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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/sanchit-gandhi/notebooks/blob/main/multi_dataset_evaluation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Multi-Dataset Evaluation with 🤗 Transformers and Datasets\n",
        "\n",
        "by: [Sanchit Gandhi](https://huggingface.co/sanchit-gandhi)\n",
        "\n",
        "Automatic Speech Recogntion (ASR) models are measured by their performance on unseen audio data. In this Colab we'll measure the performance of OpenAI's [Whisper model](https://openai.com/blog/whisper/) on **8 ASR datasets** with one script. Using streaming mode, we'll require no more than 20GB of disk space to achieve this."
      ],
      "metadata": {
        "id": "-sRoTHHrVlh5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare Environment\n",
        "\n",
        "Let's begin by installing the packages we'll need to process audio datasets. We require the Unix package `ffmpeg` version 4. We'll also need the Python package `datasets`, as well as some other popular Hugging Face libraries like `transformers` and `evaluate` for our ASR pipeline.\n",
        "\n",
        "*Note*: Do make sure to select a GPU runtime if you haven't already!"
      ],
      "metadata": {
        "id": "ecG9wrKjV1VA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!add-apt-repository -y ppa:jonathonf/ffmpeg-4 && apt update && apt install -y ffmpeg\n",
        "!pip install --quiet datasets transformers evaluate huggingface_hub jiwer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yF4v_-jQpziy",
        "outputId": "3ed33a1e-d8ab-4551-b89e-d317dc0b11f5",
        "collapsed": true
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r0% [Working]\r            \rGet:1 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B]\n",
            "\r0% [Connecting to archive.ubuntu.com (185.125.190.36)] [Connecting to security.\r0% [Connecting to archive.ubuntu.com (185.125.190.36)] [Connecting to security.\r0% [1 InRelease gpgv 3,626 B] [Connecting to archive.ubuntu.com (185.125.190.36\r                                                                               \rGet:2 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n",
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            "\r0% [1 InRelease gpgv 3,626 B] [Waiting for headers] [2 InRelease 14.2 kB/88.7 k\r                                                                               \rHit:4 http://archive.ubuntu.com/ubuntu bionic InRelease\n",
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            "Hit:14 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n",
            "Get:15 http://ppa.launchpad.net/jonathonf/ffmpeg-4/ubuntu bionic InRelease [15.9 kB]\n",
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            "Get:23 http://ppa.launchpad.net/jonathonf/ffmpeg-4/ubuntu bionic/main amd64 Packages [12.5 kB]\n",
            "Fetched 15.3 MB in 3s (5,413 kB/s)\n",
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            "Hit:14 http://ppa.launchpad.net/jonathonf/ffmpeg-4/ubuntu bionic InRelease\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "33 packages can be upgraded. Run 'apt list --upgradable' to see them.\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'apt autoremove' to remove it.\n",
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            "  libavutil56 libcodec2-0.7 liblilv-0-0 libmysofa1 libpocketsphinx3\n",
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            "  libx264-155 libx265-192 libzimg2\n",
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            "  ffmpeg-doc serdi sordi\n",
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            "  | pocketsphinx-hmm-en-tidigits pocketsphinx-lm-en-hub4\n",
            "  | pocketsphinx-lm-zh-hans-gigatdt | pocketsphinx-lm-zh-hant-gigatdt\n",
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            "  libsratom-0-0 libsrt1-gnutls libswresample3 libswscale5 libvidstab1.1\n",
            "  libx264-155 libx265-192 libzimg2\n",
            "The following packages will be upgraded:\n",
            "  ffmpeg\n",
            "1 upgraded, 24 newly installed, 0 to remove and 32 not upgraded.\n",
            "Need to get 13.4 MB of archives.\n",
            "After this operation, 49.3 MB of additional disk space will be used.\n",
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            "Get:6 http://archive.ubuntu.com/ubuntu bionic/universe amd64 liblilv-0-0 amd64 0.24.2~dfsg0-1 [38.0 kB]\n",
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            "Get:8 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libpocketsphinx3 amd64 0.8.0+real5prealpha-1ubuntu2 [122 kB]\n",
            "Get:9 http://ppa.launchpad.net/jonathonf/ffmpeg-4/ubuntu bionic/main amd64 libaom0 amd64 1.0.0.errata1-3~18.04.york0 [1,165 kB]\n",
