm(self.fc)
|
489 |
+
|
490 |
+
def forward(self, x, mask=None):
|
491 |
+
d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
|
492 |
+
sz_b, len_x, _ = x.size()
|
493 |
+
|
494 |
+
residual = x
|
495 |
+
|
496 |
+
q = self.w_qs(x).view(sz_b, len_x, n_head, d_k)
|
497 |
+
k = self.w_ks(x).view(sz_b, len_x, n_head, d_k)
|
498 |
+
v = self.w_vs(x).view(sz_b, len_x, n_head, d_v)
|
499 |
+
q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_k) # (n*b) x lq x dk
|
500 |
+
k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_k) # (n*b) x lk x dk
|
501 |
+
v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_v) # (n*b) x lv x dv
|
502 |
+
|
503 |
+
if mask is not None:
|
504 |
+
slf_mask = mask.repeat(n_head, 1, 1) # (n*b) x .. x ..
|
505 |
+
else:
|
506 |
+
slf_mask = None
|
507 |
+
output, attn = self.attention(q, k, v, mask=slf_mask)
|
508 |
+
|
509 |
+
output = output.view(n_head, sz_b, len_x, d_v)
|
510 |
+
output = (
|
511 |
+
output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_x, -1)
|
512 |
+
) # b x lq x (n*dv)
|
513 |
+
|
514 |
+
output = self.fc(output)
|
515 |
+
|
516 |
+
output = self.dropout(output) + residual
|
517 |
+
return output, attn
|
518 |
+
|
519 |
+
|
520 |
+
class ScaledDotProductAttention(nn.Module):
|
521 |
+
"""Scaled Dot-Product Attention"""
|
522 |
+
|
523 |
+
def __init__(self, temperature, dropout):
|
524 |
+
super().__init__()
|
525 |
+
self.temperature = temperature
|
526 |
+
self.softmax = nn.Softmax(dim=2)
|
527 |
+
self.dropout = nn.Dropout(dropout)
|
528 |
+
|
529 |
+
def forward(self, q, k, v, mask=None):
|
530 |
+
attn = torch.bmm(q, k.transpose(1, 2).contiguous())
|
531 |
+
attn = attn / self.temperature
|
532 |
+
|
533 |
+
if mask is not None:
|
534 |
+
attn = attn.masked_fill(mask, -np.inf)
|
535 |
+
|
536 |
+
attn = self.softmax(attn)
|
537 |
+
p_attn = self.dropout(attn)
|
538 |
+
|
539 |
+
output = torch.bmm(p_attn, v)
|
540 |
+
return output, attn
|
541 |
+
|
542 |
+
|
543 |
+
class Conv1dGLU(nn.Module):
|
544 |
+
"""
|
545 |
+
Conv1d + GLU(Gated Linear Unit) with residual connection.
|
546 |
+
For GLU refer to https://arxiv.org/abs/1612.08083 paper.
|
547 |
+
"""
|
548 |
+
|
549 |
+
def __init__(self, in_channels, out_channels, kernel_size, dropout):
|
550 |
+
super(Conv1dGLU, self).__init__()
|
551 |
+
self.out_channels = out_channels
|
552 |
+
self.conv1 = ConvNorm(in_channels, 2 * out_channels, kernel_size=kernel_size)
|
553 |
+
self.dropout = nn.Dropout(dropout)
|
554 |
+
|
555 |
+
def forward(self, x):
|
556 |
+
residual = x
|
557 |
+
x = self.conv1(x)
|
558 |
+
x1, x2 = torch.split(x, split_size_or_sections=self.out_channels, dim=1)
|
559 |
+
x = x1 * torch.sigmoid(x2)
|
560 |
+
x = residual + self.dropout(x)
|
561 |
+
return x
|
module/resblocks.py
ADDED
@@ -0,0 +1,150 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.nn as nn
|
3 |
+
import torch.nn.functional as F
|
4 |
+
from torch.nn import Conv1d
|
5 |
+
from torch.nn.utils import remove_weight_norm, weight_norm
|
6 |
+
|
7 |
+
from . import LRELU_SLOPE
|
8 |
+
from tools.commons import get_padding, init_weights
|
9 |
+
|
10 |
+
|
11 |
+
class ResBlock1(torch.nn.Module):
|
12 |
+
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
|
13 |
+
super(ResBlock1, self).__init__()
|
14 |
+
self.convs1 = nn.ModuleList(
|
15 |
+
[
|
16 |
+
weight_norm(
|
17 |
+
Conv1d(
|
18 |
+
channels,
|
19 |
+
channels,
|
20 |
+
kernel_size,
|
21 |
+
1,
|
22 |
+
dilation=dilation[0],
|
23 |
+
padding=get_padding(kernel_size, dilation[0]),
|
24 |
+
)
|
25 |
+
),
|
26 |
+
weight_norm(
|
27 |
+
Conv1d(
|
28 |
+
channels,
|
29 |
+
channels,
|
30 |
+
kernel_size,
|
31 |
+
1,
|
32 |
+
dilation=dilation[1],
|
33 |
+
padding=get_padding(kernel_size, dilation[1]),
|
34 |
+
)
|
35 |
+
),
|
36 |
+
weight_norm(
|
37 |
+
Conv1d(
|
38 |
+
channels,
|
39 |
+
channels,
|
40 |
+
kernel_size,
|
41 |
+
1,
|
42 |
+
dilation=dilation[2],
|
43 |
+
padding=get_padding(kernel_size, dilation[2]),
|
44 |
+
)
|
45 |
+
),
|
46 |
+
]
|
47 |
+
)
|
48 |
+
self.convs1.apply(init_weights)
|
49 |
+
|
50 |
+
self.convs2 = nn.ModuleList(
|
51 |
+
[
|
52 |
+
weight_norm(
|
53 |
+
Conv1d(
|
54 |
+
channels,
|
55 |
+
channels,
|
56 |
+
kernel_size,
|
57 |
+
1,
|
58 |
+
dilation=1,
|
59 |
+
padding=get_padding(kernel_size, 1),
|
60 |
+
)
|
61 |
+
),
|
62 |
+
weight_norm(
|
63 |
+
Conv1d(
|
64 |
+
channels,
|
65 |
+
channels,
|
66 |
+
kernel_size,
|
67 |
+
1,
|
68 |
+
dilation=1,
|
69 |
+
padding=get_padding(kernel_size, 1),
|
70 |
+
)
|
71 |
+
),
|
72 |
+
weight_norm(
|
73 |
+
Conv1d(
|
74 |
+
channels,
|
75 |
+
channels,
|
76 |
+
kernel_size,
|
77 |
+
1,
|
78 |
+
dilation=1,
|
79 |
+
padding=get_padding(kernel_size, 1),
|
80 |
+
)
|
81 |
+
),
|
82 |
+
]
|
83 |
+
)
|
84 |
+
self.convs2.apply(init_weights)
|
85 |
+
|
86 |
+
def forward(self, x, x_mask=None):
|
87 |
+
for c1, c2 in zip(self.convs1, self.convs2):
|
88 |
+
xt = F.leaky_relu(x, LRELU_SLOPE)
|
89 |
+
if x_mask is not None:
|
90 |
+
xt = xt * x_mask
|
91 |
+
xt = c1(xt)
|
92 |
+
xt = F.leaky_relu(xt, LRELU_SLOPE)
|
93 |
+
if x_mask is not None:
|
94 |
+
xt = xt * x_mask
|
95 |
+
xt = c2(xt)
|
96 |
+
x = xt + x
|
97 |
+
if x_mask is not None:
|
98 |
+
x = x * x_mask
|
99 |
+
return x
|
100 |
+
|
101 |
+
def remove_weight_norm(self):
|
102 |
+
for l in self.convs1:
|
103 |
+
remove_weight_norm(l)
|
104 |
+
for l in self.convs2:
|
105 |
+
remove_weight_norm(l)
|
106 |
+
|
107 |
+
|
108 |
+
class ResBlock2(torch.nn.Module):
|
109 |
+
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
|
110 |
+
super(ResBlock2, self).__init__()
|
111 |
+
self.convs = nn.ModuleList(
|
112 |
+
[
|
113 |
+
weight_norm(
|
114 |
+
Conv1d(
|
115 |
+
channels,
|
116 |
+
channels,
|
117 |
+
kernel_size,
|
118 |
+
1,
|
119 |
+
dilation=dilation[0],
|
120 |
+
padding=get_padding(kernel_size, dilation[0]),
|
121 |
+
)
|
122 |
+
),
|
123 |
+
weight_norm(
|
124 |
+
Conv1d(
|
125 |
+
channels,
|
126 |
+
channels,
|
127 |
+
kernel_size,
|
128 |
+
1,
|
129 |
+
dilation=dilation[1],
|
130 |
+
padding=get_padding(kernel_size, dilation[1]),
|
131 |
+
)
|
132 |
+
),
|
133 |
+
]
|
134 |
+
)
|
135 |
+
self.convs.apply(init_weights)
|
136 |
+
|
137 |
+
def forward(self, x, x_mask=None):
|
138 |
+
for c in self.convs:
|
139 |
+
xt = F.leaky_relu(x, LRELU_SLOPE)
|
140 |
+
if x_mask is not None:
|
141 |
+
xt = xt * x_mask
|
142 |
+
xt = c(xt)
|
143 |
+
x = xt + x
|
144 |
+
if x_mask is not None:
|
145 |
+
x = x * x_mask
|
146 |
+
return x
|
147 |
+
|
148 |
+
def remove_weight_norm(self):
|
149 |
+
for l in self.convs:
|
150 |
+
remove_weight_norm(l)
|
src/__init__.py
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from .campplus import CAMPPlus
|
2 |
+
from .flow import ResidualCouplingBlock as Flow
|
3 |
+
from .hifi_gan import Generator as HiFiGAN
|
4 |
+
from .hubert_posterior import PosteriorHubert
|
src/attentions.py
ADDED
@@ -0,0 +1,219 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import math
|
2 |
+
|
3 |
+
import torch
|
4 |
+
from torch import nn
|
5 |
+
from torch.nn import functional as F
|
6 |
+
|
7 |
+
import tools.commons as commons
|
8 |
+
|
9 |
+
|
10 |
+
class MultiHeadAttention(nn.Module):
|
11 |
+
def __init__(
|
12 |
+
self,
|
13 |
+
channels,
|
14 |
+
out_channels,
|
15 |
+
n_heads,
|
16 |
+
p_dropout=0.0,
|
17 |
+
window_size=None,
|
18 |
+
heads_share=True,
|
19 |
+
block_length=None,
|
20 |
+
proximal_bias=False,
|
21 |
+
proximal_init=False,
|
22 |
+
):
|
23 |
+
super().__init__()
|
24 |
+
assert channels % n_heads == 0
|
25 |
+
|
26 |
+
self.channels = channels
|
27 |
+
self.out_channels = out_channels
|
28 |
+
self.n_heads = n_heads
|
29 |
+
self.p_dropout = p_dropout
|
30 |
+
self.window_size = window_size
|
31 |
+
self.heads_share = heads_share
|
32 |
+
self.block_length = block_length
|
33 |
+
self.proximal_bias = proximal_bias
|
34 |
+
self.proximal_init = proximal_init
|
35 |
+
self.attn = None
|
36 |
+
|
37 |
+
self.k_channels = channels // n_heads
|
38 |
+
self.conv_q = nn.Conv1d(channels, channels, 1)
|
39 |
+
self.conv_k = nn.Conv1d(channels, channels, 1)
|
40 |
+
self.conv_v = nn.Conv1d(channels, channels, 1)
|
41 |
+
self.conv_o = nn.Conv1d(channels, out_channels, 1)
|
42 |
+
self.drop = nn.Dropout(p_dropout)
|
43 |
+
|
44 |
+
if window_size is not None:
|
45 |
+
n_heads_rel = 1 if heads_share else n_heads
|
46 |
+
rel_stddev = self.k_channels**-0.5
|
47 |
+
self.emb_rel_k = nn.Parameter(
|
48 |
+
torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
|
49 |
+
* rel_stddev
|
50 |
+
)
|
51 |
+
self.emb_rel_v = nn.Parameter(
|
52 |
+
torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
|
53 |
+
* rel_stddev
|
54 |
+
)
|
55 |
+
|
56 |
+
nn.init.xavier_uniform_(self.conv_q.weight)
|
57 |
+
nn.init.xavier_uniform_(self.conv_k.weight)
|
58 |
+
nn.init.xavier_uniform_(self.conv_v.weight)
|
59 |
+
if proximal_init:
|
60 |
+
with torch.no_grad():
|
61 |
+
self.conv_k.weight.copy_(self.conv_q.weight)
|
62 |
+
self.conv_k.bias.copy_(self.conv_q.bias)
|
63 |
+
|
64 |
+
def forward(self, x, c, attn_mask=None):
|
65 |
+
q = self.conv_q(x)
|
66 |
+
k = self.conv_k(c)
|
67 |
+
v = self.conv_v(c)
|
68 |
+
|
69 |
+
x, self.attn = self.attention(q, k, v, mask=attn_mask)
|
70 |
+
|
71 |
+
x = self.conv_o(x)
|
72 |
+
return x
|
73 |
+
|
74 |
+
def attention(self, query, key, value, mask=None):
|
75 |
+
# reshape [b, d, t] -> [b, n_h, t, d_k]
|
76 |
+
b, d, t_s, t_t = (*key.size(), query.size(2))
|
77 |
+
query = (
|
78 |
+
query.view(b, self.n_heads, self.k_channels, t_t)
|
79 |
+
.transpose(2, 3)
|
80 |
+
.contiguous()
|
81 |
+
)
|
82 |
+
key = (
|
83 |
+
key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3).contiguous()
|
84 |
+
)
|
85 |
+
value = (
|
86 |
+
value.view(b, self.n_heads, self.k_channels, t_s)
|
87 |
+
.transpose(2, 3)
|
88 |
+
.contiguous()
|
89 |
+
)
|
90 |
+
|
91 |
+
scores = torch.matmul(
|
92 |
+
query / math.sqrt(self.k_channels), key.transpose(-2, -1).contiguous()
|
93 |
+
)
|
94 |
+
if self.window_size is not None:
|
95 |
+
assert (
|
96 |
+
t_s == t_t
|
97 |
+
), "Relative attention is only available for self-attention."
