Fine_tuning_Wav2Vec2_for_English_ASR.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RajGothi/100-days-of-machine-learning/blob/main/Fine_tuning_Wav2Vec2_for_English_ASR.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LBSYoWbi-45k"
      },
      "source": [
        "# **Fine-tuning Wav2Vec2 for English ASR with 🤗 Transformers**"
      ]
    },
    {
      "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.\n",
        "\n",
        "Using a novel contrastive pretraining objective, Wav2Vec2 learns powerful speech representations from more than 50.000 hours 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/wav2vec2.png)\n",
        "\n",
        "For the first time, it has been shown that pretraining, followed by fine-tuning on very little labeled speech data achieves competitive results to state-of-the-art ASR systems. Using as little as 10 minutes of labeled data, Wav2Vec2 yields a word error rate (WER) of less than 5% on the clean test set of [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) - *cf.* with Table 9 of the [paper](https://arxiv.org/pdf/2006.11477.pdf)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nT_QrfWtsxIz"
      },
      "source": [
        "In this notebook, we will give an in-detail explanation of how Wav2Vec2's pretrained checkpoints can be fine-tuned on any English ASR dataset. Note that in this notebook, we will fine-tune Wav2Vec2 without making use of a language model. It is much simpler to use Wav2Vec2 without a language model as an end-to-end ASR system and it has been shown that a standalone Wav2Vec2 acoustic model achieves impressive results. For demonstration purposes, we fine-tune the \"base\"-sized [pretrained checkpoint](https://huggingface.co/facebook/wav2vec2-base) on the rather small [Timit](https://huggingface.co/datasets/timit_asr) dataset that contains just 5h of training data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gx9OdDYrCtQ1"
      },
      "source": [
        "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": "qW3J3rBizeds"
      },
      "source": [
        "First, let's try to get a good GPU in our colab! With Google Colab's free version it's sadly becoming much harder to get access to a good GPU. With Google Colab Pro, one has a much easier time getting access to a V100 or P100 GPU however."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SLAufgh_xxj7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b83d8c97-c180-4afe-b391-d202315606aa"
      },
      "source": [
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Not connected to a GPU')\n",
        "else:\n",
        "  print(gpu_info)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wed Jun  7 05:47:53 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   53C    P8     9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e335hPmdtASZ"
      },
      "source": [
        "Before we start, let's install both `datasets` and `transformers` from master. Also, we need the `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",
      "metadata": {
        "id": "c8eh87Hoee5d"
      },
      "source": [
        "%%capture\n",
        "!pip install datasets==1.18.3\n",
        "!pip install transformers==4.17.0\n",
        "!pip install jiwer"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8_6kYmDMH9lR"
      },
      "source": [
        "Next we strongly suggest to upload your training checkpoints directly to the [🤗 Hub](https://huggingface.co/) while training. The [🤗 Hub](https://huggingface.co/) has integrated version control so you can be sure that no model checkpoint is getting lost during training. \n",
        "\n",
        "To do so you have to store your authentication token from the Hugging Face website (sign up [here](https://huggingface.co/join) if you haven't already!)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
        "id": "zFLBDyzQIA3R",
        "outputId": "74d10b2c-9891-47ea-dc29-05edd3c9b1c2"
      },
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zCyw5D23IQ1F"
      },
      "source": [
        "\n",
        "Then you need to install Git-LFS to upload your model checkpoints:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9BnQDhOITBC"
      },
      "source": [
        "%%capture\n",
        "!apt install git-lfs"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mn9swf6EQ9Vd"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "${}^1$ Timit is usually 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 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 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 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": "bee4g9rpLxll"
      },
      "source": [
        "Let's start by loading the dataset and taking a look at its structure."
      ]
    },
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            "22986ff797624e92a5fc22374bd76d0d",
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            "c412a1665b2147f4816d0d991fd547a4",
            "f3d61caab9c34f5589c4dac483af1423",
            "24bb10e6d4b44e90b191879f21164a04",
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            "9ab1df7626a74e7fb627f8db4a45fecf",
            "4f434cbc01f74207bc2e2c1377936d91"
          ]
        },
        "outputId": "ec693308-d41f-4fff-e95a-50a9115a11de"
      },
      "source": [
        "from datasets import load_dataset, load_metric\n",
        "\n",
        "timit = load_dataset(\"timit_asr\")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/2.40k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0a51c3c66c2d46ca8efe94e4a9693214"
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          "metadata": {}
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        {
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          "data": {
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              "Downloading:   0%|          | 0.00/1.06k [00:00<?, ?B/s]"
            ],
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset timit_asr/clean (download: 828.75 MiB, generated: 7.90 MiB, post-processed: Unknown size, total: 836.65 MiB) to /root/.cache/huggingface/datasets/timit_asr/clean/2.0.1/b11b576ddcccbcefa7c9f0c4e6c2a43756f3033adffe0fb686aa61043d0450ad...\n"
          ]
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          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/869M [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
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            "text/plain": [
              "0 examples [00:00, ? examples/s]"
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              "0 examples [00:00, ? examples/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset timit_asr downloaded and prepared to /root/.cache/huggingface/datasets/timit_asr/clean/2.0.1/b11b576ddcccbcefa7c9f0c4e6c2a43756f3033adffe0fb686aa61043d0450ad. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "74edbbdbb84146d69f231225dcf10557"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QbIM-L0xdvf4",
        "outputId": "21742c58-098b-44d5-e5ae-266b065b3f35"
      },
      "source": [
        "timit"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],\n",
              "        num_rows: 4620\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],\n",
              "        num_rows: 1680\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ri5y5N_HMANq"
      },
      "source": [
        "Many ASR datasets only provide the target text, `'text'` for each audio `'audio'` and file `'file'`. Timit actually provides much more information about each audio file, such as the `'phonetic_detail'`, etc., which is why many researchers choose to evaluate their models on phoneme classification instead of speech recognition when working with Timit. 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",
      "metadata": {
        "id": "kbyq6lDgQc2a"
      },
      "source": [
        "timit = timit.remove_columns([\"phonetic_detail\", \"word_detail\", \"dialect_region\", \"id\", \"sentence_type\", \"speaker_id\"])"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "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",
      "metadata": {
        "id": "72737oog2F6U"
      },
      "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()))"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "K_JUmf3G3b9S",
        "outputId": "491d3202-2b4a-438e-c27c-2a923ffc3991"
      },
      "source": [
        "show_random_elements(timit[\"train\"].remove_columns([\"audio\", \"file\"]), num_examples=10)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "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>Bagpipes and bongos are musical instruments.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>She encouraged her children to make their own Halloween costumes.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The great black leopards.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>She had your dark suit in greasy wash water all year.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>One can only speak of what is in front of him, and that now is simply the mess.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Otherwise, the outlook could be dark indeed.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Gregory and Tom chose to watch cartoons in the afternoon.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>She had your dark suit in greasy wash water all year.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Would a tomboy often play outdoors?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>The annoying raccoons slipped into Phil's garden every night.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fowcOllGNNju"
      },
      "source": [
        "Alright! The transcriptions look very clean and the language seems to correspond more to written text than dialogue. This makes sense taking into account that [Timit](https://huggingface.co/datasets/timit_asr) is a 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",
      "metadata": {
        "id": "svKzVJ_hQGK6"
      },
      "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[\"text\"]).lower() + \" \"\n",
        "    return batch"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "64099fae283c43f3acd144a5c486710a",
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            "f1ae533541b34ba7b4148b774b96ba3a",
            "ab01f967648b42bd8e3fd846efe19677",
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            "7f7f212dae194257b4ce5feee2389f3c",
            "aede41a7f6c64dfbad3aa14f0a1edf53",
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            "152a3cf0ca1a4771a8fab10480b03724",
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          ]
        },
        "id": "XIHocAuTQbBR",
        "outputId": "cf65f90c-62ea-487a-f238-04c539ba67cf"
      },
      "source": [
        "timit = timit.map(remove_special_characters)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.fingerprint:Parameter 'function'=<function remove_special_characters at 0x7f3f9b087ac0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0ex [00:00, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "64099fae283c43f3acd144a5c486710a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0ex [00:00, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d13b91eb39354dbd817f39ab8e148bd5"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "RBDRAAYxRE6n",
        "outputId": "fb9e0cd6-724e-4327-beb9-022082bb43b4"
      },
      "source": [
        "show_random_elements(timit[\"train\"].remove_columns([\"audio\", \"file\"]))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "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>this is going to be a language lesson and you can master it in a few minutes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>my ideal morning begins with hot coffee</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>jeff's toy gocart never worked</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>don't ask me to carry an oily rag like that</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>the high security prison was surrounded by barbed wire</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>the coyote bobcat and hyena are wild animals</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>etiquette mandates compliance with existing regulations</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>he murmured to himself with firmness no surrender</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>don't ask me to carry an oily rag like that</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>children can consume many fruit candies in one sitting</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "LwCshNbbeRZR"
      },
      "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]}"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "timit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RqmDC-AadoOn",
        "outputId": "f196d906-6dea-49ba-8db8-22a0aeee40ef"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['file', 'audio', 'text'],\n",
              "        num_rows: 4620\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['file', 'audio', 'text'],\n",
              "        num_rows: 1680\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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            "248e8cc682f84a19872e85e56de23e48",
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            "d43767eadb394ec9a0a1b1944a0341df",
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          ]
        },
        "id": "_m6uUjjcfbjH",
        "outputId": "b58e8ed1-dfdd-4dbe-f413-0433890175ba"
      },
      "source": [
        "vocabs = timit.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=timit.column_names[\"train\"])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f4be05755be5494a920cd8b3e3b7a8e3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "b69ce7c695dd41378fb651a6a29564de"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "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",
      "source": [
        "vocabs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cIGTBdxrd0iv",
        "outputId": "ce18c6df-d1d5-4956-db1a-47a74e512c9d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['vocab', 'all_text'],\n",
              "        num_rows: 1\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['vocab', 'all_text'],\n",
              "        num_rows: 1\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vocabs[\"test\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C4eROYKVd5Ja",
        "outputId": "e1a12346-6250-48e2-d580-5ea184d7caa5"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'vocab': ['k',\n",
              "  'f',\n",
              "  'j',\n",
              "  'e',\n",
              "  \"'\",\n",
              "  'w',\n",
              "  'v',\n",
              "  'z',\n",
              "  'l',\n",
              "  'm',\n",
              "  'h',\n",
              "  'y',\n",
              "  's',\n",
              "  'b',\n",
              "  'u',\n",
              "  'g',\n",
              "  ' ',\n",
              "  'r',\n",
              "  'd',\n",
              "  't',\n",
              "  'q',\n",
              "  'o',\n",
              "  'a',\n",
              "  'n',\n",
              "  'i',\n",
              "  'c',\n",
              "  'p',\n",
              "  'x'],\n",
              " 'all_text': \"the bungalow was pleasantly situated near the shore  don't ask me to carry an oily rag like that  are you looking for employment  she had your dark suit in greasy wash water all year  at twilight on the twelfth day we'll have chablis  eating spinach nightly increases strength miraculously  got a heck of a buy on this dirt cheap  the scalloped edge is particularly appealing  a big goat idly ambled through the farmyard  this group is secularist and their program tends to be technological  we can get it if we dig he said patiently  the best way to learn is to solve extra problems  don't ask me to carry an oily rag like that  do without fancy tablecloths  she had your dark suit in greasy wash water all year  ralph controlled the stopwatch from the bleachers  we may say that his problem was diagnosed but that he refused treatment  why charge money for such garbage  this brochure is particularly informative for prospective buyer  she wore warm fleecy woolen overalls  the bungalow was pleasantly situated near the shore  a note of awe came into his voice  don't ask me to carry an oily rag like that  are you looking for employment  she had your dark suit in greasy wash water all year  set aside to dry with lid on sugar bowl  at twilight on the twelfth day we'll have chablis  eating spinach nightly increases strength miraculously  does society really exist as an entity over and above the agglomeration of man  a big goat idly ambled through the farmyard  a spring trap for solid mounting and a regular hand trap are also available  steve wore a bright red cashmere sweater  the gunman kept his victim cornered at gunpoint for three hours  she is thinner than i am  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  regulative norms governing subgroups and roles  eyes like hot honey eyes that sizzled  did you buy any corduroy overalls  iguanas and alligators are tropical reptiles  if people were more generous there would be no need for welfare  the ramifications of the issue are enormous  barb burned paper and leaves in a big bonfire  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  basketball can be an entertaining sport  trim excess clay from around lip of mold and set aside while assembling lid  how good is your endurance  they don't go for bulllike muscle as a rule  december and january are nice months to spend in miami  weatherproof galoshes are very useful in seattle  young children should avoid exposure to contagious diseases  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  he admitted his behavior and defended it  pam gives driving lessons on thursdays  new ideas for improving nutrition came with the study of soil treatment  keep the desserts simple fruit does nicely  aluminum silverware can often be flimsy  she slipped and sprained her ankle on the steep slope  he lived and breathed for the mining company  the best way to learn is to solve extra problems  the shelter shown would provide almost absolute fallout protection  bring me the firecrackers  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  ralph controlled the stopwatch from the bleachers  why charge money for such garbage  this brochure is particularly informative for a prospective buyer  she wore warm fleecy woolen overalls  he will allow a rare lie  kindergarten children decorate their classrooms for all holidays  right now may not be the best time for business mergers  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  all the while she sat there her sinewy arms swirled before her chest  withdraw all phony accusations at once  the target chart quickly and briefly tells you which additives do what  why should this be so  cory and trish played tag with beach balls for hours  do you know what they think of me  guess the question from the answer  don't ask me to carry an oily rag like that  who authorized the unlimited expense account  she had your dark suit in greasy wash water all year  why yell or worry over silly items  lori's costume needed black gloves to be completely elegant  this is not a program of socialized medicine  to further his prestige he occasionally reads the wall street journal  one wonders about its applicability to people  do avoid puncturing or cutting into meats test them  swing your arm as high as you can  call an ambulance for medical assistance  a boring novel is a superb sleeping pill  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  why buy oil when you always use mine  should she wake him  some of these speculations may have some merit others are somewhat ambiguous  planned parenthood organizations promote birth control  bricks are an alternative  weatherproof galoshes are very useful in seattle  young children should avoid exposure to contagious diseases  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  pam gives driving lessons on thursdays  aluminum silverware can often be flimsy  it suffers from a lack of unity of purpose and respect for heroic leadership  she slipped and sprained her ankle on the steep slope  fat showed in loose rolls beneath the shirt  he will allow a rare lie  come home right away  solar activities could presumably bring long periods of flood or drought  kindergarten children decorate their classrooms for all holidays  right now may not be the best time for business mergers  