Rock_Melody_Transformer.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Rock Melody Transformer (ver. 1.0)\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 2024\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/Rock-Melody-Transformer\n",
        "!pip install huggingface_hub\n",
        "!pip install einops\n",
        "!pip install torch-summary\n",
        "!apt install fluidsynth #Pip does not work for some reason. Only apt works"
      ]
    },
    {
      "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 Rock Melody Transformer modules...')\n",
        "\n",
        "import os\n",
        "import copy\n",
        "import pickle\n",
        "import secrets\n",
        "import statistics\n",
        "from time import time\n",
        "import tqdm\n",
        "\n",
        "print('=' * 70)\n",
        "print('Loading main Rock Melody Transformer modules...')\n",
        "import torch\n",
        "\n",
        "%cd /content/Rock-Melody-Transformer\n",
        "\n",
        "import TMIDIX\n",
        "\n",
        "from midi_to_colab_audio import midi_to_colab_audio\n",
        "\n",
        "from x_transformer_1_23_2 import *\n",
        "\n",
        "import random\n",
        "\n",
        "%cd /content/\n",
        "print('=' * 70)\n",
        "print('Loading aux Rock Melody Transformer modules...')\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from torchsummary import summary\n",
        "from sklearn import metrics\n",
        "\n",
        "from IPython.display import Audio, display\n",
        "\n",
        "from huggingface_hub import hf_hub_download\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 Load Rock Melody Transformer Pre-Trained Model\n",
        "\n",
        "#@markdown Model precision option\n",
        "\n",
        "model_precision = \"bfloat16\" # @param [\"bfloat16\", \"float16\"]\n",
        "\n",
        "#@markdown bfloat16 == Half precision/faster speed (if supported, otherwise the model will default to float16)\n",
        "\n",
        "#@markdown float16 == Full precision/fast speed\n",
        "\n",
        "plot_tokens_embeddings = False # @param {type:\"boolean\"}\n",
        "\n",
        "print('=' * 70)\n",
        "print('Loading Rock Melody Transformer Pre-Trained Model...')\n",
        "print('Please wait...')\n",
        "print('=' * 70)\n",
        "\n",
        "full_path_to_models_dir = \"/content/Rock-Melody-Transformer/Model\"\n",
        "\n",
        "model_checkpoint_file_name = 'Rock_Melody_Transformer_Medium_Trained_Model_21749_steps_0.7973_loss_0.7597_acc.pth'\n",
        "model_path = full_path_to_models_dir+'/'+model_checkpoint_file_name\n",
        "if os.path.isfile(model_path):\n",
        "  print('Model already exists...')\n",
        "\n",
        "else:\n",
        "  hf_hub_download(repo_id='asigalov61/Rock-Melody-Transformer',\n",
        "                  filename=model_checkpoint_file_name,\n",
        "                  local_dir='/content/Rock-Melody-Transformer/Model',\n",
        "                  local_dir_use_symlinks=False)\n",
        "\n",
        "print('=' * 70)\n",
        "print('Instantiating model...')\n",
        "\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",
        "ptdtype = {'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]\n",
        "ctx = torch.amp.autocast(device_type=device_type, dtype=ptdtype)\n",
        "\n",
        "SEQ_LEN = 4096 # Models seq len\n",
        "PAD_IDX = 2081 # Models pad index\n",
        "\n",
        "# instantiate the model\n",
        "\n",
        "model = TransformerWrapper(\n",
        "    num_tokens = PAD_IDX+1,\n",
        "    max_seq_len = SEQ_LEN,\n",
        "    attn_layers = Decoder(dim = 1024, depth = 24, heads = 16, attn_flash = True)\n",
        "    )\n",
        "\n",
        "model = AutoregressiveWrapper(model, ignore_index = PAD_IDX, pad_value=PAD_IDX)\n",
        "\n",
        "model.cuda()\n",
        "print('=' * 70)\n",
        "\n",
        "print('Loading model checkpoint...')\n",
        "\n",
        "model.load_state_dict(torch.load(model_path))\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",
        "if plot_tokens_embeddings:\n",
        "  tok_emb = model.net.token_emb.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/Rock-Melody-Transformer-Tokens-Embeddings-Plot.png\", bbox_inches=\"tight\")"
      ],
      "metadata": {
