07_03_lstm_compose_predict.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-19 07:50:57.944470: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import pickle as pkl\n",
    "import time\n",
    "import os\n",
    "import numpy as np\n",
    "import sys\n",
    "from music21 import instrument, note, stream, chord, duration\n",
    "from models.RNNAttention import create_network, sample_with_temp\n",
    "\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# run params\n",
    "section = 'compose'\n",
    "run_id = '0009'\n",
    "music_name = 'cello'\n",
    "run_folder = 'run/{}/'.format(section)\n",
    "run_folder += '_'.join([run_id, music_name])\n",
    "\n",
    "# model params\n",
    "embed_size = 100\n",
    "rnn_units = 128\n",
    "use_attention = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load the lookup tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "store_folder = os.path.join(run_folder, 'store')\n",
    "\n",
    "with open(os.path.join(store_folder, 'distincts'), 'rb') as filepath:\n",
    "    distincts = pkl.load(filepath)\n",
    "    note_names, n_notes, duration_names, n_durations = distincts\n",
    "\n",
    "with open(os.path.join(store_folder, 'lookups'), 'rb') as filepath:\n",
    "    lookups = pkl.load(filepath)\n",
    "    note_to_int, int_to_note, duration_to_int, int_to_duration = lookups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-19 07:51:10.496025: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-01-19 07:51:11.179450: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
      "2024-01-19 07:51:11.179520: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 76714 MB memory:  -> device: 0, name: NVIDIA A100-SXM4-80GB, pci bus id: 0000:01:00.0, compute capability: 8.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, None)]       0           []                               \n",
      "                                                                                                  \n",
      " input_2 (InputLayer)           [(None, None)]       0           []                               \n",
      "                                                                                                  \n",
      " embedding (Embedding)          (None, None, 100)    138900      ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " embedding_1 (Embedding)        (None, None, 100)    1700        ['input_2[0][0]']                \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)      (None, None, 200)    0           ['embedding[0][0]',              \n",
      "                                                                  'embedding_1[0][0]']            \n",
      "                                                                                                  \n",
      " lstm (LSTM)                    (None, None, 128)    168448      ['concatenate[0][0]']            \n",
      "                                                                                                  \n",
      " dropout (Dropout)              (None, None, 128)    0           ['lstm[0][0]']                   \n",
      "                                                                                                  \n",
      " lstm_1 (LSTM)                  (None, None, 128)    131584      ['dropout[0][0]']                \n",
      "                                                                                                  \n",
      " dropout_1 (Dropout)            (None, None, 128)    0           ['lstm_1[0][0]']                 \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, None, 1)      129         ['dropout_1[0][0]']              \n",
      "                                                                                                  \n",
      " reshape (Reshape)              (None, None)         0           ['dense[0][0]']                  \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, None)         0           ['reshape[0][0]']                \n",
      "                                                                                                  \n",
      " repeat_vector (RepeatVector)   (None, 128, None)    0           ['activation[0][0]']             \n",
      "                                                                                                  \n",
      " permute (Permute)              (None, None, 128)    0           ['repeat_vector[0][0]']          \n",
      "                                                                                                  \n",
      " multiply (Multiply)            (None, None, 128)    0           ['dropout_1[0][0]',              \n",
      "                                                                  'permute[0][0]']                \n",
