File size: 90,758 Bytes
685ecb2 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 |
# cp from https://github.com/jasonppy/VoiceCraft/blob/master/models/voicecraft.py
import random
import numpy as np
import logging
import argparse, copy
from typing import Dict, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchmetrics.classification import MulticlassAccuracy
from .codebooks_patterns import DelayedPatternProvider
from ...utils.util import make_pad_mask
from .embedding import SinePositionalEmbedding, TokenEmbedding
from .transformer import (
LayerNorm,
TransformerEncoder,
TransformerEncoderLayer,
)
from argparse import Namespace
from huggingface_hub import PyTorchModelHubMixin
def top_k_top_p_filtering(
logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1
):
"""Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
Args:
logits: logits distribution shape (batch size, vocabulary size)
if top_k > 0: keep only top k tokens with highest probability (top-k filtering).
if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
Make sure we keep at least min_tokens_to_keep per batch example in the output
From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
"""
if top_k > 0:
top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1)) # Safety check
# Remove all tokens with a probability less than the last token of the top-k
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
logits[indices_to_remove] = filter_value
if top_p < 1.0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
# Remove tokens with cumulative probability above the threshold (token with 0 are kept)
sorted_indices_to_remove = cumulative_probs > top_p
if min_tokens_to_keep > 1:
# Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
sorted_indices_to_remove[..., :min_tokens_to_keep] = 0
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
# scatter sorted tensors to original indexing
indices_to_remove = sorted_indices_to_remove.scatter(
1, sorted_indices, sorted_indices_to_remove
)
logits[indices_to_remove] = filter_value
return logits
def topk_sampling(logits, top_k=10, top_p=1.0, temperature=1.0):
# temperature: (`optional`) float
# The value used to module the next token probabilities. Must be strictly positive. Default to 1.0.
# top_k: (`optional`) int
# The number of highest probability vocabulary tokens to keep for top-k-filtering. Between 1 and infinity. Default to 50.
# top_p: (`optional`) float
# The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Must be between 0 and 1. Default to 1.
# Temperature (higher temperature => more likely to sample low probability tokens)
if temperature != 1.0:
logits = logits / temperature
# Top-p/top-k filtering
logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
# Sample
token = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1)
return token
class VoiceCraft(
nn.Module,
PyTorchModelHubMixin,
library_name="voicecraft",
repo_url="https://github.com/jasonppy/VoiceCraft",
tags=["text-to-speech"],
):
def __new__(
cls, args: Optional[Namespace] = None, config: Optional[Dict] = None, **kwargs
) -> "VoiceCraft":
# If initialized from Namespace args => convert to dict config for 'PyTorchModelHubMixin' to serialize it as config.json
# Won't affect instance initialization
if args is not None:
if config is not None:
raise ValueError("Cannot provide both `args` and `config`.")
config = vars(args)
return super().__new__(cls, args=args, config=config, **kwargs)
def __init__(self, args: Optional[Namespace] = None, config: Optional[Dict] = None):
super().__init__()
# If loaded from HF Hub => convert config.json to Namespace args before initializing
if args is None:
if config is None:
raise ValueError("Either `args` or `config` must be provided.")
args = Namespace(**config)
self.args = copy.copy(args)
self.pattern = DelayedPatternProvider(n_q=self.args.n_codebooks)
if not getattr(self.args, "special_first", False):
self.args.special_first = 0
if not getattr(self.args, "n_special", False):
self.args.n_special = 3
self.args.eos = getattr(self.args, "eos", -1)
self.eog = nn.Parameter(
torch.full((self.args.n_codebooks, 1), self.args.eog, dtype=torch.long),
requires_grad=False,
) # [K 1]
if self.args.eos > 0:
assert (
self.args.eos != self.args.audio_pad_token
and self.args.eos != self.args.empty_token
), self.args.eos
self.eos = nn.Parameter(
torch.full((self.args.n_codebooks, 1), self.args.eos, dtype=torch.long),
requires_grad=False,
) # [K 1]
if isinstance(self.args.audio_vocab_size, str):
self.args.audio_vocab_size = eval(self.args.audio_vocab_size)
self.n_text_tokens = self.args.text_vocab_size + 1
assert (
self.args.text_pad_token == self.args.text_vocab_size
), f"self.args.text_vocab_size: {self.args.text_vocab_size}, self.args.text_pad_token: {self.args.text_pad_token}"
self.n_audio_tokens = [
self.args.audio_vocab_size + self.args.n_special
] * self.args.n_codebooks # special tokens: empty token, EOG token, audio pad token
assert (
self.args.audio_vocab_size == self.args.empty_token
), self.args.empty_token
assert self.args.eog == self.args.audio_vocab_size + 1, self.args.eog
assert (
self.args.audio_pad_token == self.args.audio_vocab_size + 2
), self.args.audio_pad_token
self.text_embedding = TokenEmbedding(
dim_model=self.args.d_model,
vocab_size=self.n_text_tokens,
dropout=self.args.text_embedding_dropout,
)
self.audio_embedding = nn.ModuleList(
[
TokenEmbedding(
dim_model=self.args.audio_embedding_dim,
vocab_size=self.n_audio_tokens[k],
dropout=self.args.audio_embedding_dropout,
)
for k in range(self.args.n_codebooks)
]
)
self.mask_embedding = nn.Parameter(
torch.randn(self.args.max_n_spans, self.args.d_model), requires_grad=True
)
self.text_positional_embedding = SinePositionalEmbedding(
self.args.d_model,
dropout=self.args.text_positional_embedding_dropout,
scale=False,
alpha=True, # learnable scaler, scale the volume of positional embedding
)
self.audio_positional_embedding = SinePositionalEmbedding(
self.args.d_model,
dropout=self.args.audio_positional_embedding_dropout,
scale=False,
alpha=True, # learnable scaler, scale the volume of positional embedding
)
dec_layer = TransformerEncoderLayer(
self.args.d_model,
self.args.nhead,
dim_feedforward=self.args.d_model * 4,
dropout=self.args.trm_dropout,
batch_first=True,
norm_first=True,
layer_norm_cls=LayerNorm,
)
self.decoder = TransformerEncoder(
dec_layer,
num_layers=self.args.num_decoder_layers,
norm=LayerNorm(self.args.d_model),
)
self.predict_layer = nn.ModuleList(
[
nn.Sequential(
nn.Linear(self.args.d_model, self.args.audio_vocab_size // 2),
nn.GELU(),
nn.Linear(self.args.audio_vocab_size // 2, self.n_audio_tokens[k]),
)
for k in range(self.args.n_codebooks)
]
)
self.accuracy_metrics = nn.ModuleList(
[
MulticlassAccuracy(
self.n_audio_tokens[k],
top_k=10,
average="micro",
multidim_average="global",
ignore_index=None,
)
for k in range(self.args.n_codebooks)
]
)
def prepare_mask_intervals(self, y_lens):
mask_intervals = []
non_mask_intervals = []
for i, y_len in enumerate(y_lens):
if self.args.mask_sample_dist == "uniform":
n_spans = random.choice(range(1, self.args.max_n_spans + 1))
elif "poisson" in self.args.mask_sample_dist.lower():
param = float(self.args.mask_sample_dist[len("poisson") :])
poisson_sample = torch.poisson(torch.tensor([param]))
n_spans = int(poisson_sample.clamp(1, self.args.max_n_spans).item())
