Los_Angeles_Music_Composer.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Los Angeles Music Composer (ver. 4.5)\n",
        "\n",
        "***\n",
        "\n",
        "Powered by tegridy-tools: https://github.com/asigalov61/tegridy-tools\n",
        "\n",
        "***\n",
        "\n",
        "WARNING: This complete implementation is a functioning model of the Artificial Intelligence. Please excercise great humility, care, and respect. https://www.nscai.gov/\n",
        "\n",
        "***\n",
        "\n",
        "#### Project Los Angeles\n",
        "\n",
        "#### Tegridy Code 2023\n",
        "\n",
        "***"
      ],
      "metadata": {
        "id": "gpy3qsulqHa5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# (GPU CHECK)"
      ],
      "metadata": {
        "id": "W_So4w8fqPGL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X3rABEpKCO02",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title NVIDIA GPU check\n",
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# (SETUP ENVIRONMENT)"
      ],
      "metadata": {
        "id": "C0XxnXGFqVyh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vK40g6V_BTNj",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title Install dependencies\n",
        "!git clone --depth 1 https://github.com/asigalov61/Los-Angeles-Music-Composer\n",
        "!pip install torch\n",
        "!pip install einops\n",
        "!pip install torch-summary\n",
        "!pip install tqdm\n",
        "!pip install matplotlib\n",
        "!apt install fluidsynth #Pip does not work for some reason. Only apt works\n",
        "!pip install midi2audio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DzCOZU_gBiQV",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title Import modules\n",
        "\n",
        "print('=' * 70)\n",
        "print('Loading core Los Angeles Music Composer modules...')\n",
        "\n",
        "import os\n",
        "import pickle\n",
        "import random\n",
        "import secrets\n",
        "import statistics\n",
        "from time import time\n",
        "import tqdm\n",
        "\n",
        "print('=' * 70)\n",
        "print('Loading main Los Angeles Music Composer modules...')\n",
        "import torch\n",
        "\n",
        "%cd /content/Los-Angeles-Music-Composer\n",
        "\n",
        "import TMIDIX\n",
        "from lwa_transformer import *\n",
        "\n",
        "%cd /content/\n",
        "print('=' * 70)\n",
        "print('Loading aux Los Angeles Music Composer modeules...')\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from torchsummary import summary\n",
        "from sklearn import metrics\n",
        "\n",
        "from midi2audio import FluidSynth\n",
        "from IPython.display import Audio, display\n",
        "\n",
        "from google.colab import files\n",
        "\n",
        "print('=' * 70)\n",
        "print('Done!')\n",
        "print('Enjoy! :)')\n",
        "print('=' * 70)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eI3aQtHzqSnp"
      },
      "source": [
        "# (LOAD MODEL)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Unzip Pre-Trained Los Angeles Music Composer Model\n",
        "print('=' * 70)\n",
        "%cd /content/Los-Angeles-Music-Composer/Model\n",
        "\n",
        "print('=' * 70)\n",
        "print('Unzipping pre-trained Los Angeles Music Composer model...Please wait...')\n",
        "\n",
        "!cat /content/Los-Angeles-Music-Composer/Model/Los_Angeles_Music_Composer_Trained_Model.zip* > /content/Los-Angeles-Music-Composer/Model/Los_Angeles_Music_Composer_Trained_Model.zip\n",
        "print('=' * 70)\n",
        "\n",
        "!unzip -j /content/Los-Angeles-Music-Composer/Model/Los_Angeles_Music_Composer_Trained_Model.zip\n",
        "print('=' * 70)\n",
        "\n",
        "print('Done! Enjoy! :)')\n",
        "print('=' * 70)\n",
        "%cd /content/\n",
        "print('=' * 70)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "SqbxcFYVq63r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rDquonbXC2je",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title Load Los Angeles Music Composer Model\n",
        "full_path_to_model_checkpoint = \"/content/Los-Angeles-Music-Composer/Model/Los_Angeles_Music_Composer_Model_88835_steps_0.643_loss.pth\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown Model precision option\n",
        "\n",
        "model_precision = \"bfloat16\" # @param [\"bfloat16\", \"float16\", \"float32\"]\n",
        "\n",
        "#@markdown bfloat16 == Third precision/triple speed (if supported, otherwise the model will default to float16)\n",
        "\n",
        "#@markdown float16 == Half precision/double speed\n",
        "\n",
        "#@markdown float32 == Full precision/normal speed\n",
        "\n",
        "plot_tokens_embeddings = False # @param {type:\"boolean\"}\n",
        "\n",
        "print('=' * 70)\n",
        "print('Loading Los Angeles Music Composer Pre-Trained Model...')\n",
        "print('Please wait...')\n",
        "print('=' * 70)\n",
        "print('Instantiating model...')\n",
        "\n",
        "torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul\n",
        "torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn\n",
        "device_type = 'cuda'\n",
        "\n",
        "if model_precision == 'bfloat16' and torch.cuda.is_bf16_supported():\n",
        "  dtype = 'bfloat16'\n",
        "else:\n",
        "  dtype = 'float16'\n",
        "\n",
        "if model_precision == 'float16':\n",
        "  dtype = 'float16'\n",
        "\n",
        "if model_precision == 'float32':\n",
        "  dtype = 'float32'\n",
        "\n",
        "ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]\n",
        "ctx = torch.amp.autocast(device_type=device_type, dtype=ptdtype)\n",
        "\n",
        "SEQ_LEN = 4096\n",
        "\n",
        "# instantiate the model\n",
        "\n",
        "model = LocalTransformer(\n",
        "    num_tokens = 2831,\n",
        "    dim = 1024,\n",
        "    depth = 36,\n",
