diff --git "a/.ipynb_checkpoints/Fine_Tune_XLSR_Wav2Vec2_on_Greek_ASR_with_🤗_Transformers-checkpoint.ipynb" "b/.ipynb_checkpoints/Fine_Tune_XLSR_Wav2Vec2_on_Greek_ASR_with_🤗_Transformers-checkpoint.ipynb"
new file mode 100644--- /dev/null
+++ "b/.ipynb_checkpoints/Fine_Tune_XLSR_Wav2Vec2_on_Greek_ASR_with_🤗_Transformers-checkpoint.ipynb"
@@ -0,0 +1,10561 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LBSYoWbi-45k"
+   },
+   "source": [
+    "# **Fine-tuning XLSR-Wav2Vec2 for Multi-Lingual ASR with 🤗 Transformers**"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "Disclaimer: This notebook has been created by Patrick von Platen [HuggingFace] and was adapted by me for the Greek use case. The original notebook can be found here:\n",
+    "https://huggingface.co/blog/fine-tune-xlsr-wav2vec2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "V7YOT2mnUiea"
+   },
+   "source": [
+    "Wav2Vec2 is a pretrained model for Automatic Speech Recognition (ASR) and was released in [September 2020](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) by Alexei Baevski, Michael Auli, and Alex Conneau.  Soon after the superior performance of Wav2Vec2 was demonstrated on the English ASR dataset LibriSpeech, *Facebook AI* presented XLSR-Wav2Vec2 (click [here](https://arxiv.org/abs/2006.13979)). XLSR stands for *cross-lingual  speech representations* and refers to XLSR-Wav2Vec2`s ability to learn speech representations that are useful across multiple languages.\n",
+    "\n",
+    "Similar to Wav2Vec2, XLSR-Wav2Vec2 learns powerful speech representations from hundreds of thousands of hours of speech in more than 50 languages of unlabeled speech. Similar, to [BERT's masked language modeling](http://jalammar.github.io/illustrated-bert/), the model learns contextualized speech representations by randomly masking feature vectors before passing them to a transformer network.\n",
+    "\n",
+    "![wav2vec2_structure](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/xlsr_wav2vec2.png)\n",
+    "\n",
+    "The authors show for the first time that massively pretraining an ASR model on cross-lingual unlabeled speech data, followed by language-specific fine-tuning on very little labeled data achieves state-of-the-art results. See Table 1-5 of the official [paper](https://arxiv.org/pdf/2006.13979.pdf)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "nT_QrfWtsxIz"
+   },
+   "source": [
+    "In this notebook, we will give an in-detail explanation of how XLSR-Wav2Vec2's pretrained checkpoint can be fine-tuned on a low-resource ASR dataset of any language. Note that in this notebook, we will fine-tune XLSR-Wav2Vec2 without making use of a language model. It is much simpler and more efficient to use XLSR-Wav2Vec2 without a language model, but better results can be achieved by including a language model. \n",
+    "\n",
+    "For demonstration purposes, we fine-tune the [wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the low resource Greek ASR dataset of [Common Voice](https://huggingface.co/datasets/common_voice) that contains just ~6h of validated training data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Gx9OdDYrCtQ1"
+   },
+   "source": [
+    "XLSR-Wav2Vec2 is fine-tuned using Connectionist Temporal Classification (CTC), which is an algorithm that is used to train neural networks for sequence-to-sequence problems and mainly in Automatic Speech Recognition and handwriting recognition. \n",
+    "\n",
+    "I highly recommend reading the blog post [Sequence Modeling with CTC (2017)](https://distill.pub/2017/ctc/) very well-written blog post by Awni Hannun."
+   ]
+  },
+  {
+   "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": {
+    "ExecuteTime": {
+     "start_time": "2021-03-14T09:54:55.056Z"
+    },
+    "id": "c8eh87Hoee5d"
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "!pip install git+https://github.com/huggingface/datasets.git\n",
+    "!pip install git+https://github.com/huggingface/transformers.git\n",
+    "!pip install torchaudio\n",
+    "!pip install librosa\n",
+    "!pip install jiwer"
+   ]
+  },
+  {
+   "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, the XLSR-Wav2Vec2 model is thus accompanied by both a tokenizer, called [Wav2Vec2CTCTokenizer](https://huggingface.co/transformers/master/model_doc/wav2vec2.html#wav2vec2ctctokenizer), and a feature extractor, called [Wav2Vec2FeatureExtractor](https://huggingface.co/transformers/master/model_doc/wav2vec2.html#wav2vec2featureextractor).\n",
+    "\n",
+    "Let's start by creating the tokenizer responsible for decoding the model's predictions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sEXEWEJGQPqD"
+   },
+   "source": [
+    "### Create Wav2Vec2CTCTokenizer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tWmMikuNEKl_"
+   },
+   "source": [
+    "The [pretrained Wav2Vec2 checkpoint]( ) maps the speech signal to a sequence of context representations as illustrated in the figure above. A fine-tuned XLSR-Wav2Vec2 checkpoint needs to map this sequence of context representations to its corresponding transcription so that a linear layer has to be added on top of the transformer block (shown in yellow). This linear layer is used to classifies each context representation to a token class analogous how, *e.g.*, after pretraining a linear layer is added on top of BERT's embeddings for further classification - *cf.* with *\"BERT\"* section of this [blog post](https://huggingface.co/blog/warm-starting-encoder-decoder).\n",
+    "\n",
+    "The output size of this layer corresponds to the number of tokens in the vocabulary, which does **not** depend on XLSR-Wav2Vec2's pretraining task, but only on the labeled dataset used for fine-tuning. So in the first step, we will take a look at Timit and define a vocabulary based on the dataset's transcriptions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "idBczw8mWzgt"
+   },
+   "source": [
+    "First, let's go to [Common Voice](https://commonvoice.mozilla.org/en) and pick your favorite language you would like to fine-tune XLSR-Wav2Vec2 on. For this notebook, we will use Greek. \n",
+    "\n",
+    "**Note**: Most likely, the common voice link has expired. In this case, just go to [Common Voice's dataset website](https://commonvoice.mozilla.org/en/datasets), select your language, *e.g.* `Greek`, enter your email address to get the \"*Download*\" button, click right, and click `Copy link address` to fill it in the cell below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2021-03-13T13:41:41.939Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "afNe1N8mXNfp",
+    "outputId": "b81ad1ee-4f06-46f4-9942-1c561354faa7"
+   },
+   "outputs": [],
+   "source": [
