Custom_pipeline_for_data_preparation_and_CNN_training.ipynb

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      "cell_type": "markdown",
      "source": [
        "#Preparing a custom pipeline for data preparation and CNN training.\n",
        "\n",
        "For the dataset:\n",
        "\n",
        "Use the TinySOL dataset: https://zenodo.org/records/3685367.\n",
        "\n",
        "You can download this by using the following commands:\n",
        "\n",
        "```\n",
        " user:~$ wget https://zenodo.org/records/3685367/files/TinySOL.tar.gz\n",
        " user:~$ wget https://zenodo.org/records/3685367/files/TinySOL_metadata.csv\n",
        " user:~$ unzip TinySOL.tar.gz && rm TinySOL.tar.gz\n",
        "```\n",
        "\n"
      ],
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        "id": "WVMgAQuj0roK"
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      "execution_count": null,
      "metadata": {
        "id": "P7SnVvfpLG1g"
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      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torchaudio\n",
        "from torchaudio.transforms import MelSpectrogram\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class SimpleCNN(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(SimpleCNN, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
        "        self.fc1 = nn.Linear(16 * 125 * 125, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv1(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.pool(x)\n",
        "        x = x.view(x.size(0), -1)\n",
        "        x = self.fc1(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "jgtUDf5MLYjI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import torchaudio\n",
        "from torchaudio.transforms import MelSpectrogram\n",
        "\n",
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, csv_path, audio_folder):\n",
        "        self.df = pd.read_csv(csv_path)\n",
        "        self.audio_folder = audio_folder\n",
        "        self.mel_transform = MelSpectrogram(sample_rate=22050, n_fft=400, hop_length=160, n_mels=128)\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.df)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        file_name = self.df.iloc[index]['Path']\n",
        "        audio_path = os.path.join(self.audio_folder, file_name)\n",
        "\n",
        "        waveform, sample_rate = torchaudio.load(audio_path, normalize=True)\n",
        "\n",
        "        mel_spectrogram = self.mel_transform(waveform)\n",
        "\n",
        "        ground_truth = None\n",
        "        pseudo_label = None\n",
        "\n",
        "        return {\n",
        "            'file': file_name,\n",
        "            'audio': waveform,\n",
        "            'mel': mel_spectrogram,\n",
        "            'gt': ground_truth,\n",
        "            'pseudo': pseudo_label\n",
        "        }\n"
      ],
      "metadata": {
        "id": "mRcjEzqcLhvH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('TinySOL_metadata.csv')\n",
        "print(df.columns)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZaFDdSXMB_GR",
        "outputId": "f3046f9d-0113-4a4b-866d-5aebf436acad"
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      "outputs": [
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          "name": "stdout",
          "text": [
            "Index(['Path', 'Fold', 'Family', 'Instrument (abbr.)', 'Instrument (in full)',\n",
            "       'Technique (abbr.)', 'Technique (in full)', 'Pitch', 'Pitch ID',\n",
            "       'Dynamics', 'Dynamics ID', 'Instance ID', 'String ID (if applicable)',\n",
            "       'Needed digital retuning'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n"
      ],
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              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
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            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36m_upload_files\u001b[0;34m(multiple)\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m   \u001b[0;32mwhile\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'action'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'complete'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m     result = _output.eval_js(\n\u001b[0m\u001b[1;32m    165\u001b[0m         'google.colab._files._uploadFilesContinue(\"{output_id}\")'.format(\n\u001b[1;32m    166\u001b[0m             \u001b[0moutput_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_id\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/google/colab/output/_js.py\u001b[0m in \u001b[0;36meval_js\u001b[0;34m(script, ignore_result, timeout_sec)\u001b[0m\n\u001b[1;32m     38\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mignore_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0m_message\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\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     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_next_input_message\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     95\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_NOT_READY\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\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;32m---> 96\u001b[0;31m       \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.025\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     97\u001b[0m       \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m     if (\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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    {
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "import io\n",
        "\n",
        "zip_filename = 'TinySQL.zip'\n",
        "\n",
        "with zipfile.ZipFile(zip_filename, 'r') as zip_ref:\n",
        "    zip_ref.extractall('TinySQL')\n"
      ],
      "metadata": {
        "id": "4b5IW3pTEoDz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = CustomDataset(csv_path='TinySOL_metadata.csv', audio_folder='TinySOL/audio')\n",
        "pseudo_dataset = CustomDataset(csv_path='TinySOL_metadata.csv', audio_folder='TinySOL/audio')\n",
        "\n",
