Ultimate_Drums_Transformer_Ver_2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Ultimate Drums Transformer (ver. 2.0)\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/Ultimate-Drums-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 Ultimate Drums 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 Ultimate Drums Transformer modules...')\n",
        "import torch\n",
        "\n",
        "%cd /content/Ultimate-Drums-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 Ultimate Drums 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 Ultimate Drums 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 Ultimate Drums Transformer Pre-Trained Model...')\n",
        "print('Please wait...')\n",
        "print('=' * 70)\n",
        "\n",
        "full_path_to_models_dir = \"/content/Ultimate-Drums-Transformer/Models\"\n",
        "\n",
        "model_checkpoint_file_name = 'Ultimate_Drums_Transformer_Small_Trained_Model_V2_NV_6769_steps_0.5871_loss_0.813_acc.pth'\n",
        "model_path = full_path_to_models_dir+'/Small_NV/'+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/Ultimate-Drums-Transformer',\n",
        "                  filename=model_checkpoint_file_name,\n",
        "                  local_dir='/content/Ultimate-Drums-Transformer/Models/Small_NV',\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 = 8192 # Models seq len\n",
        "PAD_IDX = 385 # 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 = 4, heads = 8, attn_flash = True)\n",
        "    )\n",
        "\n",
        "model = AutoregressiveWrapper(model, ignore_index = 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/Ultimate-Drums-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": [
        "# (IMPROV)"
      ],
      "metadata": {
        "id": "BxepTeHVmmKO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Standard Improv Generator\n",
        "\n",
        "#@markdown Generation settings\n",
        "\n",
        "melody_MIDI_patch_number = 0 # @param {type:\"slider\", min:0, max:127, step:1}\n",
        "number_of_tokens_tp_generate = 258 # @param {type:\"slider\", min:30, max:8190, 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:0.05}\n",
        "\n",
        "#@markdown Other settings\n",
        "\n",
        "render_MIDI_to_audio = True # @param {type:\"boolean\"}\n",
        "\n",
        "print('=' * 70)\n",
        "print('Ultimate Drums Transformer Standard Improv Model Generator')\n",
        "print('=' * 70)\n",
        "\n",
        "outy = [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_tp_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",
        "      dtime = 0\n",
        "      ntime = 0\n",
        "      dur = 128\n",
        "      vel = 90\n",
        "      vels = [90, 110]\n",
        "      pitch = 0\n",
        "      channel = 0\n",
        "\n",
        "      patches = [0] * 16\n",
        "      patches[0] = melody_MIDI_patch_number\n",
        "\n",
        "      for ss in song:\n",
        "\n",
        "          if 0 <= ss < 128:\n",
        "\n",
        "              time += ss * 32\n",
        "\n",
        "              dtime = time\n",
        "\n",
        "              song_f.append(['note', time, dur, 0, random.choice([60, 62, 64]), 80, 0 ])\n",
        "\n",
        "          if 128 <= ss < 256:\n",
        "\n",
        "              dtime += (ss-128) * 32\n",
        "\n",
        "          if 256 <= ss < 384:\n",
        "\n",
        "              pitch = (ss-256)\n",
        "\n",
        "              if dtime == time:\n",
        "                song_f.append(['note', time, dur, 9, pitch, vels[pitch % 2], 128 ])\n",
        "              else:\n",
        "                song_f.append(['note', dtime, dur, 9, pitch, vels[pitch % 2], 128 ])\n",
        "\n",
        "      data = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,\n",
        "                                                      output_signature = 'Ultimate Drums Transformer',\n",
        "                                                      output_file_name = '/content/Ultimate-Drums-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/Ultimate-Drums-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": [
        "# (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\", \"Ultimate-Drums-Transformer-Melody-Seed-1\", \"Ultimate-Drums-Transformer-Melody-Seed-2\", \"Ultimate-Drums-Transformer-Melody-Seed-3\", \"Ultimate-Drums-Transformer-Melody-Seed-4\", \"Ultimate-Drums-Transformer-Melody-Seed-5\", \"Ultimate-Drums-Transformer-Melody-Seed-6\", \"Ultimate-Drums-Transformer-MI-Seed-1\", \"Ultimate-Drums-Transformer-MI-Seed-2\", \"Ultimate-Drums-Transformer-MI-Seed-3\", \"Ultimate-Drums-Transformer-MI-Seed-4\"]\n",
        "render_MIDI_to_audio = False # @param {type:\"boolean\"}\n",
        "\n",
        "print('=' * 70)\n",
        "print('Ultimate Drums 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/Ultimate-Drums-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",
        "  #=======================================================\n",
        "  # PRE-PROCESSING\n",
        "\n",
        "  #===============================================================================\n",
        "  # Augmented enhanced score notes\n",
        "\n",
        "  escore_notes = [e for e in escore_notes if e[3] != 9]\n",
        "\n",
        "  escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes)\n",
        "\n",
        "  patches = TMIDIX.patch_list_from_enhanced_score_notes(escore_notes)\n",
        "\n",
        "  dscore = TMIDIX.delta_score_notes(escore_notes, compress_timings=True, even_timings=True)\n",
        "\n",
        "  cscore = TMIDIX.chordify_score([d[1:] for d in dscore])\n",
        "\n",
        "  cscore_melody = [c[0] for c in cscore]\n",
        "\n",
        "  comp_times = [0] + [t[1] for t in dscore if t[1] != 0]\n",