        "cellView": "form",
        "id": "V4s_G8yUL0cH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# (GENERATE)"
      ],
      "metadata": {
        "id": "7xNyANjZsCOi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# (SETUP MODEL CHANNELS MIDI PATCHES)"
      ],
      "metadata": {
        "id": "VqCDSYHVMFCO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Setup and load model channels MIDI patches\n",
        "\n",
        "model_channel_0_piano_family = \"Acoustic Grand\" # @param [\"Acoustic Grand\", \"Bright Acoustic\", \"Electric Grand\", \"Honky-Tonk\", \"Electric Piano 1\", \"Electric Piano 2\", \"Harpsichord\", \"Clav\"]\n",
        "model_channel_1_chromatic_percussion_family = \"Music Box\" # @param [\"Celesta\", \"Glockenspiel\", \"Music Box\", \"Vibraphone\", \"Marimba\", \"Xylophone\", \"Tubular Bells\", \"Dulcimer\"]\n",
        "model_channel_2_organ_family = \"Church Organ\" # @param [\"Drawbar Organ\", \"Percussive Organ\", \"Rock Organ\", \"Church Organ\", \"Reed Organ\", \"Accordion\", \"Harmonica\", \"Tango Accordion\"]\n",
        "model_channel_3_guitar_family = \"Acoustic Guitar(nylon)\" # @param [\"Acoustic Guitar(nylon)\", \"Acoustic Guitar(steel)\", \"Electric Guitar(jazz)\", \"Electric Guitar(clean)\", \"Electric Guitar(muted)\", \"Overdriven Guitar\", \"Distortion Guitar\", \"Guitar Harmonics\"]\n",
        "model_channel_4_bass_family = \"Fretless Bass\" # @param [\"Acoustic Bass\", \"Electric Bass(finger)\", \"Electric Bass(pick)\", \"Fretless Bass\", \"Slap Bass 1\", \"Slap Bass 2\", \"Synth Bass 1\", \"Synth Bass 2\"]\n",
        "model_channel_5_strings_family = \"Violin\" # @param [\"Violin\", \"Viola\", \"Cello\", \"Contrabass\", \"Tremolo Strings\", \"Pizzicato Strings\", \"Orchestral Harp\", \"Timpani\"]\n",
        "model_channel_6_ensemble_family = \"Choir Aahs\" # @param [\"String Ensemble 1\", \"String Ensemble 2\", \"SynthStrings 1\", \"SynthStrings 2\", \"Choir Aahs\", \"Voice Oohs\", \"Synth Voice\", \"Orchestra Hit\"]\n",
        "model_channel_7_brass_family = \"Trumpet\" # @param [\"Trumpet\", \"Trombone\", \"Tuba\", \"Muted Trumpet\", \"French Horn\", \"Brass Section\", \"SynthBrass 1\", \"SynthBrass 2\"]\n",
        "model_channel_8_reed_family = \"Alto Sax\" # @param [\"Soprano Sax\", \"Alto Sax\", \"Tenor Sax\", \"Baritone Sax\", \"Oboe\", \"English Horn\", \"Bassoon\", \"Clarinet\"]\n",
        "model_channel_9_pipe_family = \"Flute\" # @param [\"Piccolo\", \"Flute\", \"Recorder\", \"Pan Flute\", \"Blown Bottle\", \"Skakuhachi\", \"Whistle\", \"Ocarina\"]\n",
        "model_channel_10_drums_family = \"Drums\" # @param [\"Drums\"]\n",
        "\n",
        "print('=' * 70)\n",
        "print('Setting up patches...')\n",
        "print('=' * 70)\n",
        "\n",
        "instruments = [v[1] for v in TMIDIX.Number2patch.items()]\n",
        "\n",
        "patches = [instruments.index(model_channel_0_piano_family),\n",
        "                       instruments.index(model_channel_1_chromatic_percussion_family),\n",
        "                       instruments.index(model_channel_2_organ_family),\n",
        "                       instruments.index(model_channel_3_guitar_family),\n",
        "                       instruments.index(model_channel_4_bass_family),\n",
        "                       instruments.index(model_channel_5_strings_family),\n",
        "                       instruments.index(model_channel_6_ensemble_family),\n",
        "                       instruments.index(model_channel_7_brass_family),\n",
        "                       instruments.index(model_channel_8_reed_family),\n",
        "                       9, # Drums patch\n",
        "                       instruments.index(model_channel_9_pipe_family),\n",
        "                       ] + [0, 0, 0, 0, 0]\n",
        "\n",
        "print('Done!')\n",
        "print('=' * 70)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "2ueVakPcMBN4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# (IMPROV)"
      ],
      "metadata": {
        "id": "BxepTeHVmmKO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Standard Improv Generator\n",
        "\n",
        "#@markdown Generation settings\n",
        "\n",
        "number_of_tokens_to_generate = 512 # @param {type:\"slider\", min:40, max:8192, step:4}\n",
        "number_of_batches_to_generate = 4 #@param {type:\"slider\", min:1, max:16, step:1}\n",