            "Get:10 http://ppa.launchpad.net/jonathonf/ffmpeg-4/ubuntu bionic/main amd64 libavutil56 amd64 7:4.3.2-0york0~18.04 [295 kB]\n",
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            "Get:18 http://ppa.launchpad.net/jonathonf/ffmpeg-4/ubuntu bionic/main amd64 libpostproc55 amd64 7:4.3.2-0york0~18.04 [65.0 kB]\n",
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            "Get:24 http://ppa.launchpad.net/jonathonf/ffmpeg-4/ubuntu bionic/main amd64 libavresample4 amd64 7:4.3.2-0york0~18.04 [67.3 kB]\n",
            "Get:25 http://ppa.launchpad.net/jonathonf/ffmpeg-4/ubuntu bionic/main amd64 ffmpeg amd64 7:4.3.2-0york0~18.04 [1,556 kB]\n",
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            "(Reading database ... 124015 files and directories currently installed.)\n",
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            "Setting up libpocketsphinx3:amd64 (0.8.0+real5prealpha-1ubuntu2) ...\n",
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            "Setting up libavresample4:amd64 (7:4.3.2-0york0~18.04) ...\n",
            "Setting up libsrt1-gnutls:amd64 (1.4.1-5~18.04.york0) ...\n",
            "Setting up libmysofa1:amd64 (1.0~dfsg0-2~18.04.york0) ...\n",
            "Setting up libvidstab1.1:amd64 (1.1.0-2~18.04.york1) ...\n",
            "Setting up libcodec2-0.7:amd64 (0.7-1) ...\n",
            "Setting up librabbitmq4:amd64 (0.8.0-1ubuntu0.18.04.2) ...\n",
            "Setting up libaom0:amd64 (1.0.0.errata1-3~18.04.york0) ...\n",
            "Setting up libserd-0-0:amd64 (0.28.0~dfsg0-1) ...\n",
            "Setting up libx265-192:amd64 (3.4-0york0~18.04) ...\n",
            "Setting up libswscale5:amd64 (7:4.3.2-0york0~18.04) ...\n",
            "Setting up libswresample3:amd64 (7:4.3.2-0york0~18.04) ...\n",
            "Setting up libsord-0-0:amd64 (0.16.0~dfsg0-1) ...\n",
            "Setting up libsratom-0-0:amd64 (0.6.0~dfsg0-1) ...\n",
            "Setting up libavcodec58:amd64 (7:4.3.2-0york0~18.04) ...\n",
            "Setting up liblilv-0-0 (0.24.2~dfsg0-1) ...\n",
            "Setting up libavformat58:amd64 (7:4.3.2-0york0~18.04) ...\n",
            "Setting up libavfilter7:amd64 (7:4.3.2-0york0~18.04) ...\n",
            "Setting up libavdevice58:amd64 (7:4.3.2-0york0~18.04) ...\n",
            "Setting up ffmpeg (7:4.3.2-0york0~18.04) ...\n",
            "Removing obsolete conffile /etc/ffserver.conf ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.6) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "\u001b[K     |████████████████████████████████| 451 kB 29.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 5.8 MB 62.4 MB/s \n",
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            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We strongly advise you link the notebook to the [Hugging Face Hub](https://huggingface.co). This enables you to login and access \"gated\" datasets on the Hub.\n",
        "\n",
        "Linking the notebook to the Hub is straightforward - it simply requires entering your Hub authentication token when prompted. Find your Hub authentication token [here](https://huggingface.co/settings/tokens):"
      ],
      "metadata": {
        "id": "DqO7h-G3LSE_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import login\n",
        "\n",
        "login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332,
          "referenced_widgets": [
            "4ef5121e6cb2492abd8eef32a9ee0c22",
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        },
        "id": "9E7zQZ0kMtln",
        "outputId": "d557c22d-8a64-4dee-c4aa-63030a0083a5"
      },
      "execution_count": 4,
      "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": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ef5121e6cb2492abd8eef32a9ee0c22"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Datasets\n",
        "\n",
        "Audio datasets are very large. This causes two issues:\n",
        "1. They require a significant amount of *storage* to download\n",
        "2. They take a significant amount of *time* to download and process\n",
        "\n",
        "The storage and time requirements present limitations to most speech researchers. However, both can be solved with 🤗 Datasets.\n",
        "\n",
        "With streaming mode, we can load and prepare samples as we iterate over the dataset. Since the samples are loaded progressively, we can get started with a dataset without waiting for the entire dataset to download. This way, we only have the data when we need it, and not when we don't!\n",
        "\n",
        "Using streaming mode, we'll evaluate the Whisper model on the nine test sets from the[End-to-end Speech Benchmark (ESB)](https://arxiv.org/abs/2210.13352). Typically, this would require **several hundred** GigaBytes worth of storage space to download. With streaming mode, this is all possible on a single Google Colab."
