Spaces:
Running on Zero
Running on Zero
File size: 15,023 Bytes
64ec292 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 | from functools import partial
import torch
import torch.nn.functional as F
from beartype import beartype
from beartype.typing import Callable, Optional, Tuple
from conformer import Conformer
from einops import rearrange, reduce, repeat
from librosa import filters
from torch import nn
from torch.nn import Module, ModuleList
# helper functions
def exists(val):
return val is not None
def default(v, d):
return v if exists(v) else d
class RMSNorm(Module):
def __init__(self, dim):
super().__init__()
self.scale = dim**0.5
self.gamma = nn.Parameter(torch.ones(dim))
def forward(self, x):
return F.normalize(x, dim=-1) * self.scale * self.gamma
# attention
def MLP(dim_in, dim_out, dim_hidden=None, depth=1, activation=nn.Tanh):
dim_hidden = default(dim_hidden, dim_in)
net = []
dims = (dim_in, *((dim_hidden,) * depth), dim_out)
for ind, (layer_dim_in, layer_dim_out) in enumerate(zip(dims[:-1], dims[1:])):
is_last = ind == (len(dims) - 2)
net.append(nn.Linear(layer_dim_in, layer_dim_out))
if is_last:
continue
net.append(activation())
return nn.Sequential(*net)
class MaskEstimator(Module):
@beartype
def __init__(self, dim, dim_inputs: Tuple[int, ...], depth, mlp_expansion_factor=4):
super().__init__()
self.dim_inputs = dim_inputs
self.to_freqs = ModuleList([])
dim_hidden = dim * mlp_expansion_factor
for dim_in in dim_inputs:
net = []
mlp = nn.Sequential(
MLP(dim, dim_in * 2, dim_hidden=dim_hidden, depth=depth), nn.GLU(dim=-1)
)
self.to_freqs.append(mlp)
def forward(self, x):
# split along band dimension and run per-band MLP
x = x.unbind(dim=-2)
outs = []
for band_features, mlp in zip(x, self.to_freqs):
freq_out = mlp(band_features)
outs.append(freq_out)
return torch.cat(outs, dim=-1)
class BandSplit(Module):
@beartype
def __init__(self, dim, dim_inputs: Tuple[int, ...]):
super().__init__()
self.dim_inputs = dim_inputs
self.to_features = ModuleList([])
for dim_in in dim_inputs:
net = nn.Sequential(RMSNorm(dim_in), nn.Linear(dim_in, dim))
self.to_features.append(net)
def forward(self, x):
# split input into predefined frequency-band chunks
x = x.split(self.dim_inputs, dim=-1)
outs = []
for split_input, to_feature in zip(x, self.to_features):
split_output = to_feature(split_input)
outs.append(split_output)
# stack back as (bands) axis
return torch.stack(outs, dim=-2)
class MelBandConformer(nn.Module):
def __init__(
self,
dim: int,
*,
depth: int,
stereo: bool = False,
num_stems: int = 1,
time_conformer_depth: int = 2,
freq_conformer_depth: int = 2,
num_bands: int = 60,
dim_head: int = 64,
heads: int = 8,
# Conformer params
ff_mult: int = 4,
conv_expansion_factor: int = 2,
conv_kernel_size: int = 31,
attn_dropout: float = 0.0,
ff_dropout: float = 0.0,
conv_dropout: float = 0.0,
# STFT
dim_freqs_in: int = 1025,
sample_rate: int = 44100,
stft_n_fft: int = 2048,
stft_hop_length: int = 512,
stft_win_length: int = 2048,
stft_normalized: bool = False,
stft_window_fn: Optional[Callable] = None,
# Loss
mask_estimator_depth: int = 1,
multi_stft_resolution_loss_weight: float = 1.0,
multi_stft_resolutions_window_sizes: Tuple[int, ...] = (
4096,
2048,
1024,
512,
256,
),
multi_stft_hop_size: int = 147,
multi_stft_normalized: bool = False,
multi_stft_window_fn: Callable = torch.hann_window,
match_input_audio_length: bool = False,
use_torch_checkpoint: bool = False,
skip_connection: bool = False,
):
super().__init__()
self.stereo = stereo
self.audio_channels = 2 if stereo else 1
self.num_stems = num_stems
self.use_torch_checkpoint = use_torch_checkpoint
self.skip_connection = skip_connection
self.layers = nn.ModuleList([])
# Layers per block: [ time-Conformer, freq-Conformer ]
conformer_kwargs = dict(
dim=dim,
dim_head=dim_head,
heads=heads,
ff_mult=ff_mult,
conv_expansion_factor=conv_expansion_factor,
conv_kernel_size=conv_kernel_size,
