process_train.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "2xLSTM.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lxr1Ni8JrH21",
        "outputId": "042ff132-c788-4813-f98d-e16cbdcbe532"
      },
      "source": [
        "!pip install music21"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: music21 in /usr/local/lib/python3.7/dist-packages (5.5.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d0tfLzUk7Zuq",
        "outputId": "986c73b0-47be-473c-c022-3527c4d9d9c3"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dyJB2-vWTOHt",
        "outputId": "bf217e11-90d5-4ced-fd9e-b9a4736dd7d5"
      },
      "source": [
        "import string\n",
        "# Load notes document, extract\n",
        "# tokens and create sequences\n",
        "\n",
        "# load notes document into memory\n",
        "def load_doc(filename):\n",
        "\tfile = open(filename, 'r')\n",
        "\ttext = file.read()\n",
        "\tfile.close()\n",
        "\treturn text\n",
        "\n",
        "# turn the document into clean tokens\n",
        "def clean_doc(doc):\n",
        "\t# split into tokens by space and make lower case\n",
        "\ttokens = doc.split(' ')\n",
        "\ttokens = [word.lower() for word in tokens]\n",
        "\treturn tokens\n",
        "\n",
        "# save tokens to file, one sequence per line\n",
        "def save_doc(lines, filename):\n",
        "\tdata = '\\n'.join(lines)\n",
        "\tfile = open(filename, 'w')\n",
        "\tfile.write(data)\n",
        "\tfile.close()\n",
        "\n",
        "# load document\n",
        "in_filename = '/content/drive/MyDrive/13.02 colab/huge_notes_offset_augmented_corrected.txt'\n",
        "doc = load_doc(in_filename)\n",
        "print(doc[:200])\n",
        "\n",
        "# clean document\n",
        "tokens = clean_doc(doc)\n",
        "\n",
        "# print details\n",
        "print('Total Tokens: %d' % len(tokens))\n",
        "print('Unique Tokens: %d' % len(set(tokens)))\n",
        "\n",
        "\"\"\"\n",
        "# statictics\n",
        "stats = dict()\n",
        "for t in tokens:\n",
        "  if t in stats:\n",
        "    stats[t] += 1\n",
        "  else:\n",
        "    stats[t] = 1\n",
        "stats = dict(sorted(stats.items(), key=lambda item: item[1]))\n",
        "file_stats = open('statistics.txt', 'w')\n",
        "for s in stats:\n",
        "  file_stats.write(str(stats[s]) + '   ' +  str(s) +'\\n')\n",
        "file_stats.close()\n",
        "\"\"\"\n",
        "# print first 200 token\n",
        "print(tokens[:200])\n",
        "\n",
        "# organize into sequences of tokens\n",
        "length = 50 + 1 # input:100 target:1\n",
        "sequences = list()\n",
        "for i in range(length, len(tokens)):\n",
        "\t# select sequence of tokens\n",
        "\tseq = tokens[i-length:i]\n",
        "\t# convert into a string line\n",
        "\tline = ' '.join(seq)\n",
        "\t# append\n",
        "\tsequences.append(line)\n",
        " \n",
        " # print # of total sequences\n",
        "print('Total Sequences: %d' % len(sequences))\n",
        "\n",
        "# save sequences to file\n",
        "out_filename = 'sequences.txt'\n",
        "save_doc(sequences, out_filename)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "67o2 48o52o2 48o52o2 48o52o1 48o53o1 69o71o1 71o1 50o53o1 60o71o1 53o67o2 52o72o1 76o1 52o74o1 72o1 52o71o1 69o1 67o1 50o59o2 50o59o2 50o59o2 60o65o2 65o1 67o1 55o59o1 57o62o1 62o1 64o1 52o55o2 53o59o\n",
            "Total Tokens: 250798\n",
            "Unique Tokens: 5353\n",
            "['67o2', '48o52o2', '48o52o2', '48o52o1', '48o53o1', '69o71o1', '71o1', '50o53o1', '60o71o1', '53o67o2', '52o72o1', '76o1', '52o74o1', '72o1', '52o71o1', '69o1', '67o1', '50o59o2', '50o59o2', '50o59o2', '60o65o2', '65o1', '67o1', '55o59o1', '57o62o1', '62o1', '64o1', '52o55o2', '53o59o2', '53o59o1', '48o52o2', '62o1', '43o59o4', '79o2', '72o84o1', '76o1', '48o1', '76o1', '72o84o1', '76o1', '48o1', '83o1', '77o81o1', '83o86o1', '77o83o1', '72o79o1', '77o1', '79o84o1', '72o88o1', '76o1', '72o86o1', '76o84o1', '83o1', '72o1', '76o81o1', '71o79o1', '74o1', '71o1', '74o1', '71o1', '74o1', '72o76o1', '77o1', '81o1', '77o1', '52o59o1', '67o1', '69o73o1', '74o1', '77o1', '74o1', '52o55o2', '74o76o1', '73o1', '74o77o1', '74o1', '55o59o1', '67o1', '52o72o4', '48o1', '49o1', '50o2', '53o65o1', '50o62o1', '47o59o1', '55o1', '48o64o1', '60o1', '55o1', '67o1', '64o1', '60o1', '53o69o2', '72o1', '69o1', '65o1', '60o1', '52o67o1', '64o1', '60o1', '72o1', '64o67o1', '59o74o3', '77o1', '74o1', '71o1', '67o1', '55o76o1', '72o1', '67o1', '79o1', '76o1', '72o1', '84o1', '79o1', '76o1', '88o1', '84o1', '79o1', '84o1', '79o1', '76o1', '79o1', '76o1', '72o76o1', '72o1', '67o1', '60o72o1', '55o67o1', '52o64o1', '55o67o1', '52o1', '48o1', '64o1', '52o1', '48o60o1', '55o1', '43o1', '48o60o1', '55o1', '52o1', '40o1', '43o55o1', '52o1', '36o48o1', '43o1', '31o4', '50o2', '55o2', '55o2', '55o62o2', '54o69o1', '57o69o1', '54o1', '50o69o2', '57o59o1', '67o1', '66o1', '67o1', '59o1', '67o1', '59o62o2', '57o72o2', '48o1', '57o1', '54o72o1', '62o72o1', '69o71o1', '50o59o1', '74o1', '71o1', '62o67o1', '50o52o1', '55o1', '60o76o1', '59o1', '57o76o1', '55o1', '69o76o2', '54o81o1', '79o1', '54o78o1', '76o1', '69o74o1', '47o1', '59o74o1', '72o1', '57o59o1', '54o69o1', '52o1', '67o74o1', '79o1', '52o1', '78o1', '52o76o1', '74o1', '67o72o1', '45o1', '57o72o1', '55o1']\n",