|
98 |
+
key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
|
99 |
+
rel_logits = self._matmul_with_relative_keys(
|
100 |
+
query / math.sqrt(self.k_channels), key_relative_embeddings
|
101 |
+
)
|
102 |
+
scores_local = self._relative_position_to_absolute_position(rel_logits)
|
103 |
+
scores = scores + scores_local
|
104 |
+
if self.proximal_bias:
|
105 |
+
assert t_s == t_t, "Proximal bias is only available for self-attention."
|
106 |
+
scores = scores + self._attention_bias_proximal(t_s).to(
|
107 |
+
device=scores.device, dtype=scores.dtype
|
108 |
+
)
|
109 |
+
if mask is not None:
|
110 |
+
scores = scores.masked_fill(mask == 0, -1e4)
|
111 |
+
if self.block_length is not None:
|
112 |
+
assert (
|
113 |
+
t_s == t_t
|
114 |
+
), "Local attention is only available for self-attention."
|
115 |
+
block_mask = (
|
116 |
+
torch.ones_like(scores)
|
117 |
+
.triu(-self.block_length)
|
118 |
+
.tril(self.block_length)
|
119 |
+
)
|
120 |
+
scores = scores.masked_fill(block_mask == 0, -1e4)
|
121 |
+
p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
|
122 |
+
p_attn = self.drop(p_attn)
|
123 |
+
output = torch.matmul(p_attn, value)
|
124 |
+
if self.window_size is not None:
|
125 |
+
relative_weights = self._absolute_position_to_relative_position(p_attn)
|
126 |
+
value_relative_embeddings = self._get_relative_embeddings(
|
127 |
+
self.emb_rel_v, t_s
|
128 |
+
)
|
129 |
+
output = output + self._matmul_with_relative_values(
|
130 |
+
relative_weights, value_relative_embeddings
|
131 |
+
)
|
132 |
+
output = (
|
133 |
+
output.transpose(2, 3).contiguous().view(b, d, t_t).contiguous()
|
134 |
+
) # [b, n_h, t_t, d_k] -> [b, d, t_t]
|
135 |
+
return output, p_attn
|
136 |
+
|
137 |
+
def _matmul_with_relative_values(self, x, y):
|
138 |
+
"""
|
139 |
+
x: [b, h, l, m]
|
140 |
+
y: [h or 1, m, d]
|
141 |
+
ret: [b, h, l, d]
|
142 |
+
"""
|
143 |
+
ret = torch.matmul(x, y.unsqueeze(0))
|
144 |
+
return ret
|
145 |
+
|
146 |
+
def _matmul_with_relative_keys(self, x, y):
|
147 |
+
"""
|
148 |
+
x: [b, h, l, d]
|
149 |
+
y: [h or 1, m, d]
|
150 |
+
ret: [b, h, l, m]
|
151 |
+
"""
|
152 |
+
ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1).contiguous())
|
153 |
+
return ret
|
154 |
+
|
155 |
+
def _get_relative_embeddings(self, relative_embeddings, length):
|
156 |
+
max_relative_position = 2 * self.window_size + 1
|
157 |
+
# Pad first before slice to avoid using cond ops.
|
158 |
+
pad_length = max(length - (self.window_size + 1), 0)
|
159 |
+
slice_start_position = max((self.window_size + 1) - length, 0)
|
160 |
+
slice_end_position = slice_start_position + 2 * length - 1
|
161 |
+
if pad_length > 0:
|
162 |
+
padded_relative_embeddings = F.pad(
|
163 |
+
relative_embeddings,
|
164 |
+
commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
|
165 |
+
)
|
166 |
+
else:
|
167 |
+
padded_relative_embeddings = relative_embeddings
|
168 |
+
used_relative_embeddings = padded_relative_embeddings[
|
169 |
+
:, slice_start_position:slice_end_position
|
170 |
+
]
|
171 |
+
return used_relative_embeddings
|
172 |
+
|
173 |
+
def _relative_position_to_absolute_position(self, x):
|
174 |
+
"""
|
175 |
+
x: [b, h, l, 2*l-1]
|
176 |
+
ret: [b, h, l, l]
|
177 |
+
"""
|
178 |
+
batch, heads, length, _ = x.size()
|
179 |
+
# Concat columns of pad to shift from relative to absolute indexing.
|
180 |
+
x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
|
181 |
+
|
182 |
+
# Concat extra elements so to add up to shape (len+1, 2*len-1).
|
183 |
+
x_flat = x.view([batch, heads, length * 2 * length])
|
184 |
+
x_flat = F.pad(
|
185 |
+
x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
|
186 |
+
)
|
187 |
+
|
188 |
+
# Reshape and slice out the padded elements.
|
189 |
+
x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
|
190 |
+
:, :, :length, length - 1 :
|
191 |
+
]
|
192 |
+
return x_final
|
193 |
+
|
194 |
+
def _absolute_position_to_relative_position(self, x):
|
195 |
+
"""
|
196 |
+
x: [b, h, l, l]
|
197 |
+
ret: [b, h, l, 2*l-1]
|
198 |
+
"""
|
199 |
+
batch, heads, length, _ = x.size()
|
200 |
+
# padd along column
|
201 |
+
x = F.pad(
|
202 |
+
x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
|
203 |
+
)
|
204 |
+
x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
|
205 |
+
# add 0's in the beginning that will skew the elements after reshape
|
206 |
+
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
|
207 |
+
x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
|
208 |
+
return x_final
|
209 |
+
|
210 |
+
def _attention_bias_proximal(self, length):
|
211 |
+
"""Bias for self-attention to encourage attention to close positions.
|
212 |
+
Args:
|
213 |
+
length: an integer scalar.
|
214 |
+
Returns:
|
215 |
+
a Tensor with shape [1, 1, length, length]
|
216 |
+
"""
|
217 |
+
r = torch.arange(length, dtype=torch.float32)
|
218 |
+
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
|
219 |
+
return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
|
src/campplus.py
ADDED
@@ -0,0 +1,407 @@
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|
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|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright (c) 2023 Hongji Wang (jijijiang77@gmail.com)
|
2 |
+
#
|
3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
+
# you may not use this file except in compliance with the License.
|
5 |
+
# You may obtain a copy of the License at
|
6 |
+
#
|
7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
+
#
|
9 |
+
# Unless required by applicable law or agreed to in writing, software
|
10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
+
# See the License for the specific language governing permissions and
|
13 |
+
# limitations under the License.
|
14 |
+
|
15 |
+
"""
|
16 |
+
This implementation is adapted from github repo:
|
17 |
+
https://github.com/alibaba-damo-academy/3D-Speaker
|
18 |
+
|
19 |
+
Some modifications:
|
20 |
+
1. Reuse the pooling layers in wespeaker
|
21 |
+
2. Remove the memory_efficient mechanism to meet the torch.jit.script
|
22 |
+
export requirements
|
23 |
+
|
24 |
+
Reference:
|
25 |
+
[1] Hui Wang, Siqi Zheng, Yafeng Chen, Luyao Cheng and Qian Chen.
|
26 |
+
"CAM++: A Fast and Efficient Network for Speaker Verification
|
27 |
+
Using Context-Aware Masking". arXiv preprint arXiv:2303.00332
|
28 |
+
"""
|
29 |
+
|
30 |
+
from collections import OrderedDict
|
31 |
+
|
32 |
+
import torch
|
33 |
+
import torch.nn as nn
|
34 |
+
import torch.nn.functional as F
|
35 |
+
|
36 |
+
from .wespeaker_campplus import pooling_layers
|
37 |
+
from .wespeaker_campplus.fbank_feature_extractor import FbankFeatureExtractor
|
38 |
+
|
39 |
+
|
40 |
+
def get_nonlinear(config_str, channels):
|
41 |
+
nonlinear = nn.Sequential()
|
42 |
+
for name in config_str.split("-"):
|
43 |
+
if name == "relu":
|
44 |
+
nonlinear.add_module("relu", nn.ReLU(inplace=True))
|
45 |
+
elif name == "prelu":
|
46 |
+
nonlinear.add_module("prelu", nn.PReLU(channels))
|
47 |
+
elif name == "batchnorm":
|
48 |
+
nonlinear.add_module("batchnorm", nn.BatchNorm1d(channels))
|
49 |
+
elif name == "batchnorm_":
|
50 |
+
nonlinear.add_module("batchnorm", nn.BatchNorm1d(channels, affine=False))
|
51 |
+
else:
|
52 |
+
raise ValueError("Unexpected module ({}).".format(name))
|
53 |
+
return nonlinear
|
54 |
+
|
55 |
+
|
56 |
+
class TDNNLayer(nn.Module):
|
57 |
+
def __init__(
|
58 |
+
self,
|
59 |
+
in_channels,
|
60 |
+
out_channels,
|
61 |
+
kernel_size,
|
62 |
+
stride=1,
|
63 |
+
padding=0,
|
64 |
+
dilation=1,
|
65 |
+
bias=False,
|
66 |
+
config_str="batchnorm-relu",
|
67 |
+
):
|
68 |
+
super(TDNNLayer, self).__init__()
|
69 |
+
if padding < 0:
|
70 |
+
assert (
|
71 |
+
kernel_size % 2 == 1
|
72 |
+
), "Expect equal paddings, \
|
73 |
+
but got even kernel size ({})".format(
|
74 |
+
kernel_size
|
75 |
+
)
|
76 |
+
padding = (kernel_size - 1) // 2 * dilation
|
77 |
+
self.linear = nn.Conv1d(
|
78 |
+
in_channels,
|
79 |
+
out_channels,
|
80 |
+
kernel_size,
|
81 |
+
stride=stride,
|
82 |
+
padding=padding,
|
83 |
+
dilation=dilation,
|
84 |
+
bias=bias,
|
85 |
+
)
|
86 |
+
self.nonlinear = get_nonlinear(config_str, out_channels)
|
87 |
+
|
88 |
+
def forward(self, x):
|
89 |
+
x = self.linear(x)
|
90 |
+
x = self.nonlinear(x)
|
91 |
+
return x
|
92 |
+
|
93 |
+
|
94 |
+
class CAMLayer(nn.Module):
|
95 |
+
def __init__(
|
96 |
+
self,
|
97 |
+
bn_channels,
|
98 |
+
out_channels,
|
99 |
+
kernel_size,
|
100 |
+
stride,
|
101 |
+
padding,
|
102 |
+
dilation,
|
103 |
+
bias,
|
104 |
+
reduction=2,
|
105 |
+
):
|
106 |
+
super(CAMLayer, self).__init__()
|
107 |
+
self.linear_local = nn.Conv1d(
|
108 |
+
bn_channels,
|
109 |
+
out_channels,
|
110 |
+
kernel_size,
|
111 |
+
stride=stride,
|
112 |
+
padding=padding,
|
113 |
+
dilation=dilation,
|
114 |
+
bias=bias,
|
115 |
+
)
|
116 |
+
self.linear1 = nn.Conv1d(bn_channels, bn_channels // reduction, 1)
|
117 |
+
self.relu = nn.ReLU(inplace=True)
|
118 |
+
self.linear2 = nn.Conv1d(bn_channels // reduction, out_channels, 1)
|
119 |
+
self.sigmoid = nn.Sigmoid()
|
120 |
+
|
121 |
+
def forward(self, x):
|
122 |
+
y = self.linear_local(x)
|
123 |
+
context = x.mean(-1, keepdim=True) + self.seg_pooling(x)
|
124 |
+
context = self.relu(self.linear1(context))
|
125 |
+
m = self.sigmoid(self.linear2(context))
|
126 |
+
return y * m
|
127 |
+
|
128 |
+
def seg_pooling(self, x, seg_len: int = 100, stype: str = "avg"):
|
129 |
+
if stype == "avg":
|
130 |
+
seg = F.avg_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True)
|
131 |
+
elif stype == "max":
|
132 |
+
seg = F.max_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True)
|
133 |
+
else:
|
134 |
+
raise ValueError("Wrong segment pooling type.")
