Add files using upload-large-folder tool

WABAD.py

@@ -1,72 +1,72 @@
-import datasets
-import pandas as pd
-import os
-import ast
-
-
-class WABADBuilderConfig(datasets.BuilderConfig):
-    def __init__(self, location_dir, **kwargs):
-        super().__init__(**kwargs)
-        self.location_dir = location_dir
-
-class WABADDataset(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        WABADBuilderConfig(name="BAM", location_dir="BAM", description="BAM location"),
-        WABADBuilderConfig(name="ARD", location_dir="ARD", description="ARD location"),
-        # add more locations here
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            features=datasets.Features({
-                "audio": datasets.Audio(), 
-                "labels": datasets.Sequence(datasets.Value("int32")),
-                "site_ID": datasets.Value("string"),
-                # any other metadata
-            })
-        )
-
-    def _split_generators(self, dl_manager):
-        config_dir = self.config.location_dir
-        
-        # Use the correct dataset URL format and download specific files
-        train_url = f"https://huggingface.co/datasets/benmcewen/WABAD/resolve/main/{config_dir}/{config_dir}_metadata_train.parquet"
-        test_url = f"https://huggingface.co/datasets/benmcewen/WABAD/resolve/main/{config_dir}/{config_dir}_metadata_test.parquet"
-        
-        # Download the parquet files
-        train_file = dl_manager.download(train_url)
-        test_file = dl_manager.download(test_url)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "parquet_file": train_file,
-                    "config_dir": config_dir
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "parquet_file": test_file,
-                    "config_dir": config_dir
-                },
-            )
-        ]
-
-    def _generate_examples(self, parquet_file, config_dir):
-        df = pd.read_parquet(parquet_file)
-        for idx, row in df.iterrows():
-            # Download audio file on demand
-            audio_url = f"https://huggingface.co/datasets/benmcewen/WABAD/resolve/main/{config_dir}/audio/{os.path.basename(row['filepath'])}"
-
-            labels = row["labels"]
-            if isinstance(labels, str):
-                labels = ast.literal_eval(labels) 
-            
-            yield idx, { 
-                "audio": {"path": audio_url, "bytes": None},  # Let datasets handle the download
-                "labels": labels,
-                "site_ID": row.get("site_ID", self.config.name),
-                # other metadata
+import datasets
+import pandas as pd
+import os
+import ast
+
+
+class WABADBuilderConfig(datasets.BuilderConfig):
+    def __init__(self, location_dir, **kwargs):
+        super().__init__(**kwargs)
+        self.location_dir = location_dir
+
+class WABADDataset(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        WABADBuilderConfig(name="BAM", location_dir="BAM", description="BAM location"),
+        WABADBuilderConfig(name="ARD", location_dir="ARD", description="ARD location"),
+        # add more locations here
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features({
+                "audio": datasets.Audio(), 
+                "labels": datasets.Sequence(datasets.Value("int32")),
+                "site_ID": datasets.Value("string"),
+                # any other metadata
+            })
+        )
+
+    def _split_generators(self, dl_manager):
+        config_dir = self.config.location_dir
+        
+        # Use the correct dataset URL format and download specific files
+        train_url = f"https://huggingface.co/datasets/benmcewen/WABAD/resolve/main/{config_dir}/{config_dir}_metadata_train.parquet"
+        test_url = f"https://huggingface.co/datasets/benmcewen/WABAD/resolve/main/{config_dir}/{config_dir}_metadata_test.parquet"
+        
+        # Download the parquet files
+        train_file = dl_manager.download(train_url)
+        test_file = dl_manager.download(test_url)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "parquet_file": train_file,
+                    "config_dir": config_dir
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "parquet_file": test_file,
+                    "config_dir": config_dir
+                },
+            )
+        ]
+
+    def _generate_examples(self, parquet_file, config_dir):
+        df = pd.read_parquet(parquet_file)
+        for idx, row in df.iterrows():
+            # Download audio file on demand
+            audio_url = f"https://huggingface.co/datasets/benmcewen/WABAD/resolve/main/{config_dir}/audio/{os.path.basename(row['filepath'])}"
+
+            labels = row["labels"]
+            if isinstance(labels, str):
+                labels = ast.literal_eval(labels) 
+            
+            yield idx, { 
+                "audio": {"path": audio_url, "bytes": None},  # Let datasets handle the download
+                "labels": labels,
+                "site_ID": row.get("site_ID", self.config.name),
+                # other metadata
             }