            "Total Sequences: 250747\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lIunryvHk-Jj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7815c2f9-4c58-4fc6-91ae-e6cd4d7aeba9"
      },
      "source": [
        "from numpy import array\n",
        "from pickle import dump\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.python.keras.utils.np_utils import to_categorical\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM, Dropout, Bidirectional, BatchNormalization\n",
        "from keras.layers import Embedding\n",
        "#from keras.optimizers import Adam\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from keras.callbacks import History, EarlyStopping, ReduceLROnPlateau\n",
        "import keras.backend as K\n",
        "\n",
        "# load document into memory\n",
        "def load_doc(filename):\n",
        "\tfile = open(filename, 'r')\n",
        "\ttext = file.read()\n",
        "\tfile.close()\n",
        "\treturn text\n",
        "\n",
        "# load\n",
        "in_filename = 'sequences.txt'\n",
        "doc = load_doc(in_filename)\n",
        "# get lines\n",
        "lines = doc.split('\\n')\n",
        "print(lines[0])\n",
        "\n",
        "# split train and test sets\n",
        "test_split = 245000\n",
        "test_lines = lines[test_split:]\n",
        "lines = lines[:test_split]\n",
        "print(\"train size: \", len(lines))\n",
        "print(\"test  size: \", len(test_lines))\n",
        "\n",
        "# integer encode sequences of words\n",
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(lines)\n",
        "print(tokenizer.word_index)\n",
        "sequences = tokenizer.texts_to_sequences(lines)\n",
        "\n",
        "# vocabulary size\n",
        "vocab_size = len(tokenizer.word_index) + 1\n",
        "\n",
        "# separate into input and output\n",
        "sequences = array(sequences)\n",
        "X, y = sequences[:,:-1], sequences[:,-1]\n",
        "y = to_categorical(y, num_classes=vocab_size)\n",
        "seq_length = X.shape[1]\n",
        "\n",
        "# define model\n",
        "model = Sequential()\n",
        "model.add(Embedding(vocab_size, 25, input_length=seq_length))\n",
        "model.add(LSTM(512, return_sequences=True))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(BatchNormalization())\n",
        "model.add(LSTM(512))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(vocab_size, activation='softmax'))\n",
        "print(model.summary())\n",
        "\n",
        "# compile model\n",
        "opt = Adam(learning_rate=0.001)\n",
        "model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\n",
        "print(K.eval(model.optimizer.lr))\n",
        "\n",
        "# fit model\n",
        "history = History()\n",
        "earlystop = EarlyStopping(monitor='loss', patience= 15, min_delta = 0.1 , verbose=0, mode= 'min')\n",
        "hist = model.fit(X, y, batch_size=64, epochs=75, callbacks=[earlystop, history])\n",
        "\n",
        "# save the model and tokenizer to file\n",
        "model.save('model.h5') \n",
        "dump(tokenizer, open('tokenizer.pkl', 'wb'))\n",
        "\n",
        "# download file\n",
        "from google.colab import files\n",
        "files.download('model.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "67o2 48o52o2 48o52o2 48o52o1 48o53o1 69o71o1 71o1 50o53o1 60o71o1 53o67o2 52o72o1 76o1 52o74o1 72o1 52o71o1 69o1 67o1 50o59o2 50o59o2 50o59o2 60o65o2 65o1 67o1 55o59o1 57o62o1 62o1 64o1 52o55o2 53o59o2 53o59o1 48o52o2 62o1 43o59o4 79o2 72o84o1 76o1 48o1 76o1 72o84o1 76o1 48o1 83o1 77o81o1 83o86o1 77o83o1 72o79o1 77o1 79o84o1 72o88o1 76o1 72o86o1\n",
            "train size:  245000\n",
            "test  size:  5747\n",
