UVR5 / demucs /tasnet.py
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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
# Created on 2018/12
# Author: Kaituo XU
# Modified on 2019/11 by Alexandre Defossez, added support for multiple output channels
# Here is the original license:
# The MIT License (MIT)
#
# Copyright (c) 2018 Kaituo XU
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from .utils import capture_init
EPS = 1e-8
def overlap_and_add(signal, frame_step):
outer_dimensions = signal.size()[:-2]
frames, frame_length = signal.size()[-2:]
subframe_length = math.gcd(frame_length, frame_step) # gcd=Greatest Common Divisor
subframe_step = frame_step // subframe_length
subframes_per_frame = frame_length // subframe_length
output_size = frame_step * (frames - 1) + frame_length
output_subframes = output_size // subframe_length
subframe_signal = signal.view(*outer_dimensions, -1, subframe_length)
frame = torch.arange(0, output_subframes,
device=signal.device).unfold(0, subframes_per_frame, subframe_step)
frame = frame.long() # signal may in GPU or CPU
frame = frame.contiguous().view(-1)
result = signal.new_zeros(*outer_dimensions, output_subframes, subframe_length)
result.index_add_(-2, frame, subframe_signal)
result = result.view(*outer_dimensions, -1)
return result
class ConvTasNet(nn.Module):
@capture_init
def __init__(self,
N=256,
L=20,
B=256,
H=512,
P=3,
X=8,
R=4,
C=4,
audio_channels=1,
samplerate=44100,
norm_type="gLN",
causal=False,
mask_nonlinear='relu'):
"""
Args:
N: Number of filters in autoencoder
L: Length of the filters (in samples)
B: Number of channels in bottleneck 1 × 1-conv block
H: Number of channels in convolutional blocks
P: Kernel size in convolutional blocks
X: Number of convolutional blocks in each repeat
R: Number of repeats
C: Number of speakers
norm_type: BN, gLN, cLN
causal: causal or non-causal
mask_nonlinear: use which non-linear function to generate mask
"""
super(ConvTasNet, self).__init__()
# Hyper-parameter
self.N, self.L, self.B, self.H, self.P, self.X, self.R, self.C = N, L, B, H, P, X, R, C
self.norm_type = norm_type
self.causal = causal
self.mask_nonlinear = mask_nonlinear
self.audio_channels = audio_channels
self.samplerate = samplerate
# Components
self.encoder = Encoder(L, N, audio_channels)
self.separator = TemporalConvNet(N, B, H, P, X, R, C, norm_type, causal, mask_nonlinear)
self.decoder = Decoder(N, L, audio_channels)
# init
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_normal_(p)
def valid_length(self, length):
return length
def forward(self, mixture):
"""
Args:
mixture: [M, T], M is batch size, T is #samples
Returns:
est_source: [M, C, T]
"""
mixture_w = self.encoder(mixture)
est_mask = self.separator(mixture_w)
est_source = self.decoder(mixture_w, est_mask)
# T changed after conv1d in encoder, fix it here
T_origin = mixture.size(-1)
T_conv = est_source.size(-1)
est_source = F.pad(est_source, (0, T_origin - T_conv))
return est_source
class Encoder(nn.Module):
"""Estimation of the nonnegative mixture weight by a 1-D conv layer.
