uvr5 / demucs /demucs.py

init

54c22e4 7 months ago

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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 typing as tp

	import julius
	import torch
	from torch import nn
	from torch.nn import functional as F

	from .states import capture_init
	from .utils import center_trim, unfold


	class BLSTM(nn.Module):
	"""
	BiLSTM with same hidden units as input dim.
	If `max_steps` is not None, input will be splitting in overlapping
	chunks and the LSTM applied separately on each chunk.
	"""
	def __init__(self, dim, layers=1, max_steps=None, skip=False):
	super().__init__()
	assert max_steps is None or max_steps % 4 == 0
	self.max_steps = max_steps
	self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)
	self.linear = nn.Linear(2 * dim, dim)
	self.skip = skip

	def forward(self, x):
	B, C, T = x.shape
	y = x
	framed = False
	if self.max_steps is not None and T > self.max_steps:
	width = self.max_steps
	stride = width // 2
	frames = unfold(x, width, stride)
	nframes = frames.shape[2]
	framed = True
	x = frames.permute(0, 2, 1, 3).reshape(-1, C, width)

	x = x.permute(2, 0, 1)

	x = self.lstm(x)[0]
	x = self.linear(x)
	x = x.permute(1, 2, 0)
	if framed:
	out = []
	frames = x.reshape(B, -1, C, width)
	limit = stride // 2
	for k in range(nframes):
	if k == 0:
	out.append(frames[:, k, :, :-limit])
	elif k == nframes - 1:
	out.append(frames[:, k, :, limit:])
	else:
	out.append(frames[:, k, :, limit:-limit])
	out = torch.cat(out, -1)
	out = out[..., :T]
	x = out
	if self.skip:
	x = x + y
	return x


	def rescale_conv(conv, reference):
	"""Rescale initial weight scale. It is unclear why it helps but it certainly does.
	"""
	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, nn.Conv2d, nn.ConvTranspose2d)):
	rescale_conv(sub, reference)


	class LayerScale(nn.Module):
	"""Layer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf).
	This rescales diagonaly residual outputs close to 0 initially, then learnt.
	"""
	def __init__(self, channels: int, init: float = 0):
	super().__init__()
	self.scale = nn.Parameter(torch.zeros(channels, requires_grad=True))
	self.scale.data[:] = init

	def forward(self, x):
	return self.scale[:, None] * x


	class DConv(nn.Module):
	"""
	New residual branches in each encoder layer.
	This alternates dilated convolutions, potentially with LSTMs and attention.
	Also before entering each residual branch, dimension is projected on a smaller subspace,
	e.g. of dim `channels // compress`.
	"""
	def __init__(self, channels: int, compress: float = 4, depth: int = 2, init: float = 1e-4,
	norm=True, attn=False, heads=4, ndecay=4, lstm=False, gelu=True,
	kernel=3, dilate=True):
	"""
	Args:
	channels: input/output channels for residual branch.
	compress: amount of channel compression inside the branch.
	depth: number of layers in the residual branch. Each layer has its own
	projection, and potentially LSTM and attention.
	init: initial scale for LayerNorm.
	norm: use GroupNorm.
	attn: use LocalAttention.
	heads: number of heads for the LocalAttention.
	ndecay: number of decay controls in the LocalAttention.
	lstm: use LSTM.
	gelu: Use GELU activation.
	kernel: kernel size for the (dilated) convolutions.
	dilate: if true, use dilation, increasing with the depth.
	"""

	super().__init__()
	assert kernel % 2 == 1
	self.channels = channels
	self.compress = compress
	self.depth = abs(depth)
	dilate = depth > 0

	norm_fn: tp.Callable[[int], nn.Module]
	norm_fn = lambda d: nn.Identity() # noqa
	if norm:
	norm_fn = lambda d: nn.GroupNorm(1, d) # noqa

	hidden = int(channels / compress)

	act: tp.Type[nn.Module]
	if gelu:
	act = nn.GELU
	else:
	act = nn.ReLU

	self.layers = nn.ModuleList([])
	for d in range(self.depth):
	dilation = 2 ** d if dilate else 1
	padding = dilation * (kernel // 2)
	mods = [
	nn.Conv1d(channels, hidden, kernel, dilation=dilation, padding=padding),
	norm_fn(hidden), act(),
	nn.Conv1d(hidden, 2 * channels, 1),
	norm_fn(2 * channels), nn.GLU(1),
	LayerScale(channels, init),
	]
	if attn:
	mods.insert(3, LocalState(hidden, heads=heads, ndecay=ndecay))
	if lstm:
	mods.insert(3, BLSTM(hidden, layers=2, max_steps=200, skip=True))
	layer = nn.Sequential(*mods)
	self.layers.append(layer)

	def forward(self, x):
	for layer in self.layers:
	x = x + layer(x)
	return x


	class LocalState(nn.Module):
	"""Local state allows to have attention based only on data (no positional embedding),
	but while setting a constraint on the time window (e.g. decaying penalty term).