            "{'74o1': 1, '76o1': 2, '72o1': 3, '67o1': 4, '71o1': 5, '69o1': 6, '79o1': 7, '64o1': 8, '77o1': 9, '81o1': 10, '62o1': 11, '60o1': 12, '57o1': 13, '65o1': 14, '55o1': 15, '59o1': 16, '52o1': 17, '83o1': 18, '48o1': 19, '84o1': 20, '50o1': 21, '66o1': 22, '78o1': 23, '68o1': 24, '86o1': 25, '53o1': 26, '52o55o1': 27, '55o59o1': 28, '70o1': 29, '55o59o4': 30, '72o2': 31, '67o2': 32, '48o52o1': 33, '76o2': 34, '64o2': 35, '74o2': 36, '69o2': 37, '55o2': 38, '53o57o1': 39, '62o2': 40, '48o57o1': 41, '75o1': 42, '71o2': 43, '60o2': 44, '73o1': 45, '53o57o4': 46, '52o2': 47, '56o59o1': 48, '50o59o1': 49, '80o1': 50, '55o59o2': 51, '52o55o2': 52, '57o2': 53, '88o1': 54, '52o57o1': 55, '59o2': 56, '47o1': 57, '56o1': 58, '45o1': 59, '50o53o1': 60, '53o57o2': 61, '43o1': 62, '52o57o4': 63, '54o1': 64, '79o2': 65, '54o57o1': 66, '52o55o4': 67, '63o1': 68, '65o2': 69, '48o52o2': 70, '77o2': 71, '55o58o1': 72, '50o2': 73, '48o2': 74, '58o1': 75, '48o57o2': 76, '57o59o1': 77, '40o1': 78, '60o76o1': 79, '61o1': 80, '82o1': 81, '57o72o1': 82, '59o74o1': 83, '52o57o2': 84, '55o71o1': 85, '56o59o2': 86, '81o2': 87, '53o2': 88, '55o67o1': 89, '64o72o1': 90, '53o56o1': 91, '89o1': 92, '51o1': 93, '55o58o2': 94, '49o1': 95, '55o4': 96, '53o55o1': 97, '55o3': 98, '50o53o2': 99, '55o57o1': 100, '48o55o1': 101, '52o72o1': 102, '50o59o2': 103, '57o59o4': 104, '54o57o2': 105, '52o59o1': 106, '60o72o1': 107, '55o57o4': 108, '85o1': 109, '56o59o4': 110, '60o69o1': 111, '57o69o1': 112, '67o4': 113, '55o76o1': 114, '52o56o1': 115, '57o72o2': 116, '36o1': 117, '43o2': 118, '67o79o1': 119, '62o77o1': 120, '91o1': 121, '62o71o1': 122, '72o74o1': 123, '71o72o1': 124, '55o72o1': 125, '59o67o1': 126, '64o76o1': 127, '47o2': 128, '72o4': 129, '38o1': 130, '74o76o1': 131, '53o59o1': 132, '41o1': 133, '66o2': 134, '68o2': 135, '50o57o1': 136, '65o74o1': 137, '72o76o1': 138, '45o2': 139, '49o52o1': 140, '48o52o4': 141, '59o71o1': 142, '67o76o1': 143, '76o4': 144, '64o79o1': 145, '50o55o1': 146, '53o69o1': 147, '57o59o2': 148, '59o79o1': 149, '69o4': 150, '62o74o1': 151, '78o2': 152, '64o4': 153, '33o1': 154, '59o74o2': 155, '48o64o1': 156, '55o64o1': 157, '55o74o1': 158, '53o74o1': 159, '83o2': 160, '60o4': 161, '70o2': 162, '87o1': 163, '54o59o1': 164, '52o64o1': 165, '84o2': 166, '48o4': 167, '51o54o1': 168, '60o64o1': 169, '64o72o2': 170, '52o67o1': 171, '74o4': 172, '72o3': 173, '57o4': 174, '69o72o1': 175, '79o4': 176, '60o76o2': 177, '67o3': 178, '52o4': 179, '93o1': 180, '44o1': 181, '69o71o1': 182, '57o74o1': 183, '48o50o1': 184, '64o67o1': 185, '52o55o3': 186, '65o77o1': 187, '55o59o3': 188, '50o71o1': 189, '62o4': 190, '51o55o1': 191, '55o79o1': 192, '49o57o1': 193, '57o77o1': 194, '55o58o4': 195, '71o74o1': 196, '74o3': 197, '69o3': 198, '60o81o1': 199, '75o2': 200, '76o79o1': 201, '57o76o1': 202, '50o54o1': 203, '73o2': 204, '53o59o2': 205, '48o53o1': 206, '48o76o1': 207, '64o72o4': 208, '54o2': 209, '59o76o1': 210, '76o3': 211, '53o59o4': 212, '52o60o1': 213, '56o2': 214, '53o58o1': 215, '54o57o4': 216, '50o4': 217, '55o71o2': 218, '46o1': 219, '53o55o2': 220, '48o69o1': 221, '50o62o1': 222, '65o4': 223, '50o52o1': 224, '79o81o1': 225, '64o3': 226, '52o72o2': 227, '57o81o1': 228, '53o57o3': 229, '71o3': 230, '69o72o2': 231, '58o2': 232, '57o65o1': 233, '48o60o1': 234, '48o51o1': 235, '57o69o2': 236, '67o69o1': 237, '52o59o2': 238, '52o79o1': 239, '53o56o2': 240, '59o67o2': 241, '57o3': 242, '69o81o1': 243, '60o69o2': 244, '55o57o2': 245, '77o4': 246, '55o76o2': 247, '57o60o1': 248, '74o77o1': 249, '59o77o1': 250, '90o1': 251, '52o53o1': 252, '60o77o1': 253, '95o1': 254, '50o77o1': 255, '52o3': 256, '59o4': 257, '42o1': 258, '50o65o1': 259, '55o77o1': 260, '52o76o1': 261, '67o76o2': 262, '60o79o1': 263, '71o4': 264, '59o62o1': 265, '50o55o2': 266, '53o65o1': 267, '50o58o1': 268, '52o54o1': 269, '48o72o1': 270, '51o56o1': 271, '48o57o4': 272, '62o3': 273, '86o2': 274, '52o69o1': 275, '65o81o1': 276, '80o2': 277, '48o55o2': 278, '77o3': 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'35o53o2': 5043, '35o45o1': 5044, '32o52o2': 5045, '65o80o3': 5046, '46o60o2': 5047, '56o75o4': 5048, '67o85o2': 5049, '70o85o1': 5050, '68o79o4': 5051, '63o73o4': 5052, '45o70o3': 5053, '34o74o4': 5054, '31o71o4': 5055, '41o55o4': 5056, '45o54o4': 5057, '43o66o4': 5058, '40o66o1': 5059, '49o74o4': 5060, '47o72o4': 5061, '64o86o3': 5062, '68o81o1': 5063, '40o55o3': 5064, '50o75o2': 5065, '36o40o4': 5066, '34o74o1': 5067, '61o85o1': 5068, '36o38o4': 5069, '28o72o1': 5070, '28o67o1': 5071, '26o71o1': 5072, '43o83o4': 5073, '31o55o3': 5074, '52o61o3': 5075, '36o60o3': 5076, '41o61o1': 5077, '29o81o1': 5078, '29o86o1': 5079, '26o89o2': 5080, '32o86o1': 5081, '32o83o1': 5082, '41o45o2': 5083, '41o86o2': 5084, '45o47o2': 5085, '30o32o1': 5086, '33o48o1': 5087, '34o52o4': 5088, '45o63o4': 5089, '28o56o4': 5090, '42o49o3': 5091, '42o49o2': 5092, '46o54o3': 5093, '76o87o2': 5094, '40o83o2': 5095, '83o85o4': 5096, '85o87o3': 5097, '40o88o3': 5098, '29o56o1': 5099, '89o91o2': 5100, '29o75o3': 5101, '28o76o1': 5102, '26o77o3': 5103, '78o80o4': 5104, '32o40o2': 5105, '32o88o1': 5106, '43o45o4': 5107, '33o73o2': 5108, '33o50o2': 5109, '33o38o1': 5110, '33o75o2': 5111, '39o42o1': 5112, '33o53o2': 5113, '35o76o1': 5114, '35o78o1': 5115, '33o86o1': 