modified dataset building script to load embeddings as List[Value(float)] -> ds.to_pandas() compatible

dcase23-task2-enriched.py

@@ -298,15 +298,6 @@ class DCASE2023Task2DatasetConfig(datasets.BuilderConfig):
                 raise NotImplementedError
 
         if type(data) == datasets.Dataset:
-            # remove embedding columns first -> throws error in .to_pandas()
-            embeddings = {}
-            emb_features = [key for key, val in data.features.items() if type(val) == datasets.Array2D]
-            if len(emb_features) > 0:
-                embeddings = {
-                    key: [np.asarray(emb).reshape(-1,) for emb in data[key].copy()] for key in emb_features
-                }
-                data = data.remove_columns(emb_features)
-
             # retrieve split
             df = data.to_pandas()
             df["split"] = data.split._name if "+" not in data.split._name else df["path"].map(get_split)
@@ -315,10 +306,6 @@ class DCASE2023Task2DatasetConfig(datasets.BuilderConfig):
             # get clearnames for classes
             class_names = data.features["class"].names
             df["class_name"] = df["class"].apply(lambda x: class_names[x])
-
-            # append embeddings
-            for emb_name, emb_list in embeddings.items():
-                df[emb_name] = emb_list
         elif type(data) == pd.DataFrame:
             df = data
         else:
@@ -341,7 +328,7 @@ class DCASE2023Task2Dataset(datasets.GeneratorBasedBuilder):
     """Dataset for the DCASE 2023 Challenge Task 2 "First-Shot Unsupervised Anomalous Sound Detection
     for Machine Condition Monitoring"."""
 
-    VERSION = datasets.Version("0.0.3")
+    VERSION = datasets.Version("0.0.4")
 
     DEFAULT_CONFIG_NAME = "dev"
 
@@ -365,7 +352,7 @@ class DCASE2023Task2Dataset(datasets.GeneratorBasedBuilder):
         features = {
                     "audio": datasets.Audio(sampling_rate=16_000),
                     "path": datasets.Value("string"),
-                    "section": datasets.Value("int64"),
+                    "section": datasets.Value("uint32"),
                     "domain": datasets.ClassLabel(num_classes=2, names=["source", "target"]),
                     "label": datasets.ClassLabel(num_classes=_NUM_TARGETS, names=_TARGET_NAMES),
                     "class": datasets.ClassLabel(num_classes=_NUM_CLASSES, names=_CLASS_NAMES),
@@ -378,7 +365,7 @@ class DCASE2023Task2Dataset(datasets.GeneratorBasedBuilder):
                 }
         if self.config.embeddings_urls is not None:
             features.update({
-                emb_name: datasets.Array2D(shape=emb["size"], dtype=emb["dtype"]) for emb_name, emb in self.config.embeddings_urls.items()
+                emb_name: [datasets.Value(emb["dtype"])] for emb_name, emb in self.config.embeddings_urls.items()
             })
         features = datasets.Features(features)
 
@@ -408,7 +395,7 @@ class DCASE2023Task2Dataset(datasets.GeneratorBasedBuilder):
                 audio_path[split] = dl_manager.download(self.config.data_urls[split])
                 local_extracted_archive[split] = dl_manager.extract(
                     audio_path[split]) if not dl_manager.is_streaming else None
-                if self.config.embeddings_urls is not None and not dl_manager.is_streaming:
+                if self.config.embeddings_urls is not None:
                     for emb_name, emb_data in self.config.embeddings_urls.items():
                         downloaded_embeddings = dl_manager.download(emb_data[split])
                         embeddings[split][emb_name] = np.load(downloaded_embeddings, allow_pickle=True)["arr_0"].item()
@@ -447,7 +434,7 @@ class DCASE2023Task2Dataset(datasets.GeneratorBasedBuilder):
                 result = {field: None for field in data_fields}
                 result.update(metadata[metadata["path"] == lookup].T.squeeze().to_dict())
                 for emb_key in embeddings.keys():
-                    result[emb_key] = embeddings[emb_key][lookup]
+                    result[emb_key] = np.asarray(embeddings[emb_key][lookup]).squeeze().tolist()
                 result["path"] = path
                 yield id_, {**result, "audio": audio}
                 id_ += 1