|
135 |
+
shape = seg.shape
|
136 |
+
seg = (
|
137 |
+
seg.unsqueeze(-1)
|
138 |
+
.expand(shape[0], shape[1], shape[2], seg_len)
|
139 |
+
.reshape(shape[0], shape[1], -1)
|
140 |
+
)
|
141 |
+
seg = seg[..., : x.shape[-1]]
|
142 |
+
return seg
|
143 |
+
|
144 |
+
|
145 |
+
class CAMDenseTDNNLayer(nn.Module):
|
146 |
+
def __init__(
|
147 |
+
self,
|
148 |
+
in_channels,
|
149 |
+
out_channels,
|
150 |
+
bn_channels,
|
151 |
+
kernel_size,
|
152 |
+
stride=1,
|
153 |
+
dilation=1,
|
154 |
+
bias=False,
|
155 |
+
config_str="batchnorm-relu",
|
156 |
+
):
|
157 |
+
super(CAMDenseTDNNLayer, self).__init__()
|
158 |
+
assert (
|
159 |
+
kernel_size % 2 == 1
|
160 |
+
), "Expect equal paddings, \
|
161 |
+
but got even kernel size ({})".format(
|
162 |
+
kernel_size
|
163 |
+
)
|
164 |
+
padding = (kernel_size - 1) // 2 * dilation
|
165 |
+
self.nonlinear1 = get_nonlinear(config_str, in_channels)
|
166 |
+
self.linear1 = nn.Conv1d(in_channels, bn_channels, 1, bias=False)
|
167 |
+
self.nonlinear2 = get_nonlinear(config_str, bn_channels)
|
168 |
+
self.cam_layer = CAMLayer(
|
169 |
+
bn_channels,
|
170 |
+
out_channels,
|
171 |
+
kernel_size,
|
172 |
+
stride=stride,
|
173 |
+
padding=padding,
|
174 |
+
dilation=dilation,
|
175 |
+
bias=bias,
|
176 |
+
)
|
177 |
+
|
178 |
+
def bn_function(self, x):
|
179 |
+
return self.linear1(self.nonlinear1(x))
|
180 |
+
|
181 |
+
def forward(self, x):
|
182 |
+
x = self.bn_function(x)
|
183 |
+
x = self.cam_layer(self.nonlinear2(x))
|
184 |
+
return x
|
185 |
+
|
186 |
+
|
187 |
+
class CAMDenseTDNNBlock(nn.ModuleList):
|
188 |
+
def __init__(
|
189 |
+
self,
|
190 |
+
num_layers,
|
191 |
+
in_channels,
|
192 |
+
out_channels,
|
193 |
+
bn_channels,
|
194 |
+
kernel_size,
|
195 |
+
stride=1,
|
196 |
+
dilation=1,
|
197 |
+
bias=False,
|
198 |
+
config_str="batchnorm-relu",
|
199 |
+
):
|
200 |
+
super(CAMDenseTDNNBlock, self).__init__()
|
201 |
+
for i in range(num_layers):
|
202 |
+
layer = CAMDenseTDNNLayer(
|
203 |
+
in_channels=in_channels + i * out_channels,
|
204 |
+
out_channels=out_channels,
|
205 |
+
bn_channels=bn_channels,
|
206 |
+
kernel_size=kernel_size,
|
207 |
+
stride=stride,
|
208 |
+
dilation=dilation,
|
209 |
+
bias=bias,
|
210 |
+
config_str=config_str,
|
211 |
+
)
|
212 |
+
self.add_module("tdnnd%d" % (i + 1), layer)
|
213 |
+
|
214 |
+
def forward(self, x):
|
215 |
+
for layer in self:
|
216 |
+
x = torch.cat([x, layer(x)], dim=1)
|
217 |
+
return x
|
218 |
+
|
219 |
+
|
220 |
+
class TransitLayer(nn.Module):
|
221 |
+
def __init__(
|
222 |
+
self, in_channels, out_channels, bias=True, config_str="batchnorm-relu"
|
223 |
+
):
|
224 |
+
super(TransitLayer, self).__init__()
|
225 |
+
self.nonlinear = get_nonlinear(config_str, in_channels)
|
226 |
+
self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias)
|
227 |
+
|
228 |
+
def forward(self, x):
|
229 |
+
x = self.nonlinear(x)
|
230 |
+
x = self.linear(x)
|
231 |
+
return x
|
232 |
+
|
233 |
+
|
234 |
+
class DenseLayer(nn.Module):
|
235 |
+
def __init__(
|
236 |
+
self, in_channels, out_channels, bias=False, config_str="batchnorm-relu"
|
237 |
+
):
|
238 |
+
super(DenseLayer, self).__init__()
|
239 |
+
self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias)
|
240 |
+
self.nonlinear = get_nonlinear(config_str, out_channels)
|
241 |
+
|
242 |
+
def forward(self, x):
|
243 |
+
if len(x.shape) == 2:
|
244 |
+
x = self.linear(x.unsqueeze(dim=-1)).squeeze(dim=-1)
|
245 |
+
else:
|
246 |
+
x = self.linear(x)
|
247 |
+
x = self.nonlinear(x)
|
248 |
+
return x
|
249 |
+
|
250 |
+
|
251 |
+
"""Note: The stride used here is different from that in Resnet
|
252 |
+
"""
|
253 |
+
|
254 |
+
|
255 |
+
class BasicResBlock(nn.Module):
|
256 |
+
expansion = 1
|
257 |
+
|
258 |
+
def __init__(self, in_planes, planes, stride=1):
|
259 |
+
super(BasicResBlock, self).__init__()
|
260 |
+
self.conv1 = nn.Conv2d(
|
261 |
+
in_planes, planes, kernel_size=3, stride=(stride, 1), padding=1, bias=False
|
262 |
+
)
|
263 |
+
self.bn1 = nn.BatchNorm2d(planes)
|
264 |
+
self.conv2 = nn.Conv2d(
|
265 |
+
planes, planes, kernel_size=3, stride=1, padding=1, bias=False
|
266 |
+
)
|
267 |
+
self.bn2 = nn.BatchNorm2d(planes)
|
268 |
+
|
269 |
+
self.shortcut = nn.Sequential()
|
270 |
+
if stride != 1 or in_planes != self.expansion * planes:
|
271 |
+
self.shortcut = nn.Sequential(
|
272 |
+
nn.Conv2d(
|
273 |
+
in_planes,
|
274 |
+
self.expansion * planes,
|
275 |
+
kernel_size=1,
|
276 |
+
stride=(stride, 1),
|
277 |
+
bias=False,
|
278 |
+
),
|
279 |
+
nn.BatchNorm2d(self.expansion * planes),
|
280 |
+
)
|
281 |
+
|
282 |
+
def forward(self, x):
|
283 |
+
out = F.relu(self.bn1(self.conv1(x)))
|
284 |
+
out = self.bn2(self.conv2(out))
|
285 |
+
out += self.shortcut(x)
|
286 |
+
out = F.relu(out)
|
287 |
+
return out
|
288 |
+
|
289 |
+
|
290 |
+
class FCM(nn.Module):
|
291 |
+
def __init__(self, block, num_blocks, m_channels=32, feat_dim=80):
|
292 |
+
super(FCM, self).__init__()
|
293 |
+
self.in_planes = m_channels
|
294 |
+
self.conv1 = nn.Conv2d(
|
295 |
+
1, m_channels, kernel_size=3, stride=1, padding=1, bias=False
|
296 |
+
)
|
297 |
+
self.bn1 = nn.BatchNorm2d(m_channels)
|
298 |
+
|
299 |
+
self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=2)
|
300 |
+
self.layer2 = self._make_layer(block, m_channels, num_blocks[0], stride=2)
|
301 |
+
|
302 |
+
self.conv2 = nn.Conv2d(
|
303 |
+
m_channels, m_channels, kernel_size=3, stride=(2, 1), padding=1, bias=False
|
304 |
+
)
|
305 |
+
self.bn2 = nn.BatchNorm2d(m_channels)
|
306 |
+
self.out_channels = m_channels * (feat_dim // 8)
|
307 |
+
|
308 |
+
def _make_layer(self, block, planes, num_blocks, stride):
|
309 |
+
strides = [stride] + [1] * (num_blocks - 1)
|
310 |
+
layers = []
|
311 |
+
for stride in strides:
|
312 |
+
layers.append(block(self.in_planes, planes, stride))
|
313 |
+
self.in_planes = planes * block.expansion
|
314 |
+
return nn.Sequential(*layers)
|
315 |
+
|
316 |
+
def forward(self, x):
|
317 |
+
x = x.unsqueeze(1)
|
318 |
+
out = F.relu(self.bn1(self.conv1(x)))
|
319 |
+
out = self.layer1(out)
|
320 |
+
out = self.layer2(out)
|
321 |
+
out = F.relu(self.bn2(self.conv2(out)))
|
322 |
+
|
323 |
+
shape = out.shape
|
324 |
+
out = out.reshape(shape[0], shape[1] * shape[2], shape[3])
|
325 |
+
return out
|
326 |
+
|
327 |
+
|
328 |
+
class CAMPPlus(nn.Module):
|
329 |
+
def __init__(
|
330 |
+
self,
|
331 |
+
feat_dim=80,
|
332 |
+
embed_dim=512,
|
333 |
+
pooling_func="TSTP",
|
334 |
+
growth_rate=32,
|
335 |
+
bn_size=4,
|
336 |
+
init_channels=128,
|
337 |
+
config_str="batchnorm-relu",
|
338 |
+
):
|
339 |
+
super(CAMPPlus, self).__init__()
|
340 |
+
|
341 |
+
self.feature_extractor = FbankFeatureExtractor(feat_dim=80)
|
342 |
+
self.head = FCM(block=BasicResBlock, num_blocks=[2, 2], feat_dim=feat_dim)
|
343 |
+
channels = self.head.out_channels
|
344 |
+
|
345 |
+
self.xvector = nn.Sequential(
|
346 |
+
OrderedDict(
|
347 |
+
[
|
348 |
+
(
|
349 |
+
"tdnn",
|
350 |
+
TDNNLayer(
|
351 |
+
channels,
|
352 |
+
init_channels,
|
353 |
+
5,
|
354 |
+
stride=2,
|
355 |
+
dilation=1,
|
356 |
+
padding=-1,
|
357 |
+
config_str=config_str,
|
358 |
+
),
|
359 |
+
),
|
360 |
+
]
|
361 |
+
)
|
362 |
+
)
|
363 |
+
channels = init_channels
|
364 |
+
for i, (num_layers, kernel_size, dilation) in enumerate(
|
365 |
+
zip((12, 24, 16), (3, 3, 3), (1, 2, 2))
|
366 |
+
):
|
367 |
+
block = CAMDenseTDNNBlock(
|
368 |
+
num_layers=num_layers,
|
369 |
+
in_channels=channels,
|
370 |
+
out_channels=growth_rate,
|
371 |
+
bn_channels=bn_size * growth_rate,
|
372 |
+
kernel_size=kernel_size,
|
373 |
+
dilation=dilation,
|
374 |
+
config_str=config_str,
|
375 |
+
)
|
376 |
+
self.xvector.add_module("block%d" % (i + 1), block)
|
377 |
+
channels = channels + num_layers * growth_rate
|
378 |
+
self.xvector.add_module(
|
379 |
+
"transit%d" % (i + 1),
|
380 |
+
TransitLayer(
|
381 |
+
channels, channels // 2, bias=False, config_str=config_str
|
382 |
+
),
|
383 |
+
)
|
384 |
+
channels //= 2
|
385 |
+
|
386 |
+
self.xvector.add_module("out_nonlinear", get_nonlinear(config_str, channels))
|
387 |
+
|
388 |
+
self.pool = getattr(pooling_layers, pooling_func)(in_dim=channels)
|
389 |
+
self.pool_out_dim = self.pool.get_out_dim()
|
390 |
+
self.xvector.add_module("stats", self.pool)
|
391 |
+
self.xvector.add_module(
|
392 |
+
"dense", DenseLayer(self.pool_out_dim, embed_dim, config_str="batchnorm_")
|
393 |
+
)
|
394 |
+
|
395 |
+
for m in self.modules():
|
396 |
+
if isinstance(m, (nn.Conv1d, nn.Linear)):
|
397 |
+
nn.init.kaiming_normal_(m.weight.data)
|
398 |
