EchoAI_LSTM_LocalAttention.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "A100",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "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_LocalAttention.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": "rbGWTugp8Y2Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from keras.utils import to_categorical\n",
        "import tensorflow.keras.backend as K\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Input, LSTM, Bidirectional, Dense, Concatenate\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\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",
        "from pathlib import Path\n",
        "import random\n",
        "from music21 import converter, instrument, note, chord, stream\n",
        "import pickle\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "1oFKUj3ubFIZ"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Mounting"
      ],
      "metadata": {
        "id": "LLguBP958dcR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xkI2x0kkavqM",
        "outputId": "904ac41e-b011-46b3-93d6-f079b04b8d31"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Load preprocessed data"
      ],
      "metadata": {
        "id": "Io5kSBNo8fZ9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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 note in notes:\n",
        "    if isinstance(note, list):\n",
        "        flat_notes.extend(note)\n",
        "    else:\n",
        "        flat_notes.append(note)\n",
        "\n",
        "flat_notes = flat_notes[:50000]\n",
        "\n",
        "print(\"There are \" + str(len(flat_notes)) + \" many notes\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5BX57EHhZDdC",
        "outputId": "db98b8ad-deb1-43ea-9f2b-0e5d5db476d2"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "File content length: 23275087 bytes\n",
            "b'\\x80\\x04\\x95\\x01\\x00\\x01\\x00\\x00\\x00\\x00\\x00]\\x94(\\x8c\\x039.2\\x94\\x8c\\x02D4\\x94\\x8c\\x03F#4\\x94\\x8c\\x039.2\\x94\\x8c\\x02A4\\x94\\x8c\\x03F#4\\x94\\x8c\\x039.2\\x94\\x8c\\x02D5\\x94\\x8c\\x02A4\\x94\\x8c\\x039.2\\x94\\x8c\\x03F#5\\x94\\x8c\\x02D5\\x94\\x8c\\x039.2\\x94\\x8c\\x02D6\\x94\\x8c\\x02A5\\x94\\x8c\\x039'\n",
            "Loaded successfully.\n",
            "There are 50000 many notes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Prepare sequences\n",
        "This snippet prepares sequences in an expanded format as specified by \"Everybody Compose: Deep Beats to Music\" https://arxiv.org/pdf/2306.06284"
      ],
      "metadata": {
        "id": "k0i5dgl58h2Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare_sequences_expanded(notes, seq_length=100, num_classes=None):\n",
        "    pitch_set = sorted(set(notes))\n",
        "    note_to_int = {note: number for number, note in enumerate(pitch_set)}\n",
        "\n",
        "    inputs, outputs = [], []\n",
        "\n",
        "    for i in range(0, len(notes) - seq_length, 1):\n",
        "        sequence_in = notes[i:i + seq_length]\n",
        "        sequence_out = notes[i:i + seq_length]\n",
        "        inputs.append([note_to_int[char] for char in sequence_in])\n",
        "        outputs.append([note_to_int[char] for char in sequence_out])\n",
        "\n",
        "    num_patterns = len(inputs)\n",
        "\n",
        "    inputs = np.reshape(inputs, (num_patterns, seq_length, 1))\n",
        "    inputs = inputs / float(num_classes)\n",
        "    outputs = to_categorical(outputs, num_classes=num_classes)\n",
        "\n",
        "    return inputs, outputs\n",
        "\n",
        "sequence_length = 64\n",
        "num_vocab = len(set(flat_notes))\n",
        "network_input, network_output = prepare_sequences_expanded(flat_notes, sequence_length, num_vocab)"
      ],
      "metadata": {
        "id": "Ig9PriAfe_4e"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model"
      ],
      "metadata": {
        "id": "2oRdXqT18s7I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_input, val_input, train_output, val_output = train_test_split(\n",
        "    network_input, network_output, test_size=0.2, random_state=42)\n",
        "\n",
        "print(f\"Training input shape: {train_input.shape}\")\n",
        "print(f\"Validation input shape: {val_input.shape}\")\n",
        "print(f\"Training output shape: {train_output.shape}\")\n",
        "print(f\"Validation output shape: {val_output.shape}\")\n",
        "\n",
        "# Parameters setup\n",
        "sequence_length = 64  # Length of the sequence\n",
        "n_features = 1        # Number of input features per timestep (e.g., pitch of the note)\n",
        "num_vocab = 967         # Total number of possible output classes (different possible notes or musical symbols)"
      ],
      "metadata": {
        "id": "TUwNYdeu6DAD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1166d162-81f0-4634-863e-c10d42f7b2f8"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training input shape: (39948, 64, 1)\n",
            "Validation input shape: (9988, 64, 1)\n",
            "Training output shape: (39948, 64, 967)\n",
            "Validation output shape: (9988, 64, 967)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class EchoAI_LSTM_LocalAttention:\n",
