{ "cells": [ { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2916657\n" ] } ], "source": [ "# can we intersect this with the discogs subset metas\n", "from suno_utils.utils.text import read_jsonl, write_jsonl\n", "\n", "discogs_subset_metas_filepath = \"/home/christian/code/christian/metadata/v4/discogs_subset_metas_raw.jsonl\"\n", "discogs_subset_metas = read_jsonl(discogs_subset_metas_filepath)\n", "print(len(discogs_subset_metas))\n" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2090009\n" ] } ], "source": [ "genius_metas_filepath = \"/home/christian/code/christian/metadata/genius_hq_metas.jsonl\"\n", "genius_metas = read_jsonl(genius_metas_filepath)\n", "print(len(genius_metas))" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2101712\n" ] } ], "source": [ "youtube_music_metas_filepath = \"/home/christian/code/christian/metadata/youtube_music_metas.jsonl\"\n", "youtube_music_metas = read_jsonl(youtube_music_metas_filepath)\n", "print(len(youtube_music_metas))\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# can we intersect this with the discogs subset metas\n", "from suno_utils.utils.text import read_jsonl, write_jsonl" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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chart_weekcurrent_weektitleperformerlast_weekpeak_poswks_on_chartsource
348662024-05-0467The Dark Side Of The MoonPink Floyd0.01990Billboard 200
345532024-04-20154The Dark Side Of The MoonPink Floyd0.01989Billboard 200
315542024-01-06155The Dark Side Of The MoonPink Floyd114.01988Billboard 200
313132023-12-30114The Dark Side Of The MoonPink Floyd149.01987Billboard 200
311482023-12-23149The Dark Side Of The MoonPink Floyd0.01986Billboard 200
307922023-12-09193The Dark Side Of The MoonPink Floyd0.01985Billboard 200
299942023-11-11195The Dark Side Of The MoonPink Floyd145.01984Billboard 200
297442023-11-04145The Dark Side Of The MoonPink Floyd54.01983Billboard 200
294532023-10-2854The Dark Side Of The MoonPink Floyd0.01982Billboard 200
245902023-05-06191The Dark Side Of The MoonPink Floyd200.01981Billboard 200
\n", "
" ], "text/plain": [ " chart_week current_week title performer \\\n", "34866 2024-05-04 67 The Dark Side Of The Moon Pink Floyd \n", "34553 2024-04-20 154 The Dark Side Of The Moon Pink Floyd \n", "31554 2024-01-06 155 The Dark Side Of The Moon Pink Floyd \n", "31313 2023-12-30 114 The Dark Side Of The Moon Pink Floyd \n", "31148 2023-12-23 149 The Dark Side Of The Moon Pink Floyd \n", "30792 2023-12-09 193 The Dark Side Of The Moon Pink Floyd \n", "29994 2023-11-11 195 The Dark Side Of The Moon Pink Floyd \n", "29744 2023-11-04 145 The Dark Side Of The Moon Pink Floyd \n", "29453 2023-10-28 54 The Dark Side Of The Moon Pink Floyd \n", "24590 2023-05-06 191 The Dark Side Of The Moon Pink Floyd \n", "\n", " last_week peak_pos wks_on_chart source \n", "34866 0.0 1 990 Billboard 200 \n", "34553 0.0 1 989 Billboard 200 \n", "31554 114.0 1 988 Billboard 200 \n", "31313 149.0 1 987 Billboard 200 \n", "31148 0.0 1 986 Billboard 200 \n", "30792 0.0 1 985 Billboard 200 \n", "29994 145.0 1 984 Billboard 200 \n", "29744 54.0 1 983 Billboard 200 \n", "29453 0.0 1 982 Billboard 200 \n", "24590 200.0 1 981 Billboard 200 " ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "billboard_filepaths = [\n", " (\"/home/christian/code/christian/metadata/popularity/billboard-200-current.csv\", \"Billboard 200\"),\n", " (\"/home/christian/code/christian/metadata/popularity/hot-100-current.csv\", \"Hot 100\"),\n", "]\n", "\n", "billboard_dfs = []\n", "for filepath, name in billboard_filepaths:\n", " df = pd.read_csv(filepath)\n", " df[\"source\"] = name\n", " billboard_dfs.append(df)\n", "\n", "billboard_df = pd.concat(billboard_dfs)\n", "# sort by wks_on_chart\n", "billboard_df = billboard_df.sort_values(by=\"wks_on_chart\", ascending=False)\n", "billboard_df.head(10)\n" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Unique artists: 16932\n", "Unique songs: 56862\n" ] } ], "source": [ "\n", "# lets count unique artists (\"performer\")\n", "unique_artists = billboard_df[\"performer\"].nunique()\n", "print(f\"Unique artists: {unique_artists}\")\n", "\n", "# lets count unique songs (\"song\")\n", "unique_songs = billboard_df[\"title\"].nunique()\n", "print(f\"Unique songs: {unique_songs}\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'id': 'UldJ9AnalEY', 'title': 'Ee Janumave Ahaa', 'artists': [{'name': 'Kailash Kher', 'id': 'UCNeP2HGGg3xquVicp-lUbsg'}], 'album': 'Oggarane', 's3_filepath': 's3://suno-data/shared/nfdg/UldJ9AnalEY/artists/songs/audio/UldJ9AnalEY/audio.webm', 'keywords': ['Prakash Rai', 'Prakash Raj', 'Sneha', 'Oggarane', 'Video songs', '2013 top songs', '2014 top songs', 'junglee music', 'Times music', 'love is cooking', 'Sneha (Award Winner)', 'Ilaiyaraaja (Composer)'], 'lyrics': 'ಈ ಜನುಮವೇ ಆಹಾ! ದೊರಕಿದೆ ರುಚಿ ಸವಿಯಲು\\nಆಹಾ!\\nಆಹಾ!\\nಈ ಜಗವಿದೆ ನವರಸಗಳ ಉಣ ಬಡಿಸಲು\\nಬಿಸಿ ಬಿಸಿ ಇದೆ\\nಬಿಸಿ ಬಿಸಿ ಇದೆ ನಳಪಾಕ ದಿನ ದಿನ ನೂತನ\\nಈ ಬಿಸಿಯು ಆರುವ ಮೊದಲೇ ಎಲ್ಲರ ಜೊತೆಯೇ ಮೆಲ್ಲುವ ಕಲೆಯೇ ಜೀವನ\\n\\nಜನುಮವೇ\\nಈ ಜನುಮವೇ ಆಹಾ! ದೊರಕಿದೆ ರುಚಿ ಸವಿಯಲು\\nಈ ಜನುಮವೇ ಆಹಾ! ದೊರಕಿದೆ ರುಚಿ ಸವಿಯಲು\\nಈ ಜಗವಿದೆ ನವರಸಗಳ ಉಣ ಬಡಿಸಲು\\n\\nಉಚ್ಚೆಳ್ಳು ಗಂಜಿ ಉಪ್ಪಿನಕಾಯಿ ಹೇಗೆ ಮಾಡಿವೆ ಗೆಳೆತನ\\nತುಪ್ಪಕ್ಕೆ ಬಿದ್ದ ಹೋಳಿಗೆಗೀಗ ಬೇಕು ಮಾವಿನ ರಸಾಯನ\\nಹುಬ್ಬಳ್ಳಿ ರೊಟ್ಟಿಕಾಳು\\nತಿಂದವ್ನೆ ಗಟ್ಟಿಯಾಳು\\nಮಂಡ್ಯದ ಮುದ್ದೆ ಬಾಡು\\nಉಂಡು ನೀ ನಿದ್ದೆ ಮಾಡು\\nಅಂಕೋಲ ಕೋಳಿ ಸಾರು\\nಮೂಗಲ್ಲು ನೀರು ಜೋರು\\nಕುಂದಾಪುರ ಕಾನೆ ಮೀನು\\nಇನ್ನೆಲ್ಲೂ ಕಾಣೆ ನೀನು\\nಹಂಚಿ ಉಂಡರೆ ಅಧಿಕವಾಗುವ ಸರಳ ಸ್ನೇಹದ ವಿರಳ ರುಚಿಯಿದು\\n\\nಜನುಮವೇ\\nಆಹಾಹಾಆಹಾಹಾಹಾಹಾಆಹಾಹಾ\\nಈ ಜಗವಿದೆ\\nಈ ಜನುಮವೇ ಆಹಾ! ದೊರಕಿದೆ ರುಚಿ ಸವಿಯಲು\\n\\nದುಡಿವ ಜನರು ಬಿಡುವಿನಲ್ಲಿ ತೆರೆವ ಬುತ್ತಿಯೇ ಬಲು ರುಚಿ\\nಬೆರಳು ಚೀಪಿ ಉಂಡ ಮನಕೆ ಬದುಕಿನಲ್ಲಿಯು ಅಭಿರುಚಿ\\nಮಾವಿನ್ಕಾಯಿ ಚಿತ್ರಾನ್ನಾನ್ನ\\nಮೈಸೂರಲ್ ತಿಂದೋನು ಜಾಣ\\nಮನೆಗೆಷ್ಟೇ ನೆಂಟ್ರು ಬರಲಿ\\nಮಲ್ನಾಡಲ್ ತಾಜ ತಂಬ್ಲಿ\\nದಾವಣ್ಗೆರೆ ಬೆಣ್ಣೆ ದೋಸೆ\\nಬೀರೂರಲ್ ತಿನ್ನೋ ಆಸೆ\\nಪಟ್ಟೇಗಾರ್ ಬಿರಿಯಾನಿಗೆ\\nಬಾ ಬಾರೋ ಬೆಳಗಾವಿಗೆ\\nಓಲೆಯ ಮುಂದೆ ಉರಿವ ಅಮ್ಮನ ಮಮತೆಯಿಂದಲೇ ಕಲಿತ ಫಲವಿದು\\n\\nಜನುಮವೇ ಆಹಾ! ದೊರಕಿದೆ ರುಚಿ ಸವಿಯಲು\\nಬಿಸಿ ಬಿಸಿ ಇದೆ\\nಬಿಸಿ ಬಿಸಿ ಇದೆ ನಳಪಾಕ ದಿನ ದಿನ ನೂತನ\\nಈ ಬಿಸಿಯು ಆರುವ ಮೊದಲೇ ಎಲ್ಲರ ಜೊತೆಯೇ ಮೆಲ್ಲುವ ಕಲೆಯೇ ಜೀವನ\\nಜನುಮವೇ ಆಹಾ! ದೊರಕಿದೆ ರುಚಿ ಸವಿಯಲು\\nಈ ಜನುಮವೇ ಆಹಾ! ದೊರಕಿದೆ ರುಚಿ ಸವಿಯಲು, ಸವಿಯಲು, ಸವಿಯಲು', 'duration_s': 216, 'views': 860294, 'playlist_ids': ['PLzmeBYOsjwtm6wlMu99kWX9Jpr6ahZAjh', 'PLbgY1FtaABEJNvDGX2YZenXpP6XEi2pmj', 'PLbCAObZ2pUfIODCidDHfjMD_ATVBcyVEy']}\n" ] } ], "source": [ "print(discogs_subset_metas[0])" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'billboard_df' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[44], line 39\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m matches\n\u001b[1;32m 38\u001b[0m \u001b[38;5;66;03m# first collopse billboard df to remove duplicated artist and title\u001b[39;00m\n\u001b[0;32m---> 39\u001b[0m billboard_df \u001b[38;5;241m=\u001b[39m \u001b[43mbillboard_df\u001b[49m\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtitle\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mperformer\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;241m.