Add files using upload-large-folder tool

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upload.py

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+from huggingface_hub import HfApi
+from huggingface_hub import login
+
+login()
+api = HfApi()
+
+api.upload_large_folder(
+    repo_id="labhamlet/GRAM-Naturalistic-Scenes-Ambisonics",
+    repo_type="dataset",
+    folder_path=".",
+)

wds_converter.py

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+#!/usr/bin/env python3
+"""
+Convert [NPY] folder to WebDataset format for reading.
+
+This script is optimized to use all available CPU cores for parallel processing
+and includes a progress bar for monitoring.
+"""
+
+import tarfile
+import numpy as np
+from pathlib import Path
+import io
+import concurrent.futures
+from tqdm import tqdm
+
+def _create_shard(args):
+    """
+    Worker function to create a single WebDataset shard from a list of .npy files.
+    This function is designed to be called by a process pool.
+    """
+    shard_id, npy_files_chunk, output_path = args
+    shard_name = f"shard-{shard_id:06d}.tar"
+    shard_path = output_path / shard_name
+
+    with tarfile.open(shard_path, 'w') as current_tar:
+        for npy_file in npy_files_chunk:
+            try:
+                # Load numpy array
+                array = np.load(npy_file)
+                
+                # Create sample key (filename without extension)
+                sample_key = npy_file.stem
+                
+                # Add numpy array as .npy file in tar
+                npy_buffer = io.BytesIO()
+                np.save(npy_buffer, array)
+                npy_buffer.seek(0)
+                
+                tarinfo = tarfile.TarInfo(name=f"{sample_key}.npy")
+                tarinfo.size = npy_buffer.getbuffer().nbytes
+                current_tar.addfile(tarinfo, npy_buffer)
+
+            except Exception as e:
+                print(f"Error processing {npy_file} for shard {shard_id}: {e}")
+                continue
+    return shard_path
+
+def create_webdataset_from_npy(
+    input_dir: str = "[NPY]",
+    output_dir: str = "webdataset_output",
+    shard_size: int = 1000
+):
+    """
+    Convert .npy files from an input folder to WebDataset format in parallel.
+    
+    Args:
+        input_dir (str): Directory containing .npy files.
+        output_dir (str): Directory to save WebDataset tar files.
+        shard_size (int): Number of samples per shard.
+    """
+    input_path = Path(input_dir)
+    output_path = Path(output_dir)
+    output_path.mkdir(parents=True, exist_ok=True)
+    
+    # Get and sort all .npy files for consistent ordering
+    npy_files = sorted(list(input_path.glob("*.npy")), key=lambda x: x.name)
+    
+    if not npy_files:
+        raise ValueError(f"No .npy files found in {input_dir}")
+        
+    print(f"Found {len(npy_files)} .npy files to process.")
+
+    # Split files into chunks for each shard
+    file_chunks = [
+        npy_files[i:i + shard_size] 
+        for i in range(0, len(npy_files), shard_size)
+    ]
+    
+    num_shards = len(file_chunks)
+    print(f"Data will be split into {num_shards} shards.")
+
+    # Prepare arguments for the worker function
+    tasks = [
+        (shard_id, chunk, output_path) 
+        for shard_id, chunk in enumerate(file_chunks)
+    ]
+
+    with concurrent.futures.ProcessPoolExecutor() as executor:
+        # Use tqdm to create a progress bar
+        results = list(tqdm(
+            executor.map(_create_shard, tasks), 
+            total=num_shards, 
+            desc="Creating Shards"
+        ))
+
+    print(f"\nConversion complete! Created {len(results)} shards in '{output_dir}'.")
+    return len(results)
+
+
+if __name__ == "__main__":
+    num_created_shards = create_webdataset_from_npy(
+        input_dir="raw",
+        output_dir="webdataset_output",
+        shard_size=1000  # Each .tar file will contain 1000 samples
+    )
+    
+    print(f"\nSuccessfully created {num_created_shards} shards.")