        "temperature = 0.9 # @param {type:\"slider\", min:0.1, max:1, step:0.05}\n",
        "\n",
        "#@markdown Other settings\n",
        "\n",
        "render_MIDI_to_audio = True # @param {type:\"boolean\"}\n",
        "\n",
        "print('=' * 70)\n",
        "print('Rock Melody Transformer Standard Improv Model Generator')\n",
        "print('=' * 70)\n",
        "\n",
        "outy = [random.randint(12, 24)+2056] + [0]\n",
        "\n",
        "print('Selected Improv sequence:')\n",
        "print(outy)\n",
        "print('=' * 70)\n",
        "\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "inp = [outy] * number_of_batches_to_generate\n",
        "\n",
        "inp = torch.LongTensor(inp).cuda()\n",
        "\n",
        "with ctx:\n",
        "  out = model.generate(inp,\n",
        "                        number_of_tokens_to_generate,\n",
        "                        temperature=temperature,\n",
        "                        return_prime=True,\n",
        "                        verbose=True)\n",
        "\n",
        "out0 = out.tolist()\n",
        "\n",
        "print('=' * 70)\n",
        "print('Done!')\n",
        "print('=' * 70)\n",
        "\n",
        "torch.cuda.empty_cache()\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(out1) != 0:\n",
        "\n",
        "      song = out1\n",
        "      song_f = []\n",
        "\n",
        "      time = 0\n",
        "      dur = 32\n",
        "      channel = 0\n",
        "      pitch = 60\n",
        "      vel = 90\n",
        "\n",
        "      for ss in song:\n",
        "\n",
        "          if 0 <= ss < 256:\n",
        "\n",
        "              time += ss * 16\n",
        "\n",
        "          if 256 < ss < 512:\n",
        "\n",
        "              dur =  (ss-256) * 16\n",
        "\n",
        "          if 512 < ss < 2048:\n",
        "\n",
        "              chan = (ss-512) // 128\n",
        "\n",
        "              if chan == 11:\n",
        "                channel = 9\n",
        "              else:\n",
        "                if chan > 8:\n",
        "                  channel = chan + 1\n",
        "                else:\n",
        "                  channel = chan\n",
        "\n",
        "              if channel == 9:\n",
        "                patch = 128\n",
        "              else:\n",
        "                patch = channel * 8\n",
        "\n",
        "              pitch = (ss-512) % 128\n",
        "\n",
        "          if 2048 < ss < 2056:\n",
        "              vel = ((ss - 2048)+1) * 15\n",
        "\n",
        "              song_f.append(['note', time, dur, channel, pitch, vel, patch])\n",
        "\n",
        "      data = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,\n",
        "                                                      output_signature = 'Rock Melody Transformer',\n",
        "                                                      output_file_name = '/content/Rock-Melody-Transformer-Composition_'+str(i),\n",
        "                                                      track_name='Project Los Angeles',\n",
        "                                                      list_of_MIDI_patches=patches\n",
        "                                                      )\n",
        "\n",
        "\n",
        "      print('=' * 70)\n",
        "      print('Displaying resulting composition...')\n",
        "      print('=' * 70)\n",
        "\n",
        "      fname = '/content/Rock-Melody-Transformer-Composition_'+str(i)\n",
        "\n",
        "      if render_MIDI_to_audio:\n",
        "        midi_audio = midi_to_colab_audio(fname + '.mid')\n",
        "        display(Audio(midi_audio, rate=16000, normalize=False))\n",
        "\n",
        "      TMIDIX.plot_ms_SONG(song_f, plot_title=fname)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "Jwxz-eaF0K1y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# (ROCK MELODY COMPOSITION GENERATION)"
      ],
      "metadata": {
        "id": "fmm3KjOtoVp9"
      }
    },
    {
      "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\", \"Rock-Melody-Transformer-Melody-Seed-1\", \"Rock-Melody-Transformer-Melody-Seed-2\", \"Rock-Melody-Transformer-Melody-Seed-3\", \"Rock-Melody-Transformer-Melody-Seed-4\", \"Rock-Melody-Transformer-Melody-Seed-5\", \"Rock-Melody-Transformer-Melody-Seed-6\", \"Rock-Melody-Transformer-Melody-Seed-7\"]\n",
