Refactor README.md to improve dataset structure, description, and metadata organization

README.md

@@ -1,60 +1,102 @@
-pretty_name: Meter2800tags:audiomusicclassificationmetermulti-class-classificationmulti-label-classificationdatasets:GTZANMAGFMAOWNlicense: mitlanguages:enconfigs:data_test_4_classesdata_test_2_classesdata_train_4_classesdata_train_2_classesdata_val_4_classesdata_val_2_classesDataset Card for Meter2800Dataset DescriptionThe Meter2800 dataset is a collection of music audio files intended for tasks such as music genre classification and meter classification. It comprises 2800 audio samples, categorized into various genres and annotated with meter information. This dataset is designed to facilitate research in music information retrieval, particularly focusing on rhythmic and structural aspects of music.Dataset StructureThe dataset is organized as follows within the repository:Meter2800/
-├── GTZAN/            # Contains WAV audio files from the GTZAN dataset
-├── MAG/              # Contains WAV audio files from the MAG dataset
-├── OWN/              # Contains WAV audio files from the OWN dataset
-├── FMA/              # Contains WAV audio files from the FMA dataset
-├── data_test_4_classes.csv
-├── data_test_2_classes.csv
-├── data_train_4_classes.csv
-├── data_train_2_classes.csv
-├── data_val_4_classes.csv
-├── data_val_2_classes.csv
-└── README.md         # This file
+---
+pretty_name: "Meter2800"
+language:
+  - en
+tags:
+  - audio
+  - music-classification
+  - meter-classification
+  - multi-class-classification
+  - multi-label-classification
+license: mit
+task_categories:
+  - audio-classification
+  - audio-tagging
+dataset_info:
+  size_categories:
+    - 1K<n<10K
+  source_datasets:
+    - gtzan
+    - mag
+    - own
+    - fma
+---
 
-All audio files are in .wav format, having been pre-processed and converted from their original formats (e.g., MP3) to ensure uniformity.Splits and ClassesThe dataset is divided into training, validation, and testing sets, available in two different class configurations:4-Class Classification:data_train_4_classes.csv: Training set metadata.data_val_4_classes.csv: Validation set metadata.data_test_4_classes.csv: Test set metadata.2-Class Classification:data_train_2_classes.csv: Training set metadata.data_val_2_classes.csv: Validation set metadata.data_test_2_classes.csv: Test set metadata.Each CSV file contains columns such as filename, label, meter, and alt_meter. The filename column specifies the relative path to the audio file within the dataset repository (e.e.g., /GTZAN/rock.00056.wav).Total Number of Files: Across the data_test_4_classes.csv, data_train_4_classes.csv, and data_val_4_classes.csv splits, there is a combined total of 2800 unique audio entries.Audio FilesThe audio files are 16-bit WAV files. The datasets library handles the loading of these audio files automatically when referenced in the filename column of the CSVs.Loading the DatasetYou can easily load this dataset using the Hugging Face datasets library.PrerequisitesEnsure you have the necessary libraries installed:pip install datasets pandas soundfile librosa
+# Meter2800
 
-Additionally, the datasets library relies on ffmpeg for audio processing. Please ensure ffmpeg is installed on your system and accessible in your environment's PATH.Loading Code ExampleTo load a specific split (e.g., train_4_classes), you can use the following Python code:from datasets import load_dataset, Audio
+**Dataset for music genre and meter (rhythm) classification**, combining tracks from GTZAN, MAG, OWN, and FMA.
 
-# Define the dataset name on Hugging Face Hub
-dataset_name = "pianistprogrammer/Meter2800"
+## Dataset Description
 
-# Define the paths to your CSV files within the Hugging Face repository
-# These paths are relative to the root of the dataset folder on the Hub.
-data_files = {
-    'train_4_classes': 'data_train_4_classes.csv',
-    'val_4_classes': 'data_val_4_classes.csv',
-    'test_4_classes': 'data_test_4_classes.csv',
-    'train_2_classes': 'data_train_2_classes.csv',
-    'val_2_classes': 'data_val_2_classes.csv',
-    'test_2_classes': 'data_2_classes.csv',
-}
+Meter2800 is a curated collection of 2,800 `.wav` music audio samples, each annotated with **genre** and **meter** (and optionally `alt_meter`). It supports both:
+
+- **4-class classification** (e.g., 4 genres),
+- **2-class classification** (binary genre labeling).
+
+Split into train/val/test sets with clear metadata in CSV.
+
+Intended for music information retrieval tasks like rhythmic / structural analysis and genre prediction.
+
+## Supported Tasks and Usage  
+
+Load the dataset via the `datasets` library with automatic audio decoding:
 
