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Master_MIDI_Dataset_GPU_Search_and_Filter.ipynb

@@ -0,0 +1,574 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "gradient": {
+          "editing": false,
+          "id": "ac5a4cf0-d9d2-47b5-9633-b53f8d99a4d2",
+          "kernelId": ""
+        },
+        "id": "SiTIpPjArIyr"
+      },
+      "source": [
+        "# Master MIDI Dataset GPU Search and Filter (ver. 2.0)\n",
+        "\n",
+        "***\n",
+        "\n",
+        "Powered by tegridy-tools: https://github.com/asigalov61/tegridy-tools\n",
+        "\n",
+        "***\n",
+        "\n",
+        "#### Project Los Angeles\n",
+        "\n",
+        "#### Tegridy Code 2024\n",
+        "\n",
+        "***"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "gradient": {
+          "editing": false,
+          "id": "fa0a611c-1803-42ae-bdf6-a49b5a4e781b",
+          "kernelId": ""
+        },
+        "id": "gOd93yV0sGd2"
+      },
+      "source": [
+        "# (SETUP ENVIRONMENT)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# ( GPU CHECK)"
+      ],
+      "metadata": {
+        "id": "0rMwKVc9FFRw"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title NVIDIA GPU Check\n",
+        "!nvidia-smi"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "dVSaUaEZFIip"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# (SETUP ENVIRONMENT)"
+      ],
+      "metadata": {
+        "id": "YRTt3Hx0FQeu"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "gradient": {
+          "editing": false,
+          "id": "a1a45a91-d909-4fd4-b67a-5e16b971d179",
+          "kernelId": ""
+        },
+        "id": "fX12Yquyuihc"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Install all dependencies (run only once per session)\n",
+        "\n",
+        "!git clone --depth 1 https://github.com/asigalov61/Los-Angeles-MIDI-Dataset\n",
+        "!pip install huggingface_hub"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gradient": {
+          "editing": false,
+          "id": "b8207b76-9514-4c07-95db-95a4742e52c5",
+          "kernelId": ""
+        },
+        "id": "z7n9vnKmug1J",
+        "cellView": "form"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Import all needed modules\n",
+        "\n",
+        "print('Loading core modules... Please wait...')\n",
+        "\n",
+        "import os\n",
+        "import copy\n",
+        "from collections import Counter\n",
+        "import random\n",
+        "import pickle\n",
+        "from tqdm import tqdm\n",
+        "import pprint\n",
+        "import statistics\n",
+        "import shutil\n",
+        "\n",
+        "import cupy as cp\n",
+        "\n",
+        "from huggingface_hub import hf_hub_download\n",
+        "\n",
+        "print('Loading TMIDIX module...')\n",
+        "os.chdir('/content/Los-Angeles-MIDI-Dataset')\n",
+        "\n",
+        "import TMIDIX\n",
+        "\n",
+        "os.chdir('/content/')\n",
+        "\n",
+        "print('Creating IO dirs... Please wait...')\n",
+        "\n",
+        "if not os.path.exists('/content/Master-MIDI-Dataset'):\n",
+        "    os.makedirs('/content/Master-MIDI-Dataset')\n",
+        "\n",
+        "if not os.path.exists('/content/Master-MIDI-Dataset'):\n",
+        "    os.makedirs('/content/Master-MIDI-Dataset')\n",
+        "\n",
+        "if not os.path.exists('/content/Output-MIDI-Dataset'):\n",
+        "    os.makedirs('/content/Output-MIDI-Dataset')\n",
+        "\n",
+        "print('Done!')\n",
+        "print('Enjoy! :)')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "gradient": {
+          "editing": false,
+          "id": "20b8698a-0b4e-4fdb-ae49-24d063782e77",
+          "kernelId": ""
+        },
+        "id": "ObPxlEutsQBj"
+      },
+      "source": [
+        "# (PREP MAIN MIDI DATASET)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Download Los Angeles MIDI Dataset\n",
+        "print('=' * 70)\n",
+        "print('Downloading Los Angeles MIDI Dataset...Please wait...')\n",
+        "print('=' * 70)\n",
+        "\n",
+        "hf_hub_download(repo_id='projectlosangeles/Los-Angeles-MIDI-Dataset',\n",
+        "                filename='Los-Angeles-MIDI-Dataset-Ver-4-0-CC-BY-NC-SA.zip',\n",
+        "                repo_type=\"dataset\",\n",