        "render_MIDI_to_audio = False # @param {type:\"boolean\"}\n",
        "\n",
        "print('=' * 70)\n",
        "print('Rock Melody Transformer 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/Rock-Melody-Transformer/Seeds/'+select_seed_MIDI+'.mid'\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",
        "    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",
        "  #===============================================================================\n",
        "  # Raw single-track ms score\n",
        "\n",
        "  raw_score = TMIDIX.midi2single_track_ms_score(f)\n",
        "\n",
        "  #===============================================================================\n",
        "  # Enhanced score notes\n",
        "\n",
        "  escore_notes = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)[0]\n",
        "\n",
        "  escore_notes = [e for e in escore_notes if e[6] < 80 or e[6] == 128]\n",
        "\n",
        "  #===============================================================================\n",
        "  # Augmented enhanced score notes\n",
        "\n",
        "  escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes)\n",
        "\n",
        "  #===============================================================================\n",
        "  # Chordified score\n",
        "  dcscore = TMIDIX.chordify_score([1000, escore_notes])\n",
        "\n",
        "  #===============================================================================\n",
        "\n",
        "  output_score = []\n",
        "  comp_tokens = []\n",
        "\n",
        "  pc = dcscore[0]\n",
        "  pc.sort(key=lambda x: x[4])\n",
        "\n",
        "  for i, c in enumerate(dcscore[:-1]):\n",
        "\n",
        "    c.sort(key=lambda x: x[4], reverse=True)\n",
        "    cchans = sorted(set([x[3] for x in c]))\n",
        "\n",
        "    nc = dcscore[i+1]\n",
        "    nc.sort(key=lambda x: x[4], reverse=True)\n",
        "    ncchans = sorted(set([x[3] for x in nc]))\n",
        "\n",
        "    dtime = max(0, min(255, (c[0][1] - pc[0][1])))\n",
        "\n",
        "    if 9 in ncchans:\n",
        "      ncdrums = 1\n",
        "    else:\n",
        "      ncdrums = 0\n",
        "\n",
        "    if ncchans == [9]:\n",
        "      nctone = 24\n",
        "    else:\n",
        "      nctone = (12 * ncdrums) + (nc[0][4] % 12)\n",
        "\n",
        "    output_score.append(nctone+2056)\n",
        "\n",
        "    output_score.append(dtime)\n",
        "\n",
        "    toks = []\n",
        "\n",
        "    toks.extend([nctone+2056, dtime])\n",
        "\n",
        "    for i, e in enumerate(c):\n",
        "\n",
        "        dur = max(1, min(255, e[2]))\n",
        "\n",
        "        if e[3] != 9:\n",
        "          chan = e[6] // 8\n",
        "        else:\n",
        "          chan = 11\n",
        "\n",
        "        ptc = max(1, min(127, e[4]))\n",
        "\n",
        "        cha_ptc = (128 * chan) + ptc\n",
        "\n",
        "        velocity = max(8, min(127, e[5]))\n",
        "        vel = round(velocity / 15)-1\n",
        "\n",
        "        output_score.extend([dur+256, cha_ptc+512, vel+2048])\n",
        "\n",
        "        if i == 0:\n",
        "          toks.extend([dur+256, cha_ptc+512, vel+2048])\n",
        "\n",
        "    comp_tokens.append(toks)\n",
        "\n",
        "    pc = c\n",
        "\n",
        "  comp_tokens.append(toks)\n",
        "\n",
        "  #=======================================================\n",
        "\n",
        "  song_f = escore_notes\n",
        "\n",
        "  comp_patches = sorted(set([e[6] for e in song_f]))\n",
        "\n",
        "  for s in song_f:\n",
        "\n",
        "    s[1] *= 16\n",
        "    s[2] *= 16\n",
        "\n",
        "  detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,\n",
        "                                                            output_signature = 'Rock Melody Transformer',\n",
        "                                                            output_file_name = '/content/Rock-Melody-Transformer-Seed-Composition',\n",
        "                                                            track_name='Project Los Angeles',\n",
