Add script

music_classification.py

@@ -0,0 +1,101 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""The Kaggle Music Genre Prediction Challenge."""
+
+
+import os
+from pathlib import Path
+
+import datasets
+import pandas as pd
+
+_CITATION = """
+"""
+
+
+_DESCRIPTION = """\
+"""
+
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+_URL = ""
+
+genres_df = pd.read_csv("data/genres.csv")
+GENRES = genres_df["genre"].tolist()
+
+
+class MusicClassification(datasets.GeneratorBasedBuilder):
+    """The Kaggle Music Genre Prediction Challenge"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "song_id": datasets.Value("int32"),
+                    "file": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=22_050),
+                    "genre_id": datasets.ClassLabel(names=GENRES),
+                    "genre": datasets.Value("string"),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        train_path = dl_manager.download_and_extract("data/train.zip")
+        test_path = dl_manager.download_and_extract("data/test.zip")
+        metadata_train = Path("data/train.csv")
+        metadata_test = Path("data/test.csv")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"audio_path": train_path, "metadata_path": metadata_train, "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"audio_path": test_path, "metadata_path": metadata_test, "split": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, audio_path, metadata_path, split):
+        print(audio_path)
+        print(metadata_path)
+        print(split)
+        metadata_df = pd.read_csv(metadata_path)
+
+        if split == "train":
+            for idx_, row in metadata_df.iterrows():
+                yield idx_, {
+                    "song_id": row["song_id"],
+                    "file": os.path.join(audio_path, row["filepath"]),
+                    "audio": os.path.join(audio_path, row["filepath"]),
+                    "genre_id": row["genre_id"],
+                    "genre": row["genre"],
+                }
+        else:
+            for idx_, row in metadata_df.iterrows():
+                yield idx_, {
+                    "song_id": row["song_id"],
+                    "file": os.path.join(audio_path, row["filepath"]),
+                    "audio": os.path.join(audio_path, row["filepath"]),
+                    "genre_id": -1,
+                    "genre": "NA",
+                }