5116, '33o87o1': 5117, '33o75o1': 5118, '33o81o1': 5119, '45o87o1': 5120, '40o85o1': 5121, '33o83o1': 5122, '38o90o1': 5123, '41o88o1': 5124, '33o85o1': 5125, '33o80o1': 5126, '28o59o2': 5127, '40o50o1': 5128, '35o67o2': 5129, '42o71o2': 5130, '41o61o3': 5131, '42o56o2': 5132, '42o58o2': 5133, '45o58o4': 5134, '41o55o2': 5135, '45o59o4': 5136, '44o51o2': 5137, '39o51o2': 5138, '49o60o2': 5139, '53o63o2': 5140, '32o61o1': 5141, '35o40o4': 5142, '41o45o4': 5143, '41o44o4': 5144, '40o43o4': 5145, '40o49o4': 5146, '33o49o4': 5147, '25o49o4': 5148, '35o49o4': 5149, '23o57o2': 5150, '25o34o1': 5151, '22o34o4': 5152, '44o47o4': 5153, '43o86o2': 5154, '43o89o2': 5155, '76o82o3': 5156, '80o83o3': 5157, '71o78o3': 5158, '63o81o3': 5159, '49o71o3': 5160, '44o71o4': 5161, '63o68o4': 5162, '44o46o3': 5163, '27o56o4': 5164, '47o86o3': 5165, '43o89o3': 5166, '51o77o3': 5167, '56o82o3': 5168, '53o63o3': 5169, '52o65o4': 5170, '44o83o3': 5171, '40o86o3': 5172, '38o80o1': 5173, '45o89o2': 5174, '46o81o1': 5175, '36o63o3': 5176, '51o89o1': 5177, '48o86o4': 5178, '34o55o2': 5179, '80o86o4': 5180, '33o69o3': 5181, '67o81o3': 5182, '41o81o3': 5183, '58o81o4': 5184, '69o82o2': 5185, '63o74o4': 5186, '43o82o4': 5187, '35o74o4': 5188, '56o66o4': 5189, '78o91o1': 5190, '57o91o4': 5191, '36o61o1': 5192, '85o86o4': 5193, '49o77o3': 5194, '56o61o2': 5195, '51o80o3': 5196, '51o84o3': 5197, '44o80o4': 5198, '63o82o2': 5199, '75o77o3': 5200, '55o87o4': 5201, '63o73o3': 5202, '43o69o4': 5203, '81o88o2': 5204, '74o94o1': 5205, '33o40o2': 5206, '78o83o3': 5207, '75o82o3': 5208, '87o90o4': 5209, '49o90o1': 5210, '76o86o3': 5211, '87o91o1': 5212, '51o94o1': 5213, '82o83o2': 5214, '50o87o2': 5215, '66o93o1': 5216, '87o94o1': 5217, '78o94o1': 5218, '85o95o1': 5219, '102o3': 5220, '28o54o1': 5221, '17o3': 5222, '40o44o2': 5223, '17o44o1': 5224, '17o1': 5225, '77o88o2': 5226, '98o103o1': 5227, '94o99o1': 5228, '96o101o1': 5229, '85o91o1': 5230, '83o99o1': 5231, '77o97o1': 5232, '74o92o1': 5233, '36o42o1': 5234, '26o30o1': 5235, '26o3': 5236, '39o44o1': 5237, '91o93o2': 5238, '53o64o3': 5239, '47o84o2': 5240, '47o75o4': 5241, '62o84o3': 5242, '47o76o3': 5243, '59o105o1': 5244, '68o104o1': 5245, '51o103o1': 5246, '53o91o2': 5247, '51o96o1': 5248, '56o98o2': 5249, '56o101o2': 5250, '56o104o4': 5251, '51o87o2': 5252, '67o96o1': 5253, '53o95o2': 5254, '58o103o2': 5255, '57o101o2': 5256, '43o88o1': 5257, '47o95o1': 5258, '59o104o1': 5259, '43o96o1': 5260, '96o100o1': 5261, '57o101o1': 5262, '57o98o1': 5263, '53o86o3': 5264, '36o52o3': 5265, '38o70o3': 5266, '91o95o4': 5267, '83o91o2': 5268, '82o91o3': 5269, '44o53o3': 5270, '33o62o2': 5271, '36o51o3': 5272, '31o59o4': 5273, '40o62o4': 5274, '39o84o2': 5275, '39o83o2': 5276, '33o49o2': 5277, '49o62o2': 5278, '46o54o2': 5279, '38o54o4': 5280, '38o57o4': 5281, '30o64o1': 5282, '66o89o1': 5283, '69o88o3': 5284, '64o91o2': 5285, '65o93o2': 5286, '72o82o3': 5287, '82o84o4': 5288, '70o86o4': 5289, '77o87o2': 5290, '72o93o1': 5291, '70o85o4': 5292, '70o91o2': 5293, '80o86o2': 5294, '79o85o1': 5295, '75o77o4': 5296, '84o91o4': 5297, '86o88o4': 5298, '42o84o3': 5299, '74o75o3': 5300, '66o67o3': 5301, '73o82o4': 5302, '48o82o4': 5303, '72o92o1': 5304, '53o92o1': 5305, '42o90o1': 5306, '47o90o1': 5307, '54o88o3': 5308, '42o62o3': 5309, '31o44o1': 5310, '21o45o1': 5311, '39o45o2': 5312, '20o23o1': 5313, '49o59o4': 5314, '23o56o2': 5315, '20o1': 5316, '58o68o2': 5317, '53o61o3': 5318, '62o75o2': 5319, '37o73o1': 5320, '37o80o1': 5321, '46o71o1': 5322, '46o57o3': 5323, '30o43o1': 5324, '38o73o1': 5325, '38o73o2': 5326, '74o75o2': 5327, '70o71o3': 5328, '63o64o2': 5329, '19o4': 5330, '43o46o3': 5331, '36o46o4': 5332, '19o1': 5333, '30o33o2': 5334, '27o32o1': 5335, '36o46o2': 5336, '22o62o2': 5337, '21o3': 5338, '19o21o2': 5339, '39o41o1': 5340, '74o85o1': 5341}\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 50, 25)            133550    \n",
            "_________________________________________________________________\n",
            "lstm (LSTM)                  (None, 50, 512)           1101824   \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 50, 512)           0         \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 50, 512)           2048      \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 512)               2099200   \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 512)               2048      \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 5342)              2740446   \n",
            "=================================================================\n",
            "Total params: 6,079,116\n",
            "Trainable params: 6,077,068\n",
            "Non-trainable params: 2,048\n",
            "_________________________________________________________________\n",
            "None\n",
