Spaces:
Sleeping
Sleeping
File size: 224,865 Bytes
32fbd07 |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"analyzer_report_fn = \"/home/michal/Development/github/pl-asr-bigos-tools/data/analyzer-reports/bigos-20240425.json\"\n",
"\n",
"# read json\n",
"import json\n",
"with open(analyzer_report_fn, \"r\") as f:\n",
" analyzer_report = json.load(f)\n",
"\n",
"print(analyzer_report)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_num_of_samples_per_split(dataset_hf):\n",
" # input - huggingface dataset object\n",
" # output - dictionary with statistics about number of samples per split\n",
" out_dict = {}\n",
" # number of samples per subset and split\n",
" metric = \"samples_count\"\n",
" print(\"Calculating {}\".format(metric))\n",
"\n",
" out_dict[metric] = {}\n",
" for split in dataset_hf.keys():\n",
" samples = dataset_hf[split].num_rows\n",
" print(split, samples)\n",
" out_dict[metric][split] = samples\n",
" # add number of samples for all splits\n",
" out_dict[metric][\"all_splits\"] = sum(out_dict[metric].values())\n",
"\n",
" return out_dict\n",
"\n",
"def get_audio_duration_per_split(dataset_hf):\n",
" # input - huggingface dataset object\n",
" # output - dictionary with statistics about audio duration per split\n",
" out_dict = {}\n",
" metric = \"audio_duration[h]\"\n",
" print(\"Calculating {}\".format(metric))\n",
"\n",
" out_dict[metric] = {}\n",
" for split in dataset_hf.keys():\n",
" #sampling_rate = dataset_hf[split][\"sampling_rate\"][0]\n",
" #audio_total_length_samples = 0\n",
" #audio_total_length_samples = sum(len(audio_file[\"array\"]) for audio_file in dataset_hf[\"test\"][\"audio\"])\n",
" audio_total_length_seconds = sum(len(audio_file[\"array\"]) / audio_file[\"sampling_rate\"] for audio_file in dataset_hf[split][\"audio\"])\n",
" audio_total_length_hours = round(audio_total_length_seconds / 3600,2)\n",
" out_dict[metric][split] = audio_total_length_hours\n",
" print(split, audio_total_length_hours)\n",
" # add number of samples for all splits\n",
" out_dict[metric][\"all_splits\"] = sum(out_dict[metric].values())\n",
" return out_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_speakers_per_split(dataset_hf):\n",
" # input - huggingface dataset object\n",
" # output - dictionary with statistics about audio duration per split\n",
" out_dict = {}\n",
" metric = \"speakers_count\"\n",
" print(\"Calculating {}\".format(metric))\n",
"\n",
" out_dict[metric] = {}\n",
" for split in dataset_hf.keys():\n",
" # extract speakers from file_id \n",
" speakers_ids_all = [str(fileid).split(\"-\")[4] for fileid in dataset_hf[split][\"audioname\"]]\n",
" speakers_ids_uniq = list(set(speakers_ids_all))\n",
" speakers_count = len(speakers_ids_uniq)\n",
" print(split, speakers_count)\n",
" # add number of samples for all splits\n",
" out_dict[metric][\"all_splits\"] = sum(out_dict[metric].values())\n",
" return out_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_uniq_utts_per_split(dataset_hf):\n",
" # input - huggingface dataset object\n",
" # output - dictionary with statistics about audio duration per split\n",
" out_dict = {}\n",
" metric = \"utterances_unique_count\"\n",
" print(\"Calculating {}\".format(metric))\n",
"\n",
" out_dict[metric] = {}\n",
" for split in dataset_hf.keys():\n",
" # extract speakers from file_id \n",
" utts_all = dataset_hf[split][\"ref_orig\"]\n",
" utts_uniq = list(set(utts_all))\n",
" utts_uniq_count = len(utts_uniq)\n",
" print(split, utts_uniq_count)\n",
" # add number of samples for all splits\n",
" out_dict[metric][\"all_splits\"] = \"N/A\"\n",
" return out_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_words_per_split(dataset_hf):\n",
" # input - huggingface dataset object\n",
" # output - dictionary with statistics about audio duration per split\n",
" out_dict = {}\n",
" metric = \"words_count\"\n",
" print(\"Calculating {}\".format(metric))\n",
"\n",
" out_dict[metric] = {}\n",
" for split in dataset_hf.keys():\n",
" # extract speakers from file_id \n",
" utts_all = dataset_hf[split][\"ref_orig\"]\n",
" utts_lenghts = [len(utt.split(\" \")) for utt in utts_all]\n",
" words_all_count = sum(utts_lenghts)\n",
" print(split, words_all_count)\n",
" # add number of samples for all splits\n",
" out_dict[metric][\"all_splits\"] = sum(out_dict[metric].values())\n",
" return out_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_unique_words_per_split(dataset_hf):\n",
" # input - huggingface dataset object\n",
" # output - dictionary with statistics about audio duration per split\n",
" out_dict = {}\n",
" out_words_list = []\n",
" metric = \"words_unique\"\n",
" print(\"Calculating {}\".format(metric))\n",
"\n",
" out_dict[metric] = {}\n",
" for split in dataset_hf.keys():\n",
" # extract speakers from file_id \n",
" utts_all = dataset_hf[split][\"ref_orig\"]\n",
" words_all = \" \".join(utts_all).split(\" \")\n",
" words_uniq = list(set(words_all))\n",
" out_words_list = out_words_list + words_uniq\n",
" words_uniq_count = len(words_uniq)\n",
" print(split, words_uniq_count)\n",
" # add number of samples for all splits\n",
" out_words_uniq = list(set((out_words_list)))\n",
" out_words_uniq_count = len(out_words_uniq)\n",
" out_dict[metric][\"all_splits\"] = out_words_uniq_count\n",
" print(\"all\", out_words_uniq_count)\n",
"\n",
" return out_dict, out_words_uniq"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_chars_per_split(dataset_hf):\n",
" # input - huggingface dataset object\n",
" # output - dictionary with statistics about audio duration per split\n",
" out_dict = {}\n",
"\n",
" metric = \"chars\"\n",
" print(\"Calculating {}\".format(metric))\n",
"\n",
