{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Team একুশ - Training Notebook - Wav2Vec2\n\n- Team Members: `Sanjay Acharjee` | `Sawradip Saha` | `Shahriar Kabir Nahin` | `Shahruk Hossain`\n\n## Table of Contents\n\n- [1. Introduction](#0)\n- [2. Installing Dependencies](#1)\n- [3. Training Configuration](#2)\n- [4. Function and Class definitions](#3)\n  - [4.1 Filtering Dataset](#3-1)\n  - [4.2 Audio Loader](#3-2)\n- [5. Main](#4)\n  - [5.1 Building LM](#4-1)\n  - [5.2 Loading Base Wav2Vec2 Model](#4-2)\n  - [5.3 Adapting Base Wav2Vec2Model](#4-3)\n\n<a id=\"0\"></a> <br>\n## Introduction\n\n- This notebook contains the steps followed for adapting a pretrained Wav2Vec2 model on the provided dataset.\n\n- We adapted a Wav2Vec2 model that was already pretrained on the OpenSLR53 Bengali dataset and available on HuggingFace (https://huggingface.co/arijitx/wav2vec2-xls-r-300m-bengali). The model checkpoitn was downloaded and added as a dataset for this notebook so this could be run without internet enabled.\n\n- We empolyed several audio augmentation techniques during training including:\n  - Speed perturbation (0.9x 1.1x)\n  - Volume perturbation (0.125x ~ 2.0x)\n  - Adding background noise using the other audio files from the dataset.\n  - Adding reverberation.\n\n- We first trained the base model for 100.8k steps (early stopping) with an intial learning rate of 3e-5, following cosine decay schedule. We enable a small proportion of different types of dropout during this stage.\n\n- Next we trained the model for 40k steps at a constant learning rage of 1e-7; this time we disabled all dropout.\n\n- The best checkpoint from the second stage of training was paired with a 5-gram language model built from the training text (and validation text when inferring on test set).\n\n- The best model with decoding parameters tuned on the validation set acheived the following LB score (mean Levenshtein distance):\n  - Public:  1.46527\n  - Private: 1.47186","metadata":{}},{"cell_type":"markdown","source":"<a id=\"1\"></a> <br>\n## Installing and Importing Dependencies","metadata":{}},{"cell_type":"code","source":"!cp -r ../input/csefest2022dlsprintdeps ./deps","metadata":{"execution":{"iopub.status.busy":"2022-08-25T04:59:33.732447Z","iopub.execute_input":"2022-08-25T04:59:33.733660Z","iopub.status.idle":"2022-08-25T04:59:35.895896Z","shell.execute_reply.started":"2022-08-25T04:59:33.733525Z","shell.execute_reply":"2022-08-25T04:59:35.894603Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install ./deps/pygtrie-2.5.0/pygtrie-2.5.0\n!pip install ./deps/exceptiongroup-1.0.0rc8-py3-none-any.whl\n!pip install ./deps/hypothesis-6.54.4-py3-none-any.whl\n!pip install ./deps/pyctcdecode-0.4.0-py2.py3-none-any.whl\n!pip install ./deps/pypi-kenlm-0.1.20220713/pypi-kenlm-0.1.20220713\n!pip install ./deps/bnunicodenormalizer-0.0.23/bnunicodenormalizer-0.0.23\n!pip install ./deps/python-Levenshtein-0.12.2/python-Levenshtein-0.12.2\n!pip install ./deps/jiwer-2.3.0-py3-none-any.whl\n\n!chmod +x ./deps/kenlm/kenlm/bin/lmplz","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-08-25T04:59:45.407674Z","iopub.execute_input":"2022-08-25T04:59:45.408391Z","iopub.status.idle":"2022-08-25T05:04:34.635498Z","shell.execute_reply.started":"2022-08-25T04:59:45.408350Z","shell.execute_reply":"2022-08-25T05:04:34.634132Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from typing import Dict, List, Tuple, Any, Union, Optional\n\nimport os\nimport re\nimport json\nimport random\nfrom pprint import pprint\n\nimport unicodedata\nfrom bnunicodenormalizer import Normalizer \n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport pandas as pd\nfrom pandarallel import pandarallel\nfrom tqdm.auto import tqdm\n\nimport torch\nimport torchaudio\nimport torchaudio.functional as F\nimport torchaudio.transforms as T\nfrom torch.utils.data import Dataset, DataLoader, IterableDataset\n\nimport transformers\nfrom transformers import Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, Wav2Vec2ForCTC\nfrom transformers import TrainingArguments, Trainer\n\nfrom datasets import load_dataset, load_metric\nfrom dataclasses import dataclass, field\n\nfrom IPython.display import display, Audio, HTML, Markdown\n\nbnorm = Normalizer()\npandarallel.initialize(progress_bar=True,nb_workers=os.cpu_count())\ntqdm.pandas()","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:05:34.523538Z","iopub.execute_input":"2022-08-25T05:05:34.524199Z","iopub.status.idle":"2022-08-25T05:05:42.959558Z","shell.execute_reply.started":"2022-08-25T05:05:34.524158Z","shell.execute_reply":"2022-08-25T05:05:42.957413Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"os.environ[\"WANDB_DISABLED\"] = \"true\"","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:05:42.961728Z","iopub.execute_input":"2022-08-25T05:05:42.962606Z","iopub.status.idle":"2022-08-25T05:05:42.968440Z","shell.execute_reply.started":"2022-08-25T05:05:42.962565Z","shell.execute_reply":"2022-08-25T05:05:42.967061Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"2\"></a> <br>\n## Configuration","metadata":{}},{"cell_type":"code","source":"# Training config class.\nclass Config:\n    # Path to audio data directories. Change to directory containing pre-converted and\n    # resampled wav files if available, along with audio_ext.\n    train_path = \"../input/dlsprint/train_files\"\n    valid_path = \"../input/dlsprint/validation_files\"\n    audio_ext = \"mp3\"\n    sample_rate = 16000\n    \n    # Path to csv metadata files.\n    train_csv_path = \"../input/dlsprint/train.csv\"\n    valid_csv_path = \"../input/dlsprint/validation.csv\"\n    \n    # Directory where language models are written to.\n    language_model_dir = \"./langauge_models\"\n    \n    # n-gram order of language model.\n    ngram_order = 5\n\n    # If True, enables all audio augmentations.\n    use_augmentation = True\n    \n    # Dropout configs for pretrained wav2vec2 model.\n    attention_dropout = 0.1\n    hidden_dropout = 0.1\n    feat_proj_dropout = 0.1\n    mask_time_prob = 0.05\n    layerdrop = 0.1\n        \n    # Early stopping.\n    early_stopping_patience = 10\n\n    # Trainer arugments.\n    trainer = TrainingArguments(\n      output_dir=\"./run-003-wav2vec2-fulldata-cosine-lr3e-5\",\n      group_by_length=False,\n      per_device_train_batch_size=16,\n      per_device_eval_batch_size=16,\n      gradient_accumulation_steps=1,\n      evaluation_strategy=\"steps\",\n      num_train_epochs=10,\n      gradient_checkpointing=True,\n      fp16=True,\n      save_steps=400,\n      eval_steps=400,\n      logging_steps=400,\n      learning_rate=3e-5,\n      dataloader_num_workers=os.cpu_count(),\n      warmup_steps=300,\n      save_total_limit=10,\n      push_to_hub=False,\n      run_name=\"run-003-wav2vec2-fulldata-cosine-lr3e-5\",\n      load_best_model_at_end=True,\n      lr_scheduler_type=\"cosine\",\n      resume_from_checkpoint=True,\n    )","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:27:58.379302Z","iopub.execute_input":"2022-08-25T05:27:58.379702Z","iopub.status.idle":"2022-08-25T05:27:58.395301Z","shell.execute_reply.started":"2022-08-25T05:27:58.379666Z","shell.execute_reply":"2022-08-25T05:27:58.394212Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Vocabulary of the pretrained model we use.\nvocab_dict_cn = {\n  \"|\": 0,\n  \"_\": 1,\n  \"a\": 2,\n  \"b\": 3,\n  \"c\": 4,\n  \"d\": 5,\n  \"e\": 6,\n  \"f\": 7,\n  \"g\": 8,\n  \"h\": 9,\n  \"i\": 10,\n  \"j\": 11,\n  \"k\": 12,\n  \"l\": 13,\n  \"m\": 14,\n  \"n\": 15,\n  \"o\": 16,\n  \"p\": 17,\n  \"r\": 18,\n  \"s\": 19,\n  \"t\": 20,\n  \"u\": 21,\n  \"v\": 22,\n  \"w\": 23,\n  \"x\": 24,\n  \"y\": 25,\n  \"z\": 26,\n  '\\x93': 27,\n  '\\x94': 28,\n  \"œ\": 29,\n  \"।