        "                                                            list_of_MIDI_patches=patches\n",
        "                                                            )\n",
        "  #=======================================================\n",
        "\n",
        "  print('=' * 70)\n",
        "  print('Composition stats:')\n",
        "  print('Composition has', len(dcscore), 'chords')\n",
        "  print('Composition MIDI patches:', comp_patches)\n",
        "  print('=' * 70)\n",
        "\n",
        "  print('Displaying resulting composition...')\n",
        "  print('=' * 70)\n",
        "\n",
        "  fname = '/content/Rock-Melody-Transformer-Seed-Composition'\n",
        "\n",
        "  if render_MIDI_to_audio:\n",
        "    midi_audio = midi_to_colab_audio(fname + '.mid')\n",
        "    display(Audio(midi_audio, rate=16000, normalize=False))\n",
        "\n",
        "  TMIDIX.plot_ms_SONG(song_f, plot_title=fname)\n",
        "\n",
        "else:\n",
        "  print('=' * 70)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Rock composition generation\n",
        "\n",
        "#@markdown NOTE: You can stop the generation at any time to render partial results\n",
        "\n",
        "#@markdown Generation settings\n",
        "\n",
        "generate_improv_intro = False # @param {type:\"boolean\"}\n",
        "conditioning_type = \"Tones-Times-Durations\" # @param [\"Tones\", \"Tones-Times\", \"Tones-Times-Durations\", \"Tones-Times-Durations-Pitches\"]\n",
        "number_of_chords_to_generate = 128 # @param {type:\"slider\", min:4, max:8192, step:4}\n",
        "max_number_of_notes_per_chord = 10 # @param {type:\"slider\", min:1, max:16, step:1}\n",
        "number_of_memory_tokens = 4096 # @param {type:\"slider\", min:32, max:8188, step:16}\n",
        "temperature = 0.9 # @param {type:\"slider\", min:0.1, max:1, step:0.05}\n",
        "\n",
        "#@markdown Other settings\n",
        "render_MIDI_to_audio = True # @param {type:\"boolean\"}\n",
        "\n",
        "print('=' * 70)\n",
        "print('Rock Melody Transformer Model Generator')\n",
        "print('=' * 70)\n",
        "\n",
        "#===============================================================================\n",
        "\n",
        "def generate_intro(num_tokens=128,\n",
        "                   temperature=0.9\n",
        "                   ):\n",
        "\n",
        "  outy = [random.randint(12, 24)+2056] + [0]\n",
        "\n",
        "  inp = [outy] * 1\n",
        "\n",
        "  inp = torch.LongTensor(inp).cuda()\n",
        "\n",
        "  with ctx:\n",
        "    out = model.generate(inp,\n",
        "                          num_tokens,\n",
        "                          temperature=temperature,\n",
        "                          return_prime=True,\n",
        "                          verbose=True)\n",
        "\n",
        "\n",
        "  out1 = out.tolist()[0]\n",
        "\n",
        "  tidx = [i for i in range(len(out1[::-1])) if out1[::-1][i] < 256][0]\n",
        "\n",
        "  out2 = out1[:-tidx-2]\n",
        "\n",
        "  return out2\n",
        "\n",
        "#===============================================================================\n",
        "\n",
        "def generate_chords(input_seq,\n",
        "                    next_time=255,\n",
        "                    max_notes_limit = 10,\n",
        "                    num_memory_tokens = 4096,\n",
        "                    temperature=0.9):\n",
        "\n",
        "    input_seq = input_seq[-num_memory_tokens:]\n",
        "\n",
        "    x = torch.tensor([input_seq] * 1, dtype=torch.long, device='cuda')\n",
        "\n",
        "    o = 0\n",
        "\n",
        "    ncount = 0\n",
        "\n",
        "    time = 0\n",
        "\n",
        "    if next_time < 256:\n",
        "\n",
        "      ntime = next_time\n",
        "\n",
        "      while (o < 2056 or o == 2080) and ncount < max_notes_limit and time < ntime:\n",
        "        with ctx:\n",
        "          out = model.generate(x,\n",
        "                              1,\n",
        "                              temperature=temperature,\n",
        "                              return_prime=False,\n",
        "                              verbose=False)\n",
        "\n",
        "        o = out.tolist()[0][0]\n",
        "\n",
        "        if 0 < o < 256:\n",
