Spaces:
Sleeping
Sleeping
File size: 32,352 Bytes
7417a6a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 |
Loading pretrained HTDemucs...
Loading CLAP model...
Model Summary:
AudioTextHTDemucs(
(htdemucs): HTDemucs(
(encoder): ModuleList(
(0): HEncLayer(
(conv): Conv2d(4, 48, kernel_size=(8, 1), stride=(4, 1), padding=(2, 0))
(norm1): Identity()
(rewrite): Conv2d(48, 96, kernel_size=(1, 1), stride=(1, 1))
(norm2): Identity()
(dconv): DConv(
(layers): ModuleList(
(0): Sequential(
(0): Conv1d(48, 6, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(1, 6, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(6, 96, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 96, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
(1): Sequential(
(0): Conv1d(48, 6, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
(1): GroupNorm(1, 6, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(6, 96, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 96, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
)
)
)
(1): HEncLayer(
(conv): Conv2d(48, 96, kernel_size=(8, 1), stride=(4, 1), padding=(2, 0))
(norm1): Identity()
(rewrite): Conv2d(96, 192, kernel_size=(1, 1), stride=(1, 1))
(norm2): Identity()
(dconv): DConv(
(layers): ModuleList(
(0): Sequential(
(0): Conv1d(96, 12, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(1, 12, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(12, 192, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 192, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
(1): Sequential(
(0): Conv1d(96, 12, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
(1): GroupNorm(1, 12, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(12, 192, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 192, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
)
)
)
(2): HEncLayer(
(conv): Conv2d(96, 192, kernel_size=(8, 1), stride=(4, 1), padding=(2, 0))
(norm1): Identity()
(rewrite): Conv2d(192, 384, kernel_size=(1, 1), stride=(1, 1))
(norm2): Identity()
(dconv): DConv(
(layers): ModuleList(
(0): Sequential(
(0): Conv1d(192, 24, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(1, 24, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(24, 384, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 384, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
(1): Sequential(
(0): Conv1d(192, 24, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
(1): GroupNorm(1, 24, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(24, 384, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 384, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
)
)
)
(3): HEncLayer(
(conv): Conv2d(192, 384, kernel_size=(8, 1), stride=(4, 1), padding=(2, 0))
(norm1): Identity()
(rewrite): Conv2d(384, 768, kernel_size=(1, 1), stride=(1, 1))
(norm2): Identity()
(dconv): DConv(
(layers): ModuleList(
(0): Sequential(
(0): Conv1d(384, 48, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(1, 48, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(48, 768, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 768, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
(1): Sequential(
(0): Conv1d(384, 48, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
(1): GroupNorm(1, 48, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(48, 768, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 768, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
)
)
)
)
(decoder): ModuleList(
(0): HDecLayer(
(conv_tr): ConvTranspose2d(384, 192, kernel_size=(8, 1), stride=(4, 1))
(norm2): Identity()
(rewrite): Conv2d(384, 768, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(norm1): Identity()
(dconv): DConv(
(layers): ModuleList(
(0): Sequential(
(0): Conv1d(384, 48, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(1, 48, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(48, 768, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 768, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
(1): Sequential(
(0): Conv1d(384, 48, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
(1): GroupNorm(1, 48, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(48, 768, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 768, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
)
)
)
(1): HDecLayer(
(conv_tr): ConvTranspose2d(192, 96, kernel_size=(8, 1), stride=(4, 1))
(norm2): Identity()
(rewrite): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(norm1): Identity()
(dconv): DConv(
(layers): ModuleList(
(0): Sequential(
(0): Conv1d(192, 24, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(1, 24, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(24, 384, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 384, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
(1): Sequential(
(0): Conv1d(192, 24, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
(1): GroupNorm(1, 24, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(24, 384, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 384, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
)
)
)
(2): HDecLayer(
(conv_tr): ConvTranspose2d(96, 48, kernel_size=(8, 1), stride=(4, 1))
(norm2): Identity()
(rewrite): Conv2d(96, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(norm1): Identity()
(dconv): DConv(
(layers): ModuleList(
(0): Sequential(