        "    def __init__(self, sequence_length, n_features, num_vocab):\n",
        "        self.sequence_length = sequence_length\n",
        "        self.n_features = n_features\n",
        "        self.num_vocab = num_vocab\n",
        "        self.model = self._build_model()\n",
        "\n",
        "    def _build_model(self):\n",
        "        inputs = Input(shape=(self.sequence_length, self.n_features))\n",
        "\n",
        "        # Bidireectional LSTM\n",
        "        bidirectional_lstm = Bidirectional(LSTM(1024, return_sequences=True, return_state=True))\n",
        "        x, forward_h, forward_c, backward_h, backward_c = bidirectional_lstm(inputs)\n",
        "\n",
        "        state_size = 1024\n",
        "        state_h = Concatenate()([forward_h, backward_h])\n",
        "        state_c = Concatenate()([forward_c, backward_c])\n",
        "        state_h = Dense(state_size, activation='tanh')(state_h)\n",
        "        state_c = Dense(state_size, activation='tanh')(state_c)\n",
        "\n",
        "        # Second LSTM layer\n",
        "        second_lstm = LSTM(state_size, return_sequences=True)\n",
        "        second_lstm_outputs = second_lstm(x, initial_state=[state_h, state_c])\n",
        "\n",
        "        # Output layer (num_vocab units)\n",
        "        outputs = Dense(self.num_vocab, activation='softmax')(second_lstm_outputs)\n",
        "\n",
        "        model = Model(inputs=inputs, outputs=outputs)\n",
        "        model.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "        return model\n",
        "\n",
        "    def summary(self):\n",
        "        self.model.summary()\n",
        "\n",
        "    def train(self, network_input, network_output, val_input, val_output, epochs=50, batch_size=64, callbacks=None):\n",
        "        history = self.model.fit(network_input, network_output, epochs=epochs,\n",
        "                                 batch_size=batch_size, validation_data=(val_input, val_output), 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)\n",
        "\n",
        "model = EchoAI_LSTM_LocalAttention(sequence_length, n_features, num_vocab)\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8SBbYBM6Z_Bb",
        "outputId": "f901d1d6-9b82-4f3e-b06c-934724b3ea8b"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_2 (InputLayer)        [(None, 64, 1)]              0         []                            \n",
            "                                                                                                  \n",
            " bidirectional_1 (Bidirecti  [(None, 64, 2048),           8404992   ['input_2[0][0]']             \n",
            " onal)                        (None, 1024),                                                       \n",
            "                              (None, 1024),                                                       \n",
            "                              (None, 1024),                                                       \n",
            "                              (None, 1024)]                                                       \n",
            "                                                                                                  \n",
            " concatenate_2 (Concatenate  (None, 2048)                 0         ['bidirectional_1[0][1]',     \n",
            " )                                                                   'bidirectional_1[0][3]']     \n",
            "                                                                                                  \n",
            " concatenate_3 (Concatenate  (None, 2048)                 0         ['bidirectional_1[0][2]',     \n",
            " )                                                                   'bidirectional_1[0][4]']     \n",
            "                                                                                                  \n",
            " dense_3 (Dense)             (None, 1024)                 2098176   ['concatenate_2[0][0]']       \n",
            "                                                                                                  \n",
            " dense_4 (Dense)             (None, 1024)                 2098176   ['concatenate_3[0][0]']       \n",
            "                                                                                                  \n",
            " lstm_3 (LSTM)               (None, 64, 1024)             1258700   ['bidirectional_1[0][0]',     \n",
            "                                                          8          'dense_3[0][0]',             \n",
            "                                                                     'dense_4[0][0]']             \n",
            "                                                                                                  \n",
            " dense_5 (Dense)             (None, 64, 967)              991175    ['lstm_3[0][0]']              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 26179527 (99.87 MB)\n",
            "Trainable params: 26179527 (99.87 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "\n",