+
if m.bias is not None:
|
399 |
+
nn.init.zeros_(m.bias)
|
400 |
+
|
401 |
+
def forward(self, x):
|
402 |
+
x = self.feature_extractor(x)
|
403 |
+
# x = x.permute(0, 2, 1) # (B,T,F) => (B,F,T)
|
404 |
+
x = self.head(x)
|
405 |
+
x = self.xvector(x)
|
406 |
+
|
407 |
+
return x
|
src/flow.py
ADDED
@@ -0,0 +1,47 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch.nn as nn
|
2 |
+
import module as modules
|
3 |
+
|
4 |
+
|
5 |
+
class ResidualCouplingBlock(nn.Module):
|
6 |
+
def __init__(
|
7 |
+
self,
|
8 |
+
channels,
|
9 |
+
hidden_channels,
|
10 |
+
kernel_size,
|
11 |
+
dilation_rate,
|
12 |
+
n_layers,
|
13 |
+
n_flows=4,
|
14 |
+
gin_channels=0,
|
15 |
+
):
|
16 |
+
super().__init__()
|
17 |
+
self.channels = channels
|
18 |
+
self.hidden_channels = hidden_channels
|
19 |
+
self.kernel_size = kernel_size
|
20 |
+
self.dilation_rate = dilation_rate
|
21 |
+
self.n_layers = n_layers
|
22 |
+
self.n_flows = n_flows
|
23 |
+
self.gin_channels = gin_channels
|
24 |
+
|
25 |
+
self.flows = nn.ModuleList()
|
26 |
+
for i in range(n_flows):
|
27 |
+
self.flows.append(
|
28 |
+
modules.ResidualCouplingLayer(
|
29 |
+
channels,
|
30 |
+
hidden_channels,
|
31 |
+
kernel_size,
|
32 |
+
dilation_rate,
|
33 |
+
n_layers,
|
34 |
+
gin_channels=gin_channels,
|
35 |
+
mean_only=True,
|
36 |
+
)
|
37 |
+
)
|
38 |
+
self.flows.append(modules.Flip())
|
39 |
+
|
40 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
41 |
+
if not reverse:
|
42 |
+
for flow in self.flows:
|
43 |
+
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
44 |
+
else:
|
45 |
+
for flow in reversed(self.flows):
|
46 |
+
x = flow(x, x_mask, g=g, reverse=reverse)
|
47 |
+
return x
|
src/hifi_gan.py
ADDED
@@ -0,0 +1,86 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.nn as nn
|
3 |
+
from torch.nn import Conv1d, ConvTranspose1d
|
4 |
+
from torch.nn import functional as F
|
5 |
+
from torch.nn.utils import remove_weight_norm, weight_norm
|
6 |
+
|
7 |
+
import module as modules
|
8 |
+
from module.resblocks import ResBlock1, ResBlock2
|
9 |
+
from tools.commons import init_weights
|
10 |
+
|
11 |
+
|
12 |
+
class Generator(torch.nn.Module):
|
13 |
+
def __init__(
|
14 |
+
self,
|
15 |
+
initial_channel,
|
16 |
+
resblock,
|
17 |
+
resblock_kernel_sizes,
|
18 |
+
resblock_dilation_sizes,
|
19 |
+
upsample_rates,
|
20 |
+
upsample_initial_channel,
|
21 |
+
upsample_kernel_sizes,
|
22 |
+
gin_channels,
|
23 |
+
activation="snakebeta",
|
24 |
+
snake_logscale=True,
|
25 |
+
):
|
26 |
+
super(Generator, self).__init__()
|
27 |
+
self.num_kernels = len(resblock_kernel_sizes)
|
28 |
+
self.num_upsamples = len(upsample_rates)
|
29 |
+
self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, 3)
|
30 |
+
resblock = ResBlock1 if resblock == "1" else ResBlock2
|
31 |
+
|
32 |
+
self.ups = nn.ModuleList()
|
33 |
+
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
34 |
+
self.ups.append(
|
35 |
+
weight_norm(
|
36 |
+
ConvTranspose1d(
|
37 |
+
in_channels=upsample_initial_channel // (2**i),
|
38 |
+
out_channels=upsample_initial_channel // (2 ** (i + 1)),
|
39 |
+
kernel_size=k,
|
40 |
+
stride=u,
|
41 |
+
padding=(k - u) // 2,
|
42 |
+
)
|
43 |
+
)
|
44 |
+
)
|
45 |
+
|
46 |
+
self.resblocks = nn.ModuleList()
|
47 |
+
for i in range(len(self.ups)):
|
48 |
+
ch = upsample_initial_channel // (2 ** (i + 1))
|
49 |
+
for j, (k, d) in enumerate(
|
50 |
+
zip(resblock_kernel_sizes, resblock_dilation_sizes)
|
51 |
+
):
|
52 |
+
self.resblocks.append(resblock(ch, k, d))
|
53 |
+
|
54 |
+
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
|
55 |
+
self.ups.apply(init_weights)
|
56 |
+
|
57 |
+
if gin_channels != 0:
|
58 |
+
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
|
59 |
+
|
60 |
+
def forward(self, x, g=None):
|
61 |
+
x = self.conv_pre(x)
|
62 |
+
if g is not None:
|
63 |
+
x = x + self.cond(g)
|
64 |
+
|
65 |
+
for i in range(self.num_upsamples):
|
66 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
67 |
+
x = self.ups[i](x)
|
68 |
+
xs = None
|
69 |
+
for j in range(self.num_kernels):
|
70 |
+
if xs is None:
|
71 |
+
xs = self.resblocks[i * self.num_kernels + j](x)
|
72 |
+
else:
|
73 |
+
xs += self.resblocks[i * self.num_kernels + j](x)
|
74 |
+
x = xs / self.num_kernels
|
75 |
+
x = F.leaky_relu(x)
|
76 |
+
x = self.conv_post(x)
|
77 |
+
x = torch.tanh(x)
|
78 |
+
|
79 |
+
return x
|
80 |
+
|
81 |
+
def remove_weight_norm(self):
|
82 |
+
print("Removing weight norm...")
|
83 |
+
for l in self.ups:
|
84 |
+
remove_weight_norm(l)
|
85 |
+
for l in self.resblocks:
|
86 |
+
l.remove_weight_norm()
|
src/hubert_posterior.py
ADDED
@@ -0,0 +1,59 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.nn as nn
|
3 |
+
|
4 |
+
from .inference_hubert import InferenceHubertBase
|
5 |
+
from .vae_memory_bank import VAEMemoryBank
|
6 |
+
|
7 |
+
|
8 |
+
def create_padding_mask(waveforms_lengths: torch.Tensor = None):
|
9 |
+
if waveforms_lengths is None:
|
10 |
+
return None
|
11 |
+
batch = waveforms_lengths.shape[0]
|
12 |
+
max_len = waveforms_lengths.max()
|
13 |
+
device = waveforms_lengths.device
|
14 |
+
padding_mask = torch.ones(batch, max_len, dtype=torch.bool, device=device)
|
15 |
+
for idx, length in enumerate(waveforms_lengths):
|
16 |
+
padding_mask[idx, :length] = 0
|
17 |
+
return padding_mask
|
18 |
+
|
19 |
+
|
20 |
+
def unfreeze_layers(model: nn.Module, root_name: str):
|
21 |
+
for name, param in model.named_parameters():
|
22 |
+
if root_name in name[: len(root_name)]:
|
23 |
+
param.requires_grad = True
|
24 |
+
|
25 |
+
|
26 |
+
class PosteriorHubert(nn.Module):
|
27 |
+
def __init__(
|
28 |
+
self, out_channels, feature_channels, downsample_channels, output_layer=11
|
29 |
+
) -> None:
|
30 |
+
super().__init__()
|
31 |
+
self.out_channels = out_channels
|
32 |
+
self.feature_channels = feature_channels
|
33 |
+
self.downsample_channels = downsample_channels
|
34 |
+
self.output_layer = output_layer
|
35 |
+
|
36 |
+
self.hubert = InferenceHubertBase()
|
37 |
+
self.memory_bank = VAEMemoryBank(
|
38 |
+
n_hidden_dims=feature_channels,
|
39 |
+
bank_size=1000,
|
40 |
+
output_channels=downsample_channels,
|
41 |
+
)
|
42 |
+
|
43 |
+
self.proj = nn.Conv1d(downsample_channels, out_channels * 2, 1)
|
44 |
+
|
45 |
+
def forward(self, waveforms: torch.Tensor, waveforms_lengths: torch.Tensor, g=None):
|
46 |
+
features, features_mask = self.hubert.extract_features(
|
47 |
+
source=waveforms,
|
48 |
+
padding_mask=create_padding_mask(waveforms_lengths),
|
49 |
+
output_layer=self.output_layer,
|
50 |
+
)
|
51 |
+
x = self.memory_bank(features.transpose(1, 2))
|
52 |
+
x_mask = (~features_mask).unsqueeze(1).to(torch.float32)
|
53 |
+
x = x[:, :, : x_mask.shape[-1]]
|
54 |
+
|
55 |
+
stats = self.proj(x) * x_mask
|
56 |
+
m, logs = torch.split(stats, self.out_channels, dim=1)
|
57 |
+
|
58 |
+
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
59 |
+
return z, m, logs, x_mask
|
src/inference_hubert/__init__.py
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
from .hubert import InferenceHubertBase
|
src/inference_hubert/fairseq_modules.py
ADDED
@@ -0,0 +1,52 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
"""
|
2 |
+
Classes reused from:
|
3 |
+
1. https://github.com/facebookresearch/fairseq/blob/main/fairseq/modules/fp32_group_norm.py
|
4 |
+
2. https://github.com/facebookresearch/fairseq/blob/main/fairseq/modules/same_pad.py
|
5 |
+
3. https://github.com/facebookresearch/fairseq/blob/main/fairseq/modules/fairseq_dropout.py
|
6 |
+
"""
|
7 |
+
|
8 |
+
import torch.nn as nn
|
9 |
+
import torch.nn.functional as F
|
10 |
+
|
11 |
+
|
12 |
+
class Fp32GroupNorm(nn.GroupNorm):
|
13 |
+
def __init__(self, *args, **kwargs):
|
14 |
+
super().__init__(*args, **kwargs)
|
15 |
+
|
16 |
+
def forward(self, input):
|
17 |
+
output = F.group_norm(
|
18 |
+
input.float(),
|
19 |
+
self.num_groups,
|
20 |
+
self.weight.float() if self.weight is not None else None,
|
21 |
+
self.bias.float() if self.bias is not None else None,
|
22 |
+
self.eps,
|
23 |
+
)
|
24 |
+
return output.type_as(input)
|
25 |
+
|
26 |
+
|
27 |
+
class SamePad(nn.Module):
|
28 |
+
def __init__(self, kernel_size, causal=False):
|
29 |
+
super().__init__()
|
30 |
+
if causal:
|
31 |
+
self.remove = kernel_size - 1
|
32 |
+
else:
|
33 |
+
self.remove = 1 if kernel_size % 2 == 0 else 0
|
34 |
+
|
35 |
+
def forward(self, x):
|
36 |
+
if self.remove > 0:
|
37 |
+
x = x[:, :, : -self.remove]
|
38 |
+
return x
|
39 |
+
|
40 |
+
|
41 |
+
class FairseqDropout(nn.Module):
|
42 |
+
def __init__(self, p, module_name=None):
|
43 |
+