starts = random.sample(
range(1, y_len - 1 - self.args.mask_len_min), n_spans
)
starts = sorted(starts)
for j in range(len(starts) - 1, 0, -1):
if starts[j] - starts[j - 1] < self.args.min_gap:
del starts[j] # If elements are too close, delete the later one
assert (
len(starts) > 0
), f"there is no masked span left, y_len: {y_len}, sampled n_spans: {n_spans}"
temp_starts = starts + [y_len]
gaps = [
temp_starts[j + 1] - temp_starts[j] for j in range(len(temp_starts) - 1)
]
ends = []
for j, (start, gap) in enumerate(zip(starts, gaps)):
mask_len = random.randint(
self.args.mask_len_min, self.args.mask_len_max
)
# if mask_len > gap * self.args.max_mask_portion: # make sure the masks are not overlapping with each other
if (
mask_len > gap - 1
): # make sure the masks are not overlapping with each other
# temp_mask_start = int(0.6*gap*self.args.max_mask_portion)
# temp_mask_end = int(gap*self.args.max_mask_portion)
temp_mask_start = 1
temp_mask_end = gap - 1
mask_len = random.randint(temp_mask_start, temp_mask_end)
ends.append(start + mask_len)
mask_intervals.append([(s, e) for s, e in zip(starts, ends)])
non_mask_intervals.append(
[(ns, ne) for ns, ne in zip([0] + ends, starts + [y_len])]
)
return mask_intervals, non_mask_intervals
def rearrange(self, y, non_mask_intervals, mask_intervals):
reduced_eog = getattr(self.args, "reduced_eog", 0)
rearranged_y = []
for i in range(len(y)):
if self.args.eos > 0:
assert reduced_eog
cur_y = (
[y[i, :, item[0] : item[1]] for item in non_mask_intervals[i][:-1]]
+ [
torch.cat(
[
y[
i,
:,
non_mask_intervals[i][-1][0] : non_mask_intervals[
i
][-1][1],
],
self.eos,
],
dim=-1,
)
]
+ [
torch.cat([y[i, :, item[0] : item[1]], self.eog], dim=-1)
for item in mask_intervals[i]
]
) # only insert eog to the last non-mask-interval, which is when the utterance actual ends
else:
if reduced_eog:
cur_y = (
[
y[i, :, item[0] : item[1]]
for item in non_mask_intervals[i][:-1]
]
+ [
torch.cat(
[
y[
i,
:,
non_mask_intervals[i][-1][
0
] : non_mask_intervals[i][-1][1],
],
self.eog,
],
dim=-1,
)
]
+ [
torch.cat([y[i, :, item[0] : item[1]], self.eog], dim=-1)
for item in mask_intervals[i]
]
) # only insert eog to the last non-mask-interval, which is when the utterance actual ends
else:
cur_y = [
torch.cat([y[i, :, item[0] : item[1]], self.eog], dim=-1)
for item in non_mask_intervals[i]
] + [
torch.cat([y[i, :, item[0] : item[1]], self.eog], dim=-1)
for item in mask_intervals[i]
] # eog is added to each section TODO this is not correct, I should add eog to non_mask_intervals if that segment is not the ending segment (as there is no way for the model to predict eog for those segments, and this will do harm to tts experiment, where the model randomly output eog for the first segment)
rearranged_y.append(cur_y)
return rearranged_y
def shift(self, rearranged_y):
shifted_y = []
patterns = []
for i in range(len(rearranged_y)):
cur_patterns = [
self.pattern.get_pattern(cur_y.shape[1]) for cur_y in rearranged_y[i]
]
out = [
cur_pattern.build_pattern_sequence(
z=cur_y.unsqueeze(0).contiguous(),
special_token=self.args.empty_token,
keep_only_valid_steps=False,
)
for cur_pattern, cur_y in zip(cur_patterns, rearranged_y[i])
]
shifted_y.append(
[item[0].squeeze(0) for item in out]
) # the first item is values, later two are indexes and mask
patterns.append(cur_patterns)
return shifted_y, patterns
def insert_mask(self, shifted_y):
inserted_y = []
mask_position = []
mask_value = []
for i in range(len(shifted_y)):
num_masks = (len(shifted_y[i]) - 1) // 2
assert num_masks == (len(shifted_y[i]) - 1) / 2, len(shifted_y[i])
emb_inds = list(range(self.args.max_n_spans))
if self.args.shuffle_mask_embedding:
random.shuffle(emb_inds)
emb_inds_use = emb_inds[:num_masks]
emb_inds_use = emb_inds_use + emb_inds_use
mask_value.append(emb_inds_use)
cur_inserted_y = []
cur_mask_position = []
for j in range(len(shifted_y[i]) - 1):
cur_inserted_y.append(shifted_y[i][j])
cur_mask_position.append(
sum([item.shape[1] for item in cur_inserted_y])
) # each item is of shape [K S], so take shape[1]
cur_inserted_y.append(
self.eog
) # insert mask token of shape [K, 1], BUT we are actually using the eog token as a place holder here, as the real mask will be inserted in embed_y function
cur_inserted_y.append(shifted_y[i][-1])
inserted_y.append(cur_inserted_y)
mask_position.append(cur_mask_position)
return inserted_y, mask_position, mask_value
def cat_y(self, inserted_y, mask_position, y_lens):
reduced_eog = getattr(self.args, "reduced_eog", 0)
cated_y = []
new_y_lens = []
for i in range(len(inserted_y)):
cur_cated_y = torch.cat(inserted_y[i], dim=1) # [K S]
cur_cated_y = cur_cated_y.transpose(1, 0) # [S K]
cur_cated_y_len = cur_cated_y.shape[0]
if reduced_eog:
assert cur_cated_y_len == y_lens[i] + len(mask_position[i]) + (
len(mask_position[i]) + 1
) * self.args.n_codebooks + (
len(mask_position[i]) / 2 + 1
), f"cur_cated_y_len == {cur_cated_y_len}, but it should be y_lens[i] ({y_lens[i]}) + len(mask_position[i]) ({len(mask_position[i])}) + (len(mask_position[i]) + 1) * self.args.n_codebooks ({(len(mask_position[i]) + 1) * self.args.n_codebooks}) + (len(mask_position[i])/2 + 1) ({len(mask_position[i])/2 + 1})={y_lens[i] + len(mask_position[i]) + (len(mask_position[i]) + 1) * self.args.n_codebooks + (len(mask_position[i])/2 + 1)}"
else:
assert cur_cated_y_len == y_lens[i] + len(mask_position[i]) + (
len(mask_position[i]) + 1
) * self.args.n_codebooks + (
len(mask_position[i]) + 1
), f"cur_cated_y_len == {cur_cated_y_len}, but it should be y_lens[i] ({y_lens[i]}) + len(mask_position[i]) ({len(mask_position[i])}) + (len(mask_position[i]) + 1) * self.args.n_codebooks ({(len(mask_position[i]) + 1) * self.args.n_codebooks}) + (len(mask_position[i]) + 1) ({len(mask_position[i]) + 1})" # the last term represent the inserted eog token, originally it's inserted at the end of every token, but this is wrong
new_y_lens.append(cur_cated_y_len)
cated_y.append(cur_cated_y)
cated_y = torch.nn.utils.rnn.pad_sequence(
cated_y, batch_first=False, padding_value=self.args.audio_pad_token
)
assert cated_y.shape == torch.Size(
[max(new_y_lens), len(inserted_y), self.args.n_codebooks]
), f"cated_y.shape: {cated_y.shape}, but it should be {torch.Size([max(new_y_lens,len(inserted_y), self.args.n_codebooks)])}"
cated_y = cated_y.permute(2, 0, 1) # [T,B,K]->[K,T,B]
assert cated_y.shape[0] == self.args.n_codebooks, cated_y.shape
return cated_y, torch.LongTensor(new_y_lens).to(cated_y.device)
def embed_y(self, cated_y, mask_position, mask_value):
embedded_y = torch.stack(
[self.audio_embedding[k](cated_y[k]) for k in range(self.args.n_codebooks)],
dim=0,
) # [K, T, B, D]
assert embedded_y.shape[0] == self.args.n_codebooks, embedded_y.shape
assert embedded_y.shape[-1] == self.args.d_model, embedded_y.shape
embedded_y = embedded_y.sum(dim=0) # [K,T,B,D]->[T,B,D]
embedded_y = embedded_y.transpose(1, 0) # [T,B,D]->[B,T,D]
for i in range(len(embedded_y)):
if len(mask_position[i]) > 0:
embedded_y[i, mask_position[i]] = self.mask_embedding[mask_value[i]]
return embedded_y
def prepare_input_target(self, y, y_lens):
# rearrange y
# assume y shape: [B T K], K is n_codebooks
assert y.shape[1] == self.args.n_codebooks, y.shape
# sample mask_intervals
mask_intervals, non_mask_intervals = self.prepare_mask_intervals(y_lens)
