Librosa_Speech_Model.ipynb

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      "metadata": {
        "id": "ffRuMsYqptKc"
      },
      "outputs": [],
      "source": [
        "import librosa\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive', force_remount=True) # mount to google drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
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          "height": 1000
        },
        "id": "4354Ak7gp8Gr",
        "outputId": "a73f41d6-2003-49cc-e272-b40f411bebf8"
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
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              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        document.querySelector('#df-77fc3292-8b91-4cda-a9f3-396dd6cafa02 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "audio_files",
              "summary": "{\n  \"name\": \"audio_files\",\n  \"rows\": 31,\n  \"fields\": [\n    {\n      \"column\": \"file_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"trimmed_speech4.wav\",\n          \"steve-jobs-stanford-commencement-speech-2005_glkoTZ98.wav\",\n          \"a-meditation-on-rumi-and-the-power-of-poetry-leili-anvar-ted_hUSzceXH.wav\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"assertiveness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2,\n          3,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"enthusiasim\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0,\n          3,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clairity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          6,\n          9,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"engagement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1,\n          2,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "audio_files = pd.DataFrame(pd.read_excel(\"/content/gdrive/MyDrive/222 proj/data.xlsx\")) # audio\n",
        "audio_files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "cZ4x4DlnjKo4"
      },
      "outputs": [],
      "source": [
        "def preprocess_data(file_path):\n",
        "  audio, sr = librosa.load(file_path, sr=16000) #16000 is recommended sample rate for\n",
        "  mfccs_scaled = np.mean(librosa.feature.mfcc(y=audio, sr = 16000, n_mfcc=1000).T, axis=0)\n",
        "  return mfccs_scaled"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1bKOpWIFsSd2",
        "outputId": "b57ed27e-89ae-4288-c72c-6c1aded8b75c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Aaron_Beverly.wav\n",
            "b1.wav\n",
            "b2.wav\n",
            "champ.wav\n",
            "harvard.wav\n",
            "miss_teen.wav\n",
            "obama_2020.wav\n",
            "obama.wav\n",
            "reporter.wav\n",
            "socks.wav\n",
            "Susan_Lozier_TED.wav\n",
            "toast.wav\n",
            "trudeau.wav\n",
            "trudeau.wav\n",
            "Woman.wav\n",
            "the-price-of-shame-monica-lewinsky-ted_bPeL2a7k.wav\n",
            "steve-jobs-stanford-commencement-speech-2005_glkoTZ98.wav\n",
            "public-speaking-example-3_Jso631k6.wav\n",
            "public-speaking-example-1_RFlXC5WZ.wav\n",
            "Public Speaking Example 4.wav\n",
            "president-nelson-mandela-inauguration-speech-may-10-1994_ebVx0SSO.wav\n",
            "michelle-yeoh-addresses-the-harvard-law-school-class-of-2023_Mm1N9Uok.wav\n",
            "ego-and-math-3blue1brown-stanford-math-department-commencement-speech-2023_8tAb7ueJ.wav\n",
            "boy-gives-speech_c1bUFPNr.wav\n",
            "a-meditation-on-rumi-and-the-power-of-poetry-leili-anvar-ted_hUSzceXH.wav\n",
            "trimmed_speech1.wav\n",
            "trimmed_speech2.wav\n",
            "trimmed_speech3.wav\n",
            "trimmed_speech4.wav\n",
            "trimmed_speech5.wav\n",
            "trimmed_speech6.wav\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "mfcc_features_list = [] # list of spectrograms\n",
        "for file_name in audio_files[\"file_name\"]:\n",
        "  print(file_name)\n",
        "  audio = preprocess_data(\"/content/gdrive/MyDrive/222 proj/audio_clips/\" + file_name)\n",
        "  mfcc_features_list.append(audio) # append spectrogram audio data to audios. After the loop, audios will have the audio information of all the files as a spectrograms in different rows\n",
        "audio_files.insert(0, \"mfccs\", mfcc_features_list) # inserting new audio column to audio_files\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "C85xR1Z6vyhV",
        "outputId": "97d6ce1e-1414-4513-f6f8-b693205237c0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               mfccs          file_name  \\\n",
              "0  [-355.01233, 80.249466, -18.828096, 16.806034,...  Aaron_Beverly.wav   \n",
