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9e1bd8d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 | # SPDX-License-Identifier: Apache-2.0
# DAC-lineage audio VAE: waveform encoder + BigVGAN decoder (inference-only bundle).
import math
from typing import List
import numpy as np
import torch
from torch import nn
from torch.nn.utils.parametrizations import weight_norm
from .dac_bigvgan import BigVGAN
from .dac_attn_proj import AttnProjection
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def WNConv1d(*args, **kwargs):
return weight_norm(nn.Conv1d(*args, **kwargs))
@torch.jit.script
def snake(x, alpha):
shape = x.shape
x = x.reshape(shape[0], shape[1], -1)
x = x + (alpha + 1e-9).reciprocal() * torch.sin(alpha * x).pow(2)
x = x.reshape(shape)
return x
class Snake1d(nn.Module):
def __init__(self, channels):
super().__init__()
self.alpha = nn.Parameter(torch.ones(1, channels, 1))
def forward(self, x):
return snake(x, self.alpha)
def init_weights(m):
if isinstance(m, nn.Conv1d):
nn.init.trunc_normal_(m.weight, std=0.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
class ResidualUnit(nn.Module):
def __init__(self, dim: int = 16, dilation: int = 1):
super().__init__()
pad = ((7 - 1) * dilation) // 2
self.block = nn.Sequential(
Snake1d(dim),
WNConv1d(dim, dim, kernel_size=7, dilation=dilation, padding=pad),
Snake1d(dim),
WNConv1d(dim, dim, kernel_size=1),
)
def forward(self, x):
y = self.block(x)
pad = (x.shape[-1] - y.shape[-1]) // 2
if pad > 0:
x = x[..., pad:-pad]
return x + y
class EncoderBlock(nn.Module):
def __init__(self, dim: int = 16, stride: int = 1):
super().__init__()
self.block = nn.Sequential(
ResidualUnit(dim // 2, dilation=1),
ResidualUnit(dim // 2, dilation=3),
ResidualUnit(dim // 2, dilation=9),
Snake1d(dim // 2),
WNConv1d(
dim // 2,
dim,
kernel_size=2 * stride,
stride=stride,
padding=math.ceil(stride / 2),
),
)
def forward(self, x):
return self.block(x)
class Encoder(nn.Module):
def __init__(
self,
d_model: int = 64,
strides: list = [2, 4, 8, 8],
d_latent: int = 64,
):
super().__init__()
# Create first convolution
self.block = [WNConv1d(1, d_model, kernel_size=7, padding=3)]
# Create EncoderBlocks that double channels as they downsample by `stride`
for stride in strides:
d_model *= 2
self.block += [EncoderBlock(d_model, stride=stride)]
# Create last convolution
self.block += [
Snake1d(d_model),
WNConv1d(d_model, d_latent, kernel_size=3, padding=1),
]
# Wrap black into nn.Sequential
self.block = nn.Sequential(*self.block)
self.enc_dim = d_model
def forward(self, x):
return self.block(x)
class DacAudioVAE(nn.Module):
def __init__(
self,
encoder_dim: int = 64,
encoder_rates: List[int] = [2, 4, 8, 8],
latent_dim: int = None,
decoder_dim: int = 1536,
decoder_rates: List[int] = [8, 8, 4, 2],
sample_rate: int = 44100,
vae_latent_channels: int = 64,
attn_proj: bool = False,
decoder_type: str = "bigvgan",
):
super().__init__()
self.encoder_dim = encoder_dim
self.encoder_rates = encoder_rates
self.decoder_dim = decoder_dim
self.decoder_rates = decoder_rates
self.sample_rate = sample_rate
self.attn_proj = attn_proj
self.decoder_type = decoder_type
if latent_dim is None:
latent_dim = encoder_dim * (2 ** len(encoder_rates))
self.latent_dim = latent_dim
self.hop_length = np.prod(encoder_rates)
self.encoder = Encoder(encoder_dim, encoder_rates, latent_dim)
if latent_dim % vae_latent_channels == 0:
self.attn_proj_dim = vae_latent_channels
else:
# smallest power of two >= vae_latent_channels
self.attn_proj_dim = 2 ** int(np.ceil(np.log2(vae_latent_channels)))
self.mean_proj = nn.Conv1d(self.attn_proj_dim, vae_latent_channels, 1)
self.logs_proj = nn.Conv1d(self.attn_proj_dim, vae_latent_channels, 1)
self.dec_in_proj = nn.Conv1d(vae_latent_channels, latent_dim, 1)
if self.decoder_type == "bigvgan":
if sample_rate == 16000:
bigvgan_conf = {"resblock": "1",
"num_mels": latent_dim,
"upsample_rates": [5,5,2,2,2,2],
"upsample_kernel_sizes": [9,9,4,4,4,4],
"upsample_initial_channel": decoder_dim,
"resblock_kernel_sizes": [3,7,11],
"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
"use_tanh_at_final": False,
"use_bias_at_final": False,
"activation": "snakebeta",
"snake_logscale": True}
elif sample_rate == 32000:
bigvgan_conf = {"resblock": "1",
"num_mels": latent_dim,
"upsample_rates": [5,5,2,2,2,2,2],
"upsample_kernel_sizes": [9,9,4,4,4,4,4],
"upsample_initial_channel": decoder_dim,
"resblock_kernel_sizes": [3,7,11],
"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
"use_tanh_at_final": False,
"use_bias_at_final": False,
"activation": "snakebeta",
"snake_logscale": True}
else:
raise ValueError(f"Invalid sample_rate: {sample_rate}")
h = AttrDict(**bigvgan_conf)
self.decoder = BigVGAN(h)
else:
raise ValueError(f"Invalid decoder type: {self.decoder_type}")
if self.attn_proj:
self.pre_block = AttnProjection(latent_dim, self.attn_proj_dim, num_heads=8)
self.sample_rate = sample_rate
self.apply(init_weights)
def preprocess(self, audio_data, sample_rate):
if sample_rate is None:
sample_rate = self.sample_rate
length = audio_data.shape[-1]
right_pad = math.ceil(length / self.hop_length) * self.hop_length - length
audio_data = nn.functional.pad(audio_data, (0, right_pad))
return audio_data
def decode(self, z: torch.Tensor):
"""Decode given latent codes and return audio data
Parameters
----------
z : Tensor[B x D x T]
Continuous latent representation
Returns
-------
Tensor[B x 1 x length]
Decoded audio data.
"""
z = self.dec_in_proj(z)
return self.decoder(z)
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