Spaces:
Runtime error
Runtime error
File size: 31,227 Bytes
177499f |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading and preparing dataset common_voice_11_0/nl to /Users/hannatoenbreker/.cache/huggingface/datasets/mozilla-foundation___common_voice_11_0/nl/11.0.0/2c65b95d99ca879b1b1074ea197b65e0497848fd697fdb0582e0f6b75b6f4da0...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9dfef4ac94534c36ad01f3ba3ac8f869",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading data files: 0%| | 0/5 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b5b797b7d8a34411b7a38bdc35a1a4ad",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Extracting data files: 0%| | 0/5 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6c07d35f372a49ed84c090168e4092b1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading data files: 0%| | 0/5 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "949b41580ea24e689fb90f15c4d0f98f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Extracting data files: 0%| | 0/5 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fe273167dd27440e976da47f2e4bea8e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating train split: 0 examples [00:00, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Reading metadata...: 30318it [00:00, 49815.73it/s]\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[2], line 6\u001b[0m\n\u001b[1;32m 2\u001b[0m token \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mhf_tlYASKaBhUkcufofQwneYwTzcyjYLkAkuN\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m 4\u001b[0m common_voice \u001b[39m=\u001b[39m DatasetDict()\n\u001b[0;32m----> 6\u001b[0m common_voice[\u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m load_dataset(\u001b[39m\"\u001b[39;49m\u001b[39mmozilla-foundation/common_voice_11_0\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mnl\u001b[39;49m\u001b[39m\"\u001b[39;49m, split\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mtrain+validation\u001b[39;49m\u001b[39m\"\u001b[39;49m, use_auth_token\u001b[39m=\u001b[39;49mtoken)\n\u001b[1;32m 7\u001b[0m common_voice[\u001b[39m\"\u001b[39m\u001b[39mtest\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m load_dataset(\u001b[39m\"\u001b[39m\u001b[39mmozilla-foundation/common_voice_11_0\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mnl\u001b[39m\u001b[39m\"\u001b[39m, split\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mtest\u001b[39m\u001b[39m\"\u001b[39m, use_auth_token\u001b[39m=\u001b[39mtoken)\n\u001b[1;32m 9\u001b[0m \u001b[39mprint\u001b[39m(common_voice)\n",
"File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/datasets/load.py:1782\u001b[0m, in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)\u001b[0m\n\u001b[1;32m 1779\u001b[0m try_from_hf_gcs \u001b[39m=\u001b[39m path \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m _PACKAGED_DATASETS_MODULES\n\u001b[1;32m 1781\u001b[0m \u001b[39m# Download and prepare data\u001b[39;00m\n\u001b[0;32m-> 1782\u001b[0m builder_instance\u001b[39m.\u001b[39;49mdownload_and_prepare(\n\u001b[1;32m 1783\u001b[0m download_config\u001b[39m=\u001b[39;49mdownload_config,\n\u001b[1;32m 1784\u001b[0m download_mode\u001b[39m=\u001b[39;49mdownload_mode,\n\u001b[1;32m 1785\u001b[0m verification_mode\u001b[39m=\u001b[39;49mverification_mode,\n\u001b[1;32m 1786\u001b[0m try_from_hf_gcs\u001b[39m=\u001b[39;49mtry_from_hf_gcs,\n\u001b[1;32m 1787\u001b[0m num_proc\u001b[39m=\u001b[39;49mnum_proc,\n\u001b[1;32m 1788\u001b[0m )\n\u001b[1;32m 1790\u001b[0m \u001b[39m# Build dataset for splits\u001b[39;00m\n\u001b[1;32m 1791\u001b[0m keep_in_memory \u001b[39m=\u001b[39m (\n\u001b[1;32m 1792\u001b[0m keep_in_memory \u001b[39mif\u001b[39;00m keep_in_memory \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m is_small_dataset(builder_instance\u001b[39m.\u001b[39minfo\u001b[39m.\u001b[39mdataset_size)\n\u001b[1;32m 1793\u001b[0m )\n",
"File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/datasets/builder.py:872\u001b[0m, in \u001b[0;36mDatasetBuilder.download_and_prepare\u001b[0;34m(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\u001b[0m\n\u001b[1;32m 870\u001b[0m \u001b[39mif\u001b[39;00m num_proc \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 871\u001b[0m prepare_split_kwargs[\u001b[39m\"\u001b[39m\u001b[39mnum_proc\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m num_proc\n\u001b[0;32m--> 872\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_download_and_prepare(\n\u001b[1;32m 873\u001b[0m dl_manager\u001b[39m=\u001b[39;49mdl_manager,\n\u001b[1;32m 874\u001b[0m verification_mode\u001b[39m=\u001b[39;49mverification_mode,\n\u001b[1;32m 875\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mprepare_split_kwargs,\n\u001b[1;32m 876\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mdownload_and_prepare_kwargs,\n\u001b[1;32m 877\u001b[0m )\n\u001b[1;32m 878\u001b[0m \u001b[39m# Sync info\u001b[39;00m\n\u001b[1;32m 879\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minfo\u001b[39m.\u001b[39mdataset_size \u001b[39m=\u001b[39m \u001b[39msum\u001b[39m(split\u001b[39m.\u001b[39mnum_bytes \u001b[39mfor\u001b[39;00m split \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minfo\u001b[39m.\u001b[39msplits\u001b[39m.\u001b[39mvalues())\n",
