assets/Music-Generation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pEI6gbW9Xh2t"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import json \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dropout, TimeDistributed, Dense, Activation, Embedding\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.utils import *\n",
    "from music21 import *\n",
    "from IPython.display import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "99F2Mtptj4MS"
   },
   "outputs": [],
   "source": [
    "data_directory = \"./drive/My Drive/Data/\"\n",
    "data_file = \"Data_Tunes.txt\"\n",
    "charIndex_json = \"char_to_index.json\"\n",
    "BATCH_SIZE = 16\n",
    "SEQ_LENGTH = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 244
    },
    "colab_type": "code",
    "id": "_aebZeh6a-n6",
    "outputId": "a0b4ec86-cb8c-4098-ed95-6510fdec5839"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='./drive/My Drive/abc.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_XacfcrSX3OK"
   },
   "source": [
    "ABC Notation\n",
    "\n",
    "ABC notation of Music\n",
    "There are two parts in ABC-notation.\n",
    "\n",
    "Part-1 represents meta data. Lines in the Part-1 of the tune notation, beginning with a letter followed by a colon, indicate various aspects of the tune such as the index, when there are more than one tune in a file (X:), the title (T:), the time signature (M:), the default note length (L:), the type of tune (R:) and the key (K:).\n",
    "\n",
    "Part-2 represents the tune, which is a sequence of characters where each character represents some musical note."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kU9kGmh6RKyI"
   },
   "outputs": [],
   "source": [
    "# Function to remove Part-1 of  and append a start token i.e 'Z' for each tune in the dataset\n",
    "\n",
    "def preprocess(data):\n",
    "  list1=list(data)\n",
    "  list2=['\\n','\\n','\\n']\n",
    "  ignore=['X','T','M','S','K','P']\n",
    "  i=0\n",
    "  #to remove Part1:\n",
    "  while(i<len(list1)):\n",
    "    if(((list1[i] in ignore) and (list1[i+1]==\":\"))or list1[i]=='%' ):\n",
    "      del list2[-1]\n",
    "      while(list1[i]!='\\n'):\n",
    "        i=i+1\n",
    "    list2.append(list1[i])\n",
    "    i=i+1\n",
    "  i=0\n",
    "  #to append 'Z'(start token)\n",
    "  preprocess_data=[]\n",
    "  while(i<len(list2)):\n",
    "    if(list2[i]=='\\n'and list2[i+1]=='\\n' and list2[i+2]=='\\n'):\n",
    "      preprocess_data.append('Z')\n",
    "      i=i+3\n",
    "    else:\n",
    "      preprocess_data.append(list2[i])\n",
    "      i=i+1\n",
    "  return preprocess_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MYOozwWeDhTv"
   },
   "outputs": [],
   "source": [
    "# Function to create char_to_index and index_to_char dictionaries so as to map each character to an index and vice versa.\n",
    "# Returns all_characters_as_indices i.e an array containing all characters of the dataset replaced with their corresponding indices as per the vocabulary.\n",
    "# Also returns num_unique_chars i.e an integer equal to number of unique characters in the data.\n",
    "\n",
    "def read_data(preprocess_data):\n",
    "  char_to_index = {ch: i for (i, ch) in enumerate(sorted(list(set(preprocess_data))))}\n",
    "\n",
    "    \n",
    "  with open(os.path.join(data_directory, charIndex_json), mode = \"w\") as f:\n",
    "        json.dump(char_to_index, f)\n",
    "        \n",
    "  index_to_char = {i: ch for (ch, i) in char_to_index.items()}\n",
    "  num_unique_chars = len(char_to_index)\n",
    "  all_characters_as_indices = np.asarray([char_to_index[c] for c in preprocess_data], dtype = np.int32)\n",
    "  return all_characters_as_indices,num_unique_chars\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4FWErp9l_c1y"
   },
   "outputs": [],
   "source": [
    "# Function which returns X and Y which will be used as input and target output for training the model.\n",
    "\n",
    "def input_output(all_chars_as_indices,num_unique_chars):\n",
    "    total_length = all_chars_as_indices.shape[0]\n",
    "    num_examples=int(total_length/SEQ_LENGTH)\n",
    "    X=np.zeros((num_examples,SEQ_LENGTH))\n",
