EchoAI_LSTM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kchiem12/EchoAI/blob/main/EchoAI_LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Setup"
      ],
      "metadata": {
        "id": "OnY3vfhdvmCm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oVkOu9hnO8GX"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from keras.utils import to_categorical\n",
        "import tensorflow.keras.backend as K\n",
        "from keras import Sequential\n",
        "from keras.layers import Dropout, LSTM, Dense, Activation, TimeDistributed, BatchNormalization\n",
        "from keras.regularizers import l2\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "import random\n",
        "from music21 import converter, instrument, note, chord, stream\n",
        "import pickle\n",
        "import pandas as pd\n",
        "import os\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Mounting"
      ],
      "metadata": {
        "id": "sj2WHtatvoNz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zFNQNUXdKUm3",
        "outputId": "f3d21e8d-61a2-4d9a-d063-c5acd36a15da"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Preprocess Data"
      ],
      "metadata": {
        "id": "M1PYA219v7wz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0iSrcsv2O8GY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4defc88e-4fda-4381-da32-7a6ab42a246b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of items in folder: 5\n"
          ]
        }
      ],
      "source": [
        "songs = []\n",
        "\n",
        "# Folder containing raw midis\n",
        "folder = './drive/MyDrive/data'\n",
        "\n",
        "# Go thru folder and append song to notes array\n",
        "for root, dirs, files in os.walk(folder):\n",
        "    for file in files:\n",
        "        if file.endswith('.midi'):\n",
        "            songs.append(os.path.join(root, file))\n",
        "\n",
        "number_of_items = len(songs)\n",
        "print(f\"Number of items in folder: {number_of_items}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oF743aohO8GY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40e8f29a-90a0-4562-e556-02d586bd89f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Success!\n"
          ]
        }
      ],
      "source": [
        "result = []\n",
        "\n",
        "def parse_raw_midis():\n",
        "  for i, file in enumerate(songs):\n",
        "      print(f'Parsing file {i + 1}: {file}')\n",
        "\n",
        "      try:\n",
        "          midi = converter.parse(file)\n",
        "          parse = None\n",
        "          parts = instrument.partitionByInstrument(midi)\n",
        "\n",
        "          # If there are parts get the notes from the first part\n",
        "          if parts:\n",
        "              parse = parts.parts[0].recurse()\n",
        "          else:\n",
        "              parse = midi.flat.notes\n",
        "\n",
        "          for curr in parse:\n",
        "              if isinstance(curr, note.Note):\n",
        "                  result.append(str(curr.pitch))\n",
        "              elif isinstance(curr, chord.Chord):\n",
        "                  result.append('.'.join(str(n) for n in curr.normalOrder))\n",
        "      except:\n",
        "          print(f'Error parsing at file {i + 1}: {file}')\n",
        "\n",
        "parse_raw_midis()\n",
        "print(\"Success!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Save preprocessed data to disk"
      ],
      "metadata": {
        "id": "ayxzzt4jIK-t"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lwioyOqNSUpP"
      },
      "outputs": [],
      "source": [
        "with open('./drive/MyDrive/CS4701/notes', 'wb') as filepath:\n",
        "  pickle.dump(result, filepath)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Load preprocessed data"
      ],
      "metadata": {
        "id": "zlNDJvSU_dS8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tWuG5da7O8GY",
        "outputId": "7b2056f7-d895-4289-b354-7f89076c17c9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "File content length: 23275087 bytes\n",
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            "Loaded successfully.\n",