\": 30,\n  \"ঁ\": 31,\n  \"ং\": 32,\n  \"ঃ\": 33,\n  \"অ\": 34,\n  \"আ\": 35,\n  \"ই\": 36,\n  \"ঈ\": 37,\n  \"উ\": 38,\n  \"ঊ\": 39,\n  \"ঋ\": 40,\n  \"এ\": 41,\n  \"ঐ\": 42,\n  \"ও\": 43,\n  \"ঔ\": 44,\n  \"ক\": 45,\n  \"খ\": 46,\n  \"গ\": 47,\n  \"ঘ\": 48,\n  \"ঙ\": 49,\n  \"চ\": 50,\n  \"ছ\": 51,\n  \"জ\": 52,\n  \"ঝ\": 53,\n  \"ঞ\": 54,\n  \"ট\": 55,\n  \"ঠ\": 56,\n  \"ড\": 57,\n  \"ঢ\": 58,\n  \"ণ\": 59,\n  \"ত\": 60,\n  \"থ\": 61,\n  \"দ\": 62,\n  \"ধ\": 63,\n  \"ন\": 64,\n  \"প\": 65,\n  \"ফ\": 66,\n  \"ব\": 67,\n  \"ভ\": 68,\n  \"ম\": 69,\n  \"য\": 70,\n  \"র\": 71,\n  \"ল\": 72,\n  \"শ\": 73,\n  \"ষ\": 74,\n  \"স\": 75,\n  \"হ\": 76,\n  \"়\": 77,\n  \"া\": 78,\n  \"ি\": 79,\n  \"ী\": 80,\n  \"ু\": 81,\n  \"ূ\": 82,\n  \"ৃ\": 83,\n  \"ে\": 84,\n  \"ৈ\": 85,\n  \"ো\": 86,\n  \"ৌ\": 87,\n  \"্\": 88,\n  \"ৎ\": 89,\n  \"ৗ\": 90,\n  \"ড়\": 91,\n  \"ঢ়\": 92,\n  \"য়\": 93,\n  \"০\": 94,\n  \"১\": 95,\n  \"২\": 96,\n  \"৩\": 97,\n  \"৪\": 98,\n  \"৫\": 99,\n  \"৬\": 100,\n  \"৭\": 101,\n  \"৮\": 102,\n  \"৯\": 103,\n  \"ৰ\": 104,\n  '\\u200c': 105,\n  '\\u200d': 106,\n  '\\u200e': 107, \n  \"[UNK]\": 108,\n  \"[PAD]\": 109,\n  \"<s>\": 110,\n  \"</s>\": 111,\n}","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:05:48.256530Z","iopub.execute_input":"2022-08-25T05:05:48.257132Z","iopub.status.idle":"2022-08-25T05:05:48.270010Z","shell.execute_reply.started":"2022-08-25T05:05:48.257097Z","shell.execute_reply":"2022-08-25T05:05:48.268628Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# These files were identified has having issues due to failing text-audio alignment\n# between reference transcript and audio.\nskipFiles = [\n    \"common_voice_bn_30665285\",\n    \"common_voice_bn_30665286\",\n    \"common_voice_bn_30665287\",\n    \"common_voice_bn_30665288\",\n    \"common_voice_bn_30665290\",\n    \"common_voice_bn_30665291\",\n    \"common_voice_bn_30665294\",\n    \"common_voice_bn_30665295\",\n    \"common_voice_bn_30665296\",\n    \"common_voice_bn_30665297\",\n    \"common_voice_bn_30665298\",\n    \"common_voice_bn_30677291\",\n    \"common_voice_bn_30677292\",\n    \"common_voice_bn_30677293\",\n    \"common_voice_bn_30677294\",\n    \"common_voice_bn_30677295\",\n    \"common_voice_bn_30995469\",\n    \"common_voice_bn_30995470\",\n    \"common_voice_bn_30998934\",\n    \"common_voice_bn_31000747\",\n    \"common_voice_bn_31000750\",\n    \"common_voice_bn_31000753\",\n    \"common_voice_bn_31005676\",\n    \"common_voice_bn_31005686\",\n    \"common_voice_bn_31009001\",\n    \"common_voice_bn_31011206\",\n    \"common_voice_bn_31013329\",\n    \"common_voice_bn_31013418\",\n    \"common_voice_bn_31016821\",\n    \"common_voice_bn_31016823\",\n    \"common_voice_bn_31016825\",\n    \"common_voice_bn_31016831\",\n    \"common_voice_bn_31019660\",\n    \"common_voice_bn_31019914\",\n    \"common_voice_bn_31021589\",\n    \"common_voice_bn_31021790\",\n    \"common_voice_bn_31021831\",\n    \"common_voice_bn_31021833\",\n    \"common_voice_bn_31021834\",\n    \"common_voice_bn_31021835\",\n    \"common_voice_bn_31021847\",\n    \"common_voice_bn_31021861\",\n    \"common_voice_bn_31025585\",\n    \"common_voice_bn_31026100\",\n    \"common_voice_bn_31037299\",\n    \"common_voice_bn_31043545\",\n    \"common_voice_bn_31052304\",\n    \"common_voice_bn_31174226\",\n    \"common_voice_bn_31174227\",\n    \"common_voice_bn_31217111\",\n    \"common_voice_bn_31277603\",\n    \"common_voice_bn_31362334\",\n    \"common_voice_bn_31445621\",\n    \"common_voice_bn_31497795\",\n    \"common_voice_bn_31498452\",\n    \"common_voice_bn_31498453\",\n    \"common_voice_bn_31498455\",\n    \"common_voice_bn_31498456\",\n    \"common_voice_bn_31498467\",\n    \"common_voice_bn_31498469\",\n    \"common_voice_bn_31498470\",\n    \"common_voice_bn_31498471\",\n    \"common_voice_bn_31500246\",\n    \"common_voice_bn_31500248\",\n    \"common_voice_bn_31500250\",\n    \"common_voice_bn_31500370\",\n    \"common_voice_bn_31500372\",\n    \"common_voice_bn_31500374\",\n    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\"common_voice_bn_31753719\",\n    \"common_voice_bn_31753949\",\n    \"common_voice_bn_31753964\",\n    \"common_voice_bn_31754006\",\n    \"common_voice_bn_31754780\",\n    \"common_voice_bn_31754841\",\n    \"common_voice_bn_31755336\",\n    \"common_voice_bn_31755367\",\n    \"common_voice_bn_31755478\",\n    \"common_voice_bn_31755479\",\n    \"common_voice_bn_31755481\",\n    \"common_voice_bn_31755482\",\n    \"common_voice_bn_31755719\",\n    \"common_voice_bn_31756018\",\n    \"common_voice_bn_31756323\",\n    \"common_voice_bn_31756392\",\n    \"common_voice_bn_31756729\",\n    \"common_voice_bn_31756941\",\n    \"common_voice_bn_31757262\",\n    \"common_voice_bn_31757301\",\n    \"common_voice_bn_31757962\",\n    \"common_voice_bn_31758172\",\n    \"common_voice_bn_31758173\",\n    \"common_voice_bn_31758174\",\n    \"common_voice_bn_31758175\",\n    \"common_voice_bn_31758176\",\n    \"common_voice_bn_31758495\",\n    \"common_voice_bn_31758614\",\n    \"common_voice_bn_31758881\",\n    \"common_voice_bn_31758991\",\n    \"common_voice_bn_31758993\",\n    \"common_voice_bn_31759033\",\n    \"common_voice_bn_31759078\",\n    \"common_voice_bn_31759080\",\n    \"common_voice_bn_31759104\",\n    \"common_voice_bn_31759229\",\n    \"common_voice_bn_31759230\",\n    \"common_voice_bn_31759691\",\n    \"common_voice_bn_31760073\",\n    \"common_voice_bn_31760112\",\n    \"common_voice_bn_31760113\",\n    \"common_voice_bn_31760114\",\n    \"common_voice_bn_31760115\",\n    \"common_voice_bn_31760116\",\n    \"common_voice_bn_31760213\",\n    \"common_voice_bn_31760218\",\n    \"common_voice_bn_31760515\",\n    \"common_voice_bn_31760581\",\n    \"common_voice_bn_31761102\",\n    \"common_voice_bn_31761351\",\n    \"common_voice_bn_31761924\",\n    \"common_voice_bn_31762220\",\n    \"common_voice_bn_31762225\",\n    \"common_voice_bn_31762245\",\n    \"common_voice_bn_31762657\",\n    \"common_voice_bn_31762694\",\n    \"common_voice_bn_31762748\",\n    \"common_voice_bn_31762751\",\n    \"common_voice_bn_31762752\",\n    \"common_voice_bn_31762999\",\n    \"common_voice_bn_31763178\",\n    \"common_voice_bn_31763260\",\n    \"common_voice_bn_31763261\",\n    \"common_voice_bn_31763263\",\n    \"common_voice_bn_31763264\",\n    \"common_voice_bn_31763310\",\n    \"common_voice_bn_31763313\",\n    \"common_voice_bn_31763812\",\n    \"common_voice_bn_31763960\",\n    \"common_voice_bn_31763961\",\n    \"common_voice_bn_31763962\",\n    \"common_voice_bn_31763963\",\n    \"common_voice_bn_31763964\",\n    \"common_voice_bn_31764098\",\n    \"common_voice_bn_31764099\",\n    \"common_voice_bn_31764100\",\n    \"common_voice_bn_31764101\",\n    \"common_voice_bn_31764102\",\n    \"common_voice_bn_31764515\",\n    \"common_voice_bn_31764659\",\n    \"common_voice_bn_31765194\",\n    \"common_voice_bn_31765196\",\n    \"common_voice_bn_31765197\",\n    \"common_voice_bn_31765198\",\n    \"common_voice_bn_31765260\",\n    \"common_voice_bn_31765262\",\n    \"common_voice_bn_31765265\",\n    \"common_voice_bn_31765349\",\n    \"common_voice_bn_31765507\",\n    \"common_voice_bn_31765546\",\n    \"common_voice_bn_31765548\",\n    \"common_voice_bn_31765554\",\n    \"common_voice_bn_31765813\",\n    \"common_voice_bn_31765852\",\n    \"common_voice_bn_31765907\",\n    \"common_voice_bn_31766244\",\n    \"common_voice_bn_31766248\",\n    \"common_voice_bn_31766642\",\n    \"common_voice_bn_31767017\",\n    \"common_voice_bn_31767632\",\n    \"common_voice_bn_31767891\",\n    \"common_voice_bn_31767894\",\n    \"common_voice_bn_31768268\",\n    \"common_voice_bn_31768545\",\n    \"common_voice_bn_31768722\",\n    \"common_voice_bn_31769081\",\n    \"common_voice_bn_31769082\",\n    \"common_voice_bn_31769090\",\n    \"common_voice_bn_31769095\",\n    \"common_voice_bn_31769097\",\n    \"common_voice_bn_31769098\",\n    \"common_voice_bn_31769099\",\n    \"common_voice_bn_31769100\",\n    \"common_voice_bn_31769101\",\n    \"common_voice_bn_31769103\",\n    \"common_voice_bn_31769104\",\n    \"common_voice_bn_31769105\",\n    \"common_voice_bn_31769107\",\n    \"common_voice_bn_31769108\",\n    \"common_voice_bn_31769170\",\n    \"common_voice_bn_31769172\",\n    \"common_voice_bn_31769173\",\n    \"common_voice_bn_31769174\",\n    \"common_voice_bn_31769538\",\n    \"common_voice_bn_31769614\",\n    \"common_voice_bn_31769777\",\n    \"common_voice_bn_31769778\",\n    \"common_voice_bn_31769779\",\n    \"common_voice_bn_31769780\",\n    \"common_voice_bn_31769856\",\n    \"common_voice_bn_31769859\",\n    \"common_voice_bn_31769862\",\n    \"common_voice_bn_31769864\",\n    \"common_voice_bn_31769867\",\n    \"common_voice_bn_31769872\",\n    \"common_voice_bn_31769878\",\n    \"common_voice_bn_31769880\",\n    \"common_voice_bn_31769886\",\n    \"common_voice_bn_31769887\",\n    \"common_voice_bn_31769888\",\n    \"common_voice_bn_31769890\",\n    \"common_voice_bn_31769977\",\n    \"common_voice_bn_31770031\",\n    \"common_voice_bn_31770083\",\n    \"common_voice_bn_31770084\",\n    \"common_voice_bn_31770085\",\n    \"common_voice_bn_31770087\",\n    \"common_voice_bn_31770212\",\n    \"common_voice_bn_31770399\",\n    \"common_voice_bn_31770686\",\n    \"common_voice_bn_31770688\",\n    \"common_voice_bn_31770708\",\n    \"common_voice_bn_31770861\",\n    \"common_voice_bn_31770863\",\n    \"common_voice_bn_31770864\",\n    \"common_voice_bn_31770865\",\n    \"common_voice_bn_31771361\",\n    \"common_voice_bn_31771409\",\n    \"common_voice_bn_31771410\",\n    \"common_voice_bn_31771625\",\n    \"common_voice_bn_31771627\",\n    \"common_voice_bn_31771630\",\n    \"common_voice_bn_31771703\",\n    \"common_voice_bn_31771721\",\n    \"common_voice_bn_31771973\",\n    \"common_voice_bn_31772113\",\n    \"common_voice_bn_31772124\",\n    \"common_voice_bn_31772222\",\n    \"common_voice_bn_31772223\",\n    \"common_voice_bn_31772265\",\n    \"common_voice_bn_31772266\",\n    \"common_voice_bn_31772346\",\n    \"common_voice_bn_31772398\",\n    \"common_voice_bn_31772399\",\n    \"common_voice_bn_31772400\",\n    \"common_voice_bn_31772404\",\n    \"common_voice_bn_31772652\",\n    \"common_voice_bn_31772653\",\n    \"common_voice_bn_31772654\",\n    \"common_voice_bn_31772655\",\n    \"common_voice_bn_31772656\",\n    \"common_voice_bn_31772672\",\n    \"common_voice_bn_31772892\",\n    \"common_voice_bn_31772972\",\n    \"common_voice_bn_31773505\",\n    \"common_voice_bn_31773535\",\n    \"common_voice_bn_31773539\",\n    \"common_voice_bn_31773651\",\n    \"common_voice_bn_31773653\",\n    \"common_voice_bn_31773655\",\n    \"common_voice_bn_31773657\",\n    \"common_voice_bn_31773659\",\n    \"common_voice_bn_31773758\",\n    \"common_voice_bn_31773760\",\n    \"common_voice_bn_31773761\",\n    \"common_voice_bn_31773812\",\n    \"common_voice_bn_31773855\",\n    \"common_voice_bn_31773913\",\n    \"common_voice_bn_31773946\",\n    \"common_voice_bn_31774320\",\n    \"common_voice_bn_31774502\",\n    \"common_voice_bn_31774544\",\n    \"common_voice_bn_31774547\",\n    \"common_voice_bn_31775860\",\n    \"common_voice_bn_31775861\",\n    \"common_voice_bn_31775862\",\n    \"common_voice_bn_31775864\",\n    \"common_voice_bn_31775886\",\n    \"common_voice_bn_31775888\",\n    \"common_voice_bn_31775889\",\n    \"common_voice_bn_31775891\",\n    \"common_voice_bn_31775893\",\n    \"common_voice_bn_31775960\",\n    \"common_voice_bn_31776246\",\n    \"common_voice_bn_31776372\",\n    \"common_voice_bn_31776435\",\n    \"common_voice_bn_31776457\",\n    \"common_voice_bn_31776459\",\n    \"common_voice_bn_31776460\",\n    \"common_voice_bn_31776461\",\n    \"common_voice_bn_31776568\",\n    \"common_voice_bn_31776700\",\n    \"common_voice_bn_31777342\",\n    \"common_voice_bn_31777802\",\n    \"common_voice_bn_31777848\",\n    \"common_voice_bn_31777864\",\n    \"common_voice_bn_31777865\",\n    \"common_voice_bn_31777868\",\n    \"common_voice_bn_31777920\",\n    \"common_voice_bn_31778016\",\n    \"common_voice_bn_31778368\",\n    \"common_voice_bn_31778691\",\n    \"common_voice_bn_31778693\",\n    \"common_voice_bn_31778886\",\n    \"common_voice_bn_31779197\",\n    \"common_voice_bn_31779659\",\n    \"common_voice_bn_31779765\",\n    \"common_voice_bn_31779767\",\n    \"common_voice_bn_31780047\",\n    \"common_voice_bn_31780049\",\n    \"common_voice_bn_31780501\",\n    \"common_voice_bn_31780589\",\n    \"common_voice_bn_31780792\",\n    \"common_voice_bn_31780840\",\n    \"common_voice_bn_31780976\",\n    \"common_voice_bn_31781084\",\n    \"common_voice_bn_31781605\",\n    \"common_voice_bn_31782044\",\n    \"common_voice_bn_31782079\",\n    \"common_voice_bn_31783553\",\n    \"common_voice_bn_31784057\",\n    \"common_voice_bn_31784318\",\n    \"common_voice_bn_31784333\",\n    \"common_voice_bn_31784384\",\n    \"common_voice_bn_31784538\",\n    \"common_voice_bn_31784564\",\n    \"common_voice_bn_31784566\",\n    \"common_voice_bn_31785133\",\n    \"common_voice_bn_31785212\",\n    \"common_voice_bn_31785249\",\n    \"common_voice_bn_31785528\",\n    \"common_voice_bn_31785667\",\n    \"common_voice_bn_31785668\",\n    \"common_voice_bn_31785944\",\n    \"common_voice_bn_31785979\",\n    \"common_voice_bn_31785980\",\n    \"common_voice_bn_31785981\",\n    \"common_voice_bn_31786203\",\n    \"common_voice_bn_31786217\",\n    \"common_voice_bn_31786281\",\n    \"common_voice_bn_31786282\",\n    \"common_voice_bn_31786283\",\n    \"common_voice_bn_31786285\",\n    \"common_voice_bn_31786367\",\n    \"common_voice_bn_31786516\",\n    \"common_voice_bn_31786519\",\n    \"common_voice_bn_31786520\",\n    \"common_voice_bn_31786521\",\n    \"common_voice_bn_31786522\",\n    \"common_voice_bn_31786845\",\n    \"common_voice_bn_31787124\",\n    \"common_voice_bn_31787691\",\n    \"common_voice_bn_31788045\",\n    \"common_voice_bn_31788130\",\n    \"common_voice_bn_31788418\",\n    \"common_voice_bn_31788513\",\n    \"common_voice_bn_31788515\",\n    \"common_voice_bn_31788516\",\n    \"common_voice_bn_31788673\",\n    \"common_voice_bn_31789054\",\n    \"common_voice_bn_31789055\",\n    \"common_voice_bn_31789056\",\n    \"common_voice_bn_31789392\",\n    \"common_voice_bn_31789393\",\n    \"common_voice_bn_31789394\",\n    \"common_voice_bn_31789395\",\n    \"common_voice_bn_31789397\",\n    \"common_voice_bn_31789475\",\n    \"common_voice_bn_31789476\",\n    \"common_voice_bn_31789477\",\n    \"common_voice_bn_31789478\",\n    \"common_voice_bn_31789479\",\n    \"common_voice_bn_31789669\",\n    \"common_voice_bn_31789694\",\n    \"common_voice_bn_31790059\",\n    \"common_voice_bn_31791040\",\n    \"common_voice_bn_31791041\",\n    \"common_voice_bn_31791043\",\n    \"common_voice_bn_31791044\",\n    \"common_voice_bn_31791090\",\n    \"common_voice_bn_31791092\",\n    \"common_voice_bn_31791138\",\n    \"common_voice_bn_31791139\",\n    \"common_voice_bn_31791140\",\n    \"common_voice_bn_31791141\",\n    \"common_voice_bn_31791142\",\n    \"common_voice_bn_31791910\",\n    \"common_voice_bn_31791987\",\n    \"common_voice_bn_31792072\",\n    \"common_voice_bn_31793357\",\n    \"common_voice_bn_31793766\",\n    \"common_voice_bn_31794478\",\n    \"common_voice_bn_31794575\",\n    \"common_voice_bn_31796218\",\n    \"common_voice_bn_31796716\",\n    \"common_voice_bn_31797835\",\n    \"common_voice_bn_31797836\",\n    \"common_voice_bn_31798447\",\n    \"common_voice_bn_31798570\",\n    \"common_voice_bn_31799923\",\n    \"common_voice_bn_31800225\",\n    \"common_voice_bn_31802483\",\n    \"common_voice_bn_31803201\",\n    \"common_voice_bn_31803980\",\n    \"common_voice_bn_31804361\",\n    \"common_voice_bn_31805137\",\n    \"common_voice_bn_31805387\",\n    \"common_voice_bn_31805441\",\n    \"common_voice_bn_31805468\",\n    \"common_voice_bn_31806488\",\n    \"common_voice_bn_31806490\",\n    \"common_voice_bn_31807719\",\n    \"common_voice_bn_31808188\",\n    \"common_voice_bn_31809237\",\n    \"common_voice_bn_31810075\",\n    \"common_voice_bn_31810245\",\n    \"common_voice_bn_31810246\",\n    \"common_voice_bn_31810247\",\n    \"common_voice_bn_31810248\",\n    \"common_voice_bn_31810249\",\n    \"common_voice_bn_31810418\",\n    \"common_voice_bn_31810435\",\n    \"common_voice_bn_31810437\",\n    \"common_voice_bn_31810438\",\n    \"common_voice_bn_31810693\",\n    \"common_voice_bn_31810714\",\n    \"common_voice_bn_31811341\",\n    \"common_voice_bn_31811425\",\n    \"common_voice_bn_31811494\",\n    \"common_voice_bn_31811511\",\n    \"common_voice_bn_31811533\",\n    \"common_voice_bn_31811914\",\n    \"common_voice_bn_31815179\",\n    \"common_voice_bn_31815991\",\n    \"common_voice_bn_31816315\",\n    \"common_voice_bn_31816316\",\n    \"common_voice_bn_31816710\",\n    \"common_voice_bn_31817036\",\n    \"common_voice_bn_31817037\",\n    \"common_voice_bn_31817038\",\n    \"common_voice_bn_31817090\",\n    \"common_voice_bn_31817127\",\n    \"common_voice_bn_31817130\",\n    \"common_voice_bn_31817132\",\n    \"common_voice_bn_31817134\",\n    \"common_voice_bn_31817159\",\n    \"common_voice_bn_31818272\",\n    \"common_voice_bn_31818995\",\n    \"common_voice_bn_31818996\",\n    \"common_voice_bn_31818997\",\n    \"common_voice_bn_31818998\",\n    \"common_voice_bn_31818999\",\n    \"common_voice_bn_31819000\",\n    \"common_voice_bn_31819001\",\n    \"common_voice_bn_31819002\",\n    \"common_voice_bn_31819004\",\n    \"common_voice_bn_31819251\",\n    \"common_voice_bn_31819471\",\n    \"common_voice_bn_31819565\",\n    \"common_voice_bn_31820299\",\n    \"common_voice_bn_31828135\",\n    \"common_voice_bn_31828948\",\n    \"common_voice_bn_31828950\",\n    \"common_voice_bn_31828952\",\n    \"common_voice_bn_31828954\",\n    \"common_voice_bn_31828955\",\n    \"common_voice_bn_31831117\",\n    \"common_voice_bn_31831119\",\n    \"common_voice_bn_31832651\",\n]\n\nskipFiles = [ f\"{p}.mp3\" for p in skipFiles ]\nprint(f\"Skipping {len(skipFiles)} files from training set\")","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:05:51.836454Z","iopub.execute_input":"2022-08-25T05:05:51.836811Z","iopub.status.idle":"2022-08-25T05:05:52.229408Z","shell.execute_reply.started":"2022-08-25T05:05:51.836782Z","shell.execute_reply":"2022-08-25T05:05:52.228270Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"3\"></a> <br>\n## Function Definitions","metadata":{}},{"cell_type":"markdown","source":"<a id=\"3-1\"></a> <br>\n### Functions for filter and normalizing dataset","metadata":{}},{"cell_type":"code","source":"def filterVotes(x: pd.Series):\n    \"\"\"\n    This function returns whether x should be filtered based on\n    the ratio of up to down votes. if it should be filtered, the\n    function returns None, otherwise 1. We later use panda's dropna()\n    method to drop rows containng None.\n    \"\"\"\n    up = x[\"up_votes\"]\n    down = x[\"down_votes\"]\n    if down > 0 and up / down < 1:\n        return None\n    return 1\n\n# Regex for matching zero witdh joiner variations.\nSTANDARDIZE_ZW = re.compile(r'(?<=\\u09b0)[\\u200c\\u200d]+(?=\\u09cd\\u09af)')\n\n# Regex for removing standardized zero width joiner, except in edge cases.\nDELETE_ZW = re.compile(r'(?<!\\u09b0)[\\u200c\\u200d](?!\\u09cd\\u09af)')\n\n# Regex matching punctuations to remove.\nPUNC = re.compile(r'([\\?\\.।;:,!\"\\'])')\n\ndef removeOptionalZW(text):\n    \"\"\"\n    Removes all optional occurrences of ZWNJ or ZWJ from Bangla text.\n    \"\"\"\n    text = STANDARDIZE_ZW.sub('\\u200D', text)\n    text = DELETE_ZW.sub('', text)\n    return text\n\ndef removePunc(text):\n    \"\"\"\n    Remove for punctuations from text.\n    \"\"\"\n    text = PUNC.sub(r\"\", text)\n    return text\n\ndef normalizeUnicode(text, normalize_nukta=True):\n    \"\"\"\n    Normalizes unicode strings using the Normalization Form Canonical\n    Composition (NFC) scheme where we first decompose all characters and then\n    re-compose combining sequences in a specific order as defined by the\n    standard in unicodedata module. Finally all zero-width joiners are\n    removed.\n    \"\"\"\n    if normalize_nukta:\n        words = [ bnorm(word)['normalized']  for word in text.split() ]\n        text = \" \".join([word for word in words if word is not None])\n        text = text.replace(\"\\u2047\", \"-\")\n\n    text = text.replace(u\"\\u098c\", u\"\\u09ef\")\n    text = unicodedata.normalize(\"NFC\", text)\n    text = removeOptionalZW(text)\n    text = removePunc(text)\n\n    return text","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:05:56.740728Z","iopub.execute_input":"2022-08-25T05:05:56.741761Z","iopub.status.idle":"2022-08-25T05:05:56.753642Z","shell.execute_reply.started":"2022-08-25T05:05:56.741717Z","shell.execute_reply":"2022-08-25T05:05:56.752625Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"3-2\"></a> <br>\n### Audio Loader Class and PyTorch Dataset\n\n- This class transcodes and augments audio files and can be used with PyTorch dataloaders.","metadata":{}},{"cell_type":"code","source":"class AudioConverter:\n    \"\"\"\n    AudioConverter offers methods to load, transcode and augment\n    audio data in various ways.\n    \"\"\"\n\n    # Configurations for parameters used in torchaudio's resampling kernel.\n    resampleFilterParams = {\n        \"fast\": {  # Fast and less accurate but still MSE = ~2e-5 compared to librosa.\n            \"lowpass_filter_width\": 16,\n            \"rolloff\": 0.85,\n            \"resampling_method\": \"kaiser_window\",\n            \"beta\": 8.555504641634386,\n        },\n        \"best\": { # Twice as slow, and a little bit more accburate.\n            \"lowpass_filter_width\": 64,\n            \"rolloff\": 0.9475937167399596,\n            \"resampling_method\": \"kaiser_window\",\n            \"beta\": 14.769656459379492,       \n        },\n    }\n\n    def __init__(\n        self,\n        sampleRate: int,\n        disableAug: bool = False,\n        speedAugProb: float = 0.5,\n        volAugProb: float = 0.5,\n        reverbAugProb: float = 0.25,\n        noiseAugProb: float = 0.25,\n        speedFactors: Tuple[float, float] = None,\n        volScaleMinMax: Tuple[float, float] = None,\n        reverbRoomScaleMinMax: Tuple[float, float] = None,\n        reverbHFDampingMinMax: Tuple[float, float] = None,\n        reverbSustainMinMax: Tuple[float, float] = None,\n        noiseSNRMinMax: Tuple[float, float] = None,\n        noiseFileList: List[str] = None,\n    ):\n        \"\"\"\n        Initializes AudioConverter.\n\n        Parameters\n        ----------\n        sampleRate: int\n            Sampling rate to convert audio to, if required.\n\n        disableAug: bool, optional\n            If True, overrides all other augmentation configs and\n            disables all augmentatoins.\n\n        speedAugProb: float, optional\n            Probability that speed augmentation will be applied.\n            If <= 0, speed augmentation is disabled.\n\n        volAugProb: float, optional\n            Probability that volume augmentation will be applied.\n            If <= 0, volume augmentation is disabled.\n\n        reverbAugProb: float, optional\n            Probability that reverberation augmentation will be applied.\n            If <= 0, reverberation augmentation is disabled.\n\n        noiseAugProb: float, optional\n            Probability that noise augmentation will be applied.\n            If <= 0, noise augmentation is disabled.\n\n        speedFactors: List[float], optional\n            List of factors by which to speed up (>1) or slow down (<1)\n            audio by. One factor is chosen randomly if provided. Otherwise,\n            default speed factors are [0.9, 1.0, 1.0].\n            \n        volScaleMinMax: Tuple[float, float], optional\n            [Min, Max] range for volume scale factors. One factor is\n            chose randomly with uniform probability from this range.\n            Default range is [0.125, 2.0].\n\n        reverbRoomScaleMinMax: Tuple[float, float], optional\n            [Min, Max] range for room size percentage. Values must be\n            between 0 and 100. Larger room size results in more reverb.\n            Default range is [25, 75].\n\n        reverbHFDampingMinMax: Tuple[float, float], optional\n            [Min, Max] range for high frequency damping percentage. Values must\n            be between 0 and 100. More damping results in muffled sound.\n            Default range is [25, 75].\n        \n        reverbSustainMinMax: Tuple[float, float], optional\n            [Min, Max] range for reverberation sustain percentage. Values must\n            be between 0 and 100. More sustain results in longer lasting echoes.