    "    Y=np.zeros((num_examples,SEQ_LENGTH,num_unique_chars))\n",
    "    for i in range(num_examples):\n",
    "      for j in range(SEQ_LENGTH):\n",
    "        X[i,j]=all_chars_as_indices[i*SEQ_LENGTH+j]\n",
    "        Y[i,j,all_chars_as_indices[i*SEQ_LENGTH+j+1]]=1\n",
    "    return X,Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0H5nVWJv_ihC"
   },
   "outputs": [],
   "source": [
    "# Function to build the training model\n",
    "\n",
    "def build_model( seq_length, num_unique_chars):\n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Embedding(input_dim = num_unique_chars, output_dim = 512, input_shape = (seq_length,))) \n",
    "    \n",
    "    model.add(LSTM(256, return_sequences = True))\n",
    "    model.add(Dropout(0.2))\n",
    "    \n",
    "    model.add(LSTM(256, return_sequences = True))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(LSTM(256, return_sequences = True))\n",
    "    model.add(Dropout(0.2))\n",
    "    \n",
    "    \n",
    "    model.add(TimeDistributed(Dense(num_unique_chars)))\n",
    "\n",
    "    model.add(Activation(\"softmax\"))\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "hDNI8EFAwran",
    "outputId": "d706902f-7bcc-4add-dbe8-f00338ecf25f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='./drive/My Drive/build.png') #image of training model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2bF_Z6KIbqw2"
   },
   "source": [
    "stateful:\n",
    "\n",
    " If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eGgrLrX4BnHh"
   },
   "outputs": [],
   "source": [
    "#Function which builds model for generating music sequences.\n",
    "\n",
    "def make_model(num_unique_chars):\n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Embedding(input_dim = num_unique_chars, output_dim = 512, batch_input_shape = (1, 1))) \n",
    "  \n",
    "# stateful: If True, the last state for each sample at index i in a batch will be used \n",
    "# as initial state for the sample of index i in the following batch.\n",
    "    model.add(LSTM(256, return_sequences = True, stateful = True))\n",
    "    model.add(Dropout(0.2))\n",
    "    \n",
    "    model.add(LSTM(256, return_sequences = True, stateful = True))\n",
    "    model.add(Dropout(0.2))\n",
    "    \n",
    "    model.add(LSTM(256,return_sequences=True, stateful = True)) \n",
    "    model.add(Dropout(0.2))\n",
    "    \n",
    "    model.add((Dense(num_unique_chars)))\n",
    "    model.add(Activation(\"softmax\"))\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "O6VNDGiQw5KY",
    "outputId": "90706643-8f6a-414b-f7a5-fdf55cbf3bef"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='./drive/My Drive/generate.png') #image showing music generation model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iw446L1OBzzl"
   },
   "outputs": [],
   "source": [
    "# Function which generates music sequences of length=gen_seq_length.\n",
    "def generate_sequence(gen_seq_length):\n",
    "    with open(os.path.join(data_directory, charIndex_json)) as f:\n",
    "        char_to_index = json.load(f)\n",
    "    index_to_char = {i:ch for ch, i in char_to_index.items()}\n",
    "    num_unique_chars = len(index_to_char)\n",
    "    \n",
    "    model = make_model(num_unique_chars)\n",
    "    model.load_weights(\"./drive/My Drive/weights.80.hdf5\")\n",
    "     \n",
    "    sequence_index = [char_to_index['Z']]\n",
    "\n",
    "    for _ in range(gen_seq_length):\n",
    "        batch = np.zeros((1, 1))\n",
    "        batch[0, 0] = sequence_index[-1]\n",
    "        \n",
    "        predicted_probs = model.predict_on_batch(batch).ravel()\n",
    "        sample = np.random.choice(range(num_unique_chars), size = 1, p = predicted_probs)\n",
    "        \n",
    "        \n",
    "        sequence_index.append(sample[0])\n",
    "    \n",
    "        \n",
    "    \n",
    "    seq = ''.join(index_to_char[c] for c in sequence_index)\n",
    "    seq='M:6/8\\n'+str(seq)\n",
    "    return seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kveYEr8HCtcZ"
   },
   "outputs": [],
   "source": [
    "# function to create a midi file given a music sequence in abc notation.\n",
    "def convert_to_midi(abc):\n",
    "    c = converter.subConverters.ConverterABC()\n",
