File size: 145,265 Bytes
287c28c |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"import librosa\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"from scipy.stats import norm\n",
"from tqdm import tqdm_notebook as tqdm\n",
"from multiprocessing import Pool\n",
"from matplotlib import pylab as plt\n",
"from collections import Counter\n",
"from TTS.config.shared_configs import BaseDatasetConfig\n",
"from TTS.tts.datasets import load_tts_samples\n",
"from TTS.tts.datasets.formatters import *\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"NUM_PROC = 8\n",
"DATASET_CONFIG = BaseDatasetConfig(\n",
" name=\"ai4b\", meta_file_train=\"metadata.csv\", path=\"/home/gokulkarthikk/datasets/indictts/ta\",\n",
" language='ta'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def formatter_ai4b(root_path, meta_file, **kwargs): # pylint: disable=unused-argument\n",
" txt_file = os.path.join(root_path, meta_file)\n",
" items = []\n",
" with open(txt_file, \"r\", encoding=\"utf-8\") as ttf:\n",
" for line in ttf:\n",
" cols = line.split(\"|\")\n",
" wav_file = os.path.join(root_path, \"wavs\", cols[0] + \".wav\")\n",
" text = cols[1].strip()\n",
" speaker_name = cols[2].strip()\n",
" items.append({\"text\": text, \"audio_file\": wav_file, \"speaker_name\": speaker_name})\n",
" return items"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" | > Found 6960 files in /home/gokulkarthikk/datasets/indictts/ta\n",
" > Number of audio files: 6960\n",
"{'text': 'ஊர்வலத்தின் முன்னணியில் ஒரு யானை தன் தும்பிக்கையில் மாலை ஏந்தி நடந்து வந்து கொண்டிருந்தது.', 'audio_file': '/home/gokulkarthikk/datasets/indictts/ta/wavs/train_tamilmale_02356.wav', 'speaker_name': 'male', 'language': 'ta'}\n"
]
}
],
"source": [
"# use your own preprocessor at this stage - TTS/datasets/proprocess.py\n",
"train_samples, eval_samples = load_tts_samples(DATASET_CONFIG, eval_split=True, formatter=formatter_ai4b)\n",
"items = train_samples + eval_samples\n",
"print(\" > Number of audio files: {}\".format(len(items)))\n",
"print(items[1])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6891 69\n"
]
}
],
"source": [
"print(len(train_samples), len(eval_samples))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# check wavs if exist\n",
"wav_files = []\n",
"for item in items:\n",
" wav_file = item['audio_file'].strip()\n",
" wav_files.append(wav_file)\n",
" if not os.path.exists(wav_file):\n",
" print(wav_file)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[]\n"
]
}
],
"source": [
"# show duplicate items\n",
"c = Counter(wav_files)\n",
"print([item for item, count in c.items() if count > 1])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'text': 'சுந்தர சோழருக்கு முன்னால், அரசு புரிந்த கண்டராதித்தர், சிவபக்தியில் திளைத்து, சிவஞான கண்டராதித்தர் என்று புகழ் பெற்றவர்.',\n",
" 'audio_file': '/home/gokulkarthikk/datasets/indictts/ta/wavs/train_tamilfemale_01045.wav',\n",
" 'speaker_name': 'female',\n",
" 'language': 'ta'}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"item"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 6960/6960 [02:58<00:00, 39.04it/s]\n"
]
}
],
"source": [
"def load_item(item):\n",
" text = item['text'].strip()\n",
" file_name = item['audio_file'].strip()\n",
" audio, sr = librosa.load(file_name, sr=None)\n",
" audio_len = len(audio) / sr\n",
" text_len = len(text)\n",
" return file_name, text, text_len, audio, audio_len\n",
"\n",
"# This will take a while depending on size of dataset\n",
"if NUM_PROC == 1:\n",
" data = []\n",
" for m in tqdm(items):\n",
" data += [load_item(m)]\n",
"else:\n",
" with Pool(8) as p:\n",
" data = list(tqdm(p.imap(load_item, items), total=len(items)))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('/home/gokulkarthikk/datasets/indictts/ta/wavs/train_tamilmale_03163.wav',\n",
" 'பில்லியன் மக்களை தன்னகத்தே கொண்ட சாதிய சமுதாய அமைப்புகளின் மருபணு இயற்கையை அறிய மாதிரிகளைக் கொண்ட ஒரு ஆய்வு எவ்வளவு உதவ முடியும் சில சாதி பிரிவுகளின் மாதிரி அளவு வெறும் இந்திய புள்ளியியல் நிறுவனத்தைச் சார்ந்த மஜும்தார் இவ்வாராய்வின் இத்தகைய பல தவறான போக்குகளை சுட்டிக்காட்டியுள்ளார்.',\n",
" 283,\n",
" array([-3.0517578e-05, 0.0000000e+00, 0.0000000e+00, ...,\n",
" 0.0000000e+00, 6.1035156e-05, -1.2207031e-04], dtype=float32),\n",