"File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/datasets/builder.py:1649\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verification_mode, **prepare_splits_kwargs)\u001b[0m\n\u001b[1;32m 1648\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_download_and_prepare\u001b[39m(\u001b[39mself\u001b[39m, dl_manager, verification_mode, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mprepare_splits_kwargs):\n\u001b[0;32m-> 1649\u001b[0m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m_download_and_prepare(\n\u001b[1;32m 1650\u001b[0m dl_manager,\n\u001b[1;32m 1651\u001b[0m verification_mode,\n\u001b[1;32m 1652\u001b[0m check_duplicate_keys\u001b[39m=\u001b[39;49mverification_mode \u001b[39m==\u001b[39;49m VerificationMode\u001b[39m.\u001b[39;49mBASIC_CHECKS\n\u001b[1;32m 1653\u001b[0m \u001b[39mor\u001b[39;49;00m verification_mode \u001b[39m==\u001b[39;49m VerificationMode\u001b[39m.\u001b[39;49mALL_CHECKS,\n\u001b[1;32m 1654\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mprepare_splits_kwargs,\n\u001b[1;32m 1655\u001b[0m )\n",
"File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/datasets/builder.py:967\u001b[0m, in \u001b[0;36mDatasetBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verification_mode, **prepare_split_kwargs)\u001b[0m\n\u001b[1;32m 963\u001b[0m split_dict\u001b[39m.\u001b[39madd(split_generator\u001b[39m.\u001b[39msplit_info)\n\u001b[1;32m 965\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 966\u001b[0m \u001b[39m# Prepare split will record examples associated to the split\u001b[39;00m\n\u001b[0;32m--> 967\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_prepare_split(split_generator, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mprepare_split_kwargs)\n\u001b[1;32m 968\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mOSError\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 969\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mOSError\u001b[39;00m(\n\u001b[1;32m 970\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mCannot find data file. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 971\u001b[0m \u001b[39m+\u001b[39m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmanual_download_instructions \u001b[39mor\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 972\u001b[0m \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39mOriginal error:\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m 973\u001b[0m \u001b[39m+\u001b[39m \u001b[39mstr\u001b[39m(e)\n\u001b[1;32m 974\u001b[0m ) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39m\n",
"File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/datasets/builder.py:1488\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._prepare_split\u001b[0;34m(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)\u001b[0m\n\u001b[1;32m 1486\u001b[0m gen_kwargs \u001b[39m=\u001b[39m split_generator\u001b[39m.\u001b[39mgen_kwargs\n\u001b[1;32m 1487\u001b[0m job_id \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[0;32m-> 1488\u001b[0m \u001b[39mfor\u001b[39;00m job_id, done, content \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_prepare_split_single(\n\u001b[1;32m 1489\u001b[0m gen_kwargs\u001b[39m=\u001b[39mgen_kwargs, job_id\u001b[39m=\u001b[39mjob_id, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39m_prepare_split_args\n\u001b[1;32m 1490\u001b[0m ):\n\u001b[1;32m 1491\u001b[0m \u001b[39mif\u001b[39;00m done:\n\u001b[1;32m 1492\u001b[0m result \u001b[39m=\u001b[39m content\n",
