diff --git "a/train_notebook.ipynb" "b/train_notebook.ipynb"
new file mode 100644--- /dev/null
+++ "b/train_notebook.ipynb"
@@ -0,0 +1,10533 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LBSYoWbi-45k"
+ },
+ "source": [
+ "# **Fine-tuning Multi-Lingual Speech Model with 🤗 Transformers**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nT_QrfWtsxIz"
+ },
+ "source": [
+ "This notebook shows how to fine-tune multi-lingual pretrained speech models for Automatic Speech Recognition."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OK7AOAkwVOrx"
+ },
+ "source": [
+ "This notebook is built to run on the [Common Voice dataset](https://huggingface.co/datasets/common_voice) with any multi-lingual speech model checkpoint from the [Model Hub](https://huggingface.co/models?language=multilingual&pipeline_tag=automatic-speech-recognition&sort=downloads) as long as that model has a version with a Connectionist Temporal Classification (CTC) head. Depending on the model and the GPU you are using, you might need to adjust the batch size to avoid out-of-memory errors. Set those two parameters, then the rest of the notebook should run smoothly:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "i69NoS4Kvh_f"
+ },
+ "outputs": [],
+ "source": [
+ "\n",
+ "model_checkpoint = \"facebook/wav2vec2-xls-r-300m\"\n",
+ "batch_size = 16"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "fw_GGnvwVjOl"
+ },
+ "outputs": [],
+ "source": [
+ "model_checkpoint = \"facebook/wav2vec2-large-xlsr-53\"\n",
+ "batch_size = 16"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZUUqzyKnVpvc"
+ },
+ "source": [
+ "For a more in-detail explanation of how multi-lingual pretrained speech models function, please take a look at the [🤗 Blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "e335hPmdtASZ"
+ },
+ "source": [
+ "Before we start, let's install both `datasets` and `transformers` from master. Also, we need the `torchaudio` and `librosa` package to load audio files and the `jiwer` to evaluate our fine-tuned model using the [word error rate (WER)](https://huggingface.co/metrics/wer) metric ${}^1$."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "c8eh87Hoee5d"
+ },
+ "outputs": [],
+ "source": [
+ "%%capture\n",
+ "!pip install datasets==1.18.3\n",
+ "#common_voice 7 below\n",
+ "#!pip install datasets==1.14\n",
+ "!pip install transformers==4.11.3\n",
+ "\n",
+ "!pip install torchaudio\n",
+ "!pip install librosa\n",
+ "!pip install jiwer"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0xxt_LwxDQlO"
+ },
+ "source": [
+ "Next we strongly suggest to upload your training checkpoints directly to the [🤗 Hub](https://huggingface.co/) while training. The [🤗 Hub](https://huggingface.co/) has integrated version control so you can be sure that no model checkpoint is getting lost during training. \n",
+ "\n",
+ "To do so you have to store your authentication token from the Hugging Face website (sign up [here](https://huggingface.co/join) if you haven't already!)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 359,
+ "referenced_widgets": [
+ "f806b26119884e688585835e33bd9cda",
+ "62281d26fca2464b891e4e8dced07110",
+ "e8ed03cfa00c4941a2351dde3c2e4cb7",
+ "5f40b4c187664350ad4f7bce35518d47",
+ "400d6ff9e84d476cadeaddfc57ba7f31",
+ "68ab1ecb6d724fc29777d7216081a667",
+ "4cc4957451364dfab93f760d0f8cd0b6",
+ "32c43163ae214a73871c7952f91c1b79",
+ "343e824ec8014081aa0dc4656e5c1247",
+ "168bb48911bd4b398e1bef1e57282a4a",
+ "589efd9025484b199b2a6fe6f6b06027",
+ "8dcd01c2904745a3a5f9cfbe2339c344",
+ "888e4ce603eb413dbe1affc067555376",
+ "7f838aab3d2c4d98a50652fb30493b10",
+ "958d9ceb018941378ce9e62cafcc2930",
+ "40a18d78a96f450a96d127a2504c18cf",
+ "6a3e10c948c94bf5b4f35496f22feb4d"
+ ]
+ },
+ "id": "mlMSH3T3EazV",
+ "outputId": "ad0ddaaf-b3c8-4a55-b5f2-c655b3a32ba0"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Login successful\n",
+ "Your token has been saved to /root/.huggingface/token\n",
+ "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
+ "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
+ "\n",
+ "git config --global credential.helper store\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "from huggingface_hub import notebook_login\n",
+ "\n",
+ "notebook_login()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ujdZ2TxhElk6"
+ },
+ "source": [
+ "\n",
+ "Then you need to install Git-LFS to upload your model checkpoints:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "WcR-d83OEkqb"
+ },
+ "outputs": [],
+ "source": [
+ "%%capture\n",
+ "!apt install git-lfs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Mn9swf6EQ9Vd"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "\n",
+ "---\n",
+ "\n",
+ "${}^1$ In the [paper](https://arxiv.org/pdf/2006.13979.pdf), the model was evaluated using the phoneme error rate (PER), but by far the most common metric in ASR is the word error rate (WER). To keep this notebook as general as possible we decided to evaluate the model using WER."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0mW-C1Nt-j7k"
+ },
+ "source": [
+ "## Prepare Data, Tokenizer, Feature Extractor"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BeBosnY9BH3e"
+ },
+ "source": [
+ "ASR models transcribe speech to text, which means that we both need a feature extractor that processes the speech signal to the model's input format, *e.g.* a feature vector, and a tokenizer that processes the model's output format to text. \n",
+ "\n",
+ "In 🤗 Transformers, speech recognition models are thus accompanied by both a tokenizer, and a feature extractor.\n",
+ "\n",
+ "Let's start by creating the tokenizer responsible for decoding the model's predictions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sEXEWEJGQPqD"
+ },
+ "source": [
+ "### Create Tokenizer for Speech Recognition"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "idBczw8mWzgt"
+ },
+ "source": [
+ "First, let's go to [Common Voice](https://commonvoice.mozilla.org/en/datasets) and pick a language to fine-tune XLSR-Wav2Vec2 on. For this notebook, we will use Turkish. \n",
+ "\n",
+ "For each language-specific dataset, you can find a language code corresponding to your chosen language. On [Common Voice](https://commonvoice.mozilla.org/en/datasets), look for the field \"Version\". The language code then corresponds to the prefix before the underscore. For Turkish, *e.g.* the language code is `\"tr\"`.\n",
+ "\n",
+ "Great, now we can use 🤗 Datasets' simple API to download the data. The dataset name will be `\"common_voice\"`, the config name corresponds to the language code - `\"tr\"` in our case."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bee4g9rpLxll"
+ },
+ "source": [
+ "Common Voice has many different splits including `invalidated`, which refers to data that was not rated as \"clean enough\" to be considered useful. In this notebook, we will only make use of the splits `\"train\"`, `\"validation\"` and `\"test\"`. \n",
+ "\n",
+ "Because the Turkish dataset is so small, we will merge both the validation and training data into a training dataset and simply use the test data for validation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Swi42bp-hrvF",
+ "outputId": "2de050e3-97f1-447b-c807-c4c98b88e198"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1.18.3\n"
+ ]
+ }
+ ],
+ "source": [
+ "import datasets\n",
+ "print(datasets.__version__)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 217,
+ "referenced_widgets": [
+ "23bdf3807fe34d63a600fcea26520f78",
+ "2f73de7bd34b4377a14b90ccf962d327",
+ "cdcd548b670e4fdbb061a7a38f305fcd",
+ "bbc251ada7504759b531d9b846b06944",
+ "51bc2d6b195142149a6d224b38bb3c5e",
+ "50ab93d93cb64e19aea03166480ecbaf",
+ "8f308646f3634822ab957547ee9b9908",
+ "b52235b5022744a996059fe98fee4dd6",
+ "b1b9a92b0495411182ff7e8db879c344",
+ "0a526f64df90463383682a3aae6885d9",
+ "aa8bde4a54724abdb5f8c8afba39f898",
+ "170bfecf226142cdb87d0bf03be43842",
+ "9ec2db1ab1524dd5a5afad2e5ddb359f",
+ "f7c38c359728400e89b60af16013db3d",
+ "52401e8b880740e4ac1b43f3844c12e0",
+ "26525cfe8560466a84c0e16abb165fe5",
+ "70e5a9ef53a74510a04ced4f5f2a27f5",
+ "5da881b52302403db72ba1196dc9bc9f",
+ "2ffb88a2e26642e3b814e11b4e40abe0",
+ "1aa7a917a2cb4175adf869bd123108d5",
+ "6a0470c75f9d428b86406c502a15d934",
+ "e454ba90eb5d4cd8a2511004c1a6459c",
+ "a4416a28b7434c1196e1e1ee78a6524e",
+ "216bdc1df5074ad890005ab4c3cc430d",
+ "633c5b60966a4f54897c46f31b5aa519",
+ "00205bf74060408d937ef70d9dab37eb",
+ "3083964c340f463aad68704998106ce3",
+ "ccf257a118a44f9180dcfb6ab1a4ea3b",
+ "baf7a967c7554036a833a20dccf69b78",
+ "bcc0f0635473429c82e402b2a8e42f3e",
+ "59d3b516d58744c6b853a517585dde3d",
+ "ef9faa9bd0d14ddca4dabfc8fe344416",
+ "f3feaffb727f4eb0833b98d7f7d94687",
+ "1e97ed9ce1a342ffb1bc35dfca37ce8c",
+ "f707cca6e67f4562922c83e96d448d8f",
+ "a7673268a04c461da202565d6df0eea2",
+ "bc5ab57b856d45c8a05aea643621be29",
+ "136775a4af24494dbcbb8a26c557ff58",
+ "0183d4af17a24bc7951ad5d9c308acfd",
+ "e1da95804e38463da33aabfd34eaf038",
+ "348c3778b1984b7fb5a1f99d2dcd5d43",
+ "42668932cd7c478db86dd01620959ad6",
+ "4f2e8ff469b64b2d8aae0daba054f992",
+ "d94834ae37514f2a8fad199ebcc1b5e0",
+ "83985afb3b9b47a8a51ed826637d7ec3",
+ "8a8a52acf6314c47bd07cfcca47481e0",
+ "fdb6d241936c4bcc949101a3abdadc2a",
+ "7e7ac42e73974bc0a5080d2f68a95805",
+ "b109d9651a1f42089ff90ceed18550be",
+ "e541b0405b654a628643cbe8be11efe3",
+ "a41911630cba40e18799e467a00aa186",
+ "f2f67f2ee2d04b109e08510dbbecf7da",
+ "893ad16a62fa463982f2d302bba8ff8b",
+ "7d91952369dd49638b025241a51714a7",
+ "9bfa87491ded49fe9f528ab156019e49",
+ "becbd6e66ac84539828e278831b47711",
+ "bae3eaf9948c40438d70f852e96c39ee",
+ "a0cf5e9552fb4925a85440d8d936477d",
+ "2dc560e4c8b4411bbc1ac4745b0afe3d",
+ "2079848a4e064e23a465efc0e8b98298",
+ "f49e625b1e0545fcae35f3b38b421fe7",
+ "3336f37aa22443adb2e0dee5231625c2",
+ "b3c878fc99cc419d9a2c41c02b5363e6",
+ "904534fe556845379b0e23869b3c923a",
+ "df70cc4b5d0b4843a8bc9c9069261c19",
+ "ab93e17d9f31428fa3f1b650aaa19e3e",
+ "c161b2e6759043eb9e3cf56faaa46905",
+ "424e6fc321b14cabbe7f039373c5ff2f",
+ "75506937f12647769085ec8a6600a291",
+ "e38cab52706447b6ad4a30add3e52810",
+ "27eab27656254686a977caf7cffb9151",
+ "310142f6ad0b42c9a9d0b4a503edc5ac",
+ "f53f8de89a8b45ec84cb54192eb2a4f0",
+ "1d1c780f5f6e4166b993d4ccebceabc8",
+ "575f85f09cdf4913ad2239dbf35652e4",
+ "047b9191a9004c868eeb99a86933dcd7",
+ "378b7ab2af4048aeb9c6caca0d911ea4",
+ "1f7352d8f3724c338ec344933005834f",
+ "9caf41ec348640ac87d40c42d013296b",
+ "4966c62d4a5f4ff09256fca2d564832d",
+ "265a9350979d48bf95f821618a06e929",
+ "2b6cf7d6a8e44f3f90caad42e5ab4047",
+ "0a42f004666f4f2aa53cda3724dc974d",
+ "1ee83dcc3dcd416da37221a50c8c3aae",
+ "270657838f0a45bb92e692503d2606ec",
+ "c78b913a99d2496b9d1a00ea326bd6e2",
+ "a5b340de26464dd5aa481e3f9178db6e",
+ "2c8b77eeb22b4193b0fa343b33fceb89",
+ "6b5174e3338843408714243f6b60072f",
+ "7110a31dd43945c8a65e0f3e2f806bca",
+ "d2c676501a8842efbbd9a24620a1fe9e",
+ "4fe0d5443bbd4295b407ba807df9141b",
+ "5692a73de14b4a6e88bd59bc7bfe0a02",
+ "0e4a34472820466da3d414ba6f43db26",
+ "b0c11305ef7b4d909659a4732f0f050f",
+ "b058118bfff44c72bcd3309201fab9b1",
+ "2ede5c651c4c4a1ba36d48fcdb25d2dc",
+ "e155b7e1b96a410db90a35ffbd7e513b",
+ "83150dc5c23b4a549efdc36910618a47"
+ ]
+ },
+ "id": "YZXnJqOgJ4LS",
+ "outputId": "5f47edd6-bd71-4702-8fee-e8f5bbd54e03"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "23bdf3807fe34d63a600fcea26520f78",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/10.1k [00:00<?, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "170bfecf226142cdb87d0bf03be43842",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/2.98k [00:00<?, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a4416a28b7434c1196e1e1ee78a6524e",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/53.1k [00:00<?, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Downloading and preparing dataset common_voice/tr to /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/tr/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8...\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1e97ed9ce1a342ffb1bc35dfca37ce8c",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/1.55G [00:00<?, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "83985afb3b9b47a8a51ed826637d7ec3",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "0 examples [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "becbd6e66ac84539828e278831b47711",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "0 examples [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "c161b2e6759043eb9e3cf56faaa46905",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "0 examples [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1f7352d8f3724c338ec344933005834f",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "0 examples [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "6b5174e3338843408714243f6b60072f",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "0 examples [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Dataset common_voice downloaded and prepared to /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/tr/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8. Subsequent calls will reuse this data.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Reusing dataset common_voice (/root/.cache/huggingface/datasets/mozilla-foundation___common_voice/tr/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8)\n"
+ ]
+ }
+ ],
+ "source": [
+ "from datasets import load_dataset, load_metric, Audio\n",
+ "\n",
+ "common_voice_train = load_dataset(\"mozilla-foundation/common_voice_8_0\", \"tr\", use_auth_token=True, split=\"train+validation\")\n",
+ "common_voice_test = load_dataset(\"mozilla-foundation/common_voice_8_0\", \"tr\", use_auth_token=True, split=\"test\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 53
+ },
+ "id": "2MMXcWFFgCXU",
+ "outputId": "02d30af9-8201-4232-e2fa-c34b9f9b4000"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'\\nfrom datasets import load_dataset, load_metric, Audio\\n\\ncommon_voice_train = load_dataset(\"common_voice\", \"tr\", split=\"train+validation\")\\ncommon_voice_test = load_dataset(\"common_voice\", \"tr\", split=\"test\")\\n'"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 7
+ }
+ ],
+ "source": [
+ "'''\n",
+ "from datasets import load_dataset, load_metric, Audio\n",
+ "\n",
+ "common_voice_train = load_dataset(\"common_voice\", \"tr\", split=\"train+validation\")\n",
+ "common_voice_test = load_dataset(\"common_voice\", \"tr\", split=\"test\")\n",
+ "'''"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ri5y5N_HMANq"
+ },
+ "source": [
+ "Many ASR datasets only provide the target text, `'sentence'` for each audio array `'audio'` and file `'path'`. Common Voice actually provides much more information about each audio file, such as the `'accent'`, etc. However, we want to keep the notebook as general as possible, so that we will only consider the transcribed text for fine-tuning.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "kbyq6lDgQc2a"
+ },
+ "outputs": [],
+ "source": [
+ "common_voice_train = common_voice_train.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"segment\", \"up_votes\"])\n",
+ "common_voice_test = common_voice_test.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"segment\", \"up_votes\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Go9Hq4e4NDT9"
+ },
+ "source": [
+ "Let's write a short function to display some random samples of the dataset and run it a couple of times to get a feeling for the transcriptions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "72737oog2F6U"
+ },
+ "outputs": [],
+ "source": [
+ "from datasets import ClassLabel\n",
