notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import re \n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import music21\n",
    "from glob import glob\n",
    "import IPython\n",
    "from tqdm import tqdm\n",
    "import pickle\n",
    "from keras.utils import np_utils\n",
    "from music21 import converter, instrument, note, chord, stream\n",
    "import tensorflow as tf\n",
    "\n",
    "import mitdeeplearning as mdl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [],
   "source": [
    "songs = glob('midiclassics/*/*.mid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [],
   "source": [
    "song = songs[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['midiclassics\\\\Albéniz\\\\Aragon (Fantasia) Op.47 part 6.mid',\n",
       " 'midiclassics\\\\Albéniz\\\\Castilla (Seguidillas) Op.47 part 7.mid',\n",
       " 'midiclassics\\\\Albéniz\\\\Cataluna (Curranda), No.2 from Suite Espanola.mid']"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "song"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1239, 1000,  580,  936,  753,  416,   70,  797,  329, 1231, 1215,\n",
       "        701,  636, 1513,  108,  190, 1116,  619, 1115, 1502, 1303, 1124,\n",
       "       1198,  734,  871,  618,  189,  472,  945,  757,  770,    6, 1304,\n",
       "       1103, 1470,  966,   88,  974, 1491, 1504, 1352,  483,  178,   39,\n",
       "       1593,  312, 1195,  581, 1570, 1338,  955,  601, 1045,  426, 1027,\n",
       "       1373,  567,  899,  892, 1596, 1567,  111,  251, 1174,  608, 1485,\n",
       "        927, 1067, 1084,   77,  115, 1314, 1746, 1429,  148,  393,  557,\n",
       "       1690, 1155, 1302,  559,  446,  980,   98,  680,  417,  933,   13,\n",
       "        528,  288, 1387,  664, 1157, 1053,  366, 1051, 1789, 1165, 1461,\n",
       "       1311])"
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "song_index = np.random.choice(len(songs), 100 , replace=False)\n",
    "song_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_notes():\n",
    "    notes = []\n",
    "    for i in tqdm(song_index):\n",
    "        # converting .mid file to stream object\n",
    "        try:\n",
    "            midi = converter.parse(songs[i])\n",
    "            notes_to_parse = []\n",
    "            try:\n",
    "                # Given a single stream, partition into a part for each unique instrument\n",
    "                parts = instrument.partitionByInstrument(midi)\n",
    "            except:\n",
    "                pass\n",
    "            if parts: # if parts has instrument parts \n",
    "                notes_to_parse = parts.parts[0].recurse()\n",
    "            else:\n",
    "                notes_to_parse = midi.flat.notes\n",
    "        \n",
    "            for element in notes_to_parse: \n",
    "                if isinstance(element, note.Note):\n",
    "                    # if element is a note, extract pitch\n",
    "                    notes.append(str(element.pitch))\n",
    "                elif(isinstance(element, chord.Chord)):\n",
    "                    # if element is a chord, append the normal form of the \n",
    "                    # chord (a list of integers) to the list of notes. \n",
    "                    notes.append('.'.join(str(n) for n in element.normalOrder))\n",
    "        except:\n",
    "            pass\n",
    "    with open('data/notes', 'wb') as filepath:\n",
    "        pickle.dump(notes, filepath)\n",
    "    \n",
    "    return notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_notes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/notes', 'rb') as filepath:\n",
    "        notes = pickle.load(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 539 unique characters in the dataset\n"
     ]
    }
   ],
   "source": [
    "# Find all unique characters in the joined string\n",
    "vocab = sorted(set(notes))\n",
