use webdataset

_info.json

@@ -0,0 +1,57 @@
+{
+  "name": "commavq-wds",
+  "splits": {
+    "train": {
+      "name": "train",
+      "filenames": [
+        "data-0000.tar.gz",
+        "data-0001.tar.gz",
+        "data-0002.tar.gz",
+        "data-0003.tar.gz",
+        "data-0004.tar.gz",
+        "data-0005.tar.gz",
+        "data-0006.tar.gz",
+        "data-0007.tar.gz",
+        "data-0008.tar.gz",
+        "data-0009.tar.gz",
+        "data-0010.tar.gz",
+        "data-0011.tar.gz",
+        "data-0012.tar.gz",
+        "data-0013.tar.gz",
+        "data-0014.tar.gz",
+        "data-0015.tar.gz",
+        "data-0016.tar.gz",
+        "data-0017.tar.gz",
+        "data-0018.tar.gz",
+        "data-0019.tar.gz",
+        "data-0020.tar.gz",
+        "data-0021.tar.gz",
+        "data-0022.tar.gz",
+        "data-0023.tar.gz",
+        "data-0024.tar.gz",
+        "data-0025.tar.gz",
+        "data-0026.tar.gz",
+        "data-0027.tar.gz",
+        "data-0028.tar.gz",
+        "data-0029.tar.gz",
+        "data-0030.tar.gz",
+        "data-0031.tar.gz",
+        "data-0032.tar.gz",
+        "data-0033.tar.gz",
+        "data-0034.tar.gz",
+        "data-0035.tar.gz",
+        "data-0036.tar.gz",
+        "data-0037.tar.gz",
+        "data-0038.tar.gz",
+        "data-0039.tar.gz"
+      ],
+      "shard_lengths": [
+        2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500,
+        2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500,
+        2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500,
+        2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500
+      ],
+      "num_samples": 100000
+    }
+  }
+}

commavq.py

@@ -1,41 +0,0 @@
-import datasets
-import os
-import numpy as np
-import itertools
-
-SHARD_SIZE = 2500
-NUM_SHARDS = 40
-_DATA_FILES = [
-      f'data_{i*SHARD_SIZE}_to_{(i+1)*SHARD_SIZE}.zip' for i in range(NUM_SHARDS)
-    ] + [ 'val.zip' ]
-
-_DESCRIPTION = """\
-TODO
-"""
-
-class CommaVQ(datasets.GeneratorBasedBuilder):
-
-  def _info(self):
-    return datasets.DatasetInfo(
-      description=_DESCRIPTION,
-      features=datasets.Features(
-        {"path": datasets.Value("string")}
-      )
-    )
-
-  def _split_generators(self, dl_manager):
-    """Returns SplitGenerators."""
-    dl_manager.download_config.ignore_url_params = True
-    to_dl = list(itertools.chain(*[x[1] for x in self.config.data_files.items()])) if self.config.data_files is not None else _DATA_FILES
-    downloaded_files = dl_manager.download(to_dl)
-    local_extracted_archive = dl_manager.extract(downloaded_files) if not dl_manager.is_streaming else [None]*len(downloaded_files)
-    return [
-      datasets.SplitGenerator(
-        name=str(i),
-        gen_kwargs={"local_extracted_archive":local_extracted_archive[i], "files": dl_manager.iter_archive(downloaded_files[i])}
-        ) for i in range(len(downloaded_files))]
-
-  def _generate_examples(self, local_extracted_archive, files):
-    for path_in_archive, f in files:
-      path = os.path.join(local_extracted_archive, path_in_archive) if local_extracted_archive is not None else path_in_archive
-      yield path_in_archive, {'path': path}

data_15000_to_17500.zip → data-0000.tar.gz

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