+    "!wget \"https://mozilla-common-voice-datasets.s3.dualstack.us-west-2.amazonaws.com/cv-corpus-6.1-2020-12-11/el.tar.gz?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIAQ3GQRTO3DNLLRSFD%2F20210312%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20210312T191930Z&X-Amz-Expires=43200&X-Amz-Security-Token=FwoGZXIvYXdzEJT%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaDNI554%2Bz27lYa%2BFqqCKSBAGgZVMcme7qLQ5PD40LwxdEa8PnLDUQe2tOhNe%2B6fQNbvV5BgEsm70cYNJ1he%2BNSyCfmOrRVvL94LKt7Uk9RIKbCzffJgiHaS7YoZCOUoaRs%2BdWu9YHy6B0%2Foy20Iq%2FrTTZunotevuVwLUb6QOq56mpHtJGTUM4j9zk9nfdKUxqwBUuvGwd2g8sTY9fo7i5f%2FpptprVRi7Z56wp6WFgh8XOsa0iQBUfg9Y2HW6U3acLAxuYzAvp8GkG4tTEoW85zcfS2%2FH%2BC3dGE6AGMYOLkqAbhLWtOKAg1GRahwYumZlTcBtfjAMnGnP%2F7tnlkb5kFR4gUAcn3K1nUkEUXxI0NfJfErnOowjwg6Y0IzYrFG6hvoavWIszZa6FctFHRzdNfrqW6YO%2FeI9mF4uwYwJAdgoBpGHsVSlI3db5LtFkN%2FgJPb7pHIH9VuxSQ%2Batatk4BbcasLhMGjGt28TIjMeGmNanAdLPppGZFaQ%2Fn4B4jiTWwX6hV%2BjXqBIwPsa0imrvYjZa17ywRys7aRtcj8y3CW9vZqM3jog2WzPvSPIuabV4hbC5wSqAIXIeqCqcUXFw0WhYXZgaz%2FpRwQK0fXGy27Hc2kciQQ2NFU6lCNo1LldvBzAcDMWJNaz3QD1qKQ%2BoaDNvTTkdyCtlm1t1Xm59G6iGrYFbdIV%2FLOjVmFIy4BP%2BGoTDYOSH%2FXtt1cMc2SW4DggwKK3oroIGMip6c8biz1p8VYoKEt0vcmNTx82BY6OEuh4LjsdaeBw7Uuk4IBSPp0nENtk%3D&X-Amz-Signature=5d919315c954f7482d6601987c3591142a4667f7d6042ffdd7006c9766f04c99&X-Amz-SignedHeaders=host\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "E0nV6zlzX-M5"
+   },
+   "source": [
+    "Cool, let's copy the downloaded file name from the output of the cell above and unzip it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-12T19:24:23.464365Z",
+     "start_time": "2021-03-12T19:24:21.255676Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "bTjNp2KUYAl8",
+    "outputId": "fb9add44-a182-491c-c664-40dd180d5055"
+   },
+   "outputs": [],
+   "source": [
+    "!tar -xvzf \"el.tar.gz?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIAQ3GQRTO3DNLLRSFD%2F20210312%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20210312T191930Z&X-Amz-Expires=43200&X-Amz-Security-Token=FwoGZXIvYXdzEJT%2F%2F%2F%2F%2F%2F%2F%2F%2F%\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DxO8BRTgYayX"
+   },
+   "source": [
+    "The data is now saved under `./cv-corpus-6.1-2020-12-11`. We can leverage `datasets` now to preprocess the dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bee4g9rpLxll"
+   },
+   "source": [
+    "Let's start by loading the dataset and taking a look at its structure. [Common Voice](https://huggingface.co/datasets/common_voice) has many different splits including `invalidated`, which refers to data that was not rated as \"clean enough\" to be considered useful. Because the Greek 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": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:46.227453Z",
+     "start_time": "2021-03-14T10:06:40.271948Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 227,
+     "referenced_widgets": [
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+     ]
+    },
+    "id": "2MMXcWFFgCXU",
+    "outputId": "82b573b6-fc21-465d-9029-f5490814169e"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using custom data configuration el-afd0a157f05ee080\n",
+      "Reusing dataset common_voice (/home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564)\n",
+      "Using custom data configuration el-afd0a157f05ee080\n",
+      "Reusing dataset common_voice (/home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset, load_metric\n",
+    "\n",
+    "common_voice_train = load_dataset(\"common_voice\", \"el\", data_dir=\"cv-corpus-6.1-2020-12-11\", split=\"train+validation\")\n",
+    "common_voice_test = load_dataset(\"common_voice\", \"el\", data_dir=\"cv-corpus-6.1-2020-12-11\", split=\"test\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ri5y5N_HMANq"
+   },
+   "source": [
+    "Many ASR datasets only provide the target text, `'sentence'` for each audio 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": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:46.251880Z",
+     "start_time": "2021-03-14T10:06:46.247618Z"
+    },
+    "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": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:46.273881Z",
+     "start_time": "2021-03-14T10:06:46.271270Z"
+    },
+    "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": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:46.307239Z",
+     "start_time": "2021-03-14T10:06:46.295588Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 669
+    },
+    "id": "K_JUmf3G3b9S",
+    "outputId": "a8fe6d21-b0ce-4d5b-e3a2-abe08ae551f7"
+   },
+   "outputs": [
+    {
+     "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>Σηκώθηκε το Βασιλόπουλο και πήρε πάλι το δρόμο του.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>στο τέλος έβγαλε</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Τέσσερις λέξεις έχουν αρνητικά σηματοδοτήσει</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Σε μερικά χρόνια δηλαδή;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>αποκρίθηκε λαχανιασμένος.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Πού να μας ακολουθήσει;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>πριν ξεβραστούν στο χαρτί τα καινούρια τους αδελφάκια</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Και είδα πρόσωπα γελαστά, και άκουσα τραγούδια παντού, και δεν είδα ζητιάνο κανένα.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Μα πεσμένο χάμω</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>\"Ξέρω 'γω! Έκλεψε, λένε, κάτι κότες.\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>δεν τραγουδώ παρά γιατί μ’ αγάπησες.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Ψήσε φαγί για περισσότερους</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Η Ειρηνούλα κοίταζε με θαυμασμό και απορία.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Της Πικρόχολης έστειλε μια φούστα</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>κι ένα γράμμα που σου έφερα.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>και σκούπισε το μέτωπο του με το μανίκι του ποκαμίσου του</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Αλλά η Γνώση λέγει πως έχει ένα γιατρικό και θα μας το δώσει.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>απομάκρυνε τις φελούκες του από την όχθη.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Το Βασιλόπουλο έβγαλε τα ξυλαράκια από την τσέπη του και το μάθημα άρχισε.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Στο βάθος ερχόταν ένας τύπος με πατίνι</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_random_elements(common_voice_train.remove_columns([\"path\"]), num_examples=20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fowcOllGNNju"
+   },
+   "source": [