              "1  [-176.20737, 65.394585, -16.727531, 17.33147, ...             b1.wav   \n",
              "2  [-377.40625, 90.72757, 5.678156, 7.091702, -3....             b2.wav   \n",
              "3  [-236.3935, 67.31595, -19.487085, 20.926512, -...          champ.wav   \n",
              "4  [-306.0925, 125.22217, -57.953915, 22.362713, ...        harvard.wav   \n",
              "\n",
              "   assertiveness  enthusiasim  clairity  engagement  \n",
              "0              8            6         9           9  \n",
              "1              3            3         5           2  \n",
              "2              6            7         5           7  \n",
              "3              9           10         9          10  \n",
              "4              8            7         8           8  "
            ],
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-4410e14f-a051-46a2-a280-7d1525868825 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4410e14f-a051-46a2-a280-7d1525868825');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        document.querySelector('#df-14ed4947-cdab-4f20-b41c-0ac83d1f752b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "audio_files",
              "summary": "{\n  \"name\": \"audio_files\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"mfccs\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"file_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"Aaron_Beverly.wav\",\n          \"trimmed_speech4.wav\",\n          \"trimmed_speech2.wav\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"assertiveness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2,\n        \"max\": 10,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3,\n          7,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"enthusiasim\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8,\n          3,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clairity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          7,\n          5,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"engagement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          5,\n          2,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 189
        }
      ],
      "source": [
        "audio_files.head() # check what audio_files looks like"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "yVA4HC5Y9UKz",
        "outputId": "40e22c58-10b0-4eba-ef2e-c7fc241586ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='enthusiasim', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn\n",
        "from scipy import stats\n",
        "\n",
        "quality = \"enthusiasim\" # THIS IS WHERE TO CHOOSE WHICH QUALITY WE WANT TO TRAIN THE MODEL ON\n",
        "seaborn.histplot(data=audio_files[quality])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "j7Vlm6UUDGBL"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(audio_files['mfccs'], audio_files[quality], test_size=0.25, random_state=48) # split into 80% 20% training testing datasets\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JNsWaI-MWRvZ",
        "outputId": "b5c5b8e6-15a3-457b-f51d-c5a4295c6862"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12     7\n",
              "18     6\n",
              "3     10\n",
              "11    10\n",
              "10     7\n",
              "13     5\n",
              "15     8\n",
              "2      7\n",
              "14     7\n",
              "6      7\n",
              "19     1\n",
              "16     3\n",
              "4      7\n",
              "17    10\n",
              "0      6\n",
              "Name: enthusiasim, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "pTkk9UaXS4o8"
      },
      "outputs": [],
      "source": [
        "from tensorflow.python.framework import ops\n",
        "# convert training and testing data lists into np array\n",
        "x_train = np.asarray(x_train)\n",
        "x_test = np.asarray(x_test)\n",
        "y_train = np.asarray(y_train)\n",
        "y_test = np.asarray(y_test)\n",
        "# convert to tensor\n",
        "x_train = np.array([tensor for tensor in x_train])\n",
        "x_test = np.array([tensor for tensor in x_test])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sMhPwr42SgFn",
        "outputId": "2a47d82d-5677-49c9-890e-66780bd6a055"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-2.31108292e+02,  8.73163834e+01, -2.39999809e+01, ...,\n",
              "         1.36617705e-01,  9.59681422e-02,  1.28089175e-01],\n",
              "       [-3.43350861e+02,  7.73025894e+01, -3.21493721e+01, ...,\n",
              "         4.65764612e-01,  6.17544204e-02, -3.59438300e-01],\n",
              "       [-2.20075836e+02,  9.98268738e+01,  1.49030304e+01, ...,\n",