super().__init__()
|
44 |
+
self.p = p
|
45 |
+
self.module_name = module_name
|
46 |
+
self.apply_during_inference = False
|
47 |
+
|
48 |
+
def forward(self, x, inplace: bool = False):
|
49 |
+
if self.p > 0 and (self.training or self.apply_during_inference):
|
50 |
+
return F.dropout(x, p=self.p, training=True, inplace=inplace)
|
51 |
+
else:
|
52 |
+
return x
|
src/inference_hubert/hubert.py
ADDED
@@ -0,0 +1,281 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
"""
|
2 |
+
The `InferenceHubertBase` class is a lightweight version of the model from this repository:
|
3 |
+
https://github.com/facebookresearch/fairseq/blob/main/fairseq/models/hubert/hubert.py#L248C5-L248C6
|
4 |
+
"""
|
5 |
+
|
6 |
+
import math
|
7 |
+
from typing import Optional, Tuple
|
8 |
+
|
9 |
+
import torch
|
10 |
+
import torch.nn as nn
|
11 |
+
import torch.nn.functional as F
|
12 |
+
from torch import Tensor
|
13 |
+
|
14 |
+
from .fairseq_modules import Fp32GroupNorm, SamePad, FairseqDropout
|
15 |
+
|
16 |
+
|
17 |
+
class InferenceHubertBase(nn.Module):
|
18 |
+
def __init__(self, *args, **kwargs) -> None:
|
19 |
+
super().__init__(*args, **kwargs)
|
20 |
+
self.feature_extractor = ConvFeatureExtractor()
|
21 |
+
self.layer_norm = nn.LayerNorm((512,), eps=1e-05, elementwise_affine=True)
|
22 |
+
self.post_extract_proj = nn.Linear(in_features=512, out_features=768, bias=True)
|
23 |
+
self.dropout_input = nn.Dropout(p=0.1, inplace=False)
|
24 |
+
self.dropout_features = nn.Dropout(p=0.1, inplace=False)
|
25 |
+
self.encoder = TransformerEncoder()
|
26 |
+
|
27 |
+
def extract_features(
|
28 |
+
self,
|
29 |
+
source: Tensor,
|
30 |
+
padding_mask: Optional[Tensor] = None,
|
31 |
+
output_layer: int = 12,
|
32 |
+
) -> Tuple[Tensor, Tensor]:
|
33 |
+
features = self.feature_extractor(source).transpose(1, 2)
|
34 |
+
features = self.layer_norm(features)
|
35 |
+
if padding_mask is not None:
|
36 |
+
padding_mask = self.__apply_padding_mask(features, padding_mask)
|
37 |
+
features = self.post_extract_proj(features)
|
38 |
+
features = self.dropout_input(features)
|
39 |
+
features = self.encoder(
|
40 |
+
features, padding_mask=padding_mask, tgt_layer=output_layer - 1
|
41 |
+
)
|
42 |
+
return features, padding_mask
|
43 |
+
|
44 |
+
def __apply_padding_mask(self, features: Tensor, padding_mask: Tensor) -> Tensor:
|
45 |
+
extra = padding_mask.size(1) % features.size(1)
|
46 |
+
if extra > 0:
|
47 |
+
padding_mask = padding_mask[:, :-extra]
|
48 |
+
padding_mask = padding_mask.view(padding_mask.size(0), features.size(1), -1)
|
49 |
+
padding_mask = padding_mask.all(-1)
|
50 |
+
return padding_mask
|
51 |
+
|
52 |
+
|
53 |
+
class ConvFeatureExtractor(nn.Module):
|
54 |
+
def __init__(self, *args, **kwargs) -> None:
|
55 |
+
super().__init__(*args, **kwargs)
|
56 |
+
conv_layers = [
|
57 |
+
nn.Sequential(
|
58 |
+
nn.Conv1d(1, 512, kernel_size=(10,), stride=(5,), bias=False),
|
59 |
+
nn.Dropout(p=0.0, inplace=False),
|
60 |
+
Fp32GroupNorm(512, 512, eps=1e-05, affine=True),
|
61 |
+
nn.GELU(approximate="none"),
|
62 |
+
),
|
63 |
+
*[
|
64 |
+
nn.Sequential(
|
65 |
+
nn.Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False),
|
66 |
+
nn.Dropout(p=0.0, inplace=False),
|
67 |
+
nn.GELU(approximate="none"),
|
68 |
+
)
|
69 |
+
for _ in range(4)
|
70 |
+
],
|
71 |
+
*[
|
72 |
+
nn.Sequential(
|
73 |
+
nn.Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False),
|
74 |
+
nn.Dropout(p=0.0, inplace=False),
|
75 |
+
nn.GELU(approximate="none"),
|
76 |
+
)
|
77 |
+
for _ in range(2)
|
78 |
+
],
|
79 |
+
]
|
80 |
+
self.conv_layers = nn.ModuleList(conv_layers)
|
81 |
+
|
82 |
+
def forward(self, x: Tensor):
|
83 |
+
x = x.unsqueeze(1)
|
84 |
+
for conv in self.conv_layers:
|
85 |
+
x = conv(x)
|
86 |
+
return x
|
87 |
+
|
88 |
+
|
89 |
+
class TransformerEncoder(nn.Module):
|
90 |
+
def __init__(
|
91 |
+
self, dropout=0.1, required_seq_len_multiple=2, *args, **kwargs
|
92 |
+
) -> None:
|
93 |
+
super().__init__(*args, **kwargs)
|
94 |
+
self.dropout = dropout # 0.1
|
95 |
+
self.required_seq_len_multiple = required_seq_len_multiple # 2
|
96 |
+
|
97 |
+
pos_conv = nn.Conv1d(
|
98 |
+
768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16
|
99 |
+
)
|
100 |
+
self.pos_conv = nn.Sequential(
|
101 |
+
nn.utils.weight_norm(pos_conv, name="weight", dim=2),
|
102 |
+
SamePad(128),
|
103 |
+
nn.GELU(approximate="none"),
|
104 |
+
)
|
105 |
+
self.layers = nn.ModuleList(
|
106 |
+
[TransformerSentenceEncoderLayer() for _ in range(12)]
|
107 |
+
)
|
108 |
+
self.layer_norm = nn.LayerNorm((768,), eps=1e-05, elementwise_affine=True)
|
109 |
+
|
110 |
+
@torch.no_grad()
|
111 |
+
def forward(self, x: Tensor, padding_mask=None, tgt_layer=None):
|
112 |
+
if padding_mask is not None:
|
113 |
+
# x = index_put(x, padding_mask, 0)
|
114 |
+
x[padding_mask] = 0
|
115 |
+
|
116 |
+
x_conv = self.pos_conv(x.transpose(1, 2))
|
117 |
+
x_conv = x_conv.transpose(1, 2)
|
118 |
+
x = x + x_conv
|
119 |
+
|
120 |
+
x = self.layer_norm(x)
|
121 |
+
|
122 |
+
# pad to the sequence length dimension
|
123 |
+
x, pad_length = pad_to_multiple(
|
124 |
+
x, self.required_seq_len_multiple, dim=-2, value=0
|
125 |
+
)
|
126 |
+
if pad_length > 0 and padding_mask is None:
|
127 |
+
padding_mask = x.new_zeros((x.size(0), x.size(1)), dtype=torch.bool)
|
128 |
+
padding_mask[:, -pad_length:] = True
|
129 |
+
else:
|
130 |
+
padding_mask, _ = pad_to_multiple(
|
131 |
+
padding_mask, self.required_seq_len_multiple, dim=-1, value=True
|
132 |
+
)
|
133 |
+
x = F.dropout(x, p=self.dropout, training=self.training)
|
134 |
+
|
135 |
+
# B x T x C -> T x B x C
|
136 |
+
x = x.transpose(0, 1)
|
137 |
+
|
138 |
+
for i, layer in enumerate(self.layers):
|
139 |
+
x, _ = layer(x, self_attn_padding_mask=padding_mask, need_weights=False)
|
140 |
+
if i == tgt_layer:
|
141 |
+
break
|
142 |
+
|
143 |
+
# T x B x C -> B x T x C
|
144 |
+
x = x.transpose(0, 1)
|
145 |
+
return x
|
146 |
+
|
147 |
+
|
148 |
+
class TransformerSentenceEncoderLayer(nn.Module):
|
149 |
+
def __init__(
|
150 |
+
self,
|
151 |
+
embedding_dim: float = 768,
|
152 |
+
ffn_embedding_dim: float = 3072,
|
153 |
+
num_attention_heads: int = 12,
|
154 |
+
dropout: float = 0.1,
|
155 |
+
attention_dropout: float = 0.1,
|
156 |
+
activation_dropout: float = 0.1,
|
157 |
+
layer_norm_first: bool = False,
|
158 |
+
*args,
|
159 |
+
**kwargs,
|
160 |
+
) -> None:
|
161 |
+
super().__init__(*args, **kwargs)
|
162 |
+
self.embedding_dim = embedding_dim
|
163 |
+
self.ffn_embedding_dim = ffn_embedding_dim
|
164 |
+
self.num_attention_heads = num_attention_heads
|
165 |
+
|
166 |
+
self.self_attn = MultiheadAttention(
|
167 |
+
self.embedding_dim, # 768
|
168 |
+
num_attention_heads, # 12
|
169 |
+
dropout=attention_dropout, # 0.1
|
170 |
+
)
|
171 |
+
self.dropout1 = nn.Dropout(dropout)
|
172 |
+
self.dropout2 = nn.Dropout(activation_dropout)
|
173 |
+
self.dropout3 = nn.Dropout(dropout)
|
174 |
+
self.layer_norm_first = layer_norm_first
|
175 |
+
self.self_attn_layer_norm = nn.LayerNorm(self.embedding_dim)
|
176 |
+
self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim)
|
177 |
+
self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim)
|
178 |
+
self.final_layer_norm = nn.LayerNorm(self.embedding_dim)
|
179 |
+
|
180 |
+
def forward(
|
181 |
+
self,
|
182 |
+
x: torch.Tensor,
|
183 |
+
self_attn_mask: torch.Tensor = None,
|
184 |
+
self_attn_padding_mask: torch.Tensor = None,
|
185 |
+
need_weights: bool = False,
|
186 |
+
att_args=None,
|
187 |
+
):
|
188 |
+
residual = x
|
189 |
+
x, attn = self.self_attn(
|
190 |
+
query=x,
|
191 |
+
key=x,
|
192 |
+
value=x,
|
193 |
+
key_padding_mask=self_attn_padding_mask,
|
194 |
+
need_weights=False,
|
195 |
+
)
|
196 |
+
|
197 |
+
x = self.dropout1(x)
|
198 |
+
x = residual + x
|
199 |
+
|
200 |
+
x = self.self_attn_layer_norm(x)
|
201 |
+
|
202 |
+
residual = x
|
203 |
+
x = F.gelu(self.fc1(x).float()).type_as(x)
|
204 |
+
x = self.dropout2(x)
|
205 |
+
x = self.fc2(x)
|
206 |
+
|
207 |
+
layer_result = x
|
208 |
+
|
209 |
+
x = self.dropout3(x)
|
210 |
+
x = residual + x
|
211 |
+
x = self.final_layer_norm(x)
|
212 |
+
|
213 |
+
return x, (attn, layer_result)
|
214 |
+
|
215 |
+
|
216 |
+
class MultiheadAttention(nn.Module):
|
217 |
+
def __init__(
|
218 |
+
self, embed_dim: int, num_heads: int, dropout=0.1, bias=True, *args, **kwargs
|
219 |
+
) -> None:
|
220 |
+
super().__init__(*args, **kwargs)
|
221 |
+
self.embed_dim = embed_dim
|
222 |
+
self.num_heads = num_heads
|
223 |
+
|
224 |
+
self.dropout_module = FairseqDropout(p=dropout)
|
