added lof scores computed with dev train and add train data separately.

dcase23-task2-enriched.py

@@ -367,6 +367,10 @@ class DCASE2023Task2Dataset(datasets.GeneratorBasedBuilder):
                     "d2v": datasets.Value("string"),
                     "d3p": datasets.Value("string"),
                     "d3v": datasets.Value("string"),
+                    "dev_train_lof_anomaly": datasets.Value("int64"),
+                    "dev_train_lof_anomaly_score": datasets.Value("float32"),
+                    "add_train_lof_anomaly": datasets.Value("int64"),
+                    "add_train_lof_anomaly_score": datasets.Value("float32"),
                 }
         if self.config.embeddings_urls is not None:
             features.update({
@@ -419,6 +423,7 @@ class DCASE2023Task2Dataset(datasets.GeneratorBasedBuilder):
                     "audio_files": dl_manager.iter_archive(audio_path[split]),
                     "embeddings": embeddings[split],
                     "metadata_file": dl_manager.download_and_extract(self.config.data_urls["metadata"]) if self.config.data_urls["metadata"] is not None else None,
+                    "scores_file": dl_manager.download_and_extract("data/scores.csv"),
                     "is_streaming": dl_manager.is_streaming,
                 },
             ) for split in split_type if split in self.config.splits
@@ -431,11 +436,14 @@ class DCASE2023Task2Dataset(datasets.GeneratorBasedBuilder):
         audio_files: Optional[Iterable],
         embeddings: Optional[Dict],
         metadata_file: Optional[str],
+        scores_file: Optional[str],
         is_streaming: Optional[bool],
     ):
         """Yields examples."""
         if metadata_file is not None:
             metadata = pd.read_csv(metadata_file)
+        if scores_file is not None:
+            scores = pd.read_csv(scores_file)
         data_fields = list(self._info().features.keys())
 
         id_ = 0
@@ -450,6 +458,8 @@ class DCASE2023Task2Dataset(datasets.GeneratorBasedBuilder):
                 result = {field: None for field in data_fields}
                 if metadata_file is not None:
                     result.update(metadata[metadata["path"] == lookup].T.squeeze().to_dict())
+                if scores is not None:
+                    result.update(scores[scores["path"] == lookup].T.squeeze().to_dict())
                 for emb_key in embeddings.keys():
                     result[emb_key] = np.asarray(embeddings[emb_key][lookup]).squeeze().tolist()
                 result["path"] = path