+ "import random\n",
+ "import pandas as pd\n",
+ "from IPython.display import display, HTML\n",
+ "\n",
+ "def show_random_elements(dataset, num_examples=10):\n",
+ " assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n",
+ " picks = []\n",
+ " for _ in range(num_examples):\n",
+ " pick = random.randint(0, len(dataset)-1)\n",
+ " while pick in picks:\n",
+ " pick = random.randint(0, len(dataset)-1)\n",
+ " picks.append(pick)\n",
+ " \n",
+ " df = pd.DataFrame(dataset[picks])\n",
+ " display(HTML(df.to_html()))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 363
+ },
+ "id": "K_JUmf3G3b9S",
+ "outputId": "ccf74737-e6eb-4b91-93e2-a1a91acbf2a7"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>sentence</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>El terazi, göz mizan.</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Daha yüz yirmi getireceksin!</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>\"Adalet'in gerdanı açıktı.\"</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>Haydi, al şu yirmi beşi de, bu hesabı kapayalım…</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>Evrakı ve raporları savcılık kaleminde duruyor, takip eden olmadığı için sıra bekliyordu.</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>Yandık!</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>Anlat bakalım.</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>Ak akçe kara gün içindir.</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>Bindim vapura geldim. Hemen bara yerleştim. Beş on kuruş kazandım.</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>Ahlak sohbetleri.</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "show_random_elements(common_voice_train.remove_columns([\"path\", \"audio\"]), num_examples=10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fowcOllGNNju"
+ },
+ "source": [
+ "Alright! The transcriptions look fairly clean. Having translated the transcribed sentences, it seems that the language corresponds more to written-out text than noisy dialogue. This makes sense considering that [Common Voice](https://huggingface.co/datasets/common_voice) is a crowd-sourced read speech corpus."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vq7OR50LN49m"
+ },
+ "source": [
+ "We can see that the transcriptions contain some special characters, such as `,.?!;:`. Without a language model, it is much harder to classify speech chunks to such special characters because they don't really correspond to a characteristic sound unit. *E.g.*, the letter `\"s\"` has a more or less clear sound, whereas the special character `\".\"` does not.\n",
+ "Also in order to understand the meaning of a speech signal, it is usually not necessary to include special characters in the transcription.\n",
+ "\n",
+ "In addition, we normalize the text to only have lower case letters and append a word separator token at the end."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "svKzVJ_hQGK6"
+ },
+ "outputs": [],
+ "source": [
+ "import re\n",
+ "chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�]'\n",
+ "\n",
+ "def remove_special_characters(batch):\n",
+ " batch[\"sentence\"] = re.sub(chars_to_ignore_regex, '', batch[\"sentence\"]).lower() + \" \"\n",
+ " return batch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 81,
+ "referenced_widgets": [
+ "da1051f673f747e4955f863369212aeb",
+ "d4d7845f9b8a49f592b61cd51e439e6d",
+ "9ec68324ba8f427eab819c06ea2e6c5f",
+ "ca95007c72854a118eb2826170a7e5ee",
+ "62c200f505f64e939ceaaea4cf5c1f1f",
+ "3df6c9b2730e4b10aaa46d59ef6db333",
+ "76c4fe0608734983a22e47799cb1c383",
+ "e951dfe6a3b845069aca8a81c7248232",
+ "4f35d24404484227801de58669037020",
+ "a1aee963e2764fbca86cf242647022e0",
+ "caa9274766ed4102988e69986c44aa0a",
+ "19ebe7b3d430420cb28da897a88c092c",
+ "d889e7cfdf9b4c758eae00b2e303a021",
+ "612da199c8dc4ef48d26b26e4fcfcd55",
+ "11cde7ee4f5b4a6796391b53bfb2b7da",
+ "d0487450690a41da805bc8b48c72271b",
+ "69d46dd2eea14bd88faf5a1ca4cfed7c",
+ "6b00d315427a4c9cb833605dc07801f6",
+ "ebdf3801c865463a9294893f62fd62f3",
+ "728ea53bb2954d4ba98c0e3898f381e5",
+ "682910d92af240b5942e46fbdf134421",
+ "4ad9488f342c4ff8bcb71def50f081ef"
+ ]
+ },
+ "id": "XIHocAuTQbBR",
+ "outputId": "2df8c280-45fd-4941-8f03-4d098e86e1a7"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "da1051f673f747e4955f863369212aeb",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "0ex [00:00, ?ex/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "19ebe7b3d430420cb28da897a88c092c",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "0ex [00:00, ?ex/s]"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "common_voice_train = common_voice_train.map(remove_special_characters)\n",
+ "common_voice_test = common_voice_test.map(remove_special_characters)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 363
+ },
+ "id": "RBDRAAYxRE6n",
+ "outputId": "ff1fdea5-bde6-48ff-b4f2-775cce206ccf"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>sentence</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>koca adama bir şeyler oluyor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>güney istikametinde gidiyordum</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>sorma</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>boş ver onu</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>sana ihtiyacımız var</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>sonradan gelen devlet devlet değildir</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>bana öyle gelmiyor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>bize de üsküdar'da toptaşı'na yakın ahşap bir ev bıraktı</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>akıllıca</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>diğerleri…</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "show_random_elements(common_voice_train.remove_columns([\"path\",\"audio\"]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jwfaptH5RJwA"
+ },
+ "source": [
+ "Good! This looks better. We have removed most special characters from transcriptions and normalized them to lower-case only.\n",
+ "\n",
+ "In CTC, it is common to classify speech chunks into letters, so we will do the same here. \n",
+ "Let's extract all distinct letters of the training and test data and build our vocabulary from this set of letters.\n",
+ "\n",
+ "We write a mapping function that concatenates all transcriptions into one long transcription and then transforms the string into a set of chars. \n",
+ "It is important to pass the argument `batched=True` to the `map(...)` function so that the mapping function has access to all transcriptions at once."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "LwCshNbbeRZR"
+ },
+ "outputs": [],
+ "source": [
+ "def extract_all_chars(batch):\n",
+ " all_text = \" \".join(batch[\"sentence\"])\n",
+ " vocab = list(set(all_text))\n",
+ " return {\"vocab\": [vocab], \"all_text\": [all_text]}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 81,
+ "referenced_widgets": [
+ "04805b7e6dbd4e878543b035c6ac9ffd",
+ "e30ada8532d34e279a5c917187030a35",
+ "88ba207d0a65433ca0e5ea1298055795",
+ "573756adc4da43ebbff370e6c5a6cee1",
+ "f13c45348bc346f8b0acdaa00b4fb75b",
+ "f223dcd00b224f229ffa30efb18a21bb",
+ "444a0eabada2472a8a0293d89d3056d4",
+ "f1909a70344a49849911d5221fe2e09c",
+ "5d497cd406e6459797fc16df24d06f39",
+ "24f87018abdd4a05ae26a0b123b81d4a",
+ "37d657c09da14715826ea3da6a3c8bf1",
+ "80d72bcc95bb4a2aaf27d16bc81c013f",
+ "ab6646153a4a49e6b468b2f2f1711605",
+ "c921133532a7448f90c9add21fb9098f",
+ "2154a5d07421448aaf6fe53c5c9568a4",
+ "7ef4dfd22751446fb255f9102f3419f9",
+ "e68c242c463e4e6895dad01ca33c9326",
+ "f6129bdabec34a01af416293eeb47ad7",
+ "b1909d011ebd4129b4992347797ef0a4",
+ "36228aad1ec24e5c97fc0d046e00561e",
+ "f0b1028065cd4765b310b7af3ae26b4b",
+ "7d5c696360e347cd8e00ef545f08ef43"
+ ]
+ },
+ "id": "_m6uUjjcfbjH",
+ "outputId": "77777471-db25-402e-d0a6-0eb95544d515"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "04805b7e6dbd4e878543b035c6ac9ffd",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ " 0%| | 0/1 [00:00<?, ?ba/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "80d72bcc95bb4a2aaf27d16bc81c013f",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ " 0%| | 0/1 [00:00<?, ?ba/s]"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "vocab_train = common_voice_train.map(\n",
+ " extract_all_chars, batched=True, \n",
+ " batch_size=-1, keep_in_memory=True, \n",
+ " remove_columns=common_voice_train.column_names\n",
+ ")\n",
+ "vocab_test = common_voice_test.map(\n",
+ " extract_all_chars, batched=True, \n",
+ " batch_size=-1, keep_in_memory=True, \n",
+ " remove_columns=common_voice_test.column_names\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7oVgE8RZSJNP"
+ },
+ "source": [
+ "Now, we create the union of all distinct letters in the training dataset and test dataset and convert the resulting list into an enumerated dictionary."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "aQfneNsmlJI0"
+ },
+ "outputs": [],
+ "source": [
+ "vocab_list = list(set(vocab_train[\"vocab\"][0]) | set(vocab_test[\"vocab\"][0]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "_0kRndSvqaKk",
+ "outputId": "f0fb07c2-4cff-4524-eeed-3f5b7b51dde2"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{' ': 8,\n",
+ " \"'\": 6,\n",
+ " '(': 35,\n",
+ " ')': 34,\n",
+ " 'a': 2,\n",
+ " 'b': 0,\n",
+ " 'c': 23,\n",
+ " 'd': 42,\n",
+ " 'e': 12,\n",
+ " 'f': 20,\n",
+ " 'g': 5,\n",
+ " 'h': 24,\n",
+ " 'i': 22,\n",
+ " 'j': 38,\n",
+ " 'k': 26,\n",
+ " 'l': 36,\n",
+ " 'm': 40,\n",
+ " 'n': 39,\n",
+ " 'o': 18,\n",
+ " 'p': 14,\n",
+ " 'q': 19,\n",
+ " 'r': 7,\n",
+ " 's': 29,\n",
+ " 't': 10,\n",
+ " 'u': 21,\n",
+ " 'v': 32,\n",
+ " 'w': 13,\n",
+ " 'x': 4,\n",
+ " 'y': 3,\n",
+ " 'z': 30,\n",
+ " 'â': 9,\n",
+ " 'ç': 28,\n",
+ " 'é': 25,\n",
+ " 'ë': 41,\n",
+ " 'î': 16,\n",
+ " 'ö': 43,\n",
+ " 'û': 11,\n",
+ " 'ü': 27,\n",
+ " 'ğ': 31,\n",
+ " 'ı': 37,\n",
+ " 'ş': 15,\n",
+ " '̇': 1,\n",
+ " '’': 17,\n",
+ " '…': 33}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 17
+ }
+ ],
+ "source": [
+ "vocab_dict = {v: k for k, v in enumerate(vocab_list)}\n",
+ "vocab_dict"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JOSzbvs9SXT1"
+ },
+ "source": [
+ "Cool, we see that all letters of the alphabet occur in the dataset (which is not really surprising) and we also extracted the special characters `\" \"` and `'`. Note that we did not exclude those special characters because: \n",
+ "\n",
+ "- The model has to learn to predict when a word is finished or else the model prediction would always be a sequence of chars which would make it impossible to separate words from each other.\n",
+ "- From the transcriptions above it seems that words that include an apostrophe, such as `maktouf'un` do exist in Turkish, so I decided to keep the apostrophe in the dataset. This might be a wrong assumption though.\n",
+ "\n",
+ "One should always keep in mind that the data-preprocessing is a very important step before training your model. E.g., we don't want our model to differentiate between `a` and `A` just because we forgot to normalize the data. The difference between `a` and `A` does not depend on the \"sound\" of the letter at all, but more on grammatical rules - *e.g.* use a capitalized letter at the beginning of the sentence. So it is sensible to remove the difference between capitalized and non-capitalized letters so that the model has an easier time learning to transcribe speech. \n",
+ "\n",
+ "It is always advantageous to get help from a native speaker of the language you would like to transcribe to verify whether the assumptions you made are sensible, *e.g.* I should have made sure that keeping `'`, but removing other special characters is a sensible choice for Turkish. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "b1fBRCn-TRaO"
+ },
+ "source": [
+ "To make it clearer to the reader that `\" \"` has its own token class, we give it a more visible character `|`. In addition, we also add an \"unknown\" token so that the model can later deal with characters not encountered in Common Voice's training set. \n",
+ "\n",
+ "Finally, we also add a padding token that corresponds to CTC's \"*blank token*\". The \"blank token\" is a core component of the CTC algorithm. For more information, please take a look at the \"Alignment\" section [here](https://distill.pub/2017/ctc/)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "npbIbBoLgaFX"
+ },
+ "outputs": [],
+ "source": [
+ "vocab_dict[\"|\"] = vocab_dict[\" \"]\n",
+ "del vocab_dict[\" \"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "znF0bNunsjbl",
+ "outputId": "c65f4b9f-1865-4362-ccef-4691a163427f"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "46"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 19
+ }
+ ],
+ "source": [
+ "vocab_dict[\"[UNK]\"] = len(vocab_dict)\n",
+ "vocab_dict[\"[PAD]\"] = len(vocab_dict)\n",
+ "len(vocab_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SFPGfet8U5sL"
+ },
+ "source": [
+ "Cool, now our vocabulary is complete and consists of 40 tokens, which means that the linear layer that we will add on top of the pretrained XLSR-Wav2Vec2 checkpoint will have an output dimension of 40."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1CujRgBNVRaD"
+ },
+ "source": [
+ "Let's now save the vocabulary as a json file."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ehyUoh9vk191"
+ },
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "with open('vocab.json', 'w') as vocab_file:\n",
+ " json.dump(vocab_dict, vocab_file)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SHJDaKlIVVim"
+ },
+ "source": [
+ "In a final step, we use the json file to instantiate a tokenizer object with the just created vocabulary file. The correct `tokenizer_type` can be retrieved from the model configuration. If a `tokenizer_class` is defined in the config, we can use it, else we assume the `tokenizer_type` corresponds to the `model_type`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "c0e164ae6da6409983df9cd716084642",
+ "a26e8267388b4b6fb72738b8bf015fec",
+ "1b14adb2df3f4dd6bc463b388996b1ba",
+ "fa59e61e499d4d44ae6c32e89be231dd",
+ "64cffbbc242a4201a3f842bc3081da3e",
+ "3f018a8c91924b85919b8713b591fd62",
+ "6393cf9b22e04d75a12f74c9dd7ef31c",
+ "749100e315ff432294bece9cad2417b7",
+ "1a463985b65a436e8880f28001f3f9ca",
+ "12455d049390474c98fb06298c5e7958",
+ "fc47bddc681a4e5b8c352cbcf232853c"
+ ]
+ },
+ "id": "1VKVaSZm7Clh",
+ "outputId": "a2286edb-c25c-4b36-c6d2-8cd6da2418d4"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "c0e164ae6da6409983df9cd716084642",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/1.53k [00:00<?, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "from transformers import AutoConfig\n",
+ "\n",
+ "config = AutoConfig.from_pretrained(model_checkpoint)\n",
+ "\n",
+ "tokenizer_type = config.model_type if config.tokenizer_class is None else None\n",
+ "config = config if config.tokenizer_class is not None else None"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "P5B_bVhf7H_K"
+ },
+ "source": [
+ "Now we can instantiate a tokenizer using `AutoTokenizer`. Additionally, we set the tokenizer's special tokens."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "xriFGEWQkO4M",
+ "outputId": "d6803d15-96ec-4562-ff4b-cd38e954b367"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "file ./config.json not found\n",
+ "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from transformers import AutoTokenizer\n",
+ "\n",
+ "tokenizer = AutoTokenizer.from_pretrained(\n",
+ " \"./\",\n",
+ " config=config,\n",