        "filepath = \"./weights/weights-improvement-{epoch:02d}-{loss:.4f}-bigger_1.keras\"\n",
        "checkpoint = ModelCheckpoint(\n",
        "    \"best_model.keras\",\n",
        "    monitor='loss',\n",
        "    verbose=0,\n",
        "    save_best_only=True,\n",
        "    mode='min'\n",
        ")\n",
        "\n",
        "callbacks_list = [checkpoint]\n",
        "\n",
        "history = model.train(train_input, train_output, val_input, val_output, epochs=100,  batch_size=512)"
      ],
      "metadata": {
        "id": "tS0hsRL2kLw8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82434a11-bdc9-44f4-b25e-ca8e645131dd"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "79/79 [==============================] - 18s 168ms/step - loss: 5.3918 - accuracy: 0.0194 - val_loss: 5.3497 - val_accuracy: 0.0207\n",
            "Epoch 2/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 5.3406 - accuracy: 0.0197 - val_loss: 5.3322 - val_accuracy: 0.0207\n",
            "Epoch 3/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 5.1810 - accuracy: 0.0228 - val_loss: 5.0137 - val_accuracy: 0.0255\n",
            "Epoch 4/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 4.8656 - accuracy: 0.0308 - val_loss: 4.7957 - val_accuracy: 0.0296\n",
            "Epoch 5/100\n",
            "79/79 [==============================] - 10s 126ms/step - loss: 4.5030 - accuracy: 0.0506 - val_loss: 4.3681 - val_accuracy: 0.0562\n",
            "Epoch 6/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 4.3128 - accuracy: 0.0635 - val_loss: 4.1005 - val_accuracy: 0.0792\n",
            "Epoch 7/100\n",
            "79/79 [==============================] - 10s 126ms/step - loss: 4.0544 - accuracy: 0.0810 - val_loss: 3.8579 - val_accuracy: 0.1018\n",
            "Epoch 8/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 3.9303 - accuracy: 0.0882 - val_loss: 3.9980 - val_accuracy: 0.0740\n",
            "Epoch 9/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 3.8671 - accuracy: 0.0950 - val_loss: 4.0338 - val_accuracy: 0.0721\n",
            "Epoch 10/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 3.7086 - accuracy: 0.1117 - val_loss: 4.3690 - val_accuracy: 0.0367\n",
            "Epoch 11/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 3.5287 - accuracy: 0.1305 - val_loss: 3.4542 - val_accuracy: 0.1317\n",
            "Epoch 12/100\n",
            "79/79 [==============================] - 10s 123ms/step - loss: 3.5094 - accuracy: 0.1334 - val_loss: 3.2756 - val_accuracy: 0.1634\n",
            "Epoch 13/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 3.4079 - accuracy: 0.1431 - val_loss: 3.2214 - val_accuracy: 0.1713\n",
            "Epoch 14/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 3.2253 - accuracy: 0.1650 - val_loss: 3.1047 - val_accuracy: 0.1875\n",
            "Epoch 15/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 3.1099 - accuracy: 0.1804 - val_loss: 3.0496 - val_accuracy: 0.1845\n",
            "Epoch 16/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 2.9468 - accuracy: 0.2081 - val_loss: 2.8406 - val_accuracy: 0.2347\n",
            "Epoch 17/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 2.9518 - accuracy: 0.2107 - val_loss: 2.8159 - val_accuracy: 0.2305\n",
            "Epoch 18/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 2.7932 - accuracy: 0.2347 - val_loss: 2.8214 - val_accuracy: 0.2166\n",
            "Epoch 19/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 2.8256 - accuracy: 0.2340 - val_loss: 2.7475 - val_accuracy: 0.2376\n",
            "Epoch 20/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 2.7361 - accuracy: 0.2522 - val_loss: 4.1451 - val_accuracy: 0.0806\n",
            "Epoch 21/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 2.8038 - accuracy: 0.2414 - val_loss: 2.4709 - val_accuracy: 0.3130\n",
            "Epoch 22/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 2.4855 - accuracy: 0.3049 - val_loss: 2.3441 - val_accuracy: 0.3399\n",
            "Epoch 23/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 2.6663 - accuracy: 0.2771 - val_loss: 2.4226 - val_accuracy: 0.3092\n",
            "Epoch 24/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 2.4257 - accuracy: 0.3141 - val_loss: 2.5070 - val_accuracy: 0.2831\n",
            "Epoch 25/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 2.2648 - accuracy: 0.3564 - val_loss: 2.8220 - val_accuracy: 0.2346\n",
            "Epoch 26/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 2.1870 - accuracy: 0.3730 - val_loss: 2.1137 - val_accuracy: 0.3903\n",
            "Epoch 27/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 2.1348 - accuracy: 0.3821 - val_loss: 2.0141 - val_accuracy: 0.4173\n",
            "Epoch 28/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 2.2900 - accuracy: 0.3467 - val_loss: 2.1120 - val_accuracy: 0.3884\n",
            "Epoch 29/100\n",
            "79/79 [==============================] - 10s 126ms/step - loss: 2.1547 - accuracy: 0.3965 - val_loss: 2.6476 - val_accuracy: 0.2419\n",
            "Epoch 30/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 2.0615 - accuracy: 0.4060 - val_loss: 1.8966 - val_accuracy: 0.4503\n",