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'7.11', '6.7.9', '2.4.9', 'C#4', '2', '6.9', '2.6', '6.7.9', '5.7.11', '6.7.9', '2.4.9', 'C#4', '2', '2.6.9', '2.6', '6.9', 'G2', 'B3', '6.7.9', 'E3', '9.2', 'C#3', '2.4', 'D2', '2.4', '2.4', 'D3', 'D3', 'D2', 'G#5', 'F4', 'C#5', 'A3', 'B-3', 'G#5', 'F4', 'C#5', 'C#4', 'B-3', '6', 'C#3', 'B-3', 'C5', 'C#3', '1.6', 'F#2', '1.6', '6', 'C#5', 'G#3', 'A3', 'C#4', 'A3', 'E5', 'C#3', 'C#4', '4.8', 'C#4', 'C#3', 'A4', 'F#2', 'C#5', 'F#4', 'D4', 'G#3', 'A3', 'D4', 'C#5', 'F#4', 'D4', 'A3', 'B4', 'B2', 'C#4', 'B2', 'D4', 'F#2', 'F#4', '4.7', '5.8', 'F4', 'E-5', 'B-4', '7.11', 'C#3', '10.0', 'B3', 'F4', 'C#3', '8.1', 'F4', 'F#2', 'G#5', 'C#5', 'F4', 'A3', 'B-3', 'C#4', 'G#5', 'C#5', 'B-3', 'B-3', '6', 'C#3', 'C5', 'B-3', 'C#3', '1.6', 'F#2', '6', 'C#5', 'G#3', '6', 'C#5', 'A3', 'C#4', 'A3', 'E5', 'C#3', 'C#4', '4.8', 'C#3', 'A4', 'F#2', 'A4', 'C#5', 'D4', 'F#4', 'F#4', 'G#3', 'A3', 'D4', 'A3', 'B4', 'B2', 'F#4', 'C#4']\n",
            "3696777\n"
          ]
        }
      ],
      "source": [
        "notes = None\n",
        "file_path = './drive/MyDrive/CS4701/notes_new'\n",
        "\n",
        "with open(file_path, 'rb') as file:\n",
        "    file_content = file.read()\n",
        "\n",
        "print(f\"File content length: {len(file_content)} bytes\")\n",
        "print(file_content[:100])\n",
        "\n",
        "try:\n",
        "    with open(file_path, 'rb') as file:\n",
        "        buffer = []\n",
        "        while True:\n",
        "            try:\n",
        "                buffer.append(pickle.load(file))\n",
        "            except EOFError:\n",
        "                break\n",
        "    notes = buffer\n",
        "    print(\"Loaded successfully.\")\n",
        "except EOFError:\n",
        "    print(\"EOFError: The file might be incomplete.\")\n",
        "except pickle.UnpicklingError:\n",
        "    print(\"UnpicklingError: The file might be corrupted.\")\n",
        "except Exception as e:\n",
        "    print(f\"Unexpected error: {e}\")\n",
        "\n",
        "# Flatten the list if it contains nested lists\n",
        "flat_notes = []\n",
        "\n",
        "for item in notes:\n",
        "    if isinstance(item, list):\n",
        "        flat_notes.extend(item)\n",
        "    else:\n",
        "        flat_notes.append(item)\n",
        "\n",
        "print(flat_notes[:1000])\n",
        "print(len(flat_notes))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l8SMs40eNiWa"
      },
      "source": [
        "# Sequence creation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dj5dnr9RO8GZ"
      },
      "outputs": [],
      "source": [
        "def prepare_sequences(notes, num_vocab, seq_length):\n",
        "    pitch_set = sorted(set(notes))\n",
        "    pitch_to_int = {pitch: idx for idx, pitch in enumerate(pitch_set)}\n",
        "\n",
        "    inputs, outputs = [], []\n",
        "\n",
        "    for i in range(0, len(notes) - seq_length, 1):\n",
        "        input_seq = notes[i:i + seq_length]\n",
        "        output_seq = notes[i + seq_length]\n",
        "\n",
        "        inputs.append([pitch_to_int[pitch] for pitch in input_seq])\n",
        "        outputs.append(pitch_to_int[output_seq])\n",
        "\n",
        "    num_patterns = len(inputs)\n",
        "\n",
        "    # Reshape and normalize input sequences\n",
        "    inputs = np.reshape(inputs, (num_patterns, seq_length, 1))\n",
        "    inputs = inputs / float(num_vocab)\n",
        "\n",
        "    # One hot encode output sequences\n",
        "    outputs = to_categorical(outputs, num_classes=num_vocab)\n",
        "\n",
        "    return inputs, outputs\n",
        "\n",
        "num_vocab = len(set(flat_notes[:50000]))\n",
        "network_input, network_output = prepare_sequences(flat_notes[:50000], num_vocab, 64)\n",
        "pitchnames = sorted(set(item for item in flat_notes[:50000]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1CLXUeVBNlJK"
      },
      "source": [
        "# Model / Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bBBW1776O8GZ",