              "         1.18775509e-01,  2.40192860e-02, -1.43464819e-01],\n",
              "       ...,\n",
              "       [-2.90033722e+02,  8.22706909e+01, -6.71874046e+00, ...,\n",
              "         6.13703132e-01,  3.21277231e-01,  4.75857556e-02],\n",
              "       [-3.26815765e+02,  8.95835342e+01,  1.29874027e+00, ...,\n",
              "        -1.15541525e-01,  5.81549406e-01,  5.45862466e-02],\n",
              "       [-3.55012329e+02,  8.02494659e+01, -1.88280964e+01, ...,\n",
              "         1.46538168e-02, -1.50094911e-01,  2.49945149e-02]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "cNaGXhtH2J0n"
      },
      "outputs": [],
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Activation, Flatten, Conv1D, MaxPooling1D\n",
        "\n",
        "# create model\n",
        "model = Sequential()\n",
        "model.add(Dense(19, activation='relu'))\n",
        "model.add(Dense(15, activation='relu'))\n",
        "\n",
        "model.add(Dense(1, activation='relu'))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VDquf85d2U64",
        "outputId": "253352b2-2721-4ad1-fb6a-be89c8b04ce6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/120\n",
            "1/1 [==============================] - 1s 1s/step - loss: 22.2509 - mae: 3.6678 - mse: 22.2509 - val_loss: 14.5971 - val_mae: 3.1786 - val_mse: 14.5971\n",
            "Epoch 2/120\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 16.9588 - mae: 3.1962 - mse: 16.9588 - val_loss: 12.4265 - val_mae: 2.9812 - val_mse: 12.4265\n",
            "Epoch 3/120\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 13.0548 - mae: 2.8517 - mse: 13.0548 - val_loss: 10.9641 - val_mae: 2.7944 - val_mse: 10.9641\n",
            "Epoch 4/120\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 10.4746 - mae: 2.5927 - mse: 10.4746 - val_loss: 10.0923 - val_mae: 2.5847 - val_mse: 10.0923\n",
            "Epoch 5/120\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 9.0825 - mae: 2.4408 - mse: 9.0825 - val_loss: 9.7470 - val_mae: 2.5128 - val_mse: 9.7470\n",
            "Epoch 6/120\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 8.4264 - mae: 2.3731 - mse: 8.4264 - val_loss: 9.7824 - val_mae: 2.4980 - val_mse: 9.7824\n",
            "Epoch 7/120\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 8.3290 - mae: 2.3648 - mse: 8.3290 - val_loss: 10.0073 - val_mae: 2.5083 - val_mse: 10.0073\n",
            "Epoch 8/120\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 8.4451 - mae: 2.3777 - mse: 8.4451 - val_loss: 10.3240 - val_mae: 2.5839 - val_mse: 10.3240\n",
            "Epoch 9/120\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 8.5186 - mae: 2.4046 - mse: 8.5186 - val_loss: 10.4138 - val_mae: 2.6261 - val_mse: 10.4138\n",
            "Epoch 10/120\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 8.3739 - mae: 2.4037 - mse: 8.3739 - val_loss: 10.4784 - val_mae: 2.6582 - val_mse: 10.4784\n",
            "Epoch 11/120\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 7.9302 - mae: 2.3663 - mse: 7.9302 - val_loss: 10.6520 - val_mae: 2.7042 - val_mse: 10.6520\n",
            "Epoch 12/120\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 7.4474 - mae: 2.3060 - mse: 7.4474 - val_loss: 10.7123 - val_mae: 2.7382 - val_mse: 10.7123\n",
            "Epoch 13/120\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 6.7935 - mae: 2.2124 - mse: 6.7935 - val_loss: 10.8107 - val_mae: 2.7685 - val_mse: 10.8107\n",
            "Epoch 14/120\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 6.1755 - mae: 2.1124 - mse: 6.1755 - val_loss: 10.8171 - val_mae: 2.7772 - val_mse: 10.8171\n",
            "Epoch 15/120\n",
            "1/1 [==============================] - 0s 82ms/step - loss: 5.6691 - mae: 2.0269 - mse: 5.6691 - val_loss: 10.7843 - val_mae: 2.7709 - val_mse: 10.7843\n",
            "Epoch 16/120\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 5.2898 - mae: 1.9442 - mse: 5.2898 - val_loss: 10.8889 - val_mae: 2.7712 - val_mse: 10.8889\n",
            "Epoch 17/120\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 5.0724 - mae: 1.8794 - mse: 5.0724 - val_loss: 11.0985 - val_mae: 2.7791 - val_mse: 11.0985\n",
            "Epoch 18/120\n",
            "1/1 [==============================] - 0s 71ms/step - loss: 4.9984 - mae: 1.8274 - mse: 4.9984 - val_loss: 11.2743 - val_mae: 2.8067 - val_mse: 11.2743\n",
            "Epoch 19/120\n",
            "1/1 [==============================] - 0s 88ms/step - loss: 5.0140 - mae: 1.7862 - mse: 5.0140 - val_loss: 11.4319 - val_mae: 2.8283 - val_mse: 11.4319\n",
            "Epoch 20/120\n",
            "1/1 [==============================] - 0s 95ms/step - loss: 5.0461 - mae: 1.7500 - mse: 5.0461 - val_loss: 11.5372 - val_mae: 2.8541 - val_mse: 11.5372\n",