            "0.001\n",
            "Epoch 1/75\n",
            "3829/3829 [==============================] - 308s 78ms/step - loss: 5.6274 - accuracy: 0.0874\n",
            "Epoch 2/75\n",
            "3829/3829 [==============================] - 299s 78ms/step - loss: 4.8226 - accuracy: 0.1477\n",
            "Epoch 3/75\n",
            "3829/3829 [==============================] - 300s 78ms/step - loss: 4.3681 - accuracy: 0.1918\n",
            "Epoch 4/75\n",
            "3829/3829 [==============================] - 300s 78ms/step - loss: 3.9969 - accuracy: 0.2303\n",
            "Epoch 5/75\n",
            "3829/3829 [==============================] - 298s 78ms/step - loss: 3.6944 - accuracy: 0.2644\n",
            "Epoch 6/75\n",
            "3829/3829 [==============================] - 298s 78ms/step - loss: 3.4379 - accuracy: 0.2982\n",
            "Epoch 7/75\n",
            "3829/3829 [==============================] - 299s 78ms/step - loss: 3.2235 - accuracy: 0.3291\n",
            "Epoch 8/75\n",
            "3829/3829 [==============================] - 300s 78ms/step - loss: 3.0374 - accuracy: 0.3549\n",
            "Epoch 9/75\n",
            "3829/3829 [==============================] - 299s 78ms/step - loss: 2.8793 - accuracy: 0.3774\n",
            "Epoch 10/75\n",
            "3829/3829 [==============================] - 300s 78ms/step - loss: 2.7423 - accuracy: 0.3998\n",
            "Epoch 11/75\n",
            "3829/3829 [==============================] - 299s 78ms/step - loss: 2.6212 - accuracy: 0.4181\n",
            "Epoch 12/75\n",
            "3829/3829 [==============================] - 299s 78ms/step - loss: 2.5159 - accuracy: 0.4346\n",
            "Epoch 13/75\n",
            "3829/3829 [==============================] - 298s 78ms/step - loss: 2.4224 - accuracy: 0.4496\n",
            "Epoch 14/75\n",
            "3829/3829 [==============================] - 299s 78ms/step - loss: 2.3395 - accuracy: 0.4634\n",
            "Epoch 15/75\n",
            "3829/3829 [==============================] - 298s 78ms/step - loss: 2.2595 - accuracy: 0.4768\n",
            "Epoch 16/75\n",
            "3829/3829 [==============================] - 299s 78ms/step - loss: 2.1933 - accuracy: 0.4881\n",
            "Epoch 17/75\n",
            "3829/3829 [==============================] - 303s 79ms/step - loss: 2.1388 - accuracy: 0.4953\n",
            "Epoch 18/75\n",
            "3829/3829 [==============================] - 303s 79ms/step - loss: 2.0875 - accuracy: 0.5057\n",
            "Epoch 19/75\n",
            "3829/3829 [==============================] - 303s 79ms/step - loss: 2.0380 - accuracy: 0.5138\n",
            "Epoch 20/75\n",
            "3829/3829 [==============================] - 305s 80ms/step - loss: 1.9922 - accuracy: 0.5212\n",
            "Epoch 21/75\n",
            "3829/3829 [==============================] - 304s 79ms/step - loss: 1.9533 - accuracy: 0.5289\n",
            "Epoch 22/75\n",
            "3829/3829 [==============================] - 304s 79ms/step - loss: 1.9111 - accuracy: 0.5366\n",
            "Epoch 23/75\n",
            "3829/3829 [==============================] - 304s 79ms/step - loss: 1.8785 - accuracy: 0.5416\n",
            "Epoch 24/75\n",
            "3829/3829 [==============================] - 304s 80ms/step - loss: 1.8498 - accuracy: 0.5475\n",
            "Epoch 25/75\n",
            "1359/3829 [=========>....................] - ETA: 3:15 - loss: 1.6715 - accuracy: 0.5819"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JvMDQZJ6tVkC"
      },
      "source": [
        "#Save model\n",
        "with open('model_clsf.json', \"w\") as json_file:\n",
        "\tjson_file.write(model.to_json())\n",
        "model.save_weights(\"weights_clsf.h5\")\n",
        "model.summary()\n",
        "print(\"Model saved.\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S8WapCLZwMkq"
      },
      "source": [
        "# plot hyperparams\n",
        "from matplotlib import pyplot\n",
        "pyplot.figure(figsize=(8, 3))\n",
        "pyplot.plot(hist.history['loss'])\n",
        "pyplot.title('training history')\n",
        "pyplot.ylabel('value')\n",
        "pyplot.xlabel('epoch')\n",
        "pyplot.legend(['Kayıp', 'accuracy'], loc='upper right')\n",
        "pyplot.show()\n",
        "pyplot.savefig('history.png')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-sExjzRLpsQ5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3271d4bb-30f4-43f7-a3de-00455666cf5d"
      },
      "source": [
        "from random import randint\n",
        "from pickle import load\n",
        "from keras.models import load_model\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "# load doc into memory\n",
        "def load_doc(filename):\n",
        "\tfile = open(filename, 'r')\n",
