File size: 28,958 Bytes
7934b29 |
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 |
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"accelerator": "GPU",
"colab": {
"name": "CTC_Segmentation_Tutorial_update.ipynb",
"private_outputs": true,
"provenance": [],
"collapsed_sections": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.7"
}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "d4KCUoxSpdoZ"
},
"source": [
"BRANCH = 'r1.17.0'\n",
"\n",
"\"\"\"\n",
"You can run either this notebook locally (if you have all the dependencies and a GPU) or on Google Colab.\n",
"\n",
"Instructions for setting up Colab are as follows:\n",
"1. Open a new Python 3 notebook.\n",
"2. Import this notebook from GitHub (File -> Upload Notebook -> \"GITHUB\" tab -> copy/paste GitHub URL)\n",
"3. Connect to an instance with a GPU (Runtime -> Change runtime type -> select \"GPU\" for hardware accelerator)\n",
"4. Run this cell to set up dependencies.\n",
"\"\"\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "JDk9zxC6pdod"
},
"source": [
"import os\n",
"# either provide a path to local NeMo repository with NeMo already installed or git clone\n",
"\n",
"# option #1: local path to NeMo repo with NeMo already installed\n",
"NEMO_DIR_PATH = \"NeMo\"\n",
"\n",
"# option #2: download NeMo repo\n",
"if 'google.colab' in str(get_ipython()) or not os.path.exists(NEMO_DIR_PATH):\n",
" ! git clone -b $BRANCH https://github.com/NVIDIA/NeMo\n",
" % cd NeMo\n",
" ! python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "CH7yR7cSwPKr"
},
"source": [
"import json\n",
"import os\n",
"import wget\n",
"\n",
"from IPython.display import Audio\n",
"import numpy as np\n",
"import scipy.io.wavfile as wav\n",
"\n",
"! pip install pandas\n",
"! pip install plotly\n",
"from plotly import graph_objects as go"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "xXRARM8XtK_g"
},
"source": [
"# 1. Introduction\n",
"End-to-end Automatic Speech Recognition (ASR) systems surpassed traditional systems in performance but require large amounts of labeled data for training. \n",
"\n",
"This tutorial will show how to use a pre-trained with Connectionist Temporal Classification (CTC) ASR model, such as [QuartzNet Model](https://arxiv.org/abs/1910.10261) or [Citrinet](https://arxiv.org/abs/2104.01721) to split long audio files and the corresponding transcripts into shorter fragments that are suitable for an ASR model training. \n",
"\n",
"We're going to use [ctc-segmentation](https://github.com/lumaku/ctc-segmentation) Python package based on the algorithm described in [CTC-Segmentation of Large Corpora for German End-to-end Speech Recognition](https://arxiv.org/pdf/2007.09127.pdf)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "8FAZKakrIyGI"
},
"source": [
"requirements = f'https://raw.githubusercontent.com/NVIDIA/NeMo/{BRANCH}/tools/ctc_segmentation/requirements.txt'\n",
"wget.download(requirements)\n",
"! pip install -r requirements.txt\n",
"! apt-get install -y ffmpeg"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "S1DZk-inQGTI"
},
"source": [
"`TOOLS_DIR` contains scripts that we are going to need during the next steps, all necessary scripts could be found [here](https://github.com/NVIDIA/NeMo/tree/main/tools/ctc_segmentation/scripts)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "1C9DdMfvRFM-"
},
"source": [
"if 'google.colab' in str(get_ipython()):\n",
" NEMO_DIR_PATH = \"/content/NeMo\"\n",
"elif not os.path.exists(NEMO_DIR_PATH):\n",
" NEMO_DIR_PATH = \"NeMo\"\n",
" \n",
"TOOLS_DIR = f'{NEMO_DIR_PATH}/tools/ctc_segmentation/scripts'\n",
"print(TOOLS_DIR)\n",
"! ls -l $TOOLS_DIR"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "XUEncnqTIzF6"
},
"source": [
"# 2. Data Download\n",
"First, let's download audio and text data (data source: [https://librivox.org/](https://librivox.org/) and [http://www.gutenberg.org](http://www.gutenberg.org)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "bkeKX2I_tIgV"
},
"source": [
"## create data directory and download an audio file\n",
"WORK_DIR = 'WORK_DIR'\n",
"DATA_DIR = WORK_DIR + '/DATA'\n",
"os.makedirs(DATA_DIR, exist_ok=True)\n",
"\n",
"print('downloading audio samples...')\n",