+    "Alright! The transcriptions look fairly clean. Having translated the transcribed sentences (I'm sadly not a native speaker in Greek), it seems that the language corresponds more to written text than noisy dialogue. This makes sense taking into account 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": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:46.328556Z",
+     "start_time": "2021-03-14T10:06:46.326639Z"
+    },
+    "id": "svKzVJ_hQGK6"
+   },
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�]'\n",
+    "\n",
+    "def remove_special_characters(batch):\n",
+    "    batch[\"text\"] = re.sub(chars_to_ignore_regex, '', batch[\"sentence\"]).lower() + \" \"\n",
+    "    return batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:46.355968Z",
+     "start_time": "2021-03-14T10:06:46.349638Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 117,
+     "referenced_widgets": [
+      "92a35db69bbf4ad6af44c53aa3870be5",
+      "389e43e47a734193a507817ebad955f7",
+      "b94e374d92c146009447c3827b977267",
+      "1f1faa7b986e475c9e6839b2e7b55c74",
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+      "b177a8cb85a24b88ab6d56205b630f1d",
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+      "6abfbe44a1bd4518b41f5f53f920e936",
+      "33c92b5afd1d4b6d88016aabfb434194",
+      "998676d59f9c464e8463d65baad6448b",
+      "45f0a2f361da4608bc8314a27657a56c",
+      "3dc9ea77b842455a91f00e9ca9f41948",
+      "7bf5b2b625764f63ad57a360c3fd0a61",
+      "7b4cfd2b448643b8a4409dd612aef0d1"
+     ]
+    },
+    "id": "XIHocAuTQbBR",
+    "outputId": "cc1a70b2-7b4d-410b-f997-1f1c47c3c9e5"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-8c6db5e938951602.arrow\n",
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-0ce2ebca66096fff.arrow\n"
+     ]
+    }
+   ],
+   "source": [
+    "common_voice_train = common_voice_train.map(remove_special_characters, remove_columns=[\"sentence\"])\n",
+    "common_voice_test = common_voice_test.map(remove_special_characters, remove_columns=[\"sentence\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:46.383256Z",
+     "start_time": "2021-03-14T10:06:46.378748Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 359
+    },
+    "id": "RBDRAAYxRE6n",
+    "outputId": "c3a72eaa-8ddc-4283-ccb8-52e50215b84d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>κι έφθασε την ώρα που το παιδί του κλέφτη ξέκοβε στο δάσος</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ελάτε πατριώτες πάμε να σκάψομε τα χωράφια</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>και πως αντί να κουτσοζεί κατάφερε να καλοζεί</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>μ' αυτός ήλθε ευθύς και μου είπε</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>μόνο γιατί μ’ αγάπησες γεννήθηκα</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>έλεγε αναστενάζοντας ο άνθρωπος</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>άνοιξε η όρεξη μου μιλώντας για δουλειές</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>να επιστρέψει τον κλεμμένο σάκο για να σωπάσει ο κακομοιρίδης</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>με σένα πλάγι μου νιώθω πως θα εκτελέσω το σκοπό μου</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>ο κακομοιρίδης χαμογέλασε πικρά</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_random_elements(common_voice_train.remove_columns([\"path\"]))"
+   ]
+  },
+  {
+   "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": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:46.405883Z",
+     "start_time": "2021-03-14T10:06:46.404039Z"
+    },
+    "id": "LwCshNbbeRZR"
+   },
+   "outputs": [],
+   "source": [
+    "def extract_all_chars(batch):\n",
+    "  all_text = \" \".join(batch[\"text\"])\n",
+    "  vocab = list(set(all_text))\n",
+    "  return {\"vocab\": [vocab], \"all_text\": [all_text]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:46.835943Z",
+     "start_time": "2021-03-14T10:06:46.431012Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 117,
+     "referenced_widgets": [
+      "304e9130c12f4110941bfbd3db49a28c",
+      "5e6bae23461b4378b6e5fc890fe6bc97",
+      "75e00c38605f44cfb584067db1160349",
+      "8c07a528fc4a4e108b393ab117fe2e46",
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+      "bd0958ea97b141b1aca367c71721c549",
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+      "aafd4b2c56db43378c4627797665db17",
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+      "ccb720bf256e42f7b0251494ccb7741f",
+      "ad428686450f4ebdb50a0356b2e5a8c4"
+     ]
+    },
+    "id": "_m6uUjjcfbjH",
+    "outputId": "75a1a23f-a9c7-4c8b-8777-dad120a9aa9a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50501fbad21d4061a0c5780a25aed098",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8c10d5f8b754e7bb0559212b945c3ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "vocab_train = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_train.column_names)\n",
+    "vocab_test = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_test.column_names)"
+   ]
+  },
+  {
+   "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": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:46.861391Z",
+     "start_time": "2021-03-14T10:06:46.858610Z"
+    },
+    "id": "aQfneNsmlJI0"
+   },
+   "outputs": [],
+   "source": [
+    "vocab_list = list(set(vocab_train[\"vocab\"][0]) | set(vocab_test[\"vocab\"][0]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:46.921226Z",
+     "start_time": "2021-03-14T10:06:46.918222Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "_0kRndSvqaKk",
+    "outputId": "29f5d23f-75b1-44d0-9975-87f9ec4c0aa5"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ώ': 0,\n",
+       " 'γ': 1,\n",
+       " 'n': 2,\n",
+       " 'ϋ': 3,\n",
+       " 'κ': 4,\n",
+       " 'e': 5,\n",
+       " 'ξ': 6,\n",
+       " \"'\": 7,\n",
+       " 'θ': 8,\n",
+       " '’': 9,\n",
+       " 'σ': 10,\n",
+       " 'η': 11,\n",
+       " 'ι': 12,\n",
+       " 'α': 13,\n",
+       " 'ε': 14,\n",
+       " 'υ': 15,\n",
+       " 'v': 16,\n",
+       " 'μ': 17,\n",
+       " 'ο': 18,\n",
+       " '«': 19,\n",
+       " '»': 20,\n",
+       " 'έ': 21,\n",
+       " 'ν': 22,\n",
+       " ' ': 23,\n",
+       " 'ά': 24,\n",
+       " 'o': 25,\n",
+       " 'ζ': 26,\n",
+       " 'β': 27,\n",
+       " 'τ': 28,\n",
+       " 'π': 29,\n",
+       " 'ή': 30,\n",
+       " 'ψ': 31,\n",
+       " 'ΐ': 32,\n",
+       " 'ό': 33,\n",
+       " 'h': 34,\n",
+       " 'ύ': 35,\n",
+       " 'ω': 36,\n",
+       " '´': 37,\n",
+       " 'χ': 38,\n",
+       " 'ϊ': 39,\n",
+       " 'ρ': 40,\n",
+       " 'a': 41,\n",
+       " 'ς': 42,\n",
+       " 'r': 43,\n",
+       " 'g': 44,\n",
+       " 'm': 45,\n",
+       " 'λ': 46,\n",
+       " '́': 47,\n",
+       " 'ί': 48,\n",
+       " 'φ': 49,\n",