+ " tokenizer_type=tokenizer_type,\n",
+ " unk_token=\"[UNK]\",\n",
+ " pad_token=\"[PAD]\",\n",
+ " word_delimiter_token=\"|\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KvL12DrNV4cx"
+ },
+ "source": [
+ "If one wants to re-use the just created tokenizer with the fine-tuned model of this notebook, it is strongly advised to upload the `tokenizer` to the [🤗 Hub](https://huggingface.co/). Let's call the repo to which we will upload the files\n",
+ "`\"wav2vec2-large-xlsr-turkish-demo-colab\"`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "A1XApZBAF2zr"
+ },
+ "outputs": [],
+ "source": [
+ "model_checkpoint_name = model_checkpoint.split(\"/\")[-1]\n",
+ "repo_name = f\"{model_checkpoint_name}-tr-CV8-v1\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "B1BiezWZF16d"
+ },
+ "source": [
+ "and upload the tokenizer to the [🤗 Hub](https://huggingface.co/)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 160
+ },
+ "id": "zytE1175GAKM",
+ "outputId": "a22ad4b6-a5b2-47c2-b088-120f2e2be53e"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.7/dist-packages/huggingface_hub/hf_api.py:1004: FutureWarning: `create_repo` now takes `token` as an optional positional argument. Be sure to adapt your code!\n",
+ " FutureWarning,\n",
+ "Cloning https://huggingface.co/emre/wav2vec2-xls-r-300m-tr-CV8-v1 into local empty directory.\n",
+ "To https://huggingface.co/emre/wav2vec2-xls-r-300m-tr-CV8-v1\n",
+ " e1e801f..b9dfb7e main -> main\n",
+ "\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'https://huggingface.co/emre/wav2vec2-xls-r-300m-tr-CV8-v1/commit/b9dfb7ea3acf138a98773a2e1cea53ec73cbf18b'"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 24
+ }
+ ],
+ "source": [
+ "tokenizer.push_to_hub(repo_name)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SwQM8lH_GGuP"
+ },
+ "source": [
+ "Great, you can see the just created repository under `https://huggingface.co/<your-username>/wav2vec2-large-xlsr-turkish-demo-colab` ."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YFmShnl7RE35"
+ },
+ "source": [
+ "### Preprocess Data\n",
+ "\n",
+ "So far, we have not looked at the actual values of the speech signal but just the transcription. In addition to `sentence`, our datasets include two more column names `path` and `audio`. `path` states the absolute path of the audio file. Let's take a look.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "id": "TTCS7W6XJ9BG",
+ "outputId": "64e75171-4e98-429a-b6fe-42ee19686281"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'cv-corpus-8.0-2022-01-19/tr/clips/common_voice_tr_17528071.mp3'"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 25
+ }
+ ],
+ "source": [
+ "common_voice_train[0][\"path\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "T6ndIjHGFp0W"
+ },
+ "source": [
+ "`XLSR-Wav2Vec2` expects the input in the format of a 1-dimensional array of 16 kHz. This means that the audio file has to be loaded and resampled.\n",
+ "\n",
+ " Thankfully, `datasets` does this automatically by calling the other column `audio`. Let try it out. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "qj_z5Zc3GAs9",
+ "outputId": "ad749bc4-29e6-40fe-e529-26eefdd4f09a"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
+ " -2.9087067e-05, -2.5093555e-05, -5.6624413e-06], dtype=float32),\n",
+ " 'path': 'cv-corpus-8.0-2022-01-19/tr/clips/common_voice_tr_17528071.mp3',\n",
+ " 'sampling_rate': 48000}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 26
+ }
+ ],
+ "source": [
+ "common_voice_train[0][\"audio\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WUUTgI1bGHW-"
+ },
+ "source": [
+ "Great, we can see that the audio file has automatically been loaded. This is thanks to the new [`\"Audio\"` feature](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=audio#datasets.Audio) introduced in `datasets == 1.13.3`, which loads and resamples audio files on-the-fly upon calling.\n",
+ "\n",
+ "In the example above we can see that the audio data is loaded with a sampling rate of 48kHz whereas 16kHz are expected by the model. We can set the audio feature to the correct sampling rate by making use of [`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "rrv65aj7G95i"
+ },
+ "outputs": [],
+ "source": [
+ "common_voice_train = common_voice_train.cast_column(\"audio\", Audio(sampling_rate=16_000))\n",
+ "common_voice_test = common_voice_test.cast_column(\"audio\", Audio(sampling_rate=16_000))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PcnO4x-NGBEi"
+ },
+ "source": [
+ "Let's take a look at `\"audio\"` again."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "aKtkc1o_HWHC",
+ "outputId": "0db16df8-99c0-4bef-eb9d-bf49ee04c676"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n",
+ " -1.4103199e-05, 4.2269539e-06, -2.5639036e-05], dtype=float32),\n",
+ " 'path': 'cv-corpus-8.0-2022-01-19/tr/clips/common_voice_tr_17528071.mp3',\n",
+ " 'sampling_rate': 16000}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 28
+ }
+ ],
+ "source": [
+ "common_voice_train[0][\"audio\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SOckzFd4Mbzq"
+ },
+ "source": [
+ "This seemed to have worked! Let's listen to a couple of audio files to better understand the dataset and verify that the audio was correctly loaded. \n",
+ "\n",
+ "**Note**: *You can click the following cell a couple of times to listen to different speech samples.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 93
+ },
+ "id": "dueM6U7Ev0OA",
+ "outputId": "6cc8d624-0884-454f-fe7b-d1181e395026"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "haşan yolunu kesmiş emine demiş bu dünyada gönlüne karşı gelen babayiğit çıkmamış \n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ " <audio controls=\"controls\" autoplay=\"autoplay\">\n",
+ " <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
+ " Your browser does not support the audio element.\n",
+ " </audio>\n",
+ " "
+ ],
+ "text/plain": [
+ "<IPython.lib.display.Audio object>"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 29
+ }
+ ],
+ "source": [
+ "import IPython.display as ipd\n",
+ "import numpy as np\n",
+ "import random\n",
+ "\n",
+ "rand_int = random.randint(0, len(common_voice_train)-1)\n",
+ "\n",
+ "print(common_voice_train[rand_int][\"sentence\"])\n",
+ "ipd.Audio(data=common_voice_train[rand_int][\"audio\"][\"array\"], autoplay=True, rate=16000)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gY8m3vARHYTa"
+ },
+ "source": [
+ "It seems like the data is now correctly loaded and resampled. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1MaL9J2dNVtG"
+ },
+ "source": [
+ "It can be heard, that the speakers change along with their speaking rate, accent, and background environment, etc. Overall, the recordings sound acceptably clear though, which is to be expected from a crowd-sourced read speech corpus.\n",
+ "\n",
+ "Let's do a final check that the data is correctly prepared, by printing the shape of the speech input, its transcription, and the corresponding sampling rate.\n",
+ "\n",
+ "**Note**: *You can click the following cell a couple of times to verify multiple samples.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "1Po2g7YPuRTx",
+ "outputId": "3a09dd08-01a8-4977-fe03-7e50a8c8c020"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Target text: o hemen yerinden fırladı yeleğinin cebinden kibritini çıkardı \n",
+ "Input array shape: (89280,)\n",
+ "Sampling rate: 16000\n"
+ ]
+ }
+ ],
+ "source": [
+ "rand_int = random.randint(0, len(common_voice_train)-1)\n",
+ "\n",
+ "print(\"Target text:\", common_voice_train[rand_int][\"sentence\"])\n",
+ "print(\"Input array shape:\", common_voice_train[rand_int][\"audio\"][\"array\"].shape)\n",
+ "print(\"Sampling rate:\", common_voice_train[rand_int][\"audio\"][\"sampling_rate\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "M9teZcSwOBJ4"
+ },
+ "source": [
+ "Good! Everything looks fine - the data is a 1-dimensional array, the sampling rate always corresponds to 16kHz, and the target text is normalized.\n",
+ "\n",
+ "Next, we should process the data with the model's feature extractor. Let's load the feature extractor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "2a2cbbf93c8c4ad69052b1b0478acfea",
+ "a4c449e803d942d7a4bdc9f5c923107d",
+ "475e841d2f254301a9c4a5da9be07bfe",
+ "189dcc43f91946669a77445e3acb1143",
+ "75c01c6a6b094b13995c8c061749e127",
+ "f1d9f253969b446faeaca0167b84afb8",
+ "210d7c933d564b769964e68668491128",
+ "feccd14aa429488fb5194cf7f18ddc05",
+ "06ecae394d624765baf9bdbaddde248e",
+ "da191a839e85403c8e0f9e27144b7a69",
+ "9b45662a7c774bf8913b547705021a5f"
+ ]
+ },
+ "id": "UuA-9bgBYT4x",
+ "outputId": "420cad05-e704-4253-a8d4-8a3d728df53a"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2a2cbbf93c8c4ad69052b1b0478acfea",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/212 [00:00<?, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "from transformers import AutoFeatureExtractor\n",
+ "\n",
+ "feature_extractor = AutoFeatureExtractor.from_pretrained(model_checkpoint)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VgcfxUSAYaCQ"
+ },
+ "source": [
+ "and wrap it into a `Wav2Vec2Processor` together with the tokenizer."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "byshSGTjYcdu"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import Wav2Vec2Processor\n",
+ "\n",
+ "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GNFuvi26Yiw6"
+ },
+ "source": [
+ "Finally, we can leverage `Wav2Vec2Processor` to process the data to the format expected by the model for training. To do so let's make use of Dataset's [`map(...)`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=map#datasets.DatasetDict.map) function.\n",
+ "\n",
+ "First, we load and resample the audio data, simply by calling `batch[\"audio\"]`.\n",
+ "Second, we extract the `input_values` from the loaded audio file. In our case, the `Wav2Vec2Processor` only normalizes the data. For other speech models, however, this step can include more complex feature extraction, such as [Log-Mel feature extraction](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum). \n",
+ "Third, we encode the transcriptions to label ids."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "eJY7I0XAwe9p"
+ },
+ "outputs": [],
+ "source": [
+ "def prepare_dataset(batch):\n",
+ " audio = batch[\"audio\"]\n",
+ "\n",
+ " # batched output is \"un-batched\"\n",
+ " batch[\"input_values\"] = processor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_values[0]\n",
+ " batch[\"input_length\"] = len(batch[\"input_values\"])\n",
+ " \n",
+ " with processor.as_target_processor():\n",
+ " batch[\"labels\"] = processor(batch[\"sentence\"]).input_ids\n",
+ " return batch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "q6Pg_WR3OGAP"
+ },
+ "source": [
+ "Let's apply the data preparation function to all examples."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 81,
+ "referenced_widgets": [
+ "a3606e7e00e54b3a98cf6d999519a559",
+ "9580a891a8964def85c2b4c20720bc12",
+ "d85dfe5ecb754a57ac52a593f3da5bce",
+ "6a5594fe7fdc451cb3b8c075638e046c",
+ "c674f0a97f67412b8a9ff5a3316074ac",
+ "d9132d9c79874897a7da4ffffbd374d3",
+ "d7e6852b58704eab8541e4250e229f98",
+ "8aab01eaed564597993f0095f4afeb62",
+ "56c2bd8eff2b45849191764a68a17826",
+ "be891c23f24145cbb6db1a24f66ecd16",
+ "72206d82d0ba4b1586da4598eb3b0202",
+ "f439211c04884bea98fe4afba8e7ca4a",
+ "45e0c7a8c1fa427cad3c9565cd068251",
+ "e04bd4466f384471a39f32f55d2780f6",
+ "33797b5723ac435ba7e42fcfaec4756d",
+ "486ebf97b1474c37b28107bfb23a0703",
+ "eab6aa2773994c44b4e9ee8c43eaaae5",
+ "c685d717186a493486a1a5b8afcece4e",
+ "29741efbd6e64365bcd010a066a09b34",
+ "8df4bdc8aae64be38bfaf68ddbbfb4be",
+ "87cc8498063f4fb6b1a2db3556f896ce",
+ "cadf486284ad4b1ea64ec93b3f7a938c"
+ ]
+ },
+ "id": "-np9xYK-wl8q",
+ "outputId": "81648a84-61a8-42e2-d6ae-6c79d170890d"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a3606e7e00e54b3a98cf6d999519a559",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "0ex [00:00, ?ex/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "f439211c04884bea98fe4afba8e7ca4a",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "0ex [00:00, ?ex/s]"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "common_voice_train = common_voice_train.map(prepare_dataset, remove_columns=common_voice_train.column_names)\n",
+ "common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nKcEWHvKI1by"
+ },
+ "source": [
+ "**Note**: Currently `datasets` make use of [`torchaudio`](https://pytorch.org/audio/stable/index.html) and [`librosa`](https://librosa.org/doc/latest/index.html) for audio loading and resampling. If you wish to implement your own costumized data loading/sampling, feel free to just make use of the `\"path\"` column instead and disregard the `\"audio\"` column."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "24CxHd5ewI4T"
+ },
+ "source": [
+ "Long input sequences require a lot of memory. Since speech models in `transformers` are based on `self-attention` the memory requirement scales quadratically with the input length for long input sequences (*cf.* with [this](https://www.reddit.com/r/MachineLearning/comments/genjvb/d_why_is_the_maximum_input_sequence_length_of/) reddit post). For this demo, let's filter all sequences that are longer than 5\n",
+ " seconds out of the training dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "77494b9ce086467bbac1cff55ab6e715",
+ "f305d2a7984c4b7d84e7458307ded2d2",
+ "d4cb7c13043c405fa2185f61b0b064ca",
+ "6245e8f435cb44a0a403362788f5f0df",
+ "857ed4101a09450e850f3f56072102f9",
+ "0fd358405d93489dba3efd6643120ebf",
+ "4994d7b0efe04c03a6bface4e986fd61",
+ "db7f11ef346042dc9d89e59750e0bca9",
+ "d9cfb56b15b44940be99fb5842608182",
+ "d4ce7ffc6596478d998dd614429f21d1",
+ "e18fb65bbfd247a997a22ce6f2e940de"
+ ]
+ },
+ "id": "tdHfbUJ_09iA",
+ "outputId": "a96b1a04-2de1-4211-8a3d-c5718451827b"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "77494b9ce086467bbac1cff55ab6e715",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ " 0%| | 0/26 [00:00<?, ?ba/s]"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "max_input_length_in_sec = 5.0\n",
+ "common_voice_train = common_voice_train.filter(lambda x: x < max_input_length_in_sec * processor.feature_extractor.sampling_rate, input_columns=[\"input_length\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1ZWDCCKqwcfS"
+ },
+ "source": [
+ "Awesome, now we are ready to start training!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gYlQkKVoRUos"
+ },
+ "source": [
+ "## Training\n",
+ "\n",
+ "The data is processed so that we are ready to start setting up the training pipeline. We will make use of 🤗's [Trainer](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer) for which we essentially need to do the following:\n",
+ "\n",
+ "- Define a data collator. In contrast to most NLP models, speech models usually have a much larger input length than output length. *E.g.*, a sample of input length 50000 for XLSR-Wav2Vec2 has an output length of no more than 100. Given the large input sizes, it is much more efficient to pad the training batches dynamically meaning that all training samples should only be padded to the longest sample in their batch and not the overall longest sample. Therefore, fine-tuning speech models requires a special padding data collator, which we will define below\n",
+ "\n",
+ "- Evaluation metric. During training, the model should be evaluated on the word error rate. We should define a `compute_metrics` function accordingly\n",
+ "\n",
+ "- Load a pretrained checkpoint. We need to load a pretrained checkpoint and configure it correctly for training.\n",
+ "\n",