"wget.download(\"https://multilangaudiosamples.s3.us-east-2.amazonaws.com/audio_samples.zip\", DATA_DIR)\n",
"! unzip -o $DATA_DIR/audio_samples.zip -d $DATA_DIR\n",
"! rm $DATA_DIR/audio_samples.zip\n",
"\n",
"DATA_DIR = os.path.join(DATA_DIR, \"audio_samples\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "2JAv7ePmpdok"
},
"source": [
"We downloaded audio and text samples in `English` and `Spanish`:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Y6VYVk9mpdol"
},
"source": [
"! ls $DATA_DIR"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "-_XE9MkKuAA7"
},
"source": [
"Data folder for each language contains both audio and text files. Note, the text file and the audio file share the same base name. For example, an audio file `example.wav` should have a corresponding text file called `example.txt`."
]
},
{
"cell_type": "code",
"metadata": {
"id": "IGhijb-Bpdol"
},
"source": [
"! ls $DATA_DIR/es/audio/ $DATA_DIR/es/text/"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "FWqlbSryw_WL"
},
"source": [
"# 3. Segmentation of a single file (Spanish sample)\n",
"\n",
"Let's listen to our Spanish audio sample:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ulkPrqwipdom"
},
"source": [
"base_name_es = \"el19demarzoyel2demayo_03_perezgaldos\"\n",
"Audio(f\"{DATA_DIR}/es/audio/{base_name_es}.wav\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "RzlLsmMXpdom"
},
"source": [
"Let's take a look at the ground truth text:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "9Qfp10Xnpdom"
},
"source": [
"text = f\"{DATA_DIR}/es/text/{base_name_es}.txt\"\n",
"! cat $text"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "RMT5lkPYzZHK"
},
"source": [
"As one probably noticed, the audio file contains a prologue and an epilogue that are missing in the corresponding text. The segmentation algorithm could handle extra audio fragments at the end and the beginning of the audio, but prolonged untranscribed audio segments in the middle of the file could deteriorate segmentation results. That is why, it is recommended to normalize text, so that transcripts contain spoken equivalents of abbreviations and numbers.\n",
"\n",
"## 3.1. Prepare Text and Audio\n",
"\n",
"We're going to use `prepare_data.py` script to prepare both text and audio data for segmentation.\n",
"\n",
"### Text preprocessing:\n",
"* the text will be roughly split into sentences and stored under '$OUTPUT_DIR/processed/*.txt' where each sentence is going to start with a new line (we're going to find alignments for these sentences in the next steps)\n",
"* to change the lengths of the final sentences/fragments, specify additional punctuation marks to split the text into fragments, use `--additional_split_symbols` argument. Use `|` as a separator between symbols, for example: `--additional_split_symbols=;|:`\n",
"* `max_length` argument - max number of words in a segment for alignment (used only if there are no punctuation marks present in the original text. Long non-speech segments are better for segments split and are more likely to co-occur with punctuation marks. Random text split could deteriorate the quality of the alignment.\n",
"* out-of-vocabulary words will be removed based on pre-trained ASR model vocabulary, and the text will be changed to lowercase \n",
"* sentences for alignment with the original punctuation and capitalization will be stored under `$OUTPUT_DIR/processed/*_with_punct.txt`\n",
"* numbers will be converted from written to their spoken form with `num2words` package. For English, it's recommended to use NeMo normalization tool use `--use_nemo_normalization` argument (not supported if running this segmentation tutorial in Colab, see the text normalization tutorial: [`tutorials/text_processing/Text_Normalization.ipynb`](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/text_processing/Text_Normalization.ipynb) for more details). Even `num2words` normalization is usually enough for proper segmentation. However, it does not take audio into account. NeMo supports audio-based normalization for English, German and Russian languages that can be applied to the segmented data as a post-processing step. Audio-based normalization produces multiple normalization options. For example, `901` could be normalized as `nine zero one` or `nine hundred and one`. The audio-based normalization chooses the best match among the possible normalization options and the transcript based on the character error rate. Note, the audio-based normalization of long audio samples is not supported due to multiple normalization options. See [NeMo/nemo_text_processing/text_normalization/normalize_with_audio.py](https://github.com/NVIDIA/NeMo/blob/stable/nemo_text_processing/text_normalization/normalize_with_audio.py) for more details.\n",