      "                                                                                                  \n",
      " lambda (Lambda)                (None, 128)          0           ['multiply[0][0]']               \n",
      "                                                                                                  \n",
      " pitch (Dense)                  (None, 1389)         179181      ['lambda[0][0]']                 \n",
      "                                                                                                  \n",
      " duration (Dense)               (None, 17)           2193        ['lambda[0][0]']                 \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 622,135\n",
      "Trainable params: 622,135\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/keras/optimizers/optimizer_v2/rmsprop.py:143: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super().__init__(name, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "weights_folder = os.path.join(run_folder, 'weights')\n",
    "weights_file = 'weights.h5'\n",
    "\n",
    "model, att_model = create_network(n_notes, n_durations, embed_size, rnn_units, use_attention)\n",
    "\n",
    "# Load the weights to each node\n",
    "weight_source = os.path.join(weights_folder,weights_file)\n",
    "model.load_weights(weight_source)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## build your own phrase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# prediction params\n",
    "notes_temp=0.2\n",
    "duration_temp = 0.1\n",
    "max_extra_notes = 5*16\n",
    "max_seq_len = 32\n",
    "seq_len = 32\n",
    "\n",
    "notes = ['G2', 'D3', 'B3', 'A3', 'B3', 'D3','B3', 'D3', 'G2', 'D3', 'B3', 'A3','B3', 'D3', 'B3', 'D3']\n",
    "durations = [ 0.25] * 16\n",
    "\n",
    "#notes = [ 'B4', 'A4', 'G#4', 'A4', 'C5', 'E4', 'D5', 'C5', 'B4', 'C5' ]\n",
    "#durations = [ 0.25 ] * 4 + [ 0.5, 0.5 ] + [ 0.25 ] * 4 \n",
    "\n",
    "# notes = ['START', 'F#3', 'G#3', 'F#3', 'E3', 'F#3', 'G#3', 'F#3', 'E3', 'F#3', 'G#3', 'F#3', 'E3','F#3', 'G#3', 'F#3', 'E3', 'F#3', 'G#3', 'F#3', 'E3', 'F#3', 'G#3', 'F#3', 'E3']\n",
    "# durations = [0, 0.75, 0.25, 1, 1, 1, 2, 0.75, 0.25, 1, 1, 1, 2, 0.75, 0.25, 1, 1, 1, 2, 0.75, 0.25, 1, 1, 1, 2]\n",
    "\n",
    "\n",
    "#notes = ['START']\n",
    "#durations = [0]\n",
    "\n",
    "if seq_len is not None:\n",
    "    notes = ['START'] * (seq_len - len(notes)) + notes\n",
    "    durations = [0] * (seq_len - len(durations)) + durations\n",
    "\n",
    "\n",
    "sequence_length = len(notes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate notes from the neural network based on a sequence of notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated sequence of 112 notes\n"
     ]
    }
   ],
   "source": [
    "prediction_output = []\n",
    "notes_input_sequence = []\n",
    "durations_input_sequence = []\n",
    "\n",
    "overall_preds = []\n",
    "\n",
    "for n, d in zip(notes,durations):\n",
    "    note_int = note_to_int[n]\n",
    "    duration_int = duration_to_int[d]\n",
    "    \n",
    "    notes_input_sequence.append(note_int)\n",
    "    durations_input_sequence.append(duration_int)\n",
    "    \n",
    "    prediction_output.append([n, d])\n",
    "    \n",
    "    if n != 'START':\n",
    "        midi_note = note.Note(n)\n",
    "\n",
    "        new_note = np.zeros(128)\n",
    "        new_note[midi_note.pitch.midi] = 1\n",
    "        overall_preds.append(new_note)\n",
    "\n",
    "\n",
    "att_matrix = np.zeros(shape = (max_extra_notes+sequence_length, max_extra_notes))\n",
    "\n",
    "for note_index in range(max_extra_notes):\n",
    "\n",
    "    prediction_input = [\n",
    "        np.array([notes_input_sequence])\n",
    "        , np.array([durations_input_sequence])\n",
    "       ]\n",
    "\n",
    "    notes_prediction, durations_prediction = model.predict(prediction_input, verbose=0)\n",
    "    if use_attention:\n",
    "        att_prediction = att_model.predict(prediction_input, verbose=0)[0]\n",
    "        att_matrix[(note_index-len(att_prediction)+sequence_length):(note_index+sequence_length), note_index] = att_prediction\n",
    "    \n",
    "    new_note = np.zeros(128)\n",
    "    \n",
    "    for idx, n_i in enumerate(notes_prediction[0]):\n",
    "        try:\n",
    "            note_name = int_to_note[idx]\n",
    "            midi_note = note.Note(note_name)\n",
    "            new_note[midi_note.pitch.midi] = n_i\n",
    "            \n",
    "        except:\n",
    "            pass\n",
    "        \n",
    "    overall_preds.append(new_note)\n",
    "            \n",
    "    \n",
    "    i1 = sample_with_temp(notes_prediction[0], notes_temp)\n",