\n            Default range is [25, 75].\n            \n        noiseSNRMinMax: Tuple[float, float], optional\n            [Min, Max] range for signal-to-noise ratio when adding noise. One\n            factor is chose randomly with uniform probability from this range.\n            Lower SNR results in louder noise. Default range is [10.0, 30.0].\n\n        noiseFileList: List[str], optional\n            List of paths to audio files to use as noise samples. If None is provided,\n            noise augmentation will be disabled. Otherwise, the audio files will be assumed\n            to be sources of noise, and be mixed in with speech audio on-the-fly.\n        \"\"\"\n        self.sampleRate = sampleRate\n        \n        enableAug = not disableAug\n        self.speedAugProb = speedAugProb if enableAug else -1\n        self.volAugProb = volAugProb if enableAug else -1\n        self.reverbAugProb = reverbAugProb if enableAug else -1\n        self.noiseAugProb = noiseAugProb if enableAug else -1\n        \n        # Factors by which audio speed is perturbed.\n        self.speedFactors = speedFactors\n        if speedFactors is None:\n            self.speedFactors = [0.9, 1.0, 1.1]\n        \n        # [Min, Max] Volume scale range.\n        self.volScaleRange = volScaleMinMax\n        if volScaleMinMax is None:\n            self.volScaleRange = [0.125, 2.0]\n        \n        # [Min, Max] Room size as a percentage, higher = more reverb\n        self.reverbRoomScaleRange = reverbRoomScaleMinMax\n        if reverbRoomScaleMinMax is None:\n            self.reverbRoomScaleRange = [25, 75]\n        \n        # [Min, Max] High frequency damping as a percentage, higher = more damping.\n        self.reverbHFDampingRange = reverbHFDampingMinMax\n        if reverbHFDampingMinMax is None:\n            self.reverbHFDampingRange = [25, 75]\n        \n        # [Min, Max] How long reverb is sustained as a percentage, higher = lasts longer.\n        self.reverbSustainRange = reverbSustainMinMax \n        if reverbSustainMinMax is None:\n            self.reverbSustainRange = [25, 75]       \n\n        # Audio files to use as source of noise.\n        self.noiseFiles = noiseFileList\n        if self.noiseFiles is None or len(self.noiseFiles) == 0:\n            self.noiseAugProb = -1\n\n        # [Min, Max] Signal to noise ratio range for adding noise to audio.\n        # Lower SNR = noise is more prominent, i.e. speech is more noisy.\n        self.noiseSNRRange = noiseSNRMinMax\n        if noiseSNRMinMax is None:\n            self.noiseSNRRange = [10.0, 30.0]\n        \n        self.validateConfig()\n        \n    def validateConfig(self):\n        \"\"\"\n        Checks configured options and raises an error if they\n        are not consistent with what is expected.\n        \"\"\"\n        if len(self.volScaleRange) != 2:\n            raise ValueError(\"volume scale range must be provided as [min, max]\")\n        if len(self.reverbRoomScaleRange) != 2:\n            raise ValueError(\"reverb room scale range must be provided as [min, max]\")\n        if len(self.reverbHFDampingRange) != 2:\n            raise ValueError(\"reverb high frequency dampling range must be provided as [min, max]\")\n        if len(self.reverbSustainRange) != 2:\n            raise ValueError(\"reverb sustain range must be provided as [min, max]\")\n        if len(self.noiseSNRRange) != 2:\n            raise ValueError(\"noise SNR range must be provided as [min, max]\")\n            \n        for v in self.reverbRoomScaleRange:\n            if v > 100 or v < 0:\n                raise ValueError(\"reverb room scale must be between 0 and 100\")\n        for v in self.reverbHFDampingRange:\n            if v > 100 or v < 0:\n                raise ValueError(\"reverb high frequency dampling must be between 0 and 100\")\n        for v in self.reverbSustainRange:\n            if v > 100 or v < 0:\n                raise ValueError(\"reverb sustain range must be between 0 and 100\")\n\n    @classmethod\n    def loadAudio(\n        cls, audioPath: str, sampleRate: int = None, returnTensor: bool = True, resampleType: str = \"fast\",\n    ) -> Union[torch.Tensor, np.ndarray]:\n        \"\"\"\n        Uses torchaudio to load and resample (if necessary) audio files and returns\n        audio samples as either a numpy.float32 array or a torch.Tensor.\n        \n        Parameters\n        ----------\n        audioPath: str\n            Path to audio file file (wav / mp3 / flac).\n        \n        sampleRate: int, optional\n            Sampling rate to convert audio to. If None,\n            audio is not resampled.\n        \n        returnTensor: bool, optional\n            If True, the audio samples are returned as a torch.Tensor.\n            Otherwise, the samples are returned as a numpy.float32 array.\n            \n        resampleType: str, optional\n            Either \"fast\" or \"best\" - sets the quality of resampling.\n            \"best\" is twice as slow as \"fast\" but more accurate. \"fast\"\n            is still comparable to librosa's resampled output though,\n            in terms of MSE.\n\n        Returns\n        -------\n        Union[torch.Tensor, np.ndarray]\n            Audio waveform scaled between +/- 1.0 as either a numpy.float32 array,\n            or torch.Tensor, with shape (channels, numSamples)\n        \"\"\"\n        x, sr = torchaudio.load(audioPath)\n        if sampleRate is not None or sr != sampleRate:\n            x = F.resample(x, sr, sampleRate, **cls.resampleFilterParams[resampleType])\n        \n        if returnTensor:\n            return x\n        \n        return x.numpy()\n\n    def getAudio(self, audioPath: str, returnTensor: bool = False) -> Union[np.ndarray, torch.Tensor]:\n        \"\"\"\n        Loads audio from specified path and applies augmentations randomly\n        on-the-fly. Audio samples scaled between -1.0 and +1.0 are returned\n        as a numpy.float32 array or torch.Tensor with shape (numSamples,).\n\n        Parameters\n        ----------\n        audioPath: str\n            Path to audio file file (wav / mp3 / flac).\n        \n        returnTensor: bool, optional\n            If True, the audio samples are returned as a torch.Tensor.\n            Otherwise, the samples are returned as a numpy.float32 array.\n        \n        Returns\n        ------- \n        Union[torch.Tensor, np.ndarray]\n            Audio waveform scaled between +/- 1.0 as either a numpy.float32 array,\n            or torch.Tensor, with shape (channels, numSamples)\n        \"\"\"\n        wav = self.loadAudio(\n            audioPath, sampleRate=self.sampleRate, returnTensor=True, resampleType=\"fast\",\n        )\n\n        # Applying sox-based effects first.\n        effects = []\n        \n        if random.uniform(0, 1) <= self.speedAugProb:\n            effects.extend([\n                [\"speed\", f\"{random.choice(self.speedFactors)}\"],\n                [\"rate\", f\"{self.sampleRate}\"],\n            ])\n\n        if random.uniform(0, 1) <= self.reverbAugProb:\n            effects.append([\n                \"reverb\",\n                f\"{random.uniform(*self.reverbSustainRange)}\",\n                f\"{random.uniform(*self.reverbHFDampingRange)}\",\n                f\"{random.uniform(*self.reverbRoomScaleRange)}\",\n            ])\n        \n        # If no effects are selected, this is a no-op.\n        wav = self.applySoxEffects(wav, effects)\n\n        if random.uniform(0, 1) <= self.noiseAugProb:\n            noiseFile = random.choice(self.noiseFiles)\n            noiseSNR = random.uniform(*self.noiseSNRRange)\n            wav = self.addNoiseFromFile(wav, noiseFile, noiseSNR)\n\n        if random.uniform(0, 1) <= self.volAugProb:\n            volScale = random.uniform(*self.volScaleRange)\n            wav = self.scaleVolume(wav, volScale)\n        \n        if returnTensor:\n            return wav\n        \n        return wav.numpy()\n\n\n    def scaleVolume(self, wav: Union[np.ndarray, torch.Tensor], scale: float) -> torch.Tensor:\n        \"\"\"\n        Scales the amplitude (with clipping) of the provided audio signal\n        by the given scale factor.