      ],
      "metadata": {
        "id": "Qq53b1A2tRz2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, we'll load the nine test sets from the ESB benchmark in streaming mode:"
      ],
      "metadata": {
        "id": "dSXV-XPXM2wF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "librispeech_clean = load_dataset(\"librispeech_asr\", \"all\", split=\"test.clean\", streaming=True)\n",
        "librispeech_other = load_dataset(\"librispeech_asr\", \"all\", split=\"test.other\", streaming=True)\n",
        "\n",
        "common_voice = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"en\", revision=\"streaming\", split=\"test\", streaming=True, use_auth_token=True)\n",
        "\n",
        "voxpopuli = load_dataset(\"facebook/voxpopuli\", \"en\", split=\"test\", streaming=True)\n",
        "\n",
        "tedlium = load_dataset(\"LIUM/tedlium\", \"release3\", split=\"test\", streaming=True)\n",
        "\n",
        "gigaspeech = load_dataset(\"speechcolab/gigaspeech\", \"xs\", split=\"test\", streaming=True, use_auth_token=True)\n",
        "\n",
        "spgispeech = load_dataset(\"kensho/spgispeech\", \"S\", split=\"test\", streaming=True, use_auth_token=True)\n",
        "\n",
        "earnings22 = load_dataset(\"anton-l/earnings22_baseline_5_gram\", split=\"test\", streaming=True)\n",
        "\n",
        "ami = load_dataset(\"edinburghcstr/ami\", \"ihm\", split=\"test\", streaming=True)"
      ],
      "metadata": {
        "id": "pkyYctmItQTF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674,
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      "source": [
        "Next, we create a dictionary of dataset names and dataset objects. This gives us an easy lookup table in our evaluation loop."
      ],
      "metadata": {
        "id": "109nsEXFF-sd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "esb_datasets = {\n",
        "    \"LibriSpeech Clean\": librispeech_clean,\n",
        "    \"LibriSpeech Other\": librispeech_other,\n",
        "    \"Common Voice\": common_voice,\n",
        "    \"VoxPopuli\": voxpopuli,\n",
        "    \"TEDLIUM\": tedlium,\n",
        "    \"GigaSpeech\": gigaspeech,\n",
        "    \"SPGISpeech\": spgispeech,\n",
        "    \"Earnings-22\": earnings22,\n",
        "    \"AMI\": ami\n",
        "}"
      ],
      "metadata": {
        "id": "BmcVXn4amR69"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We then define a 'helper function' that gets the correct transcription column from our dataset. We'll use this function to automatically get the right column names when we perform multi-dataset evaluation:"
      ],
      "metadata": {
        "id": "CSrpJNhUI9mI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_text(sample):\n",
        "    if \"text\" in sample:\n",
        "        return sample[\"text\"]\n",
        "    elif \"sentence\" in sample:\n",
        "        return sample[\"sentence\"]\n",
        "    elif \"normalized_text\" in sample:\n",
        "        return sample[\"normalized_text\"]\n",
        "    elif \"transcript\" in sample:\n",
        "        return sample[\"transcript\"]\n",
        "    else:\n",
        "        raise ValueError(f\"Sample: {sample.keys()} has no transcript.\")"
      ],
      "metadata": {
        "id": "FjBVrFve1iXF"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Whisper Model\n",
        "\n",
        "We'll create an ASR evaluation pipeline using 🤗 Transformers [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines) method. `pipeline` will take care of the data pre-processing and the text generation. All we have to do is pass the audio inputs to pipeline and assess the returned predictions against the reference transcriptions.\n",
        "\n",
        "We'll evaluate the official OpenAI [Whisper tiny.en](https://huggingface.co/openai/whisper-tiny.en) checkpoint. Let's load it with `pipeline`:"
      ],
      "metadata": {
        "id": "CK0k8vH4Mkf8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "whisper_asr = pipeline(\n",
        "    \"automatic-speech-recognition\", model=\"openai/whisper-tiny.en\", device=0\n",
        ")"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "## Load the Word Error Rate metric\n",
        "\n",
        "We'll assess our system using the [Word Error Rate (WER)](https://huggingface.co/spaces/evaluate-metric/wer) metric, the 'de-facto' metric for assessing ASR systems. We'll load the WER metric from the 🤗 Evaluate library:"