    "    c.registerOutputExtensions = (\"midi\", )\n",
    "    c.parseData(abc)\n",
    "    s = c.stream\n",
    "    s.write('midi', fp='demos1.mid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "yI3ceWwF_XL6",
    "outputId": "e2d1a356-5176-4b18-93d0-266a742127c8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of preprocess_data-116963\n",
      "vocab_size=59\n",
      "all_characters=[33 44 57 ... 15 20 57]\n",
      "length of all_characters-116963\n",
      "shape of X=(1827, 64)\n",
      "shape of Y=(1827, 64, 59)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "file = open(os.path.join(data_directory, data_file), mode = 'r')\n",
    "data = file.read()\n",
    "file.close()\n",
    "preprocess_data=preprocess(data)\n",
    "all_characters_as_indices,num_unique_chars=read_data(preprocess_data)\n",
    "X,Y=input_output(all_characters_as_indices,num_unique_chars)\n",
    "print(\"length of preprocess_data-{}\".format(len(preprocess_data)))\n",
    "print(\"vocab_size={}\".format(num_unique_chars))\n",
    "print(\"all_characters={}\".format(all_characters_as_indices))\n",
    "print(\"length of all_characters-{}\".format(len(all_characters_as_indices)))\n",
    "print(\"shape of X={}\".format(X.shape))\n",
    "print(\"shape of Y={}\".format(Y.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "_TNCaeqyAda9",
    "outputId": "174462fc-4e1d-424c-c0b6-a743cdfc7bcf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 64, 512)           30208     \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 64, 256)           787456    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 64, 256)           0         \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 64, 256)           525312    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 64, 256)           0         \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 64, 256)           525312    \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 64, 256)           0         \n",
      "_________________________________________________________________\n",
      "time_distributed_1 (TimeDist (None, 64, 59)            15163     \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 64, 59)            0         \n",
      "=================================================================\n",
      "Total params: 1,883,451\n",
      "Trainable params: 1,883,451\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/indexed_slices.py:434: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/80\n",
      "1827/1827 [==============================] - 56s 31ms/step - loss: 3.0364 - accuracy: 0.1966\n",
      "Epoch 2/80\n",
      "1827/1827 [==============================] - 54s 30ms/step - loss: 2.1573 - accuracy: 0.3917\n",
      "Epoch 3/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 1.8398 - accuracy: 0.4435\n",
      "Epoch 4/80\n",
      "1827/1827 [==============================] - 54s 29ms/step - loss: 1.6679 - accuracy: 0.4740\n",
      "Epoch 5/80\n",
      "1827/1827 [==============================] - 54s 29ms/step - loss: 1.5664 - accuracy: 0.4956\n",
      "Epoch 6/80\n",
      "1827/1827 [==============================] - 54s 29ms/step - loss: 1.4949 - accuracy: 0.5152\n",
      "Epoch 7/80\n",
      "1827/1827 [==============================] - 54s 30ms/step - loss: 1.4356 - accuracy: 0.5334\n",
      "Epoch 8/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 1.3794 - accuracy: 0.5510\n",
      "Epoch 9/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 1.3255 - accuracy: 0.5664\n",
      "Epoch 10/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 1.2850 - accuracy: 0.5787\n",
      "Epoch 11/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 1.2474 - accuracy: 0.5887\n",
      "Epoch 12/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 1.2149 - accuracy: 0.5991\n",
      "Epoch 13/80\n",
      "1827/1827 [==============================] - 54s 29ms/step - loss: 1.1850 - accuracy: 0.6084\n",
      "Epoch 14/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 1.1542 - accuracy: 0.6182\n",
      "Epoch 15/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 1.1260 - accuracy: 0.6267\n",
      "Epoch 16/80\n",