\n        \n        Parameters\n        ----------\n        wav: Union[np.ndarray, torch.Tensor]\n             Audio samples scaled between -1.0 and +1.0, with shape\n             (channels, numSamples).\n\n        Returns\n        -------\n        torch.Tensor\n            Audio samples with perturbed volume.\n        \"\"\"\n        if scale == 1.0:\n            return wav\n\n        return torch.clamp(wav * scale, -1.0, 1.0)\n\n    def addNoiseFromFile(\n        self, wav: Union[np.ndarray, torch.Tensor], noiseFile: str, snr: float,\n    ) -> torch.Tensor:\n        \"\"\"\n        Adds noise signal from provided noise audio file at the \n        specified SNR to the speech signal.\n        \n        Parameters\n        ----------\n        wav: Union[np.ndarray, torch.Tensor]\n             Audio samples scaled between -1.0 and +1.0, with shape\n             (channels, numSamples).\n\n        snr: float\n            Signal-to-Noise ratio at which to mix in the noise signal.\n        \n        Returns\n        -------\n        torch.Tensor\n            Audio samples with noise added at specified SNR.\n        \"\"\"\n        # Loading noise signal.\n        noiseSig = self.loadAudio(\n            noiseFile, sampleRate=self.sampleRate, returnTensor=True, resampleType=\"fast\",\n        )\n\n        # Computing noise power.\n        noisePower = torch.mean(torch.pow(noiseSig, 2))\n        \n        # Computing signal power.\n        signalPower = torch.mean(torch.pow(wav, 2))\n\n        # Noise Coefficient for target SNR; amplitude coeff is sqrt of power coeff.\n        noiseScale = torch.sqrt((signalPower / noisePower) / (10 ** (snr / 20.0)))\n        \n        # Add noise at random location in speech signal.\n        nWav, nNoise = wav.shape[-1], noiseSig.shape[-1]\n\n        if nWav < nNoise:\n            a = random.randint(0, nNoise-nWav)\n            b = a + nWav\n            return wav + (noiseSig[..., a:b] * noiseScale)\n        \n        a = random.randint(0, nWav-nNoise)\n        b = a + nNoise          \n        wav[..., a:b] += (noiseSig * noiseScale)\n\n        return wav\n    \n        \n    def applySoxEffects(self, wav: Union[np.ndarray, torch.Tensor], effects: List[List[str]]) -> torch.Tensor:\n        \"\"\"\n        Applies different audio manipulation effects to provided audio, like\n        speed and volume perturbation, reverberation etc. For a full list of\n        supported effects, check torchaudio.sox_effects.\n\n        Parameters\n        ----------\n        wav: Union[np.ndarray, torch.Tensor]\n             Audio samples scaled between -1.0 and +1.0, with shape\n             (channels, numSamples).\n        \n        effects: List[List[str]]\n            List of sox effects and associated arguments, example:\n            '[ [\"speed\", \"1.2\"], [\"vol\", \"0.5\"] ]'\n\n        Returns\n        -------\n        torch.Tensor\n            Audio samples with effects applied. May not be the same\n            number of samples as input sample array, depending on types\n            of effects applied (e.g. speed perturbation may reduce or\n            increase the number of samples).\n        \"\"\"\n        if effects is None or len(effects) == 0:\n            return wav\n\n        wav, _ = torchaudio.sox_effects.apply_effects_tensor(\n            wav, sample_rate=self.sampleRate, effects=effects,\n        )\n\n        return wav\n    \n    def perturbSpeed(self, wav: Union[np.ndarray, torch.Tensor], factor: float) -> torch.Tensor:\n        \"\"\"\n        Perturbs the speed of the provided audio signal by the given factor.\n        \n        Parameters\n        ----------\n        wav: Union[np.ndarray, torch.Tensor]\n             Audio samples scaled between -1.0 and +1.0, with shape\n             (channels, numSamples).\n\n        Returns\n        -------\n        torch.Tensor\n            Audio samples with perturbed speed. Will have more or less\n            samples than input depending on whether slowed down or\n            sped up.\n        \"\"\"\n        effects = [\n            [\"speed\", f\"{factor}\"],\n            [\"rate\", f\"{self.sampleRate}\"],\n        ]\n        \n        return self.applySoxEffects(wav, effects)\n    \n    def addReverb(\n        self, wav: Union[np.ndarray, torch.Tensor], roomSize: float, hfDamping: float, sustain: float,\n    ) -> torch.Tensor:\n        \"\"\"\n        Adds reverberation to the provided audio signal using given parameters.\n        \n        Parameters\n        ----------\n        wav: Union[np.ndarray, torch.Tensor]\n             Audio samples scaled between -1.0 and +1.0, with shape\n             (channels, numSamples).\n        \n        roomSize: float\n            Room size as a percentage between 0 and 100,\n            higher = more reverb\n\n        hfDamping: float\n            High Frequency damping as a percentage between 0 and 100,\n            higher = more damping.\n\n        sustain: float\n            How long reverb is sustained as a percentage between 0 and 100,\n            higher = lasts longer.\n\n        Returns\n        -------\n        torch.Tensor\n            Audio samples with reverberated audio.\n        \"\"\"\n        effects = [[\"reverb\", f\"{roomSize}\", f\"{hfDamping}\", f\"{sustain}\"]]\n        return self.applySoxEffects(wav, effects)","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:06:00.932841Z","iopub.execute_input":"2022-08-25T05:06:00.933328Z","iopub.status.idle":"2022-08-25T05:06:00.973496Z","shell.execute_reply.started":"2022-08-25T05:06:00.933294Z","shell.execute_reply":"2022-08-25T05:06:00.972313Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- This PyTorch Dataset class uses the AudioConverter class to load audio files parallely.","metadata":{}},{"cell_type":"code","source":"class SprintDataset(Dataset):\n        \n    def __init__(self, df, processor, audioConverter, loopDataset=1):\n        self.df = df\n        self.paths = df['path']\n        self.sentences = df['sentence']\n        self.len = len(self.df) * loopDataset\n\n        self.processor = processor\n        self.ac = audioConverter\n\n    def __len__(self):\n        return self.len\n\n    def loadSample(self, idx):\n        idx %= len(self.df)\n        audio_path = self.paths[idx]\n        sentence = self.sentences[idx]\n\n        wave = self.ac.getAudio(audio_path)[0]\n        input_values = processor(wave, sampling_rate=16000).input_values[0]\n\n        input_length = len(input_values)\n        with self.processor.as_target_processor():\n            labels = self.processor(sentence).input_ids\n\n        return {\n            'input_values':input_values,\n            'input_length':input_length,\n            'labels':labels\n        }\n\n    def __getitem__(self, idx): \n        if idx >= self.len:\n            raise IndexError('index out of range')\n        return self.loadSample(idx)","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:06:02.124273Z","iopub.execute_input":"2022-08-25T05:06:02.124654Z","iopub.status.idle":"2022-08-25T05:06:02.134230Z","shell.execute_reply.started":"2022-08-25T05:06:02.124598Z","shell.execute_reply":"2022-08-25T05:06:02.132902Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"@dataclass\nclass DataCollatorCTCWithPadding:\n    \"\"\"\n    Data collator that will dynamically pad the inputs received.\n    Args:\n        processor (:class:`~transformers.Wav2Vec2Processor`)\n            The processor used for proccessing the data.\n        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):\n            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)\n            among:\n            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single\n              sequence if provided).\n            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the\n              maximum acceptable input length for the model if that argument is not provided.\n            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of\n              different lengths).