" 20.5855625)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[0]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20.590784531250023\n"
]
}
],
"source": [
"durs = [item[-1] for item in data]\n",
"print(sum(durs)/(60*60))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 6960/6960 [00:00<00:00, 171093.74it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" > Number of words: 37080\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# count words in the dataset\n",
"w_count = Counter()\n",
"for item in tqdm(data):\n",
" text = item[1].lower().strip()\n",
" for word in text.split():\n",
" w_count[word] += 1\n",
"print(\" > Number of words: {}\".format(len(w_count)))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 6960/6960 [00:00<00:00, 613800.59it/s]\n"
]
}
],
"source": [
"text_vs_durs = {} # text length vs audio duration\n",
"text_len_counter = Counter() # number of sentences with the keyed length\n",
"for item in tqdm(data):\n",
" text = item[1].lower().strip()\n",
" text_len = len(text)\n",
" text_len_counter[text_len] += 1\n",
" audio_len = item[-1]\n",
" try:\n",
" text_vs_durs[text_len] += [audio_len]\n",
" except:\n",
" text_vs_durs[text_len] = [audio_len]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# text_len vs avg_audio_len, median_audio_len, std_audio_len\n",
"text_vs_avg = {}\n",
"text_vs_median = {}\n",
"text_vs_std = {}\n",
"for key, durs in text_vs_durs.items():\n",
" text_vs_avg[key] = np.mean(durs)\n",
" text_vs_median[key] = np.median(durs)\n",
" text_vs_std[key] = np.std(durs)"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"### Avg audio length per char"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"for item in data:\n",
" if item[-1] <= 1:\n",
" print(item)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"sec_per_chars = []\n",
"for item in data:\n",
" text = item[1]\n",
" dur = item[-1]\n",
" sec_per_char = dur / len(text)\n",
" sec_per_chars.append(sec_per_char)\n",
"# sec_per_char /= len(data)\n",
"# print(sec_per_char)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.08241033481853367\n",
"0.011320645779761917\n"
]
}
],
"source": [
"mean = np.mean(sec_per_chars)\n",
"std = np.std(sec_per_chars)\n",
"print(mean)\n",
"print(std)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"dist = norm(mean, std)\n",
"\n",
"# find irregular instances long or short voice durations\n",
"for item in data:\n",
" text = item[1]\n",
" dur = item[-1]\n",
" sec_per_char = dur / len(text)\n",
" pdf =norm.pdf(sec_per_char)\n",
" if pdf < 0.39:\n",
" #print(item)\n",
" pass"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"### Plot Dataset Statistics"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fe09646ac40>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.title(\"text length vs mean audio duration\")\n",
"plt.scatter(list(text_vs_avg.keys()), list(text_vs_avg.values()))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fe091968400>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.title(\"text length vs median audio duration\")\n",
"plt.scatter(list(text_vs_median.keys()), list(text_vs_median.values()))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fe09191d670>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.title(\"text length vs STD\")\n",
"plt.scatter(list(text_vs_std.keys()), list(text_vs_std.values()))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fe0918ce850>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.title(\"text length vs # instances\")\n",
"plt.scatter(list(text_len_counter.keys()), list(text_len_counter.values()))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 6960/6960 [00:00<00:00, 1778070.16it/s]\n"
]
},
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'audio length distribution')"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"audio_lens = []\n",
"for item in tqdm(data):\n",
" audio_len = item[-1]\n",
" audio_lens.append(audio_len)\n",
"\n",
"sns.displot(audio_lens)\n",
"plt.title(\"audio length distribution\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"### Check words frequencies"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"w_count_df = pd.DataFrame.from_dict(w_count, orient='index')\n",
"w_count_df.sort_values(0, ascending=False, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"Collapsed": "false",
"scrolled": true
},