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  withdraw all phony accusations at once  cory and trish played tag with beach balls for hours  day after day some new episode is reported  he will allow a rare lie  kindergarten children decorate their classrooms for all holidays  right now may not be the best time for business mergers  presently his water brother said breathlessly  is the mother of an autistic child at fault  don't ask me to carry an oily rag like that  properly used the present book is an excellent instrument of enlightenment  she had your dark suit in greasy wash water all year  withdraw all phony accusations at once  cory and trish played tag with beach balls for hours  it can project longrange goals for itself  he was gifted with animal magnetism and a potent allure for women of any race  don't ask me to carry an oily rag like that  daphne's swedish needlepoint scarf matched her skirt  she had your dark suit in greasy wash water all year  even a simple vocabulary contains symbols  highway and freeway mean the same thing  the sound of jennifer's bugle scared the antelope  very austere yet racy  the fog prevented them from arriving on time  are your grades higher or lower than nancy's  project development was proceeding too slowly  that noise problem grows more annoying each day  you certainly can't expect the infield to do any better than it did last year  that'll be plenty to eat by  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  what factors condition the degree of realization at various times and places  serve the coleslaw after i add the oil  the oasis was a mirage  guess the question from the answer  don't ask me to carry an oily rag like that  who authorized the unlimited expense account  yet they thrived on it  she had your dark suit in greasy wash water all year  you were in a hurry weren't you  this is my hen ledger he informed him in an absorbed way  why yell or worry over silly items  lori's costume needed black gloves to be completely elegant  to further his prestige he occasionally reads the wall street journal  nurses' training here doesn't cost anything  a muscular abdomen is good for your back  don't ask me to carry an oily rag like that  the causeway ended abruptly at the shore  it all seemed rather hopeless  she had your dark suit in greasy wash water all year  cereal grains have been used for centuries to prepare fermented beverages  the carpet cleaners shampooed our oriental rug  please take this dirty table cloth to the cleaners for me  those musicians harmonize marvelously  the clumsy customer spilled some expensive perfume  we can die too we can die like real people  poets moreover dwell on human passions  don't ask me to carry an oily rag like that  grandmother outgrew her upbringing in petticoats  she had your dark suit in greasy wash water all year  the viewpoint overlooked the ocean  i'd ride the subway but i haven't enough change  it was not exactly panic they gave way to but they could not just sit there  please dig my potatoes up before frost  the theme may be the formation of a shape from which other shapes evolve  cliff was soothed by the luxurious massage  he spoke soothingly  each untimely income loss coincided with the breakdown of a heating system part  jane may earn more money by working hard  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  the cat's meow always hurts my ears  youngsters love common candy as treats  here's where luck would normally step in  medieval society was based on hierarchies  what shall these effects be  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  destroy every file related to my audits  these programs emphasize the acceptance of biracial classrooms peacefully  where were you while we were away  bones and cartilage transmit poorly and tend to reflect the ultrasonic signals  bob papered over the living room murals  alice's ability to work without supervision is noteworthy  no political order or economic system is so clearly contrary to nature  greg buys fresh milk each weekday morning  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  catastrophic economic cutbacks neglect the poor  allow leeway here but rationalize all errors  he was only vaguely aware of the sluicing rain  agricultural products are unevenly distributed  many scouring powders now also contain phosphates  the nearest synagogue may not be within walking distance  the cranberry bog gets very pretty in autumn  the clearly identifiable enemy continued on as if no one else were around  don't ask me to carry an oily rag like that  laugh dance and sing if fortune smiles upon you  she had your dark suit in greasy wash water all year  mom strongly dislikes appetizers  it's healthier to cook without sugar  this is known as conformational entropy  tradition requires parental approval for underage marriage  comparable trends can be noted elsewhere  not surprisingly this approach did not work  in short and to borrow an arboreal phrase slash timber  don't ask me to carry an oily rag like that  daphne's swedish needlepoint scarf matched her skirt  she had your dark suit in greasy wash water all year  even a simple vocabulary contains symbols  highway and freeway mean the same thing  the sound of jennifer's bugle scared the antelope  the fog prevented them from arriving on time  one thing he thought nobody knows about it yet  the bungalow was pleasantly situated near the shore  he remembered them well from his happy period  students' choices of ideal educational goals are not arbitrary or whimsical  don't ask me to carry an oily rag like that  are you looking for employment  she had your dark suit in greasy wash water all year  at twilight on the twelfth day we'll have chablis  eating spinach nightly increases strength miraculously  more he could take at leisure  a big goat idly ambled through the farmyard  we of the liberalled world got all set for peace and rehabilitation  the cranberry bog gets very pretty in autumn  his sarcasm was followed by a stupid grin of his thick mouth and bad teeth  don't ask me to carry an oily rag like that  laugh dance and sing if fortune smiles upon you  she had your dark suit in greasy wash water all year  mom strongly dislikes appetizers  it's healthier to cook without sugar  officials meet on rubbish  tradition requires parental approval for underage marriage  many selections are themselves convincing contributions to this appraisal  guess the question from the answer  don't ask me to carry an oily rag like that  who authorized the unlimited expense account  she had your dark suit in greasy wash water all year  why yell or worry over silly items  never happier in my life  they went downstairs loaded a tray lavishly  lori's costume needed black gloves to be completely elegant  to further his prestige he occasionally reads the wall street journal  bridges tunnels and ferries are the most common methods of river crossings  greg buys fresh milk each weekday morning  don't ask me to carry an oily rag like that  they also want money prestige and security  she had your dark suit in greasy wash water all year  catastrophic economic cutbacks neglect the poor  allow leeway here but rationalize all errors  agricultural products are unevenly distributed  a good morrow to you my boy  the nearest synagogue may not be within walking distance  the cranberry bog gets very pretty in autumn  don't ask me to carry an oily rag like that  the plaintiff in school desegregation cases  laugh dance and sing if fortune smiles upon you  she had your dark suit in greasy wash water all year  mom strongly dislikes appetizers  trim away excess clay reinforce seam with a coil of clay  it's healthier to cook without sugar  they came upon cheetal deer at woodland pools  tradition requires parental approval for underage marriage  i'd rather not buy these shoes than be overcharged  beg that guard for one gallon of gas  actually they said he did sleep but so shortly and fitfully that he forgot  at right is a casual style in a crushed unlined white leather  don't ask me to carry an oily rag like that  her right hand aches whenever the barometric pressure changes  she had your dark suit in greasy wash water all year  it is aweinspiring  rob sat by the pond and sketched the stray geese  combine all the ingredients in a large bowl  medieval society was based on hierarchies  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  accident murder suicide  take your pick  destroy every file related to my audits  in short scientific sampling was introduced in place of subjective sampling  where were you while we were away  bob papered over the living room murals  alice's ability to work without supervision is noteworthy  best solution is to find an area that is predominantly sunlight or shade  he may toss coins he may take slips paper from a grab bag  steve wore a bright red cashmere sweater  one validated acts of school districts  the gunman kept his victim cornered at gunpoint for three hours  she is thinner than i am  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  did you buy any corduroy overalls  iguanas and alligators are tropical reptiles  within this framework what followed was strained even macabre  weatherproof galoshes are very useful in seattle  young children should avoid exposure to contagious diseases  if he ever did such a thing again she'd die of shame  what it does stimulates rumen activity  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  pam gives driving lessons on thursdays  aluminum silverware can often be flimsy  she slipped and sprained her ankle on the steep slope  this is especially true for knitted fabrics  muscular abdomen is good for your back  he also reminded himself that he had an unusual number of possibilities  don't ask me to carry an oily rag like that  the causeway ended abruptly at the shore  she had your dark suit in greasy wash water all year  the carpet cleaners shampooed our oriental rug  widow nice sort of woman  please take this dirty table cloth to the cleaners for me  yet he remains the fiercest of competitors  those musicians harmonize marvelously  he trembled lest his piece should fail  steve wore a bright red cashmere sweater  his work began just six days after the flood  the gunman kept his victim cornered at gunpoint for three hours  she is thinner than i am  don't ask me to carry an oily rag like that  no the man was not drunk he wondered how he got tied up with this stranger  she had your dark suit in greasy wash water all year  did you buy any corduroy overalls  iguanas and alligators are tropical reptiles  i'd rather not buy these shoes than be overcharged  beg that guard for one gallon of gas  morphophonemic rules may be thought of as joining certain points in the system  they were both very fluent  don't ask me to carry an oily rag like that  her right hand aches whenever the barometric pressure changes  she had your dark suit in greasy wash water all year  rob sat by the pond and sketched the stray geese  combine all the ingredients in a large bowl  he swung up over the wheel  weatherproof galoshes are very useful in seattle  young children should avoid exposure to contagious diseases  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  pam gives driving lessons on thursdays  y'all should have let me do it  aluminum silverware can often be flimsy  my mother never wops me  she slipped and sprained her ankle on the steep slope  obviously the bridal pair has many adjustments to make to their new situation  in a way he couldn't blame her  a muscular abdomen is good for your back  do buy allpurpose mugs or cups  don't ask me to carry an oily rag like that  the causeway ended abruptly at the shore  she had your dark suit in greasy wash water all year  each form represented by the dictionary is looked up in the textform list  the carpet cleaners shampooed our oriental rug  please take this dirty table cloth to the cleaners for me  those musicians harmonize marvelously  he will allow a rare lie  kindergarten children decorate their classrooms for all holidays  we're here to transact business  right now may not be the best time for business mergers  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  her legs hung down long and thin as she sat on the high stool  withdraw all phony accusations at once  oh he'll be a plumber came the answer  cory and trish played tag with beach balls for hours  originally the main types used were various compositions of polyesters  he stole a dime from a beggar  before thursday's exam review every formula  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  john cleans shellfish for a living  several firms are merchandising enzyme preparation through feed manufacturers  what about a tea room then  they enjoy it when i audition  jeff thought you argued in favor of a centrifuge purchase  is it because of my slovenliness that hair grows on my face  cliff was soothed by the luxurious massage  take care of yourself then  he peered ahead and grinned as the railroad tracks came into view again below  each untimely income loss coincided with the breakdown of a heating system part  jane may earn more money by working hard  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  the cat's meow always hurts my ears  youngsters love common candy as treats  i'd rather not buy these shoes than be overcharged  beg that guard for one gallon of gas  look sweetheart some fool was  don't ask me to carry an oily rag like that  her right hand aches whenever the barometric pressure changes  she had your dark suit in greasy wash water all year  view takes in five counties two bedrooms  rob sat by the pond and sketched the stray geese  combine all the ingredients in a large bowl  it was a fairly modern motel with quite a bit of electrical display in front  will robin wear a yellow lily  do line barbecue fire bowl with heavy foil to reflect heat  special task forces rescue hostages from kidnappers  that diagram makes sense only after much study  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  or more rhythmically the swarthy witches are laughing  straw hats are out of fashion this year  after that we began to get visitors to class  the tooth fairy forgot to come when roger's tooth fell out  medieval society was based on hierarchies  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  destroy every file related to my audits  where were you while we were away  bob papered over the living room murals  he further proposed grants of an unspecified sum for experimental hospitals  alice's ability to work without supervision is noteworthy  deans can form an important bridge between the president and the faculty  perhaps things were even worse then  a muscular abdomen is good for your back  don't ask me to carry an oily rag like that  the causeway ended abruptly at the shore  she had your dark suit in greasy wash water all year  he then offered his own estimate of the weather which was unenthusiastic  the carpet cleaners shampooed our oriental rug  less personal salesmanship  please take this dirty table cloth to the cleaners for me  we can however maximize its expected value  those musicians harmonize marvelously  family rationing probably will be necessary  it was there that she would have to enact her renunciation beg forgiveness  the emblem depicts the acropolis all aglow  military personnel are expected to obey government orders  on these they feel they can rely  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  his shoulder felt as if it were broken  only lawyers love millionaires  if carol comes tomorrow have her arrange for a meeting at two  she blew her nose on a tissue and opened the coke bottle  a chosen few will become generals  the small boy put the worm on the hook  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  there is not space to hold or force to guard any increased number of prisoners  yes sir she said is that definite  the fish began to leap frantically on the surface of the small lake  artificial intelligence is for real  only the most accomplished artists obtain popularity  if people were more generous there would be no need for welfare  barb burned paper and leaves in a big bonfire  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  basketball can be an entertaining sport  how good is your endurance  it was exposed to a high velocity gas jet  this is no spectatortype experience everyone is to be a participant  wait a little while  december and january are nice months to spend in miami  sulphur oil and copra make the kind of tinder any firebug dreams of  cheap stockings run the first time they're worn  there need be no squeamishness about admitting this  don't ask me to carry an oily rag like that  instructions for preparing clay drying glazing and firing are not given  she had your dark suit in greasy wash water all year  the morning dew on the spider web glistened in the sun  calcium makes bones and teeth strong  objects made of pewter are beautiful  don't do charlie's dirty dishes  but a somewhat more detailed analysis of this process may be illuminating  they took it away overalls or something  are your grades higher or lower than nancy's  project development was proceeding too slowly  that noise problem grows more annoying each day  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  serve the coleslaw after i add the oil  nothing has been done yet to take advantage of the enabling legislation  the oasis was a mirage  he will allow a rare lie  clever light songs were overly coy tragic songs a little too melodramatic  a warm breeze played across it moving it like waves  kindergarten children decorate their classrooms for all holidays  right now may not be the best time for business mergers  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  withdraw all phony accusations at once  do not draw yarn too tight  cory and trish played tag with beach balls for hours  let me quote him even more fully for his analysis is important to my theme  if people were more generous there would be no need for welfare  bake slowly at least onehalf hour longer  he said the crazy fool half aloud  barb burned paper and leaves in a big bonfire  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  basketball can be an entertaining sport  how good is your endurance  december and january are nice months to spend in miami  what elements of our behavior are decisive  a new idea drifted in from nowhere  swing your arm as high as you can  call an ambulance for medical assistance  a boring novel is a superb sleeping pill  don't ask me to carry an oily rag like that  could the ambassador himself be the man on this side the prime minister feared  she had your dark suit in greasy wash water all year  why buy oil when you always use mine  planned parenthood organizations promote birth control  such optimism was completely unjustified  the emblem depicts the acropolis all aglow  military personnel are expected to obey government orders  it all did look very efficient and shipshape  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  his shoulder felt as if it were broken  hay and pasture are the other chief sources of livestock feed  only lawyers love millionaires  if carol comes tomorrow have her arrange for a meeting at two  swing your arm as high as you can  call an ambulance for medical assistance  a boring novel is a superb sleeping pill  nobody else showed pleasure  said you wore yourself out cleaning all these empty rooms  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  why buy oil when you always use mine  seeds of soybean cotton corn sesame and rape yield semidrying oils  planned parenthood organizations promote birth control  guess the question from the answer  don't ask me to carry an oily rag like that  who authorized the unlimited expense account  she had your dark suit in greasy wash water all year  why yell or worry over silly items  lori's costume needed black gloves to be completely elegant  they make us conformists look good  we always thought we would die with our boots on  to further his prestige he occasionally reads the wall street journal  a carriage crunched by its dim lights filtering through the gloom  these include leaves of absences illnesses and layoffs  perhaps it was right perhaps it was just  greg buys fresh milk each weekday morning  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  catastrophic economic cutbacks neglect the poor  allow leeway here but rationalize all errors  he paused then added everything on a ship is a weapon  agricultural products are unevenly distributed  the nearest synagogue may not be within walking distance  will robin wear a yellow lily  the pulsing glow of a cigarette  special task forces rescue hostages from kidnappers  their props were two stepladders