Chords_Progressions_Transformer_Melody.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Chords Progressions Transformer Melody (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/Chords-Progressions-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 Chords Progressions 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 Chords Progressions Transformer modules...')\n",
        "import torch\n",
        "\n",
        "%cd /content/Chords-Progressions-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 Chords Progressions 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 Chords Progressions 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 Chords Progressions Transformer Melody Pre-Trained Model...')\n",
        "print('Please wait...')\n",
        "print('=' * 70)\n",
        "\n",
        "full_path_to_models_dir = \"/content/Chords-Progressions-Transformer/Models\"\n",
        "\n",
        "dim = 1024\n",
        "depth = 4\n",
        "heads = 8\n",
        "\n",
        "model_checkpoint_file_name = 'Chords_Progressions_Transformer_Melody_Trained_Model_31061_steps_0.3114_loss_0.9002_acc.pth'\n",
        "model_path = full_path_to_models_dir+'/Melody/'+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/Chords-Progressions-Transformer',\n",
        "                  filename=model_checkpoint_file_name,\n",
        "                  local_dir='/content/Chords-Progressions-Transformer/Models/Melody',\n",
        "                  local_dir_use_symlinks=False)\n",
        "\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 = 449 # 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 = dim, depth = depth, heads = heads, 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/Chords-Progressions-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": "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\", \"Chords-Progressions-Transformer-Piano-Seed-1\", \"Chords-Progressions-Transformer-Piano-Seed-2\", \"Chords-Progressions-Transformer-Piano-Seed-3\", \"Chords-Progressions-Transformer-Piano-Seed-4\", \"Chords-Progressions-Transformer-Piano-Seed-5\", \"Chords-Progressions-Transformer-Piano-Seed-6\", \"Chords-Progressions-Transformer-MI-Seed-1\", \"Chords-Progressions-Transformer-MI-Seed-2\", \"Chords-Progressions-Transformer-MI-Seed-3\", \"Chords-Progressions-Transformer-MI-Seed-4\", \"Chords-Progressions-Transformer-MI-Seed-5\", \"Chords-Progressions-Transformer-MI-Seed-6\"]\n",
        "render_MIDI_to_audio = False # @param {type:\"boolean\"}\n",
        "\n",
        "print('=' * 70)\n",
        "print('Chords Progressions 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/Chords-Progressions-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",
        "  raw_score = TMIDIX.midi2single_track_ms_score(open(f, 'rb').read())\n",
        "\n",
        "  raw_escore = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)[0]\n",
        "\n",
        "  raw_escore = [e for e in raw_escore if e[3] != 9]\n",
        "\n",
        "  escore = TMIDIX.augment_enhanced_score_notes(raw_escore)\n",
        "\n",
        "  cscore = TMIDIX.chordify_score([1000, escore])\n",
        "\n",
        "  chords_tokens = []\n",
        "  cho_toks = []\n",
        "\n",
        "  for c in cscore:\n",
        "    tones_chord = sorted(set([t[4] % 12 for t in c]))\n",
        "\n",
        "    try:\n",
        "      chord_token = TMIDIX.ALL_CHORDS_SORTED.index(tones_chord)\n",
        "    except:\n",
        "      chord_token = TMIDIX.ALL_CHORDS_SORTED.index(TMIDIX.check_and_fix_tones_chord(tones_chord))\n",
        "\n",
        "    cho_toks.append(chord_token+128)\n",
        "\n",
        "    if cho_toks:\n",
        "      if len(cho_toks) > 1:\n",
        "\n",
        "        chords_tokens.append(cho_toks)\n",
        "        cho_toks = [cho_toks[-1]]\n",
        "\n",
        "  cho_toks = cho_toks + cho_toks\n",
        "\n",
        "  chords_tokens.append(cho_toks)\n",
        "  #=======================================================\n",
        "\n",
        "  song = raw_escore\n",
        "  song_f = []\n",
        "\n",
        "  time = 0\n",
        "  dur = 0\n",
        "  vel = 90\n",
        "  pitch = 0\n",
        "  channel = 0\n",
        "\n",
        "  patches = [0] * 16\n",
        "\n",
        "  channel = 0\n",
        "\n",
        "  for ss in song:\n",
        "\n",
        "    time = ss[1]\n",
        "\n",
        "    dur = ss[2]\n",
        "\n",
        "    pitch = ss[4]\n",
        "\n",
        "    vel = ss[5]\n",
        "\n",
        "    song_f.append(['note', time, dur, channel, pitch, vel, 0])\n",
        "\n",
        "  detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,\n",
        "                                                          output_signature = 'Chords Progressions Transformer',\n",
        "                                                          output_file_name = '/content/Chords-Progressions-Transformer-Seed-Composition',\n",
        "                                                          track_name='Project Los Angeles',\n",
        "                                                          list_of_MIDI_patches=patches\n",
        "                                                          )\n",
        "\n",
        "  #=======================================================\n",
        "\n",
        "  print('=' * 70)\n",