        "outputId": "2f0b140e-6762-4842-8c07-69eace54f543"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_6 (LSTM)               (None, 64, 1024)          4202496   \n",
            "                                                                 \n",
            " lstm_7 (LSTM)               (None, 64, 1024)          8392704   \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 64, 1024)          0         \n",
            "                                                                 \n",
            " lstm_8 (LSTM)               (None, 1024)              8392704   \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 1024)              1049600   \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 1024)              0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 967)               991175    \n",
            "                                                                 \n",
            " activation_2 (Activation)   (None, 967)               0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 23028679 (87.85 MB)\n",
            "Trainable params: 23028679 (87.85 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "class EchoAI_LSTM:\n",
        "    def __init__(self, input_shape, num_vocab):\n",
        "        self.input_shape = input_shape\n",
        "        self.num_vocab = num_vocab\n",
        "        self.model = self._build_model()\n",
        "\n",
        "    def _build_model(self):\n",
        "        model = Sequential()\n",
        "        model.add(LSTM(\n",
        "            1024,\n",
        "            input_shape=self.input_shape,\n",
        "            return_sequences=True\n",
        "        ))\n",
        "        model.add(LSTM(1024, return_sequences=True))\n",
        "        model.add(Dropout(0.3))\n",
        "        model.add(LSTM(1024))\n",
        "        model.add(Dense(1024))\n",
        "        model.add(Dropout(0.3))\n",
        "        model.add(Dense(self.num_vocab))\n",
        "        model.add(Activation('softmax'))\n",
        "        return model\n",
        "\n",
        "    def compile_model(self, loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']):\n",
        "        self.model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n",
        "\n",
        "    def summary(self):\n",
        "        self.model.summary()\n",
        "\n",
        "    def train(self, network_input, network_output, epochs=50, batch_size=64, validation_split=0.2, callbacks=None):\n",
        "        history = self.model.fit(network_input, network_output, epochs=epochs,\n",
        "                                 batch_size=batch_size, validation_split=validation_split, callbacks=callbacks)\n",
        "        return history\n",
        "\n",
        "    def evaluate(self, test_input, test_output):\n",
        "        return self.model.evaluate(test_input, test_output)\n",
        "\n",
        "    def predict(self, input_data):\n",
        "        return self.model.predict(input_data, verbose=0)\n",
        "\n",
        "input_shape = (network_input.shape[1], network_input.shape[2])\n",
        "\n",
        "lstm = EchoAI_LSTM(input_shape, num_vocab)\n",
        "lstm.compile_model()\n",
        "lstm.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9caIV3VbO8GZ",
        "outputId": "15bccd02-25fe-4f73-ed4f-687e0b5fbcd7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "79/79 [==============================] - 12s 94ms/step - loss: 5.6773 - accuracy: 0.0136 - val_loss: 5.6928 - val_accuracy: 0.0129\n",
            "Epoch 2/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 5.4346 - accuracy: 0.0164 - val_loss: 5.2657 - val_accuracy: 0.0141\n",
            "Epoch 3/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 5.3363 - accuracy: 0.0188 - val_loss: 7.2687 - val_accuracy: 0.0157\n",
            "Epoch 4/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 5.4934 - accuracy: 0.0188 - val_loss: 5.0152 - val_accuracy: 0.0252\n",
            "Epoch 5/100\n",
            "79/79 [==============================] - 6s 79ms/step - loss: 5.2924 - accuracy: 0.0193 - val_loss: 5.1532 - val_accuracy: 0.0043\n",
            "Epoch 6/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 5.2785 - accuracy: 0.0203 - val_loss: 5.0730 - val_accuracy: 0.0164\n",
