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# 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.
# This code is modified from https://github.com/descriptinc/descript-audio-codec/blob/main/dac/model/dac.py
import math
from typing import List
from typing import Union
import numpy as np
import torch
from audiotools import AudioSignal
from audiotools.ml import BaseModel
from torch import nn
from .base import CodecMixin
from ..nn.layers import Snake1d
from ..nn.layers import WNConv1d
from ..nn.layers import WNConvTranspose1d
from ..nn.quantize import ResidualVectorQuantize
from .encodec import SConv1d, SConvTranspose1d, SLSTM
def init_weights(m):
if isinstance(m, nn.Conv1d):
nn.init.trunc_normal_(m.weight, std=0.02)
nn.init.constant_(m.bias, 0)
class ResidualUnit(nn.Module):
def __init__(self, dim: int = 16, dilation: int = 1, causal: bool = False):
super().__init__()
conv1d_type = SConv1d # if causal else WNConv1d
pad = ((7 - 1) * dilation) // 2
self.block = nn.Sequential(
Snake1d(dim),
conv1d_type(
dim,
dim,
kernel_size=7,
dilation=dilation,
padding=pad,
causal=causal,
norm="weight_norm",
),
Snake1d(dim),
conv1d_type(dim, dim, kernel_size=1, causal=causal, norm="weight_norm"),
)
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, causal: bool = False):
super().__init__()
conv1d_type = SConv1d # if causal else WNConv1d
self.block = nn.Sequential(
ResidualUnit(dim // 2, dilation=1, causal=causal),
ResidualUnit(dim // 2, dilation=3, causal=causal),
ResidualUnit(dim // 2, dilation=9, causal=causal),
Snake1d(dim // 2),
conv1d_type(
dim // 2,
dim,
kernel_size=2 * stride,
stride=stride,
padding=math.ceil(stride / 2),
causal=causal,
norm="weight_norm",
),
)
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,
causal: bool = False,
lstm: int = 2,
):
super().__init__()
conv1d_type = SConv1d # if causal else WNConv1d
# Create first convolution
self.block = [
conv1d_type(
1, d_model, kernel_size=7, padding=3, causal=causal, norm="weight_norm"
)
]
# Create EncoderBlocks that double channels as they downsample by `stride`
for stride in strides:
d_model *= 2
self.block += [EncoderBlock(d_model, stride=stride, causal=causal)]
# Add LSTM if needed
self.use_lstm = lstm
if lstm:
self.block += [SLSTM(d_model, lstm)]
# Create last convolution
self.block += [
Snake1d(d_model),
conv1d_type(
d_model,
d_latent,
kernel_size=3,
padding=1,
causal=causal,
norm="weight_norm",
),
]
# 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 DecoderBlock(nn.Module):
def __init__(
self,
input_dim: int = 16,
output_dim: int = 8,
stride: int = 1,
causal: bool = False,
):
super().__init__()
conv1d_type = SConvTranspose1d # if causal else WNConvTranspose1d
self.block = nn.Sequential(
Snake1d(input_dim),
conv1d_type(
input_dim,
output_dim,
kernel_size=2 * stride,
stride=stride,
padding=math.ceil(stride / 2),
causal=causal,
norm="weight_norm",
),
ResidualUnit(output_dim, dilation=1, causal=causal),
ResidualUnit(output_dim, dilation=3, causal=causal),
ResidualUnit(output_dim, dilation=9, causal=causal),
)
def forward(self, x):
return self.block(x)
class Decoder(nn.Module):
def __init__(
self,
input_channel,
channels,
rates,
d_out: int = 1,
causal: bool = False,
lstm: int = 2,
):
super().__init__()
conv1d_type = SConv1d # if causal else WNConv1d
# Add first conv layer
layers = [
conv1d_type(
input_channel,
channels,
kernel_size=7,
padding=3,
causal=causal,
norm="weight_norm",
)
]
if lstm:
layers += [SLSTM(channels, num_layers=lstm)]
# Add upsampling + MRF blocks
for i, stride in enumerate(rates):
input_dim = channels // 2**i
output_dim = channels // 2 ** (i + 1)
layers += [DecoderBlock(input_dim, output_dim, stride, causal=causal)]
# Add final conv layer
layers += [
Snake1d(output_dim),
conv1d_type(
output_dim,
d_out,
kernel_size=7,
padding=3,
causal=causal,
norm="weight_norm",
),
nn.Tanh(),
]
self.model = nn.Sequential(*layers)
def forward(self, x):
return self.model(x)
class DAC(BaseModel, CodecMixin):
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],
n_codebooks: int = 9,
codebook_size: int = 1024,
codebook_dim: Union[int, list] = 8,
quantizer_dropout: bool = False,
sample_rate: int = 44100,
lstm: int = 2,
causal: bool = False,
):
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
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, causal=causal, lstm=lstm
)
self.n_codebooks = n_codebooks
self.codebook_size = codebook_size
self.codebook_dim = codebook_dim
self.quantizer = ResidualVectorQuantize(
input_dim=latent_dim,
n_codebooks=n_codebooks,
codebook_size=codebook_size,
codebook_dim=codebook_dim,
quantizer_dropout=quantizer_dropout,
)
self.decoder = Decoder(
latent_dim,
decoder_dim,
decoder_rates,
lstm=lstm,
causal=causal,
)
self.sample_rate = sample_rate
self.apply(init_weights)
self.delay = self.get_delay()
def preprocess(self, audio_data, sample_rate):
if sample_rate is None:
sample_rate = self.sample_rate
assert 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 encode(
self,
audio_data: torch.Tensor,
n_quantizers: int = None,
):
"""Encode given audio data and return quantized latent codes
Parameters
----------
audio_data : Tensor[B x 1 x T]
Audio data to encode
n_quantizers : int, optional
Number of quantizers to use, by default None
If None, all quantizers are used.
