| --- |
| 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 |
|
|
| ```python |
| 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. |
|
|
