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mrfakename
2025-05-06 18:59:28 -07:00
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acestep/music_dcae/__init__.py
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acestep/music_dcae/music_dcae_pipeline.py
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import os
import torch
from diffusers import AutoencoderDC
import torchaudio
import torchvision.transforms as transforms
from diffusers.models.modeling_utils import ModelMixin
from diffusers.loaders import FromOriginalModelMixin
from diffusers.configuration_utils import ConfigMixin, register_to_config
try:
from .music_vocoder import ADaMoSHiFiGANV1
except ImportError:
from music_vocoder import ADaMoSHiFiGANV1
root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
DEFAULT_PRETRAINED_PATH = os.path.join(root_dir, "checkpoints", "music_dcae_f8c8")
VOCODER_PRETRAINED_PATH = os.path.join(root_dir, "checkpoints", "music_vocoder")
class MusicDCAE(ModelMixin, ConfigMixin, FromOriginalModelMixin):
@register_to_config
def __init__(self, source_sample_rate=None, dcae_checkpoint_path=DEFAULT_PRETRAINED_PATH, vocoder_checkpoint_path=VOCODER_PRETRAINED_PATH):
super(MusicDCAE, self).__init__()
self.dcae = AutoencoderDC.from_pretrained(dcae_checkpoint_path)
self.vocoder = ADaMoSHiFiGANV1.from_pretrained(vocoder_checkpoint_path)
if source_sample_rate is None:
source_sample_rate = 48000
self.resampler = torchaudio.transforms.Resample(source_sample_rate, 44100)
self.transform = transforms.Compose([
transforms.Normalize(0.5, 0.5),
])
self.min_mel_value = -11.0
self.max_mel_value = 3.0
self.audio_chunk_size = int(round((1024 * 512 / 44100 * 48000)))
self.mel_chunk_size = 1024
self.time_dimention_multiple = 8
self.latent_chunk_size = self.mel_chunk_size // self.time_dimention_multiple
self.scale_factor = 0.1786
self.shift_factor = -1.9091
def load_audio(self, audio_path):
audio, sr = torchaudio.load(audio_path)
return audio, sr
def forward_mel(self, audios):
mels = []
for i in range(len(audios)):
image = self.vocoder.mel_transform(audios[i])
mels.append(image)
mels = torch.stack(mels)
return mels
@torch.no_grad()
def encode(self, audios, audio_lengths=None, sr=None):
if audio_lengths is None:
audio_lengths = torch.tensor([audios.shape[2]] * audios.shape[0])
audio_lengths = audio_lengths.to(audios.device)
# audios: N x 2 x T, 48kHz
device = audios.device
dtype = audios.dtype
if sr is None:
sr = 48000
resampler = self.resampler
else:
resampler = torchaudio.transforms.Resample(sr, 44100).to(device).to(dtype)
audio = resampler(audios)
max_audio_len = audio.shape[-1]
if max_audio_len % (8 * 512) != 0:
audio = torch.nn.functional.pad(audio, (0, 8 * 512 - max_audio_len % (8 * 512)))
mels = self.forward_mel(audio)
mels = (mels - self.min_mel_value) / (self.max_mel_value - self.min_mel_value)
mels = self.transform(mels)
latents = []
for mel in mels:
latent = self.dcae.encoder(mel.unsqueeze(0))
latents.append(latent)
latents = torch.cat(latents, dim=0)
latent_lengths = (audio_lengths / sr * 44100 / 512 / self.time_dimention_multiple).long()
latents = (latents - self.shift_factor) * self.scale_factor
return latents, latent_lengths
@torch.no_grad()
def decode(self, latents, audio_lengths=None, sr=None):
latents = latents / self.scale_factor + self.shift_factor
pred_wavs = []
for latent in latents:
mels = self.dcae.decoder(latent.unsqueeze(0))
mels = mels * 0.5 + 0.5
mels = mels * (self.max_mel_value - self.min_mel_value) + self.min_mel_value
wav = self.vocoder.decode(mels[0]).squeeze(1)
if sr is not None:
resampler = torchaudio.transforms.Resample(44100, sr).to(latents.device).to(latents.dtype)
wav = resampler(wav)
else:
sr = 44100
pred_wavs.append(wav)
if audio_lengths is not None:
pred_wavs = [wav[:, :length].cpu() for wav, length in zip(pred_wavs, audio_lengths)]
return sr, pred_wavs
def forward(self, audios, audio_lengths=None, sr=None):