        "    causal = True,\n",
        "    local_attn_window_size = 512,\n",
        "    max_seq_len = SEQ_LEN\n",
        ")\n",
        "\n",
        "model = torch.nn.DataParallel(model)\n",
        "\n",
        "model.cuda()\n",
        "print('=' * 70)\n",
        "\n",
        "print('Loading model checkpoint...')\n",
        "\n",
        "model.load_state_dict(torch.load(full_path_to_model_checkpoint))\n",
        "print('=' * 70)\n",
        "\n",
        "model.eval()\n",
        "\n",
        "print('Done!')\n",
        "print('=' * 70)\n",
        "\n",
        "print('Model will use', dtype, 'precision...')\n",
        "print('=' * 70)\n",
        "\n",
        "# Model stats\n",
        "print('Model summary...')\n",
        "summary(model)\n",
        "\n",
        "# Plot Token Embeddings\n",
        "\n",
        "if plot_tokens_embeddings:\n",
        "  tok_emb = model.module.token_emb.weight.detach().cpu().tolist()\n",
        "\n",
        "  cos_sim = metrics.pairwise_distances(\n",
        "    tok_emb, metric='cosine'\n",
        "  )\n",
        "  plt.figure(figsize=(7, 7))\n",
        "  plt.imshow(cos_sim, cmap=\"inferno\", interpolation=\"nearest\")\n",
        "  im_ratio = cos_sim.shape[0] / cos_sim.shape[1]\n",
        "  plt.colorbar(fraction=0.046 * im_ratio, pad=0.04)\n",
        "  plt.xlabel(\"Position\")\n",
        "  plt.ylabel(\"Position\")\n",
        "  plt.tight_layout()\n",
        "  plt.plot()\n",
        "  plt.savefig(\"/content/Los-Angeles-Music-Composer-Tokens-Embeddings-Plot.png\", bbox_inches=\"tight\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# (GENERATE)"
      ],
      "metadata": {
        "id": "7xNyANjZsCOi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# (IMPROV)"
      ],
      "metadata": {
        "id": "6Y7s6izpWxG7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Improv Generation\n",
        "\n",
        "#@markdown Improv settings\n",
        "\n",
        "number_of_instruments = 1 #@param {type:\"slider\", min:1, max:12, step:1}\n",
        "lead_instrument = \"Piano\" #@param [\"Piano\", \"Guitar\", \"Bass\", \"Violin\", \"Cello\", \"Harp\", \"Trumpet\", \"Sax\", \"Flute\", \"Drums\", \"Choir\", \"Organ\"]\n",
        "add_drums = False #@param {type:\"boolean\"}\n",
        "\n",
        "#@markdown Generation settings\n",
        "\n",
        "number_of_tokens_tp_generate = 201 # @param {type:\"slider\", min:33, max:2045, step:3}\n",
        "number_of_batches_to_generate = 4 #@param {type:\"slider\", min:1, max:16, step:1}\n",
        "temperature = 1 #@param {type:\"slider\", min:0.1, max:1, step:0.1}\n",
        "\n",
        "#@markdown Other settings\n",
        "\n",
        "allow_model_to_stop_generation_if_needed = False #@param {type:\"boolean\"}\n",
        "render_MIDI_to_audio = True # @param {type:\"boolean\"}\n",
        "\n",
        "print('=' * 70)\n",
        "print('Los Angeles Music Composer Improv Model Generator')\n",
        "print('=' * 70)\n",
        "\n",
        "if allow_model_to_stop_generation_if_needed:\n",
        "  min_stop_token = 2816\n",
        "else:\n",
        "  min_stop_token = 0\n",
        "\n",
        "if add_drums:\n",
        "  drumsp = 1\n",
        "else:\n",
        "  drumsp = 0\n",
        "\n",
        "outy = [2816, 2817+drumsp, 2819+((number_of_instruments)-1)]\n",
        "\n",
        "instruments_list = [\"Piano\", \"Guitar\", \"Bass\", \"Violin\", \"Cello\", \"Harp\", \"Trumpet\", \"Sax\", \"Flute\", 'Drums', \"Choir\", \"Organ\"]\n",
        "lead_instrument_number = instruments_list.index(lead_instrument)\n",
        "\n",
        "\n",
        "time = 0\n",
        "dur_vel = (10 * 8) + (7)\n",
        "cha_ptc = (lead_instrument_number * 128) + 72\n",
        "\n",
        "outy.extend([time, dur_vel+128, cha_ptc+1152])\n",
        "\n",
        "print('Selected Improv sequence:')\n",
        "print(outy)\n",
        "print('=' * 70)\n",
        "\n",
        "inp = [outy] * number_of_batches_to_generate\n",
        "\n",
        "inp = torch.LongTensor(inp).cuda()\n",
        "\n",
        "with ctx:\n",
        "  out = model.module.generate(inp,\n",
        "                        number_of_tokens_tp_generate,\n",
        "                        temperature=temperature,\n",
        "                        return_prime=True,\n",
        "                        min_stop_token=min_stop_token,\n",
        "                        verbose=True)\n",
        "\n",
        "out0 = out.tolist()\n",
        "\n",
        "print('=' * 70)\n",
        "print('Done!')\n",
        "print('=' * 70)\n",
        "\n",
        "#======================================================================\n",
        "\n",
        "print('Rendering results...')\n",
        "\n",
        "for i in range(number_of_batches_to_generate):\n",
        "\n",
        "  print('=' * 70)\n",
        "  print('Batch #', i)\n",
        "  print('=' * 70)\n",
        "\n",
        "  out1 = out0[i]\n",
        "\n",
        "  print('Sample INTs', out1[:12])\n",
        "  print('=' * 70)\n",
        "\n",
        "  if len(out) != 0:\n",
        "\n",
        "      song = out1\n",
        "      song_f = []\n",
        "      tim = 0\n",
        "      dur = 0\n",
        "      vel = 0\n",
        "      pitch = 0\n",
        "      channel = 0\n",
        "\n",
        "      son = []\n",
        "      song1 = []\n",
        "\n",
        "      for s in song:\n",
        "        if s >= 128 and s < (12*128)+1152:\n",
        "          son.append(s)\n",
        "        else:\n",
        "          if len(son) == 3:\n",
        "            song1.append(son)\n",
        "          son = []\n",
        "          son.append(s)\n",
        "\n",
        "      for ss in song1:\n",
        "\n",
        "        tim += ss[0] * 10\n",
        "\n",
        "        dur = (((ss[1]-128) // 8)+1) * 20\n",
        "        vel = (((ss[1]-128) % 8)+1) * 15\n",
        "\n",
        "        channel = (ss[2]-1152) // 128\n",
