Add files using upload-large-folder tool

batch_split.py

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+import os
+import shutil
+import pandas as pd
+from pathlib import Path
+import argparse
+import csv
+
+# ===================== CONFIGURATION ===================== #
+parser = argparse.ArgumentParser(description="Batch split files and clean metadata.")
+parser.add_argument("main_dir", type=str, help="Path to the main directory containing files.")
+args = parser.parse_args()
+
+MAIN_DIR = args.main_dir
+CSV_PATH = f"{MAIN_DIR}/metadata.csv"     # change this
+BATCH_SIZE = 9000
+# ========================================================== #
+
+# ---- Load metadata ----
+main_path = Path(MAIN_DIR)
+csv_path = Path(CSV_PATH)
+
+# ===================================================
+# STEP 1 — Load metadata safely
+# ===================================================
+clean_rows = []
+bad_rows = []
+
+with open(csv_path, "r", encoding="utf-8", errors="replace", newline="") as f:
+    reader = csv.reader(f, delimiter=",", quotechar='"')
+    header = next(reader)
+    n_cols = len(header)
+    clean_rows.append(header)
+
+    for row in reader:
+        # remove null bytes
+        row = [col.replace("\x00", "").replace("\\", "_").replace("/", "_").replace(":", "_")
+               .replace("*", "_").replace("?", "_").replace("\"", "_").replace("<", "_")
+               .replace(">", "_").replace("|", "_") for col in row]
+
+        if len(row) == n_cols:
+            clean_rows.append(row)
+        else:
+            bad_rows.append(row)
+
+print(f"Header columns: {n_cols}")
+print(f"Total valid rows: {len(clean_rows)-1}")
+print(f"Bad rows skipped: {len(bad_rows)}")
+
+# Convert CSV to list of dicts for easier manipulation
+data = [dict(zip(header, row)) for row in clean_rows[1:]]
+
+# ----------------- Step 2: Remove files not in metadata ----------------- #
+metadata_filenames = {row["name"]+".mp3" for row in data}  # use the 'name' column
+actual_files = {f.name for f in main_path.iterdir() if f.is_file()}
+
+files_to_remove = actual_files - metadata_filenames
+for fname in files_to_remove:
+    print(f"Removing unlisted file: {fname}")
+    (main_path / fname).unlink()
+
+# refresh actual files
+actual_files = {f.name for f in main_path.iterdir() if f.is_file()}
+
+# ----------------- Step 3: Remove metadata rows where file does not exist ----------------- #
+data = [row for row in data if row["name"] in actual_files]
+
+# ----------------- Step 4: Split into batches ----------------- #
+sorted_files = sorted(actual_files)
+file_to_batch = {}
+batch_num = 1
+
+for i in range(0, len(sorted_files), BATCH_SIZE):
+    batch_files = sorted_files[i:i + BATCH_SIZE]
+    batch_dir = main_path / f"batch_{batch_num:03d}"
+    batch_dir.mkdir(exist_ok=True)
+    print(f"Creating {batch_dir} with {len(batch_files)} files")
+
+    for fname in batch_files:
+        src = main_path / fname
+        dst = batch_dir / fname
+        shutil.move(str(src), str(dst))
+        file_to_batch[fname] = batch_num
+
+    batch_num += 1
+
+# ----------------- Step 5: Add batch prefix to metadata ----------------- #
+for row in data:
+    bn = file_to_batch.get(row["name"])
+    if bn is None:
+        raise ValueError(f"Missing batch info for file: {row['name']}")
+    row["name"] = f"batch_{bn:03d}/{row['name']}"
+
+# ----------------- Step 6: Save cleaned CSV ----------------- #
+output_csv = csv_path.with_name(csv_path.stem + "_clean.csv")
+
+with open(output_csv, "w", newline="", encoding="utf-8") as f:
+    writer = csv.DictWriter(f, fieldnames=header)
+    writer.writeheader()
+    writer.writerows(data)
+
+# ===================================================
+# STEP 6 — Save cleaned metadata CSV
+# ===================================================
+
+
+print(f"Number of lines in cleaned CSV: {len(data)}")
+print(f"Cleaned CSV saved to: {output_csv}")
+