            "Epoch 31/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 1.8577 - accuracy: 0.4631 - val_loss: 1.7994 - val_accuracy: 0.4819\n",
            "Epoch 32/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 2.0000 - accuracy: 0.4181 - val_loss: 1.8971 - val_accuracy: 0.4436\n",
            "Epoch 33/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 2.1570 - accuracy: 0.3987 - val_loss: 1.9103 - val_accuracy: 0.4461\n",
            "Epoch 34/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 1.7808 - accuracy: 0.4850 - val_loss: 1.7036 - val_accuracy: 0.5053\n",
            "Epoch 35/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 1.8116 - accuracy: 0.4731 - val_loss: 2.0888 - val_accuracy: 0.3749\n",
            "Epoch 36/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 1.7370 - accuracy: 0.4921 - val_loss: 1.6723 - val_accuracy: 0.5096\n",
            "Epoch 37/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 1.6264 - accuracy: 0.5246 - val_loss: 1.5455 - val_accuracy: 0.5475\n",
            "Epoch 38/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 1.8567 - accuracy: 0.4741 - val_loss: 1.5710 - val_accuracy: 0.5431\n",
            "Epoch 39/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 1.6197 - accuracy: 0.5280 - val_loss: 1.5067 - val_accuracy: 0.5603\n",
            "Epoch 40/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 1.5953 - accuracy: 0.5325 - val_loss: 1.4541 - val_accuracy: 0.5762\n",
            "Epoch 41/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 1.4855 - accuracy: 0.5658 - val_loss: 1.4326 - val_accuracy: 0.5809\n",
            "Epoch 42/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 1.5073 - accuracy: 0.5605 - val_loss: 1.8480 - val_accuracy: 0.4524\n",
            "Epoch 43/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 1.9135 - accuracy: 0.4811 - val_loss: 1.7918 - val_accuracy: 0.4769\n",
            "Epoch 44/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 1.4593 - accuracy: 0.5753 - val_loss: 1.4706 - val_accuracy: 0.5615\n",
            "Epoch 45/100\n",
            "79/79 [==============================] - 10s 126ms/step - loss: 1.3833 - accuracy: 0.5944 - val_loss: 1.3238 - val_accuracy: 0.6132\n",
            "Epoch 46/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 1.4769 - accuracy: 0.5749 - val_loss: 1.3786 - val_accuracy: 0.5938\n",
            "Epoch 47/100\n",
            "79/79 [==============================] - 10s 123ms/step - loss: 1.4450 - accuracy: 0.5783 - val_loss: 1.2909 - val_accuracy: 0.6202\n",
            "Epoch 48/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 1.2145 - accuracy: 0.6500 - val_loss: 1.4315 - val_accuracy: 0.5780\n",
            "Epoch 49/100\n",
            "79/79 [==============================] - 10s 123ms/step - loss: 1.4938 - accuracy: 0.5742 - val_loss: 1.1952 - val_accuracy: 0.6523\n",
            "Epoch 50/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 1.4632 - accuracy: 0.5797 - val_loss: 1.2645 - val_accuracy: 0.6255\n",
            "Epoch 51/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 1.1368 - accuracy: 0.6725 - val_loss: 1.1097 - val_accuracy: 0.6794\n",
            "Epoch 52/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 1.3459 - accuracy: 0.6112 - val_loss: 1.2242 - val_accuracy: 0.6398\n",
            "Epoch 53/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 1.2159 - accuracy: 0.6497 - val_loss: 1.5775 - val_accuracy: 0.5141\n",
            "Epoch 54/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 1.2716 - accuracy: 0.6310 - val_loss: 1.3926 - val_accuracy: 0.5690\n",
            "Epoch 55/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 1.1854 - accuracy: 0.6515 - val_loss: 1.0414 - val_accuracy: 0.6986\n",
            "Epoch 56/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 1.2314 - accuracy: 0.6450 - val_loss: 1.1609 - val_accuracy: 0.6504\n",
            "Epoch 57/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 1.0911 - accuracy: 0.6806 - val_loss: 1.4069 - val_accuracy: 0.5744\n",
            "Epoch 58/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 1.0758 - accuracy: 0.6867 - val_loss: 0.9881 - val_accuracy: 0.7126\n",
            "Epoch 59/100\n",
            "79/79 [==============================] - 10s 126ms/step - loss: 0.9989 - accuracy: 0.7112 - val_loss: 1.0116 - val_accuracy: 0.6995\n",
            "Epoch 60/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 1.2672 - accuracy: 0.6458 - val_loss: 0.9291 - val_accuracy: 0.7358\n",
            "Epoch 61/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 1.0404 - accuracy: 0.7063 - val_loss: 1.6918 - val_accuracy: 0.4876\n",
            "Epoch 62/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 1.1791 - accuracy: 0.6563 - val_loss: 1.0089 - val_accuracy: 0.7024\n",
            "Epoch 63/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 0.9241 - accuracy: 0.7324 - val_loss: 0.9029 - val_accuracy: 0.7374\n",
            "Epoch 64/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 0.9035 - accuracy: 0.7385 - val_loss: 0.8622 - val_accuracy: 0.7527\n",
            "Epoch 65/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 0.9626 - accuracy: 0.7333 - val_loss: 1.6216 - val_accuracy: 0.5424\n",