+       " 'δ': 50}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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 Greek, 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 Greek. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "b1fBRCn-TRaO"
+   },
+   "source": [
+    "To make it clearer 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 Timit'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": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:46.944995Z",
+     "start_time": "2021-03-14T10:06:46.943544Z"
+    },
+    "id": "npbIbBoLgaFX"
+   },
+   "outputs": [],
+   "source": [
+    "vocab_dict[\"|\"] = vocab_dict[\" \"]\n",
+    "del vocab_dict[\" \"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:46.971142Z",
+     "start_time": "2021-03-14T10:06:46.969054Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "znF0bNunsjbl",
+    "outputId": "6dd50862-f4c5-4a05-87a7-da03d157e30e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "53"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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 39 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 39."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1CujRgBNVRaD"
+   },
+   "source": [
+    "Let's now save the vocabulary as a json file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:46.995090Z",
+     "start_time": "2021-03-14T10:06:46.993446Z"
+    },
+    "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 an object of the `Wav2Vec2CTCTokenizer` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:47.031444Z",
+     "start_time": "2021-03-14T10:06:47.018317Z"
+    },
+    "id": "xriFGEWQkO4M"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import Wav2Vec2CTCTokenizer\n",
+    "\n",
+    "tokenizer = Wav2Vec2CTCTokenizer(\"./vocab.json\", unk_token=\"[UNK]\", pad_token=\"[PAD]\", word_delimiter_token=\"|\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KvL12DrNV4cx"
+   },
+   "source": [
+    "Next, we will create the feature extractor."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mYcIiR2FQ96i"
+   },
+   "source": [
+    "### Create XLSR-Wav2Vec2 Feature Extractor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Y6mDEyW719rx"
+   },
+   "source": [
+    "Speech is a continuous signal and to be treated by computers, it first has to be discretized, which is usually called **sampling**. The sampling rate hereby plays an important role in that it defines how many data points of the speech signal are measured per second. Therefore, sampling with a higher sampling rate results in a better approximation of the *real* speech signal but also necessitates more values per second.\n",
+    "\n",
+    "A pretrained checkpoint expects its input data to have been sampled more or less from the same distribution as the data it was trained on. The same speech signals sampled at two different rates have a very different distribution, *e.g.*, doubling the sampling rate results in data points being twice as long. Thus, \n",
+    "before fine-tuning a pretrained checkpoint of an ASR model, it is crucial to verify that the sampling rate of the data that was used to pretrain the model matches the sampling rate of the dataset used to fine-tune the model.\n",
+    "\n",
+    "XLSR-Wav2Vec2 was pretrained on the audio data of [Babel](https://huggingface.co/datasets/librispeech_asr), \n",
+    "[Multilingual LibriSpeech (MLS)](https://ai.facebook.com/blog/a-new-open-data-set-for-multilingual-speech-research/), and [Common Voice](https://huggingface.co/datasets/common_voice). Most of those datasets were sampled at 16kHz, so that Common Voice, sampled at 48kHz, has to be downsampled to 16kHz for training. Therefore, we will have to downsample our fine-tuning data to 16kHz in the following.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KuUbPW7oV-B5"
+   },
+   "source": [
+    "A XLSR-Wav2Vec2 feature extractor object requires the following parameters to be instantiated:\n",
+    "\n",
+    "- `feature_size`: Speech models take a sequence of feature vectors as an input. While the length of this sequence obviously varies, the feature size should not. In the case of Wav2Vec2, the feature size is 1 because the model was trained on the raw speech signal ${}^2$.\n",
+    "- `sampling_rate`: The sampling rate at which the model is trained on.\n",
+    "- `padding_value`: For batched inference, shorter inputs need to be padded with a specific value\n",
+    "- `do_normalize`: Whether the input should be *zero-mean-unit-variance* normalized or not. Usually, speech models perform better when normalizing the input\n",
+    "- `return_attention_mask`: Whether the model should make use of an `attention_mask` for batched inference. In general, XLSR-Wav2Vec2 models should **always** make use of the `attention_mask`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:47.057924Z",
+     "start_time": "2021-03-14T10:06:47.054784Z"
+    },
+    "id": "kAR0-2KLkopp"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import Wav2Vec2FeatureExtractor\n",
+    "\n",
+    "feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qUETetgqYC3W"
+   },
+   "source": [
+    "Great, XLSR-Wav2Vec2's feature extraction pipeline is thereby fully defined!\n",
+    "\n",
+    "To make the usage of XLSR-Wav2Vec2 as user-friendly as possible, the feature extractor and tokenizer are *wrapped* into a single `Wav2Vec2Processor` class so that one only needs a `model` and `processor` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:47.083673Z",
+     "start_time": "2021-03-14T10:06:47.081127Z"
+    },
+    "id": "KYZtoW-tlZgl"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import Wav2Vec2Processor\n",
+    "\n",
+    "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fTfFDSS0YfMN"
+   },
+   "source": [
+    "If one wants to re-use the just created processor and the fine-tuned model of this notebook, one can mount his/her google drive to the notebook and save all relevant files there. To do so, please uncomment the following lines. \n",
+    "\n",
+    "We will give the fine-tuned model the name `\"wav2vec2-large-xlsr-Greek-demo\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:47.112744Z",
+     "start_time": "2021-03-14T10:06:47.111476Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "yq7Bzuzz4zjQ",
+    "outputId": "419faa94-b24f-4044-877a-ce511978c97d"
+   },
+   "outputs": [],
+   "source": [
+    "# from google.colab import drive\n",
+    "# drive.mount('/content/gdrive/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:47.141369Z",
+     "start_time": "2021-03-14T10:06:47.138818Z"
+    },
+    "id": "Par9rpypPsml"
+   },
+   "outputs": [],
+   "source": [
+    "processor.save_pretrained(\"wav2vec2-large-xlsr-greek/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DrKnYuvDIoOO"
+   },
+   "source": [
+    "Next, we can prepare the dataset."