"File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/datasets/builder.py:1625\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._prepare_split_single\u001b[0;34m(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\u001b[0m\n\u001b[1;32m 1615\u001b[0m shard_id \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m 1616\u001b[0m writer \u001b[39m=\u001b[39m writer_class(\n\u001b[1;32m 1617\u001b[0m features\u001b[39m=\u001b[39mwriter\u001b[39m.\u001b[39m_features,\n\u001b[1;32m 1618\u001b[0m path\u001b[39m=\u001b[39mfpath\u001b[39m.\u001b[39mreplace(\u001b[39m\"\u001b[39m\u001b[39mSSSSS\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mshard_id\u001b[39m:\u001b[39;00m\u001b[39m05d\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\u001b[39m.\u001b[39mreplace(\u001b[39m\"\u001b[39m\u001b[39mJJJJJ\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mjob_id\u001b[39m:\u001b[39;00m\u001b[39m05d\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1623\u001b[0m embed_local_files\u001b[39m=\u001b[39membed_local_files,\n\u001b[1;32m 1624\u001b[0m )\n\u001b[0;32m-> 1625\u001b[0m example \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49minfo\u001b[39m.\u001b[39;49mfeatures\u001b[39m.\u001b[39;49mencode_example(record) \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minfo\u001b[39m.\u001b[39mfeatures \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m record\n\u001b[1;32m 1626\u001b[0m writer\u001b[39m.\u001b[39mwrite(example, key)\n\u001b[1;32m 1627\u001b[0m num_examples_progress_update \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n",
"File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/datasets/features/features.py:1808\u001b[0m, in \u001b[0;36mFeatures.encode_example\u001b[0;34m(self, example)\u001b[0m\n\u001b[1;32m 1797\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mencode_example\u001b[39m(\u001b[39mself\u001b[39m, example):\n\u001b[1;32m 1798\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 1799\u001b[0m \u001b[39m Encode example into a format for Arrow.\u001b[39;00m\n\u001b[1;32m 1800\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1806\u001b[0m \u001b[39m `dict[str, Any]`\u001b[39;00m\n\u001b[1;32m 1807\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1808\u001b[0m example \u001b[39m=\u001b[39m cast_to_python_objects(example)\n\u001b[1;32m 1809\u001b[0m \u001b[39mreturn\u001b[39;00m encode_nested_example(\u001b[39mself\u001b[39m, example)\n",
"File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/datasets/features/features.py:439\u001b[0m, in \u001b[0;36mcast_to_python_objects\u001b[0;34m(obj, only_1d_for_numpy, optimize_list_casting)\u001b[0m\n\u001b[1;32m 419\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcast_to_python_objects\u001b[39m(obj: Any, only_1d_for_numpy\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, optimize_list_casting\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Any:\n\u001b[1;32m 420\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 421\u001b[0m \u001b[39m Cast numpy/pytorch/tensorflow/pandas objects to python lists.\u001b[39;00m\n\u001b[1;32m 422\u001b[0m \u001b[39m It works recursively.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 437\u001b[0m \u001b[39m casted_obj: the casted object\u001b[39;00m\n\u001b[1;32m 438\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 439\u001b[0m \u001b[39mreturn\u001b[39;00m _cast_to_python_objects(\n\u001b[1;32m 440\u001b[0m obj, only_1d_for_numpy\u001b[39m=\u001b[39;49monly_1d_for_numpy, optimize_list_casting\u001b[39m=\u001b[39;49moptimize_list_casting\n\u001b[1;32m 441\u001b[0m )[\u001b[39m0\u001b[39m]\n",
"File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/datasets/features/features.py:384\u001b[0m, in \u001b[0;36m_cast_to_python_objects\u001b[0;34m(obj, only_1d_for_numpy, optimize_list_casting)\u001b[0m\n\u001b[1;32m 382\u001b[0m output \u001b[39m=\u001b[39m {}\n\u001b[1;32m 383\u001b[0m \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m obj\u001b[39m.\u001b[39mitems():\n\u001b[0;32m--> 384\u001b[0m casted_v, has_changed_v \u001b[39m=\u001b[39m _cast_to_python_objects(\n\u001b[1;32m 385\u001b[0m v, only_1d_for_numpy\u001b[39m=\u001b[39;49monly_1d_for_numpy, optimize_list_casting\u001b[39m=\u001b[39;49moptimize_list_casting\n\u001b[1;32m 386\u001b[0m )\n\u001b[1;32m 387\u001b[0m has_changed \u001b[39m|\u001b[39m\u001b[39m=\u001b[39m has_changed_v\n\u001b[1;32m 388\u001b[0m output[k] \u001b[39m=\u001b[39m casted_v\n",
"File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/datasets/features/features.py:327\u001b[0m, in \u001b[0;36m_cast_to_python_objects\u001b[0;34m(obj, only_1d_for_numpy, optimize_list_casting)\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 318\u001b[0m \u001b[39mreturn\u001b[39;00m (\n\u001b[1;32m 319\u001b[0m [\n\u001b[1;32m 320\u001b[0m _cast_to_python_objects(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[39mTrue\u001b[39;00m,\n\u001b[1;32m 326\u001b[0m )\n\u001b[0;32m--> 327\u001b[0m \u001b[39melif\u001b[39;00m config\u001b[39m.\u001b[39mTF_AVAILABLE \u001b[39mand\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mtensorflow\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m sys\u001b[39m.\u001b[39mmodules \u001b[39mand\u001b[39;00m \u001b[39misinstance\u001b[39m(obj, tf\u001b[39m.\u001b[39mTensor):\n\u001b[1;32m 328\u001b[0m \u001b[39mif\u001b[39;00m obj\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m 329\u001b[0m \u001b[39mreturn\u001b[39;00m obj\u001b[39m.\u001b[39mnumpy()[()], \u001b[39mTrue\u001b[39;00m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"from datasets import load_dataset, DatasetDict\n",