        "\ttext = file.read()\n",
        "\tfile.close()\n",
        "\treturn text\n",
        "\n",
        "# generate a sequence\n",
        "def generate_seq(model, tokenizer, seq_length, seed_text, n_words):\n",
        "\tresult = list()\n",
        "\tin_text = seed_text\n",
        "\t# generate a fixed number of words\n",
        "\tfor _ in range(n_words):\n",
        "\t\t# encode the text as integer\n",
        "\t\tencoded = tokenizer.texts_to_sequences([in_text])[0]\n",
        "\t\t# truncate sequences to a fixed length\n",
        "\t\tencoded = pad_sequences([encoded], maxlen=seq_length, truncating='pre')\n",
        "\t\t# predict probabilities for each word\n",
        "\t\tyhat = model.predict_classes(encoded, verbose=0)\n",
        "\t\t# map predicted word index to word\n",
        "\t\tout_word = ''\n",
        "\t\tfor word, index in tokenizer.word_index.items():\n",
        "\t\t\tif index == yhat:\n",
        "\t\t\t\tout_word = word\n",
        "\t\t\t\tbreak\n",
        "\t\t# append to input\n",
        "\t\tin_text += ' ' + out_word\n",
        "\t\tresult.append(out_word)\n",
        "\treturn ' '.join(result)\n",
        "\n",
        "# load cleaned text sequences\n",
        "in_filename = 'sequences.txt'\n",
        "doc = load_doc(in_filename)\n",
        "lines = doc.split('\\n')\n",
        "seq_length = len(lines[0].split()) - 1\n",
        "\n",
        "# load model and tokenizer\n",
        "model = load_model('model.h5')\n",
        "tokenizer = load(open('tokenizer.pkl', 'rb'))\n",
        "\n",
        "# select a seed text\n",
        "seed_text = test_lines[randint(0,len(test_lines))]\n",
        "seed_elems = seed_text.split(' ')\n",
        "seed_text = ' '.join(seed_elems[:20])\n",
        "print(seed_text)\n",
        "\n",
        "# generate new text\n",
        "generated = generate_seq(model, tokenizer, seq_length, seed_text, 100)\n",
        "print(generated, '\\n')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "81o1 72o4 71o76o1 74o1 64o72o1 67o1 72o1 76o1 72o1 76o1 79o1 76o1 79o1 84o1 86o1 84o1 60o82o1 81o1 79o1 77o1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "76o1 74o1 72o4 62o71o4 64o69o1 67o1 65o1 64o1 63o1 64o1 65o1 67o1 69o1 71o1 72o1 71o1 69o1 67o1 65o1 48o64o2 52o76o4 52o72o2 72o74o1 71o1 72o1 50o74o1 76o1 53o77o1 74o1 52o72o2 53o59o1 52o53o1 50o59o1 52o72o1 71o1 53o74o1 77o1 48o76o1 74o1 52o72o2 55o72o2 48o79o2 52o2 55o77o1 76o1 50o74o1 76o1 50o77o4 48o55o3 55o77o1 81o1 55o1 77o1 76o1 74o1 52o72o4 59o76o1 74o1 55o72o2 55o72o2 48o76o2 47o74o1 76o1 74o1 72o1 71o1 72o1 74o1 76o1 77o1 79o1 81o1 83o1 84o1 52o79o4 48o76o4 50o79o1 77o1 60o76o1 77o1 55o79o1 77o1 57o76o1 74o1 60o72o1 67o79o3 67o79o1 81o1 55o72o4 60o79o3 83o1 81o1 79o1 77o1 76o1 74o1 72o1 71o1 69o1 67o1 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "qjOGFNnrBXD_",
        "outputId": "9ba53085-a75e-4800-c232-19d1a047a58b"
      },
      "source": [
        "# create midi file from text data\n",
        "# NOT OFFSET SENSITIVE\n",
        "import music21\n",
        "\n",
        "# split words\n",
        "events = generated.split()\n",
        "# convert to notes\n",
        "offset = 0\n",
        "output_stream = []\n",
        "for ev in events:\n",
        "  if 'o' in ev: # if chord\n",
        "    splitted = [int(i) for i in ev.split('o')]\n",
        "    chord = music21.chord.Chord(splitted)\n",
        "    output_stream.append(chord)\n",
        "  else: # if note\n",
        "    note = music21.note.Note(int(ev))\n",
        "    note.offset = offset\n",
        "    output_stream.append(note)\n",
        "    offset += 0.5\n",
        "# write into stream object\n",
        "s = music21.stream.Stream()\n",
        "for i in output_stream:\n",
        "  s.append(i)\n",
        "print(output_stream)\n",
        "# write midi file and save\n",
        "score = music21.stream.Score(output_stream)\n",
        "score.write('midi', 'generated.mid')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[<music21.chord.Chord B3 D>, <music21.chord.Chord A2 A3 D>, <music21.chord.Chord E3 C4 E>, <music21.chord.Chord D3 B3 D>, <music21.chord.Chord C4 E>, <music21.chord.Chord A2 A3 D>, <music21.chord.Chord B3 D>, <music21.chord.Chord E3 C4 D>, <music21.chord.Chord D3 B3 D>, <music21.chord.Chord C4 E>, <music21.chord.Chord A2 A3 D>, <music21.chord.Chord E3 E>, <music21.chord.Chord E2 D>, <music21.chord.Chord G3 E>, <music21.chord.Chord C3 C4 D>, <music21.chord.Chord C4 E4 E>, <music21.chord.Chord F3 D4 D>, <music21.chord.Chord E4 D>, <music21.chord.Chord D4 D>, <music21.chord.Chord C3 C4 D>, <music21.chord.Chord C3 F-4 F->, <music21.chord.Chord G4 D>, <music21.chord.Chord G3 E4 G#>, <music21.chord.Chord E3 C5 E>, <music21.chord.Chord G3 D5 E>, <music21.chord.Chord G3 E5 E>, <music21.chord.Chord C4 E5 