"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></th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>என்று</th>\n",
" <td>1068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ஒரு</th>\n",
" <td>906</td>\n",
" </tr>\n",
" <tr>\n",
" <th>நான்</th>\n",
" <td>449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>அந்த</th>\n",
" <td>397</td>\n",
" </tr>\n",
" <tr>\n",
" <th>இந்த</th>\n",
" <td>381</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ஊற்றுகளில்,</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>அம்மம்மா</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>பயங்கரப்படுத்தியது</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>கடகடா,</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>திளைத்து,</th>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>37080 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" 0\n",
"என்று 1068\n",
"ஒரு 906\n",
"நான் 449\n",
"அந்த 397\n",
"இந்த 381\n",
"... ...\n",
"ஊற்றுகளில், 1\n",
"அம்மம்மா 1\n",
"பயங்கரப்படுத்தியது 1\n",
"கடகடா, 1\n",
"திளைத்து, 1\n",
"\n",
"[37080 rows x 1 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w_count_df"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/plain": [
"449"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check a certain word\n",
"w_count_df.at['நான்', 0]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fdfc348bb20>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"w_count_counter = Counter(w_count.values())\n",
"plt.figure(figsize=(15,5))\n",
"plt.scatter(list(w_count_counter.keys()), list(w_count_counter.values()))"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# fequency bar plot - samples\n",
"import matplotlib as mpl\n",
"plt.figure()\n",
"#plt.rcParams.update({'font.family': 'Vijaya'}) \n",
"mpl.rcParams['font.sans-serif'] = ['Source Han Sans TW',\n",
" 'sans-serif',\n",
" \"Arial Unicode MS\" # fc-list :lang=hi family\n",
" ]\n",
"#plt.rc('font', family='Vijaya')\n",
"ax = w_count_df.iloc[::400].plot.bar(figsize=(15,5))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fdeac8140a0>"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/gokul_kumar/.local/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 2962 missing from current font.\n",
" font.set_text(s, 0.0, flags=flags)\n",
"/home/gokul_kumar/.local/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 2959 missing from current font.\n",
" font.set_text(s, 0.0, flags=flags)\n",
"/home/gokul_kumar/.local/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 3010 missing from current font.\n",
" font.set_text(s, 0.0, flags=flags)\n",
"/home/gokul_kumar/.local/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 2962 missing from current font.\n",
" font.set_text(s, 0, flags=flags)\n",
"/home/gokul_kumar/.local/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 2959 missing from current font.\n",
" font.set_text(s, 0, flags=flags)\n",
"/home/gokul_kumar/.local/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 3010 missing from current font.\n",
" font.set_text(s, 0, flags=flags)\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# fequency bar plot - first 100\n",
"w_count_df.iloc[:100].plot.bar(figsize=(15,5))"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fdeac641400>"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/gokul_kumar/.local/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 2947 missing from current font.\n",
" font.set_text(s, 0.0, flags=flags)\n",
"/home/gokul_kumar/.local/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 2954 missing from current font.\n",
" font.set_text(s, 0.0, flags=flags)\n",
"/home/gokul_kumar/.local/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 2947 missing from current font.\n",
" font.set_text(s, 0, flags=flags)\n",
"/home/gokul_kumar/.local/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 2954 missing from current font.\n",
" font.set_text(s, 0, flags=flags)\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# fequency bar plot - tlastop 100\n",
"w_count_df.iloc[-100:].plot.bar(figsize=(15,5))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "tts-env1",
"language": "python",
"name": "tts-env1"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
},
"vscode": {
"interpreter": {
"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|