            "Epoch 7/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 5.5734 - accuracy: 0.0202 - val_loss: 5.0492 - val_accuracy: 0.0132\n",
            "Epoch 8/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 5.2646 - accuracy: 0.0207 - val_loss: 5.2571 - val_accuracy: 0.0144\n",
            "Epoch 9/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 5.2551 - accuracy: 0.0209 - val_loss: 5.0183 - val_accuracy: 0.0188\n",
            "Epoch 10/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 5.2419 - accuracy: 0.0204 - val_loss: 5.0636 - val_accuracy: 0.0230\n",
            "Epoch 11/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 5.2311 - accuracy: 0.0216 - val_loss: 5.2118 - val_accuracy: 0.0180\n",
            "Epoch 12/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 5.2260 - accuracy: 0.0202 - val_loss: 5.0189 - val_accuracy: 0.0083\n",
            "Epoch 13/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 5.2143 - accuracy: 0.0223 - val_loss: 5.2060 - val_accuracy: 0.0227\n",
            "Epoch 14/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 5.2136 - accuracy: 0.0208 - val_loss: 5.0920 - val_accuracy: 0.0182\n",
            "Epoch 15/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 5.2049 - accuracy: 0.0220 - val_loss: 5.2823 - val_accuracy: 0.0147\n",
            "Epoch 16/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 5.1956 - accuracy: 0.0222 - val_loss: 5.9737 - val_accuracy: 0.0154\n",
            "Epoch 17/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 5.2050 - accuracy: 0.0220 - val_loss: 5.5440 - val_accuracy: 0.0056\n",
            "Epoch 18/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 5.1847 - accuracy: 0.0222 - val_loss: 5.0336 - val_accuracy: 0.0139\n",
            "Epoch 19/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 5.1626 - accuracy: 0.0239 - val_loss: 5.2806 - val_accuracy: 0.0189\n",
            "Epoch 20/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 5.1689 - accuracy: 0.0237 - val_loss: 5.1320 - val_accuracy: 0.0191\n",
            "Epoch 21/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 5.1401 - accuracy: 0.0258 - val_loss: 5.2770 - val_accuracy: 0.0148\n",
            "Epoch 22/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 5.1379 - accuracy: 0.0256 - val_loss: 5.0783 - val_accuracy: 0.0196\n",
            "Epoch 23/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 5.1165 - accuracy: 0.0264 - val_loss: 5.0496 - val_accuracy: 0.0242\n",
            "Epoch 24/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 5.0975 - accuracy: 0.0269 - val_loss: 5.3818 - val_accuracy: 0.0132\n",
            "Epoch 25/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 5.0869 - accuracy: 0.0283 - val_loss: 5.0406 - val_accuracy: 0.0224\n",
            "Epoch 26/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 5.0589 - accuracy: 0.0299 - val_loss: 5.2803 - val_accuracy: 0.0204\n",
            "Epoch 27/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 5.0397 - accuracy: 0.0317 - val_loss: 5.1383 - val_accuracy: 0.0272\n",
            "Epoch 28/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 5.0072 - accuracy: 0.0343 - val_loss: 5.1212 - val_accuracy: 0.0150\n",
            "Epoch 29/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 4.9805 - accuracy: 0.0364 - val_loss: 5.3500 - val_accuracy: 0.0176\n",
            "Epoch 30/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 4.9427 - accuracy: 0.0403 - val_loss: 5.2965 - val_accuracy: 0.0185\n",
            "Epoch 31/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 4.9007 - accuracy: 0.0428 - val_loss: 5.1897 - val_accuracy: 0.0155\n",
            "Epoch 32/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 4.8547 - accuracy: 0.0462 - val_loss: 5.3476 - val_accuracy: 0.0183\n",
            "Epoch 33/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 4.8196 - accuracy: 0.0488 - val_loss: 5.2099 - val_accuracy: 0.0203\n",
            "Epoch 34/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 4.7437 - accuracy: 0.0558 - val_loss: 5.3034 - val_accuracy: 0.0163\n",