            "Epoch 66/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 0.9825 - accuracy: 0.7121 - val_loss: 0.8460 - val_accuracy: 0.7576\n",
            "Epoch 67/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 0.8608 - accuracy: 0.7517 - val_loss: 0.8066 - val_accuracy: 0.7698\n",
            "Epoch 68/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 0.9586 - accuracy: 0.7288 - val_loss: 0.9499 - val_accuracy: 0.7193\n",
            "Epoch 69/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 0.8584 - accuracy: 0.7528 - val_loss: 0.8759 - val_accuracy: 0.7413\n",
            "Epoch 70/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 0.7668 - accuracy: 0.7830 - val_loss: 0.7556 - val_accuracy: 0.7851\n",
            "Epoch 71/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 0.8956 - accuracy: 0.7452 - val_loss: 0.7577 - val_accuracy: 0.7820\n",
            "Epoch 72/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 1.1576 - accuracy: 0.6819 - val_loss: 1.1155 - val_accuracy: 0.6724\n",
            "Epoch 73/100\n",
            "79/79 [==============================] - 10s 126ms/step - loss: 0.7625 - accuracy: 0.7845 - val_loss: 0.6984 - val_accuracy: 0.8072\n",
            "Epoch 74/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 0.8028 - accuracy: 0.7702 - val_loss: 0.7566 - val_accuracy: 0.7791\n",
            "Epoch 75/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 0.6943 - accuracy: 0.8049 - val_loss: 0.9767 - val_accuracy: 0.6979\n",
            "Epoch 76/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 0.9243 - accuracy: 0.7378 - val_loss: 1.0563 - val_accuracy: 0.6763\n",
            "Epoch 77/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 0.8127 - accuracy: 0.7657 - val_loss: 0.7853 - val_accuracy: 0.7651\n",
            "Epoch 78/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 0.6497 - accuracy: 0.8184 - val_loss: 0.6300 - val_accuracy: 0.8250\n",
            "Epoch 79/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 0.6309 - accuracy: 0.8243 - val_loss: 0.6118 - val_accuracy: 0.8310\n",
            "Epoch 80/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 0.7603 - accuracy: 0.7838 - val_loss: 0.6212 - val_accuracy: 0.8251\n",
            "Epoch 81/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 0.8625 - accuracy: 0.7625 - val_loss: 0.6814 - val_accuracy: 0.8049\n",
            "Epoch 82/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 0.6220 - accuracy: 0.8258 - val_loss: 0.5805 - val_accuracy: 0.8403\n",
            "Epoch 83/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 0.7918 - accuracy: 0.7785 - val_loss: 0.5889 - val_accuracy: 0.8355\n",
            "Epoch 84/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 0.6967 - accuracy: 0.8030 - val_loss: 0.5601 - val_accuracy: 0.8458\n",
            "Epoch 85/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 0.6610 - accuracy: 0.8153 - val_loss: 0.6238 - val_accuracy: 0.8238\n",
            "Epoch 86/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 0.6486 - accuracy: 0.8216 - val_loss: 0.9524 - val_accuracy: 0.7110\n",
            "Epoch 87/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 0.6459 - accuracy: 0.8153 - val_loss: 0.8138 - val_accuracy: 0.7614\n",
            "Epoch 88/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 0.5284 - accuracy: 0.8542 - val_loss: 0.5081 - val_accuracy: 0.8602\n",
            "Epoch 89/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 0.4840 - accuracy: 0.8688 - val_loss: 0.5064 - val_accuracy: 0.8589\n",
            "Epoch 90/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 0.5635 - accuracy: 0.8429 - val_loss: 0.7762 - val_accuracy: 0.7592\n",
            "Epoch 91/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 0.6695 - accuracy: 0.8107 - val_loss: 0.5943 - val_accuracy: 0.8261\n",
            "Epoch 92/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 0.5113 - accuracy: 0.8568 - val_loss: 0.5008 - val_accuracy: 0.8578\n",
            "Epoch 93/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 0.4415 - accuracy: 0.8812 - val_loss: 0.4574 - val_accuracy: 0.8741\n",
            "Epoch 94/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 0.6953 - accuracy: 0.8065 - val_loss: 0.4984 - val_accuracy: 0.8580\n",
            "Epoch 95/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 0.5394 - accuracy: 0.8486 - val_loss: 0.4453 - val_accuracy: 0.8782\n",
            "Epoch 96/100\n",
            "79/79 [==============================] - 10s 125ms/step - loss: 0.5095 - accuracy: 0.8585 - val_loss: 0.5139 - val_accuracy: 0.8523\n",
            "Epoch 97/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 0.4143 - accuracy: 0.8882 - val_loss: 0.4159 - val_accuracy: 0.8880\n",
            "Epoch 98/100\n",
            "79/79 [==============================] - 10s 126ms/step - loss: 0.5219 - accuracy: 0.8683 - val_loss: 2.1729 - val_accuracy: 0.4816\n",
            "Epoch 99/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 0.6912 - accuracy: 0.8141 - val_loss: 0.7992 - val_accuracy: 0.7779\n",
            "Epoch 100/100\n",
