mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-11-28 01:10:56 +01:00
649 lines
26 KiB
Python
649 lines
26 KiB
Python
# Copyright (c) Meta, 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.
|
|
# First author is Simon Rouard.
|
|
"""
|
|
This code contains the spectrogram and Hybrid version of Demucs.
|
|
"""
|
|
import math
|
|
|
|
from .filtering import wiener
|
|
import torch
|
|
from torch import nn
|
|
from torch.nn import functional as F
|
|
from fractions import Fraction
|
|
from einops import rearrange
|
|
|
|
from .transformer import CrossTransformerEncoder
|
|
|
|
from .demucs import rescale_module
|
|
from .states import capture_init
|
|
from .spec import spectro, ispectro
|
|
from .hdemucs import pad1d, ScaledEmbedding, HEncLayer, MultiWrap, HDecLayer
|
|
|
|
|
|
class HTDemucs(nn.Module):
|
|
"""
|
|
Spectrogram and hybrid Demucs model.
|
|
The spectrogram model has the same structure as Demucs, except the first few layers are over the
|
|
frequency axis, until there is only 1 frequency, and then it moves to time convolutions.
|
|
Frequency layers can still access information across time steps thanks to the DConv residual.
|
|
|
|
Hybrid model have a parallel time branch. At some layer, the time branch has the same stride
|
|
as the frequency branch and then the two are combined. The opposite happens in the decoder.
|
|
|
|
Models can either use naive iSTFT from masking, Wiener filtering ([Ulhih et al. 2017]),
|
|
or complex as channels (CaC) [Choi et al. 2020]. Wiener filtering is based on
|
|
Open Unmix implementation [Stoter et al. 2019].
|
|
|
|
The loss is always on the temporal domain, by backpropagating through the above
|
|
output methods and iSTFT. This allows to define hybrid models nicely. However, this breaks
|
|
a bit Wiener filtering, as doing more iteration at test time will change the spectrogram
|
|
contribution, without changing the one from the waveform, which will lead to worse performance.
|
|
I tried using the residual option in OpenUnmix Wiener implementation, but it didn't improve.
|
|
CaC on the other hand provides similar performance for hybrid, and works naturally with
|
|
hybrid models.
|
|
|
|
This model also uses frequency embeddings are used to improve efficiency on convolutions
|
|
over the freq. axis, following [Isik et al. 2020] (https://arxiv.org/pdf/2008.04470.pdf).
|
|
|
|
Unlike classic Demucs, there is no resampling here, and normalization is always applied.
|
|
"""
|
|
|
|
@capture_init
|
|
def __init__(
|
|
self,
|
|
sources,
|
|
# Channels
|
|
audio_channels=2,
|
|
channels=48,
|
|
channels_time=None,
|
|
growth=2,
|
|
# STFT
|
|
nfft=4096,
|
|
wiener_iters=0,
|
|
end_iters=0,
|
|
wiener_residual=False,
|
|
cac=True,
|
|
# Main structure
|
|
depth=4,
|
|
rewrite=True,
|
|
# Frequency branch
|
|
multi_freqs=None,
|
|
multi_freqs_depth=3,
|
|
freq_emb=0.2,
|
|
emb_scale=10,
|
|
emb_smooth=True,
|
|
# Convolutions
|
|
kernel_size=8,
|
|
time_stride=2,
|
|
stride=4,
|
|
context=1,