            "Epoch 35/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 4.6803 - accuracy: 0.0616 - val_loss: 5.2630 - val_accuracy: 0.0083\n",
            "Epoch 36/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 4.6134 - accuracy: 0.0650 - val_loss: 5.5792 - val_accuracy: 0.0159\n",
            "Epoch 37/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 4.5259 - accuracy: 0.0741 - val_loss: 5.3732 - val_accuracy: 0.0233\n",
            "Epoch 38/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 4.4332 - accuracy: 0.0802 - val_loss: 5.5120 - val_accuracy: 0.0156\n",
            "Epoch 39/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 4.3422 - accuracy: 0.0873 - val_loss: 5.3990 - val_accuracy: 0.0209\n",
            "Epoch 40/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 4.2356 - accuracy: 0.0981 - val_loss: 5.6228 - val_accuracy: 0.0182\n",
            "Epoch 41/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 4.1376 - accuracy: 0.1074 - val_loss: 5.6775 - val_accuracy: 0.0119\n",
            "Epoch 42/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 4.0212 - accuracy: 0.1190 - val_loss: 5.8136 - val_accuracy: 0.0152\n",
            "Epoch 43/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 3.9055 - accuracy: 0.1294 - val_loss: 5.5698 - val_accuracy: 0.0187\n",
            "Epoch 44/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 3.7916 - accuracy: 0.1422 - val_loss: 5.9234 - val_accuracy: 0.0155\n",
            "Epoch 45/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 3.6768 - accuracy: 0.1524 - val_loss: 5.8710 - val_accuracy: 0.0162\n",
            "Epoch 46/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 3.5610 - accuracy: 0.1643 - val_loss: 5.9221 - val_accuracy: 0.0140\n",
            "Epoch 47/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 3.4418 - accuracy: 0.1808 - val_loss: 5.8030 - val_accuracy: 0.0157\n",
            "Epoch 48/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 3.3117 - accuracy: 0.1950 - val_loss: 5.9225 - val_accuracy: 0.0188\n",
            "Epoch 49/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 3.1868 - accuracy: 0.2112 - val_loss: 6.2783 - val_accuracy: 0.0217\n",
            "Epoch 50/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 3.0751 - accuracy: 0.2255 - val_loss: 6.2651 - val_accuracy: 0.0201\n",
            "Epoch 51/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 2.9672 - accuracy: 0.2412 - val_loss: 6.4655 - val_accuracy: 0.0114\n",
            "Epoch 52/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 2.8341 - accuracy: 0.2601 - val_loss: 6.4665 - val_accuracy: 0.0187\n",
            "Epoch 53/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 2.7115 - accuracy: 0.2823 - val_loss: 6.3190 - val_accuracy: 0.0148\n",
            "Epoch 54/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 2.5953 - accuracy: 0.2992 - val_loss: 6.5225 - val_accuracy: 0.0194\n",
            "Epoch 55/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 2.4884 - accuracy: 0.3181 - val_loss: 6.6694 - val_accuracy: 0.0156\n",
            "Epoch 56/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 2.3805 - accuracy: 0.3389 - val_loss: 6.7269 - val_accuracy: 0.0189\n",
            "Epoch 57/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 2.2769 - accuracy: 0.3601 - val_loss: 6.9962 - val_accuracy: 0.0107\n",
            "Epoch 58/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 2.1686 - accuracy: 0.3821 - val_loss: 6.9510 - val_accuracy: 0.0134\n",
            "Epoch 59/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 2.0683 - accuracy: 0.4018 - val_loss: 6.9199 - val_accuracy: 0.0171\n",
            "Epoch 60/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 1.9717 - accuracy: 0.4226 - val_loss: 7.2044 - val_accuracy: 0.0160\n",
            "Epoch 61/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 1.8673 - accuracy: 0.4487 - val_loss: 7.4387 - val_accuracy: 0.0198\n",
            "Epoch 62/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 1.7857 - accuracy: 0.4682 - val_loss: 7.3204 - val_accuracy: 0.0182\n",
            "Epoch 63/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 1.6791 - accuracy: 0.4948 - val_loss: 7.6252 - val_accuracy: 0.0159\n",