+ "- Define the training configuration.\n",
+ "\n",
+ "After having fine-tuned the model, we will correctly evaluate it on the test data and verify that it has indeed learned to correctly transcribe speech."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Slk403unUS91"
+ },
+ "source": [
+ "### Set-up Trainer\n",
+ "\n",
+ "Let's start by defining the data collator. The code for the data collator was copied from [this example](https://github.com/huggingface/transformers/blob/9a06b6b11bdfc42eea08fa91d0c737d1863c99e3/examples/research_projects/wav2vec2/run_asr.py#L81).\n",
+ "\n",
+ "Without going into too many details, in contrast to the common data collators, this data collator treats the `input_values` and `labels` differently and thus applies to separate padding functions on them. This is necessary because in speech input and output are of different modalities meaning that they should not be treated by the same padding function.\n",
+ "Analogous to the common data collators, the padding tokens in the labels with `-100` so that those tokens are **not** taken into account when computing the loss."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "tborvC9hx88e"
+ },
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "\n",
+ "from dataclasses import dataclass, field\n",
+ "from typing import Any, Dict, List, Optional, Union\n",
+ "\n",
+ "@dataclass\n",
+ "class 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",
+ " max_length (:obj:`int`, `optional`):\n",
+ " Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).\n",
+ " max_length_labels (:obj:`int`, `optional`):\n",
+ " Maximum length of the ``labels`` returned list and optionally padding length (see above).\n",
+ " pad_to_multiple_of (:obj:`int`, `optional`):\n",
+ " If set will pad the sequence to a multiple of the provided value.\n",
+ " This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=\n",
+ " 7.5 (Volta).\n",
+ " \"\"\"\n",
+ "\n",
+ " processor: Wav2Vec2Processor\n",
+ " padding: Union[bool, str] = True\n",
+ " max_length: Optional[int] = None\n",
+ " max_length_labels: Optional[int] = None\n",
+ " pad_to_multiple_of: Optional[int] = None\n",
+ " pad_to_multiple_of_labels: Optional[int] = None\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",
+ " max_length=self.max_length,\n",
+ " pad_to_multiple_of=self.pad_to_multiple_of,\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",
+ " max_length=self.max_length_labels,\n",
+ " pad_to_multiple_of=self.pad_to_multiple_of_labels,\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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "lbQf5GuZyQ4_"
+ },
+ "outputs": [],
+ "source": [
+ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xO-Zdj-5cxXp"
+ },
+ "source": [
+ "Next, the evaluation metric is defined. As mentioned earlier, the \n",
+ "predominant metric in ASR is the word error rate (WER), hence we will use it in this notebook as well."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "3584a24565254be4a665568c91261a40",
+ "ecc615a58d1a45cbadde4079b14b2a8c",
+ "608329d74f56402fae1bd3ba3318a0ff",
+ "3ddeca68918e44eaa65a975acba73081",
+ "71f27170a8064045826d41aa63fbf29e",
+ "0fc84d9b108c4cb38c0f18b4d1a3d27a",
+ "542355f122e248bfb7f6a8d902459e16",
+ "bf1febb1eb594bbd90c9cb94363536a1",
+ "01fa773649da4e3fa955110d97a57793",
+ "10073907af5a4e9b9fd03f41221c328c",
+ "98cbd815a3f4437bbf80f67e0964d2bb"
+ ]
+ },
+ "id": "9Xsux2gmyXso",
+ "outputId": "e9c74e6e-b2ea-4a53-8d46-ea9748ea3dc3"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "3584a24565254be4a665568c91261a40",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/1.90k [00:00<?, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "wer_metric = load_metric(\"wer\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "E1qZU5p-deqB"
+ },
+ "source": [
+ "The model will return a sequence of logit vectors:\n",
+ "$\\mathbf{y}_1, \\ldots, \\mathbf{y}_m$ with $\\mathbf{y}_1 = f_{\\theta}(x_1, \\ldots, x_n)[0]$ and $n >> m$.\n",
+ "\n",
+ "A logit vector $\\mathbf{y}_1$ contains the log-odds for each word in the vocabulary we defined earlier, thus $\\text{len}(\\mathbf{y}_i) =$ `config.vocab_size`. We are interested in the most likely prediction of the model and thus take the `argmax(...)` of the logits. Also, we transform the encoded labels back to the original string by replacing `-100` with the `pad_token_id` and decoding the ids while making sure that consecutive tokens are **not** grouped to the same token in CTC style ${}^1$."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "1XZ-kjweyTy_"
+ },
+ "outputs": [],
+ "source": [
+ "def 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",
+ " pred_str = processor.batch_decode(pred_ids)\n",
+ " # we do not want to group tokens when computing the metrics\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",
+ "\n",
+ " return {\"wer\": wer}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Xmgrx4bRwLIH"
+ },
+ "source": [
+ "Now, we can load the pretrained speech checkpoint. The tokenizer's `pad_token_id` must be to define the model's `pad_token_id` or in the case of a CTC speech model also CTC's *blank token* ${}^2$.\n",
+ "\n",
+ "Because the dataset is quite small (~6h of training data) and because Common Voice is quite noisy, fine-tuning might require some hyper-parameter tuning, which is why a couple of hyperparameters are set in the following.\n",
+ "\n",
+ "**Note**: When using this notebook to train speech models on another language of Common Voice those hyper-parameter settings might not work very well. Feel free to adapt those depending on your use case. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 156,
+ "referenced_widgets": [
+ "e204b2ca0ef444779bf10e8d37943284",
+ "46ad1ddaa7814ff38877f14a4f3167fc",
+ "114d8e5de5a94e1d8a61fad25bfce8f9",
+ "ce0f4b3a12f74e9d9dbcfdf07de58c83",
+ "c9d344680b9f428a8398c4c492fe60d8",
+ "50123cd248334539882bc450f0c76315",
+ "bc3f2ee267264173ba9bc54d1ebf5d0b",
+ "f58a3b6fc7d54398a502b620e9180751",
+ "62853e9a99f3490b9b5b268d52c2102d",
+ "98d1ddd134964990b74392bfc94f6bb6",
+ "c5ad073383524924be6e8ce7c1d9a1d5"
+ ]
+ },
+ "id": "e7cqAWIayn6w",
+ "outputId": "ce06bac2-ff48-409b-c34f-216531b9cffe"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "e204b2ca0ef444779bf10e8d37943284",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "Downloading: 0%| | 0.00/1.18G [00:00<?, ?B/s]"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Some weights of the model checkpoint at facebook/wav2vec2-xls-r-300m were not used when initializing Wav2Vec2ForCTC: ['project_hid.bias', 'project_q.bias', 'quantizer.codevectors', 'quantizer.weight_proj.weight', 'project_q.weight', 'project_hid.weight', 'quantizer.weight_proj.bias']\n",
+ "- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+ "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-xls-r-300m and are newly initialized: ['lm_head.bias', 'lm_head.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from transformers import AutoModelForCTC\n",
+ "\n",
+ "model = AutoModelForCTC.from_pretrained(\n",
+ " model_checkpoint,\n",
+ " attention_dropout=0.094,\n",
+ " hidden_dropout=0.047,\n",
+ " feat_proj_dropout=0.04,\n",
+ " mask_time_prob=0.4,\n",
+ " layerdrop=0.041,\n",
+ " ctc_loss_reduction=\"mean\", \n",
+ " pad_token_id=processor.tokenizer.pad_token_id,\n",
+ " vocab_size=len(processor.tokenizer)\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1DwR3XLSzGDD"
+ },
+ "source": [
+ "The first component of most transformer-based speech models consists of a stack of CNN layers that are used to extract acoustically meaningful - but contextually independent - features from the raw speech signal. This part of the model has already been sufficiently trained during pretraining and as stated in the [paper](https://arxiv.org/pdf/2006.13979.pdf) does not need to be fine-tuned anymore. \n",
+ "Thus, we can set the `requires_grad` to `False` for all parameters of the *feature extraction* part."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "oGI8zObtZ3V0"
+ },
+ "outputs": [],
+ "source": [
+ "if hasattr(model, \"freeze_feature_extractor\"):\n",
+ " model.freeze_feature_extractor()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lD4aGhQM0K-D"
+ },
+ "source": [
+ "In a final step, we define all parameters related to training. \n",
+ "To give more explanation on some of the parameters:\n",
+ "- `group_by_length` makes training more efficient by grouping training samples of similar input length into one batch. This can significantly speed up training time by heavily reducing the overall number of useless padding tokens that are passed through the model\n",
+ "- `learning_rate` and `weight_decay` were heuristically tuned until fine-tuning has become stable. Note that those parameters strongly depend on the Common Voice dataset and might be suboptimal for other speech datasets.\n",
+ "\n",
+ "For more explanations on other parameters, one can take a look at the [docs](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer#trainingarguments).\n",
+ "\n",
+ "During training, a checkpoint will be uploaded asynchronously to the hub every 400 training steps. It allows you to also play around with the demo widget even while your model is still training.\n",
+ "\n",
+ "**Note**: If one does not want to upload the model checkpoints to the hub, simply set `push_to_hub=False`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "KbeKSV7uzGPP"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import TrainingArguments\n",
+ "\n",
+ "training_args = TrainingArguments(\n",
+ " output_dir=repo_name,\n",
+ " group_by_length=True,\n",
+ " per_device_train_batch_size=batch_size,\n",
+ " gradient_accumulation_steps=2,\n",
+ " evaluation_strategy=\"steps\",\n",
+ " num_train_epochs=30,\n",
+ " gradient_checkpointing=True,\n",
+ " fp16=True,\n",
+ " save_steps=500,\n",
+ " eval_steps=500,\n",
+ " logging_steps=500,\n",
+ " learning_rate=1e-4,\n",
+ " warmup_steps=300,\n",
+ " save_total_limit=1,\n",
+ " push_to_hub=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 140
+ },
+ "id": "-ptmRKjAd4Vr",
+ "outputId": "66187b79-442b-4fc6-d6a2-85bdb3d77e9b"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "' hyperparameters\\ntraining_args = TrainingArguments(\\n output_dir=repo_name,\\n group_by_length=True,\\n #per_device_train_batch_size=batch_size,\\n gradient_accumulation_steps=2,\\n evaluation_strategy=\"steps\",\\n num_train_epochs=5,\\n per_device_eval_batch_size=\"8\",\\n per_device_train_batch_size=\"32\",\\n gradient_checkpointing=True,\\n fp16=True,\\n save_steps=100,\\n eval_steps=100,\\n logging_steps=100,\\n learning_rate=1e-4,\\n warmup_steps=300,\\n save_total_limit=2,\\n push_to_hub=True,\\n freeze_feature_extractor=True,\\n save_total_limit=\"1\",\\n feat_proj_dropout=\"0.04\",\\n layerdrop=\"0.041\",\\n attention_dropout=\"0.094\",\\n activation_dropout=\"0.055\",\\n hidden_dropout=\"0.047\",\\n mask_time_prob=\"0.4\",\\n dataloader_num_workers=\"8\"\\n)\\n'"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 43
+ }
+ ],
+ "source": [
+ "''' hyperparameters\n",
+ "training_args = TrainingArguments(\n",
+ " output_dir=repo_name,\n",
+ " group_by_length=True,\n",
+ " #per_device_train_batch_size=batch_size,\n",
+ " gradient_accumulation_steps=2,\n",
+ " evaluation_strategy=\"steps\",\n",
+ " num_train_epochs=5,\n",
+ " per_device_eval_batch_size=\"8\",\n",
+ " per_device_train_batch_size=\"32\",\n",
+ " gradient_checkpointing=True,\n",
+ " fp16=True,\n",
+ " save_steps=100,\n",
+ " eval_steps=100,\n",
+ " logging_steps=100,\n",
+ " learning_rate=1e-4,\n",
+ " warmup_steps=300,\n",
+ " save_total_limit=2,\n",
+ " push_to_hub=True,\n",
+ " freeze_feature_extractor=True,\n",
+ " save_total_limit=\"1\",\n",
+ " feat_proj_dropout=\"0.04\",\n",
+ " layerdrop=\"0.041\",\n",
+ " attention_dropout=\"0.094\",\n",
+ " activation_dropout=\"0.055\",\n",
+ " hidden_dropout=\"0.047\",\n",
+ " mask_time_prob=\"0.4\",\n",
+ " dataloader_num_workers=\"8\"\n",
+ ")\n",
+ "'''"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OsW-WZcL1ZtN"
+ },
+ "source": [
+ "Now, all instances can be passed to Trainer and we are ready to start training!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "rY7vBmFCPFgC",
+ "outputId": "63d63141-ac40-4d03-d984-726733f8ca00"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/content/wav2vec2-xls-r-300m-tr-CV8-v1 is already a clone of https://huggingface.co/emre/wav2vec2-xls-r-300m-tr-CV8-v1. Make sure you pull the latest changes with `repo.git_pull()`.\n",
+ "Using amp fp16 backend\n"
+ ]
+ }
+ ],
+ "source": [
+ "from transformers import Trainer\n",
+ "\n",
+ "trainer = Trainer(\n",
+ " model=model,\n",
+ " data_collator=data_collator,\n",
+ " args=training_args,\n",
+ " compute_metrics=compute_metrics,\n",
+ " train_dataset=common_voice_train,\n",
+ " eval_dataset=common_voice_test,\n",
+ " tokenizer=processor.feature_extractor,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "UoXBx1JAA0DX"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "---\n",
+ "\n",
+ "${}^1$ To allow models to become independent of the speaker rate, in CTC, consecutive tokens that are identical are simply grouped as a single token. However, the encoded labels should not be grouped when decoding since they don't correspond to the predicted tokens of the model, which is why the `group_tokens=False` parameter has to be passed. If we wouldn't pass this parameter a word like `\"hello\"` would incorrectly be encoded, and decoded as `\"helo\"`.\n",
+ "\n",
+ "${}^2$ The blank token allows the model to predict a word, such as `\"hello\"` by forcing it to insert the blank token between the two l's. A CTC-conform prediction of `\"hello\"` of our model would be `[PAD] [PAD] \"h\" \"e\" \"e\" \"l\" \"l\" [PAD] \"l\" \"o\" \"o\" [PAD]`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rpvZHM1xReIW"
+ },
+ "source": [
+ "### Training"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "j-3oKSzZ1hGq"
+ },
+ "source": [
+ "Training will take multiple hours depending on the GPU allocated to this notebook. Every `save_steps`, the current checkpoint will be uploaded to the Hub."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "d2G6z5RGAq5p",
+ "outputId": "f385f8ee-aba8-4945-db45-d1f5ef6682e9"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "The following columns in the training set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running training *****\n",
+ " Num examples = 20916\n",
+ " Num Epochs = 30\n",
+ " Instantaneous batch size per device = 16\n",
+ " Total train batch size (w. parallel, distributed & accumulation) = 32\n",
+ " Gradient Accumulation steps = 2\n",
+ " Total optimization steps = 19620\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/feature_extraction_utils.py:158: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:201.)\n",
+ " tensor = as_tensor(value)\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "\n",
+ " <div>\n",
+ " \n",
+ " <progress value='14501' max='19620' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+ " [14501/19620 19:34:03 < 6:54:30, 0.21 it/s, Epoch 22.17/30]\n",
+ " </div>\n",
+ " <table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: left;\">\n",
+ " <th>Step</th>\n",
+ " <th>Training Loss</th>\n",
+ " <th>Validation Loss</th>\n",
+ " <th>Wer</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <td>500</td>\n",
+ " <td>6.611400</td>\n",
+ " <td>2.852730</td>\n",
+ " <td>0.999977</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1000</td>\n",
+ " <td>1.439500</td>\n",
+ " <td>0.500843</td>\n",
+ " <td>0.712646</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1500</td>\n",
