Datasets:

trojblue
/

million-song-subset

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "see: http://millionsongdataset.com/pages/getting-dataset/#subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !wget http://labrosa.ee.columbia.edu/~dpwe/tmp/millionsongsubset.tar.gz\n",
+    "# !tar -xvzf millionsongsubset.tar.gz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install pandas h5py pyarrow fastparquet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import h5py\n",
+    "import pandas as pd\n",
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "import unibox as ub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a7418816c46f4f5b95a8c7e307b6e569",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Listing local files: 0files [00:00, ?files/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "10000"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(ub.ls(\"../data/MillionSongSubset\", [\".h5\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 10000/10000 [00:39<00:00, 250.36it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import hdf5_getters\n",
+    "import h5py\n",
+    "from tqdm import tqdm\n",
+    "from concurrent.futures import ProcessPoolExecutor\n",
+    "\n",
+    "# Define dataset path\n",
+    "dataset_path = \"/lv0/yada/dataproc5/data/MillionSongSubset\"\n",
+    "\n",
+    "# Function to extract all available fields from an HDF5 file\n",
+    "def extract_song_data(file_path):\n",
+    "    \"\"\"Extracts all available fields from an HDF5 song file using hdf5_getters.\"\"\"\n",
+    "    song_data = {}\n",
+    "\n",
+    "    try:\n",
+    "        with hdf5_getters.open_h5_file_read(file_path) as h5:\n",
+    "            # Get all getter functions from hdf5_getters\n",
+    "            getters = [func for func in dir(hdf5_getters) if func.startswith(\"get_\")]\n",
+    "\n",
+    "            for getter in getters:\n",
+    "                try:\n",
+    "                    # Dynamically call each getter function\n",
+    "                    value = getattr(hdf5_getters, getter)(h5)\n",
+    "\n",
+    "                    # Optimize conversions\n",
+    "                    if isinstance(value, np.ndarray):\n",
+    "                        value = value.tolist()\n",
+    "                    elif isinstance(value, bytes):\n",
+    "                        value = value.decode()\n",
+    "\n",
+    "                    # Store in dictionary with a cleaned-up key name\n",
+    "                    song_data[getter[4:]] = value\n",
+    "\n",
+    "                except Exception:\n",
+    "                    continue  # Skip errors but don't slow down\n",
+    "\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error processing {file_path}: {e}\")\n",
+    "    \n",
+    "    return song_data\n",
+    "\n",
+    "# Function to process multiple files in parallel\n",
+    "def process_files_in_parallel(h5_files, num_workers=8):\n",
+    "    \"\"\"Processes multiple .h5 files in parallel.\"\"\"\n",
+    "    all_songs = []\n",
+    "\n",
+    "    with ProcessPoolExecutor(max_workers=num_workers) as executor:\n",
+    "        for song_data in tqdm(executor.map(extract_song_data, h5_files), total=len(h5_files)):\n",
+    "            if song_data:\n",
+    "                all_songs.append(song_data)\n",
+    "    \n",
+    "    return all_songs\n",
+    "\n",
+    "# Find all .h5 files\n",
+    "h5_files = [os.path.join(root, file) for root, _, files in os.walk(dataset_path) for file in files if file.endswith(\".h5\")]\n",
+    "\n",
+    "# Process files in parallel\n",
+    "all_songs = process_files_in_parallel(h5_files, num_workers=24)\n",
+    "\n",
+    "# Convert to Pandas DataFrame\n",
+    "df = pd.DataFrame(all_songs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10000, 55)\n",
+      "Index(['analysis_sample_rate', 'artist_7digitalid', 'artist_familiarity',\n",
+      "       'artist_hotttnesss', 'artist_id', 'artist_latitude', 'artist_location',\n",
+      "       'artist_longitude', 'artist_mbid', 'artist_mbtags',\n",
+      "       'artist_mbtags_count', 'artist_name', 'artist_playmeid', 'artist_terms',\n",
+      "       'artist_terms_freq', 'artist_terms_weight', 'audio_md5',\n",
+      "       'bars_confidence', 'bars_start', 'beats_confidence', 'beats_start',\n",