+        "                local_dir='/content/Main-MIDI-Dataset',\n",
+        "                local_dir_use_symlinks=False)\n",
+        "print('=' * 70)\n",
+        "print('Done! Enjoy! :)')\n",
+        "print('=' * 70)"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "7aItlhq9cRxZ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Unzip Los Angeles MIDI Dataset\n",
+        "%cd /content/Main-MIDI-Dataset/\n",
+        "\n",
+        "print('=' * 70)\n",
+        "print('Unzipping Los Angeles MIDI Dataset...Please wait...')\n",
+        "!unzip 'Los-Angeles-MIDI-Dataset-Ver-4-0-CC-BY-NC-SA.zip'\n",
+        "print('=' * 70)\n",
+        "\n",
+        "print('Done! Enjoy! :)')\n",
+        "print('=' * 70)\n",
+        "%cd /content/"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "zMF4vdMNDYYg"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Create Los Angeles MIDI Dataset files list\n",
+        "print('=' * 70)\n",
+        "print('Creating dataset files list...')\n",
+        "dataset_addr = \"/content/Main-MIDI-Dataset/MIDIs\"\n",
+        "\n",
+        "# os.chdir(dataset_addr)\n",
+        "filez = list()\n",
+        "for (dirpath, dirnames, filenames) in os.walk(dataset_addr):\n",
+        "    filez += [os.path.join(dirpath, file) for file in filenames]\n",
+        "\n",
+        "if filez == []:\n",
+        "    print('Could not find any MIDI files. Please check Dataset dir...')\n",
+        "    print('=' * 70)\n",
+        "\n",
+        "print('=' * 70)\n",
+        "print('Randomizing file list...')\n",
+        "random.shuffle(filez)\n",
+        "print('=' * 70)\n",
+        "\n",
+        "LAMD_files_list = []\n",
+        "\n",
+        "for f in tqdm(filez):\n",
+        "  LAMD_files_list.append([f.split('/')[-1].split('.mid')[0], f])\n",
+        "print('Done!')\n",
+        "print('=' * 70)"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "btrUDk8MDfdw"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Load Los Angeles MIDI Dataset Signatures Data\n",
+        "\n",
+        "print('=' * 70)\n",
+        "print('Loading LAMDa Signatures Data...')\n",
+        "sigs_data = pickle.load(open('/content/Main-MIDI-Dataset/SIGNATURES_DATA/LAMDa_SIGNATURES_DATA.pickle', 'rb'))\n",
+        "print('=' * 70)\n",
+        "\n",
+        "print('Prepping signatures...')\n",
+        "print('=' * 70)\n",
+        "\n",
+        "random.shuffle(sigs_data)\n",
+        "\n",
+        "signatures_file_names = []\n",
+        "sigs_matrixes = [ [0]*(len(TMIDIX.ALL_CHORDS)+128) for i in range(len(sigs_data))]\n",
+        "\n",
+        "idx = 0\n",
+        "for s in tqdm(sigs_data):\n",
+        "\n",
+        "  signatures_file_names.append(s[0])\n",
+        "\n",
+        "  counts_sum = sum([c[1] for c in s[1]])\n",
+        "\n",
+        "  for ss in s[1]:\n",
+        "    sigs_matrixes[idx][ss[0]] = ss[1] / counts_sum\n",
+        "\n",
+        "  idx += 1\n",
+        "\n",
+        "print('=' * 70)\n",
+        "print('Loading signatures...')\n",
+        "print('=' * 70)\n",
+        "\n",
+        "signatures_data = cp.array(sigs_matrixes)\n",
+        "\n",
+        "print('Done!')\n",
+        "print('=' * 70)"
+      ],
+      "metadata": {
+        "id": "Mv-pjxbrIqi2",
+        "cellView": "form"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# (SEARCH AND FILTER)\n",
+        "\n",
+        "### DO NOT FORGET TO UPLOAD YOUR MASTER DATASET TO \"Master-MIDI-Dataset\" FOLDER"
+      ],
+      "metadata": {
+        "id": "iaeqXuIHI0_T"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Master MIDI Dataset Search and Filter\n",
+        "\n",
+        "#@markdown NOTE: You can stop the search at any time to render partial results\n",
+        "\n",
+        "number_of_top_matches_MIDIs_to_collect = 20 #@param {type:\"slider\", min:5, max:50, step:1}\n",
+        "search_matching_type = \"ratios\" # @param [\"ratios\", \"distances\"]\n",
+        "distances_norm_order = 3 # @param {type:\"slider\", min:1, max:10, step:1}\n",
+        "maximum_match_ratio_to_search_for = 0.999 #@param {type:\"slider\", min:0, max:1, step:0.001}\n",
+        "\n",
+        "print('=' * 70)\n",
+        "print('Master MIDI Dataset GPU Search and Filter')\n",
+        "print('=' * 70)\n",
+        "\n",