            "Epoch 21/120\n",
            "1/1 [==============================] - 0s 68ms/step - loss: 5.0472 - mae: 1.7158 - mse: 5.0472 - val_loss: 11.5715 - val_mae: 2.8642 - val_mse: 11.5715\n",
            "Epoch 22/120\n",
            "1/1 [==============================] - 0s 87ms/step - loss: 4.9772 - mae: 1.6862 - mse: 4.9772 - val_loss: 11.5362 - val_mae: 2.8576 - val_mse: 11.5362\n",
            "Epoch 23/120\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 4.8284 - mae: 1.6587 - mse: 4.8284 - val_loss: 11.4444 - val_mae: 2.8554 - val_mse: 11.4444\n",
            "Epoch 24/120\n",
            "1/1 [==============================] - 0s 71ms/step - loss: 4.6322 - mae: 1.6369 - mse: 4.6322 - val_loss: 11.3173 - val_mae: 2.8496 - val_mse: 11.3173\n",
            "Epoch 25/120\n",
            "1/1 [==============================] - 0s 73ms/step - loss: 4.4134 - mae: 1.6170 - mse: 4.4134 - val_loss: 11.1822 - val_mae: 2.8408 - val_mse: 11.1822\n",
            "Epoch 26/120\n",
            "1/1 [==============================] - 0s 72ms/step - loss: 4.2076 - mae: 1.5993 - mse: 4.2076 - val_loss: 11.0588 - val_mae: 2.8306 - val_mse: 11.0588\n",
            "Epoch 27/120\n",
            "1/1 [==============================] - 0s 72ms/step - loss: 4.0361 - mae: 1.5917 - mse: 4.0361 - val_loss: 10.9603 - val_mae: 2.8394 - val_mse: 10.9603\n",
            "Epoch 28/120\n",
            "1/1 [==============================] - 0s 84ms/step - loss: 3.9092 - mae: 1.5838 - mse: 3.9092 - val_loss: 10.8791 - val_mae: 2.8486 - val_mse: 10.8791\n",
            "Epoch 29/120\n",
            "1/1 [==============================] - 0s 80ms/step - loss: 3.8267 - mae: 1.5749 - mse: 3.8267 - val_loss: 10.8274 - val_mae: 2.8571 - val_mse: 10.8274\n",
            "Epoch 30/120\n",
            "1/1 [==============================] - 0s 73ms/step - loss: 3.7759 - mae: 1.5651 - mse: 3.7759 - val_loss: 10.8037 - val_mae: 2.8651 - val_mse: 10.8037\n",
            "Epoch 31/120\n",
            "1/1 [==============================] - 0s 85ms/step - loss: 3.7384 - mae: 1.5638 - mse: 3.7384 - val_loss: 10.8010 - val_mae: 2.8725 - val_mse: 10.8010\n",
            "Epoch 32/120\n",
            "1/1 [==============================] - 0s 216ms/step - loss: 3.6958 - mae: 1.5648 - mse: 3.6958 - val_loss: 10.8130 - val_mae: 2.8793 - val_mse: 10.8130\n",
            "Epoch 33/120\n",
            "1/1 [==============================] - 0s 108ms/step - loss: 3.6332 - mae: 1.5607 - mse: 3.6332 - val_loss: 10.8358 - val_mae: 2.8854 - val_mse: 10.8358\n",
            "Epoch 34/120\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 3.5445 - mae: 1.5449 - mse: 3.5445 - val_loss: 10.8695 - val_mae: 2.8915 - val_mse: 10.8695\n",
            "Epoch 35/120\n",
            "1/1 [==============================] - 0s 76ms/step - loss: 3.4323 - mae: 1.5166 - mse: 3.4323 - val_loss: 10.9130 - val_mae: 2.8940 - val_mse: 10.9130\n",
            "Epoch 36/120\n",
            "1/1 [==============================] - 0s 84ms/step - loss: 3.3137 - mae: 1.4794 - mse: 3.3137 - val_loss: 10.9670 - val_mae: 2.8952 - val_mse: 10.9670\n",
            "Epoch 37/120\n",
            "1/1 [==============================] - 0s 144ms/step - loss: 3.2026 - mae: 1.4440 - mse: 3.2026 - val_loss: 11.0278 - val_mae: 2.8958 - val_mse: 11.0278\n",
            "Epoch 38/120\n",
            "1/1 [==============================] - 0s 119ms/step - loss: 3.1123 - mae: 1.4082 - mse: 3.1123 - val_loss: 11.0931 - val_mae: 2.8962 - val_mse: 11.0931\n",
            "Epoch 39/120\n",
            "1/1 [==============================] - 0s 170ms/step - loss: 3.0379 - mae: 1.3753 - mse: 3.0379 - val_loss: 11.1583 - val_mae: 2.9007 - val_mse: 11.1583\n",
            "Epoch 40/120\n",
            "1/1 [==============================] - 0s 146ms/step - loss: 2.9782 - mae: 1.3456 - mse: 2.9782 - val_loss: 11.2166 - val_mae: 2.9162 - val_mse: 11.2166\n",
            "Epoch 41/120\n",
            "1/1 [==============================] - 0s 124ms/step - loss: 2.9279 - mae: 1.3189 - mse: 2.9279 - val_loss: 11.2459 - val_mae: 2.9266 - val_mse: 11.2459\n",
            "Epoch 42/120\n",
            "1/1 [==============================] - 0s 84ms/step - loss: 2.8799 - mae: 1.2956 - mse: 2.8799 - val_loss: 11.2534 - val_mae: 2.9307 - val_mse: 11.2534\n",
            "Epoch 43/120\n",
            "1/1 [==============================] - 0s 140ms/step - loss: 2.8277 - mae: 1.2773 - mse: 2.8277 - val_loss: 11.2424 - val_mae: 2.9305 - val_mse: 11.2424\n",
            "Epoch 44/120\n",
            "1/1 [==============================] - 0s 130ms/step - loss: 2.7679 - mae: 1.2628 - mse: 2.7679 - val_loss: 11.2212 - val_mae: 2.9272 - val_mse: 11.2212\n",
            "Epoch 45/120\n",