+      "       'danceability', 'duration', 'end_of_fade_in', 'energy', 'key',\n",
+      "       'key_confidence', 'loudness', 'mode', 'mode_confidence', 'num_songs',\n",
+      "       'release', 'release_7digitalid', 'sections_confidence',\n",
+      "       'sections_start', 'segments_confidence', 'segments_loudness_max',\n",
+      "       'segments_loudness_max_time', 'segments_loudness_start',\n",
+      "       'segments_pitches', 'segments_start', 'segments_timbre',\n",
+      "       'similar_artists', 'song_hotttnesss', 'song_id', 'start_of_fade_out',\n",
+      "       'tatums_confidence', 'tatums_start', 'tempo', 'time_signature',\n",
+      "       'time_signature_confidence', 'title', 'track_7digitalid', 'track_id',\n",
+      "       'year'],\n",
+      "      dtype='object')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>analysis_sample_rate</th>\n",
+       "      <th>artist_7digitalid</th>\n",
+       "      <th>artist_familiarity</th>\n",
+       "      <th>artist_hotttnesss</th>\n",
+       "      <th>artist_id</th>\n",
+       "      <th>artist_latitude</th>\n",
+       "      <th>artist_location</th>\n",
+       "      <th>artist_longitude</th>\n",
+       "      <th>artist_mbid</th>\n",
+       "      <th>artist_mbtags</th>\n",
+       "      <th>...</th>\n",
+       "      <th>start_of_fade_out</th>\n",
+       "      <th>tatums_confidence</th>\n",
+       "      <th>tatums_start</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "      <th>time_signature_confidence</th>\n",
+       "      <th>title</th>\n",
+       "      <th>track_7digitalid</th>\n",
+       "      <th>track_id</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>22050</td>\n",
+       "      <td>174717</td>\n",
+       "      <td>0.450743</td>\n",
+       "      <td>0.331215</td>\n",
+       "      <td>AR1DGSO1187FB59B15</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td></td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>fe4e71a9-ddb9-47b5-9e2e-ec53862a91c6</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>...</td>\n",
+       "      <td>266.879</td>\n",
+       "      <td>[0.0, 0.0, 0.896, 0.819, 0.664, 0.693, 0.67, 0...</td>\n",
+       "      <td>[0.16738, 0.44887, 0.73036, 1.09072, 1.44407, ...</td>\n",
+       "      <td>107.053</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.657</td>\n",
+       "      <td>Jody</td>\n",
+       "      <td>2555900</td>\n",
+       "      <td>TRAHHUN128F4227029</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>22050</td>\n",
+       "      <td>7173</td>\n",
+       "      <td>0.392710</td>\n",
+       "      <td>0.311789</td>\n",
+       "      <td>ARO6WZY1187FB3A86E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td></td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>23f7ad3f-a189-4a1c-9991-4763ded495a7</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>...</td>\n",
+       "      <td>321.300</td>\n",
+       "      <td>[0.451, 0.426, 0.396, 0.32, 0.255, 0.204, 0.15...</td>\n",
+       "      <td>[0.05024, 0.25641, 0.46357, 0.66974, 0.87691, ...</td>\n",
+       "      <td>149.853</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>Turntable Terrorist</td>\n",
+       "      <td>5591259</td>\n",
+       "      <td>TRAHHMM128F932D5D9</td>\n",
+       "      <td>1995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>22050</td>\n",
+       "      <td>2759</td>\n",
+       "      <td>0.602767</td>\n",
+       "      <td>0.463193</td>\n",
+       "      <td>ARH1LE01187B98D68D</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td></td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3df3a779-a7b1-4362-a8b4-9ae6c7eb623d</td>\n",
+       "      <td>[b'american', b'soundtrack']</td>\n",
+       "      <td>...</td>\n",
+       "      <td>67.895</td>\n",
+       "      <td>[0.056, 0.058, 0.056, 0.059, 0.097, 0.093, 0.0...</td>\n",
+       "      <td>[0.54095, 0.86496, 1.20205, 1.52933, 1.85662, ...</td>\n",
+       "      <td>91.249</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.568</td>\n",
+       "      <td>Porcelain Man</td>\n",
+       "      <td>7341937</td>\n",
+       "      <td>TRAHHJY12903CA73BD</td>\n",
