Fastaudio_Audio_Classification.ipynb

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      "source": [
        "# Fastaudio Audio Classification\n",
        "\n",
        "from the [fastaudio repository](https://github.com/fastaudio/fastaudio)."
      ]
    },
    {
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      "execution_count": null,
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      "source": [
        "!pip install git+https://github.com/fastaudio/fastaudio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M7Uvul-TJ6rd"
      },
      "source": [
        "**COLAB USERS: Before you continue and import the lib, go to the `Runtime` menu and select `Restart Runtime`.**"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SrILnEFRJ6rd"
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      "source": [
        "from fastai.vision.all import *\n",
        "from fastaudio.core.all import *\n",
        "from fastaudio.augment.all import *\n",
        "from fastaudio.ci import skip_if_ci"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hO6U5rytJ6re"
      },
      "source": [
        "## Download and load data-set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8MqIC-SpJ6rf"
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      "outputs": [],
      "source": [
        "#The first time this will download a dataset that is ~650mb\n",
        "path = untar_data(URLs.ESC50, dest=\"ESC50\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rboni1gSJ6rf"
      },
      "source": [
        "The audio files are inside a subfolder `audio/`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "2iyRihIsJ6rg",
        "outputId": "cd316ea3-a797-44db-9f1b-82e015010853"
      },
      "outputs": [],
      "source": [
        "(path/\"audio\").ls()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-tZad1zBJ6ri"
      },
      "source": [
        "And there's another folder `meta/` with some metadata about all the files and the labels"
      ]
    },
    {
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        "outputId": "b089a3df-c271-4ea2-e834-6ec398f056c4"
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      "outputs": [],
      "source": [
        "(path/\"meta\").ls()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TVWaTLWuJ6rj"
      },
      "source": [
        "Opening the metadata file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "scrolled": true
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      "outputs": [],
      "source": [
        "df = pd.read_csv(path/\"meta\"/\"esc50.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pu0tXTLpJ6rj"
      },
      "source": [
        "## Datablock and Dataloader preparation"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tnc574sjJ6rk"
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      "source": [
        "# Helper function to split the data\n",
        "def CrossValidationSplitter(col='fold', fold=1):\n",
        "    \"Split `items` (supposed to be a dataframe) by fold in `col`\"\n",
        "    def _inner(o):\n",
        "        assert isinstance(o, pd.DataFrame), \"ColSplitter only works when your items are a pandas DataFrame\"\n",
        "        col_values = o.iloc[:,col] if isinstance(col, int) else o[col]\n",
        "        valid_idx = (col_values == fold).values.astype('bool')\n",
        "        return IndexSplitter(mask2idxs(valid_idx))(o)\n",
        "    return _inner"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lWkJGmKZJ6rk"
      },
      "source": [
        "Creating the Audio to Spectrogram transform from a predefined config."
      ]
    },
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      "execution_count": null,
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      "source": [
        "cfg = AudioConfig.BasicMelSpectrogram(n_fft=512)\n",
        "a2s = AudioToSpec.from_cfg(cfg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WTfC668wJ6rl"
      },
      "source": [
        "Creating the Datablock"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dG4FGbEgJ6rl"
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      "source": [
        "auds = DataBlock(blocks = (AudioBlock, CategoryBlock),  \n",
        "                 get_x = ColReader(\"filename\", pref=path/\"audio\"), \n",
        "                 splitter = CrossValidationSplitter(fold=1),\n",
        "                 batch_tfms = [a2s],\n",
        "                 get_y = ColReader(\"category\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rPjz2mEQJ6rl"
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      "source": [
        "dbunch = auds.dataloaders(df, bs=64)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "44o0RURfJ6rm"
      },
      "source": [
        "Visualizing one batch of data. Notice that the title of each Spectrogram is the corresponding label."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "outputId": "b086e09b-0496-4c40-83d5-7d51201f479b",
        "scrolled": false
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      "outputs": [],
      "source": [
        "dbunch.show_batch(figsize=(10, 5))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hIVk5JNyJ6rm"
      },
      "source": [
        "## Learner and Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "While creating the learner, we need to indicate that our input spectrograms only have one channel. Besides that, it's the usual vision learner."
      ]
    },
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        "learn = cnn_learner(dbunch, \n",
        "            resnet18,\n",
        "            n_in=1,  # <- This is the only audio specific modification here\n",
        "            loss_func=CrossEntropyLossFlat(),\n",
        "            metrics=[accuracy])"
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      "source": [
        "@skip_if_ci\n",
        "def run_learner():\n",
        "    # epochs are a bit longer due to the chosen melspectrogram settings\n",
        "    learn.fine_tune(10)\n",
        "\n",
        "# We only validate the model when running in CI\n",
        "run_learner()"
      ]
    }
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