Using_model.ipynb

{
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   "cell_type": "code",
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   "id": "fbb9c398-62a1-49bb-aa1e-88645f0f5094",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "import numpy as np\n",
    "import random\n",
    "from music21 import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "33b2fa4a-8cc8-49b8-8140-c5db2b66b057",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[326, 185, 102, 180, 82, 6, 118, 6, 165, 129, 165, 308, 118, 326, 68, 163, 68, 113, 321, 6, 163, 326, 326, 326, 326, 129, 104, 242, 172, 341, 77, 165, 77, 165, 136, 329, 329, 327, 104, 185, 321, 321, 321, 321, 6, 125, 6, 23, 145, 83, 269, 89, 337, 82, 177, 108, 177, 148, 177, 108, 108, 332, 337, 163, 104, 329, 329, 125, 163, 321, 321, 321, 321, 125, 321, 327, 308, 308, 23, 286, 290, 290, 23, 286, 23, 92, 290, 122, 326, 290, 23, 290, 290, 23, 290, 23, 290, 23, 92, 92, 23, 118, 23, 303, 337, 337, 23, 303, 57, 231, 337, 23, 303, 57, 231, 337, 23, 135, 92, 23, 23, 337, 23, 303, 57, 231, 337, 23, 303, 57, 231, 303, 23, 303, 57, 231, 337, 23, 303, 57, 231, 337, 23, 135, 92, 23, 23, 337, 23, 303, 57, 231, 303, 303, 23, 303, 57, 231, 303, 23, 303, 57, 231, 337, 23, 135, 92, 23, 23, 337, 23, 303, 57, 231, 337, 23, 303, 57, 231, 303, 23, 303, 57, 231, 337, 23, 303, 57, 231, 337, 23, 135, 92, 23, 23, 337, 23, 303, 57, 231, 303, 303, 23, 303, 57, 231, 303, 23, 303, 57, 231, 337, 23, 135, 92, 23, 23, 337, 23, 303, 57, 231, 337, 23, 303, 57, 231, 303, 23, 303, 57, 231, 337, 23, 303, 57, 231, 337, 23, 135, 92, 23, 23, 337, 23, 303, 57, 231, 303, 303, 23, 303, 57, 231, 303, 23, 303, 57, 231, 337, 23, 135, 92, 23, 23, 337, 23, 303, 57, 231, 337, 23, 303, 57, 231, 303, 23, 303, 57, 231, 337, 23, 303, 57, 231, 337, 23, 135, 92, 23, 23, 337, 23, 303, 57, 231, 303, 303, 23, 303, 57, 231, 303, 23, 303, 57, 231, 337, 23, 135, 92, 23, 23, 337, 23, 303, 57, 231, 337, 23, 303, 57, 231, 303, 23, 303, 57, 231, 337, 23, 303, 57, 231, 337, 23, 135, 92, 23, 23, 337, 23, 303, 57, 231, 303, 303, 23, 303, 57, 231, 303, 23, 303, 57, 231, 337, 23, 135, 92, 23, 23, 337, 23, 303, 57, 231, 337, 23, 303, 57, 231, 303, 23, 303, 57, 231, 337, 23, 303, 57, 231, 337, 23, 135, 92, 23, 23, 337, 23, 303, 57, 231, 303, 303, 23, 303, 57, 231, 303, 23, 303, 57, 231, 337, 23, 135, 92, 23, 23, 337, 23, 303, 57, 231, 337, 23, 303, 57, 231, 303, 23, 303, 57, 231, 337, 23, 303, 57, 231, 337, 23, 135, 92, 23, 23, 337, 23, 303, 57, 231, 303, 303, 23, 303, 57, 231, 303, 23, 303, 57, 231, 337, 23, 135, 92, 23, 23, 337, 23, 303, 57, 231, 337, 23, 303, 57, 231, 303, 23, 303, 57, 231, 337, 23, 303, 57, 231, 337, 23, 135, 92, 23, 23, 337, 23, 303, 57, 231, 303, 303, 23, 303, 57, 231, 303, 23, 303, 57, 231, 337]\n"
     ]
    }
   ],
   "source": [
    "ind = np.random.randint(0,500)\n",
    "model = load_model(\"model.h5\")\n",
    "random_music = [137, 6, 6,137, 6, 137, 6, 327, 103, 6, 338, 103, 249, 185, 338, 338, 338, 338, 118, 347, 338, 249, 119, 351, 351, 351, 129, 107, 23, 118, 351, 129]\n",
    "random_music = np.array(random_music)\n",
    "num_of_steps = 32\n",
    "predictions=[]\n",
    "for i in range(500):\n",
    "\n",
    "    random_music = random_music.reshape(1,num_of_steps,1)\n",
    "\n",
    "    prob  = model.predict(random_music)[0]\n",
    "    y_pred= np.argmax(prob,axis=0)\n",
    "    predictions.append(y_pred)\n",
    "\n",
    "    random_music = np.insert(random_music[0],len(random_music[0]),y_pred)\n",
    "    random_music = random_music[1:]\n",
    "    \n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "926ae0d6-28d4-460e-83d9-1a53ee5db97e",
   "metadata": {},
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   "source": [
    "def convert_to_midi(prediction_output):\n",
    "   \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",
    "        \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",
    "                \n",
    "                cn=int(current_note)\n",
    "                new_note = note.Note(cn)\n",
    "                new_note.storedInstrument = instrument.Piano()\n",
    "                notes.append(new_note)\n",
    "                \n",
    "            new_chord = chord.Chord(notes)\n",
    "            new_chord.offset = offset\n",
    "            output_notes.append(new_chord)\n",
    "            \n",
    "        # pattern is a note\n",
    "        else:\n",
    "            \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 += 1\n",
    "    midi_stream = stream.Stream(output_notes)\n",
    "    midi_stream.write('midi', fp='music.mid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9831731a-9bc0-4f4a-b023-d033c55e44ab",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "argument of type 'numpy.int64' is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-71df9e7f3e79>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mconvert_to_midi\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-3-3f459ae808f8>\u001b[0m in \u001b[0;36mconvert_to_midi\u001b[1;34m(prediction_output)\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[1;31m# pattern is a chord\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'.'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpattern\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mpattern\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misdigit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m             \u001b[0mnotes_in_chord\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpattern\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m             \u001b[0mnotes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: argument of type 'numpy.int64' is not iterable"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1249b317-f4ab-4a15-918f-ab4629e64ce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(\"unique_notes.pkl\",\"rb\") as p:\n",
    "    unique_notes = pickle.load(p)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9c27176c-b55a-4b2b-8d61-fcbcacb5c31e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_int_to_note = dict((number, note_) for number, note_ in enumerate(unique_notes)) \n",
    "predicted_notes = [x_int_to_note[i] for i in predictions]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "880f5685-6c63-4bb2-a18e-72a2ef53532e",
   "metadata": {},
   "outputs": [],
   "source": [
    "convert_to_midi(predicted_notes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "daf242a1-f22f-4966-af6b-974c4874e3bf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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