        "        pitch = (ss[2]-1152) % 128\n",
        "\n",
        "        song_f.append(['note', tim, dur, channel, pitch, vel ])\n",
        "\n",
        "      detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,\n",
        "                                                                output_signature = 'Los Angeles Music Composer',\n",
        "                                                                output_file_name = '/content/Los-Angeles-Music-Composer-Music-Composition_'+str(i),\n",
        "                                                                track_name='Project Los Angeles',\n",
        "                                                                list_of_MIDI_patches=[0, 24, 32, 40, 42, 46, 56, 71, 73, 0, 53, 19, 0, 0, 0, 0]\n",
        "                                                                )\n",
        "\n",
        "\n",
        "      print('=' * 70)\n",
        "      print('Displaying resulting composition...')\n",
        "      print('=' * 70)\n",
        "\n",
        "      fname = '/content/Los-Angeles-Music-Composer-Music-Composition_'+str(i)\n",
        "\n",
        "      x = []\n",
        "      y =[]\n",
        "      c = []\n",
        "\n",
        "      colors = ['red', 'yellow', 'green', 'cyan', 'blue', 'pink', 'orange', 'purple', 'gray', 'white', 'gold', 'silver']\n",
        "\n",
        "      for s in song_f:\n",
        "        x.append(s[1] / 1000)\n",
        "        y.append(s[4])\n",
        "        c.append(colors[s[3]])\n",
        "\n",
        "      if render_MIDI_to_audio:\n",
        "        FluidSynth(\"/usr/share/sounds/sf2/FluidR3_GM.sf2\", 16000).midi_to_audio(str(fname + '.mid'), str(fname + '.wav'))\n",
        "        display(Audio(str(fname + '.wav'), rate=16000))\n",
        "\n",
        "      plt.figure(figsize=(14,5))\n",
        "      ax=plt.axes(title=fname)\n",
        "      ax.set_facecolor('black')\n",
        "\n",
        "      plt.scatter(x,y, c=c)\n",
        "      plt.xlabel(\"Time\")\n",
        "      plt.ylabel(\"Pitch\")\n",
        "      plt.show()"
      ],
      "metadata": {
        "cellView": "form",
        "id": "Jwxz-eaF0K1y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# (CUSTOM MIDI)"
      ],
      "metadata": {
        "id": "Gt03VtO6uKkb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4QXbFLsKqSnt",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title Load Seed MIDI\n",
        "\n",
        "#@markdown Press play button to to upload your own seed MIDI or to load one of the provided sample seed MIDIs from the dropdown list below\n",
        "\n",
        "select_seed_MIDI = \"Upload your own custom MIDI\" #@param [\"Upload your own custom MIDI\", \"Los-Angeles-Music-Composer-Piano-Seed-1\", \"Los-Angeles-Music-Composer-Piano-Seed-2\", \"Los-Angeles-Music-Composer-Piano-Seed-3\", \"Los-Angeles-Music-Composer-Piano-Seed-4\", \"Los-Angeles-Music-Composer-Piano-Seed-5\", \"Los-Angeles-Music-Composer-MI-Seed-1\", \"Los-Angeles-Music-Composer-MI-Seed-2\", \"Los-Angeles-Music-Composer-MI-Seed-3\", \"Los-Angeles-Music-Composer-MI-Seed-4\", \"Los-Angeles-Music-Composer-MI-Seed-5\"]\n",
        "render_MIDI_to_audio = False # @param {type:\"boolean\"}\n",
        "\n",
        "print('=' * 70)\n",
        "print('Los Angeles Music Composer Seed MIDI Loader')\n",
        "print('=' * 70)\n",
        "\n",
        "f = ''\n",
        "\n",
        "if select_seed_MIDI != \"Upload your own custom MIDI\":\n",
        "  print('Loading seed MIDI...')\n",
        "  f = '/content/Los-Angeles-Music-Composer/Seeds/'+select_seed_MIDI+'.mid'\n",
        "  score = TMIDIX.midi2single_track_ms_score(open(f, 'rb').read(), recalculate_channels=False)\n",
        "\n",
        "else:\n",
        "  print('Upload your own custom MIDI...')\n",
        "  print('=' * 70)\n",
        "  uploaded_MIDI = files.upload()\n",
        "  if list(uploaded_MIDI.keys()):\n",
        "    score = TMIDIX.midi2single_track_ms_score(list(uploaded_MIDI.values())[0], recalculate_channels=False)\n",
        "    f = list(uploaded_MIDI.keys())[0]\n",
        "\n",
        "if f != '':\n",
        "\n",
        "  print('=' * 70)\n",
        "  print('File:', f)\n",
        "  print('=' * 70)\n",
        "\n",
        "  #=======================================================\n",
        "  # START PROCESSING\n",
        "\n",
        "  # INSTRUMENTS CONVERSION CYCLE\n",
        "  events_matrix = []\n",
        "  melody_chords_f = []\n",
        "  melody_chords_f1 = []\n",
        "\n",
        "  itrack = 1\n",
        "\n",
        "  patches = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
        "\n",
        "  patch_map = [\n",
        "              [0, 1, 2, 3, 4, 5, 6, 7], # Piano\n",
        "              [24, 25, 26, 27, 28, 29, 30], # Guitar\n",
        "              [32, 33, 34, 35, 36, 37, 38, 39], # Bass\n",
        "              [40, 41], # Violin\n",
        "              [42, 43], # Cello\n",
        "              [46], # Harp\n",
        "              [56, 57, 58, 59, 60], # Trumpet\n",
        "              [64, 65, 66, 67, 68, 69, 70, 71], # Sax\n",
        "              [72, 73, 74, 75, 76, 77, 78], # Flute\n",
        "              [-1], # Drums\n",
        "              [52, 53], # Choir\n",
        "              [16, 17, 18, 19, 20] # Organ\n",
        "              ]\n",
        "\n",
        "  while itrack < len(score):\n",
        "    for event in score[itrack]:\n",
        "        if event[0] == 'note' or event[0] == 'patch_change':\n",
        "            events_matrix.append(event)\n",
        "    itrack += 1\n",
        "\n",
        "  events_matrix.sort(key=lambda x: x[1])\n",
        "\n",
        "  events_matrix1 = []\n",
        "\n",
        "  for event in events_matrix:\n",
        "    if event[0] == 'patch_change':\n",
        "        patches[event[2]] = event[3]\n",
        "\n",
        "    if event[0] == 'note':\n",
        "        event.extend([patches[event[3]]])\n",
        "        once = False\n",