+        "###########\n",
+        "\n",
+        "print('Loading MIDI files...')\n",
+        "print('This may take a while on a large dataset in particular.')\n",
+        "\n",
+        "dataset_addr = \"/content/Master-MIDI-Dataset\"\n",
+        "\n",
+        "filez = list()\n",
+        "\n",
+        "for (dirpath, dirnames, filenames) in os.walk(dataset_addr):\n",
+        "    for file in filenames:\n",
+        "        if file.endswith(('.mid', '.midi', '.kar')):\n",
+        "            filez.append(os.path.join(dirpath, file))\n",
+        "\n",
+        "print('=' * 70)\n",
+        "\n",
+        "if filez:\n",
+        "\n",
+        "  print('Randomizing file list...')\n",
+        "  random.shuffle(filez)\n",
+        "  print('=' * 70)\n",
+        "\n",
+        "  ###################\n",
+        "\n",
+        "  if not os.path.exists('/content/Output-MIDI-Dataset'):\n",
+        "      os.makedirs('/content/Output-MIDI-Dataset')\n",
+        "\n",
+        "  ###################\n",
+        "\n",
+        "  input_files_count = 0\n",
+        "  files_count = 0\n",
+        "\n",
+        "  for f in filez:\n",
+        "    try:\n",
+        "\n",
+        "          input_files_count += 1\n",
+        "\n",
+        "          fn = os.path.basename(f)\n",
+        "          fn1 = os.path.splitext(fn)[0]\n",
+        "          ext = os.path.splitext(f)[1]\n",
+        "\n",
+        "          print('Processing MIDI File #', files_count+1, 'out of', len(filez))\n",
+        "          print('MIDI file name', fn)\n",
+        "          print('-' * 70)\n",
+        "\n",
+        "          #=======================================================\n",
+        "\n",
+        "          raw_score = TMIDIX.midi2single_track_ms_score(open(f, 'rb').read())\n",
+        "          escore = TMIDIX.advanced_score_processor(raw_score, return_score_analysis=False, return_enhanced_score_notes=True)[0]\n",
+        "\n",
+        "          for e in escore:\n",
+        "            e[1] = int(e[1] / 16)\n",
+        "            e[2] = int(e[2] / 16)\n",
+        "\n",
+        "          src_sigs = []\n",
+        "\n",
+        "          for i in range(-6, 6):\n",
+        "\n",
+        "            escore_copy = copy.deepcopy(escore)\n",
+        "\n",
+        "            for e in escore_copy:\n",
+        "              e[4] += i\n",
+        "\n",
+        "            cscore = TMIDIX.chordify_score([1000, escore_copy])\n",
+        "\n",
+        "            sig = []\n",
+        "\n",
+        "            for c in cscore:\n",
+        "\n",
+        "              pitches = sorted(set([p[4] for p in c if p[3] != 9]))\n",
+        "\n",
+        "              if pitches:\n",
+        "                if len(pitches) > 1:\n",
+        "                  tones_chord = sorted(set([p % 12 for p in pitches]))\n",
+        "                  checked_tones_chord = TMIDIX.check_and_fix_tones_chord(tones_chord)\n",
+        "\n",
+        "                  sig_token = TMIDIX.ALL_CHORDS.index(checked_tones_chord) + 128\n",
+        "\n",
+        "                elif len(pitches) == 1:\n",
+        "                  sig_token = pitches[0]\n",
+        "\n",
+        "                sig.append(sig_token)\n",
+        "\n",
+        "            fsig = [list(v) for v in Counter(sig).most_common()]\n",
+        "\n",
+        "            src_sig_mat = [0] * (len(TMIDIX.ALL_CHORDS)+128)\n",
+        "\n",
+        "            counts_sum = sum([c[1] for c in fsig])\n",
+        "\n",
+        "            for s in fsig:\n",
+        "\n",
+        "              src_sig_mat[s[0]] = s[1] / counts_sum\n",
+        "\n",
+        "            src_sigs.append(src_sig_mat)\n",
+        "\n",
+        "          src_signatures = cp.stack(cp.array(src_sigs))\n",
+        "\n",
+        "          #=======================================================\n",
+        "\n",
+        "          print('Searching for matches...Please wait...')\n",
+        "          print('-' * 70)\n",
+        "\n",
+        "          lower_threshold = 0.0\n",
+        "          upper_threshold = maximum_match_ratio_to_search_for\n",
+        "          filter_size = number_of_top_matches_MIDIs_to_collect\n",
+        "\n",
+        "          final_ratios = []\n",