            "79/79 [==============================] - 10s 124ms/step - loss: 0.5714 - accuracy: 0.8430 - val_loss: 0.4278 - val_accuracy: 0.8850\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.model.save('./drive/MyDrive/Models/echoai_lstm_localattn_model.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-IN4irHD30G",
        "outputId": "e55974cc-6cd2-4496-b852-104168bad26d"
      },
      "execution_count": 26,
      "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": "1iqty6Cg8xCn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "total_loss = [train + val for train, val in zip(history.history['loss'], history.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(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.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 Local Attention LSTM')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "lt7j40iW0sd4",
        "outputId": "082aa98d-4080-4ce2-847a-baa986a2a62a"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min loss 0.8301381468772888 at 96\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
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Accuracy Over Epochs for Local Attention LSTM')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "hF_NykdDmZLy",
        "outputId": "dd477805-7e52-4906-ff67-d52b774ef519"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "image/png": {
              "width": 576,
              "height": 454
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Create predictions"
      ],
      "metadata": {
        "id": "TqCDTOQL81uf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "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",
        "\n",
        "    if not np.isclose(np.sum(predictions), 1):\n",
        "        predictions /= np.sum(predictions)\n",
        "\n",
        "    probas = np.random.multinomial(1, predictions, 1)\n",
        "    return np.argmax(probas)\n",
        "\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 = {number: note for number, note in enumerate(pitchnames)}\n",
        "\n",
        "    pattern = network_input[start]\n",
        "    prediction_output = []\n",
        "\n",
        "    for note_index in range(25):\n",
        "        prediction_input = np.reshape(pattern, (1, -1, 1))  # Reshape for prediction\n",
        "\n",
        "        predictions = model.predict(prediction_input)\n",
        "        predictions = predictions[0]  # First sequence of predictions\n",
        "\n",
        "        # Loop through each timestep's predictions in the sequence\n",
        "        results = []\n",
        "        for timestep_prediction in predictions:\n",
        "            index = sample_with_temperature(timestep_prediction, temperature)\n",
        "            result = int_to_note[index]\n",
        "            results.append(result)\n",
        "\n",
        "        # Use the last predicted note to continue the sequence generation\n",
        "        prediction_output.extend(results)\n",
        "        preds_to_inputs = [(np.argmax(curr) / float(num_vocab)) for curr in predictions]\n",
        "        pattern = preds_to_inputs\n",
        "        #last_note_index = np.argmax(predictions[-1])  # Use the last timestep prediction for continuing the sequence\n",
        "        #pattern = np.append(pattern[1:], last_note_index / float(num_vocab))  # Update the pattern\n",
        "\n",
        "    return prediction_output\n",
        "\n",
        "\n",
        "# Usage of the function\n",
        "num_vocab = len(set(flat_notes))  # Total number of unique notes\n",
        "pitchnames = sorted(set(flat_notes))  # Sorted list of unique notes\n",
        "prediction_output = generate_notes(model, train_input, pitchnames, num_vocab, 0)\n",
        "print(\"Generated Sequence of Notes:\", prediction_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XBMKXGXXjepL",
        "outputId": "2df66b9d-6432-4126-a818-0d5225b1383d"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "Generated Sequence of Notes: ['C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C3', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C4', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9', 'C5', 'C3', 'C5', 'D4', 'A4', 'C3', 'A3', 'C5', 'B3', 'D5', 'C5', 'C5', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '2.5', '5.9', 'C3', '4.7', 'C2', '11.2', '2.5', '0.4', 'C3', 'C2', '11.2', '2.5', '2.5', '0.4', '0.4.7', '7.11.2', '0.4.7', '9.11', '8.9', 'D5', '5.11', '5.11', '7.9', '6.7', 'C5', '4.7', '4.7', 'E6', 'E6', '2.5', '1.2', 'C4', 'B3', 'A3', 'D5', 'G3', 'E5', '0.2', '4.5', 'G4', 'C6', 'G#4', '9.2', 'C6', 'F5', '5.9']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(prediction_output)"
      ],
      "metadata": {