+   ]
+  },
+  {
+   "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 kept the path to its file in the dataset. `XLSR-Wav2Vec2` expects the audio file in the format of a 1-dimensional array, so in the first step, let's load all audio files into the dataset object.\n",
+    "\n",
+    "Let's first check the serialization format of the downloaded audio files by looking at the first training sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:47.698154Z",
+     "start_time": "2021-03-14T10:06:47.695685Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TTCS7W6XJ9BG",
+    "outputId": "9c3b8cc0-3bcd-43fe-87ca-2825239b365a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'path': '/home/earendil/Desktop/ML_playground/wav2vec2-large-xlsr-53-greek/cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20430274.mp3',\n",
+       " 'text': 'το βασιλόπουλο έβγαλε τα ξυλαράκια από την τσέπη του και το μάθημα άρχισε '}"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "common_voice_train[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wSBIGEiaKHMn"
+   },
+   "source": [
+    "Alright, the audio file is saved in the `.mp3` format. The `.mp3` format is usually not the easiest format to deal with. We found that the [`torchaudio`](https://pytorch.org/audio/stable/index.html) library works best for reading in `.mp3` data. \n",
+    "\n",
+    "An audio file usually stores both its values and the sampling rate with which the speech signal was digitalized. We want to store both in the dataset and write a `map(...)` function accordingly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:48.542682Z",
+     "start_time": "2021-03-14T10:06:48.513736Z"
+    },
+    "id": "al9Luo4LPpwJ"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/earendil/anaconda3/envs/cuda110/lib/python3.8/site-packages/torchaudio/backend/utils.py:53: UserWarning: \"sox\" backend is being deprecated. The default backend will be changed to \"sox_io\" backend in 0.8.0 and \"sox\" backend will be removed in 0.9.0. Please migrate to \"sox_io\" backend. Please refer to https://github.com/pytorch/audio/issues/903 for the detail.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torchaudio\n",
+    "\n",
+    "def speech_file_to_array_fn(batch):\n",
+    "    speech_array, sampling_rate = torchaudio.load(batch[\"path\"])\n",
+    "    batch[\"speech\"] = speech_array[0].numpy()\n",
+    "    batch[\"sampling_rate\"] = sampling_rate\n",
+    "    batch[\"target_text\"] = batch[\"text\"]\n",
+    "    return batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:48.781708Z",
+     "start_time": "2021-03-14T10:06:48.755556Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 117,
+     "referenced_widgets": [
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+      "b0479f03f96241ca959f6fe7bcbd1aba"
+     ]
+    },
+    "id": "afeicUeWlrRL",
+    "outputId": "d5e4d41a-61d6-4094-eba9-a5bbed02cedc"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-d93f28c9964eab5e.arrow\n",
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-38a09981767eff59.arrow\n"
+     ]
+    }
+   ],
+   "source": [
+    "common_voice_train = common_voice_train.map(speech_file_to_array_fn, remove_columns=common_voice_train.column_names)\n",
+    "common_voice_test = common_voice_test.map(speech_file_to_array_fn, remove_columns=common_voice_test.column_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ki5sXmzucc9Q"
+   },
+   "source": [
+    "Great, now we've successfully read in all the audio files, but since we know that Common Voice is sampled at 48kHz, we need to resample the audio files to 16kHz. \n",
+    "\n",
+    "Let's make use of the [`librosa`](https://github.com/librosa/librosa) library to downsample the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:49.580796Z",
+     "start_time": "2021-03-14T10:06:49.185101Z"
+    },
+    "id": "6Y6AK3Z-kHwP"
+   },
+   "outputs": [],
+   "source": [
+    "import librosa\n",
+    "import numpy as np\n",
+    "\n",
+    "def resample(batch):\n",
+    "    batch[\"speech\"] = librosa.resample(np.asarray(batch[\"speech\"]), 48_000, 16_000)\n",
+    "    batch[\"sampling_rate\"] = 16_000\n",
+    "    return batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:50.667773Z",
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+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-3c241372ac077a03.arrow\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-adc5c7b231c12387.arrow\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-8b677f7acb15af63.arrow\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-b301abaf3851c052.arrow\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-6e39c70c57299786.arrow\n",
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-6d7e0aa094522c60.arrow\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-872d739fcf9fc10a.arrow\n",
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-20a401885e80295d.arrow\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-ba8c6dd59eb8ccf2.arrow\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-2e240883a5f827fd.arrow\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-485c00dc9048ed50.arrow\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-923d905502a8661d.arrow\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-062aeafc3b8816c1.arrow\n",
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-bb54bb00dae79669.arrow\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-44bf1791baae8e2e.arrow\n",
+      "Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-ecc0dfac5615a58e.arrow\n"
+     ]
+    }
+   ],
+   "source": [
+    "common_voice_train = common_voice_train.map(resample, num_proc=8)\n",
+    "common_voice_test = common_voice_test.map(resample, num_proc=8)"
+   ]
+  },
+  {
+   "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": 25,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:50.721070Z",
+     "start_time": "2021-03-14T10:06:50.694488Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
+    },
+    "id": "dueM6U7Ev0OA",
+    "outputId": "1a3e579d-213e-4c7a-b2ec-9a7725d95afc"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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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>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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))\n",
+    "\n",
+    "ipd.Audio(data=np.asarray(common_voice_train[rand_int][\"speech\"]), autoplay=True, rate=16000)"
+   ]
+  },
+  {
+   "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": 26,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:50.779682Z",
+     "start_time": "2021-03-14T10:06:50.745322Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1Po2g7YPuRTx",
+    "outputId": "96b0b82c-a5df-4ae6-d17b-9c7d4f710b42"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Target text: ξεπούλησε τα κτήματα μας \n",
+      "Input array shape: (47232,)\n",
+      "Sampling rate: 16000\n"
+     ]
+    }
+   ],
+   "source": [
+    "rand_int = random.randint(0, len(common_voice_train))\n",
+    "\n",
+    "print(\"Target text:\", common_voice_train[rand_int][\"target_text\"])\n",
+    "print(\"Input array shape:\", np.asarray(common_voice_train[rand_int][\"speech\"]).shape)\n",
+    "print(\"Sampling rate:\", common_voice_train[rand_int][\"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."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "k3Pbn5WvOYZF"
+   },
+   "source": [
+    "Finally, we can process the dataset to the format expected by the model for training. We will again make use of the `map(...)` function.\n",
+    "\n",
+    "First, we check that the data samples have the same sampling rate of 16kHz.\n",
+    "Second, we extract the `input_values` from the loaded audio file. In our case, this includes only normalization, but for other speech models, this step could correspond to extracting, *e.g.* [Log-Mel features](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum). \n",
+    "Third, we encode the transcriptions to label ids.\n",
+    "\n",
+    "**Note**: This mapping function is a good example of how the `Wav2Vec2Processor` class should be used. In \"normal\" context, calling `processor(...)` is redirected to `Wav2Vec2FeatureExtractor`'s call method. When wrapping the processor into the `as_target_processor` context, however, the same method is redirected to `Wav2Vec2CTCTokenizer`'s call method.\n",