# need to have EOG in each section (SOG will be generated by the pattern class)
# but mask can be inserted later after we have shifted the input
# y could be rearranged in this way:
# [
# [tensor[4, 12], tensor[4, 45], tensor[4, 102], tensor[4, 32]], tensor[4, 22]],
# [tensor[4, 44], tensor[4, 56], tensor[4, 19]],
# ...
# ]
# for the first list of tensors (4 tensors), first 3 tensors are non_masked part, last 2 are masked part.
# NOTE #non_masked_part = #masked_part + 1
# NOTE *these are also the targets*
# added eog at the end of each segment (masked segment and unmasked segment)
rearranged_y = self.rearrange(y, non_mask_intervals, mask_intervals)
targets = rearranged_y # each element in each sample is of shape [K T]
assert targets[0][0].shape[0] == self.args.n_codebooks, targets[0][0].shape
# next we need to apply pattern shifting to each tensor, after which, we'll replace the starting tokens of each section with a token that's different from the special padding token
# [[5, 1, 2, 3, 4, 5, 5],
# [5, 5, 1, 2, 3, 4, 5],
# [5, 5, 5, 1, 2, 3, 4]]
shifted_y, patterns = self.shift(rearranged_y) # each element [K S]
assert shifted_y[0][0].shape[0] == self.args.n_codebooks, shifted_y[0][0].shape[
0
]
# then, insert mask token at the intersection of each tensor (we want to decide the arrangement of the mask (shuffle or not)), we better have a separate nn.embedding for it
# we also need to record the position of the inserted mask
inserted_y, mask_position, mask_value = self.insert_mask(shifted_y)
assert inserted_y[0][0].shape[0] == self.args.n_codebooks, inserted_y[0][
0
].shape[0]
assert inserted_y[0][1].shape == torch.Size(
(self.args.n_codebooks, 1)
), f"this should be a mask, so should have shape {(self.args.n_codebooks, 1)}, but it's {inserted_y[0][1].shape}"
# then concat tensors that belong to the same sample (in order) then get the length of each sample, and then stack them in batch dimension, pad them with pad_token
cated_y, new_y_lens = self.cat_y(inserted_y, mask_position, y_lens) # KTB
assert cated_y.shape == torch.Size(
(self.args.n_codebooks, cated_y.shape[1], len(inserted_y))
)
# embed remember to separately embed the mask tokens
embedded_y = self.embed_y(cated_y, mask_position, mask_value) # BTD
assert embedded_y.shape[1:] == torch.Size(
(max(new_y_lens), self.args.d_model)
), embedded_y.shape
# positional embedding
y_input = self.audio_positional_embedding(embedded_y)
# make attention mask and padding mask
y_padding_mask = make_pad_mask(new_y_lens).to(y.device)
y_attention_mask = (
torch.triu(torch.ones(y_input.shape[1], y_input.shape[1]), diagonal=1)
.bool()
.to(y_padding_mask.device)
)
return (
y_input,
new_y_lens,
targets,
y_padding_mask,
y_attention_mask,
mask_position,
patterns,
)
def remove_mask(self, logits, mask_position, new_y_lens):
# logits: [B K S card]
logits_use = []
for i in range(len(logits)):
non_mask_positions = [-1] + mask_position[i] + [new_y_lens[i]]
non_mask_intervals = [
[non_mask_positions[i] + 1, non_mask_positions[i + 1]]
for i in range(len(non_mask_positions) - 1)
]
cur_logits_use = [logits[i, :, l:r] for l, r in non_mask_intervals]
logits_use.append(cur_logits_use)
return logits_use
def revert_pattern(self, patterns, logits_use):
logits_final = []
logit_masks = []
for i in range(len(logits_use)):
cur_logits = [
item.unsqueeze(0).permute(0, 3, 1, 2).contiguous()
for item in logits_use[i]
] # each item is of shape [1 K S card] [1 card K S]
cur_logits_final = [
cur_pattern.revert_pattern_logits(item, 0, keep_only_valid_steps=False)
for cur_pattern, item in zip(patterns[i], cur_logits)
] # if input output order doesn't match, this step will give an error
cur_logits_final_ret = [
item[0].permute(0, 2, 3, 1).squeeze(0) for item in cur_logits_final
] # each element is of shape [K,T,card]
logits_final.append(cur_logits_final_ret)
logit_masks.append([item[2] for item in cur_logits_final])
return logits_final, logit_masks
def dec_forward(
self,
x_input,
x_lens,
x_attention_mask,
x_padding_mask,
y_input,
new_y_lens,
y_attention_mask,
y_padding_mask,
past=None,
last_3_tokens=False,
):
x_attn_mask = F.pad(
x_attention_mask,
(0, new_y_lens.max()),
value=True,
) # x attn to all x, doesn't attn to any y, this follow figure 3 of the valle paper
y_attn_mask = F.pad(
y_attention_mask,
(x_lens.max(), 0), # y is padded at the front
value=False,
) # y attn to all x, for y itself use lower triangle mask to ensure autoregressive
xy_attn_mask = torch.concat([x_attn_mask, y_attn_mask], dim=0)
# merge key padding and attention masks
bsz, src_len = x_input.shape[0], x_lens.max() + new_y_lens.max()
xy_padding_mask = torch.concat([x_padding_mask, y_padding_mask], dim=1)
_xy_padding_mask = (
xy_padding_mask.view(bsz, 1, 1, src_len)
.expand(-1, self.args.nhead, -1, -1)
.reshape(bsz * self.args.nhead, 1, src_len)
)
# Check shapes and resize+broadcast as necessary
if xy_attn_mask.shape != _xy_padding_mask.shape:
assert (
xy_attn_mask.ndim + 1 == _xy_padding_mask.ndim
), f"xy_attn_mask.shape: {xy_attn_mask.shape}, _xy_padding_mask: {_xy_padding_mask.shape}"
xy_attn_mask = xy_attn_mask.unsqueeze(0).repeat(
_xy_padding_mask.shape[0], 1, 1
) # Example approach
xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask)
new_attn_mask = torch.zeros_like(xy_attn_mask)
new_attn_mask.masked_fill_(xy_attn_mask, float("-inf"))
xy_attn_mask = new_attn_mask
xy_input = torch.cat([x_input, y_input], dim=1)
if past == None: # do not use kvcache
out, _ = self.decoder((xy_input, None), mask=xy_attn_mask)
return out[:, x_lens.max() :], None
else: # use kvcache
if (
past.ndim > 3
): # uses kvcache, only need to pass the last tokens, this doesn't work with multi-span speech editing yet
if last_3_tokens:
xy_input = xy_input[:, -3:]
xy_attn_mask = xy_attn_mask[:, -3:]
else:
xy_input = xy_input[:, -1:]
xy_attn_mask = xy_attn_mask[:, -1:]
out, present = self.decoder((xy_input, None), mask=xy_attn_mask, past=past)
if isinstance(out, tuple): # get rid of stage_embedding
out = out[0]
if out.shape[1] > x_lens.max(): # the first pass, not kvcache yet
return out[:, x_lens.max() :], present
else: # used kvcache
return out, present
def forward(self, batch):
"""
Args:
x:
A 2-D tensor of shape (N, S).
x_lens:
A 1-D tensor of shape (N,). It contains the number of tokens in `x`
before padding.
y:
A 3-D tensor of shape (N, K, T).
where K is the number of codebooks
y_lens:
A 1-D tensor of shape (N,). It contains the number of tokens in `x`
before padding.
"""
x, x_lens, y, y_lens = batch["x"], batch["x_lens"], batch["y"], batch["y_lens"]
if len(x) == 0:
return None
x = x[
:, : x_lens.max()
] # this deal with gradient accumulation, where x_lens.max() might not be longer than the length of the current slice of x
y = y[:, :, : y_lens.max()]
assert x.ndim == 2, x.shape
assert x_lens.ndim == 1, x_lens.shape
assert y.ndim == 3 and y.shape[1] == self.args.n_codebooks, y.shape
assert y_lens.ndim == 1, y_lens.shape
# makes attention mask and padding mask for x
x_padding_mask = make_pad_mask(x_lens).to(x.device)
x_attention_mask = (
torch.triu(torch.ones(x.shape[1], x.shape[1]), diagonal=1)
.bool()
.to(x_padding_mask.device)
)
x_input = self.text_embedding(x)
x_input = self.text_positional_embedding(x_input)
(
y_input,
new_y_lens,
targets,
y_padding_mask,
y_attention_mask,
mask_position,
patterns,
) = self.prepare_input_target(y, y_lens)
y_out = self.dec_forward(
x_input,
x_lens,
x_attention_mask,
x_padding_mask,
y_input,
new_y_lens,
y_attention_mask,
y_padding_mask,
)
y_out = y_out[0] # no kv-caching during training
assert (
y_out.shape == y_input.shape
), f"y_out.shape: {y_out.shape}, y_input.shape: {y_input.shape}" # [B S D]
logits = torch.stack(
[self.predict_layer[i](y_out) for i in range(self.args.n_codebooks)], dim=1
) # [B K S card]
# take out the mask token (using mask_position and new_y_lens) and revert (using function provided by self.pattern)
assert (
logits.shape[1] == self.args.n_codebooks
and logits.shape[3] == self.n_audio_tokens[0]
), logits.shape
logits_use = self.remove_mask(logits, mask_position, new_y_lens)
# revert the pattern shift for each logits section in each sample
logits_final, logit_masks = self.revert_pattern(patterns, logits_use)
assert (
logits_final[0][0].shape[0] == self.args.n_codebooks
and logits_final[0][0].shape[2] == self.n_audio_tokens[0]
), f"it is: {logits_final[0][0].shape}, but should be [K, T, card]"
# testing
sample_to_test = 0
assert len(logits_final[sample_to_test]) == len(
targets[sample_to_test]
), f"{len(logits_final[sample_to_test])}, {len(targets[sample_to_test])}"
temp = sum(
[
logits_final[sample_to_test][i].shape[:-1]
!= targets[sample_to_test][i].shape
for i in range(len(targets[sample_to_test]))
]
)
assert (
temp == 0
), f"none equal positions: {temp}, total number of elements: {len(targets[sample_to_test])}"
logit_masked = sum(
[(item == False).any() for cur_mask in logit_masks for item in cur_mask]
)
assert logit_masked == 0, logit_masks
logits = torch.cat(
[torch.cat(item, dim=1) for item in logits_final], dim=1
) # [K, T1+T2+T3+..., card]
targets = torch.cat(
[torch.cat(item, dim=1) for item in targets], dim=1
) # [K, T1+T2+T3+...]
assert targets.shape[0] == logits.shape[0], f"{targets.shape}, {logits.shape}"
loss = []
ntokens = []
top10acc = []
for k, (logit, target) in enumerate(zip(logits, targets)):
loss.append(F.cross_entropy(logit, target, reduction="mean"))
top10acc.append(self.accuracy_metrics[k](logit.detach(), target))
ntokens.append(len(logit))
all_ntokens = sum(ntokens)
if self.args.codebook_weight != None:
codebook_weight = eval(self.args.codebook_weight)
else:
codebook_weight = [1.0] * self.args.n_codebooks
loss = sum([l * nt * cw for l, nt, cw in zip(loss, ntokens, codebook_weight)])
top10acc_by_codebook = [t10a * nt for t10a, nt in zip(top10acc, ntokens)]
top10acc = sum(top10acc_by_codebook)
ntokens = torch.tensor(all_ntokens).to(logits.device)
return {
"loss": loss,
"top10acc": top10acc,
"top10acc_by_codebook": top10acc_by_codebook,
"effective_ntoken": ntokens,
}
def inference(
self,
x: torch.Tensor,
x_lens: torch.Tensor,
y: torch.Tensor,
mask_interval: list[torch.Tensor],
top_k: int = -100,
top_p: float = 1.0,
temperature: float = 1.0,
stop_repetition: int = -1,
kvcache: int = 1,
silence_tokens: list[int] = [1388, 1898, 131],
) -> torch.Tensor:
"""
Args:
x:
A 2-D tensor of shape (1, L).