            "1/1 [==============================] - 0s 141ms/step - loss: 2.7008 - mae: 1.2527 - mse: 2.7008 - val_loss: 11.1841 - val_mae: 2.9203 - val_mse: 11.1841\n",
            "Epoch 46/120\n",
            "1/1 [==============================] - 0s 120ms/step - loss: 2.6299 - mae: 1.2462 - mse: 2.6299 - val_loss: 11.1310 - val_mae: 2.9097 - val_mse: 11.1310\n",
            "Epoch 47/120\n",
            "1/1 [==============================] - 0s 130ms/step - loss: 2.5598 - mae: 1.2423 - mse: 2.5598 - val_loss: 11.0778 - val_mae: 2.8980 - val_mse: 11.0778\n",
            "Epoch 48/120\n",
            "1/1 [==============================] - 0s 111ms/step - loss: 2.4951 - mae: 1.2398 - mse: 2.4951 - val_loss: 11.0284 - val_mae: 2.8883 - val_mse: 11.0284\n",
            "Epoch 49/120\n",
            "1/1 [==============================] - 0s 107ms/step - loss: 2.4380 - mae: 1.2375 - mse: 2.4380 - val_loss: 10.9849 - val_mae: 2.8869 - val_mse: 10.9849\n",
            "Epoch 50/120\n",
            "1/1 [==============================] - 0s 106ms/step - loss: 2.3884 - mae: 1.2342 - mse: 2.3884 - val_loss: 10.9479 - val_mae: 2.8854 - val_mse: 10.9479\n",
            "Epoch 51/120\n",
            "1/1 [==============================] - 0s 147ms/step - loss: 2.3443 - mae: 1.2289 - mse: 2.3443 - val_loss: 10.9170 - val_mae: 2.8835 - val_mse: 10.9170\n",
            "Epoch 52/120\n",
            "1/1 [==============================] - 0s 134ms/step - loss: 2.3028 - mae: 1.2211 - mse: 2.3028 - val_loss: 10.8942 - val_mae: 2.8814 - val_mse: 10.8942\n",
            "Epoch 53/120\n",
            "1/1 [==============================] - 0s 109ms/step - loss: 2.2622 - mae: 1.2103 - mse: 2.2622 - val_loss: 10.8782 - val_mae: 2.8791 - val_mse: 10.8782\n",
            "Epoch 54/120\n",
            "1/1 [==============================] - 0s 140ms/step - loss: 2.2177 - mae: 1.1958 - mse: 2.2177 - val_loss: 10.8687 - val_mae: 2.8767 - val_mse: 10.8687\n",
            "Epoch 55/120\n",
            "1/1 [==============================] - 0s 119ms/step - loss: 2.1701 - mae: 1.1781 - mse: 2.1701 - val_loss: 10.8655 - val_mae: 2.8742 - val_mse: 10.8655\n",
            "Epoch 56/120\n",
            "1/1 [==============================] - 0s 106ms/step - loss: 2.1218 - mae: 1.1581 - mse: 2.1218 - val_loss: 10.8686 - val_mae: 2.8717 - val_mse: 10.8686\n",
            "Epoch 57/120\n",
            "1/1 [==============================] - 0s 98ms/step - loss: 2.0750 - mae: 1.1362 - mse: 2.0750 - val_loss: 10.8770 - val_mae: 2.8709 - val_mse: 10.8770\n",
            "Epoch 58/120\n",
            "1/1 [==============================] - 0s 130ms/step - loss: 2.0321 - mae: 1.1132 - mse: 2.0321 - val_loss: 10.8860 - val_mae: 2.8774 - val_mse: 10.8860\n",
            "Epoch 59/120\n",
            "1/1 [==============================] - 0s 121ms/step - loss: 1.9949 - mae: 1.0936 - mse: 1.9949 - val_loss: 10.8932 - val_mae: 2.8827 - val_mse: 10.8932\n",
            "Epoch 60/120\n",
            "1/1 [==============================] - 0s 74ms/step - loss: 1.9604 - mae: 1.0783 - mse: 1.9604 - val_loss: 10.8966 - val_mae: 2.8864 - val_mse: 10.8966\n",
            "Epoch 61/120\n",
            "1/1 [==============================] - 0s 74ms/step - loss: 1.9266 - mae: 1.0640 - mse: 1.9266 - val_loss: 10.8944 - val_mae: 2.8880 - val_mse: 10.8944\n",
            "Epoch 62/120\n",
            "1/1 [==============================] - 0s 75ms/step - loss: 1.8943 - mae: 1.0521 - mse: 1.8943 - val_loss: 10.8863 - val_mae: 2.8878 - val_mse: 10.8863\n",
            "Epoch 63/120\n",
            "1/1 [==============================] - 0s 111ms/step - loss: 1.8603 - mae: 1.0417 - mse: 1.8603 - val_loss: 10.8731 - val_mae: 2.8857 - val_mse: 10.8731\n",
            "Epoch 64/120\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 1.8242 - mae: 1.0324 - mse: 1.8242 - val_loss: 10.8565 - val_mae: 2.8824 - val_mse: 10.8565\n",
            "Epoch 65/120\n",
            "1/1 [==============================] - 0s 80ms/step - loss: 1.7881 - mae: 1.0241 - mse: 1.7881 - val_loss: 10.8397 - val_mae: 2.8786 - val_mse: 10.8397\n",
            "Epoch 66/120\n",
            "1/1 [==============================] - 0s 124ms/step - loss: 1.7538 - mae: 1.0162 - mse: 1.7538 - val_loss: 10.8257 - val_mae: 2.8752 - val_mse: 10.8257\n",
            "Epoch 67/120\n",
            "1/1 [==============================] - 0s 78ms/step - loss: 1.7229 - mae: 1.0088 - mse: 1.7229 - val_loss: 10.8162 - val_mae: 2.8731 - val_mse: 10.8162\n",
            "Epoch 68/120\n",
            "1/1 [==============================] - 0s 75ms/step - loss: 1.6947 - mae: 1.0012 - mse: 1.6947 - val_loss: 10.8119 - val_mae: 2.8724 - val_mse: 10.8119\n",