    "print(\"There are\", len(vocab), \"unique characters in the dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Vectorize the text\n",
    "char2idx = {u:i for i, u in enumerate(vocab)}\n",
    "\n",
    "# Create a mapping from indices to characters. This is\n",
    "#   the inverse of char2idx and allows us to convert back\n",
    "#   from unique index to the character in our vocabulary.\n",
    "idx2char = np.array(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vectorizing whole song data\n",
    "def vectorize_string(string):\n",
    "  vectorized_output = np.array([char2idx[char] for char in string])\n",
    "  return vectorized_output\n",
    "\n",
    "# def vectorize_string(string):\n",
    "\n",
    "vectorized_songs = vectorize_string(notes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77972"
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vectorized_songs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Batch definition to create training examples ###\n",
    "\n",
    "def get_batch(vectorized_songs, seq_length, batch_size):\n",
    "  # the length of the vectorized songs string\n",
    "  n = vectorized_songs.shape[0] - 1\n",
    "  # randomly choose the starting indices for the examples in the training batch\n",
    "  idx = np.random.choice(n-seq_length, batch_size)\n",
    "\n",
    "  '''TODO: construct a list of input sequences for the training batch'''\n",
    "  input_batch = [vectorized_songs[i : i+seq_length] for i in idx]\n",
    "  \n",
    "  '''TODO: construct a list of output sequences for the training batch'''\n",
    "  output_batch = [vectorized_songs[i+1 : i+seq_length+1] for i in idx]\n",
    "  \n",
    "\n",
    "  # x_batch, y_batch provide the true inputs and targets for network training\n",
    "  x_batch = np.reshape(input_batch, [batch_size, seq_length])\n",
    "  y_batch = np.reshape(output_batch, [batch_size, seq_length])\n",
    "  return x_batch, y_batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step   0\n",
      "  input: [ 77  55 535 353  57] (array(['1.5', '1.3', 'G5', '7.10', '1.3.5'], dtype='<U14'))\n",
      "  expected output: [ 55 535 353  57  77] (array(['1.3', 'G5', '7.10', '1.3.5', '1.5'], dtype='<U14'))\n",
      "Step   1\n",
      "  input: [493 493 155   8 154] (array(['D4', 'D4', '2.3', '0.2', '2'], dtype='<U14'))\n",
      "  expected output: [493 155   8 154 493] (array(['D4', '2.3', '0.2', '2', 'D4'], dtype='<U14'))\n"
     ]
    }
   ],
   "source": [
    "#sample check\n",
    "x_batch, y_batch = get_batch(vectorized_songs, seq_length=5, batch_size=2)\n",
    "\n",
    "for i, (input_idx, target_idx) in enumerate(zip(np.squeeze(x_batch), np.squeeze(y_batch))):\n",
    "    print(\"Step {:3d}\".format(i))\n",
    "    print(\"  input: {} ({:s})\".format(input_idx, repr(idx2char[input_idx])))\n",
    "    print(\"  expected output: {} ({:s})\".format(target_idx, repr(idx2char[target_idx])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LSTM(rnn_units): \n",
    "  return tf.keras.layers.LSTM(\n",
    "    rnn_units, \n",
    "    return_sequences=True, \n",
    "    recurrent_initializer='glorot_uniform',\n",
    "    recurrent_activation='sigmoid',\n",
    "    stateful=True,\n",
    "  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Defining the RNN Model ###\n",
    "\n",
    "'''Add LSTM and Dense layers to define the RNN model using the Sequential API.'''\n",
    "def build_model(vocab_size, embedding_dim, rnn_units, batch_size):\n",
    "  model = tf.keras.Sequential([\n",
    "    # Layer 1: Embedding layer to transform indices into dense vectors \n",
    "    #   of a fixed embedding size\n",
    "    tf.keras.layers.Embedding(vocab_size, embedding_dim, batch_input_shape=[batch_size, None]),\n",
    "\n",
    "    # Layer 2: LSTM with `rnn_units` number of units. \n",
    "    #  Call the LSTM function defined above to add this layer.\n",
    "    LSTM(rnn_units), \n",