" out_dict[metric] = {}\n",
" for split in dataset_hf.keys():\n",
" # extract speakers from file_id \n",
" utts_all = dataset_hf[split][\"ref_orig\"]\n",
" words_all = \" \".join(utts_all).split(\" \")\n",
" chars_all = \" \".join(words_all)\n",
" chars_all_count = len(chars_all)\n",
" print(split, chars_all_count)\n",
" # add number of samples for all splits\n",
" out_dict[metric][\"all_splits\"] = sum(out_dict[metric].values())\n",
" return out_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_unique_chars_per_split(dataset_hf):\n",
" # input - huggingface dataset object\n",
" # output - dictionary with statistics about audio duration per split\n",
" out_dict = {}\n",
" out_chars_list = []\n",
" metric = \"chars_unique\"\n",
" print(\"Calculating {}\".format(metric))\n",
"\n",
" out_dict[metric] = {}\n",
" for split in dataset_hf.keys():\n",
" # extract speakers from file_id \n",
" utts_all = dataset_hf[split][\"ref_orig\"]\n",
" words_all = \" \".join(utts_all).split(\" \")\n",
" words_uniq = list(set(words_all))\n",
" chars_uniq = list(set(\"\".join(words_uniq)))\n",
" chars_uniq_count = len(chars_uniq)\n",
" print(split, chars_uniq_count)\n",
" out_chars_list = out_chars_list + chars_uniq\n",
" # add number of samples for all splits\n",
" out_chars_uniq = list(set((out_chars_list)))\n",
" out_chars_uniq_count = len(out_chars_uniq)\n",
" out_dict[metric][\"all_splits\"] = out_chars_uniq_count\n",
" print(\"all\", out_chars_uniq_count)\n",
"\n",
" return out_dict, out_chars_uniq"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_meta_coverage_sex_per_split(dataset_hf):\n",
" # input - huggingface dataset object\n",
" # output - dictionary with statistics about audio duration per split\n",
" out_dict = {}\n",
" metric = \"meta_coverage_sex\"\n",
" print(\"Calculating {}\".format(metric))\n",
"\n",
" out_dict[metric] = {}\n",
" for split in dataset_hf.keys():\n",
" \n",
" # extract speakers from file_id\n",
" meta_info = dataset_hf[split][\"speaker_sex\"]\n",
"\n",
" # calculate coverage\n",
" meta_info_count = len(meta_info)\n",
" meta_info_not_null_count = len([x for x in meta_info if x != \"N/A\"])\n",
" meta_info_coverage = round(meta_info_not_null_count / meta_info_count, 2)\n",
" print(split, meta_info_coverage)\n",
"\n",
" # add number of samples for all splits\n",
" out_dict[metric][split] = meta_info_coverage\n",
"\n",
" # add number of samples for all splits\n",
" out_dict[metric][\"all_splits\"] = \"N/A\"\n",
" return out_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_meta_coverage_age_per_split(dataset_hf):\n",
" # input - huggingface dataset object\n",
" # output - dictionary with statistics about audio duration per split\n",
" out_dict = {}\n",
" metric = \"meta_coverage_age\"\n",
" print(\"Calculating {}\".format(metric))\n",
"\n",
" out_dict[metric] = {}\n",
" for split in dataset_hf.keys():\n",
" meta_info = dataset_hf[split][\"speaker_age\"]\n",
" \n",
" # calculate coverage\n",
" meta_info_count = len(meta_info)\n",
" meta_info_not_null_count = len([x for x in meta_info if x != \"N/A\"])\n",
" meta_info_coverage = round(meta_info_not_null_count / meta_info_count, 2)\n",
" print(split, meta_info_coverage)\n",
"\n",
" # add number of samples for all splits\n",
" out_dict[metric][split] = meta_info_coverage\n",
"\n",
" # add number of samples for all splits\n",
" out_dict[metric][\"all_splits\"] = \"N/A\"\n",
" return out_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def speech_rate_per_split(dataset_hf):\n",
" # input - huggingface dataset object\n",
" # output - dictionary with statistics about audio duration per split\n",
" out_dict = {}\n",
" metric = \"speech_rate\"\n",
" print(\"Calculating {}\".format(metric))\n",
"\n",
" out_dict[metric] = {}\n",
" for split in dataset_hf.keys():\n",
" # extract speakers from file_id \n",
" utts_all = dataset_hf[split][\"ref_orig\"]\n",
" words_all = \" \".join(utts_all).split(\" \")\n",
" words_all_count = len(words_all)\n",
" audio_total_length_seconds = sum(len(audio_file[\"array\"]) / audio_file[\"sampling_rate\"] for audio_file in dataset_hf[split][\"audio\"])\n",
" speech_rate = round(words_all_count / audio_total_length_seconds, 2)\n",
" print(split, speech_rate)\n",
" out_dict[metric][split] = speech_rate\n",
" # add number of samples for all splits\n",
" out_dict[metric][\"all_splits\"] = \"N/A\"\n",
" return out_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# distribution of speaker age\n",
"def get_speaker_age_distribution(dataset_hf):\n",
" no_meta=False\n",
" age_buckets = ['teens','twenties', 'thirties', 'fourties', 'fifties', 'sixties', 'seventies', 'eighties', 'nineties']\n",
" # input - huggingface dataset object\n",
" # output - dictionary with statistics about audio duration per split\n",
" out_dict = {}\n",
" metric = \"speaker_age_distribution\"\n",
" print(\"Calculating {}\".format(metric))\n",
"\n",
" out_dict[metric] = {}\n",
" values_count_total = {}\n",
" for age in age_buckets:\n",
" values_count_total[age]=0\n",
" for split in dataset_hf.keys():\n",
" meta_info = dataset_hf[split][\"speaker_age\"]\n",
" meta_info_not_null = [x for x in meta_info if x != \"N/A\"]\n",
" out_dict[metric][split] = {}\n",
"\n",
" if len(meta_info_not_null) == 0:\n",
" out_dict[metric][split][age]=\"N/A\"\n",
" no_meta=True\n",
" continue\n",
" for age in age_buckets:\n",
" values_count = meta_info_not_null.count(age)\n",
" values_count_total[age] += values_count\n",
" out_dict[metric][split][age] = round(values_count/len(meta_info_not_null),2)\n",
" print(split, out_dict[metric][split])\n",
" \n",
" # add number of samples for all splits\n",
" if (no_meta):\n",
