File size: 6,540 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 |
# Copyright (c) 2024 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from concurrent.futures import ALL_COMPLETED
import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
from einops import rearrange, repeat
from models.codec.amphion_codec.quantize import ResidualVQ
from models.codec.kmeans.vocos import VocosBackbone
def init_weights(m):
if isinstance(m, nn.Conv1d):
nn.init.trunc_normal_(m.weight, std=0.02)
nn.init.constant_(m.bias, 0)
if isinstance(m, nn.Linear):
nn.init.trunc_normal_(m.weight, std=0.02)
nn.init.constant_(m.bias, 0)
def compute_codebook_perplexity(indices, codebook_size):
indices = indices.flatten()
prob = torch.bincount(indices, minlength=codebook_size).float() / indices.size(0)
perp = torch.exp(-torch.sum(prob * torch.log(prob + 1e-10)))
return perp
class RepCodec(nn.Module):
def __init__(
self,
codebook_size=8192,
hidden_size=1024,
codebook_dim=8,
vocos_dim=384,
vocos_intermediate_dim=2048,
vocos_num_layers=12,
num_quantizers=1,
downsample_scale=1,
cfg=None,
):
super().__init__()
codebook_size = (
cfg.codebook_size
if cfg is not None and hasattr(cfg, "codebook_size")
else codebook_size
)
codebook_dim = (
cfg.codebook_dim
if cfg is not None and hasattr(cfg, "codebook_dim")
else codebook_dim
)
hidden_size = (
cfg.hidden_size
if cfg is not None and hasattr(cfg, "hidden_size")
else hidden_size
)
vocos_dim = (
cfg.vocos_dim
if cfg is not None and hasattr(cfg, "vocos_dim")
else vocos_dim
)
vocos_intermediate_dim = (
cfg.vocos_intermediate_dim
if cfg is not None and hasattr(cfg, "vocos_dim")
else vocos_intermediate_dim
)
vocos_num_layers = (
cfg.vocos_num_layers
if cfg is not None and hasattr(cfg, "vocos_dim")
else vocos_num_layers
)
num_quantizers = (
cfg.num_quantizers
if cfg is not None and hasattr(cfg, "num_quantizers")
else num_quantizers
)
downsample_scale = (
cfg.downsample_scale
if cfg is not None and hasattr(cfg, "downsample_scale")
else downsample_scale
)
self.codebook_size = codebook_size
self.codebook_dim = codebook_dim
self.hidden_size = hidden_size
self.vocos_dim = vocos_dim
self.vocos_intermediate_dim = vocos_intermediate_dim
self.vocos_num_layers = vocos_num_layers
self.num_quantizers = num_quantizers
self.downsample_scale = downsample_scale
if self.downsample_scale != None and self.downsample_scale > 1:
self.down = nn.Conv1d(
self.hidden_size, self.hidden_size, kernel_size=3, stride=2, padding=1
)
self.up = nn.Conv1d(
self.hidden_size, self.hidden_size, kernel_size=3, stride=1, padding=1
)
self.encoder = nn.Sequential(
VocosBackbone(
input_channels=self.hidden_size,
dim=self.vocos_dim,
intermediate_dim=self.vocos_intermediate_dim,
num_layers=self.vocos_num_layers,
adanorm_num_embeddings=None,
),
nn.Linear(self.vocos_dim, self.hidden_size),
)
self.decoder = nn.Sequential(
VocosBackbone(
input_channels=self.hidden_size,
dim=self.vocos_dim,
intermediate_dim=self.vocos_intermediate_dim,
num_layers=self.vocos_num_layers,
adanorm_num_embeddings=None,
),
nn.Linear(self.vocos_dim, self.hidden_size),
)
self.quantizer = ResidualVQ(
input_dim=hidden_size,
num_quantizers=num_quantizers,
codebook_size=codebook_size,
codebook_dim=codebook_dim,
quantizer_type="fvq",
quantizer_dropout=0.0,
commitment=0.15,
codebook_loss_weight=1.0,
use_l2_normlize=True,
)
self.reset_parameters()
def forward(self, x):
# downsample
if self.downsample_scale != None and self.downsample_scale > 1:
x = x.transpose(1, 2)
x = self.down(x)
x = F.gelu(x)
x = x.transpose(1, 2)
# encoder
x = self.encoder(x.transpose(1, 2)).transpose(1, 2)
# vq
(
quantized_out,
all_indices,
all_commit_losses,
all_codebook_losses,
_,
) = self.quantizer(x)
# decoder
x = self.decoder(quantized_out)
# up
if self.downsample_scale != None and self.downsample_scale > 1:
x = x.transpose(1, 2)
x = F.interpolate(x, scale_factor=2, mode="nearest")
x_rec = self.up(x).transpose(1, 2)
codebook_loss = (all_codebook_losses + all_commit_losses).mean()
all_indices = all_indices
return x_rec, codebook_loss, all_indices
def quantize(self, x):
if self.downsample_scale != None and self.downsample_scale > 1:
x = x.transpose(1, 2)
x = self.down(x)
x = F.gelu(x)
x = x.transpose(1, 2)
x = self.encoder(x.transpose(1, 2)).transpose(1, 2)
(
quantized_out,
all_indices,
all_commit_losses,
all_codebook_losses,
_,
) = self.quantizer(x)
if all_indices.shape[0] == 1:
return all_indices.squeeze(0), quantized_out.transpose(1, 2)
return all_indices, quantized_out.transpose(1, 2)
def reset_parameters(self):
self.apply(init_weights)
if __name__ == "__main__":
repcodec = RepCodec(vocos_dim=1024, downsample_scale=2)
print(repcodec)
print(sum(p.numel() for p in repcodec.parameters()) / 1e6)
x = torch.randn(5, 10, 1024)
x_rec, codebook_loss, all_indices = repcodec(x)
print(x_rec.shape, codebook_loss, all_indices.shape)
vq_id, emb = repcodec.quantize(x)
print(vq_id.shape, emb.shape)
|