|
|
context_enc=0,
|
|
# Normalization
|
|
norm_starts=4,
|
|
norm_groups=4,
|
|
# DConv residual branch
|
|
dconv_mode=1,
|
|
dconv_depth=2,
|
|
dconv_comp=8,
|
|
dconv_init=1e-3,
|
|
# Before the Transformer
|
|
bottom_channels=0,
|
|
# Transformer
|
|
t_layers=5,
|
|
t_emb="sin",
|
|
t_hidden_scale=4.0,
|
|
t_heads=8,
|
|
t_dropout=0.0,
|
|
t_max_positions=10000,
|
|
t_norm_in=True,
|
|
t_norm_in_group=False,
|
|
t_group_norm=False,
|
|
t_norm_first=True,
|
|
t_norm_out=True,
|
|
t_max_period=10000.0,
|
|
t_weight_decay=0.0,
|
|
t_lr=None,
|
|
t_layer_scale=True,
|
|
t_gelu=True,
|
|
t_weight_pos_embed=1.0,
|
|
t_sin_random_shift=0,
|
|
t_cape_mean_normalize=True,
|
|
t_cape_augment=True,
|
|
t_cape_glob_loc_scale=[5000.0, 1.0, 1.4],
|
|
t_sparse_self_attn=False,
|
|
t_sparse_cross_attn=False,
|
|
t_mask_type="diag",
|
|
t_mask_random_seed=42,
|
|
t_sparse_attn_window=500,
|
|
t_global_window=100,
|
|
t_sparsity=0.95,
|
|
t_auto_sparsity=False,
|
|
# ------ Particuliar parameters
|
|
t_cross_first=False,
|
|
# Weight init
|
|
rescale=0.1,
|
|
# Metadata
|
|
samplerate=44100,
|
|
segment=10,
|
|
use_train_segment=True,
|
|
):
|
|
"""
|
|
Args:
|
|
sources (list[str]): list of source names.
|
|
audio_channels (int): input/output audio channels.
|
|
channels (int): initial number of hidden channels.
|
|
channels_time: if not None, use a different `channels` value for the time branch.
|
|
growth: increase the number of hidden channels by this factor at each layer.
|
|
nfft: number of fft bins. Note that changing this require careful computation of
|
|
various shape parameters and will not work out of the box for hybrid models.
|
|
wiener_iters: when using Wiener filtering, number of iterations at test time.
|
|
end_iters: same but at train time. For a hybrid model, must be equal to `wiener_iters`.
|
|
wiener_residual: add residual source before wiener filtering.
|
|
cac: uses complex as channels, i.e. complex numbers are 2 channels each
|
|
in input and output. no further processing is done before ISTFT.
|
|
depth (int): number of layers in the encoder and in the decoder.
|
|
rewrite (bool): add 1x1 convolution to each layer.
|
|
multi_freqs: list of frequency ratios for splitting frequency bands with `MultiWrap`.
|
|
multi_freqs_depth: how many layers to wrap with `MultiWrap`. Only the outermost
|
|
layers will be wrapped.
|
|
freq_emb: add frequency embedding after the first frequency layer if > 0,
|
|
the actual value controls the weight of the embedding.
|
|
emb_scale: equivalent to scaling the embedding learning rate
|
|
emb_smooth: initialize the embedding with a smooth one (with respect to frequencies).
|
|
kernel_size: kernel_size for encoder and decoder layers.
|
|
stride: stride for encoder and decoder layers.
|
|
time_stride: stride for the final time layer, after the merge.
|
|
context: context for 1x1 conv in the decoder.
|
|
context_enc: context for 1x1 conv in the encoder.
|
|
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.