latents, latent_lengths = self.encode(audios=audios, audio_lengths=audio_lengths, sr=sr)
sr, pred_wavs = self.decode(latents=latents, audio_lengths=audio_lengths, sr=sr)
return sr, pred_wavs, latents, latent_lengths
if __name__ == "__main__":
audio, sr = torchaudio.load("test.wav")
audio_lengths = torch.tensor([audio.shape[1]])
audios = audio.unsqueeze(0)
# test encode only
model = MusicDCAE()
# latents, latent_lengths = model.encode(audios, audio_lengths)
# print("latents shape: ", latents.shape)
# print("latent_lengths: ", latent_lengths)
# test encode and decode
sr, pred_wavs, latents, latent_lengths = model(audios, audio_lengths, sr)
print("reconstructed wavs: ", pred_wavs[0].shape)
print("latents shape: ", latents.shape)
print("latent_lengths: ", latent_lengths)
print("sr: ", sr)
torchaudio.save("test_reconstructed.flac", pred_wavs[0], sr)
print("test_reconstructed.flac")
acestep/music_dcae/music_log_mel.py
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import torch
import torch.nn as nn
from torch import Tensor
from torchaudio.transforms import MelScale
class LinearSpectrogram(nn.Module):
def __init__(
self,
n_fft=2048,
win_length=2048,
hop_length=512,
center=False,
mode="pow2_sqrt",
):
super().__init__()
self.n_fft = n_fft
self.win_length = win_length
self.hop_length = hop_length
self.center = center
self.mode = mode
self.register_buffer("window", torch.hann_window(win_length))
def forward(self, y: Tensor) -> Tensor:
if y.ndim == 3:
y = y.squeeze(1)
y = torch.nn.functional.pad(
y.unsqueeze(1),
(
(self.win_length - self.hop_length) // 2,
(self.win_length - self.hop_length + 1) // 2,
),
mode="reflect",
).squeeze(1)
dtype = y.dtype
spec = torch.stft(
y.float(),
self.n_fft,
hop_length=self.hop_length,
win_length=self.win_length,
window=self.window,
center=self.center,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=True,
)
spec = torch.view_as_real(spec)
if self.mode == "pow2_sqrt":
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
spec = spec.to(dtype)
return spec
class LogMelSpectrogram(nn.Module):
def __init__(
self,
sample_rate=44100,
n_fft=2048,
win_length=2048,
hop_length=512,
n_mels=128,
center=False,
f_min=0.0,
f_max=None,
):
super().__init__()
self.sample_rate = sample_rate
self.n_fft = n_fft
self.win_length = win_length
self.hop_length = hop_length
self.center = center
self.n_mels = n_mels
self.f_min = f_min
self.f_max = f_max or sample_rate // 2
self.spectrogram = LinearSpectrogram(n_fft, win_length, hop_length, center)
self.mel_scale = MelScale(
self.n_mels,
self.sample_rate,
self.f_min,
self.f_max,
self.n_fft // 2 + 1,
"slaney",
"slaney",
)
def compress(self, x: Tensor) -> Tensor:
return torch.log(torch.clamp(x, min=1e-5))
def decompress(self, x: Tensor) -> Tensor:
return torch.exp(x)
def forward(self, x: Tensor, return_linear: bool = False) -> Tensor:
linear = self.spectrogram(x)
x = self.mel_scale(linear)
x = self.compress(x)
# print(x.shape)
if return_linear:
return x, self.compress(linear)
return x
acestep/music_dcae/music_vocoder.py
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import librosa
import torch
from torch import nn
from functools import partial
from math import prod
from typing import Callable, Tuple, List
import numpy as np
import torch.nn.functional as F
from torch.nn import Conv1d
from torch.nn.utils import weight_norm
from torch.nn.utils.parametrize import remove_parametrizations as remove_weight_norm
from diffusers.models.modeling_utils import ModelMixin
from diffusers.loaders import FromOriginalModelMixin
from diffusers.configuration_utils import ConfigMixin, register_to_config
try:
from music_log_mel import LogMelSpectrogram
except ImportError:
from .music_log_mel import LogMelSpectrogram
def drop_path(
x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True
):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
'survival rate' as the argument.