x_lens:
A 1-D tensor of shape (1,). It contains the number of tokens in `x`
before padding.
y:
A 3-D tensor of shape (1, T, K).
mask_interval:
a list of tensors of shape (M, 2). contains M mask_start and mask_end. list length is actually 1, because we only support single sample inference for now
top_k: (`optional`) int
The number of highest probability tokens to keep for top-k-filtering. Default to -100.
top_p: (`optional`) float
For Neucleus sampling
temperature: (`optional`) float
The value used to module the next token probabilities. Must be strictly positive. Default to 1.0.
eog_coef: (`optional`) float
if 0, no change to eog token logits, otherwise, will adjust eog token logit based on the difference between acoustic token and phn token length
stop_repetition (`optional`) int
if not -1, will set the logits of a token that repeated this many times to be -100000, to avoid generating it again. This only apply to tokens from the first codebook
allowed_repeat_tokens (`optional`) list of ints
by inspecting the validation set, get a few tokens that indeed repeat a significant amount of time, and exclude those tokens from prevent repetition
ultimate_stop_repetition (`optional`) int
no matter that token it is, stop repetition once after this number
"""
assert x.ndim == 2, x.shape
assert x_lens.ndim == 1, x_lens.shape
assert y.ndim == 3, y.shape
if self.args.special_first:
y = y + int(self.args.n_special)
y = y.transpose(2, 1) # [1,T,K] -> [1,K,T]
assert (
y.shape[0] == 1 and y.shape[1] == self.args.n_codebooks
), y.shape # there is no padding
assert mask_interval.shape == torch.Size(
(1, mask_interval.shape[1], 2)
), mask_interval
# make x attention mask and x_input
x_attention_mask = (
torch.triu(torch.ones(x.shape[1], x.shape[1]), diagonal=1)
.bool()
.to(x.device)
)
# x_attention_mask = torch.zeros(x.shape[1], x.shape[1]).bool().to(x.device)
x_input = self.text_embedding(x)
x_input = self.text_positional_embedding(x_input)
# make initial y_input
# make mask_interval and non_mask_interval
y_len = y.shape[2]
y_lens = torch.LongTensor([y_len]).to(y.device)
mask_interval = mask_interval[0]
starts = [item[0].item() for item in mask_interval] + [y_len]
ends = [0] + [item[1].item() for item in mask_interval]
mask_intervals = [
[(item[0].item(), item[1].item()) for item in mask_interval]
] # a werid name change, mask_interval is input, now is mask_intervals, with one more dimension
non_mask_intervals = [[(ns, ne) for ns, ne in zip(ends, starts)]]
# rearrange y
# will add have EOG in each section (SOG will be generated by the pattern class)
# but mask can be inserted later after we have shifted the input
# y could be rearranged in this way:
# [
# [tensor[4, 12], tensor[4, 45], tensor[4, 102], tensor[4, 32]], tensor[4, 22]],
# [tensor[4, 44], tensor[4, 56], tensor[4, 19]],
# ...
# ]
# for the first list of tensors (4 tensors), first 3 tensors are non_masked part, last 2 are masked part.
# NOTE #non_masked_part = #masked_part + 1
rearranged_y = self.rearrange(y, non_mask_intervals, mask_intervals)
assert rearranged_y[0][0].shape[0] == self.args.n_codebooks, rearranged_y[0][
0
].shape
# shift each element of y
# next we need to apply pattern shifting to each tensor, after which, we'll replace the starting tokens of each section with a token that's different from the special padding token
# [
# [empty, 1, 2, 3, eog, empty, empty, empty],
# [empty, empty, 1, 2, 3, eog, empty, empty],
# [empty, empty, empty, 1, 2, 3, eog, empty],
# [empty, empty, empty, empty, 1, 2, 3, eog]
# ]
shifted_y, patterns = self.shift(
rearranged_y
) # each element [K S], patterns is not used, as we directly use the original input y
assert shifted_y[0][0].shape[0] == self.args.n_codebooks, shifted_y[0][0].shape
# insert mask token at the intersction of each tensor, but *actually inserted eog as place holder*
# the position of inserted mask is also recorded
# and the mask_value, the index of the mask emb is recorded
inserted_y, mask_position, mask_value = self.insert_mask(shifted_y)
assert inserted_y[0][0].shape[0] == self.args.n_codebooks, inserted_y[0][
0
].shape[0]
assert inserted_y[0][1].shape == torch.Size(
(self.args.n_codebooks, 1)
), f"this should be a mask, so should have shape {(self.args.n_codebooks, 1)}, but it's {inserted_y[0][1].shape}"
# then concat tensors that belong to the same sample (in order) then get the length of each sample, and then stack them in batch dimension, pad them with pad_token
cated_y, new_y_lens = self.cat_y(inserted_y, mask_position, y_lens) # KTB
assert cated_y.shape == torch.Size(
(self.args.n_codebooks, cated_y.shape[1], len(inserted_y))
)
assert not (cated_y == self.args.audio_pad_token).any(), cated_y
### NOTE this is different from forward, as we will remove the masked tokens
### say there are two masked region
### the cated_y should be like
### [empty a a a a mask0 empty b b b mask1 empty c c mask0 empty]
### which means we need to take the part after the last empty out
num_mask = len(mask_position[0]) // 2
assert num_mask == len(mask_position[0]) / 2, mask_position
cated_y = cated_y[:, : mask_position[0][num_mask] + 2] # of shape [K,T,B]
# logging.info(f"mask_position[0][num_mask]+2: {mask_position[0][num_mask]+2}")
more_mask_value = mask_value[0][
num_mask + 1 :
] # NOTE this will be used in the generation loop for reference for inserting mask embedding
new_y_lens[0] = mask_position[0][num_mask] + 2
mask_position[0] = mask_position[0][: num_mask + 1]
assert (
mask_position[0][num_mask] + 2 == cated_y.shape[1]
), f"num_mask: {num_mask}, mask_position: {mask_position}, cated_y.shape: {cated_y.shape}"
# embed: remember to separately embed the mask tokens
embedded_y = self.embed_y(
cated_y, mask_position, [mask_value[0][: num_mask + 1]]
) # BTD
# assert embedded_y.shape == torch.Size((y.shape[0], max(new_y_lens), self.args.d_model)), embedded_y.shape
# positional embedding
y_input = self.audio_positional_embedding(embedded_y)
# make attention mask and padding mask
y_attention_mask = (
torch.triu(torch.ones(y_input.shape[1], y_input.shape[1]), diagonal=1)
.bool()
.to(y.device)
)
# y_lens = torch.LongTensor([y_input.shape[1]]).to(y.device)
x_padding_mask = torch.full((1, x_lens[0]), False).to(x.device)
y_padding_mask = torch.full((1, new_y_lens[0]), False).to(y.device)
codebook_eog = [False] * self.args.n_codebooks
generated = [] # doesn't contain any empty_token, contains eog
cur_generated = []
# say 0 is empty, 4 is eog
# tensor([[ 1, 2, 3, 4, 0, 0],
# [ 0, 1, 2, 3, 4, 0],
# [ 0, 0, 1, 2, 3, 4]])
num_gen = []
cur_num_gen = 0
##################### silence repetition handling #####################
##################### silence repetition handling #####################
logging.info(
f"silence tokens: {silence_tokens}, note that if you are not using the pretrained encodec 6f79c6a8, make sure you specified it yourself, rather than using the default"
)
consec_silence_count = 0
prev_token = None
##################### silence repetition handling #####################
##################### silence repetition handling #####################
# prepare the cache placeholder
# n_layers, 2, bsz, num_heads, src_len, head_dim
past = (
torch.ones(
[self.args.num_decoder_layers, 2, x.shape[0]],
device=x.device,
dtype=torch.float32,
)
if kvcache
else None
)
# handle multi-span kv-cache
new_masked_span = False
def sample_helper(
n_eog,
logits,
codebook_eog,
top_k,
top_p,
temperature,
prev_token,
consec_silence_count,
stop_repetition,
silence_tokens,
cur_num_gen,
):
if n_eog == 0:
logits_adjust = logits
for jj in range(1, self.args.n_codebooks):
logits_adjust[jj][self.args.eog] = -10000
logits_adjust[jj][self.args.empty_token] = -10000
##################### silence repetition handling #####################
if (
stop_repetition > 0
and prev_token in silence_tokens
and consec_silence_count > stop_repetition
):
if logits_adjust[0, prev_token] < 0:
logits_adjust[0, prev_token] = logits_adjust[0, prev_token] * (
consec_silence_count - (stop_repetition - 1)
)
else:
logits_adjust[0, prev_token] = logits_adjust[0, prev_token] / (
consec_silence_count - (stop_repetition - 1)
)
##################### silence repetition handling #####################
if type(logits_adjust) == list:
samples_list = []
for logit in logits_adjust:
# print(logit)
# print(logit.shape)
cur_sample = topk_sampling(
logit.unsqueeze(0),
top_k=top_k,
top_p=top_p,
temperature=temperature,
) # [1, 1]
samples_list.append(cur_sample)