|
|
bottom_channels: if >0 it adds a linear layer (1x1 Conv) before and after the
|
|
transformer in order to change the number of channels
|
|
t_layers: number of layers in each branch (waveform and spec) of the transformer
|
|
t_emb: "sin", "cape" or "scaled"
|
|
t_hidden_scale: the hidden scale of the Feedforward parts of the transformer
|
|
for instance if C = 384 (the number of channels in the transformer) and
|
|
t_hidden_scale = 4.0 then the intermediate layer of the FFN has dimension
|
|
384 * 4 = 1536
|
|
t_heads: number of heads for the transformer
|
|
t_dropout: dropout in the transformer
|
|
t_max_positions: max_positions for the "scaled" positional embedding, only
|
|
useful if t_emb="scaled"
|
|
t_norm_in: (bool) norm before addinf positional embedding and getting into the
|
|
transformer layers
|
|
t_norm_in_group: (bool) if True while t_norm_in=True, the norm is on all the
|
|
timesteps (GroupNorm with group=1)
|
|
t_group_norm: (bool) if True, the norms of the Encoder Layers are on all the
|
|
timesteps (GroupNorm with group=1)
|
|
t_norm_first: (bool) if True the norm is before the attention and before the FFN
|
|
t_norm_out: (bool) if True, there is a GroupNorm (group=1) at the end of each layer
|
|
t_max_period: (float) denominator in the sinusoidal embedding expression
|
|
t_weight_decay: (float) weight decay for the transformer
|
|
t_lr: (float) specific learning rate for the transformer
|
|
t_layer_scale: (bool) Layer Scale for the transformer
|
|
t_gelu: (bool) activations of the transformer are GeLU if True, ReLU else
|
|
t_weight_pos_embed: (float) weighting of the positional embedding
|
|
t_cape_mean_normalize: (bool) if t_emb="cape", normalisation of positional embeddings
|
|
see: https://arxiv.org/abs/2106.03143
|
|
t_cape_augment: (bool) if t_emb="cape", must be True during training and False
|
|
during the inference, see: https://arxiv.org/abs/2106.03143
|
|
t_cape_glob_loc_scale: (list of 3 floats) if t_emb="cape", CAPE parameters
|
|
see: https://arxiv.org/abs/2106.03143
|
|
t_sparse_self_attn: (bool) if True, the self attentions are sparse
|
|
t_sparse_cross_attn: (bool) if True, the cross-attentions are sparse (don't use it
|
|
unless you designed really specific masks)
|
|
t_mask_type: (str) can be "diag", "jmask", "random", "global" or any combination
|
|
with '_' between: i.e. "diag_jmask_random" (note that this is permutation
|
|
invariant i.e. "diag_jmask_random" is equivalent to "jmask_random_diag")
|
|
t_mask_random_seed: (int) if "random" is in t_mask_type, controls the seed
|
|
that generated the random part of the mask
|
|
t_sparse_attn_window: (int) if "diag" is in t_mask_type, for a query (i), and
|
|
a key (j), the mask is True id |i-j|<=t_sparse_attn_window
|
|
t_global_window: (int) if "global" is in t_mask_type, mask[:t_global_window, :]
|
|
and mask[:, :t_global_window] will be True
|
|
t_sparsity: (float) if "random" is in t_mask_type, t_sparsity is the sparsity
|
|
level of the random part of the mask.
|
|
t_cross_first: (bool) if True cross attention is the first layer of the
|
|
transformer (False seems to be better)
|
|
rescale: weight rescaling trick
|
|
use_train_segment: (bool) if True, the actual size that is used during the