a chair and a palm fan  that diagram makes sense only after much study  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  flies swarmed around the bowl of lentils in which he mopped thick black bread  straw hats are out of fashion this year  the tooth fairy forgot to come when roger's tooth fell out  cliff was soothed by the luxurious massage  each untimely income loss coincided with the breakdown of a heating system part  jane may earn more money by working hard  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  the cat's meow always hurts my ears  her study of history was persistently pursued  quietly he determined to foil her  youngsters love common candy as treats  so if all these beers was to get me in bed man you just spent a lot of money  when suitably lighted what would it look like  he stole a dime from a beggar  all are well written and are overwritten  before thursday's exam review every formula  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  john cleans shellfish for a living  they enjoy it when i audition  jeff thought you argued in favor of a centrifuge purchase  it was applied with a compost shredder made from a converted manure spreader  he will allow a rare lie  this is followed by a boom in conferences  kindergarten children decorate their classrooms for all holidays  how long would it be occupied  right now may not be the best time for business mergers  it gave the rubberized fabric a dull gleam like metal  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  withdraw all phony accusations at once  cory and trish played tag with beach balls for hours  drop five forms in the box before you go out  her wardrobe consists of only skirts and blouses  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  put the butcher block table in the garage  elderly people are often excluded  psychologically the reception should be the climax following the sermon  pizzerias are convenient for a quick lunch  beat me to death in front of a hundred people  a detailed study of this latter phenomenon was not attempted in this paper  a chosen few will become generals  the small boy put the worm on the hook  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  our key salesmen are in appliances and cosmetics  the fish began to leap frantically on the surface of the small lake  or does he get exercise  artificial intelligence is for real  this brings us to the question of accreditation of art schools in general  only the most accomplished artists obtain popularity  swing your arm as high as you can  he wadded his sweat shirt into a ball and stripped down to his swimming trunks  call an ambulance for medical assistance  do you love to run up a hem sew on buttons make neat buttonholes  then he  then what  a boring novel is a superb sleeping pill  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  why buy oil when you always use mine  planned parenthood organizations promote birth control  let's be fine superior people of great dignity he said as if he were joking  he stole a dime from a beggar  before thursday's exam review every formula  don't ask me to carry an oily rag like that  he had plunged into the dark woods beyond  she had your dark suit in greasy wash water all year  john cleaned shellfish for a living  one had to believe in final events or one was stranded in the abyss of nothing  they enjoy it when i audition  jeff thought you argued in favor of a centrifuge purchase  steve wore a bright red cashmere sweater  the gunman kept his victim cornered at gunpoint for three hours  she is thinner than i am  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  he is a man of major talent  but a man of solitary uncertain impulses  did you buy any corduroy overalls  they were a vagabond lot and considered to be shady and undesirable characters  iguanas and alligators are tropical reptiles  ballets were used in opera from its beginning  i'd rather not buy these shoes than be overcharged  beg that guard for one gallon of gas  increasing tempo of new product development  don't ask me to carry an oily rag like that  her right hand aches whenever the barometric pressure changes  he gave a short hard laugh and looked at her knowingly  she had your dark suit in greasy wash water all year  rob sat by the pond and sketched the stray geese  combine all the ingredients in a large bowl  you don't eat enough honey  greg buys fresh milk each weekday morning  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  catastrophic economic cutbacks neglect the poor  vases make same as salt and pepper shakers leaving off top pieces  allow leeway here but rationalize all errors  now that this is at odds with our meaning may be shown as follows  agricultural products are unevenly distributed  the nearest synagogue may not be within walking distance  our boy didn't chicken out no sir  medieval society was based on hierarchies  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  destroy every file related to my audits  where were you while we were away  bob papered over the living room murals  compulsory retirement at sixtyfive looms on our horizon  upstairs busy feet showering like raindrops pattered around her room  alice's ability to work without supervision is noteworthy  fists pummeled him as he staggered forward  guess the question from the answer  don't ask me to carry an oily rag like that  who authorized the unlimited expense account  how's it strike you foul or fair  she had your dark suit in greasy wash water all year  weakness in leadership  why yell or worry over silly items  lori's costume needed black gloves to be completely elegant  to further his prestige he occasionally reads the wall street journal  poach the apples in this syrup for twelve minutes drain them and cool  pretty soon a woman came along carrying a folded umbrella as a walking stick  if people were more generous there would be no need for welfare  barb burned paper and leaves in a big bonfire  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  basketball can be an entertaining sport  how good is your endurance  if the farm is rented the rent must be paid  laboratory astrophysics  december and january are nice months to spend in miami  he took his mask from his forehead and threw it unexpectedly across the deck  the clumsy customer spilled some expensive perfume  don't ask me to carry an oily rag like that  grandmother outgrew her upbringing in petticoats  make lid for sugar bowl the same as jar lids omitting design disk  she had your dark suit in greasy wash water all year  the viewpoint overlooked the ocean  do they make classbiased decisions  i'd ride the subway but i haven't enough change  please dig my potatoes up before frost  and possessed himself  how  the emblem depicts the acropolis all aglow  military personnel are expected to obey government orders  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  his shoulder felt as if it were broken  what obsessions had she picked up during these long nights of talk  only lawyers love millionaires  if carol comes tomorrow have her arrange for a meeting at two  they will take a wedding trip later  in fact our whole defensive unit did a good job  weatherproof galoshes are very useful in seattle  young children should avoid exposure to contagious diseases  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  pam gives driving lessons on thursdays  this does not allow the mystery to invade us  aluminum silverware can often be flimsy  shrugs met that from room clerks from bellhops  she slipped and sprained her ankle on the steep slope  will robin wear a yellow lily  poultry results of irradiation tests with poultry have been quite successful  special task forces rescue hostages from kidnappers  a sleepy voice answered  that diagram makes sense only after much study  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  you're boiling milk ain't you  straw hats are out of fashion this year  the tooth fairy forgot to come when roger's tooth fell out  steve wore a bright red cashmere sweater  the gunman kept his victim cornered at gunpoint for three hours  she is thinner than i am  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  he makes me uncomfortable  he swung around eyes toward the bedroom some fifteen feet away  at different times he served as gleeclub and choir leader and as organist  did you buy any corduroy overalls  iguanas and alligators are tropical reptiles  shaving cream is a popular item on halloween  this can be done to a high degree of accuracy by existing surveying techniques  help greg to pick a peck of potatoes  they own a big house in the remote countryside  don't ask me to carry an oily rag like that  practically all forecasts mention new and exciting products on the horizon  but she suffered in her offduty hours  she had your dark suit in greasy wash water all year  michael colored the bedroom wall with crayons  the misquote was retracted with an apology  shaving cream is a popular item on halloween  help greg to pick a peck of potatoes  they own a big house in the remote countryside  most of us brush our teeth by hand  don't ask me to carry an oily rag like that  his body processes only slowed down enough for him to dispel fatigue poisons  she had your dark suit in greasy wash water all year  they close sometime after eight  michael colored the bedroom wall with crayons  the misquote was retracted with an apology  the emblem depicts the acropolis all aglow  military personnel are expected to obey government orders  don't ask me to carry an oily rag like that  passing dogs were greeted with a cordial bark  she had your dark suit in greasy wash water all year  his shoulder felt as if it were broken  stacked heels are also popular on dressy or tailored shoes  he wasn't likely to come down with hot chills and puzzling striped fever  only lawyers love millionaires  if carol comes tomorrow have her arrange for a meeting at two  if people were more generous there would be no need for welfare  princes and factions clashed in the open street and died on the open scaffold  barb burned paper and leaves in a big bonfire  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  basketball can be an entertaining sport  how good is your endurance  just long enough to make you feel important  there were other farmhouses nearby  december and january are nice months to spend in miami  he will allow a rare lie  a few years later the dome fell in  kindergarten children decorate their classrooms for all holidays  right now may not be the best time for business mergers  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  lot of people will roam the streets in costumes and masks and having a ball  withdraw all phony accusations at once  but in this one section we welcomed auditors  cory and trish played tag with beach balls for hours  will robin wear a yellow lily  you may amaze yourself and acquire a real knack for it  special task forces rescue hostages from kidnappers  strength began to zip up and down his chest  the feet wore army shoes in obvious disrepair  that diagram makes sense only after much study  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  straw hats are out of fashion this year  the tooth fairy forgot to come when roger's tooth fell out  steve wore a bright red cashmere sweater  the gunman kept his victim cornered at gunpoint for three hours  but how little love we give him  she is thinner than i am  some of them ignored the text and had apparently memorized the words long ago  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  did you buy any corduroy overalls  iguanas and alligators are tropical reptiles  diversity of perception yes diversity of fact yes  another memo for sightseers bring your camera along to museums  are your grades higher or lower than nancy's  progress is being made too in improving motorists' access to many turnpikes  project development was proceeding too slowly  that noise problem grows more annoying each day  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  serve the coleslaw after i add the oil  it provides a frame for the sampling census  the oasis was a mirage  a chosen few will become generals  the small boy put the worm on the hook  the jaded amorist conjured up pictures of the blasphemous rites with relish  don't ask me to carry an oily rag like that  his salary had reached the ten thousand mark  she had your dark suit in greasy wash water all year  the fish began to leap frantically on the surface of the small lake  artificial intelligence is for real  each is still glorified as a national hero  only the most accomplished artists obtain popularity  lighted windows glowed jewelbright through the downpour  the clumsy customer spilled some expensive perfume  salvation reconsidered  indeed it is possible to separate electron paramagnetic from nuclear effects  don't ask me to carry an oily rag like that  grandmother outgrew her upbringing in petticoats  she had your dark suit in greasy wash water all year  the viewpoint overlooked the ocean  i'd ride the subway but i haven't enough change  please dig my potatoes up before frost  cliff was soothed by the luxurious massage  each untimely income loss coincided with the breakdown of a heating system part  jane may earn more money by working hard  theirs is a sacrificial life by earthly standards  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  the cat's meow always hurts my ears  she looked crestfallen as if he had somehow disappointed the whole human race  it had a teeny envelope tied to its wrist  youngsters love common candy as treats  will robin wear a yellow lily  he drove and the road wobbled familiar scenes crept past on either side  yet we no longer feel uneasy  special task forces rescue hostages from kidnappers  that diagram makes sense only after much study  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  straw hats are out of fashion this year  the tooth fairy forgot to come when roger's tooth fell out  it also provides for funds to clear slums and help colleges build dormitories  i'd rather not buy these shoes than be overcharged  asked why he replied primly because that's no activity for a gentleman  he says he'll be here on the oneo'clock plane  beg that guard for one gallon of gas  we would establish no censorship  don't ask me to carry an oily rag like that  her right hand aches whenever the barometric pressure changes  she had your dark suit in greasy wash water all year  rob sat by the pond and sketched the stray geese  combine all the ingredients in a large bowl  shaving cream is a popular item on halloween  help greg to pick a peck of potatoes  you would drive us into shame he declared  they own a big house in the remote countryside  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  fold into whipped cream and add a dash of salt and sprinkling of paprika  michael colored the bedroom wall with crayons  the clouds bulged downward and burst suddenly into a great black funnel  the misquote was retracted with an apology  are your grades higher or lower than nancy's  project development was proceeding too slowly  that noise problem grows more annoying each day  heavy industry slanted toward inexhaustible government coffers attracted him  don't ask me to carry an oily rag like that  next the hen was nested and all seemed well  she had your dark suit in greasy wash water all year  serve the coleslaw after i add the oil  soon the office work claimed all her time  the oasis was a mirage  i'd rather not buy these shoes than be overcharged  beg that guard for one gallon of gas  don't ask me to carry an oily rag like that  her right hand aches whenever the barometric pressure changes  she had your dark suit in greasy wash water all year  rob sat by the pond and sketched the stray geese  combine all the ingredients in a large bowl  it circled her thighs exploring with its icy tentacles  he drew a deep breath  a portable electric heater is advisable for shelters in cold climates  guess the question from the answer  don't ask me to carry an oily rag like that  who authorized the unlimited expense account  she had your dark suit in greasy wash water all year  okay take the tray  why yell or worry over silly items  or the bay of female dogs in heat  lori's costume needed black gloves to be completely elegant  it will however inhibit the sprouting of potatoes and other root vegetables  to further his prestige he occasionally reads the wall street journal  the best way to learn is to solve extra problems  don't ask me to carry an oily rag like that  maybe today'll be a goodnews day  she had your dark suit in greasy wash water all year  ralph controlled the stopwatch from the bleachers  why charge money for such garbage  too many new things are happening for it to be a complete erotic fulfillment  this brochure is particularly informative for a prospective buyer  she spouted a mouthful of water into the air  she wore warm fleecy woolen overalls  shaving cream is a popular item on halloween  help greg to pick a peck of potatoes  they own a big house in the remote countryside  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  michael colored the bedroom wall with crayons  every movement she made seemed unnecessarily noisy  the misquote was retracted with an apology  his history is his alone yet each man must recognize his own history in it  a flame would use up air  drop five forms in the box before you go out  her wardrobe consists of only skirts and blouses  this we can sympathetically understand  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  my own experience has followed simpler lines  put the butcher block table in the garage  elderly people are often excluded  pizzerias are convenient for quick lunch  she was ready for her great adventures and the arrival of her mobile partner  you've got no business up here  the shutter consists of two parts a fastopening part and a fastclosing part  guess the question from the answer  don't ask me to carry an oily rag like that  who authorized the unlimited expense account  she had your dark suit in greasy wash water all year  this theory eventually proved inexact  why yell or worry over silly items  lori's costume needed black gloves to be completely elegant  to further his prestige he occasionally reads the wall street journal  cheap stockings run the first time they're worn  if you are a life tenant you deduct allowable depreciation and depletion  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  the morning dew on the spider web glistened in the sun  calcium makes bones and teeth strong  objects made of pewter are beautiful  getting the boss rattlesnake to help you  look at the things we do to escape our fears and to forget our worries  don't do charlie's dirty dishes  medieval society was based on hierarchies  don't ask me to carry an oily rag like that  the tale he thought would become less gay  she had your dark suit in greasy wash water all year  now the school was indeed bereft  destroy every file related to my audits  where only one club existed before he says two will flourish henceforth  where were you while we were away  bob papered over the living room murals  alice's ability to work without supervision is noteworthy  shaving cream is a popular item on halloween  the structures housing the apartments are of masonry and frame construction  help greg to pick a peck of potatoes  a wheel squeaked on a hub was still and squeaked again  they own a big house in the remote countryside  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  michael colored the bedroom wall with crayons  the misquote was retracted with an apology  oh the time of death  should the accelerating growth of technology then warn us  after that violence was exultantly easy  greg buys fresh milk each weekday morning  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  catastrophic economic cutbacks neglect the poor  allow leeway here but rationalize all errors  agricultural products are unevenly distributed  the nearest synagogue may not be within walking distance  there are more obvious nymphomaniacs on any privateeye series  the family requests that flowers be omitted  cheap stockings run the first time they're worn  to date the one meat showing favorable results at sterilization doses is pork  don't ask me to carry an oily rag like that  old people have their idiosyncrasies  she had your dark suit in greasy wash water all year  the morning dew on the spider web glistened in the sun  calcium makes bones and teeth strong  objects made of pewter are beautiful  don't do charlie's dirty dishes  don't ask me to carry an oily rag like that  daphne's swedish needlepoint scarf matched her skirt  she had your dark suit in greasy wash water all