\u001b[39magg({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwks_on_chart\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfirst\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msource\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfirst\u001b[39m\u001b[38;5;124m'\u001b[39m})\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[1;32m 40\u001b[0m \u001b[38;5;66;03m# convert the billboard df to a list of dicts\u001b[39;00m\n\u001b[1;32m 41\u001b[0m billboard_df_list \u001b[38;5;241m=\u001b[39m billboard_df\u001b[38;5;241m.\u001b[39mto_dict(orient\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrecords\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "\u001b[0;31mNameError\u001b[0m: name 'billboard_df' is not defined" ] } ], "source": [ "from rapidfuzz import process, fuzz\n", "from tqdm import tqdm\n", "\n", "def create_song_lookups(metas):\n", " combined = {}\n", " for meta in metas:\n", " # Skip if title or artists is missing/empty\n", " if not meta.get('title') or not meta.get('artists'):\n", " continue\n", " # Skip if artists is empty list\n", " if len(meta['artists']) == 0:\n", " continue\n", " # Create combined string and add to lookup\n", " combined[f\"{str(meta['title'])} {str(meta['artists'][0]['name'])}\".lower().strip()] = meta\n", " return {'combined': combined}\n", "\n", "def find_matches(chart_songs, library_songs, threshold=85):\n", " library_lookups = create_song_lookups(library_songs)\n", " matches = []\n", " \n", " for chart_song in tqdm(chart_songs):\n", " search_string = f\"{str(chart_song['title'])} {str(chart_song['performer'])}\".lower().strip()\n", " combined_matches = process.extract(search_string,\n", " library_lookups['combined'].keys(),\n", " scorer=fuzz.ratio,\n", " score_cutoff=threshold,\n", " limit=3)\n", " \n", " for match_str, score, _ in combined_matches:\n", " matches.append({\n", " 'chart_song': chart_song,\n", " 'library_song': library_lookups['combined'][match_str],\n", " 'match_score': score\n", " })\n", " \n", " return matches\n", "\n", "# first collopse billboard df to remove duplicated artist and title\n", "billboard_df = billboard_df.groupby(['title', 'performer']).agg({'wks_on_chart': 'first', 'source': 'first'}).reset_index()\n", "# convert the billboard df to a list of dicts\n", "billboard_df_list = billboard_df.to_dict(orient=\"records\")\n", "print(len(billboard_df_list))\n", "\n", "billboard_metas = []\n", "matches = find_matches(billboard_df_list, discogs_subset_metas)\n", "print(len(matches))\n", "for match in matches:\n", " print(match)\n", " billboard_meta = {\n", " \"id\": match[\"library_song\"][\"id\"],\n", " \"title\": match[\"library_song\"][\"title\"],\n", " \"artist\": match[\"library_song\"][\"artists\"][0][\"name\"],\n", " \"source\": match[\"chart_song\"][\"source\"],\n", " \"match_score\": match[\"match_score\"]\n", " }\n", " billboard_metas.append(billboard_meta)\n" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "from rapidfuzz import process, fuzz\n", "from rapidfuzz.process import cdist\n", "from multiprocessing import Pool\n", "import numpy as np\n", "from tqdm import tqdm\n", "\n", "def create_song_lookups(metas, dataset=\"discogs\"):\n", " song_lookups = {}\n", " if dataset == \"discogs\":\n", " song_lookups['combined'] = {\n", " f\"{meta['title']} - {meta['artists'][0]['name']}\".lower().strip(): meta \n", " for meta in metas \n", " if meta.get('title') and meta.get('artists') and meta['artists']\n", " }\n", " elif dataset == \"youtube\":\n", " song_lookups['combined'] = {\n", " f\"{meta['title']} - {meta['artists']}\".lower().strip(): meta \n", " for meta in metas \n", " if meta.get('title') and meta.get('artists') and meta['artists']\n", " }\n", " elif dataset == \"genius\":\n", " song_lookups['combined'] = {\n", " f\"{meta['youtube_title']}\".lower().strip(): meta \n", " for meta in metas \n", " if meta.get('youtube_title')\n", " }\n", "\n", " # remove any songs that have \"cover\" in the title\n", " song_lookups['combined'] = {k: v for k, v in song_lookups['combined'].items() if \"cover\" not in k.lower()}\n", "\n", " return song_lookups\n", "\n", "def process_chunk(args):\n", " chunk, library_keys, threshold = args\n", " return process.extract(chunk, library_keys, scorer=fuzz.ratio, \n", " score_cutoff=threshold, limit=3)\n", "\n", "def find_matches_parallel(chart_songs, library_songs, threshold=85, dataset=\"genius\"):\n", " library_lookups = create_song_lookups(library_songs)\n", " library_keys = list(library_lookups['combined'].keys())\n", " \n", " search_strings = [f\"{song['title']} - {song['performer']}\".lower().strip() \n", " for song in chart_songs]\n", "\n", " chunks = [(s, library_keys, threshold) for s in search_strings]\n", " \n", " matches = []\n", " with Pool() as pool:\n", " for i, combined_matches in enumerate(tqdm(\n", " pool.imap(process_chunk, chunks), \n", " total=len(chunks)\n", " )):\n", " for match_str, score, _ in combined_matches:\n", " matches.append({\n", " 'chart_song': chart_songs[i],\n", " 'library_song': library_lookups['combined'][match_str],\n", " 'match_score': score\n", " })\n", " return matches\n", "\n", "def find_matches_vectorized(chart_songs, library_songs, threshold=90, dataset=\"discogs\"):\n", " library_lookups = create_song_lookups(library_songs, dataset=dataset)\n", " library_keys = list(library_lookups['combined'].keys())\n", " \n", " search_strings = [f\"{song['title']} - {song['performer']}\".lower().strip() \n", " for song in chart_songs]\n", "\n", " distances = cdist(search_strings, library_keys, scorer=fuzz.ratio, workers=128)\n", " \n", " matches = []\n", " for i, row in enumerate(distances):\n", " match_indices = np.where(row >= threshold)[0][:3]\n", " \n", " for idx in match_indices:\n", " match_str = library_keys[idx]\n", " matches.append({\n", " 'chart_song': chart_songs[i],\n", " 'library_song': library_lookups['combined'][match_str],\n", " 'match_score': row[idx]\n", " })\n", " \n", " return matches\n", "\n", "# Usage example:\n", "def