            "Epoch 64/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 1.6312 - accuracy: 0.5079 - val_loss: 7.6257 - val_accuracy: 0.0157\n",
            "Epoch 65/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 1.5174 - accuracy: 0.5348 - val_loss: 7.7905 - val_accuracy: 0.0145\n",
            "Epoch 66/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 1.4526 - accuracy: 0.5527 - val_loss: 7.7385 - val_accuracy: 0.0181\n",
            "Epoch 67/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 1.3923 - accuracy: 0.5715 - val_loss: 7.9786 - val_accuracy: 0.0155\n",
            "Epoch 68/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 1.3442 - accuracy: 0.5839 - val_loss: 8.2650 - val_accuracy: 0.0148\n",
            "Epoch 69/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 1.2487 - accuracy: 0.6120 - val_loss: 8.4645 - val_accuracy: 0.0170\n",
            "Epoch 70/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 1.2044 - accuracy: 0.6217 - val_loss: 8.3857 - val_accuracy: 0.0152\n",
            "Epoch 71/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 1.1255 - accuracy: 0.6474 - val_loss: 8.4326 - val_accuracy: 0.0141\n",
            "Epoch 72/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 1.0814 - accuracy: 0.6576 - val_loss: 8.4643 - val_accuracy: 0.0186\n",
            "Epoch 73/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 1.0175 - accuracy: 0.6807 - val_loss: 8.6249 - val_accuracy: 0.0116\n",
            "Epoch 74/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 0.9859 - accuracy: 0.6905 - val_loss: 8.5251 - val_accuracy: 0.0123\n",
            "Epoch 75/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 0.9475 - accuracy: 0.7028 - val_loss: 8.9967 - val_accuracy: 0.0149\n",
            "Epoch 76/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 0.8680 - accuracy: 0.7234 - val_loss: 8.9477 - val_accuracy: 0.0171\n",
            "Epoch 77/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 0.8453 - accuracy: 0.7309 - val_loss: 9.3243 - val_accuracy: 0.0133\n",
            "Epoch 78/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 0.8246 - accuracy: 0.7406 - val_loss: 9.2074 - val_accuracy: 0.0107\n",
            "Epoch 79/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 0.7810 - accuracy: 0.7516 - val_loss: 9.0855 - val_accuracy: 0.0142\n",
            "Epoch 80/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 0.7485 - accuracy: 0.7637 - val_loss: 9.3608 - val_accuracy: 0.0127\n",
            "Epoch 81/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 0.7082 - accuracy: 0.7745 - val_loss: 9.2609 - val_accuracy: 0.0193\n",
            "Epoch 82/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 0.6574 - accuracy: 0.7912 - val_loss: 9.5969 - val_accuracy: 0.0130\n",
            "Epoch 83/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 0.6497 - accuracy: 0.7950 - val_loss: 9.5755 - val_accuracy: 0.0110\n",
            "Epoch 84/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 0.6389 - accuracy: 0.7991 - val_loss: 9.7664 - val_accuracy: 0.0135\n",
            "Epoch 85/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 0.5928 - accuracy: 0.8128 - val_loss: 9.8344 - val_accuracy: 0.0150\n",
            "Epoch 86/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 0.5621 - accuracy: 0.8237 - val_loss: 9.6961 - val_accuracy: 0.0168\n",
            "Epoch 87/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 0.5692 - accuracy: 0.8201 - val_loss: 10.1226 - val_accuracy: 0.0131\n",
            "Epoch 88/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 0.5303 - accuracy: 0.8337 - val_loss: 10.0402 - val_accuracy: 0.0139\n",
            "Epoch 89/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 0.5221 - accuracy: 0.8382 - val_loss: 9.8349 - val_accuracy: 0.0174\n",
            "Epoch 90/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 0.5094 - accuracy: 0.8410 - val_loss: 10.2153 - val_accuracy: 0.0157\n",
            "Epoch 91/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 0.4854 - accuracy: 0.8506 - val_loss: 10.2604 - val_accuracy: 0.0135\n",