+ " <td>0.810500</td>\n",
+ " <td>0.388432</td>\n",
+ " <td>0.645293</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2000</td>\n",
+ " <td>0.685500</td>\n",
+ " <td>0.343037</td>\n",
+ " <td>0.610986</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2500</td>\n",
+ " <td>0.638700</td>\n",
+ " <td>0.306737</td>\n",
+ " <td>0.584678</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3000</td>\n",
+ " <td>0.593800</td>\n",
+ " <td>0.304234</td>\n",
+ " <td>0.580553</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3500</td>\n",
+ " <td>0.566700</td>\n",
+ " <td>0.298875</td>\n",
+ " <td>0.563640</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4000</td>\n",
+ " <td>0.534800</td>\n",
+ " <td>0.290669</td>\n",
+ " <td>0.556330</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4500</td>\n",
+ " <td>0.525000</td>\n",
+ " <td>0.298471</td>\n",
+ " <td>0.551884</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5000</td>\n",
+ " <td>0.501200</td>\n",
+ " <td>0.292787</td>\n",
+ " <td>0.552090</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5500</td>\n",
+ " <td>0.483500</td>\n",
+ " <td>0.279511</td>\n",
+ " <td>0.541755</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6000</td>\n",
+ " <td>0.475100</td>\n",
+ " <td>0.277353</td>\n",
+ " <td>0.540884</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6500</td>\n",
+ " <td>0.459600</td>\n",
+ " <td>0.265267</td>\n",
+ " <td>0.529631</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7000</td>\n",
+ " <td>0.443500</td>\n",
+ " <td>0.267604</td>\n",
+ " <td>0.530434</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7500</td>\n",
+ " <td>0.428900</td>\n",
+ " <td>0.264186</td>\n",
+ " <td>0.520006</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8000</td>\n",
+ " <td>0.431900</td>\n",
+ " <td>0.270382</td>\n",
+ " <td>0.518517</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8500</td>\n",
+ " <td>0.416900</td>\n",
+ " <td>0.260475</td>\n",
+ " <td>0.519250</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9000</td>\n",
+ " <td>0.403800</td>\n",
+ " <td>0.255564</td>\n",
+ " <td>0.513200</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9500</td>\n",
+ " <td>0.401300</td>\n",
+ " <td>0.267153</td>\n",
+ " <td>0.504217</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10000</td>\n",
+ " <td>0.387900</td>\n",
+ " <td>0.249567</td>\n",
+ " <td>0.502933</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10500</td>\n",
+ " <td>0.380700</td>\n",
+ " <td>0.247131</td>\n",
+ " <td>0.496815</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11000</td>\n",
+ " <td>0.375400</td>\n",
+ " <td>0.247230</td>\n",
+ " <td>0.496792</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11500</td>\n",
+ " <td>0.372200</td>\n",
+ " <td>0.241458</td>\n",
+ " <td>0.495348</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12000</td>\n",
+ " <td>0.357100</td>\n",
+ " <td>0.244072</td>\n",
+ " <td>0.495325</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12500</td>\n",
+ " <td>0.352100</td>\n",
+ " <td>0.235727</td>\n",
+ " <td>0.493240</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13000</td>\n",
+ " <td>0.343200</td>\n",
+ " <td>0.231585</td>\n",
+ " <td>0.487579</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13500</td>\n",
+ " <td>0.342800</td>\n",
+ " <td>0.235534</td>\n",
+ " <td>0.483271</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14000</td>\n",
+ " <td>0.328200</td>\n",
+ " <td>0.240142</td>\n",
+ " <td>0.481208</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table><p>\n",
+ " <div>\n",
+ " \n",
+ " <progress value='908' max='1043' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+ " [ 908/1043 12:56 < 01:55, 1.17 it/s]\n",
+ " </div>\n",
+ " "
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-500\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-500/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-500/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-500/preprocessor_config.json\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/preprocessor_config.json\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1000\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1000/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-500] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1500\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1500/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1500/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1000] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2000\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2000/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-1500] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2500\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2500/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2500/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2000] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3000\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3000/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-2500] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3500\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3500/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3500/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3000] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4000\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4000/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-3500] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4500\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4500/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4500/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4000] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5000\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5000/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-4500] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5500\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5500/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5500/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5000] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6000\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6000/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-5500] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6500\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6500/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6500/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6000] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7000\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7000/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-6500] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7500\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7500/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7500/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7000] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8000\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8000/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-7500] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8500\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8500/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8500/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8000] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9000\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9000/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-8500] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9500\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9500/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9500/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9000] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10000\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10000/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-9500] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10500\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10500/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10500/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10000] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11000\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11000/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-10500] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11500\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11500/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11500/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11000] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12000\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12000/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-11500] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12500\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12500/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12500/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12000] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13000\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13000/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-12500] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13500\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13500/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13500/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13500/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13000] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n",
+ "Saving model checkpoint to wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-14000\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-14000/config.json\n",
+ "Model weights saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-14000/pytorch_model.bin\n",
+ "Configuration saved in wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-14000/preprocessor_config.json\n",
+ "Deleting older checkpoint [wav2vec2-xls-r-300m-tr-CV8-v1/checkpoint-13500] due to args.save_total_limit\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:882: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " return (input_length - kernel_size) // stride + 1\n",
+ "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 8339\n",
+ " Batch size = 8\n"
+ ]
+ }
+ ],
+ "source": [
+ "trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kPNxoqifM7R3"
+ },
+ "source": [
+ "You can now upload the final result of the training to the Hub. Just execute this instruction:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "FKgqc1FhAxAP",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 165
+ },
+ "outputId": "8b001341-0116-4451-b5fc-92bfc3beb4e2"
+ },
+ "outputs": [
+ {
+ "output_type": "error",
+ "ename": "NameError",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-1-8fcc5527db7e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m: name 'trainer' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "trainer.push_to_hub()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gKSOruDqNJxO"
+ },
+ "source": [
+ "You can now share this model with all your friends, family, favorite pets: they can all load it with the identifier \"your-username/the-name-you-picked\" so for instance:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2x-GybJDNQoH"
+ },
+ "source": [
+ "```python\n",
+ "from transformers import AutoModelForCTC, Wav2Vec2Processor\n",
+ "\n",
+ "model = AutoModelForCTC.from_pretrained(\"patrickvonplaten/wav2vec2-large-xlsr-turkish-demo-colab\")\n",
+ "processor = Wav2Vec2Processor.from_pretrained(\"patrickvonplaten/wav2vec2-large-xlsr-turkish-demo-colab\")\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RHIVc44_fY2N"
+ },
+ "source": [
+ "To fine-tune larger models on larger datasets using CTC loss, one should take a look at the official speech-recognition examples [here](https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition#connectionist-temporal-classification-without-language-model-ctc-wo-lm) 🤗."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "8Yj_7_SS2XMb"
+ },
+ "outputs": [],
+ "source": [
+ "import argparse\n",
+ "import re\n",
+ "from typing import Dict\n",
+ "\n",
+ "import torch\n",
+ "from datasets import Audio, Dataset, load_dataset, load_metric\n",
+ "\n",
+ "from transformers import AutoFeatureExtractor, pipeline\n",
+ "\n",
+ "\n",
+ "def log_results(result: Dataset, args: Dict[str, str]):\n",
+ " \"\"\"DO NOT CHANGE. This function computes and logs the result metrics.\"\"\"\n",
+ "\n",
+ " log_outputs = args.log_outputs\n",
+ " dataset_id = \"_\".join(args.dataset.split(\"/\") + [args.config, args.split])\n",
+ "\n",
+ " # load metric\n",
+ " wer = load_metric(\"wer\")\n",
+ " cer = load_metric(\"cer\")\n",
+ "\n",
+ " # compute metrics\n",
+ " wer_result = wer.compute(references=result[\"target\"], predictions=result[\"prediction\"])\n",
+ " cer_result = cer.compute(references=result[\"target\"], predictions=result[\"prediction\"])\n",
+ "\n",
+ " # print & log results\n",
+ " result_str = f\"WER: {wer_result}\\n\" f\"CER: {cer_result}\"\n",
+ " print(result_str)\n",
+ "\n",
+ " with open(f\"{dataset_id}_eval_results.txt\", \"w\") as f:\n",
+ " f.write(result_str)\n",
+ "\n",
+ " # log all results in text file. Possibly interesting for analysis\n",
+ " if log_outputs is not None:\n",
+ " pred_file = f\"log_{dataset_id}_predictions.txt\"\n",
+ " target_file = f\"log_{dataset_id}_targets.txt\"\n",
+ "\n",
+ " with open(pred_file, \"w\") as p, open(target_file, \"w\") as t:\n",
+ "\n",
+ " # mapping function to write output\n",
+ " def write_to_file(batch, i):\n",
+ " p.write(f\"{i}\" + \"\\n\")\n",
+ " p.write(batch[\"prediction\"] + \"\\n\")\n",
+ " t.write(f\"{i}\" + \"\\n\")\n",
+ " t.write(batch[\"target\"] + \"\\n\")\n",
+ "\n",
+ " result.map(write_to_file, with_indices=True)\n",
+ "\n",
+ "\n",
+ "def normalize_text(text: str) -> str:\n",
+ " \"\"\"DO ADAPT FOR YOUR USE CASE. this function normalizes the target text.\"\"\"\n",
+ "\n",
+ " chars_to_ignore_regex = '[,?.!\\-\\;\\:\"“%‘”�—’…–]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training\n",
+ "\n",
+ " text = re.sub(chars_to_ignore_regex, \"\", text.lower())\n",
+ "\n",
+ " # In addition, we can normalize the target text, e.g. removing new lines characters etc...\n",
+ " # note that order is important here!\n",
+ " token_sequences_to_ignore = [\"\\n\\n\", \"\\n\", \" \", \" \"]\n",
+ "\n",
+ " for t in token_sequences_to_ignore:\n",
+ " text = \" \".join(text.split(t))\n",
+ "\n",
+ " return text\n",
+ "\n",
+ "\n",
+ "def main(args):\n",
+ " # load dataset\n",
+ " dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True)\n",
+ "\n",
+ " # for testing: only process the first two examples as a test\n",
+ " # dataset = dataset.select(range(10))\n",
+ "\n",
+ " # load processor\n",
+ " feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)\n",
+ " sampling_rate = feature_extractor.sampling_rate\n",
+ "\n",
+ " # resample audio\n",
+ " dataset = dataset.cast_column(\"audio\", Audio(sampling_rate=sampling_rate))\n",
+ "\n",
+ " # load eval pipeline\n",
+ " if args.device is None:\n",
+ " args.device = 0 if torch.cuda.is_available() else -1\n",
+ " asr = pipeline(\"automatic-speech-recognition\", model=args.model_id, device=args.device)\n",
+ "\n",
+ " # map function to decode audio\n",
+ " def map_to_pred(batch):\n",
+ " prediction = asr(\n",
+ " batch[\"audio\"][\"array\"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s\n",
+ " )\n",
+ "\n",
+ " batch[\"prediction\"] = prediction[\"text\"]\n",
+ " batch[\"target\"] = normalize_text(batch[\"sentence\"])\n",
+ " return batch\n",
+ "\n",
+ " # run inference on all examples\n",