process_billboard_matches(billboard_df, subset_metas, method='vectorized', dataset=\"discogs\"): \n", " billboard_df_list = billboard_df.to_dict(orient=\"records\")\n", " \n", " if method == 'parallel':\n", " matches = find_matches_parallel(billboard_df_list, subset_metas, dataset=dataset)\n", " else:\n", " matches = find_matches_vectorized(billboard_df_list, subset_metas, dataset=dataset)\n", " \n", " billboard_metas = []\n", " for match in matches:\n", " source = match[\"chart_song\"][\"source\"]\n", " source_artist = match[\"chart_song\"][\"performer\"]\n", " source_title = match[\"chart_song\"][\"title\"]\n", " if isinstance(source, list):\n", " source = source[0]\n", " elif isinstance(source, np.ndarray):\n", " source = source[0]\n", " billboard_meta = {\n", " \"id\": match[\"library_song\"][\"id\"],\n", " \"title\": match[\"library_song\"].get(\"title\", \"\"),\n", " \"artist\": match[\"library_song\"].get(\"artists\", [{}]),\n", " \"youtube_title\": match[\"library_song\"].get(\"youtube_title\", \"\"),\n", " \"source\": source,\n", " \"match_score\": match[\"match_score\"],\n", " \"source_artist\": source_artist,\n", " \"source_title\": source_title,\n", " }\n", "\n", "\n", " # add tags\n", " billboard_meta[\"tags\"] = [\"Billboard\"]\n", " #billboard_meta[\"tags\"].append(source)\n", " if \"chart_dates\" in match[\"chart_song\"]:\n", " chart_date_tag = f\"{source}\"\n", " for chart_date in match[\"chart_song\"][\"chart_dates\"]:\n", " chart_date_tag += f\" {chart_date}\"\n", " billboard_meta[\"tags\"].append(chart_date_tag)\n", " if \"source\" in match[\"chart_song\"]:\n", " #for source in match[\"chart_song\"][\"source\"]:\n", " billboard_meta[\"tags\"].append(source)\n", " billboard_metas.append(billboard_meta)\n", " \n", " return billboard_metas" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15\n" ] } ], "source": [ "billboard_df = billboard_df.groupby(['title', 'performer']).agg({\n", " 'wks_on_chart': 'first', \n", " 'source': 'first'\n", "}).reset_index()\n", "billboard_metas = process_billboard_matches(billboard_df, discogs_subset_metas[:1000])\n", "print(len(billboard_metas))\n" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [], "source": [ "for billboard_meta in billboard_metas:\n", " billboard_meta[\"match_score\"] = float(billboard_meta[\"match_score\"])\n" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [], "source": [ "write_jsonl(billboard_metas, \"/home/christian/code/christian/metadata/popularity/discogs_subset_billboard_metas.jsonl\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Empty DataFrame\n", "Columns: [title, performer, year, source]\n", "Index: []\n" ] }, { "data": { "text/html": [ "
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titleperformersource
264Blinding LightsThe Weeknd[Gen Z, TikTok, Workout, Viral, Audiophile]
164Bad GuyBillie Eilish[Workout, Reddit, Viral, Audiophile]
1717Something Just Like ThisThe Chainsmokers & Coldplay[Reddit, Viral, Duet, Audiophile]
1567Rolling in the DeepAdele[Workout, Reddit, Viral, Audiophile]
917In My FeelingsDrake[Gen Z, Meme Song, Viral, Audiophile]
747God's PlanDrake[Gen Z, Reddit, Viral, Audiophile]
1196Midnight CityM83[Gen Z, TikTok, Audiophile]
549DynamiteBTS[Gen Z, TikTok, Viral]
1137Lucid DreamsJuice WRLD[Gen Z, Viral, Audiophile]
282BodyMegan Thee Stallion[Gen Z, TikTok, Viral]
\n", "
" ], "text/plain": [ " title performer \\\n", "264 Blinding Lights The Weeknd \n", "164 Bad Guy Billie Eilish \n", "1717 Something Just Like This The Chainsmokers & Coldplay \n", "1567 Rolling in the Deep Adele \n", "917 In My Feelings Drake \n", "747 God's Plan Drake \n", "1196 Midnight City M83 \n", "549 Dynamite BTS \n", "1137 Lucid Dreams Juice WRLD \n", "282 Body Megan Thee Stallion \n", "\n", " source \n", "264 [Gen Z, TikTok, Workout, Viral, Audiophile] \n", "164 [Workout, Reddit, Viral, Audiophile] \n", "1717 [Reddit, Viral, Duet, Audiophile] \n", "1567 [Workout, Reddit, Viral, Audiophile] \n", "917 [Gen Z, Meme Song, Viral, Audiophile] \n", "747 [Gen Z, Reddit, Viral, Audiophile] \n", "1196 [Gen Z, TikTok, Audiophile] \n", "549 [Gen Z, TikTok, Viral] \n", "1137 [Gen Z, Viral, Audiophile] \n", "282 [Gen Z, TikTok, Viral] " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "gpt_df = pd.read_csv(\"/home/christian/code/christian/metadata/popularity/chatgpt.csv\")\n", "\n", "# check for rows with NaN in the source column\n", "print(gpt_df[gpt_df[\"source\"].isna()])\n", "\n", "# count the number of songs with \"Audiophile\" in the Source column\n", "audiophile_count = gpt_df[gpt_df[\"source\"].str.contains(\"Audiophile\")].shape[0]\n", "\n", "# look for repeated rows with the same title and performer, take all the sources and merge them into a list\n", "gpt_df = gpt_df.groupby([\"title\", \"performer\"]).agg({\"source\": \"unique\"}).reset_index()\n", "\n", "# sort by the length of the sources\n", "gpt_df = gpt_df.sort_values(by=\"source\", key=lambda x: x.str.len(), ascending=False)\n", "\n", "\n", "gpt_df.head(10)\n" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1455\n" ] } ], "source": [ "# find matches\n", "gpt_metas = process_billboard_matches(gpt_df, discogs_subset_metas[:3_000_000])\n", "print(len(gpt_metas))\n", "\n", "for gpt_meta in gpt_metas:\n", " gpt_meta[\"match_score\"] = float(gpt_meta[\"match_score\"])\n", "\n", "write_jsonl(gpt_metas, \"/home/christian/code/christian/metadata/popularity/discogs_subset_gpt_metas.jsonl\")\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "169909\n", "id 6KbQ3uYMLKb5jDxLF7wYDD\n", "name Singende Bataillone 1. Teil\n", "artists ['Carl Woitschach']\n", "duration_ms 158648\n", "release_date 1928\n", "year 1928\n", "acousticness 0.995\n", "danceability 0.708\n", "energy 0.195\n", "instrumentalness 0.563\n", "liveness 0.151\n", "loudness -12.428\n", "speechiness 0.0506\n", "tempo 118.469\n", "valence 0.779\n", "mode 1\n", "key 10\n", "popularity 0\n", "explicit 0\n", "source Popularity\n", "chart_dates {0}\n", "title Singende Bataillone 1. Teil\n", "performer ['Carl Woitschach']\n", "Name: 0, dtype: object\n" ] } ], "source": [ "import pandas as pd\n", "# spotify data\n", "spotify_df = pd.read_csv(\"/home/christian/code/christian/metadata/popularity/spotify-data.csv\")\n", "spotify_df.sort_values(by=\"popularity\", ascending=False).head(10)\n", "\n", "# add source to the df\n", "spotify_df[\"source\"] = \"Popularity\"\n", "\n", "# add chart_dates to the df using the popularity