" out_dict[metric][\"all_splits\"] = \"N/A\"\n",
" return out_dict\n",
" \n",
" out_dict[metric][\"all_splits\"] = {}\n",
" # calculate total number of samples in values_count_total\n",
" for age in age_buckets:\n",
" total_samples = sum(values_count_total.values())\n",
" out_dict[metric][\"all_splits\"][age] = round(values_count_total[age]/total_samples,2)\n",
" return out_dict\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# distribution of speaker age\n",
"def get_speaker_sex_distribution(dataset_hf):\n",
" no_meta=False\n",
" sex_types = ['male', 'female']\n",
"\n",
" # input - huggingface dataset object\n",
" # output - dictionary with statistics about audio duration per split\n",
" out_dict = {}\n",
" metric = \"speaker_sex_distribution\"\n",
" print(\"Calculating {}\".format(metric))\n",
" out_dict[metric] = {}\n",
" values_count_total = {}\n",
" for sex in sex_types:\n",
" values_count_total[sex]=0\n",
" for split in dataset_hf.keys():\n",
" meta_info = dataset_hf[split][\"speaker_sex\"]\n",
" meta_info_not_null = [x for x in meta_info if x != \"N/A\"]\n",
" out_dict[metric][split] = {}\n",
"\n",
" if len(meta_info_not_null) == 0:\n",
" out_dict[metric][split][sex]=\"N/A\"\n",
" no_meta=True\n",
" continue\n",
" for sex in sex_types:\n",
" values_count = meta_info_not_null.count(sex)\n",
" values_count_total[sex] += values_count\n",
" out_dict[metric][split][sex] = round(values_count/len(meta_info_not_null),2)\n",
" print(split, out_dict[metric][split])\n",
" \n",
" # add number of samples for all splits\n",
" if (no_meta):\n",
" out_dict[metric][\"all_splits\"] = \"N/A\"\n",
" return out_dict\n",
" \n",
" out_dict[metric][\"all_splits\"] = {}\n",
" # calculate total number of samples in values_count_total\n",
" for sex in sex_types:\n",
" total_samples = sum(values_count_total.values())\n",
" out_dict[metric][\"all_splits\"][sex] = round(values_count_total[sex]/total_samples,2)\n",
" return out_dict\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"recordings_per_speaker_stats_dict = {}\n",
"def recordings_per_speaker_stats(dataset_hf):\n",
" # input - huggingface dataset object\n",
" # output - dictionary with statistics about audio duration per split\n",
" out_dict = {}\n",
" metric = \"recordings_per_speaker\"\n",
" print(\"Calculating {}\".format(metric))\n",
" \n",
" recordings_per_speaker_stats_dict = {}\n",
"\n",
" out_dict[metric] = {}\n",
" for split in dataset_hf.keys():\n",
" # extract speakers from file_id \n",
" audiopaths = dataset_hf[split][\"audioname\"]\n",
" speaker_prefixes = [str(fileid).split(\"-\")[0:5] for fileid in audiopaths]\n",
"\n",
" # create dictionary with list of audio paths matching speaker prefix\n",
" speakers_dict = {}\n",
" # Create initial dictionary keys from speaker prefixes\n",
" for speaker_prefix in speaker_prefixes:\n",
" speaker_prefix_str = \"-\".join(speaker_prefix)\n",
" speakers_dict[speaker_prefix_str] = []\n",
"\n",
" # Populate the dictionary with matching audio paths\n",
" for audio_path in audiopaths:\n",
" for speaker_prefix_str in speakers_dict.keys():\n",
" if speaker_prefix_str in audio_path:\n",
" speakers_dict[speaker_prefix_str].append(audio_path)\n",
"\n",
"\n",
" # todo calculate recordings_per_speaker_stats_dict\n",
" # iterate of speaker_dict prefixes and calculate number of recordings per speaker.\n",
" recordings_per_speaker_stats_dict = {}\n",
" for speaker_prefix_str in speakers_dict.keys():\n",
" recordings_per_speaker_stats_dict[speaker_prefix_str] = len(speakers_dict[speaker_prefix_str])\n",
" out_dict[metric][split] = {}\n",
" \n",
" out_dict[metric][split][\"recordings_per_speaker_list\"] = recordings_per_speaker_stats_dict \n",
" \n",
" # use recordings_per_speaker_stats to calculate statistics like min, max, avg, median, std\n",
" out_dict[metric][split][\"recordings_per_speaker_stats\"] = {}\n",
" speakers = len(list(recordings_per_speaker_stats_dict.keys()))\n",
" recordings_total = len(audiopaths)\n",
" average_recordings_per_speaker = round( recordings_total / speakers,2)\n",
" out_dict[metric][split][\"recordings_per_speaker_stats\"][\"average\"] = average_recordings_per_speaker\n",
" out_dict[metric][split][\"recordings_per_speaker_stats\"][\"std\"] = round(np.std(list(recordings_per_speaker_stats_dict.values())),2)\n",
" out_dict[metric][split][\"recordings_per_speaker_stats\"][\"median\"] = np.median(list(recordings_per_speaker_stats_dict.values()))\n",
"\n",
" out_dict[metric][split][\"recordings_per_speaker_stats\"][\"min\"] = min(recordings_per_speaker_stats_dict.values())\n",
" out_dict[metric][split][\"recordings_per_speaker_stats\"][\"max\"] = max(recordings_per_speaker_stats_dict.values())\n",
"\n",
" # add number of samples for all splits\n",
" out_dict[metric][\"all_splits\"] = \"N/A\"\n",
" return out_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from datasets import load_dataset\n",
"import os\n",
"\n",
"def distribution_audio_duration(dataset_hf, output_dir, metric = \"audio_duration_seconds\", dimension = \"speaker_sex\"):\n",
" # input - huggingface dataset object\n",
" # output - figure with distribution of audio duration per sex\n",
" out_dict = {}\n",
"\n",
" print(\"Calculating {}\".format(metric))\n",
" out_dict[metric] = {}\n",
" # drop samples for which dimension column values are equal to \"N/A\"\n",
" for split in dataset_hf.keys():\n",
" df_dataset = pd.DataFrame(dataset_hf[split])\n",
" df_dataset = df_dataset.drop(columns=[\"audio\"])\n",
" \n",
" # remove values equal to \"N/A\" for column dimension\n",
" df_filtered = df_dataset[df_dataset[dimension] != \"N/A\"] \n",
" df_filtered = df_filtered[df_filtered[dimension] != \"other\"]\n",