+ " result = dataset.map(map_to_pred, remove_columns=dataset.column_names)\n",
+ "\n",
+ " # compute and log_results\n",
+ " # do not change function below\n",
+ " log_results(result, args)\n",
+ "\n",
+ "'''\n",
+ "if __name__ == \"__main__\":\n",
+ " parser = argparse.ArgumentParser()\n",
+ "\n",
+ " parser.add_argument(\n",
+ " \"--model_id\", type=str, required=True, help=\"Model identifier. Should be loadable with 🤗 Transformers\"\n",
+ " )\n",
+ " parser.add_argument(\n",
+ " \"--dataset\",\n",
+ " type=str,\n",
+ " required=True,\n",
+ " help=\"Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets\",\n",
+ " )\n",
+ " parser.add_argument(\n",
+ " \"--config\", type=str, required=True, help=\"Config of the dataset. *E.g.* `'en'` for Common Voice\"\n",
+ " )\n",
+ " parser.add_argument(\"--split\", type=str, required=True, help=\"Split of the dataset. *E.g.* `'test'`\")\n",
+ " parser.add_argument(\n",
+ " \"--chunk_length_s\", type=float, default=None, help=\"Chunk length in seconds. Defaults to 5 seconds.\"\n",
+ " )\n",
+ " parser.add_argument(\n",
+ " \"--stride_length_s\", type=float, default=None, help=\"Stride of the audio chunks. Defaults to 1 second.\"\n",
+ " )\n",
+ " parser.add_argument(\n",
+ " \"--log_outputs\", action=\"store_true\", help=\"If defined, write outputs to log file for analysis.\"\n",
+ " )\n",
+ " parser.add_argument(\n",
+ " \"--device\",\n",
+ " type=int,\n",
+ " default=None,\n",
+ " help=\"The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.\",\n",
+ " )\n",
+ " args = parser.parse_args()\n",
+ "\n",
+ " main(args)"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [],
+ "machine_shape": "hm",
+ "name": "Emre-Turkish-Fine-Tune Multi-Lingual Speech Recognition Model with 🤗 Transformers using CTC.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "f806b26119884e688585835e33bd9cda": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "VBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "VBoxView",
+ "_dom_classes": [],
+ "_model_name": "VBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_62281d26fca2464b891e4e8dced07110",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_e8ed03cfa00c4941a2351dde3c2e4cb7",
+ "IPY_MODEL_5f40b4c187664350ad4f7bce35518d47",
+ "IPY_MODEL_400d6ff9e84d476cadeaddfc57ba7f31",
+ "IPY_MODEL_68ab1ecb6d724fc29777d7216081a667",
+ "IPY_MODEL_4cc4957451364dfab93f760d0f8cd0b6"
+ ]
+ }
+ },
+ "62281d26fca2464b891e4e8dced07110": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": "column",
+ "width": "50%",
+ "min_width": null,
+ "border": null,
+ "align_items": "center",
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": "flex",
+ "left": null
+ }
+ },
+ "e8ed03cfa00c4941a2351dde3c2e4cb7": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_32c43163ae214a73871c7952f91c1b79",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "<center>\n<img src=https://huggingface.co/front/assets/huggingface_logo-noborder.svg alt='Hugging Face'>\n<br>\nCopy a token from <a href=\"https://huggingface.co/settings/token\" target=\"_blank\">your Hugging Face tokens page</a> and paste it below.\n<br>\nImmediately click login after copying your token or it might be stored in plain text in this notebook file.\n</center>",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_343e824ec8014081aa0dc4656e5c1247"
+ }
+ },
+ "5f40b4c187664350ad4f7bce35518d47": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "PasswordModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "PasswordView",
+ "style": "IPY_MODEL_168bb48911bd4b398e1bef1e57282a4a",
+ "_dom_classes": [],
+ "description": "Token:",
+ "_model_name": "PasswordModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "",
+ "_view_count": null,
+ "disabled": false,
+ "_view_module_version": "1.5.0",
+ "continuous_update": true,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_589efd9025484b199b2a6fe6f6b06027"
+ }
+ },
+ "400d6ff9e84d476cadeaddfc57ba7f31": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ButtonModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ButtonView",
+ "style": "IPY_MODEL_8dcd01c2904745a3a5f9cfbe2339c344",
+ "_dom_classes": [],
+ "description": "Login",
+ "_model_name": "ButtonModel",
+ "button_style": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "tooltip": "",
+ "_view_count": null,
+ "disabled": false,
+ "_view_module_version": "1.5.0",
+ "layout": "IPY_MODEL_888e4ce603eb413dbe1affc067555376",
+ "_model_module": "@jupyter-widgets/controls",
+ "icon": ""
+ }
+ },
+ "68ab1ecb6d724fc29777d7216081a667": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_7f838aab3d2c4d98a50652fb30493b10",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "\n<b>Pro Tip:</b> If you don't already have one, you can create a dedicated 'notebooks' token with 'write' access, that you can then easily reuse for all notebooks.\n<br>\n<i>Logging in with your username and password is deprecated and won't be possible anymore in the near future. You can still use them for now by clicking below.</i>\n</center>",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_958d9ceb018941378ce9e62cafcc2930"
+ }
+ },
+ "4cc4957451364dfab93f760d0f8cd0b6": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ButtonModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ButtonView",
+ "style": "IPY_MODEL_40a18d78a96f450a96d127a2504c18cf",
+ "_dom_classes": [],
+ "description": "Use password",
+ "_model_name": "ButtonModel",
+ "button_style": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "tooltip": "",
+ "_view_count": null,
+ "disabled": false,
+ "_view_module_version": "1.5.0",
+ "layout": "IPY_MODEL_6a3e10c948c94bf5b4f35496f22feb4d",
+ "_model_module": "@jupyter-widgets/controls",
+ "icon": ""
+ }
+ },
+ "32c43163ae214a73871c7952f91c1b79": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "343e824ec8014081aa0dc4656e5c1247": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "168bb48911bd4b398e1bef1e57282a4a": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "589efd9025484b199b2a6fe6f6b06027": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "8dcd01c2904745a3a5f9cfbe2339c344": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ButtonStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ButtonStyleModel",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "button_color": null,
+ "font_weight": "",
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "888e4ce603eb413dbe1affc067555376": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "7f838aab3d2c4d98a50652fb30493b10": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "958d9ceb018941378ce9e62cafcc2930": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "40a18d78a96f450a96d127a2504c18cf": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ButtonStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ButtonStyleModel",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "button_color": null,
+ "font_weight": "",
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "6a3e10c948c94bf5b4f35496f22feb4d": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "23bdf3807fe34d63a600fcea26520f78": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_2f73de7bd34b4377a14b90ccf962d327",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_cdcd548b670e4fdbb061a7a38f305fcd",
+ "IPY_MODEL_bbc251ada7504759b531d9b846b06944",
+ "IPY_MODEL_51bc2d6b195142149a6d224b38bb3c5e"
+ ]
+ }
+ },
+ "2f73de7bd34b4377a14b90ccf962d327": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "cdcd548b670e4fdbb061a7a38f305fcd": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_50ab93d93cb64e19aea03166480ecbaf",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "Downloading: 100%",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_8f308646f3634822ab957547ee9b9908"
+ }
+ },
+ "bbc251ada7504759b531d9b846b06944": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_b52235b5022744a996059fe98fee4dd6",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "success",
+ "max": 10069,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 10069,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_b1b9a92b0495411182ff7e8db879c344"
+ }
+ },
+ "51bc2d6b195142149a6d224b38bb3c5e": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_0a526f64df90463383682a3aae6885d9",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 10.1k/10.1k [00:00<00:00, 393kB/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_aa8bde4a54724abdb5f8c8afba39f898"
+ }
+ },
+ "50ab93d93cb64e19aea03166480ecbaf": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "8f308646f3634822ab957547ee9b9908": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "b52235b5022744a996059fe98fee4dd6": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "b1b9a92b0495411182ff7e8db879c344": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "0a526f64df90463383682a3aae6885d9": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "aa8bde4a54724abdb5f8c8afba39f898": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "170bfecf226142cdb87d0bf03be43842": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_9ec2db1ab1524dd5a5afad2e5ddb359f",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_f7c38c359728400e89b60af16013db3d",
+ "IPY_MODEL_52401e8b880740e4ac1b43f3844c12e0",
+ "IPY_MODEL_26525cfe8560466a84c0e16abb165fe5"
+ ]
+ }
+ },
+ "9ec2db1ab1524dd5a5afad2e5ddb359f": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "f7c38c359728400e89b60af16013db3d": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_70e5a9ef53a74510a04ced4f5f2a27f5",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "Downloading: 100%",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_5da881b52302403db72ba1196dc9bc9f"
+ }
+ },
+ "52401e8b880740e4ac1b43f3844c12e0": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_2ffb88a2e26642e3b814e11b4e40abe0",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "success",
+ "max": 2984,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 2984,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_1aa7a917a2cb4175adf869bd123108d5"
+ }
+ },
+ "26525cfe8560466a84c0e16abb165fe5": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_6a0470c75f9d428b86406c502a15d934",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 2.98k/2.98k [00:00<00:00, 116kB/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_e454ba90eb5d4cd8a2511004c1a6459c"
+ }
+ },
+ "70e5a9ef53a74510a04ced4f5f2a27f5": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "5da881b52302403db72ba1196dc9bc9f": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "2ffb88a2e26642e3b814e11b4e40abe0": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "1aa7a917a2cb4175adf869bd123108d5": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "6a0470c75f9d428b86406c502a15d934": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "e454ba90eb5d4cd8a2511004c1a6459c": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "a4416a28b7434c1196e1e1ee78a6524e": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_216bdc1df5074ad890005ab4c3cc430d",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_633c5b60966a4f54897c46f31b5aa519",
+ "IPY_MODEL_00205bf74060408d937ef70d9dab37eb",
+ "IPY_MODEL_3083964c340f463aad68704998106ce3"
+ ]
+ }
+ },
+ "216bdc1df5074ad890005ab4c3cc430d": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "633c5b60966a4f54897c46f31b5aa519": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_ccf257a118a44f9180dcfb6ab1a4ea3b",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "Downloading: 100%",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_baf7a967c7554036a833a20dccf69b78"
+ }
+ },
+ "00205bf74060408d937ef70d9dab37eb": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_bcc0f0635473429c82e402b2a8e42f3e",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "success",
+ "max": 53072,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 53072,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_59d3b516d58744c6b853a517585dde3d"
+ }
+ },
+ "3083964c340f463aad68704998106ce3": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_ef9faa9bd0d14ddca4dabfc8fe344416",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 53.1k/53.1k [00:00<00:00, 228kB/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_f3feaffb727f4eb0833b98d7f7d94687"
+ }
+ },
+ "ccf257a118a44f9180dcfb6ab1a4ea3b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "baf7a967c7554036a833a20dccf69b78": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "bcc0f0635473429c82e402b2a8e42f3e": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "59d3b516d58744c6b853a517585dde3d": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "ef9faa9bd0d14ddca4dabfc8fe344416": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "f3feaffb727f4eb0833b98d7f7d94687": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "1e97ed9ce1a342ffb1bc35dfca37ce8c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_f707cca6e67f4562922c83e96d448d8f",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_a7673268a04c461da202565d6df0eea2",
+ "IPY_MODEL_bc5ab57b856d45c8a05aea643621be29",
+ "IPY_MODEL_136775a4af24494dbcbb8a26c557ff58"
+ ]
+ }
+ },
+ "f707cca6e67f4562922c83e96d448d8f": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "a7673268a04c461da202565d6df0eea2": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_0183d4af17a24bc7951ad5d9c308acfd",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "Downloading: 100%",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_e1da95804e38463da33aabfd34eaf038"
+ }
+ },
+ "bc5ab57b856d45c8a05aea643621be29": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_348c3778b1984b7fb5a1f99d2dcd5d43",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "success",
+ "max": 1550870485,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 1550870485,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_42668932cd7c478db86dd01620959ad6"
+ }
+ },
+ "136775a4af24494dbcbb8a26c557ff58": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_4f2e8ff469b64b2d8aae0daba054f992",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 1.55G/1.55G [00:33<00:00, 46.5MB/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_d94834ae37514f2a8fad199ebcc1b5e0"
+ }
+ },
+ "0183d4af17a24bc7951ad5d9c308acfd": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "e1da95804e38463da33aabfd34eaf038": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "348c3778b1984b7fb5a1f99d2dcd5d43": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "42668932cd7c478db86dd01620959ad6": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "4f2e8ff469b64b2d8aae0daba054f992": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "d94834ae37514f2a8fad199ebcc1b5e0": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "83985afb3b9b47a8a51ed826637d7ec3": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_8a8a52acf6314c47bd07cfcca47481e0",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_fdb6d241936c4bcc949101a3abdadc2a",
+ "IPY_MODEL_7e7ac42e73974bc0a5080d2f68a95805",
+ "IPY_MODEL_b109d9651a1f42089ff90ceed18550be"
+ ]
+ }
+ },
+ "8a8a52acf6314c47bd07cfcca47481e0": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "fdb6d241936c4bcc949101a3abdadc2a": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_e541b0405b654a628643cbe8be11efe3",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_a41911630cba40e18799e467a00aa186"
+ }
+ },
+ "7e7ac42e73974bc0a5080d2f68a95805": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_f2f67f2ee2d04b109e08510dbbecf7da",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "info",
+ "max": 1,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 1,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_893ad16a62fa463982f2d302bba8ff8b"
+ }
+ },