year  in what ways then did the patient's psychical blindness manifest itself  even a simple vocabulary contains symbols  let all projects dry slowly for several days  highway and freeway mean the same thing  the sound of jennifer's bugle scared the antelope  it would be a colossal shame to throw away a story like this  the fog prevented them from arriving on time  an octillion atoms  now let us see where this power comes from  don't ask me to carry an oily rag like that  it sounded silly why go on  daphne's swedish needlepoint scarf matched her skirt  she had your dark suit in greasy wash water all year  even a simple vocabulary contains symbols  highway and freeway mean the same thing  for the unseen ghostlike rifleman aimed a little higher the third time  the sound of jennifer's bugle scared the antelope  the fog prevented them from arriving on time  are your grades higher or lower than nancy's  but what has been happening recently might be described as creeping mannerism  project development was proceeding too slowly  there would still be plenty of moments of regret and sadness and guilty relief  that noise problem grows more annoying each day  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  serve the coleslaw after i add the oil  vapor pressure method  the oasis was a mirage  that added traffic means rising streams of dimes and quarters at toll gates  steve wore a bright red cashmere sweater  coverage of primary literature will follow  the gunman kept his victim cornered at gunpoint for three hours  we knew that it was as reassurance the ironic fruit of a deeply moral nature  she is thinner than i am  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  did you buy any corduroy overalls  iguanas and alligators are tropical reptiles  the bungalow was pleasantly situated near the shore  put on his old brown corduroy coat and it was already soaked  the better quality products in both of these lines contain phosphate builders  don't ask me to carry an oily rag like that  are you looking for employment  she had your dark suit in greasy wash water all year  at twilight on the twelfth day we'll have chablis  why pick me for your patsy  eating spinach nightly increases strength miraculously  a big goat idly ambled through the farmyard  the cranberry bog gets very pretty in autumn  don't ask me to carry an oily rag like that  laugh dance and sing if fortune smiles upon you  rector was often curious often tempted to ask questions but he never did  she had your dark suit in greasy wash water all year  brown eyes eyebrow mustache  mom strongly dislikes appetizers  it's healthier to cook without sugar  few rural areas are protected by zoning  tradition requires parental approval for underage marriage  are your grades higher or lower than nancy's  his dark brows which had been lowered in anger smoothed  project development was proceeding too slowly  to put it bluntly you are getting outmoded  that noise problem grows more annoying each day  he wondered if it wore the old anxiety or the old taut stoicism  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  serve the coleslaw after i add the oil  the oasis was a mirage  swing your arm as high as you can  call an ambulance for a medical assistance  as a rule the autistic child doesn't enjoy physical contact with others  you too were a stupid jackass until you poisoned me  a boring novel is a superb sleeping pill  don't ask me to carry an oily rag like that  it is the obverse of triviality shallowness emotional anaesthesia  she had your dark suit in greasy wash water all year  why buy oil when you always use mine  planned parenthood organizations promote birth control  drop five forms in the box before you go out  propriety was synonymous with ritual observance the mark of a true gentleman  her wardrobe consists of only skirts and blouses  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  put the butcher block table in the garage  jars are assembled in bowl of butter mold  elderly people are often excluded  pizzerias are convenient for a quick lunch  nowadays we talk as though the blitz were just a short skirmish  he stole a dime from a beggar  before thursday's exam review every formula  there results a study of literature freed from the tyranny of the contemporary  afterwards you did well enough  or certain words or rituals that child and adult go through may do the trick  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  john cleaned shellfish for a living  they enjoy it when i audition  jeff thought you argued in favor of a centrifuge purchase  cheap stockings run the first time they're worn  the lower portion provides a rating at any stage between infancy and adulthood  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  the morning dew on the spider web glistened in the sun  the purchasers rejected the order in two letters written in vigorous terms  calcium makes bones and teeth strong  why accuse a shipmate of being behind this  objects made of pewter are beautiful  don't do charlie's dirty dishes  shaving cream is a popular item on halloween  help greg to pick a peck of potatoes  displayed as lamps the puppets delight the children and are decorative accent  them new to the country were referred to as pilgrims  they own a big house in the remote countryside  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  michael colored the bedroom wall with crayons  the misquote was retracted with an apology  it's a formality you know  the cranberry bog gets very pretty in autumn  these were heroes nine feet tall to him  he saw the dangers not the glories of being identified as a mutineer  don't ask me to carry an oily rag like that  laugh dance and sing if fortune smiles upon you  she had your dark suit in greasy wash water all year  mom strongly dislikes appetizers  it's healthier to cook without sugar  otherwise you may be saddled with a goodsize milk bill by milk drinkers  tradition requires parental approval for underage marriage  the gallant halfcity is dying on its feet  the redcoats ran like rabbits  don't ask me to carry an oily rag like that  daphne's swedish needlepoint scarf matched her skirt  she had your dark suit in greasy wash water all year  even a simple vocabulary contains symbols  highway and freeway mean the same thing  the sound of jennifer's bugle scared the antelope  thus events occurred and victims changed in appearance and new ones arrived  the fog prevented them from arriving on time  the clumsy customer spilled some expensive perfume  how much will it cost to do any necessary modernizing and redecorating  oval face without an expression in the world  don't ask me to carry an oily rag like that  grandmother outgrew her upbringing in petticoats  she had your dark suit in greasy wash water all year  the viewpoint overlooked the ocean  i'd ride the subway but i haven't enough change  yes because you didn't run off to use that new toothbrush  please dig my potatoes up before frost  the best way to learn is to solve extra problems  nobody in his right mind punishes a quartercenturyold dereliction  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  ralph controlled the stopwatch from the bleachers  why charge money for such garbage  this brochure is particularly informative for a prospective buyer  a quick touchdown resulted  she wore warm fleecy woolen overalls  as she goes she herself will be illuminated  unfortunately there is still little demand for broccoli and cauliflower  drop five forms in the box before you go out  her wardrobe consists of only skirts and blouses  he could pick up another pack on his way home if he were going home  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  put the butcher block table in the garage  elderly people are often excluded  pizzerias are convenient for a quick lunch  that's where device quackery can lead  his election on the other hand would unquestionably strengthen the regulars  he stole a dime from a beggar  before thursday's exam review every formula  she sounded as though they already existed  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  john cleans shellfish for a living  palm oil protects the surfaces of steel sheets before they are plated with tin  they enjoy it when i audition  jeff thought you argued in favor of a centrifuge purchase  the best way to learn is to solve extra problems  next best to destroying an army is to deprive it of its freedom of action  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  ralph controlled the stopwatch from the bleachers  have you got our keys handy  why charge money for such garbage  a range of bluffs hugged the horizon the color of decaying moss  this brochure is particularly informative for a prospective buyer  she wore warm fleecy woolen overalls  northerners indulge in an extremely dangerous luxury  my mother was beside herself with curiosity  greg buys fresh milk each weekday morning  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  catastrophic economic cutbacks neglect the poor  allow leeway here but rationalize all errors  agricultural products are unevenly distributed  the nearest synagogue may not be within walking distance  got no business over here on a stakeout anyway  the cranberry bog gets very pretty in autumn  don't ask me to carry an oily rag like that  laugh dance and sing if fortune smiles upon you  she had your dark suit in greasy wash water all year  the revised procedure was acclaimed as a longoverdue reform  one could hear a very faint ladylike sigh of relief  mom strongly dislikes appetizers  it's healthier to cook without sugar  tradition requires parental approval for underage marriage  he held his arms close to his sides and made himself as small as possible  the bungalow was pleasantly situated near the shore  a noncontributory plan usually won't pay off for the worker until he retires  don't ask me to carry an oily rag like that  are you looking for employment  she had your dark suit in greasy wash water all year  at twilight on the twelfth day we'll have chablis  eating spinach nightly increases strength miraculously  the instinct to discipline has been lost  at law school the same  a big goat idly ambled through the farmyard  the clumsy customer spilled some expensive perfume  don't ask me to carry an oily rag like that  grandmother outgrew her upbringing in petticoats  she had your dark suit in greasy wash water all year  the viewpoint overlooked the ocean  i'd ride the subway but i haven't enough change  please dig my potatoes up before frost  to create such a lamp order a wired pedestal from any lamp shop  their work mirrors the mentality of the psychopath rootless and irresponsible  others invoked technology and common sense  swing your arm as high as you can  call an ambulance for medical assistance  when he saw me coming he turned his radio off  the armchair traveler preserves his illusions  a boring novel is a superb sleeping pill  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  why buy oil when you always use mine  selected bibliographies and tables of available data are now in preparation  planned parenthood organizations promote birth control  the clumsy customer spilled some expensive perfume  choose carefully between contributory or noncontributory pension plans  careful don't disturb her  don't ask me to carry an oily rag like that  grandmother outgrew her upbringing in petticoats  she had your dark suit in greasy wash water all year  the viewpoint overlooked the ocean  i'd ride the subway but i haven't enough change  please dig my potatoes up before frost  poverty imposes a kind of chastity on the ambitious  but such cases were in the past unusual  shaving cream is a popular item on halloween  you saw them always together those years  help greg to pick a peck of potatoes  they own a big house in the remote countryside  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  visually these approximated what he was feeling within himself  michael colored the bedroom wall with crayons  the misquote was retracted with an apology  are your grades higher or lower than nancy's  project development was proceeding too slowly  meats the radiation processing of meat has received extensive investigation  that noise problem grows more annoying each day  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  serve the coleslaw after i add the oil  he would not carry a brief case  by that one feels that magnetic forces are as general as electrical forces  the oasis was a mirage  the bungalow was pleasantly situated near the shore  it may be hard to decide what this labor is worth but charge something for it  exboyfriend meet exhusband  don't ask me to carry an oily rag like that  are you looking for employment  she had your dark suit in greasy wash water all year  at twilight on the twelfth day we'll have chablis  eating spinach nightly increases strength miraculously  a big goat idly ambled through the farmyard  a bullet she answered  the job also covers a number of other items  i'd rather not buy these shoes than be overcharged  beg that guard for one gallon of gas  one looked down on a sea of leaves a breaking wave of flower  don't ask me to carry an oily rag like that  her right hand aches whenever the barometric pressure changes  she had your dark suit in greasy wash water all year  rob sat by the pond and sketched the stray geese  combine all the ingredients in a large bowl  the manybodied girl lay near him  give me your machine gun ammo  drop five forms in the box before you go out  her wardrobe consists of only skirts and blouses  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  put the butcher block table in the garage  elderly people are often excluded  for an instant the old aunt felt something indefinable flash through her smile  pizzerias are convenient for a quick lunch  aim to balance your employee benefit package  somebody with no vices  for once radicalism was a recommendation  don't ask me to carry an oily rag like that  daphne's swedish needlepoint scarf matched her skirt  she had your dark suit in greasy wash water all year  even a simple vocabulary contains symbols  highway and freeway mean the same thing  historians have had two reasons for persisting so long in their investigations  the sound of jennifer's bugle scared the antelope  the fog prevented them from arriving on time  weatherproof galoshes are very useful in seattle  young children should avoid exposure to contagious diseases  it is not a pleasant or easy position but one we have endeavored to maintain  all nut kernels are rich in protein  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  pam gives driving lessons on thursdays  lovie you make me feel naked  aluminum silverware can often be flimsy  she slipped and sprained her ankle on the steep slope  a chosen few will become generals  aw shut up he said  the small boy put the worm on the hook  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  the fish began to leap frantically on the surface of the small lake  a profession that brought me as good an income as mine wasn't to be sneezed at  aligning all the teeth may take a year or more  artificial intelligence is for real  only the most accomplished artists obtain popularity  no return address whatsoever  he always seemed to have money in his pocket  greg buys fresh milk each weekday morning  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  catastrophic economic cutbacks neglect the poor  allow leeway here but rationalize all errors  as such it was beyond politics and had no need of justification by a message  agricultural products are unevenly distributed  the nearest synagogue may not be within walking distance  drop five forms in the box before you go out  if dark came they would lose her  her wardrobe consists of only skirts and blouses  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  put the butcher block table in the garage  elderly people are often excluded  those were especially the ones that all other grownups laughed at loudest  he thought he saw a pale face at a window  pizzerias are convenient for quick lunch  the clumsy customer spilled some expensive perfume  don't ask me to carry an oily rag like that  as a result he was persuaded out to dinner  grandmother outgrew her upbringing in petticoats  he not only knew soldiering but mathematics history and literature as well  she had your dark suit in greasy wash water all year  the viewpoint overlooked the ocean  i'd ride the subway but i haven't enough change  the courtyard is magnificently decorated  please dig my potatoes up before frost  thoroughly wised up  cliff was soothed by the luxurious massage  substances all crystalline substances and other solidstate materials  each untimely income loss coincided with the breakdown of a heating system part  jane may earn more money by working hard  how come you're riding the rods kids like you  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  the cat's meow always hurts my ears  youngsters love common candy as treats  if people were more generous there would be no need for welfare  this is taxation without representation  barb burned paper and leaves in a big bonfire  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  basketball can be an entertaining sport  how good is your endurance  the farmer's life must be arranged to meet the demands of crops and livestock  religion thus becomes integrated with life  december and january are nice months to spend in miami  it's a streamlined rifle fast and wellmade  cliff was soothed by the luxurious massage  each untimely income loss coincided with the breakdown of a heating system part  jane may earn more money by working hard  don't ask me to carry an oily rag like that  within the larger social system are the structural and functional subsystems  she had your dark suit in greasy wash water all year  the cat's meow always hurts my ears  and what eyes they were  youngsters love common candy as treats  will robin wear a yellow lily  special task forces rescue hostages from kidnappers  we've all been rich and spoiled long enough to hate the machine age  that diagram makes sense only after much study  don't ask me to carry an oily rag like that  fuss fuss old man  she had your dark suit in greasy wash water all year  straw hats are out of fashion this year  we produce peanut oil but to a much greater extent we eat the entire seed  the tooth fairy forgot to come when roger's tooth fell out  he stole a dime from a beggar  before thursday's exam review every formula  american newspaper reviewers like to call his plays nihilistic  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  john cleans shellfish for a living  but the ships are very slow now and we don't get so many sailors any more  they enjoy it when i audition  jeff thought you argued in favor of a centrifuge purchase  the prospect of cutting back spending is an unpleasant one for any governor  cheap stockings run the first time they're worn  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  the morning dew on the spider web glistened in the sun  calcium makes bones and teeth strong  husbandry was bounded by snakerail fences and there were grazing cattle  objects made of pewter are beautiful  authorities say that oldsters are a prime target  he had fallen into a soft job and now the job was gone and he was stranded  don't do charlie's dirty dishes  surely this is a reality we all acknowledge  a muscular abdomen is good for your back  living in a shelter the radioactivity of fallout decays rapidly at first  about dawn he got up to blow  don't ask me to carry an oily rag like that  the causeway ended abruptly at the shore  she had your dark suit in greasy wash water all year  the carpet cleaners shampooed our oriental rug  please take this dirty table cloth to the cleaners for me  those musicians harmonize marvelously  will robin wear a yellow lily  special task forces rescue hostages from kidnappers  eternity is no time for recriminations  that diagram makes sense only after much study  let us differentiate a few of these ideas  don't ask me to carry an oily rag like that  well junior didn't he eat only one and mr henry didn't even do that well  she had your dark suit in greasy wash water all year  straw hats are out of fashion this year  the tooth fairy forgot to come when roger's tooth fell out  a chosen few will become generals  the small boy put the worm on the hook  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  her tail no longer lashed  there's still such a thing as mental illness  but even mother's loving attitude will not always prevent misbehavior  the fish began to leap frantically on the surface of the small lake  artificial intelligence is for real  only the most accomplished