        "\n",
        "        for p in patch_map:\n",
        "            if event[6] in p and event[3] != 9: # Except the drums\n",
        "                event[3] = patch_map.index(p)\n",
        "                once = True\n",
        "\n",
        "        if not once and event[3] != 9: # Except the drums\n",
        "            event[3] = 15 # All other instruments/patches channel\n",
        "            event[5] = max(80, event[5])\n",
        "\n",
        "        if event[3] < 12: # We won't write chans 12-16 for now...\n",
        "            events_matrix1.append(event)\n",
        "\n",
        "  #=======================================================\n",
        "  # PRE-PROCESSING\n",
        "\n",
        "  # checking number of instruments in a composition\n",
        "  instruments_list_without_drums = list(set([y[3] for y in events_matrix1 if y[3] != 9]))\n",
        "\n",
        "  if len(events_matrix1) > 0 and len(instruments_list_without_drums) > 0:\n",
        "\n",
        "    # recalculating timings\n",
        "    for e in events_matrix1:\n",
        "        e[1] = int(e[1] / 10) # Max 1 seconds for start-times\n",
        "        e[2] = int(e[2] / 20) # Max 2 seconds for durations\n",
        "\n",
        "    # Sorting by pitch, then by start-time\n",
        "    events_matrix1.sort(key=lambda x: x[4], reverse=True)\n",
        "    events_matrix1.sort(key=lambda x: x[1])\n",
        "\n",
        "    #=======================================================\n",
        "    # FINAL PRE-PROCESSING\n",
        "\n",
        "    melody_chords = []\n",
        "\n",
        "    pe = events_matrix1[0]\n",
        "\n",
        "    for e in events_matrix1:\n",
        "      if e[1] >= 0 and e[2] >= 0:\n",
        "\n",
        "        # Cliping all values...\n",
        "        tim = max(0, min(127, e[1]-pe[1]))\n",
        "        dur = max(1, min(127, e[2]))\n",
        "        cha = max(0, min(11, e[3]))\n",
        "        ptc = max(1, min(127, e[4]))\n",
        "        vel = max(8, min(127, e[5]))\n",
        "\n",
        "        velocity = round(vel / 15)\n",
        "\n",
        "        # Writing final note\n",
        "        melody_chords.append([tim, dur, cha, ptc, velocity])\n",
        "\n",
        "        pe = e\n",
        "\n",
        "  instruments_list = list(set([y[2] for y in melody_chords]))\n",
        "  num_instr = len(instruments_list)\n",
        "\n",
        "  #=======================================================\n",
        "  # FINAL PROCESSING\n",
        "  #=======================================================\n",
        "\n",
        "  # Break between compositions / Intro seq\n",
        "\n",
        "  if 9 in instruments_list:\n",
        "    drums_present = 2818 # Yes\n",
        "  else:\n",
        "    drums_present = 2817 # No\n",
        "\n",
        "  melody_chords_f.extend([2816, drums_present, 2819+(num_instr-1)])\n",
        "\n",
        "  #=======================================================\n",
        "\n",
        "  # Composition control seq\n",
        "  intro_mode_time = statistics.mode([0] + [y[0] for y in melody_chords if y[2] != 9 and y[0] != 0])\n",
        "  intro_mode_dur = statistics.mode([y[1] for y in melody_chords if y[2] != 9])\n",
        "  intro_mode_pitch = statistics.mode([y[3] for y in melody_chords if y[2] != 9])\n",
        "  intro_mode_velocity = statistics.mode([y[4] for y in melody_chords if y[2] != 9])\n",
        "\n",
        "  # Instrument value 12 is reserved for composition control seq\n",
        "  intro_dur_vel = (intro_mode_dur * 8) + (intro_mode_velocity-1)\n",
        "  intro_cha_ptc = (12 * 128) + intro_mode_pitch\n",
        "\n",
        "  melody_chords_f.extend([intro_mode_time, intro_dur_vel+128, intro_cha_ptc+1152])\n",
        "\n",
        "  # TOTAL DICTIONARY SIZE 2831\n",
        "\n",
        "  #=======================================================\n",
        "  # MAIN PROCESSING CYCLE\n",
        "  #=======================================================\n",
        "\n",
        "  for m in melody_chords:\n",
        "\n",
        "    # WRITING EACH NOTE HERE\n",
        "    dur_vel = (m[1] * 8) + (m[4]-1)\n",
        "    cha_ptc = (m[2] * 128) + m[3]\n",
        "\n",
        "    melody_chords_f.extend([m[0], dur_vel+128, cha_ptc+1152])\n",
        "    melody_chords_f1.append([m[0], dur_vel+128, cha_ptc+1152])\n",
        "\n",
        "  #=======================================================\n",
        "\n",
        "  song = melody_chords_f\n",
        "  song_f = []\n",
        "  tim = 0\n",
        "  dur = 0\n",
        "  vel = 0\n",
        "  pitch = 0\n",
        "  channel = 0\n",
        "\n",
        "  son = []\n",
        "  song1 = []\n",
        "\n",
        "  for s in song:\n",
        "    if s >= 128 and s < (12*128)+1152:\n",
        "      son.append(s)\n",
        "    else:\n",
        "      if len(son) == 3:\n",
        "        song1.append(son)\n",
        "      son = []\n",
        "      son.append(s)\n",
        "\n",
        "  for ss in song1:\n",
        "\n",
        "    tim += ss[0] * 10\n",
        "\n",
        "    dur = ((ss[1]-128) // 8) * 20\n",
        "    vel = (((ss[1]-128) % 8)+1) * 15\n",
        "\n",
        "    channel = (ss[2]-1152) // 128\n",
        "    pitch = (ss[2]-1152) % 128\n",
        "\n",
        "    song_f.append(['note', tim, dur, channel, pitch, vel ])\n",
        "\n",
        "  detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,\n",
        "                                                            output_signature = 'Los Angeles Music Composer',\n",
        "                                                            output_file_name = '/content/Los-Angeles-Music-Composer-Seed-Composition',\n",
        "                                                            track_name='Project Los Angeles',\n",
        "                                                            list_of_MIDI_patches=[0, 24, 32, 40, 42, 46, 56, 71, 73, 0, 53, 19, 0, 0, 0, 0]\n",