"\n",
"common_voice = DatasetDict()\n",
"\n",
"common_voice[\"train\"] = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"nl\", split=\"train+validation\", use_auth_token=True)\n",
"common_voice[\"test\"] = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"nl\", split=\"test\", use_auth_token=True)\n",
"\n",
"print(common_voice)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Token is valid.\n",
"Your token has been saved in your configured git credential helpers (osxkeychain).\n",
"Your token has been saved to /Users/hannatoenbreker/.cache/huggingface/token\n",
"Login successful\n"
]
}
],
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"common_voice = common_voice.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"path\", \"segment\", \"up_votes\"])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepare feature extractor and tokenizer \n",
"### The ASR pipeline can be de-composed into three components:\n",
"\n",
"- A feature extractor which pre-processes the raw audio-inputs\n",
"- The model which performs the sequence-to-sequence mapping\n",
"- A tokenizer which post-processes the model outputs to text format\n",
"\n",
"In 🤗 Transformers, the Whisper model has an associated feature extractor and tokenizer, called WhisperFeatureExtractor and WhisperTokenizer respectively."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from transformers import WhisperFeatureExtractor\n",
"\n",
"feature_extractor = WhisperFeatureExtractor.from_pretrained(\"openai/whisper-small\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from transformers import WhisperTokenizer\n",
"\n",
"tokenizer = WhisperTokenizer.from_pretrained(\"openai/whisper-small\", language=\"Dutch\", task=\"transcribe\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"input_str = common_voice[\"train\"][0][\"sentence\"]\n",
"labels = tokenizer(input_str).input_ids\n",
"decoded_with_special = tokenizer.decode(labels, skip_special_tokens=False)\n",
"decoded_str = tokenizer.decode(labels, skip_special_tokens=True)\n",
"\n",
"print(f\"Input: {input_str}\")\n",
"print(f\"Decoded w/ special: {decoded_with_special}\")\n",
"print(f\"Decoded w/out special: {decoded_str}\")\n",
"print(f\"Are equal: {input_str == decoded_str}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from transformers import WhisperProcessor\n",
"\n",
"processor = WhisperProcessor.from_pretrained(\"openai/whisper-small\", language=\"Dutch\", task=\"transcribe\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepare our data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(common_voice[\"train\"][0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from datasets import Audio\n",
"\n",
"common_voice = common_voice.cast_column(\"audio\", Audio(sampling_rate=16000))\n",
"print(common_voice[\"train\"][0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def prepare_dataset(batch):\n",
" # load and resample audio data from 48 to 16kHz\n",
" audio = batch[\"audio\"]\n",
"\n",
" # compute log-Mel input features from input audio array \n",
" batch[\"input_features\"] = feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\n",
"\n",
" # encode target text to label ids \n",
" batch[\"labels\"] = tokenizer(batch[\"sentence\"]).input_ids\n",
" return batch\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names[\"train\"], num_proc=4)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define a Data Collator\n",
"### We can leverage the WhisperProcessor we defined earlier to perform both the feature extractor and the tokenizer operations"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"\n",
"from dataclasses import dataclass\n",
"from typing import Any, Dict, List, Union\n",
"\n",
"@dataclass\n",
"class DataCollatorSpeechSeq2SeqWithPadding:\n",
" processor: Any\n",
"\n",
" def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
" # split inputs and labels since they have to be of different lengths and need different padding methods\n",
" # first treat the audio inputs by simply returning torch tensors\n",
" input_features = [{\"input_features\": feature[\"input_features\"]} for feature in features]\n",
" batch = self.processor.feature_extractor.pad(input_features, return_tensors=\"pt\")\n",
"\n",
" # get the tokenized label sequences\n",
" label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
" # pad the labels to max length\n",
" labels_batch = self.processor.tokenizer.pad(label_features, return_tensors=\"pt\")\n",
"\n",
" # replace padding with -100 to ignore loss correctly\n",
" labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n",
"\n",
" # if bos token is appended in previous tokenization step,\n",
" # cut bos token here as it's append later anyways\n",
" if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():\n",
" labels = labels[:, 1:]\n",
"\n",
" batch[\"labels\"] = labels\n",
"\n",
" return batch"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluate our results with WER"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import evaluate\n",
"\n",
"metric = evaluate.load(\"wer\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def compute_metrics(pred):\n",
" pred_ids = pred.predictions\n",
" label_ids = pred.label_ids\n",
"\n",
" # replace -100 with the pad_token_id\n",
" label_ids[label_ids == -100] = tokenizer.pad_token_id\n",
"\n",
" # we do not want to group tokens when computing the metrics\n",
" pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n",
" label_str = tokenizer.batch_decode(label_ids, skip_special_tokens=True)\n",
"\n",
" wer = 100 * metric.compute(predictions=pred_str, references=label_str)\n",
"\n",
" return {\"wer\": wer}"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initializing our model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from transformers import WhisperForConditionalGeneration\n",
"\n",
"model = WhisperForConditionalGeneration.from_pretrained(\"openai/whisper-small\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.config.forced_decoder_ids = None\n",
"model.config.suppress_tokens = []"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training our model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from transformers import Seq2SeqTrainingArguments\n",
"\n",
"training_args = Seq2SeqTrainingArguments(\n",
" output_dir=\"./whisper-dutch\", # change to a repo name of your choice\n",
" per_device_train_batch_size=8,\n",
" gradient_accumulation_steps=2, # increase by 2x for every 2x decrease in batch size\n",
" learning_rate=1e-5,\n",
" warmup_steps=500,\n",
" max_steps=4000,\n",
" gradient_checkpointing=True,\n",
" fp16=False,\n",
" evaluation_strategy=\"steps\",\n",
" per_device_eval_batch_size=8,\n",
" predict_with_generate=True,\n",
" generation_max_length=225,\n",
" save_steps=1000,\n",
" eval_steps=1000,\n",
" logging_steps=25,\n",
" report_to=[\"tensorboard\"],\n",
" load_best_model_at_end=True,\n",
" metric_for_best_model=\"wer\",\n",
" greater_is_better=False,\n",
" push_to_hub=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from transformers import Seq2SeqTrainer\n",
"\n",
"trainer = Seq2SeqTrainer(\n",
" args=training_args,\n",
" model=model,\n",
" train_dataset=common_voice[\"train\"],\n",
" eval_dataset=common_voice[\"test\"],\n",
" data_collator=data_collator,\n",
" compute_metrics=compute_metrics,\n",
" tokenizer=processor.feature_extractor,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"trainer.train()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Share our results\n",
"### If we would like to share our training results on the hub\n",
"#### Keep in mind that we have to change the argument \"push to hub\" two blocks above this code to true"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"kwargs = {\n",
" \"dataset_tags\": \"mozilla-foundation/common_voice_11_0\",\n",
" \"dataset\": \"Common Voice 11.0\", # a 'pretty' name for the training dataset\n",
" \"dataset_args\": \"config: nl, split: test\",\n",
" \"language\": \"nl\",\n",
" \"model_name\": \"Whisper Dutch - RTL\", # a 'pretty' name for your model\n",
" \"finetuned_from\": \"openai/whisper-small\",\n",
" \"tasks\": \"automatic-speech-recognition\",\n",
" \"tags\": \"hf-asr-leaderboard\",\n",
"}\n",
"\n",
"trainer.push_to_hub(**kwargs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from transformers import WhisperForConditionalGeneration, WhisperProcessor\n",
"\n",
"model = WhisperForConditionalGeneration.from_pretrained(\"hannatoenbreker/whisper-dutch\")\n",
"processor = WhisperProcessor.from_pretrained(\"hannatoenbreker/whisper-dutch\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from transformers import pipeline\n",
"import gradio as gr\n",
"\n",
"pipe = pipeline(model=\"hanna/whisper-dutch\") # change to \"your-username/the-name-you-picked\"\n",
"\n",
"def transcribe(audio):\n",
" text = pipe(audio)[\"text\"]\n",
" return text\n",
"\n",
"iface = gr.Interface(\n",
" fn=transcribe, \n",
" inputs=gr.Audio(source=\"microphone\", type=\"filepath\"), \n",
" outputs=\"text\",\n",
" title=\"Whisper Small Dutch\",\n",
" description=\"Realtime demo for Dutch speech recognition using a fine-tuned Whisper small model.\",\n",
")\n",
"\n",
"iface.launch()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.10.0"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
|