Spaces:
Runtime error
Runtime error
File size: 6,114 Bytes
c968fc3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 |
# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
import math
class FiLM(nn.Module):
def __init__(self, in_dim, cond_dim):
super().__init__()
self.gain = Linear(cond_dim, in_dim)
self.bias = Linear(cond_dim, in_dim)
nn.init.xavier_uniform_(self.gain.weight)
nn.init.constant_(self.gain.bias, 1)
nn.init.xavier_uniform_(self.bias.weight)
nn.init.constant_(self.bias.bias, 0)
def forward(self, x, condition):
gain = self.gain(condition)
bias = self.bias(condition)
if gain.dim() == 2:
gain = gain.unsqueeze(-1)
if bias.dim() == 2:
bias = bias.unsqueeze(-1)
return x * gain + bias
class Mish(nn.Module):
def forward(self, x):
return x * torch.tanh(F.softplus(x))
def Conv1d(*args, **kwargs):
layer = nn.Conv1d(*args, **kwargs)
nn.init.kaiming_normal_(layer.weight)
return layer
def Linear(*args, **kwargs):
layer = nn.Linear(*args, **kwargs)
layer.weight.data.normal_(0.0, 0.02)
return layer
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
device = x.device
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
emb = x[:, None] * emb[None, :]
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
class ResidualBlock(nn.Module):
def __init__(self, hidden_dim, attn_head, dilation, drop_out, has_cattn=False):
super().__init__()
self.hidden_dim = hidden_dim
self.dilation = dilation
self.has_cattn = has_cattn
self.attn_head = attn_head
self.drop_out = drop_out
self.dilated_conv = Conv1d(
hidden_dim, 2 * hidden_dim, 3, padding=dilation, dilation=dilation
)
self.diffusion_proj = Linear(hidden_dim, hidden_dim)
self.cond_proj = Conv1d(hidden_dim, hidden_dim * 2, 1)
self.out_proj = Conv1d(hidden_dim, hidden_dim * 2, 1)
if self.has_cattn:
self.attn = nn.MultiheadAttention(
hidden_dim, attn_head, 0.1, batch_first=True
)
self.film = FiLM(hidden_dim * 2, hidden_dim)
self.ln = nn.LayerNorm(hidden_dim)
self.dropout = nn.Dropout(self.drop_out)
def forward(self, x, x_mask, cond, diffusion_step, spk_query_emb):
diffusion_step = self.diffusion_proj(diffusion_step).unsqueeze(-1) # (B, d, 1)
cond = self.cond_proj(cond) # (B, 2*d, T)
y = x + diffusion_step
if x_mask != None:
y = y * x_mask.to(y.dtype)[:, None, :] # (B, 2*d, T)
if self.has_cattn:
y_ = y.transpose(1, 2)
y_ = self.ln(y_)
y_, _ = self.attn(y_, spk_query_emb, spk_query_emb) # (B, T, d)
y = self.dilated_conv(y) + cond # (B, 2*d, T)
if self.has_cattn:
y = self.film(y.transpose(1, 2), y_) # (B, T, 2*d)
y = y.transpose(1, 2) # (B, 2*d, T)
gate, filter_ = torch.chunk(y, 2, dim=1)
y = torch.sigmoid(gate) * torch.tanh(filter_)
y = self.out_proj(y)
residual, skip = torch.chunk(y, 2, dim=1)
if x_mask != None:
residual = residual * x_mask.to(y.dtype)[:, None, :]
skip = skip * x_mask.to(y.dtype)[:, None, :]
return (x + residual) / math.sqrt(2.0), skip
class WaveNet(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.in_dim = cfg.input_size
self.hidden_dim = cfg.hidden_size
self.out_dim = cfg.out_size
self.num_layers = cfg.num_layers
self.cross_attn_per_layer = cfg.cross_attn_per_layer
self.dilation_cycle = cfg.dilation_cycle
self.attn_head = cfg.attn_head
self.drop_out = cfg.drop_out
self.in_proj = Conv1d(self.in_dim, self.hidden_dim, 1)
self.diffusion_embedding = SinusoidalPosEmb(self.hidden_dim)
self.mlp = nn.Sequential(
Linear(self.hidden_dim, self.hidden_dim * 4),
Mish(),
Linear(self.hidden_dim * 4, self.hidden_dim),
)
self.cond_ln = nn.LayerNorm(self.hidden_dim)
self.layers = nn.ModuleList(
[
ResidualBlock(
self.hidden_dim,
self.attn_head,
2 ** (i % self.dilation_cycle),
self.drop_out,
has_cattn=(i % self.cross_attn_per_layer == 0),
)
for i in range(self.num_layers)
]
)
self.skip_proj = Conv1d(self.hidden_dim, self.hidden_dim, 1)
self.out_proj = Conv1d(self.hidden_dim, self.out_dim, 1)
nn.init.zeros_(self.out_proj.weight)
def forward(self, x, x_mask, cond, diffusion_step, spk_query_emb):
"""
x: (B, 128, T)
x_mask: (B, T), mask is 0
cond: (B, T, 512)
diffusion_step: (B,)
spk_query_emb: (B, 32, 512)
"""
cond = self.cond_ln(cond)
cond_input = cond.transpose(1, 2)
x_input = self.in_proj(x)
x_input = F.relu(x_input)
diffusion_step = self.diffusion_embedding(diffusion_step).to(x.dtype)
diffusion_step = self.mlp(diffusion_step)
skip = []
for _, layer in enumerate(self.layers):
x_input, skip_connection = layer(
x_input, x_mask, cond_input, diffusion_step, spk_query_emb
)
skip.append(skip_connection)
x_input = torch.sum(torch.stack(skip), dim=0) / math.sqrt(self.num_layers)
x_out = self.skip_proj(x_input)
x_out = F.relu(x_out)
x_out = self.out_proj(x_out) # (B, 128, T)
return x_out
|