attn_dropout=attn_dropout,
ff_dropout=ff_dropout,
conv_dropout=conv_dropout,
)
for _ in range(depth):
time_block = Conformer(depth=time_conformer_depth, **conformer_kwargs)
freq_block = Conformer(depth=freq_conformer_depth, **conformer_kwargs)
self.layers.append(nn.ModuleList([time_block, freq_block]))
self.stft_window_fn = partial(
stft_window_fn or torch.hann_window, stft_win_length
)
self.stft_kwargs = dict(
n_fft=stft_n_fft,
hop_length=stft_hop_length,
win_length=stft_win_length,
normalized=stft_normalized,
)
# number of frequency bins produced by STFT (ignoring complex axis)
freqs = torch.stft(
torch.randn(1, 4096),
**self.stft_kwargs,
window=torch.ones(stft_n_fft),
return_complex=True,
).shape[1]
# build mel filter bank to define band grouping
mel_filter_bank_numpy = filters.mel(
sr=sample_rate, n_fft=stft_n_fft, n_mels=num_bands
)
mel_filter_bank = torch.from_numpy(mel_filter_bank_numpy)
# ensure coverage at the boundaries
mel_filter_bank[0][0] = 1.0
mel_filter_bank[-1, -1] = 1.0
freqs_per_band = mel_filter_bank > 0
assert freqs_per_band.any(dim=0).all(), (
"all frequency bins must be covered by bands"
)
repeated_freq_indices = repeat(torch.arange(freqs), "f -> b f", b=num_bands)
freq_indices = repeated_freq_indices[freqs_per_band]
if stereo:
# duplicate indices for stereo by interleaving channels along the freq axis
freq_indices = repeat(freq_indices, "f -> f s", s=2)
freq_indices = freq_indices * 2 + torch.arange(2)
freq_indices = rearrange(freq_indices, "f s -> (f s)")
self.register_buffer("freq_indices", freq_indices, persistent=False)
self.register_buffer("freqs_per_band", freqs_per_band, persistent=False)
num_freqs_per_band = reduce(freqs_per_band, "b f -> b", "sum")
num_bands_per_freq = reduce(freqs_per_band, "b f -> f", "sum")
self.register_buffer("num_freqs_per_band", num_freqs_per_band, persistent=False)
self.register_buffer("num_bands_per_freq", num_bands_per_freq, persistent=False)
# BandSplit and MaskEstimator — same structure as your original
freqs_per_bands_with_complex = tuple(
2 * f * self.audio_channels for f in num_freqs_per_band.tolist()
)
self.band_split = BandSplit(dim=dim, dim_inputs=freqs_per_bands_with_complex)
self.mask_estimators = nn.ModuleList(
[
MaskEstimator(
dim=dim,
dim_inputs=freqs_per_bands_with_complex,
depth=mask_estimator_depth,
mlp_expansion_factor=4, # could be exposed as a parameter
)
for _ in range(num_stems)
]
)
# multi-resolution STFT loss setup
self.multi_stft_resolution_loss_weight = multi_stft_resolution_loss_weight
self.multi_stft_resolutions_window_sizes = multi_stft_resolutions_window_sizes
self.multi_stft_n_fft = stft_n_fft
self.multi_stft_window_fn = multi_stft_window_fn
self.multi_stft_kwargs = dict(
hop_length=multi_stft_hop_size, normalized=multi_stft_normalized
)
self.match_input_audio_length = match_input_audio_length
def forward(
self,
raw_audio: torch.Tensor,
target: Optional[torch.Tensor] = None,
return_loss_breakdown: bool = False,
):
"""
b - batch
f - freq
t - time
s - audio channel (1 mono / 2 stereo)
n - stems
c - complex (2)
d - feature dim
"""
device = raw_audio.device
if raw_audio.ndim == 2:
raw_audio = rearrange(raw_audio, "b t -> b 1 t")
batch, channels, raw_audio_length = raw_audio.shape
istft_length = raw_audio_length if self.match_input_audio_length else None
assert (not self.stereo and channels == 1) or (self.stereo and channels == 2), (
"set stereo=True for stereo input (C=2), stereo=False for mono (C=1)"
)
# --- STFT ---
raw_audio_flat, packed_shape = (
raw_audio.reshape(-1, raw_audio.shape[-1]),
raw_audio.shape[:2],
)
stft_window = self.stft_window_fn(device=device)
stft_repr = torch.stft(
raw_audio_flat, **self.stft_kwargs, window=stft_window, return_complex=True
)
stft_repr = torch.view_as_real(stft_repr) # (B*C, F, T, 2)
stft_repr = stft_repr.view(
*packed_shape, *stft_repr.shape[1:]
) # (b, s, f, t, c)