            "Epoch 69/120\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 1.6667 - mae: 0.9927 - mse: 1.6667 - val_loss: 10.8128 - val_mae: 2.8734 - val_mse: 10.8128\n",
            "Epoch 70/120\n",
            "1/1 [==============================] - 0s 102ms/step - loss: 1.6382 - mae: 0.9831 - mse: 1.6382 - val_loss: 10.8190 - val_mae: 2.8760 - val_mse: 10.8190\n",
            "Epoch 71/120\n",
            "1/1 [==============================] - 0s 98ms/step - loss: 1.6092 - mae: 0.9724 - mse: 1.6092 - val_loss: 10.8300 - val_mae: 2.8800 - val_mse: 10.8300\n",
            "Epoch 72/120\n",
            "1/1 [==============================] - 0s 110ms/step - loss: 1.5800 - mae: 0.9609 - mse: 1.5800 - val_loss: 10.8418 - val_mae: 2.8842 - val_mse: 10.8418\n",
            "Epoch 73/120\n",
            "1/1 [==============================] - 0s 93ms/step - loss: 1.5530 - mae: 0.9498 - mse: 1.5530 - val_loss: 10.8530 - val_mae: 2.8878 - val_mse: 10.8530\n",
            "Epoch 74/120\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 1.5278 - mae: 0.9394 - mse: 1.5278 - val_loss: 10.8626 - val_mae: 2.8906 - val_mse: 10.8626\n",
            "Epoch 75/120\n",
            "1/1 [==============================] - 0s 71ms/step - loss: 1.5025 - mae: 0.9290 - mse: 1.5025 - val_loss: 10.8661 - val_mae: 2.8920 - val_mse: 10.8661\n",
            "Epoch 76/120\n",
            "1/1 [==============================] - 0s 72ms/step - loss: 1.4771 - mae: 0.9186 - mse: 1.4771 - val_loss: 10.8663 - val_mae: 2.8923 - val_mse: 10.8663\n",
            "Epoch 77/120\n",
            "1/1 [==============================] - 0s 99ms/step - loss: 1.4514 - mae: 0.9084 - mse: 1.4514 - val_loss: 10.8654 - val_mae: 2.8917 - val_mse: 10.8654\n",
            "Epoch 78/120\n",
            "1/1 [==============================] - 0s 80ms/step - loss: 1.4255 - mae: 0.8986 - mse: 1.4255 - val_loss: 10.8636 - val_mae: 2.8904 - val_mse: 10.8636\n",
            "Epoch 79/120\n",
            "1/1 [==============================] - 0s 87ms/step - loss: 1.4011 - mae: 0.8901 - mse: 1.4011 - val_loss: 10.8615 - val_mae: 2.8888 - val_mse: 10.8615\n",
            "Epoch 80/120\n",
            "1/1 [==============================] - 0s 87ms/step - loss: 1.3773 - mae: 0.8819 - mse: 1.3773 - val_loss: 10.8593 - val_mae: 2.8870 - val_mse: 10.8593\n",
            "Epoch 81/120\n",
            "1/1 [==============================] - 0s 81ms/step - loss: 1.3540 - mae: 0.8738 - mse: 1.3540 - val_loss: 10.8574 - val_mae: 2.8852 - val_mse: 10.8574\n",
            "Epoch 82/120\n",
            "1/1 [==============================] - 0s 80ms/step - loss: 1.3317 - mae: 0.8658 - mse: 1.3317 - val_loss: 10.8563 - val_mae: 2.8834 - val_mse: 10.8563\n",
            "Epoch 83/120\n",
            "1/1 [==============================] - 0s 68ms/step - loss: 1.3099 - mae: 0.8580 - mse: 1.3099 - val_loss: 10.8453 - val_mae: 2.8804 - val_mse: 10.8453\n",
            "Epoch 84/120\n",
            "1/1 [==============================] - 0s 122ms/step - loss: 1.2892 - mae: 0.8504 - mse: 1.2892 - val_loss: 10.8441 - val_mae: 2.8791 - val_mse: 10.8441\n",
            "Epoch 85/120\n",
            "1/1 [==============================] - 0s 117ms/step - loss: 1.2687 - mae: 0.8428 - mse: 1.2687 - val_loss: 10.8526 - val_mae: 2.8796 - val_mse: 10.8526\n",
            "Epoch 86/120\n",
            "1/1 [==============================] - 0s 68ms/step - loss: 1.2473 - mae: 0.8349 - mse: 1.2473 - val_loss: 10.8678 - val_mae: 2.8814 - val_mse: 10.8678\n",
            "Epoch 87/120\n",
            "1/1 [==============================] - 0s 77ms/step - loss: 1.2264 - mae: 0.8271 - mse: 1.2264 - val_loss: 10.8831 - val_mae: 2.8834 - val_mse: 10.8831\n",
            "Epoch 88/120\n",
            "1/1 [==============================] - 0s 88ms/step - loss: 1.2060 - mae: 0.8192 - mse: 1.2060 - val_loss: 10.8978 - val_mae: 2.8852 - val_mse: 10.8978\n",
            "Epoch 89/120\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 1.1861 - mae: 0.8114 - mse: 1.1861 - val_loss: 10.9094 - val_mae: 2.8864 - val_mse: 10.9094\n",
            "Epoch 90/120\n",
            "1/1 [==============================] - 0s 76ms/step - loss: 1.1665 - mae: 0.8038 - mse: 1.1665 - val_loss: 10.9206 - val_mae: 2.8872 - val_mse: 10.9206\n",
            "Epoch 91/120\n",
            "1/1 [==============================] - 0s 84ms/step - loss: 1.1476 - mae: 0.7965 - mse: 1.1476 - val_loss: 10.9312 - val_mae: 2.8877 - val_mse: 10.9312\n",
            "Epoch 92/120\n",
            "1/1 [==============================] - 0s 78ms/step - loss: 1.1291 - mae: 0.7892 - mse: 1.1291 - val_loss: 10.9412 - val_mae: 2.8878 - val_mse: 10.9412\n",
            "Epoch 93/120\n",
            "1/1 [==============================] - 0s 84ms/step - loss: 1.1114 - mae: 0.7825 - mse: 1.1114 - val_loss: 10.9502 - val_mae: 2.8875 - val_mse: 10.9502\n",