Returns
-------
dict
A dictionary with the following keys:
"z" : Tensor[B x D x T]
Quantized continuous representation of input
"codes" : Tensor[B x N x T]
Codebook indices for each codebook
(quantized discrete representation of input)
"latents" : Tensor[B x N*D x T]
Projected latents (continuous representation of input before quantization)
"vq/commitment_loss" : Tensor[1]
Commitment loss to train encoder to predict vectors closer to codebook
entries
"vq/codebook_loss" : Tensor[1]
Codebook loss to update the codebook
"length" : int
Number of samples in input audio
"""
z = self.encoder(audio_data)
z, codes, latents, commitment_loss, codebook_loss = self.quantizer(
z, n_quantizers
)
return z, codes, latents, commitment_loss, codebook_loss
def decode(self, z: torch.Tensor):
"""Decode given latent codes and return audio data
Parameters
----------
z : Tensor[B x D x T]
Quantized continuous representation of input
length : int, optional
Number of samples in output audio, by default None
Returns
-------
dict
A dictionary with the following keys:
"audio" : Tensor[B x 1 x length]
Decoded audio data.
"""
return self.decoder(z)
def forward(
self,
audio_data: torch.Tensor,
sample_rate: int = None,
n_quantizers: int = None,
):
"""Model forward pass
Parameters
----------
audio_data : Tensor[B x 1 x T]
Audio data to encode
sample_rate : int, optional
Sample rate of audio data in Hz, by default None
If None, defaults to `self.sample_rate`
n_quantizers : int, optional
Number of quantizers to use, by default None.
If None, all quantizers are used.
Returns
-------
dict
A dictionary with the following keys:
"z" : Tensor[B x D x T]
Quantized continuous representation of input
"codes" : Tensor[B x N x T]
Codebook indices for each codebook
(quantized discrete representation of input)
"latents" : Tensor[B x N*D x T]
Projected latents (continuous representation of input before quantization)
"vq/commitment_loss" : Tensor[1]
Commitment loss to train encoder to predict vectors closer to codebook
entries
"vq/codebook_loss" : Tensor[1]
Codebook loss to update the codebook
"length" : int
Number of samples in input audio
"audio" : Tensor[B x 1 x length]
Decoded audio data.
"""
length = audio_data.shape[-1]
audio_data = self.preprocess(audio_data, sample_rate)
z, codes, latents, commitment_loss, codebook_loss = self.encode(
audio_data, n_quantizers
)
x = self.decode(z)
return {
"audio": x[..., :length],
"z": z,
"codes": codes,
"latents": latents,
"vq/commitment_loss": commitment_loss,
"vq/codebook_loss": codebook_loss,
}
if __name__ == "__main__":
import numpy as np
from functools import partial
model = DAC().to("cpu")
for n, m in model.named_modules():
o = m.extra_repr()
p = sum([np.prod(p.size()) for p in m.parameters()])
fn = lambda o, p: o + f" {p/1e6:<.3f}M params."
setattr(m, "extra_repr", partial(fn, o=o, p=p))
print(model)
print("Total # of params: ", sum([np.prod(p.size()) for p in model.parameters()]))
length = 88200 * 2
x = torch.randn(1, 1, length).to(model.device)
x.requires_grad_(True)
x.retain_grad()
# Make a forward pass
out = model(x)["audio"]
print("Input shape:", x.shape)
print("Output shape:", out.shape)
# Create gradient variable
grad = torch.zeros_like(out)
grad[:, :, grad.shape[-1] // 2] = 1
# Make a backward pass
out.backward(grad)
# Check non-zero values
gradmap = x.grad.squeeze(0)
gradmap = (gradmap != 0).sum(0) # sum across features
rf = (gradmap != 0).sum()
print(f"Receptive field: {rf.item()}")
x = AudioSignal(torch.randn(1, 1, 44100 * 60), 44100)
model.decompress(model.compress(x, verbose=True), verbose=True)
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