225 |
+
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
|
226 |
+
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
|
227 |
+
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
|
228 |
+
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
|
229 |
+
|
230 |
+
def forward(
|
231 |
+
self,
|
232 |
+
query: Tensor,
|
233 |
+
key: Tensor,
|
234 |
+
value: Tensor,
|
235 |
+
key_padding_mask: Optional[Tensor] = None,
|
236 |
+
need_weights: bool = False,
|
237 |
+
attn_mask: Optional[Tensor] = None,
|
238 |
+
) -> Tuple[Tensor, Optional[Tensor]]:
|
239 |
+
|
240 |
+
tgt_len, bsz, embed_dim = query.size()
|
241 |
+
src_len = tgt_len
|
242 |
+
assert embed_dim == self.embed_dim, f"query dim {embed_dim} != {self.embed_dim}"
|
243 |
+
src_len, key_bsz, _ = key.size()
|
244 |
+
assert src_len, key_bsz == value.shape[:2]
|
245 |
+
return F.multi_head_attention_forward(
|
246 |
+
query,
|
247 |
+
key,
|
248 |
+
value,
|
249 |
+
self.embed_dim,
|
250 |
+
self.num_heads,
|
251 |
+
torch.empty([0]),
|
252 |
+
torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)),
|
253 |
+
None,
|
254 |
+
None,
|
255 |
+
False,
|
256 |
+
self.dropout_module.p,
|
257 |
+
self.out_proj.weight,
|
258 |
+
self.out_proj.bias,
|
259 |
+
self.training or self.dropout_module.apply_during_inference,
|
260 |
+
key_padding_mask.bool() if key_padding_mask is not None else None,
|
261 |
+
need_weights,
|
262 |
+
attn_mask,
|
263 |
+
use_separate_proj_weight=True,
|
264 |
+
q_proj_weight=self.q_proj.weight,
|
265 |
+
k_proj_weight=self.k_proj.weight,
|
266 |
+
v_proj_weight=self.v_proj.weight,
|
267 |
+
)
|
268 |
+
|
269 |
+
|
270 |
+
def pad_to_multiple(x, multiple, dim=-1, value=0):
|
271 |
+
# Inspired from https://github.com/lucidrains/local-attention/blob/master/local_attention/local_attention.py#L41
|
272 |
+
if x is None:
|
273 |
+
return None, 0
|
274 |
+
tsz = x.size(dim)
|
275 |
+
m = tsz / multiple
|
276 |
+
remainder = math.ceil(m) * multiple - tsz
|
277 |
+
if m.is_integer():
|
278 |
+
return x, 0
|
279 |
+
pad_offset = (0,) * (-1 - dim) * 2
|
280 |
+
|
281 |
+
return F.pad(x, (*pad_offset, 0, remainder), value=value), remainder
|
src/vae_memory_bank.py
ADDED
@@ -0,0 +1,40 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.nn as nn
|
3 |
+
|
4 |
+
from .attentions import MultiHeadAttention
|
5 |
+
|
6 |
+
|
7 |
+
class VAEMemoryBank(nn.Module):
|
8 |
+
def __init__(
|
9 |
+
self,
|
10 |
+
bank_size=1000,
|
11 |
+
n_hidden_dims=512,
|
12 |
+
n_attn_heads=2,
|
13 |
+
init_values=None,
|
14 |
+
output_channels=192,
|
15 |
+
):
|
16 |
+
super().__init__()
|
17 |
+
|
18 |
+
self.bank_size = bank_size
|
19 |
+
self.n_hidden_dims = n_hidden_dims
|
20 |
+
self.n_attn_heads = n_attn_heads
|
21 |
+
|
22 |
+
self.encoder = MultiHeadAttention(
|
23 |
+
channels=n_hidden_dims,
|
24 |
+
out_channels=n_hidden_dims,
|
25 |
+
n_heads=n_attn_heads,
|
26 |
+
)
|
27 |
+
|
28 |
+
self.memory_bank = nn.Parameter(torch.randn(n_hidden_dims, bank_size))
|
29 |
+
self.proj = nn.Conv1d(n_hidden_dims, output_channels, 1)
|
30 |
+
if init_values is not None:
|
31 |
+
with torch.no_grad():
|
32 |
+
self.memory_bank.copy_(init_values)
|
33 |
+
|
34 |
+
def forward(self, z: torch.Tensor):
|
35 |
+
b, _, _ = z.shape
|
36 |
+
ret = self.encoder(
|
37 |
+
z, self.memory_bank.unsqueeze(0).repeat(b, 1, 1), attn_mask=None
|
38 |
+
)
|
39 |
+
ret = self.proj(ret)
|
40 |
+
return ret
|
src/wespeaker_campplus/__init__.py
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
"""
|
2 |
+
The methods in this module are reused from this repository
|
3 |
+
https://github.com/wenet-e2e/wespeaker
|
4 |
+
"""
|
src/wespeaker_campplus/fbank_feature_extractor.py
ADDED
@@ -0,0 +1,38 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch.nn as nn
|
2 |
+
import torch
|
3 |
+
import torch.nn.functional as F
|
4 |
+
import torchaudio
|
5 |
+
|
6 |
+
class PreEmphasis(torch.nn.Module):
|
7 |
+
|
8 |
+
def __init__(self, coef: float = 0.97):
|
9 |
+
super().__init__()
|
10 |
+
self.coef = coef
|
11 |
+
self.register_buffer(
|
12 |
+
'flipped_filter', torch.FloatTensor([-self.coef, 1.]).unsqueeze(0).unsqueeze(0)
|
13 |
+
)
|
14 |
+
|
15 |
+
def forward(self, input: torch.tensor) -> torch.tensor:
|
16 |
+
input = input.unsqueeze(1)
|
17 |
+
input = F.pad(input, (1, 0), 'reflect')
|
18 |
+
return F.conv1d(input, self.flipped_filter).squeeze(1)
|
19 |
+
|
20 |
+
class FbankFeatureExtractor(nn.Module):
|
21 |
+
"""Some Information about MyModule"""
|
22 |
+
def __init__(self, feat_dim = 80, f_max = 7600, **kwargs):
|
23 |
+
super(FbankFeatureExtractor, self, ).__init__()
|
24 |
+
|
25 |
+
self.torchfbank = torch.nn.Sequential(
|
26 |
+
PreEmphasis(),
|
27 |
+
torchaudio.transforms.MelSpectrogram(sample_rate=16000, n_fft=512, win_length=400, hop_length=160, \
|
28 |
+
f_min = 20, f_max = f_max, window_fn=torch.hamming_window, n_mels=feat_dim),
|
29 |
+
)
|
30 |
+
|
31 |
+
self.instance_norm = nn.InstanceNorm1d(feat_dim)
|
32 |
+
|
33 |
+
def forward(self, x):
|
34 |
+
with torch.no_grad():
|
35 |
+
x = self.torchfbank(x)+1e-6
|
36 |
+
x = x.log()
|
37 |
+
x = x - torch.mean(x, dim=-1, keepdim=True)
|
38 |
+
return x
|
src/wespeaker_campplus/pooling_layers.py
ADDED
@@ -0,0 +1,287 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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 |
+
# Copyright (c) 2021 Shuai Wang (wsstriving@gmail.com)
|
2 |
+
#
|
3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
+
# you may not use this file except in compliance with the License.
|
5 |
+
# You may obtain a copy of the License at
|
6 |
+
#
|
7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
+
#
|
9 |
+
# Unless required by applicable law or agreed to in writing, software
|
10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
+
# See the License for the specific language governing permissions and
|
13 |
+
# limitations under the License.
|
14 |
+
"""
|
15 |
+
Pooling functions to aggregate frame-level deep features
|
16 |
+
into segment-level speaker embeddings
|
17 |
+
|
18 |
+
High-order statistics are surprisingly effective, TSDP acts similarly as TSTP,
|
19 |
+
even though we remove the mean statistic, on Voxceleb.
|
20 |
+
"""
|
21 |
+
|
22 |
+
import torch
|
23 |
+
import torch.nn as nn
|
24 |
+
import torch.nn.functional as F
|
25 |
+
|
26 |
+
|
27 |
+
class TAP(nn.Module):
|
28 |
+
"""
|
29 |
+
Temporal average pooling, only first-order mean is considered
|
30 |
+
"""
|
31 |
+
|
32 |
+
def __init__(self, in_dim=0, **kwargs):
|
33 |
+
super(TAP, self).__init__()
|
34 |
+
self.in_dim = in_dim
|
35 |
+
|
36 |
+
def forward(self, x):
|
37 |
+
pooling_mean = x.mean(dim=-1)
|
38 |
+
# To be compatable with 2D input
|
39 |
+
pooling_mean = pooling_mean.flatten(start_dim=1)
|
40 |
+
return pooling_mean
|
41 |
+
|
42 |
+
def get_out_dim(self):
|
43 |
+
self.out_dim = self.in_dim
|
44 |
+
return self.out_dim
|
45 |
+
|
46 |
+
|
47 |
+
class TSDP(nn.Module):
|
48 |
+
"""
|
49 |
+
Temporal standard deviation pooling, only second-order std is considered
|
50 |
+
"""
|
51 |
+
|
52 |
+
def __init__(self, in_dim=0, **kwargs):
|
53 |
+
super(TSDP, self).__init__()
|
54 |
+
self.in_dim = in_dim
|
55 |
+
|
56 |
+
def forward(self, x):
|
57 |
+
# The last dimension is the temporal axis
|
58 |
+
pooling_std = torch.sqrt(torch.var(x, dim=-1) + 1e-7)
|
59 |
+
pooling_std = pooling_std.flatten(start_dim=1)
|
60 |
+
return pooling_std
|
61 |
+
|
62 |
+
def get_out_dim(self):
|
63 |
+
self.out_dim = self.in_dim
|
64 |
+
return self.out_dim
|
65 |
+
|
66 |
+
|
67 |
+
class TSTP(nn.Module):
|
68 |
+
"""
|
69 |
+
Temporal statistics pooling, concatenate mean and std, which is used in
|
70 |
+
x-vector
|
71 |
+
Comment: simple concatenation can not make full use of both statistics
|
72 |
+
"""
|
73 |
+
|
74 |
+
def __init__(self, in_dim=0, **kwargs):
|
75 |
+
super(TSTP, self).__init__()
|
76 |
+
self.in_dim = in_dim
|
77 |
+
|
78 |
+
def forward(self, x):
|
79 |
+
# The last dimension is the temporal axis
|
80 |
+
pooling_mean = x.mean(dim=-1)
|
81 |
+
pooling_std = torch.sqrt(torch.var(x, dim=-1) + 1e-7)
|
82 |
+
pooling_mean = pooling_mean.flatten(start_dim=1)
|
83 |
+
pooling_std = pooling_std.flatten(start_dim=1)
|
84 |
+
stats = torch.cat((pooling_mean, pooling_std), 1)
|
85 |
+
return stats
|
86 |
+
|
87 |
+
def get_out_dim(self):
|
88 |
+
self.out_dim = self.in_dim * 2
|
89 |
+
return self.out_dim
|
90 |
+
|
91 |
+
|
92 |
+
class ASTP(nn.Module):
|
93 |
+
"""Attentive statistics pooling: Channel- and context-dependent
|
94 |
+
statistics pooling, first used in ECAPA_TDNN.