    "    i2 = sample_with_temp(durations_prediction[0], duration_temp)\n",
    "    \n",
    "\n",
    "    note_result = int_to_note[i1]\n",
    "    duration_result = int_to_duration[i2]\n",
    "    \n",
    "    prediction_output.append([note_result, duration_result])\n",
    "\n",
    "    notes_input_sequence.append(i1)\n",
    "    durations_input_sequence.append(i2)\n",
    "    \n",
    "    if len(notes_input_sequence) > max_seq_len:\n",
    "        notes_input_sequence = notes_input_sequence[1:]\n",
    "        durations_input_sequence = durations_input_sequence[1:]\n",
    "        \n",
    "#     print(note_result)\n",
    "#     print(duration_result)\n",
    "        \n",
    "    if note_result == 'START':\n",
    "        break\n",
    "\n",
    "overall_preds = np.transpose(np.array(overall_preds)) \n",
    "print('Generated sequence of {} notes'.format(len(prediction_output)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 552,
       "width": 1218
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "ax.set_yticks([int(j) for j in range(35,70)])\n",
    "\n",
    "plt.imshow(overall_preds[35:70,:], origin=\"lower\", cmap='coolwarm', vmin = -0.5, vmax = 0.5, extent=[0, max_extra_notes, 35,70]\n",
    "          \n",
    "          )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## convert the output from the prediction to notes and create a midi file from the notes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'run/compose/0009_cello/output/output-20240119-080011.mid'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_folder = os.path.join(run_folder, 'output')\n",
    "\n",
    "midi_stream = stream.Stream()\n",
    "\n",
    "# create note and chord objects based on the values generated by the model\n",
    "for pattern in prediction_output:\n",
    "    note_pattern, duration_pattern = pattern\n",
    "    # pattern is a chord\n",
    "    if ('.' in note_pattern):\n",
    "        notes_in_chord = note_pattern.split('.')\n",
    "        chord_notes = []\n",
    "        for current_note in notes_in_chord:\n",
    "            new_note = note.Note(current_note)\n",
    "            new_note.duration = duration.Duration(duration_pattern)\n",
    "            new_note.storedInstrument = instrument.Violoncello()\n",
    "            chord_notes.append(new_note)\n",
    "        new_chord = chord.Chord(chord_notes)\n",
    "        midi_stream.append(new_chord)\n",
    "    elif note_pattern == 'rest':\n",
    "    # pattern is a rest\n",
    "        new_note = note.Rest()\n",
    "        new_note.duration = duration.Duration(duration_pattern)\n",
    "        new_note.storedInstrument = instrument.Violoncello()\n",
    "        midi_stream.append(new_note)\n",
    "    elif note_pattern != 'START':\n",
    "    # pattern is a note\n",
    "        new_note = note.Note(note_pattern)\n",
    "        new_note.duration = duration.Duration(duration_pattern)\n",
    "        new_note.storedInstrument = instrument.Violoncello()\n",
    "        midi_stream.append(new_note)\n",
    "\n",
    "\n",
    "\n",
    "midi_stream = midi_stream.chordify()\n",
    "timestr = time.strftime(\"%Y%m%d-%H%M%S\")\n",
    "midi_stream.write('midi', fp=os.path.join(output_folder, 'output-' + timestr + '.mid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1600,
       "width": 1563
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## attention plot\n",
    "if use_attention:\n",
    "    fig, ax = plt.subplots(figsize=(20,20))\n",
    "\n",
    "    im = ax.imshow(att_matrix[(seq_len-2):,], cmap='coolwarm', interpolation='nearest')\n",
    "\n",
    "\n",
    "    \n",
    "\n",
    "    # Minor ticks\n",
    "    ax.set_xticks(np.arange(-.5, len(prediction_output)- seq_len, 1), minor=True);\n",
    "    ax.set_yticks(np.arange(-.5, len(prediction_output)- seq_len, 1), minor=True);\n",
    "\n",
    "    # Gridlines based on minor ticks\n",
    "    ax.grid(which='minor', color='black', linestyle='-', linewidth=1)\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    # We want to show all ticks...\n",
    "    ax.set_xticks(np.arange(len(prediction_output) - seq_len))\n",
    "    ax.set_yticks(np.arange(len(prediction_output)- seq_len+2))\n",
    "    # ... and label them with the respective list entries\n",
    "    ax.set_xticklabels([n[0] for n in prediction_output[(seq_len):]])\n",
    "    ax.set_yticklabels([n[0] for n in prediction_output[(seq_len - 2):]])\n",
    "\n",
    "    # ax.grid(color='black', linestyle='-', linewidth=1)\n",
    "\n",
    "    ax.xaxis.tick_top()\n",
    "\n",
    "\n",
    "    \n",
    "    plt.setp(ax.get_xticklabels(), rotation=90, ha=\"left\", va = \"center\",\n",
    "             rotation_mode=\"anchor\")\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}