artists obtain popularity  he stole a dime from a beggar  before thursday's exam review every formula  if necessary to replace both halves on grill sear cuts and allot extra time  don't ask me to carry an oily rag like that  we came on as soon as we could  she had your dark suit in greasy wash water all year  john cleans shellfish for a living  but within that framework he allowed for as much flexibility as possible  they enjoy it when i audition  jeff thought you argued in favor of a centrifuge purchase  drop five forms in the box before you go out  the reasons for this dive seemed foolish now  her wardrobe consists of only skirts and blouses  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  production may fall far below expectations  put the butcher block table in the garage  elderly people are often excluded  his captain was thin and haggard and his beautiful boots were worn and shabby  pizzerias are convenient for a quick lunch  you took me by surprise  medieval society was based on hierarchies  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  destroy every file related to my audits  the heavens refused to give up their weeping  where were you while we were away  bob papered over living room murals  the idea of a central tank with lines to each house is not in itself a novelty  alice's ability to work without supervision is noteworthy  a muscular abdomen is good for your back  don't ask me to carry an oily rag like that  the causeway ended abruptly at the shore  however the litter remained augmented by several dozen lunchroom suppers  she had your dark suit in greasy wash water all year  the carpet cleaners shampooed our oriental rug  books are for schnooks  to many experts this trend was inevitable  please take this dirty table cloth to the cleaners for me  those musicians harmonize marvelously  he had left her inviolate thinking familiarity would gentle her in time  the emblem depicts the acropolis all aglow  broil or toast as usual  military personnel are expected to obey government orders  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  his shoulder felt as if it were broken  often dominant stress simply indicates a centering of attention or emotion  only lawyers love millionaires  if carol comes tomorrow have her arrange for a meeting at two  a chosen few will become generals  heave on those ropes the boat's come unstuck  in wage negotiations the industry bargains as a unit with a single union  the small boy put the worm on the hook  don't ask me to carry an oily rag like that  materials ceramic modeling clay red white or buff  she had your dark suit in greasy wash water all year  the fish began to leap frantically on the surface of the small lake  artificial intelligence is for real  only the most accomplished artists obtain popularity  cheap stockings run the first time they're worn  ahah he thought a lush divorcee at last  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  the morning dew on the spider web glistened in the sun  calcium makes bones and teeth strong  objects made of pewter are beautiful  husky young man he said with mock distaste  don't do charlie's dirty dishes  thus technical efficiency is achieved at the expense of actual experience  the best way to learn is to solve extra problems  how did one join them  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  ralph controlled the stopwatch from the bleachers  dogs did something to one's ego  the socalled vegetable ivory is the hard endosperm of the eggsized seed  why charge money for such garbage  this brochure is particularly informative for a prospective buyer  she wore warm fleecy woolen overalls  cliff was soothed by the luxurious massage  each untimely income loss coincided with the breakdown of a heating system part  jane may earn more money by working hard  don't ask me to carry an oily rag like that  here he is quite persuasively the very embodiment of meanness and slyness  she had your dark suit in greasy wash water all year  the cat's meow always hurts my ears  youngsters love common candy as treats  dances alternated with sung or spoken verses  my father ran him off here six years ago  the bungalow was pleasantly situated near the shore  be excited and don't identify yourself  sometimes he coincided with my father's being at home  don't ask me to carry an oily rag like that  are you looking for employment  she had your dark suit in greasy wash water all year  at twilight on the twelfth day we'll have chablis  he has never himself done anything for which to be hated  which of us has  eating spinach nightly increases strength miraculously  a big goat idly ambled through the farmyard  cheap stockings run the first time they're worn  at the base of the rocky hillside they left their horses and climbed on foot  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  the morning dew on the spider web glistened in the sun  calcium makes bones and teeth strong  a woman met a famous author at a literary tea  objects made of pewter are beautiful  you young men get to be my age you won't take flu so lightly  don't do charlie's dirty dishes  the best way to learn is to solve extra problems  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  ralph controlled the stopwatch from the bleachers  why charge money for such garbage  no one material is best for all situations  he swung in through his own wide gateway  this brochure is particularly informative for a prospective buyer  the electron optical system may be either a magnetic or electrostatic one  she wore warm fleecy woolen overalls  the emblem depicts the acropolis all aglow  military personnel are expected to obey government orders  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  milk production may be increased by the antiinfective properties of this drug  his shoulder felt as if it were broken  only lawyers love millionaires  she said sharks have no bones and shrimp swam backward  while he was in custody his wife divorced him  if carol comes tomorrow have her arrange for a meeting at two  check your cafeteria location to make sure it's convenient for most employees  by that time perhaps something better can be done  don't ask me to carry an oily rag like that  daphne's swedish needlepoint scarf matched her skirt  she had your dark suit in greasy wash water all year  this tragic lad had forged his own shackles  even a simple vocabulary contains symbols  highway and freeway mean the same thing  the sound of jennifer's bugle scared the antelope  the fog prevented them from arriving on time  medieval society was based on hierarchies  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  destroy every file related to my audits  there forces are more latent than in electricity and less than in magnetism  they had slapped their thighs  where were you while we were away  bob papered over the living room murals  chief interest in radiosterilization resides in the military services  alice's ability to work without supervision is noteworthy  the cranberry bog gets very pretty in autumn  maybe he will help to turn our fair city into a ghost town  don't ask me to carry an oily rag like that  laugh dance and sing if fortune smiles upon you  she had your dark suit in greasy wash water all year  mom strongly dislikes appetizers  it's healthier to cook without sugar  only those story tellers will remain who can imitate the style of the virtuous  she radiated warmth and good fellowship  tradition requires parental approval for underage marriage  if people were more generous there would be no need for welfare  there are also honest seekers after truth  stake me she said and let me at those dice  barb burned paper and leaves in a big bonfire  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  basketball can be an entertaining sport  how good is your endurance  encourage them to exercise their benefits  december and january are nice months to spend in miami  swing your arm as high as you can  call an ambulance for medical assistance  urethane foam as an insulator is also coming in for a good deal of attention  a boring novel is a superb sleeping pill  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  the drug is also incorporated in supplements  why buy oil when you always use mine  above him slid the evasive shadow of a storm cloud  planned parenthood organizations promote birth control  a muscular abdomen is good for your back  he'd collared one of his generals in public  don't ask me to carry an oily rag like that  the causeway ended abruptly at the shore  she had your dark suit in greasy wash water all year  the carpet cleaners shampooed our oriental rug  heels place emphasis on the long legged silhouette  sonic electronic galvanic  all blank  please take this dirty table cloth to the cleaners for me  those musicians harmonize marvelously  a chosen few will become generals  further research we believe will develop important commercial applications  the small boy put the worm on the hook  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  the fish began to leap frantically on the surface of the small lake  our enemy is also threatening to devour us  why single me out on this permit deal  artificial intelligence is for real  only the most accomplished artists obtain popularity  the emblem depicts the acropolis all aglow  military personnel are expected to obey government orders  don't ask me to carry an oily rag like that  she had your dark suit in greasy wash water all year  my dad did it and he lived to a great age  the rustling problem was by no means solved  his shoulder felt as if it were broken  salesmanship is still necessary but it's a different brand of salesmanship  only lawyers love millionaires  if carol comes tomorrow have her arrange for a meeting at two  weatherproof galoshes are very useful in seattle  young children should avoid exposure to contagious diseases  don't ask me to carry an oily rag like that  a second twitched his shirtsleeve and he felt a brief burn on his upper arm  she had your dark suit in greasy wash water all year  pam gives driving lessons on thursdays  he rubbed his eyes sleepily with one huge paw  eight field guns were captured in position  aluminum silverware can often be flimsy  she slipped and sprained her ankle on the steep slope \"}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQfneNsmlJI0"
      },
      "source": [
        "vocab_list = list(set(vocabs[\"train\"][\"vocab\"][0]) | set(vocabs[\"test\"][\"vocab\"][0]))"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vocab_list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N3xpArTiejdU",
        "outputId": "a1048930-e7c0-43cc-f111-0eba486fefa9"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['f',\n",
              " 'k',\n",
              " 'j',\n",
              " 'e',\n",
              " \"'\",\n",
              " 'w',\n",
              " 'v',\n",
              " 'z',\n",
              " 'l',\n",
              " 'm',\n",
              " 'h',\n",
              " 'y',\n",
              " 's',\n",
              " 'b',\n",
              " 'u',\n",
              " 'g',\n",
              " ' ',\n",
              " 'r',\n",
              " 'd',\n",
              " 't',\n",
              " 'q',\n",
              " 'o',\n",
              " 'a',\n",
              " 'n',\n",
              " 'i',\n",
              " 'c',\n",
              " 'p',\n",
              " 'x']"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_0kRndSvqaKk",
        "outputId": "63538f14-7ff1-4e34-8061-c41219875179"
      },
      "source": [
        "vocab_dict = {v: k for k, v in enumerate(vocab_list)}\n",
        "vocab_dict"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'f': 0,\n",
              " 'k': 1,\n",
              " 'j': 2,\n",
              " 'e': 3,\n",
              " \"'\": 4,\n",
              " 'w': 5,\n",
              " 'v': 6,\n",
              " 'z': 7,\n",
              " 'l': 8,\n",
              " 'm': 9,\n",
              " 'h': 10,\n",
              " 'y': 11,\n",
              " 's': 12,\n",
              " 'b': 13,\n",
              " 'u': 14,\n",
              " 'g': 15,\n",
              " ' ': 16,\n",
              " 'r': 17,\n",
              " 'd': 18,\n",
              " 't': 19,\n",
              " 'q': 20,\n",
              " 'o': 21,\n",
              " 'a': 22,\n",
              " 'n': 23,\n",
              " 'i': 24,\n",
              " 'c': 25,\n",
              " 'p': 26,\n",
              " 'x': 27}"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "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 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",
        "- In English, we need to keep the `'` character to differentiate between words, *e.g.*, `\"it's\"` and `\"its\"` which have very different meanings."
      ]
    },
    {
      "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",
      "metadata": {
        "id": "npbIbBoLgaFX"
      },
      "source": [
        "vocab_dict[\"|\"] = vocab_dict[\" \"]\n",
        "del vocab_dict[\" \"]"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "znF0bNunsjbl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13d41a75-a723-46e4-a98a-b6501b5597eb"
      },
      "source": [
        "vocab_dict[\"[UNK]\"] = len(vocab_dict)\n",
        "vocab_dict[\"[PAD]\"] = len(vocab_dict)\n",
        "len(vocab_dict)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SFPGfet8U5sL"
      },
      "source": [
        "Cool, now our vocabulary is complete and consists of 30 tokens, which means that the linear layer that we will add on top of the pretrained Wav2Vec2 checkpoint will have an output dimension of 30."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vocab_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "juD8yp4zfACv",
        "outputId": "3b9df67a-c513-4f13-e223-312a18fc987a"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'f': 0,\n",
              " 'k': 1,\n",
              " 'j': 2,\n",
              " 'e': 3,\n",
              " \"'\": 4,\n",
              " 'w': 5,\n",
              " 'v': 6,\n",
              " 'z': 7,\n",
              " 'l': 8,\n",
              " 'm': 9,\n",
              " 'h': 10,\n",
              " 'y': 11,\n",
              " 's': 12,\n",
              " 'b': 13,\n",
              " 'u': 14,\n",
              " 'g': 15,\n",
              " 'r': 17,\n",
              " 'd': 18,\n",
              " 't': 19,\n",
              " 'q': 20,\n",
              " 'o': 21,\n",
              " 'a': 22,\n",
              " 'n': 23,\n",
              " 'i': 24,\n",
              " 'c': 25,\n",
              " 'p': 26,\n",
              " 'x': 27,\n",
              " '|': 16,\n",
              " '[UNK]': 28,\n",
              " '[PAD]': 29}"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1CujRgBNVRaD"
      },
      "source": [
        "Let's now save the vocabulary as a json file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ehyUoh9vk191"
      },
      "source": [
        "import json\n",
        "with open('vocab.json', 'w') as vocab_file:\n",
        "    json.dump(vocab_dict, vocab_file)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "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",
      "metadata": {
        "id": "xriFGEWQkO4M"
      },
      "source": [
        "from transformers import Wav2Vec2CTCTokenizer\n",
        "\n",
        "tokenizer = Wav2Vec2CTCTokenizer(\"./vocab.json\", unk_token=\"[UNK]\", pad_token=\"[PAD]\", word_delimiter_token=\"|\")"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C4e_c4kbig2a",
        "outputId": "1e59535c-7f8c-4684-bb75-bf63fd607fad"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PreTrainedTokenizer(name_or_path='', vocab_size=30, model_max_len=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '[UNK]', 'pad_token': '[PAD]'})"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P6L2EVWwIazG"
      },
      "source": [
        "If one wants to re-use the just created tokenizer with the fine-tuned model of this notebook, it is strongly advised to upload the `tokenizer` to the [🤗 Hub](https://huggingface.co/). Let's call the repo to which we will upload the files\n",
        "`\"wav2vec2-base-timit-demo-colab\"`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1mbffBdxIl0M"
      },
      "source": [
        "repo_name = \"wav2vec2-base-timit-demo-google-colab\""
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.save_pretrained(\"Fine-Tune-Model-Wav2vec2-English\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AoSx1FlMTzx2",
        "outputId": "92091b47-62d0-4eab-e1e3-a4f1a6153f5c"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "tokenizer config file saved in Fine-Tune-Model-Wav2vec2-English/tokenizer_config.json\n",
            "Special tokens file saved in Fine-Tune-Model-Wav2vec2-English/special_tokens_map.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('Fine-Tune-Model-Wav2vec2-English/tokenizer_config.json',\n",
              " 'Fine-Tune-Model-Wav2vec2-English/special_tokens_map.json',\n",
              " 'Fine-Tune-Model-Wav2vec2-English/vocab.json',\n",
              " 'Fine-Tune-Model-Wav2vec2-English/added_tokens.json')"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pmpG2ftFIu3B"
      },
      "source": [
        "and upload the tokenizer to the [🤗 Hub](https://huggingface.co/)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "ScBMUz8jIxJi",
        "outputId": "07bd205b-234e-4363-ffe2-d8398a985ed0"
      },
      "source": [
        "tokenizer.push_to_hub(repo_name)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-84-e531bb070692>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepo_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/file_utils.py\u001b[0m in \u001b[0;36mpush_to_hub\u001b[0;34m(self, repo_path_or_name, repo_url, use_temp_dir, commit_message, organization, private, use_auth_token, **model_card_kwargs)\u001b[0m\n\u001b[1;32m   2899\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2900\u001b[0m         \u001b[0;31m# Create or clone the repo. If the repo is already cloned, this just retrieves the path to the repo.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2901\u001b[0;31m         repo = self._create_or_get_repo(\n\u001b[0m\u001b[1;32m   2902\u001b[0m             \u001b[0mrepo_path_or_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrepo_path_or_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2903\u001b[0m             \u001b[0mrepo_url\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrepo_url\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/file_utils.py\u001b[0m in \u001b[0;36m_create_or_get_repo\u001b[0;34m(cls, repo_path_or_name, repo_url, organization, private, use_auth_token)\u001b[0m\n\u001b[1;32m   2983\u001b[0m             \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmakedirs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepo_path_or_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2984\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2985\u001b[0;31m         \u001b[0mrepo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRepository\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepo_path_or_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclone_from\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrepo_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_auth_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_auth_token\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2986\u001b[0m         \u001b[0mrepo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgit_pull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2987\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mrepo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m             \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmoothly_deprecate_use_auth_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhas_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/repository.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, local_dir, clone_from, repo_type, token, git_user, git_email, revision, skip_lfs_files, client)\u001b[0m\n\u001b[1;32m    519\u001b[0m                 \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[Repository] is a valid git repo\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    520\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"If not specifying `clone_from`, you need to pass Repository a valid git clone.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhuggingface_token\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mgit_email\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mgit_user\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: If not specifying `clone_from`, you need to pass Repository a valid git clone."