""" # noqa: E501
if drop_prob == 0.0 or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (
x.ndim - 1
) # work with diff dim tensors, not just 2D ConvNets
random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
if keep_prob > 0.0 and scale_by_keep:
random_tensor.div_(keep_prob)
return x * random_tensor
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" # noqa: E501
def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
self.scale_by_keep = scale_by_keep
def forward(self, x):
return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
def extra_repr(self):
return f"drop_prob={round(self.drop_prob,3):0.3f}"
class LayerNorm(nn.Module):
r"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
shape (batch_size, height, width, channels) while channels_first corresponds to inputs
with shape (batch_size, channels, height, width).
""" # noqa: E501
def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
super().__init__()
self.weight = nn.Parameter(torch.ones(normalized_shape))
self.bias = nn.Parameter(torch.zeros(normalized_shape))
self.eps = eps
self.data_format = data_format
if self.data_format not in ["channels_last", "channels_first"]:
raise NotImplementedError
self.normalized_shape = (normalized_shape,)
def forward(self, x):
if self.data_format == "channels_last":
return F.layer_norm(
x, self.normalized_shape, self.weight, self.bias, self.eps
)
elif self.data_format == "channels_first":
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None] * x + self.bias[:, None]
return x
class ConvNeXtBlock(nn.Module):
r"""ConvNeXt Block. There are two equivalent implementations:
(1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
(2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
We use (2) as we find it slightly faster in PyTorch
Args:
dim (int): Number of input channels.
drop_path (float): Stochastic depth rate. Default: 0.0
layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0.
kernel_size (int): Kernel size for depthwise conv. Default: 7.
dilation (int): Dilation for depthwise conv. Default: 1.
""" # noqa: E501
def __init__(
self,
dim: int,
drop_path: float = 0.0,
layer_scale_init_value: float = 1e-6,
mlp_ratio: float = 4.0,
kernel_size: int = 7,
dilation: int = 1,
):
super().__init__()
self.dwconv = nn.Conv1d(
dim,
dim,
kernel_size=kernel_size,
padding=int(dilation * (kernel_size - 1) / 2),
groups=dim,
) # depthwise conv
self.norm = LayerNorm(dim, eps=1e-6)
self.pwconv1 = nn.Linear(
dim, int(mlp_ratio * dim)
) # pointwise/1x1 convs, implemented with linear layers
self.act = nn.GELU()
self.pwconv2 = nn.Linear(int(mlp_ratio * dim), dim)
self.gamma = (
nn.Parameter(layer_scale_init_value *
torch.ones((dim)), requires_grad=True)
if layer_scale_init_value > 0
else None
)
self.drop_path = DropPath(
drop_path) if drop_path > 0.0 else nn.Identity()
def forward(self, x, apply_residual: bool = True):
input = x
x = self.dwconv(x)
x = x.permute(0, 2, 1) # (N, C, L) -> (N, L, C)
x = self.norm(x)
x = self.pwconv1(x)
x = self.act(x)
x = self.pwconv2(x)
if self.gamma is not None:
x = self.gamma * x
x = x.permute(0, 2, 1) # (N, L, C) -> (N, C, L)
x = self.drop_path(x)
if apply_residual:
x = input + x
return x
class ParallelConvNeXtBlock(nn.Module):
def __init__(self, kernel_sizes: List[int], *args, **kwargs):
super().__init__()
self.blocks = nn.ModuleList(
[
ConvNeXtBlock(kernel_size=kernel_size, *args, **kwargs)
for kernel_size in kernel_sizes
]
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return torch.stack(
[block(x, apply_residual=False) for block in self.blocks] + [x],
dim=1,
).sum(dim=1)
class ConvNeXtEncoder(nn.Module):
def __init__(
self,
input_channels=3,
depths=[3, 3, 9, 3],