E>, <music21.chord.Chord C5 E->, <music21.chord.Chord C4 D5 D->, <music21.chord.Chord B3 D5 E>, <music21.chord.Chord B3 D5 E>, <music21.chord.Chord D5 D>, <music21.chord.Chord E4 C5 D>, <music21.chord.Chord B3 D5 E>, <music21.chord.Chord F4 A5 E>, <music21.chord.Chord G4 E>, <music21.chord.Chord D5 D>, <music21.chord.Chord C5 D->, <music21.chord.Chord D5 E>, <music21.chord.Chord D5 D>, <music21.chord.Chord A4 C5 E->, <music21.chord.Chord E5 C#>, <music21.chord.Chord C5 D->, <music21.chord.Chord D5 C#>, <music21.chord.Chord G4 E5 C#>, <music21.chord.Chord C5 E>, <music21.chord.Chord D5 E>, <music21.chord.Chord C5 D->, <music21.chord.Chord E5 C#>, <music21.chord.Chord D5 C#>, <music21.chord.Chord C4 F-5 D->, <music21.chord.Chord B4 D5 G#>, <music21.chord.Chord C4 E5 E>, <music21.chord.Chord F#4 F#5 E>, <music21.chord.Chord G#5 E>, <music21.chord.Chord D5 E>, <music21.chord.Chord D5 E>, <music21.chord.Chord E4 C#>, <music21.chord.Chord D4 C#>, <music21.chord.Chord C4 D->, <music21.chord.Chord B3 C#>, <music21.chord.Chord A3 C#>, <music21.chord.Chord G3 C#>, <music21.chord.Chord A3 C#>, <music21.chord.Chord F#3 C#>, <music21.chord.Chord D3 C#>, <music21.chord.Chord D3 C#>, <music21.chord.Chord D3 C#>, <music21.chord.Chord D3 C#>, <music21.chord.Chord D3 C#>, <music21.chord.Chord F#3 C#>, <music21.chord.Chord D3 C#>, <music21.chord.Chord D3 C#>, <music21.chord.Chord G3 C#>, <music21.chord.Chord F#3 C#>, <music21.chord.Chord D3 C#>, <music21.chord.Chord D3 C#>, <music21.chord.Chord F#3 C#>, <music21.chord.Chord D3 C#>, <music21.chord.Chord B4 E5 C#>, <music21.chord.Chord B4 E5 D>, <music21.chord.Chord A4 F#5 F#>, <music21.chord.Chord C#5 E5 E#>, <music21.chord.Chord A1 A2 D#>, <music21.chord.Chord E5 G5 E->, <music21.chord.Chord D5 F#5 C#>, <music21.chord.Chord G2 C#>, <music21.chord.Chord A2 D->, <music21.chord.Chord B2 C#>, <music21.chord.Chord D3 E->, <music21.chord.Chord A2 D->, <music21.chord.Chord D3 C#>, <music21.chord.Chord D3 E->, <music21.chord.Chord A4 A5 E>, <music21.chord.Chord F#2 F#3 E>, <music21.chord.Chord D2 D3 D>, <music21.chord.Chord A3 C#>, <music21.chord.Chord F#3 C#>, <music21.chord.Chord A3 C#>, <music21.chord.Chord D5 B5 C#>, <music21.chord.Chord D5 A5 C#>, <music21.chord.Chord D2 C#>, <music21.chord.Chord D3 C#>, <music21.chord.Chord F#5 A5 C#>, <music21.chord.Chord D3 C#>, <music21.chord.Chord F#3 C#>, <music21.chord.Chord B5 D6 C#>, <music21.chord.Chord E5 E6 C#>, <music21.chord.Chord D3 C#>, <music21.chord.Chord G3 C#>, <music21.chord.Chord A4 A5 C#>, <music21.chord.Chord B5 D6 C#>, <music21.chord.Chord D3 C#>, <music21.chord.Chord F3 D->, <music21.chord.Chord A3 C#>, <music21.chord.Chord D6 G6 D->, <music21.chord.Chord A2 D->, <music21.chord.Chord D3 C#>, <music21.chord.Chord D5 D6 C#>, <music21.chord.Chord G3 G6 D->]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'000.mid'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rmtAD6rr3dSJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "eb3969a1-5f14-441f-baf9-99f43a03868f"
      },
      "source": [
        "# create midi file from text data\n",
        "# OFFSET SENSITIVE\n",
        "import music21\n",
        "\n",
        "# split words\n",
        "events = generated.split()\n",
        "# convert to notes\n",
        "offset = 0\n",
        "output_stream = []\n",
        "for ev in events:\n",
        "  splitted = [int(i) for i in ev.split('o')]\n",
        "  # get offset\n",
        "  q_length = splitted[-1]\n",
        "  # get notes\n",
        "  splitted = splitted[:-1]\n",
        "  if len(splitted) > 1: # if chord\n",
        "    chord = music21.chord.Chord(splitted, quarterLength = q_length)\n",
        "    chord.offset = offset\n",
        "    output_stream.append(chord)\n",
        "  else: # if note\n",
        "    note = music21.note.Note(splitted[0], quarterLength = q_length)\n",
        "    note.offset = offset\n",
        "    output_stream.append(note)\n",
        "  # offset\n",
        "  offset += q_length\n",
        "s = music21.stream.Stream()\n",
        "# write into stream object\n",
        "for i in output_stream:\n",
        "  s.append(i)\n",
        "print(output_stream)\n",
        "# write midi file and save\n",
        "score = music21.stream.Score(output_stream)\n",