samples = torch.cat(samples_list, dim=0) # [K, 1]
else:
samples = topk_sampling(
logits_adjust, top_k=top_k, top_p=top_p, temperature=temperature
) # [K, 1]
assert samples.shape == torch.Size(
(self.args.n_codebooks, 1)
), f"samples.shape: {samples.shape}"
if cur_num_gen < self.args.n_codebooks - 1:
for jj in range(1, self.args.n_codebooks - cur_num_gen):
samples[-jj, 0] = self.args.empty_token
if (
samples[0, 0] == self.args.eog
or torch.argmax(logits[0], dim=-1) == self.args.eog
or y_input.shape[1] > x_lens[0] * 10
): # last one means y is already too long, shouldn't happen, but put it here
samples[0, 0] = self.args.eog
codebook_eog[0] = True
##################### silence repetition handling #####################
##################### silence repetition handling #####################
if samples[0, 0] in silence_tokens and samples[0, 0] == prev_token:
consec_silence_count += 1
else:
consec_silence_count = 0
prev_token = samples[0, 0]
##################### silence repetition handling #####################
##################### silence repetition handling #####################
return samples, codebook_eog, prev_token, consec_silence_count
else:
assert (
sum(codebook_eog[i] for i in range(n_eog)) == n_eog
), f"codebook_eog: {codebook_eog}, but n_eog: {n_eog}"
logits_adjust = logits
for jj in range(n_eog + 1, self.args.n_codebooks):
logits_adjust[jj][self.args.eog] = -10000
logits_adjust[jj][self.args.empty_token] = -10000
if type(logits_adjust) == list:
samples_list = []
for logit in logits_adjust:
cur_sample = topk_sampling(
logit.unsqueeze(0),
top_k=top_k,
top_p=top_p,
temperature=temperature,
) # [1, 1]
samples_list.append(cur_sample)
samples = torch.cat(samples_list, dim=0) # [K, 1]
else:
samples = topk_sampling(
logits_adjust, top_k=top_k, top_p=top_p, temperature=temperature
) # [K, 1]
for jj in range(n_eog):
samples[jj, 0] = self.args.empty_token
samples[n_eog, 0] = self.args.eog
codebook_eog[n_eog] = True
return samples, codebook_eog, prev_token, consec_silence_count
while True:
y_out, present = self.dec_forward(
x_input,
x_lens,
x_attention_mask,
x_padding_mask,
y_input,
new_y_lens,
y_attention_mask,
y_padding_mask,
past=past,
last_3_tokens=new_masked_span,
)
if new_masked_span:
new_masked_span = False
if past != None:
past = (
torch.cat([past, present.to(past.dtype)], dim=-2)
if past.ndim > 3
else present.to(past.dtype)
)
y_out = y_out[:, -1:] # only take the last one
logits = torch.stack(
[self.predict_layer[i](y_out) for i in range(self.args.n_codebooks)],
dim=1,
) # [B K S card], B==S==1, so [1 K 1 card]
logits = logits.squeeze(0).squeeze(1) # [K card]
assert logits.shape == torch.Size(
(self.args.n_codebooks, self.n_audio_tokens[0])
), f"{logits.shape}"
n_eog = sum(codebook_eog)
assert n_eog < self.args.n_codebooks
if (
self.args.eos > 0
): # eos stands for end-of-sentence, which shouldn't be used as we are doing speech editing
for jj in range(self.args.n_codebooks):
logits[jj][self.args.eos] = -10000.0
# need to use a helper function to hand different n_eog cases
samples, codebook_eog, prev_token, consec_silence_count = sample_helper(
n_eog,
logits,
codebook_eog,
top_k,
top_p,
temperature,
prev_token,
consec_silence_count,
stop_repetition,
silence_tokens,
cur_num_gen,
)
cur_num_gen += 1
cur_generated.append(samples.squeeze(-1)) # [K,1] -> [K]
# get samples_emb
samples_emb = torch.stack(
[
self.audio_embedding[k](samples[k])
for k in range(self.args.n_codebooks)
],
dim=0,
) # [K,1,D]
samples_emb = samples_emb.sum(dim=0, keepdim=True) # [1,1,D]
if (
sum(codebook_eog) == self.args.n_codebooks
): # generation for the current span is done
# re-init
codebook_eog = [False] * self.args.n_codebooks
num_gen.append(cur_num_gen)
cur_num_gen = 0
generated.append(cur_generated)
cur_generated = []
# if the current mask span is the last span, then all done
# else
# append the next mask token and the four empty tokens to start the next generation
if len(more_mask_value) > 0:
next_mask_ind = more_mask_value.pop(0)
mask_emb = (
self.mask_embedding[next_mask_ind].unsqueeze(0).unsqueeze(0)
) # [1,1,D]
assert mask_emb.shape == torch.Size(
(1, 1, self.args.d_model)
), mask_emb.shape
empty_token = torch.LongTensor([self.args.empty_token]).to(y.device)
empty_emb = torch.stack(
[
self.audio_embedding[k](empty_token)
for k in range(self.args.n_codebooks)
],
dim=0,
).sum(
dim=0, keepdim=True
) # [1,1,D]
assert empty_emb.shape == torch.Size(
(1, 1, self.args.d_model)
), empty_emb.shape
extra_emb = torch.cat([mask_emb, empty_emb], dim=1) # [1,2,D]
samples_emb = torch.cat(
[samples_emb, extra_emb], dim=1
) # [1,3,D] # prev_last_token, mask_token, empty token
assert samples_emb.shape == torch.Size(
(1, 3, self.args.d_model)
), f"samples_emb.shape: {samples_emb.shape}"
##################### silence repetition handling #####################
##################### silence repetition handling #####################
consec_silence_count = 0
prev_token = None
##################### silence repetition handling #####################
##################### silence repetition handling #####################
# handling kv-caching for multi-span editing
new_masked_span = True
else:
break
else:
assert samples_emb.shape == torch.Size(
(1, 1, self.args.d_model)
), f"samples_emb.shape: {samples_emb.shape}"
embedded_y = torch.cat([embedded_y, samples_emb], dim=1)
# positional embedding
y_input = self.audio_positional_embedding(embedded_y) # [B T D]
# make attention mask and padding mask
y_attention_mask = (
torch.triu(torch.ones(y_input.shape[1], y_input.shape[1]), diagonal=1)
.bool()
.to(y.device)
)
new_y_lens = torch.LongTensor([y_input.shape[1]]).to(y.device)
y_padding_mask = torch.full((1, new_y_lens[0]), False).to(y.device)
assert (
len(generated) == num_mask
), f"len(generated): {len(generated)}, num_mask: {num_mask}"
# # combine non_masked_span with generated spans
# first need to shift the generated part back
flatten_gen = []
for l, orig_span in enumerate(generated):
span = torch.stack(orig_span, dim=0) # [T K]
span = span.transpose(1, 0) # [K, T]
assert span.shape[0] == self.args.n_codebooks, span.shape
unshifted_span = []
for j, s in enumerate(span):
start_from = j
end_at = -(self.args.n_codebooks - start_from)
unshifted_span.append(s[start_from:end_at])
unshifted_span = torch.stack(unshifted_span, dim=0)
assert (
unshifted_span.shape[1] == num_gen[l] - self.args.n_codebooks
), f"len(unshifted_spans[0]): {len(unshifted_span[0])}, num_gen[l]: {num_gen[l]}"
flatten_gen.append(unshifted_span)
# logging.info(f"unshfited_span: {unshifted_span.shape}")
# raise
assert len(non_mask_intervals[0]) - 1 == len(
flatten_gen
), f"len(non_mask_intervals[0]): {len(non_mask_intervals[0])}, len(flatten_gen): {len(flatten_gen)}"
res = []
for orig_interval, gen in zip(non_mask_intervals[0], flatten_gen):
res.append(y[0, :, orig_interval[0] : orig_interval[1]])
res.append(gen)
res.append(y[0, :, non_mask_intervals[0][-1][0] : non_mask_intervals[0][-1][1]])
res = torch.cat(res, dim=1).unsqueeze(0) # [K,new_T] -> [1, K, new_T]
expected_y_len = (
y_len
- sum([item[1] - item[0] for item in mask_intervals[0]])
+ sum([item - self.args.n_codebooks for item in num_gen])
)
assert res.shape == torch.Size(
(1, self.args.n_codebooks, expected_y_len)
), f"res.shape: {res.shape}, expected_y_len: {expected_y_len}. y_len - sum([item[1] - item[0] for item in mask_interval]) + sum([item - self.args.n_codebooks for item in num_gen]): {y_len}-{sum([item[1] - item[0] for item in mask_interval])} + {sum([item - self.args.n_codebooks for item in num_gen])}"
if self.args.special_first:
res = res - int(self.args.n_special)
return res
def inference_tts(
self,
x: torch.Tensor,
x_lens: torch.Tensor,
y: torch.Tensor,
top_k: int = -100,
top_p: float = 1.0,
temperature: float = 1.0,
stop_repetition: int = 3,
kvcache: int = 1,
silence_tokens: list[int] = [1388, 1898, 131],
*kargs,
) -> torch.Tensor:
"""
different from inference_tts, this implementation uses kvcache, which should have significant speed up
Args:
x:
A 2-D tensor of shape (1, L).
x_lens:
A 1-D tensor of shape (1,). It contains the number of tokens in `x`
before padding.
y:
A 3-D tensor of shape (1, T, K).
top_k: (`optional`) int
The number of highest probability tokens to keep for top-k-filtering. Default to -100.
top_p: (`optional`) float
For Neucleus sampling
temperature: (`optional`) float
The value used to module the next token probabilities. Must be strictly positive. Default to 1.0.
