diff --git "a/notebooks/AISF War Room.ipynb" "b/notebooks/AISF War Room.ipynb" new file mode 100644--- /dev/null +++ "b/notebooks/AISF War Room.ipynb" @@ -0,0 +1,19077 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "079d4a35", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d0ff8f00", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('..')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "992d200a", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import matplotlib.pyplot as plt\n", + "from datetime import datetime\n", + "from IPython.display import Audio\n", + "\n", + "\n", + "# torch\n", + "import torch\n", + "import torchaudio\n", + "from torch.utils.data import DataLoader\n", + "\n", + "# model training\n", + "from cnn import CNNetwork\n", + "from dataset import VoiceDataset\n", + "from train import train, validate_epoch\n", + "\n", + "# api\n", + "from server.preprocess import process_from_url, librosa, wget" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "46f8e561", + "metadata": {}, + "outputs": [], + "source": [ + "BATCH_SIZE = 128\n", + "EPOCHS = 10\n", + "LEARNING_RATE = 0.001\n", + "\n", + "TRAIN_PATH = \"../data/aisf/train\"\n", + "TEST_PATH = \"../data/aisf/test\"\n", + "DEV_PATH = \"../data/aisf/dev\"\n", + "SAMPLE_RATE=48000\n", + "\n", + "MEL_SPEC = torchaudio.transforms.MelSpectrogram(\n", + " sample_rate=SAMPLE_RATE,\n", + " n_fft=2048,\n", + " hop_length=512,\n", + " n_mels=128,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "1f5155c2", + "metadata": {}, + "source": [ + "# Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "656f56e0", + "metadata": {}, + "outputs": [], + "source": [ + "torch.set_printoptions(profile=\"default\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c528dcef", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using cpu device.\n" + ] + } + ], + "source": [ + "if torch.cuda.is_available():\n", + " device = \"cuda\"\n", + "else:\n", + " device = \"cpu\"\n", + "print(f\"Using {device} device.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "20808900", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "21" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(train_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "fa129eec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training batch...: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17/17 [00:37<00:00, 2.20s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Loss: 1.18, Training Accuracy 0.37722854552780016\n", + "-------------------------------------------- \n", + "\n", + "Epoch 2/10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training batch...: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17/17 [00:33<00:00, 2.00s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Loss: 1.17, Training Accuracy 0.3792871172441579\n", + "-------------------------------------------- \n", + "\n", + "Epoch 3/10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training batch...: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17/17 [00:36<00:00, 2.13s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Loss: 1.17, Training Accuracy 0.3792871172441579\n", + "-------------------------------------------- \n", + "\n", + "Epoch 4/10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training batch...: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17/17 [00:34<00:00, 2.02s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Loss: 1.17, Training Accuracy 0.3827495467365028\n", + "-------------------------------------------- \n", + "\n", + "Epoch 5/10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training batch...: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17/17 [00:35<00:00, 2.08s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Loss: 1.17, Training Accuracy 0.3792871172441579\n", + "-------------------------------------------- \n", + "\n", + "Epoch 6/10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training batch...: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17/17 [00:32<00:00, 1.93s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Loss: 1.17, Training Accuracy 0.3806720890410959\n", + "-------------------------------------------- \n", + "\n", + "Epoch 7/10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training batch...: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17/17 [00:32<00:00, 1.89s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Loss: 1.17, Training Accuracy 0.3813645749395649\n", + "-------------------------------------------- \n", + "\n", + "Epoch 8/10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training batch...: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17/17 [00:34<00:00, 2.02s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Loss: 1.17, Training Accuracy 0.3813645749395649\n", + "-------------------------------------------- \n", + "\n", + "Epoch 9/10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training batch...: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17/17 [00:35<00:00, 2.10s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Loss: 1.17, Training Accuracy 0.3803258460918614\n", + "-------------------------------------------- \n", + "\n", + "Epoch 10/10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training batch...: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17/17 [00:33<00:00, 1.99s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Loss: 1.17, Training Accuracy 0.38101833199033036\n", + "-------------------------------------------- \n", + "\n", + "---- Finished Training ----\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Datasets\n", + "train_dataset = VoiceDataset(TRAIN_PATH, MEL_SPEC, device, SAMPLE_RATE, time_limit_in_secs=3)\n", + "test_dataset = VoiceDataset(TEST_PATH, MEL_SPEC, device, SAMPLE_RATE, time_limit_in_secs=3)\n", + "dev_dataset = VoiceDataset(DEV_PATH, MEL_SPEC, device, SAMPLE_RATE, time_limit_in_secs=3)\n", + "\n", + "# Dataloaders\n", + "train_dataloader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n", + "test_dataloader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)\n", + "dev_dataloader = DataLoader(dev_dataset, batch_size=BATCH_SIZE, shuffle=True)\n", + "\n", + "model = CNNetwork().to(device)\n", + "\n", + "loss_fn = torch.nn.CrossEntropyLoss()\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n", + "# optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n", + "\n", + "history = train(\n", + " model,\n", + " test_dataloader,\n", + " loss_fn,\n", + " optimizer,\n", + " device,\n", + " 10,\n", + "# test_dataloader\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "424422e9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Testing batch...: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 5.97it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.5514446496963501, 1.0)" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "validate_epoch(model, dev_dataloader, loss_fn, device)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "171304c0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trained void model saved at ../models/aisf/void_20230517_113634.pth\n" + ] + } + ], + "source": [ + "now = datetime.now()\n", + "now = now.strftime(\"%Y%m%d_%H%M%S\")\n", + "model_filename = f\"../models/aisf/void_{now}.pth\"\n", + "torch.save(model.state_dict(), model_filename)\n", + "print(f\"Trained void model saved at {model_filename}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "d2cfee0e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 0% [ ] 0 / 116590\r", + " 7% [..... ] 8192 / 116590\r", + " 14% [.......... ] 16384 / 116590\r", + " 21% [................ ] 24576 / 116590\r", + " 28% [..................... ] 32768 / 116590\r", + " 35% [.......................... ] 40960 / 116590\r", + " 42% [................................ ] 49152 / 116590\r", + " 49% [..................................... ] 57344 / 116590\r", + " 56% [.......................................... ] 65536 / 116590\r", + " 63% [................................................ ] 73728 / 116590\r", + " 70% [..................................................... ] 81920 / 116590\r", + " 77% 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{ + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[0., 0., 1.]], grad_fn=)\n", + "2\n" + ] + } + ], + "source": [ + "model = CNNetwork()\n", + "sd = torch.load('../models/aisf/void_20230517_115313.pth')\n", + "model.load_state_dict(sd)\n", + "\n", + "i = 2100\n", + "ex = test_dataset[i][0]\n", + "expected = test_dataset[i][1]\n", + "ex = ex.unsqueeze(0)\n", + "print(model(ex))\n", + "print(expected)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "a2a41ad5", + "metadata": {}, + "outputs": [], + "source": [ + "og = test_dataset[0][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "3e03cb8b", + "metadata": {}, + "outputs": [], + "source": [ + "from server.preprocess import process_from_url" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "3154fc0d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 0% [ ] 0 / 280670\r", + " 2% [.. ] 8192 / 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\u001b[43mprocess_from_url\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhttps://cdn.filestackcontent.com/A6gvnfdrQyWG3sX5G6Tz\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/ml-sandbox/VoID/notebooks/../server/preprocess.py:17\u001b[0m, in \u001b[0;36mprocess_from_url\u001b[0;34m(url)\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprocess_from_url\u001b[39m(url):\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# download UI audio\u001b[39;00m\n\u001b[1;32m 16\u001b[0m filename \u001b[38;5;241m=\u001b[39m wget\u001b[38;5;241m.\u001b[39mdownload(url)\n\u001b[0;32m---> 17\u001b[0m audio \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 18\u001b[0m wavfile\u001b[38;5;241m.\u001b[39mwrite(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtemp.wav\u001b[39m\u001b[38;5;124m'\u001b[39m, DEFAULT_SAMPLE_RATE, audio)\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# remove wget file\u001b[39;00m\n", + "File \u001b[0;32m~/anaconda3/envs/void/lib/python3.9/site-packages/torch/serialization.py:815\u001b[0m, in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, weights_only, **pickle_load_args)\u001b[0m\n\u001b[1;32m 813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 814\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m pickle\u001b[38;5;241m.\u001b[39mUnpicklingError(UNSAFE_MESSAGE \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(e)) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 815\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_legacy_load\u001b[49m\u001b[43m(\u001b[49m\u001b[43mopened_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmap_location\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpickle_module\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpickle_load_args\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/void/lib/python3.9/site-packages/torch/serialization.py:1033\u001b[0m, in \u001b[0;36m_legacy_load\u001b[0;34m(f, map_location, pickle_module, **pickle_load_args)\u001b[0m\n\u001b[1;32m 1027\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(f, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mreadinto\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m0\u001b[39m) \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m sys\u001b[38;5;241m.\u001b[39mversion_info \u001b[38;5;241m<\u001b[39m (\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m2\u001b[39m):\n\u001b[1;32m 1028\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 1029\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch.load does not work with file-like objects that do not implement readinto on Python 3.8.0 and 3.8.1. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1030\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReceived object of type \u001b[39m\u001b[38;5;130;01m\\\"\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(f)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\\"\u001b[39;00m\u001b[38;5;124m. Please update to Python 3.8.2 or newer to restore this \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1031\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunctionality.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1033\u001b[0m magic_number \u001b[38;5;241m=\u001b[39m \u001b[43mpickle_module\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpickle_load_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1034\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m magic_number \u001b[38;5;241m!=\u001b[39m MAGIC_NUMBER:\n\u001b[1;32m 1035\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid magic number; corrupt file?\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mUnpicklingError\u001b[0m: unpickling stack underflow" + ] + } + ], + "source": [ + "wav = process_from_url(\"https://cdn.filestackcontent.com/A6gvnfdrQyWG3sX5G6Tz\")" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "306f7ed0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[[False, False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False, False,\n", + " False, 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0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00]]])" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wav" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "id": "679f7316", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def foo():\n", + " for i in range(len(dev_dataset)):\n", + " if torch.all(dev_dataset[i][0] == wav):\n", + " return True\n", + "\n", + " return False\n", + "\n", + "foo()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e23718f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "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": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}