      ],
      "metadata": {
        "id": "9Qari1Lu0Dxl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "\n",
        "wer_metric = evaluate.load(\"wer\")"
      ],
      "metadata": {
        "id": "XXEe44U1z7Te",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
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        "outputId": "bfca731a-caf4-4474-c55d-addc78d355a2"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.49k [00:00<?, ?B/s]"
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    {
      "cell_type": "markdown",
      "source": [
        "Bonus: You can also try other evaluation methods like the [Character Error Rate (CER)](https://huggingface.co/spaces/evaluate-metric/cer). For the CER, update the above statement to `evaluate.load(\"cer\")`"
      ],
      "metadata": {
        "id": "ia6-sRSClqqh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Normalisation\n",
        "\n",
        "The [Whisper paper](https://cdn.openai.com/papers/whisper.pdf) demonstrates the drastic effect that normalising the text outputs have on WER. The normalisation step is important as it removes errors unrelated to the speech recognition task, such as casing and punctuation. It also makes the formatting consistent between references and predictions by converting spelled out numbers to symbollic form (e.g. \"two\" -> \"2\") and British spellings to American (e.g. \"grey\" -> \"gray\").\n",
        "\n",
        "We first write a function to normalise the reference of a single sample according to the Whisper English text normaliser:"
      ],
      "metadata": {
        "id": "63i2XhSSr3xk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "whisper_norm = whisper_asr.tokenizer._normalize\n",
        "\n",
        "def normalise(batch):\n",
        "    batch[\"norm_text\"] = whisper_norm(get_text(batch))\n",
        "    return batch"
      ],
      "metadata": {
        "id": "TZ83oC4ZihEq"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We'll apply this function to our data using 🤗 Datasets' [`.map`](https://huggingface.co/docs/datasets/process#map) method in our evaluation pipeline.\n",
        "\n",
        "We also need to remove any empty reference transcriptions from our dataset, as these will give a divide by 0 error in the WER calculation.\n",
        "\n",
        "We write a function that indicates which samples to keep, and which to discard. This function, `is_target_text_in_range`, returns a boolean: reference transcriptions that are not empty return True, and those are empty return False:"
      ],
      "metadata": {
        "id": "cx4jbs1Epv4k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "filter_sequences = [\"ignore time segment in scoring\", \"\"]\n",
        "\n",
        "def is_target_text_in_range(ref):\n",
        "    ref = ref.strip()\n",
        "    return ref not in filter_sequences"
      ],
      "metadata": {
        "id": "p_IohuslpiTi"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Again, we'll apply this function to our data using 🤗 Datasets' [`.map`](https://huggingface.co/docs/datasets/process#map) method in our evaluation pipeline."
      ],
      "metadata": {
        "id": "HJhrDucxQfYX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Multi-Dataset Evaluation\n",
        "\n",
        "In this final section, we combine everything together to form the multi-dataset evaluation loop for the Whisper model.\n",
        "\n",
        "First, we define a generator that iterates over the dataset and yields the audio samples and reference text ready for our model:"
      ],
      "metadata": {
        "id": "R7Ncs3EsPe7Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def data(dataset):\n",
        "    for i, item in enumerate(dataset):\n",
        "        yield {**item[\"audio\"], \"reference\": item[\"norm_text\"]}"
      ],
      "metadata": {
        "id": "_rQ8s45sNYfu"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We then loop over the ESB datasets and compute the individual WER scores, combining the single-dataset evaluation steps into one loop. We store the WER results in a separate list to display later.\n",
        "\n",
        "We only evaluate on the first 128 samples for each dataset to demonstrate how this script can be used for multi-dataset evluation with streaming mode. If you want to evaluate on the entire dataset, comment out or remove this line."