|
95 |
+
"""
|
96 |
+
|
97 |
+
def __init__(self, in_dim, bottleneck_dim=128, global_context_att=False, **kwargs):
|
98 |
+
super(ASTP, self).__init__()
|
99 |
+
self.in_dim = in_dim
|
100 |
+
self.global_context_att = global_context_att
|
101 |
+
|
102 |
+
# Use Conv1d with stride == 1 rather than Linear, then we don't
|
103 |
+
# need to transpose inputs.
|
104 |
+
if global_context_att:
|
105 |
+
self.linear1 = nn.Conv1d(
|
106 |
+
in_dim * 3, bottleneck_dim, kernel_size=1
|
107 |
+
) # equals W and b in the paper
|
108 |
+
else:
|
109 |
+
self.linear1 = nn.Conv1d(
|
110 |
+
in_dim, bottleneck_dim, kernel_size=1
|
111 |
+
) # equals W and b in the paper
|
112 |
+
self.linear2 = nn.Conv1d(
|
113 |
+
bottleneck_dim, in_dim, kernel_size=1
|
114 |
+
) # equals V and k in the paper
|
115 |
+
|
116 |
+
def forward(self, x):
|
117 |
+
"""
|
118 |
+
x: a 3-dimensional tensor in tdnn-based architecture (B,F,T)
|
119 |
+
or a 4-dimensional tensor in resnet architecture (B,C,F,T)
|
120 |
+
0-dim: batch-dimension, last-dim: time-dimension (frame-dimension)
|
121 |
+
"""
|
122 |
+
if len(x.shape) == 4:
|
123 |
+
x = x.reshape(x.shape[0], x.shape[1] * x.shape[2], x.shape[3])
|
124 |
+
assert len(x.shape) == 3
|
125 |
+
|
126 |
+
if self.global_context_att:
|
127 |
+
context_mean = torch.mean(x, dim=-1, keepdim=True).expand_as(x)
|
128 |
+
context_std = torch.sqrt(
|
129 |
+
torch.var(x, dim=-1, keepdim=True) + 1e-7
|
130 |
+
).expand_as(x)
|
131 |
+
x_in = torch.cat((x, context_mean, context_std), dim=1)
|
132 |
+
else:
|
133 |
+
x_in = x
|
134 |
+
|
135 |
+
# DON'T use ReLU here! ReLU may be hard to converge.
|
136 |
+
alpha = torch.tanh(self.linear1(x_in)) # alpha = F.relu(self.linear1(x_in))
|
137 |
+
alpha = torch.softmax(self.linear2(alpha), dim=2)
|
138 |
+
mean = torch.sum(alpha * x, dim=2)
|
139 |
+
var = torch.sum(alpha * (x**2), dim=2) - mean**2
|
140 |
+
std = torch.sqrt(var.clamp(min=1e-7))
|
141 |
+
return torch.cat([mean, std], dim=1)
|
142 |
+
|
143 |
+
def get_out_dim(self):
|
144 |
+
self.out_dim = 2 * self.in_dim
|
145 |
+
return self.out_dim
|
146 |
+
|
147 |
+
|
148 |
+
class MHASTP(torch.nn.Module):
|
149 |
+
"""Multi head attentive statistics pooling
|
150 |
+
Reference:
|
151 |
+
Self Multi-Head Attention for Speaker Recognition
|
152 |
+
https://arxiv.org/pdf/1906.09890.pdf
|
153 |
+
"""
|
154 |
+
|
155 |
+
def __init__(
|
156 |
+
self, in_dim, layer_num=2, head_num=2, d_s=1, bottleneck_dim=64, **kwargs
|
157 |
+
):
|
158 |
+
super(MHASTP, self).__init__()
|
159 |
+
assert (
|
160 |
+
in_dim % head_num
|
161 |
+
) == 0 # make sure that head num can be divided by input_dim
|
162 |
+
self.in_dim = in_dim
|
163 |
+
self.head_num = head_num
|
164 |
+
d_model = int(in_dim / head_num)
|
165 |
+
channel_dims = [bottleneck_dim for i in range(layer_num + 1)]
|
166 |
+
if d_s > 1:
|
167 |
+
d_s = d_model
|
168 |
+
else:
|
169 |
+
d_s = 1
|
170 |
+
self.d_s = d_s
|
171 |
+
channel_dims[0], channel_dims[-1] = d_model, d_s
|
172 |
+
heads_att_trans = []
|
173 |
+
for i in range(self.head_num):
|
174 |
+
att_trans = nn.Sequential()
|
175 |
+
for i in range(layer_num - 1):
|
176 |
+
att_trans.add_module(
|
177 |
+
"att_" + str(i),
|
178 |
+
nn.Conv1d(channel_dims[i], channel_dims[i + 1], 1, 1),
|
179 |
+
)
|
180 |
+
att_trans.add_module("tanh" + str(i), nn.Tanh())
|
181 |
+
att_trans.add_module(
|
182 |
+
"att_" + str(layer_num - 1),
|
183 |
+
nn.Conv1d(channel_dims[layer_num - 1], channel_dims[layer_num], 1, 1),
|
184 |
+
)
|
185 |
+
heads_att_trans.append(att_trans)
|
186 |
+
self.heads_att_trans = nn.ModuleList(heads_att_trans)
|
187 |
+
|
188 |
+
def forward(self, input):
|
189 |
+
"""
|
190 |
+
input: a 3-dimensional tensor in xvector architecture
|
191 |
+
or a 4-dimensional tensor in resnet architecture
|
192 |
+
0-dim: batch-dimension, last-dim: time-dimension (frame-dimension)
|
193 |
+
"""
|
194 |
+
if len(input.shape) == 4: # B x F x T
|
195 |
+
input = input.reshape(
|
196 |
+
input.shape[0], input.shape[1] * input.shape[2], input.shape[3]
|
197 |
+
)
|
198 |
+
assert len(input.shape) == 3
|
199 |
+
bs, f_dim, t_dim = input.shape
|
200 |
+
chunks = torch.chunk(input, self.head_num, 1)
|
201 |
+
# split
|
202 |
+
chunks_out = []
|
203 |
+
# for i in range(self.head_num):
|
204 |
+
# att_score = self.heads_att_trans[i](chunks[i])
|
205 |
+
for i, layer in enumerate(self.heads_att_trans):
|
206 |
+
att_score = layer(chunks[i])
|
207 |
+
alpha = F.softmax(att_score, dim=-1)
|
208 |
+
mean = torch.sum(alpha * chunks[i], dim=2)
|
209 |
+
var = torch.sum(alpha * chunks[i] ** 2, dim=2) - mean**2
|
210 |
+
std = torch.sqrt(var.clamp(min=1e-7))
|
211 |
+
chunks_out.append(torch.cat((mean, std), dim=1))
|
212 |
+
out = torch.cat(chunks_out, dim=1)
|
213 |
+
return out
|
214 |
+
|
215 |
+
def get_out_dim(self):
|
216 |
+
self.out_dim = 2 * self.in_dim
|
217 |
+
return self.out_dim
|
218 |
+
|
219 |
+
|
220 |
+
class MQMHASTP(torch.nn.Module):
|
221 |
+
"""An attentive pooling
|
222 |
+
Reference:
|
223 |
+
multi query multi head attentive statistics pooling
|
224 |
+
https://arxiv.org/pdf/2110.05042.pdf
|
225 |
+
Args:
|
226 |
+
in_dim: the feature dimension of input
|
227 |
+
layer_num: the number of layer in the pooling layer
|
228 |
+
query_num: the number of querys
|
229 |
+
head_num: the number of heads
|
230 |
+
bottleneck_dim: the bottleneck dimension
|
231 |
+
|
232 |
+
SA (H = 1, Q = 1, n = 2, d_s = 1) ref:
|
233 |
+
https://www.danielpovey.com/files/2018_interspeech_xvector_attention.pdf
|
234 |
+
MHA (H > 1, Q = 1, n = 1, d_s = 1) ref:
|
235 |
+
https://arxiv.org/pdf/1906.09890.pdf
|
236 |
+
AS (H = 1, Q > 1, n = 2, d_s = 1) ref:
|
237 |
+
https://arxiv.org/pdf/1803.10963.pdf
|
238 |
+
VSA (H = 1, Q > 1, n = 2, d_s = d_h) ref:
|
239 |
+
http://www.interspeech2020.org/uploadfile/pdf/Mon-2-10-5.pdf
|
240 |
+
"""
|
241 |
+
|
242 |
+
def __init__(
|
243 |
+
self,
|
244 |
+
in_dim,
|
245 |
+
layer_num=2,
|
246 |
+
query_num=2,
|
247 |
+
head_num=8,
|
248 |
+
d_s=2,
|
249 |
+
bottleneck_dim=64,
|
250 |
+
**kwargs
|
251 |
+
):
|
252 |
+
super(MQMHASTP, self).__init__()
|
253 |
+
self.n_query = nn.ModuleList(
|
254 |
+
[
|
255 |
+
MHASTP(
|
256 |
+
in_dim,
|
257 |
+
layer_num=layer_num,
|
258 |
+
head_num=head_num,
|
259 |
+
d_s=d_s,
|
260 |
+
bottleneck_dim=bottleneck_dim,
|
261 |
+
)
|
262 |
+
for i in range(query_num)
|
263 |
+
]
|
264 |
+
)
|
265 |
+
self.query_num = query_num
|
266 |
+
self.in_dim = in_dim
|
267 |
+
|
268 |
+
def forward(self, input):
|
269 |
+
"""
|
270 |
+
input: a 3-dimensional tensor in xvector architecture
|
271 |
+
or a 4-dimensional tensor in resnet architecture
|
272 |
+
0-dim: batch-dimension, last-dim: time-dimension (frame-dimension)
|
273 |
+
"""
|
274 |
+
if len(input.shape) == 4: # B x F x T
|
275 |
+
input = input.reshape(
|
276 |
+
input.shape[0], input.shape[1] * input.shape[2], input.shape[3]
|
277 |
+
)
|
278 |
+
assert len(input.shape) == 3
|
279 |
+
res = []
|
280 |
+
for i, layer in enumerate(self.n_query):
|
281 |
+
res.append(layer(input))
|
282 |
+
out = torch.cat(res, dim=-1)
|
283 |
+
return out
|
284 |
+
|
285 |
+
def get_out_dim(self):
|
286 |
+
self.out_dim = self.in_dim * 2 * self.query_num
|
287 |
+
return self.out_dim
|
tools/commons.py
ADDED
@@ -0,0 +1,181 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import math
|
2 |
+
|
3 |
+
import numpy as np
|
4 |
+
import torch
|
5 |
+
from torch import nn
|
6 |
+
from torch.nn import functional as F
|
7 |
+
|
8 |
+
|
9 |
+
def init_weights(m, mean=0.0, std=0.01):
|
10 |
+
classname = m.__class__.__name__
|
11 |
+
if classname.find("Conv") != -1:
|
12 |
+
try:
|
13 |
+
m.weight.data.normal_(mean, std)
|
14 |
+
except:
|
15 |
+
try:
|
16 |
+
m.conv.weight.data.normal_(mean, std)
|
17 |
+
except:
|
18 |
+
m.conv.conv.weight.data.normal_(mean, std)
|
19 |
+
|
20 |
+
|
21 |
+
def get_padding(kernel_size, dilation=1):
|
22 |
+
return int((kernel_size * dilation - dilation) / 2)
|
23 |
+
|
24 |
+
|
25 |
+
def convert_pad_shape(pad_shape):
|
26 |
+
l = pad_shape[::-1]
|
27 |
+
pad_shape = [item for sublist in l for item in sublist]
|
28 |
+
return pad_shape
|
29 |
+
|
30 |
+
|
31 |
+
def intersperse(lst, item):
|
32 |
+
result = [item] * (len(lst) * 2 + 1)
|
33 |
+
result[1::2] = lst
|
34 |
+
return result
|
35 |
+
|
36 |
+
|
37 |
+
def kl_divergence(m_p, logs_p, m_q, logs_q):
|
38 |
+
"""KL(P||Q)"""
|
39 |
+
kl = (logs_q - logs_p) - 0.5
|
40 |
+
kl += (
|
41 |
+
0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
|
42 |
+
)
|
43 |
+
return kl
|
44 |
+
|
45 |
+
|
46 |
+
def rand_gumbel(shape):
|
47 |
+
"""Sample from the Gumbel distribution, protect from overflows."""
|
48 |
+
uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
|
49 |
+
return -torch.log(-torch.log(uniform_samples))
|
50 |
+
|
51 |
+
|
52 |
+
def rand_gumbel_like(x):
|
53 |
+
g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
|
54 |
+
return g
|
55 |
+
|
56 |
+
|
57 |
+
def slice_segments(x, ids_str, segment_size=4):
|
58 |
+
ret = torch.zeros_like(x[:, :, :segment_size])
|
59 |
+
for i in range(x.size(0)):
|
60 |
+
idx_str = ids_str[i]
|
61 |
+
idx_end = idx_str + segment_size
|
62 |
+
ret[i] = x[i, :, idx_str:idx_end]
|
63 |
+
return ret
|
64 |
+
|
65 |
+
|
66 |
+
def rand_slice_segments(x, x_lengths=None, segment_size=4):
|
67 |
+
b, d, t = x.size()
|
68 |
+
if x_lengths is None:
|
69 |
+
x_lengths = t
|
70 |
+
ids_str_max = x_lengths - segment_size + 1
|
71 |
+
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
|
72 |
+
ret = slice_segments(x, ids_str, segment_size)
|
73 |
+
return ret, ids_str
|
74 |
+
|
75 |
+
|
76 |
+
def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
|
77 |
+
position = torch.arange(length, dtype=torch.float)
|
78 |
+
num_timescales = channels // 2
|
79 |
+
log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
|
80 |
+
num_timescales - 1
|
81 |
+
)
|
82 |
+
inv_timescales = min_timescale * torch.exp(
|
83 |
+
torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
|
84 |
+
)
|
85 |
+
scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
|
86 |
+
signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
|
87 |
+
signal = F.pad(signal, [0, 0, 0, channels % 2])
|
88 |
+
signal = signal.view(1, channels, length)
|
89 |
+
return signal
|
90 |
+
|
91 |
+
|
92 |
+
def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
|
93 |
+
b, channels, length = x.size()
|
94 |
+
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
95 |
+
return x + signal.to(dtype=x.dtype, device=x.device)
|
96 |
+
|
97 |
+
|
98 |
+
def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
|
99 |
+
b, channels, length = x.size()
|
100 |
+
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
101 |
+
return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
|
102 |
+
|
103 |
+
|
104 |
+
def subsequent_mask(length):
|
105 |
+
mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
|
106 |
+
return mask
|
107 |
+
|
108 |
+
|
109 |
+
@torch.jit.script
|
110 |
+
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
111 |
+
n_channels_int = n_channels[0]
|
112 |
+
in_act = input_a + input_b
|
113 |
+
t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
114 |
+
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
115 |
+
acts = t_act * s_act
|
116 |
+
return acts
|
117 |
+
|
118 |
+
|
119 |
+
def convert_pad_shape(pad_shape):
|
120 |
+
l = pad_shape[::-1]
|
121 |
+
pad_shape = [item for sublist in l for item in sublist]
|
122 |
+
return pad_shape
|
123 |
+
|
124 |
+
|
125 |
+
def shift_1d(x):
|
126 |
+
x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
|
127 |
+
return x
|
128 |
+
|
129 |
+
|
130 |
+
def sequence_mask(length: torch.LongTensor, max_length: int = None):
|
131 |
+
"""_summary_
|
132 |
+
|
133 |
+
Args:
|
134 |
+
length (torch.LongTensor): 1d sequence of lengths of some sequence [BATCH_SIZE]
|
135 |
+
max_length (int, optional) : max length of sequence. Defaults to None.