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KvL12DrNV4cx"
      },
      "source": [
        "Great, you can see the just created repository under `https://huggingface.co/<your-username>/wav2vec2-base-timit-demo-colab`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mYcIiR2FQ96i"
      },
      "source": [
        "### Create 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",
        "Wav2Vec2 was pretrained on the audio data of [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) and LibriVox which both were sampling with 16kHz. Our fine-tuning dataset, [Timit](hhtps://huggingface.co/datasets/timit_asr), was luckily also sampled with 16kHz. If the fine-tuning dataset would have been sampled with a rate lower or higher than 16kHz, we first would have had to up or downsample the speech signal to match the sampling rate of the data used for pretraining. \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KuUbPW7oV-B5"
      },
      "source": [
        "A 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, models should **always** make use of the `attention_mask` to mask padded tokens. However, due to a very specific design choice of `Wav2Vec2`'s \"base\" checkpoint, better results are achieved when using no `attention_mask`. This is **not** recommended for other speech models. For more information, one can take a look at [this](https://github.com/pytorch/fairseq/issues/3227) issue. **Important** If you want to use this notebook to fine-tune [large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60), this parameter should be set to `True`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kAR0-2KLkopp"
      },
      "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=False)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qUETetgqYC3W"
      },
      "source": [
        "Great, Wav2Vec2's feature extraction pipeline is thereby fully defined!\n",
        "\n",
        "To make the usage of 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",
      "source": [
        "feature_extractor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1R6MLJvckBaf",
        "outputId": "1afa371a-adf3-40f2-c338-c14318902d88"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Wav2Vec2FeatureExtractor {\n",
              "  \"do_normalize\": true,\n",
              "  \"feature_extractor_type\": \"Wav2Vec2FeatureExtractor\",\n",
              "  \"feature_size\": 1,\n",
              "  \"padding_side\": \"right\",\n",
              "  \"padding_value\": 0.0,\n",
              "  \"return_attention_mask\": false,\n",
              "  \"sampling_rate\": 16000\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KYZtoW-tlZgl"
      },
      "source": [
        "from transformers import Wav2Vec2Processor\n",
        "\n",
        "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "processor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Uzv6HjPkH6I",
        "outputId": "7ae010c9-36f4-4de5-a7ad-50a5c6513acc"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Wav2Vec2Processor:\n",
              "- feature_extractor: Wav2Vec2FeatureExtractor {\n",
              "  \"do_normalize\": true,\n",
              "  \"feature_extractor_type\": \"Wav2Vec2FeatureExtractor\",\n",
              "  \"feature_size\": 1,\n",
              "  \"padding_side\": \"right\",\n",
              "  \"padding_value\": 0.0,\n",
              "  \"return_attention_mask\": false,\n",
              "  \"sampling_rate\": 16000\n",
              "}\n",
              "\n",
              "- tokenizer: PreTrainedTokenizer(name_or_path='', vocab_size=30, model_max_len=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '[UNK]', 'pad_token': '[PAD]'})"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "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 the transcription. In addition to `'text'`, our datasets include two more column names `'file'` and `'audio'`. `'file'` states the absolute path of the audio file. Let's take a look."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "TTCS7W6XJ9BG",
        "outputId": "3d2b65f8-47aa-427e-ab78-ec739f17cbc3"
      },
      "source": [
        "timit[\"train\"][0][\"file\"]"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/root/.cache/huggingface/datasets/downloads/extracted/404950a46da14eac65eb4e2a8317b1372fb3971d980d91d5d5b221275b1fd7e0/data/TRAIN/DR4/MMDM0/SI681.WAV'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BwxprOw4Nzrl"
      },
      "source": [
        "`Wav2Vec2` expects the input in the format of a 1-dimensional array of 16 kHz. This means that the audio file has to be loaded and resampled.\n",
        "\n",
        " Thankfully, `datasets` does this automatically when calling the column `audio`. Let try it out. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mk9QHuSsN7lf",
        "outputId": "240d4f97-3a56-45c5-c996-69ac739cc101"
      },
      "source": [
        "timit[\"train\"][0][\"audio\"]"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'path': '/root/.cache/huggingface/datasets/downloads/extracted/404950a46da14eac65eb4e2a8317b1372fb3971d980d91d5d5b221275b1fd7e0/data/TRAIN/DR4/MMDM0/SI681.WAV',\n",
              " 'array': array([-2.1362305e-04,  6.1035156e-05,  3.0517578e-05, ...,\n",
              "        -3.0517578e-05, -9.1552734e-05, -6.1035156e-05], dtype=float32),\n",
              " 'sampling_rate': 16000}"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wSBIGEiaKHMn"
      },
      "source": [
        "We can see that the audio file has automatically been loaded. This is thanks to the new [`\"Audio\"` feature](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=audio#datasets.Audio) introduced in `datasets == 4.13.3`, which loads and resamples audio files on-the-fly upon calling.\n",
        "\n",
        "The sampling rate is set to 16kHz which is what `Wav2Vec2` expects as an input."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SOckzFd4Mbzq"
      },
      "source": [
        "Great, 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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 93
        },
        "id": "dueM6U7Ev0OA",
        "outputId": "2e1da726-2019-4fa1-f647-d4cfe45e447f"
      },
      "source": [
        "import IPython.display as ipd\n",
        "import numpy as np\n",
        "import random\n",
        "\n",
        "rand_int = random.randint(0, len(timit[\"train\"]))\n",
        "\n",
        "print(timit[\"train\"][rand_int][\"text\"])\n",
        "ipd.Audio(data=np.asarray(timit[\"train\"][rand_int][\"audio\"][\"array\"]), autoplay=True, rate=16000)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "in developing film many toxic chemicals are used \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
              "\n",
              "                <audio  controls=\"controls\" autoplay=\"autoplay\">\n",
              "                    <source 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\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1MaL9J2dNVtG"
      },
      "source": [
        "It can be heard, that the speakers change along with their speaking rate, accent, etc. Overall, the recordings sound relatively clear though, which is to be expected from a 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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Po2g7YPuRTx",
        "outputId": "913e25ca-4abe-4348-a01a-0b0383659b79"
      },
      "source": [
        "rand_int = random.randint(0, len(timit[\"train\"]))\n",
        "\n",
        "print(\"Target text:\", timit[\"train\"][rand_int][\"text\"])\n",
        "print(\"Input array shape:\", np.asarray(timit[\"train\"][rand_int][\"audio\"][\"array\"]).shape)\n",
        "print(\"Sampling rate:\", timit[\"train\"][rand_int][\"audio\"][\"sampling_rate\"])"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Target text: my desires are simple give me one informative paragraph on the subject \n",
            "Input array shape: (76596,)\n",
            "Sampling rate: 16000\n"
          ]
        }
      ]
    },
    {
      "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 make use of the `map(...)` function.\n",
        "\n",
        "First, we load and resample the audio data, simply by calling `batch[\"audio\"]`.\n",
        "Second, we extract the `input_values` from the loaded audio file. In our case, the `Wav2Vec2Processor` only normalizes the data. For other speech models, however, this step can include more complex feature extraction, such as [Log-Mel feature extraction](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum).\n",
        "Third, we encode the transcriptions to label ids.\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",
      "metadata": {
        "id": "eJY7I0XAwe9p"
      },
      "source": [
        "def prepare_dataset(batch):\n",
        "    audio = batch[\"audio\"]\n",
        "\n",
        "    # batched output is \"un-batched\" to ensure mapping is correct\n",
        "    batch[\"input_values\"] = processor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_values[0]\n",
        "    batch[\"input_length\"] = len(batch[\"input_values\"])\n",
        "    \n",
        "    with processor.as_target_processor():\n",
        "        batch[\"labels\"] = processor(batch[\"text\"]).input_ids\n",
        "    return batch"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hVMZhH4-nP8-"
      },
      "source": [
        "Let's apply the data preparation function to all examples."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-np9xYK-wl8q"
      },
      "source": [
        "timit = timit.map(prepare_dataset, remove_columns=timit.column_names[\"train\"], num_proc=4)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "timit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ioxB_lWunhW9",
        "outputId": "59effda6-aad7-4016-e2a9-143fa1365f6a"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_values', 'input_length', 'labels'],\n",
              "        num_rows: 4620\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input_values', 'input_length', 'labels'],\n",
              "        num_rows: 1680\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "timit[\"train\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IYoAzmcenu44",
        "outputId": "dd028088-7ef5-4dfe-b5cc-b84136cded7f"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  ...],\n",
              " 'input_length': 39936,\n",
              " 'labels': [5,\n",
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              "  16]}"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_MuJSH8nTuQ"
      },
      "source": [
        "**Note**: Currently `datasets` make use of [`torchaudio`](https://pytorch.org/audio/stable/index.html) and [`librosa`](https://librosa.org/doc/latest/index.html) for audio loading and resampling. If you wish to implement your own costumized data loading/sampling, feel free to just make use of the `\"path\"` column instead and disregard the `\"audio\"` column."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M4J0bU1WsvAg"
      },
      "source": [
        "Long input sequences require a lot of memory. Since `Wav2Vec2` is based on `self-attention` the memory requirement scales quadratically with the input length for long input sequences (*cf.* with [this](https://www.reddit.com/r/MachineLearning/comments/genjvb/d_why_is_the_maximum_input_sequence_length_of/) reddit post). For this demo, let's filter all sequences that are longer than 4 seconds out of the training dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "5b5df7c405b44790a47c8cbfe493b8c8",
            "75d4e406435f4f10978a01b382497255",
            "2de65cbd62b54d17999048b49371fd0a",
            "1760f047b26543f39736a46394fc05c5",
            "9e892e4ce94a48c28a4c36b90a27ce6a",
            "996e7553cd3d4702882db54c91ea3ffc",
            "f8eaeedd83c04788b86d98c4e53df99b",
            "2ebd5b316d794183bd2541a0b106270d",
            "75b5e86bad1f4e17afbbc232df26023a",
            "2ab19a36a456411390569c48d4af2385",
            "c7173f8c13324a31bb9d1ca2fb4b0e55"
          ]
        },
        "id": "nqGobEPUvG3v",
        "outputId": "4fff8e08-6b7a-4ff4-8ee5-d6813e5ed8a9"
      },
      "source": [
        "max_input_length_in_sec = 4.0\n",
        "timit[\"train\"] = timit[\"train\"].filter(lambda x: x < max_input_length_in_sec * processor.feature_extractor.sampling_rate, input_columns=[\"input_length\"])"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5b5df7c405b44790a47c8cbfe493b8c8"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "25Genil2v_Br"
      },
      "source": [
        "Awesome, now we are ready to start training!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gYlQkKVoRUos"
      },
      "source": [
        "## Training & Evaluation\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, 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 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 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",
      "metadata": {
        "id": "tborvC9hx88e"
      },
      "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",
        "    \"\"\"\n",
        "\n",
        "    processor: Wav2Vec2Processor\n",
        "    padding: Union[bool, str] = True\n",
        "\n",
        "    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
        "        # split inputs and labels since they have to be of different lenghts and need\n",
        "        # different padding methods\n",
        "        input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n",
        "        label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
        "\n",
        "        batch = self.processor.pad(\n",
        "            input_features,\n",
        "            padding=self.padding,\n",
        "            return_tensors=\"pt\",\n",
        "        )\n",
        "        with self.processor.as_target_processor():\n",
        "            labels_batch = self.processor.pad(\n",
        "                label_features,\n",
        "                padding=self.padding,\n",
        "                return_tensors=\"pt\",\n",
        "            )\n",
        "\n",
        "        # replace padding with -100 to ignore loss correctly\n",
        "        labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n",
        "\n",
        "        batch[\"labels\"] = labels\n",
        "\n",
        "        return batch"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lbQf5GuZyQ4_"
      },
      "source": [
        "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "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",
      "source": [
        "data_collator"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XAVYH7WXq3nq",
        "outputId": "2834ba3d-543c-4f69-8ac7-0ed3bc63ace8"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataCollatorCTCWithPadding(processor=Wav2Vec2Processor:\n",
              "- feature_extractor: Wav2Vec2FeatureExtractor {\n",
              "  \"do_normalize\": true,\n",
              "  \"feature_extractor_type\": \"Wav2Vec2FeatureExtractor\",\n",
              "  \"feature_size\": 1,\n",
              "  \"padding_side\": \"right\",\n",
              "  \"padding_value\": 0.0,\n",
              "  \"return_attention_mask\": false,\n",
              "  \"sampling_rate\": 16000\n",
              "}\n",
              "\n",
              "- tokenizer: PreTrainedTokenizer(name_or_path='', vocab_size=30, model_max_len=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '[UNK]', 'pad_token': '[PAD]'}), padding=True)"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Xsux2gmyXso",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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            "228312b2a2784fbcac3e56d93dfe1a5f",
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            "e8c6daf844734ac39feb680f944c3750",
            "6b2a6ec68f0045d0b511ed5d0934977a",
            "de8fa9f2dfbb4991801016c9b15b43fd",
            "34c8d4e524c441fb9ae3ce0a902480af",
            "13722121bca94b2bb82fd2f820bdaf89",
            "7238f31f92834923afbe10635fa72119",
            "9e2520599da74306a7a77377b8d3f032",
            "12e39e66102447beb3d882ada19b4502"
          ]
        },
        "outputId": "f75e6688-d015-49d5-a8ef-6b5f7c1097ee"
      },
      "source": [
        "wer_metric = load_metric(\"wer\")"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.90k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8e34781efe55481cbb0cea15d6b7a39b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "1XZ-kjweyTy_"
      },
      "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}"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xmgrx4bRwLIH"
      },
      "source": [
        "Now, we can load the pretrained `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*\"."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222,
          "referenced_widgets": [
            "e87c75ec66e0491db3fb1fcc38927051",
            "a354090c70744cd684e072758af7679d",
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            "eadeec8bff8442c49235b0e01c5b7aaf",
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            "cdba57e5c37e49d5b2104034ffd6a4e0",
            "b4b948ed627e452b8c16701ce3b3e64a",
            "aa9064ef4de94b0f91b93d64006b6175",
            "65f84cfab8d44685912dcc621bfb4cc1",
            "ec40bf2c85bb4f26a556e5d9d3a92792",
            "e054ec07ec7c49b8b07f0848550c43d2",
            "74ddecbb561643d2a752023e933265a5",
            "d9b312ea95ea4ffba878d1de113043ff",
            "40bd9d02ce6c4325a4f1b8f4c1110d7f",
            "60c595f5ee9749d39d93a22c0319c739",
            "1259bcf402f1438292bba5dc03ef2d98",
            "a27a9770c0bb48569769db56562774c4",
            "94596a711c5d49c6a335b5e4a2a768f5",
            "48a5937aff264d8eb769471208ecfa89",
            "f6dbba829feb41618ba2c15d9955d86a",
            "5b0b73966343451096d5025b1c128b80",
            "67a0de79c8ee4fb79dc140a7860a1956"
          ]
        },
        "id": "e7cqAWIayn6w",
        "outputId": "a3b66684-e74f-4d66-8eec-a53cfe0d9646"
      },
      "source": [
        "from transformers import Wav2Vec2ForCTC\n",
        "\n",
        "model = Wav2Vec2ForCTC.from_pretrained(\n",
        "    \"facebook/wav2vec2-base\",\n",
        "    ctc_loss_reduction=\"mean\", \n",
        "    pad_token_id=processor.tokenizer.pad_token_id,\n",
        ")"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.80k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e87c75ec66e0491db3fb1fcc38927051"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py:356: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/363M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "74ddecbb561643d2a752023e933265a5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at facebook/wav2vec2-base were not used when initializing Wav2Vec2ForCTC: ['project_hid.weight', 'project_q.weight', 'quantizer.weight_proj.bias', 'quantizer.weight_proj.weight', 'quantizer.codevectors', 'project_q.bias', 'project_hid.bias']\n",
            "- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-base and are newly initialized: ['lm_head.bias', 'lm_head.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1DwR3XLSzGDD"
      },
      "source": [
        "The first component of 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 pretrainind and as stated in the [paper](https://arxiv.org/abs/2006.11477) 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",
      "source": [
        "model.freeze_feature_encoder()"
      ],
      "metadata": {
        "id": "Et_NUAZjWppc"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "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",
        "During training, a checkpoint will be uploaded asynchronously to the hub every 400 training steps. It allows you to also play around with the demo widget even while your model is still training.\n",
        "\n",
        "**Note**: If one does not want to upload the model checkpoints to the hub, simply set `push_to_hub=False`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbeKSV7uzGPP"
      },
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "  output_dir=repo_name,\n",
        "  group_by_length=True,\n",
        "  per_device_train_batch_size=8,\n",
        "  evaluation_strategy=\"steps\",\n",
        "  num_train_epochs=30,\n",
        "  fp16=True,\n",
        "  gradient_checkpointing=True,\n",
        "  save_steps=500,\n",
        "  eval_steps=500,\n",
        "  logging_steps=500,\n",
        "  learning_rate=1e-4,\n",
        "  weight_decay=0.005,\n",
        "  warmup_steps=1000,\n",
        "  save_total_limit=2,\n",
        ")"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "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",
      "metadata": {
        "id": "rY7vBmFCPFgC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb27af66-6ff8-4d5b-cd74-9e050fe34b42"
      },
      "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=timit[\"train\"],\n",
        "    eval_dataset=timit[\"test\"],\n",
        "    tokenizer=processor.feature_extractor,\n",