"""
def __init__(self, L, N, audio_channels):
super(Encoder, self).__init__()
# Hyper-parameter
self.L, self.N = L, N
# Components
# 50% overlap
self.conv1d_U = nn.Conv1d(audio_channels, N, kernel_size=L, stride=L // 2, bias=False)
def forward(self, mixture):
"""
Args:
mixture: [M, T], M is batch size, T is #samples
Returns:
mixture_w: [M, N, K], where K = (T-L)/(L/2)+1 = 2T/L-1
"""
mixture_w = F.relu(self.conv1d_U(mixture)) # [M, N, K]
return mixture_w
class Decoder(nn.Module):
def __init__(self, N, L, audio_channels):
super(Decoder, self).__init__()
# Hyper-parameter
self.N, self.L = N, L
self.audio_channels = audio_channels
# Components
self.basis_signals = nn.Linear(N, audio_channels * L, bias=False)
def forward(self, mixture_w, est_mask):
"""
Args:
mixture_w: [M, N, K]
est_mask: [M, C, N, K]
Returns:
est_source: [M, C, T]
"""
# D = W * M
source_w = torch.unsqueeze(mixture_w, 1) * est_mask # [M, C, N, K]
source_w = torch.transpose(source_w, 2, 3) # [M, C, K, N]
# S = DV
est_source = self.basis_signals(source_w) # [M, C, K, ac * L]
m, c, k, _ = est_source.size()
est_source = est_source.view(m, c, k, self.audio_channels, -1).transpose(2, 3).contiguous()
est_source = overlap_and_add(est_source, self.L // 2) # M x C x ac x T
return est_source
class TemporalConvNet(nn.Module):
def __init__(self, N, B, H, P, X, R, C, norm_type="gLN", causal=False, mask_nonlinear='relu'):
"""
Args:
N: Number of filters in autoencoder
B: Number of channels in bottleneck 1 × 1-conv block
H: Number of channels in convolutional blocks
P: Kernel size in convolutional blocks
X: Number of convolutional blocks in each repeat
R: Number of repeats
C: Number of speakers
norm_type: BN, gLN, cLN
causal: causal or non-causal
mask_nonlinear: use which non-linear function to generate mask
"""
super(TemporalConvNet, self).__init__()
# Hyper-parameter
self.C = C
self.mask_nonlinear = mask_nonlinear
# Components
# [M, N, K] -> [M, N, K]
layer_norm = ChannelwiseLayerNorm(N)
# [M, N, K] -> [M, B, K]
bottleneck_conv1x1 = nn.Conv1d(N, B, 1, bias=False)
# [M, B, K] -> [M, B, K]
repeats = []
for r in range(R):
blocks = []
for x in range(X):
dilation = 2**x
padding = (P - 1) * dilation if causal else (P - 1) * dilation // 2
blocks += [
TemporalBlock(B,
H,
P,
stride=1,
padding=padding,
dilation=dilation,
norm_type=norm_type,
causal=causal)
]
repeats += [nn.Sequential(*blocks)]
temporal_conv_net = nn.Sequential(*repeats)
# [M, B, K] -> [M, C*N, K]
mask_conv1x1 = nn.Conv1d(B, C * N, 1, bias=False)
# Put together
self.network = nn.Sequential(layer_norm, bottleneck_conv1x1, temporal_conv_net,
mask_conv1x1)
def forward(self, mixture_w):
"""
Keep this API same with TasNet
Args:
mixture_w: [M, N, K], M is batch size
returns:
est_mask: [M, C, N, K]
"""
M, N, K = mixture_w.size()
score = self.network(mixture_w) # [M, N, K] -> [M, C*N, K]
score = score.view(M, self.C, N, K) # [M, C*N, K] -> [M, C, N, K]
if self.mask_nonlinear == 'softmax':
est_mask = F.softmax(score, dim=1)
elif self.mask_nonlinear == 'relu':
est_mask = F.relu(score)
else:
raise ValueError("Unsupported mask non-linear function")
return est_mask
class TemporalBlock(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
norm_type="gLN",
causal=False):
super(TemporalBlock, self).__init__()
# [M, B, K] -> [M, H, K]
conv1x1 = nn.Conv1d(in_channels, out_channels, 1, bias=False)
prelu = nn.PReLU()
norm = chose_norm(norm_type, out_channels)
# [M, H, K] -> [M, B, K]
dsconv = DepthwiseSeparableConv(out_channels, in_channels, kernel_size, stride, padding,
dilation, norm_type, causal)
# Put together
self.net = nn.Sequential(conv1x1, prelu, norm, dsconv)
def forward(self, x):
"""
Args:
x: [M, B, K]
Returns:
[M, B, K]
"""
residual = x
out = self.net(x)
# TODO: when P = 3 here works fine, but when P = 2 maybe need to pad?
return out + residual # look like w/o F.relu is better than w/ F.relu
# return F.relu(out + residual)
class DepthwiseSeparableConv(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
norm_type="gLN",
causal=False):
super(DepthwiseSeparableConv, self).__init__()
# Use `groups` option to implement depthwise convolution
# [M, H, K] -> [M, H, K]
depthwise_conv = nn.Conv1d(in_channels,
in_channels,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=in_channels,
bias=False)
if causal:
chomp = Chomp1d(padding)
prelu = nn.PReLU()
norm = chose_norm(norm_type, in_channels)
# [M, H, K] -> [M, B, K]
pointwise_conv = nn.Conv1d(in_channels, out_channels, 1, bias=False)
# Put together
if causal:
self.net = nn.Sequential(depthwise_conv, chomp, prelu, norm, pointwise_conv)
else:
self.net = nn.Sequential(depthwise_conv, prelu, norm, pointwise_conv)
def forward(self, x):
"""
Args:
x: [M, H, K]
Returns:
result: [M, B, K]
"""
return self.net(x)
class Chomp1d(nn.Module):
"""To ensure the output length is the same as the input.