        "train_dataset, val_dataset = train_test_split(train_dataset, test_size=0.2, random_state=42)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)\n",
        "pseudo_loader = DataLoader(pseudo_dataset, batch_size=32, shuffle=True)\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "Vn3CfTayz0cl",
        "outputId": "7d0f5f90-5f63-4270-b879-c52f3857bd10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchaudio/functional/functional.py:584: UserWarning: At least one mel filterbank has all zero values. The value for `n_mels` (128) may be set too high. Or, the value for `n_freqs` (201) may be set too low.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchaudio/functional/functional.py:584: UserWarning: At least one mel filterbank has all zero values. The value for `n_mels` (128) may be set too high. Or, the value for `n_freqs` (201) may be set too low.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "Failed to open the input \"TinySOL/audio/Winds/Sax_Alto/ordinario/ASax-ord-D#4-pp-N-N.wav\" (No such file or directory).",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-8c16c2f24ade>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Split dataset into train and validation sets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\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      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# Create data loaders\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/sklearn/model_selection/_split.py\u001b[0m in \u001b[0;36mtrain_test_split\u001b[0;34m(test_size, train_size, random_state, shuffle, stratify, *arrays)\u001b[0m\n\u001b[1;32m   2583\u001b[0m         \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstratify\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   2584\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2585\u001b[0;31m     return list(\n\u001b[0m\u001b[1;32m   2586\u001b[0m         chain.from_iterable(\n\u001b[1;32m   2587\u001b[0m             \u001b[0;34m(\u001b[0m\u001b[0m_safe_indexing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_safe_indexing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\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/sklearn/model_selection/_split.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   2585\u001b[0m     return list(\n\u001b[1;32m   2586\u001b[0m         chain.from_iterable(\n\u001b[0;32m-> 2587\u001b[0;31m             \u001b[0;34m(\u001b[0m\u001b[0m_safe_indexing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_safe_indexing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2588\u001b[0m         )\n\u001b[1;32m   2589\u001b[0m     )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/__init__.py\u001b[0m in \u001b[0;36m_safe_indexing\u001b[0;34m(X, indices, axis)\u001b[0m\n\u001b[1;32m    356\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_array_indexing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices_dtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\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--> 358\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_list_indexing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices_dtype\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    359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    360\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/__init__.py\u001b[0m in \u001b[0;36m_list_indexing\u001b[0;34m(X, key, key_dtype)\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\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    211\u001b[0m     \u001b[0;31m# key is a integer array-like of key\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkey\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    213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/__init__.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\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    211\u001b[0m     \u001b[0;31m# key is a integer array-like of key\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkey\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    213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-11-34d12ad10c9c>\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;31m# Load audio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m         \u001b[0mwaveform\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_rate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorchaudio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maudio_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0;31m# Extract Mel spectrogram\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/torchaudio/_backend/utils.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(uri, frame_offset, num_frames, normalize, channels_first, format, buffer_size, backend)\u001b[0m\n\u001b[1;32m    201\u001b[0m         \"\"\"\n\u001b[1;32m    202\u001b[0m         \u001b[0mbackend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdispatcher\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muri\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 203\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muri\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframe_offset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_frames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchannels_first\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer_size\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    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mload\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/torchaudio/_backend/ffmpeg.