+    "For more information please check the [docs](https://huggingface.co/transformers/master/model_doc/wav2vec2.html#transformers.Wav2Vec2Processor.__call__)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:06:51.188480Z",
+     "start_time": "2021-03-14T10:06:51.185722Z"
+    },
+    "id": "eJY7I0XAwe9p"
+   },
+   "outputs": [],
+   "source": [
+    "def prepare_dataset(batch):\n",
+    "    # check that all files have the correct sampling rate\n",
+    "    assert (\n",
+    "        len(set(batch[\"sampling_rate\"])) == 1\n",
+    "    ), f\"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.\"\n",
+    "\n",
+    "    batch[\"input_values\"] = processor(batch[\"speech\"], sampling_rate=batch[\"sampling_rate\"][0]).input_values\n",
+    "    \n",
+    "    with processor.as_target_processor():\n",
+    "        batch[\"labels\"] = processor(batch[\"target_text\"]).input_ids\n",
+    "    return batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:07:12.818162Z",
+     "start_time": "2021-03-14T10:06:51.733417Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 509,
+     "referenced_widgets": [
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+     "output_type": "stream",
+     "text": [
+      "/home/earendil/anaconda3/envs/cuda110/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
+      "  return array(a, dtype, copy=False, order=order)\n"
+     ]
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+    {
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " "
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1087e951f9854d3f832e841838929931",
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+       "HBox(children=(IntProgress(value=0, description='#0', max=59, style=ProgressStyle(description_width='initial')…"
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "       "
+     ]
+    },
+    {
+     "data": {
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+       "HBox(children=(IntProgress(value=0, description='#1', max=24, style=ProgressStyle(description_width='initial')…"
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+    {
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+       "HBox(children=(IntProgress(value=0, description='#2', max=24, style=ProgressStyle(description_width='initial')…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      ]
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+    },
+    {
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " "
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c4d007a3cf14faaa37d656e2eb1da34",
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+       "HBox(children=(IntProgress(value=0, description='#7', max=24, style=ProgressStyle(description_width='initial')…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "HBox(children=(IntProgress(value=0, description='#6', max=24, style=ProgressStyle(description_width='initial')…"
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c34f0a513be54597b83ba64aa3819d61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, description='#5', max=24, style=ProgressStyle(description_width='initial')…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e00e506138d046d1ab41aa243ced2680",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, description='#0', max=24, style=ProgressStyle(description_width='initial')…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "common_voice_train = common_voice_train.map(prepare_dataset, remove_columns=common_voice_train.column_names, batch_size=8, num_proc=8, batched=True)\n",
+    "common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names, batch_size=8, num_proc=8, batched=True)"
+   ]
+  },
+  {
+   "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, XLSR-Wav2Vec2 has a much larger input length than output length. *E.g.*, a sample of input length 50000 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 XLSR-Wav2Vec2 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 (again making use of XLSR-Wav2Vec2's context manager). 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": 29,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:07:12.850867Z",
+     "start_time": "2021-03-14T10:07:12.844816Z"
+    },
+    "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": 30,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:07:12.883379Z",
+     "start_time": "2021-03-14T10:07:12.881910Z"
+    },
+    "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": 31,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:07:15.225831Z",
+     "start_time": "2021-03-14T10:07:12.909977Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 67,
+     "referenced_widgets": [
+      "d5fb02debe4347e781543a996ce39be3",
+      "da12267c52144292adb9896249d61a6a",
+      "8e1a85a4a6214a16b7431da23a301ada",
+      "57198e3250374e1fa64c5a4be255861e",
+      "a3c04c75ec9743feb795f69e7c4dff4f",
+      "d453bd4a35e54bfba20dac1fe86c60c1",
+      "969080b52ad44ee997829986d198c505",
+      "8f737725708c4097a8caaf4da6229636"
+     ]
+    },
+    "id": "9Xsux2gmyXso",
+    "outputId": "58e0e6f5-6131-4147-bccb-6c42223833db"
+   },
+   "outputs": [],
+   "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": 32,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:07:15.267780Z",
+     "start_time": "2021-03-14T10:07:15.265135Z"
+    },
+    "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 `XLSR-Wav2Vec2` checkpoint. The tokenizer's `pad_token_id` must be to define the model's `pad_token_id` or in the case of `Wav2Vec2ForCTC` also CTC's *blank token* ${}^2$. To save GPU memory, we enable PyTorch's [gradient checkpointing](https://pytorch.org/docs/stable/checkpoint.html) and also set the loss reduction to \"*mean*\".\n",
+    "\n",
+    "Because the dataset is quite small (~6h of training data) and because Common Voice is quite noisy, fine-tuning Facebook's [wav2vec2-large-xlsr-53 checkpoint](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) seems to require some hyper-parameter tuning. Therefore, I had to play around a bit with different values for dropout, [SpecAugment](https://arxiv.org/abs/1904.08779)'s masking dropout rate, layer dropout, and the learning rate until training seemed to be stable enough. \n",
+    "\n",
+    "**Note**: When using this notebook to train XLSR-Wav2Vec2 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": 33,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:07:23.145790Z",
+     "start_time": "2021-03-14T10:07:15.296367Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "e7cqAWIayn6w",
+    "outputId": "0a5ab559-6c38-47c6-b4f5-64480ed1df65"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-large-xlsr-53 and are newly initialized: ['lm_head.weight', 'lm_head.bias']\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 Wav2Vec2ForCTC\n",
+    "\n",
+    "model = Wav2Vec2ForCTC.from_pretrained(\n",
+    "    \"facebook/wav2vec2-large-xlsr-53\", \n",
+    "    attention_dropout=0.1,\n",
+    "    hidden_dropout=0.1,\n",
+    "    feat_proj_dropout=0.0,\n",
+    "    mask_time_prob=0.05,\n",
+    "    layerdrop=0.1,\n",
+    "    gradient_checkpointing=True, \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": [
+    "NOTE: Since Greek is not one of the 53 languages that XLSR-Wav2Vec2 had been pretrained on, we may or may not follow the below suggestion of freezing the CNN layers. \n",
+    "\n",
+    "Original text: The first component of XLSR-Wav2Vec2 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": 34,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:07:23.193215Z",
+     "start_time": "2021-03-14T10:07:23.191472Z"
+    },
+    "id": "oGI8zObtZ3V0"
+   },
+   "outputs": [],
+   "source": [
+    "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 Timit 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",
+    "**Note**: If one wants to save the trained models in his/her google drive the commented-out `output_dir` can be used instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:07:23.256610Z",
+     "start_time": "2021-03-14T10:07:23.234660Z"
+    },
+    "id": "KbeKSV7uzGPP"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import TrainingArguments\n",
+    "\n",
+    "training_args = TrainingArguments(\n",
+    "  output_dir=\"wav2vec2-large-xlsr-greek\",\n",
+    "  # output_dir=\"./wav2vec2-large-xlsr-Greek-demo\",\n",
+    "  group_by_length=True,\n",
+    "  per_device_train_batch_size=6,\n",
+    "  gradient_accumulation_steps=2,\n",
+    "  evaluation_strategy=\"steps\",\n",
+    "  num_train_epochs=60,\n",
+    "  fp16=True,\n",
+    "  save_steps=400,\n",
