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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"gradient": {
"editing": false,
"id": "ac5a4cf0-d9d2-47b5-9633-b53f8d99a4d2",
"kernelId": ""
},
"id": "SiTIpPjArIyr"
},
"source": [
"# Los Angeles MIDI Dataset Metadata Maker (ver. 3.0)\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 2023\n",
"\n",
"***"
]
},
{
"cell_type": "markdown",
"metadata": {
"gradient": {
"editing": false,
"id": "fa0a611c-1803-42ae-bdf6-a49b5a4e781b",
"kernelId": ""
},
"id": "gOd93yV0sGd2"
},
"source": [
"# (SETUP ENVIRONMENT)"
]
},
{
"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 https://github.com/asigalov61/tegridy-tools\n",
"!pip install tqdm"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"gradient": {
"editing": false,
"id": "b8207b76-9514-4c07-95db-95a4742e52c5",
"kernelId": ""
},
"id": "z7n9vnKmug1J"
},
"outputs": [],
"source": [
"#@title Import all needed modules\n",
"\n",
"print('Loading needed modules. Please wait...')\n",
"import os\n",
"\n",
"import math\n",
"import statistics\n",
"import random\n",
"from collections import Counter\n",
"import pickle\n",
"\n",
"from tqdm import tqdm\n",
"\n",
"if not os.path.exists('/content/Dataset'):\n",
" os.makedirs('/content/Dataset')\n",
"\n",
"print('Loading TMIDIX module...')\n",
"os.chdir('/content/tegridy-tools/tegridy-tools')\n",
"\n",
"import TMIDIX\n",
"\n",
"print('Done!')\n",
"\n",
"os.chdir('/content/')\n",
"print('Enjoy! :)')"
]
},
{
"cell_type": "markdown",
"metadata": {
"gradient": {
"editing": false,
"id": "20b8698a-0b4e-4fdb-ae49-24d063782e77",
"kernelId": ""
},
"id": "ObPxlEutsQBj"
},
"source": [
"# (DOWNLOAD SOURCE MIDI DATASET)"
]
},
{
"cell_type": "code",
"source": [
"#@title Download original LAKH MIDI Dataset\n",
"\n",
"%cd /content/Dataset/\n",
"\n",
"!wget 'http://hog.ee.columbia.edu/craffel/lmd/lmd_full.tar.gz'\n",
"!tar -xvf 'lmd_full.tar.gz'\n",
"!rm 'lmd_full.tar.gz'\n",
"\n",
"%cd /content/"
],
"metadata": {
"cellView": "form",
"id": "7aItlhq9cRxZ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "S69mWHAcn5Bg"
},
"outputs": [],
"source": [
"#@title Mount Google Drive\n",
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JwrqQeie08t0"
},
"source": [
"# (FILE LIST)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "DuVWtdDNcqKh"
},
"outputs": [],
"source": [
"#@title Save file list\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/Dataset\"\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",
"print('=' * 70)\n",
"\n",
"if filez == []:\n",
" print('Could not find any MIDI files. Please check Dataset dir...')\n",
" print('=' * 70)\n",
"\n",
"print('Randomizing file list...')\n",
"random.shuffle(filez)\n",
"\n",
"TMIDIX.Tegridy_Any_Pickle_File_Writer(filez, '/content/filez')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "qI_adhjojrJ9"
},
"outputs": [],
"source": [
"#@title Load file list\n",
"filez = TMIDIX.Tegridy_Any_Pickle_File_Reader('/content/filez')\n",
"print('Done!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FLxHvO-wlwfU"
},
"source": [
"# (PROCESS)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CeGo7CruaCJQ",
"cellView": "form"
},
"outputs": [],
"source": [
"#@title Process MIDIs with TMIDIX MIDI processor\n",
"\n",
"print('=' * 70)\n",
"print('TMIDIX MIDI Processor')\n",
"print('=' * 70)\n",
"print('Starting up...')\n",
"print('=' * 70)\n",
"\n",
"###########\n",
"\n",
"START_FILE_NUMBER = 0\n",