        "                                                            )\n",
        "\n",
        "  #=======================================================\n",
        "\n",
        "  print('=' * 70)\n",
        "  print('Composition stats:')\n",
        "  print('Composition has', len(melody_chords_f1), 'notes')\n",
        "  print('Composition has', len(melody_chords_f), 'tokens')\n",
        "  print('=' * 70)\n",
        "\n",
        "  print('Displaying resulting composition...')\n",
        "  print('=' * 70)\n",
        "\n",
        "  fname = '/content/Los-Angeles-Music-Composer-Seed-Composition'\n",
        "\n",
        "  x = []\n",
        "  y =[]\n",
        "  c = []\n",
        "\n",
        "  colors = ['red', 'yellow', 'green', 'cyan', 'blue', 'pink', 'orange', 'purple', 'gray', 'white', 'gold', 'silver']\n",
        "\n",
        "  for s in song_f:\n",
        "    x.append(s[1] / 1000)\n",
        "    y.append(s[4])\n",
        "    c.append(colors[s[3]])\n",
        "\n",
        "  if render_MIDI_to_audio:\n",
        "    FluidSynth(\"/usr/share/sounds/sf2/FluidR3_GM.sf2\", 16000).midi_to_audio(str(fname + '.mid'), str(fname + '.wav'))\n",
        "    display(Audio(str(fname + '.wav'), rate=16000))\n",
        "\n",
        "  plt.figure(figsize=(14,5))\n",
        "  ax=plt.axes(title=fname)\n",
        "  ax.set_facecolor('black')\n",
        "\n",
        "  plt.scatter(x,y, c=c)\n",
        "  plt.xlabel(\"Time\")\n",
        "  plt.ylabel(\"Pitch\")\n",
        "  plt.show()\n",
        "\n",
        "else:\n",
        "  print('=' * 70)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# (CONTINUATION)"
      ],
      "metadata": {
        "id": "f4jrChTacXWV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dkvXYwR_qSnx",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title Standard/Simple Continuation\n",
        "\n",
        "#@markdown Generation settings\n",
        "\n",
        "number_of_prime_tokens = 351 # @param {type:\"slider\", min:3, max:2045, step:3}\n",
        "number_of_tokens_to_generate = 222 # @param {type:\"slider\", min:33, max:2045, step:3}\n",
        "number_of_batches_to_generate = 4 #@param {type:\"slider\", min:1, max:16, step:1}\n",
        "temperature = 1 #@param {type:\"slider\", min:0.1, max:1, step:0.1}\n",
        "\n",
        "#@markdown Other settings\n",
        "\n",
        "include_prime_tokens_in_generated_output = True #@param {type:\"boolean\"}\n",
        "allow_model_to_stop_generation_if_needed = False #@param {type:\"boolean\"}\n",
        "render_MIDI_to_audio = True # @param {type:\"boolean\"}\n",
        "\n",
        "print('=' * 70)\n",
        "print('Los Angeles Music Composer Standard Model Generator')\n",
        "print('=' * 70)\n",
        "\n",
        "if allow_model_to_stop_generation_if_needed:\n",
        "  min_stop_token = 2816\n",
        "else:\n",
        "  min_stop_token = 0\n",
        "\n",
        "outy = melody_chords_f[:number_of_prime_tokens]\n",
        "\n",
        "inp = [outy] * number_of_batches_to_generate\n",
        "\n",
        "inp = torch.LongTensor(inp).cuda()\n",
        "\n",
        "with ctx:\n",
        "  out = model.module.generate(inp,\n",
        "                        number_of_tokens_to_generate,\n",
        "                        temperature=temperature,\n",
        "                        return_prime=include_prime_tokens_in_generated_output,\n",
        "                        min_stop_token=min_stop_token,\n",
        "                        verbose=True)\n",
        "\n",
        "out0 = out.tolist()\n",
        "print('=' * 70)\n",
        "print('Done!')\n",
        "print('=' * 70)\n",
        "\n",
        "#======================================================================\n",
        "print('Rendering results...')\n",
        "\n",
        "for i in range(number_of_batches_to_generate):\n",
        "\n",
        "  print('=' * 70)\n",
        "  print('Batch #', i)\n",
        "  print('=' * 70)\n",
        "\n",
        "  out1 = out0[i]\n",
        "\n",
        "  print('Sample INTs', out1[:12])\n",
        "  print('=' * 70)\n",
        "\n",
        "  if len(out) != 0:\n",
        "\n",
        "      song = out1\n",
        "      song_f = []\n",
        "      tim = 0\n",
        "      dur = 0\n",
        "      vel = 0\n",
        "      pitch = 0\n",
        "      channel = 0\n",
        "\n",
        "      son = []\n",
        "      song1 = []\n",
        "\n",
        "      for s in song:\n",
        "        if s >= 128 and s < (12*128)+1152:\n",
        "          son.append(s)\n",
        "        else:\n",
        "          if len(son) == 3:\n",
        "            song1.append(son)\n",
        "          son = []\n",
        "          son.append(s)\n",
        "\n",
        "      for ss in song1:\n",
        "\n",
        "        tim += ss[0] * 10\n",
        "\n",
        "        dur = ((ss[1]-128) // 8) * 20\n",
        "        vel = (((ss[1]-128) % 8)+1) * 15\n",
        "\n",
        "        channel = (ss[2]-1152) // 128\n",
        "        pitch = (ss[2]-1152) % 128\n",
        "\n",
        "        song_f.append(['note', tim, dur, channel, pitch, vel ])\n",
        "\n",
        "      detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,\n",
        "                                                                output_signature = 'Los Angeles Music Composer',\n",
        "                                                                output_file_name = '/content/Los-Angeles-Music-Composer-Music-Composition_'+str(i),\n",
        "                                                                track_name='Project Los Angeles',\n",
        "                                                                list_of_MIDI_patches=[0, 24, 32, 40, 42, 46, 56, 71, 73, 0, 53, 19, 0, 0, 0, 0]\n",
        "                                                                )\n",
        "      print('=' * 70)\n",