|
|
training is used during inference.
|
|
"""
|
|
super().__init__()
|
|
self.cac = cac
|
|
self.wiener_residual = wiener_residual
|
|
self.audio_channels = audio_channels
|
|
self.sources = sources
|
|
self.kernel_size = kernel_size
|
|
self.context = context
|
|
self.stride = stride
|
|
self.depth = depth
|
|
self.bottom_channels = bottom_channels
|
|
self.channels = channels
|
|
self.samplerate = samplerate
|
|
self.segment = segment
|
|
self.use_train_segment = use_train_segment
|
|
self.nfft = nfft
|
|
self.hop_length = nfft // 4
|
|
self.wiener_iters = wiener_iters
|
|
self.end_iters = end_iters
|
|
self.freq_emb = None
|
|
assert wiener_iters == end_iters
|
|
|
|
self.encoder = nn.ModuleList()
|
|
self.decoder = nn.ModuleList()
|
|
|
|
self.tencoder = nn.ModuleList()
|
|
self.tdecoder = nn.ModuleList()
|
|
|
|
chin = audio_channels
|
|
chin_z = chin # number of channels for the freq branch
|
|
if self.cac:
|
|
chin_z *= 2
|
|
chout = channels_time or channels
|
|
chout_z = channels
|
|
freqs = nfft // 2
|
|
|
|
for index in range(depth):
|
|
norm = index >= norm_starts
|
|
freq = freqs > 1
|
|
stri = stride
|
|
ker = kernel_size
|
|
if not freq:
|
|
assert freqs == 1
|
|
ker = time_stride * 2
|
|
stri = time_stride
|
|
|
|
pad = True
|
|
last_freq = False
|
|
if freq and freqs <= kernel_size:
|
|
ker = freqs
|
|
pad = False
|
|
last_freq = True
|
|
|
|
kw = {
|
|
"kernel_size": ker,
|
|
"stride": stri,
|
|
"freq": freq,
|
|
"pad": pad,
|
|
"norm": norm,
|
|
"rewrite": rewrite,
|
|
"norm_groups": norm_groups,
|
|
"dconv_kw": {
|
|
"depth": dconv_depth,
|
|
"compress": dconv_comp,
|
|
"init": dconv_init,
|
|
"gelu": True,
|
|
},
|
|
}
|
|
kwt = dict(kw)
|
|
kwt["freq"] = 0
|
|
kwt["kernel_size"] = kernel_size
|
|
kwt["stride"] = stride
|
|
kwt["pad"] = True
|
|
kw_dec = dict(kw)
|
|
multi = False
|
|
if multi_freqs and index < multi_freqs_depth:
|
|
multi = True
|
|
kw_dec["context_freq"] = False
|
|
|
|
if last_freq:
|
|
chout_z = max(chout, chout_z)
|
|
chout = chout_z
|
|
|
|
enc = HEncLayer(
|
|
chin_z, chout_z, dconv=dconv_mode & 1, context=context_enc, **kw
|
|
)
|
|
if freq:
|
|
tenc = HEncLayer(
|
|
chin,
|
|
chout,
|
|
dconv=dconv_mode & 1,
|
|
context=context_enc,
|
|
empty=last_freq,
|
|
**kwt
|
|
)
|
|
self.tencoder.append(tenc)
|
|
|
|
if multi:
|
|
enc = MultiWrap(enc, multi_freqs)
|
|
self.encoder.append(enc)
|
|
if index == 0:
|
|
chin = self.audio_channels * len(self.sources)
|
|
chin_z = chin
|
|
if self.cac:
|
|
chin_z *= 2
|
|
dec = HDecLayer(
|
|
chout_z,
|
|
chin_z,
|
|
dconv=dconv_mode & 2,
|
|
last=index == 0,
|
|
context=context,
|
|
**kw_dec
|
|
)
|
|
if multi:
|
|
dec = MultiWrap(dec, multi_freqs)
|
|
if freq:
|
|
tdec = HDecLayer(
|
|
chout,
|
|
chin,
|
|
dconv=dconv_mode & 2,
|
|
empty=last_freq,
|
|
last=index == 0,
|
|
context=context,
|
|
**kwt
|
|
)
|
|
self.tdecoder.insert(0, tdec)
|
|
self.decoder.insert(0, dec)
|
|
|
|
chin = chout
|
|
chin_z = chout_z
|
|
chout = int(growth * chout)
|
|
chout_z = int(growth * chout_z)
|
|
if freq:
|
|
if freqs <= kernel_size:
|
|
freqs = 1
|
|
else:
|
|
freqs //= stride
|
|
if index == 0 and freq_emb:
|
|
self.freq_emb = ScaledEmbedding(
|
|
freqs, chin_z, smooth=emb_smooth, scale=emb_scale
