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# 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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