"\n",
"### Audio preprocessing:\n",
"* non '.wav' audio files will be converted to `.wav` format\n",
"* audio files will be resampled to 16kHz (sampling rate used during training NeMo ASR models)\n",
"* stereo tracks will be converted to mono\n",
"* In some cases, if an audio contains a very long untranscribed prologue, increasing `--cut_prefix` value might help improve segmentation quality.\n",
"\n",
"\n",
"The `prepare_data.py` will preprocess all `.txt` files found in the `--in_text=$DATA_DIR` and all audio files located at `--audio_dir=$DATA_DIR`.\n",
"\n",
"We are going to use [Spanish Citrinet-512 model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_es_citrinet_512)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "u4zjeVVv-UXR"
},
"source": [
"MODEL = \"stt_es_citrinet_512\" \n",
"OUTPUT_DIR = WORK_DIR + \"/es_output\"\n",
"\n",
"! rm -rf $OUTPUT_DIR\n",
"\n",
"! python $TOOLS_DIR/prepare_data.py \\\n",
"--in_text=$DATA_DIR/es/text \\\n",
"--output_dir=$OUTPUT_DIR/processed/ \\\n",
"--language='en' \\\n",
"--model=$MODEL \\\n",
"--audio_dir=$DATA_DIR/es/audio"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "kmDTCuTLH7pm"
},
"source": [
"The following four files should be generated and stored at the `$OUTPUT_DIR/processed` folder:\n",
"\n",
"* el19demarzoyel2demayo_03_perezgaldos.txt (lower cased and normalized text with punctuation removed, each line represents an utterance for alignment)\n",
"* el19demarzoyel2demayo_03_perezgaldos.wav (.wav mono file, 16kHz)\n",
"* el19demarzoyel2demayo_03_perezgaldos_with_punct.txt (raw utterances for alignment with punctuation and case preserved)\n",
"* el19demarzoyel2demayo_03_perezgaldos_with_punct_normalized.txt (normalized utterances for alignment utterance for alignment with punctuation and case preserved)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "6R7OKAsYH9p0"
},
"source": [
"! ls $OUTPUT_DIR/processed"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "bIvKBwRcH_9W"
},
"source": [
"The `.txt` file without punctuation contains preprocessed text phrases that we're going to align within the audio file. Here, we split the text into sentences. Each line should contain a text snippet for alignment."
]
},
{
"cell_type": "code",
"metadata": {
"id": "74GLpMgoICmk"
},
"source": [
"! head $OUTPUT_DIR/processed/el19demarzoyel2demayo_03_perezgaldos.txt"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "QrvZAjeoR9U1"
},
"source": [
"## 3.2. Run CTC-Segmentation\n",
"\n",
"In this step, we're going to use the [`ctc-segmentation`](https://github.com/lumaku/ctc-segmentation) to find the start and end time stamps for the segments we created during the previous step.\n",
"\n",
"\n",
"As described in the [CTC-Segmentation of Large Corpora for German End-to-end Speech Recognition](https://arxiv.org/pdf/2007.09127.pdf), the algorithm is relying on a CTC-based ASR model to extract utterance segments with exact time-wise alignments. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "xyKtaqAd-Tvk"
},
"source": [
"WINDOW = 8000\n",
"\n",
"! python $TOOLS_DIR/run_ctc_segmentation.py \\\n",
"--output_dir=$OUTPUT_DIR \\\n",
"--data=$OUTPUT_DIR/processed \\\n",
"--model=$MODEL \\\n",
"--window_len=$WINDOW "
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "wY27__e3HmhH"
},
"source": [
"`WINDOW` parameter might need to be adjusted depending on the length of the utterance one wants to align, the default value should work in most cases. By default, if the alignment is not found for the initial `WINDOW` size, the window size will be doubled a few times to re-try backtracing. \n",
"\n",
"Let's take a look at the generated alignments."