"""
def __init__(self, chomp_size):
super(Chomp1d, self).__init__()
self.chomp_size = chomp_size
def forward(self, x):
"""
Args:
x: [M, H, Kpad]
Returns:
[M, H, K]
"""
return x[:, :, :-self.chomp_size].contiguous()
def chose_norm(norm_type, channel_size):
"""The input of normlization will be (M, C, K), where M is batch size,
C is channel size and K is sequence length.
"""
if norm_type == "gLN":
return GlobalLayerNorm(channel_size)
elif norm_type == "cLN":
return ChannelwiseLayerNorm(channel_size)
elif norm_type == "id":
return nn.Identity()
else: # norm_type == "BN":
# Given input (M, C, K), nn.BatchNorm1d(C) will accumulate statics
# along M and K, so this BN usage is right.
return nn.BatchNorm1d(channel_size)
# TODO: Use nn.LayerNorm to impl cLN to speed up
class ChannelwiseLayerNorm(nn.Module):
"""Channel-wise Layer Normalization (cLN)"""
def __init__(self, channel_size):
super(ChannelwiseLayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1]
self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1]
self.reset_parameters()
def reset_parameters(self):
self.gamma.data.fill_(1)
self.beta.data.zero_()
def forward(self, y):
"""
Args:
y: [M, N, K], M is batch size, N is channel size, K is length
Returns:
cLN_y: [M, N, K]
"""
mean = torch.mean(y, dim=1, keepdim=True) # [M, 1, K]
var = torch.var(y, dim=1, keepdim=True, unbiased=False) # [M, 1, K]
cLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta
return cLN_y
class GlobalLayerNorm(nn.Module):
"""Global Layer Normalization (gLN)"""
def __init__(self, channel_size):
super(GlobalLayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1]
self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1]
self.reset_parameters()
def reset_parameters(self):
self.gamma.data.fill_(1)
self.beta.data.zero_()
def forward(self, y):
"""
Args:
y: [M, N, K], M is batch size, N is channel size, K is length
Returns:
gLN_y: [M, N, K]
"""
# TODO: in torch 1.0, torch.mean() support dim list
mean = y.mean(dim=1, keepdim=True).mean(dim=2, keepdim=True) # [M, 1, 1]
var = (torch.pow(y - mean, 2)).mean(dim=1, keepdim=True).mean(dim=2, keepdim=True)
gLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta
return gLN_y
if __name__ == "__main__":
torch.manual_seed(123)
M, N, L, T = 2, 3, 4, 12
K = 2 * T // L - 1
B, H, P, X, R, C, norm_type, causal = 2, 3, 3, 3, 2, 2, "gLN", False
mixture = torch.randint(3, (M, T))
# test Encoder
encoder = Encoder(L, N)
encoder.conv1d_U.weight.data = torch.randint(2, encoder.conv1d_U.weight.size())
mixture_w = encoder(mixture)
print('mixture', mixture)
print('U', encoder.conv1d_U.weight)
print('mixture_w', mixture_w)
print('mixture_w size', mixture_w.size())
# test TemporalConvNet
separator = TemporalConvNet(N, B, H, P, X, R, C, norm_type=norm_type, causal=causal)
est_mask = separator(mixture_w)
print('est_mask', est_mask)
# test Decoder
decoder = Decoder(N, L)
est_mask = torch.randint(2, (B, K, C, N))
est_source = decoder(mixture_w, est_mask)
print('est_source', est_source)
# test Conv-TasNet
conv_tasnet = ConvTasNet(N, L, B, H, P, X, R, C, norm_type=norm_type)
est_source = conv_tasnet(mixture)
print('est_source', est_source)
print('est_source size', est_source.size())