"""
eog_inference = self.args.eos if self.args.eos > 0 else self.args.eog
assert x.ndim == 2, x.shape
assert x_lens.ndim == 1, x_lens.shape
assert y.ndim == 3, y.shape
if self.args.special_first:
y = y + int(self.args.n_special)
y = y.transpose(2, 1) # [1,T,K] -> [1,K,T]
assert (
y.shape[0] == 1 and y.shape[1] == self.args.n_codebooks
), y.shape # there is no padding
# make x attention mask and x_input
x_attention_mask = (
torch.triu(torch.ones(x.shape[1], x.shape[1]), diagonal=1)
.bool()
.to(x.device)
)
# x_attention_mask = torch.zeros(x.shape[1], x.shape[1]).bool().to(x.device)
x_input = self.text_embedding(x)
x_input = self.text_positional_embedding(x_input)
y_len = y.shape[2]
y_lens = torch.LongTensor([y_len]).to(y.device)
# rearrange y, we don't add eog to the end, this doesn't actually do anything in the tts scenario
rearranged_y = [[y[0]]]
assert rearranged_y[0][0].shape[0] == self.args.n_codebooks, rearranged_y[0][
0
].shape
# shift y to create the delayed pattern
shifted_y, patterns = self.shift(
rearranged_y
) # each element [K S], patterns is not used, as we directly use the original input y
assert shifted_y[0][0].shape[0] == self.args.n_codebooks, shifted_y[0][0].shape
assert len(shifted_y[0]) == 1, len(shifted_y[0])
# below is different from forward or inference
# where we cut this shifted part
shifted_y[0][0] = shifted_y[0][0][:, : -(self.args.n_codebooks - 1)]
assert (
not (
shifted_y[0][0][self.args.n_codebooks :] == self.args.empty_token
).any()
and not (shifted_y[0][0][self.args.n_codebooks :] == self.args.eog).any()
), shifted_y[0][0]
# next section in inference is insert mask at the intersection of each tensor in a sample, but we don't need to do that
# next section is concate tensors of each sample to one tensor, which we also don't need
cated_y = shifted_y[0][0].unsqueeze(-1) # [K,S]->[K,S,B]
new_y_lens = torch.LongTensor([cated_y.shape[1]]).to(cated_y.device)
assert cated_y.shape == torch.Size((self.args.n_codebooks, cated_y.shape[1], 1))
assert not (cated_y == self.args.audio_pad_token).any(), cated_y
# replace tokens in y with the embeddings, add sum codebooks up
embedded_y = torch.stack(
[self.audio_embedding[k](cated_y[k]) for k in range(self.args.n_codebooks)],
dim=0,
) # [K, S, B, D]
assert embedded_y.shape[0] == self.args.n_codebooks, embedded_y.shape
assert embedded_y.shape[-1] == self.args.d_model, embedded_y.shape
embedded_y = embedded_y.sum(dim=0) # [K,S,B,D]->[S,B,D]
embedded_y = embedded_y.transpose(1, 0) # [S,B,D]->[B,S,D]
# positional embedding
y_input = self.audio_positional_embedding(embedded_y)
# make attention mask and padding mask
y_attention_mask = (
torch.triu(torch.ones(y_input.shape[1], y_input.shape[1]), diagonal=1)
.bool()
.to(y.device)
)
x_padding_mask = torch.full((1, x_lens[0]), False).to(x.device)
y_padding_mask = torch.full((1, new_y_lens[0]), False).to(y.device)
# entering the generation stage
# starting from line 708
codebook_eog = [False] * self.args.n_codebooks
generated = [] # doesn't contain any empty token, contain eog
cur_generated = []
# say 0 is empty, 4 is eog
# tensor([[ 1, 2, 3, 4, 0, 0],
# [ 0, 1, 2, 3, 4, 0],
# [ 0, 0, 1, 2, 3, 4]])
num_gen = []
cur_num_gen = 0
##################### silence repetition handling #####################
##################### silence repetition handling #####################
logging.info(
f"silence tokens: {silence_tokens}, note that if you are not using the pretrained encodec 6f79c6a8, make sure you specified it yourself, rather than using the default"
)
consec_silence_count = 0
prev_token = None
##################### silence repetition handling #####################
##################### silence repetition handling #####################
# prepare the cache placeholder
# n_layers, 2, bsz, num_heads, src_len, head_dim
past = (
torch.ones(
[self.args.num_decoder_layers, 2, x.shape[0]],
device=x.device,
dtype=torch.float32,
)
if kvcache
else None
)
# logging.info(f"number of decoder layers: {self.args.num_decoder_layers}")
# logging.info(f"number of decoder layers: {self.args.num_decoder_layers}")
# logging.info(f"number of decoder layers: {self.args.num_decoder_layers}")
def sample_helper(
n_eog,
logits,
codebook_eog,
top_k,
top_p,
temperature,
prev_token,
consec_silence_count,
stop_repetition,
silence_tokens,
cur_num_gen,
):
if n_eog == 0:
logits_adjust = logits
for jj in range(1, self.args.n_codebooks):
logits_adjust[jj][eog_inference] = -10000
logits_adjust[jj][self.args.empty_token] = -10000
if (
cur_num_gen <= self.args.encodec_sr // 5
): # this shouldn't happen, but just in case the model stopped too early
logits_adjust[0][eog_inference] = -10000
##################### silence repetition handling #####################
if (
stop_repetition > 0
and prev_token in silence_tokens
and consec_silence_count > stop_repetition
):
if logits_adjust[0, prev_token] < 0:
logits_adjust[0, prev_token] = logits_adjust[0, prev_token] * (
consec_silence_count - (stop_repetition - 1)
)
else:
logits_adjust[0, prev_token] = logits_adjust[0, prev_token] / (
consec_silence_count - (stop_repetition - 1)
)
##################### silence repetition handling #####################
samples = topk_sampling(
logits_adjust, top_k=top_k, top_p=top_p, temperature=temperature
) # [K, 1]
assert samples.shape == torch.Size(
(self.args.n_codebooks, 1)
), f"samples.shape: {samples.shape}"
if cur_num_gen < self.args.n_codebooks - 1:
for jj in range(1, self.args.n_codebooks - cur_num_gen):
samples[-jj, 0] = self.args.empty_token
if (
samples[0, 0] == eog_inference
or torch.argmax(logits[0], dim=-1) == eog_inference
or y_input.shape[1] > x_lens[0] * (self.args.encodec_sr // 5)
): # last one means y is already too long, shouldn't happen, but put it here
samples[0, 0] = eog_inference
codebook_eog[0] = True
##################### silence repetition handling #####################
if samples[0, 0] in silence_tokens and samples[0, 0] == prev_token:
consec_silence_count += 1
else:
consec_silence_count = 0
prev_token = samples[0, 0]
##################### silence repetition handling #####################
return samples, codebook_eog, prev_token, consec_silence_count
else:
assert (
sum(codebook_eog[i] for i in range(n_eog)) == n_eog
), f"codebook_eog: {codebook_eog}, but n_eog: {n_eog}"
logits_adjust = logits
for jj in range(n_eog + 1, self.args.n_codebooks):
logits_adjust[jj][eog_inference] = -10000
logits_adjust[jj][self.args.empty_token] = -10000
samples = topk_sampling(
logits_adjust, top_k=top_k, top_p=top_p, temperature=temperature
) # [K, 1]
for jj in range(n_eog):
samples[jj, 0] = self.args.empty_token
samples[n_eog, 0] = eog_inference
codebook_eog[n_eog] = True
return samples, codebook_eog, prev_token, consec_silence_count
while True:
y_out, present = self.dec_forward(
x_input,
x_lens,
x_attention_mask,
x_padding_mask,
y_input,
new_y_lens,
y_attention_mask,
y_padding_mask,
past=past,
)
if past != None:
past = (
torch.cat([past, present.to(past.dtype)], dim=-2)
if past.ndim > 3
else present.to(past.dtype)
)
y_out = y_out[:, -1:] # only take the last token
logits = torch.stack(
[self.predict_layer[i](y_out) for i in range(self.args.n_codebooks)],
dim=1,
) # [B K S card], B==S==1, so [1 K 1 card]
logits = logits.squeeze(0).squeeze(1) # [K card]
assert logits.shape == torch.Size(
(self.args.n_codebooks, self.n_audio_tokens[0])
), f"{logits.shape}"
n_eog = sum(codebook_eog)
assert n_eog < self.args.n_codebooks
if (
self.args.eos > 0
): # if we are using end-of-sentence token (which is used by default), eog shouldn't be used here, as there is no masked spans
for jj in range(self.args.n_codebooks):
logits[jj][self.args.eog] = -10000.0
samples, codebook_eog, prev_token, consec_silence_count = sample_helper(
n_eog,
logits,
codebook_eog,
top_k,
top_p,
temperature,
prev_token,
consec_silence_count,
stop_repetition,
silence_tokens,
cur_num_gen,
)
cur_num_gen += 1
cur_generated.append(samples.squeeze(-1)) # [K,1] -> [K]
# samples.shape is [K,1]
# ge samples_emb
samples_emb = torch.stack(
[
self.audio_embedding[k](samples[k])
for k in range(self.args.n_codebooks)
],
dim=0,
) # [K,1,D]
samples_emb = samples_emb.sum(dim=0, keepdim=True) # [1,1,D]
if (
sum(codebook_eog) == self.args.n_codebooks
): # generation for the current span is done