        "id": "VVpd5vdJ07mA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6abe98fa-7a05-46ca-c812-5a6fa155c408"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['B5', 'C5', 'G4', 'E4', 'E4', 'B-4', 'A5', 'E-5', 'E-5', 'F#3', 'E-5', 'F4', 'C4', 'G#4', 'C5', 'G#5', '3.6.7', 'E5', '11.1.4', 'F4', 'E5', 'E4', 'B-2', 'E-5', 'D4', '8.11.2', 'E4', 'B3', 'F5', 'B3', 'G#2', 'A3', 'B-2', 'D5', 'D5', 'E-5', 'F5', 'B4', 'G4', 'A3', '3.6', 'B-4', '8.10', 'G#3', '11.3.6', 'F#3', 'E4', '4.9', 'G3', '7.8', 'F#5', 'C#4', 'F5', '8.9', 'D4', 'B-2', '11.2', 'B4', 'B-3', '0.4', 'G#5', 'D4', 'B-3', '1.7', 'E-4', 'D4', 'F3', 'G5', 'G3', 'F4', 'A4', 'F3', 'B-3', 'F#4', 'G#2', 'A2', 'B-5', 'G3', 'C4', 'F#4', '11', 'A2', '2.4', 'A4', 'G4', 'G#2', 'A3', 'D4', 'E4', 'C#5', 'E3', 'C#5', 'F4', 'B-3', 'G#2', 'C#4', 'C#5', 'D5', 'F#3', 'B4', 'A4', 'G5', 'D4', 'C#4', '4.7', 'E4', 'E-3', 'G#4', '2.4', 'F5', 'C#5', '6.10.0', 'D5', '4.7.10', 'D4', 'F4', 'F3', 'E3', 'E-5', 'F#2', '11.2', 'F#4', 'A4', '1.3', 'F5', 'C2', 'C4', '10.1', 'G#4', 'B4', 'G#3', 'G#3', 'E-4', 'E-4', 'C3', 'A3', 'B3', 'D4', 'G#4', 'F4', 'B2', 'G4', 'E4', 'C#3', '11.0', 'F#5', '11.1', 'D3', 'F#6', 'F#4', 'B-4', 'E-4', 'C4', 'C#4', 'F#5', 'F#4', 'D5', 'D4', 'D4', 'E5', 'A4', 'D2', 'G#4', 'F#5', 'F5', 'F#4', 'F#4', 'E3', '11.3', 'E-5', 'F#4', 'G#1', '2.6', 'G#4', 'F4', '7.10', 'F#5', '8.11', 'G#4', 'E5', 'C#5', 'B-3', 'G#4', 'D5', '2.3', 'F#3', 'F#2', '1.3', 'F#3', 'G4', 'B-4', '0.3', 'G5', 'A4', 'G4', 'G3', 'F3', 'F4', 'C#4', 'G#4', 'B3', 'G4', 'D4', 'E-5', 'A4', 'G3', 'G#6', 'B-3', '3', 'B-3', '10', 'G#3', 'F#5', 'E4', 'E-3', 'G4', 'C3', 'C#4', 'F2', 'G#3', 'G#5', 'B-4', 'E3', 'G#1', 'C#4', 'B2', 'G4', 'B3', 'B-4', 'G4', 'F#4', 'C5', '3.8', 'G#5', 'G#4', 'E5', '11.3', 'F#5', 'D5', '7.0', 'G4', '6.8', 'F4', 'G4', 'F4', 'C#3', 'C#5', 'B-4', '11.1.4', 'E4', '9.0', '1.2', 'G3', 'B-3', 'D5', '0.4', 'E3', 'D6', 'G3', 'G4', 'G2', 'C5', 'D3', 'F4', 'A3', 'C2', 'G#4', 'G3', 'B2', 'F#2', 'F3', 'D5', '2.4.8', 'E-5', '2.4', 'C3', 'G3', 'F5', 'C#4', 'G4', 'F#5', 'C5', 'E4', 'C4', 'G#5', 'C4', 'D5', 'E4', 'A2', 'F#3', 'G5', 'E5', 'F3', 'E4', 'F4', 'B4', '3', 'E4', 'G4', 'G4', '11.4', 'D5', 'G#2', 'A4', 'F3', '7.10', 'G#5', 'F5', 'C5', 'E5', 'F#3', 'A3', '2.6', 'E-5', 'B-4', '1.2.4', 'G2', 'A3', 'E5', '1.5.8', 'B-4', 'A4', 'F#3', 'F#3', 'A2', 'D4', 'B3', 'E-5', 'G3', 'G4', 'C5', 'F5', 'B3', 'E3', 'F#4', 'C#4', '2.4', 'B3', '2.4', 'B4', 'F4', 'B-5', 'C#5', 'C5', 'E4', 'A4', 'C6', 'C#5', 'E5', 'E-3', 'G3', 'G#4', 'B-3', 'E4', 'F#4', 'B-3', 'F#5', 'G#4', 'C#5', 'A4', '3.5', 'E5', 'G#4', 'C2', '2.5.9', 'G4', 'F#4', '5.9', 'F#4', 'A5', 'F4', 'F3', 'G#1', 'A4', 'E-5', 'B4', '2.8', 'E3', 'B-4', '0.4.5.7', 'G3', 'D4', 'A4', '0.3', 'G#4', 'C#4', 'F4', 'G5', 'D4', 'E4', 'A5', 'F#4', 'B3', 'D5', 'G5', 'G4', 'B-5', 'C#3', 'D4', 'G3', '4.6', 'F5', '2.5', 'A5', 'F#4', 'G4', 'A4', 'E4', 'D5', 'B-3', 'F4', 'G3', 'B4', 'B-4', 'F#4', 'B3', 'C#2', 'C#4', 'F#3', 'F2', 'E4', 'G#2', 'E3', 'E-3', '2.6.9', 'G#3', 'E3', 'G#4', '2.6', 'F3', 'E-3', '7.10', 'F2', '8.11', 'F5', 'F#3', 'D6', 'B4', 'G#4', 'A3', '11.4', 'G4', 'B-3', '11.1', 'F3', 'B1', 'A2', '0.3', 'G4', 'B-3', 'G4', 'D4', 'C#6', 'G4', 'B3', 'F3', 'C5', 'E5', 'G#3', 'G#2', 'C#4', 'G4', 'B4', 'F#4', '4.10', 'G#4', '2.5.8', 'D5', 'F3', 'F#5', 'F#4', 'F#5', 'G4', 'C#2', 'G5', 'C5', 'F#5', 'G#3', 'F#4', 'E5', 'C#5', 'D4', 'E-5', 'D2', 'C4', 'G3', 'F4', 'A3', '5.11', 'G4', 'E3', 'E4', '4', 'E5', 'D5', '7.10', 'G3', '7', 'G#6', 'E3', 'D5', 'C#5', 'G#2', 'B-3', '2.4', 'F2', 'C#5', '10.1.2.3', 'E4', 'C5', 'C#4', '1.3', 'G#2', 'B-4', 'G5', 'G#2', 'B4', 'G2', 'B3', 'G4', 'A5', 'E-3', 'D5', 'F#3', 'C#2', 'E5', 'E4', 'G#5', '2.5', 'F4', '3.5', 'D4', 'G#3', 'F#2', 'C#3', 'F4', 'F#3', 'C#3', 'F3', 'G#2', 'E-5', 'B-4', 'G4', 'D4', 'D4', 'D3', 'G4', 'E-2', 'E3', 'G3', 'F4', 'B4', '2.8', 'G#4', 'D4', 'G#4', '3.9', 'G4', 'D4', '5.9', 'G#1', '5.8', 'G4', 'G#4', 'G#2', 'A4', 'D4', 'C3', '2.6', 'F#3', 'E-5', '0', 'F4', 'A4', 'F#6', '10.2', 'G#4', 'E-5', 'G#4', 'B2', 'C#3', 'C4', 'D5', 'B2', 'F4', 'F#2', 'D5', 'G5', 'A4', 'E4', 'F2', 'E-5', '11', 'C5', '11.3', 'C5', 'A2', 'G#5', 'C5', 'G4', 'E4', 'C#3', 'F#3', 'E5', 'G4', 'A4', 'E5', 'C2', 'C4', 'C#5', 'F3', 'D3', 'F#3', 'C5', 'F3', 'A4', '3.8', 'G4', 'G3', 'A4', '2.6', 'F3', 'E-5', '6.8', 'F4', '6.10.0', 'F#5', 'F#3', 'E4', 'E-5', 'G#5', 'B3', '3.9', 'G#3', 'B4', '11.2', 'F5', 'F#5', 'B3', '0.2', 'B-4', 'C#4', 'E4', 'F5', 'G4', 'E3', 'B4', 'G3', 'C#2', 'G3', 'G4', 'G4', 'D2', 'G5', 'F#4', 'D5', '3.7', 'B5', '2.5', 'G3', 'F4', 'C5', 'D5', 'F#5', 'A5', 'A5', 'G#4', 'E4', 'E-3', 'A4', 'C#3', 'D4', 'B1', 'C#5', 'D3', 'D5', 'F#4', 'E-5', 'F3', 'B-3', '3.6', 'F4', 'G3', 'G#4', '2.3', 'E-4', 'A4', '9.0', 'F#2', '9.1', 'G5', 'E4', 'D5', 'B0', 'E5', 'C#3', '2.4', 'F3', 'B3', '1.3', 'G5', 'B4', 'C#4', '0.4', 'F#4', 'C#5', 'E5', 'G#5', 'D4', 'G4', 'C4', 'G#5', 'E-5', 'G#6', 'G4', 'E3', 'G2', 'G5', 'G3', 'E3', '11.3', 'F#4', '2.5', 'F#3', 'G3', 'C#3', 'E-3', 'F4', 'B4', 'B4', 'F#4', 'F4', 'D5', 'F#6', 'E4', 'F#2', 'D3', 'C5', 'G#3', 'B4', 'G2', 'G3', 'C5', 'B3', '4.7', 'F2', 'G3', 'E3', '2.4', 'F5', 'D4', '5.7', 'G#5', '7.0', 'G4', 'F#5', 'F#5', 'D4', 'F#2', 'B4', '4.5', 'G#3', 'D4', '0.4', 'F#4', 'E-4', 'G#4', '1', 'G#3', 'A4', 'F#3', 'E4', 'E4', 'G#3', 'B-4', 'G2', 'B-4', 'G4', 'G3', 'G4', 'B-3', 'D5', 'F6', 'B3', '2.7', 'F3', '11.3.6', 'C#5', 'G#2', 'F2', 'A4', 'E5', 'E5', 'B-3', 'G5', 'D3', 'F#5', 'C#4', 'E5', 'E3', 'C#5', 'B3', 'F4', 'G#3', 'F4', 'F#5', 'D4', 'B3', '4.7', 'G5', 'A4', 'E5', '3.4', 'G#2', 'B3', '6.11', 'E-5', '8.10', 'G4', 'G5', 'F#3', 'E4', 'F4', 'C4', '5.11', 'G4', 'C#5', '10.1.4.6', 'F4', 'B3', 'E3', '0.4.7', 'F4', 'C#5', 'F5', 'C#4', 'E4', 'G5', 'C5', 'F4', 'C5', 'G4', 'C#3', 'G3', 'B-4', 'G4', 'G#5', 'E-3', '11.3', 'E-4', '2.4', 'A3', 'F#3', 'E3', 'C#3', 'G4', 'G4', 'G#1', 'G4', 'G#2', 'G3', 'D2', 'D3', 'D5', 'B-5', 'E-5', 