+        "\n",
+        "          avg_idxs = []\n",
+        "\n",
+        "          all_filtered_means = []\n",
+        "          all_filtered_idxs = []\n",
+        "          all_filtered_tvs = []\n",
+        "\n",
+        "          tv_idx = -6\n",
+        "\n",
+        "          for target_sig in tqdm(src_signatures):\n",
+        "\n",
+        "            if search_matching_type == 'ratios':\n",
+        "\n",
+        "              ratios = cp.where(target_sig != 0, cp.divide(cp.minimum(signatures_data, target_sig), cp.maximum(signatures_data, target_sig)), 0)\n",
+        "              max_comp_lengths = cp.maximum(cp.repeat(cp.sum(target_sig != 0), signatures_data.shape[0]), cp.sum(signatures_data != 0, axis=1))\n",
+        "\n",
+        "              results = cp.divide(cp.sum(ratios, axis=1), max_comp_lengths)\n",
+        "\n",
+        "            elif search_matching_type == 'distances':\n",
+        "\n",
+        "              distances = cp.power(cp.sum(cp.power(cp.abs(signatures_data - target_sig), distances_norm_order), axis=1), 1 / distances_norm_order)\n",
+        "\n",
+        "              results = cp.max(distances) - distances\n",
+        "\n",
+        "            unique_means = cp.unique(results)\n",
+        "            sorted_means = cp.sort(unique_means)[::-1]\n",
+        "\n",
+        "            filtered_means = sorted_means[(sorted_means >= lower_threshold) & (sorted_means <= upper_threshold)][:filter_size]\n",
+        "\n",
+        "            filtered_idxs = cp.where(cp.in1d(results, filtered_means))[0]\n",
+        "\n",
+        "            all_filtered_means.extend(results[cp.in1d(results, filtered_means)].tolist())\n",
+        "\n",
+        "            all_filtered_idxs.extend(filtered_idxs.tolist())\n",
+        "\n",
+        "            filtered_tvs = [tv_idx] * filtered_idxs.shape[0]\n",
+        "\n",
+        "            all_filtered_tvs.extend(filtered_tvs)\n",
+        "\n",
+        "            tv_idx += 1\n",
+        "\n",
+        "          filtered_results = sorted(zip(all_filtered_means, all_filtered_idxs, all_filtered_tvs), key=lambda x: x[0], reverse=True)[:filter_size]\n",
+        "\n",
+        "          #=======================================================\n",
+        "\n",
+        "          print('Done!')\n",
+        "          print('-' * 70)\n",
+        "          print('Max match ratio:', filtered_results[0][0])\n",
+        "          print('Max match transpose value:', filtered_results[0][2])\n",
+        "          print('Max match signature index:', filtered_results[0][1])\n",
+        "          print('Max match file name:', signatures_file_names[filtered_results[0][1]])\n",
+        "          print('-' * 70)\n",
+        "          print('Copying max ratios MIDIs...')\n",
+        "\n",
+        "          for fr in filtered_results:\n",
+        "\n",
+        "            max_ratio_index = fr[1]\n",
+        "\n",
+        "            ffn = signatures_file_names[fr[1]]\n",
+        "            ffn_idx = [y[0] for y in LAMD_files_list].index(ffn)\n",
+        "\n",
+        "            ff = LAMD_files_list[ffn_idx][1]\n",
+        "\n",
+        "            #=======================================================\n",
+        "\n",
+        "            dir_str = str(fn1)\n",
+        "            copy_path = '/content/Output-MIDI-Dataset/'+dir_str\n",
+        "            if not os.path.exists(copy_path):\n",
+        "                os.mkdir(copy_path)\n",
+        "\n",
+        "            fff = str(fr[0] * 100) + '_' + str(fr[2]) + '_' + ffn + '.mid'\n",
+        "\n",
+        "            shutil.copy2(ff, copy_path+'/'+fff)\n",
+        "\n",
+        "          shutil.copy2(f, copy_path+'/'+fn)\n",
+        "\n",
+        "          #======================================================='''\n",
+        "          print('Done!')\n",
+        "          print('=' * 70)\n",
+        "\n",
+        "          #=======================================================\n",
+        "\n",
+        "          # Processed files counter\n",
+        "          files_count += 1\n",
+        "\n",
+        "    except KeyboardInterrupt:\n",
+        "        print('Quitting...')\n",
