ASR

asr.ipynb

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    "colab": {
      "name": "ASR",
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      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/gist/MittalShruti/cc5edd986e461bcfd3633afa438f9b4e/asr.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "DzrgTEIvw-Ph",
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      "source": [
        "def asr_learner(data:DataBunch, loss_func, number_of_classes, window_size=255,**kwargs)->Learner:\n",
        "    model = DeepSpeech(num_classes=number_of_classes, window_size=window_size)\n",
        "\n",
        "    apply_init(model, nn.init.kaiming_normal_)\n",
        "    \n",
        "    w = WerMetric(EPSTOK, hi_vocab); w.__name__ = \"wer\"\n",
        "    c = CerMetric(EPSTOK, hi_vocab); c.__name__ = \"cer\"\n",
        "    metrics = [w, c]\n",
        "    \n",
        "    x, y = db.one_batch()\n",
        "    pred = model(x)\n",
        "    \n",
        "    learn = Learner(data, model, loss_func(pred, *y), callback_fns=ShowGraph, metrics=metrics, **kwargs)\n",
        "    \n",
        "    return learn"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      "cell_type": "code",
      "metadata": {
        "id": "AKHE9oEEyl2l",
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      },
      "source": [
        "\n",
        "blank_id = hi_vocab.numericalize(EPSTOK)[0]\n",
        "num_saidas = len(list(hi_lbls))\n",
        "\n",
        "loss_func = CTC(blank=blank_id, post_reduction=lambda loss: torch.sum(loss))\n",
        "\n",
        "learn = asr_learner(db, loss_func, num_saidas)\n",
        "learn.opt_func = optim.Adam"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "iZruuUwJzWeR",
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      },
      "source": [
        ""
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      "execution_count": 0,
      "outputs": []
    }
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