" # if df_filtered is empty, skip violin plot generation for this split and dimension\n",
" if df_filtered.empty:\n",
" print(\"No data for split {} and dimension {}\".format(split, dimension))\n",
" continue\n",
" plt.figure(figsize=(15, 10))\n",
" sns.violinplot(data = df_filtered, hue=dimension, x='dataset', y=metric, split=True, fill = False, inner=\"box\", legend='auto', common_norm=True)\n",
" plt.title('Violin plot of {} by {} for split {}'.format(metric, dimension, split))\n",
" plt.xlabel(dimension)\n",
" plt.ylabel(metric)\n",
" plt.show()\n",
" # save figure to file\n",
" os.makedirs(output_dir, exist_ok=True)\n",
" output_fn = os.path.join(output_dir, metric + \"-\" + dimension + \"-\" + split + \".png\") \n",
" plt.savefig(output_fn)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from datasets import load_dataset\n",
"from datasets import get_dataset_config_names\n",
"dataset_name = \"amu-cai/pl-asr-bigos-v2\"\n",
"# get dataset config names\n",
"dataset_config_names = get_dataset_config_names(dataset_name)\n",
"# load dataset\n",
"dataset_hf = load_dataset(dataset_name, \"all\")\n",
"\n",
"dataset_statistics={}\n",
"dataset_contents = {}\n",
"output_dir_plots = \"./plots\"\n",
"os.makedirs(output_dir_plots, exist_ok=True)\n",
"output_dir_reports = \"./reports\"\n",
"os.makedirs(output_dir_plots, exist_ok=True)\n",
"\n",
"for config_name in dataset_config_names :\n",
" print(config_name)\n",
" dataset_hf_subset = load_dataset(dataset_name, config_name) \n",
" #dataset_statistics[config_name] = get_num_of_samples_per_split(dataset_hf_subset)\n",
" #dataset_statistics[config_name] = get_uniq_utts_per_split(dataset_hf_subset)\n",
" #dataset_statistics[config_name] = get_words_per_split(dataset_hf_subset)\n",
" \n",
" #dataset_contents[config_name] = {}\n",
"\n",
" #dataset_statistics[config_name], dataset_contents[config_name][\"unique_words\"] = get_unique_words_per_split(dataset_hf_subset)\n",
"\n",
" #dataset_statistics[config_name] = get_chars_per_split(dataset_hf_subset)\n",
" #dataset_statistics[config_name], dataset_contents[config_name][\"unique_chars\"] = get_unique_chars_per_split(dataset_hf_subset)\n",
"\n",
" #dataset_statistics[config_name] = get_audio_duration_per_split(dataset_hf_subset)\n",
" #dataset_statistics[config_name] = get_speakers_per_split(dataset_hf_subset)\n",
"\n",
" #dataset_statistics[config_name] = get_meta_coverage_sex_per_split(dataset_hf_subset)\n",
" #dataset_statistics[config_name] = get_meta_coverage_age_per_split(dataset_hf_subset)\n",
" # metadata coverage per subset in percent - speaker accent\n",
"\n",
" # speech rate per subset\n",
" #dataset_statistics[config_name] = speech_rate_per_split(dataset_hf_subset)\n",
" #dataset_statistics[config_name] = get_speaker_age_distribution(dataset_hf_subset)\n",
" #dataset_statistics[config_name] = get_speaker_sex_distribution(dataset_hf_subset)\n",
" #print(dataset_statistics[config_name])\n",
" \n",
" dataset_statistics[config_name] = recordings_per_speaker_stats(dataset_hf_subset)\n",
" #dataset_statistics[config_name] = uniq_utterances_per_speaker_stats(dataset_hf_subset)\n",
" # number of words per speaker (min, max, med, avg, std)\n",
"\n",
"\n",
" # distribution\n",
" # distribution of audio duration per subset\n",
" output_dir_plots_subset = os.path.join(output_dir_plots, config_name)\n",
" dataset_statistics[config_name] = distribution_audio_duration(dataset_hf_subset, output_dir_plots_subset, 'audio_duration_seconds', 'speaker_sex')\n",
" \n",
" # distribution of audio duration per age\n",
" dataset_statistics[config_name] = distribution_audio_duration(dataset_hf_subset, output_dir_plots_subset, 'audio_duration_seconds', 'speaker_age')\n",
"\n",
" \n",
" # distribution of speaking rate per subset\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/michal/.pyenv/versions/3.10.11/envs/streamlit/lib/python3.10/site-packages/datasets/load.py:1486: FutureWarning: The repository for amu-cai/pl-asr-bigos-v2 contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/amu-cai/pl-asr-bigos-v2\n",
"You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
"Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset({\n",
" features: ['audioname', 'split', 'dataset', 'speaker_id', 'ref_orig', 'audio', 'audio_duration_samples', 'audio_duration_seconds', 'samplingrate_orig', 'sampling_rate', 'audiopath_bigos', 'audiopath_local', 'speaker_age', 'speaker_sex'],\n",
" num_rows: 44\n",
"})\n"
]
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: './reports/pelcra/pl-asr-pelcra-for-bigos/dataset_contents.json'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[2], line 22\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(json_stats_secret, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m file:\n\u001b[1;32m 20\u001b[0m stats_dict_secret \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mload(file)\n\u001b[0;32m---> 22\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mjson_contents_public\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m file:\n\u001b[1;32m 23\u001b[0m contents_dict_public \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mload(file)\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(json_stats_public, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m file:\n",
"File \u001b[0;32m~/.pyenv/versions/3.10.11/envs/streamlit/lib/python3.10/site-packages/IPython/core/interactiveshell.py:324\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 319\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 320\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 321\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 322\u001b[0m )\n\u001b[0;32m--> 324\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './reports/pelcra/pl-asr-pelcra-for-bigos/dataset_contents.json'"