column\n", "spotify_df[\"chart_dates\"] = spotify_df[\"popularity\"].apply(lambda x: set([str(x)]))\n", "\n", "# add title and performer to the df\n", "spotify_df[\"title\"] = spotify_df[\"name\"]\n", "spotify_df[\"performer\"] = spotify_df[\"artists\"]\n", "\n", "# can use for popularity values\n", "print(len(spotify_df))\n", "print(spotify_df.iloc[0])\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "performer ['Carl Woitschach']\n", "title Singende Bataillone 1. Teil\n", "source Popularity\n", "chart_dates {0}\n", "Name: 0, dtype: object" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spotify_df.iloc[0][[\"performer\", \"title\", \"source\", \"chart_dates\"]]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "ename": "", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", "\u001b[1;31mClick here for more info. \n", "\u001b[1;31mView Jupyter log for further details." ] } ], "source": [ "# find matches\n", "spotify_metas = process_billboard_matches(spotify_df, discogs_subset_metas)\n", "print(len(spotify_metas))\n", "\n", "for spotify_meta in spotify_metas:\n", " spotify_meta[\"match_score\"] = float(spotify_meta[\"match_score\"])\n", "\n", "write_jsonl(spotify_metas, \"/home/christian/code/christian/metadata/popularity/discogs_subset_spotify_metas.jsonl\")\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Billboard Hot Country Songs before merge: 151593, after merge: 12436\n", "Billboard Hot Dance/Electronic Songs before merge: 15500, after merge: 1499\n", "Billboard Hot R&B/Hip-Hop Songs before merge: 150990, after merge: 14361\n", "Billboard Hot 100 before merge: 315185, after merge: 27940\n", "Billboard Japan Hot 100 before merge: 38690, after merge: 10575\n", "Billboard Hot Latin Songs before merge: 77496, after merge: 6272\n", "Billboard Hot Pop Songs before merge: 54800, after merge: 3610\n", "Billboard Hot Rock Songs before merge: 24900, after merge: 2280\n", "Billboard YouTube Music Charts before merge: 9600, after merge: 864\n", "Total rows: 79837\n" ] }, { "data": { "text/html": [ "
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titleperformerchart_datessource
0\"Never More\" Quote The RavenStonewall Jackson{1969}Billboard Hot Country Songs
1\"You've Got\" The TouchAlabama{1987}Billboard Hot Country Songs
2'57 ChevroletBillie Jo Spears{1978}Billboard Hot Country Songs
3'Cause I Have YouWynn Stewart{1967}Billboard Hot Country Songs
4'Fore She Was MamaClay Walker{2006, 2007}Billboard Hot Country Songs
5'Gator HollowLefty Frizzell{1965}Billboard Hot Country Songs
6'Round HereSawyer Brown{1995, 1996}Billboard Hot Country Songs
7'Tater Raisin' ManDick Curless{1965}Billboard Hot Country Songs
8'Til A Tear Becomes A RoseKeith Whitley & Lorrie Morgan{1990}Billboard Hot Country Songs
9'Til A Tear Becomes A RoseLeon Everette{1985}Billboard Hot Country Songs
\n", "
" ], "text/plain": [ " title performer chart_dates \\\n", "0 \"Never More\" Quote The Raven Stonewall Jackson {1969} \n", "1 \"You've Got\" The Touch Alabama {1987} \n", "2 '57 Chevrolet Billie Jo Spears {1978} \n", "3 'Cause I Have You Wynn Stewart {1967} \n", "4 'Fore She Was Mama Clay Walker {2006, 2007} \n", "5 'Gator Hollow Lefty Frizzell {1965} \n", "6 'Round Here Sawyer Brown {1995, 1996} \n", "7 'Tater Raisin' Man Dick Curless {1965} \n", "8 'Til A Tear Becomes A Rose Keith Whitley & Lorrie Morgan {1990} \n", "9 'Til A Tear Becomes A Rose Leon Everette {1985} \n", "\n", " source \n", "0 Billboard Hot Country Songs \n", "1 Billboard Hot Country Songs \n", "2 Billboard Hot Country Songs \n", "3 Billboard Hot Country Songs \n", "4 Billboard Hot Country Songs \n", "5 Billboard Hot Country Songs \n", "6 Billboard Hot Country Songs \n", "7 Billboard Hot Country Songs \n", "8 Billboard Hot Country Songs \n", "9 Billboard Hot Country Songs " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "# other charts\n", "chart_filepaths = [\n", " (\"/home/christian/code/christian/metadata/popularity/All_Country_Songs_from_2011-04-09_to_2018-12-31.csv\", \"Billboard Hot Country Songs\"),\n", " (\"/home/christian/code/christian/metadata/popularity/All_Dance_Electronic_Songs_from_2013-01-26_to_2018-12-31.csv\", \"Billboard Hot Dance/Electronic Songs\"),\n", " (\"/home/christian/code/christian/metadata/popularity/All_Hip_Hop_Songs_from_1958-10-20_to_2018-12-31.csv\", \"Billboard Hot R&B/Hip-Hop Songs\"),\n", " (\"/home/christian/code/christian/metadata/popularity/All_Hot_100_from_1958-08-04_to_2018-12-31.csv\", \"Billboard Hot 100\"),\n", " (\"/home/christian/code/christian/metadata/popularity/All_Japan_Hot_100_from_2011-04-09_to_2018-12-31.csv\", \"Billboard Japan Hot 100\"),\n", " (\"/home/christian/code/christian/metadata/popularity/All_Latin_Songs_from_1986-09-20_to_2018-12-31.csv\", \"Billboard Hot Latin Songs\"),\n", " (\"/home/christian/code/christian/metadata/popularity/All_Pop_Songs_from_1992-10-03_to_2018-12-31.csv\", \"Billboard Hot Pop Songs\"),\n", " (\"/home/christian/code/christian/metadata/popularity/All_Rock_Songs_from_2009-06-20_to_2018-12-31.csv\", \"Billboard Hot Rock Songs\"),\n", " (\"/home/christian/code/christian/metadata/popularity/All_YouTube_chart_from_2011-08-27_to_2018-12-31.csv\", \"Billboard YouTube Music Charts\"),\n", "]\n", "\n", "chart_dfs = []\n", "for filepath, name in chart_filepaths:\n", " df = pd.read_csv(filepath)\n", " before_merge = len(df)\n", " df_merged = df.groupby(['title', 'artist'])['chart_date'].agg(list).reset_index()\n", "\n", " # If you need the dates as strings, split on \"-\" in each string and take the first part \n", " df_merged['chart_dates'] = df_merged['chart_date'].apply(lambda x: set([str(date).split(\"-\")[0] for date in x]))\n", " after_merge = len(df_merged)\n", "\n", " # drop chart_date\n", " df_merged.drop(columns=[\"chart_date\"], inplace=True)\n", "\n", " print(f\"{name} before merge: {before_merge}, after merge: {after_merge}\")\n", " # add source to the df\n", " df_merged[\"source\"] = name\n", "\n", "\n", " # rename artist to performer\n", " df_merged.rename(columns={\"artist\": \"performer\"}, inplace=True)\n", " chart_dfs.append(df_merged)\n", "\n", "\n", "chart_df = pd.concat(chart_dfs)\n", "chart_df.head(10)\n", "total_rows = len(chart_df)\n", "print(f\"Total rows: {total_rows}\")\n", "chart_df.head(10)\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"You'll Accomp'ny Me\"" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "genius_metas[0][\"youtube_title\"]" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "41028\n" ] } ], "source": [ "\n", "chart_metas = process_billboard_matches(chart_df, discogs_subset_metas, dataset=\"discogs\")\n", "print(len(chart_metas))\n" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'id': '89719ef9-c93c-472f-9d1f-f233009b52c2', 'title': '', 'artist': [{}], 'youtube_title': \"'Til I Gain Control Again - Crystal Gayle\", 'source': 'Billboard Hot Country Songs', 'match_score': 100.0, 'source_artist': 'Crystal Gayle', 'source_title': \"'til I Gain Control Again\", 'tags': ['Billboard', 'Billboard Hot Country Songs 1983 1982', 'Billboard Hot Country Songs']}\n", "{'id': '8bd9be23-2b38-4247-97dc-01bedae1608e', 'title': '', 'artist': [{}], 'youtube_title': '15 Minutes - Rodney Atkins', 'source': 'Billboard Hot Country Songs', 'match_score': 100.0, 'source_artist': 'Rodney Atkins', 'source_title': '15 Minutes', 'tags': ['Billboard', 'Billboard Hot Country Songs 2009', 'Billboard Hot Country Songs']}\n", "{'id': '94583033-631d-4932-9859-f077025dc366', 'title': '', 'artist': [{}], 'youtube_title': '8th of November - Big & Rich', 'source': 'Billboard Hot Country Songs', 'match_score': 100.0, 'source_artist': 'Big & Rich', 'source_title': '8th Of November', 'tags': ['Billboard', 'Billboard Hot Country Songs 2006', 'Billboard Hot Country Songs']}\n", "{'id': 'ce0d066a-0871-4bf1-805a-e9c61a1c4337', 'title': '', 'artist': [{}], 'youtube_title': 'A LONG and LASTING LOVE - CRYSTAL GAYLE', 'source': 'Billboard Hot Country Songs', 'match_score': 100.0, 'source_artist': 'Crystal Gayle', 'source_title': 'A Long And Lasting Love', 'tags': ['Billboard', 'Billboard Hot Country Songs 1985', 'Billboard Hot Country Songs']}\n", "{'id': '7cb0d7d7-2c14-46dd-a988-84a66ee9e87c', 'title': '', 'artist': [{}], 'youtube_title': 'a man holding on (to a woman letting go) - Ty Herndon', 'source': 'Billboard Hot Country Songs', 'match_score': 96.22642, 'source_artist': 'Ty Herndon', 'source_title': \"A Man Holdin' On (To A Woman Lettin' Go)\", 'tags': ['Billboard', 'Billboard Hot Country Songs 1998', 'Billboard Hot Country Songs']}\n", "{'id': '5f5404ae-42d0-4ba2-bbcf-fe86e9945137', 'title': '', 'artist': [{}], 'youtube_title': 'Ala-Freakin-Bama By Trace Adkins', 'source': 'Billboard Hot Country Songs', 'match_score': 95.2381, 'source_artist': 'Trace Adkins', 'source_title': 'Ala-Freakin-Bama', 'tags': ['Billboard', 'Billboard Hot Country Songs 2010', 'Billboard Hot Country Songs']}\n", "{'id': 'bf648b11-6e3a-4be8-809e-fdfae8845ca9', 'title': '', 'artist': [{}], 'youtube_title': 'All I Can Do - Dolly Parton', 'source': 'Billboard Hot Country Songs', 'match_score': 100.0, 'source_artist': 'Dolly Parton', 'source_title': 'All I Can Do', 'tags': ['Billboard', 'Billboard Hot Country Songs 1976', 'Billboard Hot Country Songs']}\n", "{'id': 'b7acd627-b025-4f07-9d3a-8d985b36b667', 'title': '', 'artist': [{}], 'youtube_title': 'America Will Survive - Hank Williams Jr', 'source': 'Billboard Hot Country Songs', 'match_score': 98.73418, 'source_artist': 'Hank Williams Jr.', 'source_title': 'America Will Survive', 'tags': ['Billboard', 'Billboard Hot Country Songs 2001', 'Billboard Hot Country Songs']}\n", "{'id': 'd5dc1fa6-1c65-4402-94c4-c4a16ac0fa59', 'title': '', 'artist': [{}], 'youtube_title': 'another world-crystal gayle and gary morris', 'source': 'Billboard Hot Country Songs', 'match_score': 97.72727, 'source_artist': 'Crystal Gayle And Gary Morris', 'source_title': 'Another World', 'tags': ['Billboard', 'Billboard Hot Country Songs 1987', 'Billboard Hot Country Songs']}\n", "{'id': '8eb31cbd-5b84-44c8-9994-9da50a37bbc3', 'title': '', 'artist': [{}], 'youtube_title': 'Any Way You Want Me (Gene Watson)', 'source': 'Billboard Hot Country Songs', 'match_score': 93.93939, 'source_artist': 'Gene Watson', 'source_title': 'Any Way You Want Me', 'tags': ['Billboard', 'Billboard Hot Country Songs 1981', 'Billboard Hot Country Songs']}\n", "{'id': '9197f979-8961-46fd-bdac-3a03c6c95b51', 'title': '', 'artist': [{}], 'youtube_title': 'Are You Ever Gonna Love Me - Holly Dunn', 'source': 'Billboard Hot Country Songs', 'match_score': 100.0, 'source_artist': 'Holly Dunn', 'source_title': 'Are You Ever Gonna Love Me', 'tags': ['Billboard', 'Billboard Hot Country Songs 1989', 'Billboard Hot Country Songs']}\n", "{'id': '9783c2c2-26ba-4c37-8bc8-afe2e585a51a', 'title': '', 'artist': [{}], 'youtube_title': 'Baby Blue - George Strait', 'source': 'Billboard Hot Country Songs', 'match_score': 100.0, 'source_artist': 'George Strait', 'source_title': 'Baby Blue', 'tags': ['Billboard', 'Billboard Hot Country Songs 1988', 'Billboard Hot Country Songs']}\n", "{'id': 'c4c2bfff-754b-4713-9971-e856c8567be6', 'title': '', 'artist': [{}], 'youtube_title': 'Back In My Arms Again-Kenny Chesney', 'source': 'Billboard Hot Country Songs', 'match_score': 97.22222, 'source_artist': 'Kenny Chesney', 'source_title': 'Back In My Arms Again', 'tags': ['Billboard', 'Billboard Hot Country Songs 1996', 'Billboard Hot Country Songs']}\n", "{'id': '62be6725-4d57-4e81-a191-4f6be31d7a85', 'title': '', 'artist': [{}], 'youtube_title': 'Back Seat of a Greyhound Bus - 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Hole', 'source': 'Billboard Hot Rock Songs', 'match_score': 100.0, 'source_artist': 'Hole', 'source_title': 'Skinny Little Bitch', 'tags': ['Billboard', 'Billboard Hot Rock Songs 2010', 'Billboard Hot Rock Songs']}\n", "{'id': '3a6d042a-2af3-484c-b3d9-4e86caa86aab', 'title': '', 'artist': [{}], 'youtube_title': 'Sober - blink-182', 'source': 'Billboard Hot Rock Songs', 'match_score': 100.0, 'source_artist': 'Blink-182', 'source_title': 'Sober', 'tags': ['Billboard', 'Billboard Hot Rock Songs 2016', 'Billboard Hot Rock Songs']}\n", "{'id': 'dc0c6f8f-9b3b-4f3b-9c2a-882fe3bd409a', 'title': '', 'artist': [{}], 'youtube_title': 'Somebody Else - The 1975', 'source': 'Billboard Hot Rock Songs', 'match_score': 100.0, 'source_artist': 'The 1975', 'source_title': 'Somebody Else', 'tags': ['Billboard', 'Billboard Hot Rock Songs 2017 2016', 'Billboard Hot Rock Songs']}\n", "{'id': '553fa5e3-a0fb-47fc-8525-886ce6092a43', 'title': '', 'artist': [{}], 'youtube_title': 'Sweater Weather - The Neighbourhood', 'source': 'Billboard Hot Rock Songs', 'match_score': 100.0, 'source_artist': 'The Neighbourhood', 'source_title': 'Sweater Weather', 'tags': ['Billboard', 'Billboard Hot Rock Songs 2014 2013', 'Billboard Hot Rock Songs']}\n", "{'id': 'a7e3ba9a-a384-4db2-b85b-2031594c91a4', 'title': '', 'artist': [{}], 