            "Epoch 94/120\n",
            "1/1 [==============================] - 0s 98ms/step - loss: 1.0939 - mae: 0.7758 - mse: 1.0939 - val_loss: 10.9584 - val_mae: 2.8866 - val_mse: 10.9584\n",
            "Epoch 95/120\n",
            "1/1 [==============================] - 0s 84ms/step - loss: 1.0762 - mae: 0.7690 - mse: 1.0762 - val_loss: 10.9634 - val_mae: 2.8851 - val_mse: 10.9634\n",
            "Epoch 96/120\n",
            "1/1 [==============================] - 0s 71ms/step - loss: 1.0585 - mae: 0.7620 - mse: 1.0585 - val_loss: 10.9645 - val_mae: 2.8828 - val_mse: 10.9645\n",
            "Epoch 97/120\n",
            "1/1 [==============================] - 0s 80ms/step - loss: 1.0414 - mae: 0.7551 - mse: 1.0414 - val_loss: 10.9677 - val_mae: 2.8824 - val_mse: 10.9677\n",
            "Epoch 98/120\n",
            "1/1 [==============================] - 0s 229ms/step - loss: 1.0249 - mae: 0.7485 - mse: 1.0249 - val_loss: 10.9729 - val_mae: 2.8825 - val_mse: 10.9729\n",
            "Epoch 99/120\n",
            "1/1 [==============================] - 0s 233ms/step - loss: 1.0085 - mae: 0.7416 - mse: 1.0085 - val_loss: 10.9799 - val_mae: 2.8827 - val_mse: 10.9799\n",
            "Epoch 100/120\n",
            "1/1 [==============================] - 0s 115ms/step - loss: 0.9922 - mae: 0.7346 - mse: 0.9922 - val_loss: 10.9882 - val_mae: 2.8828 - val_mse: 10.9882\n",
            "Epoch 101/120\n",
            "1/1 [==============================] - 0s 147ms/step - loss: 0.9763 - mae: 0.7277 - mse: 0.9763 - val_loss: 10.9956 - val_mae: 2.8830 - val_mse: 10.9956\n",
            "Epoch 102/120\n",
            "1/1 [==============================] - 0s 217ms/step - loss: 0.9608 - mae: 0.7217 - mse: 0.9608 - val_loss: 11.0019 - val_mae: 2.8833 - val_mse: 11.0019\n",
            "Epoch 103/120\n",
            "1/1 [==============================] - 0s 86ms/step - loss: 0.9455 - mae: 0.7156 - mse: 0.9455 - val_loss: 11.0071 - val_mae: 2.8837 - val_mse: 11.0071\n",
            "Epoch 104/120\n",
            "1/1 [==============================] - 0s 69ms/step - loss: 0.9306 - mae: 0.7096 - mse: 0.9306 - val_loss: 11.0111 - val_mae: 2.8844 - val_mse: 11.0111\n",
            "Epoch 105/120\n",
            "1/1 [==============================] - 0s 68ms/step - loss: 0.9157 - mae: 0.7036 - mse: 0.9157 - val_loss: 11.0138 - val_mae: 2.8852 - val_mse: 11.0138\n",
            "Epoch 106/120\n",
            "1/1 [==============================] - 0s 85ms/step - loss: 0.9012 - mae: 0.6976 - mse: 0.9012 - val_loss: 11.0170 - val_mae: 2.8862 - val_mse: 11.0170\n",
            "Epoch 107/120\n",
            "1/1 [==============================] - 0s 76ms/step - loss: 0.8867 - mae: 0.6917 - mse: 0.8867 - val_loss: 11.0232 - val_mae: 2.8873 - val_mse: 11.0232\n",
            "Epoch 108/120\n",
            "1/1 [==============================] - 0s 83ms/step - loss: 0.8727 - mae: 0.6861 - mse: 0.8727 - val_loss: 11.0308 - val_mae: 2.8882 - val_mse: 11.0308\n",
            "Epoch 109/120\n",
            "1/1 [==============================] - 0s 80ms/step - loss: 0.8591 - mae: 0.6806 - mse: 0.8591 - val_loss: 11.0400 - val_mae: 2.8888 - val_mse: 11.0400\n",
            "Epoch 110/120\n",
            "1/1 [==============================] - 0s 84ms/step - loss: 0.8453 - mae: 0.6754 - mse: 0.8453 - val_loss: 11.0504 - val_mae: 2.8891 - val_mse: 11.0504\n",
            "Epoch 111/120\n",
            "1/1 [==============================] - 0s 78ms/step - loss: 0.8321 - mae: 0.6708 - mse: 0.8321 - val_loss: 11.0654 - val_mae: 2.8899 - val_mse: 11.0654\n",
            "Epoch 112/120\n",
            "1/1 [==============================] - 0s 129ms/step - loss: 0.8188 - mae: 0.6663 - mse: 0.8188 - val_loss: 11.0788 - val_mae: 2.8904 - val_mse: 11.0788\n",
            "Epoch 113/120\n",
            "1/1 [==============================] - 0s 105ms/step - loss: 0.8059 - mae: 0.6618 - mse: 0.8059 - val_loss: 11.0899 - val_mae: 2.8908 - val_mse: 11.0899\n",
            "Epoch 114/120\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.7933 - mae: 0.6572 - mse: 0.7933 - val_loss: 11.0985 - val_mae: 2.8911 - val_mse: 11.0985\n",
            "Epoch 115/120\n",
            "1/1 [==============================] - 0s 84ms/step - loss: 0.7807 - mae: 0.6524 - mse: 0.7807 - val_loss: 11.1023 - val_mae: 2.8912 - val_mse: 11.1023\n",
            "Epoch 116/120\n",
            "1/1 [==============================] - 0s 78ms/step - loss: 0.7683 - mae: 0.6473 - mse: 0.7683 - val_loss: 11.1043 - val_mae: 2.8912 - val_mse: 11.1043\n",
            "Epoch 117/120\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.7561 - mae: 0.6421 - mse: 0.7561 - val_loss: 11.1050 - val_mae: 2.8910 - val_mse: 11.1050\n",