]
},
{
"cell_type": "code",
"metadata": {
"id": "ktBAsfJRVCwI"
},
"source": [
"alignment_file = f\"{WINDOW}_{base_name_es}_segments.txt\"\n",
"! head -n 3 $OUTPUT_DIR/segments/$alignment_file"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "xCwEFefHZz1C"
},
"source": [
"The expected output for our audio sample looks like this:\n",
"\n",
"```\n",
"<PATH_TO>/processed/el19demarzoyel2demayo_03_perezgaldos.wav\n",
"11.518331881862931 25.916246734191596 -1.2629864680237006 | entraron en la habitaci贸n donde est谩bamos y al punto que d mauro vio a su sobrina dirigiose a ella con los brazos abiertos y al estrecharla en ellos exclam贸 endulzando la voz 隆in茅s de mi alma inocente hija de mi prima juana | Entraron en la habitaci贸n donde est谩bamos, y al punto que D. Mauro vio a su sobrina dirigiose a ella con los brazos abiertos, y al estrecharla en ellos, exclam贸 endulzando la voz: -隆In茅s de mi alma, inocente hija de mi prima Juana! | Entraron en la habitaci贸n donde est谩bamos, y al punto que D. Mauro vio a su sobrina dirigiose a ella con los brazos abiertos, y al estrecharla en ellos, exclam贸 endulzando la voz: - In茅s de mi alma, inocente hija de mi prima Juana!\n",
"25.756269902499053 28.155922377887165 -0.0003830735786323203 | al fin al fin te veo | Al fin, al fin te veo. | Al fin, al fin te veo.\n",
"...\n",
"```\n",
"\n",
"**Details of the file content**:\n",
"- the first line of the file contains the path to the original audio file\n",
"- all subsequent lines contain:\n",
" * the first number is the start of the segment (in seconds)\n",
" * the second one is the end of the segment (in seconds)\n",
" * the third value - alignment confidence score (in log space)\n",
" * text fragments corresponding to the timestamps\n",
" * original text without pre-processing\n",
" * normalized text\n",
"\n",
"Finally, we're going to split the original audio file into segments based on the found alignments. We're going to save only segments with alignment score above the threshold value (default threshold=-2:\n",
"* high scored clips (segments with the segmentation score above the threshold value)\n",
"* low scored clips (segments with the segmentation score below the threshold)\n",
"* deleted segments (segments that were excluded during the alignment. For example, in our sample audio file, the prologue and epilogue that don't have the corresponding transcript were excluded. Oftentimes, deleted files also contain such things as clapping, music, or hard breathing. \n",
"\n",
"The alignment score values depend on the pre-trained model quality and the dataset.\n",
"\n",
"Also note, that the `OFFSET` parameter is something one might want to experiment with since timestamps have a delay (offset) depending on the model.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "6YM64RPlitPL"
},
"source": [
"OFFSET = 0\n",
"THRESHOLD = -2\n",
"\n",
"! python $TOOLS_DIR/cut_audio.py \\\n",
"--output_dir=$OUTPUT_DIR \\\n",
"--alignment=$OUTPUT_DIR/segments/ \\\n",
"--threshold=$THRESHOLD \\\n",
"--offset=$OFFSET"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "QoyS0T8AZxcx"
},
"source": [
"## 3.3. Transcribe segmented audio\n",
"\n",
"The transcripts will be saved in a new manifest file in `pred_text` field."
