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import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import deque
from .separation import SeparationNet
import typing as tp
import math
class Swish(nn.Module):
def forward(self, x):
return x * x.sigmoid()
class ConvolutionModule(nn.Module):
"""
Convolution Module in SD block.
Args:
channels (int): input/output channels.
depth (int): number of layers in the residual branch. Each layer has its own
compress (float): amount of channel compression.
kernel (int): kernel size for the convolutions.
"""
def __init__(self, channels, depth=2, compress=4, kernel=3):
super().__init__()
assert kernel % 2 == 1
self.depth = abs(depth)
hidden_size = int(channels / compress)
norm = lambda d: nn.GroupNorm(1, d)
self.layers = nn.ModuleList([])
for _ in range(self.depth):
padding = (kernel // 2)
mods = [
norm(channels),
nn.Conv1d(channels, hidden_size*2, kernel, padding = padding),
nn.GLU(1),
nn.Conv1d(hidden_size, hidden_size, kernel, padding = padding, groups = hidden_size),
norm(hidden_size),
Swish(),
nn.Conv1d(hidden_size, channels, 1),
]
layer = nn.Sequential(*mods)
self.layers.append(layer)
def forward(self, x):
for layer in self.layers:
x = x + layer(x)
return x
class FusionLayer(nn.Module):
"""
A FusionLayer within the decoder.
Args:
- channels (int): Number of input channels.
- kernel_size (int, optional): Kernel size for the convolutional layer, defaults to 3.
- stride (int, optional): Stride for the convolutional layer, defaults to 1.
- padding (int, optional): Padding for the convolutional layer, defaults to 1.
"""
def __init__(self, channels, kernel_size=3, stride=1, padding=1):
super(FusionLayer, self).__init__()
self.conv = nn.Conv2d(channels * 2, channels * 2, kernel_size, stride=stride, padding=padding)
def forward(self, x, skip=None):
if skip is not None:
x += skip
x = x.repeat(1, 2, 1, 1)
x = self.conv(x)
x = F.glu(x, dim=1)
return x
class SDlayer(nn.Module):
"""
Implements a Sparse Down-sample Layer for processing different frequency bands separately.
Args:
- channels_in (int): Input channel count.
- channels_out (int): Output channel count.
- band_configs (dict): A dictionary containing configuration for each frequency band.
Keys are 'low', 'mid', 'high' for each band, and values are
dictionaries with keys 'SR', 'stride', and 'kernel' for proportion,
stride, and kernel size, respectively.
"""
def __init__(self, channels_in, channels_out, band_configs):
super(SDlayer, self).__init__()
# Initializing convolutional layers for each band
self.convs = nn.ModuleList()
self.strides = []
self.kernels = []
for config in band_configs.values():
self.convs.append(nn.Conv2d(channels_in, channels_out, (config['kernel'], 1), (config['stride'], 1), (0, 0)))
self.strides.append(config['stride'])
self.kernels.append(config['kernel'])
# Saving rate proportions for determining splits
self.SR_low = band_configs['low']['SR']
self.SR_mid = band_configs['mid']['SR']
def forward(self, x):
B, C, Fr, T = x.shape
# Define splitting points based on sampling rates
splits = [
(0, math.ceil(Fr * self.SR_low)),
(math.ceil(Fr * self.SR_low), math.ceil(Fr * (self.SR_low + self.SR_mid))),
(math.ceil(Fr * (self.SR_low + self.SR_mid)), Fr)
]
# Processing each band with the corresponding convolution
outputs = []
original_lengths=[]
for conv, stride, kernel, (start, end) in zip(self.convs, self.strides, self.kernels, splits):
extracted = x[:, :, start:end, :]
original_lengths.append(end-start)
current_length = extracted.shape[2]
# padding
if stride == 1:
total_padding = kernel - stride
else:
total_padding = (stride - current_length % stride) % stride
pad_left = total_padding // 2
pad_right = total_padding - pad_left
padded = F.pad(extracted, (0, 0, pad_left, pad_right))
output = conv(padded)
outputs.append(output)
return outputs, original_lengths
class SUlayer(nn.Module):
"""
Implements a Sparse Up-sample Layer in decoder.