            "Epoch 92/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 0.4515 - accuracy: 0.8587 - val_loss: 10.2093 - val_accuracy: 0.0161\n",
            "Epoch 93/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 0.4418 - accuracy: 0.8603 - val_loss: 10.3016 - val_accuracy: 0.0125\n",
            "Epoch 94/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 0.4364 - accuracy: 0.8635 - val_loss: 10.5129 - val_accuracy: 0.0113\n",
            "Epoch 95/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 0.4163 - accuracy: 0.8690 - val_loss: 10.9669 - val_accuracy: 0.0161\n",
            "Epoch 96/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 0.4037 - accuracy: 0.8740 - val_loss: 10.6564 - val_accuracy: 0.0140\n",
            "Epoch 97/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 0.3875 - accuracy: 0.8783 - val_loss: 10.5861 - val_accuracy: 0.0139\n",
            "Epoch 98/100\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 0.3685 - accuracy: 0.8842 - val_loss: 10.6894 - val_accuracy: 0.0172\n",
            "Epoch 99/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 0.3570 - accuracy: 0.8892 - val_loss: 10.9657 - val_accuracy: 0.0153\n",
            "Epoch 100/100\n",
            "79/79 [==============================] - 6s 71ms/step - loss: 0.3651 - accuracy: 0.8880 - val_loss: 11.2364 - val_accuracy: 0.0124\n"
          ]
        }
      ],
      "source": [
        "num_epochs = 100\n",
        "\n",
        "filepath = \"./weights/weights-improvement-{epoch:02d}-{loss:.4f}-bigger_1.h5\"\n",
        "checkpoint = ModelCheckpoint(\n",
        "    \"best_model.keras\",\n",
        "    monitor='val_accuracy',\n",
        "    verbose=0,\n",
        "    save_best_only=True,\n",
        "    mode='min'\n",
        ")\n",
        "\n",
        "callbacks_list = [checkpoint]\n",
        "\n",
        "model = lstm.train(network_input, network_output, epochs=num_epochs, validation_split=0.2, batch_size=512, callbacks=callbacks_list)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lstm.model.save('./drive/MyDrive/Models/echoai_lstm_model.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OWwfG-5oCxI3",
        "outputId": "3fae47da-6cad-427d-e2e1-dcda576cf481"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Results"
      ],
      "metadata": {
        "id": "LzrwmvTy1AM_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "_kLsvjIjEolk",
        "outputId": "85a849f9-37e5-48e5-ee41-a7fa54a38021"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min loss 8.988272905349731 at 58\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 562,
              "height": 454
            }
          }
        }
      ],
      "source": [
        "total_loss = [train + val for train, val in zip(model.history['loss'], model.history['val_loss'])]\n",
        "\n",
        "min_total_loss = min(total_loss)\n",
        "min_epoch = total_loss.index(min_total_loss)\n",
        "print(f\"Min loss {min_total_loss} at {min_epoch}\")\n",
        "\n",
        "plt.plot(model.history['loss'], label='Training Loss')\n",
        "plt.plot(model.history['val_loss'], label='Validation Loss')\n",
        "plt.plot(total_loss, label='Total Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Loss Over Epochs for Regular LSTM')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(model.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.plot(model.history['accuracy'], label='Training Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Accuracy Over Epochs for Regular LSTM')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "DpJpqtCVfDcg",
        "outputId": "c9d7ec02-f8da-46cf-d8a4-f41ed638413b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 567,
              "height": 454
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8vsuqq0ANnWO"
      },
      "source": [
        "# Create predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fZpS9oEUO8GZ"
      },
      "outputs": [],
      "source": [
        "def sample_with_temperature(predictions, temperature=1.0):\n",
        "    predictions = np.asarray(predictions).astype('float64')\n",
        "\n",
        "    if temperature==0.0:\n",
        "      return np.argmax(predictions)\n",
        "\n",