        "      print('Displaying resulting composition...')\n",
        "      print('=' * 70)\n",
        "\n",
        "      fname = '/content/Los-Angeles-Music-Composer-Music-Composition_'+str(i)\n",
        "\n",
        "      x = []\n",
        "      y =[]\n",
        "      c = []\n",
        "\n",
        "      colors = ['red', 'yellow', 'green', 'cyan', 'blue', 'pink', 'orange', 'purple', 'gray', 'white', 'gold', 'silver']\n",
        "\n",
        "      for s in song_f:\n",
        "        x.append(s[1] / 1000)\n",
        "        y.append(s[4])\n",
        "        c.append(colors[s[3]])\n",
        "\n",
        "      if render_MIDI_to_audio:\n",
        "        FluidSynth(\"/usr/share/sounds/sf2/FluidR3_GM.sf2\", 16000).midi_to_audio(str(fname + '.mid'), str(fname + '.wav'))\n",
        "        display(Audio(str(fname + '.wav'), rate=16000))\n",
        "\n",
        "      plt.figure(figsize=(14,5))\n",
        "      ax=plt.axes(title=fname)\n",
        "      ax.set_facecolor('black')\n",
        "\n",
        "      plt.scatter(x,y, c=c)\n",
        "      plt.xlabel(\"Time\")\n",
        "      plt.ylabel(\"Pitch\")\n",
        "      plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# (INSTRUMENTS INPAINTING)"
      ],
      "metadata": {
        "id": "jUdZ93-K9ABR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Pitches/Instruments Inpainting\n",
        "\n",
        "#@markdown Inpainting settings\n",
        "\n",
        "#@markdown Select desired instruments to inpaint.\n",
        "\n",
        "#@markdown Selected instruments MUST BE present in the composition for inpainting to work\n",
        "\n",
        "#@markdown You can stop the inpainting at any time to render partial results\n",
        "\n",
        "Piano = False #@param {type:\"boolean\"}\n",
        "Guitar = False #@param {type:\"boolean\"}\n",
        "Bass = False #@param {type:\"boolean\"}\n",
        "Violin = False #@param {type:\"boolean\"}\n",
        "Cello = False #@param {type:\"boolean\"}\n",
        "Harp = False #@param {type:\"boolean\"}\n",
        "Trumpet = False #@param {type:\"boolean\"}\n",
        "Clarinet = False #@param {type:\"boolean\"}\n",
        "Flute = False #@param {type:\"boolean\"}\n",
        "Choir = False #@param {type:\"boolean\"}\n",
        "Organ = False #@param {type:\"boolean\"}\n",
        "\n",
        "#@markdown Generation settings\n",
        "\n",
        "number_of_prime_notes = 64 #@param {type:\"slider\", min:1, max:512, step:1}\n",
        "number_of_memory_tokens = 4095 # @param {type:\"slider\", min:6, max:4095, step:3}\n",
        "number_of_samples_per_inpainted_note = 1 #@param {type:\"slider\", min:1, max:16, step:1}\n",
        "temperature = 1 #@param {type:\"slider\", min:0.1, max:1, step:0.1}\n",
        "\n",
        "#@markdown Other settings\n",
        "render_MIDI_to_audio = True # @param {type:\"boolean\"}\n",
        "\n",
        "\n",
        "inpaint_instrument = []\n",
        "\n",
        "if Piano:\n",
        "  inpaint_instrument.append(0)\n",
        "\n",
        "if Guitar:\n",
        "  inpaint_instrument.append(1)\n",
        "\n",
        "if Bass:\n",
        "  inpaint_instrument.append(2)\n",
        "\n",
        "if Violin:\n",
        "  inpaint_instrument.append(3)\n",
        "\n",
        "if Cello:\n",
        "  inpaint_instrument.append(4)\n",
        "\n",
        "if Harp:\n",
        "  inpaint_instrument.append(5)\n",
        "\n",
        "if Trumpet:\n",
        "  inpaint_instrument.append(6)\n",
        "\n",
        "if Clarinet:\n",
        "  inpaint_instrument.append(7)\n",
        "\n",
        "if Flute:\n",
        "  inpaint_instrument.append(8)\n",
        "\n",
        "if Choir:\n",
        "  inpaint_instrument.append(10)\n",
        "\n",
        "if Organ:\n",
        "  inpaint_instrument.append(11)\n",
        "\n",
        "print('=' * 70)\n",
        "print('Los Angeles Music Composer Inpainting Model Generator')\n",
        "print('=' * 70)\n",
        "\n",
        "out2 = []\n",
        "\n",
        "for m in melody_chords_f1[:number_of_prime_notes]:\n",
        "  out2.extend(m)\n",
        "\n",
        "for i in tqdm.tqdm(range(number_of_prime_notes, len(melody_chords_f1))):\n",
        "\n",
        "  try:\n",
        "\n",
        "    if ((melody_chords_f1[i][2]-1152) // 128) in inpaint_instrument:\n",
        "\n",
        "      out2.extend(melody_chords_f1[i][:2])\n",
        "\n",
        "      samples = []\n",
        "\n",
        "      for j in range(number_of_samples_per_inpainted_note):\n",
        "\n",
        "        inp = torch.LongTensor([out2[-number_of_memory_tokens:]]).cuda()\n",
        "\n",
        "        with ctx:\n",
        "          out1 = model.module.generate(inp,\n",
        "                                1,\n",
        "                                temperature=temperature,\n",
        "                                return_prime=True,\n",
        "                                verbose=False)\n",
        "\n",
        "          with torch.no_grad():\n",
        "            with ctx:\n",
        "              test_loss, test_acc = model(out1)\n",
        "\n",
        "        samples.append([out1.tolist()[0][-1], test_acc.tolist()])\n",
        "\n",
        "      accs = [y[1] for y in samples]\n",
        "      max_acc = max(accs)\n",
        "      max_acc_sample = samples[accs.index(max_acc)][0]\n",
        "\n",
        "      out2.extend([max_acc_sample])\n",
        "    else:\n",
        "      out2.extend(melody_chords_f1[i])\n",
        "\n",
        "  except KeyboardInterrupt:\n",
        "    print('Stopping inpainting...')\n",
        "    break\n",
        "\n",
        "  except Exception as e:\n",
        "    print('Error', e)\n",
        "    break\n",
        "\n",
        "print('=' * 70)\n",
        "print('Done!')\n",
        "print('=' * 70)\n",
        "\n",
        "#==================================================\n",
        "\n",
        "print('Rendering results...')\n",