"LAST_SAVED_BATCH_COUNT = 0\n",
"\n",
"input_files_count = START_FILE_NUMBER\n",
"files_count = LAST_SAVED_BATCH_COUNT\n",
"\n",
"melody_chords_f = []\n",
"\n",
"stats = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"\n",
"print('Processing MIDI files. Please wait...')\n",
"print('=' * 70)\n",
"\n",
"for f in tqdm(filez[START_FILE_NUMBER:]):\n",
" try:\n",
" input_files_count += 1\n",
"\n",
" fn = os.path.basename(f)\n",
" fn1 = fn.split('.mid')[0]\n",
"\n",
" #=======================================================\n",
" # START PROCESSING\n",
" \n",
" opus = TMIDIX.midi2opus(open(f, 'rb').read())\n",
" \n",
" opus_events_matrix = []\n",
" \n",
" itrack0 = 1\n",
" \n",
" while itrack0 < len(opus):\n",
" for event in opus[itrack0]: \n",
" opus_events_matrix.append(event)\n",
" itrack0 += 1\n",
" \n",
" #=======================================================\n",
" \n",
" ms_score = TMIDIX.opus2score(TMIDIX.to_millisecs(opus))\n",
"\n",
" ms_events_matrix = []\n",
" \n",
" itrack1 = 1\n",
" \n",
" while itrack1 < len(ms_score):\n",
" for event in ms_score[itrack1]: \n",
" if event[0] == 'note':\n",
" ms_events_matrix.append(event)\n",
" itrack1 += 1\n",
"\n",
" ms_events_matrix.sort(key=lambda x: x[1])\n",
" \n",
" #=======================================================\n",
"\n",
" # Convering MIDI to score with MIDI.py module\n",
" score = TMIDIX.opus2score(opus)\n",
"\n",
" # INSTRUMENTS CONVERSION CYCLE\n",
"\n",
" events_matrix = []\n",
" full_events_matrix = []\n",
" \n",
" itrack = 1\n",
" patches = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"\n",
" while itrack < len(score):\n",
" for event in score[itrack]: \n",
" if event[0] == 'note' or event[0] == 'patch_change':\n",
" events_matrix.append(event)\n",
" full_events_matrix.append(event)\n",
" itrack += 1\n",
" \n",
" full_events_matrix.sort(key=lambda x: x[1])\n",
" events_matrix.sort(key=lambda x: x[1])\n",
" \n",
" events_matrix1 = []\n",
"\n",
" for event in events_matrix:\n",
" if event[0] == 'patch_change':\n",
" patches[event[2]] = event[3]\n",
"\n",
" if event[0] == 'note':\n",
" event.extend([patches[event[3]]])\n",
" events_matrix1.append(event)\n",
"\n",
" if len(events_matrix1) > 32: \n",
" \n",
" events_matrix1.sort(key=lambda x: x[1])\n",
"\n",
" for e in events_matrix1:\n",
" if e[0] == 'note':\n",
" if e[3] == 9:\n",
" e[4] = ((abs(e[4]) % 128) + 128)\n",
" else:\n",
" e[4] = (abs(e[4]) % 128)\n",
"\n",
" pitches_counts = [[y[0],y[1]] for y in Counter([y[4] for y in events_matrix1]).most_common()]\n",
" pitches_counts.sort(key=lambda x: x[0], reverse=True)\n",
" \n",
" patches = sorted([y[6] for y in events_matrix1])\n",
" patches_counts = [[y[0], y[1]] for y in Counter(patches).most_common()]\n",
" patches_counts.sort(key = lambda x: x[0])\n",
" \n",
" midi_patches = sorted(list(set([y[3] for y in events_matrix if y[0] == 'patch_change'])))\n",
" if len(midi_patches) == 0:\n",
" midi_patches = [0]\n",
" \n",
" times = []\n",
" pt = ms_events_matrix[0][1]\n",
" start = True\n",
" for e in ms_events_matrix:\n",
" if (e[1]-pt) != 0 or start == True:\n",
" times.append((e[1]-pt))\n",
" start = False\n",
" pt = e[1]\n",
" \n",
" times_sum = min(10000000, sum(times))\n",
" \n",
" durs = [e[2] for e in ms_events_matrix]\n",
" vels = [e[5] for e in ms_events_matrix]\n",
" \n",
" avg_time = int(sum(times) / len(times))\n",
" avg_dur = int(sum(durs) / len(durs))\n",