+        "        print('Total number of processed MIDI files', files_count)\n",
+        "        print('=' * 70)\n",
+        "        break\n",
+        "\n",
+        "    except Exception as ex:\n",
+        "        print('WARNING !!!')\n",
+        "        print('=' * 70)\n",
+        "        print('Bad file:', f)\n",
+        "        print('Error detected:', ex)\n",
+        "        print('=' * 70)\n",
+        "        continue\n",
+        "\n",
+        "    print('Total number of processed MIDI files', files_count)\n",
+        "    print('=' * 70)\n",
+        "\n",
+        "else:\n",
+        "  print('Could not find any MIDI files. Please check Dataset dir...')\n",
+        "  print('=' * 70)"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "M0JWCPzBGNvh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YzCMd94Tu_gz"
+      },
+      "source": [
+        "# Congrats! You did it! :)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "private_outputs": true,
+      "provenance": [],
+      "gpuType": "T4",
+      "machine_shape": "hm"
+    },
+    "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": "3.9.7"
+    },
+    "accelerator": "GPU"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

TMIDIX.py

@@ -3852,7 +3852,8 @@ ALL_CHORDS = [[0], [7], [5], [9], [2], [4], [11], [10], [8], [6], [3], [1], [0,
               [2, 5, 7, 9, 11], [1, 3, 5, 7, 10], [0, 2, 4, 7, 10], [1, 3, 5, 7, 9],
               [1, 3, 5, 9, 11], [1, 5, 7, 9, 11], [1, 3, 7, 9, 11], [3, 5, 7, 9, 11],
               [2, 4, 6, 8, 10], [0, 4, 6, 8, 10], [0, 2, 6, 8, 10], [1, 3, 5, 7, 11],
-              [0, 2, 4, 8, 10], [0, 2, 4, 6, 8], [0, 2, 4, 6, 10]]
+              [0, 2, 4, 8, 10], [0, 2, 4, 6, 8], [0, 2, 4, 6, 10], [0, 2, 4, 6, 8, 10],
+              [1, 3, 5, 7, 9, 11]]
 
 def find_exact_match_variable_length(list_of_lists, target_list, uncertain_indices):
     # Infer possible values for each uncertain index
@@ -3981,7 +3982,7 @@ def analyze_score_pitches(score, channels_to_analyze=[0]):
 
 ###################################################################################
 
-ALL_CHORDS_GROUPED = [
+ALL_CHORDS_GROUPED = [[[1, 3, 5, 7, 9, 11], [0, 2, 4, 6, 8, 10]],
     [[0, 2, 5, 7, 10], [0, 2, 4, 7, 9], [0, 2, 5, 7, 9], [1, 4, 6, 9, 11],
     [1, 3, 6, 8, 11], [1, 3, 6, 8, 10], [1, 4, 6, 8, 11], [1, 3, 5, 8, 10],
     [2, 4, 6, 9, 11], [2, 4, 7, 9, 11], [0, 3, 5, 7, 10], [0, 3, 5, 8, 10],
@@ -4510,6 +4511,33 @@ def ascii_text_words_counter(ascii_text):
     
 ###################################################################################
 
+def check_and_fix_tones_chord(tones_chord):
+
+    lst = tones_chord
+
+    if len(lst) == 2:
+      if lst[1] - lst[0] == 1:
+        return [lst[-1]]
+      else:
+        if 0 in lst and 11 in lst:
+          lst.remove(0)
+        return lst
+
+    non_consecutive = [lst[0]]
+
+    if len(lst) > 2: 
+      for i in range(1, len(lst) - 1):
+          if lst[i-1] + 1 != lst[i] and lst[i] + 1 != lst[i+1]:
+              non_consecutive.append(lst[i])
+      non_consecutive.append(lst[-1])
+
+    if 0 in non_consecutive and 11 in non_consecutive:
+      non_consecutive.remove(0)
+
+    return non_consecutive
+
+###################################################################################
+
 # This is the end of the TMIDI X Python module
 
 ###################################################################################

master_midi_dataset_gpu_search_and_filter.py

@@ -0,0 +1,399 @@
+# -*- coding: utf-8 -*-
+"""Master_MIDI_Dataset_GPU_Search_and_Filter.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/github/asigalov61/Los-Angeles-MIDI-Dataset/blob/main/Extras/Master_MIDI_Dataset_GPU_Search_and_Filter.ipynb
+
+# Master MIDI Dataset GPU Search and Filter (ver. 2.0)
+
+***
+
+Powered by tegridy-tools: https://github.com/asigalov61/tegridy-tools
+
+***
+
+#### Project Los Angeles
+
+#### Tegridy Code 2024
+
+***
+
+# (SETUP ENVIRONMENT)
+
+# ( GPU CHECK)
+"""
+
+# @title NVIDIA GPU Check
+!nvidia-smi
+
+"""# (SETUP ENVIRONMENT)"""
+
+#@title Install all dependencies (run only once per session)
+
+!git clone --depth 1 https://github.com/asigalov61/Los-Angeles-MIDI-Dataset
+!pip install huggingface_hub
+
+#@title Import all needed modules
+
+print('Loading core modules... Please wait...')