|
|
)
|
|
self.freq_emb_scale = freq_emb
|
|
|
|
if rescale:
|
|
rescale_module(self, reference=rescale)
|
|
|
|
transformer_channels = channels * growth ** (depth - 1)
|
|
if bottom_channels:
|
|
self.channel_upsampler = nn.Conv1d(transformer_channels, bottom_channels, 1)
|
|
self.channel_downsampler = nn.Conv1d(
|
|
bottom_channels, transformer_channels, 1
|
|
)
|
|
self.channel_upsampler_t = nn.Conv1d(
|
|
transformer_channels, bottom_channels, 1
|
|
)
|
|
self.channel_downsampler_t = nn.Conv1d(
|
|
bottom_channels, transformer_channels, 1
|
|
)
|
|
|
|
transformer_channels = bottom_channels
|
|
|
|
if t_layers > 0:
|
|
self.crosstransformer = CrossTransformerEncoder(
|
|
dim=transformer_channels,
|
|
emb=t_emb,
|
|
hidden_scale=t_hidden_scale,
|
|
num_heads=t_heads,
|
|
num_layers=t_layers,
|
|
cross_first=t_cross_first,
|
|
dropout=t_dropout,
|
|
max_positions=t_max_positions,
|
|
norm_in=t_norm_in,
|
|
norm_in_group=t_norm_in_group,
|
|
group_norm=t_group_norm,
|
|
norm_first=t_norm_first,
|
|
norm_out=t_norm_out,
|
|
max_period=t_max_period,
|
|
weight_decay=t_weight_decay,
|
|
lr=t_lr,
|
|
layer_scale=t_layer_scale,
|
|
gelu=t_gelu,
|
|
sin_random_shift=t_sin_random_shift,
|
|
weight_pos_embed=t_weight_pos_embed,
|
|
cape_mean_normalize=t_cape_mean_normalize,
|
|
cape_augment=t_cape_augment,
|
|
cape_glob_loc_scale=t_cape_glob_loc_scale,
|
|
sparse_self_attn=t_sparse_self_attn,
|
|
sparse_cross_attn=t_sparse_cross_attn,
|
|
mask_type=t_mask_type,
|
|
mask_random_seed=t_mask_random_seed,
|
|
sparse_attn_window=t_sparse_attn_window,
|
|
global_window=t_global_window,
|
|
sparsity=t_sparsity,
|
|
auto_sparsity=t_auto_sparsity,
|
|
)
|
|
else:
|
|
self.crosstransformer = None
|
|
|
|
def _spec(self, x):
|
|
hl = self.hop_length
|
|
nfft = self.nfft
|
|
x0 = x # noqa
|
|
|
|
# We re-pad the signal in order to keep the property
|
|
# that the size of the output is exactly the size of the input
|
|
# divided by the stride (here hop_length), when divisible.
|
|
# This is achieved by padding by 1/4th of the kernel size (here nfft).
|
|
# which is not supported by torch.stft.
|
|
# Having all convolution operations follow this convention allow to easily
|
|
# align the time and frequency branches later on.
|
|
assert hl == nfft // 4
|
|
le = int(math.ceil(x.shape[-1] / hl))
|
|
pad = hl // 2 * 3
|
|
x = pad1d(x, (pad, pad + le * hl - x.shape[-1]), mode="reflect")
|
|
|
|
z = spectro(x, nfft, hl)[..., :-1, :]
|
|
assert z.shape[-1] == le + 4, (z.shape, x.shape, le)
|
|
z = z[..., 2: 2 + le]
|
|
return z
|
|
|
|
def _ispec(self, z, length=None, scale=0):
|
|
hl = self.hop_length // (4**scale)
|
|
z = F.pad(z, (0, 0, 0, 1))
|
|
z = F.pad(z, (2, 2))
|
|
pad = hl // 2 * 3
|
|
le = hl * int(math.ceil(length / hl)) + 2 * pad
|
|
x = ispectro(z, hl, length=le)
|
|
x = x[..., pad: pad + length]
|
|
return x
|
|
|
|
def _magnitude(self, z):
|
|
# return the magnitude of the spectrogram, except when cac is True,
|
|
# in which case we just move the complex dimension to the channel one.
|
|
if self.cac:
|
|
B, C, Fr, T = z.shape
|
|
m = torch.view_as_real(z).permute(0, 1, 4, 2, 3)
|
|
m = m.reshape(B, C * 2, Fr, T)
|
|
else:
|
|
m = z.abs()
|
|
return m
|
|
|
|
def _mask(self, z, m):
