mel_spectrogram_cnn.ipynb

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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/dusarp/dance-bits-experiments/blob/main/mel_spectrogram_cnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "execution_count": null,
      "metadata": {
        "id": "SlOjECyVtgT2"
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      "source": [
        "# Imports\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
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        "id": "w6OYvbAvJib1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#mount Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_TP5rjGl76TY",
        "outputId": "6802a36c-8857-4f1e-eb47-47dd06ebeb3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
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      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
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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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              "// See the License for the specific language governing permissions and\n",
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          "metadata": {}
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving real-studio-vid1.npy to real-studio-vid1.npy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the video file\n",
        "file_path = \"/content/real-studio-vid1.npy\"\n",
        "data = np.load(file_path)"
      ],
      "metadata": {
        "id": "XH6fscaI8BCf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the data or check its shape, etc.\n",
        "print(data)\n",
        "print(data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o9GJl604JatW",
        "outputId": "1ec91712-6758-4a6c-e905-8f41ee998914"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "[[-24.857124  -15.146767  -12.073616  ... -13.662367  -11.894629\n",
            "  -19.208868 ]\n",
            " [-21.532417  -12.769215   -8.280472  ...  -7.190918   -5.780098\n",
            "  -10.4868355]\n",
            " [-20.105932  -15.604622  -15.66144   ... -15.08651   -13.744595\n",
            "  -13.981142 ]\n",
            " ...\n",
            " [-36.756767  -36.76602   -38.58355   ... -39.628185  -41.021534\n",
            "  -45.92129  ]\n",
            " [-44.233425  -40.104527  -42.53934   ... -43.948376  -45.736164\n",
            "  -50.73719  ]\n",
            " [-53.981346  -50.331394  -54.661453  ... -54.401543  -56.282005\n",
            "  -58.12305  ]]\n",
            "(128, 2801)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Normalize the spectrogram to the range [0, 1]\n",
        "mel_spec_min = data.min()\n",
        "mel_spec_max = data.max()\n",
        "mel_spec_normalized = (data - mel_spec_min) / (mel_spec_max - mel_spec_min)\n",
        "\n",
        "# Step 2: Scale the normalized values to the range [-0.5, 0.5]\n",
        "mel_spec_scaled = mel_spec_normalized - 0.5"
      ],
      "metadata": {
        "id": "oyIsv9CpJ5wr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(mel_spec_scaled)\n",
        "print(mel_spec_scaled.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bEiP19nxKFvj",
        "outputId": "2f2d9093-3372-4453-f2cb-f608530f16f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.18928593  0.31066543  0.3490798  ...  0.3292204   0.3513171\n",
            "   0.25988913]\n",
            " [ 0.2308448   0.34038484  0.3964941  ...  0.4101135   0.4277488\n",
            "   0.3689146 ]\n",
            " [ 0.24867582  0.30494225  0.304232   ...  0.31141865  0.32819253\n",
            "   0.32523572]\n",
            " ...\n",
            " [ 0.0405404   0.0404247   0.01770562 ...  0.00464767 -0.01276916\n",
            "  -0.07401615]\n",
            " [-0.05291781 -0.00130659 -0.03174177 ... -0.0493547  -0.07170206\n",
            "  -0.13421488]\n",
            " [-0.17476684 -0.12914243 -0.18326816 ... -0.18001929 -0.20352507\n",
            "  -0.22653812]]\n",
            "(128, 2801)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#unmoount Google Drive\n",
        "#drive.flush_and_unmount()"
      ],
      "metadata": {
        "id": "l8qe7kql8Bla"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MelSpectrogramCNN(nn.Module):\n",
        "    def __init__(self, num_classes=10):\n",
        "        super(MelSpectrogramCNN, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1)\n",
        "        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)\n",
        "        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)\n",
        "        self.fc1 = nn.Linear(64 * 32 * 32, 128)\n",
        "        self.fc2 = nn.Linear(128, num_classes)\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.dropout = nn.Dropout(0.25)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        x = self.pool(F.relu(self.conv2(x)))\n",
        "        x = self.pool(F.relu(self.conv3(x)))\n",
        "        x = x.view(-1, 64 * 32 * 32)  # Flatten the tensor\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = self.dropout(x)\n",
        "        x = self.fc2(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "kKKw8Vv2uNV0"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}