+
+import os
+import copy
+from collections import Counter
+import random
+import pickle
+from tqdm import tqdm
+import pprint
+import statistics
+import shutil
+
+import cupy as cp
+
+from huggingface_hub import hf_hub_download
+
+print('Loading TMIDIX module...')
+os.chdir('/content/Los-Angeles-MIDI-Dataset')
+
+import TMIDIX
+
+os.chdir('/content/')
+
+print('Creating IO dirs... Please wait...')
+
+if not os.path.exists('/content/Master-MIDI-Dataset'):
+    os.makedirs('/content/Master-MIDI-Dataset')
+
+if not os.path.exists('/content/Master-MIDI-Dataset'):
+    os.makedirs('/content/Master-MIDI-Dataset')
+
+if not os.path.exists('/content/Output-MIDI-Dataset'):
+    os.makedirs('/content/Output-MIDI-Dataset')
+
+print('Done!')
+print('Enjoy! :)')
+
+"""# (PREP MAIN MIDI DATASET)"""
+
+#@title Download Los Angeles MIDI Dataset
+print('=' * 70)
+print('Downloading Los Angeles MIDI Dataset...Please wait...')
+print('=' * 70)
+
+hf_hub_download(repo_id='projectlosangeles/Los-Angeles-MIDI-Dataset',
+                filename='Los-Angeles-MIDI-Dataset-Ver-4-0-CC-BY-NC-SA.zip',
+                repo_type="dataset",
+                local_dir='/content/Main-MIDI-Dataset',
+                local_dir_use_symlinks=False)
+print('=' * 70)
+print('Done! Enjoy! :)')
+print('=' * 70)
+
+# Commented out IPython magic to ensure Python compatibility.
+#@title Unzip Los Angeles MIDI Dataset
+# %cd /content/Main-MIDI-Dataset/
+
+print('=' * 70)
+print('Unzipping Los Angeles MIDI Dataset...Please wait...')
+!unzip 'Los-Angeles-MIDI-Dataset-Ver-4-0-CC-BY-NC-SA.zip'
+print('=' * 70)
+
+print('Done! Enjoy! :)')
+print('=' * 70)
+# %cd /content/
+
+#@title Create Los Angeles MIDI Dataset files list
+print('=' * 70)
+print('Creating dataset files list...')
+dataset_addr = "/content/Main-MIDI-Dataset/MIDIs"
+
+# os.chdir(dataset_addr)
+filez = list()
+for (dirpath, dirnames, filenames) in os.walk(dataset_addr):
+    filez += [os.path.join(dirpath, file) for file in filenames]
+
+if filez == []:
+    print('Could not find any MIDI files. Please check Dataset dir...')
+    print('=' * 70)
+
+print('=' * 70)
+print('Randomizing file list...')
+random.shuffle(filez)
+print('=' * 70)
+
+LAMD_files_list = []
+
+for f in tqdm(filez):
+  LAMD_files_list.append([f.split('/')[-1].split('.mid')[0], f])
+print('Done!')
+print('=' * 70)
+
+#@title Load Los Angeles MIDI Dataset Signatures Data
+
+print('=' * 70)
+print('Loading LAMDa Signatures Data...')