-# Load a specific split, for example, the 'train_4_classes' split
-# The 'filename' column in the CSVs will be automatically interpreted as audio paths.
-dataset_train_4_classes = load_dataset(dataset_name, data_files=data_files, split='train_4_classes')
+```python
+from datasets import load_dataset, Audio
+dataset = load_dataset(
+  "pianistprogrammer/Meter2800",
+  data_files={
+    "train_4": "data_train_4_classes.csv",
+    "val_4": "data_val_4_classes.csv",
+    "test_4": "data_test_4_classes.csv",
+    "train_2": "data_train_2_classes.csv",
+    "val_2": "data_val_2_classes.csv",
+    "test_2": "data_test_2_classes.csv"
+  }
+)
 
-print(f"Dataset loaded successfully: {dataset_train_4_classes}")
-print(f"First example from 'train_4_classes':\n{dataset_train_4_classes[0]}")
+Each entry in the dataset contains:
 
-# Accessing the audio data:
-first_example = dataset_train_4_classes[0]
-print(f"\nAudio path: {first_example['filename']}")
-print(f"Audio array (first 10 samples): {first_example['audio']['array'][:10]}")
-print(f"Sampling rate: {first_example['audio']['sampling_rate']}")
+- **filename**: Path to the audio file.
+- **label**: Genre label (multi-class or binary, depending on split).
+- **meter**: Primary meter annotation (e.g., 4/4, 3/4).
+- **alt_meter**: Optional alternative meter annotation.
+- **audio**: Audio data as a NumPy array and its sampling rate.
 
-# To load all splits as a DatasetDict:
-dataset_dict = load_dataset(dataset_name, data_files=data_files)
-print(f"\nLoaded all splits as a DatasetDict: {dataset_dict}")
+The dataset is organized into the following splits:
 
-# Accessing another split, e.g., test_2_classes
-print(f"\nFirst example from 'test_2_classes':\n{dataset_dict['test_2_classes'][0]}")
+- `train_4`, `val_4`, `test_4`: For 4-class genre classification.
+- `train_2`, `val_2`, `test_2`: For 2-class (binary) genre classification.
+
+All splits are provided as CSV files referencing the audio files in the corresponding folders (`GTZAN/`, `MAG/`, `OWN/`, `FMA/`).
+
+Example row in a CSV file:
+
+| filename                | label   | meter | alt_meter | 
+|-------------------------|---------|-------|-----------|
+| GTZAN/blues.00000.wav   | blues   | 4/4   | 12/8      |
+
+
+Meter2800/
+├── GTZAN/
+├── MAG/
+├── OWN/
+├── FMA/
+├── data_train_4_classes.csv
+├── data_val_4_classes.csv
+├── data_test_4_classes.csv
+├── data_train_2_classes.csv
+├── data_val_2_classes.csv
+├── data_test_2_classes.csv
+└── README.md
 
 
-Key Features and UsageAutomatic Audio Loading: When you access an example, the audio column will automatically contain the loaded audio as a NumPy array (array) and its sampling_rate.Flexible Splits: You can load specific splits or the entire DatasetDict to manage your training, validation, and testing pipelines.Metadata Integration: The CSVs provide rich metadata (label, meter, alt_meter) alongside the audio paths, enabling diverse machine learning tasks.Citation@misc{meter2800_dataset,
-  author = {PianistProgrammer},
-  title = {{Meter2800}: A Dataset for Music Genre and Meter Classification},
-  year = {2025},
-  publisher = {Hugging Face},
-  url = {https://huggingface.co/datasets/pianistprogrammer/Meter2800}
+@misc{meter2800_dataset,
+  author       = {PianistProgrammer},
+  title        = {{Meter2800}: A Dataset for Music Genre and Meter Classification},
+  year         = {2025},
+  publisher    = {Hugging Face},
+  url          = {https://huggingface.co/datasets/pianistprogrammer/Meter2800}
 }
 
+license: "CC0 1.0 Public Domain"