+       "      <td>1999</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 55 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   analysis_sample_rate  artist_7digitalid  artist_familiarity  \\\n",
+       "0                 22050             174717            0.450743   \n",
+       "1                 22050               7173            0.392710   \n",
+       "2                 22050               2759            0.602767   \n",
+       "\n",
+       "   artist_hotttnesss           artist_id  artist_latitude artist_location  \\\n",
+       "0           0.331215  AR1DGSO1187FB59B15              NaN                   \n",
+       "1           0.311789  ARO6WZY1187FB3A86E              NaN                   \n",
+       "2           0.463193  ARH1LE01187B98D68D              NaN                   \n",
+       "\n",
+       "   artist_longitude                           artist_mbid  \\\n",
+       "0               NaN  fe4e71a9-ddb9-47b5-9e2e-ec53862a91c6   \n",
+       "1               NaN  23f7ad3f-a189-4a1c-9991-4763ded495a7   \n",
+       "2               NaN  3df3a779-a7b1-4362-a8b4-9ae6c7eb623d   \n",
+       "\n",
+       "                  artist_mbtags  ... start_of_fade_out  \\\n",
+       "0                            []  ...           266.879   \n",
+       "1                            []  ...           321.300   \n",
+       "2  [b'american', b'soundtrack']  ...            67.895   \n",
+       "\n",
+       "                                   tatums_confidence  \\\n",
+       "0  [0.0, 0.0, 0.896, 0.819, 0.664, 0.693, 0.67, 0...   \n",
+       "1  [0.451, 0.426, 0.396, 0.32, 0.255, 0.204, 0.15...   \n",
+       "2  [0.056, 0.058, 0.056, 0.059, 0.097, 0.093, 0.0...   \n",
+       "\n",
+       "                                        tatums_start    tempo time_signature  \\\n",
+       "0  [0.16738, 0.44887, 0.73036, 1.09072, 1.44407, ...  107.053              4   \n",
+       "1  [0.05024, 0.25641, 0.46357, 0.66974, 0.87691, ...  149.853              3   \n",
+       "2  [0.54095, 0.86496, 1.20205, 1.52933, 1.85662, ...   91.249              4   \n",
+       "\n",
+       "  time_signature_confidence                title track_7digitalid  \\\n",
+       "0                     0.657                 Jody          2555900   \n",
+       "1                     1.000  Turntable Terrorist          5591259   \n",
+       "2                     0.568        Porcelain Man          7341937   \n",
+       "\n",
+       "             track_id  year  \n",
+       "0  TRAHHUN128F4227029     0  \n",
+       "1  TRAHHMM128F932D5D9  1995  \n",
+       "2  TRAHHJY12903CA73BD  1999  \n",
+       "\n",
+       "[3 rows x 55 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ub.peeks(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[37m2025-02-19 14:01:45 [INFO] HuggingFaceDatasetsBackend.data_to_hub: Uploading dataset to HF repo trojblue/million-song-subset\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6e47a2259e54cb19dc37e6762883cbc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Uploading the dataset shards:   0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f69f061bad16497ca9f9bac1ab4500c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Creating parquet from Arrow format:   0%|          | 0/20 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d998490521645edb6d4e462de6045f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Creating parquet from Arrow format:   0%|          | 0/20 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "389e657da20c4a3c8e18899e57642b96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Creating parquet from Arrow format:   0%|          | 0/20 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "767913f1d6134a888edea631657fce58",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Creating parquet from Arrow format:   0%|          | 0/20 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b59fd321ef98408880ec22deb55498c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Creating parquet from Arrow format:   0%|          | 0/20 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[37m2025-02-19 14:02:47 [INFO] saves: DataFrame saved (HF dataset) to \"hf://trojblue/million-song-subset\" in 62.75s\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "ub.saves(df, \"hf://trojblue/million-song-subset\", private=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}