        "print('=' * 70)\n",
        "\n",
        "if len(out2) != 0:\n",
        "\n",
        "    song = out2\n",
        "    song_f = []\n",
        "    time = 0\n",
        "    dur = 0\n",
        "    vel = 0\n",
        "    pitch = 0\n",
        "    channel = 0\n",
        "\n",
        "    son = []\n",
        "    song1 = []\n",
        "\n",
        "    for s in song:\n",
        "      if s >= 128 and s < (12*128)+1152:\n",
        "        son.append(s)\n",
        "      else:\n",
        "        if len(son) == 3:\n",
        "          song1.append(son)\n",
        "        son = []\n",
        "        son.append(s)\n",
        "\n",
        "    for ss in song1:\n",
        "\n",
        "      time += ss[0] * 10\n",
        "\n",
        "      dur = ((ss[1]-128) // 8) * 20\n",
        "      vel = (((ss[1]-128) % 8)+1) * 15\n",
        "\n",
        "      channel = (ss[2]-1152) // 128\n",
        "      pitch = (ss[2]-1152) % 128\n",
        "\n",
        "      song_f.append(['note', time, dur, channel, pitch, vel ])\n",
        "\n",
        "    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,\n",
        "                                                              output_signature = 'Los Angeles Music Composer',\n",
        "                                                              output_file_name = '/content/Los-Angeles-Music-Composer-Music-Composition',\n",
        "                                                              track_name='Project Los Angeles',\n",
        "                                                              list_of_MIDI_patches=[0, 24, 32, 40, 42, 46, 56, 71, 73, 0, 53, 19, 0, 0, 0, 0]\n",
        "                                                              )\n",
        "\n",
        "\n",
        "\n",
        "    print('=' * 70)\n",
        "    print('Displaying resulting composition...')\n",
        "    print('=' * 70)\n",
        "\n",
        "    fname = '/content/Los-Angeles-Music-Composer-Music-Composition'\n",
        "\n",
        "    x = []\n",
        "    y =[]\n",
        "    c = []\n",
        "\n",
        "    colors = ['red', 'yellow', 'green', 'cyan', 'blue', 'pink', 'orange', 'purple', 'gray', 'white', 'gold', 'silver']\n",
        "\n",
        "    for s in song_f:\n",
        "      x.append(s[1] / 1000)\n",
        "      y.append(s[4])\n",
        "      c.append(colors[s[3]])\n",
        "\n",
        "    if render_MIDI_to_audio:\n",
        "      FluidSynth(\"/usr/share/sounds/sf2/FluidR3_GM.sf2\", 16000).midi_to_audio(str(fname + '.mid'), str(fname + '.wav'))\n",
        "      display(Audio(str(fname + '.wav'), rate=16000))\n",
        "\n",
        "    plt.figure(figsize=(14,5))\n",
        "    ax=plt.axes(title=fname)\n",
        "    ax.set_facecolor('black')\n",
        "\n",
        "    plt.scatter(x,y, c=c)\n",
        "    plt.xlabel(\"Time\")\n",
        "    plt.ylabel(\"Pitch\")\n",
        "    plt.show()"
      ],
      "metadata": {
        "cellView": "form",
        "id": "MIh5DvbzczNM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# (HARMONIZATION)"
      ],
      "metadata": {
        "id": "-ljIsqzx0jwr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Melody Harmonization\n",
        "\n",
        "#@markdown NOTE: You can stop harmonization at any time to render partial results\n",
        "\n",
        "melody_instrument = \"Violin\" #@param [\"Piano\", \"Guitar\", \"Bass\", \"Violin\", \"Cello\", \"Harp\", \"Trumpet\", \"Sax\", \"Flute\", \"Choir\", \"Organ\"]\n",
        "number_of_prime_melody_notes = 4 # @param {type:\"slider\", min:0, max:50, step:1}\n",
        "number_of_memory_tokens = 4095 # @param {type:\"slider\", min:33, max:4095, step:3}\n",
        "temperature = 1 #@param {type:\"slider\", min:0.1, max:1, step:0.1}\n",
        "render_MIDI_to_audio = True # @param {type:\"boolean\"}\n",
        "\n",
        "print('=' * 70)\n",
        "print('Los Angeles Music Composer Melody Harmonization Model Generator')\n",
        "\n",
        "print('=' * 70)\n",
        "print('Extracting melody...')\n",
        "#=======================================================\n",
        "\n",
        "instruments_list = [\"Piano\", \"Guitar\", \"Bass\", \"Violin\", \"Cello\", \"Harp\", \"Trumpet\", \"Clarinet\", \"Flute\", 'Drums', \"Choir\", \"Organ\"]\n",
        "melody_instrument_number = instruments_list.index(melody_instrument)\n",
        "\n",
        "melody = []\n",
        "pe = events_matrix1[0]\n",
        "\n",
        "for e in events_matrix1:\n",
        "    if e[3] != 9:\n",
        "\n",
        "      # Cliping all values...\n",
        "      time = max(0, min(127, e[1]-pe[1]))\n",
        "      dur = max(1, min(127, e[2]))\n",
        "      cha = melody_instrument_number\n",
        "      ptc = max(1, min(127, e[4]))\n",
        "\n",
        "      if ptc < 60:\n",
        "        ptc_aug = (ptc % 12) + 60\n",
        "      else:\n",
        "        ptc_aug = ptc\n",
        "\n",
        "      velocity = round(vel / 15)\n",
        "\n",
        "      # WRITING EACH NOTE HERE\n",
        "      dur_vel = (dur * 8) + (velocity-1)\n",
        "      cha_ptc = (melody_instrument_number * 128) + ptc_aug\n",
        "\n",
        "      if time != 0:\n",
        "        melody.append([time, dur_vel+128, cha_ptc+1152])\n",
        "\n",
        "      pe = e\n",
        "\n",
        "melody[0][0] = 0\n",
        "\n",
        "#=======================================================\n",
        "\n",
        "print('=' * 70)\n",
        "print('Melody has', len(melody), 'notes')\n",
        "print('Melody has', len(melody)*3, 'tokens')\n",
        "\n",
        "print('=' * 70)\n",
        "print('Starting harmonization...')\n",
        "print('=' * 70)\n",
        "\n",
        "outy = []\n",
        "\n",
        "for m in melody[:number_of_prime_melody_notes]:\n",
        "  outy.extend(m)\n",
        "\n",
        "next_chord_time = 0\n",
        "chord_time_delta = 0\n",
        "cur_time = 0\n",