+    "  eval_steps=400,\n",
+    "  logging_steps=400,\n",
+    "  learning_rate=3e-4,\n",
+    "  warmup_steps=500,\n",
+    "  save_total_limit=2,\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": 36,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T10:07:24.993889Z",
+     "start_time": "2021-03-14T10:07:23.305173Z"
+    },
+    "id": "rY7vBmFCPFgC"
+   },
+   "outputs": [],
+   "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 between 180 and 240 minutes depending on the GPU allocated to this notebook. While the trained model yields somewhat satisfying results on *Common Voice*'s test data of Greek, it is by no means an optimally fine-tuned model. The purpose of this notebook is to demonstrate how XLSR-Wav2Vec2's [checkpoint](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) can be fine-tuned on a low-resource ASR dataset.\n",
+    "\n",
+    "In case you want to use this google colab to fine-tune your model, you should make sure that your training doesn't stop due to inactivity. A simple hack to prevent this is to paste the following code into the console of this tab (*right mouse click -> inspect -> Console tab and insert code*)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VYYAvgkW4P0m"
+   },
+   "source": [
+    "```javascript\n",
+    "function ConnectButton(){\n",
+    "    console.log(\"Connect pushed\"); \n",
+    "    document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click() \n",
+    "}\n",
+    "setInterval(ConnectButton,60000);\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T17:43:05.019454Z",
+     "start_time": "2021-03-14T10:07:25.037347Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 348
+    },
+    "id": "_UEjJqGsQw24",
+    "outputId": "2e23b190-ca76-48ad-8117-376d1d7c058e",
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/earendil/anaconda3/envs/cuda110/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:131: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
+      "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "        <style>\n",
+       "            /* Turns off some styling */\n",
+       "            progress {\n",
+       "                /* gets rid of default border in Firefox and Opera. */\n",
+       "                border: none;\n",
+       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "                background-size: auto;\n",
+       "            }\n",
+       "        </style>\n",
+       "      \n",
+       "      <progress value='18600' max='18600' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [18600/18600 7:32:53, Epoch 60/60]\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",
+       "      <th>Runtime</th>\n",
+       "      <th>Samples Per Second</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>400</td>\n",
+       "      <td>7.193600</td>\n",
+       "      <td>3.278684</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>123.640600</td>\n",
+       "      <td>12.310000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>800</td>\n",
+       "      <td>3.198500</td>\n",
+       "      <td>2.831500</td>\n",
+       "      <td>0.996669</td>\n",
+       "      <td>121.957800</td>\n",
+       "      <td>12.480000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1200</td>\n",
+       "      <td>1.156600</td>\n",
+       "      <td>0.779333</td>\n",
+       "      <td>0.772585</td>\n",
+       "      <td>113.901900</td>\n",
+       "      <td>13.362000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1600</td>\n",
+       "      <td>0.529700</td>\n",
+       "      <td>0.647442</td>\n",
+       "      <td>0.675179</td>\n",
+       "      <td>122.788700</td>\n",
+       "      <td>12.395000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2000</td>\n",
+       "      <td>0.372500</td>\n",
+       "      <td>0.567449</td>\n",
+       "      <td>0.607348</td>\n",
+       "      <td>114.373900</td>\n",
+       "      <td>13.307000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2400</td>\n",
+       "      <td>0.287700</td>\n",
+       "      <td>0.563049</td>\n",
+       "      <td>0.562128</td>\n",
+       "      <td>114.404000</td>\n",
+       "      <td>13.304000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2800</td>\n",
+       "      <td>0.234200</td>\n",
+       "      <td>0.547992</td>\n",
+       "      <td>0.540325</td>\n",
+       "      <td>123.026600</td>\n",
+       "      <td>12.371000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3200</td>\n",
+       "      <td>0.196700</td>\n",
+       "      <td>0.627634</td>\n",
+       "      <td>0.557787</td>\n",
+       "      <td>114.868300</td>\n",
+       "      <td>13.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3600</td>\n",
+       "      <td>0.172900</td>\n",
+       "      <td>0.536431</td>\n",
+       "      <td>0.533259</td>\n",
+       "      <td>114.600900</td>\n",
+       "      <td>13.281000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4000</td>\n",
+       "      <td>0.160700</td>\n",
+       "      <td>0.510704</td>\n",
+       "      <td>0.495306</td>\n",
+       "      <td>115.385200</td>\n",
+       "      <td>13.191000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4400</td>\n",
+       "      <td>0.138100</td>\n",
+       "      <td>0.592979</td>\n",
+       "      <td>0.522055</td>\n",
+       "      <td>123.419400</td>\n",
+       "      <td>12.332000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4800</td>\n",
+       "      <td>0.126100</td>\n",
+       "      <td>0.663255</td>\n",
+       "      <td>0.530534</td>\n",
+       "      <td>124.109600</td>\n",
+       "      <td>12.263000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5200</td>\n",
+       "      <td>0.121600</td>\n",
+       "      <td>0.611431</td>\n",
+       "      <td>0.508428</td>\n",
+       "      <td>116.885800</td>\n",
+       "      <td>13.021000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5600</td>\n",
+       "      <td>0.111900</td>\n",
+       "      <td>0.609117</td>\n",
+       "      <td>0.504189</td>\n",
+       "      <td>123.424400</td>\n",
+       "      <td>12.331000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6000</td>\n",
+       "      <td>0.107000</td>\n",
+       "      <td>0.581159</td>\n",
+       "      <td>0.495811</td>\n",
+       "      <td>131.865500</td>\n",
+       "      <td>11.542000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6400</td>\n",
+       "      <td>0.098500</td>\n",
+       "      <td>0.653878</td>\n",
+       "      <td>0.508227</td>\n",
+       "      <td>132.657900</td>\n",
+       "      <td>11.473000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6800</td>\n",
+       "      <td>0.095900</td>\n",
+       "      <td>0.602002</td>\n",
+       "      <td>0.490663</td>\n",
+       "      <td>132.224900</td>\n",
+       "      <td>11.511000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7200</td>\n",
+       "      <td>0.089900</td>\n",
+       "      <td>0.600693</td>\n",
+       "      <td>0.488140</td>\n",
+       "      <td>115.003200</td>\n",
+       "      <td>13.234000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7600</td>\n",
+       "      <td>0.086700</td>\n",
+       "      <td>0.592105</td>\n",
+       "      <td>0.487635</td>\n",
+       "      <td>115.062900</td>\n",
+       "      <td>13.228000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8000</td>\n",
+       "      <td>0.082500</td>\n",
+       "      <td>0.615173</td>\n",
+       "      <td>0.493792</td>\n",
+       "      <td>115.057900</td>\n",
+       "      <td>13.228000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8400</td>\n",
+       "      <td>0.076800</td>\n",
+       "      <td>0.608913</td>\n",
+       "      <td>0.477238</td>\n",
+       "      <td>123.812500</td>\n",
+       "      <td>12.293000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8800</td>\n",
+       "      <td>0.069800</td>\n",
+       "      <td>0.618013</td>\n",
+       "      <td>0.473302</td>\n",
+       "      <td>123.652500</td>\n",
+       "      <td>12.309000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9200</td>\n",
+       "      <td>0.075300</td>\n",
+       "      <td>0.621384</td>\n",
+       "      <td>0.486626</td>\n",
+       "      <td>115.310600</td>\n",
+       "      <td>13.199000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9600</td>\n",
+       "      <td>0.067400</td>\n",
+       "      <td>0.638355</td>\n",
+       "      <td>0.481377</td>\n",
