meter2800.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 Meter2800 dataset."""

from pathlib import Path
import datasets
import pandas as pd

_CITATION = """\
@misc{meter2800_dataset,
  author       = {PianistProgrammer},
  title        = {{Meter2800}: A Dataset for Music Time signature detection / Meter Classification},
  year         = {2025},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/pianistprogrammer/Meter2800}
}
"""

_DESCRIPTION = """\
Meter2800 is a dataset of 2,800 music audio samples for automatic meter classification.
Each audio file is annotated with a primary meter class label (e.g., 'two', 'three', 'four') 
and an alternative meter (numerical, e.g., 2, 3, 4, 6). 
It is split into training, validation, and test sets, each available in two class configurations:
2-class and 4-class. All audio is 16-bit WAV format.
"""

_HOMEPAGE = "https://huggingface.co/datasets/pianistprogrammer/Meter2800"
_LICENSE = "mit"

# Define the labels - adjust these based on your actual data
LABELS_4 = ["three", "four", "five", "seven"]
LABELS_2 = ["simple", "complex"]  # or whatever your 2-class grouping actually is

class Meter2800Config(datasets.BuilderConfig):
    """BuilderConfig for Meter2800."""
    def __init__(self, name, **kwargs):
        super(Meter2800Config, self).__init__(
            name=name,
            version=datasets.Version("1.0.0"),
            **kwargs
        )

class Meter2800(datasets.GeneratorBasedBuilder):
    """Meter2800 dataset."""
    
    BUILDER_CONFIGS = [
        Meter2800Config(
            name="4_classes",
            description="4-class meter classification"
        ),
        Meter2800Config(
            name="2_classes", 
            description="2-class meter classification"
        ),
    ]
    
    DEFAULT_CONFIG_NAME = "4_classes"

    def _info(self):
        if self.config.name == "4_classes":
            label_names = LABELS_4
        elif self.config.name == "2_classes":
            label_names = LABELS_2
        else:
            # Fallback - shouldn't happen with proper configs
            label_names = LABELS_4
            
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                "filename": datasets.Value("string"),
                "audio": datasets.Audio(sampling_rate=22_050),
                "label": datasets.ClassLabel(names=label_names),
                "meter": datasets.Value("string"),
                "alt_meter": datasets.Value("string"),
            }),
            supervised_keys=("audio", "label"),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # Get the data directory
        data_dir = dl_manager.download_and_extract("")
        
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "csv_file": f"{data_dir}/data_train_{self.config.name}.csv",
                    "data_dir": data_dir
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "csv_file": f"{data_dir}/data_val_{self.config.name}.csv",
                    "data_dir": data_dir
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "csv_file": f"{data_dir}/data_test_{self.config.name}.csv",
                    "data_dir": data_dir
                },
            ),
        ]

    def _generate_examples(self, csv_file, data_dir):
        df = pd.read_csv(csv_file)
        df = df.dropna(subset=["filename", "label", "meter"]).reset_index(drop=True)

        for idx, row in df.iterrows():
            # Construct the full audio path
            audio_path = f"{data_dir}/{row['filename']}"
            
            yield idx, {
                "filename": row["filename"],
                "audio": audio_path,
                "label": row["label"],
                "meter": str(row["meter"]),
                "alt_meter": str(row.get("alt_meter", row["meter"])),
            }