" avg_vel = int(sum(vels) / len(vels))\n",
" \n",
" mode_time = statistics.mode(times)\n",
" mode_dur = statistics.mode(durs)\n",
" mode_vel = statistics.mode(vels)\n",
" \n",
" median_time = int(statistics.median(times))\n",
" median_dur = int(statistics.median(durs))\n",
" median_vel = int(statistics.median(vels))\n",
" \n",
" text_events_list = ['text_event', \n",
" 'text_event_08', \n",
" 'text_event_09', \n",
" 'text_event_0a', \n",
" 'text_event_0b', \n",
" 'text_event_0c',\n",
" 'text_event_0d',\n",
" 'text_event_0e',\n",
" 'text_event_0f']\n",
" \n",
" text_events_count = len([e for e in full_events_matrix if e[0] in text_events_list])\n",
" lyric_events_count = len([e for e in full_events_matrix if e[0] == 'lyric'])\n",
" \n",
" chords = []\n",
" pe = ms_events_matrix[0]\n",
" cho = []\n",
" for e in ms_events_matrix:\n",
" if (e[1] - pe[1]) == 0:\n",
" if e[3] != 9:\n",
" if (e[4] % 12) not in cho:\n",
" cho.append(e[4] % 12)\n",
" else:\n",
" if len(cho) > 0:\n",
" chords.append(sorted(cho))\n",
" cho = []\n",
" if e[3] != 9:\n",
" if (e[4] % 12) not in cho:\n",
" cho.append(e[4] % 12)\n",
"\n",
" pe = e\n",
" \n",
" if len(cho) > 0:\n",
" chords.append(sorted(cho))\n",
"\n",
" ms_chords_counts = sorted([[list(key), val] for key,val in Counter([tuple(c) for c in chords if len(c) > 1]).most_common()], reverse=True, key = lambda x: x[1])\n",
" if len(ms_chords_counts) == 0:\n",
" ms_chords_counts = [[[0, 0], 0]]\n",
" \n",
" total_number_of_chords = len(set([y[1] for y in events_matrix1]))\n",
" \n",
" tempo_change_count = len([f for f in full_events_matrix if f[0] == 'set_tempo'])\n",
" \n",
" thirty_second_note = [e for e in events_matrix1][32]\n",
" thirty_second_note_idx = full_events_matrix.index(thirty_second_note)\n",
"\n",
" data = []\n",
" data.append(['total_number_of_tracks', itrack])\n",
" data.append(['total_number_of_opus_midi_events', len(opus_events_matrix)])\n",
" data.append(['total_number_of_score_midi_events', len(full_events_matrix)])\n",
" data.append(['average_median_mode_time_ms', [avg_time, median_time, mode_time]])\n",
" data.append(['average_median_mode_dur_ms', [avg_dur, median_dur, mode_dur]])\n",
" data.append(['average_median_mode_vel', [avg_vel, median_vel, mode_vel]])\n",
" data.append(['total_number_of_chords', total_number_of_chords])\n",
" data.append(['total_number_of_chords_ms', len(times)])\n",
" data.append(['ms_chords_counts', ms_chords_counts])\n",
" data.append(['pitches_times_sum_ms', times_sum])\n",
" data.append(['total_pitches_counts', pitches_counts])\n",
" data.append(['midi_patches', midi_patches])\n",
" data.append(['total_patches_counts', patches_counts])\n",
" data.append(['tempo_change_count', tempo_change_count])\n",
" data.append(['text_events_count', text_events_count])\n",
" data.append(['lyric_events_count', lyric_events_count])\n",
" data.append(['midi_ticks', score[0]])\n",
" data.extend(full_events_matrix[:thirty_second_note_idx])\n",
" data.append(full_events_matrix[-1])\n",
" \n",
" melody_chords_f.append([fn1, data])\n",
"\n",
" #=======================================================\n",
"\n",
" # Processed files counter\n",
" files_count += 1\n",
"\n",
" # Saving every 5000 processed files\n",
" if files_count % 10000 == 0:\n",
" print('SAVING !!!')\n",
" print('=' * 70)\n",
" print('Saving processed files...')\n",
" print('=' * 70)\n",
" print('Processed so far:', files_count, 'out of', input_files_count, '===', files_count / input_files_count, 'good files ratio')\n",