    "\n",
    "    # Layer 3: Dense (fully-connected) layer that transforms the LSTM output\n",
    "    #   into the vocabulary size. \n",
    "    # Add the Dense layer.\n",
    "    tf.keras.layers.Dense(vocab_size)\n",
    "  \n",
    "  ])\n",
    "\n",
    "  return model\n",
    "\n",
    "# Build a simple model with default hyperparameters. You will get the \n",
    "#   chance to change these later.\n",
    "model = build_model(len(vocab), embedding_dim=256, rnn_units=1024, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_7 (Embedding)     (32, None, 256)           137984    \n",
      "                                                                 \n",
      " lstm_7 (LSTM)               (32, None, 1024)          5246976   \n",
      "                                                                 \n",
      " dense_7 (Dense)             (32, None, 539)           552475    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 5,937,435\n",
      "Trainable params: 5,937,435\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape:       (32, 100)  # (batch_size, sequence_length)\n",
      "Prediction shape:  (32, 100, 539) # (batch_size, sequence_length, vocab_size)\n"
     ]
    }
   ],
   "source": [
    "x, y = get_batch(vectorized_songs, seq_length=100, batch_size=32)\n",
    "pred = model(x)\n",
    "print(\"Input shape:      \", x.shape, \" # (batch_size, sequence_length)\")\n",
    "print(\"Prediction shape: \", pred.shape, \"# (batch_size, sequence_length, vocab_size)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 60, 211,  75, 521,  14, 126, 529, 292, 523, 174,  67,  85, 399,\n",
       "       262,  97, 236, 352, 141, 106,  57, 469, 323, 157,  95, 350, 205,\n",
       "       154,  92, 316, 530, 342, 242, 121, 457, 387, 421,  43, 341, 538,\n",
       "       227, 429, 333, 163, 286, 111, 415, 235,  56, 413, 208, 453, 164,\n",
       "       498, 441,  16, 453, 374, 348,  92, 222, 350, 261, 280, 139, 145,\n",
       "       154, 338, 205, 404, 375, 339, 275, 465, 269, 294, 177,  18, 537,\n",
       "       505, 321, 187,  54, 131,  24, 486, 431, 454, 388, 331, 295, 118,\n",
       "       516, 496, 187, 466, 281, 504, 180,  51, 160], dtype=int64)"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_indices = tf.random.categorical(pred[0], num_samples=1)\n",
    "sampled_indices = tf.squeeze(sampled_indices,axis=-1).numpy()\n",
    "sampled_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input: \n",
      " '9.1 4.6 G5 3.6 B3 E-6 3.6 E-6 7.10.1 E6 G5 B3 F#5 E-6 3.6 E-6 7.10.1 E6 G5 11.3.6 F#5 E-6 5.8.11 D6 F5 9.0.4 E5 C6 3.6.9 E-5 C6 7.11.2 B5 D5 0.3.6 C5 A5 4.7.11 G5 6.9.0 F#5 6.9.11 E-5 4.7.11 E2 4.7.11 B4 11.0 A4 B1 B4 B3 B2 B4 9 A3 A4 G1 G4 G3 G2 11.2 7 D4 11.2 0.4 C2 E4 C3 E5 11 D4 D5 A1 C5 C4 A2 E5 0 E4 E5 D3 F5 F4 B2 D4 F4 G#2 B3 F4 F5 D3 F4 D4 B2 F4 B3 G#2 F4 D3'\n",
      "\n",
      "Next Char Predictions: \n",
      " '1.3.6.7 3.5.8.9 1.4.7.9.10 F5 0.2.5.8 11.0.3.7 G#6 5.7.9.1 F7 2.4.8.9 1.4.5 1.7 8.10.3 4.7.8.10 10.1 4.5.7.10.0 7.0.1 11.2.3.6 10.11.0 1.3.5 B-5 6.7.10.11 2.3.5 10.0.4 7 3.5 2 10.0.2.4 6.11 G#7 6.8.9.11 4.6.10 11.0.2 A2 8.0.2 9.0.2 1.2.4.7 6.8.9.1 G9 3.7.9.10 9.1.3 6.8.0 2.4.5 5.7.10 10.11.2.4 8.9.11.2.5 4.5.7 1.3.4 8.9.11 3.5.7 9.2 2.4.5.6 E-3 9.10.3 0.2.6.8 9.2 7.9.0 6.9.11 10.0.2.4 3.7.10 7 4.7.8 5.6.7.8 11.2 11.2.5 2 6.8.11.1 3.5 8.11.1.4 7.9.0.3 6.8.11.2 5.11 B-1 4.8.9 5.7.9.11 2.5.6 0.3 G7 E3 6.7.10.0 2.5.9 1.2.7.8 11.1.2.4 0.3.6.7 C3 9.10 9.2.3 8.0.3 6.7.9.11 5.8 10.3.4 F#7 D7 2.5.9 B-2 5.6.8.11.1 E2 2.5.7 1.2.6.8 2.3.6.7.10'\n"
     ]
    }
   ],
   "source": [
    "print(\"Input: \\n\", repr(\" \".join(idx2char[x[0]])))\n",
    "print()\n",
    "print(\"Next Char Predictions: \\n\", repr(\" \".join(idx2char[sampled_indices])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction shape:  (32, 100, 539)  # (batch_size, sequence_length, vocab_size)\n",
      "scalar_loss:       6.2893133\n"
