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import os
import sys
import math
import torch
import random
import numpy as np
import typing as tp
from torch import nn
from einops import rearrange
from fractions import Fraction
from torch.nn import functional as F
now_dir = os.getcwd()
sys.path.append(now_dir)
from .states import capture_init
from .demucs import rescale_module
from main.configs.config import Config
from .hdemucs import pad1d, spectro, ispectro, wiener, ScaledEmbedding, HEncLayer, MultiWrap, HDecLayer
translations = Config().translations
def create_sin_embedding(length: int, dim: int, shift: int = 0, device="cpu", max_period=10000):
assert dim % 2 == 0
pos = shift + torch.arange(length, device=device).view(-1, 1, 1)
half_dim = dim // 2
adim = torch.arange(dim // 2, device=device).view(1, 1, -1)
phase = pos / (max_period ** (adim / (half_dim - 1)))
return torch.cat([torch.cos(phase), torch.sin(phase)], dim=-1)
def create_2d_sin_embedding(d_model, height, width, device="cpu", max_period=10000):
if d_model % 4 != 0: raise ValueError(translations["dims"].format(dims=d_model))
pe = torch.zeros(d_model, height, width)
d_model = int(d_model / 2)
div_term = torch.exp(torch.arange(0.0, d_model, 2) * -(math.log(max_period) / d_model))
pos_w = torch.arange(0.0, width).unsqueeze(1)
pos_h = torch.arange(0.0, height).unsqueeze(1)
pe[0:d_model:2, :, :] = torch.sin(pos_w * div_term).transpose(0, 1).unsqueeze(1).repeat(1, height, 1)
pe[1:d_model:2, :, :] = torch.cos(pos_w * div_term).transpose(0, 1).unsqueeze(1).repeat(1, height, 1)
pe[d_model::2, :, :] = torch.sin(pos_h * div_term).transpose(0, 1).unsqueeze(2).repeat(1, 1, width)
pe[d_model + 1 :: 2, :, :] = torch.cos(pos_h * div_term).transpose(0, 1).unsqueeze(2).repeat(1, 1, width)
return pe[None, :].to(device)
def create_sin_embedding_cape( length: int, dim: int, batch_size: int, mean_normalize: bool, augment: bool, max_global_shift: float = 0.0, max_local_shift: float = 0.0, max_scale: float = 1.0, device: str = "cpu", max_period: float = 10000.0):
assert dim % 2 == 0
pos = 1.0 * torch.arange(length).view(-1, 1, 1)
pos = pos.repeat(1, batch_size, 1)
if mean_normalize: pos -= torch.nanmean(pos, dim=0, keepdim=True)
if augment:
delta = np.random.uniform(-max_global_shift, +max_global_shift, size=[1, batch_size, 1])
delta_local = np.random.uniform(-max_local_shift, +max_local_shift, size=[length, batch_size, 1])
log_lambdas = np.random.uniform(-np.log(max_scale), +np.log(max_scale), size=[1, batch_size, 1])
pos = (pos + delta + delta_local) * np.exp(log_lambdas)
pos = pos.to(device)
half_dim = dim // 2
adim = torch.arange(dim // 2, device=device).view(1, 1, -1)
phase = pos / (max_period ** (adim / (half_dim - 1)))
return torch.cat([torch.cos(phase), torch.sin(phase)], dim=-1).float()
class MyGroupNorm(nn.GroupNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, x):
x = x.transpose(1, 2)
return super().forward(x).transpose(1, 2)
class LayerScale(nn.Module):
def __init__(self, channels: int, init: float = 0, channel_last=False):
super().__init__()
self.channel_last = channel_last
self.scale = nn.Parameter(torch.zeros(channels, requires_grad=True))
self.scale.data[:] = init
def forward(self, x):
if self.channel_last: return self.scale * x
else: return self.scale[:, None] * x
class MyTransformerEncoderLayer(nn.TransformerEncoderLayer):
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation=F.relu, group_norm=0, norm_first=False, norm_out=False, layer_norm_eps=1e-5, layer_scale=False, init_values=1e-4, device=None, dtype=None, sparse=False, mask_type="diag", mask_random_seed=42, sparse_attn_window=500, global_window=50, auto_sparsity=False, sparsity=0.95, batch_first=False):