'youtube_title': 'Teenage Satellites - blink-182', 'source': 'Billboard Hot Rock Songs', 'match_score': 100.0, 'source_artist': 'Blink-182', 'source_title': 'Teenage Satellites', 'tags': ['Billboard', 'Billboard Hot Rock Songs 2016', 'Billboard Hot Rock Songs']}\n", "{'id': '7d871b55-672d-4ae6-9d4a-0028960eb437', 'title': '', 'artist': [{}], 'youtube_title': 'The Only Thing That Matters - blink-182', 'source': 'Billboard Hot Rock Songs', 'match_score': 100.0, 'source_artist': 'Blink-182', 'source_title': 'The Only Thing That Matters', 'tags': ['Billboard', 'Billboard Hot Rock Songs 2016', 'Billboard Hot Rock Songs']}\n", "{'id': '5b80e650-c131-4538-8592-5466468f7da6', 'title': '', 'artist': [{}], 'youtube_title': 'The State Of Massachusetts - Dropkick Murphys', 'source': 'Billboard Hot Rock Songs', 'match_score': 100.0, 'source_artist': 'Dropkick Murphys', 'source_title': 'The State Of Massachusetts', 'tags': ['Billboard', 'Billboard Hot Rock Songs 2013', 'Billboard Hot Rock Songs']}\n", "{'id': '4304f834-235d-497c-bae2-ba3cf72c7e51', 'title': '', 'artist': [{}], 'youtube_title': \"THIS TIME IT'S DIFFERENT (NEW Evans Blue)\", 'source': 'Billboard Hot Rock Songs', 'match_score': 92.30769, 'source_artist': 'Evans Blue', 'source_title': \"This Time It's Different\", 'tags': ['Billboard', 'Billboard Hot Rock Songs 2012', 'Billboard Hot Rock Songs']}\n", "{'id': '05f30729-3e1b-4322-8843-2bcd425dc63f', 'title': '', 'artist': [{}], 'youtube_title': 'Under Pressure- Queen Feat. David Bowie', 'source': 'Billboard Hot Rock Songs', 'match_score': 90.666664, 'source_artist': 'Queen & David Bowie', 'source_title': 'Under Pressure', 'tags': ['Billboard', 'Billboard Hot Rock Songs 2016 2018', 'Billboard Hot Rock Songs']}\n", "{'id': 'b34b37e8-9957-4d7a-b089-4deb3c929906', 'title': '', 'artist': [{}], 'youtube_title': '\"When Legends Rise\" - Godsmack', 'source': 'Billboard Hot Rock Songs', 'match_score': 96.55173, 'source_artist': 'Godsmack', 'source_title': 'When Legends Rise', 'tags': ['Billboard', 'Billboard Hot Rock Songs 2018', 'Billboard Hot Rock Songs']}\n", "{'id': 'b9ea6658-7021-49a8-afcd-c56789bc7ecc', 'title': '', 'artist': [{}], 'youtube_title': '\"Bad Romance\" - Lady Gaga', 'source': 'Billboard YouTube Music Charts', 'match_score': 95.833336, 'source_artist': 'Lady Gaga', 'source_title': 'Bad Romance', 'tags': ['Billboard', 'Billboard YouTube Music Charts 2012', 'Billboard YouTube Music Charts']}\n", "{'id': '45fbceae-9f1e-4235-8077-bdf104d84244', 'title': '', 'artist': [{}], 'youtube_title': 'Culpables - MTZ Manuel Turizo', 'source': 'Billboard YouTube Music Charts', 'match_score': 92.59259, 'source_artist': 'Manuel Turizo', 'source_title': 'Culpables', 'tags': ['Billboard', 'Billboard YouTube Music Charts 2018', 'Billboard YouTube Music Charts']}\n", "{'id': '2749a063-6d88-486f-9d4a-bf5396d5fbdd', 'title': '', 'artist': [{}], 'youtube_title': 'Evolution of Music - Pentatonix', 'source': 'Billboard YouTube Music Charts', 'match_score': 100.0, 'source_artist': 'Pentatonix', 'source_title': 'Evolution Of Music', 'tags': ['Billboard', 'Billboard YouTube Music Charts 2013', 'Billboard YouTube Music Charts']}\n", "{'id': 'cfb44ec1-5615-42c4-8e86-b7dcb6fa98ab', 'title': '', 'artist': [{}], 'youtube_title': 'Galway Girl - Ed Sheeran', 'source': 'Billboard YouTube Music Charts', 'match_score': 100.0, 'source_artist': 'Ed Sheeran', 'source_title': 'Galway Girl', 'tags': ['Billboard', 'Billboard YouTube Music Charts 2017', 'Billboard YouTube Music Charts']}\n", "{'id': '4e5fbb98-f97d-4a3e-8f6a-af321817c69c', 'title': '', 'artist': [{}], 'youtube_title': 'God is a woman - Ariana Grande', 'source': 'Billboard YouTube Music Charts', 'match_score': 100.0, 'source_artist': 'Ariana Grande', 'source_title': 'God Is A Woman', 'tags': ['Billboard', 'Billboard YouTube Music Charts 2018', 'Billboard YouTube Music Charts']}\n", "{'id': '69c33fec-a8c1-4406-87d0-3a24eb3353e5', 'title': '', 'artist': [{}], 'youtube_title': '\"Last Friday Night (TGIF)\" - Katy Perry', 'source': 'Billboard YouTube Music Charts', 'match_score': 92.5, 'source_artist': 'Katy Perry', 'source_title': 'Last Friday Night (T.G.I.F.)', 'tags': ['Billboard', 'Billboard YouTube Music Charts 2011 2013', 'Billboard YouTube Music Charts']}\n", "{'id': 'b0a0b33b-2721-4a14-9923-755d28fcfff6', 'title': '', 'artist': [{}], 'youtube_title': 'No Brainer - DJ Khaled ft. Justin Bieber, Chance the Rapper, Quavo', 'source': 'Billboard YouTube Music Charts', 'match_score': 92.086334, 'source_artist': 'DJ Khaled Featuring Justin Bieber, Chance The Rapper & Quavo', 'source_title': 'No Brainer', 'tags': ['Billboard', 'Billboard YouTube Music Charts 2018', 'Billboard YouTube Music Charts']}\n", "{'id': '15701d41-5ff4-47ee-bb28-7fe9c48d2de4', 'title': '', 'artist': [{}], 'youtube_title': 'Problem ~ Ariana Grande feat. Iggy Azalea', 'source': 'Billboard YouTube Music Charts', 'match_score': 90.69768, 'source_artist': 'Ariana Grande Featuring Iggy Azalea', 'source_title': 'Problem', 'tags': ['Billboard', 'Billboard YouTube Music Charts 2014 2015', 'Billboard YouTube Music Charts']}\n", "{'id': '7c8a85c2-dcf5-4d4f-9074-7e12e11cbfc3', 'title': '', 'artist': [{}], 'youtube_title': 'Symphony - Clean Bandit feat. Zara Larsson', 'source': 'Billboard YouTube Music Charts', 'match_score': 93.181816, 'source_artist': 'Clean Bandit Featuring Zara Larsson', 'source_title': 'Symphony', 'tags': ['Billboard', 'Billboard YouTube Music Charts 2017', 'Billboard YouTube Music Charts']}\n", "{'id': 'b89bd651-4dc1-46fd-b310-8a946a754975', 'title': '', 'artist': [{}], 'youtube_title': 'Taki Taki - DJ Snake ft. Selena Gomez, Ozuna & Cardi B', 'source': 'Billboard YouTube Music Charts', 'match_score': 92.98245, 'source_artist': 'DJ Snake Featuring Selena Gomez, Ozuna & Cardi B', 'source_title': 'Taki Taki', 'tags': ['Billboard', 'Billboard YouTube Music Charts 2018', 'Billboard YouTube Music Charts']}\n", "{'id': '534d53ae-427a-4336-96ba-06d2d3bdf170', 'title': '', 'artist': [{}], 'youtube_title': 'Too Good At Goodbyes - Sam Smith', 'source': 'Billboard YouTube Music Charts', 'match_score': 100.0, 'source_artist': 'Sam Smith', 'source_title': 'Too Good At Goodbyes', 'tags': ['Billboard', 'Billboard YouTube Music Charts 2017 2018', 'Billboard YouTube Music Charts']}\n" ] } ], "source": [ "for chart_meta in chart_metas:\n", " print(chart_meta)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "for chart_meta in chart_metas:\n", " chart_meta[\"match_score\"] = float(chart_meta[\"match_score\"])\n", "\n", "write_jsonl(chart_metas, \"/home/christian/code/christian/metadata/popularity/discogs_subset_chart_metas.jsonl\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['year', 'title', 'published_at', 'updated_at', 'category', 'nominee',\n", " 'artist', 'workers', 'img', 'winner'],\n", " dtype='object')\n", "4810\n", "year 2019\n", "title 62nd Annual GRAMMY Awards (2019)\n", "published_at 2020-05-19T05:10:28-07:00\n", "updated_at 2020-05-19T05:10:28-07:00\n", "category Song Of The Year\n", "nominee Lover\n", "artist NaN\n", "workers Taylor Swift, songwriter (Taylor Swift)\n", "img https://www.grammy.com/sites/com/files/styles/...