dims=[96, 192, 384, 768],
drop_path_rate=0.0,
layer_scale_init_value=1e-6,
kernel_sizes: Tuple[int] = (7,),
):
super().__init__()
assert len(depths) == len(dims)
self.channel_layers = nn.ModuleList()
stem = nn.Sequential(
nn.Conv1d(
input_channels,
dims[0],
kernel_size=7,
padding=3,
padding_mode="replicate",
),
LayerNorm(dims[0], eps=1e-6, data_format="channels_first"),
)
self.channel_layers.append(stem)
for i in range(len(depths) - 1):
mid_layer = nn.Sequential(
LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
nn.Conv1d(dims[i], dims[i + 1], kernel_size=1),
)
self.channel_layers.append(mid_layer)
block_fn = (
partial(ConvNeXtBlock, kernel_size=kernel_sizes[0])
if len(kernel_sizes) == 1
else partial(ParallelConvNeXtBlock, kernel_sizes=kernel_sizes)
)
self.stages = nn.ModuleList()
drop_path_rates = [
x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
]
cur = 0
for i in range(len(depths)):
stage = nn.Sequential(
*[
block_fn(
dim=dims[i],
drop_path=drop_path_rates[cur + j],
layer_scale_init_value=layer_scale_init_value,
)
for j in range(depths[i])
]
)
self.stages.append(stage)
cur += depths[i]
self.norm = LayerNorm(dims[-1], eps=1e-6, data_format="channels_first")
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, (nn.Conv1d, nn.Linear)):
nn.init.trunc_normal_(m.weight, std=0.02)
nn.init.constant_(m.bias, 0)
def forward(
self,
x: torch.Tensor,
) -> torch.Tensor:
for channel_layer, stage in zip(self.channel_layers, self.stages):
x = channel_layer(x)
x = stage(x)
return self.norm(x)
def init_weights(m, mean=0.0, std=0.01):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
m.weight.data.normal_(mean, std)
def get_padding(kernel_size, dilation=1):
return (kernel_size * dilation - dilation) // 2
class ResBlock1(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
super().__init__()
self.convs1 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[0],
padding=get_padding(kernel_size, dilation[0]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[1],
padding=get_padding(kernel_size, dilation[1]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[2],
padding=get_padding(kernel_size, dilation[2]),
)
),
]
)
self.convs1.apply(init_weights)
self.convs2 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
]
)
self.convs2.apply(init_weights)
def forward(self, x):
for c1, c2 in zip(self.convs1, self.convs2):
xt = F.silu(x)
xt = c1(xt)
xt = F.silu(xt)
xt = c2(xt)
x = xt + x
return x
def remove_weight_norm(self):
for conv in self.convs1:
remove_weight_norm(conv)
for conv in self.convs2:
remove_weight_norm(conv)
class HiFiGANGenerator(nn.Module):
def __init__(
self,
*,
hop_length: int = 512,
upsample_rates: Tuple[int] = (8, 8, 2, 2, 2),
upsample_kernel_sizes: Tuple[int] = (16, 16, 8, 2, 2),
resblock_kernel_sizes: Tuple[int] = (3, 7, 11),
resblock_dilation_sizes: Tuple[Tuple[int]] = (
(1, 3, 5), (1, 3, 5), (1, 3, 5)),
num_mels: int = 128,
upsample_initial_channel: int = 512,
use_template: bool = True,
pre_conv_kernel_size: int = 7,
post_conv_kernel_size: int = 7,
post_activation: Callable = partial(nn.SiLU, inplace=True),
):
super().__init__()
assert (
prod(upsample_rates) == hop_length
), f"hop_length must be {prod(upsample_rates)}"
self.conv_pre = weight_norm(
nn.Conv1d(
num_mels,
upsample_initial_channel,
pre_conv_kernel_size,
1,
padding=get_padding(pre_conv_kernel_size),
)
)
self.num_upsamples = len(upsample_rates)
self.num_kernels = len(resblock_kernel_sizes)
self.noise_convs = nn.ModuleList()
self.use_template = use_template
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
c_cur = upsample_initial_channel // (2 ** (i + 1))
self.ups.append(
weight_norm(
nn.ConvTranspose1d(