     ]
    }
   ],
   "source": [
    "### Defining the loss function ###\n",
    "\n",
    "'''define the loss function to compute and return the loss between\n",
    "    the true labels and predictions (logits). Set the argument from_logits=True.'''\n",
    "def compute_loss(labels, logits):\n",
    "  loss = tf.keras.losses.sparse_categorical_crossentropy(labels, logits, from_logits=True)\n",
    "  \n",
    "  return loss\n",
    "\n",
    "'''compute the loss using the true next characters from the example batch \n",
    "    and the predictions from the untrained model several cells above'''\n",
    "example_batch_loss = compute_loss(y, pred)\n",
    "\n",
    "\n",
    "print(\"Prediction shape: \", pred.shape, \" # (batch_size, sequence_length, vocab_size)\") \n",
    "print(\"scalar_loss:      \", example_batch_loss.numpy().mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Hyperparameter setting and optimization ###\n",
    "\n",
    "# Optimization parameters:\n",
    "num_training_iterations = 1000  # Increase this to train longer\n",
    "batch_size = 12  # Experiment between 1 and 64\n",
    "seq_length = 300  # Experiment between 50 and 500\n",
    "learning_rate = 5e-3  # Experiment between 1e-5 and 1e-1\n",
    "\n",
    "# Model parameters: \n",
    "vocab_size = len(vocab)\n",
    "embedding_dim = 256 \n",
    "rnn_units = 1024  # Experiment between 1 and 2048\n",
    "\n",
    "# Checkpoint location: \n",
    "checkpoint_dir = './training_checkpoints'\n",
    "checkpoint_prefix = os.path.join(checkpoint_dir, \"my_ckpt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 376
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [06:01<00:00,  2.77it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 376
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Define optimizer and training operation ###\n",
    "\n",
    "'''instantiate a new model for training using the `build_model`\n",
    "  function and the hyperparameters created above.'''\n",
    "model = build_model(vocab_size, embedding_dim, rnn_units, batch_size)\n",
    "\n",
    "\n",
    "\n",
    "'''Try using the Adam optimizer to start.'''\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate)\n",
    "\n",
    "\n",
    "@tf.function\n",
    "def train_step(x, y): \n",
    "  # Use tf.GradientTape()\n",
    "  with tf.GradientTape() as tape:\n",
    "  \n",
    "    '''feed the current input into the model and generate predictions'''\n",
    "    y_hat = model(x) \n",
    " \n",
    "  \n",
    "    ''' compute the loss!'''\n",
    "    loss = compute_loss(y, y_hat) \n",
    "    \n",
    "\n",
    "  # Now, compute the gradients \n",
    "  '''complete the function call for gradient computation. \n",
    "      Remember that we want the gradient of the loss with respect all \n",
    "      of the model parameters. \n",
    "      HINT: use `model.trainable_variables` to get a list of all model\n",
    "      parameters.'''\n",
    "  grads = tape.gradient(loss, model.trainable_variables)\n",
    "\n",
    "  \n",
    "  # Apply the gradients to the optimizer so it can update the model accordingly\n",
    "  optimizer.apply_gradients(zip(grads, model.trainable_variables))\n",
    "  return loss\n",
    "\n",
    "##################\n",
    "# Begin training!#\n",
    "##################\n",
    "\n",
    "history = []\n",
    "plotter = mdl.util.PeriodicPlotter(sec=2, xlabel='Iterations', ylabel='Loss')\n",
    "if hasattr(tqdm, '_instances'): tqdm._instances.clear() # clear if it exists\n",
    "\n",
    "for iter in tqdm(range(num_training_iterations)):\n",
    "\n",
    "  # Grab a batch and propagate it through the network\n",
    "  x_batch, y_batch = get_batch(vectorized_songs, seq_length, batch_size)\n",
    "  loss = train_step(x_batch, y_batch)\n",
    "\n",
    "  # Update the progress bar\n",
    "  history.append(loss.numpy().mean())\n",