        "          ncount = 0\n",
        "          time += o\n",
        "\n",
        "        if 2048 <= o < 2056:\n",
        "          ncount += 1\n",
        "\n",
        "        if (o < 2056 or o == 2080) and time < ntime:\n",
        "          x = torch.cat((x, out), 1)\n",
        "\n",
        "    else:\n",
        "\n",
        "      while (o < 2056 or o == 2080) and ncount < max_notes_limit:\n",
        "        with ctx:\n",
        "          out = model.generate(x,\n",
        "                              1,\n",
        "                              temperature=temperature,\n",
        "                              return_prime=False,\n",
        "                              verbose=False)\n",
        "\n",
        "        o = out.tolist()[0][0]\n",
        "\n",
        "        if 0 < o < 256:\n",
        "          ncount = 0\n",
        "\n",
        "        if 2048 <= o < 2056:\n",
        "          ncount += 1\n",
        "\n",
        "        if (o < 2056 or o == 2080):\n",
        "          x = torch.cat((x, out), 1)\n",
        "\n",
        "    return x.tolist()[0][len(input_seq):]\n",
        "\n",
        "#===============================================================================\n",
        "\n",
        "print('Generating...')\n",
        "print('=' * 70)\n",
        "\n",
        "output = []\n",
        "first_note = True\n",
        "pidx = 0\n",
        "sidx = 0\n",
        "\n",
        "out = []\n",
        "tidxs = []\n",
        "dtime = 0\n",
        "\n",
        "#===============================================================================\n",
        "\n",
        "if conditioning_type == 'Tones':\n",
        "  cond_type = 4\n",
        "\n",
        "elif conditioning_type == 'Tones-Times':\n",
        "  cond_type = 3\n",
        "\n",
        "elif conditioning_type == 'Tones-Times-Durations':\n",
        "  cond_type = 2\n",
        "\n",
        "elif conditioning_type == 'Tones-Times-Durations-Pitches':\n",
        "  cond_type = 1\n",
        "\n",
        "#===============================================================================\n",
        "\n",
        "if generate_improv_intro:\n",
        "  print('Generating intro...')\n",
        "  print('=' * 70)\n",
        "  output = generate_intro()\n",
        "  print('=' * 70)\n",
        "  print('Generating continaution...')\n",
        "  first_note = False\n",
        "  pidx = 1\n",
        "  cond_type = 4\n",
        "  sidx = 1\n",
        "  print('=' * 70)\n",
        "\n",
        "#===============================================================================\n",
        "\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "pbar = tqdm.tqdm(total=len(comp_tokens[sidx:number_of_chords_to_generate]))\n",
        "\n",
        "while pidx < len(comp_tokens[sidx:number_of_chords_to_generate])-1:\n",
        "\n",
        "  c = comp_tokens[sidx:number_of_chords_to_generate][pidx]\n",
        "  nc = comp_tokens[sidx:number_of_chords_to_generate][pidx+1]\n",
        "\n",
        "  if cond_type > 3:\n",
        "    ntime = 256\n",
        "  else:\n",
        "    ntime = nc[1]\n",
        "\n",
        "  pidx += 1\n",
        "  pbar.update(1)\n",
        "\n",
        "  try:\n",
        "\n",
        "    if output:\n",
        "      tidxs.append(len(output))\n",
        "\n",
        "    dtime = 0\n",
        "\n",
        "    if out:\n",
        "      dtime = sum([o for o in out if o < 256])\n",
        "\n",
        "    if (c[0]-2056) < 12:\n",
        "      output.extend([c[0]+12, c[1]-dtime, c[2], c[3], c[4]])\n",
        "\n",
        "    else:\n",
        "      output.extend([c[0], c[1]-dtime, c[2], c[3], c[4]])\n",
        "\n",
        "    if first_note:\n",
        "      output = output[:-1]\n",
        "      first_note = False\n",
        "    else:\n",
        "      output = output[:-cond_type]\n",
        "\n",
        "    out = []\n",
        "\n",
        "    tries = 0\n",
        "\n",
        "    while not out and tries < max_number_of_notes_per_chord:\n",
        "\n",
        "      out = generate_chords(output,\n",
        "                            next_time=ntime,\n",
        "                            temperature=temperature,\n",
        "                            max_notes_limit=max_number_of_notes_per_chord,\n",
