Update README.md

README.md

@@ -161,3 +161,79 @@ This dataset is a structured extraction of the [Million Song Subset](http://mill
 For more details, visit the [Million Song Dataset website](http://millionsongdataset.com).
 
 
+## Appendix: Processing Code
+
+The dataset was converted using the following snippet:
+
+```python
+import os
+import unibox as ub
+import pandas as pd
+import numpy as np
+import h5py
+from tqdm import tqdm
+from concurrent.futures import ProcessPoolExecutor
+
+# https://github.com/tbertinmahieux/MSongsDB/blob/0c276e289606d5bd6f3991f713e7e9b1d4384e44/PythonSrc/hdf5_getters.py
+import hdf5_getters
+
+# Define dataset path
+dataset_path = "/lv0/yada/dataproc5/data/MillionSongSubset"
+
+# Function to extract all available fields from an HDF5 file
+def extract_song_data(file_path):
+    """Extracts all available fields from an HDF5 song file using hdf5_getters."""
+    song_data = {}
+
+    try:
+        with hdf5_getters.open_h5_file_read(file_path) as h5:
+            # Get all getter functions from hdf5_getters
+            getters = [func for func in dir(hdf5_getters) if func.startswith("get_")]
+
+            for getter in getters:
+                try:
+                    # Dynamically call each getter function
+                    value = getattr(hdf5_getters, getter)(h5)
+
+                    # Optimize conversions
+                    if isinstance(value, np.ndarray):
+                        value = value.tolist()
+                    elif isinstance(value, bytes):
+                        value = value.decode()
+
+                    # Store in dictionary with a cleaned-up key name
+                    song_data[getter[4:]] = value
+
+                except Exception:
+                    continue  # Skip errors but don't slow down
+
+    except Exception as e:
+        print(f"Error processing {file_path}: {e}")
+    
+    return song_data
+
+# Function to process multiple files in parallel
+def process_files_in_parallel(h5_files, num_workers=8):
+    """Processes multiple .h5 files in parallel."""
+    all_songs = []
+
+    with ProcessPoolExecutor(max_workers=num_workers) as executor:
+        for song_data in tqdm(executor.map(extract_song_data, h5_files), total=len(h5_files)):
+            if song_data:
+                all_songs.append(song_data)
+    
+    return all_songs
+
+# Find all .h5 files
+h5_files = [os.path.join(root, file) for root, _, files in os.walk(dataset_path) for file in files if file.endswith(".h5")]
+
+# Process files in parallel
+all_songs = process_files_in_parallel(h5_files, num_workers=24)
+
+# Convert to Pandas DataFrame
+df = pd.DataFrame(all_songs)
+
+ub.saves(df, "hf://trojblue/million-song-subset", private=False)
+```
+
+