+sigs_data = pickle.load(open('/content/Main-MIDI-Dataset/SIGNATURES_DATA/LAMDa_SIGNATURES_DATA.pickle', 'rb'))
+print('=' * 70)
+
+print('Prepping signatures...')
+print('=' * 70)
+
+random.shuffle(sigs_data)
+
+signatures_file_names = []
+sigs_matrixes = [ [0]*(len(TMIDIX.ALL_CHORDS)+128) for i in range(len(sigs_data))]
+
+idx = 0
+for s in tqdm(sigs_data):
+
+  signatures_file_names.append(s[0])
+
+  counts_sum = sum([c[1] for c in s[1]])
+
+  for ss in s[1]:
+    sigs_matrixes[idx][ss[0]] = ss[1] / counts_sum
+
+  idx += 1
+
+print('=' * 70)
+print('Loading signatures...')
+print('=' * 70)
+
+signatures_data = cp.array(sigs_matrixes)
+
+print('Done!')
+print('=' * 70)
+
+"""# (SEARCH AND FILTER)
+
+### DO NOT FORGET TO UPLOAD YOUR MASTER DATASET TO "Master-MIDI-Dataset" FOLDER
+"""
+
+#@title Master MIDI Dataset Search and Filter
+
+#@markdown NOTE: You can stop the search at any time to render partial results
+
+number_of_top_matches_MIDIs_to_collect = 20 #@param {type:"slider", min:5, max:50, step:1}
+search_matching_type = "ratios" # @param ["ratios", "distances"]
+distances_norm_order = 3 # @param {type:"slider", min:1, max:10, step:1}
+maximum_match_ratio_to_search_for = 0.999 #@param {type:"slider", min:0, max:1, step:0.001}
+
+print('=' * 70)
+print('Master MIDI Dataset GPU Search and Filter')
+print('=' * 70)
+
+###########
+
+print('Loading MIDI files...')
+print('This may take a while on a large dataset in particular.')
+
+dataset_addr = "/content/Master-MIDI-Dataset"
+
+filez = list()
+
+for (dirpath, dirnames, filenames) in os.walk(dataset_addr):
+    for file in filenames:
+        if file.endswith(('.mid', '.midi', '.kar')):
+            filez.append(os.path.join(dirpath, file))
+
+print('=' * 70)
+
+if filez:
+
+  print('Randomizing file list...')
+  random.shuffle(filez)
+  print('=' * 70)
+
+  ###################
+
+  if not os.path.exists('/content/Output-MIDI-Dataset'):
+      os.makedirs('/content/Output-MIDI-Dataset')
+
+  ###################
+
+  input_files_count = 0
+  files_count = 0
+
+  for f in filez:
+    try:
+
+          input_files_count += 1
+
+          fn = os.path.basename(f)
+          fn1 = os.path.splitext(fn)[0]
+          ext = os.path.splitext(f)[1]
+
+          print('Processing MIDI File #', files_count+1, 'out of', len(filez))
+          print('MIDI file name', fn)
+          print('-' * 70)
+
+          #=======================================================
+
+          raw_score = TMIDIX.midi2single_track_ms_score(open(f, 'rb').read())
+          escore = TMIDIX.advanced_score_processor(raw_score, return_score_analysis=False, return_enhanced_score_notes=True)[0]
+
+          for e in escore:
+            e[1] = int(e[1] / 16)
+            e[2] = int(e[2] / 16)
+
+          src_sigs = []
+
+          for i in range(-6, 6):
+
+            escore_copy = copy.deepcopy(escore)
+
+            for e in escore_copy:
+              e[4] += i
+
+            cscore = TMIDIX.chordify_score([1000, escore_copy])
+
+            sig = []
+
+            for c in cscore:
+
+              pitches = sorted(set([p[4] for p in c if p[3] != 9]))
+
+              if pitches:
+                if len(pitches) > 1:
+                  tones_chord = sorted(set([p % 12 for p in pitches]))
+                  checked_tones_chord = TMIDIX.check_and_fix_tones_chord(tones_chord)
+
+                  sig_token = TMIDIX.ALL_CHORDS.index(checked_tones_chord) + 128
+
+                elif len(pitches) == 1:
+                  sig_token = pitches[0]
+
+                sig.append(sig_token)
+
+            fsig = [list(v) for v in Counter(sig).most_common()]
+
+            src_sig_mat = [0] * (len(TMIDIX.ALL_CHORDS)+128)
+
+            counts_sum = sum([c[1] for c in fsig])
+
+            for s in fsig:
+
+              src_sig_mat[s[0]] = s[1] / counts_sum
+
+            src_sigs.append(src_sig_mat)
+
+          src_signatures = cp.stack(cp.array(src_sigs))
+
+          #=======================================================
+
+          print('Searching for matches...Please wait...')