clean_dataset.py

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+import csv
+import re
+import shutil
+from pathlib import Path
+import argparse
+
+# ==========================
+# Configuration
+# ==========================
+
+parser = argparse.ArgumentParser(description="Clean dataset filenames and metadata.")
+parser.add_argument("dir_path", type=str, help="Path to the source directory containing batches and metadata.csv")
+args = parser.parse_args()
+
+dir_path = args.dir_path
+
+SOURCE_ROOT = Path(dir_path)          # folder containing batch_00x + metadata.csv
+CSV_NAME = "metadata.csv"
+
+DEST_ROOT = Path(dir_path+"_clean")
+DEST_CSV_NAME = "metadata.csv"
+
+REPLACEMENT_CHAR = "_"                 # character to replace special characters with
+
+# ==========================
+# Filename cleaning function
+# ==========================
+
+def clean_filename(filename: str, replacement: str = "_") -> str:
+    """
+    Replace special characters in filename while preserving extension.
+    Allowed: letters, numbers, dot, underscore, dash
+    """
+    p = Path(filename)
+    stem = p.stem
+    suffix = p.suffix
+
+    # Replace unwanted characters
+    cleaned_stem = re.sub(r"[^A-Za-z0-9._]", replacement, stem)
+
+    # Collapse multiple replacements
+    cleaned_stem = re.sub(rf"{re.escape(replacement)}+", replacement, cleaned_stem)
+
+    # Avoid empty names
+    if not cleaned_stem:
+        cleaned_stem = "file"
+
+    return cleaned_stem + suffix
+
+
+# ==========================
+# Main process
+# ==========================
+
+def main():
+    source_csv_path = SOURCE_ROOT / CSV_NAME
+    dest_csv_path = DEST_ROOT / DEST_CSV_NAME
+
+    DEST_ROOT.mkdir(parents=True, exist_ok=True)
+
+    # Build mapping: original filename -> cleaned filename
+    filename_map = {}
+
+    rows = []
+
+    with open(source_csv_path, newline="", encoding="utf-8") as f:
+        reader = csv.reader(f)
+        # header = next(reader)
+
+        for row in reader:
+            original_name = row[0]
+            cleaned_name = clean_filename(original_name, REPLACEMENT_CHAR)
+
+            filename_map[original_name] = cleaned_name
+
+            new_row = row.copy()
+            new_row[0] = original_name.split("/")[0] +"/" + cleaned_name
+            # import pdb; pdb.set_trace()
+            rows.append(new_row)
+
+    # Copy and rename files batch by batch
+    for batch_dir in SOURCE_ROOT.iterdir():
+        if not batch_dir.is_dir():
+            continue
+        if not batch_dir.name.startswith("batch_"):
+            continue
+
+        dest_batch_dir = DEST_ROOT / batch_dir.name
+        dest_batch_dir.mkdir(parents=True, exist_ok=True)
+
+        for file_path in batch_dir.iterdir():
+            if not file_path.is_file():
+                continue
+
+            original_name = file_path.name
+
+            if str(Path(batch_dir.name) / original_name) not in filename_map:
+                print(f"WARNING: {str(Path(batch_dir.name) / original_name)} not found in CSV. Skipping.")
+                # import pdb; pdb.set_trace()
+                continue
+
+            cleaned_name = filename_map[str(Path(batch_dir.name) / original_name)]
+            dest_file_path = dest_batch_dir / cleaned_name
+
+            shutil.copy2(file_path, dest_file_path)
+
+    # Write cleaned CSV
+    with open(dest_csv_path, "w", newline="", encoding="utf-8") as f:
+        writer = csv.writer(f)
+        # writer.writerow(header)
+        writer.writerows(rows)
+
+    print("Clean dataset created successfully:")
+    print(f"  Folder: {DEST_ROOT}")
+    print(f"  CSV:    {dest_csv_path}")
+
+
+if __name__ == "__main__":
+    main()

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