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__(d_model=d_model, nhead=nhead, dim_feedforward=dim_feedforward, dropout=dropout, activation=activation, layer_norm_eps=layer_norm_eps, batch_first=batch_first, norm_first=norm_first, device=device, dtype=dtype)
self.auto_sparsity = auto_sparsity
if group_norm:
self.norm1 = MyGroupNorm(int(group_norm), d_model, eps=layer_norm_eps, **factory_kwargs)
self.norm2 = MyGroupNorm(int(group_norm), d_model, eps=layer_norm_eps, **factory_kwargs)
self.norm_out = None
if self.norm_first & norm_out: self.norm_out = MyGroupNorm(num_groups=int(norm_out), num_channels=d_model)
self.gamma_1 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()
self.gamma_2 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()
def forward(self, src, src_mask=None, src_key_padding_mask=None):
x = src
T, B, C = x.shape
if self.norm_first:
x = x + self.gamma_1(self._sa_block(self.norm1(x), src_mask, src_key_padding_mask))
x = x + self.gamma_2(self._ff_block(self.norm2(x)))
if self.norm_out: x = self.norm_out(x)
else:
x = self.norm1(x + self.gamma_1(self._sa_block(x, src_mask, src_key_padding_mask)))
x = self.norm2(x + self.gamma_2(self._ff_block(x)))
return x
class CrossTransformerEncoder(nn.Module):
def __init__(self, dim: int, emb: str = "sin", hidden_scale: float = 4.0, num_heads: int = 8, num_layers: int = 6, cross_first: bool = False, dropout: float = 0.0, max_positions: int = 1000, norm_in: bool = True, norm_in_group: bool = False, group_norm: int = False, norm_first: bool = False, norm_out: bool = False, max_period: float = 10000.0, weight_decay: float = 0.0, lr: tp.Optional[float] = None, layer_scale: bool = False, gelu: bool = True, sin_random_shift: int = 0, weight_pos_embed: float = 1.0, cape_mean_normalize: bool = True, cape_augment: bool = True, cape_glob_loc_scale: list = [5000.0, 1.0, 1.4], sparse_self_attn: bool = False, sparse_cross_attn: bool = False, mask_type: str = "diag", mask_random_seed: int = 42, sparse_attn_window: int = 500, global_window: int = 50, auto_sparsity: bool = False, sparsity: float = 0.95):
super().__init__()
assert dim % num_heads == 0
hidden_dim = int(dim * hidden_scale)
self.num_layers = num_layers
self.classic_parity = 1 if cross_first else 0
self.emb = emb
self.max_period = max_period
self.weight_decay = weight_decay
self.weight_pos_embed = weight_pos_embed
self.sin_random_shift = sin_random_shift
if emb == "cape":
self.cape_mean_normalize = cape_mean_normalize
self.cape_augment = cape_augment
self.cape_glob_loc_scale = cape_glob_loc_scale
if emb == "scaled": self.position_embeddings = ScaledEmbedding(max_positions, dim, scale=0.2)
self.lr = lr
activation: tp.Any = F.gelu if gelu else F.relu
self.norm_in: nn.Module
self.norm_in_t: nn.Module
if norm_in:
self.norm_in = nn.LayerNorm(dim)
self.norm_in_t = nn.LayerNorm(dim)
elif norm_in_group:
self.norm_in = MyGroupNorm(int(norm_in_group), dim)
self.norm_in_t = MyGroupNorm(int(norm_in_group), dim)
else:
self.norm_in = nn.Identity()
self.norm_in_t = nn.Identity()
self.layers = nn.ModuleList()
self.layers_t = nn.ModuleList()
kwargs_common = {
"d_model": dim,
"nhead": num_heads,
"dim_feedforward": hidden_dim,
"dropout": dropout,
"activation": activation,
"group_norm": group_norm,
"norm_first": norm_first,
"norm_out": norm_out,
"layer_scale": layer_scale,
"mask_type": mask_type,
"mask_random_seed": mask_random_seed,
"sparse_attn_window": sparse_attn_window,
"global_window": global_window,
"sparsity": sparsity,
"auto_sparsity": auto_sparsity,
"batch_first": True,
}
kwargs_classic_encoder = dict(kwargs_common)
kwargs_classic_encoder.update({"sparse": sparse_self_attn})
kwargs_cross_encoder = dict(kwargs_common)