|
|
# Apply masking given the mixture spectrogram `z` and the estimated mask `m`.
|
|
# If `cac` is True, `m` is actually a full spectrogram and `z` is ignored.
|
|
niters = self.wiener_iters
|
|
if self.cac:
|
|
B, S, C, Fr, T = m.shape
|
|
out = m.view(B, S, -1, 2, Fr, T).permute(0, 1, 2, 4, 5, 3)
|
|
out = torch.view_as_complex(out.contiguous())
|
|
return out
|
|
if self.training:
|
|
niters = self.end_iters
|
|
if niters < 0:
|
|
z = z[:, None]
|
|
return z / (1e-8 + z.abs()) * m
|
|
else:
|
|
return self._wiener(m, z, niters)
|
|
|
|
def _wiener(self, mag_out, mix_stft, niters):
|
|
# apply wiener filtering from OpenUnmix.
|
|
init = mix_stft.dtype
|
|
wiener_win_len = 300
|
|
residual = self.wiener_residual
|
|
|
|
B, S, C, Fq, T = mag_out.shape
|
|
mag_out = mag_out.permute(0, 4, 3, 2, 1)
|
|
mix_stft = torch.view_as_real(mix_stft.permute(0, 3, 2, 1))
|
|
|
|
outs = []
|
|
for sample in range(B):
|
|
pos = 0
|
|
out = []
|
|
for pos in range(0, T, wiener_win_len):
|
|
frame = slice(pos, pos + wiener_win_len)
|
|
z_out = wiener(
|
|
mag_out[sample, frame],
|
|
mix_stft[sample, frame],
|
|
niters,
|
|
residual=residual,
|
|
)
|
|
out.append(z_out.transpose(-1, -2))
|
|
outs.append(torch.cat(out, dim=0))
|
|
out = torch.view_as_complex(torch.stack(outs, 0))
|
|
out = out.permute(0, 4, 3, 2, 1).contiguous()
|
|
if residual:
|
|
out = out[:, :-1]
|
|
assert list(out.shape) == [B, S, C, Fq, T]
|
|
return out.to(init)
|
|
|
|
def valid_length(self, length: int):
|
|
"""
|
|
Return a length that is appropriate for evaluation.
|
|
In our case, always return the training length, unless
|
|
it is smaller than the given length, in which case this
|
|
raises an error.
|
|
"""
|
|
if not self.use_train_segment:
|
|
return length
|
|
training_length = int(self.segment * self.samplerate)
|
|
if training_length < length:
|
|
raise ValueError(
|
|
f"Given length {length} is longer than "
|
|
f"training length {training_length}")
|
|
return training_length
|
|
|
|
def forward(self, mix):
|
|
length = mix.shape[-1]
|
|
length_pre_pad = None
|
|
if self.use_train_segment:
|
|
if self.training:
|
|
self.segment = Fraction(mix.shape[-1], self.samplerate)
|
|
else:
|
|
training_length = int(self.segment * self.samplerate)
|
|
if mix.shape[-1] < training_length:
|
|
length_pre_pad = mix.shape[-1]
|
|
mix = F.pad(mix, (0, training_length - length_pre_pad))
|
|
z = self._spec(mix)
|
|
mag = self._magnitude(z)
|
|
x = mag
|
|
|
|
B, C, Fq, T = x.shape
|
|
|
|
# unlike previous Demucs, we always normalize because it is easier.
|
|
mean = x.mean(dim=(1, 2, 3), keepdim=True)
|
|
std = x.std(dim=(1, 2, 3), keepdim=True)
|
|
x = (x - mean) / (1e-5 + std)
|
|
# x will be the freq. branch input.
|
|
|
|
# Prepare the time branch input.
|
|
xt = mix
|
|
meant = xt.mean(dim=(1, 2), keepdim=True)
|
|
stdt = xt.std(dim=(1, 2), keepdim=True)
|
|
xt = (xt - meant) / (1e-5 + stdt)
|
|
|
|
# okay, this is a giant mess I know...
|
|
saved = [] # skip connections, freq.