upsample_initial_channel // (2**i),
upsample_initial_channel // (2 ** (i + 1)),
k,
u,
padding=(k - u) // 2,
)
)
)
if not use_template:
continue
if i + 1 < len(upsample_rates):
stride_f0 = np.prod(upsample_rates[i + 1:])
self.noise_convs.append(
Conv1d(
1,
c_cur,
kernel_size=stride_f0 * 2,
stride=stride_f0,
padding=stride_f0 // 2,
)
)
else:
self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = upsample_initial_channel // (2 ** (i + 1))
for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes):
self.resblocks.append(ResBlock1(ch, k, d))
self.activation_post = post_activation()
self.conv_post = weight_norm(
nn.Conv1d(
ch,
1,
post_conv_kernel_size,
1,
padding=get_padding(post_conv_kernel_size),
)
)
self.ups.apply(init_weights)
self.conv_post.apply(init_weights)
def forward(self, x, template=None):
x = self.conv_pre(x)
for i in range(self.num_upsamples):
x = F.silu(x, inplace=True)
x = self.ups[i](x)
if self.use_template:
x = x + self.noise_convs[i](template)
xs = None
for j in range(self.num_kernels):
if xs is None:
xs = self.resblocks[i * self.num_kernels + j](x)
else:
xs += self.resblocks[i * self.num_kernels + j](x)
x = xs / self.num_kernels
x = self.activation_post(x)
x = self.conv_post(x)
x = torch.tanh(x)
return x
def remove_weight_norm(self):
for up in self.ups:
remove_weight_norm(up)
for block in self.resblocks:
block.remove_weight_norm()
remove_weight_norm(self.conv_pre)
remove_weight_norm(self.conv_post)
class ADaMoSHiFiGANV1(ModelMixin, ConfigMixin, FromOriginalModelMixin):
@register_to_config
def __init__(
self,
input_channels: int = 128,
depths: List[int] = [3, 3, 9, 3],
dims: List[int] = [128, 256, 384, 512],
drop_path_rate: float = 0.0,
kernel_sizes: Tuple[int] = (7,),
upsample_rates: Tuple[int] = (4, 4, 2, 2, 2, 2, 2),
upsample_kernel_sizes: Tuple[int] = (8, 8, 4, 4, 4, 4, 4),
resblock_kernel_sizes: Tuple[int] = (3, 7, 11, 13),
resblock_dilation_sizes: Tuple[Tuple[int]] = (
(1, 3, 5), (1, 3, 5), (1, 3, 5), (1, 3, 5)),
num_mels: int = 512,
upsample_initial_channel: int = 1024,
use_template: bool = False,
pre_conv_kernel_size: int = 13,
post_conv_kernel_size: int = 13,
sampling_rate: int = 44100,
n_fft: int = 2048,
win_length: int = 2048,
hop_length: int = 512,
f_min: int = 40,
f_max: int = 16000,
n_mels: int = 128,
):
super().__init__()
self.backbone = ConvNeXtEncoder(
input_channels=input_channels,
depths=depths,
dims=dims,
drop_path_rate=drop_path_rate,
kernel_sizes=kernel_sizes,
)
self.head = HiFiGANGenerator(
hop_length=hop_length,
upsample_rates=upsample_rates,
upsample_kernel_sizes=upsample_kernel_sizes,
resblock_kernel_sizes=resblock_kernel_sizes,
resblock_dilation_sizes=resblock_dilation_sizes,
num_mels=num_mels,
upsample_initial_channel=upsample_initial_channel,
use_template=use_template,
pre_conv_kernel_size=pre_conv_kernel_size,
post_conv_kernel_size=post_conv_kernel_size,
)
self.sampling_rate = sampling_rate
self.mel_transform = LogMelSpectrogram(
sample_rate=sampling_rate,
n_fft=n_fft,
win_length=win_length,
hop_length=hop_length,
f_min=f_min,
f_max=f_max,
n_mels=n_mels,
)
self.eval()
@torch.no_grad()
def decode(self, mel):
y = self.backbone(mel)
y = self.head(y)
return y
@torch.no_grad()
def encode(self, x):
return self.mel_transform(x)
def forward(self, mel):
y = self.backbone(mel)
y = self.head(y)
return y
if __name__ == "__main__":
import soundfile as sf
x = "test_audio.flac"
model = ADaMoSHiFiGANV1.from_pretrained("./checkpoints/music_vocoder", local_files_only=True)
wav, sr = librosa.load(x, sr=44100, mono=True)
wav = torch.from_numpy(wav).float()[None]
mel = model.encode(wav)
wav = model.decode(mel)[0].mT
sf.write("test_audio_vocoder_rec.flac", wav.cpu().numpy(), 44100)