        "    predictions = np.log(predictions + 1e-9) / temperature\n",
        "    exp_predictions = np.exp(predictions)\n",
        "    predictions = exp_predictions / np.sum(exp_predictions)\n",
        "    probas = np.random.multinomial(1, predictions, 1)\n",
        "    return np.argmax(probas)\n",
        "\n",
        "def generate_notes(model, network_input, pitchnames, num_vocab, temperature=1.0):\n",
        "    start = np.random.randint(0, len(network_input)-1)\n",
        "    int_to_note = dict((number, note)for number, note in enumerate(pitchnames))\n",
        "\n",
        "    pattern = network_input[start]\n",
        "    output = []\n",
        "\n",
        "    for note_index in range(100):\n",
        "        prediction_input = np.reshape(pattern, (1, len(pattern), 1))\n",
        "\n",
        "        prediction = model.predict(prediction_input)\n",
        "        index = sample_with_temperature(prediction[0], temperature)\n",
        "        result = int_to_note[index]\n",
        "        output.append(result)\n",
        "\n",
        "        # Update the pattern\n",
        "        pattern = np.append(pattern, (index / float(num_vocab)))\n",
        "        pattern = pattern[1:]\n",
        "\n",
        "    return output\n",
        "\n",
        "num_vocab = len(set(flat_notes[:50000]))\n",
        "pitchnames = sorted(set(item for item in flat_notes[:50000]))\n",
        "output = generate_notes(lstm, network_input, pitchnames, num_vocab, temperature=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6I6CHr8ORKT_",
        "outputId": "c218b7a3-c58d-4d37-db00-e2089191b6a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['C3', 'E4', 'E4', 'E4', 'G#3', 'G#3', 'G#3', 'G#3', 'G#3', 'G#3', 'G#3', 'G#3', 'D3', 'D3', '2.8', '2.8', '2.8', 'G#3', 'B-4', 'B-4', 'B-4', 'F4', 'G3', 'E3', 'E3', 'E3', 'E3', 'E3', 'E3', 'E3', 'C4', 'C#2', '0.5', 'F3', 'C#5', 'C#3', 'F4', 'C#4', 'C#4', 'A4', 'A4', 'D3', 'D4', 'D4', 'D5', 'D4', 'A5', 'D5', '7.9', '7.9', '7.9', '7.9', 'B4', 'C5', 'C#5', 'A2', 'A2', 'A1', 'A1', 'A1', 'A1', 'D3', '3.6.9.11', 'A3', '4.8', '11.3', '4.8', '4.8', '11.3', '11.3', 'F#5', '3', '3', '3', '3', '3', '1.3.5', '1.3.5', 'G3', 'G3', '1.4.8', '3.4.8.11', '3.7.10', '3.7.10', '8.11.2', '8.11.2', 'B4', 'B-4', 'B-4', 'B-4', '1.3', '8.11', 'B-4', 'B-4', '1.3', '1.3', 'E-2', 'G#4', '3', 'G#4']\n"
          ]
        }
      ],
      "source": [
        "print(output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SoGlB59EO8Ga"
      },
      "outputs": [],
      "source": [
        "def create_midi(output):\n",
        "    offset = 0\n",
        "    output_notes = []\n",
        "\n",
        "    # create note and chord objects based on the values generated by the model\n",
        "    for pattern in output:\n",
        "        # pattern is a chord\n",
        "        if ('.' in pattern) or pattern.isdigit():\n",
        "            notes_in_chord = pattern.split('.')\n",
        "            notes = []\n",
        "            for current_note in notes_in_chord:\n",
        "                new_note = note.Note(int(current_note))\n",
        "                new_note.storedInstrument = instrument.Piano()\n",
        "                notes.append(new_note)\n",
        "            new_chord = chord.Chord(notes)\n",
        "            new_chord.offset = offset\n",
        "            output_notes.append(new_chord)\n",
        "        # pattern is a note\n",
        "        else:\n",
        "            new_note = note.Note(pattern)\n",
        "            new_note.offset = offset\n",
        "            new_note.storedInstrument = instrument.Piano()\n",
        "            output_notes.append(new_note)\n",
        "\n",
        "        # increase offset each iteration so that notes do not stack\n",
        "        offset += 0.5\n",
        "\n",
        "    midi_stream = stream.Stream(output_notes)\n",
        "    midi_stream.write('midi', fp='output.mid')\n",
        "\n",
        "create_midi(output)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "A100",
      "machine_shape": "hm",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.7"
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}