      ],
      "metadata": {
        "id": "rmaJ0TDPE0wg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Audio\n",
        "\n",
        "# set the batch size in accordance to your device\n",
        "BATCH_SIZE = 16\n",
        "wer_results = []\n",
        "\n",
        "# loop over all the datasets in the ESB benchmark\n",
        "for dataset_name, dataset in esb_datasets.items():\n",
        "    # only for debugging, restricts the number of rows to numeric value in brackets\n",
        "    dataset = dataset.take(128)\n",
        "\n",
        "    # resample to 16kHz\n",
        "    dataset = dataset.cast_column(\"audio\", Audio(sampling_rate=16000))\n",
        "\n",
        "    # normalise references\n",
        "    dataset = dataset.map(normalise)\n",
        "\n",
        "    # remove any empty references\n",
        "    dataset = dataset.filter(is_target_text_in_range, input_columns=[\"norm_text\"])\n",
        "\n",
        "    # placeholders for predictions and references\n",
        "    predictions = []\n",
        "    references = []\n",
        "\n",
        "    # run streamed inference\n",
        "    for out in whisper_asr(data(dataset), batch_size=BATCH_SIZE):\n",
        "        predictions.append(whisper_norm(out[\"text\"]))\n",
        "        references.append(out[\"reference\"][0])\n",
        "\n",
        "    # compute the WER\n",
        "    wer = wer_metric.compute(references=references, predictions=predictions)\n",
        "    wer = round(100 * wer, 2)\n",
        "\n",
        "    wer_results.append(wer)"
      ],
      "metadata": {
        "id": "8qLWCi_MoVrv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acb3cd49-09a6-40df-df95-bbc9251c80ab"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/transformers/generation/utils.py:1387: UserWarning: Neither `max_length` nor `max_new_tokens` has been set, `max_length` will default to 448 (`self.config.max_length`). Controlling `max_length` via the config is deprecated and `max_length` will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "Reading metadata...: 16354it [00:00, 34017.57it/s]\n",
            "/usr/local/lib/python3.8/dist-packages/transformers/generation/utils.py:1387: UserWarning: Neither `max_length` nor `max_new_tokens` has been set, `max_length` will default to 448 (`self.config.max_length`). Controlling `max_length` via the config is deprecated and `max_length` will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Alright! In one code cell we managed to evaluate over nine different test sets! Let's print the results in tabular form:"
      ],
      "metadata": {
        "id": "flGyTQVUE-qg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.DataFrame({\"Dataset\": esb_datasets.keys(), \"WER\": wer_results})\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "2pw9sz3Xujdj",
        "outputId": "eae533d1-f0d9-42b6-94a4-4fdbefb06fac"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Dataset    WER\n",
              "0  LibriSpeech Clean   4.73\n",
              "1  LibriSpeech Other  16.17\n",
              "2       Common Voice  63.27\n",
              "3          VoxPopuli  10.22\n",
              "4            TEDLIUM   5.16\n",
              "5         GigaSpeech  10.62\n",
              "6         SPGISpeech   6.67\n",
              "7        Earnings-22  22.05\n",
              "8                AMI  24.93"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dataset</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
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              "      <td>16.17</td>\n",
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              "      <td>63.27</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>VoxPopuli</td>\n",
              "      <td>10.22</td>\n",
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              "      <th>5</th>\n",
              "      <td>GigaSpeech</td>\n",
              "      <td>10.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>SPGISpeech</td>\n",
              "      <td>6.67</td>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We ran the above evaluation script for the Whisper \"tiny.en\" and \"small.en\" checkpoints on the full datasets from the ESB benchmark. The results of the run were as follows:\n",
        "\n",
        "| **Dataset name**  | **Whisper tiny.en** | **Whisper small.en** |\n",
        "|-------------------|---------------------|----------------------|\n",
        "| LibriSpeech Clean | 5.66                | 3.05                 |\n",
        "| LibriSpeech Other | 15.38               | 7.53                 |\n",
        "| Common Voice      | 31.17               | 15.20                |\n",
        "| VoxPopuli         | 12.58               | 8.45                 |\n",
        "| TEDLIUM           | 14.28               | 12.21                |\n",
        "| GigaSpeech        | 14.07               | 11.36                |\n",
        "| SPGISpeech        | 5.82                | 3.63                 |\n",
        "| Earnings-22       | 13.79               | 16.40                |\n",
        "| AMI               | 24.68               | 17.88                |"
      ],
      "metadata": {
        "id": "DQrte8nKPeZJ"
      }
    }
  ]
}