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Upload HF Eval Script.ipynb
Browse files- HF Eval Script.ipynb +189 -0
HF Eval Script.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "be1c7379",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install --quiet --root-user-action=ignore --upgrade pip\n",
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"!pip install --quiet --root-user-action=ignore \"datasets>=1.18.3\" \"transformers==4.11.3\" librosa jiwer huggingface_hub \n",
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"!pip install --quiet --root-user-action=ignore https://github.com/kpu/kenlm/archive/master.zip pyctcdecode\n",
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"!pip install --quiet --root-user-action=ignore --upgrade transformers\n",
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"!pip install --quiet --root-user-action=ignore torch_audiomentations audiomentations "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "8892305a",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Reusing dataset common_voice (/ai_data/cache/common_voice/de/6.1.0/a1dc74461f6c839bfe1e8cf1262fd4cf24297e3fbd4087a711bd090779023a5e)\n",
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"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "efc316d6eedb4dcab341dfd0fe8cc926",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/15588 [00:00<?, ?ex/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from datasets import load_dataset, Audio, load_metric\n",
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"from transformers import AutoModelForCTC, Wav2Vec2ProcessorWithLM\n",
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"import torchaudio.transforms as T\n",
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"import torch\n",
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"import unicodedata\n",
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"import numpy as np\n",
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"import re\n",
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"\n",
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"# load testing dataset \n",
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"testing_dataset = load_dataset(\"common_voice\", \"de\", split=\"test\")\n",
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"\n",
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"# replace invisible characters with space\n",
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"allchars = list(set([c for t in testing_dataset['sentence'] for c in list(t)]))\n",
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"map_to_space = [c for c in allchars if unicodedata.category(c)[0] in 'PSZ' and c not in 'ʻ-']\n",
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"replacements = ''.maketrans(''.join(map_to_space), ''.join(' ' for i in range(len(map_to_space))), '\\'ʻ')\n",
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"\n",
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"def text_fix(text):\n",
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" # change ß to ss\n",
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" text = text.replace('ß','ss')\n",
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" # convert dash to space and remove double-space\n",
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" text = text.replace('-',' ').replace(' ',' ').replace(' ',' ')\n",
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" # make lowercase\n",
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" text = text.lower()\n",
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" # remap all invisible characters to space\n",
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" text = text.translate(replacements).strip()\n",
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" # for easier comparison to Zimmermeister, replace unrepresentable characters with ?\n",
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" text = re.sub(\"[âşěýňעảנźțãòàǔł̇æồאắîשðșęūāñë生בøúıśžçćńřğ]+\",\"?\",text)\n",
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" # remove multiple spaces (again)\n",
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" text = ' '.join([w for w in text.split(' ') if w != ''])\n",
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" return text\n",
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"\n",
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"# load model\n",
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"model = AutoModelForCTC.from_pretrained(\"fxtentacle/wav2vec2-xls-r-1b-tevr\")\n",
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"model.to('cuda')\n",
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"# load processor\n",
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"class HajoProcessor(Wav2Vec2ProcessorWithLM):\n",
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" @staticmethod\n",
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" def get_missing_alphabet_tokens(decoder, tokenizer):\n",
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" return []\n",
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"processor = HajoProcessor.from_pretrained(\"fxtentacle/wav2vec2-xls-r-1b-tevr\")\n",
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"\n",
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"# this function will be called for each WAV file\n",
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"def predict_single_audio(batch, image=False): \n",
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" audio = batch['audio']['array']\n",
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" # resample, if needed\n",
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" if batch['audio']['sampling_rate'] != 16000:\n",
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" audio = T.Resample(orig_freq=batch['audio']['sampling_rate'], new_freq=16000)(torch.from_numpy(audio)).numpy()\n",
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" # normalize\n",
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" audio = (audio - audio.mean()) / np.sqrt(audio.var() + 1e-7)\n",
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" # ask HF processor to prepare audio for GPU eval\n",
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" input_values = processor(audio, return_tensors=\"pt\", sampling_rate=16_000).input_values\n",
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" # call model on GPU\n",
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" with torch.no_grad():\n",
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" logits = model(input_values.to('cuda')).logits.cpu().numpy()[0]\n",
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" # ask HF processor to decode logits\n",
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" decoded = processor.decode(logits, beam_width=500)\n",
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" # return as dictionary\n",
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" return { 'groundtruth': text_fix(batch['sentence']), 'prediction': decoded.text }\n",
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"\n",
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"# process all audio files\n",
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"all_predictions = testing_dataset.map(predict_single_audio, remove_columns=testing_dataset.column_names)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "38f5481f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0 PRED: mückenstiche sollte man nicht aufkratzen\n",
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"0 GT: mückenstiche sollte man nicht aufkratzen\n",
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"1 PRED: ist diese leitung sicher\n",
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"1 GT: ist diese leitung sicher\n",
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"2 PRED: die ratten verlassen das sinkende schiff\n",
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"2 GT: die ratten verlassen das sinkende schiff\n",
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"3 PRED: ich habe eine neue arbeit\n",
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"3 GT: ich habe eine neue arbeit\n",
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"4 PRED: was sieht kamera eins gerade\n",
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"4 GT: was sieht kamera eins gerade\n",
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"5 PRED: was für ein angeber dachte horst im stillen\n",
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"5 GT: was für ein angeber dachte horst im stillen\n",
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"6 PRED: rückgängig machen\n",
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"6 GT: rückgängig machen\n",
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"7 PRED: war die integration erfolgreich\n",
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"7 GT: war die integration erfolgreich\n"
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]
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}
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],
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"source": [
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"# log example results\n",
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"for i in range(8):\n",
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" print(i,'PRED: ',all_predictions[i]['prediction'])\n",
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" print(i,' GT: ',all_predictions[i]['groundtruth'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "cbabe801",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"WER 3.6433399042523233 %\n",
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"CER 1.5398893560981173 %\n"
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]
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}
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],
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"source": [
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"# print results\n",
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"print('WER', load_metric(\"wer\").compute(predictions=all_predictions['prediction'], references=all_predictions['groundtruth'])*100.0, '%')\n",
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"print('CER', load_metric(\"cer\").compute(predictions=all_predictions['prediction'], references=all_predictions['groundtruth'])*100.0, '%')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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