    "  plotter.plot(history)\n",
    "\n",
    "  # Update the model with the changed weights!\n",
    "  if iter % 100 == 0:     \n",
    "    model.save_weights(checkpoint_prefix)\n",
    "    \n",
    "# Save the trained model and the weights\n",
    "model.save_weights(checkpoint_prefix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_13\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_13 (Embedding)    (1, None, 256)            137984    \n",
      "                                                                 \n",
      " lstm_13 (LSTM)              (1, None, 1024)           5246976   \n",
      "                                                                 \n",
      " dense_13 (Dense)            (1, None, 539)            552475    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 5,937,435\n",
      "Trainable params: 5,937,435\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "'''Rebuild the model using a batch_size=1'''\n",
    "model = build_model(vocab_size, embedding_dim, rnn_units, batch_size=1)\n",
    "\n",
    "# Restore the model weights for the last checkpoint after training\n",
    "model.load_weights(tf.train.latest_checkpoint(checkpoint_dir))\n",
    "model.build(tf.TensorShape([1, None]))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Prediction of a generated song ###\n",
    "\n",
    "def generate_text(model, start_string, generation_length=1000):\n",
    "  # Evaluation step (generating notes text using the learned RNN model)\n",
    "  \n",
    "  '''convert the start string to numbers (vectorize)'''\n",
    "  input_eval = [char2idx[s] for s in start_string] \n",
    "  \n",
    "  input_eval = tf.expand_dims(input_eval, 0)\n",
    "  \n",
    "  # Empty string to store our results\n",
    "  text_generated = []\n",
    "  \n",
    "  # Here batch size == 1\n",
    "  model.reset_states()\n",
    "  tqdm._instances.clear()\n",
    "\n",
    "  for i in tqdm(range(generation_length)):\n",
    "      ''' evaluate the inputs and generate the next character predictions'''\n",
    "      predictions = model(input_eval)\n",
    "     \n",
    "      \n",
    "      # Remove the batch dimension\n",
    "      predictions = tf.squeeze(predictions, 0)\n",
    "      \n",
    "      '''use a multinomial distribution to sample'''\n",
    "      predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy()\n",
    "      \n",
    "      \n",
    "      # Pass the prediction along with the previous hidden state\n",
    "      #   as the next inputs to the model\n",
    "      input_eval = tf.expand_dims([predicted_id], 0)\n",
    "      \n",
    "      ''' add the predicted character to the generated text!'''\n",
    "      # Hint: consider what format the prediction is in vs. the output\n",
    "      text_generated.append(idx2char[predicted_id])\n",
    " \n",
    "  return text_generated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:11<00:00, 84.50it/s]\n"
     ]
    }
   ],
   "source": [
    "generated_text = generate_text(model, start_string=[\"F#5\"], generation_length=1000) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [\"F#5\"]\n",
    "a.extend(generated_text)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1001"
      ]
     },
     "execution_count": 487,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_midi(prediction_output):\n",
    "    \"\"\" convert the output from the prediction to notes and create a midi file\n",
    "        from the notes \"\"\"\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",
    "    \n",
    "    print('Saving Output file as midi....')\n",
    "\n",
    "    midi_stream.write('midi', fp='generated_output.mid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving Output file as midi....\n"
     ]
    }
   ],
   "source": [
    "create_midi(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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