\n    \"\"\"\n\n    processor: Wav2Vec2Processor\n    padding: Union[bool, str] = True\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 lenghts and need\n        # different padding methods\n        input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n        label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n\n        batch = self.processor.pad(\n            input_features,\n            padding=self.padding,\n            return_tensors=\"pt\",\n        )\n        with self.processor.as_target_processor():\n            labels_batch = self.processor.pad(\n                label_features,\n                padding=self.padding,\n                return_tensors=\"pt\",\n            )\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        batch[\"labels\"] = labels\n\n        return batch","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:06:05.152548Z","iopub.execute_input":"2022-08-25T05:06:05.153576Z","iopub.status.idle":"2022-08-25T05:06:05.163361Z","shell.execute_reply.started":"2022-08-25T05:06:05.153532Z","shell.execute_reply":"2022-08-25T05:06:05.162057Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"4\"></a> <br>\n## Main","metadata":{}},{"cell_type":"code","source":"# Testing punctuation removal\ntext = \"আমার আ,ম; ;?অনেক?! .ভাল: 'লাগে\"\ndisplay(HTML(f\"original: {text}\"))\ndisplay(HTML(f\"puncs removed: {removePunc(text)}\"))","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:06:06.536918Z","iopub.execute_input":"2022-08-25T05:06:06.537543Z","iopub.status.idle":"2022-08-25T05:06:06.549468Z","shell.execute_reply.started":"2022-08-25T05:06:06.537508Z","shell.execute_reply":"2022-08-25T05:06:06.548342Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Loading metadata files.\ntrain_df = pd.read_csv(Config.train_csv_path)\nvalid_df = pd.read_csv(Config.valid_csv_path)\n\nprint(f\"Train utts before filtering: {len(train_df)}\")\n\n# Removing files with an up down vote ratio of less than 1.\nprint(\"Filtering training dataset ... \")\ntrain_df[\"up_votes\"] = train_df.apply(filterVotes, axis=1)\ntrain_df.dropna(subset = ['up_votes'], inplace=True)\n\n# Removing files that we encountered errors in a previous runs.\ntrain_df = train_df[~train_df.path.isin(skipFiles)]\ntrain_df = train_df.reset_index()\nvalid_df = valid_df.reset_index()\n\nprint(f\"Train utts after filtering: {len(train_df)}\")","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:06:08.635440Z","iopub.execute_input":"2022-08-25T05:06:08.635808Z","iopub.status.idle":"2022-08-25T05:06:13.261919Z","shell.execute_reply.started":"2022-08-25T05:06:08.635776Z","shell.execute_reply":"2022-08-25T05:06:13.260799Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"4-1\"></a> <br>\n### Build Language Model\n\n- The language model is built using KenLM tooklit (https://github.com/kpu/kenlm).\n- The text used to create the language model is the combination of training and validation transcripts.\n- During model selection process based on validation results, we left out validation transcripts.","metadata":{}},{"cell_type":"code","source":"lmDir = Config.language_model_dir\norder = Config.ngram_order\n\nos.makedirs(lmDir, exist_ok=True)\n\nwith open(f\"{lmDir}/train-text.txt\", 'w') as f:\n    for line in train_df[\"sentence\"]:\n        # Keeping the variants of letters where the nukta is part of the character\n        # and where it's a separate joining character since the base model was trained\n        # this way.\n        line = normalizeUnicode(line.strip(), normalize_nukta=False)\n        f.write(f\"{line}\\n\")\n\n    for line in valid_df[\"sentence\"]:\n        # Keeping the variants of letters where the nukta is part of the character\n        # and where it's a separate joining character since the base model was trained\n        # this way.\n        line = normalizeUnicode(line.strip(), normalize_nukta=False)\n        f.write(f\"{line}\\n\")\n\n!echo -e \"LM training text stats [lines words chars]:\\n$(wc {lmDir}/train-text.txt)\"\n!./deps/kenlm/kenlm/bin/lmplz -o \"{order}\" --discount_fallback < \"{lmDir}/train-text.txt\" > \"{lmDir}/dlsprint-data-lm.{order}.arpa\"","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:06:13.263958Z","iopub.execute_input":"2022-08-25T05:06:13.264692Z","iopub.status.idle":"2022-08-25T05:06:26.809343Z","shell.execute_reply.started":"2022-08-25T05:06:13.264609Z","shell.execute_reply":"2022-08-25T05:06:26.808147Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Apply unicode normalization.\nprint(\"Normalizing training and validation transcripts... \")\n# train_df[\"sentence\"] = train_df.apply(lambda x: normalizeUnicode(x[\"sentence\"]), axis=1)\n# valid_df[\"sentence\"] = valid_df.apply(lambda x: normalizeUnicode(x[\"sentence\"]), axis=1)\ntrain_df[\"sentence\"] = [ normalizeUnicode(x) for x in tqdm(train_df[\"sentence\"]) ]\nvalid_df[\"sentence\"] = [ normalizeUnicode(x) for x in tqdm(valid_df[\"sentence\"]) ]\n\n# Updating paths to point to data directories.\ntrain_df[\"path\"] = [ os.path.join(Config.train_path, x.replace(\"mp3\", Config.audio_ext)) for x in train_df['path'] ]\nvalid_df[\"path\"] = [ os.path.join(Config.valid_path, x.replace(\"mp3\", Config.audio_ext)) for x in valid_df['path'] ]\n\n# Keeping only audio filename and transcript columns.\ntrain_df = train_df[[\"path\",\"sentence\"]]\nvalid_df = valid_df[[\"path\",\"sentence\"]]","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:06:26.811562Z","iopub.execute_input":"2022-08-25T05:06:26.811941Z","iopub.status.idle":"2022-08-25T05:20:10.476124Z","shell.execute_reply.started":"2022-08-25T05:06:26.811902Z","shell.execute_reply":"2022-08-25T05:20:10.475155Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Checking a sample from the train dataframe.\nsample = train_df.iloc[random.randint(0, len(train_df))]\n\ndisplay(HTML(sample['sentence']))\n\nx = AudioConverter.loadAudio(sample['path'], sampleRate=Config.sample_rate, returnTensor=False)\ndisplay(Audio(x, rate=Config.sample_rate))","metadata":{"execution":{"iopub.status.busy":"2022-08-25T01:16:05.531506Z","iopub.execute_input":"2022-08-25T01:16:05.531944Z","iopub.status.idle":"2022-08-25T01:16:05.675136Z","shell.execute_reply.started":"2022-08-25T01:16:05.531902Z","shell.execute_reply":"2022-08-25T01:16:05.674155Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"vocab_dict_nc={v:k for k,v in vocab_dict_cn.items()}\nvocab = list(vocab_dict_cn.keys())\n\nwith open('vocab.json', 'w') as f:\n    json.dump(vocab_dict_cn, f)\n    \nprint(f\"vocabulary size = {len(vocab)}\")","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:20:10.480385Z","iopub.execute_input":"2022-08-25T05:20:10.481222Z","iopub.status.idle":"2022-08-25T05:20:10.488588Z","shell.execute_reply.started":"2022-08-25T05:20:10.481185Z","shell.execute_reply":"2022-08-25T05:20:10.487658Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(\n    \"./\",\n    unk_token=\"[UNK]\",\n    pad_token=\"[PAD]\",\n    word_delimiter_token=\"|\",\n    bos_token=\"<s>\",\n    eos_token=\"</s>\",\n)\n\nfeature_extractor = Wav2Vec2FeatureExtractor(\n    feature_size=1,\n    sampling_rate=Config.sample_rate,\n    padding_value=0.0,\n    padding_side=\"right\",\n    do_normalize=True,\n    return_attention_mask=True,\n)\n\nprocessor = Wav2Vec2Processor(\n    feature_extractor=feature_extractor,\n    tokenizer=tokenizer,\n)","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:24:25.129481Z","iopub.execute_input":"2022-08-25T05:24:25.130150Z","iopub.status.idle":"2022-08-25T05:24:25.138504Z","shell.execute_reply.started":"2022-08-25T05:24:25.130115Z","shell.execute_reply":"2022-08-25T05:24:25.137343Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Creating dataset objects for train and val.