|
136 |
+
|
137 |
+
Returns:
|
138 |
+
_type_: _description_
|
139 |
+
"""
|
140 |
+
if max_length is None:
|
141 |
+
max_length = length.max()
|
142 |
+
|
143 |
+
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
|
144 |
+
|
145 |
+
return x.unsqueeze(0) < length.unsqueeze(1)
|
146 |
+
|
147 |
+
|
148 |
+
def generate_path(duration, mask):
|
149 |
+
"""
|
150 |
+
duration: [b, 1, t_x]
|
151 |
+
mask: [b, 1, t_y, t_x]
|
152 |
+
"""
|
153 |
+
device = duration.device
|
154 |
+
|
155 |
+
b, _, t_y, t_x = mask.shape
|
156 |
+
cum_duration = torch.cumsum(duration, -1)
|
157 |
+
|
158 |
+
cum_duration_flat = cum_duration.view(b * t_x)
|
159 |
+
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
|
160 |
+
path = path.view(b, t_x, t_y)
|
161 |
+
path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
|
162 |
+
path = path.unsqueeze(1).transpose(2, 3).contiguous() * mask
|
163 |
+
return path
|
164 |
+
|
165 |
+
|
166 |
+
def clip_grad_value_(parameters, clip_value, norm_type=2):
|
167 |
+
if isinstance(parameters, torch.Tensor):
|
168 |
+
parameters = [parameters]
|
169 |
+
parameters = list(filter(lambda p: p.grad is not None, parameters))
|
170 |
+
norm_type = float(norm_type)
|
171 |
+
if clip_value is not None:
|
172 |
+
clip_value = float(clip_value)
|
173 |
+
|
174 |
+
total_norm = 0
|
175 |
+
for p in parameters:
|
176 |
+
param_norm = p.grad.data.norm(norm_type)
|
177 |
+
total_norm += param_norm.item() ** norm_type
|
178 |
+
if clip_value is not None:
|
179 |
+
p.grad.data.clamp_(min=-clip_value, max=clip_value)
|
180 |
+
total_norm = total_norm ** (1.0 / norm_type)
|
181 |
+
return total_norm
|
tools/transforms.py
ADDED
@@ -0,0 +1,209 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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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(
|
13 |
+
inputs,
|
14 |
+
unnormalized_widths,
|
15 |
+
unnormalized_heights,
|
16 |
+
unnormalized_derivatives,
|
17 |
+
inverse=False,
|
18 |
+
tails=None,
|
19 |
+
tail_bound=1.0,
|
20 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
21 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
22 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE,
|
23 |
+
):
|
24 |
+
if tails is None:
|
25 |
+
spline_fn = rational_quadratic_spline
|
26 |
+
spline_kwargs = {}
|
27 |
+
else:
|
28 |
+
spline_fn = unconstrained_rational_quadratic_spline
|
29 |
+
spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
|
30 |
+
|
31 |
+
outputs, logabsdet = spline_fn(
|
32 |
+
inputs=inputs,
|
33 |
+
unnormalized_widths=unnormalized_widths,
|
34 |
+
unnormalized_heights=unnormalized_heights,
|
35 |
+
unnormalized_derivatives=unnormalized_derivatives,
|
36 |
+
inverse=inverse,
|
37 |
+
min_bin_width=min_bin_width,
|
38 |
+
min_bin_height=min_bin_height,
|
39 |
+
min_derivative=min_derivative,
|
40 |
+
**spline_kwargs
|
41 |
+
)
|
42 |
+
return outputs, logabsdet
|
43 |
+
|
44 |
+
|
45 |
+
def searchsorted(bin_locations, inputs, eps=1e-6):
|
46 |
+
bin_locations[..., -1] += eps
|
47 |
+
return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
|
48 |
+
|
49 |
+
|
50 |
+
def unconstrained_rational_quadratic_spline(
|
51 |
+
inputs,
|
52 |
+
unnormalized_widths,
|
53 |
+
unnormalized_heights,
|
54 |
+
unnormalized_derivatives,
|
55 |
+
inverse=False,
|
56 |
+
tails="linear",
|
57 |
+
tail_bound=1.0,
|
58 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
59 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
60 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE,
|
61 |
+
):
|
62 |
+
inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
|
63 |
+
outside_interval_mask = ~inside_interval_mask
|
64 |
+
|
65 |
+
outputs = torch.zeros_like(inputs)
|
66 |
+
logabsdet = torch.zeros_like(inputs)
|
67 |
+
|
68 |
+
if tails == "linear":
|
69 |
+
unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
|
70 |
+
constant = np.log(np.exp(1 - min_derivative) - 1)
|
71 |
+
unnormalized_derivatives[..., 0] = constant
|
72 |
+
unnormalized_derivatives[..., -1] = constant
|
73 |
+
|
74 |
+
outputs[outside_interval_mask] = inputs[outside_interval_mask]
|
75 |
+
logabsdet[outside_interval_mask] = 0
|
76 |
+
else:
|
77 |
+
raise RuntimeError("{} tails are not implemented.".format(tails))
|
78 |
+
|
79 |
+
(
|
80 |
+
outputs[inside_interval_mask],
|
81 |
+
logabsdet[inside_interval_mask],
|
82 |
+
) = rational_quadratic_spline(
|
83 |
+
inputs=inputs[inside_interval_mask],
|
84 |
+
unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
|
85 |
+
unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
|
86 |
+
unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
|
87 |
+
inverse=inverse,
|
88 |
+
left=-tail_bound,
|
89 |
+
right=tail_bound,
|
90 |
+
bottom=-tail_bound,
|
91 |
+
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 |
+
|
100 |
+
def rational_quadratic_spline(
|
101 |
+
inputs,
|
102 |
+
unnormalized_widths,
|
103 |
+
unnormalized_heights,
|
104 |
+
unnormalized_derivatives,
|
105 |
+
inverse=False,
|
106 |
+
left=0.0,
|
107 |
+
right=1.0,
|
108 |
+
bottom=0.0,
|
109 |
+
top=1.0,
|
110 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
111 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
112 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE,
|
113 |
+
):
|
114 |
+
if torch.min(inputs) < left or torch.max(inputs) > right:
|
115 |
+
raise ValueError("Input to a transform is not within its domain")
|
116 |
+
|
117 |
+
num_bins = unnormalized_widths.shape[-1]
|
118 |
+
|
119 |
+
if min_bin_width * num_bins > 1.0:
|
120 |
+
raise ValueError("Minimal bin width too large for the number of bins")
|
121 |
+
if min_bin_height * num_bins > 1.0:
|
122 |
+
raise ValueError("Minimal bin height too large for the number of bins")
|
123 |
+
|
124 |
+
widths = F.softmax(unnormalized_widths, dim=-1)
|
125 |
+
widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
|
126 |
+
cumwidths = torch.cumsum(widths, dim=-1)
|
127 |
+
cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
|
128 |
+
cumwidths = (right - left) * cumwidths + left
|
129 |
+
cumwidths[..., 0] = left
|
130 |
+
cumwidths[..., -1] = right
|
131 |
+
widths = cumwidths[..., 1:] - cumwidths[..., :-1]
|
132 |
+
|
133 |
+
derivatives = min_derivative + F.softplus(unnormalized_derivatives)
|
134 |
+
|
135 |
+
heights = F.softmax(unnormalized_heights, dim=-1)
|
136 |
+
heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
|
137 |
+
cumheights = torch.cumsum(heights, dim=-1)
|
138 |
+
cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
|
139 |
+
cumheights = (top - bottom) * cumheights + bottom
|
140 |
+
cumheights[..., 0] = bottom
|
141 |
+
cumheights[..., -1] = top
|
142 |
+
heights = cumheights[..., 1:] - cumheights[..., :-1]
|
143 |
+
|
144 |
+
if inverse:
|
145 |
+
bin_idx = searchsorted(cumheights, inputs)[..., None]
|
146 |
+
else:
|
147 |
+
bin_idx = searchsorted(cumwidths, inputs)[..., None]
|
148 |
+
|
149 |
+
input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
|
150 |
+
input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
|
151 |
+
|
152 |
+
input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
|
153 |
+
delta = heights / widths
|
154 |
+
input_delta = delta.gather(-1, bin_idx)[..., 0]
|
155 |
+
|
156 |
+
input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
|
157 |
+
input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
|
158 |
+
|
159 |
+
input_heights = heights.gather(-1, bin_idx)[..., 0]
|
160 |
+
|
161 |
+
if inverse:
|
162 |
+
a = (inputs - input_cumheights) * (
|
163 |
+
input_derivatives + input_derivatives_plus_one - 2 * input_delta
|
164 |
+
) + input_heights * (input_delta - input_derivatives)
|
165 |
+
b = input_heights * input_derivatives - (inputs - input_cumheights) * (
|
166 |
+
input_derivatives + input_derivatives_plus_one - 2 * input_delta
|
167 |
+
)
|
168 |
+
c = -input_delta * (inputs - input_cumheights)
|
169 |
+
|
170 |
+
discriminant = b.pow(2) - 4 * a * c
|
171 |
+
assert (discriminant >= 0).all()
|
172 |
+
|
173 |
+
root = (2 * c) / (-b - torch.sqrt(discriminant))
|
174 |
+
outputs = root * input_bin_widths + input_cumwidths
|
175 |
+
|
176 |
+
theta_one_minus_theta = root * (1 - root)
|
177 |
+
denominator = input_delta + (
|
178 |
+
(input_derivatives + input_derivatives_plus_one - 2 * input_delta)
|
179 |
+
* theta_one_minus_theta
|
180 |
+
)
|
181 |
+
derivative_numerator = input_delta.pow(2) * (
|
182 |
+
input_derivatives_plus_one * root.pow(2)
|
183 |
+
+ 2 * input_delta * theta_one_minus_theta
|
184 |
+
+ input_derivatives * (1 - root).pow(2)
|
185 |
+
)
|
186 |
+
logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
|
187 |
+
|
188 |
+
return outputs, -logabsdet
|
189 |
+
else:
|
190 |
+
theta = (inputs - input_cumwidths) / input_bin_widths
|
191 |
+
theta_one_minus_theta = theta * (1 - theta)
|
192 |
+
|
193 |
+
numerator = input_heights * (
|
194 |
+
input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
|
195 |
+
)
|
196 |
+
denominator = input_delta + (
|
197 |
+
(input_derivatives + input_derivatives_plus_one - 2 * input_delta)
|
198 |
+
* theta_one_minus_theta
|
199 |
+
)
|
200 |
+
outputs = input_cumheights + numerator / denominator
|
201 |
+
|
202 |
+
derivative_numerator = input_delta.pow(2) * (
|
203 |
+
input_derivatives_plus_one * theta.pow(2)
|
204 |
+
+ 2 * input_delta * theta_one_minus_theta
|
205 |
+
+ input_derivatives * (1 - theta).pow(2)
|
206 |
+
)
|
207 |
+
logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
|
208 |
+
|
209 |
+
return outputs, logabsdet
|