kwargs_cross_encoder.update({"sparse": sparse_cross_attn})
for idx in range(num_layers):
if idx % 2 == self.classic_parity:
self.layers.append(MyTransformerEncoderLayer(**kwargs_classic_encoder))
self.layers_t.append(MyTransformerEncoderLayer(**kwargs_classic_encoder))
else:
self.layers.append(CrossTransformerEncoderLayer(**kwargs_cross_encoder))
self.layers_t.append(CrossTransformerEncoderLayer(**kwargs_cross_encoder))
def forward(self, x, xt):
B, C, Fr, T1 = x.shape
pos_emb_2d = create_2d_sin_embedding(C, Fr, T1, x.device, self.max_period)
pos_emb_2d = rearrange(pos_emb_2d, "b c fr t1 -> b (t1 fr) c")
x = rearrange(x, "b c fr t1 -> b (t1 fr) c")
x = self.norm_in(x)
x = x + self.weight_pos_embed * pos_emb_2d
B, C, T2 = xt.shape
xt = rearrange(xt, "b c t2 -> b t2 c")
pos_emb = self._get_pos_embedding(T2, B, C, x.device)
pos_emb = rearrange(pos_emb, "t2 b c -> b t2 c")
xt = self.norm_in_t(xt)
xt = xt + self.weight_pos_embed * pos_emb
for idx in range(self.num_layers):
if idx % 2 == self.classic_parity:
x = self.layers[idx](x)
xt = self.layers_t[idx](xt)
else:
old_x = x
x = self.layers[idx](x, xt)
xt = self.layers_t[idx](xt, old_x)
x = rearrange(x, "b (t1 fr) c -> b c fr t1", t1=T1)
xt = rearrange(xt, "b t2 c -> b c t2")
return x, xt
def _get_pos_embedding(self, T, B, C, device):
if self.emb == "sin":
shift = random.randrange(self.sin_random_shift + 1)
pos_emb = create_sin_embedding(T, C, shift=shift, device=device, max_period=self.max_period)
elif self.emb == "cape":
if self.training: pos_emb = create_sin_embedding_cape(T, C, B, device=device, max_period=self.max_period, mean_normalize=self.cape_mean_normalize, augment=self.cape_augment, max_global_shift=self.cape_glob_loc_scale[0], max_local_shift=self.cape_glob_loc_scale[1], max_scale=self.cape_glob_loc_scale[2])
else: pos_emb = create_sin_embedding_cape(T, C, B, device=device, max_period=self.max_period, mean_normalize=self.cape_mean_normalize, augment=False)
elif self.emb == "scaled":
pos = torch.arange(T, device=device)
pos_emb = self.position_embeddings(pos)[:, None]
return pos_emb
def make_optim_group(self):
group = {"params": list(self.parameters()), "weight_decay": self.weight_decay}
if self.lr is not None: group["lr"] = self.lr
return group
class CrossTransformerEncoderLayer(nn.Module):
def __init__(self, d_model: int, nhead: int, dim_feedforward: int = 2048, dropout: float = 0.1, activation=F.relu, layer_norm_eps: float = 1e-5, layer_scale: bool = False, init_values: float = 1e-4, norm_first: bool = False, group_norm: bool = False, norm_out: bool = False, sparse=False, mask_type="diag", mask_random_seed=42, sparse_attn_window=500, global_window=50, sparsity=0.95, auto_sparsity=None, device=None, dtype=None, batch_first=False):
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__()
self.auto_sparsity = auto_sparsity
self.cross_attn: nn.Module
self.cross_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first)
self.linear1 = nn.Linear(d_model, dim_feedforward, **factory_kwargs)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model, **factory_kwargs)
self.norm_first = norm_first
self.norm1: nn.Module
self.norm2: nn.Module
self.norm3: nn.Module
if group_norm:
self.norm1 = MyGroupNorm(int(group_norm), d_model, eps=layer_norm_eps, **factory_kwargs)
self.norm2 = MyGroupNorm(int(group_norm), d_model, eps=layer_norm_eps, **factory_kwargs)
self.norm3 = MyGroupNorm(int(group_norm), d_model, eps=layer_norm_eps, **factory_kwargs)
else:
self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
self.norm3 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
self.norm_out = None
if self.norm_first & norm_out:
self.norm_out = MyGroupNorm(num_groups=int(norm_out), num_channels=d_model)