      "1827/1827 [==============================] - 52s 29ms/step - loss: 1.0980 - accuracy: 0.6344\n",
      "Epoch 17/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 1.0705 - accuracy: 0.6441\n",
      "Epoch 18/80\n",
      "1827/1827 [==============================] - 52s 29ms/step - loss: 1.0463 - accuracy: 0.6513\n",
      "Epoch 19/80\n",
      "1827/1827 [==============================] - 52s 29ms/step - loss: 1.0198 - accuracy: 0.6613\n",
      "Epoch 20/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.9974 - accuracy: 0.6682\n",
      "Epoch 21/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.9758 - accuracy: 0.6754\n",
      "Epoch 22/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.9494 - accuracy: 0.6833\n",
      "Epoch 23/80\n",
      "1827/1827 [==============================] - 52s 28ms/step - loss: 0.9275 - accuracy: 0.6909\n",
      "Epoch 24/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.9029 - accuracy: 0.6992\n",
      "Epoch 25/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.8827 - accuracy: 0.7052\n",
      "Epoch 26/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.8562 - accuracy: 0.7138\n",
      "Epoch 27/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.8330 - accuracy: 0.7213\n",
      "Epoch 28/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.8134 - accuracy: 0.7285\n",
      "Epoch 29/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.7895 - accuracy: 0.7355\n",
      "Epoch 30/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.7718 - accuracy: 0.7418\n",
      "Epoch 31/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.7500 - accuracy: 0.7499\n",
      "Epoch 32/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.7297 - accuracy: 0.7559\n",
      "Epoch 33/80\n",
      "1827/1827 [==============================] - 54s 29ms/step - loss: 0.7093 - accuracy: 0.7623\n",
      "Epoch 34/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.6948 - accuracy: 0.7666\n",
      "Epoch 35/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.6728 - accuracy: 0.7729\n",
      "Epoch 36/80\n",
      "1827/1827 [==============================] - 54s 30ms/step - loss: 0.6518 - accuracy: 0.7821\n",
      "Epoch 37/80\n",
      "1827/1827 [==============================] - 54s 30ms/step - loss: 0.6419 - accuracy: 0.7852\n",
      "Epoch 38/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.6201 - accuracy: 0.7905\n",
      "Epoch 39/80\n",
      "1827/1827 [==============================] - 52s 28ms/step - loss: 0.6038 - accuracy: 0.7969\n",
      "Epoch 40/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.5901 - accuracy: 0.8006\n",
      "Epoch 41/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.5748 - accuracy: 0.8063\n",
      "Epoch 42/80\n",
      "1827/1827 [==============================] - 54s 29ms/step - loss: 0.5595 - accuracy: 0.8127\n",
      "Epoch 43/80\n",
      "1827/1827 [==============================] - 54s 29ms/step - loss: 0.5435 - accuracy: 0.8156\n",
      "Epoch 44/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.5314 - accuracy: 0.8199\n",
      "Epoch 45/80\n",
      "1827/1827 [==============================] - 54s 30ms/step - loss: 0.5215 - accuracy: 0.8236\n",
      "Epoch 46/80\n",
      "1827/1827 [==============================] - 54s 29ms/step - loss: 0.5074 - accuracy: 0.8279\n",
      "Epoch 47/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.4969 - accuracy: 0.8298\n",
      "Epoch 48/80\n",
      "1827/1827 [==============================] - 52s 28ms/step - loss: 0.4837 - accuracy: 0.8360\n",
      "Epoch 49/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.4795 - accuracy: 0.8366\n",
      "Epoch 50/80\n",
      "1827/1827 [==============================] - 54s 29ms/step - loss: 0.4619 - accuracy: 0.8430\n",
      "Epoch 51/80\n",
      "1827/1827 [==============================] - 52s 29ms/step - loss: 0.4502 - accuracy: 0.8456\n",
      "Epoch 52/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.4487 - accuracy: 0.8469\n",
      "Epoch 53/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.4373 - accuracy: 0.8510\n",
      "Epoch 54/80\n",
      "1827/1827 [==============================] - 52s 29ms/step - loss: 0.4250 - accuracy: 0.8537\n",