]
},
{
"cell_type": "code",
"metadata": {
"id": "1UaSIflBZwaV"
},
"source": [
"wget.download(f'https://raw.githubusercontent.com/NVIDIA/NeMo/{BRANCH}/examples/asr/transcribe_speech.py')\n",
"\n",
"! python transcribe_speech.py \\\n",
"pretrained_name=$MODEL \\\n",
"dataset_manifest=$OUTPUT_DIR/manifests/manifest.json \\\n",
"output_filename=$OUTPUT_DIR/manifests/manifest_transcribed.json"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "F-nPT8z_IVD-"
},
"source": [
"def plot_signal(signal, sample_rate):\n",
" \"\"\" Plot the signal in time domain \"\"\"\n",
" fig_signal = go.Figure(\n",
" go.Scatter(x=np.arange(signal.shape[0])/sample_rate,\n",
" y=signal, line={'color': 'green'},\n",
" name='Waveform',\n",
" hovertemplate='Time: %{x:.2f} s<br>Amplitude: %{y:.2f}<br><extra></extra>'),\n",
" layout={\n",
" 'height': 200,\n",
" 'xaxis': {'title': 'Time, s'},\n",
" 'yaxis': {'title': 'Amplitude'},\n",
" 'title': 'Audio Signal',\n",
" 'margin': dict(l=0, r=0, t=40, b=0, pad=0),\n",
" }\n",
" )\n",
" fig_signal.show()\n",
" \n",
"def display_samples(manifest):\n",
" \"\"\" Display audio and reference text.\"\"\"\n",
" with open(manifest, 'r') as f:\n",
" for line in f:\n",
" sample = json.loads(line)\n",
" sample_rate, signal = wav.read(sample['audio_filepath'])\n",
" plot_signal(signal, sample_rate)\n",
" display(Audio(sample['audio_filepath']))\n",
" display('Reference text: ' + sample['text_no_preprocessing'])\n",
" if 'pred_text' in sample:\n",
" display('ASR transcript: ' + sample['pred_text'])\n",
" print(f\"Score: {sample['score']}\")\n",
" print('\\n' + '-' * 110)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "S69UFA30ZvxV"
},
"source": [
"Let's examine the high scored segments we obtained.\n",
"\n",
"The `Reference text` in the next cell represents the original text without pre-processing, while `ASR transcript` is an ASR model prediction with greedy decoding. Also notice, that `ASR transcript` in some cases contains errors that could decrease the alignment score, but usually it doesn鈥檛 hurt the quality of the aligned segments.\n",
"\n",
"Displaying audio in Jupyter Notebook could be slow, it's recommended to use [Speech Data Explorer](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/tools/speech_data_explorer.html) to analyze speech data."
]
},
{
"cell_type": "code",
"metadata": {
"id": "Q45uBtsHIaAD"
},
"source": [
"# let's examine only a few first samples\n",
"! head -n 2 $OUTPUT_DIR/manifests/manifest_transcribed.json > $OUTPUT_DIR/manifests/samples.json\n",
"\n",
"display_samples(f\"{OUTPUT_DIR}/manifests/samples.json\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "yivXpD25T4Ir"
},
"source": [
"# 4. Processing of multiple files (English samples)\n",
"\n",
"Up until now, we were processing only one file at a time, but to create a large dataset processing of multiple files simultaneously could help speed up things considerably. \n",
"\n",
"Our English data folder contains 2 audio files and corresponding text files:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "KRc9yMjPXPgj"
},
"source": [
"! ls $DATA_DIR/en/audio $DATA_DIR/en/text"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "3ftilXu-5tzT"
},
"source": [
"We are going to use `run_segmentation.sh` to perform all the above steps starting from the text and audio preprocessing to segmentation and manifest creation in a single step:"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2I4w34Hepdor"
},
"source": [
"`run_segmentation.sh` script takes `DATA_DIR` argument and assumes that it contains folders `text` and `audio`.\n",
"An example of the `DATA_DIR` folder structure:\n",
"\n",
"\n",
"--DATA_DIR\n",
"\n",
" |----audio\n",
" |---1.mp3\n",
" |---2.mp3\n",
" \n",
" |-----text\n",
" |---1.txt\n",
" |---2.txt"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nYXNvBDsHMEu"
},
"source": [
"`run_segmentation.sh` could use multiple `WINDOW` sizes for segmentation, and then adds segments that were similarly aligned with various window sizes to `verified_segments` folder. This could be useful to reduce the amount of manual work while checking the alignment quality."