+       "      <td>123.632300</td>\n",
+       "      <td>12.311000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10000</td>\n",
+       "      <td>0.061700</td>\n",
+       "      <td>0.668408</td>\n",
+       "      <td>0.476734</td>\n",
+       "      <td>122.694800</td>\n",
+       "      <td>12.405000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10400</td>\n",
+       "      <td>0.059000</td>\n",
+       "      <td>0.654593</td>\n",
+       "      <td>0.467346</td>\n",
+       "      <td>115.387700</td>\n",
+       "      <td>13.190000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10800</td>\n",
+       "      <td>0.059800</td>\n",
+       "      <td>0.636886</td>\n",
+       "      <td>0.466842</td>\n",
+       "      <td>115.148800</td>\n",
+       "      <td>13.218000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11200</td>\n",
+       "      <td>0.055800</td>\n",
+       "      <td>0.646353</td>\n",
+       "      <td>0.465630</td>\n",
+       "      <td>115.132300</td>\n",
+       "      <td>13.220000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11600</td>\n",
+       "      <td>0.056800</td>\n",
+       "      <td>0.606136</td>\n",
+       "      <td>0.469668</td>\n",
+       "      <td>115.484500</td>\n",
+       "      <td>13.179000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12000</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.606987</td>\n",
+       "      <td>0.463813</td>\n",
+       "      <td>115.399700</td>\n",
+       "      <td>13.189000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12400</td>\n",
+       "      <td>0.048900</td>\n",
+       "      <td>0.643559</td>\n",
+       "      <td>0.454931</td>\n",
+       "      <td>115.288900</td>\n",
+       "      <td>13.202000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12800</td>\n",
+       "      <td>0.050800</td>\n",
+       "      <td>0.637720</td>\n",
+       "      <td>0.454931</td>\n",
+       "      <td>115.713100</td>\n",
+       "      <td>13.153000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>13200</td>\n",
+       "      <td>0.047500</td>\n",
+       "      <td>0.629017</td>\n",
+       "      <td>0.458363</td>\n",
+       "      <td>115.546300</td>\n",
+       "      <td>13.172000</td>\n",
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+       "    <tr>\n",
+       "      <td>13600</td>\n",
+       "      <td>0.041500</td>\n",
+       "      <td>0.669488</td>\n",
+       "      <td>0.452912</td>\n",
+       "      <td>115.620100</td>\n",
+       "      <td>13.164000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>14000</td>\n",
+       "      <td>0.043700</td>\n",
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+       "      <td>0.445947</td>\n",
+       "      <td>115.604300</td>\n",
+       "      <td>13.166000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>14400</td>\n",
+       "      <td>0.040500</td>\n",
+       "      <td>0.597802</td>\n",
+       "      <td>0.448572</td>\n",
+       "      <td>115.567400</td>\n",
+       "      <td>13.170000</td>\n",
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+       "    <tr>\n",
+       "      <td>14800</td>\n",
+       "      <td>0.037300</td>\n",
+       "      <td>0.616714</td>\n",
+       "      <td>0.448774</td>\n",
+       "      <td>129.269600</td>\n",
+       "      <td>11.774000</td>\n",
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+       "      <td>15200</td>\n",
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+       "      <td>132.656900</td>\n",
+       "      <td>11.473000</td>\n",
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+       "    <tr>\n",
+       "      <td>15600</td>\n",
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+       "    <tr>\n",
+       "      <td>16000</td>\n",
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+       "    <tr>\n",
+       "      <td>16400</td>\n",
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+       "    <tr>\n",
+       "      <td>16800</td>\n",
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+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "TrainOutput(global_step=18600, training_loss=0.34002172352165305, metrics={'train_runtime': 27175.8808, 'train_samples_per_second': 0.684, 'total_flos': 2.8460932280012886e+19, 'epoch': 60.0, 'init_mem_cpu_alloc_delta': 8143405, 'init_mem_gpu_alloc_delta': 1261972480, 'init_mem_cpu_peaked_delta': 18258, 'init_mem_gpu_peaked_delta': 0, 'train_mem_cpu_alloc_delta': 1358069, 'train_mem_gpu_alloc_delta': 3779249152, 'train_mem_cpu_peaked_delta': 183666894, 'train_mem_gpu_peaked_delta': 1681072128})"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RHIVc44_fY2N"
+   },
+   "source": [
+    "The training loss goes down and we can see that the WER on the test set also improves nicely. Because this notebook is just for demonstration purposes, we can stop here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2021-03-14T10:06:00.880Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "J8E8pxJ9hgZS",
+    "outputId": "5d137935-475d-4dbf-d833-d26780311e18"
+   },
+   "outputs": [],
+   "source": [
+    "model = Wav2Vec2ForCTC.from_pretrained(\"wav2vec2-large-xlsr-greek/checkpoint-18400/\").to(\"cuda\")\n",
+    "processor = Wav2Vec2Processor.from_pretrained(\"wav2vec2-large-xlsr-greek/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QsfGCQYSvY8C"
+   },
+   "source": [
+    "Now, we will just take a random example of the test set, run it through the model and take the `argmax(...)` of the logits to retrieve the predicted token ids."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T17:50:43.012433Z",
+     "start_time": "2021-03-14T17:50:20.994866Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "EA6fxk-jiHby",
+    "outputId": "8b91a977-2559-4adc-8298-b4ad6b7ff196"
+   },
+   "outputs": [],
+   "source": [
+    "input_dict = processor(common_voice_test[\"input_values\"][345], return_tensors=\"pt\", sampling_rate=16_000, padding=True)\n",
+    "\n",
+    "logits = model(input_dict.input_values.to(\"cuda\")).logits\n",
+    "\n",
+    "pred_ids = torch.argmax(logits, dim=-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1JDyVCMDvlYd"
+   },
+   "source": [
+    "We adapted `common_voice_test` quite a bit so that the dataset instance does not contain the original sentence label anymore. Thus, we re-use the original dataset to get the label of the first example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T17:47:32.341035Z",
+     "start_time": "2021-03-14T17:47:29.882908Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8dPE2GRIgtx-",
+    "outputId": "a211d1ee-d850-481d-8bac-dc46c3efa561"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using custom data configuration el-ac779bf2c9f7c09b\n",
+      "Reusing dataset common_voice (/home/earendil/.cache/huggingface/datasets/common_voice/el-ac779bf2c9f7c09b/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564)\n"
+     ]
+    }
+   ],
+   "source": [
+    "common_voice_test_transcription = load_dataset(\"common_voice\", \"el\", data_dir=\"./cv-corpus-6.1-2020-12-11\", split=\"test\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ApS3MmN4v5P7"
+   },
+   "source": [
+    "Finally, we can decode the example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-14T17:50:43.087361Z",
+     "start_time": "2021-03-14T17:50:43.083839Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Phqxa1O1jMDk",
+    "outputId": "60d48c9f-f745-45ac-9105-446dc71025ca"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction:\n",
+      "και τι να δούμε\n",
+      "\n",
+      "Reference:\n",
+      "και τι να δούμε;\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Prediction:\")\n",
+    "print(processor.decode(pred_ids[0]))\n",
+    "\n",
+    "print(\"\\nReference:\")\n",
+    "print(common_voice_test_transcription[\"sentence\"][345].lower())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4n_8vhzUv7aw"
+   },
+   "source": [
+    "Alright! The transcription can definitely be recognized from our prediction, but it is far from being perfect. Training the model a bit longer, spending more time on the data preprocessing, and especially using a language model for decoding would certainly improve the model's overall performance. \n",
+    "\n",
+    "For a demonstration model on a low-resource language, the results are acceptable, however 🤗."
+   ]
+  },
+  {
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