+          print('-' * 70)
+
+          lower_threshold = 0.0
+          upper_threshold = maximum_match_ratio_to_search_for
+          filter_size = number_of_top_matches_MIDIs_to_collect
+
+          final_ratios = []
+
+          avg_idxs = []
+
+          all_filtered_means = []
+          all_filtered_idxs = []
+          all_filtered_tvs = []
+
+          tv_idx = -6
+
+          for target_sig in tqdm(src_signatures):
+
+            if search_matching_type == 'ratios':
+
+              ratios = cp.where(target_sig != 0, cp.divide(cp.minimum(signatures_data, target_sig), cp.maximum(signatures_data, target_sig)), 0)
+              max_comp_lengths = cp.maximum(cp.repeat(cp.sum(target_sig != 0), signatures_data.shape[0]), cp.sum(signatures_data != 0, axis=1))
+
+              results = cp.divide(cp.sum(ratios, axis=1), max_comp_lengths)
+
+            elif search_matching_type == 'distances':
+
+              distances = cp.power(cp.sum(cp.power(cp.abs(signatures_data - target_sig), distances_norm_order), axis=1), 1 / distances_norm_order)
+
+              results = cp.max(distances) - distances
+
+            unique_means = cp.unique(results)
+            sorted_means = cp.sort(unique_means)[::-1]
+
+            filtered_means = sorted_means[(sorted_means >= lower_threshold) & (sorted_means <= upper_threshold)][:filter_size]
+
+            filtered_idxs = cp.where(cp.in1d(results, filtered_means))[0]
+
+            all_filtered_means.extend(results[cp.in1d(results, filtered_means)].tolist())
+
+            all_filtered_idxs.extend(filtered_idxs.tolist())
+
+            filtered_tvs = [tv_idx] * filtered_idxs.shape[0]
+
+            all_filtered_tvs.extend(filtered_tvs)
+
+            tv_idx += 1
+
+          filtered_results = sorted(zip(all_filtered_means, all_filtered_idxs, all_filtered_tvs), key=lambda x: x[0], reverse=True)[:filter_size]
+
+          #=======================================================
+
+          print('Done!')
+          print('-' * 70)
+          print('Max match ratio:', filtered_results[0][0])
+          print('Max match transpose value:', filtered_results[0][2])
+          print('Max match signature index:', filtered_results[0][1])
+          print('Max match file name:', signatures_file_names[filtered_results[0][1]])
+          print('-' * 70)
+          print('Copying max ratios MIDIs...')
+
+          for fr in filtered_results:
+
+            max_ratio_index = fr[1]
+
+            ffn = signatures_file_names[fr[1]]
+            ffn_idx = [y[0] for y in LAMD_files_list].index(ffn)
+
+            ff = LAMD_files_list[ffn_idx][1]
+
+            #=======================================================
+
+            dir_str = str(fn1)
+            copy_path = '/content/Output-MIDI-Dataset/'+dir_str
+            if not os.path.exists(copy_path):
+                os.mkdir(copy_path)
+
+            fff = str(fr[0] * 100) + '_' + str(fr[2]) + '_' + ffn + '.mid'
+
+            shutil.copy2(ff, copy_path+'/'+fff)
+
+          shutil.copy2(f, copy_path+'/'+fn)
+
+          #======================================================='''
+          print('Done!')
+          print('=' * 70)
+
+          #=======================================================
+
+          # Processed files counter
+          files_count += 1
+
+    except KeyboardInterrupt:
+        print('Quitting...')
+        print('Total number of processed MIDI files', files_count)
+        print('=' * 70)
+        break
+
+    except Exception as ex:
+        print('WARNING !!!')
+        print('=' * 70)
+        print('Bad file:', f)
+        print('Error detected:', ex)
+        print('=' * 70)
+        continue
+
+    print('Total number of processed MIDI files', files_count)
+    print('=' * 70)
+
+else:
+  print('Could not find any MIDI files. Please check Dataset dir...')
+  print('=' * 70)
+
+"""# Congrats! You did it! :)"""