\n", "winner True\n", "Name: 20, dtype: object\n" ] }, { "data": { "text/plain": [ "performer Tom Barnes, Lewis Capaldi, Pete Kelleher, Benj...\n", "title Someone You Loved\n", "source Grammy Song Of The Year\n", "chart_dates {2019}\n", "Name: 22, dtype: object" ] }, "execution_count": 132, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# load grammy data\n", "grammy_df = pd.read_csv(\"/home/christian/code/christian/metadata/popularity/the_grammy_awards.csv\")\n", "print(grammy_df.columns)\n", "print(len(grammy_df))\n", "print(grammy_df.iloc[20])\n", "\n", "# if artist is NaN replace with nominee\n", "grammy_df[\"artist\"] = grammy_df[\"artist\"].fillna(grammy_df[\"workers\"])\n", "# add source to the df, use the column \"category\" for each row\n", "grammy_df[\"source\"] = grammy_df[\"category\"].apply(lambda x: \"Grammy \" + x)\n", "grammy_df[\"title\"] = grammy_df[\"nominee\"]\n", "grammy_df[\"performer\"] = grammy_df[\"artist\"]\n", "\n", "\n", "# use the year for each row\n", "grammy_df[\"chart_dates\"] = grammy_df[\"year\"].apply(lambda x: set([str(x)]))\n", "\n", "grammy_df.iloc[22][[\"performer\", \"title\", \"source\", \"chart_dates\"]]" ] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[134], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m grammy_metas \u001b[38;5;241m=\u001b[39m \u001b[43mprocess_billboard_matches\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrammy_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdiscogs_subset_metas\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mlen\u001b[39m(grammy_metas))\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m grammy_meta \u001b[38;5;129;01min\u001b[39;00m grammy_metas:\n", "Cell \u001b[0;32mIn[102], line 72\u001b[0m, in \u001b[0;36mprocess_billboard_matches\u001b[0;34m(billboard_df, discogs_subset_metas, method)\u001b[0m\n\u001b[1;32m 70\u001b[0m matches \u001b[38;5;241m=\u001b[39m find_matches_parallel(billboard_df_list, discogs_subset_metas)\n\u001b[1;32m 71\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 72\u001b[0m matches \u001b[38;5;241m=\u001b[39m \u001b[43mfind_matches_vectorized\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbillboard_df_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdiscogs_subset_metas\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 74\u001b[0m billboard_metas \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m match \u001b[38;5;129;01min\u001b[39;00m matches:\n", "Cell \u001b[0;32mIn[102], line 49\u001b[0m, in \u001b[0;36mfind_matches_vectorized\u001b[0;34m(chart_songs, library_songs, threshold)\u001b[0m\n\u001b[1;32m 44\u001b[0m library_keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(library_lookups[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcombined\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mkeys())\n\u001b[1;32m 46\u001b[0m search_strings \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msong[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtitle\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msong[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mperformer\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mlower()\u001b[38;5;241m.\u001b[39mstrip() \n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m song \u001b[38;5;129;01min\u001b[39;00m chart_songs]\n\u001b[0;32m---> 49\u001b[0m distances \u001b[38;5;241m=\u001b[39m \u001b[43mcdist\u001b[49m\u001b[43m(\u001b[49m\u001b[43msearch_strings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlibrary_keys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscorer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfuzz\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mratio\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mworkers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m128\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 51\u001b[0m matches \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, row \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(distances):\n", "File \u001b[0;32m~/miniconda3/envs/suno_env/lib/python3.10/site-packages/rapidfuzz/process_cpp.py:70\u001b[0m, in \u001b[0;36mcdist\u001b[0;34m(queries, choices, scorer, processor, score_cutoff, score_hint, score_multiplier, dtype, workers, **kwargs)\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 68\u001b[0m dtype \u001b[38;5;241m=\u001b[39m _dtype_to_type_num(dtype)\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39masarray(\n\u001b[0;32m---> 70\u001b[0m \u001b[43m_cdist\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 71\u001b[0m \u001b[43m \u001b[49m\u001b[43mqueries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[43m \u001b[49m\u001b[43mchoices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[43m \u001b[49m\u001b[43mscorer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscorer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 74\u001b[0m \u001b[43m \u001b[49m\u001b[43mprocessor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprocessor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 75\u001b[0m \u001b[43m 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'de', 'fr', 'ca', 'au', 'source'],\n", " dtype='object')\n", "Index(['year', 'position', 'artist', 'song', 'indicativerevenue', 'us', 'uk',\n", " 'de', 'fr', 'ca', 'au', 'source'],\n", " dtype='object')\n" ] }, { "data": { "text/plain": [ "song Just Like A Pill\n", "artist P!nk\n", "us 8\n", "source song_of_year\n", "Name: 1029, dtype: object" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# load song of month and song of the year\n", "song_of_month_df = pd.read_csv(\"/home/christian/code/christian/metadata/popularity/chart2000-songmonth-0-3-0074.csv\")\n", "song_of_year_df = pd.read_csv(\"/home/christian/code/christian/metadata/popularity/chart2000-songyear-0-3-0074.csv\")\n", "\n", "song_of_month_df[\"source\"] = \"song_of_month\"\n", "song_of_year_df[\"source\"] = \"song_of_year\"\n", "\n", "print(song_of_month_df.columns)\n", "print(song_of_year_df.columns)\n", "\n", "# merge the two dfs\n", "song_of_month_df = song_of_month_df.groupby([\"song\", \"artist\"]).agg({\"us\": \"first\"}).reset_index()\n", "song_of_year_df = song_of_year_df.groupby([\"song\", \"artist\"]).agg({\"us\": \"first\"}).reset_index()\n", "\n", "song_of_month_df[\"source\"] = \"song_of_month\"\n", "song_of_year_df[\"source\"] = \"song_of_year\"\n", "\n", "# merge the two dfs\n", "songs_df = pd.concat([song_of_month_df, song_of_year_df])\n", "\n", "\n", "\n", "songs_df.iloc[5000]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# create a map of id to meta\n", "id_to_meta = {meta[\"id\"]: meta for meta in discogs_subset_metas}" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from rapidfuzz import fuzz\n", "import numpy as np\n", "import pandas as pd\n", "from tqdm import tqdm\n", "\n", "def find_matches(metas, id_to_meta, billboard_collapsed, threshold=85):\n", " matches = []\n", " \n", " # Convert billboard data to arrays for vectorization\n", " bb_titles = np.array([t.lower() for t in billboard_collapsed['title']])\n", " bb_performers = np.array([p.lower() for p in billboard_collapsed['performer']])\n", " \n", " for meta in tqdm(metas):\n", " meta_id = meta[\"id\"]\n", " base_meta = id_to_meta[meta_id]\n", " \n", " title = base_meta[\"title\"].lower()\n", " artist = base_meta[\"artists\"][0][\"name\"].lower()\n", " \n", " # Vectorized fuzzy matching\n", " title_scores = np.array([fuzz.ratio(title, bb_title) for bb_title in bb_titles])\n", " artist_scores = np.array([fuzz.ratio(artist, bb_performer) for bb_performer in bb_performers])\n", " \n", " # Find matches above threshold\n", " combined_mask = (title_scores >= threshold) & (artist_scores >= threshold)\n", " match_indices = np.where(combined_mask)[0]\n", " \n", " if len(match_indices) > 0:\n", " # Get best match based on combined score\n", " best_idx = match_indices[np.argmax(title_scores[match_indices] + artist_scores[match_indices])]\n", " matches.append({\n", " 'discogs_id': meta_id,\n", " 'billboard_match': billboard_collapsed.iloc[best_idx].to_dict(),\n", " 'title_score': title_scores[best_idx],\n", " 'artist_score': artist_scores[best_idx]\n", " })\n", " \n", " return matches" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 0%| | 1/1000 [00:00<06:26, 2.59it/s]" ] } ], "source": [ "matches = find_matches(discogs_subset_metas[1000:2000], id_to_meta, billboard_collapsed)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'discogs_id': 'AG8fugqFn9Q', 'billboard_match': {'title': 'Panic Switch', 'performer': 'Silversun Pickups', 'source': 'hot-100', 'wks_on_chart': 1, 'peak_pos': 92}, 'title_score': 100.0, 'artist_score': 100.0}\n", "{'discogs_id': '16pgjuMcs8Y', 'billboard_match': {'title': 'Pressure The Hinges', 'performer': 'Haste The Day', 'source': 'billboard-200', 'wks_on_chart': 2, 'peak_pos': 89}, 'title_score': 100.0, 'artist_score': 100.0}\n", "{'discogs_id': '4-lDHzRi6eM', 'billboard_match': {'title': 'Little Red Rooster', 'performer': 'Sam Cooke', 'source': 'hot-100', 'wks_on_chart': 10, 'peak_pos': 11}, 'title_score': 100.0, 'artist_score': 100.0}\n", "{'discogs_id': '0P2a6aLDkkM', 'billboard_match': {'title': 'Being With You', 'performer': 'Smokey Robinson', 'source': 'billboard-200', 'wks_on_chart': 28, 'peak_pos': 10}, 'title_score': 100.0, 'artist_score': 100.0}\n", "{'discogs_id': 'wm54ShkWkqE', 'billboard_match': {'title': 'Armed And Extremely Dangerous', 'performer': 'First Choice', 'source': 'hot-100', 'wks_on_chart': 14, 'peak_pos': 28}, 'title_score': 100.0, 'artist_score': 100.0}\n", "{'discogs_id': 'r0pQgOP_weo', 'billboard_match': {'title': 'I Should Be...', 'performer': 'Dru Hill', 'source': 'hot-100', 'wks_on_chart': 17, 'peak_pos': 25}, 'title_score': 100.0, 'artist_score': 100.0}\n", "{'discogs_id': 'ITUybVxghMQ', 'billboard_match': {'title': 'Go Get It', 'performer': 'T.I.', 'source': 'hot-100', 'wks_on_chart': 1, 'peak_pos': 77}, 'title_score': 100.0, 'artist_score': 100.0}\n", "{'discogs_id': 'mnkVrY12XBs', 'billboard_match': {'title': \"What Cha Gonna Do With My Lovin'\", 'performer': 'Stephanie Mills', 'source': 'billboard-200', 'wks_on_chart': 34, 'peak_pos': 22}, 'title_score': 100.0, 'artist_score': 100.0}\n", "{'discogs_id': 'cU5uVnWL1Us', 'billboard_match': {'title': 'Viral Moment', 'performer': 'Lil Durk', 'source': 'hot-100', 'wks_on_chart': 1, 'peak_pos': 91}, 'title_score': 100.0, 'artist_score': 100.0}\n", "{'discogs_id': 'Hy4DlucAGUo', 'billboard_match': {'title': 'Nights In White Satin', 'performer': 'The Moody Blues', 'source': 'hot-100', 'wks_on_chart': 18, 'peak_pos': 2}, 'title_score': 85.71428571428572, 'artist_score': 100.0}\n", "{'discogs_id': 'Zm_73Wbr3Qc', 'billboard_match': {'title': 'Ticket To Ride', 'performer': 'Carpenters', 'source': 'billboard-200', 'wks_on_chart': 16, 'peak_pos': 150}, 'title_score': 100.0, 'artist_score': 100.0}\n" ] } ], "source": [ "for match in matches:\n", " print(match)\n" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 30%|███ | 30/100 [00:37<01:24, 1.21s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "title Panic Switch\n", "performer Silversun Pickups\n", "source hot-100\n", "wks_on_chart 1\n", "peak_pos 92\n", "Name: 42933, dtype: object\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " 80%|████████ | 80/100 [01:40<00:25, 1.26s/it]\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_2679383/4066476660.py\u001b[0m in 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discogs_subset_metas_with_tags[idx][\"id\"]\n", " base_meta = id_to_meta[meta_id]\n", "\n", " # loose string match on title and artist\n", " title = base_meta[\"title\"]\n", " artist = base_meta[\"artists\"][0][\"name\"]\n", "\n", " # iterate over the billboard df and find the row that has the same title and artist\n", " for index, row in billboard_collapsed.iterrows():\n", " # make the string matching case insensitive\n", " if row[\"title\"].lower() == title.lower() and row[\"performer\"].lower() == artist.lower():\n", " print(row)\n", " break\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "suno_env", "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": "3.10.9" } }, "nbformat": 4, "nbformat_minor": 2 }