	Also a failed experiments with trying to provide some frequency based attention.
	"""
	def __init__(self, channels: int, heads: int = 4, nfreqs: int = 0, ndecay: int = 4):
	super().__init__()
	assert channels % heads == 0, (channels, heads)
	self.heads = heads
	self.nfreqs = nfreqs
	self.ndecay = ndecay
	self.content = nn.Conv1d(channels, channels, 1)
	self.query = nn.Conv1d(channels, channels, 1)
	self.key = nn.Conv1d(channels, channels, 1)
	if nfreqs:
	self.query_freqs = nn.Conv1d(channels, heads * nfreqs, 1)
	if ndecay:
	self.query_decay = nn.Conv1d(channels, heads * ndecay, 1)
	# Initialize decay close to zero (there is a sigmoid), for maximum initial window.
	self.query_decay.weight.data *= 0.01
	assert self.query_decay.bias is not None # stupid type checker
	self.query_decay.bias.data[:] = -2
	self.proj = nn.Conv1d(channels + heads * nfreqs, channels, 1)

	def forward(self, x):
	B, C, T = x.shape
	heads = self.heads
	indexes = torch.arange(T, device=x.device, dtype=x.dtype)
	# left index are keys, right index are queries
	delta = indexes[:, None] - indexes[None, :]

	queries = self.query(x).view(B, heads, -1, T)
	keys = self.key(x).view(B, heads, -1, T)
	# t are keys, s are queries
	dots = torch.einsum("bhct,bhcs->bhts", keys, queries)
	dots /= keys.shape[2]**0.5
	if self.nfreqs:
	periods = torch.arange(1, self.nfreqs + 1, device=x.device, dtype=x.dtype)
	freq_kernel = torch.cos(2 * math.pi * delta / periods.view(-1, 1, 1))
	freq_q = self.query_freqs(x).view(B, heads, -1, T) / self.nfreqs ** 0.5
	dots += torch.einsum("fts,bhfs->bhts", freq_kernel, freq_q)
	if self.ndecay:
	decays = torch.arange(1, self.ndecay + 1, device=x.device, dtype=x.dtype)
	decay_q = self.query_decay(x).view(B, heads, -1, T)
	decay_q = torch.sigmoid(decay_q) / 2
	decay_kernel = - decays.view(-1, 1, 1) * delta.abs() / self.ndecay**0.5
	dots += torch.einsum("fts,bhfs->bhts", decay_kernel, decay_q)

	# Kill self reference.
	dots.masked_fill_(torch.eye(T, device=dots.device, dtype=torch.bool), -100)
	weights = torch.softmax(dots, dim=2)

	content = self.content(x).view(B, heads, -1, T)
	result = torch.einsum("bhts,bhct->bhcs", weights, content)
	if self.nfreqs:
	time_sig = torch.einsum("bhts,fts->bhfs", weights, freq_kernel)
	result = torch.cat([result, time_sig], 2)
	result = result.reshape(B, -1, T)
	return x + self.proj(result)