" print('=' * 70)\n",
" count = str(files_count)\n",
" TMIDIX.Tegridy_Any_Pickle_File_Writer(melody_chords_f, '/content/drive/MyDrive/LAMD_META_DATA_'+count)\n",
" melody_chords_f = []\n",
" print('=' * 70)\n",
"\n",
" except KeyboardInterrupt:\n",
" print('Saving current progress and quitting...')\n",
" break \n",
"\n",
" except Exception as ex:\n",
" print('WARNING !!!')\n",
" print('=' * 70)\n",
" print('Bad MIDI:', f)\n",
" print('Error detected:', ex)\n",
" print('=' * 70)\n",
" continue\n",
"\n",
"# Saving last processed files...\n",
"print('=' * 70)\n",
"print('Saving processed files...')\n",
"print('=' * 70)\n",
"print('Processed so far:', files_count, 'out of', input_files_count, '===', files_count / input_files_count, 'good files ratio')\n",
"print('=' * 70)\n",
"count = str(files_count)\n",
"TMIDIX.Tegridy_Any_Pickle_File_Writer(melody_chords_f, '/content/drive/MyDrive/LAMD_META_DATA_'+count)\n",
"\n",
"# Displaying resulting processing stats...\n",
"print('=' * 70)\n",
"print('Done!') \n",
"print('=' * 70)\n",
"\n",
"print('Resulting Stats:')\n",
"print('=' * 70)\n",
"print('Total good processed MIDI files:', files_count)\n",
"print('=' * 70)"
]
},
{
"cell_type": "markdown",
"source": [
"# (BUILD FINAL METADATA FILE)"
],
"metadata": {
"id": "rr1IA9GwAybn"
}
},
{
"cell_type": "code",
"source": [
"#@title Build final metadata file\n",
"full_path_to_metadata_pickle_files = \"/content/drive/MyDrive\" #@param {type:\"string\"}\n",
"\n",
"print('=' * 70)\n",
"print('Los Angeles MIDI Dataset Metadata File Builder')\n",
"print('=' * 70)\n",
"print('Searching for files...')\n",
"\n",
"filez = list()\n",
"for (dirpath, dirnames, filenames) in os.walk(full_path_to_metadata_pickle_files):\n",
" filez += [os.path.join(dirpath, file) for file in filenames if file.split('.')[-1] == 'pickle']\n",
"print('=' * 70)\n",
"\n",
"filez.sort()\n",
"\n",
"print('Loading metadata files... Please wait...')\n",
"print('=' * 70)\n",
"\n",
"metadata = []\n",
"\n",
"for f in tqdm(filez):\n",
"\n",
" metadata.extend(pickle.load(open(f, 'rb')))\n",
" print('Done!')\n",
" print('=' * 70)\n",
" print('Loaded file:', f)\n",
" print('=' * 70)\n",
" \n",
"print('Done!')\n",
"print('=' * 70)\n",
"print('Randomizing metadata entries order...')\n",
"random.shuffle(metadata)\n",
"print('=' * 70)\n",
"print('Writing final metadata pickle file...Please wait...')\n",
"\n",
"with open('/content/LAMDa_META_DATA.pickle', 'wb') as handle:\n",
" pickle.dump(metadata, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
"\n",
"print('=' * 70)\n",
"print('Done!')\n",
"print('=' * 70)"
],
"metadata": {
"cellView": "form",
"id": "_uGS9wJGBoEF"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@title Zip final metadata file\n",
"print('=' * 70)\n",
"print('Zipping... Please wait...')\n",
"print('=' * 70)\n",
"!zip LAMDa_META_DATA.zip LAMDa_META_DATA.pickle\n",
"print('=' * 70)\n",
"print('Done!')\n",
"print('=' * 70)"
],
"metadata": {
"cellView": "form",
"id": "tnEgu3uYEX0a"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "YzCMd94Tu_gz"
},
"source": [
"# Congrats! You did it! :)"
]
}
],
"metadata": {
"colab": {
"machine_shape": "hm",
"private_outputs": true,
"provenance": []
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"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"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |