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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.
import math
import julius
from torch import nn
from .tasnet_v2 import ConvTasNet
from .utils import capture_init, center_trim
class BLSTM(nn.Module):
def __init__(self, dim, layers=1):
super().__init__()
self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)
self.linear = nn.Linear(2 * dim, dim)
def forward(self, x):
x = x.permute(2, 0, 1)
x = self.lstm(x)[0]
x = self.linear(x)
x = x.permute(1, 2, 0)
return x
def rescale_conv(conv, reference):
std = conv.weight.std().detach()
scale = (std / reference)**0.5
conv.weight.data /= scale
if conv.bias is not None:
conv.bias.data /= scale
def rescale_module(module, reference):
for sub in module.modules():
if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d)):
rescale_conv(sub, reference)
def auto_load_demucs_model_v2(sources, demucs_model_name):
if '48' in demucs_model_name:
channels=48
elif 'unittest' in demucs_model_name:
channels=4
else:
channels=64
if 'tasnet' in demucs_model_name:
init_demucs_model = ConvTasNet(sources, X=10)
else:
init_demucs_model = Demucs(sources, channels=channels)
return init_demucs_model
class Demucs(nn.Module):
@capture_init
def __init__(self,
sources,
audio_channels=2,
channels=64,
depth=6,
rewrite=True,
glu=True,
rescale=0.1,
resample=True,
kernel_size=8,
stride=4,
growth=2.,
lstm_layers=2,
context=3,
normalize=False,
samplerate=44100,
segment_length=4 * 10 * 44100):
"""
Args:
sources (list[str]): list of source names
audio_channels (int): stereo or mono
channels (int): first convolution channels
depth (int): number of encoder/decoder layers
rewrite (bool): add 1x1 convolution to each encoder layer
and a convolution to each decoder layer.
For the decoder layer, `context` gives the kernel size.
glu (bool): use glu instead of ReLU
resample_input (bool): upsample x2 the input and downsample /2 the output.
rescale (int): rescale initial weights of convolutions
to get their standard deviation closer to `rescale`
kernel_size (int): kernel size for convolutions
stride (int): stride for convolutions
growth (float): multiply (resp divide) number of channels by that
for each layer of the encoder (resp decoder)
lstm_layers (int): number of lstm layers, 0 = no lstm
context (int): kernel size of the convolution in the
decoder before the transposed convolution. If > 1,
will provide some context from neighboring time
steps.
samplerate (int): stored as meta information for easing
future evaluations of the model.
segment_length (int): stored as meta information for easing
future evaluations of the model. Length of the segments on which
the model was trained.
"""
super().__init__()
self.audio_channels = audio_channels
self.sources = sources
self.kernel_size = kernel_size
self.context = context
self.stride = stride
self.depth = depth
self.resample = resample
self.channels = channels
self.normalize = normalize
self.samplerate = samplerate
self.segment_length = segment_length
self.encoder = nn.ModuleList()
self.decoder = nn.ModuleList()
if glu:
activation = nn.GLU(dim=1)
ch_scale = 2
else:
activation = nn.ReLU()
ch_scale = 1
in_channels = audio_channels
for index in range(depth):
encode = []
encode += [nn.Conv1d(in_channels, channels, kernel_size, stride), nn.ReLU()]
if rewrite:
encode += [nn.Conv1d(channels, ch_scale * channels, 1), activation]
self.encoder.append(nn.Sequential(*encode))
decode = []
if index > 0:
out_channels = in_channels
else:
out_channels = len(self.sources) * audio_channels
if rewrite:
decode += [nn.Conv1d(channels, ch_scale * channels, context), activation]
decode += [nn.ConvTranspose1d(channels, out_channels, kernel_size, stride)]
if index > 0:
decode.append(nn.ReLU())
self.decoder.insert(0, nn.Sequential(*decode))
in_channels = channels
channels = int(growth * channels)
channels = in_channels
if lstm_layers:
self.lstm = BLSTM(channels, lstm_layers)
else:
self.lstm = None
if rescale:
rescale_module(self, reference=rescale)
def valid_length(self, length):
"""
Return the nearest valid length to use with the model so that
there is no time steps left over in a convolutions, e.g. for all
layers, size of the input - kernel_size % stride = 0.
If the mixture has a valid length, the estimated sources
will have exactly the same length when context = 1. If context > 1,
the two signals can be center trimmed to match.
For training, extracts should have a valid length.For evaluation
on full tracks we recommend passing `pad = True` to :method:`forward`.
"""
if self.resample:
length *= 2
for _ in range(self.depth):
length = math.ceil((length - self.kernel_size) / self.stride) + 1
length = max(1, length)
length += self.context - 1
for _ in range(self.depth):
length = (length - 1) * self.stride + self.kernel_size
if self.resample:
length = math.ceil(length / 2)
return int(length)
def forward(self, mix):
x = mix
if self.normalize:
mono = mix.mean(dim=1, keepdim=True)
mean = mono.mean(dim=-1, keepdim=True)
std = mono.std(dim=-1, keepdim=True)
else:
mean = 0
std = 1
x = (x - mean) / (1e-5 + std)
if self.resample:
x = julius.resample_frac(x, 1, 2)
saved = []
for encode in self.encoder:
x = encode(x)
saved.append(x)
if self.lstm:
x = self.lstm(x)
for decode in self.decoder:
skip = center_trim(saved.pop(-1), x)
x = x + skip
x = decode(x)
if self.resample:
x = julius.resample_frac(x, 2, 1)
x = x * std + mean
x = x.view(x.size(0), len(self.sources), self.audio_channels, x.size(-1))
return x