Create midi_dataset.py

midi_dataset.py

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+import os
+import pandas as pd
+from datasets import DatasetBuilder, GeneratorBasedBuilder, Split, Value, Features, ClassLabel
+
+class MidiDataset(GeneratorBasedBuilder):
+    """Hugging Face Dataset for MIDI files and their labels"""
+
+    def _info(self):
+        return DatasetBuilder.info(
+            features=Features({
+                "file_path": Value("string"),  # MIDI file path
+                "label": ClassLabel(names=["1", "2", "3", "4"]),  # 4Q labels as class labels
+                "annotator": Value("string")  # Annotator information
+            })
+        )
+
+    def _split_generators(self, dl_manager):
+        """Split the dataset. Assumes all files are local."""
+        # Set paths to midis/ and label.csv
+        midi_path = "midis"
+        label_path = "label.csv"
+
+        return [
+            Split(
+                name=Split.TRAIN,
+                gen_kwargs={
+                    "midi_dir": midi_path,
+                    "label_file": label_path
+                }
+            )
+        ]
+
+    def _generate_examples(self, midi_dir, label_file):
+        """Yield examples from MIDI files and the label.csv"""
+        # Read the label.csv into a pandas DataFrame
+        df = pd.read_csv(label_file)
+        
+        for index, row in df.iterrows():
+            midi_file = os.path.join(midi_dir, f"{row['ID']}.mid")
+            if os.path.exists(midi_file):
+                yield index, {
+                    "file_path": midi_file,
+                    "label": str(row["4Q"]),  # Convert label to string for ClassLabel compatibility
+                    "annotator": row["annotator"]
+                }
+
+# Usage Example:
+# You can now load the dataset using this script as follows:
+# from datasets import load_dataset
+# dataset = load_dataset("path/to/your/script", split="train")