self.gamma_1 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()
self.gamma_2 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
if isinstance(activation, str): self.activation = self._get_activation_fn(activation)
else: self.activation = activation
def forward(self, q, k, mask=None):
if self.norm_first:
x = q + self.gamma_1(self._ca_block(self.norm1(q), self.norm2(k), mask))
x = x + self.gamma_2(self._ff_block(self.norm3(x)))
if self.norm_out: x = self.norm_out(x)
else:
x = self.norm1(q + self.gamma_1(self._ca_block(q, k, mask)))
x = self.norm2(x + self.gamma_2(self._ff_block(x)))
return x
def _ca_block(self, q, k, attn_mask=None):
x = self.cross_attn(q, k, k, attn_mask=attn_mask, need_weights=False)[0]
return self.dropout1(x)
def _ff_block(self, x):
x = self.linear2(self.dropout(self.activation(self.linear1(x))))
return self.dropout2(x)
def _get_activation_fn(self, activation):
if activation == "relu": return F.relu
elif activation == "gelu": return F.gelu
raise RuntimeError(translations["activation"].format(activation=activation))
class HTDemucs(nn.Module):
@capture_init
def __init__(self, sources, audio_channels=2, channels=48, channels_time=None, growth=2, nfft=4096, wiener_iters=0, end_iters=0, wiener_residual=False, cac=True, depth=4, rewrite=True, multi_freqs=None, multi_freqs_depth=3, freq_emb=0.2, emb_scale=10, emb_smooth=True, kernel_size=8, time_stride=2, stride=4, context=1, context_enc=0, norm_starts=4, norm_groups=4, dconv_mode=1, dconv_depth=2, dconv_comp=8, dconv_init=1e-3, bottom_channels=0, 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, t_cross_first=False, rescale=0.1, samplerate=44100, segment=4 * 10, use_train_segment=True):
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
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
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):
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):
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):
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):
if not self.use_train_segment: return length
training_length = int(self.segment * self.samplerate)
if training_length < length: raise ValueError(translations["length_or_training_length"].format(length=length, 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).to(mix.device)
x = mag
B, C, Fq, T = x.shape
mean = x.mean(dim=(1, 2, 3), keepdim=True)
std = x.std(dim=(1, 2, 3), keepdim=True)
x = (x - mean) / (1e-5 + std)
xt = mix
meant = xt.mean(dim=(1, 2), keepdim=True)
stdt = xt.std(dim=(1, 2), keepdim=True)
xt = (xt - meant) / (1e-5 + stdt)
saved = []
saved_t = []
lengths = []
lengths_t = []
for idx, encode in enumerate(self.encoder):
lengths.append(x.shape[-1])
inject = None
if idx < len(self.tencoder):
lengths_t.append(xt.shape[-1])
tenc = self.tencoder[idx]
xt = tenc(xt)
if not tenc.empty: saved_t.append(xt)
else: inject = xt
x = encode(x, inject)
if idx == 0 and self.freq_emb is not None:
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))
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)
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]
device_type = x.device.type
device_load = f"{device_type}:{x.device.index}" if not device_type == "mps" else device_type
x_is_other_gpu = not device_type in ["cuda", "cpu"]
if x_is_other_gpu: x = x.cpu()
zout = self._mask(z, x)
if self.use_train_segment: x = self._ispec(zout, length) if self.training else self._ispec(zout, training_length)
else: x = self._ispec(zout, length)
if x_is_other_gpu: x = x.to(device_load)
if self.use_train_segment: xt = xt.view(B, S, -1, length) if self.training else 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 |