]
},
{
"cell_type": "code",
"metadata": {
"id": "hRFAl0gO92bp"
},
"source": [
"MODEL = \"QuartzNet15x5Base-En\" # \"stt_en_citrinet_512_gamma_0_25\" \n",
"OUTPUT_DIR_2 = WORK_DIR + \"/en_output\"\n",
"\n",
"! rm -rf $OUTPUT_DIR_2\n",
"\n",
"! bash $TOOLS_DIR/../run_segmentation.sh \\\n",
"--MODEL_NAME_OR_PATH=$MODEL \\\n",
"--DATA_DIR=$DATA_DIR/en \\\n",
"--OUTPUT_DIR=$OUTPUT_DIR_2 \\\n",
"--SCRIPTS_DIR=$TOOLS_DIR \\\n",
"--MIN_SCORE=$THRESHOLD \\\n",
"--USE_NEMO_NORMALIZATION=False"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "zzJTwKq2Kl9U"
},
"source": [
"Manifest file with segments with alignment score above the threshold values are saved in `en/output/manifests/manifest.json`.\n",
"\n",
"Next, we are going to run `run_filter.sh`. The script does the following:\n",
"* adds ASR transcripts to the manifest\n",
"* calculates and saves metrics such as Word Error Rate (WER), Character Error Rate (CER), CER at the tails of the audio file, word difference between reference and transcript, mean absolute values at the tails of the audio.\n",
"* filters out samples that do not satisfy threshold values and saves selected segments in `manifest_transcribed_metrics_filtered.json`.\n",
"\n",
"Note, it's better to analyze the manifest with metrics in Speech Data Explorer to decide on what thresholds should be used for final sample selection."
]
},
{
"cell_type": "code",
"metadata": {
"id": "xsm89hYlpdor"
},
"source": [
"! bash $TOOLS_DIR/../run_filter.sh \\\n",
"--SCRIPTS_DIR=$TOOLS_DIR \\\n",
"--MODEL_NAME_OR_PATH=stt_en_conformer_ctc_large \\\n",
"--MANIFEST=$OUTPUT_DIR_2/manifests/manifest.json \\\n",
"--INPUT_AUDIO_DIR=$DATA_DIR/en/audio/"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "nacE_iQ2_85L"
},
"source": [
"# let's examine only a few first samples\n",
"! head -n 2 $OUTPUT_DIR_2/manifests/manifest_transcribed_metrics_filtered.json > $OUTPUT_DIR_2/manifests/samples.json\n",
"\n",
"display_samples(f\"{OUTPUT_DIR_2}/manifests/samples.json\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "lcvT3P2lQ_GS"
},
"source": [
"# Next Steps\n",
"\n",
"- Check out [NeMo Speech Data Explorer tool](https://github.com/NVIDIA/NeMo/tree/main/tools/speech_data_explorer#speech-data-explorer) to interactively evaluate the aligned segments.\n",
"- Try Audio-based normalization tool."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GYylwvTX2VSF"
},
"source": [
"# References\n",
"K眉rzinger, Ludwig, et al. [\"CTC-Segmentation of Large Corpora for German End-to-End Speech Recognition.\"](https://arxiv.org/abs/2007.09127) International Conference on Speech and Computer. Springer, Cham, 2020."
]
}
]
} |