        "                            num_memory_tokens=number_of_memory_tokens\n",
        "                            )\n",
        "\n",
        "      tries += 1\n",
        "\n",
        "    if tries == max_number_of_notes_per_chord:\n",
        "      tidxs = tidxs[:-1]\n",
        "      output = output[:tidxs[-1]]\n",
        "\n",
        "      pidx -= 1\n",
        "\n",
        "      out = []\n",
        "\n",
        "      pbar.refresh()\n",
        "      pbar.reset()\n",
        "      pbar.update(pidx)\n",
        "\n",
        "    else:\n",
        "      output.extend(out)\n",
        "\n",
        "  except KeyboardInterrupt:\n",
        "    print('Stopping generation...')\n",
        "    break\n",
        "\n",
        "  except Exception as e:\n",
        "    print('Error:', e)\n",
        "    break\n",
        "\n",
        "pbar.close()\n",
        "\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "#===============================================================================\n",
        "\n",
        "print('=' * 70)\n",
        "print('Done!')\n",
        "print('=' * 70)\n",
        "\n",
        "#===============================================================================\n",
        "\n",
        "print('Rendering results...')\n",
        "\n",
        "print('=' * 70)\n",
        "print('Sample INTs', output[:12])\n",
        "print('=' * 70)\n",
        "\n",
        "if len(output) != 0:\n",
        "\n",
        "    song = output\n",
        "    song_f = []\n",
        "\n",
        "    time = 0\n",
        "    dur = 32\n",
        "    channel = 0\n",
        "    pitch = 60\n",
        "    vel = 90\n",
        "\n",
        "    for ss in song:\n",
        "\n",
        "        if 0 <= ss < 256:\n",
        "\n",
        "            time += ss * 16\n",
        "\n",
        "        if 256 < ss < 512:\n",
        "\n",
        "            dur =  (ss-256) * 16\n",
        "\n",
        "        if 512 < ss < 2048:\n",
        "\n",
        "            chan = (ss-512) // 128\n",
        "\n",
        "            if chan == 11:\n",
        "              channel = 9\n",
        "            else:\n",
        "              if chan > 8:\n",
        "                channel = chan + 1\n",
        "              else:\n",
        "                channel = chan\n",
        "\n",
        "            if channel == 9:\n",
        "              patch = 128\n",
        "            else:\n",
        "              patch = channel * 8\n",
        "\n",
        "            pitch = (ss-512) % 128\n",
        "\n",
        "        if 2048 < ss < 2056:\n",
        "            vel = ((ss - 2048)+1) * 15\n",
        "\n",
        "            song_f.append(['note', time, dur, channel, pitch, vel, patch])\n",
        "\n",
        "detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,\n",
        "                                                          output_signature = 'Rock Melody Transformer',\n",
        "                                                          output_file_name = '/content/Rock-Melody-Transformer-Composition',\n",
        "                                                          track_name='Project Los Angeles',\n",
        "                                                          list_of_MIDI_patches=patches\n",
        "                                                          )\n",
        "\n",
        "#=========================================================================\n",
        "\n",
        "print('=' * 70)\n",
        "print('Displaying resulting composition...')\n",
        "print('=' * 70)\n",
        "\n",
        "fname = '/content/Rock-Melody-Transformer-Composition'\n",
        "\n",
        "if render_MIDI_to_audio:\n",
        "  midi_audio = midi_to_colab_audio(fname + '.mid')\n",
        "  display(Audio(midi_audio, rate=16000, normalize=False))\n",
        "\n",
        "TMIDIX.plot_ms_SONG(song_f, plot_title=fname)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "dJSHwQgKPuez"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Congrats! You did it! :)"
      ],
      "metadata": {
        "id": "eoWDEy6CwDr6"
      }
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "private_outputs": true,
      "provenance": [],
      "gpuType": "T4",
      "gpuClass": "premium"
    },
    "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
}