(0): Conv1d(96, 12, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(1, 12, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(12, 192, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 192, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
(1): Sequential(
(0): Conv1d(96, 12, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
(1): GroupNorm(1, 12, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(12, 192, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 192, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
)
)
)
(3): HDecLayer(
(conv_tr): ConvTranspose2d(48, 16, kernel_size=(8, 1), stride=(4, 1))
(norm2): Identity()
(rewrite): Conv2d(48, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(norm1): Identity()
(dconv): DConv(
(layers): ModuleList(
(0): Sequential(
(0): Conv1d(48, 6, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(1, 6, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(6, 96, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 96, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
(1): Sequential(
(0): Conv1d(48, 6, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
(1): GroupNorm(1, 6, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(6, 96, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 96, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
)
)
)
)
(tencoder): ModuleList(
(0): HEncLayer(
(conv): Conv1d(2, 48, kernel_size=(8,), stride=(4,), padding=(2,))
(norm1): Identity()
(rewrite): Conv1d(48, 96, kernel_size=(1,), stride=(1,))
(norm2): Identity()
(dconv): DConv(
(layers): ModuleList(
(0): Sequential(
(0): Conv1d(48, 6, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(1, 6, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(6, 96, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 96, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
(1): Sequential(
(0): Conv1d(48, 6, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
(1): GroupNorm(1, 6, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(6, 96, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 96, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
)
)
)
(1): HEncLayer(
(conv): Conv1d(48, 96, kernel_size=(8,), stride=(4,), padding=(2,))
(norm1): Identity()
(rewrite): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
(norm2): Identity()
(dconv): DConv(
(layers): ModuleList(
(0): Sequential(
(0): Conv1d(96, 12, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(1, 12, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(12, 192, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 192, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
(1): Sequential(
(0): Conv1d(96, 12, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
(1): GroupNorm(1, 12, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(12, 192, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 192, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
)
)
)
(2): HEncLayer(
(conv): Conv1d(96, 192, kernel_size=(8,), stride=(4,), padding=(2,))
(norm1): Identity()
(rewrite): Conv1d(192, 384, kernel_size=(1,), stride=(1,))
(norm2): Identity()
(dconv): DConv(
(layers): ModuleList(
(0): Sequential(
(0): Conv1d(192, 24, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(1, 24, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(24, 384, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 384, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
(1): Sequential(
(0): Conv1d(192, 24, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
(1): GroupNorm(1, 24, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(24, 384, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 384, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
)
)
)
(3): HEncLayer(
(conv): Conv1d(192, 384, kernel_size=(8,), stride=(4,), padding=(2,))
(norm1): Identity()
(rewrite): Conv1d(384, 768, kernel_size=(1,), stride=(1,))
(norm2): Identity()
(dconv): DConv(
(layers): ModuleList(
(0): Sequential(
(0): Conv1d(384, 48, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(1, 48, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(48, 768, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 768, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
(1): Sequential(
(0): Conv1d(384, 48, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
(1): GroupNorm(1, 48, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(48, 768, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 768, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
)
)
)
)
(tdecoder): ModuleList(
(0): HDecLayer(
(conv_tr): ConvTranspose1d(384, 192, kernel_size=(8,), stride=(4,))
(norm2): Identity()
(rewrite): Conv1d(384, 768, kernel_size=(3,), stride=(1,), padding=(1,))
(norm1): Identity()
(dconv): DConv(
(layers): ModuleList(
(0): Sequential(
(0): Conv1d(384, 48, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(1, 48, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(48, 768, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 768, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
(1): Sequential(
(0): Conv1d(384, 48, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
(1): GroupNorm(1, 48, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(48, 768, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 768, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
)
)
)
(1): HDecLayer(
(conv_tr): ConvTranspose1d(192, 96, kernel_size=(8,), stride=(4,))
(norm2): Identity()
(rewrite): Conv1d(192, 384, kernel_size=(3,), stride=(1,), padding=(1,))