codebook_eog = [False] * self.args.n_codebooks
num_gen.append(cur_num_gen)
cur_num_gen = 0
generated.append(cur_generated)
cur_generated = []
break
else:
assert samples_emb.shape == torch.Size(
(1, 1, self.args.d_model)
), f"samples_emb.shape: {samples_emb.shape}"
embedded_y = torch.cat([embedded_y, samples_emb], dim=1)
y_input = self.audio_positional_embedding(embedded_y) # [B T D]
# make attention mask and padding mask
y_attention_mask = (
torch.triu(torch.ones(y_input.shape[1], y_input.shape[1]), diagonal=1)
.bool()
.to(y.device)
)
new_y_lens = torch.LongTensor([y_input.shape[1]]).to(y.device)
y_padding_mask = torch.full((1, new_y_lens[0]), False).to(y.device)
assert len(generated) == 1, f"len(generated): {len(generated)}"
# revert the pattern
flatten_gen = []
for l, orig_span in enumerate(generated):
span = torch.stack(orig_span, dim=0) # [T, K]
span = span.transpose(1, 0) # [K, T]
assert span.shape[0] == self.args.n_codebooks, span.shape
unshifted_span = []
for j, s in enumerate(span):
start_from = j
end_at = -(self.args.n_codebooks - start_from)
unshifted_span.append(s[start_from:end_at])
unshifted_span = torch.stack(unshifted_span, dim=0)
assert (
unshifted_span.shape[1] == num_gen[l] - self.args.n_codebooks
), f"len(unshifted_spans[0]): {len(unshifted_span[0])}, num_gen[l]: {num_gen[l]}"
flatten_gen.append(unshifted_span)
assert len(flatten_gen) == 1, len(flatten_gen)
# combine
res = [y[0], flatten_gen[0]]
res = torch.cat(res, dim=1).unsqueeze(0) # [K, new_t] -> [1, K, new_T]
expected_y_len = y_len + sum([item - self.args.n_codebooks for item in num_gen])
assert res.shape == torch.Size(
(1, self.args.n_codebooks, expected_y_len)
), f"res.shape: {res.shape}, expected_y_len: {expected_y_len}. y_len + sum([item - self.args.n_codebooks for item in num_gen]): {y_len} + {sum([item - self.args.n_codebooks for item in num_gen])}"
if self.args.special_first:
res = res - int(self.args.n_special)
flatten_gen = flatten_gen - int(self.args.n_special)
return res, flatten_gen[0].unsqueeze(0)
def inference_tts_batch(
self,
x: torch.Tensor,
x_lens: torch.Tensor,
y: torch.Tensor,
top_k: int = -100,
top_p: float = 1.0,
temperature: float = 1.0,
stop_repetition: int = 3,
kvcache: int = 1,
batch_size: int = 5,
silence_tokens: list[int] = [1388, 1898, 131],
*kargs,
) -> torch.Tensor:
"""
have a batch size when forward passing, but they are equivalant to same example but different random seed, therefore as long as one example generated eog, we can drop all other samlpes
different from inference_tts, this implementation uses kvcache, which should have significant speed up
Args:
x:
A 2-D tensor of shape (1, L).
x_lens:
A 1-D tensor of shape (1,). It contains the number of tokens in `x`
before padding.
y:
A 3-D tensor of shape (1, T, K).
top_k: (`optional`) int
The number of highest probability tokens to keep for top-k-filtering. Default to -100.
top_p: (`optional`) float
For Neucleus sampling
temperature: (`optional`) float
The value used to module the next token probabilities. Must be strictly positive. Default to 1.0.
"""
eog_inference = self.args.eos if self.args.eos > 0 else self.args.eog
assert x.ndim == 2, x.shape
assert x_lens.ndim == 1, x_lens.shape
assert y.ndim == 3, y.shape
if self.args.special_first:
y = y + int(self.args.n_special)
y = y.transpose(2, 1) # [1,T,K] -> [1,K,T]
assert (
y.shape[0] == 1 and y.shape[1] == self.args.n_codebooks
), y.shape # there is no padding
# make x attention mask and x_input
x_attention_mask = (
torch.triu(torch.ones(x.shape[1], x.shape[1]), diagonal=1)
.bool()
.to(x.device)
)
# x_attention_mask = torch.zeros(x.shape[1], x.shape[1]).bool().to(x.device)
x_input = self.text_embedding(x)
x_input = self.text_positional_embedding(x_input)
y_len = y.shape[2]
y_lens = torch.LongTensor([y_len]).to(y.device)
# rearrange y, we don't add eog to the end, this doesn't actually do anything in the tts scenario
rearranged_y = [[y[0]]]
assert rearranged_y[0][0].shape[0] == self.args.n_codebooks, rearranged_y[0][
0
].shape
# shift y to create the delayed pattern
shifted_y, patterns = self.shift(
rearranged_y
) # each element [K S], patterns is not used, as we directly use the original input y
assert shifted_y[0][0].shape[0] == self.args.n_codebooks, shifted_y[0][0].shape
assert len(shifted_y[0]) == 1, len(shifted_y[0])
# below is different from forward or inference
# where we cut this shifted part
shifted_y[0][0] = shifted_y[0][0][:, : -(self.args.n_codebooks - 1)]
assert (
not (
shifted_y[0][0][self.args.n_codebooks :] == self.args.empty_token
).any()
and not (shifted_y[0][0][self.args.n_codebooks :] == self.args.eog).any()
), shifted_y[0][0]
# next section in inference is insert mask at the intersection of each tensor in a sample, but we don't need to do that
# next section is concate tensors of each sample to one tensor, which we also don't need
cated_y = shifted_y[0][0].unsqueeze(-1) # [K,S]->[K,S,B]
new_y_lens = torch.LongTensor([cated_y.shape[1]]).to(cated_y.device)
assert cated_y.shape == torch.Size((self.args.n_codebooks, cated_y.shape[1], 1))
assert not (cated_y == self.args.audio_pad_token).any(), cated_y
# replace tokens in y with the embeddings, add sum codebooks up
embedded_y = torch.stack(
[self.audio_embedding[k](cated_y[k]) for k in range(self.args.n_codebooks)],
dim=0,
) # [K, S, B, D]
assert embedded_y.shape[0] == self.args.n_codebooks, embedded_y.shape
assert embedded_y.shape[-1] == self.args.d_model, embedded_y.shape
embedded_y = embedded_y.sum(dim=0) # [K,S,B,D]->[S,B,D]
embedded_y = embedded_y.transpose(1, 0) # [S,B,D]->[B,S,D]
# positional embedding
y_input = self.audio_positional_embedding(embedded_y)
# make attention mask and padding mask
y_attention_mask = (
torch.triu(torch.ones(y_input.shape[1], y_input.shape[1]), diagonal=1)
.bool()
.to(y.device)
)
x_padding_mask = torch.full((1, x_lens[0]), False).to(x.device)
y_padding_mask = torch.full((1, new_y_lens[0]), False).to(y.device)
# entering the generation stage
# starting from line 708
codebook_eog = [False] * self.args.n_codebooks
generated = [] # doesn't contain any empty token, contain eog
cur_generated = [[] for _ in range(batch_size)]
# say 0 is empty, 4 is eog
# tensor([[ 1, 2, 3, 4, 0, 0],
# [ 0, 1, 2, 3, 4, 0],
# [ 0, 0, 1, 2, 3, 4]])
num_gen = []
cur_num_gen = 0
##################### silence repetition handling #####################
##################### silence repetition handling #####################
logging.info(
f"silence tokens: {silence_tokens}, note that if you are not using the pretrained encodec 6f79c6a8, make sure you specified it yourself, rather than using the default"
)
consec_silence_counts = [0 for _ in range(batch_size)]
prev_tokens = [None for _ in range(batch_size)]
##################### silence repetition handling #####################
##################### silence repetition handling #####################
# prepare the cache placeholder
# n_layers, 2, bsz, num_heads, src_len, head_dim
past = (
torch.ones(
[self.args.num_decoder_layers, 2, x.shape[0]],
device=x.device,
dtype=torch.float32,
)
if kvcache
else None
)
# logging.info(f"number of decoder layers: {self.args.num_decoder_layers}")
# logging.info(f"number of decoder layers: {self.args.num_decoder_layers}")
# logging.info(f"number of decoder layers: {self.args.num_decoder_layers}")
keep = None # NOTE: this very important, tells which sample to keep
def sample_helper(
n_eog,
logits,
codebook_eog,
top_k,
top_p,
temperature,
prev_tokens,
consec_silence_counts,
stop_repetition,
silence_tokens,
cur_num_gen,
keep,
):
if n_eog == 0:
logits_adjust = logits
for jj in range(1, self.args.n_codebooks):
logits_adjust[:, jj, eog_inference] = -10000
logits_adjust[:, jj, self.args.empty_token] = -10000
if (
cur_num_gen <= self.args.encodec_sr // 5
): # this shouldn't happen, but just in case the model stopped too early
logits_adjust[:, :, eog_inference] = -10000
##################### silence repetition handling #####################
for b in range(batch_size):
prev_token = prev_tokens[b]
consec_silence_count = consec_silence_counts[b]
if (
stop_repetition > 0
and prev_token in silence_tokens
and consec_silence_count > stop_repetition
):
if logits_adjust[b, 0, prev_token] < 0:
logits_adjust[b, 0, prev_token] = logits_adjust[
b, 0, prev_token
] * (consec_silence_count - (stop_repetition - 1))
else:
logits_adjust[b, 0, prev_token] = logits_adjust[
b, 0, prev_token
] / (consec_silence_count - (stop_repetition - 1))
##################### silence repetition handling #####################
samples = topk_sampling(
logits_adjust.reshape(
batch_size * self.args.n_codebooks, logits_adjust.shape[-1]
),
top_k=top_k,
top_p=top_p,
temperature=temperature,
) # [B*K, 1]
samples = samples.reshape(batch_size, self.args.n_codebooks, 1)
assert samples.shape == torch.Size(
(batch_size, self.args.n_codebooks, 1)
), f"samples.shape: {samples.shape}"
for b in range(batch_size):
if cur_num_gen < self.args.n_codebooks - 1:
for jj in range(1, self.args.n_codebooks - cur_num_gen):
samples[b, -jj, 0] = self.args.empty_token
if (
samples[b, 0, 0] == eog_inference
or torch.argmax(logits[b, 0], dim=-1) == eog_inference
or y_input.shape[1] > x_lens[b] * (self.args.encodec_sr // 5)
): # last one means y is already too long, shouldn't happen, but put it here
samples[b, 0, 0] = eog_inference
codebook_eog[0] = True
keep = b # NOTE keep is a very important variable, we only return this one, note that if eog shows up in two samples, keep will be overwritten by the later one (or the last one)
##################### silence repetition handling #####################
if (
samples[b, 0, 0] in silence_tokens
and samples[b, 0, 0] == prev_tokens[b]
):
consec_silence_counts[b] += 1
else:
consec_silence_counts[b] = 0
prev_tokens[b] = samples[b, 0, 0]
##################### silence repetition handling #####################
return samples, codebook_eog, prev_tokens, consec_silence_counts, keep
else:
assert (
sum(codebook_eog[i] for i in range(n_eog)) == n_eog
), f"codebook_eog: {codebook_eog}, but n_eog: {n_eog}"
logits_adjust = logits
for jj in range(n_eog + 1, self.args.n_codebooks):
logits_adjust[:, jj, eog_inference] = -10000
logits_adjust[:, jj, self.args.empty_token] = -10000
samples = topk_sampling(
logits_adjust.reshape(
batch_size * self.args.n_codebooks, logits_adjust.shape[-1]
),
top_k=top_k,
top_p=top_p,
temperature=temperature,
) # [B, K, 1]
samples = samples.reshape(batch_size, self.args.n_codebooks, 1)
for jj in range(n_eog):
samples[keep, jj, 0] = self.args.empty_token
samples[keep, n_eog, 0] = eog_inference
codebook_eog[n_eog] = True
return samples, codebook_eog, prev_tokens, consec_silence_counts, keep
while True:
# if cur_num_gen > 0, should have everything in kvcache, so only pass in the last token
# in the first generation step, we repeat each tensor to make their first dimension of length the batch size
if cur_num_gen == 0:
assert x_input.ndim == 3 and x_input.shape[0] == 1, x_input.shape
assert (
x_padding_mask.ndim == 2 and x_padding_mask.shape[0] == 1
), x_padding_mask.shape
assert (
y_input.ndim == 3
and y_input.shape[0] == 1
and y_input.shape[1] == new_y_lens[0]
), y_input.shape
assert (
embedded_y.ndim == 3
and embedded_y.shape[0] == 1
and embedded_y.shape[1] == new_y_lens[0]
), embedded_y.shape
x_input = x_input.repeat(batch_size, 1, 1)
x_lens = x_lens.repeat(batch_size)
# x_attention_mask = x_attention_mask.repeat(batch_size, 1, 1) # no need to work with attention mask, it doesn't contain batch dimension
x_padding_mask = x_padding_mask.repeat(batch_size, 1)
y_input = y_input.repeat(batch_size, 1, 1)
new_y_lens = new_y_lens.repeat(batch_size)
# y_attention_mask = y_attention_mask.repeat(batch_size, 1, 1) # no need to work with attention mask, it doesn't contain batch dimension
y_padding_mask = y_padding_mask.repeat(batch_size, 1)
embedded_y = embedded_y.repeat(
batch_size, 1, 1
) # will be used to concat with newly generated token embedding
past = past.repeat(1, 1, batch_size) if past != None else None
else:
assert (
x_input.shape[0] == batch_size
and x_padding_mask.shape[0] == batch_size
and y_input.shape[0] == batch_size
and new_y_lens.shape[0] == batch_size
), f"x_input.shape: {x_input.shape}, x_padding_mask.shape: {x_padding_mask.shape}, y_input.shape: {y_input.shape}, new_y_lens.shape: {new_y_lens.shape}"
y_out, present = self.dec_forward(
x_input,
x_lens,
x_attention_mask,
x_padding_mask,
y_input,
new_y_lens,
y_attention_mask,
y_padding_mask,
past=past,
)
if past != None:
past = (
torch.cat([past, present.to(past.dtype)], dim=-2)
if past.ndim > 3
else present.to(past.dtype)
)
# if no eog emerges, y_out should have batch size of batch_size
if sum(codebook_eog) == 0:
assert y_out.shape[0] == batch_size and y_out.ndim == 3, y_out.shape
y_out = y_out[:, -1:] # only take the last token
logits = torch.stack(
[self.predict_layer[i](y_out) for i in range(self.args.n_codebooks)],
dim=1,
) # [B K S card], S==1, so [B K 1 card]
logits = logits.squeeze(2) # [B K card]
assert logits.shape == torch.Size(
(batch_size, self.args.n_codebooks, self.n_audio_tokens[0])
), f"{logits.shape}"
n_eog = sum(codebook_eog)
if self.args.eos > 0:
for jj in range(self.args.n_codebooks):
logits[:, jj, self.args.eog] = -10000.0
samples, codebook_eog, prev_tokens, consec_silence_counts, keep = (
sample_helper(
n_eog,
logits,
codebook_eog,
top_k,
top_p,
temperature,
prev_tokens,
consec_silence_counts,
stop_repetition,
silence_tokens,
cur_num_gen,
keep,
)
)
cur_num_gen += 1
if sum(codebook_eog) == 0: # no eog yet, keep batch_size of samples
assert keep == None
for b in range(batch_size):
cur_generated[b].append(samples[b].squeeze(-1))
elif sum(codebook_eog) == 1: # the first eog just showed up in this step
assert keep != None
cur_generated = cur_generated[keep]
cur_generated.append(samples[keep].squeeze(-1))
else: # we are generating the rest eogs for the 'keep' sample
cur_generated.append(samples[keep].squeeze(-1))
# samples.shape is [K,1]
# ge samples_emb
samples_emb = torch.stack(
[
self.audio_embedding[k](samples[:, k])
for k in range(self.args.n_codebooks)
],
dim=1,
) # [B, K,1,D]
assert samples_emb.shape == torch.Size(
[batch_size, self.args.n_codebooks, 1, self.args.d_model]
)
samples_emb = samples_emb.sum(dim=1, keepdim=False) # [B,1,D]
if (
sum(codebook_eog) == self.args.n_codebooks
): # generation for the current span is done
codebook_eog = [False] * self.args.n_codebooks
num_gen.append(cur_num_gen)
cur_num_gen = 0
generated.append(cur_generated)
cur_generated = [[] for _ in range(batch_size)]
break
else:
assert samples_emb.shape == torch.Size(
(batch_size, 1, self.args.d_model)
), f"samples_emb.shape: {samples_emb.shape}"
embedded_y = torch.cat([embedded_y, samples_emb], dim=1)
y_input = self.audio_positional_embedding(embedded_y) # [B T D]
# make attention mask and padding mask
y_attention_mask = (
torch.triu(torch.ones(y_input.shape[1], y_input.shape[1]), diagonal=1)
.bool()
.to(y.device)
)
new_y_lens = (
torch.LongTensor([y_input.shape[1]]).to(y.device).repeat(batch_size)
)
y_padding_mask = torch.full((batch_size, new_y_lens[0]), False).to(y.device)
assert len(generated) == 1, f"len(generated): {len(generated)}"
# revert the pattern
flatten_gen = []
for l, orig_span in enumerate(generated):
span = torch.stack(orig_span, dim=0) # [T, K]
span = span.transpose(1, 0) # [K, T]
assert span.shape[0] == self.args.n_codebooks, span.shape
unshifted_span = []
for j, s in enumerate(span):
start_from = j
end_at = -(self.args.n_codebooks - start_from)
unshifted_span.append(s[start_from:end_at])
unshifted_span = torch.stack(unshifted_span, dim=0)
assert (
unshifted_span.shape[1] == num_gen[l] - self.args.n_codebooks
), f"len(unshifted_spans[0]): {len(unshifted_span[0])}, num_gen[l]: {num_gen[l]}"
flatten_gen.append(unshifted_span)
assert len(flatten_gen) == 1, len(flatten_gen)
# combine
res = [y[0], flatten_gen[0]]
res = torch.cat(res, dim=1).unsqueeze(0) # [K, new_t] -> [1, K, new_T]
expected_y_len = y_len + sum([item - self.args.n_codebooks for item in num_gen])
assert res.shape == torch.Size(
(1, self.args.n_codebooks, expected_y_len)
), f"res.shape: {res.shape}, expected_y_len: {expected_y_len}. y_len + sum([item - self.args.n_codebooks for item in num_gen]): {y_len} + {sum([item - self.args.n_codebooks for item in num_gen])}"
if self.args.special_first:
res = res - int(self.args.n_special)
flatten_gen = flatten_gen - int(self.args.n_special)
return res, flatten_gen[0].unsqueeze(0)
|