	class Demucs(nn.Module):
	@capture_init
	def __init__(self,
	sources,
	# Channels
	audio_channels=2,
	channels=64,
	growth=2.,
	# Main structure
	depth=6,
	rewrite=True,
	lstm_layers=0,
	# Convolutions
	kernel_size=8,
	stride=4,
	context=1,
	# Activations
	gelu=True,
	glu=True,
	# Normalization
	norm_starts=4,
	norm_groups=4,
	# DConv residual branch
	dconv_mode=1,
	dconv_depth=2,
	dconv_comp=4,
	dconv_attn=4,
	dconv_lstm=4,
	dconv_init=1e-4,
	# Pre/post processing
	normalize=True,
	resample=True,
	# Weight init
	rescale=0.1,
	# Metadata
	samplerate=44100,
	segment=4 * 10):
	"""
	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
	growth (float): multiply (resp divide) number of channels by that
	for each layer of the encoder (resp decoder)
	depth (int): number of layers in the encoder and in the decoder.
	rewrite (bool): add 1x1 convolution to each layer.
	lstm_layers (int): number of lstm layers, 0 = no lstm. Deactivated
	by default, as this is now replaced by the smaller and faster small LSTMs
	in the DConv branches.
	kernel_size (int): kernel size for convolutions
	stride (int): stride for convolutions
	context (int): kernel size of the convolution in the
	decoder before the transposed convolution. If > 1,
	will provide some context from neighboring time steps.
	gelu: use GELU activation function.
	glu (bool): use glu instead of ReLU for the 1x1 rewrite conv.
	norm_starts: layer at which group norm starts being used.
	decoder layers are numbered in reverse order.
	norm_groups: number of groups for group norm.
	dconv_mode: if 1: dconv in encoder only, 2: decoder only, 3: both.
	dconv_depth: depth of residual DConv branch.
	dconv_comp: compression of DConv branch.
	dconv_attn: adds attention layers in DConv branch starting at this layer.
	dconv_lstm: adds a LSTM layer in DConv branch starting at this layer.
	dconv_init: initial scale for the DConv branch LayerScale.
	normalize (bool): normalizes the input audio on the fly, and scales back
	the output by the same amount.
	resample (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`.
	samplerate (int): stored as meta information for easing
	future evaluations of the model.
	segment (float): duration of the chunks of audio to ideally evaluate the model on.
	This is used by `demucs.apply.apply_model`.
	"""

	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 = segment
	self.encoder = nn.ModuleList()
	self.decoder = nn.ModuleList()
	self.skip_scales = nn.ModuleList()

	if glu:
	activation = nn.GLU(dim=1)
	ch_scale = 2
	else:
	activation = nn.ReLU()
	ch_scale = 1
	if gelu:
	act2 = nn.GELU
	else:
	act2 = nn.ReLU

	in_channels = audio_channels
	padding = 0
	for index in range(depth):
	norm_fn = lambda d: nn.Identity() # noqa
	if index >= norm_starts:
	norm_fn = lambda d: nn.GroupNorm(norm_groups, d) # noqa

	encode = []
	encode += [
	nn.Conv1d(in_channels, channels, kernel_size, stride),
	norm_fn(channels),
	act2(),
	]
	attn = index >= dconv_attn
	lstm = index >= dconv_lstm
	if dconv_mode & 1:
	encode += [DConv(channels, depth=dconv_depth, init=dconv_init,
	compress=dconv_comp, attn=attn, lstm=lstm)]
	if rewrite:
	encode += [
	nn.Conv1d(channels, ch_scale * channels, 1),
	norm_fn(ch_scale * channels), 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, 2 * context + 1, padding=context),
	norm_fn(ch_scale * channels), activation]
	if dconv_mode & 2:
	decode += [DConv(channels, depth=dconv_depth, init=dconv_init,
	compress=dconv_comp, attn=attn, lstm=lstm)]
	decode += [nn.ConvTranspose1d(channels, out_channels,
	kernel_size, stride, padding=padding)]
	if index > 0:
	decode += [norm_fn(out_channels), act2()]
	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 convolution, e.g. for all
	layers, size of the input - kernel_size % stride = 0.

	Note that input are automatically padded if necessary to ensure that the output
	has the same length as the input.
	"""
	if self.resample:
	length *= 2

	for _ in range(self.depth):
	length = math.ceil((length - self.kernel_size) / self.stride) + 1
	length = max(1, length)

	for idx 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
	length = x.shape[-1]

	if self.normalize:
	mono = mix.mean(dim=1, keepdim=True)
	mean = mono.mean(dim=-1, keepdim=True)
	std = mono.std(dim=-1, keepdim=True)
	x = (x - mean) / (1e-5 + std)
	else:
	mean = 0
	std = 1

	delta = self.valid_length(length) - length
	x = F.pad(x, (delta // 2, delta - delta // 2))

	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 = saved.pop(-1)
	skip = center_trim(skip, x)
	x = decode(x + skip)

	if self.resample:
	x = julius.resample_frac(x, 2, 1)
	x = x * std + mean
	x = center_trim(x, length)
	x = x.view(x.size(0), len(self.sources), self.audio_channels, x.size(-1))
	return x

	def load_state_dict(self, state, strict=True):
	# fix a mismatch with previous generation Demucs models.
	for idx in range(self.depth):
	for a in ['encoder', 'decoder']:
	for b in ['bias', 'weight']:
	new = f'{a}.{idx}.3.{b}'
	old = f'{a}.{idx}.2.{b}'
	if old in state and new not in state:
	state[new] = state.pop(old)
	super().load_state_dict(state, strict=strict)