'F4', 'D3', 'D5', 'G4', 'F#4', 'A3', '4.8', 'F#2', 'G4', 'F#3', '11.2', 'E5', 'G#3', '9', 'E3', 'C4', 'F#4', 'G#2', 'D5', 'A4', 'F4', 'B-2', '10.3', 'E4', 'F#5', '1.4', 'F4', 'B-3', 'E-5', '1.6', 'F4', 'B-4', 'F3', 'G4', 'F4', 'C#3', 'A5', 'E4', 'C#4', 'F3', 'F4', 'D7', 'A4', 'F4', 'G#2', 'D3', '2.5', 'E5', '2.6', 'A3', 'F4', 'G#5', 'A4', 'G5', 'D5', 'B4', 'G#3', 'G#3', 'E3', 'E4', 'E4', '9.2', 'G3', 'C4', 'G#6', 'D5', 'G#3', 'G3', 'F#5', 'A4', '2.6.9', 'F#3', 'E2', 'F#2', '11.3', 'D5', 'F#3', '8.11', 'F#4', '9.1', 'G4', 'G#4', 'E2', 'C3', 'G5', 'A4', '10.3', 'G3', 'A4', '1.4', 'G#5', 'F4', 'C#5', '0', 'E-5', 'A2', 'G5', 'E4', 'F#4', 'G#5', 'D4', 'D5', 'B-4', 'F#2', 'B1', 'D5', 'C#2', 'E3', 'G4', 'F#2', '2.6.9', '9.2', '2.7', 'E-5', 'G5', 'D4', 'E-3', 'E5', 'C#4', 'D3', 'E3', 'E4', 'F3', 'E3', 'G#3', 'F4', 'B-4', 'C4', 'F5', 'B-4', 'G#4', 'G3', 'G5', 'B1', '4', 'G4', 'G3', 'G4', '11.3', 'G4', 'F#2', '6', 'G#2', '8.1', 'G5', 'F#5', 'D4', 'A3', 'C5', 'E-5', '10.1', 'G4', 'E-2', '1.2.4', 'G2', 'A4', '8.10', '0.4', 'E4', 'A4', 'G3', 'E5', 'B-4', 'G#5', 'B-3', 'C#2', '9.2', 'G#4', 'B-2', 'G#5', 'B-4', 'F5', 'F2', 'F#3', '2.5', 'B4', '2.4.7', 'F#4', 'D4', 'C#5', 'A5', 'D5', 'F4', 'B2', 'F4', 'F#5', 'E4', 'G#1', 'E4', 'F#2', 'G#2', 'D5', 'E4', 'A4', 'C5', 'E3', 'G4', 'B4', '4.8', 'G4', 'C#5', 'F3', '11', 'F#5', 'G4', '8.0.3', 'G4', '7.9', 'G#4', 'E-4', 'G#5', 'A4', 'C5', 'C#4', '2.4', 'F#5', 'A4', '1.2', 'F3', 'E3', 'E5', '11.0', 'G#3', 'B3', 'B4', 'E4', 'F#4', 'G#3', 'A3', 'F#4', 'C#2', 'D5', 'F1', 'G#4', 'C#4', 'G5', 'C#4', 'D4', '3', 'F1', '2.4', 'B3', 'F#5', 'F5', 'B-4', 'G4', 'E5', 'C#4', 'F#5', 'E-2', 'G#2', 'A3', 'G3', 'G#1', 'D4', 'F#4', 'G#5', 'A3', 'G#4', 'G4', 'G#3', 'D3', '2.5', 'E4', 'F#3', 'F#3', '11.3.6', 'G4', 'G3', '8.10', 'F#4', '8.10', 'G4', 'G#1', 'G3', 'E-3', 'G#1', 'A4', '2.6.9', 'G#3', 'A4', '1.6', 'E5', 'A3', 'A3', '10.1', 'F5', 'C#5', 'G4', 'F4', 'D5', 'C6', 'B3', 'G#4', 'C#2', 'D5', 'E4', 'F#5', 'G#2', 'F4', 'F#3', 'D4', '3.6', 'G#3', '2.8', 'G3', 'G#4', 'G#6', 'A4', 'G3', 'C5', 'D4', 'E4', 'F#4', 'E2', 'D4', 'G3', 'F#2', 'D5', 'B4', 'F#4', 'C#5', 'G#4', 'G#2', 'F#4', 'B-3', '11.2', 'D5', 'G4', 'G4', '2.6.8', 'G#4', 'F2', '6.9.1', 'F3', '7.10', 'G4', 'E4', 'E5', 'A3', 'G3', 'B-3', '11.2.5', 'G#4', 'B3', '1.2.4', 'D4', 'F2', 'D5', '1.4', 'G4', 'B4', 'G4', 'G#4', 'F4', 'C5', 'A5', 'G4', 'A3', 'F#5', 'F4', 'E-5', 'F#5', 'F3', 'E5', 'E4', '2.4', 'E-4', '2.4', 'A3', 'G#3', 'F#4', 'B4', 'F4', 'B3', 'D3', 'E5', 'G#3', 'G4', 'B-4', 'G3', 'E-5', 'B3', 'A4', 'E3', 'E-5', 'F4', 'F3', 'E-5', 'C4', '4.7.10', 'F3', 'G#3', 'F5', '2.8', 'G2', 'G#6', '6.9', 'F#5', '5.9', 'G4', 'F#3', 'F5', 'G#2', 'G4', 'B-5', '3', 'G4', 'E4', '1.4', 'F4', 'B3', 'E-5', '0.2', 'G4', 'E5', 'F2', 'F#2', 'C4', 'B4', 'G#2', 'G4', 'C2', 'F4', 'B2', 'G3', 'B3', 'G#4', 'E4', 'E-5', '2.6', 'G4', '2.4', 'D2', 'G#4', 'G5', 'E-4', 'D4', 'F#3', 'B3', 'F#5', 'F#2', 'C#2', 'A3', 'G#2', 'D2', 'A3', 'D4', 'F3', 'A2', 'G3', 'F#3', 'G#4', 'B4', '4.7', 'G#3', 'F3', 'F#2', '2.4', 'F4', 'G#2', '7.10', 'E5', '7.0', 'A4', 'A3', 'G#3', 'E-4', 'G#2', 'C#4', '2.5', 'E4', 'E-3', '1.7', 'G#4', 'D7', 'D4', '0.4', 'E4', 'B3', 'G#4', 'G#4', 'F4', 'F4', 'A3', 'G5', 'B-3', 'F4', 'E4', 'G#1', 'E-4', 'G3', 'F6', 'F5', '2.5', 'B4', '2.5', 'C#4', 'G#2', 'D5', 'C4', 'D5', 'E4', 'C#3', 'F4', 'F4', 'G#4', 'B2', 'E-3', 'F#3', 'B4', 'B3', 'G4', 'C#4', 'G3', 'G3', 'G#4', 'D5', '2.5', 'D4', 'C4', 'E3', '11.3.6', 'C#4', 'G3', '6.9', 'E-2', '7.0', 'G2', 'F3', 'F4', 'A4', 'F2', 'G4', '2.6', 'F#3', 'A3', '0.2', 'G#4', 'B-4', 'C#4', '1.3', 'F5', 'C2', 'F4', 'G5', 'G#1', 'G3', 'B-3', 'G#4', 'E4', 'F5', 'F#4', 'D4', 'D3', 'E3', 'D5', 'G#2', '10.1', 'B3', '2.6', 'B4', 'B4', 'E-4', 'B4', 'F5', 'E4', 'A3', 'F5', 'G#3', 'E5', 'A4', 'G#4', 'F#3', 'B-1', 'E4', 'F2', 'A4', 'G#3', 'E4', 'F4', 'C#5', '3.6', 'G5', 'G4', 'G4', '11.2.5', 'E-5', 'E4', 'A4', 'F#2', '6.11', 'E4', 'G4', 'D5', 'E5', 'F4', 'B3', '2.6', 'G3', 'B-3', '0.3', 'G3', 'C5', 'C3', '0.3.6', 'G4', 'E-2', 'E3', 'G#2', 'D5', 'G#2', 'B-3', 'F#4', 'D5', 'F#4', 'E-2', 'E3', 'B2', 'D5', 'F#5', 'E4', '2.4', 'C5', '2.4', 'A6', 'C5', 'G3', 'C5', 'D5', 'G3', 'E-2', 'G5', 'E-4', 'E5', 'D3', 'G#2', 'C#4', 'A4', 'B-4', 'G4', 'E-4', 'G#2', 'G#2', 'F2', 'D5', '3.7', 'G4', 'G#4', 'F1', '3.8', 'F5', 'G3', '7.9', 'G3', 'A4', 'G#4', 'C5', 'D4', 'A4', 'G4', 'A1', '2.4', 'D5', 'D4', '1.6', 'E-5', 'B-3', 'B1', '0.4', 'F3', '9.10', 'E4', 'G5', 'D3', 'E5', 'A4', 'G#2', 'E4', 'E4', 'E4', 'C#4', 'C5', 'E4', 'F#4', 'C2', '11.2', 'D5', '5.7', '9.2', 'G5', 'F4', 'A4', 'F4', 'F4', 'B4', 'D4', 'F2', 'E4', 'A4', 'F4', 'F4', 'B-3', 'C#5', 'G4', 'B3', 'E-5', 'E5', 'F3', 'B-3', '4.6', 'G#5', 'G3', 'G#2', '2.6', 'F6', 'F3', '6.10.1', 'G4', '8.10', 'G3', 'G#4', 'D4', 'B-3', 'E5', 'C4', '3.4', 'G4', 'A4', '1.4', 'C5', 'D3', 'A4', '11']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from music21 import converter, instrument, note, chord, stream\n",
        "\n",
        "def create_midi(prediction_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 prediction_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(prediction_output)"
      ],
      "metadata": {
        "id": "p4lf3zSH2X1r"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4eYhV5sY2bVH"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}