(norm1): Identity()
(dconv): DConv(
(layers): ModuleList(
(0): Sequential(
(0): Conv1d(192, 24, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(1, 24, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(24, 384, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 384, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
(1): Sequential(
(0): Conv1d(192, 24, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
(1): GroupNorm(1, 24, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(24, 384, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 384, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
)
)
)
(2): HDecLayer(
(conv_tr): ConvTranspose1d(96, 48, kernel_size=(8,), stride=(4,))
(norm2): Identity()
(rewrite): Conv1d(96, 192, kernel_size=(3,), stride=(1,), padding=(1,))
(norm1): Identity()
(dconv): DConv(
(layers): ModuleList(
(0): Sequential(
(0): Conv1d(96, 12, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(1, 12, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(12, 192, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 192, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
(1): Sequential(
(0): Conv1d(96, 12, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
(1): GroupNorm(1, 12, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(12, 192, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 192, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
)
)
)
(3): HDecLayer(
(conv_tr): ConvTranspose1d(48, 8, kernel_size=(8,), stride=(4,))
(norm2): Identity()
(rewrite): Conv1d(48, 96, kernel_size=(3,), stride=(1,), padding=(1,))
(norm1): Identity()
(dconv): DConv(
(layers): ModuleList(
(0): Sequential(
(0): Conv1d(48, 6, kernel_size=(3,), stride=(1,), padding=(1,))
(1): GroupNorm(1, 6, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(6, 96, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 96, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
(1): Sequential(
(0): Conv1d(48, 6, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
(1): GroupNorm(1, 6, eps=1e-05, affine=True)
(2): GELU(approximate='none')
(3): Conv1d(6, 96, kernel_size=(1,), stride=(1,))
(4): GroupNorm(1, 96, eps=1e-05, affine=True)
(5): GLU(dim=1)
(6): LayerScale()
)
)
)
)
)
(freq_emb): ScaledEmbedding(
(embedding): Embedding(512, 48)
)
(channel_upsampler): Conv1d(384, 512, kernel_size=(1,), stride=(1,))
(channel_downsampler): Conv1d(512, 384, kernel_size=(1,), stride=(1,))
(channel_upsampler_t): Conv1d(384, 512, kernel_size=(1,), stride=(1,))
(channel_downsampler_t): Conv1d(512, 384, kernel_size=(1,), stride=(1,))
(crosstransformer): CrossTransformerEncoder(
(norm_in): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(norm_in_t): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(layers): ModuleList(
(0): MyTransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(linear1): Linear(in_features=512, out_features=2048, bias=True)
(dropout): Dropout(p=0.02, inplace=False)
(linear2): Linear(in_features=2048, out_features=512, bias=True)
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.02, inplace=False)
(dropout2): Dropout(p=0.02, inplace=False)
(norm_out): MyGroupNorm(1, 512, eps=1e-05, affine=True)
(gamma_1): LayerScale()
(gamma_2): LayerScale()
)
(1): CrossTransformerEncoderLayer(
(cross_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(linear1): Linear(in_features=512, out_features=2048, bias=True)
(dropout): Dropout(p=0.02, inplace=False)
(linear2): Linear(in_features=2048, out_features=512, bias=True)
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(norm_out): MyGroupNorm(1, 512, eps=1e-05, affine=True)
(gamma_1): LayerScale()
(gamma_2): LayerScale()
(dropout1): Dropout(p=0.02, inplace=False)
(dropout2): Dropout(p=0.02, inplace=False)
)
(2): MyTransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(linear1): Linear(in_features=512, out_features=2048, bias=True)
(dropout): Dropout(p=0.02, inplace=False)
(linear2): Linear(in_features=2048, out_features=512, bias=True)
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.02, inplace=False)
(dropout2): Dropout(p=0.02, inplace=False)
(norm_out): MyGroupNorm(1, 512, eps=1e-05, affine=True)
(gamma_1): LayerScale()
(gamma_2): LayerScale()
)
(3): CrossTransformerEncoderLayer(
(cross_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(linear1): Linear(in_features=512, out_features=2048, bias=True)
(dropout): Dropout(p=0.02, inplace=False)
(linear2): Linear(in_features=2048, out_features=512, bias=True)
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(norm_out): MyGroupNorm(1, 512, eps=1e-05, affine=True)
(gamma_1): LayerScale()
(gamma_2): LayerScale()
(dropout1): Dropout(p=0.02, inplace=False)
(dropout2): Dropout(p=0.02, inplace=False)
)
(4): MyTransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(linear1): Linear(in_features=512, out_features=2048, bias=True)
(dropout): Dropout(p=0.02, inplace=False)
(linear2): Linear(in_features=2048, out_features=512, bias=True)
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.02, inplace=False)
(dropout2): Dropout(p=0.02, inplace=False)
(norm_out): MyGroupNorm(1, 512, eps=1e-05, affine=True)
(gamma_1): LayerScale()
(gamma_2): LayerScale()
)
)
(layers_t): ModuleList(
(0): MyTransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(linear1): Linear(in_features=512, out_features=2048, bias=True)
(dropout): Dropout(p=0.02, inplace=False)