# fold channel into frequency axis (as in your setup)
stft_repr_fs = rearrange(stft_repr, "b s f t c -> b (f s) t c")
# index frequencies by mel bands
b_idx = torch.arange(batch, device=device)[..., None]
x = stft_repr_fs[b_idx, self.freq_indices] # (b, sum(freqs_in_bands), t, c)
x = rearrange(x, "b f t c -> b t (f c)") # flatten complex axis into features
# --- BandSplit -> (b, t, bands, dim) ---
if self.use_torch_checkpoint:
x = torch.utils.checkpoint.checkpoint(
self.band_split, x, use_reentrant=False
)
else:
x = self.band_split(x)
# --- Axial Conformer (time, then freq) ---
store = [None] * len(self.layers)
for i, (time_conf, freq_conf) in enumerate(self.layers):
# Time axis: (b, t, bands, d) -> ((b*bands), t, d)
bsz, tlen, bands, d = x.shape
x_time = rearrange(x, "b t f d -> (b f) t d")
if self.use_torch_checkpoint:
x_time = torch.utils.checkpoint.checkpoint(
time_conf, x_time, use_reentrant=False
)
else:
x_time = time_conf(x_time)
x = rearrange(x_time, "(b f) t d -> b t f d", b=bsz, f=bands)
# Freq axis: (b, t, f, d) -> ((b*t), f, d)
bsz, tlen, bands, d = x.shape
x_freq = rearrange(x, "b t f d -> (b t) f d")
if self.use_torch_checkpoint:
x_freq = torch.utils.checkpoint.checkpoint(
freq_conf, x_freq, use_reentrant=False
)
else:
x_freq = freq_conf(x_freq)
x = rearrange(x_freq, "(b t) f d -> b t f d", b=bsz, t=tlen)
if self.skip_connection:
store[i] = x if store[i] is None else store[i] + x
# --- Mask estimation ---
# (b, t, f_bands, d) -> per-stem MLP over bands
if self.use_torch_checkpoint:
masks = torch.stack(
[
torch.utils.checkpoint.checkpoint(fn, x, use_reentrant=False)
for fn in self.mask_estimators
],
dim=1,
)
else:
masks = torch.stack([fn(x) for fn in self.mask_estimators], dim=1)
masks = rearrange(masks, "b n t (f c) -> b n f t c", c=2)
# --- Complex modulation ---
stft_repr_c = rearrange(stft_repr, "b s f t c -> b 1 (f s) t c")
stft_repr_c = torch.view_as_complex(stft_repr_c) # (b, 1, F*S, T)
masks_c = torch.view_as_complex(masks) # (b, n, F*S, T)
masks_c = masks_c.type(stft_repr_c.dtype)
scatter_idx = repeat(
self.freq_indices,
"f -> b n f t",
b=batch,
n=self.num_stems,
t=stft_repr_c.shape[-1],
)
stft_repr_expanded = repeat(stft_repr_c, "b 1 ... -> b n ...", n=self.num_stems)
masks_summed = torch.zeros_like(stft_repr_expanded).scatter_add_(
2, scatter_idx, masks_c
)
denom = repeat(self.num_bands_per_freq, "f -> (f r) 1", r=self.audio_channels)
masks_averaged = masks_summed / denom.clamp(min=1e-8)
stft_mod = stft_repr_c * masks_averaged
# --- iSTFT ---
stft_mod = rearrange(
stft_mod, "b n (f s) t -> (b n s) f t", s=self.audio_channels
)
recon_audio = torch.istft(
stft_mod,
**self.stft_kwargs,
window=stft_window,
return_complex=False,
length=istft_length,
)
recon_audio = rearrange(
recon_audio,
"(b n s) t -> b n s t",
b=batch,
s=self.audio_channels,
n=self.num_stems,
)
if self.num_stems == 1:
recon_audio = rearrange(recon_audio, "b 1 s t -> b s t")
# Loss
if target is None:
return recon_audio
if self.num_stems > 1:
assert target.ndim == 4 and target.shape[1] == self.num_stems
if target.ndim == 2:
target = rearrange(target, "... t -> ... 1 t")
target = target[..., : recon_audio.shape[-1]]
loss = F.l1_loss(recon_audio, target)
multi_stft_resolution_loss = 0.0
for window_size in self.multi_stft_resolutions_window_sizes:
res_stft_kwargs = dict(
n_fft=max(window_size, self.multi_stft_n_fft),
win_length=window_size,
return_complex=True,
window=self.multi_stft_window_fn(window_size, device=device),
**self.multi_stft_kwargs,
)
recon_Y = torch.stft(
rearrange(recon_audio, "... s t -> (... s) t"), **res_stft_kwargs
)
target_Y = torch.stft(
rearrange(target, "... s t -> (... s) t"), **res_stft_kwargs
)
multi_stft_resolution_loss += F.l1_loss(recon_Y, target_Y)
total_loss = (
loss + self.multi_stft_resolution_loss_weight * multi_stft_resolution_loss
)
if not return_loss_breakdown:
return total_loss
return total_loss, (loss, multi_stft_resolution_loss)
|