]
}
],
"source": [
"import json\n",
"import pandas as pd\n",
"\n",
"#dataset_public = \"amu-cai/pl-asr-bigos-v2\"\n",
"#dataset_secret = \"amu-cai/pl-asr-bigos-v2-secret\"\n",
"\n",
"dataset_public = \"pelcra/pl-asr-pelcra-for-bigos\"\n",
"dataset_secret = \"pelcra/pl-asr-pelcra-for-bigos-secret\"\n",
"\n",
"json_contents_public = \"./reports/{}/dataset_contents.json\".format(dataset_public)\n",
"json_stats_public = \"reports/{}/dataset_statistics.json\".format(dataset_public)\n",
"\n",
"json_contents_secret = \"./reports/{}/dataset_contents.json\".format(dataset_secret)\n",
"json_stats_secret = \"reports/{}/dataset_statistics.json\".format(dataset_secret)\n",
"\n",
"with open(json_contents_secret, 'r') as file:\n",
" contents_dict_secret = json.load(file)\n",
"\n",
"with open(json_stats_secret, 'r') as file:\n",
" stats_dict_secret = json.load(file)\n",
"\n",
"with open(json_contents_public, 'r') as file:\n",
" contents_dict_public = json.load(file)\n",
"\n",
"with open(json_stats_public, 'r') as file:\n",
" stats_dict_public = json.load(file)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ul-diabiz_poleval-22\n",
"ul-spokes_mix_emo-18\n",
"ul-spokes_mix_luz-18\n",
"ul-spokes_mix_parl-18\n",
"ul-spokes_biz_bio-23\n",
"ul-spokes_biz_int-23\n",
"ul-spokes_biz_luz-23\n",
"ul-spokes_biz_pod-23\n",
"ul-spokes_biz_pres-23\n",
"ul-spokes_biz_vc-23\n",
"ul-spokes_biz_vc2-23\n",
"ul-spokes_biz_wyw-23\n",
"all\n"
]
}
],
"source": [
"# merge contents if dictionaries for fields utts, words, words_unique, chars, chars_unique and speech_rate\n",
"for dataset in stats_dict_public.keys():\n",
" print(dataset)\n",
" for metric in stats_dict_secret[dataset].keys():\n",
" for split in stats_dict_secret[dataset][metric].keys():\n",
" if split == \"test\":\n",
" stats_dict_public[dataset][metric][split] = stats_dict_secret[dataset][metric][split]\n",
" \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" value\n",
"metric split \n",
"samples test 947\n",
" train 7719\n",
" validation 284\n",
"utts_unique test 944\n",
" train 7556\n",
" validation 280\n",
"words test 12051\n",
" train 89255\n",
" validation 3900\n",
"words_unique test 2772\n",
" train 12341\n",
" validation 1209\n",
"chars test 66433\n",
" train 495454\n",
" validation 23594\n",
"audio[h] test 2.05\n",
" train 16.59\n",
" validation 1.04\n",
"speakers test 24\n",
" train 132\n",
" validation 14\n",
"speech_rate test 1.63\n",
" train 1.49\n",
" validation 1.04\n",
"meta_cov_sex test N/A\n",
" train N/A\n",
" validation N/A\n",
"meta_cov_age test N/A\n",
" train N/A\n",
" validation N/A\n",
"meta_dist_sex test N/A\n",
" train N/A\n",
" validation N/A\n",
"meta_dist_age test N/A\n",
" train N/A\n",
" validation N/A\n",
"samples_per_spk test {'average': 39.46, 'std': 52.52, 'median': 22....\n",
" train {'average': 58.48, 'std': 99.27, 'median': 24....\n",
" validation {'average': 20.29, 'std': 7.64, 'median': 19.0...\n"
]
}
],
"source": [
"# Creating a MultiIndex DataFrame\n",
"rows = []\n",
"for dataset, metrics in stats_dict_public.items():\n",
" if (dataset == \"all\"):\n",
" continue\n",
" for metric, splits in metrics.items():\n",
" for split, value in splits.items():\n",
" if (split == \"all_splits\"):\n",
" continue\n",
" rows.append((dataset, metric, split, value))\n",
"\n",
"# Convert to DataFrame\n",
"df = pd.DataFrame(rows, columns=['dataset', 'metric', 'split', 'value'])\n",
"df.set_index(['dataset', 'metric', 'split'], inplace=True)\n",
"\n",
"print(df.loc['ul-diabiz_poleval-22'])"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>metric</th>\n",
" <th>samples</th>\n",
" <th>utts_unique</th>\n",
" <th>words</th>\n",
" <th>words_unique</th>\n",
" <th>chars</th>\n",
" <th>audio[h]</th>\n",
" <th>speakers</th>\n",
" </tr>\n",
" <tr>\n",
" <th>dataset</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ul-diabiz_poleval-22</th>\n",
" <td>8950</td>\n",
" <td>8780</td>\n",
" <td>105206</td>\n",
" <td>16322</td>\n",
" <td>585481</td>\n",
" <td>19.68</td>\n",
" <td>170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ul-spokes_biz_bio-23</th>\n",
" <td>54917</td>\n",
" <td>54136</td>\n",
" <td>1278269</td>\n",
" <td>137520</td>\n",
" <td>7694395</td>\n",
" <td>275.96</td>\n",
" <td>158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ul-spokes_biz_int-23</th>\n",
" <td>1109</td>\n",
" <td>1101</td>\n",
" <td>23123</td>\n",
" <td>6665</td>\n",
" <td>141643</td>\n",
" <td>4.51</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ul-spokes_biz_luz-23</th>\n",
" <td>41966</td>\n",
" <td>41641</td>\n",
" <td>786593</td>\n",
" <td>108535</td>\n",
" <td>4490695</td>\n",
" <td>148.55</td>\n",
" <td>158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ul-spokes_biz_pod-23</th>\n",
" <td>22807</td>\n",
" <td>22762</td>\n",
" <td>605852</td>\n",
" <td>83807</td>\n",
" <td>3650700</td>\n",
" <td>110.0</td>\n",
" <td>113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ul-spokes_biz_pres-23</th>\n",
" <td>17174</td>\n",
" <td>17158</td>\n",
" <td>251841</td>\n",
" <td>54253</td>\n",
" <td>1642817</td>\n",
" <td>64.49</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ul-spokes_biz_vc-23</th>\n",
" <td>45272</td>\n",
" <td>44710</td>\n",
" <td>568780</td>\n",
" <td>77754</td>\n",
" <td>3348648</td>\n",
" <td>104.13</td>\n",
" <td>78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ul-spokes_biz_vc2-23</th>\n",
" <td>25802</td>\n",
" <td>25596</td>\n",
" <td>755885</td>\n",
" <td>99850</td>\n",