+ "b109d9651a1f42089ff90ceed18550be": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_7d91952369dd49638b025241a51714a7",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 16850/0 [00:15<00:00, 1484.26 examples/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_9bfa87491ded49fe9f528ab156019e49"
+ }
+ },
+ "e541b0405b654a628643cbe8be11efe3": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "a41911630cba40e18799e467a00aa186": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "f2f67f2ee2d04b109e08510dbbecf7da": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "893ad16a62fa463982f2d302bba8ff8b": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": "20px",
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "7d91952369dd49638b025241a51714a7": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "9bfa87491ded49fe9f528ab156019e49": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "becbd6e66ac84539828e278831b47711": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_bae3eaf9948c40438d70f852e96c39ee",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_a0cf5e9552fb4925a85440d8d936477d",
+ "IPY_MODEL_2dc560e4c8b4411bbc1ac4745b0afe3d",
+ "IPY_MODEL_2079848a4e064e23a465efc0e8b98298"
+ ]
+ }
+ },
+ "bae3eaf9948c40438d70f852e96c39ee": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "a0cf5e9552fb4925a85440d8d936477d": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_f49e625b1e0545fcae35f3b38b421fe7",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_3336f37aa22443adb2e0dee5231625c2"
+ }
+ },
+ "2dc560e4c8b4411bbc1ac4745b0afe3d": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_b3c878fc99cc419d9a2c41c02b5363e6",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "info",
+ "max": 1,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 1,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_904534fe556845379b0e23869b3c923a"
+ }
+ },
+ "2079848a4e064e23a465efc0e8b98298": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_df70cc4b5d0b4843a8bc9c9069261c19",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 8275/0 [00:13<00:00, 167.52 examples/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_ab93e17d9f31428fa3f1b650aaa19e3e"
+ }
+ },
+ "f49e625b1e0545fcae35f3b38b421fe7": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "3336f37aa22443adb2e0dee5231625c2": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "b3c878fc99cc419d9a2c41c02b5363e6": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "904534fe556845379b0e23869b3c923a": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": "20px",
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "df70cc4b5d0b4843a8bc9c9069261c19": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "ab93e17d9f31428fa3f1b650aaa19e3e": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "c161b2e6759043eb9e3cf56faaa46905": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_424e6fc321b14cabbe7f039373c5ff2f",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_75506937f12647769085ec8a6600a291",
+ "IPY_MODEL_e38cab52706447b6ad4a30add3e52810",
+ "IPY_MODEL_27eab27656254686a977caf7cffb9151"
+ ]
+ }
+ },
+ "424e6fc321b14cabbe7f039373c5ff2f": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "75506937f12647769085ec8a6600a291": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_310142f6ad0b42c9a9d0b4a503edc5ac",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_f53f8de89a8b45ec84cb54192eb2a4f0"
+ }
+ },
+ "e38cab52706447b6ad4a30add3e52810": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_1d1c780f5f6e4166b993d4ccebceabc8",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "info",
+ "max": 1,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 1,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_575f85f09cdf4913ad2239dbf35652e4"
+ }
+ },
+ "27eab27656254686a977caf7cffb9151": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_047b9191a9004c868eeb99a86933dcd7",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 8104/0 [00:13<00:00, 493.17 examples/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_378b7ab2af4048aeb9c6caca0d911ea4"
+ }
+ },
+ "310142f6ad0b42c9a9d0b4a503edc5ac": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "f53f8de89a8b45ec84cb54192eb2a4f0": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "1d1c780f5f6e4166b993d4ccebceabc8": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "575f85f09cdf4913ad2239dbf35652e4": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": "20px",
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "047b9191a9004c868eeb99a86933dcd7": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "378b7ab2af4048aeb9c6caca0d911ea4": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "1f7352d8f3724c338ec344933005834f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_9caf41ec348640ac87d40c42d013296b",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_4966c62d4a5f4ff09256fca2d564832d",
+ "IPY_MODEL_265a9350979d48bf95f821618a06e929",
+ "IPY_MODEL_2b6cf7d6a8e44f3f90caad42e5ab4047"
+ ]
+ }
+ },
+ "9caf41ec348640ac87d40c42d013296b": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "4966c62d4a5f4ff09256fca2d564832d": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_0a42f004666f4f2aa53cda3724dc974d",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_1ee83dcc3dcd416da37221a50c8c3aae"
+ }
+ },
+ "265a9350979d48bf95f821618a06e929": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_270657838f0a45bb92e692503d2606ec",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "info",
+ "max": 1,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 1,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_c78b913a99d2496b9d1a00ea326bd6e2"
+ }
+ },
+ "2b6cf7d6a8e44f3f90caad42e5ab4047": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_a5b340de26464dd5aa481e3f9178db6e",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 151/0 [00:10<00:00, 11.17 examples/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_2c8b77eeb22b4193b0fa343b33fceb89"
+ }
+ },
+ "0a42f004666f4f2aa53cda3724dc974d": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "1ee83dcc3dcd416da37221a50c8c3aae": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "270657838f0a45bb92e692503d2606ec": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "c78b913a99d2496b9d1a00ea326bd6e2": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": "20px",
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "a5b340de26464dd5aa481e3f9178db6e": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "2c8b77eeb22b4193b0fa343b33fceb89": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "6b5174e3338843408714243f6b60072f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_7110a31dd43945c8a65e0f3e2f806bca",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_d2c676501a8842efbbd9a24620a1fe9e",
+ "IPY_MODEL_4fe0d5443bbd4295b407ba807df9141b",
+ "IPY_MODEL_5692a73de14b4a6e88bd59bc7bfe0a02"
+ ]
+ }
+ },
+ "7110a31dd43945c8a65e0f3e2f806bca": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "d2c676501a8842efbbd9a24620a1fe9e": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_0e4a34472820466da3d414ba6f43db26",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_b0c11305ef7b4d909659a4732f0f050f"
+ }
+ },
+ "4fe0d5443bbd4295b407ba807df9141b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_b058118bfff44c72bcd3309201fab9b1",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "info",
+ "max": 1,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 1,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_2ede5c651c4c4a1ba36d48fcdb25d2dc"
+ }
+ },
+ "5692a73de14b4a6e88bd59bc7bfe0a02": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_e155b7e1b96a410db90a35ffbd7e513b",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 3158/0 [00:12<00:00, 34.32 examples/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_83150dc5c23b4a549efdc36910618a47"
+ }
+ },
+ "0e4a34472820466da3d414ba6f43db26": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "b0c11305ef7b4d909659a4732f0f050f": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "b058118bfff44c72bcd3309201fab9b1": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "2ede5c651c4c4a1ba36d48fcdb25d2dc": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": "20px",
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "e155b7e1b96a410db90a35ffbd7e513b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "83150dc5c23b4a549efdc36910618a47": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "da1051f673f747e4955f863369212aeb": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_d4d7845f9b8a49f592b61cd51e439e6d",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_9ec68324ba8f427eab819c06ea2e6c5f",
+ "IPY_MODEL_ca95007c72854a118eb2826170a7e5ee",
+ "IPY_MODEL_62c200f505f64e939ceaaea4cf5c1f1f"
+ ]
+ }
+ },
+ "d4d7845f9b8a49f592b61cd51e439e6d": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "9ec68324ba8f427eab819c06ea2e6c5f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_3df6c9b2730e4b10aaa46d59ef6db333",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_76c4fe0608734983a22e47799cb1c383"
+ }
+ },
+ "ca95007c72854a118eb2826170a7e5ee": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_e951dfe6a3b845069aca8a81c7248232",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "success",
+ "max": 1,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 1,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_4f35d24404484227801de58669037020"
+ }
+ },
+ "62c200f505f64e939ceaaea4cf5c1f1f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_a1aee963e2764fbca86cf242647022e0",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 25058/? [00:03<00:00, 6564.54ex/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_caa9274766ed4102988e69986c44aa0a"
+ }
+ },
+ "3df6c9b2730e4b10aaa46d59ef6db333": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "76c4fe0608734983a22e47799cb1c383": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "e951dfe6a3b845069aca8a81c7248232": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "4f35d24404484227801de58669037020": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": "20px",
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "a1aee963e2764fbca86cf242647022e0": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "caa9274766ed4102988e69986c44aa0a": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "19ebe7b3d430420cb28da897a88c092c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_d889e7cfdf9b4c758eae00b2e303a021",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_612da199c8dc4ef48d26b26e4fcfcd55",
+ "IPY_MODEL_11cde7ee4f5b4a6796391b53bfb2b7da",
+ "IPY_MODEL_d0487450690a41da805bc8b48c72271b"
+ ]
+ }
+ },
+ "d889e7cfdf9b4c758eae00b2e303a021": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "612da199c8dc4ef48d26b26e4fcfcd55": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_69d46dd2eea14bd88faf5a1ca4cfed7c",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_6b00d315427a4c9cb833605dc07801f6"
+ }
+ },
+ "11cde7ee4f5b4a6796391b53bfb2b7da": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_ebdf3801c865463a9294893f62fd62f3",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "success",
+ "max": 1,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 1,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_728ea53bb2954d4ba98c0e3898f381e5"
+ }
+ },
+ "d0487450690a41da805bc8b48c72271b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_682910d92af240b5942e46fbdf134421",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 8339/? [00:01<00:00, 6312.98ex/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_4ad9488f342c4ff8bcb71def50f081ef"
+ }
+ },
+ "69d46dd2eea14bd88faf5a1ca4cfed7c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "6b00d315427a4c9cb833605dc07801f6": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "ebdf3801c865463a9294893f62fd62f3": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "728ea53bb2954d4ba98c0e3898f381e5": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": "20px",
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "682910d92af240b5942e46fbdf134421": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "4ad9488f342c4ff8bcb71def50f081ef": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "04805b7e6dbd4e878543b035c6ac9ffd": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_e30ada8532d34e279a5c917187030a35",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_88ba207d0a65433ca0e5ea1298055795",
+ "IPY_MODEL_573756adc4da43ebbff370e6c5a6cee1",
+ "IPY_MODEL_f13c45348bc346f8b0acdaa00b4fb75b"
+ ]
+ }
+ },
+ "e30ada8532d34e279a5c917187030a35": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "88ba207d0a65433ca0e5ea1298055795": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_f223dcd00b224f229ffa30efb18a21bb",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "100%",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_444a0eabada2472a8a0293d89d3056d4"
+ }
+ },
+ "573756adc4da43ebbff370e6c5a6cee1": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_f1909a70344a49849911d5221fe2e09c",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "success",
+ "max": 1,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 1,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_5d497cd406e6459797fc16df24d06f39"
+ }
+ },
+ "f13c45348bc346f8b0acdaa00b4fb75b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_24f87018abdd4a05ae26a0b123b81d4a",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 1/1 [00:00<00:00, 1.58ba/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_37d657c09da14715826ea3da6a3c8bf1"
+ }
+ },
+ "f223dcd00b224f229ffa30efb18a21bb": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "444a0eabada2472a8a0293d89d3056d4": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "f1909a70344a49849911d5221fe2e09c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "5d497cd406e6459797fc16df24d06f39": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "24f87018abdd4a05ae26a0b123b81d4a": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "37d657c09da14715826ea3da6a3c8bf1": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "80d72bcc95bb4a2aaf27d16bc81c013f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_ab6646153a4a49e6b468b2f2f1711605",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_c921133532a7448f90c9add21fb9098f",
+ "IPY_MODEL_2154a5d07421448aaf6fe53c5c9568a4",
+ "IPY_MODEL_7ef4dfd22751446fb255f9102f3419f9"
+ ]
+ }
+ },
+ "ab6646153a4a49e6b468b2f2f1711605": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "c921133532a7448f90c9add21fb9098f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_e68c242c463e4e6895dad01ca33c9326",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "100%",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_f6129bdabec34a01af416293eeb47ad7"
+ }
+ },
+ "2154a5d07421448aaf6fe53c5c9568a4": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_b1909d011ebd4129b4992347797ef0a4",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "success",
+ "max": 1,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 1,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_36228aad1ec24e5c97fc0d046e00561e"
+ }
+ },
+ "7ef4dfd22751446fb255f9102f3419f9": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_f0b1028065cd4765b310b7af3ae26b4b",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 1/1 [00:00<00:00, 4.20ba/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_7d5c696360e347cd8e00ef545f08ef43"
+ }
+ },