        ")"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using amp half precision backend\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 90 and 270 minutes depending on the GPU allocated to this notebook. While the trained model yields satisfying results on *Timit*'s test data, it is by no means an optimally fine-tuned model. The purpose of this notebook is to demonstrate how Wav2Vec2's [base](https://huggingface.co/facebook/wav2vec2-base), [large](https://huggingface.co/facebook/wav2vec2-large), and [large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) checkpoints can be fine-tuned on any English 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": "markdown",
      "metadata": {
        "id": "PmY6yqd1zvzU"
      },
      "source": [
        "Depending on what GPU was allocated to your google colab it might be possible that you are seeing an `\"out-of-memory\"` error here. In this case, it's probably best to reduce `per_device_train_batch_size` to 16 or even less and eventually make use of [`gradient_accumulation`](https://huggingface.co/transformers/master/main_classes/trainer.html#trainingarguments)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_UEjJqGsQw24",
        "outputId": "422317f7-2a2d-4fa6-ddad-3b6ed9b89b42"
      },
      "source": [
        "trainer.train()"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the training set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "***** Running training *****\n",
            "  Num examples = 3978\n",
            "  Num Epochs = 30\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 14940\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='14940' max='14940' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [14940/14940 2:01:18, Epoch 30/30]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Wer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>3.485400</td>\n",
              "      <td>1.809138</td>\n",
              "      <td>1.043967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.883700</td>\n",
              "      <td>0.525274</td>\n",
              "      <td>0.516643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.441600</td>\n",
              "      <td>0.469110</td>\n",
              "      <td>0.470195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.314600</td>\n",
              "      <td>0.411869</td>\n",
              "      <td>0.435394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.242100</td>\n",
              "      <td>0.439705</td>\n",
              "      <td>0.405003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.205200</td>\n",
              "      <td>0.419329</td>\n",
              "      <td>0.405968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>0.164300</td>\n",
              "      <td>0.452228</td>\n",
              "      <td>0.391220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.140100</td>\n",
              "      <td>0.409126</td>\n",
              "      <td>0.390738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>0.145200</td>\n",
              "      <td>0.609423</td>\n",
              "      <td>0.460134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.110000</td>\n",
              "      <td>0.455026</td>\n",
              "      <td>0.374406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>0.099400</td>\n",
              "      <td>0.491886</td>\n",
              "      <td>0.362690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>0.090200</td>\n",
              "      <td>0.452926</td>\n",
              "      <td>0.364689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6500</td>\n",
              "      <td>0.083300</td>\n",
              "      <td>0.496041</td>\n",
              "      <td>0.361932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7000</td>\n",
              "      <td>0.073900</td>\n",
              "      <td>0.494408</td>\n",
              "      <td>0.359451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7500</td>\n",
              "      <td>0.066200</td>\n",
              "      <td>0.510382</td>\n",
              "      <td>0.365860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8000</td>\n",
              "      <td>0.061400</td>\n",
              "      <td>0.553986</td>\n",
              "      <td>0.366894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8500</td>\n",
              "      <td>0.058300</td>\n",
              "      <td>0.521811</td>\n",
              "      <td>0.360003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9000</td>\n",
              "      <td>0.054400</td>\n",
              "      <td>0.533753</td>\n",
              "      <td>0.365723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9500</td>\n",
              "      <td>0.047900</td>\n",
              "      <td>0.494559</td>\n",
              "      <td>0.350975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10000</td>\n",
              "      <td>0.042300</td>\n",
              "      <td>0.523815</td>\n",
              "      <td>0.353663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10500</td>\n",
              "      <td>0.040300</td>\n",
              "      <td>0.479494</td>\n",
              "      <td>0.346427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11000</td>\n",
              "      <td>0.035300</td>\n",
              "      <td>0.526291</td>\n",
              "      <td>0.349804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11500</td>\n",
              "      <td>0.031700</td>\n",
              "      <td>0.514158</td>\n",
              "      <td>0.347805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12000</td>\n",
              "      <td>0.032700</td>\n",
              "      <td>0.554085</td>\n",
              "      <td>0.346634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12500</td>\n",
              "      <td>0.026700</td>\n",
              "      <td>0.519511</td>\n",
              "      <td>0.339949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13000</td>\n",
              "      <td>0.023100</td>\n",
              "      <td>0.524011</td>\n",
              "      <td>0.345669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13500</td>\n",
              "      <td>0.023400</td>\n",
              "      <td>0.547113</td>\n",
              "      <td>0.339880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14000</td>\n",
              "      <td>0.022500</td>\n",
              "      <td>0.533622</td>\n",
              "      <td>0.337192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14500</td>\n",
              "      <td>0.021600</td>\n",
              "      <td>0.539717</td>\n",
              "      <td>0.337813</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-500\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-500/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-500/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-500/preprocessor_config.json\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-1000\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-1000/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-1000/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-1000/preprocessor_config.json\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-1500\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-1500/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-1500/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-1500/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-500] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-2000\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-2000/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-2000/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-2000/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-1000] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-2500\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-2500/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-2500/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-2500/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-1500] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-3000\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-3000/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-3000/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-3000/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-2000] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-3500\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-3500/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-3500/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-3500/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-2500] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-4000\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-4000/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-4000/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-4000/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-3000] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-4500\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-4500/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-4500/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-4500/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-3500] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-5000\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-5000/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-5000/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-5000/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-4000] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-5500\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-5500/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-5500/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-5500/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-4500] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-6000\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-6000/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-6000/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-6000/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-5000] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-6500\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-6500/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-6500/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-6500/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-5500] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-7000\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-7000/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-7000/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-7000/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-6000] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-7500\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-7500/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-7500/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-7500/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-6500] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-8000\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-8000/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-8000/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-8000/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-7000] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-8500\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-8500/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-8500/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-8500/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-7500] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-9000\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-9000/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-9000/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-9000/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-8000] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-9500\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-9500/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-9500/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-9500/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-8500] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-10000\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-10000/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-10000/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-10000/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-9000] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-10500\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-10500/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-10500/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-10500/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-9500] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-11000\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-11000/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-11000/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-11000/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-10000] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-11500\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-11500/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-11500/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-11500/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-10500] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-12000\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-12000/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-12000/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-12000/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-11000] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-12500\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-12500/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-12500/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-12500/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-11500] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-13000\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-13000/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-13000/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-13000/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-12000] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-13500\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-13500/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-13500/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-13500/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-12500] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-14000\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-14000/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-14000/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-14000/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-13000] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1680\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-base-timit-demo-google-colab/checkpoint-14500\n",
            "Configuration saved in wav2vec2-base-timit-demo-google-colab/checkpoint-14500/config.json\n",
            "Model weights saved in wav2vec2-base-timit-demo-google-colab/checkpoint-14500/pytorch_model.bin\n",
            "Feature extractor saved in wav2vec2-base-timit-demo-google-colab/checkpoint-14500/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-base-timit-demo-google-colab/checkpoint-13500] due to args.save_total_limit\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=14940, training_loss=0.23710141501113913, metrics={'train_runtime': 7286.8253, 'train_samples_per_second': 16.378, 'train_steps_per_second': 2.05, 'total_flos': 3.0766610595969556e+18, 'train_loss': 0.23710141501113913, 'epoch': 30.0})"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UCyp-v3n4Zlt"
      },
      "source": [
        "The final WER should be around 0.3 which is reasonable given that state-of-the-art phoneme error rates (PER) are just below 0.1 (see [leaderboard](https://paperswithcode.com/sota/speech-recognition-on-timit)) and that WER is usually worse than PER.\n",
        "\n",
        "You can now upload the result of the training to the Hub, just execute this instruction:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.save_model(\"Fine-Tune-Model-Wav2vec2-English\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "asrSnsf1EhEE",
        "outputId": "8ef651cc-0f0b-4122-d843-e8755917323a"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to Fine-Tune-Model-Wav2vec2-English\n",
            "Configuration saved in Fine-Tune-Model-Wav2vec2-English/config.json\n",
            "Model weights saved in Fine-Tune-Model-Wav2vec2-English/pytorch_model.bin\n",
            "Feature extractor saved in Fine-Tune-Model-Wav2vec2-English/preprocessor_config.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r \"/content/drive/MyDrive/Project/Fine-Tune-Model-Wav2vec2-English.zip\" \"/content/Fine-Tune-Model-Wav2vec2-English\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4a75czCPJACQ",
        "outputId": "a541a1e4-5e67-4770-9712-7d151531399e"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: content/Fine-Tune-Model-Wav2vec2-English/ (stored 0%)\n",
            "  adding: content/Fine-Tune-Model-Wav2vec2-English/config.json (deflated 67%)\n",
            "  adding: content/Fine-Tune-Model-Wav2vec2-English/special_tokens_map.json (deflated 39%)\n",
            "  adding: content/Fine-Tune-Model-Wav2vec2-English/vocab.json (deflated 50%)\n",
            "  adding: content/Fine-Tune-Model-Wav2vec2-English/preprocessor_config.json (deflated 33%)\n",
            "  adding: content/Fine-Tune-Model-Wav2vec2-English/pytorch_model.bin (deflated 9%)\n",
            "  adding: content/Fine-Tune-Model-Wav2vec2-English/tokenizer_config.json (deflated 37%)\n",
            "  adding: content/Fine-Tune-Model-Wav2vec2-English/training_args.bin (deflated 48%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "djzwS5WeNu16"
      },
      "source": [
        "You can now share this model with all your friends, family, favorite pets: they can all load it with the identifier \"your-username/the-name-you-picked\" so for instance:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Adm0LngNNxq7"
      },
      "source": [
        "```python\n",
        "from transformers import AutoModelForCTC, Wav2Vec2Processor\n",
        "\n",
        "model = AutoModelForCTC.from_pretrained(\"patrickvonplaten/wav2vec2-base-timit-demo-google-colab\")\n",
        "processor = Wav2Vec2Processor.from_pretrained(\"patrickvonplaten/wav2vec2-base-timit-demo-google-colab\")\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nD4XP5IHncsY"
      },
      "source": [
        "### Evaluate\n",
        "\n",
        "In the final part, we run our model on some of the validation data to get a feeling for how well it works.\n",
        "\n",
        "Let's load the `processor` and `model`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-rg0USDCnbo6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "289cbb4e-11dd-44c6-e198-1f251800eb2d"
      },
      "source": [
        "processor = Wav2Vec2Processor.from_pretrained(\"/content/Fine-Tune-Model-Wav2vec2-English\")"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading feature extractor configuration file /content/Fine-Tune-Model-Wav2vec2-English/preprocessor_config.json\n",
            "Feature extractor Wav2Vec2FeatureExtractor {\n",
            "  \"do_normalize\": true,\n",
            "  \"feature_extractor_type\": \"Wav2Vec2FeatureExtractor\",\n",
            "  \"feature_size\": 1,\n",
            "  \"padding_side\": \"right\",\n",
            "  \"padding_value\": 0.0,\n",
            "  \"return_attention_mask\": false,\n",
            "  \"sampling_rate\": 16000\n",
            "}\n",
            "\n",
            "Didn't find file /content/Fine-Tune-Model-Wav2vec2-English/added_tokens.json. We won't load it.\n",
            "loading file /content/Fine-Tune-Model-Wav2vec2-English/vocab.json\n",
            "loading file /content/Fine-Tune-Model-Wav2vec2-English/tokenizer_config.json\n",
            "loading file None\n",
            "loading file /content/Fine-Tune-Model-Wav2vec2-English/special_tokens_map.json\n",
            "Adding <s> to the vocabulary\n",
            "Adding </s> to the vocabulary\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sSpFW3pDn23P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d5e2499-d9d1-4648-a6f9-c3321f706c51"
      },
      "source": [
        "model = Wav2Vec2ForCTC.from_pretrained(\"/content/Fine-Tune-Model-Wav2vec2-English\").cuda()"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading configuration file /content/Fine-Tune-Model-Wav2vec2-English/config.json\n",
            "Model config Wav2Vec2Config {\n",
            "  \"_name_or_path\": \"facebook/wav2vec2-base\",\n",
            "  \"activation_dropout\": 0.0,\n",
            "  \"adapter_kernel_size\": 3,\n",
            "  \"adapter_stride\": 2,\n",
            "  \"add_adapter\": false,\n",
            "  \"apply_spec_augment\": true,\n",
            "  \"architectures\": [\n",
            "    \"Wav2Vec2ForCTC\"\n",
            "  ],\n",
            "  \"attention_dropout\": 0.1,\n",
            "  \"bos_token_id\": 1,\n",
            "  \"classifier_proj_size\": 256,\n",
            "  \"codevector_dim\": 256,\n",
            "  \"contrastive_logits_temperature\": 0.1,\n",
            "  \"conv_bias\": false,\n",
            "  \"conv_dim\": [\n",
            "    512,\n",
            "    512,\n",
            "    512,\n",
            "    512,\n",
            "    512,\n",
            "    512,\n",
            "    512\n",
            "  ],\n",
            "  \"conv_kernel\": [\n",
