music_classification.py

# 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",
                }