|
|
saved_t = [] # skip connections, time.
|
|
lengths = [] # saved lengths to properly remove padding, freq branch.
|
|
lengths_t = [] # saved lengths for time branch.
|
|
for idx, encode in enumerate(self.encoder):
|
|
lengths.append(x.shape[-1])
|
|
inject = None
|
|
if idx < len(self.tencoder):
|
|
# we have not yet merged branches.
|
|
lengths_t.append(xt.shape[-1])
|
|
tenc = self.tencoder[idx]
|
|
xt = tenc(xt)
|
|
if not tenc.empty:
|
|
# save for skip connection
|
|
saved_t.append(xt)
|
|
else:
|
|
# tenc contains just the first conv., so that now time and freq.
|
|
# branches have the same shape and can be merged.
|
|
inject = xt
|
|
x = encode(x, inject)
|
|
if idx == 0 and self.freq_emb is not None:
|
|
# add frequency embedding to allow for non equivariant convolutions
|
|
# over the frequency axis.
|
|
frs = torch.arange(x.shape[-2], device=x.device)
|
|
emb = self.freq_emb(frs).t()[None, :, :, None].expand_as(x)
|
|
x = x + self.freq_emb_scale * emb
|
|
|
|
saved.append(x)
|
|
if self.crosstransformer:
|
|
if self.bottom_channels:
|
|
b, c, f, t = x.shape
|
|
x = rearrange(x, "b c f t-> b c (f t)")
|
|
x = self.channel_upsampler(x)
|
|
x = rearrange(x, "b c (f t)-> b c f t", f=f)
|
|
xt = self.channel_upsampler_t(xt)
|
|
|
|
x, xt = self.crosstransformer(x, xt)
|
|
|
|
if self.bottom_channels:
|
|
x = rearrange(x, "b c f t-> b c (f t)")
|
|
x = self.channel_downsampler(x)
|
|
x = rearrange(x, "b c (f t)-> b c f t", f=f)
|
|
xt = self.channel_downsampler_t(xt)
|
|
|
|
for idx, decode in enumerate(self.decoder):
|
|
skip = saved.pop(-1)
|
|
x, pre = decode(x, skip, lengths.pop(-1))
|
|
# `pre` contains the output just before final transposed convolution,
|
|
# which is used when the freq. and time branch separate.
|
|
|
|
offset = self.depth - len(self.tdecoder)
|
|
if idx >= offset:
|
|
tdec = self.tdecoder[idx - offset]
|
|
length_t = lengths_t.pop(-1)
|
|
if tdec.empty:
|
|
assert pre.shape[2] == 1, pre.shape
|
|
pre = pre[:, :, 0]
|
|
xt, _ = tdec(pre, None, length_t)
|
|
else:
|
|
skip = saved_t.pop(-1)
|
|
xt, _ = tdec(xt, skip, length_t)
|
|
|
|
# Let's make sure we used all stored skip connections.
|
|
assert len(saved) == 0
|
|
assert len(lengths_t) == 0
|
|
assert len(saved_t) == 0
|
|
|
|
S = len(self.sources)
|
|
x = x.view(B, S, -1, Fq, T)
|
|
x = x * std[:, None] + mean[:, None]
|
|
|
|
zout = self._mask(z, x)
|
|
if self.use_train_segment:
|
|
if self.training:
|
|
x = self._ispec(zout, length)
|
|
else:
|
|
x = self._ispec(zout, training_length)
|
|
else:
|
|
x = self._ispec(zout, length)
|
|
|
|
if self.use_train_segment:
|
|
if self.training:
|
|
xt = xt.view(B, S, -1, length)
|
|
else:
|
|
xt = xt.view(B, S, -1, training_length)
|
|
else:
|
|
xt = xt.view(B, S, -1, length)
|
|
xt = xt * stdt[:, None] + meant[:, None]
|
|
x = xt + x
|
|
if length_pre_pad:
|
|
x = x[..., :length_pre_pad]
|
|
return x
|