            "    10,\n",
            "    3,\n",
            "    3,\n",
            "    3,\n",
            "    3,\n",
            "    2,\n",
            "    2\n",
            "  ],\n",
            "  \"conv_stride\": [\n",
            "    5,\n",
            "    2,\n",
            "    2,\n",
            "    2,\n",
            "    2,\n",
            "    2,\n",
            "    2\n",
            "  ],\n",
            "  \"ctc_loss_reduction\": \"mean\",\n",
            "  \"ctc_zero_infinity\": false,\n",
            "  \"diversity_loss_weight\": 0.1,\n",
            "  \"do_stable_layer_norm\": false,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"feat_extract_activation\": \"gelu\",\n",
            "  \"feat_extract_norm\": \"group\",\n",
            "  \"feat_proj_dropout\": 0.1,\n",
            "  \"feat_quantizer_dropout\": 0.0,\n",
            "  \"final_dropout\": 0.0,\n",
            "  \"freeze_feat_extract_train\": true,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-05,\n",
            "  \"layerdrop\": 0.0,\n",
            "  \"mask_channel_length\": 10,\n",
            "  \"mask_channel_min_space\": 1,\n",
            "  \"mask_channel_other\": 0.0,\n",
            "  \"mask_channel_prob\": 0.0,\n",
            "  \"mask_channel_selection\": \"static\",\n",
            "  \"mask_feature_length\": 10,\n",
            "  \"mask_feature_min_masks\": 0,\n",
            "  \"mask_feature_prob\": 0.0,\n",
            "  \"mask_time_length\": 10,\n",
            "  \"mask_time_min_masks\": 2,\n",
            "  \"mask_time_min_space\": 1,\n",
            "  \"mask_time_other\": 0.0,\n",
            "  \"mask_time_prob\": 0.05,\n",
            "  \"mask_time_selection\": \"static\",\n",
            "  \"model_type\": \"wav2vec2\",\n",
            "  \"no_mask_channel_overlap\": false,\n",
            "  \"no_mask_time_overlap\": false,\n",
            "  \"num_adapter_layers\": 3,\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_codevector_groups\": 2,\n",
            "  \"num_codevectors_per_group\": 320,\n",
            "  \"num_conv_pos_embedding_groups\": 16,\n",
            "  \"num_conv_pos_embeddings\": 128,\n",
            "  \"num_feat_extract_layers\": 7,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"num_negatives\": 100,\n",
            "  \"output_hidden_size\": 768,\n",
            "  \"pad_token_id\": 29,\n",
            "  \"proj_codevector_dim\": 256,\n",
            "  \"tdnn_dilation\": [\n",
            "    1,\n",
            "    2,\n",
            "    3,\n",
            "    1,\n",
            "    1\n",
            "  ],\n",
            "  \"tdnn_dim\": [\n",
            "    512,\n",
            "    512,\n",
            "    512,\n",
            "    512,\n",
            "    1500\n",
            "  ],\n",
            "  \"tdnn_kernel\": [\n",
            "    5,\n",
            "    3,\n",
            "    3,\n",
            "    1,\n",
            "    1\n",
            "  ],\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.17.0\",\n",
            "  \"use_weighted_layer_sum\": false,\n",
            "  \"vocab_size\": 32,\n",
            "  \"xvector_output_dim\": 512\n",
            "}\n",
            "\n",
            "loading weights file /content/Fine-Tune-Model-Wav2vec2-English/pytorch_model.bin\n",
            "All model checkpoint weights were used when initializing Wav2Vec2ForCTC.\n",
            "\n",
            "All the weights of Wav2Vec2ForCTC were initialized from the model checkpoint at /content/Fine-Tune-Model-Wav2vec2-English.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use Wav2Vec2ForCTC for predictions without further training.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load uploaded Model in Hugging Face"
      ],
      "metadata": {
        "id": "zEcnNXLVb7gW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "processor = Wav2Vec2Processor.from_pretrained(\"RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686,
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        "id": "rv4Pz4X0bks9",
        "outputId": "f93707f5-01de-47be-d542-8c0890200144"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
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            "storing https://huggingface.co/RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR/resolve/main/preprocessor_config.json in cache at /root/.cache/huggingface/transformers/0c2e7ee3cf8a061cd4af55768e4827814bdd909f7f6670f18ce87d6193e648b0.0e3e6656f99a6f7b9eddd943463eb7f34363640fce9e87b047ebd50d4b112b50\n",
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            "loading feature extractor configuration file https://huggingface.co/RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR/resolve/main/preprocessor_config.json from cache at /root/.cache/huggingface/transformers/0c2e7ee3cf8a061cd4af55768e4827814bdd909f7f6670f18ce87d6193e648b0.0e3e6656f99a6f7b9eddd943463eb7f34363640fce9e87b047ebd50d4b112b50\n",
            "Feature extractor Wav2Vec2FeatureExtractor {\n",
            "  \"do_normalize\": true,\n",
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            "\n",
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        {
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          "text": [
            "storing https://huggingface.co/RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR/resolve/main/special_tokens_map.json in cache at /root/.cache/huggingface/transformers/ca6bcc8694f8c9e47d6cf639505aa54bfb20394da638e677d916ef9a4bb90650.a21d51735cf8667bcd610f057e88548d5d6a381401f6b4501a8bc6c1a9dc8498\n",
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            "loading file https://huggingface.co/RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR/resolve/main/tokenizer_config.json from cache at /root/.cache/huggingface/transformers/5416f967031aa67b85853ac6177c48cdc0a422183cd0419d3dab71f8c715e7cb.59710b1a6a5501d31e746b6e464f5c44de3e55a58f80634196025936683a68a9\n",
            "loading file https://huggingface.co/RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR/resolve/main/added_tokens.json from cache at None\n",
            "loading file https://huggingface.co/RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR/resolve/main/special_tokens_map.json from cache at /root/.cache/huggingface/transformers/ca6bcc8694f8c9e47d6cf639505aa54bfb20394da638e677d916ef9a4bb90650.a21d51735cf8667bcd610f057e88548d5d6a381401f6b4501a8bc6c1a9dc8498\n",
            "Adding <s> to the vocabulary\n",
            "Adding </s> to the vocabulary\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Wav2Vec2ForCTC.from_pretrained(\"RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR\").cuda()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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          ]
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        "id": "H1hh3ZnYbyI7",
        "outputId": "22354154-21f9-47f1-e307-4a089f660f7b"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "https://huggingface.co/RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR/resolve/main/config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpfip77ngl\n"
          ]
        },
        {
          "output_type": "display_data",
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              "Downloading:   0%|          | 0.00/2.29k [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "storing https://huggingface.co/RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR/resolve/main/config.json in cache at /root/.cache/huggingface/transformers/061185028df5453e113f0375d01ece04a7e337d89b1d86e4f47aab9d9db37d42.3202909be16e9c482257d0c4d35b4f4c2558cea585027c729eed803ba4bf0d8e\n",
            "creating metadata file for /root/.cache/huggingface/transformers/061185028df5453e113f0375d01ece04a7e337d89b1d86e4f47aab9d9db37d42.3202909be16e9c482257d0c4d35b4f4c2558cea585027c729eed803ba4bf0d8e\n",
            "loading configuration file https://huggingface.co/RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/061185028df5453e113f0375d01ece04a7e337d89b1d86e4f47aab9d9db37d42.3202909be16e9c482257d0c4d35b4f4c2558cea585027c729eed803ba4bf0d8e\n",
            "Model config Wav2Vec2Config {\n",
            "  \"_name_or_path\": \"facebook/wav2vec2-base\",\n",
            "  \"activation_dropout\": 0.0,\n",
            "  \"adapter_kernel_size\": 3,\n",
            "  \"adapter_stride\": 2,\n",
            "  \"add_adapter\": false,\n",
            "  \"apply_spec_augment\": true,\n",
            "  \"architectures\": [\n",
            "    \"Wav2Vec2ForCTC\"\n",
            "  ],\n",
            "  \"attention_dropout\": 0.1,\n",
            "  \"bos_token_id\": 1,\n",
            "  \"classifier_proj_size\": 256,\n",
            "  \"codevector_dim\": 256,\n",
            "  \"contrastive_logits_temperature\": 0.1,\n",
            "  \"conv_bias\": false,\n",
            "  \"conv_dim\": [\n",
            "    512,\n",
            "    512,\n",
            "    512,\n",
            "    512,\n",
            "    512,\n",
            "    512,\n",
            "    512\n",
            "  ],\n",
            "  \"conv_kernel\": [\n",
            "    10,\n",
            "    3,\n",
            "    3,\n",
            "    3,\n",
            "    3,\n",
            "    2,\n",
            "    2\n",
            "  ],\n",
            "  \"conv_stride\": [\n",
            "    5,\n",
            "    2,\n",
            "    2,\n",
            "    2,\n",
            "    2,\n",
            "    2,\n",
            "    2\n",
            "  ],\n",
            "  \"ctc_loss_reduction\": \"mean\",\n",
            "  \"ctc_zero_infinity\": false,\n",
            "  \"diversity_loss_weight\": 0.1,\n",
            "  \"do_stable_layer_norm\": false,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"feat_extract_activation\": \"gelu\",\n",
            "  \"feat_extract_norm\": \"group\",\n",
            "  \"feat_proj_dropout\": 0.1,\n",
            "  \"feat_quantizer_dropout\": 0.0,\n",
            "  \"final_dropout\": 0.0,\n",
            "  \"freeze_feat_extract_train\": true,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-05,\n",
            "  \"layerdrop\": 0.0,\n",
            "  \"mask_channel_length\": 10,\n",
            "  \"mask_channel_min_space\": 1,\n",
            "  \"mask_channel_other\": 0.0,\n",
            "  \"mask_channel_prob\": 0.0,\n",
            "  \"mask_channel_selection\": \"static\",\n",
            "  \"mask_feature_length\": 10,\n",
            "  \"mask_feature_min_masks\": 0,\n",
            "  \"mask_feature_prob\": 0.0,\n",
            "  \"mask_time_length\": 10,\n",
            "  \"mask_time_min_masks\": 2,\n",
            "  \"mask_time_min_space\": 1,\n",
            "  \"mask_time_other\": 0.0,\n",
            "  \"mask_time_prob\": 0.05,\n",
            "  \"mask_time_selection\": \"static\",\n",
            "  \"model_type\": \"wav2vec2\",\n",
            "  \"no_mask_channel_overlap\": false,\n",
            "  \"no_mask_time_overlap\": false,\n",
            "  \"num_adapter_layers\": 3,\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_codevector_groups\": 2,\n",
            "  \"num_codevectors_per_group\": 320,\n",
            "  \"num_conv_pos_embedding_groups\": 16,\n",
            "  \"num_conv_pos_embeddings\": 128,\n",
            "  \"num_feat_extract_layers\": 7,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"num_negatives\": 100,\n",
            "  \"output_hidden_size\": 768,\n",
            "  \"pad_token_id\": 29,\n",
            "  \"proj_codevector_dim\": 256,\n",
            "  \"tdnn_dilation\": [\n",
            "    1,\n",
            "    2,\n",
            "    3,\n",
            "    1,\n",
            "    1\n",
            "  ],\n",
            "  \"tdnn_dim\": [\n",
            "    512,\n",
            "    512,\n",
            "    512,\n",
            "    512,\n",
            "    1500\n",
            "  ],\n",
            "  \"tdnn_kernel\": [\n",
            "    5,\n",
            "    3,\n",
            "    3,\n",
            "    1,\n",
            "    1\n",
            "  ],\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.17.0\",\n",
            "  \"use_weighted_layer_sum\": false,\n",
            "  \"vocab_size\": 32,\n",
            "  \"xvector_output_dim\": 512\n",
            "}\n",
            "\n",
            "https://huggingface.co/RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR/resolve/main/pytorch_model.bin not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpzamv4pf7\n"
          ]
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        {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "storing https://huggingface.co/RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR/resolve/main/pytorch_model.bin in cache at /root/.cache/huggingface/transformers/626975727b0e162a6314dfe88515517057c41c95a9aff1fe187796eba71cb736.47328aae5a3c8d2299211d725def87a9a951315dfc48aba7f81507a434bad1e1\n",
            "creating metadata file for /root/.cache/huggingface/transformers/626975727b0e162a6314dfe88515517057c41c95a9aff1fe187796eba71cb736.47328aae5a3c8d2299211d725def87a9a951315dfc48aba7f81507a434bad1e1\n",
            "loading weights file https://huggingface.co/RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/626975727b0e162a6314dfe88515517057c41c95a9aff1fe187796eba71cb736.47328aae5a3c8d2299211d725def87a9a951315dfc48aba7f81507a434bad1e1\n",
            "All model checkpoint weights were used when initializing Wav2Vec2ForCTC.\n",
            "\n",
            "All the weights of Wav2Vec2ForCTC were initialized from the model checkpoint at RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use Wav2Vec2ForCTC for predictions without further training.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TUuZpB0v5jn_"
      },
      "source": [
        "Now, we will make use of the `map(...)` function to predict the transcription of every test sample and to save the prediction in the dataset itself. We will call the resulting dictionary `\"results\"`. \n",
        "\n",
        "**Note**: we evaluate the test data set with `batch_size=1` on purpose due to this [issue](https://github.com/pytorch/fairseq/issues/3227). Since padded inputs don't yield the exact same output as non-padded inputs, a better WER can be achieved by not padding the input at all."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "40naJl53n7jT"
      },
      "source": [
        "def map_to_result(batch):\n",
        "  with torch.no_grad():\n",
        "    input_values = torch.tensor(batch[\"input_values\"], device=\"cuda\").unsqueeze(0)\n",
        "    logits = model(input_values).logits\n",
        "\n",
        "  pred_ids = torch.argmax(logits, dim=-1)\n",
        "  batch[\"pred_str\"] = processor.batch_decode(pred_ids)[0]\n",
        "  batch[\"text\"] = processor.decode(batch[\"labels\"], group_tokens=False)\n",
        "  \n",
        "  return batch"
      ],
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uPqH7gZqrGPi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "ca1d387ecc5f4d109dc4ba3619c02cee",
            "3360122900854f34b5ca03ce7283d417",
            "9949d84d22124eeea42a87b939d0334e",
            "47876f5f4fa048d18ae918979641630b",
            "b56f161cc3ce4d5498e81a02df1bcb46",
            "4dc951df22f14394973d6c8a96561cfb",
            "e5d5c7a400ae4f59a60c5d979764cf97",
            "82ce25463f6b4cb79a9f5153912d8c4f",
            "6cc2e1c50e4a4593b02d92d6a145c733",
            "b314310d78c44cbba1971f2367373829",
            "9c5a34faca4e4b00b29a71509c6715ea"
          ]
        },
        "outputId": "ac71cd37-ab5e-4c52-c0e4-03bc87c9d013"
      },
      "source": [
        "results = timit[\"test\"].map(map_to_result, remove_columns=timit[\"test\"].column_names)"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0ex [00:00, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ca1d387ecc5f4d109dc4ba3619c02cee"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-mqpdB8R6rty"
      },
      "source": [
        "Let's compute the overall WER now."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PmqAb4Isx8OK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4afad32d-7763-42d7-b2c5-057e9065e370"
      },
      "source": [
        "print(\"Test WER: {:.3f}\".format(wer_metric.compute(predictions=results[\"pred_str\"], references=results[\"text\"])))"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test WER: 0.218\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Va94d8Y7Q98"
      },
      "source": [
        "21.8% WER - not bad! Our demo model would have probably made it on the official [leaderboard](https://paperswithcode.com/sota/speech-recognition-on-timit).\n",
        "\n",
        "Let's take a look at some predictions to see what errors are made by the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "odNDiFVRy53w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "7867af01-67fa-48da-868b-8c4a399e807f"
      },
      "source": [
        "show_random_elements(results)"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pred_str</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>young children should avoide exposure to contigious diseases</td>\n",
              "      <td>young children should avoid exposure to contagious diseases</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>art official intelligence is for real</td>\n",
              "      <td>artificial intelligence is for real</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>their propes were two step laddters a chare and a polm fan</td>\n",
              "      <td>their props were two stepladders a chair and a palm fan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>if people were more generous there would be no need for wealfar</td>\n",
              "      <td>if people were more generous there would be no need for welfare</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>the fish began to leap frantically on the surface of the smallakche</td>\n",
              "      <td>the fish began to leap frantically on the surface of the small lake</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>her rite hand aques whenever the barametric pressure changes</td>\n",
              "      <td>her right hand aches whenever the barometric pressure changes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>only lowyers love milianears</td>\n",
              "      <td>only lawyers love millionaires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>the neareust sceniguad may not be within wallkin distance</td>\n",
              "      <td>the nearest synagogue may not be within walking distance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>basketball can be an entertaining spport</td>\n",
              "      <td>basketball can be an entertaining sport</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>she had your dark suit in greasy wash water all year</td>\n",
              "      <td>she had your dark suit in greasy wash water all year</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5HFCujhd9n4N"
      },
      "source": [
        "It becomes clear that the predicted transcriptions are acoustically very similar to the target transcriptions, but often contain spelling or grammatical errors. This shouldn't be very surprising though given that we purely rely on Wav2Vec2 without making use of a language model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a3ydKvUl9FTK"
      },
      "source": [
        "Finally, to better understand how CTC works, it is worth taking a deeper look at the exact output of the model. Let's run the first test sample through the model, take the predicted ids and convert them to their corresponding tokens."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AqaM45t87uM4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "b91bbef4-6509-4ee0-a17d-6960d8bd24a5"
      },
      "source": [
        "model.to(\"cuda\")\n",
        "\n",
        "with torch.no_grad():\n",
        "  logits = model(torch.tensor(timit[\"test\"][:1][\"input_values\"], device=\"cuda\")).logits\n",
        "\n",
        "pred_ids = torch.argmax(logits, dim=-1)\n",
        "\n",
        "# convert ids to tokens\n",
        "\" \".join(processor.tokenizer.convert_ids_to_tokens(pred_ids[0].tolist()))"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] t t h e | | [PAD] b b [PAD] [PAD] u u n n g g [PAD] a l l l [PAD] [PAD] [PAD] o o w | w w [PAD] a s s | | | [PAD] p [PAD] l l [PAD] e e s s s [PAD] e n t t [PAD] l l [PAD] y y | | | s s [PAD] [PAD] i i t t [PAD] [PAD] [PAD] u u u u [PAD] [PAD] [PAD] a a t t t e e e d | | n n e e e a a r r | t t h e | | s s s h h [PAD] o o [PAD] o o r r r [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] |'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tdO8E28g-n5C"
      },
      "source": [
        "The output should make it a bit clearer how CTC works in practice. The model is to some extent invariant to speaking rate since it has learned to either just repeat the same token in case the speech chunk to be classified still corresponds to the same token. This makes CTC a very powerful algorithm for speech recognition since the speech file's transcription is often very much independent of its length.\n",
        "\n",
        "I again advise the reader to take a look at [this](https://distill.pub/2017/ctc) very nice blog post to better understand CTC."
      ]
    }
  ]
}