\ndisable_aug = not Config.use_augmentation\nloop_train_dataset = 2\nloop_val_datset = 1\n\ntrain_ac = AudioConverter(sampleRate=Config.sample_rate, disableAug=disable_aug, noiseFileList=train_df['path'].tolist())\nval_ac = AudioConverter(sampleRate=Config.sample_rate, disableAug=True)\n\ntrain_dataset = SprintDataset(train_df, processor, train_ac, loop_train_dataset)\nvalid_dataset = SprintDataset(valid_df, processor, val_ac, loop_val_datset)\n\ndata_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:24:27.885553Z","iopub.execute_input":"2022-08-25T05:24:27.886148Z","iopub.status.idle":"2022-08-25T05:24:27.900757Z","shell.execute_reply.started":"2022-08-25T05:24:27.886114Z","shell.execute_reply":"2022-08-25T05:24:27.899648Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Checking sample from dataset.\nsample = train_dataset[123]\npprint(sample)\n\nx = sample['input_values']\ny = [ vocab_dict_nc[x] for x in sample['labels'] ]\n\ndisplay(HTML(str(y)))\ndisplay(Audio(x, rate=Config.sample_rate))","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:24:29.495176Z","iopub.execute_input":"2022-08-25T05:24:29.495525Z","iopub.status.idle":"2022-08-25T05:24:29.569243Z","shell.execute_reply.started":"2022-08-25T05:24:29.495495Z","shell.execute_reply":"2022-08-25T05:24:29.568322Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Callback for computing metrics","metadata":{}},{"cell_type":"code","source":"wer_metric = load_metric(\"../input/csefest2022dlsprintdeps/metrics/metrics/wer.py\")\ncer_metric = load_metric(\"../input/csefest2022dlsprintdeps/metrics/metrics/cer.py\")\n\ndef compute_metrics(pred):\n    pred_logits = pred.predictions\n    pred_ids = np.argmax(pred_logits, axis=-1)\n\n    pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n    \n    # We do not want to group tokens when computing the metrics\n    pred_str = processor.batch_decode(pred_ids)\n    label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n\n    wer = wer_metric.compute(predictions=pred_str, references=label_str)\n    cer = cer_metric.compute(predictions=pred_str, references=label_str)\n\n    return {\"wer\": wer, \"cer\": cer}","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:24:36.981325Z","iopub.execute_input":"2022-08-25T05:24:36.981683Z","iopub.status.idle":"2022-08-25T05:24:37.013501Z","shell.execute_reply.started":"2022-08-25T05:24:36.981653Z","shell.execute_reply":"2022-08-25T05:24:37.012330Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"4-2\"></a> <br>\n### Loading base Wav2Vec2 model from HuggingFace\n\n- We use a pretrained Wav2Vec2 model from Hugging Face as a starting point.\n- Model Link: https://huggingface.co/arijitx/wav2vec2-xls-r-300m-bengali\n- The model was trained on the OPENSLR SLR53 Bengali Dataset: https://www.openslr.org/53/","metadata":{}},{"cell_type":"code","source":"base_model = \"../input/csefest2022dlsprintbasemodels/models/arijitx/wav2vec2-xls-r-300m-bengali\"\n\n# Loading model.\nmodel = Wav2Vec2ForCTC.from_pretrained(\n    base_model, \n    ignore_mismatched_sizes=False,\n    attention_dropout=Config.attention_dropout,\n    hidden_dropout=Config.hidden_dropout,\n    feat_proj_dropout=Config.feat_proj_dropout,\n    mask_time_prob=Config.mask_time_prob,\n    layerdrop=Config.layerdrop,\n    ctc_loss_reduction=\"mean\", \n    pad_token_id=processor.tokenizer.pad_token_id,\n    vocab_size=len(processor.tokenizer),\n)\n\n# Freezing encoder layers.\nmodel.freeze_feature_encoder()\n\n# Printing stats.\ntotal_param = sum(p.numel() for p in model.parameters())\ntrainable_param = sum(p.numel() for p in model.parameters() if p.requires_grad)\nprint(f\"total_param = {total_param}\")\nprint(f\"trainable = {trainable_param}\")","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:24:40.831314Z","iopub.execute_input":"2022-08-25T05:24:40.831694Z","iopub.status.idle":"2022-08-25T05:24:53.497149Z","shell.execute_reply.started":"2022-08-25T05:24:40.831662Z","shell.execute_reply":"2022-08-25T05:24:53.495702Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"4-3\"></a> <br>\n### Adapting Wav2Vec2 Model\n\n- First run with cosine lr decay schedule, dropouts enabled","metadata":{}},{"cell_type":"code","source":"trainer = Trainer(\n    model=model,\n    data_collator=data_collator,\n    args=Config.trainer,\n    compute_metrics=compute_metrics,\n    train_dataset=train_dataset,\n    eval_dataset=valid_dataset,   \n    tokenizer=processor.feature_extractor,\n    callbacks=[transformers.EarlyStoppingCallback(early_stopping_patience=Config.early_stopping_patience)],\n)","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:28:12.586194Z","iopub.execute_input":"2022-08-25T05:28:12.586566Z","iopub.status.idle":"2022-08-25T05:28:12.608858Z","shell.execute_reply.started":"2022-08-25T05:28:12.586535Z","shell.execute_reply":"2022-08-25T05:28:12.607906Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"trainer.train()","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:28:14.908039Z","iopub.execute_input":"2022-08-25T05:28:14.908462Z","iopub.status.idle":"2022-08-25T05:29:01.087240Z","shell.execute_reply.started":"2022-08-25T05:28:14.908427Z","shell.execute_reply":"2022-08-25T05:29:01.084931Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- Second run with best model from first run (ckpt-100800)\n- Constant lr schedule with lower lr, dropouts disabled.","metadata":{}},{"cell_type":"code","source":"# Updating trainer config.\nConfig.trainer = TrainingArguments(\n  output_dir=\"./run-004-wav2vec2-fulldata-constant-lr1e-7\",\n  group_by_length=False,\n  per_device_train_batch_size=16,\n  per_device_eval_batch_size=16,\n  gradient_accumulation_steps=1,\n  evaluation_strategy=\"steps\",\n  num_train_epochs=15,\n  gradient_checkpointing=True,\n  fp16=True,\n  save_steps=400,\n  eval_steps=400,\n  logging_steps=400,\n  learning_rate=1e-7,\n  dataloader_num_workers=os.cpu_count(),\n  warmup_steps=0,\n  save_total_limit=10,\n  push_to_hub=False,\n  run_name=\"run-004-wav2vec2-fulldata-constant-lr1e-7\" ,\n  load_best_model_at_end=True,\n  lr_scheduler_type=\"constant\",\n  resume_from_checkpoint=True,\n)\n\n# Disabling dropouts.\nConfig.attention_dropout = 0\nConfig.hidden_dropout = 0\nConfig.feat_proj_dropout = 0\nConfig.mask_time_prob = 0\nConfig.layerdrop = 0\n\n# Increasing patience.\nConfig.early_stopping_patience = 15","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:29:05.227750Z","iopub.execute_input":"2022-08-25T05:29:05.228839Z","iopub.status.idle":"2022-08-25T05:29:05.244469Z","shell.execute_reply.started":"2022-08-25T05:29:05.228789Z","shell.execute_reply":"2022-08-25T05:29:05.243405Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Loading best model from first run.\nmodel_path = \"./run-003-wav2vec2-fulldata-cosine-lr3e-5/ckpt-100800\"\n\nmodel = Wav2Vec2ForCTC.from_pretrained(\n    model_path,\n    ignore_mismatched_sizes=False,\n    attention_dropout=Config.attention_dropout,\n    hidden_dropout=Config.hidden_dropout,\n    feat_proj_dropout=Config.feat_proj_dropout,\n    mask_time_prob=Config.mask_time_prob,\n    layerdrop=Config.layerdrop,\n    ctc_loss_reduction=\"mean\", \n    pad_token_id=processor.tokenizer.pad_token_id,\n    vocab_size=len(processor.tokenizer),\n)","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:29:07.768443Z","iopub.execute_input":"2022-08-25T05:29:07.769441Z","iopub.status.idle":"2022-08-25T05:29:27.825322Z","shell.execute_reply.started":"2022-08-25T05:29:07.769402Z","shell.execute_reply":"2022-08-25T05:29:27.823547Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"trainer = Trainer(\n    model=model,\n    data_collator=data_collator,\n    args=Config.trainer,\n    compute_metrics=compute_metrics,\n    train_dataset=train_dataset,\n    eval_dataset=valid_dataset,   \n    tokenizer=processor.feature_extractor,\n    callbacks=[transformers.EarlyStoppingCallback(early_stopping_patience=Config.early_stopping_patience)],\n)","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:29:27.826430Z","iopub.status.idle":"2022-08-25T05:29:27.828022Z","shell.execute_reply.started":"2022-08-25T05:29:27.827750Z","shell.execute_reply":"2022-08-25T05:29:27.827775Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"trainer.train()","metadata":{"execution":{"iopub.status.busy":"2022-08-25T05:29:27.829499Z","iopub.status.idle":"2022-08-25T05:29:27.830305Z","shell.execute_reply.started":"2022-08-25T05:29:27.830047Z","shell.execute_reply":"2022-08-25T05:29:27.830070Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}