" <td>4526688</td>\n",
" <td>162.08</td>\n",
" <td>84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ul-spokes_biz_wyw-23</th>\n",
" <td>11357</td>\n",
" <td>11204</td>\n",
" <td>259517</td>\n",
" <td>45114</td>\n",
" <td>1552980</td>\n",
" <td>56.41</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ul-spokes_mix_emo-18</th>\n",
" <td>24329</td>\n",
" <td>21063</td>\n",
" <td>252380</td>\n",
" <td>19819</td>\n",
" <td>1379695</td>\n",
" <td>51.23</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ul-spokes_mix_luz-18</th>\n",
" <td>20919</td>\n",
" <td>19668</td>\n",
" <td>204587</td>\n",
" <td>26106</td>\n",
" <td>1132428</td>\n",
" <td>37.48</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ul-spokes_mix_parl-18</th>\n",
" <td>8656</td>\n",
" <td>8521</td>\n",
" <td>100992</td>\n",
" <td>18681</td>\n",
" <td>669210</td>\n",
" <td>24.55</td>\n",
" <td>48</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"metric samples utts_unique words words_unique chars \\\n",
"dataset \n",
"ul-diabiz_poleval-22 8950 8780 105206 16322 585481 \n",
"ul-spokes_biz_bio-23 54917 54136 1278269 137520 7694395 \n",
"ul-spokes_biz_int-23 1109 1101 23123 6665 141643 \n",
"ul-spokes_biz_luz-23 41966 41641 786593 108535 4490695 \n",
"ul-spokes_biz_pod-23 22807 22762 605852 83807 3650700 \n",
"ul-spokes_biz_pres-23 17174 17158 251841 54253 1642817 \n",
"ul-spokes_biz_vc-23 45272 44710 568780 77754 3348648 \n",
"ul-spokes_biz_vc2-23 25802 25596 755885 99850 4526688 \n",
"ul-spokes_biz_wyw-23 11357 11204 259517 45114 1552980 \n",
"ul-spokes_mix_emo-18 24329 21063 252380 19819 1379695 \n",
"ul-spokes_mix_luz-18 20919 19668 204587 26106 1132428 \n",
"ul-spokes_mix_parl-18 8656 8521 100992 18681 669210 \n",
"\n",
"metric audio[h] speakers \n",
"dataset \n",
"ul-diabiz_poleval-22 19.68 170 \n",
"ul-spokes_biz_bio-23 275.96 158 \n",
"ul-spokes_biz_int-23 4.51 9 \n",
"ul-spokes_biz_luz-23 148.55 158 \n",
"ul-spokes_biz_pod-23 110.0 113 \n",
"ul-spokes_biz_pres-23 64.49 55 \n",
"ul-spokes_biz_vc-23 104.13 78 \n",
"ul-spokes_biz_vc2-23 162.08 84 \n",
"ul-spokes_biz_wyw-23 56.41 38 \n",
"ul-spokes_mix_emo-18 51.23 40 \n",
"ul-spokes_mix_luz-18 37.48 21 \n",
"ul-spokes_mix_parl-18 24.55 48 "
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get the total number of speakers, samples, unique utts, words, unique words, chars, unique chars and speech rate\n",
"metrics = [\"samples\", \"utts_unique\", \"words\", \"words_unique\", \"chars\", \"audio[h]\", \"speakers\"]\n",
" # unique utts, words, unique words, chars, unique chars and speech rate\n",
"# filter the multiindex dataframe to leave only specific metrics\n",
"df_total = df.loc[(slice(None), metrics), :]\n",
"df_total = df_total.unstack(level ='split')\n",
"df_total['value', 'total'] = df_total['value'].sum(axis=1)\n",
"df_total.columns = df_total.columns.droplevel(0)\n",
"columns_to_drop = ['test', 'train', 'validation']\n",
"df_total.drop(columns = columns_to_drop, inplace = True)\n",
"df_total = df_total.unstack(level ='metric')\n",
"df_total.columns = df_total.columns.droplevel(0)\n",
"df_total"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"4\" halign=\"left\">value</th>\n",
" </tr>\n",
" <tr>\n",
" <th>split</th>\n",
" <th>test</th>\n",
" <th>train</th>\n",
" <th>validation</th>\n",
" <th>total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>fair-mls-20</th>\n",
" <td>519</td>\n",
" <td>25042</td>\n",
" <td>511</td>\n",
" <td>26072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>google-fleurs-22</th>\n",
" <td>758</td>\n",
" <td>2841</td>\n",
" <td>338</td>\n",
" <td>3937</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mailabs-corpus_librivox-19</th>\n",
" <td>1501</td>\n",
" <td>11834</td>\n",
" <td>1527</td>\n",
" <td>14862</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mozilla-common_voice_15-23</th>\n",
" <td>8896</td>\n",
" <td>19119</td>\n",
" <td>8895</td>\n",
" <td>36910</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pjatk-clarin_mobile-15</th>\n",
" <td>392</td>\n",
" <td>2861</td>\n",
" <td>242</td>\n",
" <td>3495</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pjatk-clarin_studio-15</th>\n",
" <td>1404</td>\n",
" <td>44</td>\n",
" <td>40</td>\n",
" <td>1488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>polyai-minds14-21</th>\n",
" <td>53</td>\n",
" <td>462</td>\n",
" <td>47</td>\n",
" <td>562</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pwr-azon_read-20</th>\n",
" <td>586</td>\n",
" <td>1820</td>\n",
" <td>382</td>\n",
" <td>2788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pwr-azon_spont-20</th>\n",
" <td>48</td>\n",
" <td>357</td>\n",
" <td>51</td>\n",
" <td>456</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pwr-maleset-unk</th>\n",
" <td>477</td>\n",
" <td>3783</td>\n",
" <td>478</td>\n",
" <td>4738</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pwr-shortwords-unk</th>\n",
" <td>92</td>\n",
" <td>761</td>\n",
" <td>86</td>\n",
" <td>939</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pwr-viu-unk</th>\n",
" <td>267</td>\n",
" <td>2146</td>\n",
" <td>290</td>\n",
" <td>2703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Total</th>\n",
" <td>14993</td>\n",
" <td>71070</td>\n",
" <td>12887</td>\n",
" <td>98950</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" value \n",
"split test train validation total\n",
"fair-mls-20 519 25042 511 26072\n",
"google-fleurs-22 758 2841 338 3937\n",
"mailabs-corpus_librivox-19 1501 11834 1527 14862\n",
"mozilla-common_voice_15-23 8896 19119 8895 36910\n",
"pjatk-clarin_mobile-15 392 2861 242 3495\n",
"pjatk-clarin_studio-15 1404 44 40 1488\n",
"polyai-minds14-21 53 462 47 562\n",
