README.md

---
license: cc-by-4.0
task_categories:
  - audio-classification
language:
  - en
tags:
  - math-rock
  - midwest-emo
  - mbti-classification
  - music-analysis
  - multimodal
dataset_info:
  features:
    - name: artist
      dtype: string
    - name: song
      dtype: string
    - name: audio
      dtype:
        audio:
          sampling_rate: 16000
    - name: lyrics
      dtype: string
    - name: mbti
      dtype: string
    - name: emotion
      dtype: string
    - name: vibe
      dtype: string
    - name: intensity
      dtype: string
    - name: tempo
      dtype: string
    - name: file_name
      dtype: string
  splits:
    - name: train
      num_examples: 2500
configs:
  - config_name: default
    data_files:
      - split: train
        path: "data/train-*.parquet"
---

# Neural Math Rock & Midwest Emo Dataset

An audio and lyrics dataset curated specifically for Math Rock and Midwest Emo genres. Designed as the primary training data for a multimodal analysis system to classify emotion and MBTI personality using Transformer architectures and audio feature extraction.

## Dataset Description
- **Total Tracks:** 2500 full tracks.
- **Audio Format:** FLAC (Mono, 16000 Hz). Downsampled and compressed natively for WavLM compatibility and optimized PyTorch dataloader performance.
- **Total Size:** ~14.8 GB (distributed across 50 Parquet shards).
- **Data Sources:** Metadata, lyrics, and audio files extracted independently.

## Classification Labels (Ground Truth)
- **MBTI:** 16 cognitive personality types.
- **Emotion:** 28 emotion categories.
- **Vibe:** Technical, Melancholic, Atmospheric, Aggressive.
- **Intensity:** Low, Medium, High.
- **Tempo:** Slow, Moderate, Fast.

## Usage
The dataset is optimized for standard machine learning workflows. You can load it directly into memory or use `streaming=True` if you are working in environments with strict memory constraints.

```python
from datasets import load_dataset

# Load dataset
dataset = load_dataset("anggars/neural-mathrock")

# Iterate and fetch a sample
sample = dataset['train'][0]

print(f"Artist : {sample['artist']}")
print(f"Song   : {sample['song']}")
print(f"MBTI   : {sample['mbti']}")
print(f"Emotion: {sample['emotion']}")