+ "e68c242c463e4e6895dad01ca33c9326": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "f6129bdabec34a01af416293eeb47ad7": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "b1909d011ebd4129b4992347797ef0a4": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "36228aad1ec24e5c97fc0d046e00561e": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "f0b1028065cd4765b310b7af3ae26b4b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "7d5c696360e347cd8e00ef545f08ef43": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "c0e164ae6da6409983df9cd716084642": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_a26e8267388b4b6fb72738b8bf015fec",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_1b14adb2df3f4dd6bc463b388996b1ba",
+ "IPY_MODEL_fa59e61e499d4d44ae6c32e89be231dd",
+ "IPY_MODEL_64cffbbc242a4201a3f842bc3081da3e"
+ ]
+ }
+ },
+ "a26e8267388b4b6fb72738b8bf015fec": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "1b14adb2df3f4dd6bc463b388996b1ba": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_3f018a8c91924b85919b8713b591fd62",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "Downloading: 100%",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_6393cf9b22e04d75a12f74c9dd7ef31c"
+ }
+ },
+ "fa59e61e499d4d44ae6c32e89be231dd": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_749100e315ff432294bece9cad2417b7",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "success",
+ "max": 1568,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 1568,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_1a463985b65a436e8880f28001f3f9ca"
+ }
+ },
+ "64cffbbc242a4201a3f842bc3081da3e": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_12455d049390474c98fb06298c5e7958",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 1.53k/1.53k [00:00<00:00, 64.5kB/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_fc47bddc681a4e5b8c352cbcf232853c"
+ }
+ },
+ "3f018a8c91924b85919b8713b591fd62": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "6393cf9b22e04d75a12f74c9dd7ef31c": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "749100e315ff432294bece9cad2417b7": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "1a463985b65a436e8880f28001f3f9ca": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "12455d049390474c98fb06298c5e7958": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "fc47bddc681a4e5b8c352cbcf232853c": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "2a2cbbf93c8c4ad69052b1b0478acfea": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_a4c449e803d942d7a4bdc9f5c923107d",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_475e841d2f254301a9c4a5da9be07bfe",
+ "IPY_MODEL_189dcc43f91946669a77445e3acb1143",
+ "IPY_MODEL_75c01c6a6b094b13995c8c061749e127"
+ ]
+ }
+ },
+ "a4c449e803d942d7a4bdc9f5c923107d": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "475e841d2f254301a9c4a5da9be07bfe": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_f1d9f253969b446faeaca0167b84afb8",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "Downloading: 100%",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_210d7c933d564b769964e68668491128"
+ }
+ },
+ "189dcc43f91946669a77445e3acb1143": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_feccd14aa429488fb5194cf7f18ddc05",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "success",
+ "max": 212,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 212,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_06ecae394d624765baf9bdbaddde248e"
+ }
+ },
+ "75c01c6a6b094b13995c8c061749e127": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_da191a839e85403c8e0f9e27144b7a69",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 212/212 [00:00<00:00, 7.62kB/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_9b45662a7c774bf8913b547705021a5f"
+ }
+ },
+ "f1d9f253969b446faeaca0167b84afb8": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "210d7c933d564b769964e68668491128": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "feccd14aa429488fb5194cf7f18ddc05": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "06ecae394d624765baf9bdbaddde248e": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "da191a839e85403c8e0f9e27144b7a69": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "9b45662a7c774bf8913b547705021a5f": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "a3606e7e00e54b3a98cf6d999519a559": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_9580a891a8964def85c2b4c20720bc12",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_d85dfe5ecb754a57ac52a593f3da5bce",
+ "IPY_MODEL_6a5594fe7fdc451cb3b8c075638e046c",
+ "IPY_MODEL_c674f0a97f67412b8a9ff5a3316074ac"
+ ]
+ }
+ },
+ "9580a891a8964def85c2b4c20720bc12": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "d85dfe5ecb754a57ac52a593f3da5bce": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_d9132d9c79874897a7da4ffffbd374d3",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_d7e6852b58704eab8541e4250e229f98"
+ }
+ },
+ "6a5594fe7fdc451cb3b8c075638e046c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_8aab01eaed564597993f0095f4afeb62",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "success",
+ "max": 1,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 1,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_56c2bd8eff2b45849191764a68a17826"
+ }
+ },
+ "c674f0a97f67412b8a9ff5a3316074ac": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_be891c23f24145cbb6db1a24f66ecd16",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 25058/? [02:41<00:00, 98.80ex/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_72206d82d0ba4b1586da4598eb3b0202"
+ }
+ },
+ "d9132d9c79874897a7da4ffffbd374d3": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "d7e6852b58704eab8541e4250e229f98": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "8aab01eaed564597993f0095f4afeb62": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "56c2bd8eff2b45849191764a68a17826": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": "20px",
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "be891c23f24145cbb6db1a24f66ecd16": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "72206d82d0ba4b1586da4598eb3b0202": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "f439211c04884bea98fe4afba8e7ca4a": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_45e0c7a8c1fa427cad3c9565cd068251",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_e04bd4466f384471a39f32f55d2780f6",
+ "IPY_MODEL_33797b5723ac435ba7e42fcfaec4756d",
+ "IPY_MODEL_486ebf97b1474c37b28107bfb23a0703"
+ ]
+ }
+ },
+ "45e0c7a8c1fa427cad3c9565cd068251": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "e04bd4466f384471a39f32f55d2780f6": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_eab6aa2773994c44b4e9ee8c43eaaae5",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_c685d717186a493486a1a5b8afcece4e"
+ }
+ },
+ "33797b5723ac435ba7e42fcfaec4756d": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_29741efbd6e64365bcd010a066a09b34",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "success",
+ "max": 1,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 1,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_8df4bdc8aae64be38bfaf68ddbbfb4be"
+ }
+ },
+ "486ebf97b1474c37b28107bfb23a0703": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_87cc8498063f4fb6b1a2db3556f896ce",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 8339/? [01:10<00:00, 161.28ex/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_cadf486284ad4b1ea64ec93b3f7a938c"
+ }
+ },
+ "eab6aa2773994c44b4e9ee8c43eaaae5": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "c685d717186a493486a1a5b8afcece4e": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "29741efbd6e64365bcd010a066a09b34": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "8df4bdc8aae64be38bfaf68ddbbfb4be": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": "20px",
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "87cc8498063f4fb6b1a2db3556f896ce": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "cadf486284ad4b1ea64ec93b3f7a938c": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "77494b9ce086467bbac1cff55ab6e715": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_f305d2a7984c4b7d84e7458307ded2d2",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_d4cb7c13043c405fa2185f61b0b064ca",
+ "IPY_MODEL_6245e8f435cb44a0a403362788f5f0df",
+ "IPY_MODEL_857ed4101a09450e850f3f56072102f9"
+ ]
+ }
+ },
+ "f305d2a7984c4b7d84e7458307ded2d2": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "d4cb7c13043c405fa2185f61b0b064ca": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_0fd358405d93489dba3efd6643120ebf",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "100%",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_4994d7b0efe04c03a6bface4e986fd61"
+ }
+ },
+ "6245e8f435cb44a0a403362788f5f0df": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_db7f11ef346042dc9d89e59750e0bca9",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "success",
+ "max": 26,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 26,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_d9cfb56b15b44940be99fb5842608182"
+ }
+ },
+ "857ed4101a09450e850f3f56072102f9": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_d4ce7ffc6596478d998dd614429f21d1",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 26/26 [00:00<00:00, 289.35ba/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_e18fb65bbfd247a997a22ce6f2e940de"
+ }
+ },
+ "0fd358405d93489dba3efd6643120ebf": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "4994d7b0efe04c03a6bface4e986fd61": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "db7f11ef346042dc9d89e59750e0bca9": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "d9cfb56b15b44940be99fb5842608182": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "d4ce7ffc6596478d998dd614429f21d1": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "e18fb65bbfd247a997a22ce6f2e940de": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "3584a24565254be4a665568c91261a40": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_ecc615a58d1a45cbadde4079b14b2a8c",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_608329d74f56402fae1bd3ba3318a0ff",
+ "IPY_MODEL_3ddeca68918e44eaa65a975acba73081",
+ "IPY_MODEL_71f27170a8064045826d41aa63fbf29e"
+ ]
+ }
+ },
+ "ecc615a58d1a45cbadde4079b14b2a8c": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "608329d74f56402fae1bd3ba3318a0ff": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_0fc84d9b108c4cb38c0f18b4d1a3d27a",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "Downloading: ",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_542355f122e248bfb7f6a8d902459e16"
+ }
+ },
+ "3ddeca68918e44eaa65a975acba73081": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_bf1febb1eb594bbd90c9cb94363536a1",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "success",
+ "max": 1901,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 1901,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_01fa773649da4e3fa955110d97a57793"
+ }
+ },
+ "71f27170a8064045826d41aa63fbf29e": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_10073907af5a4e9b9fd03f41221c328c",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 4.48k/? [00:00<00:00, 166kB/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_98cbd815a3f4437bbf80f67e0964d2bb"
+ }
+ },
+ "0fc84d9b108c4cb38c0f18b4d1a3d27a": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "542355f122e248bfb7f6a8d902459e16": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "bf1febb1eb594bbd90c9cb94363536a1": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "01fa773649da4e3fa955110d97a57793": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "10073907af5a4e9b9fd03f41221c328c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "98cbd815a3f4437bbf80f67e0964d2bb": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "e204b2ca0ef444779bf10e8d37943284": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HBoxView",
+ "_dom_classes": [],
+ "_model_name": "HBoxModel",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "box_style": "",
+ "layout": "IPY_MODEL_46ad1ddaa7814ff38877f14a4f3167fc",
+ "_model_module": "@jupyter-widgets/controls",
+ "children": [
+ "IPY_MODEL_114d8e5de5a94e1d8a61fad25bfce8f9",
+ "IPY_MODEL_ce0f4b3a12f74e9d9dbcfdf07de58c83",
+ "IPY_MODEL_c9d344680b9f428a8398c4c492fe60d8"
+ ]
+ }
+ },
+ "46ad1ddaa7814ff38877f14a4f3167fc": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "114d8e5de5a94e1d8a61fad25bfce8f9": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_50123cd248334539882bc450f0c76315",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": "Downloading: 100%",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_bc3f2ee267264173ba9bc54d1ebf5d0b"
+ }
+ },
+ "ce0f4b3a12f74e9d9dbcfdf07de58c83": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "ProgressView",
+ "style": "IPY_MODEL_f58a3b6fc7d54398a502b620e9180751",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "FloatProgressModel",
+ "bar_style": "success",
+ "max": 1269737156,
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": 1269737156,
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "orientation": "horizontal",
+ "min": 0,
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_62853e9a99f3490b9b5b268d52c2102d"
+ }
+ },
+ "c9d344680b9f428a8398c4c492fe60d8": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "HTMLView",
+ "style": "IPY_MODEL_98d1ddd134964990b74392bfc94f6bb6",
+ "_dom_classes": [],
+ "description": "",
+ "_model_name": "HTMLModel",
+ "placeholder": "",
+ "_view_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "value": " 1.18G/1.18G [00:21<00:00, 61.1MB/s]",
+ "_view_count": null,
+ "_view_module_version": "1.5.0",
+ "description_tooltip": null,
+ "_model_module": "@jupyter-widgets/controls",
+ "layout": "IPY_MODEL_c5ad073383524924be6e8ce7c1d9a1d5"
+ }
+ },
+ "50123cd248334539882bc450f0c76315": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "bc3f2ee267264173ba9bc54d1ebf5d0b": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "f58a3b6fc7d54398a502b620e9180751": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "ProgressStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "bar_color": null,
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "62853e9a99f3490b9b5b268d52c2102d": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ },
+ "98d1ddd134964990b74392bfc94f6bb6": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_view_name": "StyleView",
+ "_model_name": "DescriptionStyleModel",
+ "description_width": "",
+ "_view_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.5.0",
+ "_view_count": null,
+ "_view_module_version": "1.2.0",
+ "_model_module": "@jupyter-widgets/controls"
+ }
+ },
+ "c5ad073383524924be6e8ce7c1d9a1d5": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_view_name": "LayoutView",
+ "grid_template_rows": null,
+ "right": null,
+ "justify_content": null,
+ "_view_module": "@jupyter-widgets/base",
+ "overflow": null,
+ "_model_module_version": "1.2.0",
+ "_view_count": null,
+ "flex_flow": null,
+ "width": null,
+ "min_width": null,
+ "border": null,
+ "align_items": null,
+ "bottom": null,
+ "_model_module": "@jupyter-widgets/base",
+ "top": null,
+ "grid_column": null,
+ "overflow_y": null,
+ "overflow_x": null,
+ "grid_auto_flow": null,
+ "grid_area": null,
+ "grid_template_columns": null,
+ "flex": null,
+ "_model_name": "LayoutModel",
+ "justify_items": null,
+ "grid_row": null,
+ "max_height": null,
+ "align_content": null,
+ "visibility": null,
+ "align_self": null,
+ "height": null,
+ "min_height": null,
+ "padding": null,
+ "grid_auto_rows": null,
+ "grid_gap": null,
+ "max_width": null,
+ "order": null,
+ "_view_module_version": "1.2.0",
+ "grid_template_areas": null,
+ "object_position": null,
+ "object_fit": null,
+ "grid_auto_columns": null,
+ "margin": null,
+ "display": null,
+ "left": null
+ }
+ }
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file