        "\n",
        "  #=======================================================\n",
        "\n",
        "  song_f = escore_notes\n",
        "\n",
        "  for s in song_f:\n",
        "    s[1] *= 16\n",
        "    s[2] *= 16\n",
        "\n",
        "  time = 0\n",
        "  dur = 0\n",
        "  vel = 90\n",
        "  pitch = 0\n",
        "  channel = 0\n",
        "\n",
        "\n",
        "  detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,\n",
        "                                                            output_signature = 'Ultimate Drums Transformer',\n",
        "                                                            output_file_name = '/content/Ultimate-Drums-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(cscore), 'chords')\n",
        "  print('Composition has', len(comp_times), 'time tokens')\n",
        "  print('Composition MIDI patches:', sorted(set(patches)))\n",
        "  print('=' * 70)\n",
        "\n",
        "  print('Displaying resulting composition...')\n",
        "  print('=' * 70)\n",
        "\n",
        "  fname = '/content/Ultimate-Drums-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": "markdown",
      "source": [
        "# (DRUMS TRACK GENERATION)"
      ],
      "metadata": {
        "id": "fmm3KjOtoVp9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dkvXYwR_qSnx",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title Drums track generation\n",
        "\n",
        "#@markdown Generation settings\n",
        "number_of_prime_chords = 128 # @param {type:\"slider\", min:4, max:8192, step:1}\n",
        "max_number_of_drums_pitches_per_step = 5 # @param {type:\"slider\", min:1, max:10, step:1}\n",
        "number_of_memory_tokens = 4096 # @param {type:\"slider\", min:32, max:8188, step:16}\n",
        "temperature = 1 # @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('Ultimate Drums Transformer Drums Track Generator')\n",
        "print('=' * 70)\n",
        "\n",
        "#===============================================================================\n",
        "\n",
        "def generate_drums(notes_times,\n",
        "                   max_drums_limit = 8,\n",
        "                   num_memory_tokens = 4096,\n",
        "                   temperature=0.9):\n",
        "\n",
        "    x = torch.tensor([notes_times] * 1, dtype=torch.long, device='cuda')\n",
        "\n",
        "    o = 128\n",
        "\n",
        "    ncount = 0\n",
        "\n",
        "    while o > 127 and ncount < max_drums_limit:\n",
        "      with ctx:\n",
        "        out = model.generate(x[-num_memory_tokens:],\n",
        "                            1,\n",
        "                            temperature=temperature,\n",
        "                            return_prime=False,\n",
        "                            verbose=False)\n",
        "\n",
        "      o = out.tolist()[0][0]\n",
        "\n",
        "      if 256 <= o < 384:\n",
        "        ncount += 1\n",
        "\n",
        "      if o > 127:\n",
        "        x = torch.cat((x, out), 1)\n",
        "\n",
        "    return x.tolist()[0][len(notes_times):]\n",
        "\n",
        "#===============================================================================\n",
        "\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "output = []\n",
        "\n",
        "for c in tqdm.tqdm(comp_times[:number_of_prime_chords]):\n",
        "  output.append(c)\n",
        "\n",
        "  out = generate_drums(output,\n",
        "                       temperature=temperature,\n",
        "                       max_drums_limit=max_number_of_drums_pitches_per_step,\n",
        "                       num_memory_tokens=number_of_memory_tokens\n",
        "                       )\n",
        "  output.extend(out)\n",
        "\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "#===============================================================================\n",
        "\n",
        "print('=' * 70)\n",
        "print('Done!')\n",
        "print('=' * 70)\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",
        "    dtime = 0\n",
        "    ntime = 0\n",
        "    dur = 32\n",
        "    vel = 90\n",
        "    vels = [100, 120]\n",
        "    pitch = 0\n",
        "    channel = 0\n",
        "\n",
        "    idx = 0\n",
        "\n",
        "    for ss in song:\n",
        "\n",
        "        if 0 <= ss < 128:\n",
        "\n",
        "            time += cscore[idx][0][0] * 32\n",
        "\n",
        "            dtime = time\n",
        "\n",
        "            for c in cscore[idx]:\n",
        "              song_f.append(['note', time, c[1] * 32, c[2], c[3], c[4], c[5]])\n",
        "\n",
        "            idx += 1\n",
        "\n",
        "        if 128 <= ss < 256:\n",
        "\n",
        "            dtime += (ss-128) * 32\n",
        "\n",
        "        if 256 <= ss < 384:\n",
        "\n",
        "            pitch = (ss-256)\n",
        "\n",
        "            if dtime == time:\n",
        "              song_f.append(['note', time, dur, 9, pitch, vels[pitch % 2], 128 ])\n",
        "            else:\n",
        "              song_f.append(['note', dtime, dur, 9, pitch, vels[pitch % 2], 128 ])\n",
        "\n",
        "detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,\n",
        "                                                          output_signature = 'Ultimate Drums Transformer',\n",
        "                                                          output_file_name = '/content/Ultimate-Drums-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/Ultimate-Drums-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)"
      ]
    },
    {
      "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"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}