      "Epoch 55/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.4177 - accuracy: 0.8567\n",
      "Epoch 56/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.4116 - accuracy: 0.8593\n",
      "Epoch 57/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.3994 - accuracy: 0.8634\n",
      "Epoch 58/80\n",
      "1827/1827 [==============================] - 52s 29ms/step - loss: 0.3933 - accuracy: 0.8656\n",
      "Epoch 59/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.3930 - accuracy: 0.8655\n",
      "Epoch 60/80\n",
      "1827/1827 [==============================] - 52s 29ms/step - loss: 0.3809 - accuracy: 0.8690\n",
      "Epoch 61/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.3760 - accuracy: 0.8717\n",
      "Epoch 62/80\n",
      "1827/1827 [==============================] - 52s 29ms/step - loss: 0.3706 - accuracy: 0.8731\n",
      "Epoch 63/80\n",
      "1827/1827 [==============================] - 52s 29ms/step - loss: 0.3598 - accuracy: 0.8759\n",
      "Epoch 64/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.3603 - accuracy: 0.8755\n",
      "Epoch 65/80\n",
      "1827/1827 [==============================] - 52s 28ms/step - loss: 0.3509 - accuracy: 0.8806\n",
      "Epoch 66/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.3461 - accuracy: 0.8807\n",
      "Epoch 67/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.3377 - accuracy: 0.8844\n",
      "Epoch 68/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.3324 - accuracy: 0.8859\n",
      "Epoch 69/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.3317 - accuracy: 0.8858\n",
      "Epoch 70/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.3225 - accuracy: 0.8887\n",
      "Epoch 71/80\n",
      "1827/1827 [==============================] - 52s 28ms/step - loss: 0.3197 - accuracy: 0.8895\n",
      "Epoch 72/80\n",
      "1827/1827 [==============================] - 52s 28ms/step - loss: 0.3196 - accuracy: 0.8893\n",
      "Epoch 73/80\n",
      "1827/1827 [==============================] - 52s 29ms/step - loss: 0.3137 - accuracy: 0.8918\n",
      "Epoch 74/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.3107 - accuracy: 0.8922\n",
      "Epoch 75/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.3062 - accuracy: 0.8944\n",
      "Epoch 76/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.3039 - accuracy: 0.8949\n",
      "Epoch 77/80\n",
      "1827/1827 [==============================] - 52s 28ms/step - loss: 0.2981 - accuracy: 0.8976\n",
      "Epoch 78/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.2958 - accuracy: 0.8976\n",
      "Epoch 79/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.2921 - accuracy: 0.8992\n",
      "Epoch 80/80\n",
      "1827/1827 [==============================] - 53s 29ms/step - loss: 0.2868 - accuracy: 0.9013\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7fda3a2c4208>"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=build_model(SEQ_LENGTH,num_unique_chars)\n",
    "model.summary()\n",
    "model.compile(loss = \"categorical_crossentropy\", optimizer = \"adam\", metrics = [\"accuracy\"])\n",
    "checkpoint=ModelCheckpoint(filepath='weights.{epoch:02d}.hdf5',monitor='loss',save_best_only=True,save_weights_only=True,period=1)\n",
    "model.fit(X,Y,batch_size=16,epochs=80,callbacks=[checkpoint])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "iWz5iH8oj3yf",
    "outputId": "bbd5e890-72b4-443d-f5f5-81a6324acc99"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MUSIC SEQUENCE GENERATED: \n",
      "M:6/8\n",
      "ZD|\"G\"G2G BAG|\"G\"d2d d2d|\"C\"cBA \"A7\"B6|\"D7\"^c2d e3|\"G\"d2B d2B|\n",
      "\"Am\"c2B A2B|\"A7\"c2c cBA|\"D\"e2d dBc|\"D\"d3 -d3|\"G\"\"g\"g3 fgB|\"Am\"c3 \"D7\"c2d|\n",
      "\"G\"e2d B^AB|\"G\"d2d BdB|\"D\"d2c \"Em\"B2c|\"A7\"A6|\"D7\"d3 ABc|\n"
     ]
    }
   ],
   "source": [
    "music = generate_sequence(192)\n",
    "print(\"\\nMUSIC SEQUENCE GENERATED: \\n{}\".format(music))\n",
    "convert_to_midi(music)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ybSsFZd6xU6f"
   },
   "source": [
    "We have finished training and generating music sequences."
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "MusGenfinal.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}