            "Epoch 118/120\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.7441 - mae: 0.6368 - mse: 0.7441 - val_loss: 11.1053 - val_mae: 2.8909 - val_mse: 11.1053\n",
            "Epoch 119/120\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.7322 - mae: 0.6314 - mse: 0.7322 - val_loss: 11.1059 - val_mae: 2.8907 - val_mse: 11.1059\n",
            "Epoch 120/120\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.7208 - mae: 0.6263 - mse: 0.7208 - val_loss: 11.1081 - val_mae: 2.8903 - val_mse: 11.1081\n"
          ]
        }
      ],
      "source": [
        "model.compile(loss='mse', optimizer='adam', metrics=['mae', 'mse'])\n",
        "\n",
        "# train the model\n",
        "history = model.fit(x_train, y_train, epochs = 120, validation_data=[x_test, y_test])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EGHGUh9sDDvM",
        "outputId": "5cdcbcc4-2dcf-463b-a49a-1d18c3bc533b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 72ms/step\n"
          ]
        }
      ],
      "source": [
        "y_pred = model.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fsOYTIscCdLD",
        "outputId": "87844e6a-f122-475c-83cf-4a76be0dee32"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.15315729959730806\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import r2_score\n",
        "print(r2_score(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for y in range(0, len(y_pred)):\n",
        "  print(\"Actual Enthusiasm\", y_test[y], \"Predicted enthusiasm:\", y_pred[y][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z9vBres3WLi5",
        "outputId": "f3613216-ea11-46a6-e947-601126d7b7b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Actual Enthusiasm 3 Predicted enthusiasm: 3.8643587\n",
            "Actual Enthusiasm 4 Predicted enthusiasm: 3.6078587\n",
            "Actual Enthusiasm 6 Predicted enthusiasm: 5.272105\n",
            "Actual Enthusiasm 9 Predicted enthusiasm: 10.404229\n",
            "Actual Enthusiasm 5 Predicted enthusiasm: 0.31234646\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S3xL6YK18m3t",
        "outputId": "6b01ec0a-8276-45b5-f9f1-74db6975c388"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nan\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/scipy/stats/_stats_py.py:5445: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
            "  warnings.warn(stats.ConstantInputWarning(warn_msg))\n"
          ]
        }
      ],
      "source": [
        "from scipy.stats import spearmanr\n",
        "coef, p = spearmanr(y_test, y_pred)\n",
        "print(coef)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir saved_model\n",
        "model.save(\"saved_models/\" + quality) # create ability to save model for any quality"
      ],
      "metadata": {
        "id": "Gf-QmrEtvIb5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r \"saved_models.zip\" \"saved_models\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-kk0BFCs34Q0",
        "outputId": "ca9cad3a-8056-48c5-a224-450ca2ab3ca9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: saved_models/ (stored 0%)\n",
            "  adding: saved_models/enthusiasim/ (stored 0%)\n",
            "  adding: saved_models/enthusiasim/fingerprint.pb (stored 0%)\n",
            "  adding: saved_models/enthusiasim/variables/ (stored 0%)\n",
            "  adding: saved_models/enthusiasim/variables/variables.data-00000-of-00001 (deflated 8%)\n",
            "  adding: saved_models/enthusiasim/variables/variables.index (deflated 64%)\n",
            "  adding: saved_models/enthusiasim/saved_model.pb (deflated 87%)\n",
            "  adding: saved_models/enthusiasim/keras_metadata.pb (deflated 90%)\n",
            "  adding: saved_models/enthusiasim/assets/ (stored 0%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# after saving all different models..."
      ],
      "metadata": {
        "id": "Y097nFQfcJXl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def test_speech(file_name, characteristic):\n",
        "  if characteristic not in (\"assertiveness\", \"enthusiasm\", \"clarity\", \"engagement\"):\n",
        "    print(\"Error: test_speech called with bad characteristic. Please use one of the following: assertiveness, enthusiasm, clarity, engagement\")\n",
        "    return -1\n",
        "\n",
        "  model = tf.keras.models.load_model('saved_models/' + quality) # load in saved model\n",
        "\n",
        "  spectrogram = get_spectrogram(\"/content/gdrive/MyDrive/222 proj/audio_clips/\" + file_name)\n",
        "  spectrogram = spectrogram[0:15000] # cut to 30 seconds\n",
        "  return model.predict(spectrogram) # return prediction"
      ],
      "metadata": {
        "id": "ls1WI9yrrlLM"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}