        "  print('Composition stats:')\n",
        "  print('Composition has', len(cscore), 'chords')\n",
        "  print('Composition has', len(chords_tokens), 'chords tokens')\n",
        "  print('=' * 70)\n",
        "\n",
        "  print('Displaying resulting composition...')\n",
        "  print('=' * 70)\n",
        "\n",
        "  fname = '/content/Chords-Progressions-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 Generate chords progressions melody from custom MIDI chords\n",
        "\n",
        "#@markdown NOTE: You can stop the generation at any time to render partial results\n",
        "\n",
        "#@markdown Generation settings\n",
        "\n",
        "melody_MIDI_patch_number = 40 # @param {type:\"slider\", min:0, max:127, step:1}\n",
        "chords_MIDI_patch_number = 0 # @param {type:\"slider\", min:0, max:127, step:1}\n",
        "chords_duration = 32 # @param {type:\"slider\", min:4, max:128, step:4}\n",
        "number_of_chords_to_generate_melody_for = 128 # @param {type:\"slider\", min:8, max:4096, step:1}\n",
        "max_number_of_melody_notes_per_chord = 4 # @param {type:\"slider\", min:1, max:10, step:1}\n",
        "number_of_memory_tokens = 4096 # @param {type:\"slider\", min:32, max:8188, step:4}\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",
        "#===============================================================================\n",
        "\n",
        "print('=' * 70)\n",
        "print('Chords Progressions Transformer Melody Model Generator')\n",
        "print('=' * 70)\n",
        "\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "output = []\n",
        "\n",
        "for i in tqdm.tqdm(range(len(chords_tokens[:number_of_chords_to_generate_melody_for]))):\n",
        "  try:\n",
        "\n",
        "    output.extend(chords_tokens[i])\n",
        "\n",
        "    o = 0\n",
        "\n",
        "    count = 0\n",
        "\n",
        "    while o < 128 and count < max_number_of_melody_notes_per_chord:\n",
        "\n",
        "      x = torch.LongTensor([[output]]).cuda()\n",
        "\n",
        "      with ctx:\n",
        "          out = model.generate(x[-number_of_memory_tokens:],\n",
        "                              1,\n",
        "                              temperature=temperature,\n",
        "                              return_prime=False,\n",
        "                              verbose=False)\n",
        "\n",
        "      o = out.tolist()[0][0]\n",
        "\n",
        "      if o < 128:\n",
        "        output.append(o)\n",
        "        count += 1\n",
        "\n",
        "  except KeyboardInterrupt:\n",
        "    print('=' * 70)\n",
        "    print('Stopping generation...')\n",
        "    break\n",
        "\n",
        "  except Exception as e:\n",
        "    print('=' * 70)\n",
        "    print('Error:', e)\n",
        "    break\n",
        "\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "#===============================================================================\n",
        "print('=' * 70)\n",
        "\n",
        "out1 = output\n",
        "\n",
        "print('Sample INTs', out1[:12])\n",
        "print('=' * 70)\n",
        "\n",
        "patches = [0] * 16\n",
        "\n",
        "patches[3] = melody_MIDI_patch_number\n",
        "patches[0] = chords_MIDI_patch_number\n",
        "\n",
        "if len(output) != 0:\n",
        "\n",
        "    song = output\n",
        "    song_f = []\n",
        "\n",
        "    time = 0\n",
        "    dur = 10\n",
        "    vel = 90\n",
        "    pitch = 0\n",
        "    channel = 0\n",
        "\n",
        "    song1 = []\n",
        "    ptc = []\n",
        "    cho = []\n",
        "\n",
        "    for s in song:\n",
        "      if s < 128:\n",
        "        ptc.append(s)\n",
        "      else:\n",
        "        if ptc:\n",
        "          grp = [cho, ptc]\n",
        "          song1.append(grp)\n",
        "          cho = []\n",
        "          ptc = []\n",
        "\n",
        "        cho.append(s)\n",
        "\n",
        "    for ss in song1:\n",
        "\n",
        "      tones_chord = TMIDIX.ALL_CHORDS_SORTED[(ss[0][0]-128)]\n",
        "\n",
        "      dur = chords_duration\n",
        "\n",
        "      for t in tones_chord:\n",
        "        song_f.append(['note', time * 16, dur * 16, 0, 60+t, vel, chords_MIDI_patch_number])\n",
        "        song_f.append(['note', time * 16, dur * 16, 0, 48+t, vel, chords_MIDI_patch_number])\n",
        "\n",
        "      ptc_count = len(ss[1])\n",
        "      ptc_time_dur = dur // ptc_count\n",
        "\n",
        "      for p in ss[1]:\n",
        "        song_f.append(['note', time * 16, ptc_time_dur * 16, 3, p, vel, melody_MIDI_patch_number])\n",
        "        time += ptc_time_dur\n",
        "\n",
        "    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,\n",
        "                                                            output_signature = 'Chords Progressions Transformer',\n",
        "                                                            output_file_name = '/content/Chords-Progressions-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/Chords-Progressions-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": "GTpejF_T7JwT"
      },
      "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": 4,
  "nbformat_minor": 0
}