Args:
- channels_in: The number of input channels.
- channels_out: The number of output channels.
- convtr_configs: Dictionary containing the configurations for transposed convolutions.
"""
def __init__(self, channels_in, channels_out, band_configs):
super(SUlayer, self).__init__()
# Initializing convolutional layers for each band
self.convtrs = nn.ModuleList([
nn.ConvTranspose2d(channels_in, channels_out, [config['kernel'], 1], [config['stride'], 1])
for _, config in band_configs.items()
])
def forward(self, x, lengths, origin_lengths):
B, C, Fr, T = x.shape
# Define splitting points based on input lengths
splits = [
(0, lengths[0]),
(lengths[0], lengths[0] + lengths[1]),
(lengths[0] + lengths[1], None)
]
# Processing each band with the corresponding convolution
outputs = []
for idx, (convtr, (start, end)) in enumerate(zip(self.convtrs, splits)):
out = convtr(x[:, :, start:end, :])
# Calculate the distance to trim the output symmetrically to original length
current_Fr_length = out.shape[2]
dist = abs(origin_lengths[idx] - current_Fr_length) // 2
# Trim the output to the original length symmetrically
trimmed_out = out[:, :, dist:dist + origin_lengths[idx], :]
outputs.append(trimmed_out)
# Concatenate trimmed outputs along the frequency dimension to return the final tensor
x = torch.cat(outputs, dim=2)
return x
class SDblock(nn.Module):
"""
Implements a simplified Sparse Down-sample block in encoder.
Args:
- channels_in (int): Number of input channels.
- channels_out (int): Number of output channels.
- band_config (dict): Configuration for the SDlayer specifying band splits and convolutions.
- conv_config (dict): Configuration for convolution modules applied to each band.
- depths (list of int): List specifying the convolution depths for low, mid, and high frequency bands.
"""
def __init__(self, channels_in, channels_out, band_configs={}, conv_config={}, depths=[3, 2, 1], kernel_size=3):
super(SDblock, self).__init__()
self.SDlayer = SDlayer(channels_in, channels_out, band_configs)
# Dynamically create convolution modules for each band based on depths
self.conv_modules = nn.ModuleList([
ConvolutionModule(channels_out, depth, **conv_config) for depth in depths
])
#Set the kernel_size to an odd number.
self.globalconv = nn.Conv2d(channels_out, channels_out, kernel_size, 1, (kernel_size - 1) // 2)
def forward(self, x):
bands, original_lengths = self.SDlayer(x)
# B, C, f, T = band.shape
bands = [
F.gelu(
conv(band.permute(0, 2, 1, 3).reshape(-1, band.shape[1], band.shape[3]))
.view(band.shape[0], band.shape[2], band.shape[1], band.shape[3])
.permute(0, 2, 1, 3)
)
for conv, band in zip(self.conv_modules, bands)
]
lengths = [band.size(-2) for band in bands]
full_band = torch.cat(bands, dim=2)
skip = full_band
output = self.globalconv(full_band)
return output, skip, lengths, original_lengths
class SCNet(nn.Module):
"""
The implementation of SCNet: Sparse Compression Network for Music Source Separation. Paper: https://arxiv.org/abs/2401.13276.pdf
Args:
- sources (List[str]): List of sources to be separated.
- audio_channels (int): Number of audio channels.
- nfft (int): Number of FFTs to determine the frequency dimension of the input.
- hop_size (int): Hop size for the STFT.
- win_size (int): Window size for STFT.
- normalized (bool): Whether to normalize the STFT.
- dims (List[int]): List of channel dimensions for each block.
- band_configs (Dict[str, Dict[str, int]]): Configuration for each frequency band, including how to divide the frequency bands,
and the settings for the upsampling/downsampling convolutional layers.
- conv_depths (List[int]): List specifying the number of convolution modules in each SD block.
- compress (int): Compression factor for convolution module.