(linear2): Linear(in_features=2048, out_features=512, bias=True)
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.02, inplace=False)
(dropout2): Dropout(p=0.02, inplace=False)
(norm_out): MyGroupNorm(1, 512, eps=1e-05, affine=True)
(gamma_1): LayerScale()
(gamma_2): LayerScale()
)
(1): CrossTransformerEncoderLayer(
(cross_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(linear1): Linear(in_features=512, out_features=2048, bias=True)
(dropout): Dropout(p=0.02, inplace=False)
(linear2): Linear(in_features=2048, out_features=512, bias=True)
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(norm_out): MyGroupNorm(1, 512, eps=1e-05, affine=True)
(gamma_1): LayerScale()
(gamma_2): LayerScale()
(dropout1): Dropout(p=0.02, inplace=False)
(dropout2): Dropout(p=0.02, inplace=False)
)
(2): MyTransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(linear1): Linear(in_features=512, out_features=2048, bias=True)
(dropout): Dropout(p=0.02, inplace=False)
(linear2): Linear(in_features=2048, out_features=512, bias=True)
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.02, inplace=False)
(dropout2): Dropout(p=0.02, inplace=False)
(norm_out): MyGroupNorm(1, 512, eps=1e-05, affine=True)
(gamma_1): LayerScale()
(gamma_2): LayerScale()
)
(3): CrossTransformerEncoderLayer(
(cross_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(linear1): Linear(in_features=512, out_features=2048, bias=True)
(dropout): Dropout(p=0.02, inplace=False)
(linear2): Linear(in_features=2048, out_features=512, bias=True)
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(norm_out): MyGroupNorm(1, 512, eps=1e-05, affine=True)
(gamma_1): LayerScale()
(gamma_2): LayerScale()
(dropout1): Dropout(p=0.02, inplace=False)
(dropout2): Dropout(p=0.02, inplace=False)
)
(4): MyTransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(linear1): Linear(in_features=512, out_features=2048, bias=True)
(dropout): Dropout(p=0.02, inplace=False)
(linear2): Linear(in_features=2048, out_features=512, bias=True)
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.02, inplace=False)
(dropout2): Dropout(p=0.02, inplace=False)
(norm_out): MyGroupNorm(1, 512, eps=1e-05, affine=True)
(gamma_1): LayerScale()
(gamma_2): LayerScale()
)
)
)
)
(clap): ClapTextModelWithProjection(
(text_model): ClapTextModel(
(embeddings): ClapTextEmbeddings(
(word_embeddings): Embedding(50265, 768, padding_idx=1)
(position_embeddings): Embedding(514, 768, padding_idx=1)
(token_type_embeddings): Embedding(1, 768)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(encoder): ClapTextEncoder(
(layer): ModuleList(
(0-11): 12 x ClapTextLayer(
(attention): ClapTextAttention(
(self): ClapTextSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): ClapTextSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): ClapTextIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): ClapTextOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
)
(pooler): ClapTextPooler(
(dense): Linear(in_features=768, out_features=768, bias=True)
(activation): Tanh()
)
)
(text_projection): ClapProjectionLayer(
(linear1): Linear(in_features=768, out_features=512, bias=True)
(activation): ReLU()
(linear2): Linear(in_features=512, out_features=512, bias=True)
)
)
(text_attn): TextCrossAttention(
(q_proj): Linear(in_features=384, out_features=384, bias=True)
(k_proj): Linear(in_features=512, out_features=384, bias=True)
(v_proj): Linear(in_features=512, out_features=384, bias=True)
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=384, out_features=384, bias=True)
)
(out_mlp): Sequential(
(0): Linear(in_features=384, out_features=384, bias=True)
(1): GELU(approximate='none')
(2): Linear(in_features=384, out_features=384, bias=True)
)
(norm_q): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
(norm_out): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
)
(freq_decoder): FreqDecoder(
(layers): ModuleList(
(0): Sequential(
(0): ConvTranspose2d(384, 192, kernel_size=(8, 1), stride=(4, 1), padding=(2, 0))
(1): GroupNorm(1, 192, eps=1e-05, affine=True)
(2): GELU(approximate='none')
)
(1): Sequential(
(0): ConvTranspose2d(192, 96, kernel_size=(8, 1), stride=(4, 1), padding=(2, 0))
(1): GroupNorm(1, 96, eps=1e-05, affine=True)
(2): GELU(approximate='none')
)
(2): Sequential(
(0): ConvTranspose2d(96, 48, kernel_size=(8, 1), stride=(4, 1), padding=(2, 0))
(1): GroupNorm(1, 48, eps=1e-05, affine=True)
(2): GELU(approximate='none')
)
(3): Sequential(
(0): ConvTranspose2d(48, 4, kernel_size=(8, 1), stride=(4, 1), padding=(2, 0))
(1): Identity()
(2): Identity()
)
)
)
(time_decoder): TimeDecoder(
(layers): ModuleList(
(0): Sequential(
(0): ConvTranspose1d(384, 192, kernel_size=(8,), stride=(4,), padding=(2,))
(1): GroupNorm(1, 192, eps=1e-05, affine=True)
(2): GELU(approximate='none')
)
(1): Sequential(
(0): ConvTranspose1d(192, 96, kernel_size=(8,), stride=(4,), padding=(2,))
(1): GroupNorm(1, 96, eps=1e-05, affine=True)
(2): GELU(approximate='none')
)
(2): Sequential(
(0): ConvTranspose1d(96, 48, kernel_size=(8,), stride=(4,), padding=(2,))
(1): GroupNorm(1, 48, eps=1e-05, affine=True)
(2): GELU(approximate='none')
)
(3): Sequential(
(0): ConvTranspose1d(48, 4, kernel_size=(8,), stride=(4,), padding=(2,))
(1): Identity()
(2): Identity()
)
)
)
(freq_out): Conv2d(4, 2, kernel_size=(1, 1), stride=(1, 1))
(time_out): Conv1d(4, 2, kernel_size=(1,), stride=(1,))
) |