        "score.write('midi', '000.mid')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[<music21.note.Note E>, <music21.note.Note D>, <music21.note.Note C>, <music21.chord.Chord D4 B4>, <music21.chord.Chord E4 A4>, <music21.note.Note G>, <music21.note.Note F>, <music21.note.Note E>, <music21.note.Note E->, <music21.note.Note E>, <music21.note.Note F>, <music21.note.Note G>, <music21.note.Note A>, <music21.note.Note B>, <music21.note.Note C>, <music21.note.Note B>, <music21.note.Note A>, <music21.note.Note G>, <music21.note.Note F>, <music21.chord.Chord C3 F-4>, <music21.chord.Chord E3 E5>, <music21.chord.Chord E3 C5>, <music21.chord.Chord C5 D5>, <music21.note.Note B>, <music21.note.Note C>, <music21.chord.Chord D3 D5>, <music21.note.Note E>, <music21.chord.Chord F3 F5>, <music21.note.Note D>, <music21.chord.Chord E3 C5>, <music21.chord.Chord F3 B3>, <music21.chord.Chord E3 F3>, <music21.chord.Chord D3 B3>, <music21.chord.Chord E3 C5>, <music21.note.Note B>, <music21.chord.Chord F3 D5>, <music21.note.Note F>, <music21.chord.Chord C3 E5>, <music21.note.Note D>, <music21.chord.Chord E3 C5>, <music21.chord.Chord G3 C5>, <music21.chord.Chord C3 G5>, <music21.note.Note E>, <music21.chord.Chord G3 F5>, <music21.note.Note E>, <music21.chord.Chord D3 D5>, <music21.note.Note E>, <music21.chord.Chord D3 F5>, <music21.chord.Chord C3 G3>, <music21.chord.Chord G3 F5>, <music21.note.Note A>, <music21.note.Note G>, <music21.note.Note F>, <music21.note.Note E>, <music21.note.Note D>, <music21.chord.Chord E3 C5>, <music21.chord.Chord B3 E5>, <music21.note.Note D>, <music21.chord.Chord G3 C5>, <music21.chord.Chord G3 C5>, <music21.chord.Chord C3 E5>, <music21.chord.Chord B2 D5>, <music21.note.Note E>, <music21.note.Note D>, <music21.note.Note C>, <music21.note.Note B>, <music21.note.Note C>, <music21.note.Note D>, <music21.note.Note E>, <music21.note.Note F>, <music21.note.Note G>, <music21.note.Note A>, <music21.note.Note B>, <music21.note.Note C>, <music21.chord.Chord E3 G5>, <music21.chord.Chord C3 E5>, <music21.chord.Chord D3 G5>, <music21.note.Note F>, <music21.chord.Chord C4 F-5>, <music21.note.Note F>, <music21.chord.Chord G3 G5>, <music21.note.Note F>, <music21.chord.Chord A3 E5>, <music21.note.Note D>, <music21.chord.Chord C4 C5>, <music21.chord.Chord G4 G5>, <music21.chord.Chord G4 G5>, <music21.note.Note A>, <music21.chord.Chord G3 C5>, <music21.chord.Chord C4 G5>, <music21.note.Note B>, <music21.note.Note A>, <music21.note.Note G>, <music21.note.Note F>, <music21.note.Note E>, <music21.note.Note D>, <music21.note.Note C>, <music21.note.Note B>, <music21.note.Note A>, <music21.note.Note G>]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'000.mid'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "AFsCddgoQCh9",
        "outputId": "815304f4-7ec9-43be-e1cc-1b2b838c0206"
      },
      "source": [
        "# create midi file from seed data\n",
        "# OFFSET SENSITIVE\n",
        "import music21\n",
        "\n",
        "# split words\n",
        "events = seed_text.split()\n",
        "# convert to notes\n",
        "offset = 0\n",
        "output_stream = []\n",
        "for ev in events:\n",
        "  splitted = [int(i) for i in ev.split('o')]\n",
        "  # get offset\n",
        "  q_length = splitted[-1]\n",
        "  # get notes\n",
        "  splitted = splitted[:-1]\n",
        "  if len(splitted) > 1: # if chord\n",
        "    chord = music21.chord.Chord(splitted, quarterLength = q_length)\n",
        "    chord.offset = offset\n",
        "    output_stream.append(chord)\n",
        "  else: # if note\n",
        "    note = music21.note.Note(splitted[0], quarterLength = q_length)\n",
        "    note.offset = offset\n",
        "    output_stream.append(note)\n",
        "  # offset\n",
        "  offset += q_length\n",
        "s = music21.stream.Stream()\n",
        "# write into stream object\n",
        "for i in output_stream:\n",
        "  s.append(i)\n",
        "print(output_stream)\n",
        "# write midi file and save\n",
        "score = music21.stream.Score(output_stream)\n",
        "score.write('midi', 'seed.mid')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[<music21.note.Note A>, <music21.note.Note C>, <music21.chord.Chord B4 E5>, <music21.note.Note D>, <music21.chord.Chord E4 C5>, <music21.note.Note G>, <music21.note.Note C>, <music21.note.Note E>, <music21.note.Note C>, <music21.note.Note E>, <music21.note.Note G>, <music21.note.Note E>, <music21.note.Note G>, <music21.note.Note C>, <music21.note.Note D>, <music21.note.Note C>, <music21.chord.Chord C4 B-5>, <music21.note.Note A>, <music21.note.Note G>, <music21.note.Note F>]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'seed.mid'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ommVRkj98Xye"
      },
      "source": [
        "# ***NOTES*** #\n",
        "\n",
        "\"\"\"\n",
        "# statictics\n",
        "stats = dict()\n",
        "for t in tokens:\n",
        "  if t in stats:\n",
        "    stats[t] += 1\n",
        "  else:\n",
        "    stats[t] = 1\n",
        "stats = dict(sorted(stats.items(), key=lambda item: item[1]))\n",
        "file_stats = open('statistics.txt', 'w')\n",
        "for s in stats:\n",
        "  file_stats.write(str(stats[s]) + '   ' +  str(s) +'\\n')\n",
        "file_stats.close()\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}