        "\n",
        "for i in tqdm.tqdm(range(number_of_prime_melody_notes, len(melody))):\n",
        "\n",
        "  try:\n",
        "    outy.extend(melody[i])\n",
        "\n",
        "    #====================================================\n",
        "\n",
        "    outy.extend([0])\n",
        "\n",
        "    inp = [outy[-number_of_memory_tokens:]]\n",
        "    inp = torch.LongTensor(inp).cuda()\n",
        "\n",
        "    with ctx:\n",
        "      out = model.module.generate(inp,\n",
        "                            2,\n",
        "                            temperature=temperature,\n",
        "                            return_prime=False,\n",
        "                            verbose=False)\n",
        "\n",
        "    out0 = out.tolist()[0]\n",
        "\n",
        "\n",
        "\n",
        "    if (((out0[1]-(1152)) // 128) != melody_instrument_number):\n",
        "      outy.extend(out0)\n",
        "    else:\n",
        "      outy.pop()\n",
        "\n",
        "    #====================================================\n",
        "\n",
        "    out0 = [0, 0, 0]\n",
        "    pout0 = []\n",
        "\n",
        "    while out0[0] == 0 and out0 != pout0:\n",
        "\n",
        "      pout0 = out0\n",
        "\n",
        "      inp = [outy[-number_of_memory_tokens:]]\n",
        "      inp = torch.LongTensor(inp).cuda()\n",
        "\n",
        "      with ctx:\n",
        "        out = model.module.generate(inp,\n",
        "                              3,\n",
        "                              temperature=temperature,\n",
        "                              return_prime=False,\n",
        "                              verbose=False)\n",
        "\n",
        "      out0 = out.tolist()[0]\n",
        "\n",
        "      if out0[0] == 0 and (((out0[2]-(1152)) // 128) != melody_instrument_number) and out0 != pout0:\n",
        "        outy.extend(out0)\n",
        "\n",
        "    #====================================================\n",
        "\n",
        "  except KeyboardInterrupt:\n",
        "    break\n",
        "\n",
        "  except Exception as e:\n",
        "    print('Error', e)\n",
        "    break\n",
        "\n",
        "print('=' * 70)\n",
        "print('Done!')\n",
        "print('=' * 70)\n",
        "\n",
        "#======================================================================\n",
        "\n",
        "print('Rendering results...')\n",
        "print('=' * 70)\n",
        "\n",
        "out1 = outy\n",
        "\n",
        "print('Sample INTs', out1[:12])\n",
        "print('=' * 70)\n",
        "\n",
        "if len(out1) != 0:\n",
        "\n",
        "    song = out1\n",
        "    song_f = []\n",
        "    time = 0\n",
        "    dur = 0\n",
        "    vel = 0\n",
        "    pitch = 0\n",
        "    channel = 0\n",
        "\n",
        "    son = []\n",
        "    song1 = []\n",
        "\n",
        "    for s in song:\n",
        "      if s >= 128 and s < (12*128)+1152:\n",
        "        son.append(s)\n",
        "      else:\n",
        "        if len(son) == 3:\n",
        "          song1.append(son)\n",
        "        son = []\n",
        "        son.append(s)\n",
        "\n",
        "    for ss in song1:\n",
        "\n",
        "      time += ss[0] * 10\n",
        "\n",
        "      dur = ((ss[1]-128) // 8) * 20\n",
        "      vel = (((ss[1]-128) % 8)+1) * 15\n",
        "\n",
        "      channel = (ss[2]-1152) // 128\n",
        "      pitch = (ss[2]-1152) % 128\n",
        "\n",
        "      song_f.append(['note', time, dur, channel, pitch, vel ])\n",
        "\n",
        "    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,\n",
        "                                                          output_signature = 'Los Angeles Music Composer',\n",
        "                                                          output_file_name = '/content/Los-Angeles-Music-Composer-Music-Composition',\n",
        "                                                          track_name='Project Los Angeles',\n",
        "                                                          list_of_MIDI_patches=[0, 24, 32, 40, 42, 46, 56, 71, 73, 0, 53, 19, 0, 0, 0, 0]\n",
        "                                                        )\n",
        "\n",
        "\n",
        "    print('=' * 70)\n",
        "    print('Displaying resulting composition...')\n",
        "    print('=' * 70)\n",
        "\n",
        "    fname = '/content/Los-Angeles-Music-Composer-Music-Composition'\n",
        "\n",
        "    x = []\n",
        "    y =[]\n",
        "    c = []\n",
        "\n",
        "    colors = ['red', 'yellow', 'green', 'cyan', 'blue', 'pink', 'orange', 'purple', 'gray', 'white', 'gold', 'silver']\n",
        "\n",
        "    for s in song_f:\n",
        "      x.append(s[1] / 1000)\n",
        "      y.append(s[4])\n",
        "      c.append(colors[s[3]])\n",
        "\n",
        "    if render_MIDI_to_audio:\n",
        "      FluidSynth(\"/usr/share/sounds/sf2/FluidR3_GM.sf2\", 16000).midi_to_audio(str(fname + '.mid'), str(fname + '.wav'))\n",
        "      display(Audio(str(fname + '.wav'), rate=16000))\n",
        "\n",
        "    plt.figure(figsize=(14,5))\n",
        "    ax=plt.axes(title=fname)\n",
        "    ax.set_facecolor('black')\n",
        "\n",
        "    plt.scatter(x,y, c=c)\n",
        "    plt.xlabel(\"Time\")\n",
        "    plt.ylabel(\"Pitch\")\n",
        "    plt.show()"
      ],
      "metadata": {
        "cellView": "form",
        "id": "tGviAhtH0nOb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Congrats! You did it! :)"
      ],
      "metadata": {
        "id": "eoWDEy6CwDr6"
      }
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "private_outputs": true,
      "provenance": [],
      "gpuClass": "premium",
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.9.13"
    }
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  "nbformat_minor": 0
}