"pwr-azon_read-20 586 1820 382 2788\n",
"pwr-azon_spont-20 48 357 51 456\n",
"pwr-maleset-unk 477 3783 478 4738\n",
"pwr-shortwords-unk 92 761 86 939\n",
"pwr-viu-unk 267 2146 290 2703\n",
"Total 14993 71070 12887 98950"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Access all data where Metric is 'utts'\n",
"df_utts = df.xs('samples', level='metric')\n",
"\n",
"# change split to columns\n",
"df_utts = df_utts.unstack(level='split')\n",
"df_utts\n",
"\n",
"# add column with total number of samples\n",
"df_utts['value', 'total'] = df_utts['value'].sum(axis=1)\n",
"df_utts\n",
"\n",
"# create a new row with total number of samples and concatenate it to the DataFrame\n",
"df_total = df_utts.sum()\n",
"df_total.name = ('Total')\n",
"df_utts = pd.concat([df_utts, pd.DataFrame(df_total).T])\n",
"df_utts\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" value\n",
"dataset metric \n",
"pjatk-clarin_mobile-15 samples 2861\n",
" utts 2857\n",
" words 74634\n",
" words_unique 23166\n",
" chars 507238\n",
"... ...\n",
"all meta_cov_sex 0.57\n",
" meta_cov_age 0.24\n",
" meta_dist_sex {'male': 0.64, 'female': 0.36}\n",
" meta_dist_age {'teens': 0.03, 'twenties': 0.43, 'thirties': ...\n",
" samples_per_spk {'average': 194.71, 'std': 689.86, 'median': 4...\n",
"\n",
"[169 rows x 1 columns]\n"
]
}
],
"source": [
"\n",
"# Access all 'train' splits across all metrics\n",
"print(df.xs('train', level='split'))\n",
"\n",
"# xs is the best for single level indexing. It can be chained, but is less effective than loc or boolean masking"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" value\n",
"dataset metric split \n",
"mozilla-common_voice_15-23 samples test 8896\n",
"mailabs-corpus_librivox-19 samples test 1501\n",
"pjatk-clarin_studio-15 samples test 1404\n",
"google-fleurs-22 samples test 758\n",
"pwr-azon_read-20 samples test 586\n",
"fair-mls-20 samples test 519\n",
"pwr-maleset-unk samples test 477\n",
"pjatk-clarin_mobile-15 samples test 392\n",
"pwr-viu-unk samples test 267\n"
]
}
],
"source": [
"# Boolean masking for a more complex condition across levels\n",
"mask_test_set = (df.index.get_level_values('metric') == 'samples') & (df.index.get_level_values('split') == 'test') \n",
"df_test = df.loc[mask_test_set]\n",
"# convert value to numbric\n",
"# sort by value\n",
"df_test = df_test.sort_values(by='value', ascending=False)\n",
"# filter out values smaller than 100\n",
"df_test = df_test[df_test['value'] > 100]\n",
"\n",
"# remove dataset \"all\"\n",
"df_test = df_test.drop('all', level='dataset')\n",
"print(df_test)\n"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">value</th>\n",
" </tr>\n",
" <tr>\n",
" <th>split</th>\n",
" <th>test</th>\n",
" <th>train</th>\n",
" <th>validation</th>\n",
" </tr>\n",
" <tr>\n",
" <th>dataset</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>fair-mls-20</th>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>google-fleurs-22</th>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mailabs-corpus_librivox-19</th>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mozilla-common_voice_15-23</th>\n",
" <td>{'teens': 0.11, 'twenties': 0.38, 'thirties': ...</td>\n",
" <td>{'teens': 0.03, 'twenties': 0.43, 'thirties': ...</td>\n",
" <td>{'teens': 0.15, 'twenties': 0.46, 'thirties': ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pjatk-clarin_mobile-15</th>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pjatk-clarin_studio-15</th>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>polyai-minds14-21</th>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pwr-azon_read-20</th>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pwr-azon_spont-20</th>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pwr-maleset-unk</th>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pwr-shortwords-unk</th>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pwr-viu-unk</th>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" <td>N/A</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" value \\\n",
"split test \n",
"dataset \n",
"fair-mls-20 N/A \n",
"google-fleurs-22 N/A \n",
"mailabs-corpus_librivox-19 N/A \n",
"mozilla-common_voice_15-23 {'teens': 0.11, 'twenties': 0.38, 'thirties': ... \n",
"pjatk-clarin_mobile-15 N/A \n",
"pjatk-clarin_studio-15 N/A \n",
"polyai-minds14-21 N/A \n",
"pwr-azon_read-20 N/A \n",
"pwr-azon_spont-20 N/A \n",
"pwr-maleset-unk N/A \n",
"pwr-shortwords-unk N/A \n",
"pwr-viu-unk N/A \n",
"\n",
" \\\n",
"split train \n",
"dataset \n",
"fair-mls-20 N/A \n",
"google-fleurs-22 N/A \n",
"mailabs-corpus_librivox-19 N/A \n",
"mozilla-common_voice_15-23 {'teens': 0.03, 'twenties': 0.43, 'thirties': ... \n",
"pjatk-clarin_mobile-15 N/A \n",
"pjatk-clarin_studio-15 N/A \n",
"polyai-minds14-21 N/A \n",
"pwr-azon_read-20 N/A \n",
"pwr-azon_spont-20 N/A \n",
"pwr-maleset-unk N/A \n",
"pwr-shortwords-unk N/A \n",
"pwr-viu-unk N/A \n",
"\n",
" \n",
"split validation \n",
"dataset \n",
"fair-mls-20 N/A \n",
"google-fleurs-22 N/A \n",
"mailabs-corpus_librivox-19 N/A \n",
"mozilla-common_voice_15-23 {'teens': 0.15, 'twenties': 0.46, 'thirties': ... \n",
"pjatk-clarin_mobile-15 N/A \n",
"pjatk-clarin_studio-15 N/A \n",
"polyai-minds14-21 N/A \n",
"pwr-azon_read-20 N/A \n",
"pwr-azon_spont-20 N/A \n",
"pwr-maleset-unk N/A \n",
"pwr-shortwords-unk N/A \n",
"pwr-viu-unk N/A "
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# What is the total distribution of age in common voice dataset and overall?\n",
"df_age = df.xs('meta_dist_age', level='metric')\n",
"df_age = df_age.unstack(level='split')\n",
"#df_age['value', 'total'] = df_age['value'].sum(axis=1)\n",