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(uri, frame_offset, num_frames, normalize, channels_first, format, buffer_size)\u001b[0m\n\u001b[1;32m    332\u001b[0m             )\n\u001b[1;32m    333\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--> 334\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mload_audio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormpath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muri\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframe_offset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_frames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchannels_first\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\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    335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\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/torchaudio/_backend/ffmpeg.py\u001b[0m in \u001b[0;36mload_audio\u001b[0;34m(src, frame_offset, num_frames, convert, channels_first, format)\u001b[0m\n\u001b[1;32m     98\u001b[0m ) -> Tuple[torch.Tensor, int]:\n\u001b[1;32m     99\u001b[0m     \u001b[0mfilter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_load_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe_offset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_frames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtorchaudio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompat_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchannels_first\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    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_ops.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    690\u001b[0m         \u001b[0;31m# We save the function ptr as the `op` attribute on\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    691\u001b[0m         \u001b[0;31m# OpOverloadPacket to access it here.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 692\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_op\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;32mor\u001b[0m \u001b[0;34m{\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[0m\u001b[1;32m    693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    694\u001b[0m     \u001b[0;31m# TODO: use this to make a __dir__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Failed to open the input \"TinySOL/audio/Winds/Sax_Alto/ordinario/ASax-ord-D#4-pp-N-N.wav\" (No such file or directory)."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = SimpleCNN()\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "train_dataset, val_dataset = train_test_split(dataset, test_size=0.2, random_state=42)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)"
      ],
      "metadata": {
        "id": "qcCgnsu2Lpzh",
        "outputId": "23a3ba52-342e-4f7f-c182-8ec1a9b5e218",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'dataset' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-07ecab34b0c9>\u001b[0m in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Split dataset into train and validation sets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\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     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# Create data loaders\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 30\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    for batch in train_loader:\n",
        "        inputs, labels = batch['mel'], batch['gt']\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "torch.save(model.state_dict(), 'correct_model.pth')\n",
        "\n",
        "model.load_state_dict(torch.load('correct_model.pth'))\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)"
      ],
      "metadata": {
        "id": "ypu2Qflj_UAw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    for batch in pseudo_loader:\n",
        "        inputs, pseudo_labels = batch['mel'], batch['pseudo']\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(inputs)\n",
        "        loss = criterion(outputs, pseudo_labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "torch.save(model.state_dict(), 'pseudo_model.pth')"
      ],
      "metadata": {
        "id": "94EJnK_s_k39"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.load_state_dict(torch.load('correct_model.pth'))\n",
        "model.eval()\n",
        "correct_labels, predictions = [], []\n",
        "with torch.no_grad():\n",
        "    for batch in val_loader:\n",
        "        inputs, labels = batch['mel'], batch['gt']\n",
        "        outputs = model(inputs)\n",
        "        _, preds = torch.max(outputs, 1)\n",
        "        correct_labels.extend(labels.numpy())\n",
        "        predictions.extend(preds.numpy())\n",
        "\n",
        "accuracy = accuracy_score(correct_labels, predictions)\n",
        "conf_matrix = confusion_matrix(correct_labels, predictions)\n",
        "\n",
        "print(f'Accuracy with correct labels: {accuracy}')\n",
        "print(f'Confusion Matrix:\\n{conf_matrix}')"
      ],
      "metadata": {
        "id": "uIjIDJoB_krR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.load_state_dict(torch.load('pseudo_model.pth'))\n",
        "model.eval()\n",
        "pseudo_labels, pseudo_predictions = [], []\n",
        "with torch.no_grad():\n",
        "    for batch in val_loader:\n",
        "        inputs, labels = batch['mel'], batch['gt']\n",
        "        outputs = model(inputs)\n",
        "        _, preds = torch.max(outputs, 1)\n",
        "        pseudo_labels.extend(labels.numpy())\n",
        "        pseudo_predictions.extend(preds.numpy())\n",
        "\n",
        "pseudo_accuracy = accuracy_score(pseudo_labels, pseudo_predictions)\n",
        "pseudo_conf_matrix = confusion_matrix(pseudo_labels, pseudo_predictions)\n",
        "\n",
        "print(f'Accuracy with pseudo labels: {pseudo_accuracy}')\n",
        "print(f'Confusion Matrix:\\n{pseudo_conf_matrix}')"
      ],
      "metadata": {
        "id": "HoiMUcp-_khW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "outputId": "18c49c50-4b42-43b1-c285-e91833d2c759"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'model' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-f796863565eb>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pseudo_model.pth'\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[0m\u001b[1;32m      2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\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      3\u001b[0m \u001b[0mpseudo_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpseudo_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\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      4\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\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      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval_loader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
          ]
        }
      ]
    }
  ]
}