Datasets:

keepsolid001
/

PMB

license: cc-by-nc-4.0
language:
  - fa
task_categories:
  - audio-classification
pretty_name: PMB
size_categories:
  - 10K<n<100K
dataset_info:
  features:
    - name: audio
      dtype:
        audio:
          sampling_rate: 32000
    - name: id
      dtype: string
    - name: artist
      dtype: string
    - name: song
      dtype: string
    - name: duration
      dtype: float64
    - name: genre_primary
      dtype: string
    - name: genre_raw
      dtype: string
    - name: key
      dtype: string
    - name: key_tonic
      dtype: string
    - name: key_mode
      dtype: string
    - name: valence_num
      dtype: float64
    - name: valence_cat
      dtype: string
    - name: arousal
      dtype: string
    - name: tempo
      dtype: string
    - name: popularity
      dtype: float64
    - name: caption_ref
      dtype: string
    - name: instruments_raw
      dtype: string
configs:
  - config_name: default
    data_files:
      - split: benchmark
        path: data/benchmark-*

PMB: a zero-shot benchmark for music understanding in Persian music

13,544 clips (~20 s, 32 kHz mono MP3) of Persian music with labels for zero-shot evaluation of audio-language models: genre (7 classes), musical key (24 classes; also tonic-only and mode-only granularities), emotion (valence 0–100 + 3-class bins; arousal 3-class), tempo (4 ordered classes), plus reference captions, Spotify popularity, and artist/song metadata.

Derived from the PMG dataset (supervised split): the persian-pop genre was downsampled to 1,000 distinct songs (one clip each), spread across 113 artists; all clips of the remaining six genres are retained.

genre	clips
afghan pop	5,449
persian rock	3,276
classic persian pop	2,299
persian traditional	1,277
persian pop	1,000
persian alternative	232
persian neo-traditional	11

Usage

from datasets import load_dataset
ds = load_dataset("keepsolid001/PMB", split="benchmark")

Label provenance & caveats

Genre/key/emotion/tempo labels are track-level, Spotify-derived metadata inherited by each clip. A classical key-detection baseline (Krumhansl-Schmuckler) agrees with the key labels at 0.37 (24-way; chance 0.04) and 0.69 (mode), validating them as benchmark gold; tempo categories are softer (beat-tracked BPM agrees at only 0.30).
valence_cat bins valence_num (0-100) at <40 / 40-60 / >60. arousal is the dataset's own 3-way energy category.
caption_ref is metadata-templated prose - suitable for attribute-coverage metrics, not as human-written caption gold.
Multiple clips of the same song share labels for the non-persian-pop genres; split by song/artist to avoid leakage when training.

Benchmark results

Eleven systems (audio-LLMs, contrastive audio-text models, and a classical DSP baseline) have been evaluated zero-shot on this set across genre, key, emotion, tempo, captioning (incl. cultural identification), language identification, instrument recognition, and stem-based hallucination tests. See the paper for full results and analysis.

License & provenance

Audio excerpts of commercial Persian music, distributed for non-commercial research only (CC-BY-NC-4.0). If you are a rights holder and want content removed, open a discussion on this repository.

Citation

Anonymous — under review. A citation will be added upon publication.