"df_age"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"from datasets import load_dataset \n",
"pelcra = load_dataset(\"pelcra/pl-asr-pelcra-for-bigos\", \"all\", split=\"test\")\n",
"df_test = pelcra.to_pandas()\n",
"df_test = df_test.drop(columns=[\"audio\"])\n",
"df_test.to_csv(\"test.tsv\", sep=\"\\t\",index=False)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1400x800 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"\n",
"# Load the data from the TSV file into a DataFrame\n",
"#file_path = '/mnt/data/test.tsv'\n",
"#data = pd.read_csv(file_path, sep='\\t')\n",
"data = df_test\n",
"# Group the data by audio duration and calculate the count and total duration for each group\n",
"duration_group = data.groupby('audio_duration_seconds').agg(\n",
" sample_count=('audio_duration_seconds', 'size'),\n",
" total_duration=('audio_duration_seconds', 'sum')\n",
").reset_index()\n",
"\n",
"# eliminate outliers - samples with duration longer than 150 seconds\n",
"duration_group = duration_group[duration_group['audio_duration_seconds'] < 120]\n",
"\n",
"# Calculate the cumulative percentage of the total duration\n",
"duration_group['cumulative_duration'] = (duration_group['total_duration'].cumsum() / \n",
" duration_group['total_duration'].sum()) * 100\n",
"\n",
"# Plotting the data\n",
"fig, ax1 = plt.subplots(figsize=(14, 8))\n",
"\n",
"# Left axis - Count of samples (blue line)\n",
"ax1.set_xlabel('Sample Duration (seconds)')\n",
"ax1.set_ylabel('Sample Count', color='blue')\n",
"ax1.plot(duration_group['audio_duration_seconds'], duration_group['sample_count'], color='blue')\n",
"ax1.tick_params(axis='y', labelcolor='blue')\n",
"\n",
"# Right axis - Total duration (orange line)\n",
"ax2 = ax1.twinx()\n",
"ax2.set_ylabel('Total Duration (hours)', color='orange')\n",
"ax2.plot(duration_group['audio_duration_seconds'], duration_group['total_duration'] / 3600, color='orange')\n",
"ax2.tick_params(axis='y', labelcolor='orange')\n",
"\n",
"# Adding Cumulative % (green dashed line)\n",
"ax3 = ax1.twinx()\n",
"ax3.spines[\"right\"].set_position((\"axes\", 1.15))\n",
"ax3.set_ylabel('Cumulative % of Corpus Total', color='green')\n",
"ax3.plot(duration_group['audio_duration_seconds'], duration_group['cumulative_duration'], color='green', linestyle='--')\n",
"ax3.tick_params(axis='y', labelcolor='green')\n",
"\n",
"# Title and legend\n",
"plt.title('Sample Duration Distributions')\n",
"fig.tight_layout() # Adjust the layout to make room for the third y-axis\n",
"\n",
"# Show plot\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/michal/.pyenv/versions/3.10.11/envs/streamlit/lib/python3.10/site-packages/datasets/load.py:1486: FutureWarning: The repository for amu-cai/pl-asr-bigos-v2 contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/amu-cai/pl-asr-bigos-v2\n",
"You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
"Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n",
" warnings.warn(\n",
"Downloading data: 100%|██████████| 976M/976M [01:07<00:00, 14.5MB/s] \n",
"Downloading data: 100%|██████████| 78.8M/78.8M [00:05<00:00, 14.6MB/s]\n",
"Downloading data: 100%|██████████| 129M/129M [00:08<00:00, 16.1MB/s] \n",
"Downloading data: 100%|██████████| 934k/934k [00:00<00:00, 11.4MB/s]\n",
"Downloading data: 100%|██████████| 77.5k/77.5k [00:00<00:00, 7.19MB/s]\n",
"Downloading data: 100%|██████████| 52.6k/52.6k [00:00<00:00, 3.63MB/s]\n",
"Generating test split: 22 examples [00:00, 206.09 examples/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generating examples\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Generating test split: 392 examples [00:01, 310.58 examples/s]\n",
"Generating train split: 36 examples [00:00, 335.23 examples/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generating examples\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Generating train split: 2861 examples [00:08, 321.16 examples/s]\n",
"Generating validation split: 34 examples [00:00, 330.58 examples/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generating examples\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Generating validation split: 242 examples [00:00, 317.93 examples/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"DatasetDict({\n",
" test: Dataset({\n",
" features: ['audioname', 'split', 'dataset', 'speaker_id', 'ref_orig', 'audio', 'audio_duration_samples', 'audio_duration_seconds', 'samplingrate_orig', 'sampling_rate', 'audiopath_bigos', 'audiopath_local', 'speaker_age', 'speaker_sex'],\n",
" num_rows: 392\n",
" })\n",
" train: Dataset({\n",
" features: ['audioname', 'split', 'dataset', 'speaker_id', 'ref_orig', 'audio', 'audio_duration_samples', 'audio_duration_seconds', 'samplingrate_orig', 'sampling_rate', 'audiopath_bigos', 'audiopath_local', 'speaker_age', 'speaker_sex'],\n",
" num_rows: 2861\n",
" })\n",
" validation: Dataset({\n",
" features: ['audioname', 'split', 'dataset', 'speaker_id', 'ref_orig', 'audio', 'audio_duration_samples', 'audio_duration_seconds', 'samplingrate_orig', 'sampling_rate', 'audiopath_bigos', 'audiopath_local', 'speaker_age', 'speaker_sex'],\n",
" num_rows: 242\n",
" })\n",
"})\n"
]
}
],
"source": [
"import os\n",
"from datasets import load_dataset\n",
"from datasets import get_dataset_config_names\n",
"dataset_name = \"amu-cai/pl-asr-bigos-v2\"\n",
"# get dataset config names\n",
"dataset_config_names = get_dataset_config_names(dataset_name)\n",
"# load dataset\n",
"dataset_hf = load_dataset(dataset_name, \"pjatk-clarin_mobile-15\")\n",
"\n",
"print(dataset_hf)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "bigos-hf",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|