- conv_kernel (int): Kernel size for convolution layer in convolution module.
- num_dplayer (int): Number of dual-path layers.
- expand (int): Expansion factor in the dual-path RNN, default is 1.
"""
def __init__(self,
sources = ['drums', 'bass', 'other', 'vocals'],
audio_channels = 2,
# Main structure
dims = [4, 32, 64, 128], # dims = [4, 64, 128, 256] in SCNet-large
# STFT
nfft = 4096,
hop_size = 1024,
win_size = 4096,
normalized = True,
# SD/SU layer
band_configs = {
'low': { 'SR': .175, 'stride': 1, 'kernel': 3 },
'mid': { 'SR': .392, 'stride': 4, 'kernel': 4 },
'high': {'SR': .433, 'stride': 16, 'kernel': 16 }
},
# Convolution Module
conv_depths = [3,2,1],
compress = 4,
conv_kernel = 3,
# Dual-path RNN
num_dplayer = 6,
expand = 1,
# mamba
use_mamba = False,
mamba_config = {
'd_stat': 16,
'd_conv': 4,
'd_expand': 2
}):
super().__init__()
self.sources = sources
self.audio_channels = audio_channels
self.dims = dims
self.band_configs = band_configs
self.hop_length = hop_size
self.conv_config = {
'compress': compress,
'kernel': conv_kernel,
}
self.stft_config = {
'n_fft': nfft,
'hop_length': hop_size,
'win_length': win_size,
'center': True,
'normalized': normalized
}
self.encoder = nn.ModuleList()
self.decoder = nn.ModuleList()
for index in range(len(dims)-1):
enc = SDblock(
channels_in = dims[index],
channels_out = dims[index+1],
band_configs = self.band_configs,
conv_config = self.conv_config,
depths = conv_depths
)
self.encoder.append(enc)
dec = nn.Sequential(
FusionLayer(channels = dims[index+1]),
SUlayer(
channels_in = dims[index+1],
channels_out = dims[index] if index != 0 else dims[index] * len(sources),
band_configs = self.band_configs,
)
)
self.decoder.insert(0, dec)
self.separation_net = SeparationNet(
channels = dims[-1],
expand = expand,
num_layers = num_dplayer,
use_mamba = use_mamba,
**mamba_config
)
def forward(self, x):
# B, C, L = x.shape
B = x.shape[0]
# In the initial padding, ensure that the number of frames after the STFT (the length of the T dimension) is even,
# so that the RFFT operation can be used in the separation network.
padding = self.hop_length - x.shape[-1] % self.hop_length
if (x.shape[-1] + padding) // self.hop_length % 2 == 0:
padding += self.hop_length
x = F.pad(x, (0, padding))
# STFT
L = x.shape[-1]
x = x.reshape(-1, L)
x = torch.stft(x, **self.stft_config, return_complex=True)
x = torch.view_as_real(x)
x = x.permute(0, 3, 1, 2).reshape(x.shape[0]//self.audio_channels, x.shape[3]*self.audio_channels, x.shape[1], x.shape[2])
B, C, Fr, T = x.shape
save_skip = deque()
save_lengths = deque()
save_original_lengths = deque()
# encoder
for sd_layer in self.encoder:
x, skip, lengths, original_lengths = sd_layer(x)
save_skip.append(skip)
save_lengths.append(lengths)
save_original_lengths.append(original_lengths)
#separation
x = self.separation_net(x)
#decoder
for fusion_layer, su_layer in self.decoder:
x = fusion_layer(x, save_skip.pop())
x = su_layer(x, save_lengths.pop(), save_original_lengths.pop())
#output
n = self.dims[0]
x = x.view(B, n, -1, Fr, T)
x = x.reshape(-1, 2, Fr, T).permute(0, 2, 3, 1)
x = torch.view_as_complex(x.contiguous())
x = torch.istft(x, **self.stft_config)
x = x.reshape(B, len(self.sources), self.audio_channels, -1)
x = x[:, :, :, :-padding]
return x |