PMG-Bench: 13,544 clips + labels (initial release)

README.md

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+---
+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.

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